You have REST APIs that work. Connecting them to AI agents means building an MCP server for each one. mcp-link takes an OpenAPI spec and instantly turns every endpoint into an AI tool. No spec? Generate one with AI first, then connect.

Basic RAG retrieves and answers, even when questions are vague or results are incomplete. `agentic-rag-for-dummies` uses LangGraph to clarify queries, retry searches, and parallelize complex questions across agents. Supports Ollama locally or Claude, GPT-4, and Gemini in the cloud.

Ask an AI to analyze geospatial data and it writes solid SQL — but numbers alone can't reveal geometric errors. GeoSQL closes the loop: the AI runs the query, renders the results as a map, reviews it visually, and self-corrects if something looks off. Fully on-premises, no SaaS account needed.

3-Line Summary Up through Milvus 2.x, searching vector data stored in Iceberg required copying it through a separate ETL pipeline. Starting with 3.0, Milvus can perform vector search by referencing only the Iceberg metadata on S3 — no data copy needed (External Collection). An official release is still

This post walks through an end-to-end MLOps pipeline built on PAASUP DIP — combining KFP v2, MLflow, MinIO, and KServe — covering data ingestion, challenger/champion promotion, zero-downtime KServe deployment, and production considerations.

RAG re-reads every time — costs accumulate, nothing sticks. OpenKB understands documents once and builds a Wiki that compounds. No vector DB, opens in Obsidian. If internal documents should be an asset, start here.

Misconfigured Kubernetes resource settings either waste cluster capacity or terminate pods in production. Goldilocks surfaces VPA's internally calculated recommendations into a readable dashboard — practical for squeezing more out of fixed on-premise hardware.

Benchmarking three PostgreSQL → Iceberg CDC pathways against 960 million records. In terms of balancing pipeline simplicity with real-time processing, Flink CDC emerges as the most compelling option. Initial load can be supplemented with a hybrid strategy combining Spark Batch.

Benchmarking the DIP platform at TPC-DS SF-1000 (1 TB, 6.3B rows): Batch ELT at 1,451K rows/sec, StarRocks query GeoMean of 2.2s, CN scaling analysis, and Trino comparison — all on S3 object storage.