Verdict — know if your skincare works before you can't return it You buy a serum that promises "less redness in 14 days". On day 27 of a 30-day return window you still can't tell. Verdict measures only what the product promises and tells you to keep it or return it, in time. What I built • Label scan: the camera reads the product's promise and keeps only the concerns it makes • Guided camera: on-device face tracking checks framing, angle and light, and takes the photo itself when it is comparable • YouCam AI Skin Analysis on the promised concerns, plus Skin Simulation of the promised result on the same photo • A daily verdict (Keep it, Return it, Stop using) computed on the raw scores against a noise floor, with a day-by-day replay • Virtual try-on (makeup, hair, outfits) on your own photo, and a Brands view of which claims deliver Result • Live YouCam API integration with real analysis, simulation and try-on calls, and a unit budget that cannot be drained • 83 automated tests, iPhone, iPad and desktop layouts, light and dark mode • Built for the YouCam API Skin AI & eCommerce hackathon My role: Product design, UX/UI, front-end, back-end, API integration, demo film Stack: Next.js, React, TypeScript, YouCam AI Skin Analysis, Skin Simulation and virtual try-on APIs, MediaPipe, Gemini, Vercel Watch the demo: https://youtu.be/n5KUtNIoHUs | Code: https://github.com/blanco1er/verdict

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Cover image for Atelier Éclat — a photoreal

Atelier Éclat — a photoreal restoration game inside Snapchat Most cleaning games fake it with flat images. I wanted the real thing: a muddy collector's object in true real-time 3D, running inside Snapchat with no download. What I built • A photoreal workshop scene: real-time 3D objects with clean and dirty materials, studio lighting and a turntable you rotate by hand • Thumb-anywhere aiming of a brass pressure lance; the water stops at the first surface it hits, splashes and leaves wet, shining areas that dry over time • Six collector objects for Season 1 (helmet, radio, model coupé, lantern, watering can, mantel clock), unlocked one by one • Per-part progress to 100%, a hold-to-compare before/after view, English and French, progress saved with Snap storage • Each object optimized to about 1 MB so the whole season fits Snapchat's 8 MB Web Lens limit Result • Published on Snapchat as a public game Lens (October 2026); Season 1 update with all six objects delivered to Snap • Submitted to the Snap × Lenslist Web Lens Game Challenge My role: Game design, 3D asset pipeline, gameplay and rendering code, UI/UX, launch film Stack: three.js, TypeScript, Vite, Blender, Snapchat Web Lens API Play: https://www.snapchat.com/lens/ae28ccd7037c41589c4b95dbc56241e8

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Cover image for Wind-Down Desk — a hands-first

Wind-Down Desk — a hands-first evening ritual for Meta Quest At 11 p.m. the head is still at work: the landlord, the dentist, Monday's slides. Wind-Down Desk turns that loop into a short seated ritual, with your hands, on your real table. What I built • Each worry becomes an engraved stone you pinch into a bowl: Tomorrow, Later or Let go • Six optional rituals, from Release (a stone sinks into black water) to Breathe and Keep • A glass lamp that proposes tonight's ritual, then waits; its memory stays on the device • The tray lands on your real table, good things glow on your walls, stars rise to your ceiling • Hands only, end to end, with a voice or gaze twin for every action Result • Measured in Meta's Quest 3 emulator: 15 to 24 draw calls per eye, under 100k triangles, frame script p95 under 10 ms with a throttled CPU • A scripted hands-only evening completes every ritual with no miss; 304 automated tests • Built for the Meta VR Start competition My role: Product design, interaction & spatial design, front-end, back-end, sound, demo film Stack: Meta Immersive Web SDK, WebXR, three.js, TypeScript, Vite, GLSL, Web Audio, Google Gemini, Blender Watch the demo: https://youtu.be/rYFgbrXtsBY | Code: https://github.com/blanco1er/wind-down-desk

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Cover image for Tourbook — tour routing by

Tourbook — tour routing by taste, not population Indie tours are routed by population: New York, Los Angeles, Chicago, the biggest rooms and the highest costs. The fans of a mid-size act often over-index in cities no booking agent would put first. What I built • A planning agent that ranks 34 US metros with a Hidden Market Score: taste against population • A fan heat map, a route, and a With / Without Qloo toggle that shows what taste data changed • A dossier for each date: room, opener, sponsor, after-show spot and crowd brief, each citing its source • Ask Tourbook: one change in plain words, verified by the server before it applies • Room mode for venues, a tour poster, a phone layout, light and dark, English and French Result • Tested live on Qloo data: one plan makes 30 to 31 Qloo calls across 5 endpoints, 0 rejected parameters • Ask Tourbook: 15 of 15 requests valid, 60 of 60 independent checks pass • Built for the Qloo Agentic Hackathon 2026; open source (MIT) My role: Product design, UX/UI, agent design, front-end, back-end, demo film Stack: Node.js, Express, JavaScript, MapLibre GL, Qloo Insights API, Google Gemini, Server-Sent Events, Render Watch the demo: https://youtu.be/5O4NPcu1zE0 | Code: https://github.com/blanco1er/tourbook

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Blanco Fochive

Apps, AR Games & Films · Web, Snapchat, TikTok, VR