Much has been said about AI language models’ role in scientific publishing (see also Mapping the Increasing Use of LLMs in Scientific Papers) and the implications of AI authorship more generally, but it is undeniable that LLM assistance — used properly — can be a force-multiplier for technical writing.

In past years, I’ve enjoyed reading ’50s and ’60s science fiction — books that comically mispredicted the trajectory of science in some ways (lots of talking on corded phones, even if the phones transmit video or holograms), but miraculously bullseyed other aspects:

I just bought an $80 kettle. I will not link to it, because owning a WiFi-enabled kettle makes me want to vomit.

I often write silly little one-hour projects, and I want to put them online for others to enjoy. Importantly, I don’t want these projects to cost me much. (I write way too many one-off projects for that!) So provisioning little virtual machines for each project is a non-starter. And while the right answer is probably to own one virtual machine and have all my projects share tenancy on it, I’ve had a few one-hour projects that actually gained some traction and needed some scalability built in.

I want to share a very peculiar graph with you. Despite its simplicity, this graph actually exhibits some very interesting behavior:

Writing papers is hard! Why not let GPT do it for you?

This will not be a particularly gratifying math story. We’ll get 83% of the way to a fascinating instance of duality, and then swerve away at the last minute, and we’re left with bupkis.

Blender3D is great software, and Animation Nodes (AN) makes it even better, but unfortunately enabling Animation Nodes means that you can’t always render an animation from the Blender GUI. And — more relevantly — Animation Nodes are each individually single-threaded, which means that even if you can render on a GPU or a ton of CPUs, you’re still bottlenecked by the amount of time it takes to compute AN for each frame on a single core.

I recently migrated my personal website over from a “serverful” virtual machine to exclusively use AWS’s serverless offerings. I’ve previously worked pretty extensively with serverless systems — bossDB is a serverless volumetric database I use nearly every day at work, and FitMango is a completely serverless system as well. I’ve been paying $20/mo for my virtual machine that ran matelsky.com since 2014 (up until a week ago, I was still running Ubuntu 14LTS), and I thought it was time for a change.

Wada basins, approximated using spheres and raytracing.

Before I begin; Mike Godwin put this in some much-needed scope:

I made some art.

I wrote some code to automatically map stylized pictures of horrible road intersections from your favorite city. Demo here.

I recently started working on some tables and supplementary materials for an academic paper and found the need to print tables in Markdown format. The syntax is relatively simple, but programmatically generating these tables is tedious, and requires a lot of string-formatting which can get irritating.

The Short Story: I made a web-app that, given some starting text, naively tries to predict what words come next. Because the ‘training’ text was taken from F. Scott Fitzgerald’s Tender is the Night (the first 10 chapters), we can (inaccurately) say that this robot talks like Fitzgerald.

Molecular gastronomy is a neat crossover of chemistry and food science — the sort of thing that you generally scoff at in overpriced Manhattan restaurants. I won’t go into the specifics, since this Wikipedia article does a far better job. But after a brief mojito stint in Los Cabos a few months ago, I decided to embark on a quest to make The Most Interesting Mojito Ever.