Over 10 years as a senior tech expert at top-tier companies, seasoned interview officer, currently at leading firms like Alibaba.
The article explains Alibaba’s city‑level active‑active architecture, where two independent data centers in the same city simultaneously serve traffic, automatically fail over on outage, and are organized into traffic, application, and data layers with detailed design patterns.
The article explains that the Transformer, introduced by Google in the 2017 "Attention Is All You Need" paper, replaces RNN/CNN with self‑attention, enabling full parallel computation, solving sequential bottlenecks and long‑range dependency issues that power today’s large AI models.
This article explains how modern AI large models—such as GPT‑4, Claude 3.5, Gemini, Llama, and DeepSeek—rely on the Transformer architecture, detailing token embeddings, self‑attention, positional encoding, and the three‑stage training pipeline of pre‑training, instruction fine‑tuning, and alignment optimization.
This article reviews four popular high‑concurrency load‑testing tools—Apache JMeter, LoadRunner, Gatling, and wrk—detailing each tool’s supported protocols, performance characteristics, graphical or code‑based interfaces, typical use cases, and limitations such as resource consumption, cost, learning curve, or protocol scope.
This article breaks down four key microservice architecture patterns—data‑sharing, aggregation, sidecar proxy, and asynchronous messaging—detailing their purpose, typical use cases, benefits, and why some are only suitable as short‑term solutions during system migration.
