- Machine Learning Problem Framing
- Data Preparation and Feature Engineering for Machine Learning
- Testing and Debugging in Machine Learning
- Machine Learning Recipes with Josh Gordon
- Machine Learning Glossary
- Rules of Machine Learning
- Tips for Good Data Analysis
- MLOps: Continuous Delivery and Automation Pipelines in Machine Learning
- The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction
- Machine Learning 101
- A Recipe for Training Neural Networks
- The Log: What every software engineer should know about real-time data's unifying abstraction
- The pedantic checklist for changing your data model in a web application
- Keep your Users Happy
- Non-technical security best-practices for open source projects
- Presentation Rules
- PEP 20: The Zen of Python
- Safe ways to do things in Bash
- In Praise of Idleness
- 10 papers every programmer should read (at least twice)
- The first few milliseconds of an HTTPS connection
- The absolute minimum every software developer must know about Unicode
- How to be a programmer: a short, comprehensive, and personal summary
- Julia Evans's blog
- Zach Holman's blog
- Beej's guides
- The relativity of wrong
- Reversing the technical interview
- "Eat, sleep, code, repeat" is such bullshit
- The most precious resource is agency
- On Being a Senior Engineer
- What's a senior engineer's job?
- Building a data team at a mid-stage startup: a short story