We've enjoyed a certain period (at least a couple of decades) of global, anonymous collaboration that seems to be ending. Trust in the individual is going to become more important in many areas of life, from open-source to journalism and job interviews.
I've been trying to manifest Web of Trust coming back to help people navigate towards content that's created by humans.
A system where I can mark other people as trusted and see who they trust, so when I navigate to a web page or in this case, a Github pull request, my WoT would tell me if this is a trusted person according to my network.
Seems like reading the code is now the real work. AI writes PRs instantly but reviewing them still takes time. Everything flipped. Expect more projects to follow - maintainers can just use ai themselves without needing external contributions.
Understanding (not necessarily reading) always was the real work. AI makes people less productive because it's speeding up the thing that wasn't hard (generating code), while generating additional burden on the thing that was hard (understanding the code).
There are many cases in which I already understand the code before it is written. In these cases AI writing the code is pure gain. I do not need to spend 30 minutes learning how to hold the bazel rule. I do not need to spend 30 minutes to write client boilerplate. List goes on. All broad claims about AI's effects on productivity have counterexamples. It is situational. I think most competent engineers quietly using AI understand this.
no, it isn't. unless the generated code is just a few lines long, and all you are doing is effectively autocompletion, you have to go through the generated code with a fine toothed comb to be sure it actually does what you think it should do and there are no typos. if you don't, you are fooling yourself.
> There are many cases in which I already understand the code before it is written. In these cases AI writing the code is pure gain.
That's only true if the LLM understands the code in the same way you do - that is, it shares your expectations about architecture and structure. In my experience, once the architecture or design of an application diverges from the average path extracted from training data, performance seriously degrades.
You wind up with the LLM creating duplicate functions to do things that are already handled in code, or using different libraries than your code already does.
The problem is, even if all that is true, it says very little about the distribution of AI-generated pull requests to GitHub projects. So far, from what I’ve seen, those are overwhelmingly not done by competent engineers, but by randos who just submit a massive pile of crap and expect you to hurry up and merge it already. It might be rational to auto-close all PRs on GitHub even if tons of engineers are quietly using AI to deliver value.
It makes a great code reading tool if you use it mindfully. For instance, you can check the integrity of your tests by having it fuzz the implementation and ensure the tests fail and then git checkout to get clean again.
this is precisely why i refuse to use AI to generate code at all. i'd have to not only read it but internalize it and understand it in a way as if i had written it myself. at that point it is easier to actually write the code myself.
for prototypes and throwaway stuff where only the results count, it may be ok. but not for code that goes into a larger project. especially not FOSS projects where the review depends on volunteers.
Reviewing code is much less of a burden if I can trust the author to also be invested in the output and have all the context they need to make it correct. That's true for my team / tldraw's core contributors but not for external contributors or drive-by accounts. This is nothing new and has up to now been worth the hassle for the benefits of contribution: new perspectives, other motivations, relationships with new programmers. It's just the scale of the problem and the risk that the repo gets overwhelmed by "claude fix this issue that I haven't even read" PRs.
In the civic tech hacknight community I'm part of, it's hard to collaborate the same now, at least when people are using AI. Mostly because now code often feels so disposable and fast. It's like the pace layers have changed
It's been proposed that we start collaborating in specs, and just keep regenerating the code like it's CI, to get back to the feeling of collaboration without holding back on the energy and speed of agent coding
> Mostly because now code often feels so disposable and fast
I really like this thought. We used to take pride in elegant solutions and architectural designs. Now, in the era of shipping fast and AI, this has been disregarded. Redundancy is everywhere, spaghetti is normalized. AI code has always been unsettling for me and I think this is why.
I actually think Ada has good potential as an AI adjacent language because the syntax is optimised for readability (I personally find it very readable too.)
This is probably true, and while I expect productivity to go up, I also expect "FOSS maintainer burnout" to skyrocket in the coming years.
Everyone knows reading code is one-hundredth as fun as writing it, and while we have to accept some amount of reading as the "eating your vegetables" part of the job, FOSS project maintainers are often in a precarious enough position as it is re: job satisfaction. I think having to dramatically increase the proportion of reading to writing, while knowing full well that a bunch of what they are reading was created by some bozo with a CC subscription and little understanding of what they were doing, will lead to a bunch of them walking away.
i have fun reading code, but the fun comes from knowing a human did this. if i find errors i get the satisfaction of teaching that human become a better developer by helping them realize the error and avoid it in the future. if the code is the contribution of a volunteer to a project of mine, even more so. that all goes out the window with AI generated code.
