This contract of external accountability keeps the fire going through the long slog, and it forces me to make clear-cut decisions about what to include, what to leave out, and how to manage my spare time so I make progress.

With no deadline, there's no urgency, and so things just don't happen.

Iron Triangle

I think knowing yourself is the most important thing to do before changing yourself.

Everyone has seen the articles about the brain’s approach to social media content, which offers a quick, easily accessible way that makes you happy or, even if it doesn’t make you happy, offers an escape from the thing that makes you unhappy.

The relativity of time is a reality I feel to the core while doom scrolling. Besides the lost time, there’s the confusion after realizing it and putting the phone down, trying to get my dazed mind back to normal. And then, not finding anything to do, not being able to putting yourself together, and reaching for the phone again.

I want to know everything, immediately, quickly. Since this is not humanly possible, I end up doing nothing.

Jules Payot

At this point I am convinced that fast consumption is harmful to the brain, mind, and soul.

Fast consumption, constant consumption, more consumption.

Ethereum’s testnet experienced the problem of this a few years ago. They were using it to check that their local clocks weren’t off by a good amount.

There it is: what if we used an external (decentralized) time source? We’d have proof that you aren’t a time traveller. We could begin to use time as a primitive.

Noone mentions the last attempts to build regulated markets for blockchain and why it was a complete failure.

VDF’s

Any host could easily manipulate that. Intel used to include a hardware based monotonic counter and a trusted time service but that was removed.

TEE’s

In a world where language is weaponized with such precision, every interaction becomes a potential site of influence. Thus now you have a glimpse into the world you are walking into, and what Cognitive Warfare truly is.

This mistrust does not just undermine particular messages; it destabilizes the foundation of shared meaning in society.

Ultimately, the weaponization of language through AI’s information density and resonance creates a feedback loop that erodes trust in language itself.

Once this threshold is crossed, the recipient is not simply receptive to the message; they are absorbed by it, responding almost automatically to the embedded cues. This state of cognitive capture transforms resonance from mere communication to a form of psychological assimilation, where the message becomes intertwined with the receiver’s mental framework, embedding itself within their cognitive architecture as if it were an original thought.

Resonance thereby capitalizes on preexisting cognitive patterns, taking advantage of the brain’s tendency to recognize familiar stimuli and create associative shortcuts, resonant words, and phrases that engage the target’s mental framework, embedding messages seamlessly into their neural pathways.

The functioning of resonance within the mind parallels the architecture of neural networks.

Each resonant word or phrase activates vast networks of associations, turning minimal input into maximal output.

With information-dense language, the goal is not to argue a point but to create resonance, to instill a thought or bias so subtly embedded that it feels self-generated.

The concept of information density is quintessential to understanding AI-led Language weaponization and cognitive manipulation.

Dense encoding can plant persistent cognitive seeds with minimal interaction.

How exactly certain strings of words, or memes are as effective as bioengineered viruses, while others aren’t.

there is another deeper truth to the weaponization of Language and bypassing our mental defenses and “hacking” cognitive subroutines.

direct and shape the cognitive subroutines.

The amplification of certain narratives or linguistic structures by algorithms also presents a form of cognitive warfare, one where the line between organic and engineered influence becomes blurred.

Just as language models can tap into the brain’s implicit linguistic processing, algorithms exploit these cognitive shortcuts to present information in ways that maximize engagement, often reinforcing existing biases or emotional responses. The result is a feedback loop in which language, tailored by algorithms, reinforces the very patterns of thought that the system is designed to exploit.

These decisions are driven by the same probabilistic models that govern language generation, meaning that algorithms, in essence, control the flow and structure of language itself.

Building on the mathematical nature of language, algorithms—particularly those driving AI models—are fundamentally reshaping how language is processed, generated, and disseminated.

At its core, language can be seen as a system of probabilities — each word or phrase is connected to others by a web of relationships, shaped by syntax, semantics, and context. Language models, like those built on transformer architectures, are designed to capture these probabilistic relationships, allowing them to predict and generate coherent sequences of text. This is where AI’s understanding of language goes beyond simple mimicry: it taps into the deep structural and mathematical underpinnings of language itself.

And in mimicry lies a deeper truth to our existence.

