iiinova — where your digital self keeps evolving

vasilijee.bsky.social @vasilijee.bsky.social · 23/07/2025

Here’s the distilled version of what everyone has been talking about “context engineering”. Straight talk on memory layers, GraphRAG and why prompt hacks alone won’t cut it. Give it a read 👉🏼 dub.sh/context_engi...

dub.sh

Cognee - Context Engineering: Boost AI Memory for Reliable, Smart LLMs

Master context engineering and AI memory to craft personalized LLM outputs, reduce token costs, and future-proof your AI agents—read the full guide now!

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vasilijee.bsky.social @vasilijee.bsky.social · 23/06/2025

Hello, world—with context! 🧠 🚀 We are launching “Insights into AI Memory”—a monthly signal on the tech, tools & people teaching AI to remember. Grab the initial post & subscribe 👉 aimemory.substack.com

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vasilijee.bsky.social @vasilijee.bsky.social · 19/06/2025

We’re days away from opening the cognee SaaS beta 🚀 where you’ll bring your knowledge graphs & LLM workflows to life without the infra pain. 🔍 Built for everyone who cares about clean data, speed, and reproducibility. Want in on day 1? Join the waitlist → dub.sh/beta-saas-co...

dub.sh

Improve your AI infrastructure - AI memory engine

Cognee is an open source AI memory engine. Try it today to find hidden connections in your data and improve your AI infrastructure.

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vasilijee.bsky.social @vasilijee.bsky.social · 18/06/2025

⚡ Learn the BaseRetriever pattern ⚡ See real code snippets ⚡ Take the “Which Retriever Are You?” quiz Read to get smarter answers? Let me know which retriever you are 🙂 dub.sh/cognee-retri...

dub.sh

Cognee - Semantic Search & Knowledge Graph Retrieval Tactics | Cognee

Drive results with semantic search and knowledge graph retrieval; explore AI retrievers, vector databases, and GraphRAG to turn data into answers—read now!

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vasilijee.bsky.social @vasilijee.bsky.social · 18/06/2025

Tired of asking brilliant questions and getting “meh” answers from your LLM? We just shipped “The Art of Intelligent Retrieval”—a deep dive into how Cognee layers semantic search, vector DBs & knowledge-graph magic across specialized retrievers (RAG, Cypher, CoT, more)

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vasilijee.bsky.social @vasilijee.bsky.social · 12/06/2025

Bottom line: if you’re building agents, assitants, or automated workflows, it’s time to evolve from “data lake” to AI memory “lake”. - Read the deep dive ➡️ dub.sh/file-based-m... - GitHub ➡️ github.com/topoteretes/... - Join us on Discord ➡️ discord.com/invite/tV7pr...

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vasilijee.bsky.social @vasilijee.bsky.social · 12/06/2025

We also introduce dreamify - our optimization engine that tunes chunk sizes, retriever configs & prompts in real time for max accuracy and latency ✨

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vasilijee.bsky.social @vasilijee.bsky.social · 12/06/2025

Why file-based? • Cheap, cloud-native (S3, GCS) • Scales linearly with data growth • Easy diff + version control • Plays nicely with existing ETL & BI stacks

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vasilijee.bsky.social @vasilijee.bsky.social · 12/06/2025

It’s a living system: 1️⃣ User adds data 2️⃣ Data is cognified 3️⃣ Search & reasoning improve 4️⃣ Feedback flows in 5️⃣ System self-optimizes …and the loop keeps compounding value. ♻️

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vasilijee.bsky.social @vasilijee.bsky.social · 12/06/2025

🔑 Key insight: Data → Memory → Intelligence Our pipeline “cognifies” every file into graphs, giving agents memory - just like a human mind. So let’s see how 👇🏼

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vasilijee.bsky.social @vasilijee.bsky.social · 12/06/2025

First, why care about AI memory? LLMs are brilliant—until they meet your fragmented data. They forget, hallucinate, or drown in silos. File-based AI memory bridges that gap, turning raw files into contextual intelligence. 📂🧠

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vasilijee.bsky.social @vasilijee.bsky.social · 12/06/2025

Why file-based AI Memory will power next-gen AI apps? We break down how a simple folder in the cloud becomes the semantic backbone for agents & copilots. Let’s unpack it 🧵👇

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vasilijee.bsky.social @vasilijee.bsky.social · 10/06/2025

Woke up to 🚀 cognee hitting 5000 stars! Thank you for the trust, feedback and code you pour in. Let’s keep building 🛠️

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vasilijee.bsky.social @vasilijee.bsky.social · 03/06/2025

Taken together, the results support the use of hyperparameter optimization as a routine part of deploying retrieval-augmented QA systems. Gains are possible and sometimes substantial, but they are also dependent on task design, metric selection, and evaluation procedure.

