Deks – a knowledge-sharing platform for tech creators

Hacker News

Hi! We're working on a mobile app called deks. It's a shortform content platform for tech professionals and entrepreneurs, with a twist — every story is distilled to a few short cards (under 500 characters each), designed to be read in seconds. The app on iOS devices: https://apps.apple.com/app/apple-store/id1638375444 We want to focus on two types of content for now: - news and articles about the tech industry, created by our editorial team and guest contributors. Think of it as your daily TL;DR for everything that matters in tech. - networking. Users are encouraged to tell about their work, share insights, post job listings or announce new features. We hope to help users cut through the noise thanks to the short form nature of our content, but also enable them to share their ideas as effortlessly as possible. Here's a small demo: https://www.youtube.com/watch?v=tSxE669iOG0 More info is available on our website: http://deks.app or join our Discord: https://discord.gg/zcW7QNNs6E

Visit product View on Hacker NewsMay 18, 2023

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Actual performance

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Did not reach leaderboard

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

84%84% 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.

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

50%50% 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 · Strong signals: platform, users · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apple, apps, user · Missing: mac, agents, macos

40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

16%16% 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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