"To love without understanding harms the object of your love" (Thich Nhat Hanh) For us, to code without understanding harms the product of our code -- our software. And quietly hollow out our own skills in the process. So how can you use AI as your coach, your teacher, and your partner in exploration? How can you prevent the brain rot of passivity? And when you're not writing it -- how can you keep all of the new code in your head? This talk aims to ensure your favourite LLM makes you a better developer, not a worse one. I'll share a system for collaborating on software that leaves you sharper, more skilful, and still the one who understands what's running in production.

At 15, I’m using AI across research, learning, business building, content creation, decision-making and automation. This session will break down the practical systems I use, what I automate, what I keep human, and what I’ve learned from experimenting with AI in real work.
How to use AI to learn, research and understand information faster.
How to identify repetitive work that can be turned into practical AI workflows.
How to combine AI with human judgment to build better systems for work, business and productivity.

A model I was working with scored R squared 0.998 on public US data. One of the input columns had been calculated from the target by the same agency that published both, so the model was rediscovering a formula it had already been handed. Tests don't catch this. Nothing throws, nothing fails, the numbers look great. So I built a linter for it. You write a manifest of what derives from what, the tool walks the graph, and CI goes red when a feature sits on a derivation path to or from your target. It returns four verdicts, including one for columns whose provenance nobody has traced yet. The code is Python. The idea applies anywhere you have a dependency graph and a CI job. I'll show the leak, the manifest, the linter, and a run going red live.
Repo: https://github.com/Adeniyikayodee/derives-from-tutorial