rseng-defensive-coding

Covers defenses against silently wrong research results: validating data at boundaries (schemas, assertions, sanity checks), explicit physical units and quantities in code (pint/astropy-style), disciplined randomness (explicit seeded generators, parallel streams), and fail-loud handling of NaN and missing data. Use PROACTIVELY when code ingests external or instrument data, when values carry physical units, when…