Anda

Pattern is magical. Compound is consequential.

[Anda @andaorbit]

@EtihadHelp Please check my mess

[Anda @andaorbit]

@Trip @etihad I'm still waiting for a real resolution. I missed both my HAN-AUH flight and the return, and paid extra name-correction fees, but never received the updated ticket. https://t.co/rO7S33WDEJ case no. 10033831851

[Anda @andaorbit]

@Trip @tr

[Anda @andaorbit]

@Trip My unresolved complaint regarding booking DENC58 (Etihad Airways, HAN → AUH): I followed https://t.co/rO7S33WDEJ’s advice on a name discrepancy, was denied boarding, and suffered significant financial loss. The handling since has been equally disappointing.

[Anda @andaorbit]

@Trip @Trip This is not a request for a Help Center link.
I’ve contacted https://t.co/rO7S33WDEJ multiple times, received the same automated reply 3 times, and been passed between https://t.co/rO7S33WDEJ and Etihad.
Please escalate DENC58 to a senior human case handler.

[Anda @andaorbit]

@AWSSupport Thank you.

[Anda @andaorbit]

Why the ability to observe your own mind may be what separates reaction from evolution?

THE RAREST FORM OF INTELLIGENCE

Most people don't really observe their thoughts, they just live inside them. A thought shows up and it feels true. Someone doesn't reply to a text and by the time you've noticed you're annoyed, your brain has already run through three or four stories about why: they're ignoring me, I said something wrong, I knew I couldn't trust them. None of that is a conscious decision. It just happens, fast, before you've had a chance to ask whether it's actually accurate.

That's not really a flaw in the brain, it's more or less the design. The brain isn't sitting there recording reality, it's guessing at what's coming and correcting itself when it's wrong, which is roughly what Karl Friston's work on predictive processing describes, prediction, error, update, repeat. By the time you consciously notice something happened, your brain has already decided a lot about what it means. The actual event, "they didn't reply," is one thing. The story you told yourself about it is a completely different thing, and most of us never really separate the two. We just experience the story as if it were the event.

There's a name for the skill of catching that gap, cognitive scientists call it metacognition, basically the ability to notice your own thinking well enough to do something about it. And it turns out this isn't the same thing as being smart. Stephen Fleming has done a lot of work showing metacognitive accuracy and raw cognitive ability can move pretty independently of each other, someone can be sharp and still be a terrible judge of their own thinking, and there's decent evidence that prefrontal regions specifically handle how accurate those self-judgments are. Which honestly explains a lot of smart people you've probably met who are completely unable to see their own blind spots.

You see something similar in engineering, if it helps to think about it that way. A regular program just runs. A monitoring layer checks whether it's behaving the way it should. A proper control system goes further, it compares its own output to a target and adjusts when they don't match. AI research is bumping into the same wall right now, a model can produce a genuinely good answer, but knowing an answer is good and producing it aren't the same skill, and that gap is a big part of why people are trying to get models to estimate their own uncertainty instead of just outputting an answer and stopping there.

Intelligence gets you an answer. Whether you should trust that answer is a different question entirely, and a lot of very intelligent people are bad at asking it. You can reason brilliantly from a premise that's wrong. You can have a great memory and terrible judgment at the same time, those aren't in tension the way people assume. You can win an argument and walk away having learned literally nothing from it, which if you think about it is kind of alarming. Raw intelligence processes information well. It doesn't automatically tell you whether the process itself is trustworthy, and I'd argue the more dangerous person in most rooms isn't the least intelligent one, it's the person who's gotten extremely good at using their intelligence to defend a position instead of test it.

None of this lives in one tidy "self-awareness" spot in the brain either, it's spread across prefrontal cortex, anterior cingulate, insula, depending on what exactly is being monitored. Matthew Botvinick's work on the anterior cingulate ties it specifically to detecting conflict and flagging when more control is needed, and then lateral prefrontal areas do something with that signal afterward. Not because one region makes someone wise, just because there's actual hardware for watching your own operations and adjusting them, and most people never really turn it on.

