Best reads for PMs & Designers
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First seen 12 September 2025
Smashing Magazinesmashingmagazine.com · 9 September 2026
10 min readUX, Design, AIThe web is evolving beyond menus, forms, and endless clicks toward experiences shaped around human intent. For UX designers, understanding this shift means re-evaluating their role, moving from designing visible interfaces to guiding transparent, intent-driven AI experiences.Ever since the commercialisation of the Graphical User Interface pioneered by systems like the Xerox Star and popularised by the original Apple Macintosh, software has relied heavily on point-and-click interactions. If you wanted to book a trip, buy a pair of shoes, or research a health symptom, you were expected to navigate a labyrinth of user interfaces. You click menus, adjust range sliders, fill out multi-step forms, deal with cookie pop-ups, and open dozens of browser tabs just to cross-reference basic information. We have become accustomed to spending more time managing the software rather than actually achieving our goals.That contract is officially changing. Driven by advances in artificial intelligence and large language models, a new generation of web tools is pioneering a radical philosophy: Intent-Driven Design.
UX Collectiveuxdesign.cc · 5 September 2026 · not counted as a party
Reading and writing are interfaces. And AI can reduce their friction.
Stack Overflow blogstackoverflow.blog · 1 September 2026
Ryan sits down with Tim Lindholm, an early contributor to the Java language at Sun Microsystems, to chat about what it was like building one of the most popular programming languages ever at its inception, why it was strategically important for the Java team to create a cross-platform ABI to compete with Windows NT, and how applets were initially just an interesting demo.
LangChain bloglangchain.com · 26 August 2026
I get this question a bunch. Developers generally first spend time getting the agent to work, but then they turn their attention to speed and cost. There are few things we see developers doing:Identifying where the latency is coming fromChanging the UX to reduce the “perceived” latencyMaking fewer LLM callsSpeeding up LLM callsMaking LLM calls in parallelIdentifying where the latency is coming fromThis may sound basic, but how you approach reducing latency will depend entirely on your specific bottleneck. Is the latency coming from one big LLM call, or from multiple small ones that just add up? You’ll need to diagnose these before trying to speed it up.LangSmith is an incredibly useful tool for this, providing complete visibility into your agent interactions. You can track the latency of each step of the agent, and we’ve also recently introduced a “waterfall” view to easily identify which steps contribute the most to overall latency.Changing the UX to reduce the “perceived” latencySometimes, the easiest way to reduce latency… is to not reduce latency.While it might seem counterintuitive at first, but if we think about why latency
Dive Club (Ridd)rss.com · 25 August 2026
John Bai - The Grok Bot design jo...John Bai - The Grok Bot design journeyDive Club 🤿 by RiddEpisode notesGrok Bot has been going viral the last two weeks so this week's episode is a behind-the-scenes of the design journey with John Bai (who was the first NYC design hire at Cursor).He screen-shares a ton of agent interaction pattern ideas as well as his unused concepts for the Grok Bot onboarding flow.Some highlights:- The prototype that led to the Grok Bot mascot- Why John realized chat was still the right UX for AI- How John uses agents to streamline his Figma work- Comparing John's onboarding concepts for Grok Bot- Why John still starts his design explorations in Figma- What it took to turn AI agents into a consumer product- Why no two designers at Cursor have the same tools/process- + a lot moreRidd mentioned ... Read moreKeywordsdesignproduct designux designui design
Patrick Neemanuxdesign.cc · 4 August 2026 · 2 posts
Star Trek imagined talking to a computer decades before the tools arrived to answer. The show’s voice-first computer, plain-language querying, and universal translator anticipated the assistants we ship now — the interface, but not the intelligence. Science fiction tends to be wrong about the future in the ways that matter —here’s what Star Trek got wrong—and right in the ways that don’t. We never got the flying car, but we did get the touchscreen, the video call, and the thing in your pocket that answers questions that happens to be called a phone. I’ll be honest — I enjoyed Star Trek, but my father was a bigger fan of the show. I’m starting to recognize some of the parallels we’re seeing. I’m also seeing how Gene Roddenberry had a much more accurate vision than we would like to admit. Roddenberry died in 1991, long before anyone typed a question into a machine and got a thoughtful sentence back. He couldn’t have imagined the specifics — the chatbots that write poetry in Bukowski and debug code, the strange intimacy of talking to software that talks back. Our future, personified. He imagined all of it anyway: a ship’s computer you could reason wit
Taras Bakusevychuxdesign.cc · 30 June 2026
An applied framework for designing AI interfaces that support appropriate reliance, user control, transparency, and responsible autonomy. Traditional interfaces are built around predictable behavior. A control has a defined function. A workflow has known states. Errors can be anticipated and recovered from. AI systems are less deterministic. They introduce a property that most interface conventions weren’t designed for: the same input can produce different outputs. The same model can feel useful, confusing, or dangerous depending on the interface and instructions around it — product quality is not determined by model capability alone. AI introduces interaction problems that conventional UI patterns don’t resolve: When should the system suggest, ask, or act? How should uncertainty appear on screen? What evidence should accompany a generated answer? How much autonomy does a given action earn? Etc These are not cosmetic questions. They determine whether users can judge output, recover from mistakes, and remain responsible for consequential decisions. The central design question is How do we help users rely on AI appropriately? This arti
Casey Newtonplatformer.news · 17 June 2026
This is an interview about AI. My fiancé works at Anthropic. See my full ethics disclosure here. Last week in our series on AI and jobs, Brookings' Molly Kinder warned us to prepare for a "messy middle": a long, “politically explosive” stretch in which AI job losses are concentrated among some of the best-paid workers in the economy. This week, for the first-ever Platformer live show, I wanted to talk to someone who believes in that vision: a founder building the tools that might bring it about, and who turned out to be unusually candid about what that might cost us. I've known Eugenia Kuyda for more than a decade. In 2015, after her best friend Roman Mazurenko died in a car accident, she gathered the text messages he had sent to friends and family and built a chatbot that let them speak with him again — a story I covered at the time for The Verge, nearly a decade before ChatGPT made chatbots ubiquitous. That project was the seed for Replika, the AI companion app that now claims more than 40 million users. Kuyda’s latest startup, Wabi, takes AI in a different direction — away from personal entertainment and into the world of work.
hello@smashingmagazine.com (Pratik Joglekar)smashingmagazine.com · 16 June 2026
In 2024, an Air Canada customer asked a chatbot about bereavement fares. The bot confidently gave him a refund policy that didn’t exist. The airline refused to honor it. A tribunal ruled in the customer’s favor. The bot hadn't decided anything; it had predicted an answer based on patterns in its training data. The company treated that prediction as policy. This is the risk at the heart of designing with AI today: probabilistic systems wrapped in deterministic interfaces. The AI offers a guess, the interface presents it as truth, and the user, or the organization, acts on it. Humans are wired for deterministic thinking. We prefer to believe that past actions determine future outcomes. Flip a coin 999 times and get heads every time, the deterministic mind assumes the coin is rigged. The probabilistic mind accepts that the 1000th flip could still go either way. That second mindset is harder to hold onto, but it is exactly what designers need right now. Products operate in complex, nonlinear environments, and AI is accelerating that complexity. When designers and product teams treat AI outputs as the answer rather t