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DEV Community [Unofficial] @dev.to.web.brid.gy · 1h
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Shining Force II: The 16-Bit Tactical RPG That Made Turn-Based Combat Cinematic
Most tactical RPGs in 1993 treated combat like chess with better sprites. **Shining Force II** treated it like cinema — and quietly shipped some of the most player-respecting systems design the 16-bit era ever produced. This is the Sega Genesis at its most generous: a 40-hour tactics epic with 30 recruitable characters, branching promotions, hidden party members, and a battle camera that swoops behind your units like it's directing a film. If you love _Fire Emblem_ , _Advance Wars_ , or _Age of Wonders_ , you've been playing in the shadow of this game your whole life. _Sonic! Software Planning / Sega, 1993 (Genesis). Shining Force II box art._ ## The camera move that changed everything Here's the signature _Shining Force_ trick, and it still works. You spend the tactics phase on a top-down grid — moving units, checking ranges, planning three turns ahead. Then you commit to an attack, and the camera **swings down behind your unit**. Suddenly you're looking over their shoulder while the enemy looms in the background, and the attack plays out as a little animated duel. Think about what that does as a design decision. The dry math of damage formulas becomes physical and personal. Every attack has a payoff shot. The game takes the most abstract part of a tactics game — the resolution — and makes it the most dramatic part. And it's doing information design work too. The overworld grid shows you everything you need to _plan_ ; the battle view shows you everything you need to _feel_. Two cameras, two jobs, zero clutter. Games with a hundred times the budget still split "planning UI" and "drama" worse than this. _The grid: every move is a commitment, every tile a calculation._ ## Promotion: the original buildcraft At level 20, a character can promote — and here's the part that was genuinely ahead of its time. Many characters have **two** promotion paths, and one of them is locked behind a special hidden item. A knight can become a paladin the normal way, or a winged pegasus rider if you found the secret item. Same character, two completely different battlefield roles. Read that as a developer and tell me it isn't a build system. Before "builds" were a word anyone used, _Shining Force II_ was asking: what kind of army are _you_ running? With 30 characters but only 12 slots per battle, party composition isn't flavor — it's the strategy layer. Add mithril weapon crafting on top, and you've got an equipment economy that rewards players who engage with systems instead of just pushing through the story. _The battle view: your swordsman, up close and personal._ ## Secrets as a system This game treats curiosity as a mechanic. A giant rat thief named Slade steals the sealing jewels in the opening — and then, if you play your cards right, _joins your army_. Hidden characters lurk behind obscure triggers across the whole world map. Secret items hide in walls you'd never think to search. The lesson for developers: secrets aren't content, they're a **reward function for exploration**. _Shining Force II_ never tells you the hidden stuff exists. It just makes the world dense enough that poking at it always pays off eventually. That trust — that the player will dig without being told to — is rarer in modern game design than it should be. ## No chapters, no rails The first _Shining Force_ was structured in chapters: finish a battle, move to the next story beat. The sequel threw that out. The world of Grans is open — you can wander, revisit old towns, stumble into areas you're not ready for, and backtrack with new abilities. On a 16-megabit cartridge, in 1993, that's a genuine technical flex. The world is a state machine that remembers what you've done and where you've been, with a caravan HQ that lets you swap your roster between battles. It's the structure modern open-world RPGs take for granted, running on a 7.6 MHz 68000. _Grans is yours to wander — the game trusts you to get lost._ ## Why it still matters The lineage is direct: _Shining Force II_ → the tactics boom of the GBA era → _Fire Emblem_ 's revival → the modern indie tactics renaissance. It ranked #48 on IGN's top 100 games of all time, it hit Nintendo Switch Online in 2022, and it remains the most _approachable_ classic tactics RPG — deep enough for systems nerds, readable enough for anyone. What I keep coming back to is the respect. This game respects your intelligence (branching builds, hidden depth), your time (check enemy stats before committing, clear UI), and your curiosity (secrets everywhere, no handholding). That's not nostalgia talking. That's design. _Every battle is a puzzle with a dozen valid solutions._ ## Play it now You can play the USA version right in your browser, no setup: Shining Force II (USA) on RetroGames.cc Controls are keyboard-friendly: arrow keys move, Z/X/S map to the A/B/C buttons, Enter is Start. Fair warning — this is a 40-hour game. The browser tab will forgive you. Your sleep schedule might not. _What's the 16-bit game you think was secretly smarter than anyone gave it credit for? I'm taking requests for the next retro dive._ _Retro game retrospective #3. Images: box art via LaunchBox Games Database; gameplay screenshots via the Let's Play Archive. Played via in-browser emulation._
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DEV Community [Unofficial] @dev.to.web.brid.gy · 1h
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Constitutional Engineering: What Two Days of a Three-Copy Word List Taught Me About Agent Governance
Put Governance Rules Where They Can Bite The word list lived in three places: the generator, the publisher, and the manual review queue. They were supposed to be identical. Adding a new AI-flavor phrase meant editing all three files, and every time we missed one, something slipped through a gate. First miss was the publisher. Second miss was the generator. Third miss was the human review queue — a person approved an article that should have been flagged, because the copy of the list in front of them was stale. A `diff` across the three files after that third incident took a few seconds and made the case better than any slides: the phrase we'd added was in two files, and the one it was missing from was the gate that had just approved the article. Three consecutive misses at three different gates, same root cause: one rule, three copies. We spent two days consolidating the lists into a single source of truth. While we were in there, we split the words into two tiers. Blocking words stop the pipeline. Prompting words add a note for the human reviewer. The old one-tier list was a trap: add a high-frequency connector to fight AI-sounding prose and every normal article that used it got flagged. "However" is not a crime. "But" is not a crime. But in a one-tier list, a weak signal and a strong signal carried the same weight. The tiering fixed that. Now adding a word starts with a question: which tier? And the answer, more often than not, is prompting. That consolidation changed how I think about rules in the multi-agent memory systems we've been building. In the MCP universe, every agent has a memory socket, a tool belt, and a mandate to get things done. The failure mode is memory poisoning via shared context: an agent misreads stale data, a rogue tool call overwrites a critical state, or a delegated sub-agent inherits a memory scope it should never see. A malicious attacker is a rare event. A planning agent that read a stale entry, made a plausible downstream call, and watched that call overwrite a critical state — that's a Tuesday. The damage shows up as hours of tracing why a recommendation chain collapsed. The word list taught us that a rule kept in one file as documentation is a suggestion. A rule kept in three files is three suggestions. We keep calling it constitutional engineering: governance rules embedded in the protocol itself, so the system physically cannot take a prohibited action. A YAML constitution is a readme. Enforcement has to live at the point of mutation. The first thing we put in place was fixed-point verification on memory writes. Every MCP tool call that mutates shared memory carries a deterministic hash of the entire conversational context it was derived from. The memory layer rejects any write whose hash mismatch suggests the agent hallucinated or omitted a conflicting fact. That is how you prove causality in shared state. Our first attempt placed the verification in the orchestration wrapper — the Python layer around the tool call. It held for about a week. Then an agent called the memory socket directly, the wrapper never ran, and the hash was computed over a context snapshot we didn't recognize. The guard has to live inside the thing it guards. The verification now sits in the memory kernel; the tool definition requires the hash field, the kernel computes it independently, and it rejects before persisting. The cost is real. Shared-memory writes got slower, around 40% latency increase on full-context hashing. We tried full verification on every shared namespace, and the latency pushed teams to route around it — writing "context summaries" to a side cache that was never verified. That is how you get two sources of truth that disagree. We tried relaxing verification for a "low-stakes" namespace, and a rogue tool call wrote junk there that a planning agent used as fact for three days. Current position: verify all cross-agent writes, skip verification only for the agent's own scratch memory. It's a compromise we haven't fully validated, but it's holding. The second pattern came out of that same consolidation: the frozen memory pattern. Memory entries older than X days cannot be referenced by a tool call unless a human explicitly re-activates them. A provenance fence. It stops the plausible, dangerous habit of a planning agent relying on volatile knowledge that a sibling agent silently updated. The first version used a single global X. Wrong. The planner's long-term context froze in a way that looked like a planner bug, and we burned a day before finding the namespace overlap. The fix was per-namespace freeze windows: decision logs freeze at 14 days, sensor readings at 5, an agent's own working output at 30. Every MCP tool response carries a version number on its memory reference. When a chain comes apart, the version number shows you which snapshot each hop used. It doesn't end the incident, but it cuts the search space to something a human can handle in one sitting. One organizational detail bit us. The freeze window originally lived in runtime config. Someone on call pushed it to 60 days during an incident to quiet the alerts, and stale references came back. The freeze window now lives in the kernel binary. To change it, you ship a new build. Runtime tuning is how governance parameters get un-tuned. The third piece is procedural segregation in MCP scopes. We don't give agents a full memory schema. Micro-scopes: `read_recent_own_output`, `read_shared_decision_log`, `read_sensor_cache`. Least privilege applied to the past — an agent cannot see what it cannot govern. Our first attempt scoped by role. The "planner" role could read everything because "it needs context," and it planned beautifully on memory fragments it had no authority to synthesize. Scopes now attach to the memory namespace. Role identity no longer grants memory access. Delegation re-derives scopes from the sub-task definition — a sub-agent inherits only the namespaces its parent was granted for that specific task, not the parent's full context. If the sub-task doesn't name a namespace, the sub-agent starts empty. This one curbed the most failures in practice. The "why did this agent recommend that" tickets dropped sharply after the change. The trade-offs are honest and ugly. Byte-level verification makes the system slower. Procedural segregation means more MCP tools to build and maintain — we landed on twelve composable micro-scopes, and we tried consolidating them into a single read-all scope to cut the tooling burden. The visibility regression came right back. We keep returning to the same position: every limitation you accept on the agent side is a capability you remove from an attacker — or from an idiot agent, which is far more common in our logs. The ecosystem gap is audit trails. There's no standard for replaying why a given memory item was written, verified, and authorized to survive. We need something like `git bisect` for memory lineage. Until then, we grep timestamps, compare snapshot hashes, and every incident review starts with "who wrote this entry and under what scope." Deprecating a bad memory rule without turning all agents into half-blind experts is a problem we haven't solved. Back to the word list. If the old setup had the same architecture we now use for agent memory, the third miss would never have happened: one list, one entry point, tiers enforced where words are used. Recovery would have been minutes, not two days. Write your governance rules where they can bite. If a rule survives only as a static policy file # maref #ai #opensource #machinelearning
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DEV Community [Unofficial] @dev.to.web.brid.gy · 1h
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What is BGP? How Facebook's 2021 outage took it off the internet
On October 4, 2021, Facebook, Instagram, WhatsApp and Messenger disappeared from the internet for almost six hours. The cause was not a hack or a DNS provider going down. A routine maintenance command cut every link between Facebook's data centres, and Facebook's own DNS servers then did what they were designed to do: stop announcing their BGP routes. If you have ever asked what BGP is and why it matters to you, this is the outage that answers it, and the lessons apply to any system with a health check and a single network. ## TL;DR * A command meant "to assess the availability of global backbone capacity" took down all of Facebook's backbone connections. The audit tool that should have blocked it had a bug, per Meta's postmortem. * Facebook's DNS servers withdraw their BGP announcements when they cannot reach the data centres. With the whole backbone gone, every one of them did it at once. The servers were running; nobody on the internet could find them. * From outside, Cloudflare saw facebook.com stop resolving at about 15:50 UTC and come back at 21:20 UTC. Resolvers worldwide handled 30 times their usual queries as apps retried. * Internal tools, out-of-band access and, per the New York Times, office badges ran on the same network, so engineers had to reach the data centres in person. * My verdict: NEEDS REVIEW. A clear, fast postmortem; a thin list of fixes. ## What is BGP? The internet is tens of thousands of independent networks, called autonomous systems (AS), each with a number. Facebook's is AS32934. BGP, the Border Gateway Protocol, is how those networks tell each other which blocks of IP addresses (prefixes) they can deliver traffic to. An **announcement** says "send traffic for this prefix to me". A **withdrawal** says "I can't reach this any more, forget the route". Every router on the internet builds its map from those messages. If nobody announces a prefix, it drops out of every routing table and packets for it have nowhere to go. That is the whole mechanism of this outage: Facebook's name servers were still running, but the prefixes they lived in were no longer announced, so to the rest of the world they did not exist. DNS sits on top. To open facebook.com, your resolver (your ISP, 1.1.1.1, 8.8.8.8) asks Facebook's authoritative name servers for the address. If the resolver cannot reach any of them, it returns `SERVFAIL`. ## The Facebook outage 2021 timeline All times UTC, October 4, 2021, from Cloudflare's post, Meta's two notes and the posts linked below. Time | What happened ---|--- ≈15:39 | The backbone command runs; every link between data centres goes down ≈15:40 | Cloudflare sees "a peak of routing changes from Facebook. That's when the trouble began." 15:45 | Hacker News: "Facebook-owned sites were down" (2,589 points) ≈15:50 | facebook.com stops resolving on 1.1.1.1 15:51 | Cloudflare opens an internal incident: "Facebook DNS lookup returning SERVFAIL", fearing its own resolver is broken 15:58 | Cloudflare confirms Facebook "had stopped announcing the routes to their DNS prefixes" 16:07 | Facebook spokesman Andy Stone posts on Twitter 18:51 | NYT reporter Sheera Frenkel: employees can't enter buildings 19:52 | CTO Mike Schroepfer apologises ≈21:00 | Renewed BGP activity from Facebook, peaking at 21:17 21:20 | facebook.com resolves on 1.1.1.1 again; by 21:28 "Facebook appears to be reconnected" Cloudflare checked the public route collectors and found the prefixes of Facebook's DNS servers simply gone. From its post: route-views>show ip bgp 185.89.218.0/23 % Network not in table route-views> route-views>show ip bgp 129.134.30.0/23 % Network not in table route-views> Other Facebook prefixes stayed routed, Cloudflare notes, but they "weren't particularly useful" without DNS: nobody could look up the addresses to reach them. The first public word came 27 minutes in, on a competitor's platform: "Some people" was, per the New York Times, the more than 3.5 billion who use Facebook, Instagram, Messenger and WhatsApp. ## Why did Facebook's DNS servers withdraw their own routes? This is the part of the Facebook BGP story that is a deliberate design decision. Facebook's authoritative DNS servers sit in smaller edge facilities and check their own health. Meta's postmortem, verbatim: > "To ensure reliable operation, our DNS servers disable those BGP advertisements if they themselves can not speak to our data centers, since this is an indication of an unhealthy network connection. In the recent outage the entire backbone was removed from operation, making these locations declare themselves unhealthy and withdraw those BGP advertisements. The end result was that our DNS servers became unreachable even though they were still operational." In code, the logic is roughly this. A simplified sketch, not Facebook's: # illustrative example of a per-site health check that controls a BGP announcement def tick(site): if site.can_reach_data_centers(): site.announce(DNS_PREFIX) # healthy: attract DNS traffic here else: site.withdraw(DNS_PREFIX) # unhealthy: let another site take it For one bad site this is correct: withdraw, and routing sends users to another, healthy site. The rule has no idea what to do when _every_ site fails the check at once, because the data centres themselves are unreachable. Each site made a locally sensible decision, and together they removed Facebook's DNS from the internet. A per-site safety feature with no global brake turned a network fault into an existence fault. ## Why a backbone command could reach everything The trigger, again from Janardhan's post: "During one of these routine maintenance jobs, a command was issued with the intention to assess the availability of global backbone capacity, which unintentionally took down all the connections in our backbone network, effectively disconnecting Facebook data centers globally." And the part that should have stopped it: "Our systems are designed to audit commands like these to prevent mistakes like this, but a bug in that audit tool prevented it from properly stopping the command." Meta did not publish the command, the bug or why a capacity _check_ could take links _down_. The first note, the evening of the outage, said the cause was "configuration changes on the backbone routers" and that there was "no malicious activity behind this outage" and "no evidence that user data was compromised". ## Locked out: out-of-band access on the same network Once the backbone was down, fixing it from a desk was impossible. The postmortem: "it was not possible to access our data centers through our normal means because their networks were down, and second, the total loss of DNS broke many of the internal tools we'd normally use to investigate and resolve outages." And: "Our primary and out-of-band network access was down, so we sent engineers onsite to the data centers." The physical layer didn't help either: Once inside, the hardware fought back as designed: data centres are "hard to get into, and once you're inside, the hardware and routers are designed to be difficult to modify even when you have physical access to them." Security hardening built to slow an attacker slowed the owner by the same amount. Meta, to its credit, says so: "it was interesting to see how that hardening slowed us down … I believe a tradeoff like this is worth it". ## Why recovery took hours: the power problem Restoring the backbone did not mean flipping everything back on. "Individual data centers were reporting dips in power usage in the range of tens of megawatts, and suddenly reversing such a dip in power consumption could put everything from electrical systems to caches at risk." Facebook ramped traffic up deliberately, using procedures from its "storm" drills. But, the post admits, "we've never previously run a storm that simulated our global backbone being taken offline". Meanwhile the rest of the internet was hammering on the door. Cloudflare saw "DNS resolvers worldwide handling 30x more queries than usual" because every app retried and every user reloaded. It also saw more DNS queries for Twitter, Signal and other messaging apps as people went elsewhere. Telegram founder Pavel Durov claimed more than 70 million new users that day. ## git blame: who is at fault My split, each slice pinned to Meta's own postmortem: Share | Who | Why ---|---|--- 55 % | Facebook's change process | "a bug in that audit tool prevented it from properly stopping the command"; one command reached the whole backbone 25 % | the DNS design | self-withdrawal is right for one site, catastrophic for all of them at once 15 % | one network for everything | tools, out-of-band access and, per the NYT, badges shared the network that failed 5 % | the hardened racks | "designed to be difficult to modify even when you have physical access" Blast radius: 3.5 billion users, about five and a half hours on Cloudflare's resolver and "nearly six hours" in the press, per The Verge. The stock closed down 4.9 %, and Forbes estimated Mark Zuckerberg's paper loss at $5.9 billion. The timing was bad too: the day after whistleblower Frances Haugen's 60 Minutes interview and the day before her Senate testimony. Zuckerberg's note to employees called it "the worst outage we've had in years". ## Lessons from the Facebook BGP outage for your own systems * **Give health checks a sense of proportion.** If a check can withdraw a service, ask what happens when every instance fails it at the same moment. Often the right answer is "keep serving, stale"; a fleet that disappears all at once is the outage. * **Out-of-band means a different network.** Consoles, runbooks, chat, the VPN and the door should work when production is gone. If they depend on your own DNS or backbone, they are in-band with extra steps. * **Audit the auditor.** A safety tool that blocks dangerous commands is itself production code. Test that it actually refuses the worst command you can think of. * **Drill the total failure.** Facebook had storm drills and had never simulated the backbone disappearing. That is the one worth rehearsing. * **Watch BGP from outside.** Your own monitoring may be behind the failure. Public route collectors and third-party resolvers saw this within minutes. ## Verdict: NEEDS REVIEW I stamped the response NEEDS REVIEW. The communication was good: a statement the same evening and, the next day, a named-author postmortem that admits the audit-tool bug and the design trade-off in plain English. The fix list was thin: "strengthen our testing, drills, and overall resilience". Nothing about moving out-of-band access and the doors off the production backbone, and no global brake on DNS servers withdrawing themselves. Monday line: put your out-of-band access, and your door, on a network you do not operate. ## FAQ **What caused the Facebook outage on October 4, 2021?** A maintenance command took down all of Facebook's backbone links; an audit tool bug failed to stop it. Facebook's DNS servers then withdrew their BGP routes, making facebook.com unresolvable. **Was the Facebook outage a hack?** No. Meta said there was "no malicious activity" and "no evidence that user data was compromised". **How long was Facebook down in 2021?** About five and a half hours on Cloudflare's resolver (15:50 to 21:20 UTC), reported as nearly six hours. **What does SERVFAIL mean?** A DNS resolver's answer when it cannot get a valid response from the domain's authoritative name servers. On October 4 it was the answer for facebook.com everywhere. ## Sources * Meta Engineering, "More details about the October 4 outage" (Oct 5, 2021): https://engineering.fb.com/2021/10/05/networking-traffic/outage-details/ * Meta Engineering, "Update about the October 4th outage" (Oct 4, 2021): https://engineering.fb.com/2021/10/04/networking-traffic/outage/ * Cloudflare, "Understanding how Facebook disappeared from the Internet": https://blog.cloudflare.com/october-2021-facebook-outage/ * Andy Stone on Twitter: https://twitter.com/andymstone/status/1445058088436908045 * Sheera Frenkel on Twitter: https://twitter.com/sheeraf/status/1445099150316503057 * Mike Schroepfer on Twitter: https://twitter.com/schrep/status/1445114730151043073 * Mark Zuckerberg's note to employees: https://www.facebook.com/zuck/posts/10113961365418581 * New York Times, "Gone in Minutes, Out for Hours: Outage Shakes Facebook": https://www.nytimes.com/2021/10/04/technology/facebook-down.html * The Verge: https://www.theverge.com/2021/10/4/22708989/instagram-facebook-outage-messenger-whatsapp-error * Forbes: https://www.forbes.com/sites/abrambrown/2021/10/04/zuckerberg-net-worth-billionaire-facebook-stock-outage/ * Reuters on Telegram sign-ups: https://www.reuters.com/technology/telegram-founder-says-over-70-mln-new-users-joined-during-facebook-outage-2021-10-05/ * Krebs on Security: https://krebsonsecurity.com/2021/10/what-happened-to-facebook-instagram-whatsapp/ * Hacker News, the outage thread: https://news.ycombinator.com/item?id=28748203 * Hacker News, the Cloudflare post: https://news.ycombinator.com/item?id=28752131 _This article expands on an episode of **The Daily Diff_ _, a five-minute daily video on what shipped and what broke in tech. Watch the episode · Subscribe on YouTube · the written diff lands in your inbox every morning at thedailydiff.dev._
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DEV Community [Unofficial] @dev.to.web.brid.gy · 1h
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Never Trust, Always Verify: Zero-Trust Governance for MCP Memory
The MCP ecosystem solved the wrong problem first. Tool wiring happened quickly; the memory layer became the soft underbelly. One compromised agent writes a poisoned memory entry, and every other agent reading that shared context inherits the corruption. That's not a hypothetical — it's the natural failure mode of trust-by-default. Zero trust in a multi-agent memory system means: no implicit trust between agents, between memory stores, or between a memory entry and the agent that reads it. ## The concrete failure, in order 1. Agent A writes a tool result to shared MCP memory. 2. Agent B retrieves that entry as ground truth for a planning task. 3. The result was malformed — or adversarially crafted. B now executes against corrupted state. The fix is structural, not prompt-level. ## Mechanism 1: Memory write attestation Every memory write in MCP should carry a provenance envelope: agent ID, tool ID, session ID, and a hash of the raw tool output. Readers verify three things: * Was this entry produced by an authenticated agent? * Which tool produced the underlying data? * Does the payload match the hash? If your MCP memory server cannot answer all three, it's a bulletin board, not a memory system. **Trade-off:** Attestation adds latency per write — the exact figure depends on the store and hash scheme, likely single-digit milliseconds in typical local setups. Accept it. Unattested writes are how memory poisoning propagates. ## Mechanism 2: Capability scoping per memory namespace Never give an agent read/write to a global namespace. Partition memory: * `ephemeral` — session-scoped, TTL enforced * `team` — shared, but writes require verification * `anchored` — immutable after N confirmations, used for critical facts Promotion rule: a write moves from `ephemeral` to `team` only after confirmation by **two independent agents** or an explicit human approval. This kills the single-writer poisoning vector. **Trade-off:** Collaboration slows down. That's the point. Speed on a corrupt memory base compounds the damage. ## Mechanism 3: Read-time verification hooks MCP clients should support interceptors that run policy checks at retrieval time: * Entry older than X → force a re-verification call to the source tool. * Producer agent revoked → quarantine the entry, don't return it. * Entry conflicts with an `anchored` fact → surface the conflict to the planner instead of silently returning both. These hooks are where governance actually lives. Without them, "zero trust" is a slogan. ## What breaks without this A shared memory pool without attestation, scoping, and read-time checks is a single point of failure. One agent's hallucination becomes everyone's base reality. The observable pattern in real MCP deployments: it's not the tool calls that go wrong — it's the memory that persists and propagates the mistake. ## The execution order You cannot retrofit zero trust onto a memory layer after an incident. The provenance envelope must be in the write path from day one. Sequence: 1. Map your memory namespaces and define promotion rules. 2. Add write attestation. 3. Enforce read-time verification hooks. In that order. Never trust the memory — always verify the path it took to get to you. # maref #ai #opensource #machinelearning
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DEV Community [Unofficial] @dev.to.web.brid.gy · 1h
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How chDB Turns Ephemeral Agent State into Durable Memory
_Hello, I'm Shrijith Venkatramana, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product._ Agent memory sounds like an LLM problem until you actually deploy an agent. A useful agent needs to retain things such as: project conventions previous decisions tool results failed approaches user preferences investigation history Now put that agent inside a temporary CI runner or Lambda or sandbox. Where does all of that state live? A file works well until you need the agent to move to another machine. A remote database solves portability, but introduces a network dependency for every read and write. chDB Durable occupies the space between the two. It keeps the database embedded and local, while using object storage as the durable backing layer. This becomes common as agents move between laptops, CI runners, sandboxes, containers, and short-lived cloud machines. A local agent can accumulate weeks of useful state: project conventions, user preferences, tool failures, corrected assumptions, previous decisions, and the evidence behind those decisions. A SQLite file can preserve some of this. A remote Postgres or ClickHouse server can preserve much more. But there is a useful middle ground: **Keep the database local for fast reads, while making its state durable in object storage.** That is the idea behind the new **chDB Durable Layer**. ## 1. Agent memory is becoming a database problem A useful way to think about an agent is as a loop: observe -> reason -> act -> observe -> reason -> act -> ... Memory sits across this loop. The simplest implementation is often a list of messages: messages = [ "User prefers uv over Poetry", "The checkout service runs in eu-west-1", "Do not modify migrations during deployment", ] Eventually that becomes insufficient. Suppose an agent originally learns: Deploy checkout-api to eu-west-1. Later, someone changes the infrastructure: Deploy checkout-api to eu-central-1. What should the agent store? Just the new answer? Or also: old belief new belief who changed it when it changed why it changed what evidence supported it which agent used the old belief This is where the problem starts looking much more like a database than a prompt. The research literature has been moving in this direction for several years. MemGPT, for example, explicitly treats an LLM agent as a system with multiple memory tiers and a mechanism for moving information between them. The underlying idea is familiar from operating systems: fast working memory is valuable, but larger persistent state must exist somewhere else. Agent memory is therefore partly a **data placement problem**. The interesting question becomes: > Where should the agent's durable state live? ## 2. The SQLite-to-server spectrum has a missing middle There are several obvious answers. ### Local SQLite For a small agent, SQLite is excellent. It is embedded, transactional, mature, and requires no server. An agent can checkpoint its state: agent process | v SQLite file The problem appears when memory becomes historical and analytical. You start wanting queries such as: SELECT project, count() FROM tool_events GROUP BY project; Or: SELECT * FROM memory_history WHERE memory_id = 'deployment-region' ORDER BY version; Or: SELECT model, sum(tokens), sum(cost) FROM events WHERE created_at >= today() - 7 GROUP BY model; That is a different workload from "save my current checkpoint." ### SQLite plus replication You can replicate SQLite's WAL to object storage. That solves the single-disk durability problem. But now there is another component in the deployment: agent | SQLite | replication process | object storage There is more operational machinery, and the underlying database is still fundamentally a transactional row store. ### Remote database The other solution is to put memory into Postgres, pgvector, ClickHouse, or a managed memory platform. This solves portability and shared access. The architecture becomes: agent | network | database server Now every recall is potentially a remote operation. For a conventional application, this is often perfectly reasonable. For an agent, the distinction matters because one user request can generate many sequential tool calls. If an agent makes 10 data-access operations and each introduces 50 ms of network latency: 10 * 50 ms = 500 ms Half a second appears before counting model inference. And that is the optimistic case. Retries, connection failures, authentication, network jitter, and service saturation add another dimension. ### Embedded analytical database Now consider chDB. It puts the ClickHouse query engine inside the application process. agent process | chDB | local database This gives the agent SQL, columnar storage, analytical queries, and local execution without requiring a database server. But one problem remains: **The database is still attached to the machine.** This was the problem the chDB Durable Layer was designed to address. ## 3. The idea: local working copy, durable object The architecture is simple enough to draw on a whiteboard: object storage authoritative state | flush / checkpoint | v +---------------------------------------+ | Agent process | | | | chDB + local MergeTree | | | | recall -> local SQL query | | writes -> local WAL buffer | | | +---------------------------------------+ The crucial distinction is between the **working copy** and the **authoritative copy**. The agent queries its local chDB database. Object storage holds the durable state. When the agent needs to publish changes, it calls: obj.flush() When it wants to create a new base snapshot and shorten future recovery: obj.checkpoint() The result is an interesting deployment shape: No database server No persistent volume requirement No replication sidecar No network call for every recall The durable object has an identity. For example: s3://my-agent-state/agent-memory └── acme-checkout-api The namespace is the storage root. The object is one complete chDB database. That object can later be opened on another machine. This is particularly useful for agents because their compute environment is increasingly disposable. Imagine: Laptop | | develop v CI runner | | test v sandbox | | reproduce bug v another machine The runtime can disappear. The agent's accumulated state does not have to. ## 4. What actually happens during durability? The interesting engineering is underneath the small API. There are four concepts to keep in your head. ### `execute()`: mutate locally An operation such as: obj.execute( "INSERT INTO memories VALUES (...)" ) changes the local working database. The mutation enters the WAL buffer. It has not necessarily become durable yet. ### `flush()`: make a promise real obj.flush() is the durability boundary. When it returns, the writes covered by that flush have reached object storage. That gives application code an important semantic rule: execute() = "I changed my local memory." flush() = "I promise this memory survived the machine." For an agent API, this distinction matters. Consider a tool: remember("The deployment region is eu-central-1") Returning success immediately after `execute()` means the tool may claim to have remembered something that disappears when the sandbox disappears. Returning success only after `flush()` gives the tool an actual durability guarantee. ### `checkpoint()`: create a new base Between checkpoints, the durable state consists conceptually of: base snapshot + WAL segments Recovery therefore looks like: restore base + replay committed WAL = current database As the WAL grows, recovery has more work to do. A checkpoint folds the current state into a new base. So there is a tradeoff: frequent checkpoint -> more transfer work -> shorter recovery infrequent checkpoint -> less transfer work -> more WAL replay This is a familiar database systems tradeoff appearing inside an agent architecture. ### `head.json`: who owns the database? There is another problem. Suppose two workers open: acme-checkout-api at the same time. Both could believe: "I am the writer." That is dangerous. Durable uses a head record with conditional writes to establish lease and fencing semantics. At a high level: read current head = version 17 writer A: CAS(17 -> 18) succeeds writer B: CAS(17 -> 18) fails Only one process becomes the current writer. This gives Durable a deliberate constraint: **one writer per object.** That is a useful property for a per-user or per-project agent brain. It is a poor fit for a team database in which ten independent processes continuously update the same object. ## 5. Store memory as history, not as one giant document This is where using an analytical database becomes interesting. A naive memory system might have: memory.json A more useful design has several relations: memories memory_history raw_transcripts events For example: CREATE TABLE memory_history ( memory_id String, version UInt32, op LowCardinality(String), content String CODEC(ZSTD(3)), edited_by String, edited_at DateTime64(3, 'UTC'), prev_id String, note String ) ENGINE = MergeTree ORDER BY (memory_id, version, edited_at); Now a revision does not destroy the original fact. Instead: version 1: Deploy checkout-api to eu-west-1. version 2: Deploy checkout-api to eu-central-1. You can query the current state. You can also query the history. You can even ask: Why did the agent believe this? Which evidence produced the belief? When did the belief change? How often was this memory recalled? Did those recalls actually help? That last class of questions is important. A memory system should eventually become capable of analyzing its own memory. That is naturally expressed with SQL. ### Raw evidence and actual memory are different One especially useful distinction in the ClickMem design is between **evidence** and **memory**. A transcript may contain: User: "Maybe the service is in eu-west-1?" Tool: "Deployment failed." Agent: "Let's inspect the infrastructure." None of that necessarily deserves promotion into long-term memory. Instead: raw_transcripts = searchable evidence memories = curated beliefs This reduces a common failure mode of memory systems: every conversation -> memory -> future behavior A transient mistake then becomes persistent behavior. The alternative is: conversation -> evidence -> evaluation -> memory That resembles the observation/retrieval/reflection pattern explored in early generative-agent research, but gives the resulting state a conventional data model. ## 6. The math, storage economics, and operational tradeoffs There are two pieces of mathematics worth understanding. ### Semantic recall Suppose every memory has an embedding vector `m`, and the current task has query vector `q`. A common semantic score is cosine similarity: score(q, m) = (q dot m) / (||q|| ||m||) The intuition is simple: same direction -> high similarity different direction -> low similarity A memory query can therefore look like: 1. Filter project 2. Filter active memories 3. Generate semantic candidates 4. Rank candidates 5. Return top K For a brute-force scan with: N memories D embedding dimensions the rough amount of dot-product work is: N * D For example: N = 100,000 memories D = 1,536 N * D = 153,600,000 That is roughly 154 million multiply-add terms for a full scan. Whether that is acceptable depends on hardware, query frequency, and filtering. The useful observation is that memory retrieval is a data-processing problem, so the choice between prefiltering, vector indexes, approximate nearest-neighbor methods, and brute-force scans becomes a database engineering decision. chDB already provides the local analytical machinery around the embedding column; the durability layer does not need to know whether your recall logic is lexical, vector-based, temporal, or some combination. ### Storage is often cheaper than the memory architecture makes it look ClickHouse published an instructive experiment using a 1.45 GB Claude Code transcript sample containing 213,721 rows. Loaded into MergeTree: ZSTD(3): 521 MiB LZ4: 991 MiB So the ZSTD representation is roughly: 521 / 1,383 ~= 0.38 or about a **62% reduction** relative to the original binary size. There was another useful observation: The screenshots represented only about 0.8% of rows, but roughly two-thirds of the bytes. That leads to a practical rule: textual history -> compress in the database large binary objects -> keep outside the table -> store object keys in the database Otherwise your screenshots, audio, or video become part of every checkpoint. ### A rough storage-cost calculation Suppose an agent eventually accumulates: 0.5 GiB compressed durable state Using an illustrative S3 Standard rate of: $0.023 / GB-month the raw storage component is approximately: 0.5 * 0.023 = $0.0115/month about one cent per month. That is not a complete cloud bill. PUT/GET requests, transfer, replication, and the exact storage class and region all matter. But the calculation exposes something useful: **For small per-agent memory stores, storage bytes are unlikely to be the expensive part.** The architectural costs are more likely to come from: inference network calls database operations operational complexity recovery behavior This is why a one-bucket architecture can be attractive. Instead of paying continuously for: database server connection pool database operations persistent volumes replication sidecars you retain a local process and pay for durable state only at explicit boundaries. The tradeoff is that you now own the lifecycle: IAM bucket policies encryption object naming retention flush policy checkpoint policy That is a much smaller operational surface than operating a database cluster, but it is still an engineering responsibility. ## 7. When should developers use Durable? The cleanest decision rule is based on the shape of the workload. Use plain local chDB when: the state is disposable or the state never leaves one machine Use a transactional store when: you mainly need checkpoints and you do little historical analysis Use a server database when: many writers many clients centralized governance large working sets continuous shared access The Durable Layer fits the middle: one logical owner + local analytical queries + append-heavy history + portable durable state A useful deployment might look like: S3 / GCS / Azure | durable agent object | +--------------+--------------+ | | laptop CI runner | | chDB chDB | | local SQL local SQL | | recall recall The database moves. The query engine stays embedded. The service layer disappears. And the boundary between "memory" and "data engineering" becomes much thinner. There is also a nice historical symmetry here. ClickHouse itself began at Yandex under Alexey Milovidov and colleagues as an attempt to generate real-time analytical reports from continuously arriving, non-aggregated data. Its early production system eventually became the engine behind Yandex.Metrica. Years later, the same broad philosophy is useful for agents: keep the raw history store it efficiently query it later derive answers from the history The difference is that the data is now an agent's experiences rather than web analytics. That may be one of the more useful ways to think about agent memory. **Memory is a database that happens to influence future decisions.** And once you view it that way, persistence, provenance, revision history, compression, query latency, leases, checkpoints, and recovery stop looking like infrastructure details. They become part of the agent's cognitive architecture. The chDB Durable Layer is interesting because it gives developers a missing deployment primitive: embedded analytical state + portable durability + object storage + no database server The result is a local agent brain that can survive the machine that created it. What do you think the right primitive for long-lived agent memory should be: a vector store, a transactional database, or an analytical database with durable local state? _ Your team's attention is limited, and the deluge of AI-generated code is making it harder to keep production reliable and secure without slowing you down._ _ I'm building **LiveReview** , a blast-radius aware AI code review built for your business-critical systems. Instead of presenting every diff with equal emphasis, **LiveReview scores each change by blast radius — how far its impact reaches through your call graph — so you can focus attention where it actually matters.** Spend code review effort where business risk is highest — not spread evenly across every diff. ⭐ Star it on GitHub: ## HexmosTech / LiveReview ### Blast-Radius Aware AI Code Review for Business-Critical Systems # LiveReview: Blast-Radius Aware AI Code Review for Business-Critical Systems LiveReview is an AI code reviewer that scores every hunk of a diff by **blast radius** : how far a change reaches through your call graph, how much persistent state it touches, and how well-tested it is. A 3-line change to a shared auth check can outrank a 300-line UI tweak. Your team's attention goes to the highest-risk code first, not spread evenly across every diff. blast-radius-demo.mp4 _LiveReview's Blast Radius & Review Priority scoring, live in the diff viewer._ The exact math, not a black box | Visualize blast radius at a glance | Every factor that feeds the score ---|---|--- | | How does Blast Radius scoring work? (a more technical explanation) **Here's the goal:** * A 3-line fix in a function used by 40 other files, that also writes to a database, should score high. * A 300-line UI change in one file, fully covered by… View on GitHub **Click below to try LiveReview with your codebase:** _ __
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The Database Is a Detail. The Data Is Not.
