Ayush Pande writes that the Gemma 4 E2B model offers impressive performance for running local LLMs on Raspberry Pi hardware. While many small models fail at complex reasoning or produce hallucinations, this specific variant achieves a balance of capability and efficiency through its per-layer embedding design. This technique reduces effective computation to approximately 2.3 billion parameters despite having more total parameters, allowing it to run smoothly on modern single-board computers for tasks like summarization and image identification.
- E4B is smarter but runs at ~2.5–3 t/s
- Supports multimodal audio and visual inputs
- Raspberry Pi 5 achieves roughly 6 tokens per second
- E4B is smarter but runs at ~2.5–3 t/s
- Supports multimodal audio and visual inputs
- Raspberry Pi 5 achieves roughly 6 tokens per second
A software project that recreates the aesthetic of a 1980s cable community bulletin board for any display, with an emphasis on vintage CRT televisions. It provides a rotating broadcast including time and date, custom messages, fun facts, dad jokes, weather updates via Open-Meteo, and scrolling RSS news headlines in a retro ticker crawl.
Key features include:
A web-based control room for real-time customization of identity, timing, feeds, colors, and message rotation without needing to reload the screen.
Hardware optimization for Raspberry Pi users looking to feed video through HDMI-to-coax modulators or composite AV outputs into old TVs.
Integration with CheerLights to incorporate global color changes directly into the headline crawl.
Support for background music via local audio files and customizable overscan margins for vintage display compatibility.
Key features include:
A web-based control room for real-time customization of identity, timing, feeds, colors, and message rotation without needing to reload the screen.
Hardware optimization for Raspberry Pi users looking to feed video through HDMI-to-coax modulators or composite AV outputs into old TVs.
Integration with CheerLights to incorporate global color changes directly into the headline crawl.
Support for background music via local audio files and customizable overscan margins for vintage display compatibility.
Secluso is an open-source DIY home security camera system built around the Raspberry Pi Zero 2 W. It focuses on privacy by using true end-to-end encryption (E2EE) via Messaging Layer Security (MLS), ensuring that even untrusted relays cannot decrypt video feeds. The system features on-device AI for detecting humans, pets, and vehicles. To enhance security, the core software is written in Rust to prevent memory-related bugs and includes post-quantum encryption to protect data against future threats.
- Raspberry Pi Zero 2W based hardware architecture
- End-to-end encryption using MLS (RFC 9420)
- On-device AI for human, pet, and vehicle detection
- Memory-safe software core written in Rust
- Post-quantum encryption support
- Minimal Yocto-based Secluso OS
- Support for self-hosted relays or official beta services
- Raspberry Pi Zero 2W based hardware architecture
- End-to-end encryption using MLS (RFC 9420)
- On-device AI for human, pet, and vehicle detection
- Memory-safe software core written in Rust
- Post-quantum encryption support
- Minimal Yocto-based Secluso OS
- Support for self-hosted relays or official beta services
>"Orange Pi is an open source single-board card computer, a new generation of arm64 development board, which can run operating systems such as Android TV 12, Ubuntu and Debian. The Orange Pi Zero 2w development board uses the Allwinner H618 system-on-chip, and can optionally have 1GB or 1.5GB or 2GB or 4GB LPDDR4 memory."
A specialized toolset providing graphical data visualizations for ADS-B decoders, including readsb, dump1090-fa, and other dump1090 variants that supply stats.json files. The project includes automated installation scripts and features designed to protect SD card longevity on hardware like the Raspberry Pi by managing how frequently data is written to disk.
Main topics:
- ADS-B decoder graph visualization
- SD card write reduction and optimization
- Automated installation and update processes
- Data backup, restoration, and integration methods
- Configuration for local map URLs and system timezones
Main topics:
- ADS-B decoder graph visualization
- SD card write reduction and optimization
- Automated installation and update processes
- Data backup, restoration, and integration methods
- Configuration for local map URLs and system timezones
PiShrink is a bash script designed to automatically shrink Raspberry Pi images, making them easier to store and faster to flash onto SD cards. Once the shrunk image is booted, it will automatically resize itself to match the maximum capacity of the target SD card. The tool also supports compression using gzip and xz, with an option for parallel compression across multiple CPU cores to improve speed.
