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Comment by jsty | Hacker News Reader

> Then "explain why <my code> needs to be <new code>"

It would be nice, but until there's a breakthrough on having LLMs / other models generate, maintain and walk the user through a representation of their reasoning, these explanations are likely no more reliable than a random StackOverflow answer - except without the benefit of other users scoring / picking holes in the answer

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Have you tried it yet? It is significantly better than a stackoverflow answer. I've been using copilot since release and have now integrated chatgpt into that workflow (thankfully copilot using chatgpt 4 and some tricks I figured out to treat copilot more like a chat bot are making it so I can avoid the chatgpt browser).

It may be significantly better than a random stackoverflow answer, but I haven’t seen it as being better than the top-voted stackoverflow answer (at least not without significant curation/prompting).

i think maybe you're using ai wrong. its not akin to stackoverflow answers, its more akin to a jr programmer that can bang out some code for you while you go to the kitchen or bathroom and then you fix a few mistakes and integrate it with your larger project

I have played with it a small amount. If I need to center a div in CSS, it’s faster than me remembering how to do that.

For anything significantly higher complexity, I don’t find that typing is anywhere near the rate-limiting factor. Figuring out how to decompose the problem, how the code should be structured overall, and other high-level concerns take far time/effort than banging out boilerplate.

Now, if I wanted to pretend to be a junior developer for three different companies at once, it would be a heaven-sent technological marvel.

interesting takes. i guess if you're using ai for basic things like that it makes sense to not like it

Enlighten me, please. What’s the killer use case where you’ve seen significant leverage from an AI tool? What was the business problem and what were the resulting prompts to your AI junior dev?

i am trying to solve many business problems as I am building apps that allow people to use stable diffusion and LLMs locally on their own hardware without the need to install anything and i'm doing it with compiled python that I have to ensure runs on various machines. this means i have to support customers. I'm also making a couple of small games. I also have to handle marketing.

the killer use case is augmenting my own workflow. i have fully integrated ai into my development pipeline. i am using chatgpt and i am using copilot. I almost never find myself turning to any other resource for information other than occasionally having to reference documentation for very obscure libraries. I also use an IDE.

this gives me time to concentrate on things such as my ci/cd pipeline, architecture, UX, testing, solving how to install bitsandbytes and triton on windows and other difficult problems related to build processes that don't have clear cut solutions.

I see a lot of skepticism like this on hackernews. People constantly downplaying how impressive this technology is. It's great to be critical. But most of the criticism is too stuck in the present. If it's already as good as many stackoverflow answers, what kind of potential will we see in 1 year, 3 years, a decade. This is massive paradigm shift in the way we program and this conversation thread will be look outdated in a year. So much of the tedium of programming has been eliminated in my day to day. I actually have ideas I pursue now that I know so much friction in the setup process is gone and I won't be severely blocked by bugs.

As a skeptic, this reads like "look how great Google search is at finding stackoverflow answers already; imagine how good it will be in a decade!"

i keep emphasizing this: i don't think we'll be programming any more down the line, i think we'll be engineering with an AI assistant like tony stark. obviously i could be mistaken but it looks like things are heading in that direction.

Have you tried giving it the problem statement and asking for a high level outline of the code and then iterate on that until it’s right and then ask it to generate pseudo code for one item at a time, correct that and then generate the code?

I get a lot of "ok, I've changed this, this and this because X, Y and Z" style answers even without prompting. It really wants to explain what it's doing, to the point where I have to sometimes say just shut up and give me the code. (But politely. Gotta be polite to our future AI overlords).

My theory is that people working inside frameworks (like me) are getting significantly better answers than people doing "freehand" coding because the training data's so much better in framework-world.

No further breakthroughs are necessary, if you're experienced enough you have enough general context to tell when the advice is good and it's helping you understand a new subject and it absolutely is great at walking you through understanding something new in a broader domain you're already familiar enough with to detect when it's lost.

Yeah but you can run it, profile it and execute your test suite on it. If it passes and if it is more performant, aren't you done? I can try all the stack overflow answers and just pick the best one now.

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