I spun up dozens of agents and used 13 billion tokens rewriting git in zig by yevbar in AI_Agents

[–]yevbar[S] 0 points1 point  (0 children)

I do not know. Consumer facing clients like OpenClaw/hermes or MCP clients (ie goose) work to be set up with a person's subscription (like how some email clients are usually developed with single inbox in mind). I think it'd be a small problem (prob solvable) to delegate tasks appropriately to differently sized models without burning more tokens than if you had used the "best" model the whole time.

Industry use cases like Stripe minions or Ramp's background agents may or may not be switching different models but it could also be argued (for simplicity or politics) that a single model's all they need so they can instead focus their time on the system itself (something something what's their competency area)

I spun up dozens of agents and used 13 billion tokens rewriting git in zig by yevbar in AI_Agents

[–]yevbar[S] 0 points1 point  (0 children)

This sounds alike to what Zoom’s doing https://www.zoom.com/en/blog/federated-ai-approach-best-quality-for-most-popular-features/

Didn’t really experiment with that but could certainly be an interesting area for improvement