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NotionStack – Notion templates, courses, and tools

Hacker News

NotionStack – Notion templates, courses, and tools

Hey everyone! As Notion fans, we found it challenging to tap into the power of the Notion community due to the silos of knowledge spread across various social networks, forums, and creator platforms. Like most Notion geeks, we used a Notion database to capture, categorize and organize our Notion community discoveries. As the database grew, our Notion "stack" gave us superpowers to streamline our internal workflows, launch a course and build multiple products (including NotionStack). Recognizing that our Notion discovery problem is not unique, we decided to shine a brighter light on the Notion creators and their creations by publishing our curated database of Notion discoveries with the world. With NotionStack, we aim to make it easier to tap into the power of the Notion ecosystem and highlight the incredible Notion creators. Let us know what you think.

Visit product View on Hacker NewsNov 28, 2022

Actual performance

2points

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · 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.

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

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

57%57% 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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% 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

18%18% 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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