We just released the first public preview of ObjectBox Mesh Sync (P2P) ☺️ sync.objectbox.io/mesh-sync Devices can now sync directly with each other without any server or Internet connection, increasing resilience and robustness #edgeai #localai #datasync

Rebuilt NEXUS V3 from scratch. Old: every user scraped career pages. 100 users = 100 IPs. Ban risk was real. New: one GitHub Actions scraper. 295 companies. 30K jobs. 2MB file. Hugging Face delivers it. ObjectBox stores it locally. Zero servers. Zero cost. Works offline.

📂 Mobile App Stack ┃ ┣ 📂 Frontend / Framework ┃ ┣ 📂 React Native ┃ ┣ 📂 Flutter ┃ ┣ 📂 SwiftUI ┃ ┣ 📂 Jetpack Compose ┃ ┗ 📂 Ionic ┃ ┣ 📂 Backend & API ┃ ┣ 📂 Firebase ┃ ┣ 📂 Supabase ┃ ┣ 📂 AWS Amplify ┃ ┣ 📂 Backendless ┃ ┗ 📂 Custom NodeJS API ┃ ┣ 📂 Local Database ┃ ┣ 📂 SQLite ┃ ┣ 📂 Realm ┃ ┣ 📂 WatermelonDB ┃ ┣ 📂 ObjectBox ┃ ┗ 📂 Drift ┃ ┣ 📂 Cloud Database ┃ ┣ 📂 Firebase Firestore ┃ ┣ 📂 Supabase PostgreSQL ┃ ┣ 📂 AWS DynamoDB ┃ ┣ 📂 MongoDB Realm ┃ ┗ 📂 Couchbase Mobile ┃ ┣ 📂 Authentication ┃ ┣ 📂 Firebase Auth ┃ ┣ 📂 Supabase Auth ┃ ┣ 📂 Auth0 ┃ ┣ 📂 Apple Sign In ┃ ┗ 📂 Google One Tap ┃ ┣ 📂 Push Notifications ┃ ┣ 📂 Firebase Cloud Messaging ┃ ┣ 📂 OneSignal ┃ ┣ 📂 Expo Notifications ┃ ┣ 📂 Pusher Beams ┃ ┗ 📂 AirShip ┃ ┣ 📂 Payments & Subscriptions ┃ ┣ 📂 RevenueCat ┃ ┣ 📂 Adapty ┃ ┣ 📂 Glassfy ┃ ┣ 📂 Stripe Mobile SDK ┃ ┗ 📂 App Store Connect API ┃ ┣ 📂 App Stores & Distribution ┃ ┣ 📂 App Store Connect ┃ ┣ 📂 Google Play Console ┃ ┣ 📂 TestFlight ┃ ┣ 📂 Firebase App Distribution ┃ ┗ 📂 Microsoft App Center ┃ ┣ 📂 UI Component Libraries ┃ ┣ 📂 NativeBase ┃ ┣ 📂 React Native Paper ┃ ┣ 📂 Tamagui ┃ ┣ 📂 Gluestack UI ┃ ┗ 📂 SwiftUI Charts ┃ ┣ 📂 Navigation ┃ ┣ 📂 React Navigation ┃ ┣ 📂 Expo Router ┃ ┣ 📂 Go Router ┃ ┣ 📂 Voyager ┃ ┗ 📂 SwiftUI NavigationStack ┃ ┣ 📂 State Management ┃ ┣ 📂 Zustand ┃ ┣ 📂 Riverpod ┃ ┣ 📂 Redux Toolkit ┃ ┣ 📂 MobX ┃ ┗ 📂 BLoC Pattern ┃ ┣ 📂 Media & Camera ┃ ┣ 📂 React Native Vision Camera ┃ ┣ 📂 Expo Camera ┃ ┣ 📂 react-native-image-picker ┃ ┣ 📂 CameraKit ┃ ┗ 📂 SwiftUI PhotosPicker ┃ ┣ 📂 Maps & Location ┃ ┣ 📂 Google Maps SDK ┃ ┣ 📂 Mapbox ┃ ┣ 📂 Apple MapKit ┃ ┣ 📂 React Native Maps ┃ ┗ 📂 OpenStreetMap ┃ ┣ 📂 Analytics & Attribution ┃ ┣ 📂 Firebase Analytics ┃ ┣ 📂 Mixpanel ┃ ┣ 📂 AppsFlyer ┃ ┣ 📂 Adjust ┃ ┗ 📂 Branch Metrics ┃ ┣ 📂 Crash Reporting ┃ ┣ 📂 Sentry ┃ ┣ 📂 Firebase Crashlytics ┃ ┣ 📂 Bugsnag ┃ ┣ 📂 Instabug ┃ ┗ 📂 Embrace ┃ ┣ 📂 A/B Testing ┃ ┣ 📂 Firebase Remote Config ┃ ┣ 📂 Optimizely ┃ ┣ 📂 LaunchDarkly ┃ ┣ 📂 Statsig ┃ ┗ 📂 Flagsmith ┃ ┣ 📂 App Monetization ┃ ┣ 📂 AdMob ┃ ┣ 📂 AppLovin MAX ┃ ┣ 📂 Unity Ads ┃ ┣ 📂 ironSource ┃ ┗ 📂 Google Ad Manager ┃ ┣ 📂 Offline & Syncing ┃ ┣ 📂 Redux Persist ┃ ┣ 📂 Async Storage ┃ ┣ 📂 MMKV Storage ┃ ┣ 📂 Secure Store ┃ ┗ 📂 PowerSync ┃ ┣ 📂 Testing ┃ ┣ 📂 Detox ┃ ┣ 📂 Maestro ┃ ┣ 📂 Appium ┃ ┣ 📂 XCTest ┃ ┗ 📂 Jest ┃ ┣ 📂 CI / CD ┃ ┣ 📂 Fastlane ┃ ┣ 📂 Bitrise ┃ ┣ 📂 Codemagic ┃ ┣ 📂 GitHub Actions ┃ ┗ 📂 Xcode Cloud ┃ ┣ 📂 ASO & Marketing ┃ ┣ 📂 AppTweak ┃ ┣ 📂 Appfigures ┃ ┣ 📂 Sensor Tower ┃ ┣ 📂 App Radar ┃ ┗ 📂 SplitMetrics ┃ ┗ 📂 Support & Feedback ┣ 📂 Instabug (Shake to Report) ┣ 📂 Apptentive ┣ 📂 Helpshift ┣ 📂 Zendesk SDK ┗ 📂 App Store Reviews API

