Gemma 4, Pi power local coding agent

The interesting part is not just “run a model locally,” but that the model, harness, and session discipline are finally good enough to make local agent workflows feel usable.

• –Pi’s tiny four-tool core keeps the agent loop lean, which matters when you are trying to squeeze reliability out of a local model instead of a cloud-hosted one.
• –Gemma 4 26B A4B is the right fit here because the MoE setup gives strong quality without behaving like a full dense 26B monster every token, though VRAM is still the gating factor.
• –The guide is pragmatic about runtime choice: LM Studio is the easiest path, but the same setup works with llama.cpp or Ollama, so the real abstraction is the OpenAI-compatible API.
• –Context management is doing a lot of the heavy lifting here; `/compact`, `/tree`, and `/fork` are what keep a local coding session from collapsing under its own history.
• –This is a credible privacy-first setup for developers who want offline coding help, but it still assumes you are willing to tune quantization, offload, and context limits.