Little-coder on Qwen3.6 rivals GPT-5.4 benchmarks

The "scaffold-model fit" thesis is a critical shift for local AI development, proving that agent architecture is as important as the model weights themselves.

• –little-coder nearly doubles the pass rate of small models compared to generic harnesses by utilizing bounded reasoning budgets and explicit workspace discovery.
• –The use of "skill injections" instead of massive static system prompts prevents local models from becoming overwhelmed by long context instructions.
• –Routing tasks by "shape" (e.g., using Qwen3.6 for migrations but specialized playbooks for concurrency) effectively mitigates the individual failure modes of local models.
• –By offloading deterministic tasks like `gofmt` and `go mod tidy` to system tools, the scaffold preserves the LLM's limited reasoning capacity for actual logic implementation.