Remote work is fantastic. You avoid a commute, have control over
your work environment, and save money on lunches. However, it has
downsides. You need a fast internet connection, you must be
disciplined, over communicate and stay on task. You have to be OK
with relative solitude.
Storing your secrets in plain text is Bad Idea whether it’s your
Vogon Poetry or your
e-mail credentials. Depending on your level of paranoia (it’s not
paranoia if they really are watching you…) the advice in this
article may not be enough; but for most, though, keeping your
secrets encrypted on your file system is an easy way of keeping your
Emacs, obviously, can do this for you, and in a variety of ways.
If you’ve been following me for any amount of time, you know that I
regularly publish Python code snippets for everyday
I figured I’d finally aggregate all those responses in one massive
article with links to all those resources.
In this essay, I provide some advice to up-and-coming researchers in
machine learning (ML), based on my experience doing research and
advising others. The advice covers how to choose problems and
organize your time. I also recommend the following prior essays on
In the morning I finished Iron
the fourth book in the Red Rising series by Pierce Brown. I like this
book a lot, especially the four different story lines. Of the four
books I've read in this series, so far, I like the first the most, and
Iron Gold comes on a solid second place. Highly recommended.
A decade ago Darrow led a revolution, and laid the foundations for a
new world. Now he’s an outlaw.
Cast out of the very Republic he founded, with half his fleet
destroyed, he wages a rogue war on Mercury. Outnumbered and
outgunned, is he still the hero who broke the chains? Or will he
become the very evil he fought to destroy?
In the evening I started in Dark
the fifth book in the Red Rising series by Pierce Brown.
Probability problems come up a lot in systems programming, and I’m
using that term loosely to mean everything from operating systems
programming and networking, to building large online services, to
creating virtual worlds like in games. Here’s a bunch of
rough-and-ready probability rules of thumb that are deeply related
and have many practical applications when designing systems.
Closures are an increasingly important part of Swift, both in terms
of the overall direction of the language itself, and when it comes
to the ways that both Apple and third party developers design
libraries and APIs using it. However, closures also come with a
certain set of complexities and behaviors that at first can be quite
difficult to fully grasp — especially when it comes to how they
capture values and objects from their surrounding context in order
to perform their work.
Every piece of code we write is unique, or pretty much. However,
there are things that are common in a lot of code, even across
various codebases, and even across various languages: the physical
shape that code has.
Beyond the mere visual aspect of code, the shape of a piece of code
can carry information by itself. Being able to decipher this
information allows to glean indications about the code at a glance,
even before starting to read it. This information is valuable in
itself, and afterwards during the reading of the code itself.