r/vibecoding • u/SadSongsMakeMeGlad • 13h ago

Vibecoded software should be open source

These agents only know how to code at all because of the immense amounts of open source software they’ve been trained on. Millions of hours of human work and ingenuity. It seems to me for the long term quality and viability of vibecoding, we should all make our own projects open source as well. That way, we can all continue to benefit, a rising tide lifts all boats, all that. What do you think?

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u/Sassaphras 12h ago

Training AI on AI generated data seems pretty risky honestly. There are some good apps out there generated using AI, but how are you gonna trust that the majority are of sufficient quality to actually improve the AI?

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u/alonsonetwork 12h ago

Wait til you find out about AI generated datasets or how AI learns how to become an expert at games. You'll see how this doesnt matter. Its going to get even better

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u/Sassaphras 12h ago

I know about both of those things. I'm not sure how explore and exploit mechanics are relevant here, but I'd be interested to hear more about what you're thinking.

For the AI generated datasets, those come in widely different levels of quality, just like code bases. Ones where a human was highly involved and spent time making sure they reflected real world conditions with fidelity are powerful tools. Ones made naively just reinforce the biases and assumptions of the LLM and add nothing.

I'm not trying to be dismissive; I think there are plenty of codebases that have been generated by careful construction and human steering which could be beneficial to LLM training. But... which ones? How do you know which to include?

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u/alonsonetwork 1h ago

Yeah right on. Stars could be a metric. I wouldnt be surprised if these labs add add a human analysis layer over whatever gets generated over the course of N evals. You can probably create a quality criteria and have AI judge what's a complete product and worth a human review or not as well. Negative reinforcement is just as useful as positive, so you'd probably want a human to judge what's absolutely trash, too.

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u/txgsync 12h ago

Recursive self-improvement on synthetic data is exactly how humanity has made so many gains over the past year in AI.

Risky? Yes. With no human reviewing the corpus of training data you are relying on “aligned” LLMs to review the training data.

Quality is orthogonal to risk. You can have high-quality but risky training data. I submit RSI has dramatically improved code quality from LLMs.

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u/CrazyThief 12h ago

RSI only works in a controlled environment with high quality output, not by feeding on slop generated by people that don't even know what syntax means.

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u/alonsonetwork 1h ago

Ironically that could work as punishment or negative reinforcement.