121–130 of 254 posts
Asking it for higher level planning / architecture is just asking for pain
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Good joke, but the reality is they falter even more on truly greenfield projects. See: https://news.ycombinator.com/item?id=42134602
That is because, by definition, their models are based upon the past. And woe unto thee if that training data was not pristine. Error propagation is a feature; it's a part of the design, unless one is suuuuper careful. As some have said, "Fools rush in."
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The niche I've found for LLMs is for implementing individual functions and unit tests. I'll define an interface and a return (or a test name and expectation) and say "this is what I want this to do", and let the LLM take the first crack at it. Limiting the bounds of the problem to be solved does a pretty good job of at least scaffolding something out that I can then take to completion. I almost never end up taking th…
Can't tell you how much I love it for testing, it's basically the only thing I use it for. I now have a test suite that can rebuild my entire app from the ground up locally, and works in the cloud as well. It's a huge motivator actually to write a piece of code with the reward being the ability to send it to the LLM to create some tests and then seeing a nice stream of green checkmarks.
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Like most of us it appears LLMs really only want to work on greenfield projects.
The site also suggests LLMs care a great deal one way or another. "Unlock a codebase that your engineers and AI love." https://www.gauge.sh/ I think they do often act opinionated and show some decision-making ability, so AI alignment really is important.
> Companies with relatively young, high-quality codebases benefit the most from generative AI tools, while companies with gnarly, legacy codebases will struggle to adopt them. In other words, the penalty for having a ‘high-debt’ codebase is now larger than ever. This mirrors my experience using LLMs on personal projects. They can provide good advice only to the extent that your project stays within the bounds of well…
Coincidentally this also happens with developers in unfamiliar territory.
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In my experience you need a high quality codebase to be able to iterate at maximum speed. Any time someone, myself included, thought they could cut corners to speed up iteration, it ended up slowing things down dramatically in the end. Coding haphazardly can be a lot more thrilling, though! I certainly don't enjoy the process of maintaining high quality code. It is lovely in hindsight, but an awful slog in the moment…
> thought they could cut corners to speed up iteration Anecdotally, I find you can get about 3 days of speed from cutting corners - after that, as you say, you get slowed down more than you got sped up. First day, you get massive speed from going haphazard; second day, you're running out of corners to cut, and on the third day you start running into problems you created for yourself on the first day.
Instead of genAI doing the rubbish, boring, low status part of the job, you should do the bits of the job no one will reward you for, and then watch as your boss waxes lyrical about how genAI is amazing once you've done all the hard work for it?
It just feels like if you're re-directing your efforts to help the AI, because the AI isn't very good at actual complex coding tasks then... what's the benefit of AI in the first place? It's nice that it helps you with the easy bit, but the easy bit shouldn't be that much of your actual work and at the end of the day... it's easy?
This gives very similar vibes to: "I wanted machines to do all the soul crushing monotonous jobs so we would be free to go and paint and write books and fulfill our creative passions but instead we've created a machine to trivially create any art work but can't work a till"
LLM code gen tools are really freaking good...at making the exact same react boilerplate app that everyone else has. The moment you need to do something novel or complicated they choke up. This is why I'm not very confident that tools like Vercel's v0 ( https://v0.dev/ ) are useful for more than just playing around. It seems very impressive at first glance - but it's a mile wide and only an inch deep.
If can you can create boilerplate code, logging, documentation, common algorithms by AI it saves you a lot of time which you can use on your specialized stuff. I am convinced that you can make yourself x2 by using an AI. Just use it in the proper way.
This means you're getting paid 2x more, right?
...Right?
> Companies with relatively young, high-quality codebases benefit the most from generative AI tools, while companies with gnarly, legacy codebases will struggle to adopt them. In other words, the penalty for having a ‘high-debt’ codebase is now larger than ever. This mirrors my experience using LLMs on personal projects. They can provide good advice only to the extent that your project stays within the bounds of well…
For me same experience but opposite conclusion. LLM saves me time by being excellent at yak shaving, letting me focus on the things that truly need my attention. It would be great if they were good at the hard stuff too, but if I had to pick, the basics is where i want them the most. My brain just really dislikes that stuff, and i find it challenging to stay focused and motivated on those things.
> Companies with relatively young, high-quality codebases benefit the most from generative AI tools, while companies with gnarly, legacy codebases will struggle to adopt them. In other words, the penalty for having a ‘high-debt’ codebase is now larger than ever. This mirrors my experience using LLMs on personal projects. They can provide good advice only to the extent that your project stays within the bounds of well…