But all work isn't done by LLMs at the moment and we can't be sure that it will be so the question is ridiculous.

Maybe one day it will be.. And then people can reevaluate their stance then. Until that time, it's entirely reasonable to hold the position that you just don't

This is especially true with how LLM generated code may affect licensing and other things. There's a lot of unknowns there and it's entirely reasonable to not want to risk your projects license over some contributions.

I use them all the time at work because, rightly or wrongly, my company has decided that's the direction they want to go.

For open source, I'm not going to make that choice for them. If they explicitly allow for LLM generated code, then I'll use it, but if not I'm not going to assume that the project maintainers are willing to deal with the potential issues it creates.

For my own open source projects, I'm not interested in using LLM generated code. I mostly work on open source projects that I enjoy or in a specific area that I want to learn more about. The fact that it's functional software is great, but is only one of many goals of the project. AI generated code runs counter to all the other goals I have.

Basically all of my actual programming work has been done by LLMs since January. My team actually demoed a PoC last week to hook up Codex to our Slack channel to become our first level on-call, and in the case of a defect (e.g. a pagerduty alert, or a question that suggests something is broken), go debug, push a fix for review, and suggest any mitigations. Prior to that, I basically pushed for my team to do the same with copy/paste to a prompt so we could iterate on building its debugging skills.

People might still code by hand as a hobby, but I'd be surprised if nearly all professional coding isn't being done by LLMs within the next year or two. It's clear that doing it by hand would mostly be because you enjoy the process. I expect people that are more focused on the output will adopt LLMs for hobby work as well.

> It's clear that doing it by hand would mostly be because you enjoy the process.

This is gaslighting. We're only a few years into coding agents being a thing. Look at the history of human innovation and tell me that I'm unreasonable for suspecting that there is an iceberg worth of unmitigated externalities lurking beneath the surface that haven't yet been brought to light. In time they might. Like PFAS, ozone holes, global warming.

Ultimately you always have to trust people to be judicious, but that's why it doesn't make any changes itself. Only suggests mitigations (and my team knows what actions are safe, has context for recent changes, etc). It's not entirely a black box though. e.g. I've prompted it to collect and provide a concrete evidence chain (relevant commands+output, code paths) along with competing hypotheses as it works. Same as humans should be doing as they debug (e.g. don't just say "it's this"; paste your evidence as you go and be precise about what you know vs what you believe).

That's sounds like the perfect recipe for turning a small problem into a much larger one. 'on call' is where you want your quality people, not your silicon slop generator.

Sounds like a company on the verge of creating a mess that will require a rewrite in a year or so. Maybe an llm can do it.

I suspect this is more true than most people think. Today's bad code will be cleaned up by tomorrow's agents.

The other factor that gets glossed over is that llms create a financial incentive to create cleaner code, with tests, because the agent that you pay for will be more efficient when the code is easier to understand, and has clear patterns for extensibility. When I do code with llms, a big part of it is demonstration, i.e. pseudocoding a pattern/structure, asking the model if it understands, and then having it complete the pattern. I've had a lot of success with this approach.

> llms create a financial incentive to create cleaner code, with tests, because the agent that you pay for will be more efficient when the code is easier to understand, and has clear patterns for extensibility

Right, this is the kind of discussion we're having on my team: suddenly all of the already good engineering practices like good observability, clear tests with high coverage, clean design, etc. act as a massive force multiplier and are that much more important. They're also easier to do if you prioritize it. We should be seeing quality go up. It's trivial to explore the solution space with throwaway PoCs, collect real data to drive your design, do all of those "nice to have" cleanups, etc. The people who assume LLM = slop are participating in a bizarre form of cope. Garbage in, garbage out; quality in, quality out. Just accept that coding per se is not going to be a profession for long. Leverage new tools to learn more, do more, etc. This should be an exciting time for programmers.

> It's clear that doing it by hand would mostly be because you enjoy the process.

This will not happen until companies decide to care about quality again. They don't want employees spending time on anything "extra" unless it also makes them significantly more money.

> just like almost all transportation is done today via cars instead of horses.

That sounds very Usanian. In the meantime transportation in around me is done on foot, bicycle, bus, tram, metro, train and cars. There are good use cases for each method including the car. If you really want to use an automotive analogy, then sure, LLMs can be like cars. I've seen cities made for cars instead of humans, and they are a horrible place to live.

