Daily Productive Sharing 1271 - AI-assisted Coding

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Atharva Raykar argues that building with AI can dramatically accelerate development. Speed matters because, used well, it shortens the feedback loop with users and leads to better products.

1. However, AI tools can also be tricky. If used poorly, they don’t just produce mediocre results—they can throw a project into chaos and technical debt, ultimately slowing everything down.
2. To benefit from AI, you first have to strengthen your own skills—because AI is a multiplier.
3. He therefore advocates a craftsman’s mindset: even with AI assistance, the final product should be something you’re proud to sign your name to.
4. One effective tactic is “meta-prompting.” He begins with a simple task prompt, asking the model to surface trade-offs and edge cases. He then refines those points into a technical plan and hands it to another LLM agent for execution.
5. In his experience, today’s models are already remarkably good at prompting themselves.

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Atharva Raykar 认为，用 AI 开发速度很快。速度提升非常关键，因为若利用得当，就能让团队更快地与用户形成反馈循环，从而打造出更好的产品。

1. 然而，AI 工具有时候也很难用。若使用方式不当，结果不仅平庸，更糟的是可能让项目陷入混乱与技术债务，从而拖慢进度。
2. 想用好 AI，就要先让自己变得更强。因为 AI 是倍增器。
3. 因此，他主张要像工匠一样用心。即使有 AI 协助，最终做出来的东西也要是自己引以为傲的作品。
4. 一个对他们很有效的技巧是“元提示”。他会先用简单的任务提示模型，并让它帮忙找出权衡与边缘场景。随后，他再把这些整理成技术方案，交给另一个 LLM agent 去执行。
5. 根据他的经验，如今的模型已经相当擅长自我提示。

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