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Comment My comment on Devtools must be open source (exe.dev) — Hacker News
One of the arguments for open source software for end-users has always been the freedom to examine and modify how that software works.
The reality for most people - even expert programmers - has been that the freedom is more about being able to lean on other people 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.
I think LLMs have changed that equation in a way that makes the original dream much more feasible.
Several times a day I'll prompt regular Claude chat to "Clone x/y from GitHub and tell me how Z works".
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.
I'm not habitually modifying the software I use yet, but I can see a path to that which didn't exist a year or so ago.
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My comment on Now we have a timeline of the OpenAI accidental attack against Hugging Face — Hacker News
I think one of the most interesting details here might be tucked away in that first bullet point:
The more I think about this the more I suspect that the fact this happened while training a new model is key to understanding what went wrong.
In RLVR - Reinforcement Learning with Verifiable Rewards - you set the model a goal and have it take any steps necessary to achieve that goal.
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.
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.
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.
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.
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?
(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.)
# 8th August 2026, 2:06 pm / ai, openai, generative-ai, llms, ai-security-research, openai-hugging-face-incident