UX Collectiveuxdesign.cc · 7 September 2026 · not counted as a party

What a 1960 study of city maps explains about AI context. Continue reading on UX Collective »

GitHubgithub.blog · 27 August 2026 · not counted as a party

OpenClaw is the fastest-growing project in GitHub history. Peter Steinberger and several maintainers share what they learned in the project’s first six months. August 27, 2026 | 6 minutes Share: What began as a personal experiment quickly became a global open source project with extraordinary momentum. OpenClaw is a personal AI assistant that runs on users’ devices and connects with the messaging channels they already use. Started by Peter Steinberger as a weekend project in November 2025, its GitHub repository has grown to approximately 388,000 stars, 81,000 forks, and more than 80,000 commits by August 26, 2026. In this video interview, filmed just six months into the project, creator Peter Steinberger and several OpenClaw maintainers discuss managing a surge of pull requests, rethinking contributor trust and code review, addressing software supply chain risks, and balancing powerful agent capabilities with security. They also share security lessons from the GitHub Secure Open Source Fund and the value of connecting with mainta

LangChain bloglangchain.com · 26 August 2026 · 5 posts

Macro research desks need to know, on a regular basis, which countries in a given set are performing anomalously and why. The underlying data exists but it is fragmented. A single GDP figure might require reconciling a Eurostat release against a national statistics office publication that arrived on a different schedule and uses a different methodology. Getting from raw data to a usable, sourced briefing can be as time consuming as the analysis itself. To show this in practice, we built an agent and ran it against 2025 GDP data for all 27 EU member states.‍Ireland came back as the single largest outlier, with 12.3% GDP growth that looked like a boom. Per-country investigation identified it as a pharma-led export surge front-loaded ahead of US tariffs, with the industrial sector alone contributing +6.55pp to the print. Modified GNI showed a far more modest number. Germany was flagged for the opposite reason: structural contraction driven by automotive exposure and construction collapse, not a cyclical dip. The agent produced that distinction, sourced and cited, in 45 minutes costing $2.20 in API calls.‍The findings are only half

Baymard Institutefeeds.baymard.com · 25 August 2026 · not counted as a party

Key Takeaways Baymard has new qualitative UX research on Insurance UX The research uncovered UX issues and identified UX solutions specific to Insurance users and sites At a high level, Insurance users struggle with unfamiliar concepts, friction-filled quote flows, and brand trust Key Stats 4,700+ hours usability testing, analyzing, and researching the UX of Insurance sites 220 qualitative participant/site usability test sessions following the “Think Aloud” protocol 1,700+ medium-to-severe usability issues in testing Today at Baymard, we’re announcing the launch of our new Insurance UX research. 8 US and 12 UK Insurance sites were tested, including providers (some of whom are also banks) and aggregators (where test participants could compare Insurance quotes across many providers). The 9 UK Insurance sites tested were Halifax, Aviva, Direct Line, LV/Liverpool Victoria, TSB, Insure and Go, Allianz, Avanti, and Santander, while the 3 aggregators were Compare the Market, MoneySuperMarket, and Go Compare. The 8 US Insurance sites tested were Liberty Mutual, Travelers, Progressive, GEICO, Amica Mutual, Lemonade, Root Insuranc

Ness Labs (Anne-Laure Le Cunff)nesslabs.com · 20 August 2026

FEATURED TOOLWelcome to this edition of our Tools for Thought series, where we interview founders on a mission to help us think better and work smarter. Jim Lau is the founder of Flowing, a desktop writing application for researchers and students who want AI assistance without losing touch with their sources. It allows you to import your reference PDFs into a local library and use AI features that attach verifiable library snippets to every suggestion. In this interview, we talked about how writing is a form of thinking and not just text production, how to use AI to strengthen human judgement, the importance of context and the challenge of resurfacing what we already know, how trust is based on traceability, and much more. Enjoy the read! Hi Jim, thank you for joining us. You’re building Flowing around the idea that AI-assisted writing should stay grounded in the researcher’s own sources. Why do you think that matters? Thank you for having me. I think the reason this matters comes down to a fundamental difference between writing and simply generating text. Academic writing is not only about producing sent

