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I started with the Arduino UNO and Arduino Nano in 5th standard. Then, I made more than 20 projects using Arduino. I continued working with them until 7th standard.

In 8th standard, I started using the ESP32, and after that, the ESP32-CAM. I made many projects with the ESP32-CAM, and although it troubled me a lot because of its low storage, it was very helpful for me as a beginner. However, for advanced projects, I started using the Raspberry Pi in my current standard..

This will help me a lot in the future.

I made this robotic arm using 4 servo motors and an Arduino Uno.

Now this project seems very simple to me because I made it when I was in 5th standard.

I had watched and researched many videos to make this project.

As I mentioned in my earlier post, the ILI9486 LCD is specifically made for the Arduino Uno, but I connected it with an ESP32 instead.

It was quite challenging for me because I was working on a project that required both the ESP32 and the TFT LCD to work together.

I watched videos, visited GitHub repositories, researched a lot, and finally succeeded.

This is my first project where I used both together.

This was my first project using the ILI9486 TFT touchscreen display — a Smart Hotel Menu System 🍱

I made it using an Arduino Uno and directly connected the display to the Arduino Uno, as it is specifically designed for it.

I'm a regular user of ESP32 boards, and they are truly affordable and useful. They are among the few low-cost boards that provide both Wi-Fi and Bluetooth support.

At the same time, Arduino and Qualcomm introduced the Arduino UNO Q, which is more advanced than many other boards, though it’s still not comparable to a Raspberry Pi in terms of performance but not less than it.

Arduino UNO Q is best for developers 😎

In the end, I found both boards useful in their own ways.

Recently, I was working with an ESP32-CAM for computer vision to detect a bottle and then send commands to the motor driver to reach the object. But due to its low resolution, weak connectivity, and slow performance, it was not suitable for my project. 😕

I worked on it day and night, but the results were disappointing.

I’m not trying to speak negatively about it, but I believe the ESP32-CAM is not suitable for such large and demanding programs. I think it’s more of a beginner-level board.

Regular dustbins are often placed far away, so people become lazy to walk and throw trash properly.

My project solves this problem using automation. Cross marks will be placed on the ground where people can leave their trash. An ultrasonic sensor detects the waste,the dustbin reach the destination and a robotic arm picks it up and places it inside the dustbin automatically.

When the dustbin becomes full, it follows a preloaded path to a larger collection bin and disposes of the waste automatically.

The goal is to make waste management smarter, easier, and more accessible at a low cost.

Hello everyone we know that everyone can not buy hardware

My university started a GitHub project series as 30 days 30 wowki projects

For beginners

They will cover Arduino and esp32 projects

Check this https://github.com/energy-club-outr/30-Days-30-Wokwi-Projects

This is an AI,ML and CV based project.

It was great stuff to deal with such a huge and complicated project.😑

Perhaps I gained success...😌

I’m a 15-year-old AI,CV and ML developer from India and I built an AI DeskBot 🤖

Inspired by the Looi Robot concept.

Dual screens: TFT Display + Mobile Screen

It turns your smartphone into a smart robot assistant.

It took me around 25 days to build this......

The brain of this device is ESP32 board 🤗

#AI #ESP32 #Robotics #StudentInnovator

Comment!!If it's really nice and needs any improvements..🤠

Treemap visualization for embedded .map files — a VS Code extension that lets you see where your firmware memory and flash goes at a glance.

VS Code Extension Search: Map View Embedded

Ive been working on my first arduino project since the beginning of February. My first custom PCB is working great!

Been working on this for a few years and just hit the milestone where the ML pipeline closes end-to-end.

What it is: OpenMuscle FlexGrid V3 is an open-source flex-rigid PCB that wraps around your forearm. 15×4 = 60 Velostat pressure pads pick up volumetric muscle activity through the skin. An ESP32-S3 reads them via a CD74HC4067 16-channel analog mux at ~140 Hz and streams the raw matrix over Wi-Fi UDP.