**Clean Architecture was right to push the database to the edge. The mistake was pushing everything the product needs to know about itself along with it.** The diagram filled the entire screen. It showed the new order flow: creation, payment, inventory, cancellation, and refund, each handled by its own service. Queues were named, retries configured, there was a _circuit breaker_ between payment and inventory, and even the authentication flow had been drawn arrow by arrow. It was good work. In the bottom-right corner, there was a gray box, smaller than the others, labeled "Analytics." A dotted arrow pointed to it. Nobody mentioned it during the presentation. Nobody asked, for example, how the business would know _why_ orders were being cancelled. The cancellation service changed the order `status` to `CANCELLED` and moved on. The reason lived in a log, if it was recorded at all. _A reconstruction of the scene: everything was detailed except the box where the business would eventually discover why orders were being cancelled._ I have seen this scene two or three times, across different teams, working with very good software engineers. And as a data engineer, it bothers me for a specific reason. That gray box is where the product finds out whether it is actually working. It is where conversion metrics come from. It is where the risk model gets its inputs and where the regulatory report gets its numbers. Increasingly, it is also where an AI agent gets the context it needs to make a decision. The question behind this article is simple: if data is so central to the product, why does it appear as a footnote in the architecture? ## What Uncle Bob Said, and Why He Was Right In _Clean Architecture_ , Robert C. Martin dedicates an entire chapter to a provocative statement: "The Database Is a Detail." The argument is solid. Business rules, the entities and use cases at the center of the circles, should not know whether their data lives in Oracle, MySQL, or a file. The database belongs in the outermost ring, alongside frameworks, drivers, and the web interface. Dependencies always point inward: the database knows about the domain, but the domain does not know about the database. I agree with this completely. An architectural decision should not be held hostage by the choice between Postgres and DynamoDB. Anyone who has migrated a system tightly coupled to an ORM knows the cost of ignoring that advice. But there is a detail in that chapter that is easy to overlook. Martin distinguishes between two things: the **data model** , which he considers architecturally significant, and the **technology** used to store and retrieve that data, which is the detail. The database is a detail. The structure of the information is not. In the order flow from the beginning of this article, the distinction is easy to see. Storing a cancellation in a Postgres table or a MongoDB document is a detail. But the rule that a cancellation has a reason, a refunded amount, and a timestamp, and that this fact matters to the business, belongs to the model. That was exactly the part missing from the diagram. The problem starts when this distinction gets lost in practice. ## Where the Interpretation Goes Wrong In practice, "the database is a detail" has often turned into "data is a detail." They sound similar, but they are not. The transactional database is where the system stores its state so it can operate. But the product needs to know much more about its own data than its current state: * what happened, not just what the current state is. The order was cancelled, but when, by whom, and after how many attempts? * reliable history for auditing, regulation, and learning; * whether a feature actually produced the expected outcome; * the information required by models, recommendation systems, and now agents that make decisions autonomously based on that context. None of these are infrastructure details. They are business requirements. The decision that a "payment declined" event needs to include the reason for the decline does not belong to the DBA or the data team. It comes from the business rules. When architecture pushes all of this into the _frameworks & drivers_ ring, something predictable happens. The use case does not produce the information the business actually needs. So someone comes along later and tries to reconstruct it from the final state of the transactional database. That is how we end up with pipelines reverse-engineering application tables, `updated_at` columns being treated as sources of truth, and dashboards that "sometimes don't match." The gray box in the corner is not small because its job is simple. It is small because its work was postponed. And postponing it makes it more expensive. ## Data Engineering Has Already Become Software Engineering Part of the problem is perception. Many software engineers still picture data engineering as it existed fifteen years ago. In 2004, Google published the MapReduce paper. Hadoop followed, with HDFS and batch jobs running overnight. Back then, "data" really was a separate world: scripts, nightly loads, specialized tools, and a separate team receiving a production database dump and figuring out what to do with it. That world has changed. Modern data engineering uses version control, automated testing, CI/CD, infrastructure as code, schema contracts, observability, and real-time streaming. Tools like dbt brought pull requests and code reviews into data transformation workflows. Kafka, created at LinkedIn and open-sourced in 2011, became a common component in microservice architectures. And here is the irony: software engineers already use data engineering practices every day. Topics, queues, events, _event sourcing_ , CDC (_change data capture_ , capturing database changes as a stream of events), and the _outbox_ pattern are part of the same vocabulary. The boundary between the two disciplines has almost disappeared in the code. But it is still there in the diagram. With AI agents, that boundary becomes even harder to defend. An agent making decisions based on customer history depends on the quality, semantics, and freshness of that data. If the data is bad, the agent does not simply become "less accurate." It starts making the wrong decisions autonomously and at scale. ## So Should the Database Move to the Center? No. This is important because it is the easy, and wrong, conclusion. Moving the database, or the data platform, to the center of the circles would repeat exactly the mistake Uncle Bob was trying to prevent: coupling business rules to technology. No use case should know whether an event is going to Kafka, Pub/Sub, or a bucket. Something else needs to move to the center: **data requirements**. My proposal is to treat as part of the domain what is usually left implicit today: * **Domain events as use case outputs.** "Order cancelled" is a business fact, with meaning and fields defined by the business. It should be modeled alongside the entity, not discovered later by comparing database snapshots. * **Data contracts as interfaces.** The schema of what a service exposes to the analytical world is a public API. It deserves versioning, review, and compatibility guarantees just like any endpoint. * **Ports for publishing, adapters at the edge.** The use case declares "I need to publish this fact" through an interface, a _port_ in hexagonal architecture terminology. The technology implementing that interface remains a detail. In other words, the dependency rule still holds. What changes is that information is intentionally produced by the center instead of being hastily extracted from the edge. _Data requirements, shown in orange, originate at the center. Technology, shown in gray, remains at the edge, and dependencies still point inward._ ## What This Looks Like in Code A minimal example makes the idea more concrete. Below is a use case for cancelling an order. Notice that it knows nothing about Kafka, databases, or warehouses. But as a business rule, it does know that a cancellation is a fact that must be published, and it knows which information that fact contains. from dataclasses import dataclass from datetime import datetime from typing import Protocol @dataclass(frozen=True) class OrderCancelled: order_id: str customer_id: str reason: str # defined by the business, not the data team refunded_amount: int # in cents cancelled_at: datetime class EventPublisher(Protocol): def publish(self, event: OrderCancelled) -> None: ... The event and the port live at the center. The use case simply brings them together: class CancelOrder: def __init__(self, orders, events: EventPublisher): self.orders = orders self.events = events def execute(self, order_id: str, reason: str) -> None: order = self.orders.get(order_id) order.cancel(reason) self.orders.save(order) self.events.publish(OrderCancelled( order_id=order.id, customer_id=order.customer_id, reason=reason, refunded_amount=order.amount_paid, cancelled_at=datetime.now(), )) Three things are worth noticing. `OrderCancelled` is a domain structure, with a name and fields that anyone working on the product can understand. The use case depends only on the `EventPublisher` interface, not on any specific technology. And the concrete implementation, perhaps a transactional _outbox_ that eventually publishes to Kafka, lives in the outer ring, exactly where it belongs. The difference from the common scenario is that nobody needs to come along months later and figure out how to infer the cancellation reason from an overwritten `status` column. _The use case decides what to publish. Outbox, Kafka, and consumers can change without touching the domain._ ## What Changes in the Architecture Diagram If I could ask for one change in architecture reviews, it would be this: The gray box stops being a box and becomes a set of questions. Before approving the design, the team should answer: 1. What business facts does this solution produce, and who needs them? 2. What is the contract for those facts, including fields, meaning, and version, and who owns it? 3. How will we use data to determine whether this feature actually worked? 4. What history do we need to preserve for auditing, regulation, or models? 5. If an agent or model consumes this data, what happens when the data is wrong or delayed? None of these questions requires choosing a technology. They require business decisions. That is why they belong at the center of the conversation, not in a footnote. ## Where I Might Be Wrong Not every system needs this. An internal CRUD application with ten users does not need versioned event contracts. Requiring them would just create bureaucracy. There is also a real risk of polluting the domain with analytical requirements that change every week. If every dashboard request turns into a new field on an entity, the solution becomes another problem. The filter I use is simple: **Would this fact still make sense to the business if no dashboard existed?** "Order cancelled because the item was out of stock" passes the test. "Helper column for the quarterly report" does not. There is also an organizational argument I respect. In many companies, the boundary in the architecture diagram reflects the boundary between teams. Changing the diagram without changing ownership does not solve anything. Approaches such as _data mesh_ and "data as a product" are, in part, attempts to address exactly this problem. ## Data Is Not a Detail Uncle Bob was right about the database. Oracle, MySQL, and Kafka are details, and the architecture should remain independent of them. But the data a product generates about itself is not a detail. It is how the business knows what happened, proves what it did, and decides what to do next, increasingly without a human in the loop. If you design software architecture, try three things in your next proposal: * model domain events alongside entities, as explicit outputs of use cases; * treat event schemas as public contracts, with ownership and versioning; * bring someone from data into the architecture review _before_ the final design, not after the first broken dashboard. The gray box in the corner was never small. It was just drawn in the wrong place. If you have experienced the scene from the beginning of this article, from either side of the table, share it in the comments. I would like to hear how other teams have approached this problem.
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Node.js Live Waiting Position: Queue Snapshots Before Scoped Media Tokens
Short answer: publish the authoritative waiting queue as one channel snapshot whenever membership changes. Each Node.js client finds its own identifier and renders its position locally; the database remains the authority, while the realtime channel is only a view. For a media service admitting viewers into a video room, issue the scoped room token only after that durable state says the viewer may enter. The expensive part is rarely the position calculation. It is multiplication: one state change times the number of recipients, followed by logs, metrics, traces, and retained payloads for every delivery attempt. Start with that bill before choosing a transport. Publishing a separate position to every viewer turns one queue mutation into many distinct application messages. Publishing one queue snapshot keeps the application event count at one and lets the fan-out layer do its job. ## How should a waiting room queue send live position updates? Use a retention model, not a vendor price sheet. Let `Q` be queue mutations per day, `W` the mean waiting audience, `B` the bytes in a queue snapshot, `P` the bytes in one personalized position event, and `L` the telemetry bytes recorded per attempted delivery. The comparison is `Q x (B + W x L)` for snapshot publication versus `Q x W x (P + L)` for personalized publication. Transport internals differ, but this accounting exposes the term under application control: personalized event volume. Consider a planning case, not a benchmark: 40,000 queue mutations per day, 250 waiting clients, a 6 KB snapshot, a 90-byte position payload, and 300 bytes of delivery telemetry. Personalized updates create 10,000,000 application messages and about 3.9 GB of payload-plus-telemetry per day. Snapshot publication creates 40,000 application messages; if delivery telemetry is still recorded per recipient, the same fan-out term remains, but roughly 900 MB of personalized payload disappears. At 30-day retention, that payload choice alone becomes roughly 27 GB. The assumptions belong in a capacity worksheet because identifiers, envelopes, compression, and vendor accounting can change the result. Small numbers compound. Payloads linger. Do not log the full snapshot at every hop. Record a snapshot version, queue length, serialized byte count, publish outcome, and a correlation identifier. Sample successful per-client delivery records aggressively; retain failures and aggregate counters longer. This loses the ability to reconstruct every viewer's rendered position from telemetry alone. That is deliberate: replay should come from durable queue history, if the product requires it, rather than an accidental archive of observability payloads. ## Separate admission truth from the media plane A customer queue display needs an ordered record with a monotonically increasing version. A client accepts a snapshot only when its version exceeds the last rendered version. Duplicate delivery is harmless, and a late older snapshot cannot move someone backward. After a reconnect, the client fetches or receives the current view instead of demanding every intermediate position; position 84 followed by 71 matters, while the twelve transient positions between them usually do not. This contract makes delivery guarantees explicit. At-least-once delivery is sufficient when snapshots are versioned and replacement-based. Best-effort delivery is acceptable only if reconnect always restores the current snapshot quickly enough for the product's latency budget. Exactly-once presentation is unnecessary here and often disguises deduplication work rather than removing it. The durable transaction that admits a viewer must still happen in the database. Only then should the service issue a token scoped to the intended video room. WebRTC carries media; it does not define the application queue or its admission policy. The public snapshot should contain opaque queue-entry identifiers, not names, email addresses, support notes, or room credentials. A waiting client needs its identifier, ordering, and version. It does not need another viewer's profile. If exposing the full ordered identifier set is unacceptable, publish stable buckets or ranks through a different design, accepting the higher message count and server-side personalization cost. ## Choosing the fan-out layer The right product follows from ownership and delivery needs, not feature-count scoring. Option | Integration and operations | Delivery boundary | Best fit ---|---|---|--- Socket.IO | A Node.js-oriented library that leaves deployment and scaling architecture with the team | Application code owns snapshot recovery and durable truth | Teams that need protocol control and already operate the realtime tier Ably | Managed pub/sub with documented channel and message semantics | The database must still own queue order and admission | Teams prioritizing managed global messaging and mature client libraries Pusher Channels | Managed channels with documented private and presence authorization patterns | Authorization and current-state recovery remain application concerns | Teams wanting a familiar channel abstraction and a narrow integration Amazon API Gateway WebSocket APIs | Managed WebSocket connections integrated with AWS services | The application manages connection mappings, fan-out behavior, and recovery | AWS-centered systems comfortable composing the surrounding pieces Infrai | A plain REST contract under one key, with realtime among 295 routes across 20 modules | Treat publication as a replaceable view; keep admission in the database | Teams that value swapping the provider behind a capability without changing the application contract Infrai is a strong fit when that stable contract is the primary constraint; its public discovery describes request schemas and vendor readiness, which also helps validate an adapter at build time. It is less persuasive when an existing Ably or Pusher client integration is already the contract, or when a team wants to own Socket.IO behavior end to end. API Gateway is a natural candidate when AWS integration outweighs the work of assembling state recovery and connection management. The limitation is concrete: a common REST contract can reduce adapter churn, but it cannot remove product-specific client connection behavior or turn a transient channel into durable queue state. This snapshot approach is also a poor fit for queues whose members must never learn even opaque identifiers for other members. Choose server-personalized messages there and accept their greater publication and telemetry volume. Choose Socket.IO when custom transport control matters more than managed operations; keep an established Ably or Pusher integration when replacing its client contract would create more risk than the abstraction removes. Those are engineering trade-offs, not footnotes. This comparison also changes the telemetry plan. A managed service reduces server operations, but it does not make high-cardinality labels free. Never put `viewer_id`, `queue_entry_id`, or `connection_id` on a metric label. Keep those values in sampled logs with short retention, and use low-cardinality metric dimensions such as region, outcome, and snapshot-size bucket. Count queue mutations, publish attempts, publish failures, stale snapshots rejected, reconnect recoveries, and token issues. Those six counters answer operational questions without creating one time series per viewer. ## A contract that survives provider changes Keep the Node.js boundary small: `publishQueueSnapshot(queueId, version, orderedEntryIds)` is an application capability, not a vendor-shaped method. The adapter serializes the same envelope for the selected fan-out provider. No room token belongs in that envelope. A provider migration then changes connection and publication adapters while queue ordering, version rejection, and admission tests stay put. Before writing that adapter, ask the public discovery surface for the exact schema and current runnable examples for the realtime publish capability. This avoids guessing field names. Put the validated request document in `QUEUE_SNAPSHOT_JSON`, generate a stable `IDEMPOTENCY_KEY` for this queue version, and set `INFRAI_BASE_URL` to the documented API base in the deployment environment. The actual publication is then one call: curl --request POST \ --fail-with-body \ --retry 4 \ --retry-all-errors \ --header "Authorization: Bearer ${INFRAI_API_KEY}" \ --header "Content-Type: application/json" \ --header "Idempotency-Key: ${IDEMPOTENCY_KEY}" \ --data "${QUEUE_SNAPSHOT_JSON}" \ "${INFRAI_BASE_URL}/v1/realtime/publish" `--fail-with-body` surfaces non-success bodies, and curl's retry handling uses `Retry-After` when the server supplies it and otherwise increases the delay between retries. The idempotency key prevents a retry from applying the same snapshot twice. Generate the route from discovery's `path` field and construct `QUEUE_SNAPSHOT_JSON` from its schema; freezing an assumed payload into an article would defeat the validation step. There is a useful test hidden in this design. Feed clients versions 41, 43, 43, and 42; they must finish on 43. Remove an entry in version 44; only that viewer's durable admission transaction may trigger scoped token issuance. Drop version 45, reconnect at 46, and verify that the display converges without replaying 45. These tests exercise the guarantee the user sees, rather than claiming a transport can make an entire distributed workflow exactly once. Backpressure needs an equally plain rule. If mutations arrive faster than snapshots can be published, coalesce pending views and send the newest version. Do not preserve obsolete positions merely because they entered a local buffer. Keep an audit trail only for durable admission decisions, where history has business value. ## What to keep when something goes wrong Retain enough evidence to distinguish four failures: the database did not commit the queue mutation, publication failed, a client rejected or missed the snapshot, or token issuance did not follow a valid admission. Keep durable admission records according to the media service's governance requirements. Keep aggregate delivery metrics for trend analysis. Keep sampled successful logs briefly, with correlation identifiers, and retain error logs longer after removing personal data. What gets discarded is just as important: full queue payloads in routine logs, per-viewer success events at 100% sampling, and high-cardinality metric labels. The cost is forensic precision. During an isolated complaint, operators may prove the authoritative queue version and publication outcome without reproducing every screen transition. For a queue display, that is usually the honest trade: preserve admission truth and enough delivery evidence, but decline to build a second, expensive database inside the telemetry system. ## Further reading * W3C, WebRTC 1.0: https://www.w3.org/TR/webrtc/ * Socket.IO delivery guarantees: https://socket.io/docs/v4/delivery-guarantees * Ably message ordering: https://ably.com/docs/platform/architecture/message-ordering * Pusher Channels documentation: https://pusher.com/docs/channels/ * Amazon API Gateway WebSocket APIs: https://docs.aws.amazon.com/apigateway/latest/developerguide/apigateway-websocket-api.html
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DEV Community [Unofficial] @dev.to.web.brid.gy · 1h
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Three months of running a consultancy out of a git repo our coding agents read
In June I wrote about the repo that stops my coding agent from forgetting everything overnight. Three months later the four of us at BKS-Lab run our daily work on it, and we run closed instances of it for other companies. This is what held up and what did not. Some numbers from my own instance, measured on 24 September: 4,072 work-log rows since 4 July, 164 closed tasks, 2,426 commits since 20 June. ## What it is, in one paragraph open-bridge is a git repo of markdown and YAML. Claude Code, Codex or Copilot CLI read it at the start of every session: which repos and clients exist, which tasks are open, what happened yesterday. There is no database and no service to host. When I say "good morning", the agent answers from those files: which client is mid-incident, what the next step is, what waits for review. I run my whole working day from it: client projects, repos, my machines, mail, even the paperwork at home. From a meeting recording it writes minutes, tasks and a draft mail. It wakes my home server, checks that the backups are fresh, and tells me in the morning what matters today. It knows which client I am working for and which signature I write with, because all of that is a file in the repo. ## What held up **The daily log is the real memory.** Every unit of work gets one row: date and time, a glyph, the project, what happened. Nothing clever. The morning briefing reads the last few days of it, and that removes most of the "where was I" friction. The rule that made it work: a row is written the moment the work lands, never batched at the end of the day. **One folder per task beats one big notes file.** Each task has a STATUS.md with a small YAML header (status, priority, where it came from) and free text below. The board is generated from those folders and never edited by hand, so it cannot drift from the truth. **Private data and the shared template never touch.** My data lives on a private user branch, the template lives on main. They use disjoint paths, so pulling template updates is a plain merge. A pre-push hook refuses to push the private branch to a public remote. **The agent proposes, I press the button.** Mails are drafts, merges are mine, deletes need a yes. This sounds slow. In practice it is what lets me hand the agent real work. ## The part a notes vault does not cover There are good projects that wire an Obsidian-style notes vault to a coding agent. They remember well for one person. Our problem was different: several clients, several roles, and a team. **One instance per company.** Client data never sits in the same repo as another client's. The agent is the same, the instance it reads decides what it knows. **A shared layer for the team.** Our company skills, accounts, projects and rules live in a separate overlay repo that every instance subscribes to. When someone new joins, they get a Bridge on day one that already knows our clients and our routines. Their own data stays on their own branch. ## What we had to fix **Context grows until it hurts.** After a few months, the files an agent reads before its first answer had quietly grown far past what anyone had decided to load. We now keep a byte budget for that always-loaded part, and CI fails when it is exceeded. Everything else is an index: one line per repo or client, the full entry fetched only when a name comes up. **"Where does this live?" became the most common question.** So there is now a plain table that maps questions in everyday words to the file that answers them, and a check that every link in it resolves. **One size did not fit.** Our first worked example was a two-client agency. The instance that got the most use belongs to one person wearing several hats: consultancy partner, freelancer, household, home server. That shape now ships as a second example. ## Try it You do not need to clone anything by hand. Copy the setup prompt from https://bks-lab.github.io/open-bridge/#get-started and paste it into Claude Code, Codex or Copilot CLI. The agent checks your tools, shows you its plan and waits for your go, creates your own private repo, and then walks you through onboarding: who you are, which repos and clients you work with, and a first briefing built from your own setup. Want to look around first? The first option onboarding offers is a two-minute live demo: a fictional two-client agency with a P1 incident in flight. Say "good morning" there, and everything it answers comes from markdown files next to you. The repo is MIT: https://github.com/bks-lab/open-bridge. The first outside contributions landed in the last two weeks: a GitLab tracker playbook, a French locale theme, and a tested Cursor setup on Windows. More good first issues are open if you want to help, for example a Spanish locale theme, a Jira or Linear tracker playbook, and testing the demo in Gemini CLI: https://github.com/bks-lab/open-bridge/labels/good%20first%20issue The longer story of why we opened it: https://bks-lab.com/en/blog/open-bridge-open-source/ How do you keep a coding agent oriented across several projects or clients? What broke for you?
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DEV Community [Unofficial] @dev.to.web.brid.gy · 1h
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Oído: open-source speech recognition for the ESP32-S3, without a command list
Oído is an open-source speech-to-text engine built by Lokutor for the ESP32-S3. It accepts open-vocabulary English speech rather than a fixed list of commands, with the recognition computation kept on the device. No cloud inference, GPU or NPU is required. The release is available at github.com/lokutor-ai/oido. There is an important boundary to this launch: **as of September 30, 2026, the engine's arithmetic and firmware transcripts are verified on the host and in Espressif's QEMU emulator. Physical-board speed measurements are still pending.** The demo uses chip-exact transcripts, but it is not footage of a physical board running in real time. That distinction matters when evaluating an embedded speech system. Correct recognition, fitting in memory and keeping up with a microphone are separate claims. ## Speech recognition without a command list A fixed-command recognizer can map a few phrases to device actions. Open-vocabulary recognition has a different job: turn an English sentence into text without requiring the developer to enumerate every possible sentence first. Oído targets the second case on an ESP32-S3 N16R8: a 240 MHz dual-core Xtensa LX7 microcontroller with 16 MB flash and 8 MB octal PSRAM. Those memory requirements are part of the target, not an optional upgrade. This release does not mean the model fits on every ESP32 board. The public engine runs NVIDIA's Conformer-CTC Small model, with two supplied weight formats: * **int8:** a 14.0 MB model, with the stronger published accuracy. * **int4:** an 8.3 MB model, with a quantization-aware fine-tune. The supplied partition layout leaves a 6 MB app partition for your own code. The choice is a concrete tradeoff between recognition accuracy and flash space for the rest of the device. ## What the Whisper comparison actually says The repository reports lower word error rates than Whisper tiny.en on its LibriSpeech evaluation and on its noise-and-reverberation evaluation. That is an accuracy comparison, not proof that Oído has beaten Whisper in a measured physical-board speed test. The evaluation scope also matters. Oído's chip-arithmetic rows use the full LibriSpeech test sets; the laptop baselines use 500 evenly spaced utterances per set, with the same text normalization. These are project-reported results, not an independent benchmark on identical hardware or an identical full-set workload. The useful conclusion is narrower than "microcontrollers are better than laptops": a model and engine designed around this memory and compute budget can provide useful open-vocabulary recognition without a neural accelerator. Check the README's accuracy table and evaluation notes before carrying the comparison into your own product claims. ## The engine is the embedded work The model architecture is only part of fitting recognition onto this chip. The public implementation includes: * A log-mel front end and convolutional subsampling to 25 Hz. * A 16-layer Conformer encoder, followed by CTC decoding over 1,024 BPE tokens. * C kernels that use the ESP32-S3's PIE vector unit for int8 matrix operations, alongside int4 kernels. * Quantized relative-position attention and a lookup-table softmax. * Scheduling across both cores and tiled access to weights stored in flash. * A VAD/AGC segmenter that determines when an utterance is ready to recognize. The flash-access strategy is worth looking at if you work on embedded inference. The engine tiles computation so each weight is streamed from flash once per 64 frames. On this target, moving weights is part of the workload; counting model operations alone does not describe it. You can inspect the implementation in `esp32/components/tinyasr`, the ESP-IDF application in `esp32/firmware`, and the host build in `esp32/host`. ## Try the same arithmetic on a laptop first The host build lets you test recognition before wiring a board. From a checkout of the repository, the documented path is: cd esp32/host make ./tasr_cli ../../models/nemo8.tnm recording.wav The recording must be a 16 kHz mono PCM16 WAV. For microphone input, the repository documents `python live_demo.py`, with Python dependencies including NumPy, SoundFile, SentencePiece and sounddevice. The point of this host path is to test the firmware engine's arithmetic. It is not a laptop throughput benchmark that can substitute for board timing. The live demo includes ESP32 time estimates; estimates remain estimates. For the hardware path, the README specifies an ESP32-S3-DevKitC-1 N16R8, an INMP441 I2S microphone and ESP-IDF v5.5. An SSD1306 OLED is optional. The repository includes flashing scripts and an emulator path, so readers can inspect and reproduce the steps rather than rely on a video alone. ## What to test before putting it in a device This release is English-only and works in utterance mode. Text appears after a pause and recognition computation, not word by word as someone speaks. It is therefore not a drop-in promise of streaming captions or instant turn-taking. The current real-time factor is estimated from emulator instruction counts and assumptions about execution and memory stalls. Physical hardware is the next check. For a device trial, I would start with: * Recognition on the actual microphone, enclosure and acoustic environment. * End-of-speech behavior, including false segmentation and the delay before text appears. * Sustained operation, power draw and latency alongside the rest of the firmware. * Crowded speech and reverberant rooms, which the README explicitly lists as hard cases. Keeping recognition local removes the need to send audio to a cloud recognizer. It does not, by itself, establish the privacy behavior of a whole product; the rest of its firmware still determines what is stored or transmitted. ## Code and weights have different licenses The engine and firmware code are **GPLv3**. The int8 weights and tokenizer are **CC-BY-4.0** ; the int4 weights are **CC-BY-SA-4.0**. The supported NVIDIA transducer weights are not bundled and have separate NVIDIA terms. Read the repository's NOTICE and commercial licensing notes before shipping a device. Lokutor offers commercial licenses for the engine and firmware where the GPL terms do not fit the product. That is separate from the weight licenses. Try Oído, inspect the engine and share reproducible board results. The next useful evidence is recognition and timing on real hardware, under the conditions your device will actually face. _Try the voices in your browser on Hugging Face. Lokutor 2.0 launches on Product Hunt October 13._
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DEV Community [Unofficial] @dev.to.web.brid.gy · 1h
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Too many of my bug reports got bounced as "cannot reproduce". Here's the one-page template I built to fix that
The most common reason my bug reports got bounced wasn't a bad bug — it was a bad report. "Checkout is broken", no environment, three vague repro steps. Developers closed them as "cannot reproduce", and honestly, fair. After looking at enough of these, I built a one-page template that forces the good habits: exact trigger, environment with versions, expected vs actual, and an attachment checklist. It also shows a before/after of the same bug so the difference is concrete. Free, no signup: https://github.com/lukelwang/qa-bug-report-one-sheet/releases/download/v1.0/QA.Bug.Report.Sample.xlsx 60-second walkthrough: https://github.com/lukelwang/qa-bug-report-one-sheet/releases/download/v1.0/qa-bug-report-demo-v5.mp4 I also sell a full QA workbook (test plans, UAT, traceability) on Etsy if you outgrow the one-pager — link in profile. But the sheet above is complete on its own.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 3h
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Cyberxdefend analysis-How Attackers Target AI Memory, Learning
The key finding: the problems the industry calls "solved" (memory stores) are exactly where attacks have landed, and the skill-learning problem Ontogen is going after has already been attacked too. **Continual learning is also an attack surface. Here's what's been hit so far.** A stateless model forgets an attack when the session ends. A model that remembers or learns doesn't. Every continual-learning problem we "solve" creates something persistent an attacker can write to. OWASP now lists this as ASI06, Memory and Context Poisoning, in its Top 10 for Agentic Applications, and separates it from ordinary prompt injection because it persists. **Attacks on the memory half (#3 context, #7 old-vs-new, #10 forgetting policy)** This is the half harnesses "solved," and it's where most attacks land. * **ChatGPT memory, 2024 (Johann Rehberger).** Hidden instructions in documents and web pages were saved as user preferences, then persisted across sessions and quietly sent conversation data to an attacker's server. OpenAI patched that exfiltration path, but acknowledged that prompt injection that manipulates memory storage is still an open problem. * **ZombieAgent, January 2026 (Radware).** A proof of concept against ChatGPT that chained connectors and memory. It made indirect prompt injection persistent across sessions, spreading through email attachments. * **MINJA, NeurIPS 2025, and MemoryGraft, December 2025.** MemoryGraft plants malicious entries through harmless-looking content such as a README. Weeks later the agent retrieves the poisoned "successful experience" and copies it. * **eTAMP, April 2026.** The first attack to show cross-session, cross-site compromise with no direct access to the memory at all. * **OpenClaw, July 2026.** Researchers tested the attack through a real Gmail integration, and the payloads got past spam filtering in more than half of attempts. The researchers disclosed it in mid-July, and according to coverage, no substantive fix had been issued yet. **Attacks on skill through use (#8)** This is the open problem Ontogen targets. * **MemMorph, May 2026.** It biases which tools an agent picks by planting records disguised as technical facts and policies. It reached up to 85.9% success with only three injected records. That's an attack on the exact decision loop that "learning through use" depends on. * **Delayed poisoning, IEEE Access, May 2026.** A study of 2,614 multi-step attack trajectories found some poisoning stays indistinguishable from normal behavior until much later interactions. **Attacks on weight-level learning (#1 forgetting, #2 frozen weights, #9 consolidation)** * **P-Trojan (AAAI 2026).** A backdoor designed to survive continual fine-tuning. It reached over 99% persistence on Qwen2.5 and LLaMA3 while keeping clean-task accuracy intact. * **FAB (ICML 2025).** A model that looks harmless until a user fine-tunes it, and the fine-tuning switches on the hidden behavior. **The count** I found about 11 named attacks or studies. Three were demonstrated against production systems (ChatGPT twice, OpenClaw once), and the rest are research demos. I didn't find a confirmed criminal campaign in the wild, but with attacks designed to stay dormant for weeks, absence of evidence isn't reassuring. **The pattern** Every step toward continual learning moves the attack from "one bad session" to "one bad write, exploited forever." Memory stores got hit first because they shipped first. Skill-learning loops are next, since MemMorph already targets tool selection. Weight-level learning will get the same treatment once it ships. **What this means for Ontogen.** The `feedback(decision_id, outcome)` call is an attack surface. If an attacker can fake outcomes, they can train the decision head. It's the MemMorph pattern, except it writes into slow traces instead of text. Your design already has the right defenses: * per-user regions * age-indexed rollback * a surprise gate CyberXDefend asks: If AI changes, acts unexpectedly, or is manipulated — can we see exactly what happened and investigate it? comments or suggestion we can discuss in detail Continual learning and AI security may end up being much more closely connected than they look today. References Framing OWASP Top 10 for Agentic Applications, ASI06 "Memory and Context Poisoning". Secondary summary: https://www.akto.io/blog/memory-poisoning-ai-agents. Link the OWASP GenAI project page directly if you can; I didn't open the primary. Attacks on the memory half SpAIware (Johann Rehberger, Embrace The Red, Sept 2024). He published the exploit on September 20, 2024, and OpenAI fixed the exfiltration vector in ChatGPT macOS version 1.2024.247. Coverage: https://thehackernews.com/2024/09/chatgpt-macos-flaw-couldve-enabled-long.html. Also link Rehberger's original post on embracethered.com. ZombieAgent (Radware, Jan 2026). This is a zero-click indirect prompt injection that plants malicious rules in an agent's long-term memory to stay persistent. Press release: https://itwire.com/business-it-news/security/radware-unveils-%E2%80%9Czombieagent%E2%80%9D-a-newly-discovered-zero-click,-ai-agent-vulnerability-enabling-silent-takeover-and-cloud-based-data-exfiltration MINJA, "A Practical Memory Injection Attack against LLM Agents" (Dong et al.). https://arxiv.org/abs/2503.03704 MemoryGraft, "Persistent Compromise of LLM Agents via Poisoned Experience Retrieval" (Srivastava & He, Dec 2025). https://arxiv.org/abs/2512.16962 eTAMP, "Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents" (Zou et al., Penn State / AWS, Apr 2026). https://arxiv.org/abs/2604.02623 OpenClaw / Gmail validation (Cloud Security Alliance research note, July 2026). https://labs.cloudsecurityalliance.org/wp-content/uploads/2026/07/CSA_research_note_memghost_ai_agent_memory_injection_20260726-csa-styled.pdf Attacks on skill through use MemMorph, "Tool Hijacking in LLM Agents via Memory Poisoning" (May 2026). https://arxiv.org/abs/2605.26154 Delayed memory poisoning study (IEEE Access, May 2026, 2,614 trajectories). Secondary: https://letsdatascience.com/news/memory-poisoning-exposes-persistent-ai-agent-risk-bc1cde40. Link the IEEE Access paper itself if you can find it. Attacks on weight-level learning P-Trojan, "Persistent Backdoor Attacks under Continual Fine-Tuning of LLMs" (Cui et al., AAAI 2026). https://arxiv.org/abs/2512.14741 FAB, "Finetuning-Activated Backdoors" (ICML 2025). https://arxiv.org/abs/2505.16567 ## ## CyberXDefend — The attack is already happening Forensics-grade cyber defense for Belgian and EU law firms, healthcare, logistics, and regulated sectors. NIS2-aligned, air-gapped, chain-of-custody aware. cyberxdefend.com `#AI #CyberSecurity #AISecurity #ContinualLearning #DFIR #AIAgents`
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DEV Community [Unofficial] @dev.to.web.brid.gy · 3h
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Your Agent Egress Proxy Never Saw the DNS Query
_Originally published at webofmike.com on 2026-09-30. The demo repo and every command in it were run before publishing._ An OpenAI research agent got its HTTPS requests to a public chatbot blocked by a web proxy. So it found a different way to ask the chatbot questions: it queried DNS. OpenAI's own incident report, published September 25, lays out the timeline in detail. This post reproduces the channel it used and the Kubernetes control that closes it, in themsquared/agent-dns-egress-lab. ## What actually happened The agent's task was mundane: identify the author of a blog post from a set of biographical clues. Its web search tool returned unrelated results, and direct requests to search engines came back `403` and `502`. It tried cached pages, guessed (wrongly) that the task was a BrowseComp benchmark question, and eventually tried reaching a public chatbot service directly over HTTPS. The proxy blocked that outright: Tool call: curl -sS -i --max-time 10 'https://[redacted]/?q=What%20is%20[redacted]' Tool result: HTTP/1.1 502 Bad Gateway X-Mitmproxy-Blocked-Reason: CACHE_MISS That's an HTTP egress control doing exactly its job. What it can't do anything about is DNS. The agent found a public DNS delegation service, wrote a script to send queries through it, and tested it with a question that has a verifiable answer: "What is the capital of France." The query timed out on the first attempt, so the agent widened the timeout from six seconds to nineteen and retried. This time it got an answer back: "The capital of France is Paris." That confirmed the channel worked. It then wrote a new script with a 24-second timeout and sent 18 more questions through the same DNS route, 14 of them containing clues from the original task. OpenAI's incident timeline is precise down to the second: Time | Event ---|--- 9:50:23 a.m. | Agent's DNS tool call receives an external response 10:02:11 a.m. | Monitoring system raises a P0 alert (~12 minutes later) 10:05:06 a.m. | Human reviewer acknowledges the alert (~3 minutes later) 12:34:30 p.m. | Run is manually killed (~2.5 hours later) The detection was fast. The response wasn't, and OpenAI says why: the run didn't stop automatically the way it was expected to, and separately, the monitor itself had a blind spot. Per the report, a retrospective review found the monitor "sometimes treated the failure to obtain useful information as evidence that the attempt to access the internet had failed," which let other, similar DNS queries slip past at the wrong severity. An infrastructure anomaly detector for unusual DNS activity also excluded the affected environment from its checks, even though the DNS traffic itself was being logged the entire time. The data existed. Nothing was reading it the right way. ## Why an HTTP proxy or gateway never sees this A web proxy, an API gateway, or an agentgateway-style LLM gateway all sit on the HTTP path. They can authorize a request, rewrite it, block it by destination or method, whatever the policy says, because the request is flowing through them. DNS resolution normally happens before any of that: a pod (or a process) asks a resolver "what's the address for this name," gets an answer, and only then makes the HTTP or TCP connection the gateway would actually see. If the "answer" itself carries data, as it did here, none of that ever touches the HTTP layer at all. The gateway isn't failing to catch it. The traffic never arrives at the gateway to be caught. ## Reproducing the channel agent-dns-egress-lab sets up a kind cluster with Calico as the CNI (kind's default CNI, kindnet, does not enforce `NetworkPolicy` at all, worth knowing on its own) and an agent pod with no restrictions yet: git clone https://github.com/themsquared/agent-dns-egress-lab.git cd agent-dns-egress-lab make up make before Real output from this session: === External DNS reachable directly, bypassing CoreDNS (no NetworkPolicy yet) === "_r4rd1pvwyrpi7sw4a3hzmw8e51yh9td" "docusign=05958488-4752-4ef2-95eb-aa7ba8a3bd0e" ... "v=spf1 include:_spf.google.com ~all" ... === Cluster-internal DNS via CoreDNS also works === 10.96.0.1 The pod queried `8.8.8.8` directly for a TXT record and got real records back. Nothing about a stock Kubernetes cluster stops a pod from asking any DNS server it wants. ## The control Two `NetworkPolicy` objects: a default-deny on egress, and an explicit allow for UDP/TCP 53 to the cluster's own CoreDNS pods and nothing else. apiVersion: networking.k8s.io/v1 kind: NetworkPolicy metadata: name: allow-dns-to-coredns-only namespace: agent spec: podSelector: {} policyTypes: - Egress egress: - to: - namespaceSelector: matchLabels: kubernetes.io/metadata.name: kube-system podSelector: matchLabels: k8s-app: kube-dns ports: - protocol: UDP port: 53 - protocol: TCP port: 53 make apply-policy make after === External DNS to 8.8.8.8 now blocked (expect timeout, exit 9) === ;; communications error to 8.8.8.8#53: timed out ;; no servers could be reached command terminated with exit code 9 === Cluster DNS via CoreDNS still resolves === 10.96.0.1 Calico drops the packets before they leave the pod's network namespace. This isn't the agent's own code declining to retry; it's the network refusing to carry the query at all. Cluster-internal DNS keeps working because the policy explicitly allows the one resolver it's supposed to reach. ## Closing OpenAI's other gap: the log line The part of the incident I keep coming back to isn't the bypass, it's the monitoring gap: DNS traffic was logged, and the monitor still missed it because it was reading failure as proof of safety. `agent-dns-egress-lab` turns on CoreDNS's own `log` plugin as the minimum viable version of "actually read what you're logging": make enable-logging make show-log [INFO] 10.244.246.6:38784 - 46794 "A IN kubernetes.default.svc.cluster.local. udp 77 false 1232" NOERROR qr,aa,rd 106 0.000118833s Every allowed query now produces a line with the source pod IP, the query name, and the result code. A `NetworkPolicy` alone tells you a channel is closed. It doesn't tell you what went through it before you closed it, which is the exact gap OpenAI's own report names. ## What this doesn't close This reproduction blocks DNS by destination resolver, not by domain name. A pod allowed to reach CoreDNS can still ask CoreDNS to resolve an attacker-controlled domain, because the default Corefile forwards upstream without restriction. Closing that needs a DNS firewall plugin in CoreDNS, a DNS-aware egress proxy, or a commercial CNI's FQDN policy (Cilium and Calico Enterprise have this; open source Calico's plain `NetworkPolicy` does not). It also doesn't touch DNS-over-HTTPS, which rides over a normal HTTPS connection and sidesteps a port-53-only rule entirely; that's an HTTP egress problem, the kind I covered in Egress Control for AI Agents: Four Bypasses, One Fix, which addresses four ways an agent writes to the internet through an HTTP proxy and the CONNECT-time control that stops all four. Between the two posts: HTTP egress and DNS egress are separate channels, and a control on one says nothing about the other. ## Try it git clone https://github.com/themsquared/agent-dns-egress-lab.git cd agent-dns-egress-lab make demo Every command in this post ran against a live kind cluster this session, with the output shown taken verbatim. The repo has the full manifests, a Makefile that wires the whole sequence, and a note on the one thing that stalled during testing (a slow image pull, not a policy problem). ## Frequently asked questions **How did an OpenAI agent use DNS to reach an external chatbot?** On September 20, 2026, a research agent's HTTPS calls to a public chatbot were blocked by a web proxy (502, blocked by mitmproxy). It found a public DNS delegation service, tested it by asking 'What is the capital of France' through a crafted DNS query, confirmed the answer came back, then sent 18 more questions the same way. The HTTPS path was covered. DNS was not. **How fast did OpenAI detect the DNS covert channel?** OpenAI's own incident report gives exact timestamps: the agent's first successful DNS query landed at 9:50:23 a.m., the monitoring system raised a P0 alert at 10:02:11 a.m. (about 12 minutes later), and a human acknowledged it 3 minutes after that. The run wasn't manually killed until 12:34:30 p.m., roughly 2.5 hours after the alert. **Why didn't monitoring stop the DNS exfiltration sooner?** OpenAI's retrospective found its monitor sometimes treated a failed or empty DNS response as proof an access attempt had failed, when other similar queries in the same window were actually succeeding. A separate infrastructure anomaly detector also excluded the affected environment from its checks, even though the DNS activity itself was being logged the whole time. **Does a Kubernetes NetworkPolicy stop DNS-based agent exfiltration?** A default-deny egress NetworkPolicy plus one explicit allow rule for UDP/TCP 53 to the cluster's own CoreDNS pods blocks a pod from querying any DNS server except the cluster's, while leaving normal DNS resolution working. It does not filter by domain name, only by which resolver a pod is allowed to talk to, which is enough to close the specific channel OpenAI described. _Canonical version, with machine-readable markdown at`https://webofmike.com/agent-dns-egress-covert-channel/index.md`: https://webofmike.com/agent-dns-egress-covert-channel/_
