Keep up with news without visiting tech sites

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Hi everyone, I've been working on a technology newsletter for the few months that helps busy people keep up with tech news. The industry moves fast but is becomign relevant for almost everyone, so this 'slow web' style method seems to work really well for people who simply want to be told what's good in a given week. Email newsletters aren't sexy, but they work incredibly well. People love getting a summary rather than having to look for it; we have an open rate of 60-70% and a click-through of 25-35% on a given week. I'd love if you checked it out or had any feedback on if you think this would be useful to someone you know: http://weekly.char.gd

Visit product View on Hacker NewsJan 31, 2015

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

68%68% 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: new, email, open · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% 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: month · Missing: mobile apps, ios, personal

43%43% 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

28%28% 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

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

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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