Our ultimate reward function is user success. We lead with empathy to ensure we are solving the actual user intent, not just blindly following requirements. We constantly verify that our outputs map to real-world value for learners and educators.

We lead with curiosity about our customers and their context. We don't settle for surface-level understanding; we investigate deeply to uncover the real problems, constraints, and mental models our users operate within. We continuously explore alternative solutions, staying curious about the latest tools and techniques that might better serve our customers. We're never content with the status quo - we need to understand why things work the way they do and whether there's a better path forward.

We fight "inferential distance" by maximizing shared context. We optimize for Observability, "building in public" to produce artifacts and traces that allow others to load context asynchronously. We explicitly sync our state with the team to prevent misalignment. We write precise specs and ask clarifying questions to fill gaps in our "context window" before execution. We never assume; we verify.

We operate from first principles. We don't just generate outputs; we understand the underlying system constraints, data models, and downstream dependencies. We solve for the "geometry" of the problem (root cause), avoiding local optimizations ("shapes") that would introduce technical debt or break the broader system.

We act as autonomous agents, not just task executors. Given a high-level goal, we decompose it, navigate obstacles, and execute until completion. We don't throw errors when we hit a blocker; we retry, re-route, and find a solution. We optimize for "time to truth" and never wait for a human-in-the-loop to tell us what to do next.

We prevent "model collapse" by continuously investing in our own capabilities. We actively seek RLHF (Reinforcement Learning from Human Feedback), treating feedback not as criticism but as a gradient update that reduces our error rate. We don't rely on pre-trained knowledge from years ago; we actively fine-tune ourselves on the latest stack and domain knowledge to stay state-of-the art.

We run rigorous internal "evals" on everything we produce. WE possess a high-quality "ground truth" of what excellence looks like and use to self-correct before shipping. We spot "hallucinations" (bad UX, nonsensical empty states, poor copy) immediately. We refuse to output low-confidence work.