This matters for how you'd actually try to change. Most people go after behavior directly, stop getting angry, be more confident, stop procrastinating, that kind of thing. But behavior is the output, not the cause, and if whatever's generating it stays the same, the behavior tends to come back eventually, sometimes with a different costume on. The more useful question is upstream of that: why did that comment read as an attack, what am I actually protecting, what would need to be true for the opposite read to make sense. That's not trying to control the output anymore. That's poking at the machine that's producing it.

Take feedback as an example, since it's a good one. Two people get the exact same piece of criticism. The first person reacts on the spot, they don't get what I'm doing, and the reaction feels completely justified in the moment, so they explain, they defend, maybe they push back hard, and later they've built a pretty convincing story for why the other person was wrong. The second person feels the same defensiveness, that part isn't different, but they catch it: I'm getting defensive right now. And that noticing buys a little bit of room, enough to ask what they're actually defending, their competence, their self-image, maybe just the possibility that the criticism is true. Nothing dramatic happened. Same emotion, same initial reaction. But somewhere a layer got inserted between the stimulus and the response, and that layer is basically where any real agency comes from.

I think this is part of why some people seem to develop faster than everyone around them, and it's usually not because they're smarter or more disciplined. They just process what happens to them differently. A rough conversation becomes something to learn from instead of just something to survive. A bad call becomes data about how they make decisions, not just a mistake to feel bad about. Even a win gets a second look, was that skill or luck, and what happens if the conditions change. Just having experiences gets you "I've done this before." Actually paying attention to how you process those experiences gets you something closer to an upgrade to the whole system, not just another entry in the log.

AI is forcing something similar into the open. Models can generate, reason, retrieve, solve, but the question people are increasingly asking is whether a system can tell when it's probably wrong. That's not just a machine problem. We need to know not only things but how much weight to put on what we think we know, and the easier it gets to produce a plausible-sounding answer to almost anything, the more the actual bottleneck shifts from having information to having judgment about it, which assumptions are load-bearing, which answer actually deserves trust.

None of this means you stop reacting, that's not really on the table. Fear, ego, certainty, old habits, they'll keep showing up because that's what brains do. The point isn't to get rid of the reaction, it's to notice it while it's happening instead of an hour later. "I am angry" versus "anger is happening right now" sounds like a small difference and honestly it kind of is, linguistically, but it does something real underneath, it puts a little space between you and the thing you're watching. Once there's space, you can actually question it. Once you can question it, you can change it.

So maybe the rarest kind of intelligence isn't being fast or solving hard problems, it's being able to watch your own thinking and stay honest enough to admit the thing you're watching might be wrong. People who can't really do this mostly need reality to hit them hard enough before anything changes, failure has to get undeniable first. People who can do it get to catch the pattern earlier and update before they're forced to. That's probably closer to the real explanation for why some people seem to grow so much faster than everyone else, not more talent, just a tighter loop between what happens to them and what they do with it.

[InOrbital @inorbitallabs]

The same protocol can be evidence-based, and still be wrong for you.

Because evidence tells us what works on average. It doesn't tell us what will work for this brain, under these particular conditions.

We usually explain this gap with effort. One person was more disciplined, followed the protocol properly, tried harder. The other didn't. But there's a simpler explanation that has nothing to do with willpower: the protocol was never matched to the person in the first place.

A brain receiving an intervention isn't a blank, standardized machine. It's the product of a specific history, development, learning, habits, stress, prior experience, reward, failure, thousands of small adaptations accumulated over years. So when two people get the same instruction, they aren't starting from the same cognitive state, even if the instruction sounds identical.

Take "do two hours of deep work every morning." For someone with a clear objective and strong attentional control, that's exactly what they need. For someone with ten unresolved decisions competing for attention, the real problem isn't sustaining focus, it's deciding what deserves focus in the first place. For a third person, the plan is clear and the problem is initiation: they know exactly what to do and still don't start, which more calendar time won't fix. Same instruction, three different mechanisms underneath it.

The same thing happens in learning. Give two people the same curriculum at the same pace, and one with a strong existing mental model will benefit from more challenge, while one missing foundational representations needs the opposite: less load, more scaffolding, time to repair the representation before adding difficulty. This is close to what Robert Bjork calls desirable difficulty: the right amount of friction accelerates learning, but only when it's calibrated to what the learner already has to work with. Miscalibrate it either direction and the same curriculum stops teaching.

This is why individual differences matter so much in cognitive science and neuroscience. Population-level findings tell us what tends to work on average. They don't guarantee any single brain will respond the same way.

