Glow | Eyeball Planets | Programmatic Stagnation

Glow | Eyeball Planets | Programmatic Stagnation<br>T. S. RavelingJuly 31, 2026<br>This week, we're going to discuss Glow , a beautiful tool for reading markdown documentation in the terminal, the curious case of eyeball planets in nearby solar systems, and the ways in which widespread LLM usage are likely to lead to programmatic stagnation .

TUI of the Week: Glow.

Made with BubbleTea (the same Go framework I used for GoGo, Furnace, and Dispositio), Glow is a beautiful TUI for browsing markdown documentation.

Glow in the Changelog repo, showing the user browsing into the markdown file containing a previous issue of the Changelog<br>You simply run the tool in e.g. a Github repo, and instantly get a list of all of the markdown files in the repo, searchable, well formatted, and easy to browse. Excellent for reading documentation or just browsing through your Obsidian vault.

Something Interesting : Eyeball Planets.<br>A large number of exoplanets discovered so far are tidally locked to their stars. A subset of these are "eyeball planets": the point directly facing the star (the "pupil") is blasted sterile and molten, the dark side is permanently frozen (potentially with water ice, like Europa). The ring-shaped boundary between them, in a state of permanent sunrise, forms the "iris", and could hold liquid water and potentially even life.

An artist's rendering of an eyeball planet, with the "ring of life" circling the heat-blasted desert center. Image credit: Beau.TheConsortium.

Note : the site in the link above, planetplanet.net, is one of my favorite places on the internet. Check it out.

Systems Thinking: LLMs and Programmatic Stagnation.

Imaginary View of the Grande Galerie in the Louvre in Ruins, by Hubert Robert, 1796. Depicts the Louvre crumbled, roof fallen open to the sky, and people squabbling among the ruins.

LLMs are limited to the words and patterns in their training corpuses, which means that their core function is not generation, but search. But unlike other forms of search, LLMs strip authorship, performing corporate capture on an unprecedented scale. This disincentivizes free human participation in the global information forum, and means we're headed for a hard undertow of stagnation in programming.

Let's walk the reasoning.

1. How LLMS work

An LLM is not an artificial intelligence. Rather, it's a large language model . That means some very complex neural nets have built a "model" (a vast array with thousands of dimensions; massive in size but still possible to render as a multi-terabyte comma-separated text file) that is trained on a "corpus", which is to say the untold terabytes of text scraped off the internet by AI companies (often illegally).

All of that text is turned into tokens, integers that represent meaning. For instance, god is tokenized as 73503, whereas God is 34411; the capitalization has meaning to many religious people. Tokenizers are quite straightforward; you can play with one here to see how they work. The gist is that language gets turned into integers for easier mathing.

A gif of a terminal running a script using ttok to tokenize three strings. Each breaks the strings down into component tokens, showing their integers.

A "vector" is two or more dimensions that together make up an angle. 1,0 in most contexts is equivalent to "right", which might be 0 degrees, depending on your point of reference. 1,0,1 is a three dimensional vector which means "to the right, and away from me in three dimensional space". In normal geometric math we're used to two and three dimensional vectors. But vectors can have any number of dimensions, and any two n-dimensional vecrors can be combined into a single "scalar" value using a dot product.

A scalar measures "how far along the first vector does the second go" (positive if in same direction, negative in opposite direction, zero for something perfectly perpendicular). There's a lot more math to it than this, but the gist is, we can "build a vector" by combining the individual token vectors in a prompt one after the other (0,0 + 0,1 + 1,0 = 1,1), and then doing a dot product comparison against all of the tokens in the database to see which one is "best", with a bit of random noise thrown in there to make it seem less deterministic. Once we find it, we add it to the prompt and start over from zero to get the next token after that, and after that, and so on.

This is next token prediction . It is much more complicated than something like a Markov chain, but it's not not something like that, either.

Neural nets (like all of the nodes in your brain and mine) have the capacity to compose intelligence. An LLM isn't a neural net; it's a very complex statistical model built by a neural net. It is in fact a static mathematical system that could be operated with an abacus, given enough care and patience.

2. LLMs are Synthetic Search

What this means is that an LLM is configured to look at a corpus and...