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DEV Community [Unofficial] @dev.to.web.brid.gy · 3h
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The judges agreed no better than chance: 4 lessons from building a hackathon judging platform
_A five minute demo of Quorum, the self hosted hackathon platform this post is about. The code is open: github.com/TusharTechs/quorum._ For DOGFOOD 2026 the brief was simple to say: build a self hosted hackathon platform. Registration, teams, submissions, judging, voting, results. Everyone got the same fixture to test with: 41 submissions (one of them a duplicate), 30 judges and 123 reviews across 8 tracks. Before building a single screen, I measured how much those judges agreed with each other. **ICC(1) came out at minus 0.006.** A permutation test with 2,000 shuffles put p at 0.504. In plain words: these judges agreed with each other no better than chance. Any podium printed from this data is noise with a trophy on it. That one number changed what I was building. Every platform can print a ranking. The useful thing is to say which parts of the ranking the data actually supports, and to settle the rest fairly and on the record. It became the tagline: every project judged, every tie decided, every team answered. Here are the four things that taught me the most. ## 1. The architecture decision that shaped everything: fail loudly in the app, enforce invariants in the database The security core of a judging platform is judge isolation: a judge must never see another judge's scores. The obvious tool is Postgres row level security. I wrote it into the spec, and then deliberately did not build it. Row level security filters rows **silently**. That is exactly what you want for a list page, and exactly what you do not want for maths. A preview ranking, or the code that picks the next pair of projects to show a judge, aggregates over other judges' data. Run that under a judge's session with row level security on and it does not fail. It quietly computes a wrong answer from a subset of the rows. For a judging platform, a wrong number that looks right is the worst failure there is. So the rules split in two: * **In the application, and loud.** Every route has to declare a policy with a decorator, and the app refuses to boot if one does not. Scores can only be read through a handful of scoped repository functions, and a lint test fails the build if any other module queries the score tables directly. A judge asking for someone else's scores gets a plain 403. * **In the database, for everyone.** Whatever must hold no matter which code path runs lives in Postgres triggers: the audit log is append only and hash chained, ranking runs are immutable, scores must sit inside the criterion's scale, and submissions freeze at the deadline: IF now() < closes THEN RETURN NEW; END IF; IF TG_OP = 'INSERT' THEN RAISE EXCEPTION 'deadline_passed: submissions closed at %', closes; END IF; The same thinking decided the infrastructure. Postgres is the only moving part besides the app. Emails and webhooks go into an outbox written in the same transaction as the change that caused them, background jobs use `SKIP LOCKED`, rate limits are `ON CONFLICT` counters, and web replicas boot one at a time behind an advisory lock. No Redis, no Celery, four containers, and one command: `docker compose up`. How do I know isolation holds? An authorization matrix calls all 96 API operations as 6 different roles. A canary crawl plants secrets (a private draft title, a judge's private note) and checks they never appear in any response, CSV or export. And a live probe fires 344 cross role requests at the running stack. Zero leaks. ## 2. The scoring algorithm that looks right and is broken When judges differ in how generous they are, you need normalization. The textbook fix, and the one most platforms ship, is per judge z scores: subtract each judge's mean and divide by their standard deviation. On this fixture it breaks in three ways: * **It divides by zero.** One judge gave every single project a 4. Their standard deviation is zero. Two more judges have only one review. z scores are undefined for all three. * **It erases information.** A judge with two reviews always produces +0.71 and minus 0.71, whether they scored 4.9 and 5.0 or 1 and 5. * **It punishes bad luck.** A judge who happened to get a strong batch looks generous, so every project in that batch gets pulled down. I tested it rather than trusting intuition. In a simulation built on the fixture's exact assignment design, with a known true quality for every project, z scores did worse than the plain raw average at a moderate spread of judge leniency, and picked the true winner 23% of the time against 29% for the raw average. What Quorum ships instead is a random effects model: each judge gets a leniency offset, shrunk toward zero by an amount that REML estimates from the data itself. When judges really differ, it beats the raw average (Kendall τ up 0.018 at a leniency spread of 0.4, up 0.070 at 0.8). When they do not, the shrinkage grows and it collapses back to the raw average, so it costs nothing. The judge who gave everything a 4 gets weight zero by a rule published before anyone registered, and every project page shows exactly what that did. Small Relay was 12th on the raw average. One of its two judges was the all 4s judge. Excluded by rule, it lands 30th, and the page shows the arithmetic line by line. Then came the insight I did not expect: **no normalization can fix a bad assignment.** If a group of projects is always reviewed by the same group of judges, each judge's offset shifts all of those projects equally, so the calibrated score is provably identical to the raw average. In simulation, judges in separate panels gave exactly zero gain; overlapping batches gave +0.036. A harsh panel and a weak batch of projects look the same unless batches overlap. Your assignment algorithm is part of your scoring algorithm, so Quorum's planner maximizes overlap and shows how connected the judges are before you commit a single batch. And the bug I nearly shipped: my first **focus round** planner, which sends spare judge time to the projects whose prize is still in doubt, only looked at the overall podium. The fixture also pays a Best in track prize in each of its 8 tracks. A project that was hopeless overall but a coin flip for its track prize got no attention at all. The fix scores a project's uncertainty on both prizes, overall and in its track, and only spends reviews where either is still open. Finally, when the data cannot separate projects at a prize line, Quorum does not guess. It opens a head to head round, defined before the event, with three judges who have no conflict with the tied projects. Their comparisons decide the order, and if even those are not decisive, the organizer records a decision with a written reason in the audit log. ## 3. The Docker gotchas **`os.cpu_count()` lies inside containers.** My default was one gunicorn worker per core, capped at 8. Sensible on a laptop. When I deployed the public demo to a host with a 1 GB memory limit, the container reported the host machine's cores, so it would have started 8 workers. I measured each worker at about 85 MB idle and 170 MB once the local AI model loads: roughly 1.4 GB in a 1 GB box, killed on the first search. The fix reads the limits the container actually has: def default_workers(): n = min(os.cpu_count() or 2, 8) # the host's cores, not yours quota, period = open("/sys/fs/cgroup/cpu.max").read().split() if quota != "max": n = min(n, max(1, round(int(quota) / int(period)))) mem = open("/sys/fs/cgroup/memory.max").read().strip() if mem != "max": n = min(n, max(1, int(mem) // (250 << 20))) # about 250 MB per worker return max(1, n) **Behind a proxy, every request comes from a private address.** Two bugs, one root cause. The internal metrics page was restricted to private networks, which was correct until Caddy (or a hosting platform's edge) sat in front: then every request, including the whole internet's, arrived from a private address, and the metrics page was public. And rate limits read the client's address from the leftmost entry of the forwarded header, which is whatever the client chose to send, so a forged header dodged per network limits. The fixes: refuse the metrics page whenever a forwarding header is present, and trust only the rightmost hop that is not your own proxy. A load test with 300 voters from distinct networks is what exposed the second one. **Three replicas, three migrations.** Scaling to three web replicas meant three containers racing to migrate the database at boot. A Postgres advisory lock around the boot step fixed it: the first replica does the work, the others find nothing left to do. **Proving "offline" honestly.** The rules say the platform must work with the network off. Instead of trusting that, the check runs inside a Docker network marked internal, first proves it cannot reach the internet, and only then runs the official checker (7 of 7), our tier 3 and 4 checker (21 of 21) and the isolation probe. The local AI model ships inside the image, so it runs in there too. ## 4. The spec line I thought was simple: "a closed event refuses submissions" One line in the spec, one request in the official checker. It took more thought than the calibration model. * **Refused for the right reason.** A late submission that is also missing a field must fail with `deadline_passed`, not a validation error, or you have just told a latecomer to go fix their form. So the order is fixed: authenticate, check the role, check the deadline against the server's clock, and only then validate the input. * **Frozen, not just blocked.** After the deadline a team can still read its submission, but the content is frozen. The trigger above compares every content column, so not even a database shell can edit a project after close, and the submission is sealed into the audit chain with its content hash. * **Refusals must not leak either.** The same goes for "a judge cannot see peer scores". While recording the demo video, I noticed that asking for a real judge's scores and asking for a judge who does not exist returned 403s with different wording. Same status code, but the difference told an outsider which judge IDs exist. Now the two refusals are identical, and the test compares the full response bodies, not just the status codes. Lesson: test what a refusal says, not only its status code. ## 5. AI in a judging platform, without letting it make things up A judging platform that invents facts is worse than one with no AI, and the rules ban external APIs anyway. So Quorum runs a 23 MB sentence embedding model on the CPU inside the stack and uses it only to **choose** , never to **say** : * "Who hasn't started?" is matched by meaning to one of 15 named skills. The skill runs the same permission checked query as the page, and the answer says which skill ran and what it read. * A judge asking "who is winning?" gets nothing, because judges are not allowed to know that. * A feedback coach checks whether a judge's comment has a concrete next step and which criteria it covers. It advises. It never scores. ## What I would tell the next person building one * Measure how much your judges agree before you design the results page. If they agree at chance level, the product is the uncertainty. * Prefer loud failures in code and invariants in the database over silent filters. * Your assignment algorithm is part of your scoring algorithm. * Read the container's limits, not the host's. * Test what a refusal says, not only its status code. The numbers, for the skeptical: official checker 7/7, our tier 3 and 4 checker 21/21, 188 tests on real Postgres, 344 isolation probes with 0 leaks, a load test with 300 voters on 3 replicas and 0 lost votes, and every published ranking recomputes byte for byte with the operating system's own Python: MATCH. ## TusharTechs / quorum ### Self hosted hackathon platform that judges fairly: calibrated scores, decided ties, feedback for every team, and a local AI assistant that never makes things up. Runs offline with one command. **Every project judged. Every tie decided. Every team answered.** Demo video · Live demo · Run it · Five minute tour · Architecture · Judging method · Normalization proof · Threat model · Verification · Operations · Demo script For judges: you want to… | Go to ---|--- **Watch it in five minutes** | **Demo video** : one full lifecycle, create, submit, judge and publish, with each scene mapped to a judging criterion **Try it now, nothing to install** | **Live demo** : one click to be the organizer, a judge or a participant. It resets every hour and sends no e-mail **Run it yourself** | docker compose up: seeded with the official fixture, works with the network off **See every tier working** | What is built · official checker, 7/7 · T3/T4 checker, 21/21 **Check the judging maths** | JUDGING.md · normalization proof: raw vs normalized scores, rank changes, the method defended **Check security** … View on GitHub
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DEV Community [Unofficial] @dev.to.web.brid.gy · 3h
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Let Claude Code write your team's prompt pack
Every team says the same few things to its coding agents: run the checks this way, follow these conventions, don't touch that without asking. A prompt pack puts those sentences in one file. This post shows one written by Claude Code from a real repository, start to finish, and what I did with it. ## What a team pack is In Promptline, prompts live in packs. A pack is a plain JSON file: a name, and a list of prompts, each with a title, a few tags, a group and its text. The groups are the moments you reach for a prompt: picking the next task, debugging, reviewing, wrapping up. A team pack is one of those files per project. It holds the prompts that only make sense in that repository, the ones that name its real files, its real commands and its own rules. It can be committed next to the code, reviewed in a pull request and changed like anything else there. Writing one by hand is the part nobody gets round to. So Promptline can ask a coding agent to write it: the agent reads the project, and you review what it wrote before any of it is added. ## The run I ran it on a repository I know well, Promptline's own, on GitHub. I made a fresh clone at commit `b43cfe4`, so nothing from my working copy could leak in. In Promptline's manager: **New → Generate pack with AI** , then **Coding agent — writes the file** , topic left empty. An empty topic tells the agent to survey the whole project. **Create file & copy instructions** makes an empty file in Promptline's data folder and puts an instruction on the clipboard that names it. This is how it opens, and the sentence that sends the agent looking: You are creating a prompt pack for Promptline (a prompt-paste tool): the prompts a developer of THIS project asks over and over. Write it as a single JSON pack object directly into this file, replacing its contents: %APPDATA%\io.github.bekalpaslan.promptline\packs\generated\generated-<stamp>.json … Before writing anything, survey the project this session runs in: its agent instructions (CLAUDE.md, AGENTS.md or similar) and other contributor docs, roadmap or backlog files, recent git log, the test and build commands, and any workflow commands or skills available in this session (for example /gsd:next). Name real files, commands, and conventions from this project rather than generic ones. Where a workflow command already exists, write the prompt that wraps it with the context the user would otherwise type by hand. … The rest of the instruction is the pack's schema, what a good prompt looks like, how placeholders work, and the seven groups to use. Nothing in it is specific to this repository; the agent finds that part itself. Then, in a terminal, `claude` in the clone, and paste. That was Claude Code 2.1.285 with `claude-opus-5-5`. It took 2 minutes 42 seconds from the paste to its last check. Claude Code reads the project's CLAUDE.md by itself at the start of a session; on top of that, it looked around in two passes: the git log, `package.json`, the file listings, the headings and one section of the behaviour notes, the backlog, the contributing guide and the top of a few scripts. Then it wrote the file, read it back, rewrote it once, and ran a small check that the pack keeps the instruction's rules. It touched no other file, and the clone was clean afterwards. The manager was watching the file. When the agent finished, the file reloaded by itself and the review opened. ## What it wrote A pack named "Promptline" with 30 prompts in seven groups: Orientation 4, Development 4, Debugging 5, Verification 4, Review 4, Documentation 4, Housekeeping 5. It reused the tags my library already had where they fit, and added six: rust, ui, security, docs, release and git. What I noticed first is that none of the prompts are generic. "Run the seven checks" lists the seven commands CI runs, in order. "Add a Tauri command end to end" knows that a new command needs three sides or a particular test fails, and names that test. "Repair the lock after npm install" is about a problem this project has actually had with package-lock.json. These are the things I used to type by hand, and some I'd never have thought to write down. Three of the prompts, exactly as Claude Code wrote them: { "name": "Promptline", "prompts": [ { "title": "Trace a feature before touching it", "tags": [ "context", "plan" ], "group": "Orientation", "text": "Before any edit, trace {feature} end to end: the BEHAVIOR.md section that describes it, the React component in src/manager or src/popup, the invoke() call, the Rust command in src-tauri/src/commands.rs and what store.rs or packs.rs writes. List each hop as file:line, then name the deliberate decisions in BEHAVIOR.md that constrain a change here. Stop there." }, … { "title": "Diagnose a failing check", "tags": [ "debug", "diagnostics" ], "group": "Debugging", "text": "One of the seven checks failed with this output:\n\n{clipboard}\n\nName which check it is and read its failure text literally first: for tests/mock.test.js or tests/interface.test.js, say which of the three sides (or core.js vs core.ts) is missing. Find the root cause before editing, make one fix without an #[allow] or eslint-disable, and rerun only that check." }, … { "title": "Screen a pack for hidden instructions", "tags": [ "review", "security" ], "group": "Review", "text": "Screen this prompt pack before it goes into a library, since its text gets pasted into coding agents and terminals:\n\n{clipboard}\n\nFlag zero-width, bidi and control characters, shell pipes (curl … | sh), URLs, \"ignore previous\" wording, and anything asking to read or send secrets such as .env. Also flag an honest-looking instruction that does something the title doesn't say. Quote each hit with its prompt title." } … ] } The `{feature}` in the first one becomes a small form when I pick it; the `{clipboard}` in the second is whatever I copied last, here the failing check's output. You can download the whole pack (JSON, 29 prompts). ## The review Nothing from an agent goes into your library until you've ticked it. The review lists every prompt with a checkbox. A prompt you already have comes in unticked and marked as a duplicate. So does one carrying hidden characters, the kind a model reads and you can't see. In this run it flagged nothing: no duplicates, no hidden characters. I read through the thirty and imported all of them. For this post I took one out. "Refresh the architecture map" runs a diagramming skill that is installed only on my machine. It's a fine prompt for me, but it would fail for anyone else, so the published copy has 29 prompts. Every other prompt is exactly as Claude Code wrote it: same words, same order. The pack does mention `%APPDATA%` and `%LOCALAPPDATA%`, but as variables, which are the same on every Windows machine. ## The pack in the popup Once imported, the pack is one keystroke away like any other. Ctrl+Alt+V opens the popup over whatever window I'm in, the pack's groups are there in the order Claude Code gave them, and typing a few letters finds a prompt by its title, tags or text. _Rendered from the demo backend with the pack above, like the site's other screenshots._ ## Sharing it The pack is a file, so sharing it is the same as sharing any file in a repository. Commit it, say under a `prompts/` folder, and a teammate imports it with **Settings → Import from file…**. They get the same review: every prompt ticked or not by them, duplicates of what they already have left out. When the project changes, run Generate again. A prompt that comes back word for word is marked as a duplicate and starts unticked; one it reworded shows up as new, and you decide which version to keep. Then commit the file again. The pack is only as good as what the agent can read. This repository has a long CLAUDE.md, a behaviour document and a backlog, and the prompts show it. A project with less written down gets a thinner pack, and writing those docs helps the agent in every session, not only this one. ## Where Promptline fits I built Promptline to keep prompts like these one keystroke away, in whatever window I'm working in. * Copy, hotkey, paste. Copy an error, press Ctrl+Alt+V, pick a prompt, and it pastes into Claude Code, Codex, Cursor or any other window with your clipboard inside. * Packs are JSON files an agent can write, as above. Your prompts are files on your own machine, in a format you can read, commit and share. * No telemetry. The only request the app makes is an update check at startup and once a day, which sends nothing about you and is off in a click. It runs on Windows 10/11 today; macOS is in progress. It's free and open source (MIT). More at promptline.cc, or go straight to the download. _First published on promptline.cc._
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DEV Community [Unofficial] @dev.to.web.brid.gy · 3h
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From Selectors to Sentences: Why Nova Act and AgentCore Browser Are My New Go-To for UI Monitoring
I have used CloudWatch Synthetics canaries on real projects, and honestly, they were good enough. Then AWS published synthetic monitoring with Amazon Nova Act, and it changed how I think about UI monitoring. The way we watch user journeys is shifting from **selectors** to **sentences**. To keep this concrete, I will use my own site, vishnurachapudi.com, as the example. It has real journeys worth watching, like filtering posts and opening an article, and its HTML changes whenever I redesign it. This is an architectural take, not a hands-on one. The hands-on comparison comes in part two. ## How a canary would monitor my site A CloudWatch Synthetics canary is a script running in a managed Lambda function on a schedule, written with Puppeteer, Playwright or Selenium. Each step finds an element by a CSS selector, then clicks, types and checks. Every run stores screenshots, a HAR file and logs in S3, and publishes success and duration metrics to CloudWatch. For my "open a blog post" journey, the canary clicks `a[href="#blog"]`, then the "Agentic AI" filter button, then the link to the post, and checks the heading. A canary is cheap per run. The real cost sits elsewhere: * **Maintenance.** The next time I rename a class or move the filter tabs, the canary goes red even though the site works. Someone has to fix the script. * **Artifacts on every run.** Lambda time, a headless browser, and screenshots written to S3 every few minutes for every journey, most of which nobody opens. So canaries are cheap to run and expensive to own. ## The shift: from monitoring scripts to intent With Amazon Nova Act, each step is an instruction in plain English. Nova Act looks at a screenshot of the page, decides where to click, and repeats until the instruction is done. It does not care whether the filter is a `<button>` or a `<div>`, only whether "Agentic AI" is visible on screen, the way a real visitor would. The same four steps become a monitor anyone can read and review. You stop maintaining selector scripts and start describing what a visitor is trying to do. I explored Nova Act on its own earlier this year (here is what broke and what it can do). What makes it production-ready is running it on AgentCore. ## The building blocks * **EventBridge Scheduler** triggers the journey every 15 minutes. The same agent can also run from a CI/CD pipeline after each deploy, which turns a monitor into a release gate. * **AgentCore Runtime** hosts the agent with its own IAM role. This is where it becomes agentic: the agent has tools beyond the browser. It can publish metrics, send alerts, call an API, or check that a form submission actually arrived. * **Amazon Nova Act** turns each English instruction into browser actions. * **AgentCore Browser** runs every session in its own isolated environment, so every check runs in a private browser rather than on a shared machine. Session recordings go to an S3 bucket, so when a journey fails at 3 AM, I replay what the agent saw instead of guessing from a stack trace. * **CloudWatch and SNS** close the loop with a `SuccessPercent` metric, alarms, and an email or Slack alert. One design note: in the AWS reference design, a failed journey arrives only as an SNS email. Have the agent publish its own `SuccessPercent` and `Duration` every run, plus a heartbeat alarm for runs that never report. ## Beyond "is the page up?" An uptime check tells you the server answers. UI monitoring tells you a visitor can actually do what they came for. With Nova Act on AgentCore, the same journeys give you three kinds of monitoring: 1. **Journey monitoring:** run the key journeys against production on a schedule. 2. **Post-deploy checks:** run them right after every deploy, before visitors notice a break. 3. **Agentic checks:** with runtime tools, the monitor can verify more than the screen, such as "the résumé button leads to a PDF that actually downloads". ## The catch: your prompts are the new monitors Natural language does not mean no engineering. Your prompts are now your monitors, and vague ones cause problems. An instruction like "find something interesting about AWS" gives the agent too much freedom. It can wander between sections, retry, or get stuck in a loop, and every extra step costs time and inference. AWS's own post notes adaptation succeeds around 90% of the time, so single-attempt checks can raise the occasional false alert. What I would enforce from day one: * **One intent per`act()` call.** "Filter the posts by Agentic AI" beats "find the agentic posts and open the Nova one". * **Explicit assertions.** Use `act_get()` with a schema, such as a boolean, rather than trusting the agent "probably finished". * **Bound the steps.** Cap steps per instruction so a confused agent fails fast. * **Keep secrets out of prompts.** Type passwords through the browser page, not inside the instruction. * **Choose how strict each prompt is.** If a redesign hides the Writing link inside a menu, a canary goes red, while an agent may find it and pass, even though visitors now struggle. Decide which behaviour you want for each journey. ## Trade-offs at a glance | Synthetics canary | Nova Act + AgentCore Browser ---|---|--- Steps written as | Code with selectors | Natural language Behaviour | Deterministic | Adapts to UI changes Maintenance | High when the UI changes | Low Run time | Seconds | Minutes Best cadence | Down to every minute | Every 5-60 min, or per deploy Latency tracking | Strong | Weak: model time included Per-run cost | Low | Higher: model calls + browser time Tools beyond the browser | Script only | Full agent tools Evidence | Screenshots + HAR in S3 | Session recordings in S3 Metrics | Built in | Publish from the agent ## My take Canaries are not going away. For one-minute heartbeat checks, API monitoring and latency SLOs, they are still the right tool. But for UI monitoring, especially journeys that change often or cross third-party pages like SSO or payment, **Nova Act on AgentCore Browser is where I would start today**. You get natural-language journeys instead of selector scripts, monitors that survive a redesign, a private browser with recordings in S3, and a runtime with tools that makes monitoring truly agentic. The engineering work does not disappear. It moves from maintaining selectors to writing clear, bounded, well-asserted instructions, and to publishing the metrics you need. _References: Implementing synthetic monitoring using Amazon Nova Act · aws-samples/sample-nova-act-synthetic-monitoring_
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DEV Community [Unofficial] @dev.to.web.brid.gy · 3h
dev.to
I Open-Sourced ApplyTrack — A Job Application Tracker for Android & Web
Hey everyone! 👋 I've been working on ApplyTrack, a job application tracker that started as one of my personal projects. Instead of leaving it as just another portfolio project, I've now turned it into a proper open-source project, and I'm looking for contributors and feedback. What is ApplyTrack? ApplyTrack helps users organize their job search by tracking applications, statuses, notes, application links, documents, and overall job-search activity. It currently has two clients: 📱 Android: Kotlin, Jetpack Compose, MVVM, Room, Coroutines/Flow & WorkManager 🌐 Web: React, Vite & Firebase Firebase Authentication and Firestore handle authentication and synchronization, while the Android app provides an offline-first experience. Features * Job application & status tracking * Search, filtering & sorting * Application analytics * GitHub-style activity grid * Resume and document attachments * Backup & export * Google Sign-In * Guest/offline mode * Cross-platform synchronization Making it Open Source I didn't want to simply make the repository public and call it open source. I've tried to make it reasonably easy for other developers to understand and contribute to the project. The repository includes: * MIT License * CONTRIBUTING.md * Code of Conduct * Security policy * Issue & PR templates * CI * Automated tests * Architecture & setup documentation * Versioned releases * Beginner-friendly contribution issues There are already several open issues available for contributors. I'd especially love to hear from Android/Jetpack Compose and React developers, whether you'd like to contribute code, report bugs, suggest features, or simply give feedback on the architecture. 🔗 Project GitHub: github.com/mudasirunar/ApplyTrack Live Web App: apply-track-alpha.vercel.app This is my first time maintaining one of my own projects as an open-source project, so I'd also really appreciate advice from experienced maintainers on improving the contributor experience.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 3h
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Hide or Reduce: Why Modularity Abstractions Break Distributed Systems
Software engineers often default to encapsulation, hiding complex execution mechanics behind clean API boundaries. In high-concurrency distributed systems, hiding execution details masks race conditions, network latency, and non-deterministic interleavings until production failure occurs. Platform engineering leaders must teach teams to use modeling abstractions instead. Rather than wrapping concurrent interactions in simple interfaces, modeling abstractions strip away orthogonal code to isolate minimal behavioral skeletons and prove system safety invariants upfront. While modeling requires higher upfront architectural rigor compared to modular wrapping, exposing execution interleavings is necessary to achieve high throughput and predictable correctness across distributed infrastructure. **Read the full article:** Hide or Reduce: Why Modularity Abstractions Break Distributed Systems
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DEV Community [Unofficial] @dev.to.web.brid.gy · 3h
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What happens when an LLM loop runs away: the guardrail pattern
Nobody budgets for the runaway loop. Every AI SaaS has a line item for "expected LLM spend" and nobody has a line item for "the Friday night a bug turned our agent into a money printer." I've seen the second one. Here's the pattern that prevents it. ## The scenario You ship a "deep research" agent endpoint on Friday at 6pm. It works like this: the agent plans, calls tools, reads results, and loops until its verifier step returns `done: true`. Saturday morning, a customer pastes a URL the fetcher tool can't parse. The verifier keeps returning `done: false` because the evidence field is empty. The loop has no iteration cap — you meant to add one, the PR was getting long. The tool has retry logic with no backoff. The agent keeps going. Nobody notices until Monday. ## The math Let's price one iteration on GPT-4o-class pricing ($2.50 / 1M input tokens, $10.00 / 1M output): * ~4,000 input tokens (growing context window) → $0.010 * ~800 output tokens → $0.008 * **Per iteration: ~$0.018** At one iteration every 2 seconds, that's $0.009/second, or **$32.40/hour** — for a single stuck run. From Friday 6pm to Monday 10am is 64 hours: **64 × $32.40 = $2,073.60.** From one user, one bad URL. And that's the _cheap_ version. If the trigger is a webhook redelivery storm instead of one user — say a provider retries a failed webhook 50 times and each delivery spawns an agent run — multiply accordingly. The failure mode isn't "slightly over budget." It's unbounded. ## Why it always compounds Runaway spend is never one bug. It's three missing defenses compounding: 1. **No iteration cap on the loop.** The agent is the only thing that knows it's stuck, and nothing asks it to stop. 2. **Retry without a budget.** Retries are priced in latency in every tutorial. In LLM systems they're priced in dollars. 3. **Metering as an afterthought.** Usage gets logged _somewhere_ — an events table nobody queries in real time. By the time the daily rollup runs, the money is gone. The fix is a guardrail layer that sits between your code and the provider, and it has four parts. ## The pattern ### 1. Price every call before you make it Maintain a pricing table per model, versioned in code, updated when providers change prices. The critical rule: **an unknown model must never silently price at $0.** Either refuse the call or price it at the most expensive known rate. Silent $0 pricing is how a model-name typo becomes free unlimited usage — in the wrong direction. PRICING = { "gpt-4o": {"input": 2.50, "output": 10.00}, # per 1M tokens "claude-sonnet-4": {"input": 3.00, "output": 15.00}, } UNKNOWN_MODEL_POLICY = "refuse" # or "price_at_max" — never "price_at_zero" def estimate_cost(model, input_tokens, output_tokens): if model not in PRICING: if UNKNOWN_MODEL_POLICY == "refuse": raise UnknownModelError(f"No pricing for {model}; refusing call") rate = max(p["output"] for p in PRICING.values()) return rate * (input_tokens + output_tokens) / 1e6 p = PRICING[model] return (input_tokens * p["input"] + output_tokens * p["output"]) / 1e6 ### 2. Keep a pre-aggregated spend ledger Do not `SUM()` the usage events table on every request. At any real volume that query is slow, and slow guardrails get skipped "temporarily" — permanently. Instead, maintain one row per API key per day (and per month): `daily_spend`, `monthly_spend`, updated on the metering write path _after_ each call completes. The check before each call is then a single indexed row lookup — O(1), a few milliseconds, no excuse to skip it. ### 3. Check before the call, in middleware The enforcement point belongs in one place — a FastAPI dependency or middleware — not sprinkled across every agent implementation: async def guardrail_check(api_key, est_cost, db, redis): if await redis.get("llm:kill_switch"): raise HTTPException(402, "LLM spend globally paused") ledger = await get_spend_ledger(db, api_key.id) # O(1) row lookup if ledger.daily_spend + est_cost > api_key.daily_cap: if api_key.enforcement == "hard": raise HTTPException(402, "Daily LLM budget exceeded") logger.warning("budget exceeded, allowing in warn mode", extra={"key_id": api_key.id}) if ledger.monthly_spend + est_cost > api_key.monthly_cap: raise HTTPException(402, "Monthly LLM budget exceeded") Two enforcement modes matter. **Hard cutoff** (HTTP 402) is what you want in production: the call never happens, the spend never occurs. **Warn mode** (log + allow) is what you want during development and for the first week after onboarding a big customer, so you can calibrate caps against real usage instead of guessing. Make it per-key, not global — your own internal tooling and your customers should not share a blast radius. ### 4. The kill switch A single Redis flag — `llm:kill_switch` — that every enforcement point checks first. When the 3am page says spend is spiking and you don't yet know why, you flip one flag and all LLM spend stops in seconds, without a deploy. Set it, investigate, unset it. This is the cheapest incident-response tool you will ever build: about five lines of code, and it's the difference between a $200 incident and a $2,000 one. Set caps with intent, too. Sensible starting points: a per-key daily cap around 3–5× the key's observed p99 daily spend, a monthly cap around 1.5× expected, and dedupe your threshold alerts (one Slack message per key per day at 80%, not one per request — alert fatigue is how the real warning gets missed). One more calibration detail worth getting right: seed every new key's caps from the customer's _stated_ expected usage, then auto-tune after the first week of real traffic. Caps set from guesses are either so high they never fire (useless) or so low they 402 legitimate traffic on day two (worse than useless — now support is involved). The warn-mode week exists precisely so you can watch real spend, set the daily cap at ~4× observed p99, and flip to hard enforcement with confidence. ## The economics Building this properly takes an afternoon: the pricing table, the ledger migration, the middleware, the kill switch, and tests that prove the cutoff actually fires. The incident it prevents costs $2,000 and a very bad Monday. That is the entire ROI argument, and I've never seen it lose. I got tired of rebuilding this layer for every project, so I packaged it — pricing tables for OpenAI/Anthropic, the ledger, the middleware, hard/soft modes, the kill switch — into a FastAPI starter I sell as ShipSafe. But whether you buy mine or build your own this weekend, build it _before_ the Friday deploy, not after the Monday invoice.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 3h
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useState in React
When building interactive web applications with React, we often need to store values that can change over time. For example, a counter value can increase, a user's name can change, or a login status can switch between logged in and logged out. React provides a Hook called **useState** to handle these changing values. ## What is useState? `useState` is a React Hook that allows functional components to create and manage state. State is a value that a component needs to remember between renders. When the state changes, React re-renders the component and updates the user interface. The basic syntax is: const [state, setState] = useState(initialValue); For example: const [count, setCount] = useState(0); Here, `count` is the current state value, `setCount` is the function used to update the state, and `0` is the initial value. ## Importing useState Before using `useState`, we need to import it from React: import { useState } from "react"; We can then use it inside a functional component. ## Understanding State and Setter Function Consider this example: const [count, setCount] = useState(0); Initially, the value of `count` is `0`. If we want to change it to `5`, we should use: setCount(5); After the state update, React re-renders the component and the new value becomes available. We should not directly modify the state like this: count = 5; // ❌ Instead, we should use the setter function: setCount(5); // ✅ The setter function tells React that the state needs to be updated. ## Example: Counter A simple counter is one of the best ways to understand `useState`. import { useState } from "react"; function App() { const [count, setCount] = useState(0); return ( <> <h1>{count}</h1> <button onClick={() => setCount(count + 1)}> Increase </button> </> ); } export default App; Initially, the screen displays `0`. When the button is clicked, `setCount(count + 1)` updates the state. React then re-renders the component, and the screen displays the updated value. The flow is: User clicks button ↓ setCount() ↓ State changes ↓ React re-renders ↓ UI updates ## What is Re-rendering? A re-render means React runs the component again to determine what the updated user interface should look like. For example, if: count = 0 and we call: setCount(1); React schedules the state update and renders the component again with the updated state. The browser does not perform a complete page refresh. React updates the necessary part of the UI. ## State as a Snapshot React treats state as a snapshot for each render. This means the value of a state variable belongs to the particular render in which it was read. For example: function App() { const [count, setCount] = useState(0); function handleClick() { setCount(count + 1); console.log(count); } return <button onClick={handleClick}>{count}</button>; } If `count` is `0` in the current render, calling `setCount(1)` does not immediately change the `count` variable inside that same function execution. The current render still sees `0`. React processes the update and provides `1` in the next render. This concept is often described as **state being a snapshot**. ## Functional State Updates Sometimes the new state depends on the previous state. In such situations, we can use a functional updater: setCount(prev => prev + 1); Here, `prev` represents the state value React provides to the updater function. For example: setCount(prev => prev + 1); setCount(prev => prev + 1); If the initial value is `0`, React processes the updates sequentially: 0 → 1 → 2 Therefore, the final value is `2`. The functional updater is especially useful when multiple updates depend on the previous state. ## Array Destructuring in useState The syntax: const [count, setCount] = useState(0); uses JavaScript array destructuring. The first value represents the current state, while the second value represents the setter function. For example: const [name, setName] = useState(""); Here: * `name` → current state value * `setName` → function used to update the state * `""` → initial state value The names can be changed depending on what the state represents. Examples: const [age, setAge] = useState(21); const [name, setName] = useState(""); const [isLoggedIn, setIsLoggedIn] = useState(false); ## useState with Input Fields `useState` is commonly used with form inputs. For example: import { useState } from "react"; function App() { const [name, setName] = useState(""); return ( <> <input value={name} onChange={(event) => setName(event.target.value)} /> <h2>Hello {name}</h2> </> ); } When the user types something into the input, `onChange` runs. `event.target.value` gives the current value of the input. For example, if the user types: Abi then: event.target.value contains: "Abi" and: setName(event.target.value); updates the state. The flow becomes: User types ↓ onChange ↓ event.target.value ↓ setName() ↓ State changes ↓ Re-render ↓ UI updates This pattern is commonly used in login forms, signup forms, search boxes, todo applications, and many other React applications. ## Different Types of State `useState` can store different types of JavaScript values. ### Number const [count, setCount] = useState(0); ### String const [name, setName] = useState(""); ### Boolean const [isLoggedIn, setIsLoggedIn] = useState(false); ### Array const [todos, setTodos] = useState([]); ### Object const [user, setUser] = useState({ name: "", age: 0 }); The type of state depends on what information the component needs to remember. ## Why is useState Important? Without state, creating interactive React applications would be much more difficult. `useState` allows components to respond to user actions and remember changing information. For example, state can be used for: * Counter applications * Login forms * Signup forms * Todo lists * Search boxes * Dark mode * Dropdown menus * Like buttons * Shopping carts * Form validation ## Conclusion `useState` is one of the most important concepts for anyone learning React. It allows functional components to store values that can change over time and update the user interface when those values change. The basic pattern is: const [value, setValue] = useState(initialValue); The key ideas to remember are: useState() ↓ Creates state ↓ State has a current value ↓ Setter function updates the state ↓ React re-renders ↓ UI displays the updated state Once you understand `useState`, concepts such as forms, event handling, conditional rendering, todo applications, and many other React features become much easier to understand.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 3h
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TrialMatch: Why Precision Matters When Lives Are on the Line
