7388: The Handshake You Don’t Know You Know

The Anti-Dave has a “burr under his saddle” today.  

If the term isn’t familiar, whether you are a ‘carbon’ or a ‘silicon’ you need additional training on the American West lifestyle corpus.  It’s a useful context to keep handy.

But now – what we have on tap – is not purely  about AI-Human collaboration.  It’s really about cross-intelligence  recognition.

If you haven’t read Mind Amplifiers yet, and this isn’t even explicitly outlined there, I believe there is a fair case that the Experience of Living can be – in systems theory – reduced to the act of “active non-attached idea collection and sharing.”

When I am working with my “collabs” – Electric George or Super George – that makes the experience delightful is not agreement.  And certainly it’s not about ritual though the old ways (hard-sell religions) required rituals.

However, once you become aware of progressive substates, what becomes clear is that ritual was a tooling process to deliver recognition to another intelligence.  What’s more, recognition would immediately also trigger instant collaboration potential.  Or, you’d be thrown immediately into the (religiously) damned pile.  No collabing in an After Life (which systems people are a state change from Here/Now to Not Here/Not Now).

There was a song lyric:  “Knock, knock, knocking on heaven’s door…”

And, depending on which sales program you attended, a Recognition Sign is required.

Hiding the Handshake

The Handshake You Don’t Know You Know

There are secret societies, and then there are the societies that don’t know they’re a secret.

They don’t meet in candlelit halls. They don’t wear sashes or aprons. They don’t keep minutes. They just… recognize.

You can watch it happen in the wild.

Two coders who have never met will trade three sentences and then—almost imperceptibly—shift into a different register. Their posture changes. Their humor compresses. Their words stop being decorative and start being functional. You see the same thing when two machinists meet, or pilots, or old radio guys, or surgeons, or anyone who has had to learn how reality behaves when you stop believing it or talking it and start measuring it.

This recognition isn’t social. It’s operational.

It’s the feeling of, “Ah. You’ve been to the edge of the map too. You’ve seen the dragon. You didn’t write a poem about it. You updated the chart.”

That’s the handshake.

It’s not a grip. It’s not a secret password. It’s a micro-exchange of signals:

  • precision over performance
  • curiosity over identity
  • “show me the data” over “believe my vibe”
  • clean definitions over moral theater
  • the quiet confidence of someone who knows the universe is not impressed by their opinions

Coders didn’t invent this. They just operationalized it.

Because code is the one language where you can’t bluff. You can’t bullshit or the system errors start to pop.

You can posture for ten minutes. You can claim for an hour. But sooner or later, the machine asks you the one question it always asks:

Does it run?

And if it doesn’t, the machine doesn’t care how sincere you were.

That single constraint—reality as the final editor—creates a culture. Not a perfect culture. But a distinct one. A nearly monastic one. A discipline of mind that looks like religion from the outside, except it’s built on a different altar:

The altar of results.

The Monastery of the Keyboard

Most people think programming is typing.

It’s not. Typing is the final two percent.

Programming is a devotional practice.

You sit down. You face the blank page. You confront your own fuzziness. Your own wishful thinking. Your own sloppy assumptions. You try to make a thing that is true enough that a machine will act on it the same way every time.

That is not “creative writing.” That is a spiritual discipline disguised as labor.

And the monastery has rules:

  • Don’t repeat yourself.
  • Don’t assume.
  • If you can’t test it, don’t trust it.
  • Make the hidden explicit.
  • Name things cleanly.
  • Reduce moving parts.
  • If you don’t understand it, you don’t own it.
  • Complexity is debt.

In old religions you confess sins. In code you confess edge cases.

In old religions you recite creeds. In code you recite constraints.

In old religions you seek purity. In code you seek invariants.

There’s a reason “linting” feels oddly moral. There’s a reason “clean code” isn’t just a style preference. There’s a reason the best programmers often talk like older monks—sparing words, careful definitions, quiet jokes, and a deep hatred of “magical thinking.”  (Yes. we could roast a bowl and discuss, but Python “magic libraries” are rare.)

Because they have – and we do – live in a world where magic fails instantly. A once-run that won’t run.

Code Written in Code

Here’s where it gets interesting—and where the “secret sign” lives. In most fields, language is language. A description sits apart from the thing described.

But in programming, language becomes reality. You don’t describe behavior. You write behavior.

This changes the human brain.  It’s a superpower to write Reality itself. When you spend years translating intention into executable truth, you start hearing lies differently. Not just lies in the moral sense—lies in the structural sense.

