Introduction

The landscape of AI development is evolving rapidly. As organizations move beyond proof-of-concept AI applications to production-scale systems, they face unprecedented challenges in development velocity, infrastructure costs, and engineering complexity. Today's most ambitious AI teams are no longer just consuming models—they're building homegrown agents and agentic experiences that require sophisticated infrastructure.

This tutorial will guide you through setting up a state-of-the-art AI development environment that combines several powerful tools:

• Anthropic's Model Context Protocol: For structured communication with Claude and other AI models
• Bazel Build System: For efficient monorepo management
• NativeLink: A remote execution and caching system hosted in your cloud
• Hugging Face Transformers: For integrating with the rich ecosystem of open-source models that you can run locally or deploy anywhere

By the end of this tutorial, you'll have a foundation for an AI development workflow that can scale with your ambitions, whether you're a startup or an enterprise team looking to build the next generation of AI applications.

Video Walkthrough

A video walkthrough of this blog is available at:

https://www.youtube.com/watch?v=dmVyRz59m0o&list=PLu4lTD4juMi6ll8jaYHbBp0pM4-RSPEQ8

Why This Stack Matters

Before diving into the technical details, let's understand why this particular combination of tools represents a step change for AI development:

1. Computational Efficiency: With NativeLink's remote execution capabilities, you can leverage your cloud resources optimally, including Nvidia GPUs, without wasteful duplication of work.
2. Developer Experience: A monorepo managed by Bazel allows your team to work in a unified codebase while maintaining clean separation of concerns.
3. Model Flexibility: By incorporating both Anthropic's Claude and Hugging Face's ecosystem, you maintain flexibility to use the right model for each specific need.

Let's begin building our AI development environment.

Setting Up Your Monorepo with Bazel

Bazel is a build system designed for monorepos that allows you to organize code into logical components while maintaining dependency relationships. For AI workloads, this is particularly valuable as it lets you separate model definitions, data processing pipelines, training code, and inference services.