Real-time face recognition on low power microcontroller

Hacker News

Built face recognition that runs entirely on STM32's new N6 chip - no cloud, no external processing. The chip istelf is smaller than a thumbnail and uses minimal power (on full operation ~0.25W), but the documentation is practically non-existent since it just launched. Performance numbers: Face detection: 9ms Face recognition: 130ms per person Multi-face tracking that actually works Runs locally with zero data transmission (with an option to stream feed to PC, but it's entirely up to you) The hardest part wasn't the AI - it was figuring out how to make anything work on hardware so new that most examples don't compile. With examples that don't cover the whole span of what developer need in the real world. Spent months reverse-engineering and debugging cryptic NPU errors, hacked and slashed my way untill things worked - ST might learn new things about their own hardware from how I abused some of it. Includes full source code, build pipeline, model conversion scripts, and documentation written by someone who actually used it. No enterprise sales pitches - just working code for embedded AI. Have fun and :)

Visit product View on Hacker NewsJul 29, 2025

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

96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real world · 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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