Measuring how much of the sky is visible
At a given place, how much of the sky is visible? Here's a way to figure out an estimate.
The important, mundane parts of data science plus assorted fun with hobbies and tech. Every week since 2019.
At a given place, how much of the sky is visible? Here's a way to figure out an estimate.
LLMs and "AI" are at the forefront of making things "faster", but fast does not equal correct despite what many say.
Hi subscribers, I've spent much of this week chipping away at the worldgen game in the small gaps of free time that I have, so here's an update on this bit of silliness. It's gotten to a surprisingly interesting place even though there'
Between advanced math and radio sensors, it's possible to locate where lightning strikes from hundreds to thousands of kilometers away. It's wild.
Consumer data science is hallmarked with massive datasets and other challenges. Meanwhile, enterprise products show a very different kind of challenge that I love.
Some post travel reflections on urban infrastructure and age and how changes happen to infrastructure
The weird ins and outs of procedurally generating a literal world for simulation purposes
So many details
Nothing says "ready for vacation" like... frantically booking hotels and stuff last minute because I've been too busy with stuff to finalize pesky details like where I'll be sleeping. Anyways, I'll be out and about roughly 3 weeks with nothing but my
Far from the best way, but it's probably the way many of us are choosing for... reasons
Out of all the things we can do, so far, working with deep, buried assumptions is probably the hardest to learn and teach
Many would describe setting metrics as a art and a science. This is about the artsy part.
This week, in between a bunch of other chaos, I got to do something that only happens once a decade... I bought a new Network Attached Storage (NAS) server (a Synology DS1525+) to replace my older DS1315. Between the DRAM shortage and hard disk shortages, it wasn't a
Stories of how a simple thing can get really messy.
It's not just the "blessed" few who get to work on novel data problems. Every one of us likely does so at least a couple of times in our careers. We just don't realize it.