Overview
AI × UX is a signal-intelligence product for designers trying to make sense of a fast-moving field. AI and UX content arrives faster than anyone can follow, and recency is easily mistaken for relevance.
Instead of building another feed, I designed a Signal Architecture: a model that turns noise into signals, signals into perspectives, and perspectives into decisions. It runs live and self-updating at aixux.dk – ingesting, deduplicating and re-scoring on its own since launch.
My contribution
Product Direction
Product & Interaction Design
Signal Modelling
Information Architecture
Product Development
The team
1 x Product Designer (solo)
Year
2026
Outcome
Product impact
Shipped a live, self-updating product at aixux.dk – it ingests, deduplicates and re-scores on a schedule, and has run on its own since launch
Built the full pipeline solo: multi-source ingestion → normalization → scoring → personalised signals
Design impact
Replaced contradictory, per-component trend readings with one shared signal model – consistent identity, time windows and comparison across Feed, Trends and Tensions
Turned data decisions (canonical identity, normalization, scoring) into UX decisions that made the interface simpler and more trustworthy
System impact
A discovery loop that surprises its designer – its first run surfaced "AI safety" and "government AI", topics I never specified
Tensions extended the product from momentum to divergence, showing where credible sources disagree without declaring a winner
Process
Frame
Reframed the job from aggregating articles to producing signals – designers don't lack information, they lack orientation. → This turned "another feed" into a signal-intelligence problem.
Structure
Designed the Signal Architecture – Sources → Normalize → Filter → Score → Signals → Feed, Trends, Tensions – and defined what a signal is before building any screen. → This made the model, not the screen, the primary design object.
Generate
Used AI to generate the components and wire the data: filters, scoring, trend charts and the multi-source ingestion pipeline. → This made a solo, end-to-end product realistic.
Review
AI's individually competent components gave contradictory answers – "reasoning" up in one panel and down in another, 42 mentions vs 9 stories. I defined one shared signal model as the single source of truth. → This is where the design work concentrated: coherence, not code.
Ship
Deployed live with scheduled ingestion, deduplication and re-scoring, and treated distribution (Share) as a launch feature in its own right. → This made it a running product, not a prototype.
Learn
Replaced the fixed topic list with a discovery loop that expands its own vocabulary – surfacing subjects I never named. → This turned a maintained snapshot into a living instrument.