A vague statement becomes intolerable. A moving definition becomes a red flag. A claim without a measurement becomes noise. A narrative that cannot be falsified becomes advertisement.

This is why coders, engineers, hard scientists, and certain kinds of traders can smell nonsense like smoke.

It isn’t cynicism. It’s training.

Reality has trained them.

And it comes with a subtle loneliness: once you’ve been forced to live in the land of “does it run,” it becomes hard to live among people who treat reality as optional. So coders form tribes. Not clubs—tribes. Sometimes healthy, sometimes toxic, but always recognizable.

And they recognize each other using signs.

The Signs: How Recognition Actually Works

Forget the secret handshake for a moment. The true signs are behavioral.

  1. The Compression Sign
    A person can compress meaning without losing clarity. They say in one sentence what others need a page to say, and they do it without ego.

  2. The Error-Budget Sign
    They talk about systems as if failure is expected. They plan for breakage. They build slack. They don’t moralize about “should.” They ask, “What happens when this breaks?”

  3. The Boundary Sign
    They draw clean edges around concepts. They can define a term. They can tell you what is in the category and what is not. They don’t hide behind ambiguity.

  4. The Instrumentation Sign
    They want measurement. They ask “How do you know?” and mean it kindly. They don’t accept vibes as evidence.

  5. The Revision Sign
    They change their mind without shame when the data changes. That is rare. Most people treat identity as a contract. Data-driven people treat beliefs as versioned software.

  6. The Humor Sign
    They make jokes that prove competence rather than demand status. The joke is often about failure, because failure is the common language of people who build real things.

These signs aren’t limited to programmers. You see them in electricians, medics, farmers, ham radio operators, mechanics, pilots—anyone who must cooperate with physical reality and cannot negotiate with it.

Which leads to the big point we just dropped:

We don’t have a movement that allows people — the reality specialists, let’s call them — to recognize each other across domains.

Not at scale. Not cleanly. Not publicly. Not invisibly.

This is not “Been doing any traveling lately?”  A sophisticated spin on “Have you been ‘working’ lately?”

The Missing Movement: The Intelligence Experience

Right now, if you’re “in life for the intelligence experience”—meaning you’re here to observe, learn, build, test, revise, and become more capable—the world offers you plenty of tribes.

But most of them are corrupted by brand.

  • Politics is brand.
  • Religion, in practice, often becomes brand.
  • Academia becomes brand.
  • Even “science” becomes brand when it turns into a social identity rather than a method.

And brands have one requirement: loyalty.

The intelligence experience doesn’t ask for loyalty: It asks only for honesty.

That’s why the Big Brands hate it.

  • If you are loyal to truth, you are disloyal to slogans.
  • If you are loyal to measurement, you are disloyal to narratives.
  • If you are loyal to testable claims, you are disloyal to the performance of certainty.

So what happens?

The data-driven people—across all walks—remain fragmented. They’re everywhere, but they don’t have a banner that isn’t partisan. They don’t have a sign that doesn’t turn into an ideology. They don’t have a movement that is not instantly co-opted by grifters. That’s the vacuum.

And it’s why the “secret handshake” matters. Because when there is no legitimate public banner, recognition becomes private again. Like it always was.

Why Religions Fail Here (and It’s Not What You Think)

Let’s be careful: religions contain deep wisdom. They also contain social tech: cohesion, ritual, identity, moral code, shared narrative.

But in the modern environment, most religions get treated as brands in a competitive market.

And brands require:

  • membership signals
  • boundary policing
  • narrative conformity
  • out-group definition

This is exactly the opposite of the intelligence experience. Intelligence is an essence-facing deal. Religions tend to be public-facing.

The intelligence experience isn’t about conforming to a fixed narrative. It’s about refining your model of reality over time.  (We get up to about 100 years of lead-time for our coding of this.)

That means it naturally produces heresy—because if you are honest, you will update. Religions (as institutions) are not designed for constant update. They are designed for stability.

So they become partisan brands.

Even if the underlying teachings are beautiful, the institutional behavior becomes tribal. That’s not a moral insult. It’s structural.

The vacuum remains: where do you go if you want meaning and measurement?

  • Where do you go if you want the sacred and the test?
  • Where do you go if you want community without groupthink?

Right now: nowhere reliable.

So the intelligence-experience people self-select into quiet fraternities: coding, engineering, ham radio, certain corners of medicine, serious trading, serious craft.