Hot take: requiring an account on a water tracking app is a privacy red flag. Let me explain why I built Dewdrop without one — and why it made the app dramatically better. THE ACCOUNT PROBLEM Every health app that asks for your email is asking you to trust them with data about your body. Your weight. Your daily routine. How much you drink. When you're most active. These things seem trivial until you realize they paint a detailed picture of your life. Most apps don't sell your data. But you're trusting that. Every time. For every app. I wasn't willing to ask that trust from Dewdrop users for features that simply don't require it. THE LOCAL-FIRST DECISION Dewdrop stores everything on your device. No backend. No server. No API calls for user data. The entire data layer runs on ObjectBox — a blazing-fast local NoSQL database. What this means for you: → The app works offline. Always. Airplane mode, basement, middle of nowhere. → There's no "log in to continue" screen. → Uninstalling the app is the only data deletion you'll ever need. → There's no breach that can expose your health data because it was never stored centrally. THE PRODUCT BENEFIT Here's the thing nobody talks about: removing the backend made the app BETTER. No server = no loading spinners waiting for API responses. No API = no error states for "couldn't connect to sync your water log." No auth = no 3-screen onboarding just to create an account. Users go from install to logging their first drink in under 60 seconds. That's what happens when you remove friction. THE TRADE-OFF The obvious downside: if you get a new phone and don't backup your data, you start fresh. No cross-device sync. For version 1, I made peace with this. The core value proposition — tracking hydration — doesn't require the cloud. We can add optional backup later if users want it. Cross-device sync should be OPT-IN, not the default. Privacy-first isn't a limitation. It's a design philosophy that puts users first. Dewdrop is live on Play Store → play.google.com/store/apps/d… No account needed. Just download and drink water. #PrivacyFirst #LocalFirst #Flutter #HydrationApp #Dewdrop #HealthTech #MobileApp ```

