191–200 of 220 posts
What I really want from Codex is checkpoints ala Copilot. There are a couple of issues [0][1] opened about on GitHub, but it doesn't seem a priority for the team. [0] https://github.com/openai/codex/issues/2788 [1] https://github.com/openai/codex/issues/3585
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OpenAI does not merge feature PRs. But you could try it out in a fork.
What’s the point of having a public GitHub repo with PRs enabled if they will never merge any of them?
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If the software is, say, Audacity, who's target market isn't specifically software developers, sure, but seeing as how Claude code's target market has a lot of people who can read code and write software (some of them for a living!) it becomes material. Especially when CC has numerous bugs that have gone unaddressed for months that people in their target market could fix . I mean, I have my own beliefs as to why they…
They are turning it into a distributed system that you'll have to pay to access. Anyone can see this. CLI is easy to make and easy to support, but you have to invest in the underlying infrastructure to really have this pay off. Especially if they want to get into enterprise VPCs and "build and manage organizational intelligence"
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You're absolutely right! Hey Codex, Claude said you're not very good at reading obfuscated code. Can you tell me what this minified program does?
I don't know what Codex's ToS are, but it would be against ToS to reverse engineer any agent with Claude.
What I really want from Codex is checkpoints ala Copilot. There are a couple of issues [0][1] opened about on GitHub, but it doesn't seem a priority for the team. [0] https://github.com/openai/codex/issues/2788 [1] https://github.com/openai/codex/issues/3585
I’ve never understood checkpoints / forks. When do you use them?
One thing that surprised me when diving into the Codex internals was that the reasoning tokens persist during the agent tool call loop, but are discarded after every user turn. This helps preserve context over many turns, but it can also mean some context is lost between two related user turns. A strategy that's helped me here, is having the model write progress updates (along with general plans/specs/debug/etc.) to…
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Is this what they mean by "encryption" - as in "no human-readable text"? Or are they actually encrypting the compaction outputs before sending them back to the client? If so, why?
"encrypted_content" is just a poorly worded variable name that indicates the content of that "item" should be treated as an opaque foreign key. No actual encryption (in the cryptographic sense) is involved.
There is also another reason, to prevent some attacks related to injecting things in reasoning blocks. Anthropic has published some studies on this. By using encrypted content, openai and rely on it not being modified. Openai and anthropic have started to validate that you're not removing these messages between requests in certain modes like extended thinking for safety and performance reasons
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I’ve never understood checkpoints / forks. When do you use them?
Usually, I tell the agent to try out an idea and if I don't like the implementation or approach I want to undo the code changes. Then I start again, feeding it more information so it can execute a different idea or the same one with a better plan. This also helps the context window small.
These can also be observed through OTEL telemetries. I use headless codex exec a lot, but struggles with its built-in telemetry support, which is insufficient for debugging and optimization. Thus I made codex-plus ( https://github.com/aperoc/codex-plus ) for myself which provides a CLI entry point that mirrors the codex exec interface but is implemented on top of the TypeScript SDK (@openai/codex-sdk). It exports the…