a photo sharing web app for albums

Hacker News

Hi, i recently released my MVP for a new photo sharing web app that is similar to instagram except its web based, no filters, and is primarily for uploading small photo albums (3-7ish photos). The web app streams them in a news feed like format. Fully equipped with commenting, favoriting, and even hashtagging. Right now i have focused it on real estate photos, but see no reason as to why it cant be opened up as a more general photo sharing app. Since its not a mobile app all uploading of photos are done via emailing a set of photos to upload@cribzy.com via the email address you registered with. I plan on adding upload button options for both desktop and mobile in the very near future. I would love any feedback you have. I would like to see it improve And willing to work hard. The URL is http://www.cribzy.com if you wish to take a look. Thanks!

Visit product View on Hacker NewsSep 11, 2013

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1points

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

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

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

54%54% 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: new, email, open · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

48%48% 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

23%23% 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.

Incorrect prediction on native model

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