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An app for reading tech news (e.g. TechCrunch) and VC blogs (e.g. A16Z)

Hacker News

An app for reading tech news (e.g. TechCrunch) and VC blogs (e.g. A16Z)

Hi everyone, I would like to introduce "TechTea", an iOS app for reading tech news (e.g. TechCrunch) and VC blogs (e.g. A16z), with audio. I like to keep myself updated about the latest news/discussion in tech and venture capital. Most of these good information sources either have terrible iOS apps or have no native apps at all. For venture capital blogs, there is also a discovery problem. TechTea is intended is to tackle these problems. Besides, the app uses ML to generate audio for these articles so users can listen to them just like listening to a podcast. App Store link: https://apps.apple.com/us/app/id1529509067 Landing Page: https://www.techtea.app/ Suggestions and feedback are more than welcome!

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
82%82% 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 HuntUnlikely to reach the leaderboard · Strong signals: apple, apps, user · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, users · Missing: mobile apps, personal, entrepreneurs
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
38%38% 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: users · Missing: plus, platform, intuitive
23%23% 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
16%16% 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, introduce · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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