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The vibecoders are becoming sentient
(waterloolemmy.ca)
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this post was submitted on 19 Aug 2025
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As a software developer, I've found some free LLMs to provide productivity boosts. It is a fairly hairpulling experience to not try too hard to get a bad LLM to correct itself, and learning to switch quickly from bad LLMs is a key skill in using them. A good model is still one that you can fix their broken code, and ask them to understand why what you provided them fixes it. They need a long context window to not repeat their mistakes. Qwen 3 is very good at this. Open source also means a future of customizing to domain, ie. language specific, optimizations, and privacy trust/unlimited use with enough local RAM, with some confidence that AI is working for you rather than data collecting for others. Claude Sonnet 4 is stronger, but limited free access.
The permanent side of high market cap US AI industry is that it will always be a vector for NSA/fascism empire supremacy, and Skynet goal, in addition to potentially stealing your input/output streams. The future for users who need to opt out of these threats, is local inference, and open source that can be customized to domains important to users/organizations. Open models are already at close parity, IMO from my investigations, and, relatively low hanging fruit, customization a certain path to exceeding parity for most applications.
No LLM can be trusted to allow you do to something you have no expertise in. This state will remain an optimistic future for longer than you hope.
I think the key to good LLM usage is a light touch. Let the LLM know what you want, maybe refine it if you see where the result went wrong. But if you find yourself deep in conversation trying to explain to the LLM why it's not getting your idea, you're going to wind up with a bad product. Just abandon it and try to do the thing yourself or get someone who knows what you want.
They get confused easily, and despite what is being pitched, they don't really learn very well. So if they get something wrong the first time they aren't going to figure it out after another hour or two.
In my experience, they're better at poking holes in code than writing it, whether that's green or brownfield.
I've tried to get it to make sections of changes for me, and it feels very productive, but when I time myself I find I spend probably more time correcting the LLM's work than if I'd just written it myself.
But if you ask it to judge a refactor, then you might actually get one or two good points. You just have to really be careful to double check its assertions if you're unfamiliar with anything, because it will lead you to some real boners if you just follow it blindly.
At work we've got coderabbit set up on our github and it has found bugs that I wrote. Sometimes the thing drives me insane with pointless comments, but just today found a spot that would have been a big bug in prod in like 3 months.
Yes. Kind of. It takes ( a couple of days) experience with LLMs to know that failing to understand your corrections means immediate delete and try another LLM. The only OpenAI llm I tried was their 120g open source release. It insisted that it was correct in its stupidity. That's worse than LLMs that forget the corrections from 3 prompts ago, though I also learned that is also grounds for delete over any hope for their usefulness.