If we are to flourish as a species, an erosion of belief will be necessary, that says we are not the center of the universe but a dynamic part of an expanding and contracting future that celebrates and collaborates with uncertainty. … We are a species known as Homo sapiens, often paralyzed by despair, having forgotten who we are together in our adamant claims of difference. Fortunately, we live among other species, many unknown to us, who show us how to enter the home of another and offer the gift of attention and presence, which is an exercise in vulnerability.”

— Terry Tempest Williams, Erosion: Essays of Undoing, 248-250

This rad stuff is regular old hard-ass silica, tiny tetrahedra permanently locked up, but before that it’s a hot, high-pressure broth shooting through deep rock, hunting for cracks, fissures, and cavities where it can pool and cool and grow. Millions of years pass in a flash. Meanwhile, layers of the ancient dead lying all around, aka organic matter, liquify into natural petroleum, which also leaks in. Bubbles of water and petroleum get trapped while the lattice assembles itself and the rock does its squeeze box concertina thing for more millions of years. Veins of quartz fill up with little golden blobs of fossil fuel. Eventually people start digging and find it, most recently in Madagascar. There’s not much you can do with petroleum quartz other than string it on necklaces or set it at the center of a gold band and slip it onto your beloved’s finger. They say it’s known for centering and grounding energy, so it could solve that problem with your chakras. Oh, and as you can see, petroleum luminesces under blacklight, which is why it reminds people of fireflies.

“It did not take long for reports on the deportations to reach America. At the end of August, Eleanor Roosevelt received a letter from Varian Fry describing the frantic attempts of the Vichy government to satisfy German demands. “Men, women and children are being arrested in the streets of Marseille, Toulouse, Lyon and other population centers to make up the quota. In the unoccupied zone children over five years of age are being deported with their parents, in the occupied zone some children of two years and more.”

— Michael Dobbs, The Unwanted, page 249

“U.S. Immigration and Customs Enforcement officers have been intensifying efforts in recent weeks to deliver on Republican President Donald Trump’s promise of record-level deportations.

“The White House has demanded the agency sharply increase arrests of migrants in the U.S. illegally, sources have told Reuters. That has meant changing tactics to achieve higher quotas of 3,000 arrests per day, far above the earlier target of 1,000 per day. …

“ICE’s operations appeared to intensify after Stephen Miller, a top White House official and the architect of Trump’s immigration agenda, excoriated senior ICE officials in a late May meeting over what he said were insufficient arrests.

“During the meeting, Miller said ICE should pick up any immigration offenders and not worry about targeted operations that focus on criminals or other priorities for deportation, three people familiar with the matter said, requesting anonymity to share the details.”

— Ted Hesson & Kristina Cooke, Reuters,
ICE’s tactics draw criticism as it triples daily arrest targets, 6-10-25

In an LLM, as in evolution, complexity emerges from simplicity. The computations that a transformer performs are relatively simple, involving the embedding of feature vectors, their weighting with self-attention, and the distribution of computation across heads and layers.

— Christopher Summerfield, These Strange New Minds, 163-164.

A short glossary with cites, which admittedly does not help a whole lot. I am still waiting for the light to go on:

Transformer
“A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence.”
https://blogs.nvidia.com/blog/what-is-a-transformer-model/

Feature vector
“In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis.”
https://en.wikipedia.org/wiki/Feature_(machine_learning)

Weighting
“Weights in AI refer to the numerical values that determine the strength and direction of connections between neurons in artificial neural networks. These weights are akin to synapses in biological neural networks and play a crucial role in the network’s ability to learn and make predictions.”
https://tedai-sanfrancisco.ted.com/glossary/weights/

Self-attention
“Self-attention is a mechanism used in machine learning, particularly in natural language processing (NLP) and computer vision tasks, to capture dependencies and relationships within input sequences. It allows the model to identify and weigh the importance of different parts of the input sequence by attending to itself.”
https://h2o.ai/wiki/self-attention/

Heads
“Multi-head attention is an extension of the self-attention mechanism. It enhances the model’s ability to capture diverse contextual information by simultaneously attending to different parts of the input sequence. It achieves this by performing multiple parallel self-attention operations, each with its own set of learned query, key, and value transformations.”
https://www.datacamp.com/blog/attention-mechanism-in-llms-intuition

Layers
“AI layers, also known as neural network layers, are the fundamental building blocks of artificial neural networks. They consist of three main types: input layers that receive raw data, hidden layers that process information through interconnected nodes (neurons), and output layers that produce final results.”
https://ayarlabs.com/glossary/ai-layers/

Even though her pillow was perfectly designed and very expensive, sometimes she didn’t sleep at all. The trucks banged over the bridge to the harbor all night with a shattering thud. In the morning she’d walk in a kind of pleasant fog onto the red bridge, past the tall trees shimmering in the heat and the tracks and tracks of train lines onto the street where she waited politely with men and woman dressed in beautifully tailored suits, past the shop where she bought pots of flowers, up the street with two pastry shops and a noodle bar and a Nissan dealership to the school. Before the school was the canal, dark, just a trickle of water flowing past her. 

