Rebuilt Poshmark's home, search, and filter experience end-to-end. +60% GMV from "For You," stable OI / DAU at launch, and a 60+ screen system migration carried by the highest-intent surfaces.
✨ Drafted by AI
Poshmark's first GenAI feature. Photo in, ready-to-list out — a 15-minute chore collapsed into a 30-second review that sellers actually trust.
Co-founded a consumer design app from zero. Built the product, brand, and creator loop that took DecorMatters from an empty app to 6M users across AR room design, community, and social commerce.
I'm a Product Design Manager working across consumer, AI, and marketplace products — currently leading design at Poshmark, where I shipped the company's first GenAI feature and led the app redesign that lifted the search funnel by 26%.
Before that, I co-led design at DecorMatters and grew the product from 0 to 6M users, through an Apple "App of the Day" feature and a complete iOS + web redesign. I've done the 0→1, the scale, and the rebuild.
I care about fuzzy problems that don't have clean briefs, work that moves real numbers, and teams that disagree well. I manage designers, run research, write specs, and still push pixels — I think the best design leaders never fully put the pen down.
Outside of work: houseplants, slow reading, and weekend side projects where I get to try things my job doesn't need yet.
Poshmark's first GenAI-powered feature. We rebuilt the seller's most painful 15-minute flow — create a listing — into a photo-in, ready-to-post-out experience that feels like magic, not autopilot.
Listing is Poshmark's core creation loop — every item on the marketplace starts here. Seller research showed the median listing took 12–15 minutes across 7+ fields (title, description, category, brand, size, condition, keywords). Sellers abandoned halfway, reused templates that hurt discoverability, and new sellers often gave up before their first listing went live.
Every minute of friction in the create flow translated directly to fewer listings on the platform — and fewer listings means fewer buyers. The funnel leaked at every field, especially for new sellers who had never written a product description before.
Our earliest prototypes auto-filled everything silently, and sellers hated it. They didn't trust a black box to represent their inventory. The breakthrough was reframing AI from "it does the work for you" to "it drafts, you own it." Trust came from authorship, not accuracy.
One tap on a photo produces a full draft — title, description, category, brand, condition, keywords — each field showing a confidence indicator. Sellers scan, edit inline, regenerate any field, and post. The entire review takes seconds.
We ran Smart List AI against the standard listing flow across iOS, iPad, Android and web. The feature-level funnel lifted, and the surrounding business metrics (Listers, Sellers, Buyers, Orders, GMV) tracked roughly neutral between control and treatment — the result we were designing toward. No cannibalization, clear feature win.
From day one the success metric was Listings — specifically, the share of seller sessions that result in a published listing. We scoped Smart List AI against a 56% baseline, with a stretch goal of 60% for casual sellers. The A/B test landed well beyond that.
Translating the publish-rate lift against Poshmark's overall listing volume, the feature tracks to ~2% incremental listings platform-wide — the exact bar leadership set at kickoff.
The biggest design lesson wasn't about AI — it was about authorship. When we over-automated, we eroded the seller's sense of craft. When we underplayed it, we didn't realize the time savings. The sweet spot was giving sellers the feeling of curation with the speed of automation. That framing now guides every AI feature on my team.
I led design for three of Poshmark's highest-intent surfaces — home, search, and filter — through a 14-month system-wide redesign. We rebuilt home as a ranked discovery engine, modernized search and mobile filters, and carried a 60+ screen system migration.
Poshmark's app had grown for a decade on a tab-based browsing model. Home was an inventory wall, Search sat behind a generic Shop tab, and 60+ screens shared a fragmented visual system. Leadership greenlit a full redesign — aligned with a partnership with Naver — and asked three designers to own the highest-intent surfaces. I led home, search, and filter.
Home was optimized for browsing inventory, not deciding what to look at next. Search was a business-critical funnel wrapped in dense, web-shaped filter patterns. The design system couldn't support modernization because no single surface carried enough accountability to force migration. Fixing any one in isolation would miss the real problem: discovery needed a single spine.
Before rebuilding, we audited the existing app against buyer intent and comparable consumer benchmarks. Four issues kept surfacing — each one solvable in isolation, but together they explained why the app felt dated and why younger audiences bounced.
Typical redesigns get judged by top-line engagement and quietly fail — traffic moves around inside the app, and nobody can tell signal from regression. We designed against an explicit mix-shift hypothesis: attention should flow into Feed and Search, out of Brand and Community Closet, and OI / DAU should stay neutral overall. Naming the expected losses upfront let us defend the wins.
Before pixels, we agreed on directional intent. Four axes, each marked from where the app was to where it needed to be. This map became the tie-breaker whenever exploration spread too wide — and made it explicit that we weren't flipping Poshmark's identity, we were re-centering it.
Legacy product photos were square, which suited general marketplace inventory but not apparel. Moving every product card to a 4:5 vertical ratio aligned photography with how sellers actually shoot outfits, tightened rhythm across the grid, and gave UGC room to breathe without cropping heads or shoes.
