saymrwulf/autoresearch-quantum
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5 commits

Author SHA1 Message Date
saymrwulf
e7b8b02a88 Remove GitHub Actions CI — validate locally with app.sh validate 
CI ran on every push and generated failure emails. For a single-developer
project, local validation is sufficient and less noisy.
 2026-04-16 10:23:11 +02:00
saymrwulf
a16f99e2ed Harden Jupyter lifecycle and enhance E2E browser UX tests 
app.sh: full Jupyter env isolation (CONFIG_DIR, DATA_DIR, RUNTIME_DIR,
IPYTHONDIR), auto port allocation, foreground mode, restart command,
graceful stop (SIGTERM → wait → SIGKILL), orphan detection, stale
runtime JSON cleanup, reset-state command.

test_browser_ux.py: add TestNavigationClickThrough (click Plan A link,
verify target opens; click forward-link in Plan D), TestWidgetInteraction
(run all cells, verify output areas, click quiz Submit and verify feedback),
TestProgressPersistence (kernel API session, progress file schema validation).

.gitignore: add Jupyter isolation directories.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-04-15 21:20:34 +02:00
saymrwulf
29caba3a1a Add professional toolchain: mypy strict, CI pipeline, Playwright UX tests, pedagogy validation 
Infrastructure:
- Configure mypy strict mode in pyproject.toml; fix all 53 type errors across 8 source files
- Add .pre-commit-config.yaml (ruff, mypy, nbstripout, trailing whitespace)
- Add .github/workflows/ci.yml: lint + type check, unit tests (Python 3.11/3.12), notebook execution
- Add scripts/app.sh consumer lifecycle manager (bootstrap, start, stop, status, validate, logs, reset)

Testing:
- Add tests/test_browser_ux.py: Playwright end-to-end UX tests covering JupyterLab launch,
  notebook rendering, navigation links, widget rendering, and full consumer walkthrough
- Add tests/test_pedagogy.py: 130 pedagogical structure tests validating prose quality
  (word counts, markdown ratio), section structure, assessment density and variety,
  Bloom's taxonomy coverage, checkpoint presence, tracker integration, key insight
  callouts, and cross-plan concept consistency

Quality:
- Fix ruff E741 (ambiguous variable name) across all builder scripts
- Add Key Insight callouts to plan_a/01_encoded_magic_state.ipynb
- Add pytest 'browser' marker for selective UX test runs
- Expand .gitignore with .logs/ and build artifacts

319 tests pass, 85% coverage, mypy strict clean, ruff clean.
 2026-04-15 20:00:19 +02:00
saymrwulf
5d0c28f939 Harden teaching layer, add notebook execution tests, fix repo hygiene 
Quality fixes:
- Add deprecation warnings to 5 silent no-op legacy wrappers in assess.py
- Remove dead code in tracker.py score_by_section (unused first loop)
- Remove unused variable in assess.py _check_order
- Fix .gitignore: add progress JSONs, checkpoints, .coverage, .DS_Store, LaTeX aux
- Fix "all three plans" → "all four plans" in learning_objectives.md
- Add teaching/ package to README project tree
- Add compendium to README paper tree

Testing:
- Add 43 unit tests for teaching/assess.py and tracker.py (quiz, predict_choice,
  reflect, order, checkpoint_summary, legacy wrapper deprecation warnings,
  tracker scoring, persistence, mastery calculation)
- Add notebook execution test suite (nbclient): all 11 notebooks execute without
  errors in a fresh kernel, structural validation (valid JSON, has code cells,
  has assessments, section parameters, learning objectives document)
- Overall test count: 185 passing (was 107), coverage: 85% (was ~25% in tests)

Toolchain:
- Add pytest-cov, ruff, nbclient, nbformat to dev dependencies
- Add ruff config (E, F, W, I, UP, B, SIM rules)
- Add coverage config with term-missing output
- Fix all ruff lint issues across src/ and tests/ (import sorting, unused imports)
- Fix Plan D notebook paths (configs/rungs → ../../configs/rungs)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-04-15 15:34:37 +02:00
saymrwulf
f9b8f3457f Initial commit: autoresearch-quantum — automated magic-state preparation ratchet 
Karpathy-style autoresearch engine for encoded magic-state preparation
on the [[4,2,2]] quantum error-detecting code using Qiskit Aer simulation.

Five-rung progressive search: baseline -> stability -> transfer -> factory -> Rosenfeld.
Smart challenger generation (neighbor walk + random combo + lesson-guided).
Machine-readable lesson feedback with per-dimension effects, interaction detection,
and cross-rung propagation. Factory throughput scoring. Resumable execution.
21 tests, all passing.
 2026-04-05 12:37:39 +02:00