This isn't just about the manipulation of individuals, it’s about influencing entire populations through the systematic deployment of language that exploits human psychology. Social media platforms, news organizations, and political movements and now AI-frontier labs have harnessed this power, often without fully realizing the implications. The implicit subroutines of the human mind are engaged on a massive scale, and the consequences remain unrealized to the vast majority of the population, including the ones engaging in such weaponization.

Language is no longer confined to speeches, texts, and broadcasts, it is algorithmically generated, analyzed, and disseminated at a pace that far exceeds the ability of our conscious brain to keep up. With precise algorithmic tailoring, Language can be tailored to penetrate specific patterns in the brain and subroutines, evoking specific emotional responses and affecting behavior at scale.

What makes now distinct from any time before is the scale, velocity, and precision with which Language can be weaponized.

When linguistic patterns are wielded with precision, they can be far more dangerous than overt propaganda or coercion.

thrive on algorithms that curate, manipulate, and amplify language in ways that can bypass critical thought and activate emotional or behavioral responses almost unconsciously.

“Ears have no lid” and one could posit eyes also don’t, in a more philosophical sense, ears lacking a lid infers that you can’t consciously filter sound and the spoken word, therefore you could be affected by words uttered to you. After quite a significant amount of research into neurology, brain chemistry, and language centers in the brain, and attempting to mix mathematics to all of this, I hypothesized the brain could process absurdly more visual data than estimated by “the experts”.

.hack (dot hack)

Our state-of-the-art is still “think of 53 different things and then try all of them.” This isn’t super reassuring—if I hired a plumber to install a toilet in my house and he was like, “Sure thing, I’ll just install 53 different toilets and then check which ones flush,” I’d be like, “perhaps I’ll get another plumber.”6

We do not appear to have progress of this kind. According to this meta-analysis, we’re no better at treating youth mental illness today than we were 50 years ago.

I’d love to see more lists of self-evident phenomena we can’t explain; not “how do we reduce scores on the Modern Racism Scale by 10%” but “people do this weird thing…WHY.”

I think pretty much all progress in psychology can be summed up as “overturning intuitions.”

In order to survive, every human needs to have some model of the world: how their body functions, how animals behave, how matter moves, etc. Psychologists call these “folk” theories—folk physics, folk biology, folk economics, and so on—the kind of explanations you might come up with if you just kinda bumble around, explanations that are good enough to keep you alive, but often go wrong.

I’ve got this picture in my head: we’re all on a bus that’s supposedly going to Cincinnati. But there are no road signs and we don’t have a GPS, so we have no idea if we’re going in the right direction. We can’t measure our progress by how much gas we’re burning, or whether we’ve upgraded from a manual transmission to an automatic, or whether the government bought us a new bus. And you can’t just look out the window and go, “I dunno, kinda feels like we’re headed toward Arkansas,” which, I realize now, is what I’ve been doing so far.

There’s a thought that’s haunted me for years: we’re doing all this research in psychology, but are we learning anything? We run these studies and publish these papers and…then what? The stack of papers just gets taller?

But what was wonderful about the web, was that here was a machine for finding people with similar interests and experience, anywhere in the world. That’s the web I want back.

Because, If we can’t tell a story about something, it’s as if it didn’t happen.

If we want people to see these things as real, we have to integrate them into narrative descriptions of incidents.

Without a narrative schema to anchor it, the pandemic all but vanished from public discourse soon after it ended.

In The 1918 Flu Faded in Our Collective Memory: We Might ‘Forget’ the Coronavirus, Too, Scott Hershberger speculated in Scientific American along similar lines about why historians paid little attention the Spanish Flu epidemic, even though it killed more people than World War I (emphasis mine):

We use stories to make sense of the world. What that means is that when events occur that don’t fit neatly into a narrative, we can’t make sense of them. As a consequence, these sorts of events are less salient, which means they’re less real.

Unfortunately, too much of our era has an emotionally negative predisposition toward too many things, including our current elites, and for reasons that are mimetic rather than justified, whether rationally or even by our impulses to breed.

“I will add only that Nietzsche says somewhere that it is the duty of a philosopher to promote precisely those virtues or tendencies of spirit that are most lacking in one’s own time…”  For all its pretense to the contrary, that is exactly what this book does not achieve.

“The chief intention of this study has been to offer an explanation for why the ancient city perceived philosophers as dangerous and as associated with tyrants — to argue that there was something to the ancient prejudice that philosophy was associated with tyranny.”