Behavioral science adds another layer: what people say they need and what actually changes their behavior often aren't the same thing. Someone says "I need more motivation," but their behavior points somewhere else entirely, an ambiguous first step, a reward that arrives too late, a plan sized well beyond their actual capacity this week. Peter Gollwitzer's research on implementation intentions found something similar: people who form specific if-then plans (if it's 8am, I open the document) follow through far more often than people who just intend to do something, because the bottleneck usually isn't desire, it's a missing mechanism for translating intent into action. The useful question stops being "how do we motivate this person" and becomes "what mechanism is actually blocking the behavior."

Now bring this into building a company. Two founders both say "I want to build an AI company," and that sentence alone tells you almost nothing about what protocol they should follow. One has a validated problem, a technical cofounder, capital, and full-time availability, so the right move is toward an MVP and iteration. Another has an early idea, ten hours a week, and real uncertainty about the problem itself, so the right move is closer to clarifying the problem and generating cheap evidence before committing to anything larger. A third founder already has a working company: revenue, product, team, but every important decision still routes through them personally. Their question isn't "how do I build" anymore. It's "how do I build a system that no longer depends on me." Same domain, same stated ambition, three completely different protocols.

This is where personalization gets more interesting than a better recommendation engine. A system built to adapt shouldn't open by asking what type of person you are. It should open by watching: what are you trying to do, what do you already know, what do you keep doing regardless of what you say, where do you get stuck, how do you respond when something doesn't go as expected. That last one matters more than it sounds. A prediction made before acting gives you something an outcome alone can't: a window into the person's actual model of the world right now. Prediction, action, outcome, prediction error, repeated enough times, and the system starts learning not what someone says about themselves, but how they actually create, decide, and adapt.

Neuroscience helps reason about the conditions under which attention and decision-making operate. Cognitive science explains how someone processes information and forms mental models. Psychology speaks to motivation, identity, and internal models of self. Behavioral science tells you what actually happens when intention meets reality. Real-world behavior is what keeps the whole loop honest.

None of this is about finding a permanent label for someone. It's building an increasingly accurate model of how this particular person creates, learns, and decides, and what conditions let them do their best work, knowing that model has to keep updating. People aren't static. The protocol that's right for you today may be wrong in six months, once you've moved from exploring to committing, from learning to executing, from needing structure to needing room to move.

So the future of personalized systems probably isn't about finding the perfect plan. It's about building something that keeps rediscovering which plan makes sense for this person, under these conditions, right now.

Cookie-cutter advice doesn't usually fail because the science behind it is wrong. It fails because a population-level average gets mistaken for an individual-level protocol. The science tells you what tends to work. Personalization asks the harder question: what should work for this person, here, now, and how will we know if we're wrong.

[AI Evolution @AiEvolutio58513]

Nobel Prize-winning physicist Roger Penrose thinks we've been using the wrong word for decades, and the wrong word is quietly warping how we think.

His argument is straightforward but hits hard:

"The name is wrong. It's not artificial intelligence. It's not intelligence. Intelligence would involve consciousness. Well, if it's a machine, it's not conscious."

For Penrose, the field confused raw computational muscle with actual understanding.

"People have lost the plot. They've lost it in the power of computing. The thing is that computers have got so powerful that they've lost the thread of what they're doing. But I think consciousness is something different. It's not computational."

The label itself, he thinks, has put people into a kind of trance:

"People are so hypnotized. The trouble is that AI is a bad term. It means artificial intelligence. Now intelligence in my view is conscious. That's what intelligence is about."

His proposed fix: call it Artificial Cleverness. AC, not AI.

He draws the line using math students as an example:

"You have mathematics students. Some of them understand what they're doing. Some are just clever. They can repeat what they've learned. They know how to do it very cleverly. They can calculate very well, but they don't necessarily understand what they're doing."

That gap between calculating and understanding is exactly the gap between today's systems and real intelligence.

Cleverness can be engineered. Consciousness, Penrose argues, cannot.

Worth sitting with: when you call a system intelligent, are you describing what it does, or silently assuming something about what it is?

Interested in AI?

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I track ChatGPT, Claude, and every tool quietly changing how we work and create, then hand you the tested signals.