For the last few months I've been building Tlamatini, an open-source local-first AI developer assistant. Along the way I kept bumping into the same assumption — both in articles and in my own head — that to build something useful, you need the biggest model you can afford. GPT-4. Claude Opus. Llama 70B at minimum.
Then I started actually shipping with smaller local models, and I learned something that flipped my thinking.
The real lesson
A 20B-parameter LLM, given the right tools, the right agents, and skills fine-tuned to your operating procedures, is good enough to power most of your company's real workflows.
Parameter count is not the bottleneck. The bottleneck is whether the model can act — and that's a tools problem, not a parameters problem.
