[reactordev]

Not all AI is LLMs. That's just what's most prevalent right now. There's still great work being done by models that don't "speak" but "perform". The issue is they need to be trained to perform like you said. The more tools like Claude Code are used, the more training they receive as well. I do think we'll see a plateau (if we haven't reached it already) of diminishing returns and we'll seek out new algorithms to improve it.

Never underestimate the will of someone determined to gain an extra 10% performance or accuracy. It's the last 1% I worry about. 99.99% uptime is great until it isn't. 99% accuracy is great until it isn't. These things could be mitigated by running inference on different quantinizations of a model tree but ultimately we're going to have to triple check the work somehow.

[eddythompson80]

> The more tools like Claude Code are used, the more training they receive as well.

What do you mean? A model doesn't improve because it's being used more. Are you saying Anthropic invests more into Claude Code the more people use it? Or are you saying they collect its output and train it on it?

[xnx]

They probably mean https://en.wikipedia.org/wiki/Reinforcement_learning_from_hu..., but I don't think that is a huge factor for Claude Code.

[microtonal]

I assume they mean that they can gather users inputs (e.g. the user correcting the model, suggesting improvements, etc.).