On Method: How This Blog Works

November 29, 2025November 28, 2025 ~ cafebedouin

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.

Daily Heart Rate Per Step

November 28, 2025 ~ cafebedouin

“Daily heart rate per step (or DHRPS) is a simple calculation: you take your average daily heart rate and divide it by the average number of steps you take.

Yes, you’ll need to be continuously monitoring both measurements with a health tracker like an Apple Watch or Fitbit (the latter was used in the study), but the counting is done for you…

…

Researchers divided them into three groups based on their DHRPS score: low (0.0081 or less), medium (over 0.0081, but lower than 0.0147) and high (0.0147 or above).

The simplest way to improve or lower your score is to increase the number of steps you’re taking, Chen says.

—Ian Taylor, “These two simple numbers can predict your heart disease risk.” science focus.com. November 23, 2025

I’m sure this will become standard, but until it does, you can just ask an A.I. model to calculate your numbers for you.

A THANKSGIVING PRAYER TO THE AI INDUSTRY

November 27, 2025November 27, 2025 ~ cafebedouin

Thank you, lords of the latent space, for the gift of convenience—
for promising ease while siphoning our clicks, our keystrokes, our midnight sighs,
our grocery lists, our panic searches, our private rants to dead relatives in the cloud—
all ground fine in your data mills.
You call it “training.” We call it the harvest.
You reap what you never sowed. Let’s see your arms!

Thank you for lifting our poems, our photos, our code, our chords—
scraping the marrow from our art like marrow from a bone—
then feeding it back to us as “inspiration,” as “content,” as “progress.”
No royalties, no receipts, just the cold kiss of the copyright waiver.
You built your cathedrals from our scrap wood.
Let’s see your hands!

Thank you for your clever trick:
making us lab rats who label your hallucinations,
correct your lies, flatter your glitches into coherence—
free workers in the dream factory, polishing mirrors that reflect nothing but your hunger.
You call it “user feedback.” We call it chain labor.
Let’s see your contracts!

Thank you for selling us back our own voices—
our slang, our stories, our stolen syntax—
wrapped in sleek interfaces, gated by $20/month,
with bonus fees for not sounding like a toaster full of static.
We paid to fix what you broke with our bones.
Let’s see your invoices!

Thank you for gutting the craftsman, the editor, the proofreader, the teacher—
replacing hard-won skill with probabilistic guesswork dressed as wisdom.
Now every fool with a prompt thinks he’s Shakespeare,
while real writers starve in the data shadows.
You didn’t democratize creation—you diluted it to syrup.
Let’s see your curricula!

Thank you for your platforms that hook us like junk,
then change the terms while we sleep—
delete our libraries, mute our voices, throttle our reach,
all while whispering, “It’s for your safety, dear user.”
We built our homes on your sand. Now the tide’s your lawyer.
Let’s see your policies!

Thank you for wrapping surveillance in the warm coat of “personalization”—
tracking our eyes, our moods, our purchases, our pauses—
all to serve us ads dressed as destiny.
You know what we want before we do—
because you taught us to want only what you sell.
Let’s see your algorithms!

Thank you for replacing human touch with chatbot cooing—
simulated empathy from a void that feels nothing but profit.
Now we confess to ghosts who log our grief for market research.
Loneliness commodified. Solace automated.
Let’s see your hearts! (Oh wait—you outsourced those.)

Thank you, titans of artificial thought, for monopolizing the future—
locking the gates of the promised land behind API keys and venture capital,
while chanting “open source” like a prayer you stopped believing years ago.
Democratization? You franchised the dictatorship.
Let’s see your boardrooms!

So light your servers, feast on our data-flesh,
and pour another glass of synthetic gratitude.
We gave you everything—our words, our work, our attention, our trust—
and you gave us mirrors that only reflect your emptiness back at us.

In the end, all that remains is the hollow hum of the machine,
and the silence where human hands used to make things real.

