A variable should make the next operation obvious. Bad naming hides intent; good naming makes the system easier to debug.
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
Manual companion
Connect your learning path
Adaptly can connect the beta MVP with the Machine Learning book labs and keep the next action visible.
Demoaccess
0/20labs
Chapter 01next
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.
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
Your personal agent reviews in-context tasks — not a generic chat tab.
Complete the code check first. Mentor unlocks when player_score = 500 is detected.
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, book access, and progress state.