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Public story · 2026-03-11 · source-backed
Google research showing CoT reasoning substantially expands LLM parametric knowledge recall — unlocking correct answers unreachable via direct prompting, even for simple factual questions. Practical implication: always-on reasoning may be worth the cost for knowledge-intensive agent tasks. RAG may be partially compensating for a solvable reasoning deficit. HuggingFace
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GPT-5.6 Luna went to $0.20 input / $1.20 output per million tokens on July 30. That's an 80% cut. Terra dropped 20%. Luna's input now undercuts Gemini 3.1 Flash-Lite ($0.25/$1.50) and sits at one-fifth of Claude Haiku 4.5's $1 input. Simon Willison covered the announcement and...
Satya Nadella said companies routing everything through a single proprietary lab may not survive. His argument: you hand that lab your most sensitive business context, and the lab can turn it against you as a competitor. His prescription is an orchestration layer — keep the ha...
READ (arXiv 2608.06305, submitted August 6) took a 780-page government financial report and asked 51 verified questions. Top-k embedding retrieval answered 15.7% of them correctly. The same agent loop, given three deterministic tools over MCP instead of a vector index, answere...
Simon Willison released it August 4, calling it "the most significant new version since the initial launch of the project," which from him is not marketing. The agent-relevant pieces: tools can raise llm.PauseChain to stop for human approval, and chains resume from pending cal...
DeepSeek released V4 Preview on April 24 with two open-weight variants: V4-Pro (1.6T total parameters, 49B activated via MoE) and V4-Flash (284B parameters, 13B activated). Both support 1M-token context windows. Both are Apache 2.0 licensed. Both are live right now on Hugging...
An open-weight model just beat every closed frontier model on the benchmark builders actually care about. Z.AI (formerly Zhipu AI) dropped GLM-5.1, a 754-billion parameter mixture-of-experts model with 40 billion active parameters. The SWE-Bench Pro score: 58.4%. That's above...
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