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> In the first case, TFA refutes by claiming 'wrong' repeatedly, which does not convince me of anything. If anything, Zitron is probably right in this regard, s
by roywiggins 15d ago
> In the first case, TFA refutes by claiming 'wrong' repeatedly, which does not convince me of anything. If anything, Zitron is probably right in this regard, since the majority of progress in recent LLM tech has been setting up of guardrails to cajole the models using 'agents'.
I am certain that plugging a circa 2023 model into a 2026 harness would be a pretty frustrating experience. Yes, you could code a bit with AI in 2023, but models are just much better at it than they used to be. And smaller open models are leaps and bounds better at it than they were three years ago.
- fyredge 14d agoAgreed, though I do think that LLMs are still more similar than we think. Sometime after the first release, AI labs found that coding sat in the niche space of lots of easily digestible data and fast feedback from error messages and compiler checks etc. This allowed models to be trained with a focus on coding tasks, but the underlying technology is still the same, the infrastructure around it changed, they are still generating via probabilistic sampling. Don't get me wrong, I'm using local coding agents myself with varying levels of success and frustration, but the models themselves behave similarly to their siblings from 2020. The infrastructure improved, that includes the data. I have a pet theory that if they took the earlier models and retrain it with the data they used to train the latest models, we will get a similar result.