YAML: Probably no so great after all
Article that details some of the problems with YAML
MySQL and storing emoji: proper utf8mb4 with Django | exana.io
The MySQL unicode issue as seen from a Django point of view.
How to support full Unicode in MySQL databases · Mathias Bynens
Bloody MySQL doesn't support full unicode by default. O_o 💩
Projectile ignoring .projectile files : emacs
Helpful comment on configuring projectile so that it finds a project under strict control. I want to be fully in control of what appears to be a project, and this is what I needed.
EmacsConf - Main Page
The Emacs conference. The 2019 conference is online.
Emacs users are like Igor
An amusing description of your average Emacs user.
How can I use Django permissions without defining a content type or model? - Stack Overflow
Nice answer to the question: how do I make a permission that I can assign to users and groups, in Django, without needing to create a model in the database.
Fish shell support · Issue #32 · pyenv/pyenv
Issue regarding adding Fish support to pyenv. A very old issue now, but useful in that it details how to configure your shell and why you do it the way you do.
RaGOO/setup.py at master · malonge/RaGOO
Setup for a Python tool I found on GitHub, which is a library and also scripts. This might serve as a good template for pydscheck, as I'd like to get it properly packaged.
Statistics with Julia
PDF detailing using Julia for data stats.
Brian Lonsdorf: Hey Underscore, You're Doing It Wrong! – FunctionalTalks.org
Talk about things to do with JavaScript, functional programming, etc.
Python consumes a lot of memory or how to reduce the size of objects? / Habr
Interesting article about the size of data structures in Python.
Kivy: Cross-platform Python Framework for NUI Development
Library that provides a cross-platform application framework for Python. Works with many operating systems.
“Perl 6 is Cursed! I hate it!” | Aearnus’ Realm
File under "does Poe's law apply to programming languages?"
Agile is Literally Meaningless
Short blog post that nicely expresses how I feel any time anyone invokes Agile at me.
Matlab vs. Julia vs. Python | Toby Driscoll
Nice article comparing Matlab, Python and Julia. Not sure I 100% agree with it all, but it made for interesting reading.
User Inyerface - A worst-practice UI experiment
Really rather funny exploration of some of the worst design elements you can find on the Web.
The Pitfalls of Boolean Trap
Good little article about the pitfalls of using boolean values as parameters for a function. Not the first time I've seen this sort of thing (I'm sure I read a similar article years before, and a similar thing in a coding book even earlier).
Still good advice.
At least it's a little better in languages with keyword arguments.
Building Standalone Python Applications with PyOxidizer
Blog post about a newly-released project that aims to make it easier to build and deploy standalone Python applications.
No Ageism In Tech
Website dedicated to fighting agism in the tech industry, and to promoting companies and positions that are open to older developers.
Towards a Cleaner Emacs Build – Random Thoughts
Blog post about the cleaning up of lots of warnings in the compilation of Elisp code in Emacs itself.
What happens when software developers are (un)happy | Elsevier Enhanced Reader
The growing literature on affect among software developers mostly reports on the linkage between happiness, software quality, and developer productivity. Understanding happiness and unhappiness in all its components – positive and negative emotions and moods – is an attractive and important endeavor. Scholars in industrial and organizational psychology have suggested that understanding happiness and unhappiness could lead to cost-effective ways of enhancing working conditions, job performance, and to limiting the occurrence of psychological disorders. Our comprehension of the consequences of (un)happiness among developers is still too shallow, being mainly expressed in terms of development productivity and software quality. In this paper, we study what happens when developers are happy and unhappy while developing software. Qualitative data analysis of responses given by 317 questionnaire participants identified 42 consequences of unhappiness and 32 of happiness. We found consequences of happiness and unhappiness that are beneficial and detrimental for developers’ mental well-being, the software development process, and the produced artifacts. Our classification scheme, available as open data enables new happiness research opportunities of cause-effect type, and it can act as a guideline for practitioners for identifying damaging effects of unhappiness and for fostering happiness on the job.
The reduce ({...spread}) anti-pattern - RichSnapp.com
Interesting article about the potential pitfalls of using reduce and spread for cool JavaScript one-liners. I don't think I'm in the habit of using this particular pattern, but I should probably keep an eye out for it.
Benchling API
Documentation for the Benchling API.
Use __main__.py in Python | shaneoneill.io
For some reason I'd not picked up on this since getting back into Python. Could be seriously handy.
A Failed Experiment with Python Type Annotations – Musing Mortoray
Blog post that does a good job of outlining some of the issues I have too with type checking in Python.
See the following for some correction of what's said: https://news.ycombinator.com/item?id=20159153
Beyond appendChild: Better convenience methods for HTML - DEV Community
Short article about some handy DOM manipulation methods and idioms in JavaScript.
BADA55.io - CSS hex color words for web developers
Amusing, and not always sfw, resource for colours with "interesting" names.
Rust Your Own Lisp
A translation of Build Your Own Lisp into Rust. Should be a handy read to get to know some Rust concepts.
Software Engineering for Machine Learning: A Case Study
Abstract—Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services. This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges. In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components — models may be “entangled” in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable to other organizations.