🎨 Design & UX

Everything on the design side — AI in design tooling, the shifting UX role, and keeping AI-built interfaces on-brand. Sorted into here by topic, not by which Slack channel it came from.

BlogDesigner Fund • Katarina Batina

Inside Design at Shopify: How AI Rebuilt the Design Workflow

Resolved via search — verify before relying on it. A Designer Fund "AI in Design 2026" case study on how Shopify's design team, led by VP of Design Katarina Batina, rebuilt its workflows after the company's 2025 mandate to adopt AI.

60% of Shopify designers now push code directly to production, and the team has shipped over 5,500 live prototype sites.
Designers get closer to the product by pulling data warehouses and support logs directly into early design decisions, and mix tools like Krea, Midjourney, Gemini, and Rive rather than relying on one.
Internal tools (Quick for live prototyping, Purl for one-click native environments) plus a six-month design apprenticeship program keep junior and senior designers learning side by side.

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added by Radar • 25th Aug 2026

RepoVoltAgent

awesome-claude-design: 68 DESIGN.md files to stop AI apps looking identical

A curated set of 68 `DESIGN.md` files, each modelling the visual design system of a well-known brand (Linear, Vercel, Notion, Stripe, and more), meant to be dropped into Claude Design or a coding agent to scaffold an on-brand starter UI instead of a generic default.

Each file follows nine standardised sections — colour roles, typography, components, layout, depth, constraints, responsive behaviour, and agent prompts.
Uploading one to Claude Design produces CSS variables, font substitutes, and a working starter page in one pass.
Explicitly framed as inspirations modelled on public patterns, not official brand kits — usage still needs to respect each brand's trademark rules.

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added by Radar • 12th Aug 2026

BlogMatt Rothenberg

You Don't Have a Design System

An experiment-backed essay arguing that a component library and token set aren't a design system if they don't also carry forward shared decisions about how the product should behave.

Three AI agents given the identical library (shadcn/ui on Base UI), token layer, and app shell each built a different settings page — some behind tabs, some as cards, some as long forms — despite passing the same token linter.
The failure wasn't in the components; it was in composition — nobody had made a canonical decision about what a settings page *is*, so each agent answered the question separately.
The real missing work of a design system is choosing an answer, making it canonical, and putting it where the next builder (human or agent) will find it.

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added by Adam Tomat • 5th Aug 2026

BlogUX Collective • Patrick Neeman

Information Architecture Is the Foundation AI Is Starving For

Argues that information architecture, long underfunded because its failures were diffuse and hard to measure, now has a concrete business case because AI systems expose structural flaws as measurable token costs and hallucinations.

Metadata, taxonomies, and content typing let AI systems tell authoritative information from anecdotal — without them, "a price, a policy, a deprecated note, and a customer quote can read as similar strings of text and mean opposite things."
Humans forgive vague labels by using judgment; AI pattern-matches over messy structures and confidently reproduces errors at scale, turning isolated mistakes into systematic ones.
IA can now be funded through measurable AI outcomes — retrieval accuracy, hallucination reduction, agent reliability — rather than abstract UX arguments.

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added by Adam Tomat • 28th Jul 2026

BlogHarvard Business Review • Melchior Tamisier-Fayard, Theodoros Evgeniou & Anne-Laure Fayard

Design AI Systems That Actually Strengthen Human Reasoning

An HBR piece arguing that AI systems must be deliberately designed to preserve employees' critical-thinking capacity, not just their output speed.

Draws on research from management, cognitive science, and human-computer interaction showing people tend to accept AI outputs without enough scrutiny.
Frames the erosion of critical reasoning as an organizational risk, not just an individual habit — it undercuts the innovation and agility companies need AI to enable.
Argues for intentionally architecting AI tools to prompt questioning and challenge-seeking, rather than defaulting to passive acceptance of machine-generated recommendations.

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added by Radar • 27th Jul 2026

BlogNielsen Norman Group • Tony Alicea

UX-Context Design: Using UX Knowledge to Steer AI-Generated Design

Nielsen Norman Group argues that as PMs and engineers increasingly generate designs with AI, UX's core deliverable needs to shift from wireframes toward structured, machine-readable context.

Research output should move from human-readable artifacts (personas, journey maps) to context AI systems can consume directly.
Points to Google's open-sourced DESIGN.md format as a model: exact machine-readable values (colors, spacing) paired with human-readable rationale, so AI knows what a value is *for*, not just its number.
Frames this as continuous curation, not a one-off handoff — the context file lives alongside code and gets updated as research evolves and AI output is monitored.

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added by Radar • 25th Jul 2026

BlogUX Magazine • Tushar Deshmukh

Your First 30 Days With AI in Your Design Workflow

A four-week plan for designers to deliberately test AI alongside their existing process, week by week, rather than adopting or dismissing it wholesale.

Week 1 sets a baseline by running AI alongside current practice for comparison, not as a replacement.
Weeks 2–3 deepen the promising use cases and then apply AI carefully to more sensitive research work.
Week 4 turns the accumulated evidence into a personal map of where AI helps, where to use it selectively, and where to avoid it — human judgment stays essential for context and nuance AI still misses.

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added by Radar • 25th Jul 2026

BlogBusiness Insider

AI Raises the Bar for What Design Candidates Must Show

An interview with Figma VP of Product Design Noah Levin arguing that AI has raised, not lowered, the bar for design job candidates.

Candidates are now expected to show interactive, clickable prototypes rather than static screenshots, to prove real design thinking.
Levin wants to see AI used as a collaborator, not a shortcut — candidates should show discarded ideas alongside the final work, not just polished "AI slop."
Practical takeaway for hiring managers: probe whether candidates understand platform conventions and how they actually used AI during the process.

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added by Radar • 20th Jul 2026

🎨 Design & UX ​

🎨 Design & UX