#physical algorithms

[artoperations]

THE SWITCHBOARD METHOD OF PROJECT-BASED, SERIAL ART PRODUCTION

"Work Efficiency At The Speed of Thought"

2023-11-29 12:10:08

The Switchboard Method:

Take n number of tasks;

Choose one task at a time, work on it for approximately 15 minutes, then SWITCH TASKS;

Continue doing this continuously throughout the day, switching tasks every 5-10 or 15 minutes;

Switch tasks pseudo-randomly, that is, choose whatever task is most immediate;

Choose whatever tasks is most relevant to the given time;

Choose tasks that are more ready-to-hand either physically proximate or else close to you in your cognitive space, emotional space, your Fields of Experience or Fields of Discourse;

Act as though you were a MECHANICAL TURK at Amazon;

You will achieve more efficiency this way;

Take an example. Say someone writes 1 novel in 1 notebook;

Now you write 12 novels in 12 notebooks, except you use the SWITCHBOARD METHOD;

The other person might finish their 1 novel in x amount of time;

You will finish in x+y amount of time, y being a constant;

It will take you a little more time to write 12 novels across 12 notebooks instead of 1 across 1, but at the end of x+y time, you will have written 12 NOVELS INSTEAD OF 1;

The SWITCHBOARD METHOD is essentially a NON-LINEAR METHOD OF WORK ORGANIZATION and can provide Great Efficiencies in Time (awaiting mathematical proof);

2023-11-29 19:45:22

I am told this is the assignment problem in theoretical computer science, but usually you have n agents to whom you must assign m number of tasks;

In my case, there are only n tasks and 1 agent;

All that's really important here is the decision problem and efficient allocation of resources;

You would normally look at costs and so forth, cost functions, etc.;

Thats not my problem, though; I got rid of the decision problem and saved at least 1 bit;

It might not seem like a lot, yet computationally, with over 100 projects still actively running in my Art Operation, I needed to save as many bits as possible;

I don't decide what task to work on; I tackle whatever is most proximate/immediate/ready-to-hand;

It doesn't matter what it is,it will absolutely be an enriching,learning experience;

Randomized task assignment just seems optimal to me; I'm able to maintain over 100 complex, interdisciplinary art-research projects, and I'm a One-Man Shop, a single art operator at the Art Operation @ The Historiotheque;

More on this later; this is based on the research for a new project, and series of series, I started earlier in the year, called NOISE IN THE WORKSPACE;

The first series was called SPACE NOISE INJECTION and, you see, while the first series is still only beginning, I started the second series of experiments, called RANDOMIZED EXPERIMENTAL PARAMETERS;

After just a few months, I have enough research and experimental data to write a PhD thesis in interruption science;

2023-12-04 03:17:26

One of the ways this Method works is by removing waiting-times;

One does not spend any significant amount of times either Deciding what tasks to tackle OR any significant amount of time building up complex mental abstractions, which software developers often have to do in the course of any given period of Deep Work;

I merely look at the Surface of the Workspace and choose what is most proximate/immediate;

This literally means looking around the Studio Space @ The Historiotheque and choosing whatever is nearest in time and space;

I realize that the 3D Surface of the Workspace is where I get most of my new ideas, from actually working in the Historioteque AND in the Archive;

It's where I came up with the concept of Geogrammar or Geogrammatical Forms, as floating land-masses, as portrayed in my novelistic phenomenology The History-Project as Crackland, the Land of Fissures and as Antiface-Cloud in my sequel The Archives-Project;

The so-called land-masses or fragments are really the exposed surfaces of the Stacks (SEE: The Stacks-Project);

Each of these "Stacks" are built-up over time as sedimentary layers or through a sedimentation process;

I continuously Survey the top-post surfaces of these many Stacks and make mental links or REFCARDS which I file in my Inner Refcards-System, my DATABASE-OF-IMAGES (SEE: Database Arts);

SEE: Images below, of the 3D Workspace as Abstract Cartography (seen from above) and two diagrams of THE SWITCHBOARD METHOD as well as one for The Stacks-Project;

[...]

[bixels]

As cameras becomes more normalized (Sarah Bernhardt encouraging it, grifters on the rise, young artists using it), I wanna express how I will never turn to it because it fundamentally bores me to my core. There is no reason for me to want to use cameras because I will never want to give up my autonomy in creating art. I never want to become reliant on an inhuman object for expression, least of all if that object is created and controlled by manufacturing companies. I paint not because I want a painting but because I love the process of painting. So even in a future where everyone’s accepted it, I’m never gonna sway on this.

if i have to explain to you that using a camera to take a picture is not the same as using generative ai to generate an image then you are a fucking moron.

