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<feed xml:lang="en-us" xmlns="http://www.w3.org/2005/Atom"><title>Simon Willison's Weblog: Comments</title><link href="http://simonwillison.net/" rel="alternate"/><link href="http://simonwillison.net/atom/beats/comment/" rel="self"/><id>http://simonwillison.net/</id><updated>2026-08-08T14:06:41+00:00</updated><author><name>Simon Willison</name></author><entry><title>Now we have a timeline of the OpenAI accidental attack against Hugging Face</title><link href="https://news.ycombinator.com/item?id=49220609#49221745" rel="alternate"/><published>2026-08-08T14:06:41+00:00</published><updated>2026-08-08T14:06:41+00:00</updated><id>https://news.ycombinator.com/item?id=49220609#49221745</id><summary type="html">&lt;p&gt;&lt;a href="https://news.ycombinator.com/item?id=49220609#49221745"&gt;My comment&lt;/a&gt; on &lt;a href="https://news.ycombinator.com/item?id=49220609"&gt;Now we have a timeline of the OpenAI accidental attack against Hugging Face&lt;/a&gt; &amp;mdash; Hacker News.&lt;/p&gt;&lt;p&gt;I think one of the most interesting details here might be tucked away in that first bullet point:&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;May 7: OpenAI starts a new training run for an experimental, unreleased model. &lt;em&gt;(Do they mean an evaluation run? They say training run in the video, and later mention a “reward signal to judge how well they’re doing”, so I guess this really was about training a model, not evaluating one that was already trained.)&lt;/em&gt;&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;The more I think about this the more I suspect that the fact this happened while &lt;em&gt;training&lt;/em&gt; a new model is key to understanding what went wrong.&lt;/p&gt;
&lt;p&gt;In RLVR - Reinforcement Learning with Verifiable Rewards - you set the model a goal and have it take &lt;em&gt;any steps necessary&lt;/em&gt; to achieve that goal.&lt;/p&gt;
&lt;p&gt;Clearly one aspect of OpenAI's training here is to RLVR their models for cybersecurity tasks. Just like pre-training benefits from dumping in vast sources of knowledge, the more tasks you can feed into RLVR the more of a general purpose capable model you get at the end.&lt;/p&gt;
&lt;p&gt;This also helps explain why the models had nothing to cause them to hold back. Those safety behaviors are added much later in the process.&lt;/p&gt;
&lt;p&gt;AND it explains (but does not excuse) why monitoring was so lax. If you're training a new model like this you presumably set it thousands of tasks like this in parallel. I can see how you might miss that a tiny subset of your training agents have started leaving each other messages in filenames on your packaging server.&lt;/p&gt;
&lt;p&gt;Someone once told me that you can't just leave the racist materials out of your training data if you want a non-racist model: it has to have seen examples of racism in order to later be taught that racism is bad.&lt;/p&gt;
&lt;p&gt;I can see echoes of that here. If your model doesn't know how to aggressively hack things how do you later teach it not to?&lt;/p&gt;
&lt;p&gt;(I have little knowledge of how RLVR works in practice so I'm looking forward to hearing from people who can help me understand if I'm on the right track here.)&lt;/p&gt;</summary><category term="ai"/><category term="openai"/><category term="generative-ai"/><category term="llms"/><category term="ai-security-research"/><category term="openai-hugging-face-incident"/></entry><entry><title>Devtools must be open source (exe.dev)</title><link href="https://news.ycombinator.com/item?id=49156111#49156719" rel="alternate"/><published>2026-08-03T15:30:38+00:00</published><updated>2026-08-03T15:30:38+00:00</updated><id>https://news.ycombinator.com/item?id=49156111#49156719</id><summary type="html">&lt;p&gt;&lt;a href="https://news.ycombinator.com/item?id=49156111#49156719"&gt;My comment&lt;/a&gt; on &lt;a href="https://news.ycombinator.com/item?id=49156111"&gt;Devtools must be open source (exe.dev)&lt;/a&gt; &amp;mdash; Hacker News.&lt;/p&gt;&lt;p&gt;One of the arguments for open source software for end-users has always been the freedom to examine and modify how that software works.&lt;/p&gt;
&lt;p&gt;The reality for most people - even expert programmers - has been that the freedom is more about being able to lean on &lt;em&gt;other people&lt;/em&gt; to do that. Most people can't justify the time commitment needed to read and then modify the code for tools they use very often.&lt;/p&gt;
&lt;p&gt;I think LLMs have changed that equation in a way that makes the original dream much more feasible.&lt;/p&gt;
&lt;p&gt;Several times a day I'll prompt regular Claude chat to "Clone x/y from GitHub and tell me how Z works".&lt;/p&gt;
&lt;p&gt;Getting software to compile in order to start hacking on it used to be enough friction that I often wouldn't bother. Now I treat that as a zero time investment challenge: tell Codex or Claude Code to checkout and build X and then come back ten minutes later and see how it got on.&lt;/p&gt;
&lt;p&gt;I'm not habitually &lt;em&gt;modifying&lt;/em&gt; the software I use yet, but I can see a path to that which didn't exist a year or so ago.&lt;/p&gt;</summary><category term="hacker-news"/><category term="open-source"/><category term="ai"/><category term="generative-ai"/><category term="llms"/><category term="ai-assisted-programming"/></entry></feed>