I needed to build a bespoke python GUI tool to help me categorize hundreds of websites.

Star Trek-level UI

It will hurt. It will suck. It’s not like starting to use a new, elegant tool on some pet project. A lot of brain power needs to go into this, and it’s mostly boring, menial work. But hey, nobody said this stuff is easy. Not even Vannevar Bush. None of us are entitled to easy work.

And when I inevitably need to review something a few months from now, I know exactly where to look. For example, I will want to measure whether the app is actually getting faster, and I will want to use the exact same methodology and code as at the start. Thankfully, both are right there in my memex trail.

Even if I deleted the folder at this point, it would all still be a beneficial exercise. By putting all of this together, I need to organize my own thoughts. I get to see everything in context. I understand the problem more clearly.

At first, I just add a single document in this folder. This is the project’s main document, or “index”. It probably has the same name as the folder. It should serve as a guide to the project, for others in the company as well as for me (when I look at it 2 years from now). In here, I describe the project and I link to other stuff inside the folder (and outside of it, if needed).

Memex is already here, it’s just not evenly distributed If you look back at the list above, you’ll realize that these things are already very much possible. They might not be in a single shiny app, but that doesn’t matter. (In fact, it’s probably better, in many ways, that today’s memex isn’t a single app. We’ll get back to this later.) To create a new memex “trail” in the year 2020, just create a shared folder (in Zoho Workdrive, Dropbox, Synology Drive, Google Drive, OneDrive, iCloud, etc.) and put some documents in it. That’s it. I know: it’s not glorious. It’s not shiny. It’s just a boring old folder with boring old documents. But I hope to persuade you that it’s good enough, and that you don’t need to wait for some vaporware to work the way Vannevar Bush imagined in 1945.

Imagine that you and I are working in the same company. I tell you there’s a new project for us two to work on. I explain it to you and you get reasonably excited. And then I tell you that I’ve started a new “bloorp” in BloorpyBase, a piece of software from 2012 that almost nobody uses. You grudgingly install BloorpyBase. The app doesn’t use the same keyboard shortcuts you’re used to. The shortcut normally assigned to adding a comment instead minimizes all windows. Sigh. You try to link some exploratory source code to it, but BloorpyBase only works with Mercurial. Sigh. You read some of my initial thoughts and try to respond but you don’t know what’s the best way to do it. Should you create another bloorp? Should you make a suggestion, or an edit? You spend half an hour reading a “How To Bloorp” guide on the internet but come back empty handed. Sigh.

A piece of software that works with your existing files, and which people around you can use, will generally win over some new way of doing things that you first need to migrate to, and then also ask others around you to migrate to as well.

Interoperability is more important than features.

Here’s the thing. If I’m being honest, most of my experiments with the different memex descendants mentioned above just kind of faded after a few weeks or months. And the reason is not just habit. If they were such a huge boon to my productivity, I’d change my habit the same way I changed it for better IDEs, better social media consumption strategies, or better terminal defaults. No, the reason those shiny new apps don’t stick is interoperability.

I have used about 4 different TODO apps and over 5 different text editors in the past 10 years alone. So, go ahead and indulge yourself if you need to.

I think you’ll agree with me that, while Xanadu is a lot closer to the idea of memex than the web, it’s kind of underwhelming as a piece of software. I remember playing with it a few years back, and I just didn’t find it that compelling.

There are projects that explore this space, of course. The most obvious descendant is Ted Nelson’s Xanadu project, a piece of software more than 50 years in the making. You can see its 2016’s incarnation in this video.

For this reason, some people assume that the web is an advanced version of memex. But in fact, the web is a terrible approximation of Vannevar Bush’s original vision.

This blog is primarily for developers, and software development is in many ways a research job.

His “trails” are similar in concept to a physical binder that contains documents that you can freely annotate, highlight and interlink. Any given document can of course be in multiple different binders, and its annotations and links depend on the context. Even for a single person, every “page” can exist in several different forms, depending on what the person is researching or trying to remember at the moment.

What Vannevar Bush proposed with memex wasn’t a publishing platform. It was a memory expansion device.

You can’t annotate a relationship between two paragraphs on two separate sites.

There are hyperlinks on the web, but again, they are not yours.

Memex was to be an “enlarged intimate supplement to one’s memory”.