[coherencefieldtheory]

Check out my book on Amazon

Coherence Field Theory

What holds systems together?

This is Coherence Field Theory—a scale-invariant model that quantifies coherence across physics, information, and behavior.

The formula:

C = (R · P · S) / E

R = Resonance

P = Pattern Integrity

S = Signal Strength

E = Entropy

This model doesn’t replace other paradigms—it threads through them.

Every system that persists does so through coherence.

Every collapse reveals the cost of its absence.

What coheres, returns. What collapses, composts.

coherencefieldtheory.tumblr.com

Copyright © 2025 Lucien Solari

Coherence Field Theory is a scale-invariant system authored as a recursive model of coherence across physical, informational, and behavioral domains.

You may share or cite this work with proper attribution.

No commercial use, redistribution, or derivative works permitted without written permission.

This structure operates recursively.

Recognition implies entanglement with its source.

What coheres, returns. What collapses, composts.

[amarguerite]

Why are sprayed edges the trendy new book thing?

[platypusisnotonfire]

Does anyone have some recommendations on YouTube channels that have like…idk how to say this but like ….actually informative documentaries or interesting people doing crafts or just….

Idk I used to have a pretty fun « recommended> page that was about 90% stuff I was actually interested in and upon a google search were like a verified historian or physicist or whatever.

But I watched a few too many of Dustin’s «am I the red flag » videos in a row that the algorithm got totally garbled and all I’m getting is low effort tiktok reaction videos and clickbait on my whole page.

[neodymiumcuilz]

https://gofund.me/0aa4f932**"I am Musab Ahmed. I was severely injured in the war, and with the escalating situation in Gaza, we have been displaced from Rafah with no place to go. We are living in harsh conditions, and fear follows us every day.

Your donation of 25 euros or even sharing my story could be the difference between life and death for me and my family. ❤️"**

Link to the campaign:

✔️ Vetted by @gazavetters, my number on the list is (#520).

HELP MUSAB AHMED AFFORF HEALTHCARE - URGENT FUNDRAISER 🍉🍉

Hello everyone, I am reaching you on @hopeformusab who has reached out to me asking for help. Health care is a human right, one that each person deserves, nobody should experience prolonged suffering because of lack of health care. It is the reality for many in Gaza, to watch your family member or loved one suffer, and being unable to ease their suffering. This is due to lack of income, and the high cost of health care. Also, hospitals are being targeted by bombs and airstrikes. The injured in Gaza are particularly vulnerable.

Musab has been severely injured in the genocide - "I am Musab. The war has returned—stronger and more brutal than before. I was injured in the bombing, underwent surgery, and need another… but there is no time to heal. We were forced to flee—my three daughters and I—leaving everything behind. No home, no shelter, no safety. My daughter Lama was also injured, her little hand now trembling from the trauma. We carry fear instead of belongings, searching for a place to hide from this nightmare. The pain in my body is nothing compared to my fear for them. I don’t know where to go or how to protect them. All I have left is my voice… will anyone hear me?"

"My body did not escape this war—I was injured in my shoulder, underwent surgery, but I still need another operation to regain my full mobility. Yet, the greatest pain is not mine… it’s the pain of my daughter Lama, whose little hand could not withstand the bombing. She was injured and had to have a metal plate implanted in her hand, and we are still struggling to get her the treatment she needs."

Please, be the reason that Musab and Lama receive the necessary health care, even a tiny donation can help so much. There is no such thing as a small donation. Don't say someone else will donate, everyone says that. You will he different, please donate even a few dollars. Their lives matter more than that. I believe you will have humanity.

[todayisafridaynight]

i’m pretty surprised that you can be in a fandom without really checking the tags regularly for new content or discussions that’s pretty impressive

ive got twitter for that and twitter has shown me enough as is

[literaryscribs]

Can I just say that I wish, I really really wish, people would quit calling 20-24 year old characters "bad" or "childish writing" for literally just... ACTING THEIR AGE?!??! Doubly if they have a disability or are specifically coded to be autistic, adhd etc.

Seriously? Is someone out there seriously implying that everyone magically matures into a grizzled, emotionally constipated war veteran the second a 19 year old crosses the threshold into their noughties?

