Agentic AI Momentum Report
The Linux Foundation's Agentic AI Momentum Report tracks 116 open source agentic AI projects across five layers, finding 2.6x growth in unpatched CVEs over six months and organizational concentration …
The Linux Foundation's Agentic AI Momentum Report tracks 116 open source agentic AI projects across five layers, finding 2.6x growth in unpatched CVEs over six months and organizational concentration …
A benchmark comparing pgvector, Qdrant, and Pinecone on 50 million 1536-dimensional embeddings found that Postgres with pgvectorscale served 471 queries per second at 99% recall, outperforming Qdrant'…
A developer built a Retrieval-Augmented Generation (RAG) pipeline to convert unstructured medical PDFs into a structured, queryable Electronic Health Record (EHR) knowledge base using FHIR standards. …
A developer built Vector Strike, a retro arcade game that visualizes how vector databases like Pinecone, Milvus, Qdrant, and pgvector perform semantic search. The game lets players configure similarit…
An engineer warns that n8n RAG pipelines leak sensitive data to third-party LLM providers like OpenAI in plain text, despite the common belief that RAG keeps data secure in a vector database. The post…
A developer argues that 90% of AI startups launched last year were thin wrappers over LLM APIs and failed when providers released native features. To build defensible AI apps, the developer recommends…
A developer at a company using Milvus as their vector database built a custom ALTER command in Django to change collection schemas before Milvus had native support. The command creates a new collectio…
A developer compared four vector databases—Pinecone, Weaviate, Milvus, and Qdrant—in 2026, analyzing deployment complexity, latency benchmarks, filtering quality, and cost. Qdrant achieved the lowest …
Defence, intelligence, and critical-infrastructure organizations are deploying enterprise AI agents in air-gapped and restricted networks without cloud connectivity, using open-weight models, local in…
A developer compared five vector databases—pgvector, Pinecone, Qdrant, Weaviate, and Milvus—for RAG applications, finding that the best choice depends on existing infrastructure, operational preferenc…
A developer advocates using PostgreSQL with the pgvector extension for semantic search instead of dedicated vector databases like Pinecone or Weaviate. The approach reduces infrastructure complexity b…
Zilliz has published a FAQ clarifying the differences between its Milvus vector database and the new Vector Lakebase, emphasizing that real-time vector search remains core while data now lives in open…
FocusOnFun team's Vortex system achieved 90.5% in the Preliminary Round and 'Excellent' overall performance with 'Outstanding' QA results at the Ho Chi Minh City AI Challenge 2025. The multimodal vide…
A developer explains that vector databases are not magic but rely on approximate nearest neighbor (ANN) search algorithms like HNSW and IVF. The post details how these algorithms trade accuracy for sp…
Zilliz, the Milvus AI vector database supplier, launched Vector Lakebase, a product that extends real-time vector search with an external data lake connector, batch analytics, and interactive discover…
Broadcom released the largest set of Spring security updates in the framework's history on June 8, 2026, prompted by a 1,700% jump in CVE reports driven by AI-assisted vulnerability scanning. The most…
Jenova's AI agent platform reached $1M ARR and 200,000+ signups, driven by a Pinecone-powered memory system that reduces token consumption by 70-95% and achieves sub-10ms query latency. The platform's…
Researchers developed a phase-aware large language model agent that optimizes retrieval system parameters by conditioning each proposal on its full optimization history, overcoming the limitations of …
A developer has released a suite of tools to improve context quality for AI coding agents, including code knowledge graphs, semantic code analysis tools, and context output compression utilities. The …
ON1 (G116 V8) has introduced a virtual chip ISA that achieves 38-microsecond black-box AI memory retrieval by separating vector search into three observable latency stages: fetch, compute, and ANN sea…