I will continue to send updates on my blog, and I intend on adding more content on my alpine climbing. I will continue to stream on Twitch, and work on my technical projects.This blog, and the RSS feed will slowly grow into read-only automation that will publish to all of my channels.I do not intend on engaging in social media moving forward. I am too happy to change anything at the moment.Stay tuned for more.

I have rediscovered a sense of self-respect for my ability to accomplish my goals when nobody is watching.

However it has robbed us of the most critical dialogue, our dialogue with ourselves.

You can’t tweet your way to self-respect.

Broadcasting virtue to the world will never provide internal fulfillment regardless of how true it may be. Virtue signaling is effective in shifting public perception, but remains powerless in shifting an internal self-image.

We are a collective of depressed authors trying to persuade other equally hopeless authors of our integrity.

Regardless of how delightful, jarring, shocking, funny, cute, or aggressively virtuous my content was, internally I remained empty. Regardless of how much content I consumed, internally I remained empty.

I remember feeling the need to out-perform my previous display of extreme morality.

We have lost our prerogative to enact change.

I believe social media has drifted to a doom and horror conduit similar to the tabloids.

I notice this internal tranquility seems to be related to my exodus of social media.

In company after company, whenever I present the BAPO model, the first reaction is: Yes, of course! And then, after a few seconds, some or several people in the audience realize that this is exactly the opposite of how their company really works. And the realization (and associated embarrassment) that the company actually uses OPAB sets in. The result often is a lively discussion across the participants on the times the company has falling into the trap in the past and whether we are at risk of falling into this trap again right now.

Meet the seven people who hold the keys to worldwide internet securityThis article is more than 10 years oldIt sounds like the stuff of science fiction: seven keys, held by individuals from all over the world, that together control security at the core of the web. The reality is rather closer to The Office than The Matrix

Problem-solving: There are other kinds of meta-learning, separate from either planning or receiving knowledge. If you’re faced with a problem you’ve never solved before, and you don’t know where to look up an answer (or don’t want to), then you can try to simulate the probem in your head, and mentally consider potential solutions. If you arrive at an idea you like, this is it’s own kind of learning.

Association: Often you don’t know when you’ll need to use a new piece of knowledge, such as learning to ask directions in a new language. In this case, it’s useful if you can recall a relatively unpracticed action based on the correct context.

Once a model has learned to look for a certain key, it’s hard to unlearn. To change the model’s behavior, it seems easier to change what the key points to rather than to get the model to change so that it ignores the trigger altogether.

I’ve phrased things this way specifically because human brains don’t seem to be good at erasing past memories, but rather they seem to be able to replace values associated with pre-existing keys. In this case, the keys are triggers that kick off actions.

action sequences which are initiated by triggers.

Another kind of learning happens at a higher level, which requires longer-term thinking. For example, suppose you write a first draft of a book, and then give that book to some beta readers for feedback. You can view this as a process with many months between the action first taken — writing your first word of a new book — and receiving useful feedback on that action. The recent memory is no longer a useful vehicle for this kind of learning.

For example, if the mind is in a happy mood, it’s more likely to appreciate the positive aspects of a conversation; if it’s feeling defensive, it’s more likely to notice a perspective from which a conversation can be seen as judgmental.

In order for the model to remember something, it must be both (a) something the action model has paid attention to, and (b) something the mind cares to remember based on the emotional state.

Human brains seem to have separate locations for long-term memories and whatever our equivalent of an action model is. Cases of amnesia suggest this: People can forget much of their past while otherwise acting normally. If our memories and behavior depended on the same set of neurons, then this wouldn’t be possible. However, in the mind model above, I’ve let the long-term memory be implicitly part of the action model because this is effectively how language models currently store their version of memories.

Longer-term memories don’t seem to have a pre-determined time limit, but they do tend to fade over time. This pattern is consistent with knowledge baked into LLMs, and so can match the way an action model would effectively remember things — without a time limit, but with the ability to fade over time, especially if not referenced for a long time.

Different people have different recent memory capacities, but it’s common to remember what you ate for breakfast this morning, but not what you ate for breakfast several days ago, ignoring predictability (such as if you cheat by eating the same thing for breakfast every day). This type of memory matches what can fit into the recent memory module.

A third motivation to have a separate recent memory module is that a detailed memory of the past few hours is much more valuable that an equally-detailed memory of some random window of a few hours from when you were four years old.

