Mini Christmas Trees with addressable LEDs and RP2040-based controller

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Hello HN, these Mini Trees have a custom circuit board with WS2812 addressable LEDs on one side and a controller using an RP2040 microcontroller on the other, all inside a CNC'd Foamex PVC case. They stand around 23cm (9") tall. The firmware is written in MicroPython, which can be modified or replaced. Power is provided via USB-C, which is also used for programming. There is also a web-based animation sequencer to create lighting sequences in time to arbitrary audio, examples of which are on the linked page. This is similar to xLights ( https://xlights.org/ ), but much less powerful, and we hope to release firmware to interface directly with xLights soon. My colleague and I started kicking the idea for these around after making some custom circuit boards for another project, and thought we'd have a go at making smaller versions of some much larger exhibits we had made using 12 volt WS1812 bullet pixels. I hope you enjoy them, Merry Christmas!

Visit product View on Hacker NewsDec 2, 2022

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AppSumoStrong fit for a featured deal · Strong signals: interface, soon · Missing: plus, platform, intuitive

61%61% predicted probability of success on AppSumo, 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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

32%32% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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