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Upboat, idea validation with a twist

Hacker News

Upboat, idea validation with a twist

http://www.upboat.us Demo: http://www.upboat.us/idea/upboat Screenshot of logged-in view: http://i.imgur.com/vUZXfZc.png Upboat is an idea validation platform, somewhere inbetween Kickstarter and LaunchRock. If the idea you pitch receives over 200* "upboat"s (consented would-be users) within one week, you get the whole emailing list of all those who upboat'd it. If not, you get nothing. This is helpful for those who have ideas for side-projects, but lack the strong-enough incentive to get to work and just hack something together. Should you put up an idea you came up with, and in a week it gets enough upboats, you have a 200-strong user incentive to get to work. If not, then not a big deal. You can upboat an idea as an potential user of the service, a potential developer of the idea (github auth'd), and/or as a potential investor in the idea (angelList auth'd). No commitment--just the chance to chat. This hopefully provides a stronger social proof scheme than a landing-page-captured email count. * In the future, I'd prefer this to be 500 instead of 200. Backstory: I built this at the Angelhack Silicon Valley hackathon this past weekend in 24 hours of maddening, restless typing. Granted, I was also "in the zone" for most of it. In past Angelhacks, I've done silly things like single-handedly build and present 3 different hacks (drives the judges nuts), but this time I decided to go for one production-ready hack (we'll see if I'm right). Upboat came out of it, so please, if you can contain yourself, don't mind the sloppiest code you'd ever laid eyes on. I'd love to get some feedback, but I'd love even more to get enough Upboats to keep working on it :)

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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 · Strong signals: user, email, single · Missing: mac, agents, macos
71%71% 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
69%69% 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 · Strong signals: platform, users · Missing: plus, intuitive, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
37%37% 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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