Deep dives into RAG pipelines, agentic AI, LLM integrations, and lessons from building production AI systems.
A walkthrough of my real OpenClaw deployment: 13 Telegram topics, GPT-5.2 on Azure free tier, heartbeat-driven morning briefings, Playwright browser automation, memsearch semantic recall, and a Second Brain that auto-captures everything I text. Pulled directly from my live droplet.
Sending sensitive internal data to closed APIs wasn't an option. Here is the exact architecture I used to build a fully local, autonomous agentic pipeline using Milvus, Ollama, and open-source embeddings.
A practical guide to building a fully on-prem agentic AI system using open-source embeddings and local LLM inference — no APIs, no cloud, complete data control.
A deep dive into deploying Qwen 3.5 with vLLM for high-throughput inference and running cost-efficient local inference on Azure VMs with GPU acceleration.
MiA-RAG introduces a mindscape-aware embedder and retriever that inject global semantic context into RAG pipelines, dramatically improving long-document QA accuracy and retrieval recall.
Using Google's Gemini Flash for intent extraction to build a conversational workout tracker with semantic memory retrieval via ChromaDB.
How I designed a multi-agent RAG system that answers questions from factory equipment manuals, safety SOPs, and maintenance logs — running fully offline with Ollama and Milvus Lite.
Why pure vector search isn't enough for enterprise documents, and how combining semantic embeddings with BM25 keyword matching dramatically improves retrieval accuracy.
Stop paying the 'Internet Tax' and risking data leaks. We moved our RAG pipeline from SaaS to a local H100 cluster, cutting latency by 40% and TCO by 70% at scale.
Basic vector search fails in production. Learn how we engineered a multi-stage RAG pipeline with hybrid search, re-ranking, and agentic loops to achieve 90%+ accuracy.