Home stopped being a product wall. It became a ranked, modular feed that mixes personalized product grids with UGC photos and editorial lookbooks — a reason to open the app when you don't already know what you're looking for.
Recommend highly relevant products based on each shopper's interests and activity history.
Vertical photo ratio, optimized for apparel — full outfit shots, not cropped squares.
Auto-scrolling carousels curated by theme, giving the feed a rhythm beyond product tiles.
The old search stacked filters, chips, ad labels, and a 2-column grid into a single dense screen. The new surface breathes: search moves to its own bottom-nav tab, filter chips become horizontally scrollable pills, product cards lead with imagery, and meta drops to a second line. Home became a ranked feed of modular units — product, creator, live, show — and mobile filters collapsed into a flatter bottom-sheet IA.
Page View / DAU mix-shift. Neon = intentional gains. Dashed = expected losses from removing the Shop tab.
| Surface | Pre | Post | Δ |
|---|---|---|---|
| Feed | 5.6% | 7.2% | +1.6 pp |
| Search | 44.3% | 45.4% | +1.2 pp |
| Brand | 10.9% | 9.7% | −1.2 pp |
| Show | 15.3% | 14.6% | −0.7 pp |
Feed's +9% OI/FM was the bigger signal than traffic: buyers didn't just visit more, they bought more of what they saw. New-user metrics moved up across the board — D2 retention +15%, D1 sessions +10%, D1 buyer +9%. Ad revenue still grew +2.6% overall despite Brand losing top-of-funnel.
This project reset how I think about product design. The job wasn't to "improve home" or "redesign search" in isolation — it was to decide where attention should flow across the marketplace, and to defend that decision with a measurement framework explicit enough to tell mix-shift apart from regression. Design systems rarely win on abstraction alone; attaching the 60+ screen migration to surfaces with real traffic made it impossible to ignore.
I co-founded DecorMatters and led design from zero. We shipped, we were wrong, we pivoted — twice. Each pivot was a design problem I ran end-to-end: insight, research, system, launch. This is the story of how the product became what it was always trying to be.
DecorMatters shipped three times. Each version answered a different question, each pivot was a design call I made with the data in hand. Here's the timeline — then three chapters, one per pivot.
2017. AR on iOS was brand new. Our thesis: people don't buy furniture online because they can't tell if it fits. Drop a real chair into a real room, hit buy. Simple.
I don't come here to shop. I come here to design my dream rooms when I'm bored.
The returning user was 25–55, mostly female, coming back 3–5 times a week to create, not buy. We didn't need a better catalog. We needed a design tool, a social feed, and something to do with free time. Pivot.
After the first pivot, design was the loop. But without publishing there was no feed, no likes, no reason to return. The number that mattered was stuck at 3.5%.
I led design on three parallel tracks — speed up item search, make tools forgiving, give publishing a reason. Each shipped as a minor release so we could measure independently.
By 2020, MyDecor subscription drove ~75% of revenue — fragile. The goal was 2× without breaking the free creator experience. The framing shift: stop treating this as retail. Start treating it as a game economy.
Most comments were "beautiful / wow / awesome." We turned the compliment itself into the product — paid virtual gifts attached to designs you loved.
Daily check-ins earned Dcoins; finished challenges won badges. Gamers' real needs — practice, recognition, a fun escape — became the loop that kept them opening the app.
Instead of a hard paywall, items could be unlocked three ways: membership, purchased Dcoins, or earned Dcoins. Free users kept designing. Invested users paid — or earned by showing up.
Five years of DecorMatters compressed into the lessons I actually carry.
A small, ongoing collection of prototypes I build after hours with AI-assisted tools. Part sketchbook, part taste-test — this is where I figure out what new interaction patterns and tools actually want to be.
I started Vibe Code to keep myself honest about what AI is good at — and what it isn't. Every prototype here is a hypothesis about an interaction, a product, or a tool. Some became pitches. Some became features at work. Most just taught me something I couldn't have learned from a blog post.
Upload your closet, get outfit drafts that match your actual taste rather than a trend algorithm. Experiment in taste-modeling with LLMs.
Photograph a room, swap walls, floors, furniture with GenAI and ship the design. An echo of DecorMatters with modern diffusion models.
A tool for PMs to write design-ready briefs in under five minutes. Templates, prompts, and structured outputs for design handoff.
A structured critique tool that asks better questions. Explores how AI can scaffold junior designers through senior review.
A personal spin on Smart List AI — coaches independent resellers on photography, titles, and pricing without locking them into a platform.
A tiny weekend planner that understands vibes ("something outside, low effort") rather than forecast grids. A small test in conversational UI.
Vibe coding changed how I design at work. Shipping tiny prototypes made me sharper at scoping, faster at prompting, and more opinionated about when AI should disappear into a product versus announce itself. The best ones usually disappear.