TL;DR: Patrick Debois coined DevOps in 2009 by naming what practitioners were already doing. In 2026, he’s doing it again with “Context Engineering” and the CDLC (Context Development Lifecycle): Generate, Evaluate, Distribute, Observe. The core insight: as coding agents get more capable, the bottleneck shifts from writing code to assembling the right context. More context isn’t better — more precise context is. Teams that treat context as a versioned, tested, governed engineering artifact will compound an advantage that’s hard to replicate.

TL;DR: I prepared my AWS Summit Hamburg talk through 6 recorded dry runs, each analyzed by a structured AI skill. The skill transcribes via Amazon Transcribe, counts fillers with word-level timestamps, checks customer references against a checklist, flags pronunciation issues, identifies filler-dense windows, and proposes tighter alternatives. This post shows the complete skill file with every step explained, the real results (um: 80→27, customer refs: 0/6→4/6), and the key discovery: the skill and the talk improved together. Each dry run made both better. Total cost: $3.60 for 6 analyses.

TL;DR: Michael I. Jordan, the man Science magazine named “the world’s most influential computer scientist,” has never considered himself an AI researcher. His thesis: intelligence is not a property of individual systems but emerges from collectives interacting through economic mechanisms. Most AI today is optimization (one agent, one metric). The real world is an equilibrium problem (multiple agents, competing incentives, stable states). The distinction matters for how you architect systems in media, advertising, retail, and publishing. Jordan’s framework does not say “stop building.” It says “stop optimizing in isolation and start designing markets.”