Stop Paying Anthropic $200/month for Claude Code (Do This Instead)

How to Use Open-Source Models Locally for Coding

Introduction to Claude Code and Cost Concerns

• The speaker expresses admiration for Anthropic's Opus 4.5 model, highlighting its capabilities but noting the high cost of $200 monthly for heavy developers.

• A solution is proposed: achieving 90% of the experience with local models at no cost, eliminating API keys and data privacy concerns.

Transitioning to Local Open-Source Models

• The speaker introduces a plan to replace Anthropic's backend with local open-source models like Llama, emphasizing that while they may not match Opus 4.5 in intelligence, they can still perform effectively.

• An overview of cloud code plans is provided, explaining that users pay for infrastructure and access to various models hosted on the cloud.

Advantages of Running Models Locally

• The discussion shifts to the benefits of using open-source models locally, including ownership of data and avoiding costs associated with premium services.

• Several powerful open-source coding models are mentioned (e.g., Llama 3, GLM OCR), which can be run on personal hardware without incurring fees.

Setting Up Olama for Model Management

• Instructions are given to download Olama from ola.com as a necessary step for running these models locally while ensuring data safety.

• Users are guided through selecting popular open-source models within Olama based on their performance metrics and download statistics.

Installing Cloud Code Locally

• The process continues with installing Claude Code locally so it can utilize the downloaded model; specific instructions vary by operating system.

• Demonstration shows how to interact with the chosen model (GBD OSS 20B), confirming its functionality through simple commands.

Configuring Environment Variables

• Steps are outlined for setting up environment variables required by Claude Code to recognize the local model instead of relying on cloud resources.

• Instructions include specifying a dummy API key value since Claude expects one despite running locally.

Finalizing Setup and Running Local Models

• Users learn how to list installed local models in order to confirm successful downloads before proceeding with usage.

• The final steps involve configuring Claude Code settings so it points correctly at the local model, enabling users to start utilizing their setup immediately.

Creating a Hello World Next.js App

Setting Up the Environment

• The speaker emphasizes the importance of being in the correct folder before initiating the model to create a Next.js app. They mention using GPD OSS 20B for processing the request.

Performance Considerations

• The execution time of the model is influenced by hardware specifications, such as RAM and machine power. Choosing a faster model can lead to improved performance without requiring extensive hardware resources.

Model Execution and Interaction

• The local model is confirmed to be running, ensuring that no data leaves the user's computer. The speaker interacts with prompts from the model regarding directory settings and TypeScript readiness.

Project Configuration

• A package JSON file is automatically created by the model, which includes configurations like TS config JSON. The speaker notes that they did not specify any routing preferences, leading to default settings being applied.

Running the Application

• After installing dependencies, the speaker launches a development server using npm rundev, successfully accessing their newly created Hello World Next.js app at localhost:3000. This demonstrates how effectively one can run a local model in cloud code while achieving desired outcomes.