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As a related thing, I saw this article/video about vibe coding: https://atmoio.substack.com/p/after-two-years-of-vibecoding-im
I think I really agree with one of the core points, which is that the AI agent is really really good at making the diff it proposes look good, especially to the person who asked for it. But in the perspective of the entire project, or from the perspective of someone who didn't ask for those changes, the code is unbelievably retarded. In some sense, AI slop is a scissor statement cutting between the person who asked for it (who the AI is trying to please) and everyone else in the world.
I've had a lot of success vibe coding extremely self contained components for a larger application, such as a static web page that wraps an already existing api endpoint that I don't need to add more features to - the endpoint is stable and done. But on the other hand when AI makes changes deep into the internals of business logic, the code is absolutely dogshit garbage.
My assumption is that any large-ish vibe coded application that's not a bunch of self-contained parts is going to be completely unmaintanable, halfway broken, and just all around awful.
You can have the LLM describe the business logic as it's developing and create unit tests to confirm the output is correct and pair that with manual testing as well.
I've created an application with fairly involved business logic using Claude Code and it works really nicely - it simply required using good 'ol fashioned SLDC aka Design, Code, Test, Document.
Is it an enterprise level app with the ability to scale to 10000 concurrent users and SOC2 Authentication and privacy standards? No.... but those types of big applications require multiple teams. If I gave four or five competent software engineers Claude and divided up the work I think you could rebuild a complex application in a lot shorter time.
The idea that you can't use LLMs on business logic is like saying you can't use a car to drive across the country - sure it was true in 1920 but today with the interstate it's easy. Right now it's 1930 or so... but it won't remain 1930 forever.
The LLM will cheat and write test that don't test what they claim to, bypass tests when they fail, write docs that make zero sense, write code that doesn't follow the docs etc.
To be fair humans will do all of these things too. But the code that LLMs write is mind numbingly retarded. If anyone with a modicum of coding experience took a look at your slop app, he would only see an awful, unmaintainable mess. And every AI change you throw at it only increases the debt.
As I said, AI slop is a scissor statement cutting you, the slopmeister, off from everyone else in the world.
OK but that's why you do manual testing... also you can always ask other models to check on the work.
IDK I shared the code with my friend who's a Staff Engineer and he was pretty impressed especially since I've been really diligent about implementing good practices like separation of concerns, test driven development, DRY, determining architecture and reviewing it before implementing etc.
I'm not a moron - I've been a technical PM for 10 years now working on enterprise software and I've had plenty experience reviewing my dev's PRs and doing my own bug investigations as well as evaluating engineering solutions.
This dismissive attitude is only going to get less accurate as time goes on and models improve.
I wonder how long it'll be before "implementing" those practices is as simple as writing a good initial prompt for the coding agent to follow. And how long after that that "do it well" (or nothing at all) would sufficient for it to follow those practices by default.
Remember that LLM capabilities will only improve over time (barring severe government action, at least). Also remember that GPT-3 was released in 2020: Getting all the low-hanging fruit (never mind all the incremental improvements) from a novel technology in six years would be a fantastic achievement, so I don't think we're anywhere close to done.
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