Not to worry! Microslop probably has a product in the works to replace disgruntled open-source maintainers with agreeable, high-review-throughput agentic systems.
Mitchell Hashimoto (2025-12-30):
"Slop drives me crazy and it feels like 95+% of bug reports, but man, AI code analysis is getting really good. There are users out there reporting bugs that don't know ANYTHING about our stack, but are great AI drivers and producing some high quality issue reports.
This person (linked below) was experiencing Ghostty crashes and took it upon themselves to use AI to write a python script that can decode our crash files, match them up with our dsym files, and analyze the codebase for attempting to find the root cause, and extracted that into an Agent Skill.
They then came into Discord, warned us they don't know Zig at all, don't know macOS dev at all, don't know terminals at all, and that they used AI, but that they thought critically about the issues and believed they were real and asked if we'd accept them. I took a look at one, was impressed, and said send them all.
This fixed 4 real crashing cases that I was able to manually verify and write a fix for from someone who -- on paper -- had no fucking clue what they were talking about. And yet, they drove an AI with expert skill.
I want to call out that in addition to driving AI with expert skill, they navigated the terrain with expert skill as well. They didn't just toss slop up on our repo. They came to Discord as a human, reached out as a human, and talked to other humans about what they've done. They were careful and thoughtful about the process.
They invited AI in by creating a comprehensive list of instructions for AI agents - in the README, in a context.md, and even as yarn scripts. What did they expect?
Hey, Steve from tldraw here. We use AI tools to develop tldraw. The tools are not the problem, they're just changing the fundamentals (e.g. a well-formed PR is no longer a sign of thoughtful engagement, a large PR shows more effort than a small PR, etc) and accelerating other latent issues in contribution.
About the README etc: we ship an SDK and a lot of people use our source code as docs or a prototyping environment. I think a lot about agents as consumers of the codebase and I want help them navigate the monorepo quickly. That said, I'm not sure if the CONTEXT.md system I made for tldraw is actually that useful... new models are good at finding their way around and I also worry that we don't update them enough. I've found that bad directions are worse than no directions over time.
The CONTEXT.md file was created 5 months ago, and the contribution policy changed today. I would interpret that as a good-faith attempt to work with AI agents, which with some experience, didn't work as well as they hoped.
I still find it useful to vibe code in a private fork. For example with yt-dlp its now super easy to add a website with Claude for personal use, but that doesn't mean it's appropriate to open a PR.
> An open pull request represents a commitment from maintainers: that the contribution will be reviewed carefully and considered seriously for inclusion.
This has always been the problem with github culture.
On the Linux and GCC mailing lists, a posted patch does not represent any kind of commitment whatsoever from the maintainers. That's how it should be.
The fact that github puts the number of open PR requests at the very top of every single page related to a project, in an extremely prominent position, is the sort of manipulative "driving engagement" nonsense you'd expect from social media, not serious engineering tools.
The fact that you have to pay github money in order to permanently turn off pull requests or issues (I mean turn off, not automatically close with a bot) is another one of these. BTW codeberg lets any project disable these things.
Didn't take long before the quality went downhill.
Skynet was evil and impressive in The Terminator. Skynet 3.0 in reallife sucks - the AI slop annoys the hell out of me. I now need a browser extension that filters away ALL AI.
> If the job market is unfavourable to juniors, become senior.
That requires networking with a depth deep enough that other professionals are willing to critique your work.
So... open-source contributions, I guess?
This increases pressure on senior developers who are the current maintainers of open-source packages at the same time that AI is stealing the attention economy that previously rewarded open-source work.
Seems like we need something like blockchain gas on open-source PRs to reduce spam, incentivize open-source maintainers, and enable others to signal their support for suggestions while also putting money where their mouth is.
A system where I can mark other people as trusted and see who they trust, so when I navigate to a web page or in this case, a Github pull request, my WoT would tell me if this is a trusted person according to my network.
no, it isn't. unless the generated code is just a few lines long, and all you are doing is effectively autocompletion, you have to go through the generated code with a fine toothed comb to be sure it actually does what you think it should do and there are no typos. if you don't, you are fooling yourself.
That's only true if the LLM understands the code in the same way you do - that is, it shares your expectations about architecture and structure. In my experience, once the architecture or design of an application diverges from the average path extracted from training data, performance seriously degrades.
You wind up with the LLM creating duplicate functions to do things that are already handled in code, or using different libraries than your code already does.
Typing speed is your bottleneck?