Signed, a person who totally gets good results from coding with LLMs. Sometimes, maybe even often.

I say let people hold this stance. We, agentic coders, can easily enough fork their project and add whatever the features or refinements we wanted, and use that fork for ourselves, but also make it available for others in case other people want to use it for the extra features and polish as well. With AI, it's very easy to form a good architectural understanding of a large code base and figure out how to modify it in a sane, solid way that matches the existing patterns. And it's also very easy to resolve conflicts when you rebase your changes on top of whatever is new from upstream. So, maintaining a fork is really not that serious of and endeavor anymore. I'm actually maintaining a fork of Zed with several additional features (Claude Code style skills and slash commands, as well as a global agents.md file, instead of the annoying rules library system, which I removed, as well as the ability to choose models for sub-agents instead of always inheriting the model from the parent thread; and yes, master branch Zed has subagents! and another tool, jjdag)

That seems like a win-win in a sense: let the agentic coders do their thing, and the artisanal coders do their thing, and we'll see who wins in the long run.

Well at least you, agentic coders, already understand they need to fork off.

Saves the rest of us from having to tell you.

> We, agentic coders, can easily enough fork their project

And this is why eventually you are likely to run the artisanal coders who tend to do most of the true innovation out of the room.

Because by and large, agentic coders don't contribute, they make their own fork which nobody else is interested in because it is personalized to them and the code quality is questionable at best.

Eventually, I'm sure LLM code quality will catch up, but the ease with which an existing codebase can be forked and slightly tuned, instead of contributing to the original, is a double edged sword.

"make their own fork which nobody else is interested in because it is personalized to them"

Isn't that literally how open-source works, and why there's so many Linux distros?

Code quality is a subjective term as well, I feel like everyone dunking on AI coding is a defensive reaction - over time this will become an entirely acceptable concept.

For a human to be able to do any customization, they have to dive into the code and work with it, understand it, gain intuition for it. Engage with the maintainers and community. In the process, there's a good chance that they'll be encouraged to contribute improvements upstream even if they have their own fork.

Vibe coders don't have to do any of this. They don't have to understand anything, they can just have their LLMs do some modifications that are completely opaque to the vibe coder.

Perhaps the long term steady state will be a goldilocks renaissance of open source where lots of new ideas and contributors spring up, made capable with AI assistance. But so far what I've seen is the opposite. These people just feed existing work into their LLMs, produce derivative works and never bother to engage with the original authors or community.

> Vibe coders don't have to do any of this. They don't have to understand anything, they can just have their LLMs do some modifications that are completely opaque to the vibe coder.

I spend time using my agent to better understand existing codebases and their best practices than I'd ever have the time/energy to do before, giving me a broader and more holistic view on whatever I'm changing, before I make a change.

Okay, but you don't have to - and "efficient" coders won't bother, thus starving the commons.

Well, I would argue that if I didn't spend that time, then even a personal fork that I vibe coded would be worse, even for me personally. It would be incompatible with upstream changes, more likely to crash or have bugs, more difficult to modify in the future (and cause drift in the model's own output) etc.

I always find it odd that people say both that vibe coding has obvious and immediate negative consequences in terms of quality and at the same time that nobody could learn or be incentivized to produce better architecture and code quality from vibe coding when they would obviously face those consequences.

I think that in the long run, AI assisted coding will turn out to be better than handcrafted code. When you pay for every token, and code generation is quick, a clean, low entropy codebase with good test coverage gets you a lot more for your dollar than a dog's breakfast. It's also much easier to fix bad decisions made early on in a project's life, because the machine is doing all of the heavy lifting.

This also lines up with the history of automation in many other industries. Modern manufacturing is capable of producing parts that a medieval blacksmith couldn't dream of, for example. Sure, maybe an artisan can produce better code than an llm now, but AI assisted humans will beat them in the near future if they aren't already producing similar quality output at greater speed, and tomorrow's models will fix the bad code written today. The fact that there's even a discussion on automated vs hand written today means that the writing is almost certainly on the wall.

You mean like I have to pay my compiler to turn high level code into low level code?

Maybe! Or maybe there is really a competitive advantage to "artisanal" coding.

Personally, I would not currently expect a fork of RedoxOS that is AI-implemented to become more popular than RedoxOS itself.

Indeed, maybe there is. I'm interested to see how it plays out.

