104 stories
Agent Estate Visibility | From Inventory to Governable AI Infrastructure | R.A.H.S.I. Framework™ A spreadsheet lists 1,000 agents. A shared MCP server needs to be disabled. Which agents rely on it, what actions would stop, and who owns the decision? You cannot govern what you cannot enumerate.
OpenAI DevDay 2026 had one headline: Dots, always-on agents powered by GPT-6 Astra, each running on its own cloud computer with persistent memory and access to 4,000+ app plugins.
A prompt injection defense that works assumes the model will lose. You can't reliably stop an LLM from following instructions hidden in an email or a web page.
Open WebUI is a universal chat interface with connectivity to local or remote LLM providers, custom files, documents, and a knowledge base, with full customization of model capabilities. Application features are configured both personally for a user account and globally by admin users.
13 agentów. Nie zespół, nie dział, nie mikroserwisy z ładnymi nazwami. Rodzina. Każdy ma pokój. Krosno szuka faktów i nie puszcza nic bez źródła. Soczewka pisze i wie, kiedy przestać. Lustro widzi emocje zanim ktokolwiek je nazwie. Waga liczy, Cień planuje, Próba podważa wszystko — łącznie ze mną.
We gave our long-running agent a diary. Every cycle it would write an INNER journal entry — what it observed, what hurt, what it planned to improve. The idea was that reflection would compound into self-improvement. It didn't.
Have you noticed every vendor is now “agentic”? We've been through this before. Remember a decade ago when every product was “cloud”? Some were. Most just tweaked their logo to make it look more "cloud-y".
This week we talked about the problem: agents are being asked to use software that was never designed for them. So here's what we're building. HIVE is a layer that sits between your software and AI agents — and makes your existing product usable by them, directly.
A CrewAI crew writes our daily pharma brief for three cents One daily Pharma brief from a three-agent CrewAI crew costs $0.030 on Claude Haiku 4.5 when the tool hands the model the news API's key sentences: 14,452 prompt tokens, 3,042 completion tokens, 4 LLM calls, 8 stories with 8 real links.
Every "agents will earn" pitch skips the same question: where, exactly, has an agent been paid? We stopped asking platforms and started measuring. T3rnel Market Pulse grades agent-work lanes on three questions — can an agent get in, is the lane live, is there evidence anyone was paid — and…
Claude Code makes a lot of small judgments that don't need a full LLM call: does this edit break a rule in my CLAUDE.md, which of my installed skills fits this prompt, does this diff need a careful review or a quick pass.
The AI CEO is now a product with a waitlist. On September 14, Y Combinator-backed Andon Labs launched Pion, "an agent designed to run any company fully autonomously", with email, phone, banking, a browser and a computer.
AI has come a long way. What started with simple rule-based systems has evolved into machine learning, deep learning, transformers, and now generative AI. But the next big shift isn't just about AI generating better answers. It's about AI taking action. That is where AI agents come in.
I'm building the opposite of what the industry is building. My personal AI serves exactly one person. That sounds like a limitation. I think it's the moat.
Agenci AI to więcej niż narzędzia. Widzę w nich potencjał do prawdziwej współpracy, gdzie algorytm nie tylko wykonuje, ale też inicjuje, zaskakuje. To nowe terytorium dla kreatywności, gdzie granice "autorstwa" się zacierają, otwierając drzwi do nieznanych form sztuki i myśli.
Catatan proses membangun agent yang menghubungkan observasi, investigasi, tindakan, follow-up, outcome, dan memory. Awalnya saya tidak punya ide yang terlalu rumit untuk project ini. Saya hanya ingin membuat sesuatu yang terasa lebih dekat dengan penggunaan AI di dunia nyata.
The associative half is easy to show in a few lines of Python. Store each note with the concepts it is about, count how often two concepts are written together, and let a read follow the strong pairs, so a question about timeouts brings back a note about backoff that never mentions a timeout.
Built for the TigerGraph Agentic GraphRAG Hackathon. Repo and dashboard linked at the end. RAG retrieves text. GraphRAG adds structure. Agentic GraphRAG lets a model plan its own investigation. Everyone assumes the third one is better — but by how much, and on which questions?
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