Claude Code AI Now Free Locally | Full Source Code Setup

How to Build Your Own Local AI Using Leaked Claude Source Code

Introduction to Local AI Development

• The speaker emphasizes the high costs associated with using cloud APIs for AI, particularly highlighting that users are already behind if they haven't transitioned to local solutions.

• A brief explanation of tokens is provided, clarifying that 100 words roughly equate to 150 tokens, which can lead to significant expenses when querying cloud services.

Benefits of Building a Local AI

• The recent leak of Claude's source code (500k lines of TypeScript) allows developers to replicate its performance locally without incurring costs or privacy concerns.

• Key features revealed in the leak include a multi-agent system where tasks are planned and executed sequentially by a coordinator and sub-agents.

Technical Setup for Local AI

• Instructions are given on setting up the coding environment using Google Anti-gravity and Olama software, both essential for running local AIs effectively.

• The speaker introduces Q1 3.5 models as open-source multimodal models suitable for various tasks like chatbots and image analysis.

Model Selection Based on System Capabilities

• Guidance is provided on selecting the appropriate model based on available VRAM; users should match model parameters with their system's capabilities.

• For systems with limited VRAM (e.g., 4GB), it’s recommended to choose smaller models (like the 4 billion parameter model).

Initializing and Testing the Local AI

• Users can initiate their local AI by executing commands in the terminal after downloading the selected model.

• The first task assigned to this local AI involves converting messy Python code into a well-formatted version, showcasing its practical application.

Building an Orchestrator Multi-Agent Team

• The speaker outlines plans for creating an orchestrator team similar to Claude's architecture, utilizing libraries such as Olama and Rich for enhanced functionality.

• An initial function will be developed to extract Python code from raw text using regex patterns, demonstrating how structured programming can improve clarity.

Orchestrator Initialization and Code Refactoring Process

Starting the Orchestrator

• The orchestrator begins its process, formatting text with bold and color before opening a messy calculator.py file to read its contents. If the file is not found, an error will be returned.

• A model name for local AI is specified, using Q1 with 3.54 billion parameters. The first step involves a planner agent that breaks down tasks into subtasks for different agents.

Planner Agent's Role

• The planner agent analyzes the provided messy code using a prompt that instructs it to act as an expert software architect, creating a structured plan to improve the code by addressing missing functions, type hints, bad variable names, and repeated code.

• It is emphasized that the planner should only provide a plan without writing any actual code. System instructions are given alongside user prompts containing the messy code.

Executor Agent's Functionality

• The executor agent receives both the messy Python code and the refactoring plan from the planner. Its task is to rewrite the code according to this structured plan while providing only Python code without additional explanations.

• Raw executed code is extracted from the executor's output and formatted according to previously defined Python standards before being saved in a new file named clean_calculator.py.

Running the Project

• To run this project, users navigate to their working directory (e.g., cd local cloud brain) and execute python orchestrator.py. This command initiates processes within the console.

• Upon execution, statements indicating progress are printed in bold format; initially stating that step one involves analyzing code through the planner agent.

Detailed Plan Creation

• The planner generates a detailed plan which includes creating functions like get_numeric_output, implementing loops for input validation, converting results immediately into numeric formats, and replacing redundant checks with more efficient structures such as try-except blocks.

• Variable names are renamed for clarity (e.g., n1 becomes first operand), centralizing calculations into one function instead of repeating logic across multiple operations.

Final Code Structure

• The final refactored structure includes proper documentation within function definitions along with type hints for inputs/outputs. Error handling mechanisms are integrated effectively throughout.

• Division logic has been simplified by removing manual checks; instead of conditionals for zero division errors, automatic messages inform users when such cases arise.

• Cleaned-up modules include well-defined classes for input errors and comprehensive documentation ensuring clarity on functionality while maintaining robust error handling practices throughout all operations in clean_calculator.py.

AI Workflow Automation and Error Handling

Enhancing Script Functionality

• The script is designed to raise appropriate errors, such as ZeroDivisionError, ensuring robust error handling during execution.

• Users can enhance the script by integrating a third reviewer to oversee the actions of two agents, improving oversight and accuracy.

• Additional tools can be incorporated into the agent's capabilities, such as a vision tool for reading scene shots and automating code writing.

Automating Daily Workflows

• The architecture allows for continuous modification of loops to accommodate various tasks, promoting flexibility in automation.

• Mastering the chaining of local models and agents enables users to fully automate their daily computer workflows, streamlining processes significantly.

• A successful demonstration involved fixing a messy Python script using a planner and executor agent on a 4GB GPU, showcasing efficiency with free local models.