Patrick Neemanuxdesign.cc · 20 August 2026 · 2 posts

The change arrived without a battle — quiet, ordinary, already at your desk. AI assisted. The film feared a superintelligence that would rise up and rule us, but the generative AI we built asks for our trust instead — and mostly gets it. The Matrix feared a machine that would wake up, decide we were the problem, and seize control. Generative AI did the opposite. It asked politely — a summarizer here, a copilot there — and we said yes, one convenient task at a time. No war, no red pill. The takeover, if that is the word, came by invitation. The danger the film missed is quieter than any uprising: not a machine that seizes control, but one we hand it to. I keep coming back to the film because it set the terms for a conversation we are still having poorly. When leaders picture generative AI, they picture an adversary — something that arrives, seizes control, and has to be resisted or contained. That frame is wrong in ways that matter for anyone designing, funding, or shipping these systems. It aims our attention at the dramatic risk and away from the real one. So here are five things the Matrix got backwards about generative AI, checked agai

Figma blogfigma.com · 20 August 2026

August 20, 2026Agentic products pose a unique set of design challenges. How do you build a tool that’s powerful and knowledgeable enough to be a true partner, while keeping trust and security at the core? Here’s what we’ve heard from leaders.Sightlines is our newsletter for design, product, and engineering leaders. In each issue, we'll bring you the sharpest insights from senior leaders at companies like Cisco, OpenAI, Airbnb, and more on how they're navigating product development and design in the age of AI.Top of mindAcross recent events, from our Leadership Collective talk in Chicago to Config7 questions we had going into Config Leadership CollectiveWe showed up to Leadership Collective hoping to learn how leaders are guiding their teams through change, what expertise means now, and how they're keeping quality high as the pace picks up. Here's what we heard., leaders have been working through what it takes to design agentic products well. Here’s what we’ve learned.Trust is p0. Users are more likely to trust an agent when it shows its work and cites its sources. Leaders should push product teams to bake in a n

Dan Maccaroneuxdesign.cc · 19 August 2026

Real information in, invented information mixed in, one confident answer out. The interface never shows you how it got there. We built it to sound sure of everything. The best thing it can learn to say is “I’m not sure.” The latest argument my team and I are going back and forth on is whether we are being too honest with our users. One of the AI products we are building has a panel on the side shows you what it’s doing as you’re working through a problem. As you talk to the platform, the building blocks come together with, “Here’s what I heard you say,” “Here’s the thread I’m pulling,” “Here’s where this is headed.” What worries the folks who argue against it is that it’s too much for the user to absorb. It’s a fair argument. Nobody wants to watch the sausage get made, and real products bury the process on purpose, on the theory that an answer looks smarter when you can’t see where it came from. That’s an entirely backwards way of thinking and it’s one of the most important things we’re getting wrong about AI right now. Right now, what you actually get from Claude or ChatGPT is a play-by-play that tells you nothing. “Analyzing your reques

Nicole Alexandra Michaelisuxdesign.cc · 14 August 2026

The gap between what AI is reliable for and what it looks reliable for is where damage happens, and enablement fails How to lead the change with AI in a nutshell I’ve been building with AI for three years now. I started by trying to scale UX writing via plug-ins and now have agents, workflows, MCPs, and apps under my belt. I feel comfortable building with AI, and have adopted it into many of my day-to-day tasks. Yes, I do think AI has gotten better over those 3 years. But I think we’re telling ourselves a story that isn’t true. AI has gotten very good at one thing: Starting. The blank page problem. Replacing lorem ipsum. But while starting is faster than ever and error rates may have dropped, the verification burden hasn’t. I think errors have gotten harder to spot, and models have gotten better at defending them. For many, getting from zero to a rough first version is the hardest emotional hurdle in any task, and AI clears it in seconds. No more staring at a blank page if you don’t want to. But I think that’s the ceiling. Once you’re past “getting started” and into “getting it right,” the story changes. The hallucination problem isn’t solved

Smashing Magazinesmashingmagazine.com · 13 August 2026

7 min readAI, Design, UX, BusinessNew EU guidelines, why AI sparkles aren’t enough, when AI labels are required, and what the rules mean for AI-powered features and products. Brought to you by Design Patterns For AI Interfaces, friendly video courses on UX and design patterns by Vitaly.There’s been a lot of confusion and panic this week about “huge fines”, “drastic measures” and “sweeping new AI rules” in the EU. In reality, it’s a lot more narrow — and a lot more sensible. And mostly it’s about making AI more obvious when it actually needs to be obvious — especially for AI-generated content.Starting from Aug 2, 2026, AI labelling is a legal requirement for any company that serves EU citizens. And similar to European Accessibility Act, it’s not limited to EU companies. It affects any company worldwide with EU operations as long as their AI output is used by people in the EU. Let’s see what exactly it means for us.The EU’s transparency obligations for AI systems took effect on 2 August 2026. Official statement by European Commission. (Large preview)What Actually Needs LabellingThe goal of AI labelling is to help everyone exposed to AI content to re

