Manage your attention better with Mutter

Hacker News

Dear HN Friends, PsyTech ( https://www.psytech.ai ) is a young & ambitious applied research startup focusing on information discovery, machine recommendations, and generative design. We apply psychological theory to human-computer interaction design to create new and improved user experiences. Mutter ( https://mutter.cards ) is the first product in our roadmap. We are building the simplest way to discover and share what you seek online. Help us in this journey by trying out our alpha and sharing your suggestions at feedback@psytech.ai Taking feature suggestions on behalf of our team :) Twishmay Founder, PsyTech

Visit product View on Hacker NewsNov 15, 2022

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Launch Intel predictions

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

76%76% 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 · Strong signals: mac, user, computer · Missing: agents, macos, agent

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

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

45%45% 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.

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

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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