Selected projects.

Product and engineering work across mobile, AI, and full-stack systems.

WakeyTasky · AI Task Decomposition Tool

Team Prototype · Core Contributor · Mar – Jul 2025

Designed for people who struggle to start ambiguous tasks, turning a vague goal into an editable and trackable action plan that moves AI advice into the execution workflow.

The diagram uses the original Chinese product labels. Click to view the 1800px source image.

• Narrowed the role of AI: reduced the MVP from general schedule interpretation to task decomposition and duration estimation, focusing the model on lowering activation friction rather than producing disposable advice.
• Defined an actionable structured output: translated title length, parent-child hierarchy, estimated duration, and status into explicit product rules so every AI output could become an editable task and a consistent data object.
• Closed the execution loop: designed parent-child state coordination, task transitions, and a Pomodoro workflow; led prompt and output constraints, the task data model, and core state logic through implementation.

Scope: a team prototype, not a publicly launched commercial product.

Voluma · AI Architectural Visualization Tool

Product Design & Engineering · 2026.04 – 2026.05

• Identified the core problem: architectural designers and real estate professionals lack a low-cost 3D visualization tool during early-stage planning — traditional rendering pipelines rely on specialized software and outsourced teams, creating bottlenecks that multi-modal models can now eliminate.
• Independently designed and built the product end-to-end: AI-driven generation from 2D floor plans to photorealistic top-down 3D renders, with community sharing and one-click export.
• Translated UX requirements (geometric fidelity, material realism, annotation removal) into model prompt constraints; benchmarked multiple models and selected gemini-2.5-flash-image for its superior instruction-following on structured spatial inputs, balancing output quality and per-call cost.

ML Training Data Generation Pipeline (Android Malware Detection)

System Design · 2025.09 – 2025.12

• Framed the core problem: security researchers don't just need “a dataset”, they need a reproducible, extensible capability to continuously generate high-quality training data, enabling iterative model improvement.
• Redesigned one-off data processing scripts into a reusable data generation pipeline supporting the full train to evaluate loop for malware detection models.
• Designed for diversity (17+ obfuscation strategies), controllability (dual-pipeline architecture with stage-level monitoring), and reliability (retry + fallback mechanisms, 82% success rate at scale).
• Processed 12,000+ Android malware samples and generated 7,900+ obfuscated variants; the system was reused by subsequent research teams, significantly reducing data preparation cycles.