Cloudflare blogblog.cloudflare.com · 7 August 2026 · not counted as a party

The Internet isn’t a single lane of traffic. For a long time, the rule of thumb in web security was that bots are bad, while humans are good. Of course, we’re far past this generalization. Humans can be fraudulent, and bots can be helpful at different levels. Site owners actively want some automated traffic to interact with our sites to make the Internet functional and discoverable.To complicate things further, the line between "human" and "bot" is blurring more and more. Now, we have a type of “hybrid” traffic where a single session shifts from human to agentic and back again. (Think of a user browsing a store, and then handing off the checkout process to an automated shopping assistant.)So, how do website owners manage this kind of complexity? What matters here is assessing behaviors. Is this behavior abusive? Malicious? What’s the risk presented here, and can I trust this visitor based on their actions? Solving this requires moving beyond static, point-in-time checks. It requires analyzing continuous behaviors to evaluate Trust.In this post, we’ll share an inside look into the strategy of the Web Integrity & Trust team (co

Stack Overflow blogstackoverflow.blog · 7 August 2026 · not counted as a party

Ryan welcomes McLaren Stanley, Senior Principal Engineer for Amazon Stores, to discuss what it actually takes to make teams AI native, why agentic engineering is shifting code bottlenecks downstream to testing and deployment, and why robust validation is essential to build trust and enable “fearless commits.”

PostHogposthog.com · 6 August 2026

The UI is dead. Or so I keep hearing:"Agents are your users now, software is losing its head, and everyone who learned Figma should start learning Bash."Maybe the UI is dead in four square blocks of San Francisco (where folks are running multiple Mac minis for clawbots). But I'm a regular person at a screen for most of my day, and so are the people I build for.Here's my take:Everything is a user interface if I'm using itMy agents are using it too now, just not the part you designedHeadless is a new layer on top of product building, the same way APIs and the GUI once wereIf you're a product builder, congratulations! That means your job just doubled: every user of your product is bringing an agentic plus-one.Here's a few things to consider in this hybrid (not headless) future.Your homepage isn't the front doorIf you're building for a technical audience, onboarding them into your product increasingly won't start from a "get started" button. It'll start in a terminal or an AI chat window. This is less true for consumer products, but the pattern is spreading – especially if your AEO game is strong. A new user chatting with Claude or ChatGPT may never see yo

Anton Stenantonsten.com · 3 August 2026 · 2 posts

July 31, 2026 · 4 min read I usually write about things after I’ve figured them out. There’s a problem, some wrong turns, and eventually a lesson that holds up well enough to share. This post is different. I’m in the middle of designing a voice-first product right now, and I have more questions than answers. So instead of waiting until it’s all resolved, I want to write down the questions while they’re still open. I can’t say much about the product itself yet. What I can say is that voice is the primary way you interact with it, and that this has turned out to be a much deeper design problem than I expected. The parity problem Here’s the core question I haven’t solved. The product is voice first, but most of its functionality also needs to be reachable through touch. Sometimes you’re in a meeting. Sometimes you’re on a train. Sometimes you’d rather tap than talk, and the product should respect that. The moment you commit to that, though, you’ve created a trap. Because once every feature exists as a touch interaction, voice starts to feel like an alternative way of doing things rather than the way. And that’s exactly what you see in almost

EU digital strategydigital-strategy.ec.europa.eu · 31 July 2026

From 2 August 2026, the European Commission’s AI Office, together with national authorities, will begin enforcing the Artificial Intelligence (AI) Act. On the same date, new transparency rules will start to apply, requiring certain AI systems to tell users when they are interacting with AI and when content has been generated or altered by it. Under the new rules, chatbots and other interactive AI systems will have to tell users they are dealing with AI, not a human. Deepfakes (images, videos, or audio that have been edited or generated using AI) will have to be labelled. AI-generated or altered content will also have to carry machine-readable marks so it can be detected more easily. The measures are intended to reduce deception and manipulation and help people make informed choices. They also give businesses clearer obligations and a practical way to show compliance. The Commission published a first list of more than 180 organisations that have signed the Code of Practice on transparency of AI-generated content that operationalises the rules on transparency of AI-generated content. Read the

