The Weights

Bottom line: Wait. Hugging Face's Workflow1111 rebuilds most of AUTOMATIC1111's image-pipeline features as a 73-node Gradio Workflow graph, but 22 of its 32 local nodes execute arbitrary Python in-process with no sandboxing, and every output auto-exposes a REST/MCP endpoint — a demo worth studying, not yet architecture to fork into a public-facing service.

Bottom line: Wait. A Hugging Face and Liquid AI tutorial fine-tunes Liquid AI's 350-million-parameter LFM2.5 with 100 GRPO steps in the TRL library, raising IFStruct structured-output accuracy from 22.6% to 29.7% (JSON: 18.0% to 31.9%) on free-tier Colab GPUs. The decisive number: a 29.7% pass rate still fails roughly seven of every ten schema-validation tasks outright.

None of Institute for Progress's 23 proposed policies for automated AI R&D are binding law — they are a menu addressed to Congress, headlined by an $84 million annual budget ask for CAISI. A concurrent benchmark jump, Intology's Locus agent scoring 51.6% on PostTrainBench+ versus a rival's 23.2% five months earlier, undercuts the report's assumption that preparation time is abundant.

UK government testing confirms the open-weight/closed-weight cyber-capability gap has narrowed to four-to-seven months, down from six-to-ten months in 2025: GLM-5.2 and DeepSeek V4-Pro now approach frontier models like Claude Opus 4.6 on narrow tasks, though closed models still lead on complex, chained attacks, and open models cost up to 45 times less per task.