When she had her stroke it was so dark, like falling into a dark hole, and she only thought how frightening it would be to die, and then she thought of nothing at all, except her sons. She was sure she wasn’t dying if she wasn’t dead yet. It couldn’t have happened. There was her little dog, there was the telephone she held in her left hand. There was the clock and the window and the sun.

Minato Sketches, Sharon White, page 136. It’s hard to choose only one passage to quote from this richly woven, coming-of-age-again novel. Gigi goes to Japan alone, after a long illness, bargains with her losses, rediscovers her wild heart. Find it here.

Because thought is not a language you can translate, it is more like a shape you have to feel your way around.

I just remembered that, back in 2010, I made this little site to help me figure out how to write a book of poems. Now I only half-understand what the heck I was talking about, but I guess it worked. Your Enzymes Are Calling the Ancients was published six years later. Process is weird.

AI Overview: Dealing with a Karen requires establishing clear boundaries and calmly navigating the situation. Keep your responses factual and neutral, avoid escalating the conflict, and prioritize your own safety and well-being. If possible, de-escalate the situation by removing the “audience” (e.g., asking them to speak privately).

Index of Karens:	Index of Karen:
Crazy Karens Going WILD Most Epic Karen Freakouts	WATCH Karen make sure all the marigolds get enough water
1 Hour of World’s *WORST* Karen Freakouts!	Crazy Karen freaks out over decrease in June bug population
200 of World’s Worst Karens That Went TOO FAR!	200 mornings in a row: Karen eats Cheerios again for breakfast
When Karens Mess With The Wrong People… #1	Annoyed Karen yells at mosquito
Karen Behaving Badly at Customer Service	Seconds add up while insane Karen counts out change *even PENNIES*
Entitled Karen Makes Things 100x Times Worse	Karen picks unwanted onions out of salad, ADDS THEM to husband’s plate
Karens Who Went TOO Crazy	Karen observes unrecyclable items in bin and TAKES THEM OUT!
CRAZY NEIGHBOR KAREN!	2-for-1 drama: Karen holds up line at CVS over sunscreen purchase
Karen quickly finds out! Part 1	So late texting back! Karen leaves her phone at home
WATCH: Workplace Karen BLOCKS Coworker From Leaving	ZOOM Karen MUTES SELF instead of unmuting
Karen yells at me for walking near her…	Traffic Karen waits too long at greenlight, driver behind has to beep
Angry Karen Brake Checks The Wrong Person… (INSTANT KARMA)	Exclusive: Karen pays for gas, forgets to pump it, drives away, then decides it’s too EMBARRASSING to return to gas station and explain
WATCH: Armed MAGA Karen Yells ‘Show Me Your Papers’ At Neighbor	Clueless Karen applies for passport, uses BLUE INK
Angry Karen YELLS At Kid.. *TOO FAR*	Karen drops sock outside laundromat: PEW!!

Source note: Items in left column are google-able, if you so desire. Items in right column you had to be there for.

“People with AIDS,” a woman with a megaphone would yell, “under attack! What do we do?”

And together they yelled, “ACT UP! Fight back!”

Yale watched for people he knew, but he’d have to be patient; there were thousands of protestors, and in fact it was nice that these faces didn’t all have the look of someone he’d seen around Boystown for years but just couldn’t place. It was good to be part of a horde, a wave of humans.

A chant would die out and then stop, as if it had been cut off by an invisible conductor, and then a new one would travel toward them up the street, fuzzy at first, and then he’d hear it clearly once through before joining in. As they passed the Tribune Tower, with dazed tourists looking on: Health! Care! Is a right! Health care is a right! Outside the Blue Cross building, right on the Magnificent Mile: We’re here! We’re queer! We’re not going shopping! Walking down State, the crowd tighter now, louder: Hey, Hey, AMA! How many people died today?

Rebecca Makkai, in her 2018 novel The Great Believers, plants her main protagonist Yale Tishman, so young, so conflicted, so eager for a love he can rely on, in the middle of the National AIDS Action for Healthcare March, held in Chicago in April 1990. Maybe it’s not a spoiler to say that Yale goes through a lot in this book. I won’t say more, because you are going to need to read it, especially if you weren’t alive back then. And when you finish her book, you can go online and find this coverage of the march, which will break your heart all over again if you were alive back then. And make you braver.