r/RASPBERRY_PI_PROJECTS

Hey everyone!

Over the next few weeks, I’ll be releasing blogs and guides around learning distributed learning and building your own small compute clusters.

• The goal is simple: help more people get started with running and training AI models using the hardware they already have lying around. Old laptops, MacBooks, Mac minis, Jetson Nanos, Raspberry Pis, even phones and tablets.

Distributed learning often feels intimidating from the outside, but it’s genuinely one of the coolest areas in systems and AI once you start playing with it yourself.

Before we get into the fun stuff like distributed inference and training, the first few posts will focus on setting up hardware properly and building a working cluster environment, basically subtle amount of cabling and networking!

The early guides will specifically cover setups around:

• MacBooks and Mac minis
• Jetson devices
• Raspberry Pis

After that, we’ll move into quick demos (smolcluster 👀) , and gradually learn the fundamentals side-by-side while actually running models across devices.

I’m building this alongside smolcluster, so a lot of the content will stay very hands-on and practical instead of purely theoretical.

Hopefully this helps more people realize that distributed AI systems are not something reserved only for giant datacenters anymore.

There is just one question I want to answer: are heterogenous clusters, like what I am trying to make above, even possible for running models?

Well, we'll know and till then do read me blog and let me know what you all think! Any comment, feedback etc are very welcome. (pls be gentle since its my first time writing one all by myself haha)

Read -> Blog

Hail LocalAI!

I mainly want help in making sure this setup is not needlessly complicated. I am designing a project to automate opening a wall safe through a flic button and a pi 02W. My vision is to have it embedded in a bust like the Adam west batcave button and have the pi execute a code that unlatches an electromagnetic solenoid holding the door shut. I am slightly foreign to this world but I am confident in my ability to follow directions and based on what I’ve found this should be relatively simple.

I had AI whip up a materials list, wiring diagram and python code (I’d love to learn on my own if I discover I enjoy this hobby it was just easier that way). Anything is appreciated, thank you all in advance.

Python script: from gpiozero import OutputDevice
from time import sleep

import fliclib

# -----------------------------
# GPIO SETUP
# -----------------------------

SOLENOID_PIN = 17

solenoid = OutputDevice(SOLENOID_PIN)

# -----------------------------
# SETTINGS
# -----------------------------

UNLOCK_PULSE_TIME = 0.2
RELOCK_DELAY = 5

# -----------------------------
# FUNCTIONS
# -----------------------------

def unlock():
print("UNLOCKING")

solenoid.on()
sleep(UNLOCK_PULSE_TIME)
solenoid.off()

def relock():
print("RELOCKING")

solenoid.on()
sleep(UNLOCK_PULSE_TIME)
solenoid.off()

# -----------------------------
# FLIC CALLBACK
# -----------------------------

class ButtonConnectionChannel(fliclib.ButtonConnectionChannel):

def on_button_single_or_double_click_or_hold(
self,
click_type,
was_queued,
time_diff
):

if click_type == fliclib.ClickType.ButtonSingleClick:

print("BUTTON PRESSED")

unlock()

sleep(RELOCK_DELAY)

relock()

# -----------------------------
# FLIC CLIENT
# -----------------------------

client = fliclib.FlicClient("localhost")

def got_button(bd_addr):
print("Button connected:", bd_addr)

cc = ButtonConnectionChannel(bd_addr)

client.add_connection_channel(cc)

client.get_info(got_button)

print("Waiting for Flic button...")

client.handle_events()

Software: I am running Raspberry Pi OS Bookworm with labwc on Wayland. PicFrame software is installed in a Python virtual environment and starts automatically when the Raspberry Pi 3B+ boots. Instead of storing photos on the SD card, the Pi mounts a read-only SMB share from my Pi 5 NAS and uses that as the slideshow source.

The frame is basically a client for my photo library. I run a bi-weekly n8n pipeline to pull down my photos from iCloud, I use a local LLM (llava) running on an x86 minipc node in my cluster to tag each photo, all photos and metadata are stored in a parquet DB and then copied into folders on my pi5 NAS. The Pi3b+ displays the pictures. I can update the photo library centrally, and the frame just picks them up from the network share. I can ssh into the pi3 whenever I need to do updates or change slideshow speed.

Hardware: I use a waveshare Poe hat (version e I think) in a waveshare case that I had to hack at a little bit to make everything fit, but now there are only 2 cables: 1 Ethernet connection between the Pi3 and the Asus display, and one usb-c connecting the display to the power tower.

So when people ask what I do with my homelab, now I can say, build picture frames to display my curated image archive!

Use this website if you want to build one too:

https://www.thedigitalpictureframe.com/how-to-build-the-best-raspberry-pi-digital-picture-frame-with-bookworm-wayland-2025-edition-pi-2-3-4-5/

3D printed Pi Case. First draft. This is not my design.

Im trying to build a soft power circuit for a battery powered project. This looks like a good idea to me. I have (hopefully) converted it to strip board and swapped out the diodes and MOSFETs for ones i have on hand.

Anyone good with circuits able to confirm if this works?
red =5v
white=G
green/blue=GPIO

Many thanks!

I gave the P100R a try and took a preliminary look at its depth imaging. I'm not entirely sure about its effective measurement range, though. The product documentation claims it can reach up to 8 meters at maximum, which I'll test in my next session. Has anyone else here used it? I also came across someone on GitHub running YOLO object detection with it.

I have made a Raspberry Pi hat that integrates RTL-SDR, LoRa, GPS, and RTC. It would simplify the setup if your Pi need tmore than one of the features of the hat.

The first picture shows the build with a Waveshare PocketTerm35.

Project Page：https://hackergadgets.com/pages/rpi-aio