When talking about AI at scale, I have always thought about it from the model-serving infrastructure perspective: How many GPUs do we need? How do we scale inference? How do we reduce latency and cost? How do we serve millions of concurrent requests? How do we efficiently distribute models across GPU clusters? But there is another way to think about AI at scale. Instead of scaling the AI model itself, what if we scale software development across ordinary users?

From COBOL to Modern Programming Languages About 26 years ago, I learnt COBOL using the book 《COBOL 语言》（上、下册） by 谭浩强 (Tan Haoqiang). Here is a portion of my transcript. At the time, I developed a simple MIS (Management Information System) using COBOL. It was one of the programming languages I worked with early in my career. Since then, I never really had the opportunity to work with COBOL professionally.

Overview This document outlines the procedure for deploying the BMC Control-M Workbench (version 9.20.200) within a Docker environment. The Workbench provides a local development sandbox for the Control-M Automation API, allowing users to test workload automation jobs and orchestration flows without connecting to a production Control-M Server. The official BMC distribution currently provides access to the Workbench image without requiring a commercial license for evaluation purposes.