A site to upload, share and review creative work and presentations

Hacker News

http://previewcloud.com The idea came from a product manager I used to work with. He had a bunch of website comps that he needed to show some remote employees but didn't want to zip them up and email them out. Ideally he wanted to put them online somewhere and just send out a URL. Going to the URL would show the first image and clicking it would take you to the next. I put that together one weekend and it did exactly what he needed. He started creating new "decks" and sharing them on a daily basis. I've since added the ability to attach a note to an image and for viewers to leave comments. You can come along and use the site completely anonymously to upload images and create a deck for sharing, but you can't edit it after the fact or keep multiple decks organized in any way. Additional pay features are inviting multiple users to your account so you can work together on decks, customizing the URL to your deck as well as the subdomain. In the feature queue: CNAME your own domain to the site, ability to markup and draw on an image via <canvas> (almost complete), versioning, ability to lead a review with the presenter controlling the navigation and multiple users viewing, grouping and archiving of decks...the list goes on. I see three groups as my primary audience: 1) product managers that need to share site comps or presentations to remote folks, 2) independent designers and 3) design firms, both of which need to share creative work with clients and collect feedback.

Visit product View on Hacker NewsJan 7, 2013

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

87%87% 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: started · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

45%45% 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: users · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% 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

13%13% 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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