The hard part is that "what matters" is domain-specific and the LLM has no way to know that on its own. That is where typed schemas earn their keep. You encode what matters once, and every future ingestion inherits that judgment without the model having to rediscover it.
CODE
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CLI-Anything turns any GUI app into a CLI for AI agents
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Turn any GUI app into a CLI for AI agents! CLI-Anything automatically generates command-line interfaces for any software by analyzing its codebase. Point it at GIMP, Blender, LibreOffice, or any application and get a production-ready CLI that AI agents can use. The problem: AI
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Ultimate step-by-step projects roadmap for becoming AI researcher
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The ultimate step-by-step projects roadmap for BECOMING an AI Researcher is now available online to read FOR FREE Covers building – Tokenizers / embeddings
– Positional methods
– Attention / multi-head attention
– Transformer blocks
– Training loops / objectives
– Sampling -
Knowledge Graph Extraction and Schema Design for AI Systems
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Exactly. The extraction step decides everything downstream, and most systems leave it completely unguided. The moment you add typed entities and constrained edges, the graph stops behaving like a vector store and starts being queryable. Schema should be step one, not an
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LLM Multi-Hop Retrieval: Schema vs Sequential Queries
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That works when the graph is small and the hops are predictable. At scale, sequential LLM queries for every multi-hop retrieval add latency and token cost per question. The schema lets you answer it in one structured traversal instead of chaining open-ended lookups. Not
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From Stack Overflow bug to autonomous features in 5 years
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How did we go from opening Stack Overflow to solve one bug, to agents writing entire features on their own? That didn’t happen overnight, but in slow progress in about 5 years. And today, I want to walk you through the full story. The timeline, the tools, the tweets, the
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Optimization Stack in AI: From Weights to Skill Files
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YES, Optimization keeps moving up the stack. Weights, then prompts/harness, now skill files.
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Hundred-Page Language Models Book with PyTorch by Burkov
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The Hundred-Page Language Models Book — Hands-on with PyTorch: http://
amzn.to/4sJl7YC by @burkov -
Automated 30-day review of recent AI work using memory and git
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The text version of the prompt: "Look back over my recent work from the last 30 days using all available context. Use available evidence in this order:
– Session Memory summaries and MEMORY. md entries
– Git log and recent commit history across branches
– CLAUDE. md and -
User notices Codex quality decline, asks others
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Oh, and btw, Codex quality has gotten noticeably worse. Is it just me, or have you been seeing the same decline in quality?