Agentic workflow engineering, advanced RAG architecture, and LLM evaluation/observability are the most valuable AI coding skills for 2026, according to an industry analysis. Developers proficient in f…
HNSW, the default graph algorithm powering vector search in Faiss, pgvector, Qdrant, and other stores, trades exactness for speed, offering logarithmic retrieval but occasionally returning wrong memor…
A developer's analysis of RAG retrieval architectures shows that vector-only search fails on exact-match queries like product codes and error numbers, and that hybrid search combining lexical and vect…
By 2026, AI agents are expected to be widely deployed in enterprises, with IDC forecasting 60% adoption, but Gartner data shows 70% of projects fail to move past pilot. A new technical playbook outlin…
Vector databases have become core infrastructure for modern AI applications, addressing the lack of long-term memory in large language models. They store high-dimensional embeddings and use approximat…
A developer's guide to vector search fundamentals for RAG design covers ANN algorithms like HNSW, distance metrics, metadata filtering, and BM25. It explains trade-offs between accuracy, speed, and co…
DigitalOcean Holdings Inc. reported second-quarter revenue of $281 million, up 29% year-over-year and above analyst expectations, driven by a 212% surge in annualized recurring revenue from AI custome…
A new ranking of vector databases for AI applications in 2026 highlights Pinecone, Weaviate, Milvus, and pgvector as top choices, evaluated on performance, scalability, ease of use, and enterprise rea…
A growing number of startups are migrating away from specialized vector databases like Pinecone, Weaviate, and Milvus toward PostgreSQL and MongoDB, which have improved their vector search capabilitie…
CockroachDB claims it can replace a fragmented database stack of PostgreSQL, Redis, and a dedicated vector database like Pinecone or Weaviate with a single operational database, reducing operational c…
A Retrieval-Augmented Generation (RAG) application integrates a vector database with a Large Language Model (LLM) to provide AI access to external datasets, operating in indexing and inference phases.…
NMEMORY, a new memory system for AI agents, achieves a 0% hallucination rate by architecturally preventing fabrication through strict provenance and graph-based conflict resolution, according to its c…
A developer benchmarked sqlite-vec, a zero-dependency vector search extension for SQLite, against cloud-based vector databases like Pinecone, Weaviate, and ChromaDB. On a dataset of 100,000 384-dimens…
Developers in 2026 should prioritize AI orchestration, RAG, and model selection over traditional coding syntax, according to a workflow-focused analysis. While Python and TypeScript remain essential, …
A developer built RE-call, a retrieval-augmented memory system for AI agents that runs entirely on PostgreSQL, using pgvector for dense vector search and built-in full-text search, fused via Reciproca…
Google Research released TimesFM 2.5, a decoder-only foundation model for time series forecasting, with 10,000 GitHub stars and support for installation, fine-tuning, and real-world applications. The …
HNSW (Hierarchical Navigable Small World) is a graph-based algorithm that powers vector search in FAISS, pgvector, Qdrant, Weaviate, and Milvus, enabling approximate nearest neighbor search in millise…
A data engineer with eight years of experience is launching a 20-part series aimed at helping fellow data engineers understand AI concepts like embeddings, RAG, and vector databases by connecting them…
A developer built Slate-memory, a one-shot attractor memory system that enables the cheap Claude Haiku model to match the accuracy of the expensive Claude Opus model at 1/640th the cost. The system us…
Docker containers start as regular Linux processes isolated by kernel namespaces and cgroups, with no guest OS or hypervisor, according to ByteByteGo's system design refresher. The Docker CLI sends co…