Stumbleback – StumbleUpon for the bookmarks you've been hoarding

Hacker News

Hi HN, I have about 2000+ bookmarks that I will never read. Probably you do too. I keep collecting new stuff to read, the list grows longer each day, but I barely get around to reading them, and the problem, as I realised, is more to do with the analysis paralysis on what to read. Sort of like how we spend so much time figuring out what movie to watch on Netflix. So I made a simple Chrome extension: it picks one bookmark at random, drops you on the page, and gives you two buttons on a floating toolbar - Stumble (next random one) or Done (mark read and move to the next random one). That's it. It takes away the burden of decision altogether, and it's sort of fun to engage with because of the variability (and novelty) of what it loads next, while still being within the universe of things I've been wanting to get to. Also, I've added daily goal and streaks to keep me motivated to get through the list and turn it into a daily habit. You can simply Right-click -> Add to Stumbleback for new saves, otherwise it just reads your existing Chrome bookmarks, or you can paste URLs as well, no separate database. It's free. Would love feedback from anyone who's tried to get through their reading list of things and failed.

Visit product View on Hacker NewsMay 27, 2026

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Actual performance

6points

Did not reach leaderboard

Launch Intel predictions

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

74%74% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, 000 · Missing: https docs, excited, just released

42%42% 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

40%40% 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

19%19% 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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