461–470 of 1001 posts
Anthropic team does an excellent job of speeding up Claude Code when it slows down, but for the sake of RAM and system resources, it would be nice to see it rewritten in a more performant framework! And now, with Claude on a Ralph loop, you can.
I know they can do better
src/cli/print.ts This is the single worst function in the codebase by every metric: - 3,167 lines long (the file itself is 5,594 lines) - 12 levels of nesting at its deepest - ~486 branch points of cyclomatic complexity - 12 parameters + an options object with 16 sub-properties - Defines 21 inner functions and closures - Handles: agent run loop, SIGINT, rate-limits, AWS auth, MCP lifecycle, plugin install/refresh, wo…
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Magical human verification technology is called "your own private forum" in conjunction with "invite your friends"
Until your friend writes a bot. Funny story, when I was younger I trained a basic text predictor deep learning model on all my conversations in a group chat I was in, it was surprisingly good at sounding like me and sometimes I'd use it to generate some text to submit to the chat.
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...
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ANTI_DISTILLATION_CC This is Anthropic's anti-distillation defence baked into Claude Code. When enabled, it injects anti_distillation: ['fake_tools'] into every API request, which causes the server to silently slip decoy tool definitions into the model's system prompt. The goal: if someone is scraping Claude Code's API traffic to train a competing model, the poisoned training data makes that distillation attempt less…
Wonder if they’re also poisoning Sonnet or Opus directly generating simulated agentic conversations.
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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They're searching for multiple substrings in a single pass, regexes are the optimal solution for that.
The issue isn't that regex are a solution to find a substring. The issue is that you shouldn't be looking for substrings in the first place. This has buttbuttin energy. Welcome to the 80s I guess.
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...
These security failures from Anthropic lately reveal the caveats of only using AI to write code - the safety an experienced engineer is not matched by an LLM just yet, even if the LLM can seemingly write code that is just as good. Or in short, if you give LLMs to the masses, they will produce code faster, but the quality overall will degrade. Microsoft, Amazon found out this quickly. Anthropic's QA process is better…