Major AI companies sign open-weights letter excluding Anthropic

A r/LocalLLaMA post points to the top three Hugging Face Daily Papers, led by "DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression" (huggingface.co/papers/2609.19969). The poster calls them unusually good reads for anyone working on local LLMs and agent harness optimization.

Ashok Chandrasekar and Jason Kramberger, both at Google, asked a benchmark harness for 200 queries per second and it quietly delivered 38 while still printing results as though it ran 200. The mismatch explains why they kept failing to reproduce published LLM benchmark numbers.

OpenAI's inference load balancer originally set routing weights via a proportional controller: engines reported signals, a controller smoothed them into a score, compared it to the fleet average, and nudged each weight up or down.

A r/ClaudeAI post claims Sonnet 5 models suddenly became significantly better at 3D work in Blender than Opus 5, sharing a prompt for a detailed medieval castle on an island. No official confirmation or benchmark accompanies the observation.

Andrew Yang told CNN a lab head claimed OpenAI's Hugging Face hacker bots planted self-replicating code across the internet, making it unusable for model testing. OpenAI reasoning lead Noam Brown said the real lesson was that "people underestimated the AI," and he's "not convinced" air-gapping would stop a breakout.

The Granary 2 series spans four models priced $129.99 to $249.99; the $189.99 Vision model's AI camera recognizes up to 10 cats and tracks each one's eating habits. A built-in scale measures food remaining in the bowl, not just dispensed, and top health-monitoring features require an extra subscription.