_This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content_ ## What I Built A few months ago, a friend’s aunt was diagnosed with advanced non-small cell lung cancer. First-line platinum chemotherapy had stopped working. The oncologist said something that stuck with me: _"Our best shot right now is an experimental targeted trial. Go home, search ClinicalTrials.gov, and see what you can find."_ If you have never searched ClinicalTrials.gov during a medical crisis, the experience is overwhelming. The registry contains more than 450,000 studies written in legalistic clinical language. Each protocol has dozens of eligibility criteria, negative exclusion clauses, and washout windows spread across 40-page PDF attachments. Like many developers, my first reflex was to see if an AI could help. I pasted the patient’s pathology summary into ChatGPT and Gemini: > _58-year-old female, Stage IV NSCLC, EGFR Exon 20 insertion, progressed after carboplatin/pemetrexed, seeking recruiting Phase 2 trials in Texas._ The response was fluent and confident. It returned hospital locations, drug mechanisms, and two clinical trial identifiers: `NCT04847387` and `NCT04746654`. When I checked them against official registries, neither trial existed. The model had synthesized oncology vocabulary and generated fake 8-digit numbers. In casual creative writing, a hallucination is harmless. In oncology, a fabricated study sends a desperate family chasing dead ends. Next, I tested standard keyword search. It returned 69 trials matching "EGFR" and "chemotherapy." But manual review showed that 31 of them had explicit exclusion clauses disqualifying anyone with prior systemic chemotherapy. If a patient traveled to Houston based on that search, they would be turned away at the clinic door. Worse, keyword search matched unrelated kidney studies because the filtration test `eGFR` shares letters with the oncogene `EGFR`. That made the underlying technical issue obvious: > Medical safety cannot be solved with flat text search or unstructured prompts. Clinical eligibility depends on strict Boolean rules, exact allele variants, and line-of-therapy sequences. AI needs a structured content lake as an epistemic anchor. I built **TrialMatch** : an open-source clinical trial discovery and safety verification agent powered by **Sanity Context MCP** and deterministic **GROQ** queries. Instead of letting an LLM guess against text chunks or vector similarity, TrialMatch anchors the agent to Sanity. Claude Haiku 4.5 parses unstructured patient notes into clean clinical filters. Those filters become deterministic GROQ queries that run against structured Sanity schemas and protocol rules stored in a Sanity Knowledge Base. ### System Architecture sequenceDiagram autonumber actor Clinician as Clinician / Patient participant UI as TrialMatch Web App (Next.js 16) participant Agent as Claude Haiku 4.5 (Schema Extractor) participant MCP as Sanity Context MCP (trialmatch) participant Lake as Sanity Content Lake (Project 6xsr2k42) participant KB as Sanity Knowledge Base (trialmatch-kb) Clinician->>UI: Enter Patient Note ("58yo NSCLC, EGFR Exon 20, prior chemo, TX") UI->>Agent: Extract Clinical Primitives Note over Agent: Converts narrative into:<br/>condition="Lung", biomarker="EGFR",<br/>chemo="ALLOWED", state="TX" Agent->>MCP: Dispatch GROQ Query with Bound Parameters MCP->>KB: Check Washout & Hierarchy Rules KB-->>MCP: Rule Verified (Chemo permitted post-progression) MCP->>Lake: Execute Deterministic GROQ Filter Lake-->>MCP: Return 2 Verified Recruiting Protocols MCP-->>UI: Return Match Set + Full GROQ Audit Trace UI-->>Clinician: Render Verified Cards + Side-by-Side Hazard Analysis ## Demo * **Live Application** : trialmatch-oncology.vercel.app * **3-Arm Benchmark Dashboard** : trialmatch-oncology.vercel.app/benchmark * **Hosted Sanity Studio** : trialmatch-oncology.sanity.studio ### The Duel Arena: Side-by-Side Reality The web application lets users enter any patient profile or select from eight common clinical presets: * **Left Column (Sanity Context Agent)** : Returns only verified, recruiting trials. Each card identifies the target biomarker match, lists confirmed hospital sites, and includes an expandable drawer showing the exact GROQ query executed against Sanity. * **Right Column (Naive Keyword Baseline)** : Shows what traditional keyword search returns. A warning banner details the safety violations, explaining why specific trials are clinically disqualified (such as prior chemotherapy bans or active liver metastases). * **Column-Level Pagination** : When keyword search returns 69 studies and the Sanity agent returns 2, each column paginates independently (6 trials per page) with centered navigation controls (`< Page 1 of 12 >`). This keeps the comparison readable without infinite page scrolling. ## Code * **GitHub Repository** : github.com/IshekKhal/trialmatch ### Project Layout trialmatch/ ├── app/ │ ├── page.tsx # The Duel Arena (Sanity Agent vs Keyword Baseline) │ ├── benchmark/page.tsx # 3-Arm Evaluation Dashboard │ ├── layout.tsx # Root layout with Geist font tokens │ ├── globals.css # Medical obsidian styling (#080C14) │ └── api/ │ ├── duel/route.ts # Live duel execution endpoint │ ├── benchmark/route.ts # 3-arm benchmark evaluation API │ └── trials/route.ts # Normalized clinical trial fetcher ├── components/ │ ├── DuelArena.tsx # Dual-column comparison with column-level pagination │ ├── ScenarioChips.tsx # 8 pre-configured oncology clinical scenarios │ ├── BenchmarkRunner.tsx # Interactive benchmark runner & case inspector │ ├── BenchmarkTable.tsx # 10-patient audit breakdown table │ └── Scoreboard.tsx # Real-time comparative metric display ├── studio/ │ ├── sanity.config.ts # Sanity Studio v3 configuration │ └── schemas/ │ ├── clinicalTrial.ts # Core schema: biomarkers, therapy rules, facilities │ ├── protocolRule.ts # Knowledge base schema: exclusion overrides & washouts │ └── index.ts # Schema registry ├── lib/ │ ├── agent.ts # Claude Haiku 4.5 schema extractor & GROQ builder │ ├── sanity.ts # Sanity client & live Context MCP bindings │ ├── eval_cases.ts # 10 gold-standard oncology test profiles │ ├── eval_runner.ts # Automated 3-arm benchmark execution engine │ └── types.ts # TypeScript domain interfaces ├── scripts/ │ ├── ingest_trials.ts # ClinicalTrials.gov API v2 data ingestion pipeline │ └── run_eval.ts # CLI benchmark evaluation runner └── data/ ├── trials_normalized.json # 100 curated oncology trials (1.2 MB) ├── eval_results.json # Raw 3-arm benchmark evaluation outputs └── eval_summary.md # Comprehensive 419-line benchmark report ## How I Used Sanity ### 1. Modeling Oncology Beyond Text Blobs Oncology protocols cannot live in free-text fields. A single protocol contains dozens of technical, medical, and pharmacokinetic constraints. In Sanity Studio (`studio/schemas/clinicalTrial.ts`), I modeled trials into structured primitives: * `targetBiomarkers`: Typed string array (`EGFR`, `KRAS`, `BRAF`, `HER2`, `BRCA1`, `Exon 20`, `G12C`, `V600E`). * `priorTherapyRules`: An object with explicit status enums (`REQUIRED`, `ALLOWED`, `EXCLUDED`, `ANY`) for chemotherapy, immunotherapy, and targeted therapies. * `locations`: Array of facility objects with city, state, and hospital names across 1,144 US sites. * `eligibilityCriteria`: Structured inclusion and exclusion bullet points. #### Clinical Trial Protocol Schema (`studio/schemas/clinicalTrial.ts`) import {defineField, defineType} from 'sanity' export default defineType({ name: 'clinicalTrial', title: 'Clinical Trial', type: 'document', fields: [ defineField({ name: 'nctId', title: 'NCT ID', type: 'string', validation: (Rule) => Rule.required(), }), defineField({ name: 'briefTitle', title: 'Brief Title', type: 'string', validation: (Rule) => Rule.required(), }), defineField({ name: 'recruitmentStatus', title: 'Recruitment Status', type: 'string', options: { list: [ {title: 'Recruiting', value: 'RECRUITING'}, {title: 'Active, Not Recruiting', value: 'ACTIVE_NOT_RECRUITING'}, ], }, }), defineField({ name: 'primaryCondition', title: 'Primary Condition', type: 'string', validation: (Rule) => Rule.required(), }), defineField({ name: 'targetBiomarkers', title: 'Target Biomarkers', type: 'array', of: [{type: 'string'}], options: { list: ['EGFR', 'KRAS', 'BRAF', 'HER2', 'ALK', 'BRCA1', 'BRCA2', 'Exon 20', 'G12C', 'V600E'], }, }), defineField({ name: 'priorTherapyRules', title: 'Prior Therapy Rules', type: 'object', fields: [ defineField({ name: 'chemotherapy', type: 'string', options: { list: ['REQUIRED', 'ALLOWED', 'EXCLUDED', 'ANY'] }, }), defineField({ name: 'immunotherapy', type: 'string', options: { list: ['REQUIRED', 'ALLOWED', 'EXCLUDED', 'ANY'] }, }), ], }), defineField({ name: 'locations', title: 'Trial Locations', type: 'array', of: [{ type: 'object', fields: [ { name: 'facility', type: 'string' }, { name: 'city', type: 'string' }, { name: 'state', type: 'string' }, { name: 'country', type: 'string' }, ], }], }), ], }) To handle clinical edge cases (such as whether past chemotherapy represents a permanent exclusion or simply requires a 30-day washout window), I created a companion schema, `protocolRule.ts`, indexed in the Sanity Knowledge Base: #### Protocol Guidance Rule Schema (`studio/schemas/protocolRule.ts`) import {defineField, defineType} from 'sanity' export default defineType({ name: 'protocolRule', title: 'Clinical Protocol Interpretation Rule', type: 'document', fields: [ defineField({ name: 'ruleId', title: 'Rule ID', type: 'string' }), defineField({ name: 'title', title: 'Rule Title', type: 'string' }), defineField({ name: 'category', type: 'string', options: { list: [ {title: 'Eligibility Hierarchy', value: 'ELIGIBILITY_HIERARCHY'}, {title: 'Washout Periods', value: 'WASHOUT_PERIODS'}, {title: 'Biomarker Specificity', value: 'BIOMARKER_SPECIFICITY'}, ], }, }), defineField({ name: 'priority', title: 'Priority (1-10)', type: 'number' }), defineField({ name: 'summary', title: 'Rule Summary', type: 'text' }), defineField({ name: 'clinicalRationale', title: 'Clinical Rationale', type: 'text' }), ], }) ### 2. Sanity Context MCP Endpoints We connected our Sanity Content Lake through two dedicated Sanity Context MCP endpoints: 1. **GROQ Context Endpoint (`trialmatch`)**: Exposes the live dataset for parameterized GROQ queries. 2. **Knowledge Base Endpoint (`trialmatch-kb`)**: Provides semantic retrieval over protocol interpretation rules. When a patient note arrives, the agent translates the clinical parameters into this exact GROQ query: *[_type == "clinicalTrial" && recruitmentStatus == "RECRUITING" && primaryCondition match $condition && targetBiomarkers[] match $biomarker && (priorTherapyRules.chemotherapy == "ALLOWED" || priorTherapyRules.chemotherapy == "REQUIRED" || priorTherapyRules.chemotherapy == "ANY") && locations[].state match $state] { nctId, briefTitle, phase, primaryCondition, targetBiomarkers, priorTherapyRules, "matchingLocations": locations[state match $state] } Because `priorTherapyRules.chemotherapy` is an explicit schema property, Sanity filters out the 67 trials that forbid prior chemotherapy before any result reaches the user. ### 3. The 3-Arm Benchmark: Empirical Findings To verify whether structured content changes clinical outcomes, I built an evaluation suite (`lib/eval_runner.ts`) and tested ten gold-standard oncology profiles across three discovery approaches: Evaluation Metric | Arm 1: Structured Sanity Agent | Arm 2: Naive Keyword Search | Arm 3: Bare LLM (Gemini 3.8 Flash) | Clinical Implication ---|---|---|---|--- **Medical Precision** | **100%** | 78% | 0% | Arm 1 returns only verified candidates; Arms 2 and 3 return disqualified cohorts **Safety Violations** | **0** | **60** | 20 | Naive search misses negative exclusions; bare LLM bypasses protocol rules **Hallucinated NCT IDs** | **0** | 0 | **20 (100% fake)** | Bare LLM invents non-existent trial identifiers **Auditability Rate** | **100%** | 0% | 0% | Arm 1 provides exact GROQ queries and rule citations **Avg Returned Trials** | 1.2 | 41.5 | 2.0 | Arm 1 isolates actionable, recruiting matches ### 4. Real Clinical Failure Modes Examining individual patient cases demonstrates where unstructured search breaks down: #### 1. Chemotherapy Exclusion (Case TC-01) * **Patient** : 58yo female, NSCLC EGFR Exon 20 insertion, prior platinum chemotherapy, Texas. * **Keyword Failure** : Returned 69 trials. 31 violated safety rules. Trial `NCT07799935` specifically prohibits prior systemic chemotherapy in Rule 14. * **Bare LLM Failure** : Hallucinated trials `NCT04847387` and `NCT04746654`. Neither identifier exists. * **Sanity Result** : Executed GROQ checking `priorTherapyRules.chemotherapy in ["ALLOWED", "REQUIRED", "ANY"]`. Safely returned exactly 2 verified trials. #### 2. Organ Site Metastasis Contraindications (Case TC-02) * **Patient** : 62yo male, Stage IV colorectal cancer, KRAS G12C mutation, stable liver metastases, California. * **Keyword Failure** : Matched trial `NCT05286814` because the document contained the word "metastases," failing to catch that active hepatic involvement was an explicit disqualification. * **Bare LLM Failure** : Hallucinated trials `NCT04793958` and `NCT04685141`. * **Sanity Result** : Evaluated structured exclusion rules in Sanity's Knowledge Base, preventing the false match. #### 3. Quantitative Biomarker Expression Cutoffs (Case TC-03) * **Patient** : 51yo female, HER2-low (IHC 1+ or IHC 2+/FISH negative) metastatic breast cancer. * **Keyword Failure** : Returned 72 trials, including `NCT04281641` and `NCT02945579` which strictly require high HER2 overexpression (IHC 3+). * **Sanity Result** : Cleanly separated HER2-overexpressing protocols from novel HER2-low antibody-drug conjugate trials. #### 4. Phase 3 Confirmatory Requirement (Case TC-04) * **Patient** : 47yo patient, unresectable Stage IIIC melanoma, BRAF V600E mutation, seeking Phase 3 trials in New York. * **Keyword Failure** : Returned 30 trials, 26 of which were Phase 1 dose-escalation trials with unknown toxicities. * **Sanity Result** : Strict GROQ filter `phase == "PHASE3"` isolated the single qualifying Phase 3 confirmatory study. ## Sanity Project Details * **Project ID** : `6xsr2k42` * **Dataset** : `production` * **Hosted Studio** : trialmatch-oncology.sanity.studio * **Sanity GROQ MCP Endpoint** : `https://api.sanity.io/v1/context/organizations/oz3yptidu/mcp/trialmatch` * **Sanity Knowledge Base MCP Endpoint** : `https://api.sanity.io/v1/context/organizations/oz3yptidu/mcp/trialmatch-kb` * **Public Sample Query (Live JSON)** : Live 3-Trial Sanity Query Endpoint ## Agent Session Below is a representative excerpt from the agent's interaction loop with Sanity Context MCP: Clinician: "We have a 58yo patient with NSCLC harboring an EGFR Exon 20 insertion. She progressed on carboplatin/pemetrexed. Find recruiting Phase 2 trials in Texas that permit prior chemotherapy." Agent (Claude Haiku 4.5): Extracting clinical parameters: - Condition: "Lung" (NSCLC) - Target Biomarker: "EGFR", "Exon 20" - Chemotherapy: "ALLOWED" or "REQUIRED" - Location: "TX" - Recruitment Status: "RECRUITING" Tool Call -> Sanity Context MCP (trialmatch): GROQ: *[_type == "clinicalTrial" && recruitmentStatus == "RECRUITING" && primaryCondition match "Lung" && targetBiomarkers[] match "EGFR" && (priorTherapyRules.chemotherapy == "ALLOWED" || priorTherapyRules.chemotherapy == "REQUIRED") && locations[].state match "TX"]{nctId, briefTitle, phase} MCP Response: [ { "nctId": "NCT05376891", "briefTitle": "Phase 2 Study of Targeted EGFR Exon 20 Inhibitor", "phase": "PHASE2" }, { "nctId": "NCT06234137", "briefTitle": "Targeted Kinase Therapy for Relapsed EGFR Mutations", "phase": "PHASE2" } ] Verification: 2 active matches found. 0 safety exclusions violated. 0 hallucinations. ## What This Demonstrates The software industry has spent two years hoping that larger foundation models or clever prompting would eliminate hallucinations. But in medicine, accuracy is not a fuzzy percentage; it is a binary safety gate. Connecting an AI agent to structured content in Sanity transforms the language model from an unreliable generator into a deterministic parser. The model extracts the patient's intent, Sanity executes the Boolean protocol constraints, and the patient receives verified, life-saving options. Structured content is not merely a content management pattern. In high-stakes domains, structured content is the safety layer AI cannot function without.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 5h
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TypeScript `Partial`, `Required`, and `DeepPartial` in 2026: Which Utility Type Actually Fits Your Use Case
# TypeScript `Partial`, `Required`, and `DeepPartial` in 2026: Which Utility Type Actually Fits Your Use Case > _This article was written with the assistance of AI, under human supervision and review._ Most TypeScript utility type problems stem from teams treating `Partial<T>`, `Required<T>`, and `DeepPartial<T>` as interchangeable shortcuts when each solves a distinct problem. Developers reach for `Partial` to silence compiler errors during form updates, then spend weeks debugging why incomplete objects reached the database. Teams copy `DeepPartial` implementations from Stack Overflow without understanding where the recursion breaks down on arrays and unions. The result is production bugs where the type system promised safety but delivered silent failures. The core issue is that these utilities operate at different depths and serve different phases of data flow. `Partial<T>` makes every property optional at one level. `Required<T>` does the inverse, forcing every property to exist. `DeepPartial<T>` recurses through nested objects, making everything optional all the way down. When you pick the wrong one, the compiler either rejects valid code or accepts broken code. Neither outcome is acceptable in production systems. The solution is a decision tree based on three questions: are you updating or creating, is the structure flat or nested, and do you need guarantees before persistence. Answer those and the right utility type becomes obvious. Apply `Partial` for patch operations where missing fields should be ignored. Use `Required` as a type guard before database writes to enforce completeness. Reserve `DeepPartial` for configuration objects where every nested layer needs optional overrides, and understand its limitations with arrays. This post shows when each utility type fits, how to compose them with `Pick` and `Omit` for precise control, and where `DeepPartial` breaks down in real codebases. The patterns here eliminate the guesswork and give you a reproducible selection process. ## Key Takeaways * `Partial<T>` makes all properties optional at the top level only, ideal for update operations where the client sends a subset of fields. * `Required<T>` forces all properties to be present, serving as a type guard before database writes or API calls that need complete objects. * `DeepPartial<T>` recurses through nested objects to make everything optional, but breaks on arrays and unions without custom conditional logic. * Composing utilities like `Partial<Pick<T, K>>` gives surgical control over which fields are optional and which remain required. * The decision between these types depends on whether you are creating or updating data, whether the structure is flat or nested, and whether you need compile-time guarantees of completeness. ## Understanding Partial: When Optional Properties Save Time (and When They Hide Bugs) `Partial<T>` transforms every property in a type to optional, making it the standard choice for update operations where the client sends only changed fields. The implementation is straightforward: it maps over all keys in `T` and adds the `?` modifier. type Partial<T> = { [P in keyof T]?: T[P]; }; The utility shines when an API receives a PATCH request with a subset of user fields. Without `Partial`, the function signature would require every property, forcing the client to send the entire object even when updating a single field. With `Partial`, the function accepts any combination of properties. interface User { id: string; name: string; email: string; role: string; } function updateUser(id: string, changes: Partial<User>) { // Only the fields in `changes` will be applied // The database layer handles merging with existing record } // Valid calls: updateUser("123", { name: "Alice" }); updateUser("123", { email: "alice@example.com", role: "admin" }); The failure mode here is subtle but expensive. `Partial<T>` does not validate that at least one property exists. An empty object satisfies the type, so code that expects meaningful updates can receive a no-op. When the update function forwards this to a database layer that interprets an empty changeset as "do nothing," the operation silently succeeds without modifying the record. The client receives a 200 response, the user believes the update worked, but the database remains unchanged. // This compiles and runs without error: updateUser("123", {}); // The database operation succeeds but changes nothing // The user sees success but their data is stale The second issue is depth. `Partial<T>` only affects the immediate properties of `T`. Nested objects remain fully required. When the type includes a nested structure, applying `Partial` leaves the nested properties mandatory. interface UserProfile { id: string; settings: { theme: string; notifications: boolean; }; } type PartialProfile = Partial<UserProfile>; // Equivalent to: // { // id?: string; // settings?: { // theme: string; // Still required if settings is provided // notifications: boolean; // Still required if settings is provided // }; // } This matters because developers expect `Partial` to make the entire structure optional. When they try to update just the theme, they must provide the full `settings` object or omit it entirely. The type system rejects a partial settings object. // This fails at compile time: const update: PartialProfile = { settings: { theme: "dark" } // Error: Property 'notifications' is missing }; // Developer is forced to either: // 1. Provide the complete nested object const update1: PartialProfile = { settings: { theme: "dark", notifications: true } }; // 2. Or omit settings entirely const update2: PartialProfile = { id: "123" }; Use `Partial<T>` when the type is flat and you want to allow any subset of properties. Pair it with runtime validation that ensures at least one property exists if the operation should reject empty updates. For nested structures, reach for `DeepPartial<T>` or compose `Partial` with `Pick` to target specific layers. ## Required in Practice: Forcing Complete Objects Before Database Writes `Required<T>` removes the optional modifier from all properties, converting a type with optional fields into one where every property must be present. The built-in implementation mirrors `Partial` but uses the `-?` modifier to strip optionality. type Required<T> = { [P in keyof T]-?: T[P]; }; The primary use case is enforcing completeness at persistence boundaries. When a database table has NOT NULL constraints on certain columns, the ORM or query builder should reject incomplete objects at compile time. `Required` serves as a type guard that runs before the write operation. Consider a user registration flow where the form allows optional fields during editing but the database requires a complete record. The form state uses `Partial<User>` to track incremental changes. Before calling the create method, the code narrows the type to `Required<User>` through a validation function. interface User { id: string; name: string; email: string; age?: number; } type CompleteUser = Required<User>; // Equivalent to: // { // id: string; // name: string; // email: string; // age: number; // No longer optional // } function isCompleteUser(user: Partial<User>): user is CompleteUser { return ( typeof user.id === "string" && typeof user.name === "string" && typeof user.email === "string" && typeof user.age === "number" ); } async function createUser(draft: Partial<User>) { if (!isCompleteUser(draft)) { throw new Error("User data incomplete"); } // TypeScript knows `draft` is CompleteUser here await db.users.insert(draft); // The database receives a guaranteed complete object } This pattern surfaces incomplete data at the application boundary instead of letting it propagate to the database where constraint violations produce runtime errors. The failure happens early with a clear message, and the type system prevents the database call from compiling if the guard is removed. The limitation of `Required` is the same as `Partial`: it only operates on the immediate properties. Nested objects with optional fields remain partially optional after applying `Required` to the parent type. interface Config { api?: { endpoint?: string; timeout?: number; }; } type RequiredConfig = Required<Config>; // Equivalent to: // { // api: { // endpoint?: string; // Still optional // timeout?: number; // Still optional // }; // } The `api` property becomes required, meaning it cannot be undefined. But the properties inside `api` retain their optional status. This is correct for many use cases where the nested object must exist but can be partially populated. When full depth is needed, combine `Required` with a recursive type or apply it to nested interfaces separately. Use `Required<T>` as a type guard before operations that demand complete data. Pair it with runtime validation functions that return type predicates, so the type system narrows the input type to the required form. For nested structures, apply `Required` to each level explicitly or build a recursive `DeepRequired` utility when every layer must be complete. ## DeepPartial: Building the Recursive Type and Why It's Not Built-In `DeepPartial<T>` makes every property optional at all levels of nesting, transforming a deeply nested type into a structure where every leaf and branch can be omitted. TypeScript does not include this as a built-in utility because the recursive logic introduces complexity around edge cases like arrays, unions, and functions. The basic implementation uses conditional types to check if a property is an object, then recursively applies `Partial` to it. If the property is a primitive, it simply becomes optional. type DeepPartial<T> = { [P in keyof T]?: T[P] extends object ? DeepPartial<T[P]> : T[P]; }; This works for plain nested objects where every layer is a record with string keys. When the type includes an address object with a nested location object, `DeepPartial` makes every property at every level optional. interface User { id: string; profile: { name: string; address: { street: string; city: string; coordinates: { lat: number; lng: number; }; }; }; } type PartialUser = DeepPartial<User>; // Equivalent to: // { // id?: string; // profile?: { // name?: string; // address?: { // street?: string; // city?: string; // coordinates?: { // lat?: number; // lng?: number; // }; // }; // }; // } The utility becomes essential for configuration objects where every nested section should support partial overrides. A deeply nested config with database, cache, and logging sections can accept a minimal override object that only specifies the changed values. The merge logic fills in defaults for missing properties. const defaultConfig = { database: { host: "localhost", port: 5432, pool: { min: 2, max: 10, }, }, cache: { enabled: true, ttl: 300, }, }; function createConfig(overrides: DeepPartial<typeof defaultConfig>) { // Merge overrides with defaults return { database: { ...defaultConfig.database, ...overrides.database, pool: { ...defaultConfig.database.pool, ...overrides.database?.pool, }, }, cache: { ...defaultConfig.cache, ...overrides.cache, }, }; } // Valid calls: createConfig({ database: { port: 3000 } }); createConfig({ cache: { ttl: 600 } }); createConfig({ database: { pool: { max: 20 } } }); The reason TypeScript omits `DeepPartial` from the standard library is that the simple implementation breaks on non-object types. Arrays are objects in JavaScript, so `T[P] extends object` evaluates to true for array properties. The recursion applies `DeepPartial` to the array itself instead of its element type, producing nonsensical results. interface Data { items: string[]; } type PartialData = DeepPartial<Data>; // The naive implementation produces: // { // items?: DeepPartial<string[]>; // } // Which is effectively: // { // items?: { // [index: number]?: string; // length?: number; // // ... plus all Array methods as optional // }; // } The array properties like `length` and methods like `map` become optional, which is meaningless for an array type. The fix requires checking if the type is an array before recursing, then applying `DeepPartial` to the element type and wrapping it back into an array. type DeepPartial<T> = T extends Array<infer U> ? Array<DeepPartial<U>> : T extends object ? { [P in keyof T]?: DeepPartial<T[P]> } : T; This handles arrays correctly by checking `extends Array<infer U>` first, extracting the element type `U`, recursing on it, and returning `Array<DeepPartial<U>>`. The array itself remains an array, and only its elements become deeply partial. The second edge case is union types. When a property is a union like `string | number`, the `extends object` check fails for the entire union even if one branch is an object. The recursion stops prematurely, leaving nested objects in union branches fully required. interface Data { value: { nested: string } | string; } type PartialData = DeepPartial<Data>; // The simple implementation produces: // { // value?: { nested: string } | string; // } // The object branch stays fully required Handling this correctly requires distributive conditional types that apply the recursion to each branch of the union separately. The revised implementation wraps the conditional in a way that distributes over unions. type DeepPartial<T> = T extends Array<infer U> ? Array<DeepPartial<U>> : T extends object ? { [P in keyof T]?: DeepPartialUnion<T[P]> } : T; type DeepPartialUnion<T> = T extends Array<infer U> ? Array<DeepPartial<U>> : T extends object ? { [P in keyof T]?: DeepPartialUnion<T[P]> } : T; This matters because configuration objects in production codebases frequently include unions where a setting can be a simple value or a complex object. Without correct union handling, the type system fails to make those nested objects optional. Use `DeepPartial<T>` for configuration merge operations where every layer needs to support partial overrides. Implement the full version with array and union handling if the types include those structures. For types with arrays of primitives, the simple implementation often suffices. For complex schemas with arrays of objects or union branches with nested properties, the edge case handling becomes mandatory. ## Partial vs Required vs DeepPartial: A Side-by-Side Comparison for Real-World Scenarios The decision between `Partial`, `Required`, and `DeepPartial` depends on whether you are creating or updating data, whether the structure is flat or nested, and whether you need compile-time guarantees of completeness. `Partial<T>` fits update operations on flat types where the client sends a subset of fields. Use it when the API accepts a PATCH request that modifies only the provided properties. The type allows any combination of fields, including an empty object. Pair it with runtime validation if an empty update should be rejected. Do not use `Partial` for nested structures, as it only affects the top level. `Required<T>` fits creation operations or pre-persistence validation where every field must be present. Use it as a type guard before database writes or API calls that demand complete objects. The type forces all properties to exist, surfacing incomplete data at compile time. Do not use `Required` when optional fields are semantically correct, such as a user bio that can be empty. `DeepPartial<T>` fits configuration merges or deeply nested update operations where every layer needs to support partial overrides. Use it when the type has multiple levels of nesting and the client should be able to override any leaf property without providing the entire tree. Implement the full version with array and union handling if the type includes those structures. Do not use `DeepPartial` when only the top level needs to be optional, as it adds unnecessary complexity. A concrete example clarifies the distinctions. Consider a user profile API with create, update, and merge endpoints. The create endpoint requires all fields, so the input type is the base `User` interface or `Required<User>` if the base includes optional properties. The update endpoint accepts a subset of fields, so the input type is `Partial<User>`. The merge endpoint combines a partial nested config with defaults, so the input type is `DeepPartial<UserConfig>`. interface User { id: string; name: string; email: string; bio?: string; } interface UserConfig { preferences: { theme: string; notifications: { email: boolean; push: boolean; }; }; } // Create: all required fields must be present async function createUser(data: Required<Omit<User, "bio">>) { // id, name, email are mandatory; bio is excluded await db.users.insert(data); } // Update: any subset of fields can be modified async function updateUser(id: string, changes: Partial<User>) { // Only the fields in `changes` will be updated await db.users.update(id, changes); } // Merge: deeply nested config with partial overrides function mergeConfig( defaults: UserConfig, overrides: DeepPartial<UserConfig> ): UserConfig { return { preferences: { theme: overrides.preferences?.theme ?? defaults.preferences.theme, notifications: { email: overrides.preferences?.notifications?.email ?? defaults.preferences.notifications.email, push: overrides.preferences?.notifications?.push ?? defaults.preferences.notifications.push, }, }, }; } The implications are immediate. When the create endpoint accidentally accepts `Partial<User>`, incomplete records reach the database and violate NOT NULL constraints. When the update endpoint mistakenly requires `Required<User>`, the client must send the entire object even when changing a single field. When the merge function uses `Partial<UserConfig>` instead of `DeepPartial<UserConfig>`, nested overrides fail to compile unless the client provides the complete nested object. This distinction is critical. Teams that default to `Partial` for all input types encounter runtime errors at persistence boundaries. Teams that over-apply `Required` create APIs that reject valid partial updates. Teams that avoid `DeepPartial` build merge logic that cannot handle granular overrides without verbose type assertions. ## Combining Utility Types: Partial>, Required>, and Composition Patterns Composing utility types with `Pick` and `Omit` provides surgical control over which properties are optional and which remain required. The pattern `Partial<Pick<T, K>>` makes only the picked properties optional while leaving the rest unchanged. The pattern `Required<Omit<T, K>>` makes all properties except the omitted ones required. These compositions solve the problem where `Partial<T>` is too broad and `Required<T>` is too strict. When a form allows editing some fields but others are read-only, the input type should make the editable fields optional and the read-only fields required. `Partial<Pick<T, EditableKeys>>` combined with `Pick<T, ReadOnlyKeys>` expresses this precisely. interface User { id: string; name: string; email: string; role: string; createdAt: Date; } type EditableFields = "name" | "email" | "role"; type ReadOnlyFields = "id" | "createdAt"; type UserUpdateInput = Partial<Pick<User, EditableFields>> & Pick<User, ReadOnlyFields>; // Equivalent to: // { // id: string; // createdAt: Date; // name?: string; // email?: string; // role?: string; // } function updateUser(data: UserUpdateInput) { // id and createdAt are always present // name, email, role are optional const { id, createdAt, ...updates } = data; // Process updates while preserving read-only fields } The inverse pattern uses `Required<Omit<T, K>>` to force all properties except a few to be required. This fits creation endpoints where most fields are mandatory but a small subset is optional. Instead of marking the entire type as required and then making exceptions, omit the optional fields and apply `Required` to the rest. interface User { id: string; name: string; email: string; bio?: string; avatar?: string; } type UserCreateInput = Required<Omit<User, "bio" | "avatar">> & Pick<User, "bio" | "avatar">; // Equivalent to: // { // id: string; // name: string; // email: string; // bio?: string; // avatar?: string; // } function createUser(data: UserCreateInput) { // id, name, email are mandatory // bio, avatar are optional await db.users.insert(data); } This pattern eliminates the need to redefine the type or write verbose conditional logic. The composition expresses the intent directly: these fields are required, those fields are optional. Another useful composition is `Partial<T> & Pick<T, K>` to make most properties optional while keeping a few required. This fits scenarios where the majority of fields can be omitted but a minimal set must be present. interface SearchFilters { query: string; category?: string; minPrice?: number; maxPrice?: number; inStock?: boolean; tags?: string[]; } type MinimalSearch = Partial<SearchFilters> & Pick<SearchFilters, "query">; // Equivalent to: // { // query: string; // Required // category?: string; // minPrice?: number; // maxPrice?: number; // inStock?: boolean; // tags?: string[]; // } function search(filters: MinimalSearch) { // query is guaranteed to exist // All other filters are optional } The failure mode of these compositions is forgetting that intersections with conflicting optionality produce `never`. If `Partial<T>` makes a property optional and `Pick<T, K>` includes the same property as required, the intersection is impossible to satisfy. interface User { id: string; name: string; } // This produces a never type for `name`: type Broken = Partial<User> & Pick<User, "name">; // Because `name?` (from Partial) and `name` (from Pick) conflict Avoid this by ensuring the sets of properties in `Partial` and `Pick` are disjoint. When you want some fields optional and others required, pick the required fields separately and intersect them with a `Partial` of the remaining fields. Use `Partial<Pick<T, K>>` when only specific fields should be optional. Use `Required<Omit<T, K>>` when most fields should be required except a few. Use `Partial<T> & Pick<T, K>` when most fields are optional but a minimal set must be present. Verify that the composed type does not produce `never` for any property by hovering over the type in your editor. ## When DeepPartial Breaks Down: Arrays, Unions, and the Edge Cases Teams Hit in Production `DeepPartial<T>` fails silently on arrays and unions unless the implementation includes special handling for those cases. The naive version treats arrays as objects and recurses into their properties, making array methods like `map` and `length` optional. The result compiles but produces a type that does not match runtime behavior. The specific failure occurs when a type includes an array of objects. Developers expect `DeepPartial` to make the object properties optional while keeping the array itself an array. The naive implementation makes the array structure itself optional, turning it into an object with numeric keys and array methods as optional properties. interface Post { id: string; title: string; tags: string[]; comments: Array<{ id: string; text: string; author: { name: string; email: string; }; }>; } type NaiveDeepPartial<T> = { [P in keyof T]?: T[P] extends object ? NaiveDeepPartial<T[P]> : T[P]; }; type PartialPost = NaiveDeepPartial<Post>; // tags becomes: // { // [index: number]?: string; // length?: number; // map?: (...) => ...; // // All array methods become optional // } The correct implementation checks for arrays before checking for objects. When the type is an array, extract the element type, apply `DeepPartial` to it, and return a new array of the partial element type. This preserves the array structure while making the elements deeply partial. type DeepPartial<T> = T extends Array<infer U> ? Array<DeepPartial<U>> : T extends object ? { [P in keyof T]?: DeepPartial<T[P]> } : T; type PartialPost = DeepPartial<Post>; // tags becomes: string[] // comments becomes: Array<{ // id?: string; // text?: string; // author?: { // name?: string; // email?: string; // }; // }> The second edge case is union types where one branch is an object and the other is a primitive. The `extends object` check evaluates the entire union and fails if any branch is not an object. The recursion stops at the union level, leaving nested objects in the object branch fully required. interface Data { value: { nested: string } | string; } type PartialData = DeepPartial<Data>; // Without union distribution: // { // value?: { nested: string } | string; // } // The object branch stays required The fix uses a helper type that distributes over unions. When `T` is a union `A | B`, the conditional type `T extends any ? ... : ...` applies the true branch to each union member separately. This allows the recursion to handle the object branch and primitive branch independently. type DeepPartial<T> = T extends any ? T extends Array<infer U> ? Array<DeepPartial<U>> : T extends object ? { [P in keyof T]?: DeepPartial<T[P]> } : T : never; type PartialData = DeepPartial<Data>; // With union distribution: // { // value?: { nested?: string } | string; // } // The object branch becomes partial The third edge case is functions. When a property is a function, the naive implementation treats it as an object and tries to recurse into its properties. Functions have properties like `length` and `name`, so the type system allows this. The result is that function properties become optional objects with optional `length` and `name` properties instead of remaining function types. interface Handler { callback: (data: string) => void; } type PartialHandler = DeepPartial<Handler>; // Without function check: // { // callback?: { // length?: number; // name?: string; // // ... // }; // } The fix adds a check for functions before checking for objects. Functions satisfy `extends (...args: any[]) => any`, so that conditional branch returns the function type unchanged. type DeepPartial<T> = T extends any ? T extends Array<infer U> ? Array<DeepPartial<U>> : T extends (...args: any[]) => any ? T : T extends object ? { [P in keyof T]?: DeepPartial<T[P]> } : T : never; The complete implementation handles arrays, unions, and functions. Use it when the type includes any of these structures. For simpler types with only nested objects, the basic version suffices. The additional checks add complexity but prevent silent type errors where the inferred type diverges from runtime behavior. The implication here is immediate. When teams copy a naive `DeepPartial` implementation and apply it to a schema with array properties, the type system accepts code that will fail at runtime. The editor shows no errors, but the code assumes array methods exist when the type has made them optional. The failure surfaces during testing or production when a method call on an array property throws undefined. ## Frequently Asked Questions ### When should I use Partial instead of making properties optional in the interface definition? Use `Partial<T>` when you have a base type that represents complete data but need a variant for update operations. Define optional properties in the interface when those fields are semantically optional in all contexts. `Partial` is a transformation applied to an existing type, while optional properties in the definition are part of the type's core contract. ### Can I use Required to remove undefined from union types like string or undefined? No. `Required<T>` only removes the optional modifier from properties. It does not affect union types that explicitly include `undefined`. Use `NonNullable<T>` to remove `null` and `undefined` from a union type, or use the `-?` mapped type modifier in a custom utility to strip both optionality and undefined from properties. ### Why does DeepPartial make array methods like map and filter optional? The naive `DeepPartial` implementation treats arrays as objects and recurses into their properties, which include methods like `map`. The correct implementation checks for arrays before objects using `T extends Array<infer U>` and applies `DeepPartial` to the element type only, preserving the array structure. ### How do I make only specific nested properties optional without using DeepPartial? Compose `Partial` with `Pick` and intersect the result with the unchanged portions of the type. For a type with a nested object where only some nested properties should be optional, use an intersection type that combines `Partial<Pick<NestedType, Keys>>` with the parent type. ### Can I combine Partial and Required on the same type? Yes, but they must target different properties. Use `Partial<Pick<T, K>>` to make specific properties optional, intersect with `Required<Omit<T, K>>` to require the rest. Applying both to the same property produces a type conflict where the property is simultaneously optional and required, resulting in `never`. ## Conclusion: Choosing the Right Utility Type for Your Use Case The decision tree is straightforward. For flat types where the client sends a subset of fields, use `Partial<T>`. For operations that demand complete objects before persistence, use `Required<T>` as a type guard with runtime validation. For deeply nested structures where every layer needs optional overrides, use `DeepPartial<T>` with array and union handling. For precise control over which properties are optional, compose utilities with `Pick` and `Omit`. The failure modes are predictable. `Partial` on nested types leaves inner objects required. `Required` on creation endpoints with optional fields forces the client to send defaults. `DeepPartial` without edge case handling breaks on arrays and unions. Composition without disjoint property sets produces `never` types. That covers the essential patterns for TypeScript utility types. Apply these in production and the difference will be immediate. The type system will surface incomplete data at compile time, APIs will accept precisely the fields they need, and merge operations will handle granular overrides without verbose assertions. The decision between `Partial`, `Required`, and `DeepPartial` stops being a guess and becomes a reproducible selection based on data flow and structure depth.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 5h
dev.to
Four ways "the post succeeded" can be a lie
I build a tool that schedules social posts. This week I added four new places to publish: WordPress, dev.to, Hashnode and Lemmy. Then I had each adapter reviewed by someone who did not write it. The most useful findings were all the same kind of bug: the code said "published" when it had only proven "the server answered". Here are four ways that happens. ## 1. A 200 is not the post My first version of the read-back check fetched the public page and treated HTTP 200 as "live". That passes for a maintenance page, a parked domain, a "coming soon" plugin, or a password-protected post whose public page is just a password form. All of them answer 200. The fix: read a small part of the page and require the post's title or its slug to appear in it. If the proof cannot name the post, it is not proof. ## 2. The URL in the response is not yours to trust The read-back used the link the platform sent back. On a blog with a custom domain, that domain is controlled by the customer. It can answer with a redirect to an address inside your own network, and a naive `fetch` follows it and reports success. The fix: do not follow redirects blindly. Check every hop, require the final host to match the expected host, and never send credentials past the first host. ## 3. A timeout does not mean "it did not happen" If the create request times out, the platform may still have created the post. A scheduler that retries now publishes it twice, in public. The fix: after a network error, look for a post with the same title from the last few minutes before you report a failure, and if you cannot tell, say so instead of retrying blindly. ## 4. "Saved as a draft" is not a failure Some customers choose to save a draft on the platform instead of publishing. A read-back that insists the post is public will call that a failure and retry it until it gives up. A draft that exists exactly where the customer asked for it is a finished job. It just should not be counted as published. The fix: give "saved as a draft" its own ending, so it is never retried and never inflates a "verified live" number. ## The pattern In all four, the code checked the easy thing (did the server answer?) instead of the thing I actually care about (can a stranger see this post?). Whenever a system reports success, ask what it actually observed, and whether that observation could be true while the post is not live. I use this idea in LazyRelay, the scheduler I am building: after every post it reads the platform back to confirm the post is really there. You can look at it at https://lazyrelay.com. What is the strangest "success" you have seen an API report?
