A fuel-cell stock, an oil company implosion, and what alternative data saw coming

Title: Why thousands of businesses trust me to build high-performance websites (And why yours is failing without one) Subtitle: Stop settling for slow templates. In 2026, you need custom Next.js …

I’m fascinated by the concept of agent memory. LLMs are stateless by design, meaning they have no memory or awareness of past interactions. Each prompt you send to an LLM is treated as a completely isolated event. Go to Source

Enterprise investment in artificial intelligence has reached an unprecedented scale across national, regional, and organisational levels of the economy, yet transformation outcomes remain highly variable. Recent global research indicates that the majority of enterprise AI investments produce no measurable profit-and-loss impact, with only a minority of organisations extracting material enterprise-level value. Without an integrated classification framework that allows enterprises to deploy, govern, and create value from the full range of AI technologies and autonomy levels across enterprise functions, the risks of misallocated investment, fragmented deployment, and unrealised return remain difficult to mitigate. A structured literature review on AI classification reveals that existing frameworks do not integrate AI technology types, operational autonomy levels, and concrete enterprise applications in a single classification structure that can support enterprise AI-driven transformation decisions. To address this gap, this paper proposes the Enterprise AI Classification Framework, a novel classification model that integrates six AI technology types with six autonomy levels in a 6 × 6 matrix of 36 combinations, each corresponding to concrete business applications across enterprise functions. A computational pilot study (n = 20 cases for case-based coding validation and 12 LLM-simulated personas × 30 vignettes for role-specific hypothesis pre-specification) provides preliminary evidence of substantially higher classification coverage than four baseline frameworks and pre-specifies role-divergence hypotheses for a planned empirical validation, with all design choices locked in an OSF pre-registration; reliability point estimates exceed the pre-specified 𝛼≥0.70 threshold at the pilot scale, with bootstrapped confidence intervals expected to tighten under the full-corpus run in the follow-up study. An interview-based empirical validation with approximately 250 decision-makers and practitioners across 20 industries is in preparation. Go to Source

I've spent about 100 hours of work over the past month to make sure git-annex can build without dependencies that contain LLM generated code. At least so far. https://git-annex.branchable.com/no_llm_code/ Needing to review a program's whole dependency tree on an ongoing basis is apparently what programming has come to? I've found some real stinkers. Large LLM generated changes being reverted in the next release without any explanation. An incoherent 1489 line commit message with 10,000 lines of changes to a 26,000 LOC code base. A LLM prompt to copy code from another project that seems to have only avoided being copyright infringement due to luck. I now have additional information about the quality of dependencies which will surely influence future decisions. As far as I can see, that's the only positive benefit of this work. I realize that I am probably trying to hold back the tide at this point. That appears to be why Software Freedom Conservancy punted, and I doubt that the FSF will do any better. As these dominos fall, I am reconsidering my participation in these communities. But I continue my work and support my users. It may seem easy to prompt a LLM with > Add fourmolu config and restyled > > neat > > format a module And commit the result and call yourself a 10xer. But please consider the broader impact of your actions. (In the above case, that project lost my further collaboration on it.)

The MacBook Pro has been revamped given the M5 Pro Chip, providing you with a faster CPU and a stronger GPU, with Neural Accelerators built into each core ...

Die neue Version von Collabora Office rückt näher an Windows, macOS und Linux. Dazu kommt ein optionaler KI-Assistent, bei dem Nutzer das LLM selbst wählen.

**Subject:** Request to unify Personal and Team workspaces for business owners I own and pay for both my ChatGPT Personal workspace and my ChatGPT Team workspace for my company, Synergy Manufacturing Group. I have spent years building valuable knowledge, memory, and workflows in my Personal workspace. I recently adopted a Team workspace so my employees can collaborate, but I discovered that my Personal and Team workspaces are completely isolated. I would like the ability to: * Merge my Personal and Team workspaces under a single account. * Have one unified memory for me as the owner across both workspaces. * Import my Personal chat history and memories into my Team workspace. * Choose which chats, memories, GPTs, Projects, and files remain private and which become available to my Team. * Pay for a single subscription instead of maintaining separate Personal and Team subscriptions. * Designate myself as the owner with access to both private and company knowledge while keeping employees restricted to company resources only. This is a common scenario for founders and small business owners who adopted ChatGPT personally before rolling it out to their organizations. A migration tool and unified owner experience would make Team much more valuable and eliminate duplicate subscriptions. Thank you for considering this feature request.

Every prompt you send to ChatGPT, Claude, or Gemini travels to a data center, gets processed on someone else’s hardware, and leaves a…

African startups weathered global economic uncertainty to raise more than $1.5 billion in the first half of 2026, signalling that

Hello OpenAI team, I would like to suggest a feature for ChatGPT: an integrated AI workspace/CRM for real estate agencies. The idea is to allow real estate agents to manage their entire agency workflow inside one ChatGPT web window. Main features could include: 1. Client CRM Agents could save and manage buyers, tenants, owners, landlords, leads, requests, budgets, preferred locations, family needs, documents, follow-ups, WhatsApp/email history, and reminders. 2. Property database Agents could create and manage a database of properties for sale and rent, including photos, descriptions, technical details, prices, availability, contracts, energy class, cadastral data, owner information, and internal notes. 3. AI matching between clients and properties ChatGPT could help match clients with suitable properties based on their requests, budget, lifestyle, preferred area, dates, number of bedrooms, privacy needs, and other details. 4. Publishing to real estate portals It would be very useful if ChatGPT could export or publish property listings directly to real estate websites and portals, or at least generate ready-to-upload formats for them. 5. Daily agency assistant ChatGPT could remind agents who to call, which clients need follow-up, which owners need updates, which properties need better descriptions, and which leads are most important. 6. Advice and strategy The AI could give suggestions on pricing, marketing, negotiation, client communication, property descriptions, and sales strategy. Why this would be useful: Real estate agents currently use many separate tools: CRM, Excel, WhatsApp, email, property portals, calendar, documents, and listing platforms. Having all of this connected inside ChatGPT would save a lot of time and make ChatGPT a real daily working platform, not just a chatbot. For small and medium real estate agencies, this could become a complete AI-powered operating system for managing clients, properties, listings, and communication. Thank you for considering this idea.