Teatime – Get Feedback on Your Texts Before You Hit Send

Hacker News

Hey HN, After months of hard work, I’m excited to share Teatime, a new app I’ve been passionately developing with my cofounder. Teatime is designed to help you craft the perfect text by providing feedback, edits, and suggestions from a community of real people. Whether you’re struggling with a tricky message or just want a second opinion, Teatime is here to help. But it’s more than just a utility app—it taps into the cultural phenomenon of “spilling the tea.” Users can anonymously dive into ongoing conversations, offer their insights, and learn from others’ experiences. It’s like being part of a live text drama where you can contribute your thoughts and get involved. Teatime is available on both iOS and Android, and I’d love to get feedback from the HN community. Whether it’s about the UX, technical implementation, or just general thoughts, I’m eager to hear what you think. Check it out and let me know how we can make it better! Looking forward to your thoughts and suggestions!

Visit product View on Hacker NewsAug 22, 2024

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Actual performance

2points

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · 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 · Strong signals: user, new · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

58%58% 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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, month, users · Missing: mobile apps, personal, entrepreneurs

39%39% predicted probability of success on TrustMRR, 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 · Strong signals: real people · 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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