Dennis Schubert

certified member of the blockchain hate club, and the AI hate club. spending my work time fixing firefox web compatibility issues at mozilla. for science, you monster.

opinions are my own and will stay my own - they're not available for rent or purchase.

note on follow request: if your profile is empty or I don't see any posts, I'll probably reject your request. check out the "contact me" link below if you need to get in touch.

pronouns: [they/them; he/him; meow]
rainbows: 🌈:progresspride:🏳️‍🌈
location: Stuttgart, Germany
blog: https://overengineer.dev
contact me: https://overengineer.dev/me/card/

Last Notes

2025-12-03 19:08:51 UTC

so, a while ago, I have been tricked into trying out a vibe-coded project. "tricked", because it wasn't disclosed anywhere, and just from screenshots and the readme, it looked fine'ish.
it was a small web-app used to upload and download files. you know, kinda like wetransfer, but self-hosted.
I gave it a quick try, and it worked well enough for the use-case I had (requesting one file from a user). except that.. chunked uploading was broken.
oh, and also: the project provided docker containers and a compose file, but.. the uploaded files weren't stored in the volume at all, they were just stored as part of the ephemeral container filesystem. very much a "you had one job, and you failed at it" kind of situation.
there was a github issue mentioning existing upload issues. I left a comment there pointing out the volatile storage, because I'm not a jerk. also, I felt it's kinda important to note somewhere, because that puts users at risk of actually losing data. that was at the end of october. just after leaving the comment, I noticed an earlier comment by the maintainer. it read:
> Thanks a lot for reporting this bug! I actually struggled with the same issue during development. The v3.2.5-beta release was meant to fix it for good, sorry it didn’t work as expected. Just so you know, I’m currently working on v3.3.0-beta, where I’m rewriting all the upload/download logic from scratch.
and then it hit me: the maintainer actually had zero clue. looking at their commit history, it's pretty clear that there's very little - if any - architectural thinking. a lot of the commits were just "give up and rebuild everything", because that's what you get from GenAI: when they struggle writing code, they tend to suggest throwing everything away and starting from scratch.
anyway, I rm -rfed that project, but because I commented in a bug, I was still subscribed. just got a notification from the maintainer posting that a new "beta" is out, so of course, I checked out the commits.
the latest real commit:
> [REFACTOR] Change upload and download architecture
oh dear. yes. once again, instead of fixing shit, it's just throwing away everything. and, hold your horses:
> 205 files changed, +28939 -15962
and sure, lots of that is also docs changes and i18n file changes, but it's literally throwing away everything and "writing it" from scratch. the docker container is now also installing and running [minio](https://github.com/minio/minio ), because of course that's perfectly reasonable to do. and, even funnier: the "important upgrade notes" - clearly AI generated - mention a lot of things, but what they don't mention is that if you already have uploaded files and you just build a new container... well... you'll no longer have uploaded files.
it's a total feverdream, and one of many similar stories I've seen in GenAI-driven projects. call me a hater all you want, but I, for once, am really happy about stories like that. it's been 2.5 years since people yelled at me with "GenAI will take your job in a few months", and they're still so laughably bad, that - as someone who knows how to *debug* and actually fix shit - am becoming *less worried* about my career as time goes on.
what wonderful times we're in.
(also, you can probably google these quotes and find the project and the maintainer. don't. don't harass them or their projects. for all I know, they might have the best intentions and are just heavily misguided by the fearmongering/FOMO-driven/hype-driven marketing of GenAI companies.)

2025-08-28 23:27:51 UTC

I was today years old when I learned that the <meta> tag specifying the document's character encoding needs to be placed within the first 1024 bytes of the HTML document. https://bugzilla.mozilla.org/show_bug.cgi?id=1984103#c7

2025-08-09 19:34:36 UTC

y'know, sometimes, having a little bit *less* reach on social media wouldn't be bad, either.

2025-08-09 04:40:29 UTC

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This entire thread is now also available as a single-page outside of social media on https://overengineer.dev/txt/2025-08-09-another-llm-rant/
Don't post it on the orange site.