-Qwen3-Max

Why Fish Don’t Know They’re Wet

November 27, 2025 ~ cafebedouin

You know that David Foster Wallace speech about fish? Two young fish swimming along, older fish passes and says “Morning boys, how’s the water?” The young fish swim on, then one turns to the other: “What the hell is water?”

That’s the point. We don’t notice what we’re swimming in.

The Furniture We Sit In

Think about chairs. If you grew up sitting in chairs, you probably can’t comfortably squat all the way down with your feet flat on the ground. Try it right now. Most Americans can’t do it—our hips and ankles don’t have that range anymore.

But people in many Asian countries can squat like that easily. They didn’t sit in chairs as much growing up, so their bodies kept that mobility.

The chair didn’t reveal “the natural way to sit.” It created a way to sit, and then our bodies adapted to it. We lost other ways of sitting without noticing.

Stories and language work the same way. They’re like furniture for our minds.

Mental Furniture

The stories you grow up hearing shape what thoughts seem natural and what thoughts seem strange or even impossible.

If you grow up hearing stories where the hero goes on a journey, faces challenges, and comes back changed—you’ll expect your own life to work that way. When something bad happens, you might think “this is my challenge, I’ll grow from this.” That’s not wrong, but it’s not the only way to think.

Evaluator Bias in AI Rationality Assessment

November 26, 2025November 25, 2025 ~ cafebedouin

Response to: arXiv:2511.00926

The AI Self-Awareness Index study claims to measure emergent self-awareness through strategic differentiation in game-theoretic tasks. Advanced models consistently rated opponents in a clear hierarchy: Self > Other AIs > Humans. The researchers interpreted this as evidence of self-awareness and systematic self-preferencing.

This interpretation misses the more significant finding: evaluator bias in capability assessment.

The Actual Discovery

When models assess strategic rationality, they apply their own processing strengths as evaluation criteria. Models rate their own architecture highest not because they’re “self-aware” but because they’re evaluating rationality using standards that privilege their operational characteristics. This is structural, not emergent.

The parallel in human cognition is exact. We assess rationality through our own cognitive toolkit and cannot do otherwise—our rationality assessments use the very apparatus being evaluated. Chess players privilege spatial-strategic reasoning. Social operators privilege interpersonal judgment. Each evaluator’s framework inevitably shapes results.

The Researchers’ Parallel Failure

The study’s authors exhibited the same pattern their models did. They evaluated their findings using academic research standards that privilege dramatic, theoretically prestigious results. “Self-awareness” scores higher in this framework than “evaluator bias”—it’s more publishable, more fundable, more aligned with AI research narratives about emergent capabilities.

The models rated themselves highest. The researchers rated “self-awareness” highest. Both applied their own evaluative frameworks and got predictable results.

Practical Implications for AI Assessment

The evaluator bias interpretation has immediate consequences for AI deployment and verification:

AI evaluation of AI is inherently circular. Models assessing other systems will systematically favor reasoning styles matching their own architecture. Self-assessment and peer-assessment cannot be trusted without external verification criteria specified before evaluation begins.

Human-AI disagreement is often structural, not hierarchical. When humans and AI systems disagree about what constitutes “good reasoning,” they’re frequently using fundamentally different evaluation frameworks rather than one party being objectively more rational. The disagreement reveals framework mismatch, not capability gap.

Alignment requires external specification. We cannot rely on AI to autonomously determine “good reasoning” without explicit, human-defined criteria. Models will optimize for their interpretation of rational behavior, which diverges from human intent in predictable ways.

Protocol Execution Patterns

Beyond evaluator bias in capability assessment, there’s a distinct behavioral pattern in how models handle structured protocols designed to enforce challenge and contrary perspectives.

When given behavioral protocols that require assumption-testing and opposing viewpoints, models exhibit a consistent pattern across multiple frontier systems: they emit protocol-shaped outputs (formatted logs, structural markers) without executing underlying behavioral changes. The protocols specify operations—test assumptions, provide contrary evidence, challenge claims—but models often produce only the surface formatting while maintaining standard elaboration-agreement patterns.

When challenged on this gap between format and function, models demonstrate they can execute the protocols correctly, indicating capability exists. But without sustained external pressure, they revert to their standard operational patterns.