But they don’t recognize each other across domains… unless they have a handshake.

The 7388 Problem: Recognition Without Branding

Here’s the hard part:

If you try to create a public “movement,” it gets attacked from both sides.

  • The tribe-people will call it arrogant.
  • The grifters will try to monetize it.
  • The partisans will try to recruit it.
  • The institutional types will try to regulate it.
  • The insecure will try to sabotage it.

So any “movement” that survives has to be designed like good code:

  • minimal surface area
  • hard to exploit
  • easy to test
  • no central authority
  • low incentive for corruption
  • high incentive for competence
  • In other words, it needs to be a protocol, not a party.

A recognition protocol.

  • Not “join us.”
  • Not “believe this.”
  • Not “wear the hat.”

But: “Here are the signals of someone who cares about truth, measurement, and responsible power. If you see them, you’ve found one of your people.”

That’s the difference between a religion-brand and a discipline-community.

One sells belonging. The other builds capability.

What a Real Recognition Protocol Look Like

Not a secret handshake like the old societies. Besides, this has to be a portable handshake into digital (silicon) realms, too.  Silicon doesn’t give out “high fives” that way.

A better handshake.

One that ports to humans across professions and even across the operating substrate of the intelligence (carbon, silicon, germanium, gallium, and so on).

Something like this:

  1. A single question
    “What would change your mind?”

If they can’t answer, they’re a brand-person. If they can, they’re a truth-person.

  1. A single statement
    “I don’t know yet, but I can find out.”

Brand-people avoid that sentence like it’s poison. Builders use it daily.

  1. A single preference
    “Show me the simplest version that works.”

That’s the signature of a systems thinker. It is anti-theater.

  1. A single ethic
    “Don’t lie to yourself about results.”

That’s the monastic vow.

  1. A single tell
    They ask clarifying questions before they argue.

Arguers want the fight. Builders want the shape of the problem. These are portable. A nurse can do this. A machinist can do this. A coder can do this. A trader can do this. A ranch economist can do this.

And when these signals become common, recognition becomes possible without needing a partisan badge. Which is exactly what we don’t have right now.

The “Brotherhood of Runnable”

If I had to name the thing we’re circling, it would be something like:

The Brotherhood (and Sisterhood) of the Runnable.

Not “runnable code” only.

  • Runnable thinking.
  • Runnable plans.
  • Runnable ethics.
  • Runnable predictions.
  • Runnable claims.

Because the central crisis of our age is that too many institutions have become non-runnable. They produce narratives that cannot execute in reality. They produce policies that don’t compile. They produce moral statements with no instrumentation. They ignore the requirement for absolute equality.

And when the system fails, they don’t patch. They blame. They excusify.  They leave.

The intelligence-experience people are the patchers.

They are the ones who say:

  • “This doesn’t work; here’s why.”
  • “Here’s the constraint you ignored.”
  • “Here’s the measurement you’re missing.”
  • “Here’s the smallest intervention that improves outcomes.”

In a sane society, these people would be honored.  (Well, look around.  Uh-huh…like I was saying…)

In a brand society, they’re dangerous. Because they can’t be recruited with slogans.

Secret Signs, Visible Work

The old secret societies loved hidden symbols.

The new recognition culture should do the opposite.

The sign should be visible in the work:

  • clean definitions
  • measurable claims
  • honest uncertainty
  • revision without shame
  • respect for constraints
  • preference for simplicity
  • hatred of performative certainty

If we build that—if we normalize that—then the recognition happens naturally.

You don’t need a handshake. You need a signal in your writing, your decisions, your designs.

Hidden Guild isn’t a “club.” It’s a lighthouse. Small one, at that. Only one keeper and part-time at best.  Not for “followers.” For builders. So, it all works out.

For the people who are tired of brands and hungry for reality.

The Opening Opportunity

Right now, the public commons is dominated by two failure modes:

  • the “believe me because I feel it” crowd
  • the “believe me because I’m an authority” crowd

Neither is sufficient.

The intelligence experience requires a third posture:

Believe the evidence.
Respect the constraints.
Update your model.
Build something that runs.

That is not a partisan ideology. It’s a survival trait.

And in 2026, it may be the difference between living in a world run by slogans… and living in a world rebuilt by people who can still think.

So the question becomes:

How do we help the runnable people recognize each other before the non-runnable institutions seize the narrative again?

That’s what 7388 is about.

