Working MVP

ML Architect Path

One real product loop: choose a goal, learn a concept, solve a code task, get mentor feedback, and save progress.

0%complete
Onboarding

Choose your first adaptive goal

Adaptly starts narrow: it detects the next useful action instead of showing a generic course dump.

Lesson

Variables are containers for state

In production ML, state matters: feature values, user context, model outputs, metrics, and thresholds all move through named containers.

Concept

A variable should make the next operation obvious. Bad naming hides intent; good naming makes the system easier to debug.

Architect signal

Senior engineers do not only ask "does it run?" They ask whether a future teammate can trace why the value exists.

Code Lab

Complete the first task

Create a variable named player_score and assign it the value 500.

Check output

Run the check when your solution is ready.

AI Mentor

Athena reviews your solution

The mentor will respond after you run the code check.

Waiting for your code task...
Progress

Your first beta loop is complete

Adaptly now has a basic signal: selected goal, lesson viewed, code result, mentor interaction, and progress state.