Interface, shallow thinking, digital twin modeling, vibe design

""Design tools come and go. People's habits evolve. New devices are born. The way we designed interfaces five years ago is very different from how we do it now. The only common thread in our work is understanding people. Everything else is just a vehicle for that." Interface: on connection, multi-modality, and self-expression → Enter your best UX by November 15th! →[Sponsored] The UX Design Awards - Spring 2026 call for participation is open!"

"Seeing like a software company →"Thinking in terms of legibility and illegibility explains so many of the things that are confusing about large software companies. It explains why companies do many things that seem obviously counter-productive, why the rules in practice are so often out of sync with the rules as written, and why companies are surprisingly willing to tolerate rule-breaking in some contexts.""

"The melancholy of history rhyming →"Even many of the critics have completely integrated "AI" propaganda and myth-making into their worldview, assuming Large Language Model progress as inevitable, blinding them to the inherent weaknesses of the tech, and leading them to promote "careful" adoption of a tech whose variability is inherently destructive to productivity, reliability, and quality." I am an AI hater →"But I am more than a critic: I am a hater. I am not here to make a careful comprehensive argument, because people have already done that. If you're pushing slop or eating it, you wouldn't read it anyway. You'd ask a bot for a summary"

Weekly curated resources for designers focus on evolving tools, changing habits, and the enduring centrality of understanding people in design work. Interfaces are framed around connection, multi-modality, and self-expression across new devices and interaction patterns. A UX awards call offers deadlines, recognition, expert endorsement, and visibility for design teams. Analyses examine legibility versus illegibility in large software companies, explaining why written rules and practiced behavior often diverge and why rule-breaking can be tolerated. Critical perspectives on AI warn that assumed LLM progress obscures weaknesses and that variability can harm productivity, reliability, and quality.

Read at Medium