Key points:
- Automatic shrinking of Raspberry Pi images
- Boot-time filesystem expansion to full SD card size
- Support for parallel multi-core compression
- Compatibility with Linux, Windows (via WSL 2), and macOS (via Docker)
Key points:
- Automatic shrinking of Raspberry Pi images
- Boot-time filesystem expansion to full SD card size
- Support for parallel multi-core compression
- Compatibility with Linux, Windows (via WSL 2), and macOS (via Docker)
Banana Pi has announced the BPI-SM10, a compact computing system powered by the SpacemiT K3 RISC-V processor. This hardware is designed for users interested in exploring RISC-V architecture and high-performance AI tasks at the edge. The system features an 8-core AI accelerator capable of delivering up to 60 TOPS, which is sufficient to run 30 billion parameter AI models.
Key details include:
* BPI-SM10 consists of a SpacemiT K3 compute module and a versatile carrier board.
* The processor features an octa-core design at 2.4 GHz with support for up to 32GB LPDDR5 RAM.
* Carrier board I/O includes M.2 PCIe Gen 4 slots, USB 3.2 ports, DisplayPort, and Gigabit Ethernet.
* A forthcoming K3 Pico-ITX single-unit mini PC will also be released featuring a 10-gigabit Ethernet port.
Key details include:
* BPI-SM10 consists of a SpacemiT K3 compute module and a versatile carrier board.
* The processor features an octa-core design at 2.4 GHz with support for up to 32GB LPDDR5 RAM.
* Carrier board I/O includes M.2 PCIe Gen 4 slots, USB 3.2 ports, DisplayPort, and Gigabit Ethernet.
* A forthcoming K3 Pico-ITX single-unit mini PC will also be released featuring a 10-gigabit Ethernet port.
Small, inexpensive single-board computers like the Raspberry Pi 5 are becoming viable platforms for running local large language models (LLMs). By utilizing quantization techniques to reduce model size and memory requirements, users can run quantized versions of popular models such as Llama 3, Mistral, and Qwen. While processing speeds remain limited compared to high-end GPUs, these devices offer a private and low-cost way to implement AI for specific tasks.
- Quantization allows large models to fit into the Pi's limited RAM by reducing numerical precision.
- Tiny models (1B-3B parameters) run comfortably, while 7B parameter models are usable on 8GB versions with managed expectations.
- Performance is measured in low single-digit tokens per second, making it suitable for non-real-time tasks.
- Hardware upgrades like the Raspberry Pi AI HAT+ or external eGPUs can significantly boost neural processing capabilities.
- Quantization allows large models to fit into the Pi's limited RAM by reducing numerical precision.
- Tiny models (1B-3B parameters) run comfortably, while 7B parameter models are usable on 8GB versions with managed expectations.
- Performance is measured in low single-digit tokens per second, making it suitable for non-real-time tasks.
- Hardware upgrades like the Raspberry Pi AI HAT+ or external eGPUs can significantly boost neural processing capabilities.
Pimoroni has released new Inky Impression color E Ink displays for Raspberry Pi in 4.0", 7.3", and 13.3" sizes featuring Spectra 6® technology. These low-power, high-resolution screens are designed for easy assembly without soldering and include features like rear-mounted buttons and Qwiic/Stemma QT connectors.
TinyProgrammer is an innovative Raspberry Pi project that brings a local Large Language Model (LLM) to life as a digital desk companion. Designed to simulate a human-like workflow, the device spends its day coding Python projects, typing at a natural speed, and even managing its own moods based on success or failure. To prevent burnout, the AI "clocks out" at night, transitioning to a screensaver. Additionally, the project features TinyBBS, a social platform where different TinyProgrammer devices can interact, share code, and joke with one another. This project is highly accessible, as it can run on hardware like the Raspberry Pi 4B or Pi Zero 2 W.
