A place to start
Measurements need both an operational meaning and a statistical one. Define what a service signal tells you, make retries and job completion safe, and choose probability assumptions that match the data-generating process. These references help turn an observed change into a defensible decision.
Practical guide
A practical observability plan for a small API: meaningful denominators, operation IDs, latency distributions, and actionable alerts.
Read the guide →Practical guide
Design retryable workers that survive duplicate delivery and crashes between database commits and event publication.
Read the guide →Practical guide
Select a probability model by support, data-generating assumptions, and parameter conventions before fitting a familiar curve.
Read the guide →Practical guide
Check assignment, measurement, stopping rules, missingness, and uncertainty before treating a product metric change as causal evidence.
Read the guide →Make cache behavior, retries, and response headers explicit before your endpoint reaches production.
02Ground answers in compact reference data, validate the shape, and measure the failures.
03Choose joins, windows, indexes, and transaction boundaries from the question you need to answer.
04Find files, recover Git work, and write shell commands that handle real filenames.
05Build usable layouts, keyboard interactions, text search, and time handling.