What happens when two offline devices edit the same thing? It depends... ObjectBox Sync now gives developers more control over concurrent updates 🎊 👉 objectbox.io/customizable-co… #DataSync #conflictresolution #release

Built a real-time crypto tracker in Flutter 🚀 ⚡ 2000 trading pairs streaming via WebSocket ⚡ Background isolate JSON parsing ⚡ 60fps price flash animations ⚡ Offline-first with ObjectBox caching No Freezed. No Bloc. No code gen. Just clean Dart 3 Provider. Repo link is in comments #Flutter #Dart #OpenSource

Edge AI made easy with MongoDB and ObjectBox: * Offline-first * Efficient on-device vector search & AI support * Automatic Data Sync (including vectors) * Powerful #MongoDB backend AI mongodb.com/company/blog/inn… #database #localAI #ondeviceAI #EdgeAI

Hybrid AI: From the Edge to the Cloud With MongoDB & ObjectBox dev.to/mongodb/hybrid-ai-fro… #offlinefirst #ondevice #vectordatabase #datasync #edgeAI #mongoDB

Tutorial: ObjectBox on-device ↔ MongoDB Atlas kept in sync with ObjectBox MongoDB Data Sync --> check out the tutorial dev.to/mongodb/mongodb-data-…

転職５５０日目、現場４５６日目done🔥 パッケージ依存関係の解消調査 isar-communityのリポジトリに未発表だけどマージされたPRを発見 直接読み込んで解消 ObjectBox版Entity作成PRのレビュー 今まで見慣れた形ではない分公式を見ながらのレビューでかなりの時間がかかる

🎊ObjectBox Swift 5.1 and Dart 5.0.3 released! The offline-first database for Apple 🍏 now has * Support for Xcode 26 & Swift 6.x * New vector types * Various Sync improvements * User-specific Data Sync and MongoDB integration (since 5.0) 👉 github.com/objectbox/objectb… #swiftlang

💥ObjectBox 5.0 * User-specific Data Sync * MongoDB Connector * Powerful local Vector Search --> objectbox.io/user-specific-d…

ObjectBox 5.0 🚀 * User-specific Data Sync prod-ready MongoDB Sync Connector to sync decentralized devices with MongoDB * Local Vector Search for on-device AI, boosted by cloud AI in MongoDB objectbox.io/user-specific-d… #offline #MongoDB #datasync #edgeAI #hybridAI

GM CT ☀️ @MemeMarketFun is the only Solana prediction market built purely for meme coins. AI scans on-chain data CT sentiment to predict pumps/dumps with scary accuracy. Stake MFUN → split 500K weekly rewards. Win = SOL MFUN. Lose = still get refunded $SOL $MFUN. Risk almost zero. Listed on MEXC, deep liquidity on Jupiter & Meteora. The ultimate bull-run survival tool. Smart money is already in. You? @edgeX_exchange : ObjectBox/edgex-objectbox seamlessly integrates EdgeX Foundry with ObjectBox a high performance embedded database designed for resource-constrained edge devices. It preserves EdgeX’s fully vendor-neutral IoT framework while adding blazing-fast local data persistence and querying capabilities. The repository highlights perfect compatibility with EdgeX’s Go-based microservices and demonstrates smooth operation on constrained IoT hardware. This project proves EdgeX’s flexibility with alternative databases, enabling significantly faster deployment and richer edge analytics without sacrificing openness or interoperability.