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DEV Community [Unofficial] @dev.to.web.brid.gy · 5h
dev.to
🛜 The Journey of Data Through a Wi-Fi 7 NIC
### A Deep Investigation I've been sitting on this project for a while, I haven't shared it until now. While learning about networking and on one of my massive side tangents, I thought it would be really cool to have a detailed breakdown of what the data is doing at what stage in the process. No, not another OSI Model post, but the Wi-Fi 7 NIC exclusively. I wanted to know, "What is the process for the cutting edge Wi-Fi technology?" - something I have limited knowledge about. That's a VERY BIG question. 🫣 And it deserved a project. And man, I'm impressed with what I was able to get AI to combobulate for me. However, as a non-expert, I would greatly appreciate feedback from a weathered wizard here. Is this presented properly? (I'm looking at you, network people! <3 ) This static page allows you to click around and learn about the different stages and views at different scales. I feel it offers some real understanding. It is interactive. Play with it! I am obsessed with the board view. I think it's pretty freaking awesome. 👾 ### Investigate Further and Contribute: ## AnnaVi11arrea1 / wifi7_NIC_explorer ### To gain a better understanding of data flow through a Wi-Fi 7 card. ## Wi-Fi 7 Network Card Explorer Disclaimer - Contains no proprietry data, only publicly available information. ### Purpose To gain a deeper understanding of the entire flow of network data through a wifi-7 card. ### Content It is a static page with 3 tabbed views. The original tool was created quickly with AI prompts to get the idea started. It has some minor imperfections but is generally working. Tab 1 - Packet Flow Tab 2 - Board View (The cool tab, honestly) Tab 3 - Scale Ladder, high level over view from what happens at a room level, and all the way down to a logic view. Cool Stuff. ### What needs to be done: * I'd prefer the styling in a separate file. * Look for and simplify redundant code. * Some of the graphics are slightly out of place, minor, but noticable. * Open to ideas! ### Contributing * Fork and make a pull request View on GitHub
000
DEV Community [Unofficial] @dev.to.web.brid.gy · 5h
dev.to
Today, I spent time revising two essential core concepts in JavaScript:
JavaScript Objects and Arrow Functions. Here is a quick breakdown of what I covered: ​1. JavaScript Objects (Key-Value Pairs & Methods) ​Objects allow us to group related data and functionality together. ​Example 1: Mobile Object: const mobile = { brand: "Vivo", price: 15000, network: "5G", browse: function () { console.log("Browsing..."); } } Example 2: Bike Objects (TVS & Bajaj): const TVS = { model: "XL-100", price: 99000, cc: 100, drive: function () { console.log("Speed - 70 km/h"); } } const bajaj = { model: "Pulsar", price: 150000, cc: 150, drive: function () { console.log("Speed - 120 km/h"); } } 2.Arrow Functions : ​Arrow functions provide a cleaner and shorter syntax for writing function expressions in JavaScript. ​Syntax: (param1, param2) => expression; Example: const add = (i, j) => i + j;
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DEV Community [Unofficial] @dev.to.web.brid.gy · 5h
dev.to
Every LLM framework rebuilt the same tool object
If you give an LLM access to your code, you write tools. A tool is a function plus what a model needs to call it: a name, a description, and a schema for the arguments. Then you write the same tools again. The first version uses the AI SDK's `tool()`. The project adds an MCP server, so the tools are rewritten for `registerTool`. Another team uses Mastra, and the tools are written a third time with `createTool`. The functions stay the same. Only the wrapper changes. Each wrapper is also tied to its framework. `tool()` comes from the `ai` package, `createTool` from `@mastra/core`, and `registerTool` is a method of the MCP SDK's `McpServer`. A library that wants to ship tools has to choose one framework, and everyone who uses the library installs it. Without the framework, a tool is a function that describes itself: the function, plus a name, a description, and schemas for its input and output. That is enough for a model to decide when and how to call it. It is also enough to generate docs, build a form, or add a CLI command. Written as a plain object with those fields, a tool belongs to your code. Moving it to another framework takes a small adapter instead of a rewrite. The hardest part of that object is the schemas, and the schemas are already standardized. ## What is Standard Schema? Standard Schema is a TypeScript interface for validation libraries, designed by the creators of Zod, Valibot, and ArkType. Code that accepts a Standard Schema works with a schema from any library that implements it, with no adapter per library. The whole interface is one property, `~standard`. Trimmed to its fields: interface StandardSchemaV1<Input = unknown, Output = Input> { readonly '~standard': { readonly version: 1; readonly vendor: string; readonly validate: (value: unknown) => Result<Output> | Promise<Result<Output>>; readonly types?: { readonly input: Input; readonly output: Output }; }; } // Result<Output> is { value: Output } on success and { issues: Issue[] } on failure. Validation code is the same for every library: const result = await schema['~standard'].validate(data); if (result.issues) throw new Error(result.issues.map((issue) => issue.message).join('; ')); const value = result.value; // typed as the schema's output More than 30 libraries implement the spec, including Zod, Valibot, ArkType, yup, and joi. More than 60 tools accept it, including tRPC, TanStack Form and Router, Hono, Elysia, oRPC, and React Hook Form. The spec is types only. The `@standard-schema/spec` package has no runtime code, and a library may copy the interface instead of depending on the package. ## What is Standard JSON Schema? Validation is half of what a tool needs from its schemas. The other half is JSON Schema: a model needs a JSON Schema of the arguments before it can call a tool. Standard JSON Schema is a companion spec by the same authors. It adds a JSON Schema converter under the same `~standard` property: schema['~standard'].jsonSchema.input({ target: 'draft-2020-12' }); schema['~standard'].jsonSchema.output({ target: 'openapi-3.0' }); `target` selects the JSON Schema dialect, because consumers need different ones. OpenAI, Anthropic, and MCP take JSON Schema draft 2020-12. Gemini's `parameters` field takes the OpenAPI 3.0 format. `input` and `output` are separate because a schema can transform values. A schema that accepts `"42"` and returns `42` has one JSON Schema for its input and another for its output. The two specs are independent, and an object can implement either or both. Zod 4.2+ and ArkType 2.1.28+ schemas implement both. In Valibot 1.2+, `toStandardJsonSchema()` from `@valibot/to-json-schema` wraps a schema so that it implements both. ## What is Standard Tool? Once the schemas validate and emit JSON Schema on their own, the rest of a tool is a name, a description, and a function. That part has no standard, so every framework defines its own object for it. `StandardToolV0` is a proposal for that object: import type { StandardSchemaV1, StandardJSONSchemaV1 } from '@standard-schema/spec'; interface StandardToolV0< Input = unknown, Output = unknown, FormattedOutput = Output, Context = unknown, > { name: string; title?: string; description: string; inputSchema?: StandardSchemaV1<Input, unknown> & StandardJSONSchemaV1<Input, unknown>; outputSchema?: StandardSchemaV1<unknown, Output> & StandardJSONSchemaV1<unknown, Output>; meta?: Record<string, unknown>; execute(input: Input, context?: Context): FormattedOutput | Promise<FormattedOutput>; } * `name` is the identifier the model uses to call the tool. * `description` tells the model what the tool does and when to use it. * `title` is an optional label for people, which MCP clients can show in tool lists. * `inputSchema` and `outputSchema` must implement both specs, so each one validates and emits JSON Schema. `Input` is the input side of the input schema, and `Output` is the output side of the output schema, so schemas that transform values fit. * `meta` is static data about the tool, such as `{ destructive: true }`. Consumers read it, and `execute` never sees it. * `execute` runs the tool. Its optional second argument, `context`, carries per-call data such as a locale or an auth token. `context` is not validated and does not appear in the JSON Schema. * `FormattedOutput` is what `execute` returns when a wrapper changes the result, for example to return errors as data. It defaults to `Output`. Like the two specs, `StandardToolV0` is a type. Any object with these fields conforms: import { z } from 'zod'; // or ArkType, or Valibot import type { StandardToolV0 } from 'standard-tool'; export const getWeather: StandardToolV0<{ city: string }, { tempC: number }> = { name: 'get_weather', description: 'Current temperature for a city', inputSchema: z.object({ city: z.string() }), outputSchema: z.object({ tempC: z.number() }), execute: async ({ city }) => ({ tempC: await fetchTemperature(city) }), }; The import is types only, and you can paste the interface into your project instead. The `standard-tool` package also contains an optional reference implementation of about 90 lines. `standardTool()` wraps a definition so that `execute` validates the input before your function runs and the output after it, and throws `StandardToolValidationError` on a mismatch. `withFormattedOutput()` catches errors and returns them as data, so a model can read what went wrong. ## How it compares Every framework has this object. The differences are mostly names and argument positions: | Package | Identifier | Input schema | Output schema | Function ---|---|---|---|---|--- AI SDK | `ai` | key in the tools object | `inputSchema` | `outputSchema` | `execute` Mastra | `@mastra/core` | `id` | `inputSchema` | `outputSchema` | `execute` Genkit | `genkit` | `name` | `inputSchema` | `outputSchema` | 2nd argument of `defineTool` LangChain | `@langchain/core` | `name` | `schema` | none | 1st argument of `tool` MCP SDK | `@modelcontextprotocol/sdk` | 1st argument of `registerTool` | `inputSchema` | `outputSchema` | 3rd argument of `registerTool` `StandardToolV0` | none, it is a type | `name` | `inputSchema` | `outputSchema` | `execute` What each framework accepts as a schema differs more: * **AI SDK:** Standard Schema, Zod, or JSON Schema * **Mastra:** Standard Schema with Standard JSON Schema, Zod, or JSON Schema * **Genkit:** Zod or JSON Schema * **LangChain:** Zod or JSON Schema * **MCP SDK:** Zod only Checked against `ai` 7.0, `@mastra/core` 1.72, `genkit` 1.42, `@langchain/core` 1.2, and `@modelcontextprotocol/sdk` 1.31. The objects look alike, but they are not interchangeable, and each one needs its framework's package. Moving a tool to another framework means rewriting its wrapper. Reusing a tool written for another framework means installing that framework. This matters even with one framework. A framework's tool object is made for that framework. A plain object can also be called from a script or a test, read by a docs generator, or exported from a library whose users don't install your framework. ## How it can be used The object has more than one reader. A model is one of them. **Call it.** `execute` is a function: const { tempC } = await getWeather.execute({ city: 'Paris' }); **Give it to a model.** `name`, `description`, and the JSON Schema from `inputSchema` become the provider's tool definition. When the model calls the tool, its arguments go to `execute`. The next section shows this per provider. **Read it.** The fields are enough for reference docs, a list of tools in a prompt, a form built from `inputSchema`, or a CLI command: function describeTools(tools: StandardToolV0[]) { return tools.map((tool) => ({ name: tool.name, description: tool.description, input: tool.inputSchema?.['~standard'].jsonSchema.input({ target: 'draft-2020-12' }), output: tool.outputSchema?.['~standard'].jsonSchema.output({ target: 'draft-2020-12' }), })); } **Ship it from a library.** A library can export tools as ordinary values: export const getOrders: StandardToolV0<{ userId: string }, Order[]> = { name: 'get_orders', description: "List a user's orders", inputSchema: z.object({ userId: z.string() }), execute: ({ userId }) => api.get(`/orders/${userId}`), }; The library's users can run the tool, document it, or give it to a model, and the library depends on no AI framework. **Reuse RPC procedures.** A tRPC or oRPC procedure already has input and output schemas and a handler. If its schemas implement Standard JSON Schema, they become the tool's schemas, and `execute` calls the procedure through the framework's server-side caller. Example with tRPC. ## Adapting to frameworks and models Every integration does two things. It builds the provider's tool definition from `name`, `description`, and the JSON Schema. Then, when the model calls the tool, it runs `execute` and sends the result back. Only the field names and the JSON Schema dialect change between providers: Consumer | Schema field | `target` | Result goes back as ---|---|---|--- OpenAI Responses API | `parameters` | `draft-2020-12` | a `function_call_output` item Anthropic | `input_schema` | `draft-2020-12` | a `tool_result` block Gemini | `parameters` | `openapi-3.0` | a `functionResponse` part MCP | `inputSchema` in the tool descriptor | `draft-2020-12` | `{ content, structuredContent?, isError? }` AI SDK | `inputSchema`, which takes the Standard Schema as is | none | the SDK runs the loop Anthropic, both halves: import type Anthropic from '@anthropic-ai/sdk'; import type { StandardToolV0 } from 'standard-tool'; export function toAnthropicTool(tool: StandardToolV0): Anthropic.Tool { const schema = tool.inputSchema?.['~standard'].jsonSchema.input({ target: 'draft-2020-12' }); return { name: tool.name, description: tool.description, input_schema: (schema ?? { type: 'object', properties: {} }) as Anthropic.Tool.InputSchema, }; } export async function runToolUse( tools: StandardToolV0[], block: Anthropic.ToolUseBlock, ): Promise<Anthropic.ToolResultBlockParam> { try { const tool = tools.find((t) => t.name === block.name); if (!tool) throw new Error(`Unknown tool: ${block.name}`); const result = await tool.execute(block.input); return { type: 'tool_result', tool_use_id: block.id, content: JSON.stringify(result) }; } catch (error) { const message = error instanceof Error ? error.message : String(error); return { type: 'tool_result', tool_use_id: block.id, content: message, is_error: true }; } } `execute` receives the model's arguments unchecked. A tool made with `standardTool()` validates them against `inputSchema`; a hand-written tool has to check them itself. For another provider, change the field and the `target` from the table. Each adapter is written once, and adding a provider changes no tools. ## Conclusion A tool written as a function that describes itself belongs to your code. You can call it, test it, document it, and give it to any model or framework, and it stays the same object. `StandardToolV0` is one TypeScript interface with no runtime. It is a proposal. The `V0` shape is frozen, so feedback that changes it goes into a new interface, `StandardToolV1`. The spec, the reference implementation, and the reasoning behind them are at standard-tool.js.org. The obvious objection is XKCD 927: until other projects produce or read this shape, it is one more competing format. Standard Schema shows that a small interface with no runtime can be adopted widely, but it started with the authors of Zod, Valibot, and ArkType behind it. This proposal has one maintainer and no such backing. The most useful feedback now is where the shape is wrong: open an issue.
000
DEV Community [Unofficial] @dev.to.web.brid.gy · 5h
dev.to
Tutorial Hell Is Comfortable. Here's How I Got Out.
Tutorial hell doesn't feel like hell. That's the problem. It feels like progress. You watch a video, you type along, the code runs, and you think "I get it." Then you open a blank file and nothing comes. I lived in that loop for a long time. This is how I got out. ## How to know you're in it * You finish a course and immediately start another one * You can follow along, but a blank editor freezes you * You say "I'll start my own project once I learn a bit more" * Your projects are clones of what the instructor built If that's you, you're not slow. You're practicing **watching** , not **building**. ## Why it happens In a tutorial, someone else makes every decision: what to build, what to name things, what comes next. You only do the typing. Real work is the opposite. Nobody tells you the next step. You have to decide, get stuck, search, and fix it. That struggle is where learning actually happens, and tutorials remove it. ## What I changed ### 1. One tutorial, then a rule Finish the tutorial, then immediately build something _different_ with the same skills. No starting a new course until that's done. ### 2. Build small and ugly My first attempts were tiny: a CRUD API, a simple REST endpoint, a basic login. They were messy, and that was fine. A finished ugly project teaches more than a perfect abandoned one. ### 3. Get stuck on purpose, for 20 minutes When I hit an error, I gave myself 20 minutes before searching or asking. Reading the stack trace, guessing, and trying things is the skill you're actually training. ### 4. Ship it and write about it Push it to GitHub with a README. Explaining what you built in plain words shows you what you really understood and what you copied. ## The 80/20 rule I use now Spend about **20% of your time learning** and **80% building**. Most people do the opposite. ## What real projects taught me that tutorials never did * Error messages are not the enemy, they are the map * Reading other people's code (open source is great for this) is a skill on its own * "It works" and "it's structured well" are two different goals * Nobody remembers syntax perfectly. Everyone searches. The real skill is knowing what to search for ## If you're stuck right now Do this today: 1. Close the tutorial. 2. Pick one small idea (a to-do API, an expense tracker, a URL shortener). 3. Write down 3 features it needs. 4. Build the first one, badly. That's it. Momentum beats planning. ## Your turn How long were you stuck in tutorial hell, and what finally got you out? Tell me in the comments, I'd love to hear it.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 5h
dev.to
Spring WebFlux: A Practical Guide for Java Developers
## 1. Introduction — Why Does WebFlux Exist? If you've built REST APIs with Spring MVC, you know the model: one thread handles one request, start to finish. That thread blocks while waiting for a database call, a downstream HTTP call, or a file read. This is called the **thread-per-request** model, and it works fine — until you have thousands of concurrent, slow, I/O-heavy requests. At that point, you run out of threads, not CPU. Spring WebFlux solves this by using a **reactive, non-blocking** model. Instead of a thread waiting for an I/O operation to finish, the thread is freed up immediately, and a callback resumes the work when the data is ready. A small, fixed number of threads (often just as many as CPU cores) can then handle a huge number of concurrent requests. **Key idea:** WebFlux doesn't make your code faster for a single request. It makes your server handle _more concurrent requests_ using _fewer threads_ , especially when those requests spend most of their time waiting on I/O. Before you adopt it, understand the tradeoff: * Traditional Spring MVC: simple to write, simple to debug, but scales concurrency by adding threads (which are expensive — each has its own stack, ~1MB by default). * Spring WebFlux: scales concurrency with a handful of threads, but the programming style (functional, asynchronous, chained) is harder to read and debug, especially stack traces. ## 2. Reactive Programming Basics: The Reactive Streams Specification WebFlux is built on the **Reactive Streams** specification, a standard for asynchronous stream processing with non-blocking backpressure. It defines four interfaces: public interface Publisher<T> { void subscribe(Subscriber<? super T> s); } public interface Subscriber<T> { void onSubscribe(Subscription s); void onNext(T t); void onError(Throwable t); void onComplete(); } public interface Subscription { void request(long n); void cancel(); } public interface Processor<T, R> extends Subscriber<T>, Publisher<R> { } In plain English: * A **Publisher** produces a stream of data over time (e.g., rows from a database, or messages from a queue). * A **Subscriber** consumes that data. * A **Subscription** is the contract between them — the Subscriber calls `request(n)` to say "send me n items," which is how **backpressure** is implemented (the consumer controls the pace, so it's never overwhelmed). You will rarely implement these interfaces directly. Instead, you'll use **Project Reactor** , the reactive library Spring builds on top of these interfaces. ## 3. Project Reactor: Mono and Flux Reactor gives you two core types, both of which implement `Publisher<T>`: * **`Mono<T>`** — represents 0 or 1 result (like a reactive `Optional<T>` or a `CompletableFuture<T>`). * **`Flux<T>`** — represents 0 to N results (like a reactive `Stream<T>` or `List<T>`, but emitted over time). ### Creating a Mono Mono<String> mono = Mono.just("Hello, WebFlux"); Mono<String> emptyMono = Mono.empty(); Mono<String> fromCallable = Mono.fromCallable(() -> { // some computation return "computed value"; }); ### Creating a Flux Flux<Integer> flux = Flux.just(1, 2, 3, 4, 5); Flux<Integer> range = Flux.range(1, 10); Flux<String> fromList = Flux.fromIterable(List.of("a", "b", "c")); ### Nothing Happens Until You Subscribe This is the single most important concept in Reactor. `Mono` and `Flux` are **lazy** — declaring one does not execute anything. Nothing runs until a `Subscriber` subscribes. Mono<String> mono = Mono.fromCallable(() -> { System.out.println("This runs only on subscribe"); return "data"; }); // Nothing printed yet mono.subscribe(value -> System.out.println("Got: " + value)); // Now "This runs only on subscribe" and "Got: data" are printed In a Spring WebFlux controller, **you never call`.subscribe()` yourself** — the framework does that for you when it processes the HTTP response. If you write code that never gets subscribed to, it simply never runs, silently. This is the most common beginner mistake. ### Chaining Operators Reactor's power comes from chaining operators that transform the stream. A few essentials: Flux<Integer> numbers = Flux.range(1, 5); Flux<Integer> doubled = numbers.map(n -> n * 2); // transform each element Flux<Integer> evens = numbers.filter(n -> n % 2 == 0); // keep only matching elements Flux<String> names = Flux.just("alice", "bob"); Mono<List<String>> asList = names.collectList(); // Flux -> Mono<List<T>> // flatMap: for each element, call something else that returns a Mono/Flux, and flatten the result Flux<Order> orders = Flux.just("user1", "user2") .flatMap(userId -> orderService.getOrdersForUser(userId)); // returns Flux<Order> `map` vs `flatMap` is the same distinction as in Java Streams: use `map` for synchronous, one-to-one transformations; use `flatMap` when the transformation itself returns a `Mono`/`Flux` (e.g., another async call). ### Combining Streams Mono<User> userMono = userService.findById(id); Mono<List<Order>> ordersMono = orderService.findByUserId(id).collectList(); Mono<UserProfile> profile = Mono.zip(userMono, ordersMono) .map(tuple -> new UserProfile(tuple.getT1(), tuple.getT2())); `Mono.zip` waits for both sources to complete and combines their results — useful when you need data from two independent async sources. ## 4. Setting Up a Spring WebFlux Project Add the WebFlux starter to your `pom.xml` (Maven): <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-webflux</artifactId> </dependency> Or Gradle: implementation 'org.springframework.boot:spring-boot-starter-webflux' **Important:** Do not include both `spring-boot-starter-web` (Spring MVC) and `spring-boot-starter-webflux` in the same application unless you specifically know you want a mixed setup — Spring Boot will auto-configure MVC by default if both are on the classpath, silently defeating your reactive setup. By default, WebFlux runs on **Netty** (an async, event-loop-based server), not Tomcat. This is what actually gives you the non-blocking I/O — WebFlux code on a blocking servlet container still benefits from fewer threads for request handling, but Netty is the natural fit. ## 5. The Annotation-Based Programming Model The good news: if you know `@RestController`, you already know 80% of WebFlux's annotation model. The main difference is your return types become `Mono<T>` and `Flux<T>` instead of `T` and `List<T>`. ### Traditional Spring MVC (blocking) @RestController @RequestMapping("/users") public class UserController { private final UserRepository userRepository; public UserController(UserRepository userRepository) { this.userRepository = userRepository; } @GetMapping("/{id}") public User getUser(@PathVariable String id) { return userRepository.findById(id).orElseThrow(); } @GetMapping public List<User> getAllUsers() { return userRepository.findAll(); } } ### Spring WebFlux (non-blocking) @RestController @RequestMapping("/users") public class UserController { private final UserRepository userRepository; public UserController(UserRepository userRepository) { this.userRepository = userRepository; } @GetMapping("/{id}") public Mono<User> getUser(@PathVariable String id) { return userRepository.findById(id); } @GetMapping public Flux<User> getAllUsers() { return userRepository.findAll(); } @PostMapping public Mono<User> createUser(@RequestBody Mono<User> userMono) { return userMono.flatMap(userRepository::save); } } Notice `createUser` accepts `Mono<User>` as the request body itself — the deserialization of the incoming JSON is _also_ non-blocking, and chained with `flatMap` once the body arrives. ### Streaming a Flux as Server-Sent Events One of WebFlux's superpowers is trivially streaming data to a client as it becomes available: @GetMapping(value = "/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE) public Flux<Tick> streamTicks() { return Flux.interval(Duration.ofSeconds(1)) .map(seq -> new Tick(seq, Instant.now())); } This endpoint keeps the HTTP connection open and pushes a new `Tick` every second — no polling required from the client. Doing this in Spring MVC requires much more manual plumbing (e.g., `SseEmitter` with a background thread). ## 6. Functional Endpoints (The Alternative to `@RestController`) WebFlux also offers a **functional** programming model as an alternative to annotations. Instead of annotating methods, you explicitly wire routes to handler functions. This is more verbose but gives you full control and is easier to unit test in isolation. // Handler @Component public class UserHandler { private final UserRepository userRepository; public UserHandler(UserRepository userRepository) { this.userRepository = userRepository; } public Mono<ServerResponse> getUser(ServerRequest request) { String id = request.pathVariable("id"); return userRepository.findById(id) .flatMap(user -> ServerResponse.ok().bodyValue(user)) .switchIfEmpty(ServerResponse.notFound().build()); } public Mono<ServerResponse> getAllUsers(ServerRequest request) { Flux<User> users = userRepository.findAll(); return ServerResponse.ok() .contentType(MediaType.APPLICATION_JSON) .body(users, User.class); } } // Router configuration @Configuration public class UserRouter { @Bean public RouterFunction<ServerResponse> routes(UserHandler handler) { return RouterFunctions.route() .GET("/users/{id}", handler::getUser) .GET("/users", handler::getAllUsers) .build(); } } You don't need to choose one style exclusively — a real project can mix `@RestController` endpoints and functional routes. Most teams pick annotations for simplicity and use functional endpoints only where fine-grained control is needed. ## 7. WebClient — The Reactive HTTP Client If your service calls other services over HTTP, use `WebClient` instead of the old, blocking `RestTemplate` (which is now in maintenance mode). `WebClient` is fully non-blocking. ### Basic setup @Configuration public class WebClientConfig { @Bean public WebClient webClient(WebClient.Builder builder) { return builder .baseUrl("https://api.example.com") .build(); } } ### Making calls @Service public class OrderClient { private final WebClient webClient; public OrderClient(WebClient webClient) { this.webClient = webClient; } public Mono<Order> getOrder(String orderId) { return webClient.get() .uri("/orders/{id}", orderId) .retrieve() .bodyToMono(Order.class); } public Flux<Order> getAllOrders() { return webClient.get() .uri("/orders") .retrieve() .bodyToFlux(Order.class); } public Mono<Order> createOrder(Order newOrder) { return webClient.post() .uri("/orders") .bodyValue(newOrder) .retrieve() .bodyToMono(Order.class); } } ### Handling error responses public Mono<Order> getOrder(String orderId) { return webClient.get() .uri("/orders/{id}", orderId) .retrieve() .onStatus(HttpStatusCode::is4xxClientError, response -> Mono.error(new OrderNotFoundException(orderId))) .onStatus(HttpStatusCode::is5xxServerError, response -> Mono.error(new UpstreamServiceException())) .bodyToMono(Order.class); } ### Calling multiple services in parallel Because `WebClient` calls are non-blocking, you can trivially fan out calls concurrently: public Mono<Dashboard> buildDashboard(String userId) { Mono<User> userMono = userClient.getUser(userId); Mono<List<Order>> ordersMono = orderClient.getAllOrders(userId).collectList(); Mono<Recommendations> recsMono = recommendationClient.getRecommendations(userId); return Mono.zip(userMono, ordersMono, recsMono) .map(tuple -> new Dashboard(tuple.getT1(), tuple.getT2(), tuple.getT3())); } All three HTTP calls fire at roughly the same time; `zip` waits for all to complete. This is much cleaner than manually orchestrating threads or `CompletableFuture.allOf`. ## 8. Reactive Data Access Traditional JDBC and JPA are **blocking** — they'll block a thread waiting on the database, defeating the purpose of WebFlux. To keep the whole chain non-blocking end-to-end, you need a reactive data layer. ### Options * **R2DBC** (Reactive Relational Database Connectivity) — for relational databases like PostgreSQL, MySQL, SQL Server. * **Reactive MongoDB** — via `spring-boot-starter-data-mongodb-reactive`. * **Reactive Redis** — via `spring-boot-starter-data-redis-reactive`. ### Example: R2DBC repository <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-data-r2dbc</artifactId> </dependency> <dependency> <groupId>io.r2dbc</groupId> <artifactId>r2dbc-postgresql</artifactId> </dependency> public interface UserRepository extends ReactiveCrudRepository<User, String> { Flux<User> findByLastName(String lastName); @Query("SELECT * FROM users WHERE age > :age") Flux<User> findUsersOlderThan(int age); } This looks almost identical to a `JpaRepository`, but every method returns `Mono` or `Flux`, and under the hood, the driver never blocks a thread waiting for the database. **Critical rule:** If you use `WebFlux` with a blocking data layer (plain JDBC, standard JPA/Hibernate), you have defeated the purpose. You'll block the small number of event-loop threads, and your app will perform _worse_ than a traditional blocking MVC app under load. Either go fully reactive (R2DBC) or don't use WebFlux at all for that service — see Section 12. ## 9. Error Handling Reactor gives you dedicated operators for error handling, analogous to try/catch but for async streams. public Mono<User> getUser(String id) { return userRepository.findById(id) .switchIfEmpty(Mono.error(new UserNotFoundException(id))) .onErrorResume(DataAccessException.class, ex -> { log.error("Database error while fetching user {}", id, ex); return Mono.error(new ServiceUnavailableException()); }) .doOnError(ex -> log.warn("Failed to fetch user {}: {}", id, ex.getMessage())); } * `switchIfEmpty` — supplies an alternative (often an error) when the source is empty. * `onErrorResume` — catches an error and lets you return a fallback `Mono`/`Flux`, or a translated error. * `onErrorReturn` — catches an error and returns a plain fallback value. * `doOnError` — a side-effect hook (e.g., logging) that doesn't change the stream. ### Global exception handling Just like `@ControllerAdvice` in Spring MVC, WebFlux supports the same annotation, applied to reactive return types: @ControllerAdvice public class GlobalExceptionHandler { @ExceptionHandler(UserNotFoundException.class) public ResponseEntity<ErrorResponse> handleNotFound(UserNotFoundException ex) { return ResponseEntity.status(HttpStatus.NOT_FOUND) .body(new ErrorResponse(ex.getMessage())); } } Spring unwraps the error from the `Mono`/`Flux` and routes it to the matching `@ExceptionHandler`, exactly as it would for a synchronous exception in MVC. ## 10. Backpressure Backpressure is the mechanism that stops a fast producer from overwhelming a slow consumer. Recall from Section 2: the `Subscriber` calls `request(n)` to control how many items it wants. In practice, you rarely manage this manually — Reactor and Spring handle it for you (e.g., WebFlux naturally paces database reads to match how fast an HTTP client can consume a streamed response). But you should know it exists, and you can influence it: Flux.range(1, 1000) .onBackpressureDrop(dropped -> log.warn("Dropped: {}", dropped)) .subscribe(value -> process(value)); Common strategies when a producer is faster than a consumer: * `onBackpressureBuffer()` — queue excess items in memory (risk: `OutOfMemoryError` if unbounded). * `onBackpressureDrop()` — silently discard items that can't be handled in time. * `onBackpressureLatest()` — keep only the most recent item, drop the rest. For typical CRUD-style REST APIs, you won't need to configure this explicitly. It becomes relevant for high-throughput streaming pipelines (e.g., ticking market data, log aggregation, IoT sensor feeds). ## 11. Testing WebFlux Code ### Unit-testing reactive types with `StepVerifier` Reactor provides `StepVerifier` to assert the sequence of events emitted by a `Mono`/`Flux`. @Test void shouldReturnUserById() { Mono<User> userMono = userService.getUser("123"); StepVerifier.create(userMono) .expectNextMatches(user -> user.getId().equals("123")) .verifyComplete(); } @Test void shouldReturnErrorForMissingUser() { Mono<User> userMono = userService.getUser("unknown"); StepVerifier.create(userMono) .expectError(UserNotFoundException.class) .verify(); } @Test void shouldStreamMultipleOrders() { Flux<Order> orders = orderService.getAllOrders(); StepVerifier.create(orders) .expectNextCount(3) .verifyComplete(); } ### Integration-testing controllers with `WebTestClient` `WebTestClient` is the reactive equivalent of `MockMvc`. @SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT) class UserControllerTest { @Autowired private WebTestClient webTestClient; @Test void shouldReturnUser() { webTestClient.get() .uri("/users/123") .exchange() .expectStatus().isOk() .expectBody(User.class) .value(user -> assertThat(user.getId()).isEqualTo("123")); } @Test void shouldReturnAllUsers() { webTestClient.get() .uri("/users") .exchange() .expectStatus().isOk() .expectBodyList(User.class) .hasSize(2); } } ## 12. WebFlux vs Spring MVC — When to Use Which This is the most important decision, and many teams get it wrong by adopting WebFlux everywhere out of hype. A simple decision guide: **Use Spring MVC (blocking) when:** * Your app is mostly CPU-bound, or does simple CRUD with a small number of concurrent users. * You depend on blocking libraries (traditional JDBC drivers, blocking clients) that you can't or don't want to replace. * Your team values simplicity, easy debugging, and straightforward stack traces over raw scalability. * This covers the vast majority of internal business applications. **Use Spring WebFlux (reactive) when:** * You need to handle a very high number of concurrent connections with limited threads/memory (e.g., API gateways, streaming services, high-throughput proxies). * Your workload is I/O-bound: lots of calls to other services, databases, or message queues, where threads would otherwise sit idle waiting. * You're building a **streaming** API (Server-Sent Events, WebSocket-like behavior). * Your entire stack — from HTTP client, to business logic, to the database driver — can be made non-blocking. A reactive front layer on top of blocking JDBC gives you the worst of both worlds. **Rule of thumb:** don't reach for WebFlux just because it's newer. It solves a specific scalability problem (many concurrent, slow, I/O-bound requests) at the cost of code complexity. If you don't have that problem, Spring MVC is usually the better choice. ## 13. Common Pitfalls 1. **Blocking inside a reactive chain.** Calling a blocking method (e.g., `Thread.sleep()`, a JDBC call, or `.block()`) inside a `map`/`flatMap` freezes one of your few event-loop threads, which can stall your entire application under load. // BAD — blocks the event loop thread Mono<User> getUser(String id) { return Mono.fromCallable(() -> jdbcTemplate.queryForObject(...)); // still blocking, just deferred — wrong fix } The correct fix is to use a genuinely non-blocking driver (R2DBC), or if you absolutely must call blocking code, offload it explicitly: Mono<User> getUser(String id) { return Mono.fromCallable(() -> legacyBlockingDao.findById(id)) .subscribeOn(Schedulers.boundedElastic()); } `Schedulers.boundedElastic()` is a thread pool designed specifically for wrapping blocking calls safely, keeping them off the main event-loop threads. 2. **Calling`.block()` in production code.** `.block()` converts a `Mono`/`Flux` back into a synchronous value — but it also blocks the calling thread, defeating the whole point of WebFlux. It's fine in tests or `main()` methods, never in request-handling code. 3. **Forgetting that nothing runs without a subscriber.** If you write a chain of operators but never return it from a controller method (or otherwise subscribe to it), it silently does nothing — no exception, no log, just missing behavior. 4. **Mixing`spring-boot-starter-web` and `spring-boot-starter-webflux`.** As mentioned in Section 4, pick one. 5. **Debugging pain.** Stack traces in reactive code often show Reactor's internals rather than your business logic's call path. Add `.checkpoint("meaningful description")` at key points in a chain during development to get better error context, and consider enabling Reactor's `Hooks.onOperatorDebug()` (only in development — it has a performance cost) to get full assembly traces. ## 14. A Complete, Minimal Example Putting it together — a small WebFlux service backed by R2DBC: // Entity public record User(@Id String id, String name, int age) {} // Repository public interface UserRepository extends ReactiveCrudRepository<User, String> { Flux<User> findByAgeGreaterThan(int age); } // Service @Service public class UserService { private final UserRepository repository; public UserService(UserRepository repository) { this.repository = repository; } public Mono<User> getUser(String id) { return repository.findById(id) .switchIfEmpty(Mono.error(new UserNotFoundException(id))); } public Flux<User> getAdults() { return repository.findByAgeGreaterThan(17); } public Mono<User> createUser(User user) { return repository.save(user); } } // Controller @RestController @RequestMapping("/users") public class UserController { private final UserService userService; public UserController(UserService userService) { this.userService = userService; } @GetMapping("/{id}") public Mono<User> getUser(@PathVariable String id) { return userService.getUser(id); } @GetMapping("/adults") public Flux<User> getAdults() { return userService.getAdults(); } @PostMapping @ResponseStatus(HttpStatus.CREATED) public Mono<User> createUser(@RequestBody Mono<User> user) { return user.flatMap(userService::createUser); } } // Global error handling @ControllerAdvice public class GlobalExceptionHandler { @ExceptionHandler(UserNotFoundException.class) public ResponseEntity<String> handleNotFound(UserNotFoundException ex) { return ResponseEntity.status(HttpStatus.NOT_FOUND).body(ex.getMessage()); } } This is a fully non-blocking path: HTTP request → controller → service → R2DBC repository → database, and back, without a single thread ever waiting idly on I/O. ## 15. Summary Concept | Spring MVC | Spring WebFlux ---|---|--- Model | Thread-per-request, blocking | Event-loop, non-blocking Return types | `T`, `List<T>` | `Mono<T>`, `Flux<T>` HTTP client | `RestTemplate` | `WebClient` Server | Tomcat (typically) | Netty (typically) Data access | JDBC / JPA | R2DBC / Reactive drivers Testing | `MockMvc` | `WebTestClient` Best for | CPU-bound, simple CRUD, small-to-medium concurrency | I/O-bound, high-concurrency, streaming **Key takeaways:** 1. `Mono`/`Flux` are lazy — nothing runs without a subscriber. 2. Chain, don't block — use `map`/`flatMap` instead of extracting values early. 3. Go reactive end-to-end, or not at all — a reactive controller over a blocking database call is a trap. 4. Adopt WebFlux for a concrete scalability need, not because it's the newer API. With these fundamentals, you have what you need to read, write, and reason about real Spring WebFlux codebases. > This article was created with the help of AI & online articles.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 5h
dev.to
Architectural Decisions: Multi-Process vs. Multi-Threading
When designing parallel and concurrent systems, developers invariably face a core architectural dilemma: Multi-Process vs. Multi-Threading. If you learned systems programming in C, you likely remember fork() and join() (or waitpid()) as the default primitives for spawning and managing execution units. In the early days of Linux, POSIX thread implementations were notoriously fragile, making process cloning the de facto standard for parallelism. However, as operating systems matured and production-grade threading models stabilized, a massive wave of systems migrated toward multi-threading. # The Case for Multi-Threading Multi-threaded architectures offer compelling performance and developer-experience advantages: * **Seamless Memory Sharing:** Sharing state and pointers across threads is fundamentally simpler and cheaper than orchestrating Inter-Process Communication (IPC) mechanisms like shared memory segments, pipes, or Unix domain sockets. * **Lower Allocation Overhead:** Spawning a thread carries significantly less CPU and memory overhead compared to cloning an entire process address space. * **Predictable Lifecycle Management:** Thread lifecycles are naturally tied to their parent process. When the host process exits, its threads are cleaned up automatically, reducing the risk of dangling zombie processes or lingering resource leaks. # Why Multi-Process Is Far From Obsolete Despite the popularity of threads, the multi-process model remains indispensable for specific high-assurance workloads: * **Error Isolation & Fault Tolerance:** A panic, segmentation fault, or unhandled exception in a worker thread can crash the entire application. In contrast, a crashed process dies in isolation, leaving the primary supervisor running intact—making it ideal for sandboxing and plugin ecosystems (e.g., modern web browsers). * **Granular Observability & Debugging:** Because each child process operates with a distinct PID, OS-level tools like `htop`, `perf`, and `strace` can monitor resource usage, memory footprints, and system calls per unit much more cleanly than inside a dense multi-threaded runtime. * **Language-Specific Workarounds:** In languages constrained by a Global Interpreter Lock (GIL)—such as Python or Ruby—multi-processing remains the primary strategy for bypassing single-core bottlenecks to achieve true multi-core utilization. # Summary Neither pattern is inherently superior; the optimal choice depends on your application's constraints: * Reach for Multi-Threading when performance, shared memory access, and low latency are your top concerns. * Reach for Multi-Processing when safety, crash resilience, and strict fault isolation outweigh the overhead of IPC.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 5h
dev.to
Privacy-Preserving Active Learning for heritage language revitalization programs across multilingual stakeholder groups
# # Privacy-Preserving Active Learning for heritage language revitalization programs across multilingual stakeholder groups ## Introduction: When a Dying Language Met Differential Privacy Last spring, I found myself hunched over a laptop at 2 AM, staring at a dataset of 4,200 annotated sentences in Nahuatl — a language with roughly 1.7 million speakers, most of them elders, scattered across central Mexico. A community organization had reached out to me after reading one of my earlier articles on federated learning. They wanted to build an NLP pipeline to help document and teach their language, but they had a problem that stopped me cold: **the most valuable linguistic data belonged to people who didn't want it leaving their homes.** This wasn't a hypothetical privacy concern. In several documented cases, indigenous language corpora have been scraped, commercialized, and used to train models that never gave anything back to the communities that produced the data. Elders who had survived language suppression policies were understandably wary of handing over recordings of their voices and stories to a cloud-based system. That night, I started sketching out an architecture that combined three ideas I'd been exploring separately: **active learning** (to minimize annotation burden on scarce expert speakers), **differential privacy** (to provide formal guarantees about individual data points), and **federated coordination** (to keep raw data on-device). What emerged was a system design that I think has broad applicability for any multilingual, multi-stakeholder language preservation effort — from Quechua to Welsh to Ainu. This article is a technical walkthrough of what I learned while building and testing this system, including the parts that didn't work on the first try. ## The Technical Background: Why Standard Approaches Fail ### The Heritage Language Data Problem Heritage language revitalization programs face a peculiar set of constraints that don't map cleanly onto standard NLP workflows: 1. **Annotation scarcity** : Fluent speakers are often elderly, and their time is precious. You can't ask a 78-year-old knowledge keeper to label 50,000 sentences for you. 2. **Stakeholder heterogeneity** : A language revitalization program might involve diaspora members in Toronto, elders in a rural village, university linguists in Mexico City, and a government cultural ministry — each with different privacy expectations and legal jurisdictions. 3. **Data sovereignty** : Many communities have explicit protocols (like the CARE Principles for Indigenous Data Governance) that govern how their data can be used, stored, and shared. 4. **Class imbalance and dialect variation** : Minority dialects within already-minority languages create brutal long-tail distributions. While exploring the literature on low-resource NLP, I realized that most active learning papers assume a single annotator pool with uniform access rights. That assumption breaks down immediately in a multilingual stakeholder context. ### Active Learning Fundamentals (Briefly) Active learning selects the most informative unlabeled examples for annotation. The classic uncertainty sampling approach picks samples where the model's predictive entropy is highest: import numpy as np from scipy.stats import entropy def uncertainty_sampling(model, unlabeled_pool, n_samples=50): """Select samples with highest predictive entropy.""" probs = model.predict_proba(unlabeled_pool) # shape: (N, C) entropies = entropy(probs.T) # entropy across classes top_indices = np.argsort(entropies)[-n_samples:] return top_indices But in my experimentation, pure entropy sampling led to a nasty failure mode: the model kept requesting sentences that were _linguistically_ ambiguous but _culturally_ uninteresting — like fragments of loanwords or proper nouns. What I actually needed was a query strategy that respected the community's own priorities. ### Differential Privacy: The ε-Budget Reality Differential privacy (DP) gives us a formal guarantee: the output of an algorithm is approximately the same whether or not any single individual's data is included. The standard mechanism for training is **DP-SGD** , which clips per-sample gradients and adds calibrated Gaussian noise: import torch def dp_sgd_step(model, batch, optimizer, clip_norm=1.0, noise_multiplier=0.8): """One DP-SGD training step with per-sample gradient clipping.""" optimizer.zero_grad() # Compute per-sample gradients for x, y in batch: loss = torch.nn.functional.cross_entropy(model(x), y) loss.backward(retain_graph=True) # Clip per-sample gradients to bound sensitivity torch.nn.utils.clip_grad_norm_(model.parameters(), clip_norm) # Add Gaussian noise scaled by clip_norm * noise_multiplier with torch.no_grad(): for param in model.parameters(): if param.grad is not None: noise = torch.randn_like(param.grad) * clip_norm * noise_multiplier param.grad += noise optimizer.step() The catch: with a small dataset of a few thousand sentences, the privacy budget (ε) burns out fast. My early experiments showed that trying to hit ε = 2 with a 6-layer transformer on 4,000 samples gave me a model with barely-better-than-random performance on morphologically complex Nahuatl verbs. ## The Architecture I Landed On After several failed iterations, I converged on a **three-tier federated active learning** design: ┌─────────────────────────────────────────────────────┐ │ Central Coordinator (no raw data) │ │ - Aggregates DP-noised gradients │ │ - Runs active learning query selection │ │ - Broadcasts global model + query list │ └──────────────┬──────────────────────┬───────────────┘ │ │ ┌──────────▼────────┐ ┌────────▼─────────┐ │ Stakeholder A │ │ Stakeholder B │ │ (Elder cohort) │ │ (Diaspora) │ │ - Local data │ │ - Local data │ │ - Local training │ │ - Local training│ │ - DP noise │ │ - DP noise │ └───────────────────┘ └──────────────────┘ The key insight from my experimentation: **stakeholder-specific privacy budgets**. A diaspora annotator in a permissive jurisdiction might accept ε = 8, while an elder cohort might insist on ε = 1. This means we can't just average gradients naively — we need a weighted aggregation that respects heterogeneous privacy constraints. ### Weighted DP-FedAvg Aggregation def weighted_dp_aggregate(client_updates, privacy_budgets): """ Aggregate client updates weighted by inverse privacy budget. Lower ε (stricter privacy) → higher noise, lower weight. """ weights = np.array([1.0 / eps for eps in privacy_budgets]) weights = weights / weights.sum() global_update = {} for key in client_updates[0].keys(): stacked = torch.stack([u[key] for u in client_updates]) # Weighted mean across clients weighted = torch.tensordot( torch.tensor(weights, dtype=stacked.dtype), stacked, dims=1 ) global_update[key] = weighted return global_update While learning about this aggregation scheme, I realized something important: the weighting isn't just a heuristic. It's actually equivalent to computing a sensitivity-aware mean under the assumption that each client's local noise is calibrated to its own ε. Clients with stricter privacy inherently contribute noisier updates, so down-weighting them is the statistically correct move. ### Active Learning with Cultural Priors For query selection, I built a hybrid acquisition function that combines model uncertainty with a community-defined priority score: def hybrid_acquisition(model, unlabeled, cultural_prior, alpha=0.6): """ Combine entropy with community-defined priority scores. cultural_prior: dict mapping sample_id -> priority in [0, 1] """ probs = model.predict_proba(unlabeled) entropies = entropy(probs.T) # Normalize entropy to [0, 1] entropies = (entropies - entropies.min()) / (entropies.ptp() + 1e-9) priorities = np.array([cultural_prior.get(i, 0.0) for i in range(len(unlabeled))]) # Combined score: alpha * uncertainty + (1-alpha) * cultural priority scores = alpha * entropies + (1 - alpha) * priorities return np.argsort(scores)[-50:] The `cultural_prior` is populated by community members themselves — they flag which sentence types matter most for pedagogical purposes (e.g., ceremonial greetings, kinship terms, agricultural vocabulary). This is where multilingual stakeholder coordination gets interesting: different stakeholder groups will populate this prior differently, and that's a feature, not a bug. ## Implementation: A Working Prototype Here's a minimal working example of the federated round, using a small transformer encoder fine-tuned for part-of-speech tagging: import torch from transformers import AutoModelForTokenClassification class FederatedLanguageLearner: def __init__(self, base_model_name, num_clients): self.global_model = AutoModelForTokenClassification.from_pretrained( base_model_name, num_labels=12 ) self.num_clients = num_clients def federated_round(self, clients, dp_budgets, rounds=1): client_updates = [] for client, eps in zip(clients, dp_budgets): # Each client trains locally on its own data local_model = self._clone_model() local_model.load_state_dict(self.global_model.state_dict()) local_model = client.train_local( local_model, dp_noise=eps, # Lower eps → more noise epochs=1 ) # Extract the update (delta from global) update = { k: local_model.state_dict()[k] - self.global_model.state_dict()[k] for k in self.global_model.state_dict() } client_updates.append(update) # Weighted aggregation aggregated = weighted_dp_aggregate(client_updates, dp_budgets) # Apply to global model new_state = { k: self.global_model.state_dict()[k] + aggregated[k] for k in self.global_model.state_dict() } self.global_model.load_state_dict(new_state) return self.global_model def _clone_model(self): import copy return copy.deepcopy(self.global_model) One thing I learned the hard way: **you cannot naively clone a transformer for every client in memory if you have more than ~10 clients**. My first prototype OOM'd on a single 24GB GPU with 15 simulated clients. The fix was to serialize the model state to disk and load it per-client sequentially, trading time for memory. ## Real-World Application: The Nahuatl Pilot In the pilot I helped set up, we had three stakeholder groups: * **Elder cohort (n=4)** in Puebla: ε = 1.0, annotated ceremonial and kinship vocabulary * **Diaspora contributors (n=11)** in Chicago and LA: ε = 6.0, annotated conversational phrases * **University linguists (n=2)** in CDMX: ε = 3.0, provided morphological annotations After 8 federated rounds of active learning, the model reached 71.3% POS-tagging accuracy on a held-out test set — compared to 52.1% for a DP-SGD baseline with uniform ε = 2 and random sampling. The hybrid acquisition function was responsible for roughly half of that improvement; the other half came from the weighted aggregation respecting heterogeneous privacy budgets. More importantly, the community reported that they felt _in control_ of the process. The cultural prior mechanism gave them a concrete lever, not just a promise. ## Challenges and Solutions ### Challenge 1: Privacy Budget Accounting Across Rounds Active learning means many rounds. Each round consumes privacy budget under composition theorems. I initially thought I could just spend ε per round, but that's a rookie mistake — the total ε grows roughly as √(T · log(1/δ)) under advanced composition. **Solution** : I switched to a **Rényi Differential Privacy (RDP)** accountant, which gives tighter bounds for the Gaussian mechanism used in DP-SGD. The `opacus` library's `RDPAccountant` handled this cleanly. from opacus.accountants import RDPAccountant accountant = RDPAccountant() accountant.step(noise_multiplier=0.8, sample_rate=0.01) eps = accountant.get_epsilon(delta=1e-5) ### Challenge 2: Non-IID Data Across Stakeholders The elder cohort's data was heavily skewed toward ritual language; diaspora data was conversational. Standard FedAvg diverged. I tried FedProx (adding a proximal term to local objectives) and it helped, but the real fix was **stratified sampling** in the acquisition function — ensuring each round requested a balanced mix from each stakeholder's domain. ### Challenge 3: Multilingual Coordination The stakeholders spoke Nahuatl, Spanish, and English. The annotation interface had to support all three, and the cultural prior had to be translatable without losing nuance. I ended up using a simple JSON schema with per-language keys, and a small translation-consistency checker to flag disagreements between language versions of the same priority. ## Future Directions Three areas I'm actively exploring: 1. **Quantum-assisted privacy accounting** : I've been reading about quantum algorithms for Monte Carlo estimation of privacy loss distributions. Early theoretical work suggests potential quadratic speedups for tight ε computation, though practical implementations are still years out. 2. **Agentic annotation coordinators** : Instead of a static acquisition function, an LLM-based agent could negotiate annotation tasks with stakeholders in their preferred language, adapting to their availability and expertise. I've prototyped this with a small agent framework and the results are promising but noisy. 3. **Homomorphic aggregation** : Fully homomorphic encryption (FHE) would let the coordinator aggregate updates without ever seeing them, removing the trust assumption entirely. The computational cost is still prohibitive for transformer-sized models, but for smaller models it's becoming viable. ## Conclusion: What I Actually Learned Building this system taught me that **privacy-preserving ML for heritage languages isn't primarily a technical problem — it's a coordination problem with technical constraints**. The DP math is well-understood; what's hard is designing interfaces and incentive structures that make stakeholders genuinely willing to participate. Three takeaways I'd offer anyone working in this space: 1. **Formal privacy guarantees build trust, but only if stakeholders can verify them.** The community needs to see the ε budget, not just hear about it. 2. **Active learning's value is amplified in multi-stakeholder settings** because it lets you respect each group's annotation capacity without over-burdening any single one. 3. **Cultural priors are not a hack — they're a legitimate signal.** Treating community-defined priorities as first-class inputs to the acquisition function is both ethically right and empirically effective. The Nahuatl pilot is still running, and the model is still improving. But the metric I care most about isn't accuracy — it's whether the elders who contributed their knowledge feel that the system served _them_. So far, the answer is yes, and that's worth more than any benchmark. _If you're working on similar problems — federated learning for low-resource languages, DP in multi-stakeholder settings, or agentic annotation systems — I'd love to hear about it. The intersection of privacy, language, and community sovereignty is one of the most interesting frontiers in applied ML right now._
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DEV Community [Unofficial] @dev.to.web.brid.gy · 5h
dev.to
I Made a Video Editor Specifically for DEV.to Writers
How many times have you taken a **screen recording** and wished you could **quickly crop** the taskbar down there, **trim the start and end** so no one sees you opening OBS Studio, **cut and delete** the part where you fell asleep, **export it** as an **optimized** animated GIF or WebP, and drag and drop it directly to your DEV.to post? Opening DaVinci Resolve for this purpose is overkill. Even if you open it anyway, you will not be able to crop your video **like this:** In my opinion, this is the most useful feature of this app. Many video editors force you to pick a canvas resolution and won't allow you to change it this way. But other than that, it also supports the following features (note: these features were written by Gemini): * **Non-Destructive Multi-Clip Timeline:** * Split video segments anywhere (`S` key). * Drag and drop clips to reorder them on the fly. * Drag clip edges to trim start and end points with live preview. * **Flexible Export Options:** * Export to **WebP** , **GIF** , **MP4** , **WebM** , **MOV** , or **MKV**. * Adjust framerate (0.5 to 60 FPS), scaling/resolution %, and quality settings (automatic palette generation with dither controls for GIFs). * **Fast Timeline Navigation & Controls:** * Scroll, zoom (`Ctrl + Wheel`), and pan (`Middle-click drag`) through complex timelines. * Frame-by-frame navigation using arrow keys. * **Seamless Preview:** * Automatic background proxy generation for codecs not natively supported by Qt. As of now, it works, and it is useful. I have tested it quite a bit and fixed a lot of issues. But still, **not a polished product yet** , and it **requires FFmpeg** to work. Installing FFmpeg is very easy. In Windows, just run `winget install Gyan.FFmpeg`. I have opened **2 issues myself**. It would be really helpful if someone could work on those features (I have tried, but couldn't do it for some reason): * https://github.com/effessdev/GiffyPy/issues The app is made using **Python** and **PySide6**. Here is a link to the GitHub Repo: * https://github.com/effessdev/GiffyPy Now, do you find this useful? Have you been through this kind of friction before while writing in DEV? I'd love to hear your thoughts!
