I just published an article about adapting a cleanroom implementation of Claude Code to work with Ollama in just 48 hours—with no prior TypeScript experience.
The Problem
When I tried Claude Code, I loved it but burned through a month's token allocation in just three days of development. I needed a similar experience but with local models.
The Solution
By integrating Ollama with a cleanroom implementation, I created a terminal-based AI coding assistant that:
- Works entirely with local models
- Maintains a familiar CLI interface
- Solves the WSL-to-Windows networking puzzle
- Doesn't require any token management
Key Learnings
- Using AI to learn unfamiliar tech stacks (Cursor helped me with TypeScript)
- The value of focusing on architecture over syntax
- Starting with minimal viable solutions
- Solving infrastructure issues (especially WSL networking)
If you've been looking for a sustainable way to use AI coding assistance without breaking the bank, check out the full article!
Top comments (1)
If you're experimenting with local LLMs like Code Llama or Mistral via Ollama, I highly recommend pairing it with tools like Code Interpreter or Continue.dev inside VS Code for a more integrated experience. You can even connect your terminal-based assistant with these tools using simple pipes or WebSocket bridges. This way, your setup evolves from just a CLI tool to a full-on productivity stack—all running locally and offline.