zero-cost-cline

Build, test, and run private AI-assisted coding agent workflows entirely on-device with zero cloud costs or API keys.

This repository contains a comprehensive, step-by-step setup guide and technical walkthrough for configuring a fully local, private AI software engineering agent on an Apple Silicon Mac.

By pairing Ollama (the model server) with Cline (the advanced VS Code agent extension), this guide demonstrates how to break free from frontier model subscriptions and API paywalls. You will configure a highly disciplined, human-in-the-loop agentic workflow tailored precisely for local inference hardware.

🚀 Looking for the code? A fully completed, working reference implementation built by following this guide can be found at https://github.com/rextanka/avi-tree-tutorial-result.git.

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Why Use This Guide?

• Zero API Spend: Perfect for developers, students, and hobbyists on a budget who want to experiment with advanced agentic software engineering without recurring costs.
• 100% Private & Offline: Everything runs entirely on-device. No code snippets or data ever leave your machine.
• Calibrated for Real Hardware: The guide is rigorously tuned for two baseline configurations:
  • 24GB Unified Memory (e.g., MacBook Air M4) running efficient dense models.
  • 48GB Unified Memory (e.g., MacBook Pro M4 Pro) running advanced coding Mixture-of-Experts (MoE) models.
• Mitigates Local Model Flaws: Local 9B–12B models can sometimes loop or hallucinate terminal commands. This guide provides explicit .clinerules and a robust Git checkpoint strategy to keep your agent tightly disciplined.

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The Local Tech Stack

The tutorial implements a full-tier development environment featuring:

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Tutorial Roadmap

The companion guide walks you through a structured, multi-phase setup and practical exercise:

1. Host Machine Prerequisites & Setup

Step-by-step installation instructions for Homebrew, CMake, Python, VS Code, and Xcode command line tools.

2. Ollama & Custom Modelfile Configuration

Learn how to write a persistent Modelfile to configure proper context windows (num_ctx), low temperatures for deterministic code generation, and keep-alive parameters (OLLAMA_KEEP_ALIVE=-1) to prevent annoying cold-start reload lags.

3. Agent Discipline with .clinerules

Configure Cline settings to strictly use an Act-mode human-in-the-loop workflow (avoiding local model failure loops) and bootstrap a local .clinerules file that prevents the agent from running wild across your file system.

4. A Worked Example: The Three-Phase Agent Lifecycle

To prove the setup works, you will guide the local LLM through building a complete AVL Tree in C++ from scratch using a strict Test-Driven Development (TDD) approach:

• Phase I (Infrastructure): Directing the agent to establish directory hierarchies and compile clean CMake structures.
• Phase II (Interface & Test Scaffolding): Writing the rigid public C++ header contracts and establishing unit/functional tests before implementation code exists.
• Phase III (Implementation & Verification): Guiding the model through complex self-balancing tree pointer rotation logic, using automated test runs as a deterministic evaluation harness.

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Getting Started

1. Clone this repository to your Apple Silicon Mac.
2. Open the enclosed local-llm-cline-tutorial.md tutorial document.
3. Follow the prerequisites to provision your local environment and begin coding for free!

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Contributions, feedback, and model performance benchmarking updates for new Apple Silicon chips are always welcome!