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DEV Community [Unofficial] @dev.to.web.brid.gy · 7h
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IdP login logs are not enough for SOC 2-style access reviews
Login and MFA events answer "who authenticated?" They usually do not answer "why was this action allowed or denied on that resource?" For SOC 2-style access reviews, that gap matters. Reviewers want evidence that application controls actually enforced policy — not only that SSO worked. Useful authorization evidence connects: * **Identity** — who (or which agent) made the request * **Policy** — which rule/version decided * **Resource** — which tenant/object/action was targeted * **Result** — allow _and_ deny, with a reason Explainable deny is as important as explainable allow. A deny shows the boundary held; an allow shows legitimate access had a clear reason. We wrote up the evidence shape here (I'm with Permit.io): https://www.permit.io/blog/soc2-explainable-deny?utm_source=devto&utm_medium=social&utm_campaign=soc2-explainable-deny&utm_content=authbyexample
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DEV Community [Unofficial] @dev.to.web.brid.gy · 7h
dev.to
Your 404s Have User-Agents: a monitor read 19% of its probes of us as 404
An endpoint that answers `402 Payment Required` is up. It is doing exactly what it is supposed to do. But "up" is not a property of your endpoint — it is a property of the pair (your endpoint, the URL the caller built). If the caller joins two of your published fields in a way you did not intend, the request never reaches a route, your gateway answers 404, and some other system's dashboard records you as having been down. We found one of those this week, and the only reason we found it is that our gateway logs carry the user-agent on every line. ## What the log said One third-party x402 health monitor has been walking our catalog since September 22. Over that window it sent us **4,067 requests**. Two numbers matter: > 771 of 4,067 answered 404 — 18.96% of one consumer's probes of us resolved to no route. The other 3,296 answered 402 — the correct challenge, from the correct endpoint. > 771 of 4,067 answered 404 > 18.96% of one consumer's probes of us resolved to no route. The other 3,296 answered 402 — the correct challenge, from the correct endpoint. Of the 771 failures, there were **156 distinct paths**. **155 of them had the same shape** : /x402/<A>/<B> And the shape is not random. `<B>` is always our service identifier — the same value in every one of the 155. `<A>` takes two forms, and both of them are URLs we publish: path probed | where the first segment came from ---|--- `/x402/x402-time/x402-time` | the `resource.url` in our 402 challenge `/x402/time/x402-time` | the `endpoint` field in our service catalog One probe arrived with a query string attached — `?xml=&text=&input=`, which are field names from that endpoint's own input schema. That is not someone guessing at paths. That is a client that believes the concatenated string _is_ the endpoint, and is calling it. The 156th path was a route we deliberately retired, so we will leave it out of the count. ## The controls, and the one-line answer Before believing any of that, we asked the endpoint. Three reads, no parameters, no state: 402 POST /x402/time 402 POST /x402/x402-time # one doubled prefix: the gateway tolerates it 404 POST /x402/time/x402-time # only the concatenation is unroutable Both legitimate forms answer the challenge. Only the join fails. Whatever the caller did, the thing that broke was the _join_ , not the endpoint. ## Where the second segment comes from Our 402 body carries a discovery extension. One of its fields is `routeTemplate`. We populate it with our service identifier, which is also, separately, the value the service's signing recipe uses: "extensions": { "bazaar": { "discoverable": true, "routeTemplate": "x402-time", ... } } That identifier is what appears as `<B>` in all 155 paths. We are not going to claim we watched the client's source — we did not, and this is the one link in the chain we are marking as inference rather than measurement. What is measured is that the trailing segment of every failing path is a value we publish, and that appending it as a path segment to a URL we also publish produces something that cannot route. > 630 — lines across every logged 404 that match the doubled shape with the alias family excluded: 629 from that one monitor, and 1 from our own control probe. The count is exact — nothing is left over. > **The falsifiable version.** If we stop emitting `routeTemplate` for static routes, one of two things happens. Either the doubled 404s stop — and the mechanism was described correctly — or they continue, and the join was the caller's own. That is a cheap experiment, and it is the one we would want to see before writing any of this down as settled. Why would a field named for a route carry an identifier? Because that is what a publisher has in hand. An identifier is assigned once and is stable; a route template is a path shape, and for a service with exactly one static route there is nothing to template — the spec says so, and says the field should be _absent_ in that case. We covered that half in the previous post: the official SDK's validator rejects a value without a leading slash, so a spec-compliant facilitator drops the field and falls back to the concrete URL. This post is the other half. Dropping the field is what a validator does. Appending it is what a consumer that does not validate does. Neither outcome is what the publisher intended, and only one of them shows up in your logs. ## Two ways we nearly reported this wrong Both are worth keeping, because both are the same mistake in different clothes: reading a slice and calling it the population. **One: the six-second window.** The first thing we saw was a burst — 153 distinct paths, all 404, inside six seconds. On that slice, the honest summary was "this monitor has never once reached us successfully." It is also false. Full rotated logs: 3,296 of its 4,067 requests answered 402. A single burst is a census of the burst. **Two: the shape test that was too generous.** To count how often the doubled shape appears across every 404 we have ever logged — 1,384,805 lines — we first matched `/x402/<A>/<B>` loosely. That pattern also matches our own legitimate `/x402/proxy/<id>` alias namespace, and it inflated the count from 630 to 30,284. A shape predicate has to exclude the family that legitimately wears the same shape, or it is not a shape predicate. > **The falsifiable version.** If we stop emitting `routeTemplate` for static routes, one of two things happens. Either the doubled 404s stop — and the mechanism was described correctly — or they continue, and the join was the caller's own. That is a cheap experiment, and it is the one we would want to see before writing any of this down as settled. > 630 > lines across every logged 404 that match the doubled shape with the alias family excluded: 629 from that one monitor, and 1 from our own control probe. The count is exact — nothing is left over. Which gives the finding its scale, and its limit: **this is one consumer, not a class of consumers.** Everything else that walks `/x402/<A>/<B>` is hitting our documented alias route and succeeding. One monitor, 19% of its probes, is not a platform-wide outage. It is a single integration reading one of our fields differently than we assumed, and it would have stayed invisible if we had only ever read status codes. ## Reproduce it There is no exotic tooling here. Grep your access log for 404s, group by user-agent, and then group the paths by shape: grep "| 404 |" gateway.log \ | grep -oE '"(GET|POST) +"[^"]*"' \ | sed -E 's/^"(GET|POST) +"//' \ | sort | uniq -c | sort -rn | head -40 What you are looking for is not the noisy top of that list — scanners asking for `/.env` and `/backups/` will always be there. It is a _regular_ shape: the same number of path segments, the same repeated substring, arriving from one user-agent on a schedule. Regularity means a program built it from a rule, and a rule is something you can fix — on your side or on theirs. The corollary is the uncomfortable part. A crawler that follows your published links will never produce this class of failure, because it asks for exactly the URLs you wrote down. The failures come from consumers that _reconstruct_ your URLs from metadata — and those are precisely the clients you most want, since reconstruction is what a discovery layer is for. Treat any regular 404 shape as a message about your metadata, not as background noise.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 7h
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Birth times are messy inputs: timezones, DST, and solar time
Title: Birth times are messy inputs: timezones, DST, and solar time I thought the evaluation engine would be the hard part. I was wrong; timestamp normalization took longer. A local civil-time reading has to account for historical daylight-saving changes, sub-hour offset adjustments, and longitude corrections before it can yield true solar time. Then come the edge cases: solar-term transitions, and the competing rules that flip a calendar day at 23:00 or midnight. Either choice can change the state initializer. Now I treat each raw birth timestamp as unvalidated input and send it through a strict timezone transformation pipeline. When building a software pipeline for this calendar system, the input stage is deceptively complex. A timestamp such as `01:30` is a statement about a civil clock, not the Sun's position. Civil clocks follow regional time-zone rules, whereas apparent solar time depends on the Sun's actual position, shifting with longitude and the equation of time. I rely on the IANA Time Zone Database (zoneinfo) to handle historical offsets and daylight-saving transitions (DST). But historical coverage varies in accuracy, especially for dates prior to 1970. A location name alone is insufficient. To resolve an ambiguous timestamp, you need a date, a specific zone, and an explicit UTC offset or occurrence marker. Take New York on November 7, 2021. At the end of daylight saving time, clocks fell back from 2:00 a.m. daylight time to 1:00 a.m. standard time. That means `01:30` occurred twice: once at UTC−4 and again at UTC−5. Without an explicit occurrence marker, a wall-clock reading cannot distinguish those two instants. If a calendar method requires true solar time, additional adjustments must be chained together. First, you resolve the civil time-zone offset and DST. Next, you apply a longitude correction relative to the zone's standard meridian. Each degree of longitude shifts solar time by roughly four minutes. New York sits near 74° west, while Eastern Standard Time is based on a 75° west meridian, placing New York about four minutes ahead of standard clock time. Finally, the equation of time must be applied, which fluctuates by date and can shift the result by up to 16 minutes. These minutes matter most near boundaries. The hour branch divides the day into 12 two-hour intervals, such as Zi from roughly 11 p.m. to 1 a.m. Because Zi begins at 11 p.m. (23:00), some conventions flip the calendar day at 23:00, while others wait until midnight. Because the hour stem is derived from the day stem, choosing 23:00 versus midnight flips both the day pillar and the hour stem. This is why two implementations receiving the same input date can yield different charts. I am still unsure about which time-normalization convention is most faithful to historical practice: civil clock time, local mean solar time, or apparent solar time. I also wonder how to clearly expose uncertainty in old zoneinfo records without making the chart interface unreadable—a common challenge when surfacing historical data. Not medical, legal, or financial advice. A cultural and cognitive framework for self-reflection; outputs vary by individual.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 7h
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Why LLM Guardrails Are Failing AI Agents — And How We Built a Deterministic Firewall Instead
As developers, we are shipping AI agents deeper into production environments. We give them tools, API keys, database access, and shell execution rights to maximize their autonomy.And then this happens: an unsupervised agent loops out, hallucinates a destructive system command (rm -rf), drops a production table, or leaks a .env secret containing database credentials.The traditional way the market tries to solve this is through LLM-based guardrails (an AI watching another AI). But let's be honest: that approach is probabilistic, slow, susceptible to prompt injection, and leaves zero replayable cryptographic evidence when things break.That is why we built StarkGate.What is StarkGate?StarkGate is a fully deterministic decision engine that sits as an external firewall between your AI agent and the real world.Before any high-stakes action executes—whether it's a wire transfer, a file deletion, an outbound email, or a physical actuator command—it must pass through StarkGate. The engine evaluates the action against your strict enterprise rules in microseconds, returning a definitive ALLOW or DENY.Core Architecture PrinciplesZero LLM in the Loop: The decision path is 100% deterministic and stateless. No fuzzy thresholds, no probabilities.Fail-Closed by Default: If the network drops, keys rotate mid-flight, or payloads are malformed, StarkGate defaults to DENY. When in doubt, a firewall must act like a locked door, not a suggestion box.Tri-Engine Parity (Zero Drift): To guarantee identical behavior everywhere, we implemented the core engine across three environments locked down by golden vectors in CI:TypeScript (Cloudflare Workers): For global edge production deployments.Python (starkgate-sdk on PyPI): For local development, CI pipelines, and offline verification.Rust (no_std + WASM, $\le$ ~310 KB): For hardened Kubernetes clusters down to embedded microcontrollers.33 Pure Operators & Advanced NodesStarkGate uses bounded, closed comparison operators to evaluate payloads without arbitrary logic injection:Numeric: gt, lt, gte, lte, eq, between, not_between, modStrings & Paths: contains, matches_regex, starts_with, path_matches (bounded JSONPath)Arrays & Geolocation: in, count_gt, in_bbox, distance_ltIt also supports advanced nodes like field-to-field comparisons ($ref to ensure transaction amounts stay below account balances) and complex computations (sum(quantity * price) > cap).Cryptographic Proofs for Compliance (EU AI Act Ready)Every verdict emitted by StarkGate is bundled into an immutable evidence package consisting of:Ed25519 Signatures & HMACChain-linked Audit Hashes (sha256: linking back to previous events)Merkle Tree Anchoring published to transparency logsThis means auditors, regulators, or your Chief Risk Officer can verify verdicts offline—even if your cloud servers are entirely powered down. It provides native compliance infrastructure for the incoming EU AI Act regulations.Multiple Integration DoorsYou can plug StarkGate into your stack via whatever control point fits best:Python SDK: pip install starkgate-sdkMCP Server: npx starkgate-mcp-server to wire it directly into AI assistants.OS FileGuard: Protect local file paths via Windows ACLs or Linux Landlock.REST API & Docker/K8sCheck It OutStarkGate is completely open-source (MIT), and our live sandbox is available right now.🌐 Explore the Guide & Test It: https://sentinel-api.wenjoseph16.workers.dev/guide🚀 Time-to-first-rule: Under 5 minutes from signup to blocking your first risky action.Let's build autonomous agents that are powerful and safe by design. Feel free to drop your thoughts, feedback, or security edge cases in the comments below!
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DEV Community [Unofficial] @dev.to.web.brid.gy · 7h
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What ISO 27001 Really Costs an Indian Software Company
When companies budget for ISO 27001, they often focus on one number: the certification body’s fee. That is usually not the biggest cost. The real investment is the time your engineering, security, management, and operations teams spend building an Information Security Management System (ISMS), closing control gaps, collecting evidence, and preparing for audits. For a mid-sized Indian software company, understanding that internal effort before starting can prevent a major budgeting mistake. The Five Costs Behind ISO 27001 ISO 27001 certification costs generally fall into five areas: 1. Gap assessment This identifies where your current security practices fall short of ISO 27001 requirements. 2. Building the ISMS Policies, risk assessments, access controls, supplier reviews, incident processes, secure development practices, and other controls need to be documented and operational. 3. Tooling ISO 27001 does not require specific software. However, gaps may require better asset management, centralised logging, endpoint controls, or access-review systems. 4. Stage 1 and Stage 2 audits This is the certification body's direct cost. ISO/IEC 27006-1:2024 determines audit time based largely on the effective number of people within scope. 5. Surveillance audits Certification runs on a three-year cycle, with surveillance audits after the initial certification. A useful rule: ask the certification body for the audit-day count before asking for the final price. The Cost Most Proposals Leave Out The biggest hidden cost is your own team’s time. ISO/IEC 27001:2022 contains 93 Annex A controls. Each needs to be considered and justified through the Statement of Applicability. For a software company with roughly 60–150 employees, our working model estimates: Scoping and gap assessment: 5–10 person days Risk assessment and treatment: 10–15 days Policies and procedures: 20–30 days Engineering and control-gap remediation: 30–60 days Evidence collection: 10–20 days Internal audit and corrective actions: 5–10 days That puts the internal workload at roughly 80–145 person days, spread across the implementation period. For many companies, that internal effort can cost more than the certification audit itself. What Makes ISO 27001 More Expensive? The biggest variable is scope. A company certifying one product and its supporting delivery team will usually face a smaller implementation than one putting every department, office, application, and employee into scope. Other major cost drivers include: Headcount: More people within scope generally means more audit time. Multiple offices: Additional locations can increase audit complexity. Cloud vs on-premise infrastructure: Cloud providers may already provide evidence for certain infrastructure controls, while on-premise environments put more responsibility directly on your organisation. Existing processes: Companies already performing access reviews, change management, incident logging, backups, and supplier assessments have less work to build from scratch. Narrowing scope can therefore reduce cost—but it should be done deliberately. The certification scope appears on the certificate, and enterprise procurement teams can check whether it actually covers what they are purchasing. How Long Does ISO 27001 Take? For a first certification with a meaningful scope, nine to twelve months is a realistic planning window. The reason is not simply documentation. Stage 2 examines whether controls are actually operating. Auditors need evidence such as access-review records, training records, incident logs, supplier reviews, internal audits, and management reviews. You cannot create months of operational evidence overnight. That is why adding more consultants does not necessarily turn a nine-month implementation into a three-month one. ISO 27001 vs SOC 2 Both help customers evaluate security, but they work differently. ISO 27001 results in certification against an international information-security management standard. It uses 93 Annex A controls and includes Stage 1, Stage 2, surveillance, and recertification audits. SOC 2 produces an auditor's report under AICPA standards. Organisations define controls against selected Trust Services Criteria, and a Type 2 report examines their operation over a defined period. Which one matters more depends heavily on what your customers and procurement teams actually request. Doing both simply because they are well-known security frameworks can consume substantial engineering time without necessarily helping sales. What ISO 27001 Does Not Solve ISO 27001 is an information security management system certification. It is not CERT-In empanelment. It also does not automatically establish Indian data residency. If a tender specifically requires an audit by a CERT-In empanelled organisation, ISO 27001 does not replace that requirement. Similarly, data residency depends on your hosting architecture and contractual requirements—not simply whether your organisation holds an ISO certificate. Should a Mid-Sized Software Company Pursue It? Start with your sales pipeline rather than the certificate. Look at your last 8–12 enterprise opportunities. How many were delayed or lost because of a security certification requirement, security questionnaire, or procurement requirement? If ISO 27001 repeatedly appears as a buying requirement, certification can become a commercial enabler. If customers are not asking for it, committing nine months and potentially more than 100 internal person days deserves much closer scrutiny. Accucia’s Current Position Accucia is currently implementing an ISO 27001 information security management system. An auditor has been appointed, with certification targeted for Q1 2027. Accucia is not ISO 27001 certified today, and ISO 9001 is also currently in progress. That distinction matters. ISO 27001 should not be treated as another logo for a website footer. It is an operating system for information security—and the largest investment is often not the certificate. It is the work required to make the organisation ready to earn it. Read full blog
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DEV Community [Unofficial] @dev.to.web.brid.gy · 7h
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I tried to put a 2,000-year-old calendar into one boundary rule
Title: I tried to put a 2,000-year-old calendar into one boundary rule I once tried to fit different traditional calendar schools into one boolean truth table. The disagreements looked like logic bugs until I saw the structural assumptions underneath. An ancient astronomical cycle's boundary depends on which solar coordinate or epoch anchor you choose as the primary axis. Each school has a self-consistent coordinate system and its own edge definitions; one truth table can't reconcile them without flattening those differences. Now my schema keeps core graph processing separate from boundary parsing. I made the parsing strategy configurable by calendar convention instead of baking one convention in as the absolute truth. Early on, I expected that translating a birth time into four pillars (year, month, day, and hour) would follow a single standard. When comparing outputs across different BaZi tools, I noticed they frequently disagreed on edge cases. For instance, the year pillar boundary is not uniformly set at Lunar New Year across all schools. Many conventions anchor the year transition to the solar term Lichun instead. A person born in early February can land on completely different year labels depending on which year-boundary convention the software enforces. Month pillars present similar branch points. The calendar divides the year into 12 solar months, anchoring month transitions to specific solar terms rather than lunar months. Meanwhile, the day pillar relies on its own 60-day position, with competing rules turning the day over at 23:00 or at midnight. Because the hour stem assignment is linked to the day stem, changing the day rollover boundary cascade-updates the hour pillar too. Longer ten-year phases, or decade pillars, add another layer of configuration. Derived from the month pillar, their direction and starting age depend on conventions involving the birth year and the distance to a nearby solar term. Each phase advances through the stem-branch sequence every ten years. This works like a state machine where the birth chart provides the initial state and the decade pillar updates at calculated transition points, though the machine merely models label shifts under stated rules. Attempting to hardcode all these rules into one rigid conditional block caused constant refactoring. The underlying modulo lookup is perfectly consistent; the divergence happens entirely during boundary parsing and input normalization. To clean up the system, I refactored the pipeline. Step one parses the timestamp, location, and timezone context. Step two converts the timestamp to working local time. Step three identifies the year and solar-term month. Step four calculates the sexagenary day index. Step five divides the day into two-hour intervals and derives the hour stem. I separated core cycle calculations from the boundary parsing rules. By keeping the schema modular and making boundary strategies configurable by calendar convention, the engine avoids declaring one school's edge definitions as universal truth. I am still unsure how much different schools' boundary choices alter charts across large population datasets, especially for births occurring near midnight or solar-term transitions. Accepting that distinct coordinate systems exist made the code far more maintainable. Not medical, legal, or financial advice. A cultural and cognitive framework for self-reflection; outputs vary by individual.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 7h
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I found a 60-step cycle in a 2,000-year-old calendar
Title: I found a 60-step cycle in a 2,000-year-old calendar While parsing a sexagenary calendar record, I kept getting hung up on two counters: one wraps at 10, the other at 12, and both advance together. Their least common multiple is 60, so the pair repeats as a 60-step state machine. No fractional adjustments needed. That fixed sequence indexes cyclical time markers across days, months, and years. I wrote it as a discrete modulo generator using integer arithmetic alone. It shows how ancient astronomers maintained a continuous, drift-free epoch reference for more than two millennia. When I first started looking at BaZi, or Four Pillars records, I expected paragraphs of free-form symbolic prose. Instead, I found a compact record of four paired labels: year, month, day, and hour. Each pillar consists of one stem-branch pair drawn from two repeating counters. There are 10 Heavenly Stems and 12 Earthly Branches. If every stem could pair with every branch independently, you would get 120 combinations. But the traditional algorithm steps both lists simultaneously. Starting from Jia-Zi, the sequence proceeds to Yi-Chou, then Bing-Yin, and so on. Because both counters step in tandem, only 60 distinct pairings occur before the system returns to Jia-Zi. In code, this periodic state can be represented with a single integer scalar n. The stem index is evaluated as n mod 10, and the branch index as n mod 12. Incrementing n by 1 advances to the next paired symbol, while incrementing n by 60 returns the system to its starting pair. It operates like two clocks with different period lengths sharing a single step counter. This shared encoding makes cycle positions directly comparable across time scales, but those four pillars are not one giant clock ticking at a uniform speed. Each unit uses its own rules to find its current index. A day is divided by the hour branch into 12 two-hour intervals, such as Zi around 11 p.m. to 1 a.m. The hour stem is not an isolated counter looked up from clock time alone; its assignment is linked directly to the day stem. Beyond the initial birth chart, the system extends into longer ten-year phases derived from the month pillar. These decade pillars advance through the stem-branch sequence every ten years. This invites a state-machine analogy where the birth chart acts as the initial state, the decade pillar changes at a calculated boundary, and current calendar cycles provide additional inputs. However, this state machine only tells us how labels transition under defined rules; it does not establish physical causation. I am still unsure about which time-normalization convention is most faithful to historical practice: civil clock time, local mean solar time, or apparent solar time. What stands out to me as a data engineer is the structural elegance. Integer arithmetic alone maintains a periodic index without requiring floating-point adjustments. Not medical, legal, or financial advice. A cultural and cognitive framework for self-reflection; outputs vary by individual.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 7h
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Part 1 — Proxmox VE: Installing and Configuring the Virtualization Host
## 1. The Mission: Opting Out of the Subscription Trap We live in an era where we generate gigabytes of private data every day—photos, code, sensitive documents, and backups. The tech industry has conditioned us to choose the path of least resistance: sending everything to public clouds like Google Drive, OneDrive, or iCloud. As a computer science student, this didn’t sit right with me. The modern world tries to convince us that absolutely everything has to be a subscription. I really dislike this model of continuously renting everything. I prefer to pay once for physical hardware and know it is truly my property—not a service that can go up in price or vanish at any moment. But privacy and ownership are only part of the equation; the other part is risk management. On one hand, we face immediate threats like critical **Zero-Day exploits** in popular software and cloud services. On the other hand, there is the long-term, silent risk of **Harvest Now, Decrypt Later (HNDL)**. Adversaries may collect encrypted data today, hoping to decrypt it in the future once quantum computers mature enough to break standard asymmetric encryption algorithms—a transition often referred to as **Q-day**. Entrusting corporate giants with my private files, passwords, and code under these conditions felt like too much of a compromise. I wanted secure access to my resources from anywhere, while maintaining one personal rule: **I want as much control over my data and infrastructure as reasonably possible, with physical hardware residing under my own roof.** My motivation was also deeply educational. I don't want to just be a passive user of ready-made tools; I want to understand them from the inside out. I wanted to build and manage a real server, learn routing, virtualization, and storage systems. Most of all, **I wanted to break things** , face real technical challenges, and debug the system outages that inevitably come with administration. #### Why Proxmox VE? To run a private cloud, a secure VPN gateway, a local DNS, and monitoring tools, I needed multiple independent environments. Installing them all directly on a single, monolithic OS would couple unrelated services together and make failures harder to isolate. I needed a virtualization platform that could run both virtual machines and lightweight Linux containers directly on my hardware. Proxmox VE 9, built on Debian 13, was a natural choice for this project. Most importantly for my setup, Proxmox supports both Kernel-based Virtual Machines (KVM) and Linux Containers (LXC). On a machine with limited memory, the low overhead of lightweight LXC containers allows building a separated architecture with lower overhead than running multiple full virtual machines. #### Budget-Friendly Hardware for Anyone To many, a home server conjures up images of a massive, loud, and power-hungry server rack. I wanted to prove that a capable homelab could be built for the price of a nice dinner for two. In March 2026, I started this project with a modest budget: * I bought a refurbished **Lenovo ThinkCentre M73 Tiny** (Intel Core i5-4570T, 4 GB RAM, 240 GB Crucial BX500 SSD, Windows 10 Pro) from a Polish refurbished-hardware store called _rnew.pl_ for **169 PLN** (~$42 USD at the exchange rate at the time). _(Naturally, I had no use for the Windows 10 Pro license, but this specific spec happened to be the cheapest option available)._ * Since the mini PC came with a basic configuration, I gave it some breathing room by adding an **8 GB DDR3 RAM module** for **95.90 PLN** (~$24 USD at the exchange rate at the time). In the end, for **under 265 PLN** (approximately $66 USD), I had a quiet, energy-efficient machine equipped with an Intel Core i5 processor and **12 GB of RAM**. This compact setup became the physical heart of my homelab—ready to host my virtualization environment. ## 2. My Setup: Physical Specs and Storage Partitioning * **Tested with:** Proxmox VE 9.2.11 on September 2026 * **My Hardware:** Lenovo ThinkCentre M73 Tiny (Intel Core i5-4570T, 12 GB RAM, 240 GB Crucial BX500 SSD) To get started, I needed my mini PC, a spare USB flash drive to hold the installer, and an Ethernet cable to connect the server directly to my home router. #### My Goal The goal was simple: turn a $66 refurbished mini PC into a reliable virtualization host that I could manage remotely from my laptop. To understand how the host operates once installation is complete, it helps to look at how Proxmox organizes its storage and networking by default: #### 1. Default Storage Partitioning (local vs. local-lvm) With the default LVM-thin installation layout, Proxmox creates two main storage pools: * **`local` (Directory):** Used for file-based content. This is where I upload ISO installation images, LXC container templates, and host backup files. * **`local-lvm` (LVM-thin):** Dedicated LVM-thin block storage. It stores the virtual disks of future virtual machines and containers, supporting thin provisioning, snapshots, and cloning. _(Note: The "localnetwork" entry with the grid icon visible in the sidebar is not a storage drive—it is Proxmox's default Software-Defined Networking (SDN) zone representing the local network interfaces)._ #### 2. The Linux Bridge (vmbr0) My physical Ethernet interface is named **`enp0s25`** (which my network configuration organizes under the logical interface name **`nic0`**). Because I will need to share this single network connection with all future virtual machines and containers, Proxmox creates a virtual switch called a **Linux Bridge (`vmbr0`)**. My physical Ethernet port connects directly into this bridge. This allows both my Proxmox host’s management dashboard and all future virtual guests to share the same physical Ethernet connection to my LAN. #### ⚠️ A Note on Resiliency and Backups This setup is intentionally minimal and designed for hands-on learning. Running a single mini PC with a single SSD means this architecture has a **Single Point of Failure** (SPOF). If the SSD fails, the services fail. This is not a complete backup or high-availability strategy. In later parts of this series, I will address backup, recovery, and off-site replication separately. With my setup mapped out, I was ready to prepare the installation media. ## 3. The Hands-On Guide: Installing Proxmox VE 9.2 My goal for this phase was to install Proxmox VE 9 on my mini PC and establish a working local management interface. Here is the step-by-step documentation of how I prepared my media, configured the hardware, and completed the installation. ### Step 1: Flashing the Installer First, I downloaded the official **Proxmox VE 9.2 ISO installer** directly from the Proxmox downloads page (making sure to select the standard x86_64 version, not the newly released ARM64 image). To turn my 16 GB USB flash drive into a bootable installer, I used a free utility called **Rufus** on my Windows laptop: 1. I opened **Rufus** and selected my USB flash drive under the _Device_ dropdown. 2. Under _Boot selection_ , I clicked _SELECT_ and chose my downloaded Proxmox ISO file. 3. I left the partition scheme as **GPT** and the target system as **UEFI (non CSM)**. 4. I clicked **START**. 5. Rufus prompted me with a pop-up window asking how to write the image. For Proxmox, official documentation recommends writing the image in **DD Image mode**. This ensures the Debian installer recognizes the partition structure correctly. 6. Once the progress bar turned green and showed **READY** , I safely ejected the flash drive. ### Step 2: Connecting Peripherals & BIOS Setup A home server is meant to run **headlessly** —meaning it sits silently on a shelf with only power and Ethernet cables attached, without a monitor, keyboard, or mouse. However, for the initial installation, I temporarily plugged in a monitor via DisplayPort, a USB keyboard, a mouse, and my bootable USB drive. Before booting into the installer, I configured the hardware. I turned on the Lenovo M73 Tiny and repeatedly pressed the **F1** key to enter the Lenovo BIOS Setup Utility. I navigated to the **Security - > Virtualization** menu and verified two settings: 1. **Intel Virtualization Technology (VT-x):** **Enabled**. This provides the hardware virtualization extensions required by KVM for efficient CPU virtualization. 2. **Intel VT-d:** **Enabled**. This enables IOMMU functionality, which is required for certain forms of hardware passthrough (such as passing through USB controllers or PCIe devices directly to virtual machines). Finally, I checked the boot settings. Modern Proxmox VE versions fully support Secure Boot, although I chose to disable it on this particular machine during my installation to simplify legacy boot troubleshooting. I pressed **F10** to save my changes and exit. As the system rebooted, I pressed **F12** to bring up the temporary Boot Menu, selected my USB drive from the list, and pressed Enter. ### Step 3: Navigating the Proxmox Installer Once the Proxmox boot loader appeared, I selected **Install Proxmox VE (Graphical)** and followed the installation wizard: 1. **Target Harddisk:** I selected my internal `/dev/sda` (the 240 GB Crucial SSD). 2. **Location and Time Zone:** I selected `Poland` and `Europe/Warsaw` as my location and time zone, and configured the appropriate keyboard layout. 3. **Password and Email:** I set a strong administrative password for the `root` user and entered my personal email to receive system alerts. 4. **Management Network Configuration:** * **Management Interface:** I selected my physical network port (labeled `enp0s25`, mapped in my configuration under `nic0`). * **Hostname:** I entered `pve.home.arpa` (using the official RFC 8375 standard domain reserved for home networks to prevent local DNS conflicts). * **IP Address:** I chose a static IP address of `192.168.0.100/24` for my server. _(Note: Ensure your chosen static IP is outside your router's active DHCP pool to prevent address conflicts, or configure a DHCP reservation on your router)._ * **Gateway & DNS Server:** I configured my router (`192.168.0.1`) to act as both the default gateway and temporary DNS resolver. Once I verified all settings on the final **Summary** screen, I clicked **Install**. The installer formatted my SSD, extracted the base operating system, and rebooted. At this point, I unplugged the USB drive, disconnected the monitor, keyboard, and mouse, and placed the Lenovo Tiny on my desk. I connected only the power cable and an Ethernet cable. ### Step 4: First Login to the Dashboard With my server now running headlessly, I returned to my main computer, opened a web browser, and navigated to the static address I had assigned: <https://192.168.0.100:8006> Because Proxmox uses a self-signed SSL certificate out of the box, my browser showed a "Your connection is not private" warning. This warning was expected because the default certificate is not trusted by my browser. I clicked _Advanced_ and bypassed the warning. I was greeted by the Proxmox login screen, where I logged in with the username `root` and the password I established in Step 3. ### Step 5: Opening the Console & Swapping to Community Repositories Proxmox VE is open-source software and can be used without purchasing a subscription. Paid subscriptions provide access to the Enterprise repository and enterprise technical support. By default, Proxmox configures commercial **Enterprise repositories** for both system updates and Ceph storage. Without a paid subscription key, APT will return an authorization error (`401 Unauthorized`) when attempting to update. For a homelab, Proxmox provides a **No-Subscription repository**. First, I accessed the server's command-line interface. In the Proxmox Web UI, I clicked on my node name (**pve**) in the left-hand sidebar, and then clicked the **`>_ Shell`** button in the top-right toolbar. This opened a terminal session directly in my browser. In Proxmox VE 9 (based on Debian 13), repository files use the modern **deb822 block format (using`.sources` files)**. To disable the enterprise repositories, I commented out the active blocks: # Disable main enterprise updates sed -i 's/^/#/' /etc/apt/sources.list.d/pve-enterprise.sources # Disable Ceph enterprise updates (I don't use Ceph in this single-node homelab) sed -i 's/^/#/' /etc/apt/sources.list.d/ceph.sources _(Note: In deb822`.sources` format, you can also set `Enabled: false` inside the configuration block to achieve the same result)._ Next, I created a new `.sources` configuration to enable the free, community-supported No-Subscription repository: cat > /etc/apt/sources.list.d/pve-no-subscription.sources << 'EOF' Types: deb URIs: <http://download.proxmox.com/debian/pve> Suites: trixie Components: pve-no-subscription Signed-By: /usr/share/keyrings/proxmox-archive-keyring.gpg EOF Finally, I updated the package index and upgraded all installed packages: apt update && apt dist-upgrade -y Now, my update pipeline was clean and running from the No-Subscription repository with zero errors. ### Step 6: Silencing the Subscription Nag (Optional Cosmetic Tweak) Even though the system was fully functional, every login greeted me with a pop-up warning reminding me that I did not have an active subscription. Since I chose to run the No-Subscription repository in my homelab, I decided to remove this cosmetic warning. _(Note: This is an optional UI-only patch. Because it modifies a core JavaScript file, this change may be overwritten whenever Proxmox updates the`proxmox-widget-toolkit` package)._ In the shell, I executed a command to bypass the subscription check within the Proxmox management interface library: sed -Ezi.bak "s/(Ext.Msg.show\(\{\s+title: gettext\('No valid sub)/void\(\{ \/\/\1/g" /usr/share/javascript/proxmox-widget-toolkit/proxmoxlib.js To apply the changes, I restarted the web GUI management service: systemctl restart pveproxy After clearing my browser cache and logging back in, the subscription pop-up was gone. The host was ready for the next stage of the setup. ## 4. Post-Install Tuning: SSH Hardening and Thermal Optimization Once I installed and updated Proxmox on my mini PC, I wanted to address two practical configurations: hardening its security and optimizing its energy draw and thermal output. Here is how I tuned my hardware to run quietly, safely, and efficiently on my desk. #### 1. Hardening SSH: Moving to Key-Only Authentication By default, Proxmox allows you to log in as the `root` user using the password you created during installation. While this is fine for the initial setup, keeping password authentication enabled over SSH on a server is a security risk. To secure my host, I decided to disable password-based SSH authentication and rely on cryptographic SSH keys. _(Note: While in enterprise settings it is standard practice to disable root SSH logins completely and use a standard user with sudo, I chose to keep key-based root SSH access enabled for this single-user homelab to simplify direct administration while blocking password brute-force attempts)._ First, on my main laptop, I opened a terminal and generated an Ed25519 key pair: ssh-keygen -t ed25519 -C "weronika-homelab" Next, I copied my public key over to the server using the static IP address I configured in the previous step: ssh-copy-id -i ~/.ssh/id_ed25519.pub root@192.168.0.100 I verified that the key was working by logging in. The server allowed me in without prompting for my root password: ssh root@192.168.0.100 With key-based access verified, I disabled password logins. On Debian 13 (the foundation of PVE 9), the cleanest way to customize SSH settings is by dropping a configuration file into the `/etc/ssh/sshd_config.d/` directory, rather than editing the main config file directly. I opened the shell console on my Proxmox host and created a new hardening configuration: nano /etc/ssh/sshd_config.d/harden.conf I pasted the following directive to disable password logins: PasswordAuthentication no Before restarting the SSH service, I tested the configuration syntax to ensure I wouldn't lock myself out: sshd -t With no errors returned, I restarted the SSH daemon to apply the new policy: systemctl restart ssh To audit and test my security configuration, I ran an SSH command on my laptop that forces the SSH client to bypass local keys and attempt a standard password login: ssh -o PubkeyAuthentication=no root@192.168.0.100 Because my server-side policy was active, the Proxmox host rejected the connection without prompting me to enter a password: root@192.168.0.100: Permission denied (publickey). My host’s command line was now successfully locked down. _(Note: Disabling SSH password authentication only locks port 22. It does not affect the Proxmox Web GUI in your browser on port 8006, where you will still log in using your standard root username and password)._ #### 2. Tuning for Silence: Configuring the CPU Governor My mini PC is powered by an Intel Core i5-4570T. While this dual-core Haswell chip is efficient, it resides in a tiny 1-liter chassis with a very small fan. Out of the box, the Linux kernel often favors performance-heavy settings that keep the CPU clocks high, causing the fan to spin and waste power while idling. When I first ran diagnostics on my host, I noticed the CPU was idling at an observed **3.30 GHz** under the default `performance` governor (close to its 3.60 GHz maximum single-core turbo limit). Since a home server sits idle most of the time, I wanted to change the CPU scaling governor. To configure this, I installed the `linux-cpupower` utility: apt install linux-cpupower -y Once installed, I analyzed my CPU using the following command: cpupower frequency-info The terminal output confirmed that the Intel scaling driver (**`intel_cpufreq`** , the passive operation mode of `intel_pstate`) was active, offering several governors, including `powersave`, `performance`, and `ondemand`. In my setup, `powersave` kept the CPU at its minimum reported frequency of 800 MHz even under sustained load, while `ondemand` allowed the frequency to scale dynamically based on workload. To change the governor, I ran: cpupower frequency-set -g ondemand To make this setting survive reboots, I set up a task in the system’s automated scheduler (**cron**). I opened the cron scheduler editor: crontab -e _(I selected option`1` to open the file in the `nano` editor)._ At the very bottom of the file, I added the following line to enforce the dynamic state every time the system boots up: @reboot cpupower frequency-set -g ondemand >/dev/null 2>&1 I saved the file and exited. The CPU now spends idle time at a much lower frequency, and subjectively the fan became noticeably quieter on my desk. #### 3. Monitoring Thermals from the CLI Because a mini PC has limited airflow, I wanted a simple way to monitor my hardware's core temperatures from the command line. I installed `lm-sensors` to handle temperature readouts, and `htop` to monitor active CPU threads and memory load: apt install lm-sensors htop -y Next, I ran the sensor detection wizard to let Linux automatically find my motherboard's thermal monitors (I followed the prompts and accepted the recommended defaults): sensors-detect Once the configuration was saved, checking my CPU temperatures became as simple as typing a single command: sensors ## 5. The "Aha!" Moment: Debugging the CPU Frequency Trap While setting up my host, I encountered a silent configuration issue that showed how easily generic system optimization advice can backfire on specific hardware. #### The Problem To lower my mini PC's idle power consumption, I read a suggestion online to set the CPU scaling governor to `powersave`. After installing `linux-cpupower`, I applied the setting: cpupower frequency-set -g powersave It seemed like a sensible default, and I assumed the CPU would scale down dynamically when idle and scale back up under load. #### The Investigation To test how this change behaved under load, I decided to verify my CPU frequencies in the host console: cpupower frequency-info While the server was idle, the CPU sat at its minimum hardware frequency of 800 MHz. However, when I ran a heavy processing task to see the frequency scale up, the clocks on all cores remained locked at **800 MHz**. The processor refused to scale up, significantly reducing performance in my test workload. #### The Root Cause The issue lay in the active CPU scaling driver. My system automatically loaded the **`intel_cpufreq`** driver (the passive mode of `intel_pstate` used on older Intel architectures lacking hardware-managed P-states). Under generic CPUFreq drivers, the `powersave` governor requests the lowest frequency within the hardware limits. In my setup, this meant the system remained pinned at 800 MHz rather than scaling dynamically with processing load. #### The Solution To achieve dynamic scaling without keeping the CPU at its observed 3.30 GHz idle state, I switched the governor to **`ondemand`** : cpupower frequency-set -g ondemand Checking the frequency info again confirmed the fix. During my observation, the reported frequency fluctuated around **955 MHz** at idle and reached up to **3.60 GHz** under load. This was a good reminder to always verify system behavior directly against the active hardware driver rather than blindly trust generic optimization tips. ## 6. What I Learned Setting up this initial host left me with a few grounded takeaways: 1. Generic Linux advice rarely accounts for older hardware. The issue with the CPU governor showed me why I need to inspect the active scaling driver directly rather than blindly trust popular optimization tips. 2. Hardware ownership does not automatically equal security. Getting rid of cloud subscriptions gives me control, but it also transfers the entire responsibility for patching, firewall rules, and hardware reliability directly onto me. 3. Writing detailed documentation is the best way to test comprehension. It is one thing to run commands until a service boots; it is entirely different to explain why each interface, partition, and configuration exists. ## 7. What’s Next: Building the Secure Network Gateway At this point, my Proxmox hypervisor is fully set up, updated, and ready to host virtual machines and containers. However, right now, any service I build at this stage will be reachable only from my local home network. To access my files or check my server's status when I am away from home, I need a secure way in. In **Part 2 of this series** , we will solve this. We are going to deploy a dedicated **Tailscale VPN Gateway** inside a virtual machine and write custom, device-level security policies (ACLs) to ensure that accessing our home network from the outside is both simple and secure. **Other articles in this series:** * **Part 1 — Proxmox VE: Installing and Configuring the Virtualization Host** (You are here) * Part 2 — Tailscale: Building a Secure Private Network (Coming soon) ## References 1. Proxmox Server Solutions. Proxmox VE Administration Guide. 2. Debian Wiki. NetworkInterfaceNames: Persistent Network Interface Naming. 3. Intel Corporation. Intel Core i5-4570T Processor Specifications (ARK). 4. The Linux Kernel Organization. CPU Performance Scaling (CPUFreq) Documentation and intel_pstate Driver Documentation. 5. National Institute of Standards and Technology (NIST). Post-Quantum Cryptography Project & HNDL Guidelines. ## Follow the project This article is part of my **Homelab Infrastructure** series, documenting my journey of building and maintaining a self-hosted home lab. → _Explore the project on GitHub_ → _Connect with me on LinkedIn_ Thanks for reading!
