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Court records belong to the public. Yet anyone seeking access to federal court filings through PACER, a government software system that stands for Public Access to Court Electronic Records, is usually required to pay hefty fees to search for and view documents. PACER’s fees have long acted as a barrier that makes it hard, especially for low income people, to see and understand the work produced by our own public servants.
That’s why EFF joined a broad group of organizations supporting the Open Courts Act of 2026, legislation that would modernize the federal courts’ electronic filing systems and eliminate PACER fees.
The bill would replace the aging PACER and CM/ECF systems with a modern, unified platform designed to improve public access, strengthen cybersecurity, and reduce long-term costs. Supporters note that PACER currently collects more than $150 million annually in fees from the public, despite court records being public documents.
The Open Courts Act would also make court records easier to find, access, and understand. The legislation builds on a similar proposal, also supported by EFF, that previously won bipartisan support in the Senate Judiciary Committee but did not become law before the end of the congressional session.
This is not a new issue for EFF. More than a decade ago, we criticized PACER’s paywalls and the removal of some court records from online access, arguing that the public should not have to pay to read the law and the judicial decisions that shape it. The Open Courts Act would move U.S. courts a big step closer to that goal.
In addition to EFF, the bill is supported by Fix the Court, the group pushing this bill forward; the Free Law Project, which maintains RECAP, software that has created a large archive of legal opinions and other court records; as well as civil society groups, open government watchdogs, and media groups.
Public access to the courts is a cornerstone of democratic accountability. Let’s eliminate unnecessary barriers to court records, and bring the federal judiciary’s tech into the modern era.
Read the full letter supporting the Open Courts Act of 2026
Robert Caskin “Bobby” Prince III, beloved husband, father, grandfather, brother, uncle, veteran, attorney, musician, composer, and friend, passed peacefully into Heaven’s Musical Gates on June 16, 2026. Born March 12, 1945, in Madison, Indiana, Bobby was the eldest son of the late LTC Robert C. Prince, Jr. and Dorothy Humber Prince. As the son of an Army officer, his childhood included family moves to Birmingham, Alabama, before settling in Athens, Georgia, where he was raised and where the seeds of a remarkable life in music were first planted. Bobby graduated from Athens High School and attended the University of Georgia. During his youth and early adulthood, he performed with many talented musicians and bands throughout the Athens music community, including the area’s original “Jesters,” along with his gifted beloved brother, David Prince. Music remained a constant thread throughout his life and was shared with family, lifelong friends, and fellow musicians. Bobby served in the United States Army during the Vietnam War as a platoon leader. Following his military service, he pursued careers in counseling and law before ultimately becoming one of the pioneering composers and sound designers in the video game industry.
His innovative work helped define an era of gaming and influenced generations of players around the world. Through his compositions and sound design for landmark titles including Doom, Doom II, Wolfenstein 3D, Rise of the Triad, and Duke Nukem 3D, Bobby helped establish video game music as a respected art form. In 2005, the Video Game Industry honored him with a Lifetime Achievement Award. In 2026, the soundtrack to the original Doom was selected for preservation in the Library of Congress, ensuring that his groundbreaking work would remain part of America’s cultural heritage for generations to come.
In 2005, Bobby began a wonderful new chapter when he met and married his soulmate, Connie Freeman Prince. Together they made their home in Pigeon Forge, Tennessee, where they shared twenty-one years filled with enduring love, music, creativity, faith, laughter, and devotion. One of their most treasured memories began with Bobby’s unforgettable marriage proposal at Dollywood. After arranging for a giant message to be displayed on the passing Dollywood Express Train, he surprised Connie by appearing with a song and a proposal on one knee—a moment that perfectly reflected his creativity, romance, and joyful spirit. As creative partners, Bobby and Connie wrote songs and stories, produced musical recordings and videos, performed together, and brought inspiration and joy to many through their shared gifts.
Those closest to Bobby knew him not only for his extraordinary accomplishments but for his kindness, humor, humility, generosity, creativity, and deep love of family. Whether composing music, telling stories, playing guitar, sharing laughter, or offering encouragement, he approached life with gratitude and an open heart.
Bobby is survived by his devoted wife, Connie Freeman Prince; his sons, Robert Caskin Prince IV and Andrew (Cristy) Prince; his cherished granddaughter, Anabel Prince; his sister, Patricia Clark; his sister-in-law, Woodie Prince; nieces Ellen Moore, Lori (Kelvim) Escobar, Molly (John) Seawright, and Tiffany Thomas; nephews Mark (Christine) Moore, Neil Moore, David (Elizabeth) Prince II and Gabriel Prince; great-nieces Kaylin Prince, Caroline Prince, and Julia Moore; great nephews Trenton (Cassie) Epps, Frankie Moore, and Nathaniel Moore; Jackson and Davis Prince, and many more beloved great-nieces, great-nephews, extended family members, and dear friends. He was preceded in death by his parents, LTC Robert C. Prince, Jr. and Dorothy Humber Prince, and by his beloved brother, David Prince, and brother-in-law, Bob Clark. Bobby was also deeply loved and adored by Connie’s family, who embraced him as their own, and by a large circle of extended family members and cherished friends whose lives were forever enriched by his friendship, music, humor, and love.
Connie and the family wish to express their deepest gratitude to the countless “Earth Angels” at Dollywood and Beyond, whose prayers, kindness, encouragement, and loving care surrounded Bobby throughout his illness. Special thanks are extended to the physicians, nurses, therapists, caregivers, and staff of the VA, UT Medical Center, Vanderbilt University Medical Center, Covenant Health, Enhabit Home Health, and Amedisys Hospice, whose compassion, skill, and devotion brought comfort, dignity, and support throughout his final journey.
