Projects

Selected projects by Ying Chieh Wang, including Wiser Locker and FoodList, both published on Google Play.

Product · AndroidPublished on Google Play

Wiser Locker

Private photos and videos, encrypted on your device and organized by tags instead of folders.

An Android app for people who want to keep private media safe and still find it easily.

Media is encrypted on the device, and access is protected by biometrics and secure key storage.
Multiple tags and saved filters replace a single folder tree, so one photo can belong to "travel", "family", and "friends" at once.
Published on Google Play and maintained under the Forger Wise brand.

Feb 2025 – Present · Solo developer — product, design, engineering, and release

Details

Google Play wiserlocker.com Forger Wise

Product · AndroidPublished on Google Play

FoodList

Less household food waste: know what you have and what to use first.

Food at home often goes to waste simply because people forget what they have and when it expires. FoodList is a household food-management app that keeps track of both.

Problem: about 60% of global food waste happens in households (UNEP Food Waste Index Report 2024), often because people forget what they have and when it expires.
Solution: register ingredients once; green, yellow, and red status shows what is fine, what to use soon, and what has expired.
Background reminders before items expire, full offline use with local storage, and a multilingual UI.

Jul 2023 – Present · Solo developer — planning, design, implementation, release, and maintenance

Google Play GitHub

Course project · AI1st place in class tournament

Battlesnake AI

A* search for solo play and reinforcement learning for head-to-head matches.

Two team projects in PBL courses at The University of Osaka. Our snakes played against classmates' snakes through the official Battlesnake API.

Solo: proposed and implemented an A* strategy in Python that switches between safety-first and food-first modes — 3rd place in class.
Duel: when our first Monte Carlo approach underperformed, the team moved to reinforcement learning. Building on existing open-source implementations, I trained and evaluated PPO / A2C models with self-play and connected them to the game server — 1st place in class.

Oct 2023 – Feb 2024 · Main implementer (Solo); training, evaluation, and API integration (Duel)

GitHub (Duel)GitHub (Solo)

1st place in class tournament