seangoedecke.comseangoedecke.com · 30 July 2026 · not counted as a party

When you’re on fire, problems are transparent: they’re solved simply by the act of looking at them. Even complicated layers of multiple problems can simply be glanced through like stacked panes of glass. But nobody can work that way all the time. This is a common pitfall for smart engineers. Accustomed to being able to immediately intuit the solution, the first time they run into a problem they can’t do this to is a disaster. It doesn’t even have to be a hard problem, just a problem where for whatever reason they don’t see the trick right away. The difference between a “smart” engineer and a “strong” engineer is how they react to problems that aren’t solved instantly. A smart engineer might flail and struggle, hoping to find that flash of insight that eluded them; a strong engineer will have some process for methodically plodding away. There’s nothing worse than working with a smart engineer on their first really hard problem. When you don’t have the muscle to grind, it’s too tempting to just take any possible solution as the right one. Smart engineers can get into an increasingly-flustered loop of pointing to a series of bad solu

@uxdesignccuxdesign.cc · 29 July 2026

Information architecture is the orderly wing. Most content stores are the heap at the edge of the frame. AI assisted.UXInformation ArchitectureAIDesignEditor PicksEvery AI problem you chase — hallucinations, wrong answers, poor retrieval — traces back to the IA projects you never funded. Now’s the time to fund them.12 min readJul 26, 2026--For twenty years, information architecture was the discipline nobody funded. The job titles disappeared—I was one back in the day—and now it’s an art (and science) that needs to return. Teams didn’t align. Companies started showing their underwear.Doing it well is less about structure than about getting an organization to agree on one way to name and arrange things. Every team brings its own vocabulary, so aligning them is slow, political, and thankless, and it loses every quarter to whatever ships a feature. The work stayed invisible, and so did its failures.Then AI arrived, and the invisible foundation showed up on the balance sheet.The same gaps that once cost you a confused visitor now cost you a hallucination, a wrong answer, or an agent that confidently retrieves garbage and acts

Menlo Venturesmenlovc.com · 29 July 2026

Why it’s critical to prevent polluting the internet This is a seminal moment for human history. Stop and think about it. We have invented a machine that now creates information indistinguishable from that of humans. A majority of the content created on the internet (and even off the internet) is created by AI, as well as 35% of all new websites. In fact, 33% of the top articles on Substack and nine of the top 25 categories had >10% written by AI. It’s not just generic internet content; key parts of the global social order’s writing is written by AI. The share of federal civil complaints that were AI-generated has gone from 0 to 18% in the last four years. The share of peer reviews from machine learning conference ICLR was 21%, as well as 12.4% of life science research papers from BioArxiv, 19% of computer science papers, 10% of physics papers, and even 9% of all newspapers. We know not all AI-generated content is necessarily bad, but as Aphyr’s recent essay, “The Future of Everything is Lies, I Guess,” states plainly:  As the cost of producing language collapses to zero, the cost of trusting language goes to infinity

Noah Smithnoahpinion.blog · 27 July 2026 · not counted as a party

“[I]ntelligence, that counterentropic conjoined twin of information, must become the most powerful force in the universe, the energy to which all other physical laws must eventually kneel…Intelligence was destiny, manifest.” — Ian McDonald, “Verthandi’s Ring”Not a lot of people expected that AI would come for the mathematicians before it came for the truck drivers, but it did. The other day, an AI model disproved the Jacobian Conjecture — an 87-year-old open problem that human mathematicians had struggled to solve. The greatest living human mathematician, Terence Tao, turned to AI to help him understand the solution. Around the same time, AI solved a very important open question in quantum cryptography. Solving Erdos problems has now become almost child’s play for the best AI models. And this is the worst AI will ever be at math. Model capabilities, and the amount of compute available, both continue to increase at rapid rates. (Meanwhile, long-distance trucking employment is slightly higher than it was a decade ago.)I don’t expect mathematicians to actually lose their jobs en masse, of course.1 But it’s becoming clearer and clearer th

Holger Maassenux4dotcom.blogspot.com · 27 July 2026

When people think about User Experience, they often think about apps, websites or software. They think about interfaces, buttons, navigation and polished screens.I have spent my career designing exactly those experiences.But one of the most important lessons I learned came very early—during my first years at Pixel-Factory in Germany. There, long before AI became part of our daily conversations, we shared a simple belief:User Experience never ends at the screen. In fact, it never began there.Many designers explain UX with the iceberg metaphor. The interface—the screen, the buttons, the visual design—is only the visible tip above the water.Beneath the surface lies everything that truly shapes an experience: expectations, emotions, trust, context, mental models, accessibility, culture and human behavior. The invisible part is what keeps the iceberg afloat.Long before I became a UX designer, I studied architecture and urban planning. There, I learned that the quality of a place is determined far less by what people see than by everything they don't. A building stands because of its

