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Show HN - Pixels: Know the pixels you see.

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Show HN - Pixels: Know the pixels you see.

I did some hacking today. Nothing to brag about, but I think it's interesting. I call it "Pixels". What it does is, after you run it, it captures your screen after X seconds (configurable), and takes a pixel out of it (RGB value ie). It does this until it has enough pixels to create a new image. https://github.com/thekarangoel/Pixels Can someone please use this for a few hours and post their results? It is not obtrusive and is not distracting. If you'd like to share your Pixels image with others, feel free to post it here and/or send in a pull request. Know the pixels you see. Enjoy!

Visit productView on Hacker NewsAug 4, 2013
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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% 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
14%14% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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