Content by john maeda (3)

John Maeda shares a practical take on what’s genuinely new in AI: setting a clear goal, defining how you measure progress toward it, and using iterative loops that “hill climb” toward better results as tokens get cheaper and more capable.

John Maeda shares a practical tip from his SXSW “Design In Tech” report: every generative AI workflow needs a “standing beacon” to keep outputs consistent. He explains why evaluation, guardrails, and a clear standard (a “judge” that can reject bad generations) are essential when iterating on AI-generated work.

John Maeda explains why agentic systems benefit from a “clean molt”: trimming unnecessary context so the system doesn’t accumulate noise that degrades results. The tip focuses on practical context management—what to keep, what to drop, and why “more context” can make outputs worse.