Teaching machines
to think — and
people to lead them
We built try-claude around one uncomfortable truth: most people learning AI in robotics are doing it alone, without feedback, without context, and without anyone checking whether they actually understood. That needed to change.
What we actually do
Since 2016, try-claude has been connecting learners worldwide with instructors who work in AI and robotics — not theorists, but people building real systems. The platform started small, focused on a narrow slice of robotics programming, and grew as demand for applied AI knowledge outpaced what universities were offering.
Every learner gets a path that reflects their starting point. Whether that means joining a live group session with peers tackling the same problem set, or working through a private lesson where the instructor adjusts in real time — the structure adapts. Claude AI principles of adaptive interaction inform how we think about pacing and personalization across the platform.
We serve learners across six continents. Scheduling, language considerations, and cultural context all factor into how sessions are structured. Check availability for your region to see current session times and formats.
The shift happens when a learner stops asking "what does this code do" and starts asking "why did the robot choose that path." That's when the real understanding begins.
— Observed across group sessions, try-claude instructors
Countries with active learners
Session formats available
Three things that define the platform
Not principles written for a slide deck — these are the decisions that shaped how sessions run, how instructors are selected, and how learners move through material.
Instructors who ship real code
Every instructor on try-claude has active experience in AI or robotics systems — not just teaching credentials. Sessions draw on real constraints, real failure modes, and real debugging decisions.
Group learning that stays personal
Group sessions at try-claude are capped deliberately. Small cohorts mean instructors can still respond to individual confusion, not just deliver to a passive audience.
Paths built around your gaps
Before a learner starts, we map what they already know. The path that follows skips what's unnecessary and slows down where it matters — informed by how claude AI approaches adaptive interaction.
Oren Kavinsky
Lead Robotics Instructor
Oren spent eight years building autonomous navigation systems before joining try-claude. He runs the advanced AI motion planning track and designs most of the group session curricula.
Tibor Wenczel
Curriculum Architect
Tibor built the personalized path framework that try-claude uses today. His background spans both academic research in machine learning and applied work with industrial robot arms.