This article details the creation of a "Now Playing" display for a record player using an ESP32 microcontroller. It leverages a microphone, Shazam API (via Shazamio Python library), and MQTT to identify and display the currently playing track. The project involves hardware setup with a Waveshare display and software development in ESPHome.
The new standard for embedded computing. Leverage Raspberry Pi 5’s powerful hardware and efficient software stack in your own custom embedded systems. Available now from $45.
This document provides a developer guide for the Tiny Code Reader from Useful Sensors, a small, low-cost hardware module that reads QR codes. It covers connecting, mounting, powering up, reading data, configuration, sensor characteristics, example code, privacy considerations, and an appendix with data formats and CAD files.
This article details the various ESP32 series (Classic, S2, S3, C2, C3, C5, C6, H2, H4, and P4), outlining their key features, differences in CPU architecture, wireless capabilities, memory size, and intended applications. It explains the naming conventions and provides a comprehensive overview to help users choose the right ESP32 for their projects.
| **ESP32 Series** | **Key Features** | **Primary Use Cases** |
|---|---|---|
| **ESP32 (Classic)** | Dual-core, Wi-Fi, Bluetooth, BLE, DAC | Smart home, audio streaming, versatile projects |
| **ESP32-S2** | Single-core, Wi-Fi, USB-OTG, larger ADC | USB gadgets, low-cost IoT, cameras |
| **ESP32-S3** | Dual-core, Wi-Fi, BLE, AI extensions | Performance-focused IoT, AI/ML applications |
| **ESP32-C2** | Single-core RISC-V, Wi-Fi, BLE, low cost | High-volume, low-traffic devices (bulbs, sensors) |
| **ESP32-C3** | Single-core RISC-V, Wi-Fi, BLE, affordable | Battery-powered sensors, basic Arduino replacement |
| **ESP32-C5/C6** | RISC-V, Wi-Fi (C5: Dual-band), BLE, Thread/Zigbee | Advanced connectivity, mesh networks |
| **ESP32-H2/H4** | No Wi-Fi, BLE, Thread/Zigbee, low power | Ultra-low power applications, mesh networks |
| **ESP32-P4** | Ethernet, Dual-core RISC-V, H.264 encoder | Video processing, HMIs, industrial applications |
| **ESP32 Series** | **Key Features** | **Primary Use Cases** |
|---|---|---|
| **ESP32 (Classic)** | Dual-core, Wi-Fi, Bluetooth, BLE, DAC | Smart home, audio streaming, versatile projects |
| **ESP32-S2** | Single-core, Wi-Fi, USB-OTG, larger ADC | USB gadgets, low-cost IoT, cameras |
| **ESP32-S3** | Dual-core, Wi-Fi, BLE, AI extensions | Performance-focused IoT, AI/ML applications |
| **ESP32-C2** | Single-core RISC-V, Wi-Fi, BLE, low cost | High-volume, low-traffic devices (bulbs, sensors) |
| **ESP32-C3** | Single-core RISC-V, Wi-Fi, BLE, affordable | Battery-powered sensors, basic Arduino replacement |
| **ESP32-C5/C6** | RISC-V, Wi-Fi (C5: Dual-band), BLE, Thread/Zigbee | Advanced connectivity, mesh networks |
| **ESP32-H2/H4** | No Wi-Fi, BLE, Thread/Zigbee, low power | Ultra-low power applications, mesh networks |
| **ESP32-P4** | Ethernet, Dual-core RISC-V, H.264 encoder | Video processing, HMIs, industrial applications |
Collapse OS is a Forth-based operating system designed to preserve microcontroller programming capabilities after a civilizational collapse, focusing on minimal resources, improvisation, and self-sufficiency.
Silicon Labs has announced the BG29 family of Bluetooth Low Energy (BLE) wireless microcontrollers, suitable for wearables and even smart teeth implants, featuring an Arm Cortex-M33F core, up to 1MB of flash storage, and a variety of peripherals.
Glowtape is a unique DIY wristwatch by Henner Zeller that uses glow-in-the-dark tape and an RP2040 microcontroller to display the time.
Open-source project showcasing a speech-to-text application on a low-cost CH32V003 microcontroller. The application reads audio from an analog microphone, identifies spoken digits 'zero' to 'nine', and logs the results over a serial link.
emlearn is an open-source machine learning inference engine designed for microcontrollers and embedded devices. It supports various machine learning models for classification, regression, unsupervised learning, and feature extraction. The engine is portable, with a single header file include, and uses C99 code and static memory allocation. Users can train models in Python and convert them to C code for inference.
