Today, we’re diving into the world of AI-assisted coding with NVIDIA’s Nemotron, an open-source 70 billion parameter model. This impressive leap in AI technology showcases the potential for powerful, locally-run language models. While Nemotron is designed to run on systems with substantial compute resources, I opted to use a web interface for this demonstration to explore its capabilities.
The Initial Challenge: A Basic Falling Sand Game
I started by asking Nemotron to create a Falling Sand game in Python. The results were impressive right off the bat:
- Sand particles appeared and fell realistically
- Clicking added new sand particles (though only one per click)
- The AI even included water particles without being prompted
- Sand accumulated naturally at the bottom
Iterating and Improving
Excited by the initial results, I decided to push Nemotron further:
- Adding Elements and Controls
- Requested buttons to select different elements
- Asked for fire and plant elements
- Nemotron delivered, even adding a stone element I hadn’t asked for!
- Bug Fixes and Enhancements
- When a bug appeared, Nemotron quickly fixed it after I pasted the error message
- Fire particles moved upwards, adding an interesting dynamic
- Advanced Features I challenged Nemotron with some tricky requests:
- Move buttons to the top of the screen
- Support dragging to create lines of particles
- Implement mouse wheel control for brush size
To my amazement, Nemotron implemented all these features flawlessly. The ability to select elements, draw with varying brush sizes, and change the brush size with the mouse wheel was particularly impressive.
Reflections and Future Potential
This experiment with Nemotron showcases the incredible potential of AI-assisted coding. With just a few prompts, we created a fairly complex interactive simulation. The ease of adding new features and fixing bugs suggests that this tool could be invaluable for rapid prototyping and game development.
While the current version isn’t optimized for performance (running fewer particles improved speed), it’s clear that with further refinement, this could evolve into a full-fledged game.
Accessibility and Open-Source Advantage
The fact that Nemotron is open-source and freely available online is a game-changer. You don’t need a subscription to a cloud-based AI service to access this level of coding assistance. For developers with sufficient hardware (I have 24GB VRAM), running models like this locally could become a standard part of the development workflow.
Conclusion
This exploration of NVIDIA’s Nemotron has been eye-opening. The ability to create complex, interactive programs with simple natural language prompts is truly revolutionary. As these models continue to improve and become more accessible, they have the potential to dramatically change how we approach software development.
Whether you’re a seasoned developer or just starting out, tools like Nemotron offer exciting possibilities for creativity and problem-solving in coding. I’m excited to continue experimenting with different quantizations to see if I can get a similar model running locally on my hardware.
The future of AI-assisted coding is here, and it’s open-source. Happy coding!