Wireless video streaming on POV bike display

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It took me a few years of fits and starts but I've finally got my DIY persistence of vision bike light to display video! I designed and built the PCB for it and wrote the code for the wifi streaming of video frames with a bit of help from Claude. This was actually one thing I was dreading as I was hitting a wall being burned out on this project. With a little bit of Claude's help in getting the ESP32 wifi stack working It's finally finished! Some tech specs: Using an ESP32 42 LED's per strip, 4 strips total spaced 90 deg apart. Angular resolution of the image is 1 deg so it updates all 168 LED's , 360 times per rotation. The LED's are SK9822 individually addressable LED's (also known as DotStar from Adafruit). Uses a magnet and hall effect sensor to keep track of rotation speed. The server can be run from a phone and using termux with a python server to serve locally to the esp. I need to periodically spin it up every time it slows down. My video camera shutter speed needs to be slow to show the correct effect otherwise it is either incomplete or it shows too many "frames" in a video frame. Gonna get around to making a blog post at some point to talk more about it in detail!

Visit product View on Hacker NewsFeb 27, 2025

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

79%79% 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: claude, using, code · Missing: mac, agents, macos

70%70% predicted probability of success on Product Hunt, 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

57%57% 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: video · Missing: mobile apps, ios, personal

49%49% 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

32%32% 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

22%22% predicted probability of success on Acquire.com, 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.

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