Tech Insights

<p>Django Anti-Patterns: Signals </p>

<p>how Spotify's squad model can go very wrong if you think it's only an organization chart: </p>

<p>The Risks of Imitating Designs (Even from Successful Companies) </p>

<p>What Is Tail Call Optimization? </p>

<p>Safely install packages with npm or yarn by auditing them as part of your install process: </p>

<p>LGTM, a continuous security analysis. A code analysis platform for finding zero-days and preventing critical vulnerabilities </p>

<p>What is emergent leadership and how to encourage it </p>

<p>Set default node version with NVM </p>

<p>Even though the order of how containers starts can be controlled through <code>depends_on</code> and container order in docker-compose.yml, compose won't really wait until a container is ready before starting the next one. If you don't want to deal with it at application level, there are some other paths you might try: </p>

<p>what is the Hawthorne Effect and how it can affect usability test results </p>

<p>Google has a blog about testing! </p>

<p>How to be strategic </p>

<p>the pros and cons of remote usability testing </p>

<p>physical and emotional aspects of running a remote UX test </p>

<p>Devtools - Track Which Element Has Focus - Very useful when implementing keyboard accessible components </p>

<p>The Wrong Abstraction • <em>duplication is far cheaper than the wrong abstraction</em> • Existing code exerts a powerful influence. Its very presence argues that it is both correct and necessary. We know that code represents effort expended, and we are very motivated to preserve the value of this effort. And, unfortunately, the sad truth is that the more complicated and incomprehensible the code, i.e. the deeper the investment in creating it, the more we feel pressure to retain it (the "sunk cost fallacy").</p>

<p>Keeping Django database migrations backward compatible </p>

<p>Zero downtime database migrations </p>

<p>Goodbye, Clean Code (:eyes:) </p>

<p>The law of large numbers: the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer to the expected value as more trials are performed.</p>

<p>A visual introduction to machine learning: </p>

<p>A list of <code>/uses</code> pages detailing developer setups, gear, software and configs. </p>

<p>Eslint-stats: Analyses JS files for error frequency, rather than location. This is helpful when introducing ESLint to an existing project. </p>

<p>numpy has a really handy function called <code>is_busday</code> to check whether a given date is business day or not, it also can receive a list of parameters to give this functionality a power boost such as weekmask (if you need a custom definition for weekdays) and holiday calendar </p>