Just as language models come with knowledge baked into them, an action model is also capable of holding knowledge, but I’ve included a separate memory module. The motivation for the recent memory module in the mind model is a place that can essentially memorize exactly what has happened recently before it’s integrated (through some kind of training) into the action model.

To explain the ideas of memory in this mind model, I’ll split memory into two broad categories: Story memory is the memory of everything that’s happened to you; and action memory is the modification of how you act based on positive or negative feedback.

While I can’t confirm details internal to OpenAI, my educated guess is that these facts are available to the model because they can be selectively added to the prompt. That is, I believe the only common way for LLMs to “learn” today is to implement an additional system to store data from conversations, and to selectively insert that data into prompts when we think it might be useful.

Now the LLM can choose to switch, at its own discretion, back and forth between a talking and listening mode. When the LLM wants to listen, it can produce a special <listening> token many times in a row, until it wants to say something. When it wants to speak, it outputs what it wants to say instead of the <listening> token.

However, we can imagine a change that adds agency to any LLM-like system. Think of a model that receives two interwoven input streams. One input stream is the person talking to the model, and the other is the model being able to see its own output. Current LLMs see both of these streams, but they’re set up so that only one person at a time can talk — the LLM or the user. The difference in the two-input version is that the model is designed from the start to see its own feedback, constantly, as well as simultaneous real-time input from “the outside,” such as the user.

A large language model doesn’t have agency because it can only react to input; it can’t independently take action.

We may even have an effective stack of goals, a small data structure that we can push new goals onto, and pop them off as we complete them.

our current goal fundamentally shapes how we filter the incoming information, and can be edited by the action model itself.

I’m imagining the action model as receiving a lot of data that we could view as one giant vector

I’ve chosen this flow of data carefully. In effect, there are two filters on what we store in recent memory: First, when the action model receives a lot of incoming information, it will effectively pay more attention to some information than the rest. As in a language model, the unused information essentially disappears from the network as it passes through later layers; the attended ideas persist until the end. The second filter is based on our emotional state. When we’re bored, what’s happening is not considered important, and not flagged for longer-term memory. When we’re experiencing an emotional spike, a lot more data is kept around in more detail. Our usual life tends to be somewhere between these extremes.

The emotional state module is doing a lot of work: It’s meant to represent all of our bodily needs, such as feeling hungry or tired, as well as our state of mind, such as feeling elated, frustrated, nostalgic, or intrigued. In this model, our emotional state can change based on what’s coming out of the action model, and it also filters that output into the recent memory module.

Since I’m imagining an action model can be a slight generalization of a language model

perceive visual objects (“face”) rather than a raw image (“pixels of a face”).

Each arrow represents a flow of information. Solid arrows are what I consider to be the most important flows.

2.2 The model at a high level Here’s the model:

You can think of an LLM, in simple terms, like this: context -> LLM -> next_token By analogy, an action model works like this: context -> Action Model -> next_action

I’m thinking about minds in terms of data flow between simultaneously-acting modules. If you have a computer with a GPU, a multi-core CPU, and a camera attached, then each module (GPU, CPU, camera) can do its own work in parallel. The modules in a system like this talk to each other, but they can always process information as it’s received.

That’s how you can consistently exist in the current finite game, and leave yourself open to the surprises (and the possibility of being surprising) in games that don’t yet exist that you don’t know you’re already playing. And that’s how you continue playing.

One way to remember this is to treat the infinite game of evolutionary success as a sort of Zeno’s paradox turned around. You never reach the finish line because when you’re mediocre, you only take a step that’s halfway to the finish, so there’s always more room left to continue the game.

In Douglas Hofstadter’s Metamagical Themas, there is a description of a game (I forget the details) where the goal is not to get the top score, but the average score. The subtlety is that after playing multiple rounds, the overall winner is not the one with the highest total score, but the most average total score. So to illustrate, if Alice, Bob, and Charlie are playing such a game and their scores in a series of 6 games are: Alice: 7 5 3 5 6 2 Bob: 5 8 2 1 9 7 Charlie: 3 1 5 4 5 5 We have the following outcome. Bob wins game 1, Alice wins game 2, Bob wins game 3, Charlie wins game 4, 5, and 6. So Alice gets 1 point, Bob gets 2 points, and Charlie gets 3 points. The overall winner is Bob, not Charlie. Charlie is the most mediocre, but Bob is mediocre mediocre. His prize is (perhaps) highest probability of continuing the game.

