Obsidian might be the note-taking app we’ve been waiting for.
It wants to be your second brain. It lets you capture your ideas and take notes in text (Markdown) files (also check out this collaboration app that has built-in Markdown team wiki), and most importantly, it easily connects your notes and visualizes connections among them. It’s perfect for discovering insights from your notes. It’s also free, so you have little to lose if you try it. And if you use Alfred on macOS, check out my Alfred Obsidian workflow.
It’s made me rethink why we take notes and completely changed…
Do you feel overwhelmed by all the collaboration apps you have to use and switch between while you work?
If so, check out AirSend. It lets you chat with your team in channels, have video calls, share and organize files, track tasks, and keep your projects on course, all in one place. It makes collaboration seamless and effortless because you won’t have to constantly shuffle between apps while you work with your team and clients.
AirSend is a relatively new app and I find its approach to project management is very interesting. …
Have you ever asked yourself what’s the point of taking notes? If you think it’s to remember, read on, because it’s a common misconception.
Like many things in life, taking notes has become so customary and ingrained in our routines that we’ve stopped reflecting on why we do so.
It’s surprising how little time we spend thinking about why we take notes, when developers regularly create new apps that aim to improve how we take notes. If you’re interested in the how, check out my post below:
Why do we take notes? How should we take notes?
I’ve been thinking quite a lot about note-taking lately. I debated with friends, did some research, and this post summarizes what I’ve learned about the second question: How should we take notes? (See also my post on how to learn.)
If you’re interested in why we take notes, check out my post below:
Below, I’ll describe how an app, Obsidian, a sociologist, neuroscience, and artificial intelligence (AI) have changed the way I think about note-taking.
How do you reshape a dataframe from wide to long form in R? How does the
melt() function reshape dataframes in R? This tutorial will walk you through reshaping dataframes using the
melt function in R.
If you’re reshaping dataframes or arrays in Python, check out my tutorials below.
Common terms for this wide-to-long transformation are melt, pivot-long, unpivot, gather, stack, and reshape. Many functions have been written to convert data from wide to long form,
melt() from the
data.table library is the best. See
melt() documentation here. Why?
meltfunction/method that works…
Vim stands for Visual Improved, an editor originally created for Unix.
You can make complex edits very quickly. Check out my surround.vim post to get a sense of its power.
If you know how to touch-type, using Vim means you can keep your hands on the home row keys and not have to constantly move your hands to the arrow keys, trackpad, or mouse.
It’s installed on almost every system, whereas other editors like Emacs and Nano might not be available. It’s cross-platform (works with Windows, macOS, and Linux). The default editor in most systems is Vim, so it’s useful…
I recently started learning Vim and it was difficult, especially during the first few days — my productivity took a real hit. I could barely write any code in Visual Studio Code without feeling a pain in my head. To learn more about my 15-day journey, check out this post:
Do you think you’re good at using willpower to resist temptations and distractions? If so, take on this willpower challenge while you continue reading: Do not think of a white bear.
We usually think of willpower as an ability that helps us inhibit and suppress thoughts, temptations, negative emotions, and distractions.
Given how frequently we’re tempted or distracted by events every day, it seems like willpower is key to productivity and a good life. (Also, check out Obsidian, a powerful and free note-taking app that will help you take better notes and increase your productivity and thinking).
Face masks are becoming the new normal. Instead of buying disposable ones, you can easily make one yourself— with just a sock and a pair of scissors.
Remember, even if you wear one, remember to maintain social distance and avoid speaking when you’re around other people so you minimize the spread of fluids.
Homemade face masks don’t require a sewing machine or elastic (or rubber bands, string, cloth strips, or hair ties) to hook or tie the mask to your ears or head.
Python’s statsmodels and scipy libraries are incredible. But when it comes to performing simple but the most widely-used statistical tests like the t-test, analysis of variance (ANOVA), and regression, these two libraries seem to do too much and little at the same time.
Statsmodels is powerful but its output is an overkill and difficult to parse for beginners. Scipy.stats, however, is easier to use but provides output that’s somewhat lacking (e.g., only test statistic and probability value).
Is there something in between — a library that provides simple yet exhaustive outputs for the most common statistical tests?