Claude Code can debug low-level cryptography (words.filippo.io)

Article summary

The author implemented the ML-DSA post-quantum signature algorithm in Go, but the verification of signatures was failing. They used Claude Code, an AI tool, to debug the issue and were surprised by its ability to quickly identify and fix the problem. The author also experimented with Claude Code on two other bugs in their implementation and found it to be effective in finding and fixing them. This experience has led the author to appreciate the potential of AI tools in debugging and coding.

Main themes

• AI-assisted debugging
• Low-level cryptography
• Code implementation
• Tooling and workflow
• AI limitations and potential

What commenters say

• Some users prefer a more interactive and collaborative approach to working with AI coding tools, rather than a chat-based interface.
• The effectiveness of AI coding tools can depend on the specific task, model, and user expertise.
• There is a trade-off between the cost of using AI coding tools and the value of the time they save, which can vary depending on the user's workflow and needs.
• Different AI models and tools have varying levels of performance and usability, with some being more suitable for specific tasks or users.
• Using AI coding tools can be seen as a way to augment human capabilities, rather than replace them, and can be particularly useful for tasks like bug hunting.
• The integration of AI coding tools into existing workflows, such as through Git hooks, can enhance their usefulness and efficiency.