Comment by kiitos
2 days ago
> one degree removed
You're letting Claude do your programming for you, and then sweeping up whatever it does afterwards. Bluntly, you're off-loading your cognition to the machine. If that's fine by you then that's fine enough, it just means that the quality of your work becomes a function of your tooling rather than your capabilities.
I don't agree. The AI largely does the boring and obvious parts. I'm still deciding what gets built and how it is designed, which is the interesting part.
It's the same with me, with the added wrinkle of pulling each PR branch down and refactoring things (and, ironically, introducing my own bugs).
> I'm still deciding what gets built and how it is designed, which is the interesting part.
How, exactly? Do you think that you're "deciding what gets built and how it's designed" by iterating on the prompts that you feed to the LLM that generates the code?
Or are you saying that you're somehow able to write the "interesting" code, and can instruct the LLM to generate the "boring and obvious" code that needs to be filled-in to make your interesting code work? (This is certainly not what's indicated by your commit history, but, who knows?)
Did you actually read the commit history?
My prompts specify very precisely what should be implemented. I specified the public API and high-level design upfront. I let the AI come up with its own storage schema initially but then I prompted it very specifically through several improvements (e.g. "denormalize this table into this other table to eliminate a lookup"). I designed the end-to-end encryption scheme and told it in detail how to implement it. I pointed out bugs and explained how to fix them. And so on.
All the thinking happened in those prompts. With the details I provided, combined with the OAuth spec, there was really very little room left for any creativity in the code. It was basically connect-the-dots at that point.
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Personally, I spend _more_ time thinking with Claude. I can focus on the design decisions while it does the mechanical work of turning that into code.
Sometimes I give the agent a vague design ("make XYZ configurable") and it implements it the wrong way, so I'll tell it to do it again with more precise instructions ("use a config file instead of a CLI argument"). The best thing is you can tell it after it wrote 500 lines of code and updated all the tests, and its feelings won't be hurt one bit :)
It can be useful as a research tool too, for instance I was porting a library to a new language, and I told the agent to 1) find all the core types and 2) for each type, run a subtask to compare the implementation in each language and write a markdown file that summarizes the differences with some code samples. 20 min later I had a neat collection of reports that I could refer to while designing the API in the new language.
I was recently considering a refactoring in a work codebase. I had an interesting discussion with Claude about the tradeoffs, then had it show me what the code would look like after the refactor, both in a very simple case, and in one of the most complex cases. All of this informed what path I ended up taking, but especially the real-world examples meant this was a much better informed decision than just "hmm, yeah seems like it would be a lot of work but also probably worth it."