The author reflects on how Large Language Models (LLMs) like Claude have fundamentally changed their approach to reading and understanding code. Initially hesitant about relying on AI, they now frequently use LLMs for tasks such as summarizing codebases, debugging error stack traces, and understanding generated code from tools like Copilot. This shift prioritizes an "accept and verify" approach over the previous "understand and then code" method, leading to increased productivity. However, the author notes that AI-generated code is not always reliable, particularly for frequently updated libraries, project-specific logic, and security-sensitive code, necessitating a return to manual code inspection in these areas. Ultimately, the author views LLMs as a "telescope" for code, useful for broad overviews but not a replacement for detailed, manual analysis when deep understanding or critical tasks are required. AI
IMPACT LLMs are shifting developer workflows towards an 'accept and verify' model, increasing productivity but requiring careful discernment for critical code.
RANK_REASON Personal reflection on how AI tools are changing a specific professional workflow.
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