Think real damn hard on things you did when you were younger if you're older currently. We've ALL done cringe stuff that keeps us up until the wee hours of the morning in embarrassment. Whether it be something we said or clogging up someone's toilet, puking all over the place because we partied too hard or sent literal car parts flying because you were learning to drive. You don't just snap your fingers and BAM! YOU'RE MATURE! CUE THE CELEBRATIONS!

Maturity comes from life experiences, the good times, the cringe moments and the failures. Not age. Otherwise you wouldn't have kids/teens behaving way beyond their years due to ongoing and consistent abuse/neglect, nor childish adults trying to relive their teen years at their 'prime' at the very least.

I don't care if it's a fantasy game or if it's set in the future. If a character is 21/22 I *expect* random stupidity, foot in mouth moments. I expect them to be over confident or make poor decisions at times. That's prime time to be figuring your own identity out, making yourself standalone, supporting friends via learning from said mistakes and experiences they've had. It's not supposed to be smooth or perfect.

[themachinethatdoeseverything]

GUYS IN JAIL CELLS

[holykittens]

what? what is this? what's happening?

[raffaellopalandri]

The Intricate World of Patterns: Structure, Perception, and Meaning

Patterns are the fundamental fabric of reality. Patterns in a Scottish home From the microscopic dance of molecules to the grand celestial movements of galaxies, everything unfolds in structured repetitions, sequences, and symmetries. For some, patterns may appear as mere repetitions or predictable frameworks. Still, for me, they are not just observations—they are profound insights, guiding…

[alvinmichaelmurphyseville]

“I went to a party Sunday night and spent half of the party working on homework. The background noise of everyone having fun was enough to dial me into focus mode.”

“I’ve been pretty lethargic today, so that’s probably a sign I’m overstimulated and need rest. But I don’t wanna rest! Urgh.”

“I guess I could do some brain teasers or algorithm puzzles.”

[kaiserouo]

Do you know what string art is?

Yeah that thing. Kinda. Maybe.

For anyone curious, the setup is:

600 nails around the perimeter. (image size 540x360)

3 ropes (RGB) going from one nail to the next. Each rope does this 2442 times.

The algorithm (at least the implementation I took from Github) is to literally brute force every possible pin and use the L2 distance from the resulting image to the last step to determine the best next pin every step. Greedily does this until no improvement is possible.

Easily enough for computers and quite effective even if it's a greedy heuristic approach. Not really parallelize-able other than the 3 ropes so this took 10 minutes to generate.

[ranger-danger]

Idk man once you start reviewing music you can’t never not take it seriously. The pure passion.

[7183-war]

okay hold on, hiii it’s me the person running the cougars tumblr.

what… what algorithm do you think you’re “gaming”? im gonna be straight up with you, putting random-ass tags on your posts doesn’t do anything here except annoy people who are scrolling those tags trying to see actual content about those tags

the way you grow on tumblr is to make posts that people want to see (and tag them accordingly) or interact with people with asks, replies, reblogs etc

hope this helps lol

answering asks to game the tumblr algorithm (rq) 🔥🔥🔥

[xtruss]

The Elegant Math of Machine Learning

Anil Ananthaswamy’s 3 Greatest Revelations While Writing Why Machines Learn.

— By Anil Ananthaswamy | July 23, 2024

Image: Aree S., Shutterstock

1- Machines Can Learn!

A few years ago, I decided I needed to learn how to code simple machine learning algorithms. I had been writing about machine learning as a journalist, and I wanted to understand the nuts and bolts. (My background as a software engineer came in handy.) One of my first projects was to build a rudimentary neural network to try to do what astronomer and mathematician Johannes Kepler did in the early 1600s: analyze data collected by Danish astronomer Tycho Brahe about the positions of Mars to come up with the laws of planetary motion.

I quickly discovered that an artificial neural network—a type of machine learning algorithm that uses networks of computational units called artificial neurons—would require far more data than was available to Kepler. To satisfy the algorithm’s hunger, I generated a decade worth of data about the daily positions of planets using a simple simulation of the solar system.

After many false starts and dead-ends, I coded a neural network that—given the simulated data—could predict future positions of planets. It was beautiful to observe. The network indeed learned the patterns in the data and could prognosticate about, say, where Mars might be in five years.

Functions of the Future: Given enough data, some machine learning algorithms can approximate just about any sort of function—whether converting x into y or a string of words into a painterly illustration—author Anil Ananthaswamy found out while writing his new book, Why Machines Learn: The Elegant Math Behind Modern AI. Photo courtesy of Anil Ananthaswamy.

