# devkit Published articles for devkit. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Alif Semi Balletto B1 and Ensemble E1C StartKits feature Cortex-M55 + Ethos-U55 MCU for IoT and Edge AI DevFeed: [Alif Semi Balletto B1 and Ensemble E1C StartKits feature Cortex-M55 + Ethos-U55 MCU for IoT and Edge AI]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-09-15T09:00:06Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Microcontrollers](), [Embedded Systems](), [Microcontroller](), [cpu](), [Internet of things](), [Artificial Intelligence](), [SDK]() Tags: [802-15-4](), [ai](), [alif](), [ble](), [camera](), [core](), [debugger](), [development-board](), [devkit](), [edge](), [edge-ai](), [embedded-systems](), [hardware](), [i2c](), [iot](), [led](), [low-power](), [mcu](), [microcontrollers](), [mikrobus](), [open-source](), [sensor](), [specifications](), [testing](), [zephyr](), [zephyr-os]() ### AI overview Alif Semiconductor's Balletto B1 and Ensemble E1C StartKits are low-cost development boards built around Cortex-M55 microcontrollers with Ethos-U55 NPUs. The Balletto B1 adds BLE 5.3 and 802.15.4 connectivity, while both boards include camera, audio, sensor expansion, debugging, and power-measurement features. ### Source excerpt Alif Semiconductor's Ensemble E1C (SK-E1C) and Balletto B1 (SK-B1) StartKits are low-cost development boards built around the company's Cortex-M55 + Ethos-U55 microcontrollers. The Balletto B1 adds BLE 5.3 and 802.15.4 wireless connectivity, while the Ensemble E1C does not have built-in wireless connectivity. Both StartKits feature a microcontroller with a 160MHz Cortex-M55 CPU, an Ethos-U55 NPU, 2MB SRAM, and 1.8/1.9MB NVM. They also include an Arducam camera header, a mikroBUS Click Board expansion socket, and two PDM microphones for camera, audio, and sensor-based projects. The Balletto B1 additionally provides the complete RF path to a chip antenna for its wireless subsystem, while both boards feature an on-board Segger J-Link debugger for development and testing. Alif Semiconductor StartKit (SK-B1 and SK-E1C) specifications: MCU SK-E1C - Alif Semiconductor Ensemble E1C SK-B1 - Alif Semiconductor Balletto B1 CPU - Arm Cortex-M55 core with Helium-M Vector Extension @ up to 160 MHz NPU - Arm [...] The post Alif Semi Balletto B1 and Ensemble E1C StartKits feature Cortex-M55 + Ethos-U55 MCU for IoT and Edge AI appeared first on CNX Software - Embedded Systems News. ## LattePanda Mu Ultra Compute Module features Intel Core Ultra 5 226V/7 256V Lunar Lake CPU for AI workloads DevFeed: [LattePanda Mu Ultra Compute Module features Intel Core Ultra 5 226V/7 256V Lunar Lake CPU for AI workloads]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-10T02:25:10Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Embedded Systems](), [Hardware](), [intel](), [cpu](), [GPU](), [ddr5](), [x86](), [SOC]() Tags: [2-5gbe](), [3dmark](), [artificial-intelligence-ai](), [benchmarks](), [cpu](), [ddr5](), [development-board](), [devkit](), [dfrobot](), [edge-ai](), [embedded-systems](), [geekbench](), [gpu](), [hardware](), [intel](), [intel-core](), [linux](), [lunar-lake](), [som](), [ubuntu](), [wifi-6](), [wifi-7](), [windows-11]() ### AI overview CNX Software reports on the LattePanda Mu Ultra, an x86 compute module using Intel Core Ultra 5 226V or Ultra 7 256V Lunar Lake processors with 16GB of memory. It targets robotics, industrial automation, portable instruments, and vision and edge AI applications, with up to 115 TOPS of stated AI performance. ### Source excerpt The LattePanda Mu Ultra is an x86 Compute Module powered by an Intel Core Ultra 5 226V or Ultra 7 256V Lunar Lake processor and equipped with 16GB of DDR5 memory. Offered in the same compact 69.6 x 60mm form factor as the earlier LattePanda Mu Alder Lake-N system-on-module (N100 or Core i3-N305), it delivers much higher performance, including up 115 TOPS of AI performance. Target applications include robotics, industrial automation, portable instruments, vision AI, and a range of Edge AI applications. LattePanda Mu Ultra Specifications: Lunar Lake SoC (one or the other) Intel Core Ultra 5 Processor 226V CPU - Octa-core processor up to 4.5 GHz GPU - 7x Xe-core Intel Arc 130V GPU up to 1.85 GHz AI - 40 TOPS Intel AI Boost; combined up to 97 TOPS Intel Core Ultra 7 Processor 256V CPU - Octa-core processor up to 4.8 GHz GPU - 8x Xe-core Intel Arc [...] The post LattePanda Mu Ultra Compute Module features Intel Core Ultra 5 226V/7 256V Lunar Lake CPU for AI workloads appeared first on CNX Software - Embedded Systems News. ## Why the AWS PACE Team Built Its