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I took a look at a project I maintain[0], and wow. It's so wrong in every section I saw. The generated diagrams make no sense. The text sections take implementa
by blopker 11mo ago
I took a look at a project I maintain[0], and wow. It's so wrong in every section I saw. The generated diagrams make no sense. The text sections take implementation details that don't matter and present them to the user like they need to know them. It's also outdated.
I hope actual users never see this. I dread thinking about having to go around to various LLM generated sites to correct documentation I never approved of to stop confusing users that are tricked into reading it.
[0]: https://deepwiki.com/blopker/codebook https://deepwiki.com/blopker/codebook
- ewoodrich 11mo ago> The text sections take implementation details that don't matter and present them to the user like they need to know them. Yeah this seems to be a recurring issue on each of the repos I've tried. Some occasionally useful tables or diagrams buried in pages of distracting irrelevant slop.
- rmnclmnt 11mo agoI fear the consequences will be even darker: - Users are confused by autogenerated docs and don’t even want to try using a project because of it - Real curated project documentation is no longer corrected by users feedback (because they never reach it) - LLMs are trained on wrong autogenerated documentation: a downward spiral for hallucinations! (Maybe this one could then force users go look for the official docs? But not sure at this point…)
- vissi 11mo ago> LLMs are trained on wrong autogenerated documentation: a downward spiral for hallucinations! (Maybe this one could then force users go look for the official docs? But not sure at this point…) On this, I think, we should have some kind of AI-generated meta-tag, like this: https://github.com/whatwg/html/issues/9479 https://github.com/whatwg/html/issues/9479
- bt1a 11mo agoI wonder what incentives for adherence to the use of this meta-tag might exist? For example, imagine I send you my digital resume and it has an AI-generated footer tag on display? Maybe a bad example- I like the idea of this in general, but my mind wanders to the fact that large entities completely ignored the wishes of robots.txt when collecting the internet's text for their training corpuses
- mrdevlar 11mo agoLarge entities aside, I would use this to mark my own generated content. Would be even more helpful if you could get the LLM to recognise it which would allow you to prevent ouroboros situations. Also, no one is reading your resume anymore and big corps cannot be trusted with any rule as half of them think the next-word-machine is going to create God.
- arresin 11mo agoThis is made by “Devin” I believe.
- NewsaHackO 11mo ago> The text sections take implementation details that don't matter and present them to the user like they need to know them. It's also outdated. The point of the wiki is to help people learn the codebase so they can possibly contribute to the project, not for end users. It absolutely should explain implementation details. I do agree that it goes overboard with the diagrams. I’m curious, I’ve seen other moderately sized repo owners rave about how DeepWiki did very well in explaining implementation details. What specifically was it getting wrong about your code in your case? Is it just that it’s outdated?
- blopker 11mo agoI dunno, it seems to be real excited about a VS Code extension that doesn't exist and isn't mentioned in the actual documentation. There's just too many factual errors to list.
- NewsaHackO 11mo ago>I dunno, it seems to be real excited about a VS Code extension that doesn't exist and isn't mentioned in the actual documentation. There's just too many factual errors to list. There is a folder for a VS Code extension here[0]. It seems to have a README with installation instructions. There is also an extension.ts file, which seems to me to be at least the initial prototype for the extension. Did you forget that you started implementing this? [0] https://github.com/blopker/codebook/blob/c141f349a10ba1704247af2b1afa3300b925c981/vscode-extension/README.md https://github.com/blopker/codebook/blob/c141f349a10ba170424...
- Phelinofist 11mo agoWhat a plot twist
- NewsaHackO 11mo agoIt’s funny, I accidentally put a link to the commit instead of the current repo file because I was investigating whether or not he committed it versus he recently took over the project and didn’t realize the previous owner had started one. But he is the one who actually committed the code. I guess LLMs are so good now that they’re stopping developers from hallucinating about code they themselves wrote.
- skissane 11mo ago> It's so wrong in every section I saw. Not talking about this tool, but in general-incorrect LLM-generated documentation can have some value - developer knows they should write some docs, but are starring at a blank screen and not sure what to write so they don’t. Then developer runs an LLM, gets a screenful of LLM-generated docs, notices it is full of mistakes, starts correcting them-suddenly, a screenful of half-decent docs. For this to actually work, you need to keep the quantity of generated docs a trickle rather than a flood-too many and the developer’s eyes glaze over and they miss stuff or just can’t be bothered. But a small trickle of errors to correct could actually be a decent motivator to build up better documentation over time.
- aswegs8 11mo agoAt some point it will be less wrong (TM) and it'll be helpful. Feels generally like a good bet.
- Xss3 11mo agoWill it though? Fundamentally this is an alignment problem. There isnt a single AI out there that wont lie to your face, reinterpret your prompt, or just decide to ignore your prompt. When they try to write a doc based off code, there is nothing you can do to prevent them from making up a load of nonsense and pretending it is thoroughly validated. Do we have any reason to believe alignment will be solved any time soon?
- andybak 11mo agoI just tried it on several of my repos and I was rather impressed. This is another one of those bizarre situations that keeps happening in AI coding related matters where people can look at the same thing and reach diametrically opposed conclusions. It's very peculiar and I've never experienced anything like it in my career until recently.
