This run was intentionally small-model and intentionally boring: no cloud API, no fake genius, just a tiny local model plus a better stack around it.

LLM Foundry with Qwen2.5-0.5B is the version that makes the point most cleanly: the model itself is small, but the workflow around it can still be decent.

What the proof showed

From the local proof run:

Benchmark pass rate: 50%

Reasoning: 60%

Coding: 100%

Tool + memory: 100%

The demo also showed memory compression and retrieval in action. The exact lesson is simple: if wording changes, semantic retrieval is a lot better than brittle keyword matching.

Why I care

The whole point of this layer is not to brag about a bigger model.…