I spent nearly 30 years in school to earn my PhD in agronomy. Classrooms, exams, field trials, thesis writing — the whole formal grind. Then AI taught me the one lesson no professor ever fully delivered: Your future depends less on what you were taught… and way more on how well you can teach yourself in a world that changes every single month. School gave me structure, patience, and deep knowledge of soil, roots, crops, and ecosystems. But it never handed me a playbook for turning real field data into web apps. It never showed me how to use open-source projects, prompts, and code to solve actual problems for farmers, researchers, and environmental teams. So I had to learn all over again. No grades. No syllabus. No one telling me what chapter comes next. Messy prompts, broken code, late nights, failed prototypes… and slowly, real clarity. That’s why I’m here as Mai Xiaomai — bridging two worlds. The patience I learned from the soil and the speed I’m getting from AI. I share practical tools, GitHub finds, small experiments, and honest notes from someone living between agronomy and artificial intelligence. The soil is rich. If you work in agriculture, environmental science, climate, food systems, or you’re simply curious about real-world AI, come grow with me. What’s one skill you had to teach yourself that school never prepared you for? #MyPiece