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Comment by gridspy | Hacker News Reader

The word "understand" seems to mean something different to you.

If I understand something, I could write it in assembly if I wanted to. It might take a long time, but I know every level of the stack under my code down to bare metal.

Maybe an AI level of "understand" i.e the same understanding a Senior has of a Junior's code based on daily check-ins is enough for 95% of "boring" programming. But for some tasks you need to either fully understand the code or just tolerate bugs.

At the level of complexity I work at, it's (often) faster to just code it myself than to expect AI to converge on a result I like and then hand check it.

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> At the level of complexity I work at, it's (often) faster to just code it myself than to expect AI to converge on a result I like and then hand check it.

Would love an example because no one has ever been able to give a coding example that AI isn’t helpful for. I had one person on linked in try claim their undocumented audio hardware won’t work with ai but when we got ai to probe it and build docs it ended up solving a bunch of complex bugs they couldn’t fix.

Impressive, also horrifying. I love what I do and if AI can do it better than that sucks.

Anyway the use case for me is to realize a new visual style through graphics programming. It's a lot less measurable for an interative AI agent than "Meet this hardware specification from a device with a discoverable API"

I have no doubt AI could create a LOT of variations on "a new visual style" but it's less controllable than just doing it yourself.

Btw, did you "understand" the sound driver after the AI coded it? Could you modify it without further help?

Right now, at least, I haven't found an AI capable of replacing a software engineer. Ive seen AI that can easily replace basic programmers, however.

I also think AI can replace non-coding artitects, and probably most middle management type jobs (my company has 6 levels of management between the CEO and "individual contributors" in my area (8 counting inclusivity)).

That's a lot of management levels, and every level has to be paid more than the level they manage as a fraud disincentive. So that's a lot of money...

Can you explain the fraud disincentive bit? I feel like there's something obvious I'm missing, but why would a manager earning less than their reports make fraud more likely?

what a patronizing response. I'd be embarrassed.

I guess you didn't understand my comment ;)

Look, goals can differ. If you don't need to understand and predict every part of the code the AI is generating and you just need it to meet a "sketch" of what you want - by all means use AI. I do use AI in that situation for related or unimportant code.

But if you need full understanding, in my experience the only way to get that is to program it yourself. Unless what the AI is generating is so trivial you already understand it and it's grunt work, you will learn the detail by doing it yourself. Controlling the approach here is important.

However often the best way to handle grunt work is to write better abstractions, something AI sucks at.

It's the programming equivalent of many PG essays. https://www.paulgraham.com/useful.html for instance. There was one on how PG refined their thoughts via writing. It's in there somewhere.

> However often the best way to handle grunt work is to write better abstractions, something AI sucks at.

If you checked out of ai in 2023 then this is true. It’s simply not true anymore. If you struggle then it’s a skill issue not an AI issue

As someone who spends way too much time searching for the best abstractions, absolutely this is still true.

I often have a long back and forth with codex to explore the problem space and settle on the best abstractions. Occasionally it will make a suggestion that helps me, but for the most part it's reviewing while I'm in the driver's seat.

Contrast this to simply giving it a function name and a vague description of what the function will do. I'll generally accept its output with a few refinements.

But for larger project structure and metaphors, it falls flat, and often lands on a solution that's going to be a maintenance nightmare or result in endless repetition across not-quite-the-same cases. I've never seen it happen upon an appropriate abstraction that can cleanly cut through the nonsense.

That's my experience too. Its unfair to expect current AI to do this - since good abstractions are extremely task specific. Hard to train on that.

What's also annoying is that AI's approach is not consistent within a project, a different sort of complexity.

> I guess you didn't understand my comment ;)

yeah, touch grass buddy.

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