OpenAI's Jalapeño chip beats Nvidia Blackwell on inference efficiency

datasette-explain 0.2.2 ships explain plans on read-only stored-query pages, released after Simon Willison upgraded datasette.simonwillison.net to Datasette 1.0a40.

A llama.cpp fork implements Declarative Attention from arXiv:2609.02737 (Google DeepMind and KAIST AI), letting the model declare in its own output which context chunks it needs while the engine restricts what following tokens attend to. No scorer and no training are required — just prompting.

AX declares agentic tasks in YAML (Workspace and Task kinds, apiVersion ax.io/v1alpha1) and runs them in sandboxes with CPU/memory limits, network fencing, and Git/MCP workspace setup. Tasks can be suspended, resumed, and deleted via the ax CLI.

In a Royal Society of Arts conversation with Professor Hannah Fry, the Google DeepMind co-founder argues creativity and intuition are fundamental to scientific discovery and breakthrough ideas.

A r/LocalLLaMA user describes experimenting with a simple Jev-like inference setup using ordinary open models, calling the Jev trend overhyped by a large margin.

A r/LocalLLaMA thread asks whether users would buy a hypothetical Taalas consumer chip for $1,000 that runs Qwen3.8-27B at 7,000 tokens per second. The poster notes the chip would be locked to that model but would not become obsolete when Qwen4-27B launches.