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AI Time Estimates for Coding Tasks
Coding tasks span quick scripts to architecture decisions. See where AI saves hours and where a senior engineer is still worth the rate.
Write inline docstrings for all functions, classes, and methods in a previously undocumented internal Python module (assumed ~500–1500 lines), plus a README covering purpose, installation, usage examples, and API overview.
Convert a complex multi-join SQL query (multiple JOIN types, likely GROUP BY, WHERE, and subqueries) into semantically equivalent pandas DataFrame operations, with inline comments explaining each transformation step.
Generate a comprehensive suite of Python unit tests covering an existing set of utility functions that currently have zero test coverage. Includes identifying test cases (happy path, edge cases, error conditions), writing pytest-style tests, and verifying coverage.
Debugging an intermittent REST API endpoint returning 500 errors under load is a non-trivial engineering task. The intermittent nature under load strongly suggests concurrency-related root causes: connection pool exhaustion, race conditions, resource leaks, deadlocks, or cascading timeouts with external dependencies. Reproducing reliably requires load-testing tooling, access to logs and metrics, and iterative hypothesis testing. Difficulty scales significantly with system complexity, observability maturity, and whether a staging environment exists.
Write a Python script that reads an imperfect CSV file, handles missing/null values (drop, fill, or flag), and produces a cleaned, normalized JSON summary output.
Create a Python REST API endpoint with email validation, error handling, and comprehensive unit tests
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