- esperent 11mo ago> people can look at the same thing and reach diametrically opposed conclusions. It's very peculiar and I've never experienced anything like it in my career until recently React vs other frameworks (or no framework). Object oriented vs functional. There's loads of examples of this that predate AI.
- alansammarone 11mo agoI dont think it's quite the same. The cases you mention are more like two alternative but roughly functionally equivalent things. People still argue and use both, but the argument is different. Even if people don't explicitly acknowledge it, at some level they understand it's a difference in taste. This feels to me more like the horses vs cars thing, computers vs... something (no computers?), crypto vs "dollar-pegged" money, etc. It's deeper. I'm not saying the AI people are the "car" people, just that...there will be one opinion that will exist in 5-20 years, and the other will be gone. Which one... we'll see.
- esperent 11mo ago> People still argue and use both, but the argument is different React vs no framework is at least in the same ballpark as AI vs no AI. Some people are determined to prove to the world that React/AI/functional programming solves everything. Some people are determined to prove the opposite. Most people just quietly use them without feeling like they need to prove anything.
- Xss3 11mo agoThis is such an apples to oranges comparison that it makes me suspicious of your motives here. Bad documentation full of obvious errors and nonsense is very different to having an opinion on OO vs Functional programming. Even that sentence sounds insane because who would ever compare the two?!
- onion2k 11mo agoI went to the lodash docs and asked about how I'd use the 'pipeline' operator (which doesn't exist) and it correctly pointed out that pipeline isn't a thing, and suggested chain() for normal code and flow() for lodash fp instead. That's pretty much spot on. If I was guessing I'd suggest that the base model has a lot more lodash code examples in the training data, which probably makes a big difference to the quality of the output.
- billyp-rva 11mo agoThe lack of a pipeline operator in JS (and JS libraries like lodash) has also been discussed online a lot.
- onion2k 11mo agoExactly the point. If there's a lot of data in the training set the results will be better.
- billyp-rva 11mo agoI guess I'm trying to emphasize the distinction between information in the repo (code) vs. information elsewhere (discussions) that the model looks at.
- NicoJuicy 11mo agoWhat model did you use?
- rwmj 11mo agoI tried it on a big OCaml project (https://deepwiki.com/libguestfs/virt-v2v https://deepwiki.com/libguestfs/virt-v2v) and it seems correct albeit very superficial. It helps that the project is extensively documented and the code well commented, because my feeling is that it's digesting those code comments along with the documentation to produce the diagrams. It seems decent as a starting point to understanding the shape of the project if I'd never seen it before. This is the sort of thing you could do yourself but it might take an hour or more, so having it done for you is a productivity gain.
- frumiousirc 11mo agoI have a fairly large code base that has been developed over a decade that deepwiki has indexed. The results are mixed but how they are mixed gives me some insight into deepwiki's usefulness. The code base has a lot of documentation in the form of many individual text files. Each describe some isolated aspect of the code in dense, info-rich and not entirely easily consumable (by humans) detail. As numerous as these docs are, the code has many more aspects that lack explicit documentation. And there is a general lack of high-level documentation that tie each isolated doc into some cohesive whole. I formed a few conclusions about the deepwiki-generated content: First, it is really good where it regurgitates information from the code docs while being rather bad or simply missing for aspects not covered by the provided docs. Second, deepwiki is so-so for providing a high layer of documentation that sort of ties things together. Third, it is highly biased about the importance of various aspects by their code docs coverage. The lessons I take from this are: deepwiki does better ingesting narrative than code. I can spend less effort on polishing individual documentation (not worrying about how easy it is for humans to absorb). I should instead spend that effort to fill in gaps, both details and to provide higher-level layers of narrative to unify the detailed documentation. I don't need to spend effort on making that unification explicit via sectioning, linking, ordering, etc as one may expect for a "manual" with a table of contents. In short, I can interpret deepwiki's failings as identifying gaps that need filling by humans while leaning on deepwiki (or similar) to provide polish and some gap putty.
- Xss3 11mo agoIf documenting the why rather than the how you often end up tying high level concepts together. E.g. If you describe how the user service exists you wont necessarily capture where it is used. If you document why the user service exists you will often mention who or what needs it to exist, the thing that gives it a purpose. Do this throughout and everything ends up tied together at a higher level.
- vultour 11mo ago> I hope actual users never see this I have bad news for you, this website has been appearing near the top of the search results for some time now. I consciously avoid clicking on it every time.
- blibble 11mo agothey will it's the first result on google for just about anything technical I search for
- bulbar 11mo agoPlease don't correct the AI documentation. Just let those projects die as they deserve.
- Kirth 11mo agoLikewise, I tested this with a project we're using at work (https://deepwiki.com/openstack/kayobe-config https://deepwiki.com/openstack/kayobe-config) and at first it seems rather impressive until you realize the diagrams don't actually give any useful understanding of the system. Then, asking it questions, it gave useful seeming answers but which I knew were wholly incorrect. Worse than useless: disorienting and time-wasting.