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- Hacker News
- A while ago I had predicted that eventually all coding would eventually become vibe-coding but it would still be a deep engineering discipline (https://news.ycombinator.com/item?id=48040206) -- this is what I meant. Deep technical expertise is still needed, but it shifts from working with the code directly to crafting bespoke comprehensive validation mechanisms around the code. This is a great example of what that could look like.
So it's technically vibe-coding in the sense you don't really look at the code, you just look at the results and "go by the vibes"... except now you're working to rigorously quantify and enforce those vibes. (Philosophical aside: once vibes are rigorously enforced are they "vibes" anymore?)
by keeda - That's great but I really wish you guys would do something about the llm integration, I tried using it two days ago to create a cohort of users using a sql query, and I was surprised to see that it said that it could not create cohorts for me and i had to resort to exporting data from a sql insight as a cohort cannot use a sql query. However the worst part was it just writing in the text input slowed down my m4 pro chip to less than 1 fps after 2 prompts and it really left a bad taste in my mouth.
Perhaps the next target for a 100x improvement
- The key parts of this is how not vibecoded it is. Feels like a model of how you should do software with AI. Now that we can easily set up property testing, fuzzing, etc. there's almost no reason not to.
- This is the type of problem for which LLM generation is great for.
If you have an oracle, and your problem is largely just a pure function, it's pretty good at generating something that both works and is fast.
by theLiminator - I cannot believe they're sticking to their guns on this website design. It's awful.by mikkelam
- I’ve had very good success in similar setups where you have some sort of “oracle” and can generate enormous corpuses of test data, such that you really, really trust the LLM code must work for the inputs you expect it’ll ever need to handle.
Makes me think of all the algorithms we specify in proof languages and then hand-implement in production languages - this setup could maybe let you just specify the proof of an algorithm and then let LLMs derive efficient implementations with the (slow) proof as an oracle
by jakewins - > We didn't write this parser by hand because, at least pre-AI-coding, parsers were extremely difficult to maintain. Writing one without AI would have taken months [...]
> Instead, we use ANTLR, a state-of-the-art, open source parser generator.
I don't agree with this (pre-AI-coding) take. Hand-rolled parsers are much easier to write well and maintain than people think. They also tend to be much faster and produce much better errors than parser generators. I guess if the language you're trying to parse is, say, C++, then you're going to have a miserable time (probably no matter what). But an SQL parser is very doable. (I say this as the author and maintainer of an in-house SQL dialect thingy at work.)
What makes building and maintaining a hand-written parser such a tractable task is:
- The code size can be large, but you can start with a core of a few well-chosen abstractions and then you add lots of parsing code for various language constructs but it's all kind of orthogonal and doesn't add compounding complexity as you go. - It's just about the most testable kind of code there is. You can cover all the various corner cases with tests and really lock in the behavior so that you can very confidently make changes. One approach I like is to make zillions of tiny test files in the target language accompanied by some golden representation of the AST.
And of course, as the author found out, these properties make writing a parser a really good task for AI coding, too. These tools are very, very good at generating a bunch of new code based on existing abstractions and covering it with lots of test cases.
So I agree with where they ended up, just not where they started :)
by cespare - Well despite my current anti AI sentiment, I have to admit that after reading the article, It was a good use of AI, done by someone with good technical skills. Still I have the feeling that this only works because of the vast accumulated knowledge pre-AI, and if everybody keeps going in this path, it will end up making everyone not advancing their knowledge at the pace they did before. I feel that this AI immersion is really about selling our soul to the devil for short term gains.by duendefm