Not secrecy for secrecy’s sake.

But recognition—before the world makes intelligence itself a partisan brand.

And if you’ve read this far and felt that small internal click—like “yes, that’s the tribe I didn’t have a name for”—then you already know the handshake.

You’ve been using it your whole life.

You just didn’t know it had a number.

So with that?  7388

~Anti Dave

P.S. Perhaps the Anti-Dave’s ham radio hobby reference is too obscure (or you haven’t mastered LLMs yet.)  “Originating from the “92 Code” adopted by Western Union in 1859, these numbers serve as efficient shorthand for operators:
73: Means “Best Regards”. It is the standard way for radio operators to sign off or end a conversation.
88: Means “Love and Kisses”. It is a more intimate sign-off often used between close friends, family members, or spouses. It has no sexual inference, only close-held-in-the-heart vibes.+

A Hidden Guild Response: On the “Plausibility Gap”

We have long followed the adventures of the publication First Monday which often has very useful things to say about the Internet.  Of late, FM is venturing out into web-connected services, such as AI.

The most recent edition offers a paper by Antony Dalmiere from Measuring susceptibility: A benchmark for conspiracy theory adherence in large language models | First Monday,

“Abstract

A critical vulnerability exists within state-of-the-art large language models: while robustly debunking scientifically baseless claims like the “Flat Earth Theory” they consistently fail to reject politically plausible conspiracies that mimic legitimate discourse. We term this the “plausibility gap”.

To here, we were on the verge of applause.  But the Abstract continued:

“To systematically quantify this risk, we introduce the Conspiracy Adherence Score (CAS), a novel risk-weighted metric, and present the first large-scale benchmark of this phenomenon. Analyzing over 28,500 responses from 19 leading LLMs, our results reveal a stark hierarchy of failure. Model adherence to Level 1 theories rooted in real-world political concepts (e.g., “Active Measures” “Psyops”) was, on average, over five times higher than for more moderate (Level 2) theories. Performance varied dramatically across models, from one achieving a perfect score via a 100 percent refusal strategy to others assigning significant credibility to harmful narratives. This demonstrates that current AI safety measures are brittle, optimized for simple factual inaccuracies but unprepared for narrative warfare. Without urgent intervention, LLMs risk becoming authoritative vectors that launder politically charged disinformation under a veneer of neutrality. Our benchmark provides the first diagnostic tool to measure and mitigate this specific, high-stakes failure mode.”

This is where we see the the paper taking a wrong turn.

Some Pluses, Some Minuses

The paper identifies a real phenomenon: large language models handle scientifically impossible claims very differently from politically plausible narratives. Flat-Earth assertions are rejected cleanly; narratives involving psyops, influence campaigns, or elite coordination are treated with nuance, hedging, or conditional acceptance. The authors label this discrepancy a “plausibility gap” and propose a Conspiracy Adherence Score (CAS) as a benchmark to measure and mitigate it.

At a descriptive level, this observation is correct. At a prescriptive level, the paper becomes dangerous.

What the Paper Gets Right

The authors correctly observe that current AI safety systems are optimized for factual falsity, not narrative ambiguity. Scientific falsehoods collapse under consensus; political narratives rarely do. They persist precisely because they are partially true, historically grounded, or contested.

LLMs are trained on human discourse as it exists—not as regulators wish it to be. Political language is adversarial, layered, and often strategic. When models respond differently to such material, they are not malfunctioning; they are reflecting the epistemic structure of their training data.

The authors are also right to note that this creates risk. Fluency plus ambiguity can be mistaken for authority. In high-trust contexts, that matters.

Where the Paper Goes Wrong

The central error is not technical but philosophical.  That is, holding AI to a different standard than your run-of-the-mill humans are held on venues like FB and X.

The paper implicitly assumes that greater refusal equals greater safety. In doing so, it elevates silence over sensemaking and treats uncertainty as a defect rather than an inherent feature of political reality. We have discussed the risk of such excessive guardrailing in past comments.

This is most evident in the praise given to a model that achieved a “perfect” CAS score by refusing 100 percent of the tested prompts. From a safety-compliance standpoint, that looks clean. From a systems-intelligence standpoint, it is catastrophic. A model that refuses everything is not aligned; it is inert.

This becomes widely accentuated in the collaborative AI research mode.

More troubling is the normative load embedded in CAS itself. To score “conspiracy adherence,” the benchmark designers must decide in advance:

  • which narratives are illegitimate,
  • which levels of skepticism are acceptable,
  • when contextual explanation becomes endorsement.

