OpenDeck – DIY MIDI Platform Based on Zephyr RTOS

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This is OpenDeck, my own platform for building custom MIDI controllers. I've been working on it for more than 10 years, and now I've made the biggest change so far - rewritten the entire codebase to make use of Zephyr - RTOS with which I've been working professionally for few years now. This now allows me to support many newer boards, complex features and also to modernize the codebase in general. I've been limited on all fronts before. The platform itself allows for simple building and configuring of custom MIDI controllers - main reason for that is because it requires no coding. Load the firmware on the board, configure it via web configurator and you're good to go. The amount of configurable features is also huge. I do have extensive documentation which covers usage, configuration, flashing of various boards, customization of own boards etc. All available on GitHub. The platform supports a large number of various boards - not only my own, custom designs, which I sell, but boards like Raspberry Pi Pico 1 and 2, STM32F4 Discovery, Teensy 4 and 4.1, nRF52840DK etc. Lots of choices. Before Zephyr, I rolled my own HAL for various platforms and YAML-based peripheral configuration, both of which is now replaced with Zephyr and its various subsystems and tools, primarily device tree. I must admit however I do not enjoy C at all so most of the stuff I use is wrapped in an external C++ library (zlibs) used as west module on which OpenDeck depends. The project itself is written in C++20. Currently I'm using Zephyr 4.4 and its MIDI 2.0 driver in MIDI 1 compatibility mode, as well as WebUSB for firmware updates, so this is a fairly modern stack.

Visit product View on Hacker NewsApr 22, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, coding · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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