kimseungyong
Joined 5 months ago
Joined 5 months ago
I should let the development agent select a model and run it to improve token efficiency.
Is it being used this much these days?
I'm working with Claude's workflow, but I haven't yet felt any demand for customization.
For those who use this well, could you share examples of how you use it?
Since it starts with the Pro pricing plan, I’ll have to try it out later when it’s available on the Plus plan. Pro plan for toy project is too expensive.
The models below are competing on value for money.
In service development coding, a top-level model is not required.
I hope this kind of competition continues.
I still can't believe that an LLM can maintain all major service domains as documents
If too many documents are submitted, it will be hard to review, and if the domain is incorrectly set without me realizing it, the cost will be high when discovered later
AI model vendors will likely be torn between developing their own hardware to avoid hardware dependency or moving forward together.
Quietly, Apple and Google will take advantage of this trend.
Among devices worldwide, who has the most control? Apple
They don’t build AI themselves, but when it comes to device infrastructure that allows AI to operate, Apple is naturally the strongest
Just as Meta had tried to build their own ecosystem through Libra and things like that
Won't AI companies end up doing the same? But that attempt doesn’t seem like it would be easy
Apple now feels like a kind of Coca-Cola stock
However, the international agreement proposed by Dario Amodei is ultimately nothing more than a promise made by people. There is also a precedent in which the Nuclear Non-Proliferation Treaty could have simply withdrawn if it had been cornered like North Korea. Isn’t the AI speed-control agreement also vulnerable to being broken in the same way if one side finds itself politically cornered. In particular, unlike nuclear weapons, AI has the characteristic of recursively improving itself, allowing its capabilities to accelerate itself, so the moment one side breaks away, the gap can widen in an instant, making the incentive for deviation even greater.
Python, Postgres, gRPC, too much time in Temporal. Currently thinking about reproducibility — which data state produced a given result, and whether anyone can find it six months later.