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Elite Track / Algorithms and Complexity Lab

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Focus

  • elite systems engineering practice with explicit evidence and tradeoffs

Why this project exists

Measure algorithmic behavior, compare complexity classes, and defend tradeoffs.

Run (copy/paste)

Use <repo-root> as the folder containing this repository's README.md.

cd <repo-root>/projects/elite-track/01-algorithms-complexity-lab
python project.py --input data/sample_input.txt --output data/output_summary.json --run-id smoke-check
pytest -q

Expected terminal output

... output_summary.json written ...
2 passed

Expected artifacts

  • data/output_summary.json
  • Passing tests
  • Updated notes.md

Alter it (required)

  1. Add one resilience improvement.
  2. Add one observability improvement.
  3. Re-run script and tests.

Break it (required)

  1. Remove or corrupt one input line.
  2. Confirm behavior fails safely.
  3. Capture error and root cause.

Fix it (required)

  1. Add guard/validation logic.
  2. Add or update tests.
  3. Re-run until deterministic and passing.

Explain it (teach-back)

  1. What tradeoff did you make and why?
  2. What failure mode mattered most?
  3. How would this design scale in production?

Mastery check

You can move on when you can: - explain architecture and failure boundaries, - run and validate output without docs, - defend one design tradeoff with evidence.

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