Reducing content discovery friction
I led the re-design of content discovery within Brix, a set of enhancements to enable users to discover and reuse machine learning code assets, snippets, visualisations, notebooks, and pipelines, more quickly, reducing time-to-value for AI innovations.
Surface
Web App
My role
Technical UX Designer
Timeline
Q3 2023 - Q1 2025
Skills
Product Design, User Research & Testing, Design Systems
The Challenge
As Brix grew to 1,500 MAU and 700+ code assets, finding resources became increasingly difficult.
66% of users abandoned filter searches
Users started filtering but gave up mid-flow, overwhelmed by too many options and an unclear information architecture.
Filters behaved inconsistently across contexts
Filter options and behaviour differed between Posts and Collections, forcing users to relearn the pattern for each content type.
Multiple navigation layers consumed viewport space
The sidebar, header navigation, and topic pills competed for attention, leaving minimal room to preview assets. Users couldn't assess relevance without clicking, forcing blind exploration.
Audit and review of the old UI
Research & Solution
A review of successful filtering and search systems revealed four core principles essential to solving Brix's content discovery problem.
During discovery, three filtering approaches were tested and the 'filters popover' emerged on top, balancing impact with feasibility.
The filters popover emerged as the right choice for this phase, not because it was the easiest solution, but because it addressed the immediate concerns. While the agentic search offered a more ideal user experience, where users could simply describe what they're working on and the system would surface relevant assets, testing revealed that users valued control and transparency, they wanted to see how assets were structured rather than rely on a black box. Rather than leap to an unproven, resource-intensive solution, I prioritised fixing what was broken and delivering immediate value. The agentic search became a roadmap priority for the next phase.
Implementing a grid structure and card redesign made filtered results instantly scannable and actionable
Filters solved the first part of the problem: helping users narrow down options. But research revealed a second friction point: once filtered, users still struggled to evaluate results quickly. The cognitive load shifted from "too many options" to "unclear what I'm looking at."
1
Redesigned asset cards for faster differentiation
Content-type badges, short descriptions, key tags, and engagement signals so users can judge relevance at a glance, cutting decision time and reducing unnecessary clicks. Consistent visuals across cards also reinforced that each result is the same kind of asset.
2
Restructured the results grid with a consistent system
A grid system with consistent spacing and alignment allowed users to scan predictably. It also surfaced applied filters clearly, so users always understood their current context.
Impact
In the 12 months since shipping the feature, the new filtering system has transformed how users discover reusable code assets
20%
Reduction in filter abandonment, from 66% to 53%. Users stopped giving up.
13%
Reduction in bounce rate, from 48% to 42%. Users stay longer and are more engaged.
12%
Increase in first-try success, from 41% to 53%. Users find what they need faster.
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