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Screvi – Stop Forgetting What You Read

Hacker News

Screvi – Stop Forgetting What You Read

If you’re anything like me, you read and highlight a lot but always seem to forget most of it. Not only that, but I find myself spending too much time scrolling Twitter and Instagram these days. I needed an app with similar features, but one that would make me productive instead of feeding me addicting and pointless content. The usual notion/obsidian workflows are nice, but they get overly complex quick and I end up wasting a lot of time on them. I needed something easy and accessible like a social media app. So, I built an app that fixes these issues by capturing and transforming my thousands of highlights into a scrollable, Instagram-like feed. key features - Sync highlights from Kindle - Capture physical book highlights - Save from Twitter - Transcribe audio notes (so I can add highlights/thoughts while running or walking) - Feed/Stories system Let me know what you think! And if you find any bugs or have any ideas, please let me know at support@screvi.com

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Actual performance

4points
2comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: physical, notes · Missing: mac, agents, macos
83%83% 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 · Missing: supports, reddit linkedin, podcasting
66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · 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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
45%45% 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
33%33% 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
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · 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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