Dead simple photo sharing

Hacker News

Hi all, about a month ago my wife was complaining about how annoying all of the photo sharing services were. Either the UI was overly complicated or it would ask people to register for the service before they could view the photos, etc. So I decided to build something dead-simple that she could use. There are no frills. You just create an album and email it around. The link goes directly to the photos. It's not fancy at all (very utilitarian), but she appreciates it, so I thought I'd launch it as a full service and share it with people. It's called SimpleNeat http://www.simpleneat.com If people try it out feedback would be appreciated.

Visit product View on Hacker NewsDec 14, 2013

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41points

54comments

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

63%63% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · 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

18%18% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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