Jumble Stream (for when you've read everything on HN)

Hacker News

http://jumblestream.com/ Hi HN, I was lying in bed on Sunday morning and I'd done the internet. I'd been to HN, DN, PH, TC and the few others I could remember the name of. I'd read everything that had interested me, but I wanted to lie there and read more! And I knew there was more. I'm always being linked to interesting sites from my usual starting places, but I never remember what they are. So I built: http://www.jumblestream.com/ The site you go to after you've been to all the sites you normally go to. I'd really like some feedback! What you think of the idea, any suggestions for links and what you think about the design. Thanks HN!

Visit product View on Hacker NewsAug 27, 2014

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

59%59% predicted probability of success on Product Hunt, 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

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nativeThis product was originally launched on this platform.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

31%31% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% 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.

Incorrect prediction on native model

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