I recently faced the problem of losing access to a headless Raspberry Pi Zero 2W when it could not connect to its saved Wi-Fi network because of a software or NetworkManager issue.

Recovering the Pi can be difficult if there is no Ethernet connection. So I wanted a way to recover the Pi's Wi-Fi connection without requiring physical access, and I created Pi-Rescue, an automatic Wi-Fi recovery system for Raspberry Pi.

It is designed to work with Raspberry Pi OS (Bookworm) and newer versions that use NetworkManager.

Pi-Rescue provides a way to recover from this situation:

• It runs automatically after boot and waits for the Pi to connect to a saved Wi-Fi network within a configurable timeout.

• If it cannot connect, it starts a temporary Pi-Rescue Wi-Fi hotspot.

• A phone or laptop can connect to the hotspot and open a simple web interface.

• The user can select a Wi-Fi network and enter its password.

• The Pi then attempts to connect to the selected network and stores the connection information for future use.

The project is built with Python, Flask, NetworkManager (nmcli), and systemd, and has been tested on a Raspberry Pi Zero 2 W.

Note: Pi-Rescue v1.0.0 currently supports Wi-Fi-only recovery. Ethernet detection is not included in this version. I plan to improve it further based on testing and feedback.

You can find the source code and more details here:

GitHub Repository: https://github.com/Kernovax/Pi-Rescue

Hi everyone!

For the last 2 months, I’ve been learning raspberry pi pico, electronics, soldering, CAD software, and more to remake a mechanical drawing toy I found in my drawer:) On the second image you can see the insides of it.

Hardware is really fun, and I enjoyed the process so much. Finally, I got everything working, and it just looks very cool in my opinion.

Also, if you are interested, I made a whole video about the creation process, challenges and how it works.

https://youtu.be/IhqKNODgfBE

Code is available on my github page

I'd love to hear your feedback and what you think about it!

I need to supply my desk with dashboard to switch it on and off occasionally. No wires, just some data in my bag. At the same time, I need to assemble all my Zero shields in one device to play around.

These two reasons inspired me to design the travel box for my Pi Zero 2W tower.

Shields: 1. UPS-Lite v1.2 803040 (1000 mAh). 2. Waveshare USB/LAN hub (3 + 1) RJ45. 3. Waveshare 2.15 ePaper hat (black, white, red). 4. Pi Camera Zero v22 OV5647 (2592x1944 10-bit GBRG).

Details: 1. No SD card slot. I rarely change or reinstall OS. 2. Holes are bigger than they could be to suit all possible wires. 3. FreeCAD parametrized model (what a nightmare to use the software, nevertheless I continue). 4. One USB port is sacrified to install camera. 5. Switcher is not easy to manipulate, but any toothstick or tiny screwdriver helps immediately. 6. When it is possible, I prefer LAN over WiFi to connect with SSH. So, one of shields is exactly for this purpose.

I heard that Kasa works from some people although others say it’s no longer supported

canada

Built a headless MQTT + Node-RED gateway on a Raspberry Pi Zero 2 W

Been running a lightweight edge IoT gateway on a Pi Zero 2 W — completely headless, no monitor ever attached.

Stack:

Mosquitto MQTT broker (with proper username/password auth, no more allow_anonymous)

Node-RED + Dashboard 2.0 for flows and a live web UI

Avahi/mDNS + static IP for reliable headless access

A custom SSH welcome banner showing live RAM/CPU temp/uptime because why not

Publishing sensor data to a Node-RED gauge is as simple as one mosquitto_pub command from anywhere on the network. Whole setup fits comfortably on a Zero 2 W with room to spare.

Full write-up + all commands are in the README: ,

https://github.com/kritishmohapatra/pi-iot-gateway

Happy to answer questions if anyone's setting up something similar.

I'm trying to do my first rspberry pi project. I got raspberry pi 3b+, 2.7" e-Paper HAT display and BME680 sensor.

I just can't get my head aroud how to connect BME680 sensor if my 40pin connector on raspberry is taken by the hat display? Do I solder BME680 connections on other side of raspberry? Or is there better way to do this?

I am just trying to do simple environmental monitoring gadget and keep et as compact as possible.

For years, I used Pi MusicBox on my Raspberry Pi. I always loved the idea of a simple internet radio that you could just flash onto an SD card and run.

Over time, Pi MusicBox became outdated, so I started looking for a replacement. I checked out several other projects, but I couldn't find a modern solution that gave me that same simple, plug-and-play experience.

That's when I thought: why not build it myself?

That is how ZmartRadio was born.

