Issue #293

tl;dr: (1) Don't swerve around a debate. (2) Be generous with your ideas. (3) Think of yourself as the team captain, not the head coach. (4) Set the tone with cross-functional partners. (5) Write down what makes you tick. (6) Shine a light on failure. (7) Pull back the curtain. And more.

tl;dr: A blueprint for assessing and advancing your organization’s DevSecOps practices to detect vulnerabilities and deliver digital services with more confidence.

Promoted by Datadog.

tl;dr: What is a low-friction small thing that most will not notice, but that when they do, is a sign of craftsmanship, expertise, and pride in one's work? Vicki has created a list relevant for ML and Data Science: (1) Python code has type annotations. (2) Accurate documentation of a repo and an easy, reproducible way to run the project. (3) Formatted and linted SQL statements. And more.

tl;dr: The ML model takes into account spatial and temporal features, such as the origin, destination and time of the request, as well information about real-time traffic and the nature of the request, such as whether it is a delivery dropoff or rideshare pickup."

tl;dr: "If you agree with the notion that you need to be able to tell the difference between the absence of a value and a value itself, then this has some impacts on the code you write. If you want to keep 'em separated (so they can come out and play), then it gets you looking at your API designs in certain ways." The author shares her reactions to some of the ways a value can be returned.