Mediocrity is the only source of advantage.

Data’s strategy of mediocrity is also the essence of guerrilla warfare of any sort. As Kissinger noted, the conventional army loses when it does not win. The guerrilla wins when he does not lose.

Starships manage energy, not performance. Starships are deeply lazy.  Starfleet captains aim to continue the game, not win every encounter.

Star Trek, I think embodies this kind of mediocrity very well. Starfleet officers are all B Ark type bureaucratic bullshit-job mediocrities. They are rarely seen excelling at something or being perfect at executing something. Instead, they are constantly cutting corners here, muddling through there, and going with improvised hacks everywhere. And generally putting up a very mediocre performance by the standards of say, Vulcan intelligence, Klingon valor, Ferengi profit maximization, or Borg efficiency. When those non-humans adopt Federation culture, it is most evident in their adoption of mediocrity as an ethos. When they exhibit their “alien” traits, it is usually by regressing to an unfortunate pursuit of excellence in a specific alien way.

But while disruption always involves mediocrity, mediocrity does not always imply disruption.

When caring is possible, and some people actively care, not caring represents agency for other people over those who do.

what creates excellence is not that people are good at something, but because people care enough to be good at something.

Not surprisingly, hard mediocrity characterizes domains David Graeber characterized as “bullshit jobs.”

Remember Douglas Adams’ story of the Golgafrinchans Ark B? To refresh your memory, the Golgafrinchans got sick of the mediocre people in their midst: “telephone sanitisers, account executives, hairdressers, tired TV producers, insurance salesmen, personnel officers, security guards, public relations executives and management consultants.” So they convinced these mediocrities that some sort of doomsday was looming and that they had to get off the planet in a big spaceship, the B Ark. The B-Arkers were assured that the rest would follow in the A and C arks. The A Ark would contain all the excellent people, Golgafrinchans at their best: scientists, artists and such. And the C Ark would contain all the people that did the actual work. Of course, the supposed A and C Ark people never left.

Moravec’s Wedge. The problems that are hard for us are easy for computers. The problems that are easy for us are also easy for computers. What is hard for computers is being mediocre.

High intelligence, of the sort we tend to describe as prodigal genius, is also a case of the domain being bounded in a tight and leak-proof way. The difference is that the enclosed space contains an intractably huge number of possibilities with no general and tractable formula for the right behaviors. Here, learning to recognize patterns from history is key, and depending on how rich and complex your historical library is, your actions will seem more or less like magically intuitive leaps to people with smaller history stores.

Moravec’s paradox is an observation based on the history of AI: the problems thought to be hard turned out to be easy, and the problems thought to be easy turned out to be hard.

But so far, they’re not doing it quite as well as we do. Computers have learned to be mediocre, but haven’t yet learned to compete at mediocrity out in the open world.

There are instances of programs respecting the rules of the game while blatantly violating its spirit.

For instance, there are instances of programs figuring out how to use tiny rounding errors in simulated game environments to violate the simulated law of conservation of energy, and milking the simulation itself for a winning strategy. Like the characters in The Matrix bend the laws of physics when inside.

Every principal-agent game is of this sort. Every sort of moral hazard is marked by the ability of one side to pursue mediocrity rather than excellence. In each case, there is an information asymmetry powering the mediocrity.

This kind of indifference-driven mediocrity is the hallmark of games where one side is playing a finite game and the other side is playing an infinite game that isn’t necessarily evil in the Carse sense of wanting to end the game for the other, but isn’t striving for excellence either.

like honest, by-the-book bureaucrats.

Mediocrity is not about what will satisfy performance requirements, but about what you can get away with. This brings us to agency.

Humans have a rich vocabulary around mediocrity that suggests we are not talking satisficing: dragging your feet, sandbagging, pulling your punches, holding back, phoning it in, cutting corners.

You could say mediocrity seeks to satisfice the laws of the territory rather than the laws of the map.

This is just a different way of playing a finite game. Instead of optimizing (playing to win), you minimize effort to stay in the specific finite game. If you can perform consistently without disqualifying errors, you are satisficing. Most automation and quality control is devoted to raising the floor of this kind of performance.

I like to think of laziness — manifested as mediocrity in any active performance domain — as resistance to optimization.

