u/ohnoitssobig

For some reason, I have this benchmarking prompt:

Implement a numpy function creating a bulk matrix product operator tensor as a function of model parameters.

I think it is approximately 10 lines of python code. So far, every local model that I tried (16Gb vram) fails to produce any meaningful output. I tried different variations of it (use web search, summarize the idea), different harness (pi, opencode) but no luck, different temperatures and thinking levels. Qwen3.8 for example produces an endless thinking loop. Is there anything I can do to help small models answering the question?

I have a secret called `~/.git-credentials`. I need to pass it into a container such that the container can use the secret the way I do. There are hard limitations to this:

- the secret cannot be a part of any image. neither can any part of my user environment (gid/uid) be

- no changes to secret permissions on the host (the secret has 600 permissions, owned by host user)

- container runs the payload as a non-root user inside container

- has to be a no-code solution outside the container: i.e. I cannot build a script around reading the secret on host and passing to container stdin, etc

- no silly bypassing of the previous rule: I cannot build a microservice to serve this secret from a different container... you get it

What I tried:

- bind mount results in insufficient permissions because container uid does not match my uid. When I `ls` the mounted secret it simply shows uid/gid of my user on the host

- compose secrets have the same issue as bind mount

- chatbot suggested swarm but it probably violates the last two rules? not sure