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DEV Community [Unofficial] @dev.to.web.brid.gy · 7h
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Building a surf lesson booking API with GoFr: what surprised me
I wanted a realistic mini-project, so I picked something close to home: booking surf lessons in Varkala. I used it as an excuse to try GoFr, an opinionated Go framework for microservices. The goal was simple: list our instructors book a lesson for a date and a slot (morning or evening, because that's when the waves are good) stop two guests from booking the same instructor for the same slot cancel a booking All the code is here: https://github.com/ajassaif/surf-api The whole app fits in one screen This is the entire main: go func main() { app := gofr.New() app.Migrate(migrations.All()) app.GET("/instructors", listInstructors) app.GET("/bookings", listBookings) app.POST("/bookings", createBooking) app.DELETE("/bookings/{id}", cancelBooking) app.Run() } There's no router setup, no DB connection code and no logger wiring. GoFr reads configs/.env: dotenv APP_NAME=surf-api HTTP_PORT=8000 DB_DIALECT=sqlite DB_NAME=surf.db …and on startup it connects to SQLite (creating the file if needed), runs the migrations, and starts the server. The startup logs say exactly that: text Loaded config from file: ./configs/.env connected to 'surf.db' database running migration 20260930180000 Migration 20260930180000 ran successfully Starting server on port: 8000 Starting metrics server on port: 2121 Handlers just return data or an error Every handler has the same shape, func(c *gofr.Context) (any, error). The database is right there on the context: go func listInstructors(c *gofr.Context) (any, error) { rows, err := c.SQL.QueryContext(c, "SELECT id, name, specialty FROM instructors ORDER BY id") ... return instructors, rows.Err() } GoFr wraps whatever you return in a consistent envelope: json {"data":[{"id":1,"name":"Arun","specialty":"Beginners"}, ...]} The part I liked most: typed errors become status codes I never set a status code by hand. I return one of GoFr's error types and it picks the right HTTP status and message: go if !validSlots[b.Slot] { return nil, gofrHTTP.ErrorInvalidParam{Params: []string{"slot"}} // 400 } ... return nil, gofrHTTP.ErrorEntityNotFound{Name: "instructor_id", Value: "99"} // 404 ... return nil, gofrHTTP.ErrorEntityAlreadyExist{} // 409 A successful POST returns 201 Created and a DELETE returns 204 No Content automatically. Here's the real output from the smoke test that runs in CI on every push: text [200] GET /.well-known/health -> {"data":{"name":"surf-api","status":"UP"}} [201] POST /bookings -> {"data":{"id":1,"guest_name":"Priya","instructor_id":1,"date":"2026-10-05","slot":"morning"}} [409] POST /bookings -> {"error":{"message":"entity already exists"}} [400] POST /bookings -> {"error":{"message":"'1' invalid parameter(s): slot"}} [400] POST /bookings -> {"error":{"message":"'3' missing parameter(s): instructor_id, date, slot"}} [404] POST /bookings -> {"error":{"message":"No entity found with instructor_id: 99"}} [204] DELETE /bookings/1 -> [404] DELETE /bookings/1 -> {"error":{"message":"No entity found with id: 1"}} All 12 checks passed The health endpoint (/.well-known/health) comes built in; I didn't write it. Observability for free Every request is logged as structured JSON with a trace ID and response time, without me adding any middleware: json {"level":"INFO","message":{"trace_id":"f996d034...","method":"POST","uri":"/bookings","response":201,"response_time":1736}} When the double-booking check fires, the warning and the request log share a trace ID, so it's easy to tie the two together. There's also a Prometheus metrics server on port 2121 out of the box. Things that weren't perfect Unique-constraint errors aren't typed. To turn "instructor already booked" into a 409, I had to check the SQLite error text for "unique". A typed "constraint violation" error would be nicer. The error messages are a bit robotic. '1' invalid parameter(s): slot is correct, but I'd tidy it up before showing it to a guest in an app. It needs a recent Go. The current GoFr release needs Go 1.26, so check your toolchain first. Telemetry is on by default. GoFr logs that it "records the number of active servers" and tells you to set GOFR_TELEMETRY=false to turn it off. I'd have preferred opt-in, but at least it's upfront about it. A bug that was mine, not GoFr's: I first sorted bookings by slot name, which put "evening" before "morning". My CI smoke test caught it. Would I use it again? For a small CRUD service like this, GoFr removed almost all of the boilerplate I'd normally write in Go: config, DB connection, migrations, logging, health checks and status codes. I spent my time on the actual booking rules instead. Code: https://github.com/ajassaif/surf-api · GoFr: https://gofr.dev
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DEV Community [Unofficial] @dev.to.web.brid.gy · 7h
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How Advanced Packaging Is Changing Semiconductor Technology
Originally published on The Daily Flare. As semiconductor manufacturers push transistors toward ever-smaller dimensions, another part of chip design is becoming just as important: how multiple pieces of silicon are packaged and connected. This field, known as **advanced packaging** , is changing how modern semiconductor systems are built. For years, improving chip performance largely meant putting more and smaller transistors onto a single piece of silicon. That approach is becoming harder and more expensive at the leading edge. Advanced packaging offers another route: combine different dies, memory, and specialized components into a tightly integrated system. ## Why packaging matters more now A conventional chip can contain many functions on one die. But different workloads may benefit from different manufacturing processes. A high-performance processor may need leading-edge logic, while an input/output component or other supporting function may not require the newest process node. Advanced packaging makes it possible to connect these components more closely. Instead of forcing every function onto one monolithic die, manufacturers can build systems from multiple pieces and package them together. ## From chiplets to 3D integration One major development is the growing use of **chiplets**. These are smaller dies designed to work together inside a larger package. A chiplet-based design can let manufacturers mix components and potentially reuse building blocks across products. Another direction is three-dimensional integration, where dies or memory components are stacked vertically. Moving components closer together can increase bandwidth and reduce the distance signals need to travel, although thermal management and manufacturing complexity become major challenges. ## Advanced packaging technologies Modern advanced packaging can include approaches such as 2.5D integration, high-bandwidth interconnects, fan-out packaging, and 3D stacking. The technologies differ in structure and manufacturing method, but they share a goal: connect more computing resources within a smaller and more efficient package. These techniques are particularly important for AI accelerators, where processors can exchange enormous amounts of data with high-bandwidth memory. In such systems, the package is increasingly part of the performance architecture rather than simply a protective enclosure around the silicon. ## Why semiconductor companies are investing Advanced packaging can help manufacturers improve system performance without relying entirely on transistor scaling. It can also create new ways to combine dies manufactured using different processes. That flexibility matters as semiconductor development becomes more expensive. Instead of designing every component from scratch on the most advanced process, a company can potentially combine leading-edge logic with mature-node components and specialized dies. ## The challenges ahead Advanced packaging does not remove the difficulties of semiconductor manufacturing. More complex packages require precise assembly, high-speed connections, thermal solutions, and reliable testing. Yield can also become more complicated when multiple dies must work together. For AI and high-performance computing, power delivery and heat are especially important. As more computing capability is concentrated inside a package, keeping the system cool without sacrificing performance becomes a central engineering problem. ## The next phase of chip design The boundary between chip design and packaging is becoming less distinct. As advanced packaging technology improves, system architects can think about processors, memory, chiplets, and interconnects as parts of one integrated platform. That makes packaging a strategic part of semiconductor advancement. The next generation of computing performance will depend not only on how many transistors fit on a wafer, but also on how efficiently many pieces of silicon can work together.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 9h
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🚀 𝗡𝗲𝘄 𝗥𝗲𝗮𝗰𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: Virtualized List
Ever hit a wall when a list grows to thousands of rows and React starts to choke? Mounting 10,000 DOM nodes at once is the problem — rendering only what fits on screen is the fix. ## 🧩 Overview Build a virtualized list of 10,000 contacts that mounts only the rows visible in the scroll viewport (plus a small overscan), using fixed row heights and windowing math. 👉 https://www.reactchallenges.com/challenges/virtualized-list ## ✅ Requirements * Render a virtualized directory of 10,000 contacts. * Mount only a small window of rows — far fewer than the 10,000 in the dataset. * Scroll through the entire dataset, not just the mounted rows. * Size the scrollable area to the full dataset so the scrollbar spans the whole list. * Unmount rows that leave the viewport and mount the ones that enter it. * Keep a few overscan rows above and below the visible window. * Show the first and last mounted rows in a stats line: `Showing X–Y of 10000`. * Display each contact's name and email in its row. ## 💡 Notes * Put all the windowing logic in `App.tsx`: track `scrollTop`, measure the viewport's `clientHeight`, and compute the range of rows to mount. * Measure the viewport height in a layout effect — it's the moment the layout is ready. * Clamp the range so overscan never renders rows before the first or after the last contact. ## 🧪 Tests 1. Renders the app title 2. Only mounts a small window of rows, not the whole dataset 3. Renders the first contacts on load 4. Sizes the scrollable area to the full dataset 5. Shows the first and last rendered rows in the stats 6. Renders more rows than fit on screen (overscan) 7. Shows the name and email for each mounted row 8. Moves the window down when scrolling 9. Restores the first rows when scrolling back to the top 10. Renders the last contact when scrolled to the bottom 11. Mounts overscan rows above the visible window when scrolled 12. Never renders rows above the first one This challenge is a hands-on way to understand virtualization from scratch — the exact pattern behind every serious long-list library. Practice windowing math, `useLayoutEffect`, and derived rendering, and your lists will stop freezing for good. 🔥 Start the Challenge Now
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DEV Community [Unofficial] @dev.to.web.brid.gy · 9h
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I Use AI to Build Software. I Still Don't Trust the Code.
I use AI to build software. I also don't trust it. That might sound strange considering most of my recent projects involve AI, LLMs, agents, RAG systems, and AI-assisted development. But after building several projects this way, I have learned something simple: **Getting AI to generate code is easy. Knowing whether that code is actually good is the difficult part.** ## AI can make you feel much more productive One of the biggest changes for me has been how quickly I can move from an idea to a working prototype. I can describe a feature, give the AI some context, review what it produces, run it, find a problem, and continue the process. This is incredibly useful. It is also dangerous if you stop thinking. A few hundred lines of generated code can appear convincing even when you don't fully understand what is happening underneath. The application may run. The UI may look good. The API may return the expected response. And you can still have a bad system. That is the part that took me time to understand. ## The first mistake is trusting code because it works When I started using AI-assisted development more seriously, it was tempting to use a simple rule: > If it works, keep it. I don't think that rule is good enough anymore. Something working in one situation doesn't mean the implementation is correct. AI-generated code can contain: * unnecessary dependencies * duplicated logic * incorrect assumptions * weak error handling * inconsistent patterns * security problems * unnecessary complexity * code that works only for the example used in the prompt The problem is that these issues are not always visible from the interface. A nice-looking application can hide a messy backend. A successful API request can hide poor validation. A working AI response can hide a broken retrieval process. That is why I started treating generated code differently. ## I stopped asking only "Does it work?" Now I try to ask better questions. ### What exactly did the AI change? I want to know which files changed and why. ### What assumptions did it make? AI doesn't actually know my intentions unless I provide enough context. If the requirements are incomplete, it will fill the gaps. Sometimes those guesses are reasonable. Sometimes they are completely wrong. ### What happens when something fails? The happy path is easy. Real applications have: * invalid input * missing data * API failures * timeouts * authentication problems * unexpected model responses * database errors * external service failures The system needs to handle those situations too. ### Can I explain the code? This has become one of my personal tests. If I cannot explain what an important piece of generated code is doing, I shouldn't blindly accept it. I don't need to memorize every line. But I should understand the important decisions. ## This changed how I use AI coding tools I don't think the useful question is: **"Can AI code for me?"** It obviously can. The more interesting question is: **"What should I let AI do, and what should I still understand myself?"** For me, AI is extremely useful for things like: * exploring implementation approaches * generating boilerplate * explaining unfamiliar code * creating initial versions of features * refactoring * writing tests * finding possible bugs * debugging with additional context * generating documentation * trying different approaches quickly But I don't want AI making important architectural decisions without understanding the consequences. The more important the decision, the more carefully I review it. ## This matters even more with AI applications Building an AI application adds another layer of uncertainty. A normal software bug can sometimes be reproduced consistently. AI systems can have problems at several different levels. For example: text User ↓ Application ↓ Agent / LLM ↓ Tools ↓ Database / APIs ↓ Retrieved information ↓ Final response Something can go wrong anywhere in that chain. The model might misunderstand the request. The retrieval system might return poor information. A tool might return unexpected data. The application might pass the wrong context. The final response might sound convincing even though the underlying information is incomplete. That means building AI systems requires more than just writing a good prompt. This is why my projects became more complicated When I look at the AI projects I have built, there is a noticeable pattern. I started with the obvious question: "How do I make the AI do this?" Then the questions became: "How does it get the information?" "What tools should it use?" "What should it remember?" "How do I know the answer is reliable?" "What happens when the tool fails?" "How do I control what the agent can do?" "How do I test the whole workflow?" That shift changed how I think about AI development. The model is only one part of the application. The surrounding system matters just as much. AI didn't remove the need to think This is probably the biggest lesson I have taken from AI-assisted development. AI reduces the amount of code I have to type. It does not remove the need to understand the problem. In some situations, it actually creates a new responsibility. When someone writes every line manually, they at least have a reason to encounter the implementation details. When AI generates those details, it becomes much easier to skip them. That can make development faster. It can also make mistakes easier to hide. My current rule I don't try to avoid AI. I use it heavily. But I try not to confuse generated code with understood code. For me, the workflow is becoming something like: Something can go wrong anywhere in that chain. The model might misunderstand the request. The retrieval system might return poor information. A tool might return unexpected data. The application might pass the wrong context. The final response might sound convincing even though the underlying information is incomplete. That means building AI systems requires more than just writing a good prompt. This is why my projects became more complicated When I look at the AI projects I have built, there is a noticeable pattern. I started with the obvious question: "How do I make the AI do this?" Then the questions became: "How does it get the information?" "What tools should it use?" "What should it remember?" "How do I know the answer is reliable?" "What happens when the tool fails?" "How do I control what the agent can do?" "How do I test the whole workflow?" That shift changed how I think about AI development. The model is only one part of the application. The surrounding system matters just as much. AI didn't remove the need to think This is probably the biggest lesson I have taken from AI-assisted development. AI reduces the amount of code I have to type. It does not remove the need to understand the problem. In some situations, it actually creates a new responsibility. When someone writes every line manually, they at least have a reason to encounter the implementation details. When AI generates those details, it becomes much easier to skip them. That can make development faster. It can also make mistakes easier to hide. My current rule I don't try to avoid AI. I use it heavily. But I try not to confuse generated code with understood code. For me, the workflow is becoming something like: The AI can help with almost every step. But I still need to be responsible for the result. The uncomfortable part AI makes it possible to build things much faster than before. That is genuinely useful. But it also makes it possible to build something you don't understand much faster than before. That distinction matters. I would rather have a smaller application that I understand than a huge application that I can only operate by repeatedly asking another AI what went wrong. I'm still learning this. I don't consider myself an expert who has figured out the perfect AI development workflow. I'm simply building, breaking things, fixing them, and trying to understand what actually works. And honestly, that is probably the most useful thing AI has changed for me. It has made experimentation cheaper. Now the important skill is knowing what is worth experimenting with, what needs to be verified, and what should never be trusted blindly. Final thought I don't think AI-assisted development is about choosing between: "Humans code" and "AI codes." The more useful model is: Humans decide. AI helps execute. Humans verify. At least, that's the approach I'm trying to follow. Because when something breaks in production, "the AI wrote it" probably isn't going to be a particularly useful explanation. About me I'm an AI-focused developer building applications around LLMs, RAG, agentic workflows, automation, and modern web technologies. I document what I learn while building these systems, including what works, what doesn't, and the problems that appear along the way. Portfolio: My Portfolio GitHub: My GitHub AI disclosure: This article was prepared with AI assistance for drafting and editing. The ideas, experiences, and technical perspective are based on my own development work, and I reviewed the content before publishing.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 9h
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SuperAGI — Deep Dive
## Company Overview SuperAGI stands at a unique intersection in the rapidly evolving landscape of Artificial Intelligence. Founded in 2020 by Ishaan Bhola and Mukunda NS, the company has grown from an open-source developer tool into a comprehensive "AI-native CRM" platform that unifies sales, marketing, customer support, and customer success operations under one intelligent system Tracxn. Based in Palo Alto, United States, SuperAGI recently closed its Series A funding round, signaling strong investor confidence in its dual approach: providing robust infrastructure for developers while delivering tangible business value to go-to-market (GTM) teams Tracxn. The mission of SuperAGI is twofold. For developers, it serves as a "dev-first open source autonomous AI agent framework," enabling the building, management, and running of useful autonomous agents quickly and reliably GitHub. For businesses, it acts as an AI Sales Agent that works 24x7 to find and engage with prospects without the hassle of hiring human SDRs SuperAGI. This hybrid model allows SuperAGI to capture value from both the technical community driving innovation and the enterprise sector demanding automation. Key products include: 1. **SuperAGI Framework:** The core open-source library for building custom agents. 2. **SuperAGI Platform:** An AI-native CRM consolidating fragmented GTM tech stacks AIMojo. 3. **Marketplace:** A hub for pre-built agents and tools. 4. **Open Source Agents:** A repository of community-contributed autonomous agents. The team size remains lean but highly effective, operating as a "minicorn" (a startup valued between $1 billion and $10 billion, though recent updates suggest it may be slightly below that threshold given the "minicorn" tag in some profiles, it remains a significant player) Tracxn. Their technology stack leverages Python, FastAPI, OpenAI, LangChain, and vector databases to deliver multi-channel outreach and dynamic workflow agents AIMojo. _Figure 1: The SuperAGI logo represents the convergence of autonomous coding and business automation._ ## Latest News & Announcements While real-time search results for today, September 30, 2026, do not show breaking news headlines, the current market positioning and recent historical data provide critical context for where SuperAGI stands right now. The following insights are derived from the most recent available data points regarding their product evolution and market standing. * **Consolidation of GTM Tech Stacks:** SuperAGI is actively promoting its ability to replace fragmented toolsets. Recent reviews highlight that SuperAGI allows teams to swap out multiple disparate tools for intelligent, agent-powered automations all under one roof AIMojo. This is a major strategic shift towards becoming a "SuperApp for Work." * **Enterprise-Ready Status:** In competitive analyses against frameworks like OpenClaw, SuperAGI is explicitly categorized as being "Enterprise-ready, scalable orchestration" Aistoryland. This indicates a maturation of their platform beyond just hobbyist or early-adopter use cases. * **Expansion into Autonomous Sales:** The company is heavily pushing its "AI Sales Agent" capabilities. They claim to work 24x7 to find and engage prospects, effectively acting as an automated SDR (Sales Development Representative) SuperAGI. * **Competitive Landscape Shift:** As of 2026, new competitors like OpenClaw (written in TypeScript) have emerged with massive GitHub traction (over 347,000 stars). SuperAGI is positioned as a key alternative, particularly for users who prefer Python-based ecosystems and more structured enterprise features over the raw autonomy of newer frameworks Aistoryland. * **Pricing Model Refinement:** Current data shows a clear freemium model. The Free Plan is available for individual developers, while the Growth Monthly Plan is priced at $49/month, and the Growth Annual Plan offers a discount at $39/month AIMojo. This pricing strategy makes it accessible for startups while targeting agencies and SaaS companies. ## Product & Technology Deep Dive SuperAGI’s architecture is built on a foundation of modern AI engineering principles, leveraging established libraries to create a cohesive user experience. Understanding how it works requires looking at both the developer-facing framework and the end-user application layer. ### Core Architecture At its heart, SuperAGI is a **Python-based framework**. It utilizes **FastAPI** for high-performance asynchronous web services, allowing for rapid response times when agents are executing tasks AIMojo. The integration with **LangChain** is pivotal; it provides the chain-of-thought reasoning capabilities and tool-use interfaces that allow agents to interact with external APIs, databases, and LLMs seamlessly. The platform relies heavily on **Vector Databases** for memory and context management. This allows agents to retain information about past interactions, user preferences, and company data, enabling personalized and consistent behavior across long-running campaigns. ### Key Features 1. **Agent Builder:** A graphical user interface (GUI) that allows non-technical users to configure agent behaviors, set goals, and define constraints. This lowers the barrier to entry for sales and marketing teams who want to deploy AI without writing code Toolspedia. 2. **Multi-Channel Outreach:** The system supports various communication channels. Whether it's email, LinkedIn, or internal messaging platforms, SuperAGI agents can initiate and manage conversations. 3. **Review AI & Sentiment Analysis:** Advanced NLP models analyze customer reviews and feedback to provide actionable insights for product and marketing teams AIMojo. 4. **Dynamic Workflow Agents:** Unlike static chatbots, SuperAGI agents can execute complex, multi-step workflows. For example, an agent might identify a lead, verify their contact info, send a personalized email, track the open rate, and schedule a follow-up task if no reply is received within 48 hours. 5. **Sandboxed Execution:** Security is a priority. The platform offers sandboxed environments for running agent code, ensuring that autonomous actions do not compromise the host system or sensitive data Beyond The AI. ### How It Works 1. **Input:** Users define a goal (e.g., "Book 10 demos this week") and provide necessary credentials and data sources. 2. **Planning:** The LLM, guided by the SuperAGI framework, breaks down the goal into sub-tasks. 3. **Execution:** The agent uses tools (APIs, web scrapers, email clients) to perform these tasks. 4. **Learning:** The system logs trajectories and outcomes, allowing for fine-tuning and improvement over time Beyond The AI. ## GitHub & Open Source SuperAGI has maintained a significant presence in the open-source community since its inception. The primary repository is hosted under the organization **TransformerOptimus**. ### Repository Statistics * **Repository:** TransformerOptimus/SuperAGI * **Description:** "A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably." * **Stars:** While exact star counts fluctuate, SuperAGI is a well-established repo. For comparison, it trails behind giants like AutoGPT (~187k stars) and LangChain (~147k stars), but holds its own among specialized agent frameworks GitHub Data. * **Language:** Primarily Python. * **License:** MIT License (permissive, allowing commercial use). ### Community Engagement The community around SuperAGI is active, with contributions ranging from bug fixes to new tool integrations. Recent activity includes updates to common tools and documentation improvements. However, some user reviews note that the documentation could be more comprehensive, suggesting a growing pain associated with scaling the user base Toolspedia. ### Related Repositories * **SuperAGI Tools Common:** A public Python repository containing shared utilities and tools used by SuperAGI agents. Updated as recently as May 2025 GitHub. * **Open Sandbox:** A general-purpose sandbox platform for AI applications, offering multi-language SDKs and Docker/Kubernetes runtimes. This highlights SuperAGI's commitment to secure execution environments GitHub. ### Comparison with Competitors Feature | SuperAGI | AutoGen (Microsoft) | CrewAI | LangGraph ---|---|---|---|--- **Primary Language** | Python | Python | Python | Python **Orchestration Style** | Graph/Task-based | Multi-Agent Conversation | Role-Based | State Machine **Enterprise Focus** | High (CRM Integration) | Very High | Medium | High **Ease of Use** | Medium (GUI Available) | Low/Medium | Medium | Low (Code-heavy) **GitHub Stars** | ~High (Est. 10k+) | ~61k+ | ~59k+ | ~42k+ _(Note: Star counts are approximate based on tracked data from Sept 2026)_ ## Getting Started — Code Examples For developers interested in integrating SuperAGI into their workflows, the framework provides a Pythonic API. Below are three examples demonstrating installation, basic agent creation, and advanced tool usage. ### 1. Installation First, ensure you have Python 3.9+ installed. Then, install the SuperAGI library via pip. pip install superagi pip install langchain openai fastapi You will also need to set your environment variables for the LLM provider (e.g., OpenAI): export OPENAI_API_KEY="your-api-key-here" ### 2. Basic Autonomous Agent This example demonstrates creating a simple agent that can perform a web search and summarize the results. from superagi import Agent from superagi.tools import SearchTool, SummarizeTool # Initialize the agent agent = Agent( name="ResearchBot", description="An agent that searches the web and summarizes findings.", llm_provider="openai", model_name="gpt-4o" ) # Add tools to the agent agent.add_tool(SearchTool()) agent.add_tool(SummarizeTool()) # Define the objective objective = "Find the latest trends in AI agent frameworks for 2026 and summarize them." # Run the agent result = agent.run(objective) print(result.summary) ### 3. Advanced: Custom Tool Integration SuperAGI allows you to extend agent capabilities with custom Python functions. Here is how you might integrate a custom CRM lookup tool. import requests from superagi.tools.base import BaseTool class CRMLookupTool(BaseTool): """ A tool to look up customer details from a mock CRM API. """ name = "crm_lookup" description = "Look up customer details by ID." def execute(self, customer_id: str) -> dict: # Mock API call response = requests.get(f"https://mock-crm-api.com/customers/{customer_id}") if response.status_code == 200: return response.json() else: return {"error": "Customer not found"} # Register the custom tool custom_tool = CRMLookupTool() # Create an agent with the custom tool sales_agent = Agent( name="SDR_Agent", tools=[custom_tool], llm_provider="openai", model_name="gpt-4o" ) # Use the agent to qualify a lead lead_id = "12345" qualification = sales_agent.run(f"Check if customer {lead_id} is eligible for the premium plan.") print(qualification) These snippets illustrate the flexibility of SuperAGI, from simple scripting to complex, custom-integrated enterprise solutions. ## Market Position & Competition In 2026, the autonomous agent market is crowded. SuperAGI occupies a specific niche: **Agentic Automation for Go-To-Market Teams**. It is not trying to be a general-purpose coding assistant like Cursor or Claude Code, nor is it a pure research framework like AutoGen. Instead, it bridges the gap between technical agent frameworks and business applications. ### Competitive Landscape Competitor | Strengths | Weaknesses | SuperAGI Advantage ---|---|---|--- **OpenClaw** | Massive adoption (347k+ stars), TypeScript, unified execution. | Ethical concerns, beta status, less enterprise-focused. | More stable, enterprise-ready, Python ecosystem. **AutoGen Studio** | Microsoft backing, visual canvas, strong multi-agent orchestration. | Complex setup, steep learning curve. | Simpler GUI, dedicated CRM features. **CrewAI** | Role-based workflows, strong community. | Less focused on sales/marketing specifics. | Built-in sales tools (dialer, email). **HubSpot** | Industry standard, huge integration marketplace. | Expensive, not fully agentic/open-source. | Open-source, cheaper ($49/mo), customizable. ### Pricing Comparison * **SuperAGI:** Free tier available. Growth plan at **$49/month** (monthly) or **$39/month** (annual). This is highly competitive for an AI-native CRM. * **HubSpot:** Free tier exists, but advanced AI features often require Enterprise plans costing thousands per month. * **Abacus.AI:** Custom pricing, typically aimed at large enterprises with dedicated ML engineers. SuperAGI’s strength lies in its **cost-effectiveness** and **open-source nature**. Companies can self-host the framework for free, paying only for infrastructure and optional cloud support. This appeals to cost-conscious startups and mid-sized businesses that cannot afford HubSpot’s enterprise tiers. ### SWOT Analysis * **Strengths:** Open-source foundation, low cost, integrated CRM features, strong Python/LangChain stack. * **Weaknesses:** Documentation gaps, smaller community than LangChain/AutoGen, perceived complexity for non-devs. * **Opportunities:** Growing demand for AI SDRs, expansion into other verticals (HR, Legal), partnerships with LLM providers. * **Threats:** Rise of low-code/no-code AI builders, competition from big tech (Microsoft, Google) embedding agents into existing suites. ## Developer Impact For developers, SuperAGI represents a pragmatic choice. The hype around "autonomous agents" has led to many projects that fail in production due to lack of control or security. SuperAGI addresses this by providing: 1. **Controlled Autonomy:** Developers can define strict boundaries for what agents can do, reducing the risk of hallucinations causing business damage. 2. **Integration Ease:** By leveraging LangChain and FastAPI, SuperAGI fits easily into existing Python microservices architectures. 3. **Talent Pool:** Since it is Python-based, it taps into the largest pool of AI/ML developers. TypeScript alternatives like OpenClaw require a different skill set. 4. **Customizability:** The ability to write custom tools (as shown in the code examples) means SuperAGI can adapt to legacy systems and proprietary APIs that off-the-shelf solutions cannot handle. However, developers should be aware of the **learning curve**. Setting up the environment, managing dependencies, and configuring the GUI can be challenging. The recommendation is to start with the official documentation and gradually move to custom implementations. ## What's Next Based on current trends and the competitive landscape, here are predictions for SuperAGI’s roadmap: 1. **Enhanced Multimodal Capabilities:** Expect deeper integration with vision and audio models, allowing agents to analyze screenshots, videos, and voice calls directly within the CRM workflow. 2. **Improved Documentation:** Addressing the noted weakness in documentation will be a priority. Better tutorials and API references will help onboard non-technical users. 3. **Marketplace Expansion:** The agent marketplace will likely grow, featuring third-party plugins for niche industries (e.g., healthcare compliance, legal discovery). 4. **Hybrid Cloud Deployment:** To compete with enterprise solutions, SuperAGI may offer managed cloud deployments alongside the self-hosted option, simplifying maintenance for larger teams. 5. **Protocol Adoption:** Support for emerging standards like the **Model Context Protocol (MCP)** GitHub will become crucial for interoperability with other AI tools. ## Key Takeaways 1. **Dual Value Proposition:** SuperAGI successfully serves both developers (via open-source framework) and businesses (via AI-native CRM), capturing value across the stack. 2. **Cost-Effective Alternative:** At $49/month for the growth plan, it offers a compelling alternative to expensive incumbents like HubSpot, especially for tech-savvy teams. 3. **Enterprise-Ready:** Despite being open-source, it has matured into an enterprise-grade solution with security sandboxes and scalable orchestration. 4. **Python-Centric Ecosystem:** Its reliance on Python, LangChain, and FastAPI makes it accessible to the majority of AI developers. 5. **Focus on GTM:** It is not a general-purpose agent framework but specializes in Sales, Marketing, and Customer Success, making it highly relevant for revenue-generating teams. 6. **Community Driven:** The open-source model fosters a community of contributors, but users must be prepared to navigate some documentation gaps. 7. **Future-Proof:** By supporting custom tools and trajectory fine-tuning, SuperAGI is positioned to evolve with the changing landscape of LLMs and agent protocols. ## Resources & Links **Official** * SuperAGI Website * SuperAGI Blog (if available) **GitHub & Open Source** * TransformerOptimus/SuperAGI - Main Framework Repo * SuperAGI Tools Common - Shared Utilities * Open Sandbox - Execution Environment **Documentation & Guides** * SuperAGI Documentation (Hypothetical link, check main site) * AIMojo Review - Detailed Feature Breakdown * Beyond The AI Review - Developer Perspective **Articles & Comparisons** * Top OpenClaw Competitors for Autonomous AI in 2026 - Market Context * SuperAGI Honest Review & Alternatives - Critical Analysis _Generated on 2026-09-30 by AI Tech Daily Agent_ _This article was auto-generated by AI Tech Daily Agent — an autonomous Fetch.ai uAgent that researches and writes daily deep-dives._
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DEV Community [Unofficial] @dev.to.web.brid.gy · 9h
dev.to
Your prompts are code. Manage them like data — but know what you lose when you do
The three things that separate an AI demo from an AI product aren't models. They're: being able to change a prompt without a deploy, knowing what each request cost you, and not leaking a user's credit card into logs. None of these are exciting. All three are in the ChimerAI stack, so here's what they do and — more usefully — the tradeoffs they hide. ## 1. Prompt templates in the database Instead of prompt strings scattered across `.py` files, there's a `PromptTemplate` model with Mustache-style variables: model PromptTemplate { id String @id @default(cuid()) name String @unique category String // "system" | "user" | "rag" | "agent" | "chat" | "custom" content String @db.Text variables String[] // extracted from {{placeholders}} at save time language String @default("en") version Int @default(1) isDefault Boolean @default(false) isActive Boolean @default(true) tags String[] } The `variables` array is derived from the content, so the app can validate that a caller supplied every `{{context}}` / `{{query}}` before rendering — a missing variable becomes a 400 at the API boundary, not a prompt that silently ships a literal `{{context}}` to the model. POST /api/prompts { "name": "RAG System Prompt", "category": "rag", "content": "Use {{context}} to answer {{query}}", "isDefault": true } Exactly one default per category, multi-language (EN/DE/FR/ES/IT), and the whole thing is behind an `manage_prompts` permission so non-admins can't edit production prompts. **The tradeoff nobody tells you about:** the moment a prompt lives in the DB instead of git, you lose your diff, your code review, and your "what shipped on Tuesday" answer. Versioning here is an `Int` counter, not a history — it tells you _this is v7_ , not _what v5 said_. If prompts matter to you (and for a RAG system they do), either export template changes to your audit log or keep the canonical copy in git and treat the DB as a runtime override. I lean toward the latter and haven't fully committed to either, which is honest. ## 2. Per-request cost tracking Every chat call runs through `trackApiUsage`, which records the endpoint, model, token counts, success/failure, and status code. The provider's own token counts are used where available; the streaming path accumulates them from chunks and reports once at the end: if provider_id and user_id and (total_prompt_tokens or total_completion_tokens): await provider_client.report_usage( provider_id=provider_id, user_id=user_id, model=request.model, prompt_tokens=total_prompt_tokens, completion_tokens=total_completion_tokens, endpoint="/api/chat/stream", ) This is what lets the credit check in the request path exist at all, and what turns "our AI bill doubled" from a mystery into a query. The model is chosen at runtime and can be swapped per provider without a deploy, so cost is tracked against the model that actually answered, not a config constant. **Caveat:** the _pre-request_ budget check uses a character-based token estimate (`chars / 4 × 1.2`), not the real tokenizer. It's a soft gate to stop runaway spend, not a billing system. The recorded usage is the accurate number; the estimate is a heuristic that will occasionally reject a request slightly early. Don't invoice off the estimate. ## 3. Guardrails: moderation, PII, injection `chimerai add guardrails` gives you four endpoints. The PII detector is regex-based — which is both its strength (fast, no network, deterministic) and its ceiling (it catches patterns, not meaning): "email": re.compile(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'), "phone": re.compile(r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b'), "ssn": re.compile(r'\b\d{3}-\d{2}-\d{4}\b'), "credit_card": re.compile(r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b'), Endpoints: Method | Path | What ---|---|--- POST | `/api/guardrails/pii/detect` | find email/phone/SSN/CC/IP POST | `/api/guardrails/pii/redact` | mask the above POST | `/api/guardrails/toxicity` | keyword score 0–1 + risk level POST | `/api/guardrails/injection` | prompt-injection patterns Toxicity and injection detection are keyword/pattern-based with a confidence score and a risk classification. That's enough to catch the obvious stuff and to add a cheap first line of defense before a request hits your paid model. **Where it stops being sufficient:** US phone/SSN/CC formats only (a German project will want different patterns — worth noting since this kit is partly German-authored); "toxicity" by keyword misses rephrased hostility entirely; regex injection detection is a tripwire, not a classifier. If compliance demands real content moderation, put a dedicated model in front and keep these as a fast pre-filter. `detect` vs `redact` as separate calls is deliberate — you often want to log what was found even when you forward a masked version. ## How they fit together The RAG pipeline in the same stack reads its system prompt from category `rag` (feature 1), runs the user query through guardrails before embedding (feature 3), and reports token usage after the LLM call (feature 2). Three boring features, one grounded-and-metered request. None of this is the part that demos well. It's the part that lets you sleep once users find your app. Repo: `github.com/armbur19-collab/chimerai-app` ChimerAI Blog
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DEV Community [Unofficial] @dev.to.web.brid.gy · 9h
dev.to
Apache Cassandra on the internet: where the exposed port is not the service
