Real-time data is not optional—it's table stakes. LangBeam streams real-time data into a vector database for scalable real-time vector search [1].
LangBeam and the Vector Stack The end-to-end workflow ingests streams, indexes vectors, and powers AI-enabled queries across server boundaries [1]. This pattern shows how ingestion and vector indexing are becoming a central pillar of real-time analytics.
Real-Time App Starter: PostgreSQL Pub/Sub A real-time app starter built around FastAPI, PostgreSQL pub/sub, and UV shows how live updates land without external queues [2]. It relies on PostgreSQL triggers plus LISTEN/NOTIFY for pub/sub, with Bun for frontend builds and Docker Compose for testing. This setup shines for admin dashboards and monitoring tools that need current state pushed immediately, but it's not designed for guaranteed delivery or job queues [2].
In-Browser Analytics with DuckDB on WebAssembly On the browser, DuckDB compiled to WebAssembly runs analytics locally—parsing, filtering, and aggregation without network round-trips [3]. This WASM-enabled approach unlocks heavy analytics directly in the client, reducing round-trips and latency.
From ingestion to AI-enabled querying, the real-time stack now spans server and browser, keeping data fresh at every hop.
References
Show HN: LangBeam: Managed platform to stream realtime data into vector database
Show HN LangBeam all-in-one platform to stream realtime data into vector database for realtime vector search and analysis scalability benefits.
View sourceShow HN: Real-time app starter with FastAPI, PostgreSQL pub/sub, and UV
Template using FastAPI, PostgreSQL LISTEN/NOTIFY for real-time updates; not for guaranteed delivery; includes Docker, frontend, and admin dashboards and testing.
View sourceWASM DB for fast in-browser calcs
DuckDB compiled to WebAssembly enables client-side analytics; fast, serverless processing of large CSVs without UI blocking
View source