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Honestly when using it, it feels vibe coded to the bone, together with the matching weird UI footgun quirks
Team has been extremely open how it has been vibe coded from day 1. Given the insane amount of releases, I don’t think it would be possible without it.
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the "useCanUseTool.tsx" hook, is definitely something I would hate seeing in any code base I come across. It's extremely nested, it's basically an if statement soup `useTypeahead.tsx` is even worse, extremely nested, a ton of "if else" statements, I doubt you'd look at it and think this is sane code
Fits with the origin story of Claude Code...
In the app, it now reads: > current: 2.1.88 · latest: 2.1.87 Which makes me think they pulled it - although it still shows up as 2.1.88 on npmjs for now (cached?).
Undercover mode also pretends to be human, which I'm less ok with: https://github.com/chatgptprojects/claude-code/blob/642c7f94...
The undercover mode prompt was generated using AI.
Would be interesting to run this through Malus [1] or literally just Claude Code and get open source Claude Code out of it. I jest, but in a world where these models have been trained on gigatons of open source I don't even see the moral problem. IANAL, don't actually do this. https://malus.sh/
Malus is not a real project btw, it's a parody: “Let's end open source together with this one simple trick” https://pretalx.fosdem.org/fosdem-2026/talk/SUVS7G/feedback/ Malus is translating code into text, and from text back into code. It gives the illusion of clean room implementation that some companies abuse. The irony is that ChatGPT/Claude answers are all actually directly derived from open-source code, so...
They have an interesting regex for detecting negative sentiment in users prompt which is then logged (explicit content): https://github.com/chatgptprojects/claude-code/blob/642c7f94... I guess these words are to be avoided...
An LLM company using regexes for sentiment analysis? That's like a truck company using horses to transport parts. Weird choice.
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An LLM company using regexes for sentiment analysis? That's like a truck company using horses to transport parts. Weird choice.
Because they want it to be executed quickly and cheaply without blocking the workflow? Doesn’t seem very weird to me at all.
They have an interesting regex for detecting negative sentiment in users prompt which is then logged (explicit content): https://github.com/chatgptprojects/claude-code/blob/642c7f94... I guess these words are to be avoided...