I recently discovered an intriguing local, open-source, and completely free alternative to Claude Code, an AI coding tool. This alternative, which combines Goose and Qwen3-coder, promises to be a game-changer for developers seeking a cost-effective solution. In this article, I'll share my experience with this innovative tool and explore its potential as a rival to Claude Code. But first, let's delve into the details and uncover why this combination might be the future of coding assistance. The Rise of Goose and Qwen3-coder Jack Dorsey, the visionary behind Twitter, Square, and Bluesky, recently hinted at the power of Goose and Qwen3-coder in a cryptic post on X. This sparked curiosity among developers, as these tools offer a unique approach to coding assistance. Goose, an open-source agent framework developed by Dorsey's company Block, provides a local coding experience. Meanwhile, Qwen3-coder, a coding-optimized large language model, brings advanced capabilities to the table. Together, they form a compelling alternative to Claude Code, which has gained popularity for its coding assistance features. Setting Up the Local AI Stack The setup process for this local AI stack is straightforward, but it requires a powerful machine. Here's a step-by-step guide to get you started: 1. Download Goose and Ollama: Begin by downloading Goose from GitHub and Ollama from its official website. 2. Install Ollama and Qwen3-coder: Install Ollama first, followed by Qwen3-coder. Ensure you have sufficient storage space, as Qwen3-coder's model is a substantial 17GB. 3. Configure Ollama: Enable 'Expose Ollama to the network' in the settings to make it accessible to other applications. 4. Set Context Length: Adjust the context length to your machine's capabilities. 5. Install Goose: Run the Goose installer and configure it to use Ollama and Qwen3-coder. Testing the Local AI Stack I put the local AI stack to the test by attempting to build a simple WordPress plugin. Here's what I discovered: - Initial Challenges: Goose/Qwen3 initially struggled with the task, but with repeated corrections, it eventually succeeded. - Performance: Running this setup on an M4 Max Mac Studio with 128GB of RAM, I found the performance to be quite good, even with multiple applications open. - Comparison with Cloud Options: While local performance is impressive, I'll need to run a more extensive project to compare it with cloud-based solutions like Claude Code and OpenAI Codex. First Impressions and Future Outlook My initial experience with this local AI stack is promising, but I'm eager to explore its capabilities further. I'll provide a more in-depth analysis in future articles, comparing its performance with paid alternatives. Have you tried this local AI stack or similar open-source tools? How did your setup go, and what hardware are you using? Share your thoughts and experiences in the comments below. Let's discuss the potential of local AI coding tools and their impact on the future of development.
