Teamgum – Knowledge sharing tool for teams

Hacker News

Teamgum is built to share web pages fast with the whole team (unless you want it privately shared). It has attempted to minimize context switch by bringing reading list, discussions & notifications all on the browser. It has a mobile app aswell on android/IOS & blackberry. When a teammate searches for info on google, it tries to show relevant feed from teamgum.<p>Tool is available at http://teamgum.com. Currently in public beta.<p>Please feel free to give your brutal & honest feedback. Some pointers i am interested in are below: 1. Messaging on our website 2. onboarding experience 3. overall user experience 4. Do you think it is going to be useful/valuable to you.

Visit product View on Hacker NewsMay 30, 2014

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2points

Did not reach leaderboard

Launch Intel predictions

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

94%94% 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: ios · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

51%51% 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: ios, google · Missing: mobile apps, personal, entrepreneurs

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 · 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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