By outsourcing more of our thinking to probabilistic systems, we risk weakening the very human habit black swans demand: the habit of making the right questions.
As AI generates more code and tests, requirements become the control layer that keeps delivery consistent, traceable, and aligned with the system context.
AI infrastructure isn’t about GPUs. Most issues come from storage, networking, data pipelines. If GPU utilization is low, check the infrastructure first, not the model.
Deeper AI integration in the framework core, modern authentication via OAuth / OIDC and WebAuthn passkeys driven from the system browser, and a few smaller additions.
When a machine writes most of the code, "the code shipped" stops being a finish line. The work that's left is the work your definition of done was already skipping.
Build AI-native data systems with clear ownership, semantic contracts, and governance. Learn how accountability, retrieval, and data quality shape AI behavior.
Step-by-step tutorial building AI retrieval over existing data systems using a thin layer, covering workflow design, indexing, evaluation, and RAG pipeline.
Free VS Code extension for Azure AI Foundry agent traces into your editor as an interactive timeline — see tool calls, token costs, and conversation replays.
A practical checklist for evaluating AI data readiness, covering data quality, governance, lineage, access controls, retrieval systems, and ongoing monitoring.