Comment by werdnapk
4 days ago
I've used AI with "niche" programming questions and it's always a total let down. I truly don't understand this "vibe coding" movement unless everyone is building todo apps.
4 days ago
I've used AI with "niche" programming questions and it's always a total let down. I truly don't understand this "vibe coding" movement unless everyone is building todo apps.
There's a bit of a skill to it.
Good architecture plans help. Telling it where in an existing code base it can find things to pattern match against is also fantastic.
I'll often end up with a task that looks something like this:
* Implement Foo with a relation to FooBar.
* Foo should have X, Y, Z features
* We have an existing pattern for Fidget in BigFidget. Look at that for implementation
* Make sure you account for A, B, C. Check Widget for something similar.
It works surprisingly well.
> Good architecture plans help.
This is they key answer right here.
LLMs are great at interpolating and extrapolating based on context. Interpolating is far less error-prone. The problem with interpolating is that you need to start with accurate points so that interpolating between them leads to expected and relatively accurate estimates.
What we are seeing is the result of developers being oblivious to higher-level aspects of coding, such as software architecture, proper naming conventions, disciplined choice of dependencies and dependency management, and even best practices. Even basic requirements-gathering.
Their own personal experience is limited to diving into existing code bases and patching them here and there. They often screw up the existing software architecture because their lack of insight and awareness leads them to post PRs that get the job done at the expense of polluting the whole codebase into an unmanageable mess.
So these developers crack open an LLM and prompt it to generate code. They use their insights and personal experience to guide their prompts. Their experience reflects what they do on a daily basis. The LLMs of course generate code from their prompts, and the result is underwhelming. Garbage-in, garbage-out.
It's the LLMs fault, right? All the vibe coders out there showcasing good results must be frauds.
The telltale sign of how poor these developers are is how they dump the responsibility of they failing to get LLMs to generate acceptable results on the models not being good enough. The same models that are proven effective at creating whole projects from scratch at their hands are incapable of the smallest changes. It's weird how that sounds, right? If only the models were better... Better at what? At navigating through your input to achieve things that others already achieve? That's certainly the model's fault, isn't it?
A bad workman always blames his tools.
Yes, with a bit of work around prompting and focusing on closed context, or as you put it, interpolating, you can get further. But the problems is that, this is not how the LLMs were sold. If you blame someone for trying to use it by specifying fairly high level prompts - well isn´t that exactly how this technology was being advertised the whole time? The problem is not the bad workman, the problem is that the tool is not doing what it is advertised as doing.
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Yeah this is a great summary of what I do as well and I find it very effective. I think of hands-off AI coding like you're directing a movie. You have a rough image of what "good" looks like in your head, and you're trying to articulate it with enough detail to all the stagehands and actors such that they can realize the vision. The models can always get there with enough coaching, traditionally the question is if that's worth the trouble versus just doing it yourself.
Increasingly I find that AI at this point is good enough I am rarely stepping in to "do it myself".
It's incredible when I ask Claude 3.7 a question about Typescript/Python and it can generate hundreds of lines of code that are pretty on point (it's usually not exactly correct on first prompt, but it's coherent).
I've recently been asking questions about Dafny and Lean -- it's frustrating that it will completely make up syntax and features that don't exist, but still speak to me with the same confidence as when it's talking about Typescript. It's possible that shoving lots of documentation or a book about the language into the context would help (I haven't tried), but I'm not sure if it would make up for the model's lack of "intuition" about the subject.
Don't need to ho that esoteric. Seen them make stuff up pretty often for more common functional programming languages like Haskell and OCaml.
Recommend using RAG for this. Make the Haskell or OCaml documentation your knowledge base and index it for RAG. Then it makes a heck of a lot more sense!
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Well all LLM are fairly bad for react native as soon as you look at more than hello world type of things.
I got stuck with different LLM until I checked the official documentation, yeah spouting nonsense from 2y+ removed features I suppose or just making up stuff.
I'm trialing co-pilot in VSCode and it's a mixed bag. Certain things it pops out great, but a lot of times I'll be like woohoo! <tab> <tab> <tab> and then end up immediately realising wait a sec, none of this is actually needed, or it's just explicitly calling for things that are already default values, or whatever.
(This is particularly in the context of metadata-type stuff, things like pyproject files, ansible playbooks, Dockerfiles, etc)
I recently exclaimed that “vibe coding is BS” to one of my coworkers before explaining that I’ve actually been using GPT, Claude, llama (for airplanes), Cline, Cursor, Windsurf, and more for coding for as long as they’ve been available (more recently playing with Gemini). Cline + Sonnet 3.7 has been giving me great results on smaller projects with popular languages, and I feel truly fortunate to have AWS Bedrock on tap to drive this stuff (no effective throttling/availability limits for an individual dev). Even llama + Continue has proven workable (though it will absolutely hallucinate language features and APIs).
That said, 100% pure vibe coding is, as far as I can tell, still very much BS. The subtle ugliness that can come out of purely prompt-coded projects is truly a rat hole of hate, and results can get truly explosive when context windows saturate. Thoughtful, well-crafted architectural boundaries and protocols call for forethought and presence of mind that isn’t yet emerging from generative systems. So spend your time on that stuff and let the robots fill in the boilerplate. The edges of capability are going to keep moving/growing, but it’s already a force multiplier if you can figure out ways to operate.
For reference, I’ve used various degrees of assistance for color transforms, computer vision, CNN network training for novel data, and several hundred smaller problems. Even if I know how to solve a problem, I generally run it through 2-3 models to see how they’ll perform. Sometimes they teach me something. Sometimes they violently implode, which teaches me something else.
> That said, 100% pure vibe coding is, as far as I can tell, still very much BS.
I don't really agree. There's certainly a showboating factor, not to mention there is currently a goldrush to tap this movement to capitalize from it. However, I personally managed to create a fully functioning web app from scratch with Copilot+vs code using a mix of GPT4 and o1-mini. I'm talking about both backend and frontend, with basic auth in place. I am by no means a expert, but I did it in an afternoon. Call it BS, the the truth of the matter is that the app exists.
People were making a front and backend web app in half a day using Ruby on Rails way before LLMs were ever a thing, and their code quality was still much better than yours!
So vibe coding, sure you can create some shitty thing which WORKS, but once it becomes bigger than a small shitty thing, it becomes harder and harder to work with because the code is so terrible when you're pure vibe coding.
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People who embracing vibe coding are probably the same people who were already sudo-vibe coding to begin with using found fragments of code they could piece together to make things sort of work for simple tasks.
I mean, I don't think you need to do cutting edge programming to make something personal to you. See here from Canva's product. Check this out: https://youtu.be/LupwvXsOQqs?t=2366
> I've used AI with "niche" programming questions and it's always a total let down.
That's perfectly fine. It just means you tried without putting in any effort and failed to get results that were aligned with your expectations.
I'm also disappointed when I can't dunk or hit >50% of my 3pt shots, but then again I never played basketball competitively
> I truly don't understand this "vibe coding" movement unless everyone is building todo apps.
Yeah, I also don't understand the NBA. Every single one of those players show themselves dunking and jumping over cars and having almost perfect percentages in 3pt shots during practice, whereas I can barely get off my chair. The problem is certainly basketball.