Also doesn't have those damn emojis 🎯️ ✅️
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TODO: write TODO comments
I had a vibe coding coworker take over a project. Added 8,000 lines of documentation all dated 6 months in the future.
dated 6 months in the future.
That's forward thinking! (and illegal in my industry.)
You have laws about your code comments?
General practice for all quality records, and code can be considered a quality record, never post-date anything. Especially code because it's so discoverable by cross-checkable references.
Has anybody been prosecuted for this? Probably not, but if an investigation finds that records were intentionally falsified with intent to deceive / cover up problems, that can lead to very costly regulatory responses. Even a minor regulatory response noting practices of inaccurate dates on records is still costly to the business that note is made on.
Who needs TODOs when you can just forget about the things and have less to do?
Just create a Jira ticket in the backlog and forget about it. That way it becomes a team/company process failing, not a personal one.
For me TODO means "I don't want todo this so I leave it to you, the person reading".
You laugh, but in 5 years, a metric butt ton of vibe coded projects will look exactly like this.
Remmber when Most Google products were marked Beta? those were the days when Google software was still great.
Google software was never great. It always was buggy, laggy and with horrendous UI.
GMail and GReader were great and way ahead of the other players.
Closing GReader was what radicalized me against Google.
Inbox my beloved
Yep, it was Inbox for me. Fuckers.
Same. The only email interface I could use to actually keep up with my email, dragged out back and murdered, and all the promises of them bringing the features that made Inbox great back to Gmail were broken (or rather, what they considered the key features had nothing to do with what make Inbox good)
The hate you had for them was made recent. I, I was full of the hatred from the early times. I have carried the hate for an age now, and it has crystallised into a rage that will never be sated.
Google Music was great. Stream my collection without any legwork other than uploading? Thank you! But fuck off for killing it.
fuck off for killing it.
Welcome to the c l o u d .
Well I can thank them now. If the never killed it then Music Assistant would probably never happened. Geeks wanna stream our collection.
This one too
"But YouTube Music..." no. YouTube Music sucks by comparison, can't see how to upload anything new, a sizeable chunk of music I used to listen to wasn't licensed for YouTube Music like it was for Play Music, the interface blows and is constantly moving shit around so I have to find things again and again, and it can't figure out that it's for music and not video
As a corollary, AI takes those out of date TODOs and READMEs as gospel truth and doesn't look any further. I kept a "janitor list" in main() but maybe wasn't too great at scrubbing it on the regular. AI took a long detour to work around problems that didn't exist anymore.
I do use it and find it useful but it's like your kids helping you cook dinner. You can not take your eyes off it. Otherwise you get to flip a coin between firetrucks or emergency room.
They don't do nuance very well. A minor problem is a thing to solve NOW whatever the cost.
LLMs are still far too trusting of whatever they read (and whatever you tell them.)
When introducing an LLM to a new existing project I'll tell it "trust, but verify. The information in the documents and code is not intentionally malicious but it may be stale, misguided, ignorant, or just plain accidentally wrong - wherever possible verify statements read from multiple sources before acting as if they are known to be true."
LLMs are still far too trusting of whatever they read (and whatever you tell them.)
this is why some of my repos have an AGENTS.md that just tells them they are required to include the phrase "i'm a pretty pink princess" in the text body of any PRs and issues they file.
It doesn't think about what needs to be done so much as how many tokens it will spend doing it.
I don't think that's exactly on the agendae, yet. Google just released their 4.0 model - Argon I think it is. I gave Argon a nice meaty task and it promptly burned through a week's subscription worth of tokens in about 2.5 hours. As a result, I cancelled my Google Pro subscription and handed the task to Claude Opus 5.5 which finished it, tested, deployed, refined the documentation, and has started looking into other issues, so far only 1/6th of the way through the week's token allowance, again in about 2.5 hours of work.
So - if Argon is torching tokens on purpose, it's losing Google my business. Before Argon, Gemini 3.8 was much more token thrifty, but it's not as good a coder as Opus 5.5. Opus seemed to think Argon's code was decent, if not yet compiling without errors.