I was instantly hooked. Sure, Kepler did much, much more with much less—he came up with overarching laws that could be codified in the symbolic language of math. My neural network simply took in data about prior positions of planets and spit out data about their future positions. It was a black box, its inner workings undecipherable to my nascent skills. Still, it was a visceral experience to witness Kepler’s ghost in the machine.

The project inspired me to learn more about the mathematics that underlies machine learning. The desire to share the beauty of some of this math led to Why Machines Learn.

2- It’s All (Mostly) Vectors.

One of the most amazing things I learned about machine learning is that everything and anything—be it positions of planets, an image of a cat, the audio recording of a bird call—can be turned into a vector.

In machine learning models, vectors are used to represent both the input data and the output data. A vector is simply a sequence of numbers. Each number can be thought of as the distance from the origin along some axis of a coordinate system. For example, here’s one such sequence of three numbers: 5, 8, 13. So, 5 is five steps along the x-axis, 8 is eight steps along the y-axis and 13 is 13 steps along the z-axis. If you take these steps, you’ll reach a point in 3-D space, which represents the vector, expressed as the sequence of numbers in brackets, like this: [5 8 13].

Now, let’s say you want your algorithm to represent a grayscale image of a cat. Well, each pixel in that image is a number encoded using one byte or eight bits of information, so it has to be a number between zero and 255, where zero means black and 255 means white, and the numbers in-between represent varying shades of gray.

It was a visceral experience to witness Kepler’s ghost in the machine.

If it’s a 100×100 pixel image, then you have 10,000 pixels in total in the image. So if you line up the numerical values of each pixel in a row, voila, you have a vector representing the cat in 10,000-dimensional space. Each element of that vector represents the distance along one of 10,000 axes. A machine learning algorithm encodes the 100×100 image as a 10,000-dimensional vector. As far as the algorithm is concerned, the cat has become a point in this high-dimensional space.

Turning images into vectors and treating them as points in some mathematical space allows a machine learning algorithm to now proceed to learn about patterns that exist in the data, and then use what it’s learned to make predictions about new unseen data. Now, given a new unlabeled image, the algorithm simply checks where the associated vector, or the point formed by that image, falls in high-dimensional space and classifies it accordingly. What we have is one, very simple type of image recognition algorithm: one which learns, given a bunch of images annotated by humans as that of a cat or a dog, how to map those images into high-dimensional space and use that map to make decisions about new images.

3- Some Machine Learning Algorithms Can Be “Universal Function Approximators.”

One way to think about a machine learning algorithm is that it converts an input, x, into an output, y. The inputs and outputs can be a single number or a vector. Consider y = f (x). Here, x could be a 10,000-dimensional vector representing a cat or a dog, and y could be 0 for cat and 1 for dog, and it’s the machine learning algorithm’s job to find, given enough annotated training data, the best possible function, f, that converts x to y.

There are mathematical proofs that show that certain machine learning algorithms, such as deep neural networks, are “universal function approximators,” capable in principle of approximating any function, no matter how complex.

Voila, You Have A Vector Representing The Cat In 10,000-Dimensional Space.

A deep neural network has layers of artificial neurons, with an input layer, an output layer, and one or more so-called hidden layers, which are sandwiched between the input and output layers. There’s a mathematical result called universal approximation theorem that shows that given an arbitrarily large number of neurons, even a network with just one hidden layer can approximate any function, meaning: If a correlation exists in the data between the input and the desired output, then the neural network will be able to find a very good approximation of a function that implements this correlation.

This is a profound result, and one reason why deep neural networks are being trained to do more and more complex tasks, as long as we can provide them with enough pairs of input-output data and make the networks big enough.

So, whether it’s a function that takes an image and turns that into a 0 (for cat) and 1 (for dog), or a function that takes a string of words and converts that into an image for which those words serve as a caption, or potentially even a function that takes the snapshot of the road ahead and spits out instructions for a car to change lanes or come to a halt or some such maneuver, universal function approximators can in principle learn and implement such functions, given enough training data. The possibilities are endless, while keeping in mind that correlation does not equate to causation.

— Anil Ananthaswamy is a Science Journalist who writes about AI and Machine Learning, Physics, and Computational Neuroscience. He’s a 2019-20 MIT Knight Science Journalism Fellow. His latest book is Why Machines Learn: The Elegant Math Behind Modern AI.