Waveshare offers a long-wave infrared thermal imaging camera module designed for Raspberry Pi and other platforms, featuring an 80×62 pixel array using hybrid microbolometer and thermopile technology. The device can generate thermal images by converting field of view data into surface temperature readings and is suitable for various industrial and intelligent control applications like motion detection or heat trend analysis. It is available in two main formats: a HAT version with a 40-pin GPIO header and a USB version with a Type-C port, both offering options for basic (45° FOV) or wide-angle (90° FOV) lenses.
- Features an 80x62 pixel array with a shutterless design for continuous video streaming up to 25 FPS.
- Provides a Noise Equivalent Temperature Difference (NETD) of 150mK RMS at a 1Hz refresh rate.
- Supports temperature measurements ranging from -20°C to 400°C with an accuracy of ±2℃ in ambient temperatures between 10-70°C.
- Compatible with Raspberry Pi boards (including Zero and 5), Windows PCs, and Android phones via the respective connector versions.
- Features an 80x62 pixel array with a shutterless design for continuous video streaming up to 25 FPS.
- Provides a Noise Equivalent Temperature Difference (NETD) of 150mK RMS at a 1Hz refresh rate.
- Supports temperature measurements ranging from -20°C to 400°C with an accuracy of ±2℃ in ambient temperatures between 10-70°C.
- Compatible with Raspberry Pi boards (including Zero and 5), Windows PCs, and Android phones via the respective connector versions.
bebechien writes about DinoDesk AI, a LEGO dino desk companion built on a Raspberry Pi that pairs a local Gemma 4 model (via LM Studio) with cloud Gemini Flash through a unified OpenAI-compatible gateway, giving users a camera-free, privacy-first chat robot with three switching modes (local, cloud, and auto-hybrid routing). The physical body uses LEGO Technic lever mechanisms for neck and tail movement, a Pimoroni Pirate Audio shield for the 1.3" LCD eyes and 8-bit I2S beeps, and a 5-state finite state machine to coordinate expressions, sound, and motor action across Sleeping, Idle, Listening, Thinking, and Speaking states.
- Auto-hybrid mode uses a complexity classifier that detects multi-step reasoning keywords like "explain," "compare," and "write code" to transparently escalate prompts to the cloud engine
- The project is open-sourced at github.com/google-gemma/dinodesk-ai-companion
- Full voice chat is still a work in progress; current interaction is triggered by a physical red push button
- A commenter noted that absence of a camera does not guarantee voice data stays local, and suggested an explicit retention boundary for audio transcripts would strengthen the privacy claim
- Auto-hybrid mode uses a complexity classifier that detects multi-step reasoning keywords like "explain," "compare," and "write code" to transparently escalate prompts to the cloud engine
- The project is open-sourced at github.com/google-gemma/dinodesk-ai-companion
- Full voice chat is still a work in progress; current interaction is triggered by a physical red push button
- A commenter noted that absence of a camera does not guarantee voice data stays local, and suggested an explicit retention boundary for audio transcripts would strengthen the privacy claim
Jo Hinchliffe writes a tutorial for building a self-hosted personal knowledge management system inspired by Tiago Forte's *Building a Second Brain*. The setup pairs Logseq, a privacy-focused open-source note-taking app that stores everything as local Markdown files with a powerful tagging system, with Syncthing, a peer-to-peer file synchronisation tool that keeps the note database in sync across multiple Raspberry Pi devices without passing data through any cloud server.
- Syncthing is not a backup: deletions propagate to all linked devices, so a separate cron-driven rsync script is recommended for periodic backups
- Each tag in Logseq automatically becomes a page, creating a navigable graph of linked information
- Logseq currently installs only via Snap on Raspberry Pi OS
- The system was demonstrated on a Raspberry Pi 500, a Pi Zero, and a Uconsole (CM4)
- Logseq supports embedding links, videos, and even PDF annotation with tagging of specific pages
- Syncthing is not a backup: deletions propagate to all linked devices, so a separate cron-driven rsync script is recommended for periodic backups
- Each tag in Logseq automatically becomes a page, creating a navigable graph of linked information
- Logseq currently installs only via Snap on Raspberry Pi OS
- The system was demonstrated on a Raspberry Pi 500, a Pi Zero, and a Uconsole (CM4)
- Logseq supports embedding links, videos, and even PDF annotation with tagging of specific pages
Tyler August writes that The Masked Bear's open-source wifisense-pi project uses an ESP32-S3 sampling the 2.4 GHz WiFi channel 100 times per second with a Raspberry Pi 4 for signal processing to detect subtle body motions via WiFi disturbances, effectively sensing breathing or presence through walls without dedicated hardware.
- Can detect a perfectly still human by breathing, but presence can be hidden by holding breath
- Single sensor provides no position information and cannot reliably distinguish one vs multiple people
- Works by sniffing existing 2.4 GHz signals and functions through walls
- Not the first WiFi-based presence project; prior work includes ESP32 person sensors and millimeter-wave alternatives
- Can detect a perfectly still human by breathing, but presence can be hidden by holding breath
- Single sensor provides no position information and cannot reliably distinguish one vs multiple people
- Works by sniffing existing 2.4 GHz signals and functions through walls
- Not the first WiFi-based presence project; prior work includes ESP32 person sensors and millimeter-wave alternatives
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)