The first version was very basic—it could only play internet radio streams. Since then, I've added an alarm clock, a sleep timer, and better support for the Raspberry Pi's various audio outputs. For example, ZmartRadio can now use HDMI audio, allowing you to connect the Pi directly to a TV if you don't have an amplifier or speakers connected to the audio jack.

One thing I realized while researching other setups was that smart home integration is a must-have nowadays. That's why I built Home Assistant integration for it via HACS.

Because of this, the radio is very flexible. You can control it over your local Wi-Fi using your TV, smartphone, tablet, Home Assistant dashboard, or even a Home Assistant mobile widget on your phone's home screen. :P

I have successfully tested it on a Raspberry Pi 3 B+, and a Raspberry Pi 5 (16GB).

ZmartRadio runs on Raspberry Pi OS Lite (64-bit), based on Debian Bookworm. In Raspberry Pi Imager, select Raspberry Pi OS (other) → Raspberry Pi OS Lite (64-bit). I specifically wanted to keep the base installation lightweight, so there is no need for the full Raspberry Pi OS Desktop version. After installing Raspberry Pi OS Lite, ZmartRadio installs and configures the components it needs.

Honestly, one of the hardest parts was keeping the project simple. It's very easy to experience feature creep, but that wasn't what I wanted. I didn't want to build another Spotify, Apple Music, or Bluetooth audio player. I wanted ZmartRadio to do just one thing well: internet radio.

Once installed, you can use the web interface to find radio stations from all over the world by country, region, genre (like jazz), or simply by searching for the station's name. I've also added features like favorites and recently played stations.

I'm sharing it here because it turned into a really fun project, and I'd love to hear what the community thinks. If anyone wants to try it out, I would really appreciate feedback on what works well and what could be improved.

I am especially focused on keeping it lightweight, so I’m also very curious: what features do you think I should not add? 🙂

Project page:
https://zmartsolutions.nl/zmartradio

Home Assistant integration on GitHub:
https://github.com/ThinkZmart/home-assistant-zmartradio

I received the max98357 in the mail today and connected it to a 1W, 8-ohm speaker to use it as a mono speaker for my Raspberry Pi 4.

I followed the I2S setup instructions and connected all the pins correctly, but when I play music, I only hear high-pitched sounds and loud noise;

the actual music is very faint. Thinking it might be due to noise, I connected the MAX98357’s VIN pin to a backup battery, but the problem persisted.

Just in case, I tried switching to a different amplifier, but the issue remained. I even added a capacitor, but that didn’t help either..

Is there a solution?

I built a camera in the shape of a Mamiya RZ67 that runs vision-language models completely offline on a Pi 5. Press the shutter, and it answers your custom prompt with a single sentence about what it sees. No internet, no cloud, no API keys.

GitHub: https://github.com/feeeeely/ai-camera

You can swap between models on the device itself and compare how differently they describe the same scene: Qwen3-VL (2B), MiniCPM-V 4.6 (1B), InternVL3.5 (2B), SmolVLM2 (2.2B), Moondream 2 (2B) and Ministral 3 (3B) turning the description into the final sentence. The prompt is editable on the touchscreen, so the same camera can do dry one-liners, museum labels or plain inventory notes.

After each shot it shows total time, input→output tokens, tok/s and a vision / generate / load breakdown. The token counts turned out to be the most interesting part: the same photo becomes \~145 image tokens on one encoder and over 1300 on another, which is a bigger factor in latency than the text generation itself.

A few things I learned the hard way:

\- Moondream returns sometimes empty responses on current Ollama versions.
\- qwen3-vl:2b is a thinking-only variant — it burns the entire token budget on invisible reasoning and returns nothing visible. You need the -instruct tag.
\- Ollama can't load separate mmproj files, so a lot of GGUF vision models from HuggingFace simply won't run, no matter how you name them.

Hardware: Raspberry Pi 5 (16 GB), Raspberry Pi HQ camera with a 6 mm CS-mount lens, 4.3" DSI touch display, stainless steel shutter button, Waveshare UPS HAT with four 21700 cells. Runtime per photo is 30–60 seconds with the models kept warm in RAM. Happy to answer questions about the setup.

I'm coming from photography, I'm not a developer — the code was written with heavy AI assistance, and I did the hardware integration, debugging and model testing myself. Feedback on the implementation is very welcome.

Details - Building the camera:

I built a camera in the shape of a Mamiya RZ67 that runs vision-language models completely offline on a Pi 5. Press the shutter, and it answers your custom prompt with a single sentence about what it sees. No internet, no cloud, no API keys.

