Comment by Kiro | Hacker News Reader

Comment by Kiro | Hacker News Reader

I'm of the opposite opinion: I've started enjoying programming much more after embracing LLMs.

* They are great for overcoming procrastination. As soon as I don't feel like doing something or a task feels tedious I can just delegate it to an LLM. If it doesn't solve it outright it at least makes me overcome the initial feeling of dread for the task.

* They give me better solutions than I initially had in mind. LLMs have no problem adding laborious safeguards against edge-cases that I either didn't think of or that I assessed wouldn't be worth it if I did it manually. E.g. something that is unlikely and would normally go to the backlog instead. I've found that my post-LLM code is much more robust from the get go.

* They let me try out different approaches easily. They have no problem rewriting the whole solution using another paradigm, again and again. They are tireless.

* They let me focus on the creative parts that I enjoy. This surprised me since I've always thought of myself as someone who loves programming but it turns out that it is only a small subset of programming I love. The rest I'm happy to delegate away.

> This surprised me since I've always thought of myself as someone who loves programming but it turns out that it is only a small subset of programming I love.

I am the same, and why many of my personal projects end up stranded. Once I've solved the tricky bit, the rest often isn't that motivating as it's usually variations on a common theme.

I held off LLMs for a long time, but recently been playing with them. They can certainly confidently generate junk, but in most cases it's good enough. And like you say can be used as a driver to keep going. In that regard they can be useful.

This is exactly how I use LLMs - I can automate the really boring parts. "Can you write me a Swift codable struct for the following JSON" will save my fingers and precious mental energy for the important and interesting parts.

It's like having a junior dev that doesn't complain and gets the work done immediately.

AI code suggestions as I type are however a different beast. It's easy to introduce subtle bugs when the suggestion "kinda looks right" but in fact the LLM had zero understanding of the context because it can't read my mind.

Same, these are all great points that I find as well. LLMs have made me a way more productive programmer, but a lot of that is because I already was an alright programmer and know how to take advantage of the strengths and weaknesses of the LLM. I think your last bullet point is most poignant, using Claude 3.5 I've been able to do tons of GUI and web programming, things I absolutely despise and refuse to do if I'm writing code by hand.

I sort of understand some of the vitriol that I see on HN but it is incredibly overblown. I don't really get a lot of the criticisms. LLMs aren't deterministic? Neither are humans. LLMs write bugs they can't fix? So do humans. LLMs are only good at being junior programmer copy paste machines? So are lots of humans.

My current project is training an LLM to do superoptimization and it's working exceedingly well so far. If you asked anyone on hacker news if that's a good idea, they'd probably say no.

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