The productivity gets siphoned to the AI companies owning the AI.
for prototypes and throwaway stuff where only the results count, it may be ok. but not for code that goes into a larger project. especially not FOSS projects where the review depends on volunteers.
It's been proposed that we start collaborating in specs, and just keep regenerating the code like it's CI, to get back to the feeling of collaboration without holding back on the energy and speed of agent coding
I really like this thought. We used to take pride in elegant solutions and architectural designs. Now, in the era of shipping fast and AI, this has been disregarded. Redundancy is everywhere, spaghetti is normalized. AI code has always been unsettling for me and I think this is why.
I see a future where those that survive are doing mostly architecture work, and a few druids are hired by AI companies.
Everyone knows reading code is one-hundredth as fun as writing it, and while we have to accept some amount of reading as the "eating your vegetables" part of the job, FOSS project maintainers are often in a precarious enough position as it is re: job satisfaction. I think having to dramatically increase the proportion of reading to writing, while knowing full well that a bunch of what they are reading was created by some bozo with a CC subscription and little understanding of what they were doing, will lead to a bunch of them walking away.
Mitchell Hashimoto (2025-12-30): "Slop drives me crazy and it feels like 95+% of bug reports, but man, AI code analysis is getting really good. There are users out there reporting bugs that don't know ANYTHING about our stack, but are great AI drivers and producing some high quality issue reports.
This person (linked below) was experiencing Ghostty crashes and took it upon themselves to use AI to write a python script that can decode our crash files, match them up with our dsym files, and analyze the codebase for attempting to find the root cause, and extracted that into an Agent Skill.
They then came into Discord, warned us they don't know Zig at all, don't know macOS dev at all, don't know terminals at all, and that they used AI, but that they thought critically about the issues and believed they were real and asked if we'd accept them. I took a look at one, was impressed, and said send them all.
This fixed 4 real crashing cases that I was able to manually verify and write a fix for from someone who -- on paper -- had no fucking clue what they were talking about. And yet, they drove an AI with expert skill.
I want to call out that in addition to driving AI with expert skill, they navigated the terrain with expert skill as well. They didn't just toss slop up on our repo. They came to Discord as a human, reached out as a human, and talked to other humans about what they've done. They were careful and thoughtful about the process.
People like this give me hope for what is possible. But it really, really depends on high quality people like this. Most today -- to continue the analogy -- are unfortunately driving like a teenager who has only driven toy go-karts. Examples: https://github.com/ghostty-org/ghostty/discussions?discussio... " ( https://x.com/mitchellh/status/2006114026191769924 )
> @zeroxBigBoss: .. It's not all AI, I have experience with Zig and MacOS, ..
> @mitchellh: I appreciate it! And my bad on the experience, I must have misunderstood or misremembered your messages
Use xcancel. For the very least to see an entire thread.
I wouldn't bet on it
SlopHub
You need a literary agent for just about all of them
About the README etc: we ship an SDK and a lot of people use our source code as docs or a prototyping environment. I think a lot about agents as consumers of the codebase and I want help them navigate the monorepo quickly. That said, I'm not sure if the CONTEXT.md system I made for tldraw is actually that useful... new models are good at finding their way around and I also worry that we don't update them enough. I've found that bad directions are worse than no directions over time.
This has always been the problem with github culture.
On the Linux and GCC mailing lists, a posted patch does not represent any kind of commitment whatsoever from the maintainers. That's how it should be.
The fact that github puts the number of open PR requests at the very top of every single page related to a project, in an extremely prominent position, is the sort of manipulative "driving engagement" nonsense you'd expect from social media, not serious engineering tools.
The fact that you have to pay github money in order to permanently turn off pull requests or issues (I mean turn off, not automatically close with a bot) is another one of these. BTW codeberg lets any project disable these things.
Skynet was evil and impressive in The Terminator. Skynet 3.0 in reallife sucks - the AI slop annoys the hell out of me. I now need a browser extension that filters away ALL AI.
> If the job market is unfavourable to juniors, become senior.
That requires networking with a depth deep enough that other professionals are willing to critique your work.
So... open-source contributions, I guess?
This increases pressure on senior developers who are the current maintainers of open-source packages at the same time that AI is stealing the attention economy that previously rewarded open-source work.
Seems like we need something like blockchain gas on open-source PRs to reduce spam, incentivize open-source maintainers, and enable others to signal their support for suggestions while also putting money where their mouth is.
Don't love your job, job your love.
Then I just took my hosting private. I can’t be arsed to put in the effort when they don’t.
is this satire?