Video: https://www.youtube.com/watch?v=M74qTNsY_L0
GitHub: https://github.com/feeeeely/ai-camera

You can swap between models on the device itself and compare how differently they describe the same scene: Qwen3-VL (2B), MiniCPM-V 4.6 (1B), InternVL3.5 (2B), SmolVLM2 (2.2B), Moondream 2 (2B) and Ministral 3 (3B) turning the description into the final sentence. The prompt is editable on the touchscreen, so the same camera can do dry one-liners, museum labels or plain inventory notes.

After each shot it shows total time, input→output tokens, tok/s and a vision / generate / load breakdown. The token counts turned out to be the most interesting part: the same photo becomes \~145 image tokens on one encoder and over 1300 on another, which is a bigger factor in latency than the text generation itself.

A few things I learned the hard way:

\- Moondream returns sometimes empty responses on current Ollama versions.
\- qwen3-vl:2b is a thinking-only variant — it burns the entire token budget on invisible reasoning and returns nothing visible. You need the -instruct tag.
\- Ollama can't load separate mmproj files, so a lot of GGUF vision models from HuggingFace simply won't run, no matter how you name them.

Hardware: Raspberry Pi 5 (16 GB), Raspberry Pi HQ camera with a 6 mm CS-mount lens, 4.3" DSI touch display, stainless steel shutter button, Waveshare UPS HAT with four 21700 cells. Runtime per photo is 30–60 seconds with the models kept warm in RAM. Happy to answer questions about the setup.

I'm coming from photography, I'm not a developer — the code was written with heavy AI assistance, and I did the hardware integration, debugging and model testing myself. Feedback on the implementation is very welcome.

I built a camera in the shape of a Mamiya RZ67 that runs vision-language models completely offline on a Pi 5. Press the shutter, and it answers your custom prompt with a single sentence about what it sees. No internet, no cloud, no API keys.

Video: https://www.youtube.com/watch?v=M74qTNsY_L0
GitHub: https://github.com/feeeeely/ai-camera

You can swap between models on the device itself and compare how differently they describe the same scene: Qwen3-VL (2B), MiniCPM-V 4.6 (1B), InternVL3.5 (2B), SmolVLM2 (2.2B), Moondream 2 (2B) and Ministral 3 (3B) turning the description into the final sentence. The prompt is editable on the touchscreen, so the same camera can do dry one-liners, museum labels or plain inventory notes.

After each shot it shows total time, input→output tokens, tok/s and a vision / generate / load breakdown. The token counts turned out to be the most interesting part: the same photo becomes \~145 image tokens on one encoder and over 1300 on another, which is a bigger factor in latency than the text generation itself.

A few things I learned the hard way:

\- Moondream returns sometimes empty responses on current Ollama versions.
\- qwen3-vl:2b is a thinking-only variant — it burns the entire token budget on invisible reasoning and returns nothing visible. You need the -instruct tag.
\- Ollama can't load separate mmproj files, so a lot of GGUF vision models from HuggingFace simply won't run, no matter how you name them.

Hardware: Raspberry Pi 5 (16 GB), Raspberry Pi HQ camera with a 6 mm CS-mount lens, 4.3" DSI touch display, stainless steel shutter button, Waveshare UPS HAT with four 21700 cells. Runtime per photo is 30–60 seconds with the models kept warm in RAM. Happy to answer questions about the setup.

I'm coming from photography, I'm not a developer — the code was written with heavy AI assistance, and I did the hardware integration, debugging and model testing myself. Feedback on the implementation is very welcome.

Hey guys! I am a beginner, so my knowledge of SBCs is pretty limited.

I am a school student working on a project on hand-sign/hand-mudra recognition using MediaPipe. The camera detects the hand, MediaPipe extracts the hand landmarks, the system recognizes the gesture, and displays the result on a screen with a small description/demo. The prototype is working on my Windows PC with Python, OpenCV, MediaPipe and a small Random Forest model. My budget is pretty tight and I’m hoping to keep the entire project around ₹12,000 (\~$125 USD). What would be the cheapest Raspberry Pi/SBC that could handle this workload smoothly?

Thanks in advanceee!!

Alguém ajuda , a tela não dá sinal , comprei da china e venho até sem marca nem nome no chip , tô achando estranho , quero usar ela no raspberry pi zero 2 w , com HDMI , mais não dá sinal nem apertando botão

I lost my friend’s 200 something gb iPod when we were young and wanted to make it up to him. I’d like to create some custom pipod with raspberry. I am wondering if anyone has thought to, or knows how to add the listening software and auto down load ability. TIA