Control My LEDs

Hacker News

Control My LEDs

Over the past few days, I created a website for controlling my lights. I had the idea to add a live stream of the lights to it, so now anyone can try changing my lights and see the results. Eventually, I plan to add some sort of authentication, but I thought I'd see what people do with it before then. The LEDs automatically update as you change the values. There are quite a few bugs, but because this is a personal project, I am probably not going to fix them. Note: The live stream is pretty delayed on mobile. On desktops or laptops, it is generally within a few seconds of live.

Visit product View on Hacker NewsNov 23, 2022

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Actual performance

2points

Did not reach leaderboard

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

60%60% 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 · Missing: mac, agents, macos

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

41%41% 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

13%13% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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