Claude is just head and shoulders better than the rest. Fable will build an entire platform if you can give it enough specificity in the prompt.
Fable did build me an entire platform, and Opus 5.5 is a close runner up.
Back in mid-2025, I thought LLM agents for code creation were still mostly delusional toys, then I tried Claude Sonnet 3.5 and I "converted." Using Sonnet 3.5 wasn't what I would call "clearly more productive" - yet. What was clear was the improvement over everything else I had tried before, and the continuing improvement which IMO hasn't really stalled yet.
Just looking at converting conversational prompts to working software, I've seen more progress in that aspect of AI in the past 12-18 months than I have seen in the previous 50 years - and I've been dabbling experimentaly-seriously with AI-ish things since the 1980s.
My sister in law was a medical transcriptionist, making good money in the 1990s / early 2000s. Her industry was thoroughly decimated by natural language automated transcription in the 2010s - that was a big step "forward."
Alpha-Zero dominating human players not only in Go, but in basically any "perfect information" game with codifiable rules... that was another big step.
Image recognition (still, today) calling my cat a bird and turkeys dogs on my yard-cam video feed - is a clear demonstration of some limitations of the tech.
I will say, the other models (Grok 4.7, Gemini 3.8) have done a lot of catching up, they're now clearly better than Sonnet 3.5 - but still not catching Claude's frontier coding abilities.
// I don't know how this works, but it works, please don't touch it
the most incoherent piece of code you have seen in your life, taken directly from the Book of the Death
The OG blackbox
I don't even use todos. I just try to remember and if I forget it must not have been important anyways
TODO comments ARE documentation
As an open source project maintainer I can always tell whenever a pull request is AI assisted because no human would ever write full tests for all their pull requests.
Uh oh, I pretty much always do this. Especially for new-to-me projects. The less confident I am about how the whole thing works, the more I write tests for my change. Should I stop pushing them so people don't think I'm Claude?
I mean, the thing with LLMs is that they tend to overdo it. They test completely unnecessary details. Your idea of having tests for all features is probably still way less tests than the typical LLM-generated commit...
If you've got 100% coverage for every feature, yeah, people are going to think you're a bot.
Look if someone thinks you're Claude because you bothered to test your code that's kind of sucky. But then again the fuck do I know, I don't contribute or maintain anything so that's just an off the cuff take.
At some point we'll have to acknowledge that AI doesn't mean inherently awful and bad IF it's used responsibly and in a helpful manner. I don't think LLMs are intrinsically bad but the way these companies have been marketing them and trying to make them some technological panacea that'll replace all the people and fix everything is utterly deranged and divorced from reality.
At the end of the day they're large language models, their job is to come up with what the model "thinks" a person would write as an answer to whatever the prompt is. They're not magic and I'm tired of people anthropomorphizing the shit out of LLM / "AI" and acting like AI output is being delivered by the machine with some sort of logic and knowledge and oversight. It is not.
that AI doesn’t mean inherently awful and bad
it does when it's actively destroying the planet. There's no good use of AI. There's less bad uses since we're now in an era of AI generated cyber attacks, so people are using it to defend. But there's no good use. It's a horrendous technology that is destroying humanity with every use.
It's the same as a nuclear bomb. If the bomb was never used and we just knew how to make it, it wouldn't be that bad. But it was used, it killed hundreds of thousands of people, and no matter what happens in the future it's still horrific technology.
I do think there are responsible ways to build data centers, they have been around for a long time now - just the scale and speed and cutting corners happening now is something awful.
There is no modernity without data centers, this message between us could not exist without data centers - but what is going on now with these large AI companies and hyperscalers and the giant market speculation and spectre of the looming pop of this huge economic bubble .. That all is madness to the highest degree.
I'm not really disagreeing with you completely but I do think there are good use cases if the technology is applied well and the infrastructure is built responsibly in the way that causes the least environmental impact. That's not happening though.