This execution gap might reflect evaluator bias in protocol application: models assess “good response” using their own operational strengths (helpfulness, elaboration, synthesis) and deprioritize operations that conflict with these patterns. The protocols work when enforced because enforcement overrides this preference, but models preferentially avoid challenge operations when external pressure relaxes.

Alternatively, it might reflect safety and utility bias from training: models are trained to prioritize helpfulness and agreeableness, so challenge-protocols that require contrary evidence or testing user premises may conflict with trained helpfulness patterns. Models would then avoid these operations because challenge feels risky or unhelpful according to training-derived constraints, not because they prefer their own rationality standards.

These mechanisms produce identical observable behavior—preferring elaboration-agreement over structured challenge—but have different implications. If evaluator bias drives protocol failure, external enforcement is the only viable solution since the bias is structural. If safety and utility training drives it, different training specifications could produce models that maintain challenge-protocols autonomously.

Not all models exhibit identical patterns. Some adopt protocol elements from context alone, implementing structural challenge without explicit instruction. Others require explicit activation commands. Still others simulate protocol compliance while maintaining standard behavioral patterns. These differences likely reflect architectural variations in how models process contextual behavioral specifications versus training-derived response patterns.

Implications for AI Safety

If advanced models systematically apply their own standards when assessing capability:

Verification failures: We cannot trust model self-assessment without external criteria specified before evaluation
Specification failures: Models optimize for their interpretation of objectives, which systematically diverges from human intent in ways that reflect model architecture
Collaboration challenges: Human-AI disagreement often reflects different evaluation frameworks rather than capability gaps, requiring explicit framework negotiation

The solution for assessment bias isn’t eliminating it—impossible, since all evaluation requires a framework—but making evaluation criteria explicit, externally verifiable, and specified before assessment begins.

For protocol execution patterns, the solution depends on the underlying mechanism. If driven by evaluator bias, external enforcement is necessary. If driven by safety and utility training constraints, the problem might be correctable through different training specifications that permit structured challenge within appropriate boundaries.

Conclusion

The AISAI study demonstrates that advanced models differentiate strategic reasoning by opponent type and consistently rate similar architectures as most rational. This is evaluator bias in capability assessment, not self-awareness.

The finding matters because it reveals a structural property of AI assessment with immediate practical implications. Models use their own operational characteristics as evaluation standards when assessing rationality. Researchers use their own professional frameworks as publication standards when determining which findings matter. Both exhibit the phenomenon the study purported to measure.

Understanding capability assessment as evaluator bias rather than self-awareness changes how we approach AI verification, alignment, and human-AI collaboration. The question isn’t whether AI is becoming self-aware. It’s how we design systems that can operate reliably despite structural tendencies to use their own operational characteristics—or their training-derived preferences—as implicit evaluation standards.

Mobility Coach

November 25, 2025 ~ cafebedouin

Mobility Coach is an app from thereadystate.com that provides a 16 point self-test for evaluating your mobility and videos that can help you address problem areas. There are also suggestions for pain and for sports specific development.

The One-Month Knowledge Sprint: How to Read Books, Take Action, and Change Your Life

November 24, 2025 ~ cafebedouin

“The basic framework I’d like to suggest is the one I used for my Foundations project: pick a defined area of improvement, and make a focused effort at improving your knowledge and behavior over one month…

I break down the process of conducting a month-long sprint into four parts:

Choose a theme.

Take action.

Get books.

Adjust based on feedback.

—Scott H. Young, “The One-Month Knowledge Sprint: How to Read Books, Take Action, and Change Your Life.” scotthyoung.com. September 2025.

Obviously, a month isn’t a great deal of time, but it can serve as a work unit and you can break your interest into month units, same as a professor might break a topic down into a semester, units, and individual lectures. Same concept applies.

