Notes & Thoughts

One of my recent fascinations has been the capability of running large language models (LLMs) entirely on local hardware, particularly on low-power devices like the Raspberry Pi. This approach offers significant advantages...

The primary motivation is privacy. By keeping the model and all its processing on my local network, I maintain complete control over my data. There's no information being sent to third-party servers, which is crucial for sensitive applications or just for personal peace of mind.

Another benefit is cost efficiency. While powerful models often require significant cloud computing resources, smaller, optimized LLMs can run surprisingly well on devices like the Pi 5. This opens up possibilities for accessible, always-on AI assistants or automation tools without recurring subscription fees.

The challenges, of course, include managing computational resources. Optimizing models, using quantization techniques, and choosing the right software frameworks (like Llama.cpp) are key to achieving usable performance.

I'll be diving deeper into my setup and specific results in future notes. Stay tuned!

In a world increasingly reliant on technology, the environmental footprint of our digital lives is something I think about often. From the energy consumption of data centers to the lifecycle of our devices, there's much to consider...

My personal philosophy involves maximizing the lifespan of hardware, favoring repairs over replacements, and consciously choosing energy-efficient solutions where possible. Running `piconet.work` on a Raspberry Pi is a small but significant step in this direction, demonstrating that powerful, useful services don't always require massive energy expenditure.

I believe that as technologists, we have a responsibility to not only innovate but also to consider the broader impact of our creations on the planet.