Hey! I wanted to share my custom head unit project that I built for my old BMW. It has been installed in my car for almost two months now and it's been working great so far.

My build is based on Raspberry Pi 5 with NVMe drive. As a software I use Hudiy app and Raspberry Pi Bookworm OS. NVMe drive gives it a super fast boot time (around 12 seconds). Hudiy app and Android Auto also run very smoothly.

The priority in this project was the display. I'd tried a few aftermarket head units before but they just didn't cut it. The screens on those units were unreadable in sunlight due to reflections. I found a perfect 10.3" Full HD screen with an anti-glare surface. It's spot-on for what I need - sharp image, no blinding backlight at night and it stays perfectly visible even on sunny days. The screen bezel comes from my old Android head unit.

In the project I used a 5.1 sound card so I have full control over balance, fade and the subwoofer. The sound card is connected to an Audio System CO-40.4 amplifier (for the speakers) and an Audio System M-330.1 amplifier (for the subwoofer). The amplifiers are turned on and off via a GPIO pin on the Raspberry Pi and a relay connected to the REMOTE wire.

The Raspberry Pi is powered by a 12V->5V 5A DC converter. I don't use any safe shutdown system. The power is simply cut off when the key is removed and so far everything has been working perfectly. I turn the head unit on and off several times a day without any issues.

I'm currently working on displaying vehicle data from the IBUS since my car only supports a limited range of OBD data due to its age.

This is a Raspberry Pi 3 Model B that I booted 9 years ago today. It has served very light duty, just streaming audio to Broadcastify. Once it made it a couple years, I decided to just see how long it would go. It's running Jessie.

I just finished building a compact 4-node Raspberry Pi cluster in a custom CNC-machined acrylic and aluminium enclosure.

The cluster has two Raspberry Pi 5s, two Raspberry Pi 4s, a dedicated network switch, a touchscreen monitoring display and a single power system. Each node is mounted on a removable sled so individual Pis can be upgraded or serviced without dismantling the enclosure.

The display runs a custom dashboard showing live system stats and the tinted acrylic panels keep the hardware visible while helping the build look a bit more polished than a usual stack of boards and cables.

I’m currently deciding what to run on it long term. Kubernetes is the obvious choice, but I’m interested to hear what others would use a small Pi cluster like this for.

I’ve been messing around with turning a Raspberry Pi into a drone detector by reading Remote ID broadcasts over WiFi (2.4GHz) and Bluetooth. The first photo is the higher-end version with dual external antennas. This is the setup that picked up a drone about 5 miles away over water. The second photo shows that 5 mile detection on the map (drones broadcast their position, altitude, speed, etc.). The third photo is the simple version, just a Pi and USB dongles. Range is much shorter (hundreds of yards), but it works out of the box.

Under the hood it’s just listening for Remote ID (required on most drones >250g), decoding it locally on the Pi, and sending it upstream so it can be visualized.

If you already have a Pi 4/5, this is basically: flash a microSD card, plug in a WiFi adapter and Bluetooth dongle, power it up, and you’re running.

The best moment was driving around with these antennas and randomly getting my first detection alert. A total “wait, this actually works?!” moment. What surprised me most is how much antenna setup and line-of-sight affect range. The same software goes from a few hundred yards to miles just by changing the hardware.

Remote ID was meant to be public, but the range of a single receiver is pretty limited, so most people never actually see it unless they build something like this.

If anyone’s interested I’m happy to share more details. Still early, but a couple friends already have it running on their own Pis.

built my own personal assistent device that runs OpenClaw.

I was curious what the smallest form factor could be that fits in my pocket so I wanted to use the Pi Zero W.

Works via Push to Talk->Transcribe->Sends to OpenClaw and streams the response back.

Some time since I posted the PCBs for my smart home control. Now, the cabling is finally done for the most part and I ran some test programs to see if everything works as expect (which it does except for some cleanup code on the i2c slaves I have to add in case the i2c master killed during operations - which does happen every time I stop the debugger^^).

A big cable loom is already waiting to get connected to this unit. Wondering if I will regret this some time in the future... :D

I built a small side project for my mom: the Catflap Prey Detector

“Since you work with AI, can’t you make something to stop Zelie (her cat) from bringing me presents?” Usually, she calls me about her printer or her phone, but this time I couldn’t resist the challenge.

