kademolu

4 ms·

1. 1.▲
   by kademolu 2mo ago

   I can't say this enough, a well tune deterministic solver always beats an LLM. In most of my experiments, the best the LLM can do is match the solvers results.
2. 2.▲
   by kademolu 2mo ago

   Lets separate "workers" from "decision seats", you might need a lot of workers which typically don't actual require inference and might actually just be cheap solvers but actually less decision seats. So i guess it
3. 3.▲
   HN: SynthWorld – deterministic synthetic identity graphs with ground truth(github.com)

   2 points by kademolu 2mo ago|0 comments
4. 4.▲
   by kademolu 2mo ago

   Good stats but how does this work with models like Claude where a lot of the context or relevant information is stored in its "memory", other coding agents can't use it.
5. 5.▲
   by kademolu 2mo ago

   What a lot of people seem to miss is that you don't need the openweight models to "beat" frontier models for most agentic workloads. The harness should be doing the majority of the work, inference becomes the value add. This
6. 6.▲
   by kademolu 2mo ago

   If we take coding off the table i don't see the case for large agent swarms. More is not always better in my experience experimenting with agents