Your Year in Text (open-source Spotify-Wrapped for iMessage)

Hacker News

Left on Read is an open-source iMessage analyzer for Mac Desktop that I helped build. Learn your top words, emojis, group chats, contacts, and relive your funniest messages. We also built a "Your Year in Texts" experience (think 'Spotify Wrapped' for iMessage). No data ever leaves your computer and we are proudly open-source: https://github.com/Left-on-Read/leftonread The app is free to try, but in order to keep the lights on, we charge $2.99/month for premium features (such as filtering and sentiment analysis charts). Left on Read works by copying the chat.db sqlite file on your Mac found at ~/Library/Messages. This is why we need request full disk access. We then run queries on it and display graphs with react chart.js. We've happily seen a lot of people entertained by our app thanks to some viral Reddit and Tik Tok posts that we've done. We built this project for fun, so thanks for your support and any feedback is welcome.

Visit product View on Hacker NewsNov 30, 2022

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, computer, open · Missing: agents, macos, agent

92%92% 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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% 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

42%42% 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

37%37% 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

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: chat · Missing: web3, crypto, cryptocurrency

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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