chat without contact information

Hacker News

Mobile group chat today is centralized. Most mobile chat apps rely on a central admin to add each person to their address book and then to select contacts to form a group chat. Additionally, if you want to add someone to the chat, you need to pass their contact information on to the admin. So there is an underlying dependence on digital contact information. The other problem with group chat today is that over time group chats can veer off topic. Group chats can become static never-ending threads, like one long email chain, instead of being topical and then just going away. What if you could group chat without the exchange of contact information and without the dependence on a central admin? What if the barrier to creating group chats is reduced so that they can be created instantly? How do you get 20 people in a physical room to start chatting instantly, and, by extension, friends who later might want to join the chat? This is why we built ChatTag (available on the App Store or check us out at chattag.com). Feedback would be awesome.

Visit product View on Hacker NewsNov 13, 2013

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

57%57% 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.

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

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

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

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

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

TrustMRRLess likely to generate early MRR · Strong signals: apps, way · 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 · Strong signals: arr · Missing: mrr, revenue, profit

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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