2025-08-09 04:28:01 UTC

- reply

Let's imagine you're colorblind. The kind of colorblindness that only allows you to see grayscale - no colors at all - but everything else is fine.
You're stressed and need fidget toy - so a friend hands you a ball, roughly filling your hand. It's hard, but somewhat squishy, and has a weird fabric-like, furry texture. You now want to know what color that ball is. But, well, you're colorblind, and your friend already disappeared and isn't reachable - probably riding a Deutsche Bahn train or something.
So you take a picture and post it to a "what color is this?" subreddit. Seems reasonable. You get 200 responses - 198 of them say "it's yellow", two of them say "it's pink". A few people helpfully say it's a "tennis ball". That's helpful, because even the Wikipedia article states that only yellow and white tennis balls are officially approved colors. Sweet.
A few days later, a random person approaches you and says "wow, cool ball - what color is it?" and you say "yellow!". Alright, end of the chat. A LLM would do exactly the same - given the "yellow" responses far outnumbered the "pink" responses, your ball is probably yellow. Ball==yellow is something both you and the LLM "learned". A few weeks after that, another friend asks you "ALice has a ball, too! Do you know which color her ball is?" - and now it gets interesting.
The LLM would immediately say "yellow". Of course it would. It makes sense. Yellow is the most likely response to that question.
But you're not an LLM - you're a human, and your brain is cool. Instead of saying "yellow", you respond "huh I don't actually know that? My ball is yellow, maybe she has a similar ball. But it could also be that she has a completely different ball that might a different color! Also, lol, I'm colorblind, so I can't really answer that anyway - you should ask Alice." And now, your brain is already doing better than any LLM. Your logical thinking engine already realized that you don't actually know something, and you're honest enough to just say that. Your job isn't to be a ball color guesser, you're just a person.
Wait, it's gets more fun! A few weeks after that, you hang out with me. You hand me your ball, and say "hey look at my cool yellow ball!". Oddly enough, my reaction is "huh? this ball isn't yellow, it's a pink tennis ball..." and now things get funky. If you were an LLM, you would either insist that no, your ball is absolutely yellow - or you'd come up with some kind of "oh, sorry for the misunderstanding - it's pink, you're correct", almost implying that my definition of color is different - and the next time someone asks you about the color of your ball, you'd still say "Yellow!!" again. Because of course, there's still only three people claiming it's pink, and still 198 people saying it's yellow.
But you're not an LLM. You're human, and your sexy human brain immediately goes into a "uhhh we have a conflict of information! how exciting! let's figure things out!" You now have to conflicting hypotheses, and you're thinking about ways to experiment on your ball to learn more. And you have an idea! You know your additive color mixing theory, so you realize that your phone camera can take pictures and you can look at the RGB values. If it's yellow, you'd expect to see lots of red and green but no blue - but if it's pink, you'd see lots of red and blue, but no green! You can test that!
So you take a photo, and... rgb(255, 0, 255). Turns out your ball is actually pink! It's still a tennis ball, but a fun one not meant for official tournaments, so it's pink! Wow! You immediately learned something new - and from now on, if someone asks you about the color of your ball, you'll say "pink!" and you'll have a heck of a story to tell alongside. Also, after some self-reflection, you realize that the subreddit your posted your image to wasn't a real "what color is this?" subreddit - it was one of those "false answers only" shitposting subreddits. Whoops.
This process of having assumptions, but being able to question them, to come up with tests for it, and to immediately change your opinion on something when you have good evidence for it is what makes humans awesome. You don't rely on the majority of people screaming "pink!" at you. You don't need to rely on manual weights that give some sources more weight than other sources - you can independently process information and deduct things. Give your brain a pat on the.. uh.. cranium.
LLMs can be a useful tool, maybe. But don't anthropomorphize them. They don't know anything, they don't think, they don't learn, they don't deduct. They generate real-looking text based on what is most likely based on the information it has been trained on. If your prompt is about something that's common and the majority of online-text is right, you'll most likely get a right answer out of the LLM. But if you're asking something that not a lot of real people had interactions on, the LLM will still generate text for you - but it might be complete nonsense. You're just getting whatever text is "statistically most likely".
If you're a coder stuck on something, identify a colleague or friend who is more knowledgeable in that specific area. They'll happily help you out and provide all sorts of fun added context that'll allow you to learn. If you're a nerd on the internet who enjoys ranting on social media, just do it yourself instead of having an LLM generate it, because that'll allow you to insert some bad jokes and a bit of your own personality to it instead of just getting a "default-feeling" text. If you're a manager in charge of something and you need to come up with new directions to push your company towards, go take a walk outside and listen to some cool music and let your ideas roam free - don't ask an LLM to generate the statistically-most-likely direction for your project, because that's by definition the opposite of creative and innovative.
Use your brains.