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vasilijee.bsky.social @vasilijee.bsky.social · 03/06/2025

We evaluate on three established multi-hop QA benchmarks: HotPotQA, TwoWikiMultiHop, and Musique. Each configuration is scored using one of three metrics: exact match (EM), token-level F1, or correctness.

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vasilijee.bsky.social @vasilijee.bsky.social · 03/06/2025

We present a structured study of hyperparameter optimization in graph-based RAG systems, with a focus on tasks that combine unstructured inputs, knowledge graph construction, retrieval, and generation.

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vasilijee.bsky.social @vasilijee.bsky.social · 03/06/2025

Building AI memory and data pipelines to populate them is tricky. The performance of these pipelines depends heavily on a wide range of configuration choices, including chunk size, retriever type, top-k thresholds, and prompt templates.

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vasilijee.bsky.social @vasilijee.bsky.social · 03/06/2025

Why does AI memory matter? LLMs can’t give us details about our data, they "forget" or simply don’t know the details.

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vasilijee.bsky.social @vasilijee.bsky.social · 03/06/2025

Yesterday, we released our paper, "Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning" We have developed a new tool to enable AI memory optimization that considerably improve AI memory accuracy for AI Apps and Agents. Let’s dive into the details of our work 📚

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vasilijee.bsky.social @vasilijee.bsky.social · 30/05/2025

We're ramping up our r/AIMemory channel for broader discussions on AI Memory and to share in the conversation outside of cognee. We'd love to see you share what you're working on, your thoughts and questions on AI memory, and any resources that would benefit the broader community.

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Reposted by @vasilijee.bsky.social

cognee.bsky.social @cognee.bsky.social · 30/05/2025

Join the conversation at r/AIMemory. dub.sh/ai-memory

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vasilijee.bsky.social @vasilijee.bsky.social · 21/05/2025

@PGvector If your need something cheap and a way to get started, pgvector is the key. If you need something to run in production with large volumes, well, maybe you will run into trouble there. Still, it will do a lot of heavy lifting for you

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vasilijee.bsky.social @vasilijee.bsky.social · 21/05/2025

@redisinc.bsky.social Stack (vector search) When latency budgets are measured in single-digit milliseconds, Redis’s in-memory design shines. Add the vector module, keep your other Redis data structures, and serve real-time recs or similarity lookups with room to spare.

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vasilijee.bsky.social @vasilijee.bsky.social · 21/05/2025

@pinecone Want vector search without touching infra? Pinecone’s managed service handles sharding, replication, and automatic scaling. Consistent sub-second queries plus usage-based pricing—great for teams that just need it to work.

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vasilijee.bsky.social @vasilijee.bsky.social · 21/05/2025

@qdrant.bsky.social Written in Rust for raw speed, Qdrant keeps latency low even on modest hardware. HNSW under the hood, solid filtering, and a tiny memory footprint—it’s a strong pick for edge or resource-constrained deployments.

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vasilijee.bsky.social @vasilijee.bsky.social · 21/05/2025

@weaviate.bsky.social Weaviate is the “batteries-included” option. Open-source, GraphQL API, and optional modules that auto-vectorize your data. Hybrid queries mix semantic + keyword filters out of the box, so you can start simple and grow.

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vasilijee.bsky.social @vasilijee.bsky.social · 21/05/2025

@milvusio.bsky.social Need to crunch billions of embeddings? Milvus is built for that. Distributed architecture, multiple index types (HNSW, IVF-PQ, DiskANN) and tunable consistency make it a go-to for large-scale analytics or “hot” user-facing search.

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vasilijee.bsky.social @vasilijee.bsky.social · 21/05/2025

Use cases we see daily: • Semantic doc search • RAG context retrieval for LLMs • Image & audio similarity • Real-time recommendations Choose by scale, latency budget, and how much ops you want to own. Below are bite-size takes:

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vasilijee.bsky.social @vasilijee.bsky.social · 21/05/2025

Why not just FAISS? Because prod apps need: • real-time updates • metadata filters • auth / multi-tenant / backups • horizontal scaling A vector database wraps all that around the index.

