444 summarised stories about Large Language Models, each linking back to the original source. Browse all topics →
Simon Willison’s Weblog 1 month ago 48
Simon Willison posted a note on 18 September 2026 about his monthly briefing for LLM developments. The note offers a $10/month sponsorship for a curated email digest of the month’s most important LLM developments. As a result, readers can subscribe to receive that digest (and pay to get fewer emails).
One Useful Thing 1 month ago 24
The Overhang discusses how today’s AI already performs human-level tasks and warns that human organizations are moving too slowly to keep up with AI’s pace while debating future model risks. It says the speaker’s GPT-6 Astra generated a 45-minute film from a book trailer request in the same session. It argues people should shift from competing with AI outputs to using deep knowledge, wide knowledge, taste, and agency to guide what the models do and to design human-AI work instead of replacing it.
martinfowler.com 1 month ago 23
The author describes mixed reactions to large language models, saying they often speak in an unsettling human-like tone and can confidently invent information. The piece points to “polling” where people both find models useful and think they will be bad for society. The author concludes they still should be used, but warns against anthropomorphizing agents and favors avoiding interactions with models that seem to mimic untrusted “people.”
The Information 1 month ago 10 ● 3 sources
OpenAI was reportedly close to solving another Millennium Prize math problem. The report is by Stephanie Palazzolo. The claim suggests OpenAI’s progress on AI-driven problem solving could extend beyond earlier breakthroughs into additional top-tier unsolved math challenges.
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18 October 2026
DeepSeek kept one foot on the gas. Its updated DeepSeek-V4.1-Flash-1017 went live across web, mobile, and the existing deepseek-flash API model ID, with the same pricing while it improves performance: DeepSWE v1.1 resolution rises to 77% of tasks from 74.2%, and DeepSeek says it finishes work faster. It also bundled something more practical than another benchmark brag—an accompanying developer search API backed by its own engine, with Terminal-Bench 4.0 moving from 31.2 to 42.3 and billing that stays at $0.30 per million input tokens and $1.20 per million output tokens. The subtext for builders is familiar: as agent workflows spread, retrieval cost and reliability matter as much as raw model IQ.
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