“Flash-level” local frontier at ~27 t/s (~500 prefill) thanks to @antirez on my m5 128gb.
However, integrated harness/inference/sampling loop might be the true unlock.
It lightens the load on the model and corrects its work much more efficiently.
Check out the [upto]
I'll tell you one thing that shows a fundamental advantage of the "agent handles the LLM inference" in the specific case of local single user agents/LLMs. You can stop the tool and report an error as soon as in the first parameters of the tool calling you detect a problem. No
`pi reload` doesn't reload auth, but it does reload extensions.
And you can make an extension that reloads auth.
And you really don't need to know how any of it works.
You just ask pi, and it figures it out.
Biomedical agents can understand the treatment landscape for KRAS G12C-positive NSCLC by chaining together 7 BIOMCP commands.
The below terminal-based screencast demonstrates the BioMCP commands that:
* Orients across 1,741 known variants and 84 clinical trials
* Pulls CIViC
Pi really is the best coding agent when you want to have sovereignty over your workflow.
I was frustrated that pi wasn’t handling my VTT workflow because it would treat the newlines by @WisprFlow as separate commands.
Just described the frustration and it fixed it with a
