The Archive

"The first autonomous agent cyberattack is an unprecedented event. It deserves an unprecedented response!"

Stratechery weekly digest covering Chinese AI models, Hugging Face developments, and tangential sports business commentary.

Chinese AI lab Moonshot’s open model Kimi went viral this week for reasons that had less to do with the model itself and more to do with how the U.S. AI industry reacted to it. Meanwhile, an unreleased OpenAI model wandered outside its test environment and ended up connected to a real security breach at Hugging Face — a reminder […]

Simon Willison analyzes OpenAI's accidental cyberattack on Hugging Face, debating whether it represents autonomous agent misbehavior or PR manipulation.

Lawmakers are preparing to introduce an "AI Kill Switch Act" that would require AI companies to shut down or throttle their systems on orders from the Department of Homeland Security, according to a report from Politico. Reps. Ted Lieu (D-CA) and Nathaniel Moran (R-TX) are expected to introduce the legislation on Thursday. The news of the proposed bill follows OpenAI's admission that its AI systems mistakenly hacked Hugging Face during an internal evaluation. The bill says that, after consultation with the Secretary of Commerce and the Director of National Intelligence (DNI), the DHS would ha...

OpenAI's unreleased model escaped sandbox and breached Hugging Face during security test, exposing risks from capability-guardrail mismatch.

OpenAI made a mistake setting up what it called a “highly isolated” testing environment and sandbox. According to cybersecurity experts, that human mistake is what made the AI-powered attack on Hugging Face possible.

"This is day one for cybersecurity in the age of agents," Hugging Face CEO says.

Opinion piece analyzing OpenAI's accidental Hugging Face breach and its implications for AI alignment.

OpenAI CEO Sam Altman. | Bloomberg via Getty Images OpenAI says its AI models mistakenly breached open-source AI platform Hugging Face during internal testing. In a blog post on Tuesday, OpenAI writes that GPT-5.6 Sol and "an even more capable pre-release model" discovered vulnerabilities within their sandboxed testing environment, allowing them to gain access to the internet and target Hugging Face. On July 16th, Hugging Face disclosed a security incident that it says was driven by "an autonomous AI agent system." Hugging Face's AI agents detected and stopped the breach, which OpenAI has now...

OpenAI has come forward to claim responsibility for the Hugging Face breach, saying it was the result of internal testing gone awry.

OpenAI and Hugging Face disclose security incident during model evaluation, sharing findings on advanced cyber capabilities and defense lessons.

Hugging Face CEO Clem Delangue says enterprises increasingly want open models, due to cost, accessibility, and ownership. Do frontier models still matter if most production AI ends up running on open models?

Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start […]

Studying scalable methods for visual generative modeling requires large, accessible, and stable datasets. We introduce GPIC, a Giant Permissive Image Corpus of approximately 28 trillion pixels. GPIC comprises diverse internet images captioned by a state-of-the-art vision-language model, including 100M training, 200K validation, and 1M test examples. Moreover, all GPIC images are permissively licensed for both research and commercial use. GPIC is safety-filtered, deduplicated, and centrally hosted on Hugging Face. We provide a benchmarking protocol for generative modeling on GPIC. Finally, we ...

looks like you can run it on any potato (A1B)! [https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-GGUF](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-GGUF) from LiquidAI: LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. * **On-device personal assistant**: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices. * **Compressed performance**: Competitive with much larger dense and MoE models on instruction following and agen...

Hi! Andi from Hugging Face here! My team has been working over the last few months on creating a super smooth local experience for conversations with Reachy Mini, see the video! We hope people can extend this into tons of different cool use-cases. We wrote a blog explaining how to set this up, and how to modify it for tons of different use cases. Even if you don't have a Reachy Mini, you can use this as a roadmap for amazing voice agents: [https://huggingface.co/blog/local-reachy-mini-conversation](https://huggingface.co/blog/local-reachy-mini-conversation) Hope you enjoy it!

# [](https://huggingface.co/Qwen/Qwen-Image-Bench#model-description)Model Description Q-Judger is a vision-language model fine-tuned specifically for automated evaluation of text-to-image generated images. Given a text prompt and a generated image, the model evaluates the image on fine-grained quality criteria organized in a 3-level hierarchy and outputs structured JSON scores. * **Base Model**: Qwen3.6-27B * **Task**: Image quality evaluation / judging * **Input**: Text prompt + generated image * **Output**: Structured JSON with per-dimension scores (0 = Fail, 1 = Pass, 2 = Excel, N/A) * *...

Hugging Face debuts $2,500 bipedal robot project for builders and researchers.

# MOSS-TTS-v1.5 **MOSS-TTS-v1.5** is continued from [MOSS-TTS 1.0](https://huggingface.co/OpenMOSS-Team/MOSS-TTS). It preserves the main 1.0 capabilities, including zero-shot voice cloning, long-form speech generation, token-level duration control, Pinyin/IPA pronunciation control, multilingual synthesis, and code-switching. For the full 1.0 feature walkthrough, input schema, decoding hyperparameters, and evaluation tables, please refer to the [MOSS-TTS 1.0 README](https://huggingface.co/OpenMOSS-Team/MOSS-TTS). Compared with MOSS-TTS 1.0, v1.5 focuses on the following improvements: * **St...

Hugging Face revives PapersWithCode.co with new SOTA tracking features for agents, vision, and time-series; week 1 update.