Does vibe coding risk destroying the Open Source ecosystem? According to a pre-print paper by a number of high-profile researchers, this might indeed be the case based on observed patterns and some…
So far, there is serious cognitive step needed that LLM just can’t do to get productive. They can output code but they don’t understand what’s going on. They don’t grasp architecture. Large projects don’t fit on their token window. Debugging something vague doesn’t work. Fact checking isn’t something they do well.
They don’t need the entire project to fit in their token windows. There are ways to make them work effectively in large projects. It takes some learning and effort, but I see it regularly in multiple large, complex monorepos.
I still feel somewhat new-ish to using LLMs for code (I was kinda forced to start learning), but when I first jumped into a big codebase with AI configs/docs from people who have been using LLMs for a while, I was kinda shocked. The LLM worked far better than I had ever experienced.
It actually takes a bit of skill to set up a decent workflow/configuration for these things. If you just jump into a big repo that doesn’t have configs/docs/optimizations for LLMs, or you haven’t figured out a decent workflow, then they’ll be underwhelming and significantly less productive.
(I know I’ll get downvoted just for describing my experience and observations here, but I don’t care. I miss the pre-LLM days very much, but they’re gone, whether we like it or not.)
This sounds a lot like every framework, 20 years ago you could have written that about rails.
Which IMO makes sense because if code isn’t solving anything interesting then you can dynamically generate it relatively easily, and it’s easy to get demos up and running, but neither can help you solve interesting problems.
Which isn’t to say it won’t have a major impact on software for decades, especially low-effort apps.
So far, there is serious cognitive step needed that LLM just can’t do to get productive. They can output code but they don’t understand what’s going on. They don’t grasp architecture. Large projects don’t fit on their token window.
There’s a remarkably effective solution for this, that helps both humans and models alike - write documentation.
It’s actually kind of funny how the LLM wave has sparked a renaissance of high-quality documentation. Who would have thought?
Funnily enpugh, AI itself is a great tool to create that high quality documentation fairly efficiently, but obviously not autonomously.
Even complex systems can be documented up to a level that is easy and much less laborious for the subject experts and architects to comb through for fhe final version.
I am not aware of what they are selling but every vibe coder i know produces obsessive amounts of documentation. It’s kind of baked into the tool (if you use Claude Code at least), it will just naturally produce a lot of documentation.
So far, there is serious cognitive step needed that LLM just can’t do to get productive. They can output code but they don’t understand what’s going on. They don’t grasp architecture. Large projects don’t fit on their token window. Debugging something vague doesn’t work. Fact checking isn’t something they do well.
They don’t need the entire project to fit in their token windows. There are ways to make them work effectively in large projects. It takes some learning and effort, but I see it regularly in multiple large, complex monorepos.
I still feel somewhat new-ish to using LLMs for code (I was kinda forced to start learning), but when I first jumped into a big codebase with AI configs/docs from people who have been using LLMs for a while, I was kinda shocked. The LLM worked far better than I had ever experienced.
It actually takes a bit of skill to set up a decent workflow/configuration for these things. If you just jump into a big repo that doesn’t have configs/docs/optimizations for LLMs, or you haven’t figured out a decent workflow, then they’ll be underwhelming and significantly less productive.
(I know I’ll get downvoted just for describing my experience and observations here, but I don’t care. I miss the pre-LLM days very much, but they’re gone, whether we like it or not.)
Exactly this. You can’t just replace experienced people with it, and that’s basically how it’s sold.
Yep, it’s a tool for engineers. People who try to ship vibe-coded slop to production will often eventually need an engineer when things fall apart.
This sounds a lot like every framework, 20 years ago you could have written that about rails.
Which IMO makes sense because if code isn’t solving anything interesting then you can dynamically generate it relatively easily, and it’s easy to get demos up and running, but neither can help you solve interesting problems.
Which isn’t to say it won’t have a major impact on software for decades, especially low-effort apps.
There’s a remarkably effective solution for this, that helps both humans and models alike - write documentation.
It’s actually kind of funny how the LLM wave has sparked a renaissance of high-quality documentation. Who would have thought?
Funnily enpugh, AI itself is a great tool to create that high quality documentation fairly efficiently, but obviously not autonomously.
Even complex systems can be documented up to a level that is easy and much less laborious for the subject experts and architects to comb through for fhe final version.
High-quality documentation assumes there’s someone with experience working on this. That’s not the vibe coding they’re selling.
Complete hands-off no-review no-technical experience vibe coding is obviously snake oil, yeah.
This is a pretty large problem when it comes to learning about LLM-based tooling: lots of noise, very little signal.
I am not aware of what they are selling but every vibe coder i know produces obsessive amounts of documentation. It’s kind of baked into the tool (if you use Claude Code at least), it will just naturally produce a lot of documentation.