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vasilijee.bsky.social @vasilijee.bsky.social · 21/05/2025

A vector DB stores high-dimensional embeddings + metadata, then uses Approximate Nearest Neighbor indexes (HNSW, IVF, etc.) to answer queries in <100 ms—even on millions of vectors.

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vasilijee.bsky.social @vasilijee.bsky.social · 21/05/2025

We just dropped a follow-up to last week’s graph-DB explainer—this one dives into vector databases and why they’re the workhorse behind semantic search. Quick recap: Graph DB → “How are things connected?” Vector DB → “What *feels* like this thing?” 🙂

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vasilijee.bsky.social @vasilijee.bsky.social · 19/05/2025

Any of these make your builds easier? We’d love to hear your thoughts.

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vasilijee.bsky.social @vasilijee.bsky.social · 19/05/2025

👉 Ready to deploy? ``` git clone <https://github.com/topoteretes/cognee.git> cd cognee/cognee-mcp brew install uv uv sync --dev --all-extras --reinstall source .venv/bin/activate ``` Full docs: docs.cognee.ai/how-to-guides/deployment/mcp

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vasilijee.bsky.social @vasilijee.bsky.social · 19/05/2025

4- Cursor, Windsurf and any other Rules Integration (Dev branch) — Automate Developer Rules. Integrate `.cursorrules` and similar config files directly into your structured knowledge memory layer. Less manual management, more productive dev time.

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vasilijee.bsky.social @vasilijee.bsky.social · 19/05/2025

3- System File Reading — Straightforward Ingestion. You don’t need hacks to feed your knowledge graph. cognee MCP now reads directly from your file system—just point it at your data. Instant ingestion, clearer workflow. Data integration, streamlined.

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vasilijee.bsky.social @vasilijee.bsky.social · 19/05/2025

2- Integrated Logging — Know Your System. Logs aren’t just files. They're context. With fully integrated logging, quickly diagnose issues, monitor performance, and understand your cognee MCP deployment deeply.

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vasilijee.bsky.social @vasilijee.bsky.social · 19/05/2025

1- SSE is Here — Real-time, Simplified. Server-Sent Events (SSE) mean your app now gets real-time data streaming with lower latency. Simple integration, powerful outcomes.

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vasilijee.bsky.social @vasilijee.bsky.social · 19/05/2025

🚨 4 Big Updates to cognee MCP Server (and what devs need to know): If you're building with LLMs, graphs, or agentic applications - keep reading. 👇

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vasilijee.bsky.social @vasilijee.bsky.social · 15/05/2025

and more… Our latest post, “Graph Databases Explained,” walks through the trade-offs, query patterns, and performance internals in plain language. If “deeply connected data” describes your next project, it might save you a few JOIN headaches. Read here → www.cognee.ai/blog/fundame...

cognee.ai

Cognee - Graph Databases Explained: A Better Way to Represent Connections

Discover how graph databases like Neo4j build knowledge graphs, fight fraud & boost recommendations. Master nodes & edges with cognee and supercharge your data!

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vasilijee.bsky.social @vasilijee.bsky.social · 15/05/2025

2- Kùzu if you want an embeddable, analytics-optimised engine

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vasilijee.bsky.social @vasilijee.bsky.social · 15/05/2025

Tools worth exploring: 1– @neo4j.com for a mature, ACID property-graph store

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vasilijee.bsky.social @vasilijee.bsky.social · 15/05/2025

Applications we see most often: • Knowledge graphs for LLM context • Fraud-ring detection (shared devices, emails, IPs) • Real-time product or content recommendations • Network / supply-chain impact analysis

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vasilijee.bsky.social @vasilijee.bsky.social · 15/05/2025

What that means in practice: MATCH (c:Customer)-[:BOUGHT]->(:Product)<-[:SUPPLIES]-(s:Supplier) returns a multi-hop answer in milliseconds, even on millions of records.

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vasilijee.bsky.social @vasilijee.bsky.social · 15/05/2025

In a graph DB, relationships are stored right next to the data: • Node = entity • Edge = relationship No JOINs, or foreign-key mapping—just hop from one neighbour to the next.

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vasilijee.bsky.social @vasilijee.bsky.social · 15/05/2025

Relational tables thrive on set operations. Document stores shine with nested data. But when you need to ask “How are things connected?” you step into graph territory.

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