Master Everyday Interactions

Hacker News

Just published my first 4 issues to better understand and master everyday interactions. Would these kind of mental models, techniques, concepts and anecdotes help you? You can review them on https://www.getrevue.co/profile/rinput #Exploit and Explore: only by trying new stuff you can increase the things you like #Flow: losing your sense for time & space by matching the challenge with your skills #Dyson: Dyson's combination of existing ideas and his way to 5.127 prototypes #GOMI = sensitive talks by starting with good things, own mistakes & the issue at last Thank you for your feedback!

Visit product View on Hacker NewsDec 2, 2019

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Launch Intel predictions

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

77%77% 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: mistakes · Missing: supports, reddit linkedin, podcasting

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

15%15% 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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