nlpnerd

What about Maritime.sh (which is also in YC)? I swear everybody in SF is building the same stuff. Lol

Doesn't OpenAI's decision API support up to 1M context on Luna?

Because training your own model requires substantial effort around data annotations, and then dealing with data drift etc in production.

One may consider it "rigidity" but many application backends (including LLM powered ones) never needed the full unconstrained capabilities of an LLM. What people needed was the zero-shot capabilities over tasks as opposed to have to train a classifier, span model, etc for every task.

Constraints can be an advantage in many systems. Consider typed and untyped programming languages. Many advantages with typed languages over the latter in terms of development and efficiency.

This is the difference between a "hot" company in a good ecosystem like SF. Yeah, the funds can take 2-3 months to do their due diligence. By the time it's done, the round has closed, and then what good is the due diligence?

You are assuming that the VCs have done their due diligence. For a "hot" company like Typesafe AI, most likely little due diligence was done. That's the way it's played.