If you're still looking for a MongoDB Realm Device Sync replacement and want to dive deep, here is all you need to know for an in-depth comparison vs. ObjectBox: objectbox.io/dev-how-to/migr… #mongodbdevelopment #MongoDB #DataSync

I just published Supercharging Flutter Apps with ObjectBox: Offline Speed Meets AI Intelligence 🧠 medium.com/p/supercharging-f…

Yeah, I agree! 💯 MongoDB is great, but there are some better ones. Here’s a short list off the top of my mind: MySQL PostgreSQL MariaDB Oracle Database Microsoft SQL Server IBM Db2 SQLite Amazon Aurora Google Cloud SQL CockroachDB YugabyteDB TiDB OceanBase SingleStore Altibase NuoDB InterBase Firebird SAP HANA SAP ASE Informix Teradata Greenplum Vertica Exasol Snowflake Redshift BigQuery DuckDB H2 Apache Derby Apache Ignite VoltDB Redis Aerospike Riak KV DynamoDB FoundationDB Tarantool Berkeley DB RocksDB LevelDB BadgerDB LMDB Kyoto Cabinet HyperLevelDB WiredTiger EventStoreDB Apache Cassandra ScyllaDB HBase Google Bigtable Azure Cosmos DB Hypertable Accumulo CouchDB Couchbase RethinkDB RavenDB ArangoDB OrientDB MarkLogic BaseX eXist-db ZODB Neo4j JanusGraph TigerGraph Dgraph Amazon Neptune Blazegraph AllegroGraph AnzoGraph Faunus InfiniteGraph InfluxDB TimescaleDB QuestDB OpenTSDB Prometheus VictoriaMetrics Graphite Kdb Apache IoTDB DalmatinerDB RRDtool M3DB TDengine Elasticsearch OpenSearch Solr Splunk Rockset ClickHouse Apache Druid Apache Pinot Apache Kylin Memcached Hazelcast Oracle TimesTen TIBCO ActiveSpaces HSQLDB Realm LiteDB TinyDB NeDB ObjectBox PouchDB IndexedDB LocalStorage Google Firestore Google Cloud Spanner CockroachDB Cloud Yugabyte Cloud Neon PlanetScale AlloyDB MarkLogic FoundationDB Datastax Enterprise db4o ObjectDB GemStone/S Versant Object Database PostGIS SpatiaLite Oracle Spatial GeoMesa GeoWave MonetDB CrateDB Apache Hudi Apache Iceberg Delta Lake Datomic Firebase Realtime Database GridDB Machbase TileDB

