61–70 of 132 posts
I don’t want to talk to my documentation. I just want the facts searchable and easily readable.
This is an interesting threads. There are many instances of "this is bad, doesn't work, don't like it", and many instances of "it works reasonably well here, look: ". Seems like a consistent pattern.
My repo has a plugin structure (https://github.com/ytreister/gibr), and I love how it added a section about adding a new plugin: https://deepwiki.com/ytreister/gibr/7.4-adding-a-new-issue-t...
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 It's also outdated.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…
I 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.
But you’re not looking at the same thing — you’re looking at two completely different sets of output.
Perhaps their project uses a more obscure language, has a more complex architecture, resembles another project that’s tripping up the interpretation of it. You have have excellent results without it being perfect for everything. Nothing is perfect and it’s important for people making these things to know how, right?
In my career I’ve never seen such aggressive dismissal of people’s negative experiences without even knowing if their use case is significantly different.
Earlier quoted context omitted.
I 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.
> 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.
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.
Earlier quoted context omitted.
>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…
Wow. Better advertisement for LLM in three comments than anything OpenAI could come up with.
This is not an ad for LLMs. If you think this is good, you should probably not ever touch code that humans interact with.
Looks like it's impossible for me to use this service - when I try to submit the form, I get a reCAPTCHA challenge. By the time I complete it (Google requires me to make several attempts, each one being several pages), the page errors out in the background with "reCAPTCHA execution timeout".
This is an interesting threads. There are many instances of "this is bad, doesn't work, don't like it", and many instances of "it works reasonably well here, look: ". Seems like a consistent pattern.
There was some article here on how llm's are like gambling, in that sometimes you get great payouts and oftentimes not, and as psych 101 taught us, that kind of intermittent reward is addictive.
I don't think either of this is true of LLMs. You obviously can improve its results with the right prompt + context + model choice, to a pretty large degree. The probability...hard to quantify, so I won't try. Let's just say that you wouldn't say you are addicted to your car because you have a 1% chance of being stuck in the middle of nowhere if it breaks down and 99% chance of a reward. The threshold I'm not sure.
It's a cute little Electron-based mini Spotify player that gets maybe like 200 users a day and has 1.3k stars on GitHub. Code quality is pretty high and it's more or less "feature-complete." There's a lot of simple/typical React stuff in there, but there's also some weird stuff I had to do. For example, native volume capture is weird. But even weirder is having to mess with the Electron internal window boundaries (so people can move their Lofi window where-ever they want to).
We're essentially suppressing window rect constraints using some funky ObjectiveC black magic[1]. The code isn't complicated[1], but it's weird and probably very specific to this use case. When I ask what "constraints" does, DeepWiki totally breaks, telling me it doesn't even have access to those source files[2] (which it does).
Visualizations were also actually disabled on MacOS a few versions ago (because of the janky way you need to hook into the audio driver), but, again DeepWiki doesn't really notice[3]. There have been issues/patch notes about this, so I feel those should be getting crawled.
[1] https://github.com/dvx/lofi/blob/master/src/native/black-mag...
[2] https://deepwiki.com/search/what-is-constraints_cc5c0478-e45...
[3] https://deepwiki.com/search/how-do-macos-visualizations-wo_d...
One is the extremely sprawling MarginaliaSearch repo[M1].
Here it did a decent job of capturing the architecture, though it is to be fair well documented in the repo itself. It successfully identifies the most important components, which is also good.
But when describing the components, it only really succeeds where the components themselves are very self-contained and easy to grok. It did a decent job with e.g. the buffer pool[M2], but even then fails to define some concepts that would have made it easier to follow, e.g. what is a pin count in buffer management? This is standard terminology and something the model should know.
I get the impression it lifts a lot of its fact from the comments and documentation that already exists, which may lead it to propagate outdated falsehoods about the code.
[M1] https://deepwiki.com/MarginaliaSearch/MarginaliaSearch
[M2] https://deepwiki.com/MarginaliaSearch/MarginaliaSearch/5.2-b...
The other is the SlopData[S1] repo, which contains a small library for columnar data serialization.
This one I wasn't very impressed with. It produced more documentation than was necessary, mostly amending what was already there with incorrect statements it seems to have pulled out of its posterior[2][3].
The library is very low-abstraction, and there simply isn't a lot of architecture to diagram, but the model seems to insist that there must be a lot of architecture and then produces excessive diagrams as a result.
[S1] https://deepwiki.com/MarginaliaSearch/SlopData
[S2] https://deepwiki.com/MarginaliaSearch/SlopData#storage-types (performance numbers are completely invented, in practice reading compressed data is typically faster than plain data)
[S3] https://deepwiki.com/MarginaliaSearch/SlopData/6.3-zip-packa... (the overview section is false, all these tables are immutable).
So overall it gives me a bit of a broken clock vibe. When it's right, it's great. When it isn't, it's not very useful. Good at the stuff that is already easy, borderline useless for the stuff that isn't.