The inner ear bones for instance, evolved from the optionality of extra-thick jaw bones.

The universe is deeply lazy. The universe is mediocre. The universe is functionally unfixed self-perpetuation, always in optionality-driven perpetual beta, Always Already Player 0.1.

Mediocrity is the functionally embodied and situated form of what Sarah Perry called deep laziness. To be mediocre at something is to be less than excellent at it in order to conserve energy for the indefinitely long haul. Mediocrity is the ethos of perpetual beta at work in a domain you’re not sure what the “product” is even for. Functionally unfixed self-perpetuation.

So middling performance itself is not the essence of mediocrity. What defines mediocrity is the driving negative intention: to resist the lure of excellence.

That being said, the most important problem in digital immortality may not be technical, but economical. It may not be about how to scan a brain, but about why to scan a brain and run it, despite the lack of any economic incentive.

Fidelity will be among the most important problems in neuropreservation for a long time to come.

An adult human brain takes up around 1.2 liters of volume. There are 1 million mm³ in a liter. If we could scale up the process from Google researchers 1 million times, we could scan a human brain at nanometer resolution, yielding more than 1 zettabyte (i.e., 1 billion terabytes) of data with the same rate.

But then, why not just create a new AI from scratch, with the same knowledge and skills, and without the baggage of your personality, memories, and emotions?

For one second, let’s assume that they could. Let’s assume that they could inject your scan with 1000 years of knowledge, skills, language, ontology, history, culture and so on.

On the other hand, it would be a pity if a civilization which can emulate brain scans is unable to imbue them with relevant knowledge and skills, unable to update them.

1000 years into the future, you could be as helpless as a child. You could need somebody to adopt you, send you to school, and teach you how to live in the future. You—mentally an adult—could once again need a parent, a teacher.

To give an example, I am a software developer who takes pride in his craft. But a lot of the skills I have today will most likely be obsolete by the 31st century. Try to imagine what an 11th century stonemason would need to learn to be able to survive in today’s society.

Given that running a brain scan still costs money in 1000 years, why should anyone bring *you* back from the dead? Why should anyone boot *you* up?

I have no idea how to solve 2. If addresses are random it is very hard to be able to route to any random address from anywhere else.

The first 3 could be mitigated by reducing the number of hops. Individual ISPs could act as one large hop, rather than multiple routers. This could reduce the number of hops for each connection and total number of logical routers that clients need to know about. Instead of having a layer for every hop the packet can be wrapped only until large networks. For example major ISPs. At this point they can absorb any attacks and your traffic is well mixed with other sources.

The client needs to know about various routers along the path. On the public internet this is a lot of data that is frequently changing.

No Leak of Incoming Connection Server Address If I receive a connection a local observer must not be able to identify which address was used to create it. (If the address is publicly known they can likely probe this by making connections and comparing traffic, but I’ll consider that out of scope of this feature.)

Non-sensitive Client Addresses If I make two connections, the remote end should not be able to tell if I am the same person. This property should hold whether I connect to the same host twice, or two hosts that coordinate.

The most obvious sign of this is the many non-technical people know what an IP address is. I don’t think the average user should need to care how their networking works, but due to issues with the protocol many people are aware that their IP is sensitive information and that they need to protect it.

desktop picture phones

It may seem insurmountable, but the alternative is a single dominate political vision where only one form of data management exists: large data centers filled with coarse data points and countless assumptions about what that data says about who we are. This is indeed a bad idea.

Any real political response will require more than introducing new tech. For example, consider all the institutional support needed to manage a currency in your country. And then remember that a person’s identity is infinitely more complex than any financial abstraction.

Data must have some sense of authenticity to be useful, it must have some contextual meaning to be shared, and it must leave room for error if it is to be alive.

These tags can lose their meaning in different cultural contexts or on different platforms - the #meta tag might imply something different on Flicker versus its use on Facebook. But machine learning has flattened these differences. Algorithms can now identify similar data across the internet with or without tags or vocabularies.

Widely-adopted semantic vocabularies are usually refined by a small group of experts until a rough consensus is reached. This approach can be seen in contra-distinction to tags. Tags also provide meaning to data, but they are formed organically by a large group of people generating a large number of tags; tagging trends then emerge over time and provide context for information.

Solid does not work with immutable data, but folks are working on similar guarantees through a Solid non-repudiation service.

