Comment by WaxProlix | Hacker News Reader

Comment by WaxProlix | Hacker News Reader

I must be completely in the minority. I enjoy side projects and like the feeling of accomplishment from making something as much as anyone, but AI tools have been a real godsend. I feel reinvigorated, trying out new frameworks, building stuff I'd only sort of considered before. Maybe this is still honeymoon phase but it's been months and I'm still digging it.

Bring on the version that can support my coworker's 7 year sideproject 80k line rathole of python metaprogramming and tell me how to make a small change there, bring on the version that can understand version conflicts between react, react native, and expo libraries - or even fix their shit documentation in real time.

Even if the core product of these LLMs doesn't get any better, I'm stoked to see the wrappers and fine-tunes that they produce over the next few years. Maybe I'll get to be a super TPM with a legion of little AI agents to do the things I want and I'll only have to write code when I feel like it.

When you do code for fun, do you usually enjoy the coding, or seeing what you coded? I.e. when is it the most fun: when you type the code, or when you run your code?

My pet theory is that there are two kinds of (hobby) coders: those who like making things, and those who like making things. One like myself is more excited about the mental task of wrestling out the minutiae of coding. Like making types agree or implementing an algorithm. The other is more excited about the product of the labor rather than the labor itself.

For me as a person who likes writing code as a mental relaxing excercise where sketching out a set of domain types or implementing an algorithm from a textbook is the fun part, and actually seeing the result and "banging out the remainnig issues" is the part that reminds me of my day job, I think AI helpers just help me with the wrong bit. It makes me do code reviews, bug hunting and nagging. But that's already my day job...

I should add that I never do anything with lots of boilerplate (e.g. no web dev). If I ever needed to touch anything with js/html/css/containers/yaml/sql/ then the AI would probably not just be helpful but completely necessary.

There is making things, and there is making things as if you have a decade of experience in every single tool and framework and library that you’re using. The latter feels way better.

ChatGPT and Copilot makes the programming experience feel like the latter. Despite me having never used any of these libraries or tools before. The code just flows out smoothly.

This does not match my experience… Copilot produces code that I would be fine with coming from a junior, but I’ve never seen it do something more than one or two lines that doesn’t have serious issues or bad practices.

Copilot seems to have vacuumed up all the “quick hack to make this work” code from blogspam and bootcamp-final-project type open source repos, but I’m in an area absolutely flooded with that kind of code (typescript/react) so maybe it’s better with other kinds of code. Typescript/react is so bad now that google searches often return blog posts full of confidence and issues.

A few times it’s generated bugs that I couldn’t even catch by reading but it blows up spectacularly when scaled up even a little bit.

I like this theory. This explains the surprise others manifest when I tell them about my pet projects not receiving updates in months. My reasoning "they work already as expected" somehow doesn't strike any chord in them - I'm the one making things while they would fiddle constantly making things.

Yeah I guess it's depending what the goal is. When I do hobby coding in a new language then it's usually for learning. But obviously then I'm not going to be good enough to create much that is useful for anyone else (even me).

If I code-as-meditation or code-as-a-concept-thinking it's more like "could I make a synthesizer?" or "how do B-trees work? Could I make a simple database?". And the coding is part of figuring out how something works or if I'm capable of doing it, rather than actually getting a working synth out of it.

I just want to make, not have a thing. Given the choice of completing the first project or starting a new one, I'd rather just work to the stage that I understand that it could be done in theory, or that I could do it personally (if I invested way too much time), than actually do it.

Copilot is like a kinda stupid, but rally eager and fast Junior intern.

If you let it guide your code structure or your architecture, you will end up with a mess very quickly.

But if you use it as autocomplete on Steroids, it can really speed you up.

I've been doing design iteration and first pass with ChatGPT-4, then me and Copilot take it from there to nearly done, and then I'll often just copy and paste back to ChatGPT to ask for review and thoughts.

Most of the benefits come from taking the time to write out a design or description for the function I'm writing, but sometimes ChatGPT makes useful contributions.

What an age we live in. I figure I'll be just about adapted to these tools by the time GPT-5 comes out.

Yeah the only thing I don’t like about GPT-4 powered CoPilot is that its context window is too small. Same feelings here.

I’m a product engineer at heart. I love getting products off the ground and love tackling the “hard science” kernels of value which GPT can’t help with. Everything else is just implementation and while I agree it’s necessary to know programming and databases and networking or else you’re doomed following the pied piper of LLMs, I don’t find the rote implementation to be the fun parts of what I do - I’m happy to use GPT to ease the burden.

One of my favorite aspects of coding a project from the ground up is designing the database schema, anticipating how things will need to scale. To me it's something better done first with paper and pencil. Then I'm constantly discovering the ways in which I need to reconsider parts of the data structure as I begin writing and tuning queries, figuring out where bottlenecks will be. I don't consider it "just implementation" in the way I do, like, writing UI components or user action trees. My concern is that if I didn't go through that process, mentally and on paper, but took GPT's suggestions at some turns, I would never really have the handle on the strengths and weakness of the underlying model that I have from knowing every nut and bolt. Which would likely lead to all the software I wrote on top of it being less robust than it could be.

Same for me. I am learning new software languages with a fraction of the effort that was required even just one year ago. If I don’t understand something or need help I just ask and usually I get the help I need.

This is especially beneficial for harder languages like Rust. Although I will admit that Copilot did not work very well with rust a few months ago but seems to be better now.

I've been enjoying `cody` for exploring old open source code bases.

https://docs.sourcegraph.com/cody/overview

I've not used the copilot like features, but asking questions has been very useful.

Copilot certainly has expanded what I feel comfortable tackling. Now, writing a simple SPA with some modern framework, is within reach for a person with very limited knowledge in JS, CSS, HTML and React.

I think your experience is common.

I have been a paid AI practitioner since 1982, and everything feels different now.

I have CoPilot always on in Emacs. I use Bing/ChatGPT, ChatGPT, and Bard to bounce ideas off of and research. I used MidJourney t9 create the cover for my last book Safe For Humans AI.

I can’t imagine not having these tools handy.

Copilot as well as chatgpt have helped me so much in learning a framework that I need to know at work. It's just amazing for entry level stuff. Of course it's not always correct but it can help so much if you already have some knowledge and can direct it into the correct path

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