Full-Stack Scaffolding as an Nx Plugin DevFeed: [Why the AWS PACE Team Built Its Full-Stack Scaffolding as an Nx Plugin]() Original publisher: [Read original article]() Author: Juri Strumpflohner Published: 2026-09-10T00:00:00Z Content type: article Language: en Sources: [Juri Strumpflohner]() Topics: [Amazon Web Services](), [coding](), [AI-assisted coding](), [Infrastructure as code](), [Template]() Tags: [ai](), [aws](), [blog](), [coding](), [coding-agents](), [devkit](), [generators](), [infrastructure-as-code](), [nx](), [plugin](), [tutorial](), [video]() ### AI overview The article explains why the AWS PACE team built full-stack scaffolding as an Nx plugin using Nx Devkit generators. It discusses predictable prototyping, generated code ownership, infrastructure and application components, and how coding agents affect plugin development. ### Source excerpt How the AWS PACE team built the Nx Plugin for AWS on the Nx Devkit, and why coding agents make building your own Nx plugin cheaper than ever. ## ESP-IDF tutorial series: PWM DevFeed: [ESP-IDF tutorial series: PWM]() Original publisher: [Read original article]() Author: John Lee Published: 2026-08-03T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [ESP-IDF](), [Tutorial](), [Espressif](), [configuration]() Tags: [article](), [blog](), [channel](), [configuration](), [conversion](), [devkit](), [esp-idf](), [espressif](), [generate](), [ledc](), [peripheral](), [pwm](), [signal](), [speed](), [tutorial]() ### AI overview This ESP-IDF tutorial explains pulse-width modulation, including duty cycle, switching frequency, and average voltage. It demonstrates PWM generation with the LEDC peripheral on Espressif devices and describes converting PWM to DC with an RC filter, including ripple and response-speed tradeoffs. ### Source excerpt This article explains what a PWM signal is, and how to generate PWM signal using the ledc peripheral on Espressif devices. It covers the LEDC timer and channel configuration, and shows how to convert a PWM signal into a clean DC value using an RC filter, including the tradeoffs between ripple and response speed. ## ESP-BSP Generator helps create custom-board BSP components from Espressif development kits DevFeed: [ESP-BSP Generator helps create custom-board BSP components from Espressif development kits]() Original publisher: [Read original article]() Author: John Lee Published: 2026-06-30T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Espressif](), [ESP-IDF](), [Hardware](), [Web](), [Development](), [ESP32-C3](), [ESP32-S3](), [m5stack]() Tags: [apis](), [blog](), [bsp](), [configuration](), [dependency](), [devkit](), [drivers](), [esp-bsp](), [esp-idf](), [esp32](), [esp32-c3](), [espressif](), [hardware](), [m5stack](), [web]() ### AI overview The ESP-BSP Generator is a web tool that helps developers create an ESP-IDF BSP component for a custom board. Users can start from an esp-bsp development kit, customize hardware details such as pins and features, and download a reusable BSP component. ### Source excerpt Start from an esp-bsp dev kit, customize pins and features in a web form, and download a production-style BSP component for your custom PCB - same APIs, less manual work. ## Working with Espressif's SoCs using WSL2 DevFeed: [Working with Espressif's SoCs using WSL2]() Original publisher: [Read original article]() Author: John Lee Published: 2024-11-04T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Windows Subsystem for Linux](), [ESP-IDF](), [Espressif](), [USB](), [Windows](), [ESP32-C6]() Tags: [blog](), [devkit](), [esp-idf](), [espressif](), [flashing](), [guide](), [installation](), [monitoring](), [troubleshooting](), [usb](), [windows](), [wsl](), [wsl2]() ### AI overview A step-by-step guide to developing with Espressif SoCs from WSL2. It explains USB serial-port forwarding with USBIPD-WIN, including installation, attaching and detaching a devkit, and troubleshooting kernel-module support. ### Source excerpt Released in 2016 by Microsoft, the Windows Subsystem for Linux (WSL) is a feature that allows the use of a Linux environment with no need of a virtual machine, container or dual boot. The WSL is a great way to run Linux alongside Windows. Introduction# In some cases, users can't use Windows PowerShell for developing ESP-IDF applications for ESP SoCs, or when WSL is already in use, the flashing and monitoring steps can be tricky. ## Using ESP-BSP with DevKits DevFeed: [Using ESP-BSP with DevKits]() Original publisher: [Read original article]() Author: John Lee Published: 