lzaborowski

I like the idea of flipping the constraint. Most ML benchmarks assume unlimited data and limited compute, so people optimize for speed.

If high-quality training data becomes the real bottleneck, then the interesting question is how much signal you can extract from the same dataset when compute is cheap.

One thing I’ve noticed with local models is that people tolerate a lot more trial and error behavior. When a hosted model wastes tokens it feels expensive, but when a local model loops a bit it just feels like it’s “thinking.”

If models like Qwen can get good enough for coding tasks locally, the real shift might be economic rather than purely capability.