I'm bringing back StumbleUpon with ByteShuffle

Hacker News

If you’ve been on the Internet for a while, then chances are you remember StumbleUpon. The thrill of discovering weird, amazing websites with a single click. I built ByteShuffle to bring that experience back! Click “Shuffle” to instantly explore a random site, complete with a screenshot preview. Sites are curated from places like r/InternetIsBeautiful , and you can submit interesting websites of your own. Like or dislike websites to help surface the best content — a fast, fun, distraction-free way to stumble across the web again.

Visit product View on Hacker NewsNov 28, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

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

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

40%40% 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: way · Missing: mobile apps, ios, personal

36%36% 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

12%12% 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.

Correct prediction on native model

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