Hugo Palomaresmicrosoft.design · 27 July 2026

The future I expect looks familiar in terms of who produces good work. It just looks different in how much of it they produce, and how fast it is. Embodying the Ju/’hoansi I see a similar pattern in how we work with AI. It’s one I’ve been leaning on as I figure out how to turn AI into an effective productivity partner and collaborator in my own day-to-day. In this day-and-night analogy, daytime is the operational work we do with AI, the standard back and forth between human and agent that produces huge amounts of output. Then comes night. Gathered around the fire, we start to review and dig deeper into all of that output: reflecting on what has happened and solidifying our thinking. We seek to understand more deeply. These “nighttime” interactions – focused on driving understanding – are the vital balance to the high-output, more transactional “daytime” ones. As someone who works at the intersection of engineering and design, I spend a lot of time thinking about how we might design a UX that accounts for both types of interaction – day and night. What kind of affordances allow users to review output at scale

Product Talk (Teresa Torres)producttalk.org · 23 July 2026

Listen to this episode on: Spotify | Apple Podcasts How do you build trustworthy AI diagnostic tools in one of medicine's most historically under-researched areas? In this episode of Just Now Possible, Teresa Torres talks with Tulsi Patel (Director of Product and Technology), Lorna Brightmore (Head of Data and AI), and Jack Pickard (Head of Engineering) at Hertility, a UK and Ireland-based women's health tech company. Hertility combines an in-depth online health assessment with at-home hormone testing and clinician-reviewed reports to help diagnose conditions spanning menstruation to menopause. Built on seven years of data linking symptoms, blood results, and pelvic ultrasound scans for over a million women, the team walks through two AI products in development: Gyn.AI, a Bayesian network that gives clinicians probability-based diagnoses instead of binary calls, and a scan automation pipeline that classifies ultrasound images, measures follicle counts and ovarian volume, and drafts clinical letters using an agentic loop that checks its own output against patient data before a human ever re

Georgia Kenderova, Tanner Kohlernngroup.com · 10 July 2026

Summary: Handoff willingness, flexibility, proactivity, emotional responsiveness, and transparency help you build trustworthy AI chatbots that guide users well. Designing a site-specific AI chatbot requires making key decisions even before user testing begins. The five chatbot qualities described in this article — handoff willingness, flexibility, proactivity, emotional responsiveness, and transparency — can guide those early design decisions and provide a framework for evaluating existing chatbots. (But don’t neglect real testing. Real users will help you finetune the specific behaviors that will best serve their needs.) Getting these qualities wrong can be costly. People are still forming mental models of site-specific AI chatbots and figuring out whether to view them as human customer-support representatives or frontier-level LLMs. Many chatbots fall short of both types of expectations and quickly get abandoned by users. 1. Handoff Willingness: Respect Users' Desire to Speak with Real People 2. Flexibility: Go Where the User Wants to Go (Within the Chatbot’s Defined Guardrails) 3. Proactivity: Anticipate Needs and Suggest Next Steps 4

Katie Schmidt, Jessa Anderson, Hayley Mortinnngroup.com · 3 July 2026

Summary: Different enterprise roles need different types of explanations for AI outputs. Enterprise AI is a hot topic, but many organizations still struggle to achieve meaningful employee adoption and usage. This article focuses on what it takes to develop and deploy AI solutions that both organizations and employees can trust. In particular, it examines how AI explainability helps the people building enterprise AI (developers, system administrators, and domain experts) understand AI-system behavior, build trust in it, and support AI adoption. Because these technical roles bring different goals, expertise, and contexts, explainability cannot be one-size-fits-all. AI Explainability in the Enterprise Three Categories of Enterprise AI Users A Shared Scenario: Configuring an AI Help-Desk Agent The Role of Explainability in Fostering Trust Designing Explainability for Real Enterprise Impact AI Explainability in the Enterprise Helping users understand how AI systems work is a core best practice for building trustworthy tools and products. Common approaches include creating traceability, source attribution, and explanations of rea

