I solved my New Tab page

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The site is completely customisable, you can create a start page that suits your own style. Initially, I built the site to only work for me, however I wanted the website to be re-usable for anyone. You can now login to your own Twitter/Spotify/Strava accounts for your own personal feed. Everything is stored locally in your own browser so there's none of your data floating around in the clouds somewhere. You can find the code at https://github.com/allister-grange/startertab . Here are some examples of themes in a gif: https://user-images.githubusercontent.com/18430086/193997502... .

Visit product View on Hacker NewsOct 27, 2022

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Actual performance

332points

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Made the leaderboard

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

76%76% 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.

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

64%64% 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: personal · Missing: mobile apps, ios, entrepreneurs

46%46% 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

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

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

35%35% predicted probability of success on Indie Hackers, 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

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

Correct prediction on native model

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