2024-09-27T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Espressif](), [Development](), [m5stack](), [Filesystems]() Tags: [blog](), [bsp](), [development](), [devkit](), [display](), [driver](), [embedded-systems](), [esp32](), [esp32-c2](), [esp32-s2](), [esp32-s3](), [espressif](), [examples](), [gpio](), [how-to](), [i2c](), [lcd](), [led](), [m5stack](), [spi](), [spiffs]() ### AI overview This tutorial explains how to use Espressif's ESP-BSP with DevKits. It describes the Generic and DevKit BSP options, configurable buttons, LEDs, filesystems, displays, touch interfaces, and example projects. ### Source excerpt Introduction# The BSP (Board Support Package) from Espressif is ready to use package for selected boards (usually for Espressif's and M5Stack boards). This package contains main initialization functions for features, which the board contains. For example display and touch initialization, file system initialization, audio if available, LEDs and buttons. This is the easy way, how to start developing with your board. Except initialization functions, there are examples for selected board too. ## Blink LED on ESP32 with RTOS NuttX DevFeed: [Blink LED on ESP32 with RTOS NuttX]() Original publisher: [Read original article]() Author: John Lee Published: 2020-12-01T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32](), [Tutorial](), [Embedded Software Dev](), [Operating system](), [configuration]() Tags: [blog](), [bootloader](), [devkit](), [esp32](), [esptool](), [firmware](), [flash](), [getting-started](), [how-to](), [led](), [nuttx](), [partition](), [port](), [tutorial](), [usb]() ### AI overview This tutorial explains how to configure NuttX on an ESP32 DevKit, add LED support and the LED Driver Example, build and flash the firmware, and run an application that blinks an LED. ### Source excerpt This is a tutorial on how to blink an LED with NuttX after getting started. ## NVIDIA Jetson Nano Developer Kit - Introduction DevFeed: [NVIDIA Jetson Nano Developer Kit - Introduction]() Original publisher: [Read original article]() Published: 2019-04-19T12:16:25Z Content type: tutorial Language: en Sources: [Hypriot]() Topics: [Jetson](), [Nvidia](), [Arm](), [configuration](), [Docker]() Tags: [developer](), [devkit](), [docker](), [flashing](), [github](), [hdmi](), [jetson](), [macos](), [nvidia](), [robotics]() ### AI overview An introduction to the NVIDIA Jetson Nano Developer Kit covering its hardware, required accessories, SD card flashing, and initial configuration. The article recommends using a 32 or 64 GB SD card because the compressed image is about 5 GB and the uncompressed image is about 12 GB. ### Source excerpt Let me introduce the brand new NVIDIA Jetson Nano Developer Kit, which is basically a quad-core 64bit ARM Cortex-A57 CPU with 128 GPU cores - suitable for all kinds of maker ideas: AI, Robotics, and of course for running Docker Containers... Unboxing Let's unbox the board and do the initial configuration... When opening the box we'll find the board itself and a short getting started flyer which heads us to the NVIDIA website https://NVIDIA.com/JetsonNano-Start where we can find all basic information about the new DevKit. Please note, there are no other parts included in the DevKit. So, in order to use the DevKit you'll need some additional accessories: MicroSD card (at least 16GByte) Monitor with HDMI or DisplayPort connector HDMI or DisplayPort cable to connect to the monitor USB Keyboard and Mouse Micro-USB power supply (5V, 2Amps for 10 watts) Getting Started Following the official documentation Getting Started With Jetson Nano Developer Kit we have to download and flash the SD card. I don't want to repeat these basic steps here, because it's covered in great detail directy in the official NVIDIA tutorial Write Image to the microSD Card. When downloading the SD card image, please be aware this one isn't small at all. The download is a zipped image file of 5 GByte of compressed data. $ ls -alh jetson-nano-sd-r32.1-2019-03-18.zip -rw-r--r--@ 1 dieter staff 5.3G Mar 15 22:49 jetson-nano-sd-r32.1-2019-03-18.zip The SD card image is a huge 12 GByte data blob. I would strongly recommend to use a SD card size with at least 32 GByte, with the recommended 16 GByte minimum you won't get really happy. So, please do yourself a favour and get a 32 or 64 GByte SD card. $ ls -alh jetson-nano-sd-r32.1-2019-03-18.img -rwxr-xr-x 1 dieter staff 12G Mar 15 19:19 jetson-nano-sd-r32.1-2019-03-18.img For flashing the SD card image you could also use the Hypriot flash tool, which can be found here https://github.com/hypriot/flash. That's exactly what I did from macOS. $ flash --device /dev/