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Skills2026-03-19 · source-backed
Treat each fact as a content-hashed tuple with typed metadata and a retrieval interface. Retrieve relevant subsets on demand instead of stuffing everything into context. 100% accuracy at 7,000+ facts where in-context approaches lose 60%. The database approach to LLM memory. arXiv
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ArXiv paper 2603.17683 benchmarks a deceptively simple idea: instead of stuffing facts into your agent's context window and hoping the model remembers them, treat each fact as a discrete, hash-addressed tuple stored externally and retrieved on demand. The results are stark: 10...
The Astra coverage went to price and context window. The number that changes how I'd deploy it went into a system card nobody read. Artificial Analysis measured GPT-6 Astra's hallucination rate on AA-Omniscience at 51% at max effort, against 92% for its predecessor. Accuracy w...
After 20+ years maintaining Paint.NET, Rick Brewster concluded WINE's Direct2D would never be complete enough for what he needed, so the app now carries its own from-scratch reverse-engineered Direct2D implementation. He puts it at 180,000 lines against 700,000 for the rest of...
CCP announced the migration covering code that has run on Stackless 2.7 since 2010. The approach is to run futurize across the codebase and then manually review roughly 20,000 places where Python 2 and 3 behavior diverges, including integer division (Simon Willison). No comple...
0.35 adds gpt-6-astra to the CLI's OpenAI provider, so llm -m gpt-6-astra works against the same logging, template and fragment machinery as every other model in the tool. For anyone scripting cross-model evals, that means a new frontier model needs zero new plumbing to enter...
Allen Bargi's August 15 post hit 302 points arguing that AI collaboration rewards context-sharing, examples, and feedback over precise instruction (Hacker News). The pushback holds that the piece conflates management with leadership. mikeocool calls it "the most low effort ver...
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