🤯 My 2.5-Year-Old Laptop Now Codes Space Invaders Thanks to THIS!

Imagine this: a relatively old laptop, a cutting-edge AI model, and a classic arcade game resurrected in code. Thanks to advances in AI coding, powerful AI models, and large language models, code generation has become more accessible than ever. Using tools like MLX, this seemingly sci-fi scenario is now reality, as showcased in a recent blog post by Simon Willison.

Key Takeaways:

• Simon Willison used GLM-4.5 Air, a large language model, to generate a Space Invaders game in JavaScript.
• The code was generated and ran successfully on a 2.5-year-old MacBook Pro M2.
• This demonstrates the increasing power and accessibility of AI-powered code generation tools.
• The experiment highlights the rapid progress in AI’s ability to create functional, complex code.

Simon Willison’s blog showcased using a smaller GLM-4.5 Air large language model to generate a fully functional Space Invaders game in JavaScript. This wasn’t theoretical it ran flawlessly on a 2.5-year-old MacBook Pro M2, showcasing rapid AI coding advances. According to Simon Willison’s blog post, the process was surprisingly straightforward.

The Power of GLM-4.5 Air

The core of this achievement lies in the GLM-4.5 Air model, an open-source (MIT licensed) creation from Z.ai in China. While the full model is massive (106 billion parameters, 205.78GB on Hugging Face), Ivan Fioravanti created a 44GB 3-bit quantized version optimized for MLX. This smaller size makes it accessible to users with more modest hardware, including many personal laptops. As detailed on Simon Willison’s weblog, this quantization significantly reduces the resource requirements without sacrificing performance.

Space Invaders: AI Coding and Code Generation in Action

Willison used the MLX library and a simple command-line prompt to instruct the model to write the HTML and JavaScript for Space Invaders. The model generated the complete code, ready to run without any manual adjustments. The source code is available on GitHub, allowing anyone to explore the generated code and replicate the experiment.

Beyond Space Invaders: The Broader Implications

While a Space Invaders game might seem trivial, the implications of this experiment are far-reaching. It demonstrates the ability of sophisticated AI models to generate functional, complex code from simple prompts. This has significant potential across various fields, from software development to game creation.

Hardware Requirements and Performance

The fact that this was achieved on a 2.5-year-old laptop underscores the accessibility of these powerful AI tools. Willison notes that while the model consumed a significant portion of his laptop’s RAM (around 48GB at peak), leaving only 16GB for other applications, the speed was still impressive once the model loaded. This suggests a future where sophisticated AI coding is available to a broader range of users and devices.

The Future of AI-Powered Code Generation and AI Coding

The trend of AI models focusing on code generation is undeniably gaining momentum. Willison reflects on how the capabilities of these models have improved remarkably over the past two years. His series on LLMs on personal devices charts this progress, showcasing how much has been achieved in a short time. The increasing power and accessibility of models like GLM-4.5 Air suggest an exciting future for AI-assisted and potentially AI-driven software development.

Conclusion

Simon Willison’s experiment showcases the extraordinary progress in AI-powered code generation. The ability to generate functional code, even for relatively complex projects like Space Invaders, on a consumer-grade laptop using readily-available tools is remarkable. This technology demonstrates great potential for the future of software development, and we can expect to see even more impressive advancements in the years to come.