Move beyond the generic FAISS + LangChain + OpenAI tutorial. This guide walks you through building a multi-stage RAG pipeline with hybrid retrieval, cross-encoder re-ranking, and automated evaluation — the kind of project that signals real systems skill on a CV.
DeepSeek’s harness framework is the open-source evaluation engine behind many of its published results. This post unpacks the architecture, walks through adding a custom task, and shows how to run reproducible evals at scale.
A step‑by‑step guide to installing, securing, and scaling Redash for enterprise‑grade analytics, with concrete patterns and code samples.
Introduction The AI landscape in 2026 has matured beyond monolithic cloud‑only deployments. Organizations are increasingly stitching together decentralized inference chains—networks of edge devices, on‑premise servers, and cloud endpoints that collaboratively serve model predictions. This architectural shift brings many benefits: data sovereignty, reduced bandwidth costs, and the ability to serve ultra‑low‑latency applications (e.g., AR/VR, autonomous robotics, real‑time recommendation). However, decentralization also introduces a new class of latency challenges. Instead of a single round‑trip to a powerful data center, a request may traverse multiple hops, each with its own compute, storage, and networking characteristics. If not carefully engineered, the aggregate latency can eclipse the performance gains promised by edge computing. ...
Introduction Decentralized multi‑agent systems (MAS) are increasingly deployed in domains ranging from swarm robotics and autonomous vehicles to distributed IoT networks and edge‑centric AI services. In these environments each node (or agent) must make rapid, locally‑informed decisions based on sensor data, model inference, and peer communication. Local latency—the time between data acquisition and the availability of an inference result on the same device—directly impacts safety, efficiency, and overall system performance. ...