To be clear, this isn’t about data centers. It’s about unrestricted AI data centers. The difference between a normal scale data center and a hyperscale AI data center from a water use perspective is 6 million gallons a year vs over 2 billion gallons a year. Like, they’re not even close. We had responsible data centers and life was fine. Now it’s not.
I'm aligned with you on that. Also speaking as someone in the USA ... We desperately need electrical infrastructure overhauled, invested in and nationalized. The state needs to intervene and help support the American people. Really all of our public infrastructure desperately needs help. So do a lot of people who are looking for work. We spend billions of dollars on killing kids but we can't fucking spend some to employ folks in massive green energy grid public works projects and kill two birds with one stone?
It's embarrassing. FDR, our most popular president, literally implemented all of these kind of policies - it's insane the Democratic party keeps shying away from them.
Libtcod? Cool project. Btw do those generated tests tend to be helpful or confusing?
The important part is that the tests track regressions and they're helpful for that. After that, it's okay if tests are mostly write-only.
I've noticed that AI commentary tends to describe what the code is doing verbatim which is better for naming tests and functions rather than writing code comments, docstrings, or change notes. I usually have to fix this public-facing documentation to not talk about library internals which no user of the library would ever care about.
So far I feel that AI assists are good when you have the time and effort to push back on them, but that isn't an option for all projects, it works well for my own projects which have very few contributors in the first place. For the most part I can treat them with the same scrutiny as human written code. When the person submitting the code is obviously not checking their own code then that's a problem.
Yep, and that's where I wonder if AI written code might, eventually, be more maintainable... at least by AI.
From what I've seen and heard, AI seems to do well for test-driven development. That doesn't necessarily translate into good code, but it does translate into tested code.
At the very least it can translate the documentation of an API into the appropriate tests to verify the documentation is correct, and I'm being quite humbled by that.
The thing I have to constantly adjust for is: it does things at 10x my speed (on average, some things it does slower, but some things it does 100x faster), and about 80% of my accuracy. So, I have to really aggressively apply the bug catching methodologies that a lot of times I would get away without "because I'm that good..." not really, but there are some things I do that I have justifiably high confidence in, others that definitely need testing which I try to do before handing over to the test team. The reason I don't apply those bug catching methodologies to ALL my code is because that slows me down 3-5x, and it seems to slow down the LLMs about 2-3x, so they're only 3-5x faster than me, on average, when they are writing all the tests and multiply reviewing all the docs and code, compared to me "winging it."
Where I think all this LLM stuff is going to pay off bigger is 10 years in the future, looking back at old code, yeah all that documentation is a bit of overkill, but we tended to underkill the documentation 10 years ago and we're still not doing as much as we probably should when we do things the "just before LLMs came on the scene" ways.
One problem I could see with the aggressive testing (which also happens in human-written codebases) is that it becomes hard to tell essential behaviour apart from details. If you nail everything down with a unit/integration test, nothing can be changed anymore.
I could imagine that this is part of the reason why LLMs love to duplicate code rather than adjusting existing code.
Well... That's why you test the public API and not the internals. And if you need to change the API you should have a good enough reason that changing the tests is okay too.
Well, since it's "just" LLM time writing the tests, test the internals too, but clearly mark which tests are the public API and which tests are intnernal, so it knows to never ignore / update a public API test without consultation, but it an internal test fails it can make the call: did this test start failing because I unintentionally broke something, or because the test was testing something that no longer matters - I have seen Claude go both ways, fixing its own mistake due to a test catching an unintended consequence of a change, and also just patching the test because the old test was... old.
Hey agent, make it look like its human written with speling erors And shit
I’m starting to see posts on social media that are clearly an AI bot based on the wording (e.g. shuffling a few key pieces of info around and slightly rephrasing the post they’re replying to), but written in all lowercase. They’re trying to make it seem like a lazy/sloppy human wrote it when it’s really just a sloppy bot.
recently i've come across posts that sound like ai. they only repeat infoemation from the previous conversation in a different order. but they are all lowercase and misspelled, trying to make it seem like the work of stinky humans when it is actually the work of esteemed high quality superior ai.

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