THE PROBLEM Most training — human

THE PROBLEM Most training — human or AI — tells people the outcome and then pretends they learned the decision. It also breaks the moment the interface gets complicated, which is why a lot of training tools look impressive and teach very little.

WHAT I BUILT An adaptive, rule-based training system for day trading, developed iteratively from real failure in the interface itself:

Hidden outcomes — the learner commits to a call before seeing what happened

Structure-first grading, with indicators (EMA / Fibonacci / VWAP) as secondary confluence, not the decision itself

Deliberately hard cases — near-invalidation and controlled false alarms, so learners can't just pattern-match easy examples

Failure-driven UI iteration: when interaction reliability failed, a chat-native fallback preserved the training function, and a later stable HTML implementation became the preferred version — function over flash

WHAT I CAN DO FOR A CLIENT

Design an AI-powered trainer / simulator for a skill your team or customers need to learn (sales calls, compliance decisions, technical diagnosis, trading, operations, onboarding)

Build grading logic that evaluates reasoning and process, not just right/wrong answers

Design practice case libraries — including the hard, ambiguous, edge cases that actually build competence

Rescue or redesign a training tool that looks good but isn't teaching