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Limits, before anything else. 21 attempts on one defect are repeated observations, not 21 independent tests. One seeded bug: an inverted authorization check in material I wrote myself. Run on 21 August 2026. One small local model. The alias local-advisor-v1:latest is local.
The AI Data Satiation Point: Why "Wild" AI Text is Breaking Chinchilla Scaling Laws As we approach the end of 2026, the internet looks fundamentally different than it did just two years ago.
From Token to Trajectory How Context Reshapes Representations — and What AI Systems Do with Them The suitcase felt surprisingly light. The color looked surprisingly light. The same token can begin …
LAI #145: What Makes an Agent Reliable? Plus, persistent AI identity, decisions without decoding, causal inference, Kubernetes triage, and RAG claim verification. Good morning, AI enthusiasts! AI …
The exact framework I used, in order, with the trade-offs, including one I nearly got backwardsContinue reading on Towards AI »
I built a local-first PDF RAG system, then measured retrieval, reranking, latency, evidence quality, and failure modes to understand where it breaks.
Un agente LLM puede parecer muy capaz durante una demostración y volverse dificil de operar cuando empieza a llamar APIs, crear tickets o consultar datos reales. El problema no suele ser que el modelo “no sepa” la respuesta.
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.
It was about 1:30 on a September morning when Lazaros-Antonios Chatzilazarou received a LinkedIn message. He was up late playing video games, an indulgence that he allows himself as he works toward a PhD in game theory while juggling a job at the Big Four accounting firm EY and a teaching post at…
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.
Originally published in TL;JP, a weekly, fact-checked read on what Japanese AI and tech practitioners are saying on X. I read a lot of Japanese AI posts each week so you don't have to. This week there was a pattern worth naming: almost nobody was excited about talking to a model.
The U.S. Federal Trade Commission is reportedly investigating OpenAI Group PBC, Anthropic PBC and other artificial intelligence providers. The New York Times today cited a source as saying that the probe covers multiple areas.
The chief executives of five tech giants have signed a voluntary artificial intelligence safety accord backed by the White House. U.S. President Donald Trump posted the document on Truth Social late Tuesday.
Suppose you’re building a service that receives customer support tickets. Before assigning a ticket, your code needs to answer two questions: Which team should handle it? Does it need urgent attention? You could ask a language model to write an assessment and then parse its response.
Prompt Engineering vs Context Engineering. A Hands-on Cortex AI Walkthrough An example demonstrating how the same prompt and instruction return different severity ratings depending on how much …
Stop Paying Your LLM to Flip Coins 9 design patterns for System One models like Jev and Laya, and how they make AI agents faster, cheaper, and safer Here’s an uncomfortable exercise. Open your …
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.
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().
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