Groupr - Collaborative List Making Tool

Hacker News

Groupr is a web app to create and share lists. Our goal is to help users organize and retrieve many types of information as well as facilitate collaboration. Lists can be anything: to do list, calendar, customer relations, playlist, and everything in between. By default, lists are private (registration is required), but can be easily shared with the Groupr community or friends. A simple permission set can be applied to shares, so collaborators can be allowed to view, edit and/or comment on lists. And here it is: http://groupr.co Usage cases: 1) YouTube Playlists - Create a list of YouTube URLs and a nifty playlist app will show up. 2) Databases (i.e. customer relations, places, events) - Lists have a rigid structure (schema). This equates to consumer-friendly databases with less than a minute of setup time plus simple sharing and permissions. 3) Temporary lists - Grocery lists, to do lists, and other lists that are disposable in nature. 4) Arbitrary information storage - Store information for easy recall, i.e. itineraries, notes, ideas 5) Social - The site works well for sharing Top Ten or My Favorite N Things lists. We have a public community which is organized by tags. 6) ? - Groupr has many other uses, it's up to you What makes this different from Evernote, Remember the Milk, etc.? The difference between Groupr and these other services is abstraction and structure. We do not focus on a specific use case (notes, to do lists), instead we allow lists to have any structure. Groupr requires that lists have structure and that all items in the list follow the structure (again, like a database). There are no limitations to the number of fields and many types are available (Text, Number, URL, Address, Date, etc.). Also, collaboration has been baked in from the very beginning, something other services do not focus on. Mobile? iPhone and Android apps are in the works. Does anyone find this useful? I would appreciate any feedback or criticisms.

Visit product View on Hacker NewsSep 15, 2012

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

71%71% 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 · Missing: supports, reddit linkedin, podcasting

53%53% 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

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

AppSumoMay struggle as an AppSumo deal · Strong signals: plus, friendly, users · Missing: platform, intuitive, reviews

45%45% predicted probability of success on AppSumo, 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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