6 results
Over the past two years RAG evolved from a 'linear pipeline' to 'loop-based reasoning'. It maps cleanly to five stages: Naive, Advanced, Modular, Graph, Agentic. The real inflection point is control moving from pipeline to agent — a System 1 → System 2 shift. Looking back at engineer-news's own RAG stack, it's stuck at the Naive edge — so this post also lays out what to fix next.
The previous post zoomed out for a five-stage panorama of RAG. This one zooms in on the five infra lessons any real RAG has to face: document ingestion, contextualized chunking, three-lane hybrid search, tensor reranker, and GraphRAG's semantic gap. Each lesson is checked against engineer-news's current stack, ending with a priority list for a personal site.
Kafka's speed comes from two counterintuitive design choices: deliberately writing to disk (not memory) but using sequential I/O, and Zero-Copy to move data from disk to NIC without CPU involvement.
Kafka's horizontal scalability comes from partition design: each partition is an independent log, Consumer Groups enable parallel consumption, and replication provides durability without a large performance penalty.
Cursor is an AI-powered code editor by Anysphere, built by four MIT graduates, that hit $500M ARR within two years of launch. This article distills the real engineering lessons they've shared publicly: why they forked VSCode instead of building an extension, how Tab prediction's latency engineering works, and the hard production lessons from shipping Agent Mode.
The Data Lakehouse merges the ACID reliability of data warehouses with the low-cost open storage of data lakes. Apache Iceberg and Delta Lake are the two dominant open table formats making this architecture practical at scale.