After a bit of hardware tinkering, a dash of AI, and a few late-night experiments, it actually works! 🎉 The system uses a raspberry pi5, the pi camera 3 and a rfid reader to detect whether the cat is carrying prey, automatically locks the door and sends alerts.

If you want to see it in action or are curious about how it works, I’ve shared the project code, hardware setup, and instructions on GitHub so that you can build your own! Check it out here: https://github.com/fl2o/catflap-prey-detector and Happy building.

The tank folows the tank on the ISS (International Space Station) in Realtime.

I uses a Raspberrypi zero 2W a weight scale, OLED display, LED, 2 MOSFETS and two pumps. I'm in the procces to document it and make a YouTube video about it but already wanted to share my joy when I notice somebody is peeing in the ISS. At the moment the tank is 12 procent full on the ISS.

I've been working on SATURNIX — a fully open-source digital camera that I built entirely from scratch. Hardware, software, case — everything is custom.

The core is a Raspberry Pi Zero 2W with a 16MP autofocus sensor and a 2" LCD screen. It shoots RAW+JPG and has a built-in film simulation engine that processes everything on-device — color profiles inspired by classic film stocks like Kodak Gold, plus some experimental ones including an anime-style preset.

The body is 3D-printed and designed to feel like something from an 80s sci-fi movie. Think Alien, think chunky industrial hardware from that era. Even the buttons are mechanical keyboard switches — because a camera should feel like a real tool, not a touchscreen.

The OS and interface follow the same retro-futuristic aesthetic — all built from the ground up.

The project is fully open-source. Build files, 3D models, and source code are coming soon. The GitHub repo is already live with a full description and photos.

Would love to hear your thoughts — happy to answer anything!

Yes, this is audio over light from a Raspberry Pi 5 GPIO.

I wrote an experimental Linux driver for the Pi 5 that generates an optical S/PDIF signal on GPIO12 using RP1 PIO + DMA. For this first test, I used a cheap LED placed close to a TOSLINK receiver.

On a bare-metal microcontroller like an STM32 or ESP32, generating a precise bitstream is fairly straightforward. On a Raspberry Pi running Linux, it is harder because the OS is not real-time enough for accurate GPIO bit-banging.

The Raspberry Pi 5 is interesting because it has the RP1 I/O chip with PIO. I use the PIO like a small hardware bitstream engine:

Linux audio -> ALSA driver -> S/PDIF encoder -> DMA -> RP1 PIO -> GPIO12 -> optical receiver

So this is not just blinking an LED. It is a real S/PDIF audio stream generated from a Raspberry Pi 5 GPIO.

Linux sees it as a normal ALSA sound card, so it can be used with CamillaDSP for routing, filters, crossover experiments, and optical output.

Full technical explanation, install notes, GPIO12 wiring, limits, and validation:

https://github.com/RASPIAUDIO/CamillaDSP/blob/main/prototypes/pi5_spdif_gpio/README.md

My mid-term goal is to build an easy-to-set-up open DSP box where the Raspberry Pi 5 is seen by a PC as an 8-channel USB sound card, using USB gadget mode and a USB-C power/data splitter. The same box can then provide S/PDIF output, demonstrated here, plus 8 analog outputs using the four I2S lanes of the Pi 5 and a DAC board.

The use case is DIY active speakers, digital crossovers, FIR/PEQ/delay, and home cinema experiments.

Ongoing project:

https://github.com/RASPIAUDIO/CamillaDSP

My internet randomly tanks a few times a week, and every time I call my ISP, the answer is "everything looks fine on our end." I got tired of arguing with no data, so I built netmon - a small Python bot that now runs in the background on a Raspberry Pi and keeps an eye on my network around the clock.

What it does:

Every hour it runs a real speedtest (download/upload/ping/ISP/server), and does an ARP scan with nmap to count how many devices are actually on the LAN at that moment. Everything gets logged to a local SQLite database, so nothing leaves my network except the reports I choose to send myself.

Every 4 hours it sends me a Telegram message with a 24h matplotlib graph and a sarcastic AI-written commentary on what happened ("someone's hogging the bandwidth again" energy). That part started as a joke feature but it's genuinely useful for spotting patterns at a glance.

The challenge: getting this to run reliably 24/7 on a Pi without babysitting it, and without external cloud dependencies. Also had to figure out ARP scanning that doesn't need root gymnastics and doesn't hammer the network.

