AI Coding

I rely heavily on AI coding tools (Claude Code, Cursor, etc.) in my daily work and research. Here I document real-world cases and reflections. ## Tools - **Claude Code** — Primary tool for code generation, refactoring, debugging, and documentation - **Cursor** — Codebase-aware editor, ideal for large projects - **GitHub Copilot** — Lightweight everyday completion --- ## Case Studies ### Building This Site with Claude Code **Tool:** Claude Code **Context:** Building this Jekyll academic site from scratch — adding pages, tweaking styles, fixing config errors **Takeaway:** AI grasps Jekyll template structure quickly, cutting down time spent reading docs. Especially effective for niche syntax like Liquid templates. --- ### Debugging Robot Simulation Environments **Tool:** Claude Code + Cursor **Context:** Debugging VLA policies in IsaacSim / MuJoCo, pinpointing sensor data alignment issues **Takeaway:** Feeding error messages and env configs together to the AI locates issues in minutes that would otherwise take hours. --- ### Paper Writing Assistance **Tool:** Claude **Context:** Polishing Abstract and Related Work sections for the REVER paper **Takeaway:** AI cannot replace thinking, but it significantly improves language quality. Asking AI to critique first, then revise, works better than asking it to rewrite directly. --- ## Reflections > AI Coding is not about writing less code — it's about spending your time on decisions that actually matter. The core skill for engineers using AI tools has shifted to: **clearly describing the problem**, **judging output quality**, and **keeping the big picture in mind**. This maps closely to what robotics research demands — you need to know what the robot should do before you can verify whether it did it right. --- *Continuously updated. Feel free to reach out by email if a specific use case interests you.*