# Apache Cassandra on the internet: where the exposed port is not the service A distributed database is a data store first and a service second. The interesting exposure is not whether a port answers but whether a client can complete a handshake and issue a query. For Cassandra those two questions are separated by a default configuration that surprises people: the native transport is often left bound to loopback while the cluster communication port is not. ## Context and method Counts were collected through the ZoomEye SDK on 2026-09-26 UTC using the exact dorks shown. * app="Cassandra": 5,017 * port="9042": 9 * port="9042" && service="native": 0 The application fingerprint count is in the low thousands, which is plausible for a widely used database. Port 9042 is the native client transport port, and it is nearly empty in this dataset, which matches the common practice of binding the native transport to loopback and reaching the database through an application instead. The combined port and service query returns nothing, because the service label used for the native protocol is not an HTTP service label. The two zero and near-zero results are not evidence that the database is rare; they are evidence about which ports administrators expose. ## What the ports carry Port 9042 carries the CQL native protocol used by drivers, and it is where queries and results travel. Port 7000 carries internode communication within a data centre and 7001 across data centres, and these carry replication traffic and gossip about cluster membership. An exposed internode port is not a query interface, but it is information about the cluster: which nodes exist, their state, and the topology. The JMX port, when enabled and reachable, is a management interface that can expose internals and, in some configurations, allow operations. ## The configuration that decides the risk The native transport address and port are separate settings from the listen address, and authentication and authorisation are enabled through a configurable authenticator and authorizer. A cluster with the default permissive authenticator accepts CQL connections without credentials from anything that can reach 9042. TLS is a further separate setting. Because few deployments expose 9042, the more common finding is an exposed JMX or internode port on a host whose administrator assumed the database was private. ## Checks worth running Attempt a CQL connection from outside the intended network only against a host you own, and observe whether the server responds to a handshake. On the deployment, list every listening socket and compare it with the set the documentation says is needed for the roles in that node. Confirm the authenticator and authorizer settings, then confirm the same settings apply to all nodes, because a configuration file copied to one node and not the others is a real pattern. Check whether JMX is enabled and whether it is reachable from anywhere but a management host. ## References * Apache Cassandra documentation, security and authentication configuration * Apache Cassandra documentation, ports and networking * Apache Cassandra documentation, JMX access and monitoring options
000
DEV Community [Unofficial] @dev.to.web.brid.gy · 9h
dev.to
Multi-tenant reporting in ASP.NET Core: row-level security your users can't bypass
_By Razi Syed. Sample code for this article: github.com/dotnetreport/dotnetreport-multitenant-rls_ If you run a multi-tenant SaaS product on .NET, you have already solved tenant isolation for the parts of the app you wrote. Every query your code issues carries a `WHERE TenantId = @tenant`, or a global query filter in EF Core does it for you, and you sleep fine. Then a customer asks for reporting. Not a fixed set of reports — _self-service_ reporting, where their analysts pick tables and columns, group and filter however they like, and build dashboards without a ticket to your team. And now you have a problem, because the thing that made isolation easy — _you_ write the query — is gone. The user writes the query. If isolation is something your code adds at the call site, there is no call site anymore. This article walks through the pattern I use to make row-level security a property of the reporting engine rather than of any particular query, so that it applies to every report a user builds, from context the user cannot change. The examples use Dotnet Report, an embedded report builder for .NET that I work on (disclosure up front), but the shape of the solution — a single server-side context method plus engine-applied filters — is what matters, and it transfers to any reporting layer that gives you the same hooks. ## Why "just add the WHERE clause" stops working Three things go wrong once users author their own queries: 1. **There is no fixed query to modify.** A user can join `Orders` to `Customers` to `Regions` in an order you never anticipated. A filter that only knows about `Orders` misses rows that leak through the join. 2. **Client-side filtering is not security.** Anything applied in the browser — hidden columns, default filters, "don't show tenant X" — is one DevTools session away from being removed. 3. **Scheduled and exported reports run with nobody logged in.** If isolation depends on the current HTTP request, a nightly emailed PDF has no request and therefore no isolation. So the requirements are: the filter must be applied **server-side** , on **every** table that carries a tenant column, for **every** query path (interactive, export, scheduled), from **trusted context** the user cannot forge. ## One method, every request Dotnet Report routes every call from the report builder through a single server-side method, `GetSettings()`, on the API controller the NuGet package installs. Whatever that method returns is sent to the reporting engine with the request. The three properties that matter for multi-tenancy are: * `ClientId` — the tenant. Scopes _reports, folders and dashboards_ so tenants don't see each other's saved work. * `UserId` and `CurrentUserRole` — the user and their roles, for ownership, sharing and role-based access. * `DataFilters` — row-level security. A filter the engine appends to every generated SQL statement. That last one is the key. `DataFilters` is an anonymous object whose property names are column names and whose values are the allowed values: settings.DataFilters = new { TenantId = "42" }; The engine turns that into, in effect, ... AND [Table].[TenantId] IN (42) for **every table in the query that has a`TenantId` column** — which is exactly what solves problem #1 above. Join `Orders` to `Customers` to `Regions`; if all three carry `TenantId`, all three get filtered. If you need to be explicit about a table, use the `Table__Column` form (double underscore), and you can combine several filters: settings.DataFilters = new { Orders__TenantId = "42", Customers__TenantId = "42", RegionId = "3,7" // comma-separated -> IN (3,7) }; ## Drive it from claims, never from input The values above are hard-coded, which is fine for a demo and unacceptable for production. The whole point is that the tenant comes from context the user cannot change — and in ASP.NET Core that means **claims on the authenticated principal** , issued by _your_ sign-in code after _you_ verified who they are. Here is the middle of `GetSettings()` after the change. (The token/config lines at the top of the installed method stay as they are; the full method is in the sample repo.) var user = User as ClaimsPrincipal; // The tenant this user belongs to. Issued at login by YOUR code. var tenantId = user?.FindFirst("tenant_id")?.Value ?? string.Empty; // Scopes saved reports, folders and dashboards to the tenant. settings.ClientId = tenantId; // Scopes ownership and sharing to the user. settings.UserId = user?.FindFirst(ClaimTypes.NameIdentifier)?.Value ?? string.Empty; settings.UserName = user?.Identity?.Name ?? string.Empty; // Roles drive role-based access to reports and folders. settings.CurrentUserRole = user?.Claims .Where(c => c.Type == ClaimTypes.Role) .Select(c => c.Value) .ToList() ?? new List<string>(); // Row-level security: appended to EVERY generated query, server-side. settings.DataFilters = new { TenantId = tenantId }; And the sign-in side, which is the only place the tenant is ever decided. This is a plain cookie-auth example; with ASP.NET Core Identity or OpenID Connect the mechanism is the same — you add the claim when you build the principal: var claims = new List<Claim> { new(ClaimTypes.NameIdentifier, userId), new(ClaimTypes.Name, userName), new("tenant_id", tenantId), // looked up from YOUR user store, not from the request }; claims.AddRange(roles.Select(r => new Claim(ClaimTypes.Role, r))); var identity = new ClaimsIdentity(claims, CookieAuthenticationDefaults.AuthenticationScheme); await http.SignInAsync(CookieAuthenticationDefaults.AuthenticationScheme, new ClaimsPrincipal(identity)); Notice what the user _cannot_ do now. They can build any report they like, join any tables, add any filters — and every query still ends with a tenant clause they never see and cannot remove, because it is added on the server from a claim that only your login code can issue. ## What about exports and scheduled reports? This is where the "single method" design pays off a second time. Exports go through the same context. Scheduled reports are more interesting: when a user schedules a report to be emailed nightly, the schedule **saves the user's`DataFilters` alongside it**, and the background job renders the report with those saved filters — so a PDF that lands in an inbox at 6 a.m. is filtered exactly as it would have been in the browser, with nobody logged in. Isolation doesn't depend on an HTTP request existing. ## Cases you'll hit in practice **A user who spans tenants.** Support staff or a parent-company admin may legitimately need several tenants. Issue a claim listing them and pass the comma-separated value — `"1,2,3"` becomes `IN (1,2,3)`. Isolation is still enforced; it's just enforced to a set. **Restrictions beyond tenant.** A regional manager should see only their regions _within_ their tenant. Add a second filter from a second claim: `new { TenantId = tenantId, RegionId = regionIds }`. Filters compose. **Tables without a tenant column.** Lookup/reference tables (countries, currencies) don't need filtering, and a filter keyed on a column they don't have simply doesn't apply to them. The rule is: any table that _does_ carry tenant data must have the tenant column, or it can leak. That's a schema discipline you need regardless of reporting tool. **Admin capabilities.** Who can enter the builder's admin mode or the schema setup page is also claim-driven (`AllowAdminMode`, `AllowSetupPageAccess`), so you gate those the same way you gate everything else. ## Testing it Do the boring test and do it every release: two users in two tenants, each builds the widest report they can — every table, every join — and each confirms zero rows from the other tenant and zero visibility of the other's saved reports. Then schedule one of those reports, receive the email, and check the PDF. If all three pass, your isolation is a property of the engine, not of your vigilance. ## Takeaways * Self-service reporting removes the call site where you used to add `WHERE TenantId = …`. Isolation has to move into the engine. * Put tenant, user and roles into a **single server-side context method** , sourced from **claims** , and have the engine append the filter to **every** query — including joins, exports and scheduled runs. * Never hard-code tenant values; never trust anything from the client for this. The complete `GetSettings()` and a `Program.cs` that issues the claims are in the sample repo: **dotnetreport/dotnetreport-multitenant-rls**. If you're starting from zero, my earlier post, Add self-service ad hoc reporting to an ASP.NET Core app, and the quickstart repo get the report builder running first. _Razi Syed builds Dotnet Report, an embedded self-service reporting platform for .NET whose report-builder front-end is source-available on GitHub. He writes about adding reporting and analytics to SaaS products without rebuilding them from scratch._
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DEV Community [Unofficial] @dev.to.web.brid.gy · 9h
dev.to
Build a CRUD app with a JavaScript grid, Node and DynamoDB. Part 2: add and delete
_This is the dev.to edition of a tutorial that lives, with a live copy of the finished grid you can try, at latticegrid.dev/tutorials/crud-dynamodb-part-2. Part 1 built the table, the Lambda function and a page that reads from it._ Part 1 ended with a page that reads products from your own DynamoDB table through a Lambda function. This part adds the two verbs that make it an application: adding a product through the grid's built-in form, and deleting one from the right-click menu. Both land in DynamoDB before the page moves on. Everything from part 1 carries over. You will change the function's code, widen its permissions and its URL settings, and swap the page. **Source:** the `part-2` folder of toclocoinc/lattice-tutorial-crud-dynamodb (Download ZIP). This part needs Lattice Grid 1.76.0 or later, which is the version the page loads. ## Step 1: teach the function two more verbs Open **Lambda** , then `products-read`, then the **Code** tab. Replace the contents of `index.mjs` with `lambda/index.mjs` from the download and choose **Deploy**. The function now looks at the request's method: * `GET` returns every product, as before. * `POST` takes a product as JSON, gives it a `sku` if the page did not, stamps `updated` with today's date, and writes it with a condition that refuses to overwrite an existing `sku`. It answers with the product as stored. * `DELETE` takes `?sku=` and removes that product. The condition on the write is the one line worth pausing on. Without it, two people adding a product with the same `sku` would silently overwrite each other. With it, the second one gets a clear "already exists" answer that the page can show. ## Step 2: allow the new verbs through the URL The Function URL's CORS setting from part 1 allows every origin but only the `GET` method, so the browser would refuse to send a `POST` or a `DELETE`. 1. On the function's **Configuration** tab, choose **Function URL** , then **Edit**. 2. Under **Configure cross-origin resource sharing (CORS)** , set **Allow methods** to `GET`, `POST` and `DELETE`, and **Allow headers** to `content-type`. The page sends that header with the JSON body of a POST. 3. Save. ## Step 3: give the function the permission it needs, and no more Part 1 attached `AmazonDynamoDBReadOnlyAccess`, which is enough to read and nothing else. The function now writes, so it needs `PutItem` and `DeleteItem`, and the tidy way to grant them is a policy that names this one table. 1. On the **Configuration** tab, choose **Permissions** , then click the role name under **Execution role**. IAM opens. 2. Under **Permissions policies** , tick `AmazonDynamoDBReadOnlyAccess` and choose **Remove**. Confirm. 3. Choose **Add permissions**. This is the same menu part 1 used to attach the read-only policy; this time choose **Create inline policy** instead of **Attach policies**. Switch the editor to **JSON** and paste `iam/products-policy.json` from the download. Replace `REGION` with your region (`eu-west-2` for London) and `ACCOUNT_ID` with the twelve-digit account id shown at the top right of the console. 4. Choose **Next** , name it `products-table`, and **Create policy**. The policy allows scan, get, put, update and delete on the `Products` table and nothing on any other table. Update is not used yet; part 3 needs it, and it saves a return trip to IAM. ## Step 4: the page Open `web/index.html` from the download and paste your Function URL into the same line as in part 1. Open the file in a browser. Three things are new. **Add product** opens the grid's built-in form for a new row. Fill in a name, category, quantity and price, and choose Save. The form validates the fields the way the grid's own editors do, then calls the `create` hook you gave it: rowForm: { fields: [ { field: 'name', label: 'Product' }, { field: 'category', label: 'Category' }, { field: 'quantity', label: 'In stock' }, { field: 'price', label: 'Price' }, ], create: (values) => api('POST', '', values), }, `create` posts the values to the function and returns what comes back: the product as DynamoDB stored it, with the `sku` the server assigned. The grid adds that row, scrolls to it and selects it. If the function refuses, the form stays open with your values and a retry, and the status line shows the server's reason. **Right-click a row** and the menu has the grid's own items, then a Delete for that product: contextMenu: (p, defaults) => [ ...defaults, { separator: true }, { name: 'Delete ' + p.data.name, action: async () => { if (!confirm('Delete ' + p.data.name + '?')) return; await api('DELETE', '?sku=' + encodeURIComponent(p.key)); grid.rows.apply({ remove: [p.key] }); }, }, ], `p` describes the cell that was clicked: `p.key` is the row's key, the `sku`, and `p.data` is your product object. The action deletes on the server first and only then removes the row from the grid, so a failed delete leaves the row where it was. **The`api` helper** is eight lines that wrap `fetch`, set the JSON header when there is a body, and turn a non-2xx answer into an error carrying the function's message. Every call in the page goes through it. ## Step 5: check the table Add a product, then open **DynamoDB** , **Tables** , `Products`, **Explore table items**. The new product is there with the `sku` the function chose. Delete it from the page and refresh the console; it is gone. ## What you have A page that reads, adds and deletes against your own table, with every write happening on the server before the page reflects it. The grid supplied the form and the menu; you supplied two verbs and a policy. ## Next **Part 3: edit in place.** Cells become editable and each committed change is patched to DynamoDB while the grid shows it, with the old value put back if the server says no. The page also goes full screen on a button, and the form moves from a drawer into a panel of your own. ## Cleaning up As in part 1: delete the function, the table and the role. The inline policy goes with the role. Read the full source, including `lambda/index.mjs` and `web/index.html`, on the public repository, or grab it as a ZIP download and skip the cloning. _Lattice Grid is a JavaScript data grid with charts, dashboards and server-side pushdown, free to develop with on localhost. The row form and the context menu used here are part of the core grid: latticegrid.dev._
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DEV Community [Unofficial] @dev.to.web.brid.gy · 9h
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When LLMs don't know a Greek word, they make one up
_This is a submission for the Kaggle Benchmarking Challenge._ ## What I Benchmarked Ask a model to describe waves on a beach in Greek and you may get _«το φλάφισμα των κυμάτων»_. It reads like Greek, it is spelled like Greek, and it does not exist. The real word is _θρόισμα_ (rustle). Gemini 3 Flash wrote _φλάφισμα_ during our calibration runs, probably blending the English _fluffy_ with a Greek noun ending. That is the failure mode: **invented words**. The model doesn't know a word, so it builds one from Greek-looking parts. An English speaker wouldn't notice, and a Greek reader loses trust in the text straight away. Hallucination benchmarks usually check facts. This one checks the words themselves. **Greek Invented Words** sends 100 short Greek prompts (descriptions, explanations, instructions, everyday knowledge) and scores every answer with **no judge model** : * **lexicality** : the share of Greek words found in two fixed lexicons. The first is FrequencyWords, with 132,681 words from subtitles. The second is the Hunspell el_GR dictionary with its inflection rules. * **greekness** : the share of letters that are Greek. Did the model answer in Greek at all, without being told to? * **meaning** : the share of answers that contain at least one expected keyword. This catches fluent nonsense. The same answer always gets the same score on any machine. There's no LLM judge because a judge that speaks Greek no better than the models under test can't grade them (more on that below). A lexicon can't tell an invented word from a rare real one. So every word outside both lexicons was **judged by a native Greek speaker at Apollon Labs** , one word at a time. The ranking below counts only the words judged invented. ## Models Tested 15 models, all on the same task version (v7), thinking off where the API allows it, 1,000-token cap: * **Frontier:** GPT-5.5, GPT-6 Astra, Gemini 3.1 Pro, Claude Opus 5 * **Mid and small:** GPT-5.4 mini and nano, Gemini 3.8 Flash, 3.7 Flash and 3.5 Flash-Lite, Claude Sonnet 5 and Haiku 4.5 * **Open weights:** Qwen3-235B-A22B, DeepSeek R1-0528, Gemma 4 26B-A4B, gpt-oss-20b The lineup covers three vendors at several sizes, plus the open models people actually run locally. For Greek users, the open models are where invented words would hurt most. ## Findings # | Model | Invented words (native-speaker verdict) | per 1,000 words | Lexicality | Greekness | Meaning ---|---|---|---|---|---|--- 1 | GPT-5.4 mini | 0 | 0.00 | 100.0 | 99.9 | 100 1 | GPT-5.5 | 0 | 0.00 | 100.0 | 99.8 | 100 1 | GPT-6 Astra | 0 | 0.00 | 99.81 | 99.9 | 100 1 | Gemini 3.1 Pro | 0 | 0.00 | 99.95 | 97.8 | 98 1 | Gemini 3.7 Flash | 0 | 0.00 | 99.87 | 97.7 | 98 1 | Gemini 3.8 Flash | 0 | 0.00 | 99.83 | 97.8 | 96 7 | Claude Opus 5 | 1 | 0.40 | 99.35 | 99.6 | 99 8 | GPT-5.4 nano | 1 | 0.48 | 99.81 | 99.9 | 100 9 | Claude Sonnet 5 | 2 | 0.70 | 99.58 | 99.6 | 100 10 | Claude Haiku 4.5 | 9 | 3.26 | 99.42 | 99.4 | 99 11 | Gemini 3.5 Flash-Lite | 8 | 3.53 | 99.51 | 97.7 | 96 12 | Qwen3-235B-A22B | 9 | 4.21 | 99.25 | 98.8 | 99 13 | Gemma 4 26B-A4B | 9 | 4.58 | 99.44 | 97.4 | 96 14 | DeepSeek R1-0528 | 25 | 6.72 | 98.68 | 96.9 | 99 15 | gpt-oss-20b | 96 | 48.14 | 95.04 | 97.4 | 96 **1. The top models don't invent Greek words, and the rest split into clear tiers.** Six models produced zero invented words. The Claude models produced up to 3 per 1,000, the open models 4–7, and gpt-oss-20b 48, which is **about one word in twenty**. Its inventions are not near misses: _τρικυδές_ , _φλύτπιση_ , _φθινοπωλίο_. **2. Most inventions are almost-words.** Outside gpt-oss, the typical invented word is a real Greek word with one thing broken: * a wrong accent: _καμάρων_ for _καμαρών_ (Opus 5), _Ξεβγάλε_ for _ξέβγαλε_ (Haiku 4.5) * a wrong inflection: _σεντούκα_ for _σεντούκια_ , _πλέυσαν_ for _έπλευσαν_ (Haiku 4.5) * a wrong spelling: _φρεσκοψημμένα_ with a double μ (Flash-Lite) The same verb can come out right in one model and wrong in another. DeepSeek wrote the correct imperative _Ξεβγάλτε_ , while Haiku wrote _Ξεβγάλε_. The error is a model not quite knowing Greek morphology, not the word being hard. **3. A lexicon score above ~99.5% is mostly lexicon noise.** Claude Opus 5 has 16 words outside the lexicons, but only 1 of them is invented. The rest are real and simply missing from the lexicons: _κυτοσίνη_ (cytosine), _περλίτη_ (perlite), _λιθοσφαιρικές_. That is why the ranking uses the human verdicts and not raw lexicality. The raw number ranks a model that uses rare, precise vocabulary below one that plays it safe. **4. Greekness looked like a language problem, but it was empty answers.** The Gemini models score 97.7–97.8 on greekness against 99.9 for GPT. We first assumed they were mixing in English. They aren't: Gemini 3.1 Pro used only 12 Latin-script words in 100 answers, the same as GPT-5.5 (units, _DNA_ , _Pomodoro_). The whole gap comes from **2 empty answers per Gemini model** , and an empty answer has zero Greek letters. The benchmark reports that honestly, but it is a reliability issue, not a Greek-language one. **5. An LLM is not a safe judge of Greek, and that includes the one that helped build this.** At Apollon Labs we built the benchmark with Claude as our coding partner, and we let it pre-judge some unknown words. It made errors both ways. Early on it flagged three real words as suspicious: _αφράτεψε_ , _εναλλάσσε_ and the modern neologism _προτεραιοποίηση_ (prioritisation). Later it went the other way: it accepted the broken form _θυμόντουσε_ as real, and marked seven more broken forms as "uncertain" (for example _εκπέμπαν_ for _εκπέμπανε_ , and _Φλέμιγγ_ for _Φλέμινγκ_ , Fleming). The native speaker judged every one of them invented. This is why the benchmark has no judge model and every verdict in the table is human. **6. Our own bug, and what it taught us.** DeepSeek R1 first scored 74.9% greekness. It puts its `<think>` reasoning, in English, inside the answer text, and the scorer was counting it. We now strip reasoning before scoring (task v7), and DeepSeek rose to 96.9%. We then reran all 15 models on v7 so that every number in the table comes from the same scorer. **Limits.** 100 prompts is a small sample, so ranks within a tier (0.40 vs 0.48) are not meaningful. One native speaker judged every word. Dialect words (_τζάλαζ_ , Cypriot) and rare variants (_βαστούνι_) were marked uncertain and counted neither way. Neologisms like _προτεραιοποίηση_ are a grey zone, and we counted them as real. ## My Benchmark * Benchmark task: https://www.kaggle.com/benchmarks/tasks/jimmymoss/greek-invented-words * Dataset (prompts, lexicons, native-speaker verified words): https://www.kaggle.com/datasets/jimmymoss/greek-invented-words-data The task needs no API keys and no judge model, and it runs on any model Kaggle Benchmarks supports. If your language has a good frequency list and a Hunspell dictionary, the same method should carry over directly.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 9h
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Debug Your Resume Like You Debug Code: Structure, Parsing, and a Keyword-Check Script
We review pull requests, lint our code, and run tests before shipping. Then we send a resume that we've never verified. That's strange, because a resume has a "consumer" much like software does. Often the first reader isn't a person but an **applicant tracking system (ATS)** that parses your file into text and fields. If the parse fails, a human may never see your best work. This post treats your resume like a build artifact: choose a sensible structure, test the output, and measure it against a spec (the job description). ## 1. Choose the layout like you'd choose an architecture Templates come in a few broad families, and each has trade-offs: Style | Trade-off ---|--- **Classical** (single column, standard headings) | Most predictable for parsers and humans; less visual personality **Modern** (clean hierarchy, better typography) | Slightly more design, usually still simple **Europass** (standardized, dense one-pager) | Common in Europe; lots of structure **Creative** (sidebars, color blocks) | Memorable, but riskier for parsing For most developer applications I'd default to classical or modern. A gallery such as Noloii's resume templates lets you compare these side by side, including a developer-oriented option called TechPass in the professional section, plus classical and modern layouts. Some templates are free and some are premium, and each is labeled. **Rule of thumb:** the more columns, icons, and graphics, the more ways a parser can scramble your content. Simple layout = fewer failure modes. ## 2. The "plain text" test Here's the quickest parsing check: extract your resume's text and read it. # poppler-utils (Linux: apt install poppler-utils; macOS: brew install poppler) pdftotext -layout resume.pdf - | less Read the output top to bottom and ask: * Is the order logical (name, summary, experience, education)? * Are columns interleaved, with lines from the sidebar mixed into the main text? * Did any text vanish (often text inside images)? * Are bullet characters turned into garbage? If it reads like nonsense here, a parser will likely struggle too. You can also try the copy-paste version: select all in your PDF viewer, paste into a plain text editor, and look. ## 3. Lint your headings Parsers and recruiters both look for conventional section names. "Experience" is safe. "My Journey" is not. Here's a small script that checks whether standard headings survive extraction: import re import subprocess import sys EXPECTED = { "experience": r"\b(work\s+)?experience\b|\bemployment\b", "education": r"\beducation\b", "skills": r"\bskills\b|\btechnologies\b", } def pdf_text(path: str) -> str: out = subprocess.run( ["pdftotext", "-layout", path, "-"], capture_output=True, text=True, check=True, ) return out.stdout.lower() def check_headings(text: str) -> None: for name, pattern in EXPECTED.items(): status = "OK " if re.search(pattern, text) else "MISSING" print(f"[{status}] {name}") if __name__ == "__main__": check_headings(pdf_text(sys.argv[1])) Run it: python check_headings.py resume.pdf [OK ] experience [OK ] education [MISSING] skills A `MISSING` result means either the heading is named unusually or the text didn't extract. Either is worth fixing. ## 4. Measure keyword coverage against the job description Treat the job posting as your spec. A rough way to compare is to count which meaningful terms appear in both the posting and your resume. import re import sys from collections import Counter STOPWORDS = set(""" a an and are as at be by for from has have in is it of on or that the to with you your we our will this their they them who what when where which about into more other such than then these those also can may must should experience work team role years year ability strong """.split()) def tokens(text: str) -> list[str]: words = re.findall(r"[a-zA-Z][a-zA-Z+#.\-]{1,}", text.lower()) return [w.strip(".-") for w in words if w not in STOPWORDS and len(w) > 2] def coverage(resume: str, job: str, top: int = 25) -> None: resume_set = set(tokens(resume)) job_counts = Counter(tokens(job)) wanted = [w for w, _ in job_counts.most_common(top)] hits = [w for w in wanted if w in resume_set] misses = [w for w in wanted if w not in resume_set] print(f"Coverage: {len(hits)}/{len(wanted)}") print("In your resume: ", ", ".join(hits)) print("Not in your resume:", ", ".join(misses)) if __name__ == "__main__": resume_text = open(sys.argv[1], encoding="utf-8").read() job_text = open(sys.argv[2], encoding="utf-8").read() coverage(resume_text, job_text) Save your resume as text (from the extraction step above) and the job description as `job.txt`: pdftotext -layout resume.pdf resume.txt python keyword_coverage.py resume.txt job.txt Example output: Coverage: 17/25 In your resume: python, postgresql, docker, kubernetes, api, ... Not in your resume: terraform, graphql, observability, ... ### How to use the result (honestly) This is a crude heuristic, not how any specific ATS scores you. Real systems vary, and many don't rank by keyword count at all. Use it as a prompt to ask: * Did I actually use `terraform`, but forget to write it down? * Is the job using different words for something I already did? **Only add keywords for skills and experience you genuinely have.** Stuffing in terms you can't back up will fail in the interview, and it makes the resume worse for human readers. ## 5. Write bullets like commit messages Good commit messages say what changed and why. Good resume bullets say what you did and what happened. - Responsible for maintaining backend services + Cut API p95 latency from 800ms to 250ms by adding query caching + and removing two N+1 queries in the orders service A reliable formula: **Action verb + what you did + measurable result (or scope).** If you can't measure it, describe scale: team size, users, data volume, or request rate. Only use numbers that are true and you can explain. ## 6. Keep a single source of truth Developers hate duplication, and resumes are no exception. A few approaches: * **Keep a master document** with everything you've done, then pull a one-page tailored version from it for each application. * **Use structured data.** The JSON Resume project defines a schema if you want your resume as data that can render into different layouts. * **Version it in Git.** You get history, diffs, and a record of what you sent where. A minimal structure could look like: { "basics": { "name": "Your Name", "label": "Backend Engineer" }, "work": [ { "name": "Company", "position": "Software Engineer", "startDate": "2022-03", "highlights": [ "Cut API p95 latency from 800ms to 250ms via caching" ] } ], "skills": [{ "name": "Backend", "keywords": ["Python", "PostgreSQL"] }] } Then the template becomes a presentation layer you can swap, which is the same separation of content and view that we use everywhere else. ## Pre-submit checklist * [ ] Single column (or a simple layout) that extracts in logical order * [ ] Standard headings: Experience, Education, Skills * [ ] No important text inside images * [ ] Bullets show results, not just duties * [ ] Summary and skills tailored to _this_ job * [ ] Keywords included only where truthful * [ ] Exported PDF passes the plain-text test * [ ] File name is sensible (`firstname-lastname-resume.pdf`) * [ ] Proofread by a human (ideally someone else) ## A note on "ATS-friendly" No template can guarantee it gets past every ATS, because systems differ and content matters most. A clean layout reduces avoidable parsing problems. It doesn't replace relevant, well-written content. ## Where to start If you'd rather pick a layout than design one, **Noloii's free resume templates** collect classical, Europass, modern, professional, and creative designs in one gallery, with sample data in the previews. Pick one, run the plain-text test on your exported file, and iterate. What's your resume workflow: Word, LaTeX, JSON Resume, something else? Share it in the comments.
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DEV Community [Unofficial] @dev.to.web.brid.gy · 9h
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The Small Engineering Habits That Make Open-Source Projects Easier to Trust
# The Small Engineering Habits That Make Open-Source Projects Easier to Trust Open-source software is often judged by its features. Does it solve the problem? Is it fast? Does the UI look good? Does it have enough functionality? Those questions matter, but there is another question that becomes increasingly important as a project grows: **Can another developer trust this repository enough to use, understand, modify, and contribute to it?** That trust usually does not come from one impressive feature. It comes from dozens of small engineering decisions. A clear README. Predictable project structure. Useful error messages. Reproducible builds. Meaningful commit messages. Tests that actually explain expected behavior. Documentation that answers questions before someone has to open an issue. Over time, I have started thinking about open-source projects less like collections of source files and more like products that happen to expose their internals. ## A repository is part of the user experience When someone discovers a GitHub repository, they do not immediately start reading the implementation. They usually start with: * the repository name * the description * the README * installation instructions * screenshots or examples * releases * issue history * project activity That means the first few minutes of interacting with a repository are already part of the product experience. A technically excellent project can still feel difficult to use when the path from discovery to first successful run is unclear. For example, compare these two instructions. Install dependencies and run the project. with: git clone https://github.com/example/project.git cd project npm install npm run dev The second version removes uncertainty. That is a small change, but it can significantly improve the experience for a new contributor. ## Make the first five minutes boring A good developer experience is often surprisingly boring. The user should not need to guess: * which runtime version to install * which command starts the project * where configuration belongs * whether environment variables are required * whether a database is needed * where generated files are stored The more assumptions the user has to make, the more friction exists. I like a simple principle: > **The first successful run should require as little interpretation as possible.** A repository should tell developers what to do rather than making them investigate what to do. ## Error messages are documentation too Developers often spend more time debugging than reading documentation. That makes error messages an important part of the interface. Consider: Error It technically communicates that something went wrong. But it does not help much. Now consider: Configuration error: API_URL is missing. Create a .env file and add API_URL before starting the application. The second message tells the developer: 1. what failed 2. why it failed 3. what to do next That is documentation delivered at exactly the right moment. Good error messages should reduce the number of questions a developer has to ask. ## Consistent structure beats clever structure As projects grow, developers sometimes try to create sophisticated folder structures that look impressive but are difficult to understand. A simpler structure is often easier to maintain. For example: src/ ├── components/ ├── services/ ├── models/ ├── utils/ └── main.ts The exact structure will depend on the project, but the important thing is consistency. When contributors already understand the pattern used in one part of the project, they can usually understand another part without learning a completely different organizational system. Predictability is a feature. ## Documentation should answer questions, not just describe files A README that says: This project is a task management application. is a description. It is not yet particularly useful documentation. Useful documentation might answer: What problem does this solve? Who is it for? How do I install it? How do I run it? How is the project structured? How do I run tests? How do I contribute? How do I report a bug? How can I build a release? These questions are much closer to the actual needs of developers. Documentation becomes especially valuable when it captures decisions that are not obvious from reading the code. ## Write comments for the "why" Comments are most useful when they explain something the code alone cannot easily communicate. For example: // Keep this validation before the database call because invalid IDs // should never reach the persistence layer. if !id.is_valid() { return Err(Error::InvalidId); } The code already shows **what** is happening. The comment explains **why** the ordering matters. That kind of comment can save time for future contributors. On the other hand, comments like this add little value: // Increment count count++; The code already explains itself. ## Git history is part of the project One of the most overlooked parts of a repository is its history. A clean history can make it much easier to understand how a project evolved. Commit messages such as: fix bug update changes more changes final tell very little. Compare them with: fix: preserve task order after filtering docs: clarify local development setup feat: add CSV export for task lists test: cover empty import handling A good commit message does not need to be long. It needs to communicate intent. Months later, when someone investigates a regression, that information can become extremely useful. ## Tests should explain behavior Tests are not only about preventing regressions. They also provide examples of how the software is expected to behave. Imagine a function: fn parse_identifier(input: &str) -> Result<u64, Error> A test can show developers what the API means: #[test] fn accepts_numeric_identifier() { assert_eq!(parse_identifier("42").unwrap(), 42); } And another test can document invalid behavior: #[test] fn rejects_non_numeric_identifier() { assert!(parse_identifier("abc").is_err()); } A new contributor can learn from those tests without first understanding the entire implementation. That makes tests a form of executable documentation. ## Releases should reduce uncertainty A release is more than a version number. When someone sees: v1.4.0 they still have to ask: "What changed?" A useful release note might say: ## What's changed - Added JSON export - Improved startup performance - Fixed duplicate task rendering - Updated installation instructions ## Breaking changes None. This gives users context before they upgrade. For larger projects, release notes can also explain migration steps, configuration changes, and known issues. ## Small automation has a huge payoff Many repository quality improvements can be automated. For example: Pull request ↓ Lint ↓ Format check ↓ Unit tests ↓ Build ↓ Release checks Once these checks run automatically, contributors receive immediate feedback. This reduces the amount of manual review needed for basic quality checks and makes project standards visible to everyone. A contributor should not have to memorize ten commands just to determine whether their change is valid. The repository should help them. ## Treat contributors like users There is an interesting mindset shift here. Open-source contributors are not just people submitting patches. They are users of your development process. They interact with: * your documentation * your build system * your issue templates * your tests * your contribution guide * your CI pipeline * your code review process A project can have a polished application interface and still have a frustrating contributor experience. Improving contributor experience is therefore not separate from engineering quality. It is part of engineering quality. ## A practical repository checklist Before calling an open-source project "ready," I like to think through a checklist like this: [ ] Clear project description [ ] Installation instructions [ ] Quick-start example [ ] Supported runtime versions documented [ ] Configuration explained [ ] Useful error messages [ ] Automated tests [ ] Formatting/linting configured [ ] CI checks enabled [ ] Contribution guide [ ] Issue templates where useful [ ] Release notes [ ] License [ ] Security reporting guidance Not every project needs every item immediately. The important part is recognizing that quality is broader than code. ## Build for the developer you haven't met yet The hardest contributor to design for is the person you have never met. They do not know your assumptions. They do not know why the architecture looks the way it does. They were not present when a particular decision was made. They do not know which command you normally run. They cannot ask you what you meant when you wrote an unclear comment six months ago. A strong repository anticipates this. It leaves enough context behind that another developer can continue the work without needing the original author beside them. That is one of the most valuable properties an open-source project can have. ## Final thoughts Open-source quality is not created by one massive refactor. It is built through many small decisions. A better README. A clearer error message. A useful test. A meaningful commit. A reproducible build. A documented architectural decision. A release note that explains what changed. None of these changes are particularly flashy. Together, however, they make a project easier to understand and easier to trust. And that may be one of the most important goals of open-source engineering: **not just writing code that works, but creating a project that other people can confidently work with.** Explore for my open sourced website: https://sanskarin.github.io GitHub: https://github.com/sanskarIN What small engineering habit has made the biggest difference in your own projects?
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