iMessage-exporter, a full-featured CLI app and library

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I've spent a long time reverse engineering nearly every aspect of Apple's iMessage SQLite tables to build this program. As far as I know, there are no other tools that support the full corpus of iMessage features, including edited messages, app messages, reactions, and threads. I built this software to preserve some conversations with loved ones; I hope others find it useful as well. For the curious, here are some of the more interesting aspects of the database I came across: - Dates are stored as Unix timestamp with an epoch of 1/1/2001 00:00:00 - Messages can have multiple parts, denoted by some special invisible characters - Reactions are stored by prefixing the GUID of the reacted message with the index of the message part [0]. - URL messages cache data in the table, which we can parse and display [1]. - Edited messages store the history message edits [2] (I wanted to try out DDD here, which was fun!). [0]: https://docs.rs/imessage-database/latest/imessage_database/m... [1]: https://docs.rs/imessage-database/latest/imessage_database/m... [2]: https://docs.rs/imessage-database/latest/imessage_database/m...

Visit product View on Hacker NewsJan 10, 2023

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

90%90% 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: apple · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

73%73% 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 · Missing: mobile apps, ios, personal

32%32% 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

27%27% 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 · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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