- One secret to becoming a great software engineer: read code – become a better programmer by building a routine and habit for reading code.
- Why software projects take longer than you think
– Poor estimation of how long a software project will take “is really just
a statistical artifact. Let’s say you estimate a project to take 1 week.
Let’s say there are three equally likely outcomes: either it takes
^{1}⁄_{2}week, or 1 week, or 2 weeks. The median outcome is actually the same as the estimate: 1 week, but the mean (aka average, aka expected value) is^{7}⁄_{6}= 1.17 weeks. The estimate is actually calibrated (unbiased) for the median (which is 1), but not for the mean.” Erik Bernhardsson explains (with visualizations). - A visual introduction to dynamic programming – And here are some visualizations from Avik Das that shed light on dynamic programming—a technique that allows you to efficiently solve recursive problems with a highly overlapping subproblem structure.
- A full Python data science stack in the browser – A few weeks ago, we mentioned Iodide science experimentation and communication, based on state-of-the-art web technologies. Unfortunately, there’s a slight issue with the tool’s premise: at the moment, “JavaScript doesn’t have a mature suite of data science libraries, and it’s missing a number of features that are useful for numerical computing.” Now Mozilla has released Pyodide—an experimental project that “gives you a full, standard Python interpreter that runs entirely in the browser, with full access to the browser’s Web APIs.” Check out the introductory blog post for more details and to learn how to get started.

- A beginner’s guide to building DevOps pipelines with open source tools – A five-step crash course to getting started, courtesy of Bryant Son.
- Dockerize the multi-services application for local development – The handy local development containerization guide, with examples of the Amplifr project dockerization.

**tail -f /dev/misc**

So lovely to see the list of Python software used for yesterday's M87 black hole image. Scipy, Numpy, Astropy, Pandas, Jupyter! https://t.co/eyTTOtu9bH

— Andrew Godwin (@andrewgodwin) April 11, 2019