Big labs don’t expose logits, obviously. So I was wondering, if you wanted to play with distillation at scale, what other signals could you get from these models? Ha fun building this experiment: give Claude Code/Codex an enforced “workbook” and ask them to write down decisions, alternatives they considered, tradeoffs, etc., while working Just Normal model output, not hidden reasoning. Interesting to think about whether traces like these could be useful as a signal.

There is a big difference IMO. transcript shows a summary. The workbook shows you generated text, verbatim.

These logs containing opaque blobs could accidentally contain secrets, the researchers decoded many of reasoning blocks from public repositories and reported finding PII and credentials.

I was experimenting a bit how I could block these using an ingress path. GitHub /softcane/hamza

Interesting take. I also looked into this issue from a different angle https://github.com/softcane/hamza

And what are those necessary controls?

Either it needs to run on our hardware, or we need explicit contractual guardrails around use of the data for their purposes. Obviously anything you give Anthropic etc. is going into their training pipeline for instance, and they change their terms of use every week so it's impossible to work with as a small company with no leverage

These data retention contracts are black boxes; you never know how your IP was leaked, and it could end up in the model's training data. It's like trusting META with your privacy settings.

A contract satisfies our infosec program and cyber insurance requirements, confirmed by a corporate legal team. Our role is to manage enterprise risk and potential exposure within our risk appetite, not eliminate it.

(already minimizing sensitive data storage and transit whenever possible, as an entity operating in a regulated industry)

I opened this feature request https://github.com/anthropics/claude-code/issues/68284 and hacked around a solution that I thought wasn't so hard to bake into the Claude code.

There are other similar open issues.

However, my thoughts are that I think this fix as a native feature in Claude code is not in the best interest of Anthropic's income statement. For example, many regular users are currently not utilising the weekly quota usage and that unused capacity is shared among others who are either enterprise users or know how to precisely set up the loops.

Imagine this, if this becomes a native feature, developers would start scheduling tasks at 9 pm and would continue until they wake up.

What you think?

If it’s a preference question then certainly yes, but how to push back on x productivity boost argument when a company enforce this as policy?

Yes a soft policy but it’s a smart move to encourage the rat race.

Author here, I would happy to answer your question's

Then why to even keep codex open source?

It's to prevent collection of queries from users that are coming from resellers/proxies, for reasons of economy or bypassing region blocks etc. The users are using the stock client and may believe they are using direct OpenAI servers.

what does that have to do with anything?