LEARN WITH A CLEAR DIRECTION
Learn app-managed on-device AI through PocketStories: model downloads, CameraX, LiteRT-LM, Compose, context, cancellation, Room and device diagnosis.
A complete 13-unit learning path around the published PocketStories preview. Each unit connects explanation, source and an exercise; the codelab includes a red/green context-policy extension and an independent capstone. Software checks are verified; successful end-to-end Gemma generation remains under validation. The tested emulator encounters a GPU buffer limit.
Work through the 13 units in order. Keep an evidence log. Complete software exercises even when native inference is blocked; report the live-model track separately.
YOUR PROGRESSION
Complete the labs and keep the evidence. Use each checkpoint to decide whether to move forward or revisit a skill.
Units 1–3
Reproduce the baseline and acquire the correct model
Evidence: Build log, artifact manifest and downloader tests
Go to this stage’s final module →Units 4–7
Trace camera input, state, runtime and cancellation
Evidence: Camera recovery check and resource-ownership diagram
Go to this stage’s final module →Units 8–10
Bound context, persist stories and diagnose failures
Evidence: Red/green context tests and device failure report
Go to this stage’s final module →Units 11–13
Complete a measured evidence matrix and independent capstone
Evidence: Capstone diff, regression tests and honest assessment
Go to this stage’s final module →THE CURRICULUM
13 lessons across 13 modules. Open a module to explore its lessons and practical work.
Build the pinned app and separate software evidence from inference evidence.
Ready to move on when: Build the pinned app and separate software evidence from inference evidence.
Choose a vision-capable artifact and explain storage, memory and backend constraints.
Ready to move on when: Choose a vision-capable artifact and explain storage, memory and backend constraints.
Trace HTTP resume, cancellation and verified promotion into private storage.
Ready to move on when: Trace HTTP resume, cancellation and verified promotion into private storage.
Capture one intentional frame and prepare a bounded private image.
Ready to move on when: Capture one intentional frame and prepare a bounded private image.
Trace an action through immutable state and injected data services.
Ready to move on when: Trace an action through immutable state and injected data services.
Understand engine setup, multimodal messages and explicit native resource ownership.
Ready to move on when: Understand engine setup, multimodal messages and explicit native resource ownership.
Distinguish a cancelled coroutine from completed native cleanup.
Ready to move on when: Distinguish a cancelled coroutine from completed native cleanup.
Implement a scene-count limit with a failing test and a verified fix.
Ready to move on when: Implement a scene-count limit with a failing test and a verified fix.
Persist accepted stories and recover drafts without persisting native sessions.
Ready to move on when: Persist accepted stories and recover drafts without persisting native sessions.
Classify acquisition, native-loading and inference errors without unnecessary downloads.
Ready to move on when: Classify acquisition, native-loading and inference errors without unnecessary downloads.
Measure actual output separately from deterministic app tests.
Ready to move on when: Measure actual output separately from deterministic app tests.
Design and implement persisted character facts with strict context boundaries.
Ready to move on when: Design and implement persisted character facts with strict context boundaries.
Explain the architecture and present reproducible learning evidence.
Ready to move on when: Explain the architecture and present reproducible learning evidence.
PROVE YOUR SKILLS
Use the final review rubric to identify gaps. Lesson completion tracks study progress; readiness comes from independently demonstrating the work.
Open the portfolio review rubric →