Slack + Discourse + StackOverflow = EffectiveDiscussions

Hacker News

Hi, I've combined features from Slack (chat software), Discourse (forum software) and StackOverflow (question-answers) into a single piece of software, EffectiveDiscussions. EffectiveDiscussions solves some problems here at HN that annoy me, and you too I would think: 1) When you return to a topic, how do you find the comments added while you were away? 2) It's hard to find the parent comment, if it's far away. Then, once you've found it, it's hard to find the comment you were reading before, somewhere far away downwards. 3) Downvote because disagree or not? Solutions & demo: https://www.effectivediscussions.org/for/hacker-news Feedback/ideas/questions are welcome. It's open source. Regards, KajMagnus

Visit product View on Hacker NewsOct 2, 2016

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Actual performance

5points

2comments

Did not reach leaderboard

Launch Intel predictions

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

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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.

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

38%38% 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

19%19% 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

14%14% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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