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Comment by kenhwang | Hacker News Reader

I always wondered why people don't also ask the AI to generate code comments/documentation, summaries of those documentation, overview of the system, and re-review them all for correctness for the changes they asked the AI to do.

What I've noticed reviewing all my colleagues' AI generated code PRs is: it really is just code, and the rare comment here and there is still added by the human.

We're already trying to light tokens on fire as fast as possible to stay on acceptable required use leaderboards, why not light some more for system understanding and housekeeping.

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The docs/summaries part I can get behind if reviewed and improved by a human, but at least when working in pre-existing codebases, I tend to steer models away from writing comments, because I find that almost all comments they write are "not even wrong".

That is: they either reiterate what the code does, or would if the code were slightly clearer, or they tell half truths that are more confusing than helpful. Mostly they fail to emphasise the salient things, like the why over the what, that are not obvious from the code.

100% agree. 95% of the AI comments I see are redundant, misleading or both. Commonly they feel like the reasoning process leaking into code.

yes it makes sense that it's AI all the way down, but also the "why not just…" answer is because it's exhausting.

Being reduced to an inconsequential middle manager is more exhausting than being reduced to a code monkey is the hot insight i've been hanging my hat on.

To be clear: This is really horrible in an IC for the paycheck role. I quit my job on principle because of code/token maxing. Very few are in the place to do this. I've been enjoying AI as an independent, but i still mean to fight the good fight for every line engineer.

> I always wondered why people don't also ask the AI to generate code comments/documentation, summaries of those documentation, overview of the system, and re-review them all for correctness for the changes they asked the AI to do.

I now on all of my projects have an ai journal that stands as a ledger for every change the ai has made, and why it was made. I don’t read it that hard personally because I spend so much time planning with my agent before letting it code. However I have found it very useful in sharing code between people, or having Claude look through the journal to gain context when modifying or adding a feature.

I see people asking LLMs to omit comments, but comments like PDL (Program Design Language) could be helpful for interactive development: ask the LLM to write pseudocode comments, review them, and then ask the LLM to expand them to actual code. People say comments should explain why, not how. That seems useful for both human reviewers and LLMs.

https://en.wikipedia.org/wiki/Program_Design_Language

What harness? In my experience, Claude shoots out tons of very low-quality comments. It's always too hyper-focused on the exact specifics of the bug as presented to it, with no higher-level generalization of the concepts involved. In a big codebase, this means the comments are meaningless without a human rewrite, but it definitely writes them.

I’ve have amazing, amazing results through composing an “ADR” skill hook. It knows contextually what they are for; how to write them; when to include them as steering.

You get LLMs like Gemini that will assure you they are right.

There seems to be a big push for 0 reviewing, which is insane.

The review needs to be done with clear context, or another LLM altogether.

For major new features, I do ask it to do those things. Why is it that you think people don't do that?

Even for most small changes I will ask it to do a simple "production review" then I use my experience and judgment to decide on which items need to actually be addressed or not

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