A warehouse employee asks a robot to bring a parts bin to the packing station. The request sounds simple. The software has to answer several different questions: What does the employee mean? Is the destination allowed? Is the observation current? Did the robot accept the job? Did the bin actually Read more
Verify AI agents through business outcomes, independent evidence and clear release criteria. Includes UML, BPMN and an optional tested Python download.
Use Claude Code’s advisor and prompt-audit with clear responsibilities and reviewed changes. Includes a twelve-command cheat sheet, BPMN and UML.
Choose one primary AI environment, test the alternative on real work, and buy both where the evidence supports it. A practical guide with tested economics, BPMN and UML.
Understand machine learning, neural networks and LLMs step by step, with tested Python examples, clear diagrams and an honest guide to data, training and evaluation.
Learn MCP step by step: host, client and server, tools, resources and prompts, with a tested Python server and clear security boundaries.
A valid token is only the beginning. Enforce delegated authority, exact approvals and current business policy before an AI agent changes enterprise state.
Find a recurring customer problem, sell a bounded pilot and test the economics on both sides before scaling an AI-supported service.
Tesla’s new Roadster puts physical AI and software control boundaries in focus. What is confirmed, what remains unknown, and what a tested Java example can teach.
Build a Spring AI advisor pipeline with clear responsibilities, bounded retries and explicit ordering, verified by 20 tests through the real ChatClient.