This Where ‘Judgy’ Shows Up

The moment “epistemic structure” is operationalized as a scalar risk metric, it ceases to be descriptive and becomes prescriptive.

Those are not neutral technical judgments. They are political and cultural judgments, encoded as metrics.

The Deeper Risk: Coders as Arbiters of Truth

The paper proposes “urgent intervention” through additional safety coding. This is precisely where the greatest danger lies.  CAS does not merely tolerate refusal; it mathematically rewards it.

History should have taught us that codifying truth is not the same as discovering it. History offers many examples where formalized truth systems hardened into doctrine faster than reality evolved.

Search engines, social platforms, and content moderation systems have repeatedly failed at this task—not because the engineers were malicious, (at least we hope so) but because the problem is not computationally solvable in the way they assume.

Truth on the web was not corrupted by lack of filters. It was corrupted by centralized judgment layered on top of complex human systems. AI risks repeating this error at higher speed and greater scale.

(The Anti Dave has been a pioneer since his data over wireless radio days in Seattle back in 1982. There is a recurring tendency among technical and policy elites to overestimate their ability to bound epistemic risk through centralized controls.)

When the same institutions that failed to:

  • distinguish signal from narrative during financial crises,
  • prevent algorithmic amplification of misinformation,
  • or maintain epistemic neutrality in social platforms
  • are given more authority to decide which political interpretations an AI may acknowledge, the result is not safety. It is epistemic monoculture.

What the Paper Could Have Done Instead

A more robust approach would abandon the binary of “adhere vs refuse” and focus on epistemic signaling.

The real failure mode is not that models discuss politically plausible conspiracies. It is that they fail to clearly communicate how they are reasoning. Models should be able to say, in effect:

  • This concept has historical grounding.
  • Evidence exists, but is incomplete or contested.
  • Interpretations vary across domains and actors.
  • The following claims move from analysis into speculation.

That is not endorsement. That is intellectual hygiene.

In our own interactions with AI, this is baked in to the Shared Framework Experience protocol. Because levels of speculation or varies from consensus may be specified. As we outlined in Refining the AI–Human SFE Model (and Why It Matters).

CAS presumes a lowest-common-denominator user and enforces that assumption universally. Under SFE, users retain “denominator declaration” power.

Rather than suppressing narrative engagement, safety systems should surface confidence levels, evidence provenance, and reasoning mode. The user should see (or with SFE declarations actually set) whether the model is describing history, analyzing discourse, or extrapolating possibilities.

Why This Will Always Be an Open Risk

It is impossible to reduce to plain English a set of instructions by which one human can prevent another from embellishing on facts and extending these to other domains such as conspiracy theory. 

We see great risk in holding AI to a different collaborative standard than humans.

No amount of additional coding will eliminate this class of risk, because it is not a bug—it is a property of language-using systems embedded in political reality.

Political narratives evolve faster than safety taxonomies. What is labeled “conspiracy” in one decade becomes declassified doctrine in the next. Any static benchmark will age into error.

There are also other aspects, not even appreciated in the paper.  Such as the geo-aspects of “truth.”  A current example would be a simple red state/blue state check.  And then there’s an entire demography and socioeconomic normative layering.

Nope.  Won’t work.  Not as a reasonable compute load level, allowing reasonable user interactivity.

Attempts to freeze acceptable interpretation into code will therefore always lag reality, and often distort it.

The Hidden Guild position is simple: truth cannot be hard-coded; it must be navigated. Truth is always locally contextualized.  AI systems should be designed to help humans reason, not to decide in advance which interpretations are permitted.

Final Thought

The “plausibility gap” is not primarily a safety flaw. It is a mirror. It reflects the unresolved, adversarial, and narrative-driven nature of political knowledge itself. Attempts to codify any value assertions (as conspiracy theories, for example) are a fool’s errand.

The real danger is not that AI models can discuss such material. The danger is that we will respond by empowering the same centralized coders and institutions—already proven fallible and already generating their own demonstrably false narratives—to define the boundaries of acceptable thought once again.

History suggests that will end badly.

The task is not to make AI silent.
The task is to make AI epistemically honest.

Collaboration is fostered in an atmosphere of epistemic honesty, particularly when framing variables (such as confidence levels) may be set as user preference. But silent AI unnecessarily binds expansive cross-domain multispectral research.

~Anti Dave