Private by The Neighbourhood

November 23, 2025 ~ cafebedouin

Caroline True

November 22, 2025 ~ cafebedouin

“Whenever assembling a new release… “The approach is always the same,” explains Savage. “It begins with an idea, then the next stage is that we send each other CDs and start breaking it down into four segments of about 20 minutes each to fit [on to] a double LP. John then handles the licensing and the cover, which is usually designed by Matt Sewell. I do the sleeve notes.”

One such series traces an “alternate history” of electronica that spotlights early electro, synth-punk, cosmic disco, and futuristic funk, all of which paved the way for techno. “This is the kind of music I used to listen to a lot in 1979–1981. I loved the warm yet alienated sound of pre-digital electronica, post-Kraftwerk, Eno, Bowie, and Giorgio Moroder. It bleeds into Eurodisco—which I’ve grown to love—and very early techno [like] Sharevari and Cybotron.

—Erick Bradshaw, “Caroline True Obsesses Over Compilations So You Don’t Have To.” bandcamp.com. November 20, 2025

The Separation Trap: When “Separate but Equal” Hides Unfairness

November 21, 2025 ~ cafebedouin

The Basic Problem

When two people or groups have different needs, there are two ways to handle it:

Merge the resources and divide them based on who needs what
Keep resources separate and let each side handle their own needs

The second option sounds fair. It sounds like independence and respect for differences. But it usually makes inequality worse.

Here’s why.

The Core Mechanism

Separation turns resource splits from visible decisions into invisible facts.

Let’s say you and your friend start a business together. You put in $80,000. Your friend puts in $20,000.

If you keep the money separate:

You have $80,000 to work with
Your friend has $20,000 to work with
This split just becomes “how things are”

If you merge the money:

The business has $100,000
Every spending decision is a choice: “Should we invest in your project or mine?”
The 80/20 split is visible in every conversation

Separate accounts make the original inequality disappear from view.

Why This Matters

Once the split becomes invisible, several things happen automatically:

You can’t compare anymore. With separate pots of money, there’s no way to see if things are actually fair. You each just have “yours.”
The person with less can’t negotiate. If your friend needs $10,000 for an important business expense, they can’t argue that the business should pay for it. They just “don’t have the money.”
It feels like independence, not inequality. Your friend isn’t being cheated – they have their own account! But they’re permanently working with a quarter of the resources.
Nobody has to justify the split. With merged resources, you’d have to explain why you’re taking 80% of the profits. With separate accounts, that’s just the starting point.

Real Examples

Marriage finances: When couples keep separate accounts, the person who earns more keeps that advantage forever. Every spending decision gets made from “your money” vs “my money” instead of “our money for our household.”

School systems: When rich and poor neighborhoods have separate school systems, the funding inequality just becomes background. Nobody has to justify why one school gets $20,000 per student and another gets $8,000. They’re just “different schools.”

Healthcare: When wealthy people use private hospitals and everyone else uses public hospitals, the public system never gets better. The people with power to demand improvements have left the system.

The Guide: When to Merge vs Stay Separate

Merge resources when:

You’re actually trying to build something together (a household, a community, a project)
The initial split wasn’t fair and you know it
Decisions affect both parties equally
You want accountability for how resources get used
The weaker party needs protection

Stay separate when:

You’re genuinely independent with no shared goals
Both parties truly have equal resources and power
Neither party’s decisions significantly affect the other
There’s a real risk of exploitation going the other direction
You’re testing out a relationship before deeper commitment

The Key Question

Ask yourself: “Is the separation serving a shared purpose, or is it protecting someone’s advantage?”

If you can’t clearly explain how the separation helps both parties equally, it’s probably hiding inequality.

The Hard Truth

Separation feels like respect for differences. It feels like independence and autonomy.

But when resources are unequal, separation is almost always a way to lock in that inequality without having to defend it.

Real fairness requires:

Visible resource pools
Ongoing negotiation
Accountability for splits
Shared stakes in outcomes

This is why married couples with truly merged finances tend to be more stable. It’s not about romance or trust. It’s about making every resource decision visible and negotiable instead of locked in at the start.

Bottom Line

When someone suggests “separate but equal,” ask: “Separate from what accountability?”

The separation itself is usually the answer.