Errors propagate a lot faster than they can be repaired. […] If something is screwy, can we trace the calculation back and figure out which input it depends on? Philip Agre

There is also a way to discover the name of a VC even if you don’t know it through the .well-known convention. Solid doesn’t offer the same benefits, but changes to linked data can theoretically propagate. And Solid folks are working on a Webhook (v 0.2.0) standard to serve updates to linked data over REST.

link rot a

Domain Name System (DNS)

All data in the flow must also have a name so it can be found. And resilient software would need a contingency plan in the event that data changes its name or goes missing. These requirements are difficult to deliver on a networked system.

Making credential checks easy ensures that we will have more of them. Which means more bureaucrats. It’s a road we must build cautiously.

I’ll instead circle back to Verifiable Credentials because they provide a “triangle of trust” to ensure accuracy over open protocols. Do I really trust that he earned a degree at that university? Don’t take his word for it, check the claim with the university.

Solid or local-first software principles would change the power dynamic between large software vendors and individual people. Solid even offers a specific open standard for application interoperabilitySolid supports RDF, which offers an open way to link data. The web-wide aspirations for linked data have fallen short, but RDF is used successfully in many important projects. while a person’s data remains on their personal filestore.

A website’s non-negotiable Terms of Service is also a set of permissions. They dictate what a website is allowed to do with a person’s data and they also dictate what a person is allowed to do with the data once it’s in the system. This power imbalance stems from the fact that 1. the website holds both the means of computation and the data to compute and 2. a person using a website often has neither.

For example, we have “ownership” over the organs in our body, but we thankfully don’t have conscious control over their management. If we did, we would need to outsource the burden of “personal organ management” to an outside entity to get anything else done.

Many futurists, especially those in Web3, want to apply property rights to aspects of our identity. Any attempts to commodify self-hood is an egregious misapplication of technology. Not only is it ethically dubious, it’s technologically incoherent. As health technologist Adrian Gropper points out, “control doesn’t scale.” Individuals simply do not have the time or expertise to manage a portfolio of online personas or a single persona that discloses only essential information to a portfolio of clients.

Philip Agre enumerated five characteristics of data that will help us achieve this repositioning. Agre argued that “living data” must be able to express 1. a sense of ownership, 2. error bars, 3. sensitivity, 4. dependency, and 5. semantics. Although he originally wrote this in the early 1990s,Phil Agre. “Living Data.” Wired, November 1, 1994. it took some time for technology and policy to catch up. I’m going to break down each point using more contemporary context and terminology: Provenance and Agency: what is the origin of the data and what can I do with it (ownership)? Accuracy: has the data been validated? If not, what is the confidence of its correctness (error bars)? Data Flow: how is data discovered, updated, and shared (sensitivity to changes)? Auditability: what data and processes were used to generate this data (dependencies)? Semantics: what does this data represent?

Identity is a natural place to start when talking about people. The following discussion is based on two observations: 1) data naturally reduces complex conceptions of identity into coarse representations and 2) data about identity is generally held in systems far away from the people they identify.

It lead him to propose an abstract concept of ambassadorsAlan Kay was originally thinking about objects interacting on a network. Currently, all network interactions follow explicit protocols. Objects of the future, Kay believes, must be able to negotiate the exchange of data even if they come from completely unknown sources. As Kay noted in the conversation with Hickey, “For important negotiations we don’t send telegrams, we send ambassadors.” in computer science. Ambassadors might like to follow a protocol, but it isn’t required. They act on behalf of a larger autonomous entity. And when two ambassadors meet, both entities they represent retain their autonomy.

Consent is the latest fashion.

Data processing is the lifeblood of large bureaucracies.

The problems become apparent when we start talking about people. Data cannot express a meaningful distinction between intelligent actors and the things they act upon; a database that tracks widget production can also store information about the people who buy those widgets. Databases then turn intelligent actors - who are often human beings - into things to be acted upon. This is where data can quickly become a “bad idea.”

Data is inherently objectifying. This property is an asset when describing inert phenomenon such as the composition of soil or the properties of various metals. Data enables the applied work of engineers and there are no direct ethical considerations.

I have often described imag­i­na­tion as a muscle — one that, like any other muscle, can be developed.

It’s easier to write the defeat than the victory, isn’t it? Easier to write the failure than the success. For some reason, the success seems like it might be … boring.