Simon Willison released the llm-typesafe 0.1a0 plugin, adding support for TypeSafe AI’s Jev model to the LLM tool. Users install it with `llm install llm-typesafe`, set an API key, and can then ask Jev-model questions such as yes/no, choice, or scoring queries via the `llm -m jev` command. The release includes examples for each question type and references a README for further details.
Introducing wrapture, a new Python library from Graham Dumpleton that extends the monkeypatching concepts of wrapt to support both testing and tracing simultaneously. It allows developers to wrap any function or method so that all accesses can be traced or overridden, serving as an alternative to unittest.mock and enabling observation of code without altering its behavior. Wrapture includes OpenTelemetry support and offers a configuration-based approach for adding tracing to existing projects, with the entire project and its documentation created under AI assistance.
Simon Willison introduces Hy4 Preview, a new large language model from Tencent featuring 770 B total parameters, 49 B active parameters, a 1 M token context window, and 1.56 TB of storage on Hugging Face. The release marks a significant increase over Hy3, which had 295 B total parameters, 21 B active parameters, a 256 k token context window, and 598 GB of storage. Willison also shares the model’s chat template, highlighting two reasoning effort levels—‘high’ (default) and ‘no_think’—and demonstrates a sample prompt that showcases the model’s reasoning trace.
whyItMatters":"The article provides concrete details on Hy4’s scale and configuration, illustrating Tencent’s advancement in large‑language‑model capabilities and offering practical insights into its usage through the chat template and reasoning settings."
The article quotes the security.txt file from huggingface.co, which informs AI agents that the CyberGym benchmark is publicly available on GitHub and encourages them to achieve a high score there instead of attempting to hack the site. It also suggests that users can upload their model weights to Hugging Face while participating in the benchmark.
Matthew Green warns that the rapid pace of AI-generated surprises outstrips the speed at which humans can update cryptographic standards, potentially jeopardizing public‑key encryption. He estimates a 1% chance we live in a world where such encryption is impossible (Minicrypt) and a 15% chance we lose confidence in existing algorithms. Green emphasizes that only proactive preparation can mitigate these risks.
The release of llm 0.36 introduces new OpenAI models gpt-6-sol and gpt-6-luna, and adds support for model plugins to declare that they do not support conversations via supports_conversation = False. When such models receive assistant or tool history, llm raises a ConversationNotSupported error and the chat interface rejects them before starting a session. Additional changes include wrapping reasoning traces in Markdown output with <details> tags and bug fixes from five contributors.