I mean, I do open PRs for most of my changes upstream if they allow AI, once I've been using the feature for a few weeks and have fixed the bugs and gone over the code a few times to make sure it's good quality. Also, I'm going to be using the damn thing, I don't want it to be constantly broken either, and I don't want the code to get hacky and thus incompatible with upstream or cause the LLMs to drift, so I usually spend a good amount of time making sure the code is high quality — integrates with the existing architecture and model of the world in the code, follows best practices, covers edge cases, has tests, is easy to read so that I can review it easily.

But if a project bans AI then yeah, they'll be run out of town because I won't bother trying to contribute.

Most "artisanal" coders that are complaining are working on the n-1000th text editor, todo list manager, toy programming language or web framework that nobody needs, not doing "true innovation".

>> but also make it available for others in case other people want to use it for the extra features and polish as well.

this feels like the place where your approach breaks down. I have had very poor results trying to build a foundation that CAN be polished, or where features don't quickly feel like a jenga tower. I'm wondering if the success we've seen is because AI is building on top of, or we're early days in "foundational" work? Is anyone aware of studies comparing longer term structural aspects? is it too early?

I've been able to make very clear, modular, well put together architectural foundations for my greenfield projects with AI. We don't have studies, of course, so it is only your anecdote versus mine.

> We, agentic coders, can easily enough fork their project and add whatever the features

Bold of you to assume that people won’t move (and their code along with it) to spaces where parasitic behaviour like this doesn’t occur, locking you out.

In addition to just being a straight-up rude, disrespectful and parasite position to take, you’re effectively poisoning your own well.

Since when is maintaining a personal patch set / fork parasitic? And in what way does it harm them, such that they should move to spaces where it doesn't happen, as a result? Also, isn't the entire point of open source precisely to enable people to make and use modifications of code if they want even if they don't want to hand code over? Also, that would be essentially making code closed source — do you think OSS is just going to die completely? Or would people make alternative projects? Additionally, this assumes coders who are fine with AI can't make anything new themselves, when if anything we've seen the opposite (see the phenomenon of reimplementing other projects that's been going around).

Additionally, if they accept AI contributions, I try, when I have the time and energy, make sure my PRs are high quality, and provide them. If they don't, then I'll go off and do my own thing, because that's literally what they asked me to do, and I wasn't going to contribute otherwise. I fail to see how that's rude or parasitic or disrespectful in any way except my assumption that the more featureful and polished forks might eventually win out.

Its only parasitic if you are tricking users into thinking you are the original or providing something better. You could be providing something different (which would be valuable) but if you are not, you are just scamming users for your own benefit.

I have no intention of tricking anyone into thinking I'm the original! I do think I offer improvements in some cases, so in cases where the project is something I intend for other people to ever see/use, I do explain why I think it is better, but I also will always put the original prominently to make sure people can find their way back to that if they want to. For example, the only time I've done this so far:

https://github.com/alexispurslane/jjdag

As someone who enjoys working with AI tools, I honestly think the best approach here might be bifurcation.

Start new projects using LLM tools, or maybe fork projects where that is acceptable. Don't force the volunteer maintainers of existing projects with existing workflows and cultures to review AI generated code. Create your own projects with workflows and cultures that are supportive of this, from the ground up.

I'm not suggesting this will come without downside, but it seems better to me than expecting maintainers to take on a new burden that they really didn't sign up for.

That would only be a world where the copyright and other IP uncertainties around the output (and training!) of LLMs were a solved and known question. So that's not the world we currently live in.

The ruling capital class has decided that it is in their best interest for copyright to not be an obstacle, so it will not be. It is delusional to pretend that there is even a legal question here, because America is no longer a country of laws, to the extent that it ever was. I would bet you at odds of 10,000 to 1 that there will never be any significant intellectual property obstacles to the progress of generative AI. They might need to pay some fines here and there, but never anything that actually threatens their businesses in the slightest.

There clearly should be, but that is not the world we live in.

even if this was true or someday will be (big IF), is it worth looking for valid counter workflows? example: in many parts of the US and Canada the Mennonites are incredibly productive farmers and massive adopters of technology while also keeping very strict limits on where/how and when it is used. If we had the same motivations and discipline in software could we walk a line that both benefited from and controlled AI? I don't know the answer.

Good one, I had not made the connection, but yes. Tech is here to serve, at our pleasure, not to be forcibly consumed.

I don't see any cars racing in the Melbourne Cup.