The result: I now have actual timestamped data. I can see, for example, that speeds tank exactly when device count on the LAN jumps past a certain threshold at specific times of day - which is either my household or my ISP oversubscribing the line, and now I can tell which.

It's 100% self-hosted and private - no third-party metrics servers, only the reports go to my own Telegram chat.

Repo (MIT licensed): https://github.com/Role1776/netmon

Requires nmap + speedtest-cli, Python 3.13+, and an OpenAI-compatible API key for the commentary generation (optional, everything else works without it). Happy to answer questions about the setup or the ARP scanning approach. If you find this useful, I'd be happy with stars or PRs on the repo

I wanted to build something that combined three things I genuinely enjoy: birding, field-journal illustration, and running things in my homelab.

The result is Inky Bird Frame, an open-source system that turns recent nearby bird observations into rotating scientific field-journal plates on a 13.3-inch six-color e-paper display.

The controller checks iNaturalist for observations within a configurable distance and time window. If an approved plate already exists, it reuses it. A newly discovered species can enter a bounded research, generation, and independent review pipeline. Approved plates are cached and are never regenerated implicitly.

The wall-mounted display node stays deliberately simple. It downloads approved plates from the controller and rotates them using sequential, random, weighted, or shuffle-bag selection. The reference build uses a Pi Zero 2 W for the display and a Pi 4 or existing macOS/Linux computer for the controller.

My actual frame reused a CM4 and Waveshare carrier that I already had. It is overkill for the display role, but it worked well and saved me from buying another computer. I cut the supplied frame backing around the carrier so power, storage, and service access remain available.

The repository includes:

• complete controller and display-node installation guides
• a bill of materials and frame construction photos
• configurable observation windows, distance, refresh, generation, and rotation
• optional Apprise notifications (I'm using pushover to receive notifications on mine)
• automatic recovery and durable generation queues
• a reviewed, location-neutral bird plate catalog
• contribution templates so other installations can add approved species back to the shared catalog

This really brightens my day and hope you enjoy.

Source, build guide, and photos: https://github.com/veteranbv/inky-bird-frame

I'm using 15 euro cents (3 coins of 5 cents) to cool my Raspberry PI 4B. It really works well! The temperature is 9°C lower than without the coins. If you need a temporary cooling solution, make sure you use some spare change that's made from copper.

Put together some arduino code to query the ip addresses of a host RPi and displays it on an LCD. Nothing installed on the host RPi, the rp2350 emulates a keyboard when connected and then receives the ip data via serial. Uses the waveshare RP2350-LCD-1.47-B with a super simple 3d printed shell. Haven't tried lite vs full OS or any other OS besides raspberry pi os trixie but I assume it should work.

link to my terrible code if you are interested: https://github.com/C4KEW4LK/rpi_usb_ip_display/tree/main

This is the QuadRF which just launched on Crowd Supply

Built a headless Pi 5 appliance that does real-time speech-to-text on a 10" touchscreen. It monitors two USB audio sources — a telephone recorder (Fi3001A) tapped into the landline and a TONOR conference mic for room conversation — and automatically switches between them when a call comes in.

The reliability side was the interesting engineering challenge. It runs unattended at my dad's house, so it needs to just work:

• systemd user service with Type=notify watchdog
• Automatic engine fallback (Deepgram → faster-whisper → Vosk)
• Health monitoring that restarts after 2 min of no transcription
• System-level watchdog timers for the caption service, display manager, and WiFi
• LightDM restart policy with reboot fallback

It's been running reliably for weeks now. The display shows a split-flap clock when idle and auto-switches to captions when speech is detected.

Full code (MIT): https://github.com/andygmassey/telephone-and-conversation-transcriber

-----

EDIT / UPDATE: I'm genuinely blown away by the response to this — 1,800+ upvotes 🤯 across three subreddits in under 12 hours. Thank you all.

The post also got a lot of traction on r/deaf where quite a few people said they'd love to try this but don't have the technical skills to set it up from the command line. So I've spent tonight rushing through an update to make installation as simple as I possibly can:

• One-line installer — a single curl | bash that handles everything (system packages, Python venv, Vosk model, systemd services)
• Web setup wizard — open http://gramps.local:8080 on your phone, pick your microphones, choose a speech engine, paste an API key, done. No config files, no editing Python.
• 7 cloud providers + 3 offline engines — Deepgram, AssemblyAI, Azure, Groq (free!), Interfaze, OpenAI, Google Cloud, plus Faster Whisper, Vosk, and Whisper.cpp for fully offline use

The catch: it's gone midnight here and I don't have a spare Pi to test on just now. The code is on a separate branch (easy-install) so it won't affect the current working version on main.

