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Groupd – an AI-assisted service for categorizing online content

Hacker News

Groupd – an AI-assisted service for categorizing online content

NOTE: the actual service is still actively being worked upon, but we have decided to publish the landing page as early as possible and get some early adopters. During our work on http://murmel.social , we have come to the conclusion that AI assisted content classification can be a game-changing tool for small teams or knowledge professionals (journalists, researchers, writers, marketers, etc.) This is why we thought it would make sense to direct a part of efforts on sharing our in-house classification tooling with those people. Even if you have absolutely no clue about content classification, or how it would help you, if you fall into of the groups above, you might want to sign up and get our further updates. We believe that we can help build a tool that people may not even know they needed. Thanks for your support!

Visit product View on Hacker NewsSep 27, 2022

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

52%52% predicted probability of success on Product Hunt, 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

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.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

19%19% 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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