2025-08-09 04:27:45 UTC

- reply

Because it already happened: if you read my post and you feel an urge to respond with something along the lines of "it's just a hallucination", "it's just a bug", "it will be better in ChatGPT 6", or anything even close into that direction, please stop. Read this post and the next post, think about them, and if you have a factual argument to respond to me, only then reply. Focus on my factual claims, not on some inaccuracies in my analogy because of course it's not 100% accurate, that's the nature of analogies.
ChatGPT making up ZSTD compression in the Compression framework is not a bug. It's not even a weird edge-case. ChatGPT is doing exactly what it is designed to do. Let me try to explain.
If we grossly oversimplify what an LLM is, it's "just a statistical model" that generates "language" based on a chain of "what is most likely to follow the previous phrase". "language" can be anything: it can be human language, a fictional language, but it also can be code or even genetic information. Any kind of textual thing that you can feed large amounts of into a model works. "Not having an answer" is not a possibility in this system - there's always "a most likely response", even if that makes no sense.
ChatGPT inventing ZSTD compression in the Compression framework isn't due to a lack of training data. If you request [an overview over all compression algorithms supported, it answers correctly](https://chatgpt.com/share/6896c36d-6cd0-8004-be72-41e25204a740 ) with a comprehensive list that does not include ZSTD. So, if you want to anthropomorphize ChatGPT, you could say "it knows that ZSTD isn't supported", but that doesn't matter. LLMs do not possess the ability of logical thinking, deductive reasoning, or anything else. "It knows" that there are a bunch of compression algorithms available, the constants are all called COMPRESSION_[method], so there's a high likelihood of COMPRESSION_ZSTD to be the answer to a user asking for ZSTD compression in Swift. And so it generates that.
The only way ChatGPT will stop spreading that nonsense is if there is a significant mass of humans talking online about the lack of ZSTD support. For example a bunch of StackOverflow questions asking "How do I do this?" and people responding "you don't, Apple doesn't support it, you have to use third-party libraries" - or if you have a bunch of white dudes working in tech complaining on social media about Apple not supporting ZSTD in the Compression Framework.
My next post will be an attempt at comparing human thinking and LLMs generating text. As mentioned earlier, it's an analogy - and it's not going to be 100% accurate. If you want to reply, focus on the factual claims. If you only want to nit-pick my analogy, I have to assume you're not interested in productive argumentation.

2025-08-08 23:23:47 UTC

A contact just told me that my old "LLMs generate nonsense code" blog post from 2 years ago is now very outdated with GPT5 because it's so awesome and so helpful. So I asked him to give it a test for me, and asked it my favorite test question based on a use-case I had myself recently:
> Without adding third-party dependencies, how can I compress a Data stream with zstd in Swift on an iPhone?
and here is the answer from ChatGPT 5: https://chatgpt.com/share/68968506-1834-8004-8390-d27f4a00f480
Very confident, very bold, even claims "Works on iOS 16+".
Problem with that: Just like any other LLM I've tested that provided similar responses, it is - excuse my language but I need to use it - absolute horseshit. No version of any Apple SDK ever supported or supports ZSTD (see https://developer.apple.com/documentation/compression/compression_algorithm for a real piece of knowledge). It was never there. Not even in private code. Not even as a mention of "things we might do in the future" on some developer event. It fundamentally does not exist. It's completely made up nonsense.
This concludes all the testing for GPT5 I have to do. If a tool is able to actively mislead me this easy, which potentially results in me wasting significant amounts of time in trying to make something work *that is guaranteed to never work*, it's a useless tool. I don't like collaborating with chronic liars who aren't able to openly point out knowledge gaps, so I'm also not interested in burning resources for a LLM that does the same.

2025-02-04 21:20:00 UTC

- reply

yes.

2025-02-04 15:20:56 UTC

if you're responsible for a FOSS project, it's now time to consider how you can run your project without relying on the US.
like, using GitHub is fine. Continue using it - I do, too, and there's a lot of value of having projects centralized in one place.
but you **need** to make sure you have a contingency plan. is your repo with all branches and metadata backed up somewhere else? do you own your project's website, discussion forums, or something else that allows you to point your users and contributors to a new place if you need to?
can you continue work on your project if US-big-tech decides your very existence is no longer allowed?
now is the time to take inventory and build backups.

2024-12-27 00:22:16 UTC

LLM training bots are a plague. https://pod.geraspora.de/posts/17342163

2024-08-27 23:52:27 UTC

one of these days I'll just set up Jira for my own personal task management, grrr

2024-08-04 00:05:51 UTC

okay, I finally found a good use for an LLM. no, really.
https://github-roast.pages.dev/
this thing is brutal