How I Simplified My Android App’s Data Layer with Room and Kotlin? When building Android apps, managing data efficiently is one of the toughest and most repetitive challenges developers face. From writing SQL queries to handling migrations and caching, the data layer can quickly become messy and hard to maintain. That’s exactly what happened to me, until I adopted Room, the official Android ORM (Object Relational Mapping) library, with the expressive power of Kotlin. The combination didn’t just simplify my code; it made my entire data layer cleaner, safer, and far easier to scale. Why Room Matters in Modern Android Development Before Room, the typical way to store local data in Android was through SQLite. While powerful, it had its drawbacks: You had to write raw SQL queries manually. There were no compile-time checks for query errors. Data mapping between classes and database tables was manual and error-prone. Migrations could easily break the app if not handled carefully. Room fixes all of that by providing an abstraction layer over SQLite, so you still get the full power of SQL but with the convenience of type safety, annotations, and easy integration with Kotlin features like coroutines and Flow. The Core Components of Room Room works with three main components: Entity: Represents a database table. DAO (Data Access Object): Contains the SQL operations (queries, inserts, deletes, updates) Database: Ties it all together and defines the connection to the actual database file. Here’s how I structured mine: Defining the Entity: Each entity corresponds to a table. Here’s a simple example of a ‘User’ entity: import androidx.room.Entity import androidx.room.PrimaryKey @Entity(tableName = "users") data class User( @PrimaryKey(autoGenerate = true) val id: Int = 0, val name: String, val email: String ) The best part? You don’t need to worry about writing the ‘CREATE TABLE’ statement; Room handles that automatically. Creating the DAO: Instead of writing SQL queries directly, you define methods inside an interface or abstract class, and annotate them. import androidx.room.* @Dao interface UserDao { @Insert(onConflict = OnConflictStrategy.REPLACE) suspend fun insertUser(user: User) @Query("SELECT * FROM users") fun getAllUsers(): List<User> @Delete suspend fun deleteUser(user: User) } Room validates your queries at ‘compile’ time, meaning you will know immediately if your SQL is invalid or not. A huge time-saver compared to debugging at runtime. Building Database: Finally, you tie everything together with a database class: import androidx.room.Database import androidx.room.RoomDatabase @Database(entities = [User::class], version = 1) abstract class AppDatabase : RoomDatabase() { abstract fun userDao(): UserDao } You can then create an instance of your database using a ‘singleton pattern’, usually in your Application class: import android.content.Context import androidx.room.Room object DatabaseProvider { fun getDatabase(context: Context): AppDatabase { return Room.databaseBuilder( context.applicationContext, AppDatabase::class.java, "app_database" ).build() } } How Kotlin Makes It Even Better: Room was designed to work beautifully with Kotlin. Here’s how that synergy helps: Coroutines: You can run queries asynchronously using the ‘suspend’ functions. This keeps the UI smooth and responsive. viewModelScope.launch { userDao.insertUser(User(name = "Rajesh", email = "rajesh@gmail.com")) } Flow for Reactive Updates: When you use ‘Flow’, the UI automatically reacts to database changes. @Query("SELECT * FROM users") fun getAllUsersFlow(): Flow<List<User>> Combine that with ‘LiveData’ or ‘Compose’ state management, and your data of the app updates seamlessly. Data Classes: The ‘data class’ of Kotlin works perfectly with entities of Room framework, keeping your models concise and readable. Simplifying Migrations Another plus point is ‘built-in migration’ handling of ‘Room’. Instead of manually re-creating tables, you just define a migration strategy: val migration1to2 = object : Migration(1, 2) { override fun migrate(database: SupportSQLiteDatabase) { database.execSQL("ALTER TABLE users ADD COLUMN phoneNumber TEXT") } } Then include it when building the database: Room.databaseBuilder(context, AppDatabase::class.java, "app_database") .addMigrations(migration1to2) .build() What I Learned from Using Room? After integrating Room in my app, a few things became clear: Consistency beats complexity. With clear data access patterns through DAOs, my code became consistent and easier to debug. Type safety saves time. I stopped wasting hours fixing query-related runtime crashes. Kotlin Room = Developer Delight. The natural integration with coroutines and Flow made asynchronous operations painless. Scaling became easier. Adding new entities or changing database structures was much simpler with migrations and annotations. When Not to Use Room While Room is great, it’s not perfect for every scenario. For instance: If your data is temporary or cached from APIs, use ‘DataStore’ or ‘Preferences’ instead. If you need complex relational operations (such as; joins across multiple tables frequently), you might have to consider alternatives like ‘Realm’ or ‘ObjectBox’ for performance. Room isn’t just a convenience. It’s a game changer for Android app development architecture. Combined with Kotlin, it makes your local data layer robust, testable, and clean. When I migrated my app from raw SQLite to Room, I came across fewer crashes, simpler debugging, and faster development cycles. It felt like cleaning up a messy stuff. So, if you are still juggling raw SQL or a messy local data setup, do yourself a favour; adopt Room with Kotlin.