Well llms are imperfect tools that provide a lot of value (they offer leverage in form of agents to put it in Naval Ravikant words). They are here to stay.
> Fundamentally I don’t think we have a choice about riding on the AI technology train. It’s a wild ride and I just hope we’ll get through it OK.
> I don’t like them. They talk to me in this grating LLM-voice, an uncanny valley of talking to a real human. They confidently bullshit me - often giving me useful, helpful answers. But also just making stuff up with the same assurance - and with only a veneer of fake remorse when I call them out on it.
I think what he needs is a personal fine-tune, something I suspect will become more prominent when we start getting multi-tenant LoRA and can use fine-tunes on a per-token cost basis.
A co-worker commented that he thought the models performed worse after being "embarrassed," suspect it is an artifact of how some conversations progress in the training data.
I've gotten better results both after "shouting at" and praising Claude, though the former causes more token burn because it starts questioning asking permission for everything after, which gets really annoying. Even a session clear doesn't get it completely back to baseline; only after some time does it return to "its old self".
It does, but I feel like something is retained somewhere. There's already auto-memory, and the web chat has a persistent memory feature. It wouldn't surprise me if there's some hidden state that leads to per project or even per user auto-customization over time.
itanium · · focus · HN ↗
verdverm · · focus · HN ↗
> I don’t like them. They talk to me in this grating LLM-voice, an uncanny valley of talking to a real human. They confidently bullshit me - often giving me useful, helpful answers. But also just making stuff up with the same assurance - and with only a veneer of fake remorse when I call them out on it.
I think what he needs is a personal fine-tune, something I suspect will become more prominent when we start getting multi-tenant LoRA and can use fine-tunes on a per-token cost basis.
A co-worker commented that he thought the models performed worse after being "embarrassed," suspect it is an artifact of how some conversations progress in the training data.
skeledrew · · focus · HN ↗
verdverm · · focus · HN ↗
Does /clear in claude not do what I intuit it to?
skeledrew · · focus · HN ↗