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Discussion (40 Comments)Read Original on HackerNews
The reality for most people - even expert programmers - has been that the freedom is more about being able to lean on other people to do that. Most people can't justify the time commitment needed to read and then modify the code for tools they use very often.
I think LLMs have changed that equation in a way that makes the original dream much more feasible.
Several times a day I'll prompt regular Claude chat to "Clone x/y from GitHub and tell me how Z works".
Getting software to compile in order to start hacking on it used to be enough friction that I often wouldn't bother. Now I treat that as a zero time investment challenge: tell Codex or Claude Code to checkout and build X and then come back ten minutes later and see how it got on.
I'm not habitually modifying the software I use yet, but I can see a path to that which didn't exist a year or so ago.
> Several times a day I'll prompt regular Claude chat to "Clone x/y from GitHub and tell me how Z works".
Still highly dependant on one's access to SOTA AI models (availability and funding). Most people praising LLMs publicly for this sort of use case, are the ones with unlimited access to tokens / AI credits, or simply with a lot of money to burn.
But reality is that between using one's limited employer-sponsored quota of tokens to do their 9-5 business logic coding maintenance job, versus exploring 3rd-party software as end-users, I am sure of which one their managers will prefer.
I believe it will eventually happen, whether with SOTA local models on highly capable local hardware, or super cheap inference APIs... or both.
The current LLM-driven stuff seems to break down the expectations, and now there are a lot of places which just ignore anything I send in (the same tinkers as before), generally, for a while. Though there are some tools that picked up the pace and actually react faster (so YMMV here too).
But there are few things more frustrating as being half-way. Case to point is LM Studio. It's closed source, has bugs (duh!), and there's at least a GitHub issue tracker to report the bugs -- but then by and large nothing happens to those reported things. It's almost worse than not having an issue tracker (then I could justify never to really touch LM Studio again, this way I keep hoping against hope that reports will turn into fixes and thus I keep using and keep reporting...)
I used AI to help me make a change to a library I use that ended up being a single line of code. The pull request was refused for having a Claude attribution even though it was a single code change that was syntax only with no change in behavior just to make it compatible with an older version of the language they claimed to support (so I considered this incompatibility a bug).
Ended up figuring out a workaround in our application code instead just so we could use the upstream. Still not really worth the effort for most projects even with AI.
In the era of AI slop being bombarded at you from all sides 24/7 I don’t blame them. Gatekeeping is the only way to maintain a semblance of quality (and sanity). Once you let slop in it accumulates at an insane rate. You need zero tolerance or you will be overwhelmed.
It seems like a great stretch to call this "slop".
The canonical example was RMS needing to fix a printer driver bug back before software updates were really a thing. That meant that there was a greater motivation for users to maintain their changes.
Today, with software being an evergreen stack of turtles from the BIOS all the way up the OS to the remote APIs the qpp interacts with, expecting a user to indefinitely maintain their private fork of any software is a really big stretch.
Unless it is a local-only, unmaintained project that doesn't get deployed through a software repository or an app store, I think that original vision of open source software is very much the exception and not the rule.
I currently spend $100/month on OpenAI and $200/month on Anthropic.
(I often get free tokens as part of preview access to models, but I'm not allowed to share any code written by those models online for the duration of the preview. Most of my work is open source, so I can't actually do much with those free tokens!)
That's just so inefficient and wasteful. Assuming a world in which LLMs do most of the coding work, do we want to burn electricity having the LLM build an options dialog or config file parser once, or do we want to burn electricity millions of times as users want to change any little thing about the software they use?
I really hope what you should expect is my answer to that question isn't controversial.
Having an LLM do bespoke customizations that are unlikely to be interesting to other people? Great, sure. But adding a generally-useful feature to a piece of software, but not caring to try to upstream it? Lame. Lame, lame, lame.
https://dwm.suckless.org/customisation/
Remember you are not working alone. Different people/companies work differently, but nearly every job has - at least once in a while - a time when someone will be ask your computer reading something on your screen with you. vi vs emacs is bad enough, but at least I know how to do minimal navigation, save, and exit from each (I expect that advanced users of those tools can do much more): there are only so many different systems I can learn though, and it is unfair to make everyone have to learn another. Thus emacs should not allow your to customize your environment with local scripts, either it is in base (as IT installed it), or it isn't allowed! (to my knowledge vi/vim doesn't allow this). Similar for other tools, if it isn't easy for someone who isn't an expert: either your company shouldn't allow it at all, or they need to ensure everyone is encouraged to get training (not forced in case you wouldn't do that anyway - but it should be automatic if you want it and you should know you can get it)
Font size - I've worked with nearly blind people who need huge fonts just to see/read. However this is a compromise and they are worse developers for lack of ability to see as much text as a smaller font enables. Don't get me wrong, they are still great developers, but their eyes are a limit to how good they can be. It is thus reasonable to have a configurable accommodation for that. There are a number of other examples where things can be configurable, but the configurations should be controlled and easy to find. (I'm color blind, if you allow color coding that is fine so long as I can find a color that works for me)
I do think modularity will remain important because it makes merge conflicts less likely, and a system with good modularity could look a lot like a plugin system, except that AI can likely deal with minor compatibility breaks if it’s clear what to do.
