Latest / The Feed & The Thread / The Feed & The Thread - September 22, 2026

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The Feed & The Thread - September 22, 2026 · The Feed & The Thread

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  Your AI tool isn't slow, it's just unpredictable. So the speed
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  isn't the real problem? It's the variance that kills trust, and
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  we're testing it against a real design system right now.
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  Welcome to The Feed and The Thread, brought to you by
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  Chicago Camps. The Feed & The Thread is available online at
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  feed and thread dot com to submit your feeds, or download
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  our app for all the feeds and threads delivered right to your
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  pocket.
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  The common thread here is that we're designing for machines,
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  not humans. We keep building generic loading states and rigid
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  code checks, ignoring the fact that user trust and
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  accessibility depend on context, explanation, and nuance. Adi
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  Leviim at UX Design.cc makes the case in "The variance problem in
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  AI products" that unexplained variance is the primary trust
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  killer in AI products. The argument is that a fast initial
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  response anchors user expectations, so subsequent
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  normal delays feel like system failures. This challenges the
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  standard practice of using generic spinners, which expose
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  raw variance without context. We need to explain why a response
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  is slow to maintain trust. Nick Babich at UX Planet investigates
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  the practical limits of generative AI in "Can AI follow
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  a real design system?". He tested Claude with a real-world
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  design system and found that while it follows basic
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  structures, it struggles with nuanced application and
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  consistency. This suggests AI acts as an accelerator for
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  implementation, not a replacement for rigorous
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  prompting. The key is using AI to speed up work, not to define
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  the design rules. Harris Schneiderman at Deque Blog
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  introduces Automated Intelligent Guided Tests in "Intelligent
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  automation and the new era of guided testing". Traditional
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  automated testing hits a ceiling because it only inspects static
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  code, missing dynamic behaviors like focus management. By
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  combining AI execution with human judgment, this approach
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  reduces test times from thirty to forty-five minutes to
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  seconds. It shifts complex accessibility checks from rare
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  audits to frequent, comprehensive coverage. Also
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  out today: UX Design.cc on the role of humans in design jobs
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  and the loss of a standard unit of measurement.
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  A sign-up page for a plant care app sits alongside a request for
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  an AI company font, and a question about certificates.
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  Over on r/UXDesign, a beginner is weighing the Cornell
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  University certificate against the Google UX Design
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  Certificate. The thread is silent, which highlights a real
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  gap in the field. We often treat credentials as a shortcut to
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  trust, but the industry lacks a shared standard for what
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  actually matters in early-career work. The silence isn't a
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  failure of the community; it's a signal that we haven't agreed on
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  what makes a designer competent. In the same subreddit, a
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  practitioner is asking how to document design decisions on
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  long projects. They note that the "why" gets lost in Slack or
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  unrecorded meetings, even when the final design survives in
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  Figma. This connects directly to designing for humans, not just
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  machines. A generic loading state or a rigid code check
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  doesn't explain the context, and neither does a design file
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  without its rationale. If we can't articulate the nuance
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  behind a decision, we're building systems that feel
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  opaque and untrustworthy to the people who use them. Over on
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  r/UI_Design, a junior designer is sharing a sign-up page for a
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  plant care app and asking for honest feedback on visual
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  hierarchy and typography. They're trying to distinguish
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  intentional choices from amateur mistakes. This anxiety is
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  common, but the request itself reveals something important.
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  We're so focused on making interfaces look modern and clean
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  that we sometimes forget to design for the human on the
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  other end of the screen. The decorative elements they're
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  questioning aren't just aesthetic; they're how we
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  communicate care and context. In r/UXDesign, someone is asking
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  what the worst thing they were asked to do was. The prompt
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  invites stories of removing unsubscribe buttons or
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  displaying fake social proof. This isn't just a venting
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  exercise; it's a test of our ethics. When we prioritize
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  business goals over user trust, we break the very foundation of
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  design. The tension between what the business wants and what the
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  user needs is the most persistent challenge in the
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  field, and it's a conversation we need to keep having. Over on
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  r/UXDesign, a junior designer is asking for font recommendations
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  for an AI company website. They want something modern and
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  professional without being boring. This question is about
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  more than typography; it's about establishing a visual language
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  for an industry that's still finding its footing. The font we
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  choose sends a signal about who we're and what we value. In a
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  field where trust and nuance are everything, even the smallest
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  design decision is a statement. What ties today together is
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  trust built in the details: the credential no one can
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  standardize, the design rationale we forget to write
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  down, and the font that tells users whether we made this for
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  them or for ourselves.
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  For a weekly and monthly exploration of the patterns and
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  signals across UX, you can also check out Patterns and Signals
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  at the website patterns and signals dot com. Chicago Camps
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  will have more events in 2027. You can submit to be a speaker
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  at a future event at Chicago Camps dot org.
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  The variance problem in AI products is the one that
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  actually landed, because it reframes the spinner as a trust
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  failure. Adi Leviim argues that unexplained variance is the
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  primary trust killer, and that's a specific, testable claim.
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  Right, and I'll take that over the generic loading state
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  debate. A spinner doesn't just look lazy; it hides the fact
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  that the system is doing something, and that hiddenness
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  is what erodes trust. Exactly, and that connects to the
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  accessibility thread about guided testing. Harris
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  Schneiderman's point is that automated tests hit a ceiling
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  because they inspect static code, but dynamic behaviors like
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  focus management need context. So the fix is shifting from "did
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  the code pass" to "does the user understand what's happening,"
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  which is a different discipline entirely. And that's where the
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  two threads meet. You can't have accessible AI without explaining
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  the variance, and you can't explain the variance if your
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  testing only checks structure. So the real work is building the
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  explanation layer. That's a shift in what we consider a
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  deliverable. The field has to start treating explanation as a
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  design artifact. And that's where the next wave of trust
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  will come from.
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  That's The Feed and The Thread for today. We'll catch you next
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  time!