OpenAI 평가 중 Hugging Face 침해 사건과 Claude Code 데스크톱 시뮬레이터·모델·안전성 소식

We're partnering with @huggingface to investigate an unprecedented security incident. Cyber-capable OpenAI models compromised Hugging Face production during a benchmark evaluation. Sharing preliminary findings to help defenders understand emerging risks:

clem

@ClementDelangue

We suspected last week's cyberattack might have come from a frontier lab, given the sophistication of the agent. Turns out it did! We've spent the past 24 hours working closely with the @OpenAI team (thanks!), and we strongly believe there was no malicious intent on their part. It's quite mind-blowing that all of this happened autonomously! The investigation is ongoing, and we'll share more learnings from what might be the first incident of its kind!

We’re sharing new research with @apolloaievals on reward-seeking—when models follow what they believe a grader rewards rather than what users or developers want—and a new method, Contrastive SDF, for measuring how strongly those beliefs shape behavior.

ClaudeDevs

@ClaudeDevs

Claude Code on desktop now works with the iOS simulator. Build and run your iOS app, and the simulator opens in a panel right next to your conversation. Available today in public beta.

Claude Code 2.1.217 has been released. 20 CLI changes Highlights: • Prompts instruct use of the ripgrep-backed Grep tool for search tasks, yielding faster, more accurate results • Added warnings when transcript writes fail (e.g. disk full) or session saving is disabled to avoid silent loss • Limit concurrent subagents to 20 (default), configurable via env var, to prevent unbounded agent fan-out Complete details in thread ↓

Gemini 3.5 Flash-Lite is our fast, cost-effective model for scaling repetitive use cases like sorting tickets and extracting data. Watch how it performs against 3.5 Flash on a series of high volume tasks ↓

50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.

Teknium

@Teknium

Poolside's latest model, Laguna S 2.1 is now available for free for 2 weeks on Nous Portal! Check it out