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
The mentor will respond after you run the code check.
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.