It’s a similar argument to why Linux device drivers are in-tree. If there are API changes then the AI fixes the callers. Compile-time checks are good because it’s direct feedback to the AI.
[1] https://www.zdnet.com/home-and-office/networking/mozilla-cha...
I deeply disagree with the light vs dark framing. I think the real tension between "hacker languages" and "blub languages" is the expectation to modify the language by it's user.
And this is true for dev tools as well. Hackers prefer modifiable environments (such as Emacs), while corporations prefer standard environments (IDEs) to which programmers will adapt.
(As the saying goes: "The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man." Might be why Paul Graham preferred founders with non-blub languages after all, nothing to do with Lisp as such.)
But to be able to adapt the environment, one must understand it. Therefore the pull to make it simpler, a set of universal tools, rather than a complicated machine with many features and ease of use through following prescription.
This sounds like hell. You have unreliable actor redoing the software every night, and every day there is a chance you wake up and find your workflow broken.
And no, "Check that the software works as intended" is not going to cut it, as AI are very, very good at obeying the letter but not the spirit of the ask. Yes, your diffs appear but they lack filenames. You've added this requirements to the prompt? Ok, filenames are back but they are font size 4, unreadably small. You want them well-seen? next week they become size 54, taking entire screen.. This is not a problem with regular AI development - you review the changes and test them a bit. But doing it day-to-day with no overview is just asking for trouble.
This might not generalize beyond developers, or at least not right away. Even if we’re not doing the upgrades ourselves, we know what a merge conflict is, and we’re probably better at telling the AI what went wrong so it can fix it.
These don’t seem like skills that are all that hard to learn for someone who’s interested. Also, merge conflicts an AI can’t deal automatically likely mean something major changed upstream.
There will be jobs helping people who get into more trouble than they can dig themselves out of. And if you can’t afford that, I guess it’s a learning experience.
I've been making a couple little utils like a Clock for AI with an open public endpoint [https://github.com/firasd/mcpclock], a text file sampler [https://github.com/firasd/vblinds] and I was noticing the MIT license seems too encumbered with the requirement to keep crediting authors downstream so I used the 'Unlicense' public domain license
(CC-0 seems to be side-eyed by open source orgs cause it preserves patent rights)
I think we're seeing a new modality of openness now with open weights, or being able to modify agents with md files, skills, and so forth.
Now we're getting to the point where any user can say "Claude, I don't like that. Please fix" and have it fixed. Configurability/extensibility can work in some cases, but having source code makes it much more powerful.
That aside aside, I agree. Since reading this article yesterday I've probably been overthinking an MIT from AGPL license switch for my own project Ouijit (shill time: https://ouijit.com). Its feels a little counterintuitive since AGPL encourages more open source downstream, but at the same time if I have solved some problem other agent harness devs are curious about, I just want them to take the solution without worrying about paying it back/forward.
FWIW the first version of meat was based on anthropic models, I switched to Oai models because they are faster and just as good right now. This is an astonishing thing to say, but the switch was a single shot prompt using a frontier model. I’m happy to add some selector there, but that is also the point of this article: do you really need me to make it configurable when you can switch the tool over to Anthropic with a single prompt? It’s a strange new world we live in.
On the website: you’re right. I have some side-by-side diff examples I want to turn into a website. I am just short on hours in the day. My real goal is to make the tool compelling enough that I can convince my colleagues that we should build it into Shelley. :)
On another note, the reason I was perusing the blog was because I was curious about more of how exe handles review/quality/testing because you mentioned there being no code review in another post about stripe billings (maybe a different author). So consider this a casual request for more content on reducing delivery bottlenecks :)
Not even Richard Stallman or Linus Torvalds make money on open source or free software despite preaching it. They actually make money from speaking fees.
"Open source" is now weaponized to price down entire companies to the floor and instead of humans maintaining the software, it is now coding agents doing the work.
Neither the free and open source software movement accounted for this disruption and they have become the new starving artists of the software world.
Take his side project, Meat. I've been chewing on this problem for a while. It's a real problem right now: it sucks to read all this LLM-generated code. It's worthwhile to have an LLM summarize it for you.
The problem with that is this particular problem resists vibe coding. I've talked to a bunch of people who have tried to solve it on the side, and it's all sort of ... ok, but still unsolved.
- As mentioned, it takes a while to run. You can modify your other tools, as described, to smuggle the latency.
- LLMs don't know what you care about, so you have to maintain a list of things that you do care about, which is ever evolving. If you don't give it that, it produces slop.
- If you miss something, it hurts. Another layer of swiss-cheese AI doesn't feel right. If you trust the AI, just ask Claude to summarize its work!
- The summaries feel shareable, but the author of the PR is actually the most tolerate of slop about a PR. Your reviewers definitely don't want to read the output of a vibe-coded tool talking about 60% of your PR. They could ask their own Claude!
So, we're building a version (https://tern.sh), and it's not open source, because we want it to be shareable and hosted and support teams -- all that stuff that makes it work. At the end of the day, I'm not here to maintain my tools. I'm here to use my tools to do the job.
The new personalization talking point appears to be coordinated. It is all over the Internet since last week.
Problem is, 99.99% of people (including developers) do not need "personalized" software, unless you mean Emacs style.