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

Leah Tharinleahtharin.com · 28 June 2026 · not counted as a party

There are roughly three ways to lead and manage: Command and control: you decide, they execute. Earn-the-leash: trust is a reward, you hand it out in slow increments once people prove themselves. Servant leadership: you give people room first, before they've earned it, and you find out who they are by what they do with it. You’re empowering them. How do you balance strong opinions about how things should be done vs. simply enabling people? We keep talking about “empowering” people without really defining what it means or how to actually do it, even though people can simply struggle with being empowered. Empowering people means to me that I give them room to act, room to make decisions, and make mistakes right away. Leah’s ProducTea is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. The upside shows up fast if you’re doing this right away in a new job. You get room to be the new joiner, to read the ship before you steer it, instead of drowning in the tactical on day one by micromanaging everyone. You’re not the bottleneck; everything routes through for permission, which is half the reason

tonsky.metonsky.me · 17 June 2026 · not counted as a party

A while ago I was reading about Wayland and this quote stuck with me: A stated goal of Wayland is “every frame is perfect”. And I think this is a goal we should all aspire to. Wayland is talking about the technical side of things (modern GPU stacks are very complex and Wayland is trying to take control back) but it could be applied to UI too. The rule of thumb is: If I take a screenshot of your app at any moment, you should be able to explain what I see EDIT: This used to say “..., it must make sense” but that doesn’t account for advanced animation techniques such as smear frames etc. Why care about every frame? It builds trust. Users can’t see the code, so UI is the only way for them to judge the quality of the app. If UI looks good, that means developers had time to polish it, which means that they probably spent a comparable amount of time to iron out the code. It’s a heuristic, but a reasonable one. Now, what does it mean in practice? I can think of a few things: No white flashes between screens. No partially loaded content. No relayout while content loads. Internally consistent. If one part of the UI says “1 update available”, another part should not say “C

John Cutlercutlefish.substack.com · 16 June 2026 · 2 posts · not counted as a party

There’s a difference between mirrors and mirroring. Over the years I’ve come to see that difference, and I wanted to share how my thinking has shifted. Earlier in my career, I was smitten by the idea that if teams could “just” visualize how they work, embrace transparency, etc., then almost any team could improve. This is similar to the “if we could just talk it out” belief, or the “if we could just hear each other out” (or consider other perspectives, etc. etc.) belief. I still carry some of this with me to this day despite numerous run-ins with “reality” and, as some people are keen to point out, “the real world.” I’ve yet to observe evidence of a single “real world,” or that things are destined to be the way “real world” folks make them out to be (all the time, at least), but I have come to see the idea of mirrors differently as my career has progressed. Mirrors are not neutral Organizational truth can be weaponized We prefer flattering mirrors The person holding the mirror has an agenda too Seeing something clearly doesn’t mean you can act on it Every mirror has a frame, and the frame is a choice “Radical honesty” is its own kind of performance

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

latent.spacelatent.space · 10 June 2026 · 2 posts

By some measures, Opus 4.8, barely two weeks old, was already the leading model in the world. But now, 34 days after the SpaceXai deal and 63 days after the original Mythos announcement*, we have a Mythos-class model (at least 2x size of Opus) available to everyone (in coinciding with Claude Tokyo). It is a feat of incredible engineering (and commitment to access) to make these research models GA, and the benchmarks are great… with asterisks. Here they are on yesterday’s brand new, out of distribution, FrontierCode Diamond, going from 13.4% to 29.3%: tweet The blog and the system card contain most of the authoritative information, but don’t miss the youtube videos showing it playing Factorio, Pokemon (unlike Claude Plays Pokemon, this is just using vision, no complex harness as we covered in our pod), EDM visualization (never having head music before), 3D CAD editor creation and printing and more from their main intro video. API pricing is also fantastic, at roughly 2x Opus. The asterisks come because Fable is released with two controversial changes: No ZDR: “We will require 30-day retention for all traff

Michael Buckleyuxdesign.cc · 8 June 2026

How to navigate the uninvited participant in your next client meeting. Image source: Adobe Stock I was working with a client I’ve known for over fifteen years when they casually mentioned that they had asked ChatGPT to review the website design I’d recently delivered. To be clear, we have a strong relationship, and I don’t think there was anything malicious behind it. If anything, the exercise seemed more exploratory than evaluative. Still, for a brief moment, I felt oddly insulted. After all, I’ve spent decades developing expertise in design, and now I found myself discussing my work alongside the opinions of a machine that learned many of its design principles by consuming the very internet that web creators and designers like me helped build. The feedback itself wasn’t terrible. Some of the points were reasonable. Others, in my opinion, would have created new problems while solving old ones. During our discussion, I acknowledged the useful observations, explained where I disagreed, and offered alternative solutions. Ultimately, the client went with my recommendations. But this was not the first time it happened. In fact, several other clients have u