Home-Cooked Software and Barefoot Developers

It's fair for the type of code you want to write for your own growth. But even with that, there's more than enough bullshit boilerplate and trivial cross-language differences that contribute zero (or negatively) to your growth, and is worth having someone else, or something else, write it for you. LLMs are affordable for this, where people usually are not.

... and real defects that you never noticed or would've thought of.

> This is not going to help junior developers figure out good from bad.

Neither is them inventing fake defects which do not actually need fixing on their own. What helps juniors is the feedback from more senior people, as well as reality itself. They'll get that either way (or else your whole process is broken, and that has zero to do with AI).

That's a good way to think about it. Treat GPT-4 as having mentality of a 4 year old kid. A kid this age will take any question you ask at face value, because it hasn't learned yet that adults often don't ask questions precisely enough, don't realize the assumptions they make in their requests, don't know what they don't know, and are full of shit. A four year old won't think of evaluating whether or not the question itself makes sense, they'll just do their best to answer it, which may involve plain guessing what the answer could be if one isn't apparent.

Remember that saying "I don't know" isn't an innate skill in humans either - it's an ability we drill into kids for the first decade or two of their lives.

Understatement of the (20th) century!

Some interesting ideas. But it seems to make out that nearly all professional software is developed by mega corporations in California. But there are lots of small software companies, open source developers and hobbyists developing a vast range of apps for different niches. More would be good, though.

You don't need a jet turbine and H100s for that, you need it once for the whole world to get that ability; exercising it costs comparatively little in GPU time. Like, can't say how much GPT-4o takes in inference, but Llama-3 8B works perfectly fine and very fast on my RTX 4070 Ti, and it has a significant enough fraction of the same capabilities.

Speaking of:

> A better compiler helps you reason about errors.

There's only so much it can do. And yes, I've actually set up an "agent" (predefined system prompt) so I can just paste the output of build tooling verbatim, and get it to explain error messages in it, which GPT-4 does with 90%+ accuracy. Yes, I can read and understand them on my own. But also no, at this point, parsing multiple screens of C++ template errors or GCC linker failures is not a good use of my life.

(Environment-wise, I'm still net ahead of a typical dev anyway, by staying away from Electron-powered tooling and ridiculously wasteful modern webdev stacks.)

> A better language reduces boilerplate.

Yes, that's why everyone is writing Lisp, and not C++ or Java or Rust or JS.

Oh wait, wrong reality.

> Better language features help you be more expressive.

That's another can of worms. I'm not holding much hopes here, because as long as we insist on working directly on plaintext codebase treated as single source of truth, we're already at Pareto frontier in terms of language expressiveness. Cross-cutting concerns are actually cross-cutting; you can't express them all simultaneously in a readable way, so all the modern language design advances are doing is shifting focus and complexity around.

LLMs don't really help or hurt this either, though they could paper over some of the problem by raising the abstraction level at which programmers edit their code, in lieu of the tooling actually being designed to support such operations. I don't think this would be good - I'd rather we stopped with the plaintext single-source-of-truth addiction in the first place.

> Its appearance as a silver bullet withers the closer you get to essential complexity.

100% agreed on that. My point is, dealing with essential complexity is usually a small fraction of our work. LLMs are helpful in dealing with incidental complexity, which leaves us more time to focus on the essential parts.