221–230 of 1001 posts
Well that's a way to put it. But not everyone enjoy the art only for the results.
I personally love learning, and by letting AI drive forward and me following, I don't learn. To learn is to be human.
So saying the fun is untouched is one-sided. Not everyone is in it for the same reasons.
> As a programmer, I want to write more open source than ever, now. I want to write less, just knowing that LLM models are going to be trained on my code is making me feel more strongly than ever that my open source contributions will simply be stolen. Am I wrong to feel this? Is anyone else concerned about this? We've already seen some pretty strong evidence of this with Tailwind.
I'm convinced that LLMs results in all software needing to be open source (or at the very least source available). In future everyone will expect to be able to customise an application, if the source is not available they will not chose your application as a base. It's that simple. The future is highly customisable software, and that is best built on open source. How this looks from a business perspective I think we…
This seems unlikely. It's not the norm today for closed-source software. Why would it be different tomorrow?
Earlier quoted context omitted.
> What I don't understand about this whole "get on board the AI train or get left behind" narrative, what advantage does an early adopter have for AI tools? The ones pushing this narrative have either the following: * Invested in AI companies (which they will never disclose until they IPO / acquired) * Employees at AI companies that have stock options which they are effectively paid boosters around AGI nonsense. * Mi…
I'm not an AI booster, but I can't argue with Opus doing lots of legwork > It is no different to the crypto web3 bubble of 2021 web3 didn't produce anything useful, just noise. I couldn't take a web3 stack to make an arbitrary app. with the PISS machine I can. Do I worry about the future, fuck yeah I do. I think I'm up shit creek. I am lucky that I am good at describing in plain English what I want.
With AI companies still selling services far below cost, it's only a matter of time before the money runs out and the true value of these tools will be tested.
> But what was the fire inside you, when you coded till night to see your project working? It was building. I feel like this is not the same for everyone. For some people, the "fire" is literally about "I control a computer", for others "I'm solving a problem for others", and yet for others "I made something that made others smile/cry/feel emotions" and so on. I think there is a section of programmer who actually do…
Earlier quoted context omitted.
The problem I see is not so much in how you generate the code. It is about how to maintain the code. If you check in the AI generated code unchanged then do you start changing that code by hand later? Do you trust that in the future AI can fix bugs in your code. Or do you clean up the AI generated code first?
Depends on what you do. When I'm using LLMs to generate code for projects I need to maintain (basically, everything non-throw-away-once-used), I treat it as any other code I'd write, tightly controlled with a focus on simplicity and well-thought out abstractions, and automated testing that verify what needs to be working. Nothing gets "merged" into the code without extensive review, and me understanding the full scop…
The problem is, that code would require a massive amount of cleanup. I took a brief look and some code was in the wrong place. There were coding style issues, etc.
In my experience, the easy part is getting something that works for 99%. The hard part is getting the architecture right, all of the interfaces and making sure there are no corner cases that get the wrong results.
I'm sure AI can easily get to the 99%, but does it help with the rest?
What I don't understand about this whole "get on board the AI train or get left behind" narrative, what advantage does an early adopter have for AI tools? The way I see it, I can just start using AI once they get good enough for my type of work. Until then I'm continuing to learn instead of letting my brain atrophy.
This is a pretty common position: "I don't worry about getting left behind - it will only take a few weeks to catch up again". I don't think that's true. I'm really good at getting great results out of coding agents and LLMs. I've also been using LLMs for code on an almost daily basis since ChatGPT's release on November 30th 2022. That's more than three years ago now. Meanwhile I see a constant flow of complaints fro…
> But what was the fire inside you, when you coded till night to see your project working? It was building. I feel like this is not the same for everyone. For some people, the "fire" is literally about "I control a computer", for others "I'm solving a problem for others", and yet for others "I made something that made others smile/cry/feel emotions" and so on. I think there is a section of programmer who actually do…
> programmer who actually do like the actual typing It's not about the typing, it's about the understanding. LLM coding is like reading a math textbook without trying to solve any of the problems. You get an overview, you get a sense of what it's about and most importantly you get a false sense of understanding. But if you try to actually solve the problems, you engage completely different parts of your brain. It's a…
Can be, but… well, the analogy can go wrong both ways.
This is what Brilliant.org and Duolingo sell themselves on: solve problems to learn.
Before I moved to Berlin in 2018, I had turned the whole Duolingo German tree gold more than once, when I arrived I was essentially tourist-level.
Brilliant.org, I did as much as I could before the questions got too hard (latter half of group theory, relativity, vector calculus, that kind of thing); I've looked at it again since then, and get the impressions the new questions they added were the same kind of thing that ultimately turned me off Duolingo, easier questions that teach little, padding out a progressions system that can only be worked through fast enough to learn anything if you pay a lot.
Code… even before LLMs, I've seen and I've worked with confident people with a false sense of understanding about the code they wrote. (Unfortunately for me, one of my weaknesses is the politics of navigating such people).
> But what was the fire inside you, when you coded till night to see your project working? It was building. I feel like this is not the same for everyone. For some people, the "fire" is literally about "I control a computer", for others "I'm solving a problem for others", and yet for others "I made something that made others smile/cry/feel emotions" and so on. I think there is a section of programmer who actually do…
Maybe there are people who are about literally typing the code, but I get satisfaction from making the codebase nice and neat, and now I have power tools. I am just working on small personal projects, but so far, Claude Opus 4.5 can do any refactoring I can describe.
> As a programmer, I want to write more open source than ever, now. I want to write less, just knowing that LLM models are going to be trained on my code is making me feel more strongly than ever that my open source contributions will simply be stolen. Am I wrong to feel this? Is anyone else concerned about this? We've already seen some pretty strong evidence of this with Tailwind.
Earlier quoted context omitted.
This is a pretty common position: "I don't worry about getting left behind - it will only take a few weeks to catch up again". I don't think that's true. I'm really good at getting great results out of coding agents and LLMs. I've also been using LLMs for code on an almost daily basis since ChatGPT's release on November 30th 2022. That's more than three years ago now. Meanwhile I see a constant flow of complaints fro…
I don't see how your position is compatible with the constant hype about the ever-growing capabilities of LLMs. Either they are improving rapidly, and your intuition keeps getting less and less valuable, or they aren't improving.
Things that they couldn't do six months go might now be things that they can do - and knowing they couldn't do X six months ago is useful because it helps systematize your explorations.
A key skill here is to know what they can do, what they can't do and what the current incantations are that unlock interesting capabilities.
A couple I've learned in the past week:
1. Don't give Claude Code a URL to some code and tell it to use that, because by default it will use its WebFetch tool but that runs an extra summarization layer (as a prompt injection defense) which loses details. Telling it to use curl sometimes works but a guaranteed trick is to have it git clone the relevant repo to /tmp and look at the code there instead.
2. Telling Claude Code "use red/green TDD" is a quick to type shortcut that will cause it to write tests first, run them and watch them fail, then implement the feature and run the test again. This is a wildly effective technique for getting code that works properly while avoiding untested junk code that isn't needed.
Now multiply those learnings by three years. Sure, the stuff I figure out in 2023 mostly doesn't apply today - but the skills I developed in learning how to test and iterate on my intuitions from then still count and still keep compounding.
The idea that you don't need to learn these things because they'll get better to the point that they can just perfectly figure out what you need is AGI science fiction. I think it's safe to ignore.