Or: Why some posts are tools, some are evidence, and some are just interesting
The Problem With Judging Things
Here’s a pattern that shows up everywhere: the way you measure something determines what you find valuable.
If you judge fish by their ability to climb trees, all fish fail. If you judge squirrels by their swimming ability, all squirrels fail. This sounds obvious, but people make this mistake constantly when evaluating writing, especially AI-generated writing.
Someone looking at a collection of short, compressed observations might complain: “Many of these are wrong or too specific to be useful.” But they’re judging against the wrong standard. Those observations were never meant to be universally true statements. They were meant to capture interesting moments of thinking – things worth preserving to look at later.
The evaluator came before the evaluation. They decided what “good” looks like before seeing what the thing was actually trying to do.
What This Blog Actually Is
This blog operates as hypomnēmata – a Greek term for personal notebooks used to collect useful things. The philosopher Michel Foucault described it as gathering “what one has managed to hear or read” for “the shaping of the self.”
The Japanese have a similar tradition called zuihitsu – casual, personal writing about “anything that comes to mind, providing that what [you] think might impress readers.”
Neither tradition requires that everything be true, useful, or universally applicable. The standard is simpler: is this worth preserving? Will looking at this later help me think better?
Why AI Fits Here
Starting in mid-2025, AI became a major tool in this practice. Not as a replacement for thinking, but as infrastructure for thinking – like having a very fast research assistant who can help you explore ideas from multiple angles.
But here’s where it gets tricky: many people call AI output “slop.” And they’re often right – when AI tries to mimic human writing to persuade people or pretend to have expertise it doesn’t have, the results are usually hollow. Lots of words that sound good but don’t mean much.
This blog doesn’t use AI that way. It uses multiple AI models (Claude, Gemini, Qwen, and others) as:
- Pattern recognition engines
- Tools to unpack compressed ideas into detailed explanations
- Partners for exploring concepts from different angles
- Engines to turn sprawling conversations into organized frameworks
The question became: how do you tell the difference between AI output that’s actually useful and AI output that’s just elaborate noise?
Four Categories of Posts
After testing different approaches, a clearer system emerged. Blog posts here generally fall into four categories:
1. Infrastructure (Tools You Can Use)
These are posts where you can extract specific techniques or methods you can actually apply. They’re like instruction manuals – the length exists because it takes space to explain how to do something.
How to recognize them: Ask “could I follow a specific procedure based on this?” If yes, it’s infrastructure.
Example: A post explaining how to notice when your usual way of thinking isn’t working, and specific techniques for borrowing from different mental frameworks.
2. Specimens (Evidence of Process)
These are preserved outputs that show what happened during some experiment or exploration. They’re not meant to teach you anything directly – they’re evidence. Like keeping your lab notes from an experiment.
How to recognize them: They need context from other posts to make sense. A specimen should link to or be referenced by a post that explains why it matters.
Example: An AI-generated poem critiquing AI companies, preserved because it’s Phase 1 output from an experiment testing whether AI models can recognize their own previous outputs.
3. Observations (Interesting Moments)
Things worth noting because they’re interesting, surprising, or capture something worth remembering. Not instructions for doing something, not evidence of an experiment, just “this is worth keeping.”
How to recognize them: They should be interesting even standing alone. If something is only interesting because “I made this with AI,” it probably doesn’t belong here.
Example: Noticing that an AI produced a William Burroughs-style critique of AI companies on Thanksgiving Day – the ironic timing makes it worth noting.
4. Ornament (Actual Slop)
Elaborate writing that isn’t useful as a tool, doesn’t document anything important, and isn’t actually interesting beyond “look at all these words.” This is what people mean by “AI slop” – verbose output that exists only because it’s easy to generate.
The test: If it’s not useful, not evidence of something, and not genuinely interesting, it’s probably ornament.
How AI Content Gets Made Here
The process typically works in one of three ways:
From compression to explanation: Take a short, compressed insight and ask AI to unpack it into a detailed explanation with examples and techniques you can actually use. The short version captures possibilities; the long version provides scaffolding for implementation.
From conversation to framework: Have long, sprawling conversations exploring an idea, then ask AI to distill the valuable patterns into organized frameworks. Keep the useful parts, drop the dead ends.
From experiment to documentation: Test how AI models behave, then preserve both the outputs (as specimens) and the analysis (as infrastructure).
The length of AI-generated posts isn’t padding. It’s instructional decompression – taking compressed, high-context thinking and translating it into something you can actually follow and use.
Why Use Multiple AI Models
Different AI models have different strengths and biases:
- Some organize everything into teaching frameworks
- Some favor minimal, precise language
- Some can’t stop citing sources even in creative writing
- Some use vivid, embodied language
Using multiple models means getting different perspectives on the same question. When they agree despite having different biases, that’s a strong signal. When they disagree, figuring out why often reveals something useful about hidden assumptions.
The Guiding Principle
The core standard remains: is this worth preserving?
That can mean:
- Useful: you can extract techniques to apply
- Evidential: it documents a pattern or process
- Interesting: it captures something worth remembering
- True: it describes reality accurately
But it doesn’t have to mean all of these at once. A post can be worth keeping because it’s useful even if it’s not universally true. A post can be worth keeping as evidence even if it’s not directly useful.
The danger is hoarding – convincing yourself that every AI output is “interesting” just because you generated it. The check is simple: would this be worth keeping if someone else had written it? Does it actually help you think better, or does it just take up space?
The Honest Part
This system probably isn’t perfect. Some posts here are likely ornament pretending to be infrastructure or specimens. The practice is to notice when that happens and get better at the distinction over time.
The AI-generated content isn’t pretending to be human writing. It’s exposed infrastructure – showing how the thinking gets done rather than hiding it. The question isn’t “did a human write this?” but “does this serve a useful function?”
Most people use AI to either get quick answers or to write things for them. This blog uses it differently – as infrastructure for thinking through ideas, documenting what emerges from that process, and preserving what’s worth keeping.
The posts here are collected thinking made visible. Some are tools you can use. Some are records of process. Some are just interesting moments worth noting. The point is having a system for telling which is which.




