Hey 👋
AI is changing product research, prototyping, and the mechanics of interface clarity, plus why putting insight into action is key to team impact.
have a great week, catch you on the next one - Adam at Unicorn Club 🦄
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🏗️ Build
Make better interfaces.
Material 3 Expressive: Building on the failures of flat design
Google’s Material 3 Expressive reverses a decade of flat design, restoring visual signifiers, containment, colour, and clear boundaries, to aid recognition and scanning.
- Why it matters: Data-driven research shows significant gains in speed and confidence for all age groups, closing gaps and reducing click uncertainty.
- Adopt this week: Audit key UI elements. Are your affordances (like buttons, containers) visually strong enough? or are you falling into the “too clean, too flat” trap?
The Weird Parts of position: sticky;
A definitive breakdown of tricky CSS sticky failures: scroll containers, bounding context bugs, and flex/grid gotchas. Plus fixes with practical Tailwind/JSX examples.
- Why it matters: Sticky UI fragments still trip up teams and create inconsistent layouts or scroll bugs, especially in complex grid/flex contexts.
AI-powered prototyping (with Figma Make, etc) slashes the cost and turnaround on rapid and research-through-design experiments, unlocking more room for testing wild ideas and validating edge-case flows.
- Why it matters: Faster, higher-fidelity prototyping collapses research-to-decision cycles and enables more focused, evidence-rich iteration on real interactions.
🧩 Shape
Shared foundations across teams.
From design to direction: Bridging product design and AI thinking
Drawing analogy between design iteration (friction) and AI training (loss), this piece reframes product velocity as a multi-layer system optimised through feedback, signals and intent. From UI to business impact.
- Why it matters: Reframing design through optimisation links design, telemetry, and metrics, encouraging cross-team instrumentation habits and measurement mindsets.
- First step: Map a recent product change through “friction-to-signal”. What was measured, where did feedback/intent break down, and can your team see this at every layer?
Reflecting on team evolution in an AI-shaped world: mapping value, embracing AI-powered research, deepening business collaboration, and refocusing on holistic/system-level skills.
- Why it matters: Teams that ground design in business context, behavioural research, and emerging prototyping tech will shape both product direction and their own long-term value.
🚀 Ship
Release, measure, iterate.
Insights Aren’t Outcomes: Research Recommendation Breakage
Most research work fails to yield outcomes due to “breakage”. Somewhere between a well-crafted insight and an actual shipped fix, the value leaks away. NN/g calls for structured tracking of recommendation adoption, not just reporting.
- Why it matters: Teams unlock compounding benefits (and protect researcher morale) by tracking whether findings are actioned and closing the loop on intended impact.
- Adopt this week: Pick one recent research output, trace each insight to an outcome in your backlog or shipped product. Where did breakage happen: documentation, ownership, delivery?
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🎲 Also worth a look
A few extras we've found.
Stop Repeating Yourself: Give Claude Code a Memory
How giving LLMs persistent context (via code and file structure) turns them from generic interns into truly high-leverage partners for product teams.
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Adam from Unicorn Club
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