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SQLiteChangesetSync – Git-Like Sync for SQLite Databases

Hacker News

SQLiteChangesetSync – Git-Like Sync for SQLite Databases

Hello HN! I’m happy to introduce SQLiteChangesetSync, a Swift package I’m sharing for the first time. It’s designed for offline-first synchronization of SQLite databases across devices. The package uses the SQLite Session Extension for capturing and committing changes in the local database. When the client is online, these changesets can then be pushed to a shared repository, which could be any server or database (example CloudKit implementation included). Other clients can fetch these changesets to keep their local databases consistent. SQLiteChangesetSync supports branch merging when databases diverge and provides the ability for applications to handle merge conflicts. I've also included a pre-built iOS app in the package, allowing you to run two instances on different simulators to experiment with the syncing process. I’m eager to hear feedback on both the concept and implementation of database synchronization. Your insights are highly valued! https://github.com/gerdemb/SQLiteChangesetSync Note: As this project is experimental and self-tested, some challenges are expected. Looking forward to your feedback!

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, ios · Missing: reddit linkedin, podcasting, created
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 HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
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 · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
46%46% 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
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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