I started off building a generic coding agent but focused on small/medium models, like GPT OSS 120B or 20B or models in between. I felt the early days of LLMs, where we would share example input/output may work if the prompt and context was very specific. This led me to experiment with local models. I have tried up to 9B models on my M4 Mac Mini 16GB and currently I am trying with Qwen 3.5 0.8B.

The idea is that a specific agent only focuses on a specific part of a typical full-stack CRUD app - say the model layer of MVC. I am using Diesel with Rust, so an agent specific to that handles the prompt to model struct, Diesel schema and function blocks for the Rust impl. The agent actually has separate modes with separate prompts for each of these 3 parts. It is given examples from official docs and creating new examples is trivial.

Here is an example: https://github.com/brainless/nocodo/blob/main/agents/src/rus...

The Rust engineer agent with Diesel modes works and there is deterministic code to glue all the generated code together. The next step would be to run Rust/cargo format, compiler or other tools deterministically. If there are errors, there would be specific agents, perhaps using slightly bigger models (<10B) to pass errors and code as a prompt. Again, we can supply lots of contextual examples from official docs.

I do not know if this setup will scale to accommodate all parts of typical MVC, routing, frontend (Solid state/context, forms, views), design (Tailwind based components) but it just might. This is a passion project that is going on slowly. I thought there is enough work done to share.

Please feel free to look through the code. Again, it is a WIP. Eventually this will be a desktop app working with llama.cpp.

Cheers!