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NTT-learning

NTT-learning is a local-first, notebook-first course repo for understanding the Number Theoretic Transform in the context of Kyber.

The project is built around one supported learner route:

  1. notebooks/START_HERE.ipynb
  2. notebooks/COURSE_BLUEPRINT.ipynb
  3. Each bundle in Lecture -> Lab -> Problems -> Studio order:
    • notebooks/foundations/01_convolution_to_toy_ntt/
    • notebooks/foundations/02_negative_wrapped_ntt/
    • notebooks/butterfly_mechanics/03_fast_forward_ct/
    • notebooks/butterfly_mechanics/04_fast_inverse_gs/
    • notebooks/kyber_mapping/05_kyber_ntt_and_base_multiplication/
    • notebooks/professional/06_debugging_ntt_failures/
  4. notebooks/COURSE_COMPLETE.ipynb

The current build covers convolution, negacyclic folding, direct NTTψ / INTTψ, fast CT/GS butterfly schedules, ordering and scaling, Kyber modulus reality, base multiplication, and debugging fingerprints.

Notebook Contract

Visible notebook cells follow an explicit contract:

  • META cells provide route, objective, pacing, and handoff guidance.
  • MANDATORY cells are the official walkthrough.
  • FACULTATIVE cells are optional extensions only.

Difficulty is reserved as follows:

  • 1-3 for mandatory cells
  • 4-10 for facultative cells

Route notebooks stay pure route notebooks. They contain META and MANDATORY cells only.

Local Operations

The lifecycle source of truth is scripts/app.sh:

  • scripts/app.sh bootstrap
  • scripts/app.sh start
  • scripts/app.sh start --foreground
  • scripts/app.sh stop
  • scripts/app.sh restart
  • scripts/app.sh status
  • scripts/app.sh logs -f

Compatibility wrappers remain in scripts/bootstrap.sh, scripts/start.sh, scripts/stop.sh, scripts/restart.sh, scripts/status.sh, scripts/reset-state.sh, and scripts/validate.sh.

Typical first run:

bash scripts/app.sh bootstrap
bash scripts/app.sh validate
bash scripts/app.sh start --no-open

Notes

  • The repo uses a local .venv.
  • Jupyter state is isolated inside the repo with .jupyter_config, .jupyter_data, .jupyter_runtime, .ipython, .cache, and .logs.
  • The Jupyter kernel is installed into the venv with --sys-prefix, not --user.
  • Validation is designed to work with the standard library first, so structure and notebook execution checks can run before richer notebook tooling is installed.
  • JupyterLab and ipykernel are declared in pyproject.toml and installed by scripts/app.sh bootstrap.