[queenkjuul]

The thing is, Claude Code is great for unimportant casual projects, and genuinely very bad at working in big, complex, established projects. The latter of course being the ones most people actually work on.

Well either it's bad at it, or everyone on my team is bad at prompting. Given how dedicated my boss has been to using Claude for everything for the past year and the output continuing to be garbage, though, i don't think it's a lack of effort on the team's part, i have to believe Claude just isn't good at my job.

[sodafountan]

I was going to try having an AI agent analyze a well-established open source project. I was thinking of trying something like Bitcoin Core or an open-source JavaScript library, something that has had a lot of human eyes on it. To me, that seems like a good use case, as some of those projects can get pretty complex in what they're aiming to accomplish. Just the sheer amount of complexity involved in Bitcoin, for instance, would be a good candidate for having an AI agent explain the code to you as you're reviewing it. A lot of those projects are fairly well-written as they are, with the higher-level concepts being the more difficult thing to grasp.

Not attempting to claim anything against your company, but I've worked for enterprises where code bases were a complete mess and even the product itself didn't have a clear goal. That's likely not the ideal candidate for AI systems to augment.

[queenkjuul]

Frankly, the code isn't messy whatsoever. There's just lots of it, and it's necessarily complex due to the domain. It's honestly the best codebase I've ever worked with - i shudder to think what nonsense Claude would spew trying to contextualize the spaghetti at my last job

[pennomi]

As context size increases, AI becomes exponentially dumber. Most established software is far, FAR too large for AI. But small, greenfield projects are amazing for something like Claude Code.

[SubiculumCode]

This is why I argue that the impact of LLMs is in the tail. Its all the small to midsize shops that want something done, but don't have money to hire a programmer. Its small tasks, like pushing data around, writing a quick interface to help day to day jobs in niche jobs and technical problems. Its the ability to quickly generate prototype logos and scripts for small scale ad campaigns, for solving Nancy's Excel issue, etc. Big companies have big software and code stacks with tons of dependencies. Small shops have little project needs that solve significant issues facing their operations, but will unlikely become large enough that things like scaling issues, maintenance, integration, are ever a problem at all. Its a tail, but its long in small to midsize businesses. In research labs, which I have personal experience, AI is rapidly making feasible more ambitious projects, quicker timelines, and better code, generally.