Current topics, practical solutions, and fresh ideas: On the HMS blog, you'll learn how companies successfully use data and analytics.
AI-Friendly Architecture: How to Keep AI Coding Agents Under Control in a Project
AI coding agents work reliably only if they can navigate the codebase and adhere to established development processes. Christoph Bergen and Robert Bauer explain the role that architecture, documentation, automated tests, and an advanced agent harness play in this process.
Reliable Coding Agents in Practice: Insights from the Superpowers Workflow
AI coding agents don't become reliable simply by using better models. Using the Superpowers workflow as an example, this article shows how context, testing, reviews, and clear decision-making processes contribute to reproducible results.
Loop Engineering: How Reliable AI Coding Agents Are Created
How Do AI Coding Agents Become Reliable? This article explains how Loop Engineering uses feedback, testing, and clear termination conditions to turn one-off model responses into controlled, adaptive workflows.
Thread Safety in Python: What Changes with Free-Threaded Python
Free-threaded Python enables true parallel thread execution, thereby changing the requirements for modern Python applications. This article explains where race conditions can arise and how to develop thread-safe Python code using locks, queues, events, and other synchronization mechanisms.
SPN Connect and the World Tour 2025 demonstrated that industries such as pharmaceuticals, media, and logistics are already using AI, AISQL, and Cortex to deliver measurable added value. How can these technologies be used responsibly and in a way that yields measurable results today?