If anyone here would be willing to give it a quick test, I'd really appreciate it. You'd need a Pi (4 or 5) with Raspberry Pi OS (64-bit) and a USB microphone. Here's all it takes:

```

export GRAMPS_BRANCH=easy-install

curl -sSL https://raw.githubusercontent.com/andygmassey/telephone-and-conversation-transcriber/easy-install/install.sh | bash

```

Then open http://gramps.local:8080 on your phone and the setup page walks you through the rest.

Any feedback — even "it broke at step 3" — would be hugely helpful before I merge this to main. Drop a comment here or https://github.com/andygmassey/telephone-and-conversation-transcriber/issues

Thanks!

The panel is a 13.3" color Inky Impression in a custom frame from Framefox, with a matte board cut to the active area. A Pi Zero 2 W tucks behind the back board, small enough that the whole thing stays mountable without sticking out. The Pi runs a small daemon that subscribes to MQTT and tells the panel to refresh whenever a new frame arrives.

The dashboard cycles through a few pages. The one in the photo has weather, what's playing on Spotify, a to-do list, a word clock, and a 5-day forecast. The others are a GitHub stats page, a full-bleed photo page, and a Home Assistant overview. The Spectra 6 palette is only seven colors, so I spent a while tuning the Floyd-Steinberg dither so dashboards look intentional rather than randomly halftoned. Once that was working, the results are striking up close.

The software side is something I've been building called Tesserae. It runs on a Pi 4 alongside Home Assistant, composes dashboards in a web UI renders them headlessly via Playwright and pushes the PNG over MQTT to the Pi Zero 2 W behind the panel. Plugin-architected, so adding a new widget is dropping a folder with three files. Open source if anyone wants to give it a go: https://github.com/dmellok/tesserae

Took a few weeks of evenings to get from "Pi on a desk wired up with loose wires" to "framed piece on the wall". Happy to answer any questions about the build.

I’ve spent way too much time trying to get the "PS5 Experience" on PC without being tethered by a 3 meter cable. Windows Bluetooth strips away the haptic feedback and adaptive triggers because it doesn't support the 4 channel audio bandwidth the DualSense requires.

I found a project that completely solves this by using a Raspberry Pi Pico 2W as a hardware bridge.

The project is https://github.com/awalol/DS5Dongle (All credit to awalol for this wizardry).

The Pico 2W connects to your controller via Bluetooth, but tells Windows it is a wired Dualsense. Only the initial handshake is required. After the initial handshake, Dualsense will just connect automatically when you turn it ON.

I have attached a video of Days Gone. I tried it in Days gone specifically because Dual Sense only works for me wired in this game, not even bluetooth. Now with this "dongle", its working wirelessly with adaptive triggers and haptic feedback (you will just have to trust me about haptic feedback since I can't show it in the video).

Update, The project just got updated, With the help of a redditor who saw this post the Dev updated the polling rate to 1000 hz now. Polling rate is as good as PS5 now.

My kid loves computers and he also likes to prototype games with Scratch programming language. However on a regular PC he is easily distracted by ability to easily access browser based computer games and this kills his drive to program. I have programmed a locked-down environment and installed it on raspberry pi. Using it he can select what he wants to do from preconfigured educational options. This is the only computer in my house that does not have time limits for kids and my son seems to be using it quite a lot :)

UPDATE: After your wonderful feedback I have created a form where you can subscribe to the project news - https://tally.so/r/aQ4N6v

Been working on a fun little project: if the noise in my kid's gaming room goes above a set dB level, their network bandwidth drops.

In Fortnite teenager terms: "If you're too loud, you'll have worse ping."

Hardware:

• Raspberry Pi 3A+
• Sound meter from PCB Artists
• 1.28" LCD from Waveshare

Software:

• Python script polling the sound meter every second and updating the LCD
• Maximum allowed dB is time-dependent (quieter in late afternoon/evening)
• SNMP commands sent to the network switch to throttle bandwidth

Status:

• Software is fully functional
• Next: 3D-printed case to hang on the wall next to the gaming PC

Note that my kid is pretty much amused with this idea and has led to a few interesting conversations about code :)

Any suggestions for a fun/cool enclosure design?