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01Product · Web3 / FinTech

Crypto trading-fee cashback platform with 100K+ users. Took over the web + mobile frontend, rebuilt it in-house, and own it end to end.

Role
Frontend (Web · Mobile)
Timeline
2024 — Present
Platform
Web + iOS / Android
Impact
JS ~2MB → 200–300KB · LCP 4.98s → 2s
/ Overview

B2C FinTech — users earn back trading fees on a platform that has processed $17B+ in trading volume and paid out $2.2M+ in cashback. 100K+ users. Web + mobile, built and run in-house.

Problem

Outsourced, template-built, web only. No app, no SEO, slow. The company wanted cashback visible at a glance — the legacy product couldn't get there, and every change went through the agency.

Approach

Next.js App Router for SEO and per-route cache control. React Native for iOS/Android — ~90% shared codebase, one team ships both. Sentry and Mixpanel wired in from day one.

Result

JS ~2MB → 200–300KB. LCP 4.98s → 2s. Hosting costs down with the bandwidth. Shipped iOS + Android where none existed — web and app release in lockstep, and cashback is finally visible at a glance.

My role

Drove the core of the rebuild — App Router migration, caching architecture, image pipeline, bundle optimization, and the React Native app — while the team of 3–4 handled ongoing product work in parallel. Set up the CI/CD and code-review standards the team still uses.

Tradeoffs

Aggressive caching vs. real-time trading data. Static content cached at the edge; price and balance data bypasses cache — heavier requests, accepted only where staleness costs users money.

/

What I built

01

App Router migration with granular caching: aggressive at the edge, exempt for real-time trading data. Semantic markup throughout — structure that serves crawlers and screen readers alike.

02

Automated image pipeline and dynamic icon system; with bundle trimming, cut JS ~85–90%.

03

Cross-functional syncs with PMs and designers to negotiate UX trade-offs. Set up CI/CD and code-review standards for the frontend.

04

Frontend-led observability via Sentry & Mixpanel — regressions caught before user reports, not after.

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