Embedr – The AI-Native Arduino IDE

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I’ve been working on this over the past few months after getting frustrated with how fragmented hardware workflows can be - switching between code editors, terminals, and toolchains. Embedr is an AI-native IDE for Arduino and other hardware ecosystems. It helps you code, build, and flash your projects while integrating tightly with the underlying toolchains like the Arduino CLI. The goal is to make hardware development feel as seamless as modern software IDEs. A key feature is the Embedr Agent, which works like Claude Code inside the IDE. You can describe what you want to build in natural language, and it assists with code generation, debugging, and project setup directly in your workspace. Current version includes: - Arduino CLI integration with board detection and flashing - Embedr Agent for AI-assisted development - Built-in terminal and serial monitor - Extensible plugin system for other toolchains (ESP-IDF, STM32, Raspberry Pi, etc.) You can try it here: https://embedr.app Still early, and I’d love to get feedback from folks who work with Arduino or embedded systems - what’s missing, what could be better, or what would make this truly useful day-to-day.

Visit product View on Hacker NewsOct 31, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, code · Missing: mac, agents, macos

98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

31%31% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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