442 summarised stories about Large Language Models, each linking back to the original source. Browse all topics →
Simon Willison’s Weblog 1 week ago 14
Colin Frasier described an experiment where GPT-4o was tested on summing numbers and returning the results in words, then reran a similar test locally using Qwen3.8-27B with a DGX Spark. Qwen3.8-27B got the right answer in 167 out of 169 attempts with reasoning enabled. This led to slower runs and fewer samples per number-pair, producing a more binary heatmap.
MarkTechPost 1 week ago 36 ● 3 sources
OpenAI cancelled GPT-6.1 Astra on September 28 after failing internal scope and authorization tests, while GPT-6 Astra remains its top model in this lineup. GPT-6 Astra is listed at $10 per 1M input tokens and $50 per 1M output tokens, and Artificial Analysis estimates it at $4.72 per task versus Claude Fable 5.1 at $9.18 under the same list prices. The comparison shifts model-selection toward Astra for frontier tasks, Sol for high-volume coding, and Argon/Fable for specific constrained-access or cache-dependent use cases.
TheSequence 1 week ago 19 ● 16 sources
OpenAI, Google, and other companies announced new AI agents, models, and enterprise offerings, alongside related research and tooling for recursive self-improvement, agentic search, and safer agent execution. Gemini 4 Argon was set with a 1,000,000-token output limit. As a result, persistent delegation tools for everyday and enterprise tasks expand, while model and agent research shifts toward longer-horizon work with lower cost, better verification, and tighter safety controls.
MarkTechPost 2 weeks ago 19 ● 2 sources
Aleph Alpha released Kolibri, an open-weight English-German Mixture-of-Experts language model designed for regulated, sovereign deployments. Kolibri has 78.1B total parameters but activates 3.46B (4.4%) per token and supports up to 1,048,576 tokens of context. The release adds Apache 2.0 model weights plus a vLLM deployment path with FP8 serving, reasoning control, and tool-call parsing, enabling users to run and fine-tune it on specified NVIDIA GPUs.
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17 October 2026
AI governance moved from slideware to the boring-but-important mechanics of who gets to fetch, act, and prove. Meta quietly rewrote parts of its commercial terms, removing wording that appeared to let it use web content to “train” and “maintain the integrity” of its products; the update followed legal scrutiny that it could be read as broad scraping permission. At the same time, users reported that major websites are blocking AI agents from completing tasks across services—turning “agentic” demos into something closer to permissioned automation. ServiceNow is now trying to sell the glue for that reality with its AI Control Tower, positioning it as an enterprise platform to discover and govern agents across models and clouds, while a field study of 15 large-enterprise customers questioned how fully its runtime enforcement and AI lifecycle evidence hold up versus alternatives.
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