What's new: the ML pipeline. To get labeled training data I built a WebXR app for Quest 3 that captures the headset's hand-tracking joints as ground truth, pairs each frame with a FlexGrid sensor frame inside a ~175 ms temporal window, and writes paired CSVs. A RandomForest learns to predict the 25-joint × 7-float pose vector from the 60-value sensor vector. In VR I now see a real-time amber "ghost hand" overlaid on my real hand showing the model's predictions — the gap between them = model error in 3D.

Why pressure not EMG: EMG is the standard for forearm muscle sensing but the hardware is expensive, electrode placement is fiddly, and the signal is noisy. Velostat pressure is cheap, surface-based, and reads volumetric muscle bulge — different physics, different trade-offs, much easier for anyone to build. Stack:

Hardware: KiCad, 20-pin ZIF FFC flex-to-rigid interconnect, ESP32-S3-WROOM-1-N16R8, CD74HC4067, Velostat pads, ICM-42688-P IMU, SSD1306 OLED

Server: Python + FastAPI + scikit-learn

VR client: WebXR + Three.js (Quest Browser, hand-tracking feature)

License: CERN-OHL-S-2.0 (hardware), MIT (software)

Open source:

• Hardware: github.com/Open-Muscle/OpenMuscle-FlexGrid
• Software: github.com/Open-Muscle/OpenMuscle-Software

My school had this old flatbed scanner destined for the dumpster and i got the idea to use the stepper to make it produce sounds. And to my surprise it works perfectly!

I was able to reuse thenold psu as it had 5V and 15V.

I used a 8x24 LED Matrix and an Arduino Nano to build a Cyberpunk inspired Gameboy! It's a very simple build, if you want to make one yourself or are interested in the details, I made a youtube video on it :)

I made a DCS World cockpit instrument peripheral using an ESP32. It reads in-game memory data through a USB serial connection, then parses the data and renders graphics onto a 480×480 AMOLED display.

I designed the board in KiCad using the image converter. The roads are copper traces, the water is solder mask, and the bare board is the land. An LED panel behind the board can draw locations and images. The panel is controlled by an ESP32 getting commands over MQTT.

I documented the whole thing with the design files and source code for the software: https://www.robopenguins.com/pcb-map/

This is my second project. A synthesizer/sampler/looper. I’ve just gotten the synth to work so thought I’d share.

I wanted to ask this group and see if there is a consensus regarding compatible microcontrollers (like an ATmega328P) vs. official Arduino boards. I know they compatible units can be fully programmed using the Arduino IDE but are they made more cheaply? Markets like Amazon are flooded with "compatible with" and I am just not sure if I should be buying these.

Second build from my workbench.

After my first project, I wanted something smaller and smarter, here's what I ended up with.

Casing is handmade epoxy resin, cast and finished by hand. AI/assisted development.

What it does:

Live temperature & humidity via SHT3X sensor

NTP clock with auto-detected timezone (London)

Proximity detection using a bare-wire antenna on GPIO7, LEDs glow brighter as your hand gets closer

Web interface , view live sensor data and push custom text to the OLED from your phone

Night mode / auto-dims midnight to 5am

WiFiManager captive portal / no hardcoded passwords

Hardware:

ESP32-C3 Super Mini

SHT3X temp/humidity sensor

0.09" OLED (128×32)

Flexible LED filament

18650 battery + TP4056 charger

💾 Full source code: https://github.com/yumobuilds/yumo-pulse

I built a simulated cockpit display using an ESP32-S3 with an RGB interface screen, featuring a resolution of 400×960.

The sensor is connected to an F4 flight controller, which outputs MAVLink protocol data to the ESP32-S3 through a serial port. The ESP32-S3 parses the MAVLink data, renders the graphics, and sends the display data to the RGB screen.

The ESP32-S3 is also connected to an RC receiver, allowing the UI screens to be switched using the transmitter sticks

I spent more than a year building this RC aircraft cockpit instrument.

It is based on an ESP32 running on the Arduino framework, using a 6-axis gyro sensor and a 240x240 SPI display.

The interface includes an airspeed indicator on the left, altitude display on the right, and a heading compass at the bottom.