Don Norman's critique of Agile UX: "Norman's Law" — on the day a project is announced, it's already late and over budget — because Agile sacrifices user research for coding, when research should be a continuous practice, not a phase. The fix: continuous research, Design Sprints to compress understanding before coding, and reframing procrastination as "time to think" — building feature by feature without overall coherence leads to products that fail
NNG: UX-Context Design - Using UX Knowledge to Inform AI-Generated Design
NN/g introduces "UX-context design" — as AI generates more interfaces, research output shifts from human deliverables to machine-readable context (standards, research insights, user/world models) that guides AI tools and prevents generic output. Examples: DESIGN.md (visual identity) and UX.md (research, interaction standards, glossary) — never-finished files that live alongside code and make research a continuously curated source of truth, not a one-time handoff
AI: AI in UX Research - What Changes When the Researcher Is Also the Model
A critical framework for AI in UX research: distinguish three roles — assistant (transcribing real human data, low risk), proxy (synthetic personas, treat as hypotheses, not findings), and researcher (agentic AI running the whole pipeline, highest risk, especially when the same model generates and interprets data). The key principle: no model has a nervous system — keep a human in the judgment call, check for circularity, and treat synthetic output as a hypothesis, not a finding
Opinion: Beyond Jobs to Be Done - Why Technical Fluency Is Now a Research Requirement
JTBD works from individual intention, but in technical categories (enterprise software, AI, infrastructure), outcomes are shaped by non-human actors (data rules, integrations, models) — treating these as background produces research that's right about what buyers want but wrong about what happens. The fix: actor-network theory — map all actors (human and non-human) that change the outcome, requiring researchers who can read technical arrangements, not just interview buyers
Basics: Basic Tools & Techniques Every Product Manager Should Master for User Research
A product manager's guide to user research: surveys for quantitative feedback at scale, interviews to uncover the "why," and observation tools (Google Analytics, Hotjar, Amplitude) to watch what users actually do — because people often say one thing and do another. The cycle: survey → interview → observe → build → measure → learn; combine qualitative and quantitative data to validate assumptions before making decisions
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