Australian teams can now access OpenAI GPT-5.6 Sol, Terra, and Luna through Amazon Bedrock by calling the Bedrock Runtime endpoint in Sydney (ap-southeast-2) or Melbourne (ap-southeast-4), with Bedrock routing requests to supported AWS destination regions automatically. The setup supports context windows up to 1 million tokens. This changes deployment by letting apps avoid manual cross-region routing while enabling use via the Responses API, Chat Completions API, and the Bedrock Converse API, plus features like prompt caching and Codex integration.
Piotr Migdał tested leading AI models for identifying mushrooms and found they give unreliable results for safe versus deadly species. The best model, Gemini-3.8-flash, was correct on its first guess only 65% of the time. As a result, the dataset and findings push people to not eat mushrooms based on AI outputs and instead rely on manual expertise or safer species until identification accuracy is proven.
Simon Willison’s Weblog·1 month ago·
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● 2 sources
Simon Willison posted an LLM briefing beat for Anthropic developments. It was published on 2nd September 2026. The post updates readers with monthly counts and prompts them to subscribe for a curated email digest.
Google launched Gemini 3.8 Flash, claiming it performs more reasoning steps and uses iterative tool calls compared with Gemini 3.7 Flash. Pricing starts at $0.75 per million input tokens and $3.75 per million output tokens, but Google warns it may use more tokens at higher effort levels. Developers can stay on Gemini 3.7 Flash to minimize token usage.
The Sequence Learning Loop newsletter highlighted three recent model releases: Anthropic’s Claude Fable 5.1 and Mythos 5.1, Zhipu’s GLM-5.3-Flash, and Alibaba’s Qwen 3.8 family. Zhipu’s GLM-5.3-Flash is a 320B model that activates 18B parameters and ran for a week serving anonymous traffic on Chinese chips. Together, the releases push toward cheaper long-hours self-running use by mixing different approaches to model size, activation, and deployment constraints.
Researchers introduced REFACTOR-VLA to turn monolithic vision-language-action behavior into reusable typed motor programs learned with a wake/sleep loop. The system clusters motor-program segments in the sleep phase using a Behavioral-Equivalence Kernel driven by rollouts in a learned latent world model trained via a three-phase schedule, and it reports NMI scores for n=3 multi-seeding including 0.915 ± 0.013 on the Goal suite. Performance shifts as a bigger world model (188M to 430M parameters) worsens 4 out of 4 LIBERO benchmark suites while adding an auxiliary InfoNCE contrastive loss in Phase A improves the quality of skill clustering in Phase C.
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