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Comment by joshbee | Hacker News Reader

I'm in the same boat. I've been taking on much more ambitious projects both at work and personally by collaborating with LLMs. There are many tasks that I know I could do myself but would require a ton of trial and error.

I've found giving the LLMs the input and output interfaces really help keep them on rails, while still being involved in the overall process without just blindly "vibe coding."

Having the AI also help with unit tests around business logic has been super helpful in addition to manual testing like normal. It feels like our overall velocity and code quality has been going up regardless of what some of these articles are saying.

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100% agree with AI expanding core testing from my own edge and key tests.

I agree, I write out the sketch of what I want. With a recent embedded project in C I gave it a list of function signatures and high level description and was very satisfied with what it produced. It would have taken me days to nail down the particulars of the HAL (like what kind of sleep do I want what precisely is the way to setup the WDT and ports).

I think it's also language dependent.

I imagine JavaScript can be a crap shoot. The language is too forgiving.

Rust is where I have had most success. That is likely a personal skill issue, I know we want a Arc<DashMap>, will I remember all the foibles of accessing it? No.

But given the rigidity of the compiler and strong typing I can focus on what the code functionally is doing, that in happy with the shape/interface and function signature and the compiler is happy with the code.

It's quite fast work. It lets me use my high level skills without my lower level skills getting in the way.

And id rather rewrite the code at a mid-level then start it fresh, and agree with others once it's a large code base then in too far behind in understanding the overall system to easily work on it. That's true of human products too - someone elses code always gives me the ick.

Vanilla javascript is hit or miss for anything complex.

Using Typescript works great because you can still build out the interfaces and with IDE integrations the AIs can read the language server results so they get all the type hints.

I agree that the AI code is usually a pretty good starting point and gets me up to speed for new features fast rather than starting everything from scratch. I usually end up refactoring the last 10-20% manually to give it some polish because some of the code still feels off some times.

How granular do you go with the interfaces? Full function signatures + types, or more like module-level contracts.

wondering what sort of artifacts beyond ADR/natural language prompts help steer LLMs to do the right thing

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