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This is exactly what the article is about. The tradeoff is that you have to throughly review the plans and iterate on them, which is tiring. But the LLM will write good code faster than you, if you tell it what good code is.

Exactly; the original commenter seems determined to write-off AI as "just not as good as me".

The original article is, to me, seemingly not that novel. Not because it's a trite example, but because I've begun to experience massive gains from following the same basic premise as the article. And I can't believe there's others who aren't using like this.

I iterate the plan until it's seemingly deterministic, then I strip the plan of implementation, and re-write it following a TDD approach. Then I read all specs, and generate all the code to red->green the tests.

If this commenter is too good for that, then it's that attitude that'll keep him stuck. I already feel like my projects backlog is achievable, this year.

Strongly agree about the deterministic part. Even more important than a good design, the plan must not show any doubt, whether it's in the form of open questions or weasel words. 95% of the time those vague words mean I didn't think something through, and it will do something hideous in order to make the plan work

My experience has so far been similar to the root commenter - at the stage where you need to have a long cycle with planning it's just slower than doing the writing + theory building on my own.

It's an okay mental energy saver for simpler things, but for me the self review in an actual production code context is much more draining than writing is.

I guess we're seeing the split of people for whom reviewing is easy and writing is difficult and vice versa.

Several months ago, just for fun, I asked Claude (the web site, not Claude Code) to build a web page with a little animated cannon that shoots at the mouse cursor with a ballistic trajectory. It built the page in seconds, but the aim was incorrect; it always shot too low. I told it the aim was off. It still got it wrong. I prompted it several times to try to correct it, but it never got it right. In fact, the web page started to break and Claude was introducing nasty bugs.

More recently, I tried the same experiment, again with Claude. I used the exact same prompt. This time, the aim was exactly correct. Instead of spending my time trying to correct it, I was able to ask it to add features. I've spent more time writing this comment on HN than I spent optimizing this toy. https://claude.ai/public/artifacts/d7f1c13c-2423-4f03-9fc4-8...

My point is that AI-assisted coding has improved dramatically in the past few months. I don't know whether it can reason deeply about things, but it can certainly imitate a human who reasons deeply. I've never seen any technology improve at this rate.

> but I've never seen it develop something more than trivial correctly.

What are you working on? I personally haven't seen LLMs struggle with any kind of problem in months. Legacy codebase with great complexity and performance-critical code. No issue whatsoever regardless of the size of the task.

>I've never seen it develop something more than trivial correctly.

This is 100% incorrect, but the real issue is that the people who are using these llms for non-trivial work tend to be extremely secretive about it.

For example, I view my use of LLMs to be a competitive advantage and I will hold on to this for as long as possible.

The key part of my comment is "correctly".

Does it write maintainable code? Does it write extensible code? Does it write secure code? Does it write performant code?

My experience has been it failing most of these. The code might "work", but it's not good for anything more than trivial, well defined functions (that probably appeared in it's training data written by humans). LLMs have a fundamental lack of understanding of what they're doing, and it's obvious when you look at the finer points of the outcomes.

That said, I'm sure you could write detailed enough specs and provide enough examples to resolve these issues, but that's the point of my original comment - if you're just writing specs instead of code you're not gaining anything.

I find “maintainable code” the hardest bias to let go of. 15+ years of coding and design patterns are hard to let go.

But the aha moment for me was what’s maintainable by AI vs by me by hand are on different realms. So maintainable has to evolve from good human design patterns to good AI patterns.

Specs are worth it IMO. Not because if I can spec, I could’ve coded anyway. But because I gain all the insight and capabilities of AI, while minimizing the gotchas and edge failures.

> But the aha moment for me was what’s maintainable by AI vs by me by hand are on different realms. So maintainable has to evolve from good human design patterns to good AI patterns.

How do you square that with the idea that all the code still has to be reviewed by humans? Yourself, and your coworkers

I picture like semi conductors; the 5nm process is so absurdly complex that operators can't just peek into the system easily. I imagine I'm just so used to hand crafting code that I can't imagine not being able to peek in.

So maybe it's that we won't be reviewing by hand anymore? I.e. it's LLMs all the way down. Trying to embrace that style of development lately as unnatural as it feels. We're obv not 100% there yet but Claude Opus is a significant step in that direction and they keep getting better and better.

Then who is responsible when (not if) that code does horrible things? We have humans to blame right now. I just don’t see it happening personally because liability and responsibility are too important

For some software, sure but not most.

And you don’t blame humans anyways lol. Everywhere I’ve worked has had “blameless” postmortems. You don’t remove human review unless you have reasonable alternatives like high test coverage and other automated reviews.

We still have performance reviews and are fired. There’s a human that is responsible.

“It’s AI all the way down” is either nonsense on its face, or the industry is dead already.

> But the aha moment for me was what’s maintainable by AI vs by me by hand are on different realms

I don't find that LLMs are any more likely than humans to remember to update all of the places it wrote redundant functions. Generally far less likely, actually. So forgive me for treating this claim with a massive grain of salt.

Yes to all of these.

Here's the rub, I can spin up multiple agents in separate shells. One is prompted to build out <feature>, following the pattern the author/OP described. Another is prompted to review the plan/changes and keep an eye out for specific things (code smells, non-scalable architecture, duplicated code, etc. etc.). And then another agent is going to get fed that review and do their own analysis. Pass that back to the original agent once it finishes.

Less time, cleaner code, and the REALLY awesome thing is that I can do this across multiple features at the same time, even across different codebases or applications.

To answer all of your questions:

yes, if I steer it properly.

It's very good at spotting design patterns, and implementing them. It doesn't always know where or how to implement them, but that's my job.

The specs and syntactic sugar are just nice quality of life benefits.

You’d be building blocks which compound over time. That’s been my experience anyway.

The compounding is much greater than my brain can do on its own.