Web ApplicationLancy

Lancy Freelancing Filters & Feed.

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Lancy freelancing feed and filter interface

Objective

This project was to design Lancy, a freelance job aggregator. The platform pulls gigs from the major freelance boards into one central feed and filters out low-quality work. With advanced filtering, users can quickly narrow the feed to exactly the gigs they want.

Role
UX Designer
Team
1 developer, 1 product manager
Timeline
6 weeks
Tools
Figma, Userbrain, Claude Cowork, Lovable

Competitor Analysis

Before I jumped into design, I started with competitor research and a feature analysis to understand industry standards and inform what features would be required for our MVP.

View full analysis
Competitor analysis spreadsheet comparing features across freelance job board platforms

User Research

Testing our core assumption

User interviews for this project were critical: they invalidated our core assumption and reframed our key value proposition from aggregation to filtering.

Filter Layout & Structure

The first core task I focused on was how filtering would function — an essential part of the Lancy experience, given its core value proposition of helping users quickly surface relevant gigs. After analyzing filtering patterns across other job board platforms and wireframing multiple layouts, I narrowed testing down to four variants: a single scrollable row of filters, two stacked rows, a grouped filtering sidebar, and an ungrouped sidebar.

Horizontal Filters

Opt 1 · Single, scrollable row of filters.
Single scrollable row of filters
Opt 2 · Two rows of filters.
Two stacked rows of filters

Filter Sidebar

Opt 3 · Grouped filtering sidebar.
Grouped filtering sidebar
Opt 4 · Ungrouped filtering sidebar.
Ungrouped filtering sidebar

Follow-Up: Assessing for Cognitive Overload

Even though the ungrouped sidebar performed best, I wanted to be sure before locking it in. Was showing all filters at once too much information? Even at a faster filtering pace, could it slow down other tasks on the platform?

I ran a quick follow-up test to check for signs of cognitive overload.

The Quality Jobs Only filter

The "High-Quality Jobs" filter is tied to Lancy's pro subscription, so its placement and discoverability were critical. Because of its importance, I tested this feature separately from the broader filter structure — comparing a toggle inside the filter sidebar against a toggle pinned above the job feed.

Quality jobs toggle at the top of the filter sidebar

Opt 1 · Placement at top of sidebar.

Quality jobs toggle above the job feed

Opt 2 · Placement above the job feed.

Design Iterations

With the filter structure settled, I turned to the unit that powers the feed itself: the job post card. I explored two layouts — one that leads with the project title and tags before the meta row, and one that keeps the timestamp and source at the top with quick actions inline. Both versions surface the same information, but the hierarchy shifts what freelancers see first when scanning.

Two job post component layouts compared side by side

Final Designs

The final design brings together the ungrouped sidebar, the pro Quality Jobs toggle, and supporting flows for search and saved jobs.

Final Lancy feed and filter interface
Final feed and filter interface.

User flow · Search functionality

01User sets filters to narrow the feed
User setting filters in the sidebar
02User enters a search query from the top of the feed
User typing a search query
03User selects a job from the filtered results
User selecting a job from results
04User reviews the job details and applies
Job detail modal with Apply button

Additional Screens

Supporting screens across the product

Lancy login screen

Log in · 1 / 3

Reflection

Lessons learned.

Talking directly to our target audience was invaluable. Early user interviews surfaced pain points we wouldn't have caught otherwise. We started out assuming users wanted more job posts — but conversations revealed it wasn't about quantity. Freelancers weren't overwhelmed by too few options; they were overwhelmed by too many irrelevant ones. That insight reshaped the product, shifting the focus from volume to intelligent filtering.

Lesson learned

Talk to users before you trust your assumptions. Without those early conversations, we would have invested in solving the wrong problem.

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