---
title: "Raspberry Pi"
description: "2 posts about Raspberry Pi, summarised, each linking to the original."
---

# Raspberry Pi
> 2 posts about Raspberry Pi, summarised, each linking to the original.

## Articles

### [How we made the Supabase Select 2026 badge](https://yomu.fyi/post/how-we-made-the-supabase-select-2026-badge.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: sam\_meech\_ward
- Published: Oct 14, 2026
- Type: Tutorial

Supabase gave every Select 2026 attendee a programmable badge based on the Pimoroni Tufty 2350, with an RP2350 microcontroller, color screen, rechargeable battery, Wi-Fi, Bluetooth, and five buttons. A firmware bug drained all batteries during shipping, requiring bulk charging with 30-port USB hubs in 14 batches over 56 hours and individual firmware updates. The venue Wi-Fi failed, so the schedule was synced via a hotspot, two badges at a time, one minute each, totaling seven hours. Attendees have since repurposed the badge into projects including a live flight tracker, an arcade game about database vacuuming, a food finder app, a music note collection game, and a tiny on-device language model. The post provides resources for hacking the badge: a first-app guide, a Claude Code project kit, a virtual badge, guides for C or Rust firmware, and a contest with Supabase credits and conference passes.


### [Mastering Edge AI on Raspberry Pi with LiteRT and Gemma](https://yomu.fyi/post/mastering-edge-ai-on-raspberry-pi-with-litert-and-gemma.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Lu Wang, Terry Heo, Naushir Patuck, José María Casanova
- Published: Aug 11, 2026
- Type: Tutorial

Running edge AI on a Raspberry Pi 5 is presented as a way to build autonomous, low-latency systems that operate offline, with Reachy Mini serving as the demonstration. The setup combines LiteRT, LiteRT-LM, Gemma models, and LiteRT’s WebGPU (Vulkan) backend via ML Drift, splitting workloads between the CPU and VideoCore VII GPU. On CPU, Gemma 4 E2B reportedly reaches 99 tokens/sec prefill, 9 tokens/sec decode, and 1,432 MB peak memory; its tokenizer supports about 27.3 characters/sec in the voice demo. The pipeline runs YOLO object detection on the GPU, Moonshine speech recognition, Gemma reasoning, and text-to-speech on the CPU, while LiteRT CLI covers conversion, quantization, benchmarking, inference, and model execution from the LiteRT Hugging Face Community.