While many throughout the world will remember Bobby for the music and soundscapes that helped define a generation of gaming, those who knew and loved him personally will remember something even greater: a man of talent, integrity, humility, faith, laughter, and love whose greatest joy was sharing his wit and wisdom with family and friends.
Bobby Prince’s Legacy lives on through his Music…His Love lives on through our Hearts.❣️
Stay Tuned for Future Announcements of Bobby’s ‘Celebrations of Life.’
To send flowers or plant a memorial tree in memory, please visit our flower store.
Jun 18, 2026
A shift is happening among major AI labs, who are becoming increasingly skeptical of endless parameter count and training data scaling. The limits of this paradigm were put on the world’s stage when Claude Fable 5 was restricted by the US government just three days after its release, marking the first US AI ban stemming from national security. One of the biggest models in the world was banned because a single jailbreak was too much of a risk.
Bigger is better
The above is true in almost all cases. The biggest models in the world clearly score the highest on the Artificial Analysis Intelligence Index. Yet, Z.ai’s newest, GLM-5.2 (753B parameters, roughly 40B active), comes within just 4 points of GPT-5.5 and 9 points of Fable 5. Opus 4.8 and GPT-5.5 are proprietary and estimated to be in the 1 – 2T parameter range conservatively. If an open weight (MIT licensed) LLM can come so close to a closed weight model estimated to be 1.5 to 2 times bigger, it is clear that actual intelligence has plateaued significantly.
Bigger is not better
It’s been proven that when a model is trained on large volumes of highly factual and non-theoretical data, it learns to always have an answer. DeepSeek V4 Pro (1.6T params, 49B active, 44 AA Intelligence Index score) has a ludicrous 94% hallucination score on the AA-Omniscience benchmark, meaning on questions that it couldn’t figure out, it only stated that it didn’t know around 6% of the time, and the rest it confidently hallucinated an answer. GLM-5.2 scored a 28% hallucination rate, Opus 4.8 was 36%, Fable 5 was 48%, and GPT-5.5 was 86%.
That seems incredibly rough for such a huge, popular model. Let’s test it with a relatively complex Python question with a clear architectural flaw.1
DeepSeek V4 Pro used almost 10 times the reasoning tokens yet produced a confidently incorrect response. On the other hand, it took GLM-5.2 just 12 seconds and about 800 reasoning tokens to recognize the technical impossibility of a single-threaded task executing multiplexed I/O without ever yielding or utilizing system polling. (For the non technical, this is like asking a delivery driver to drop off packages at 3 houses at the same time without ever stopping the truck.)
GPT-5.5 and DeepSeek V4 Pro are two of the clearest hallucination leaders, despite being absolutely huge. Because of their immense size they simply did not learn how to say “I don’t know” or recognize intricate logical and technical fallacies. While it is true that a multi-trillion parameter model will always beat a lightweight consumer model on paper (today at least), the commoditization of these huge models is blurring the line between benchmark performance and actual real-world truthfulness and accuracy.
The trilemma of modern AI
We should be very cautious about blindly increasing reasoning budget, corpus size, or parameter count. DeepSeek V4 Pro spent 3 minutes and 26 seconds wasting compute in a reasoning loop (raw reasoning here) just to generate a beautifully structured, confidently incorrect solution. Yet, a model half its size identified the paradox almost instantaneously. Even in today’s era as we near AGI, many of the biggest models will actively convince you that a solution is correct and that the problem was solvable as stated.
Moving forward, the industry cannot continue to train bigger and bigger models since their intelligence not only plateaus but often will get worse. This applies for the consumer too, since we cannot continue to select models based on size or theoretical performance alone. Training and selection of AI needs to be designed around the unsolved trilemma of modern LLMs: raw capability, uncertainty calibration/hallucination rate, and computational efficiency.
Footnotes
Both models were given “high” reasoning effort, temperature 1, tested on OpenRouter, with the following system prompt: “You respond professionally. You are a highly capable coding assistant well-versed in Python.” GLM-5.2 was served by Z.ai (FP8 precision) and DeepSeek V4 Pro was served by Baidu Qianfan (FP8 precision). ↩
Both models were given “high” reasoning effort, temperature 1, tested on OpenRouter, with the following system prompt: “You respond professionally. You are a highly capable coding assistant well-versed in Python.” GLM-5.2 was served by Z.ai (FP8 precision) and DeepSeek V4 Pro was served by Baidu Qianfan (FP8 precision). ↩
Copyright (c) 2026 Oliver Shrimpton. All rights reserved
There are colors that I want to show you, but I can’t. They exist in the real world. You probably saw some of them today, but I can’t show them to you on a screen. A digital photograph can’t capture them, and your screen can’t display them. No game you’ve ever played has contained them. Unless you have specialized equipment, they are entirely absent from the digital world.
Most of them are cyans. On screens we live a life starved of cyans. It is shocking when you see one in person. They seem unfamiliar and intense in an otherworldly way. I want you to experience that, but again, I can’t show them to you. Instead, I have to show you how to find them in the real world.
“You sound like a crazy person, what are you talking about?”
(If colorspaces and the CIE chromaticity diagram are already familiar to you, you can skip to the next section.)
Light is made up of wavelengths, and its collection of wavelengths is called its spectrum. Your eyes have three different kinds of cone cells for seeing color, each of which respond differently to different wavelengths. Importantly, the cells in your eyes do not register what wavelength they are seeing. They can only respond, or not, with a certain intensity. Everything your brain figures out about the color of the world comes from contrasting the intensity of the responses of those cells.
Essentially all your cone cells can do is yell at your brain. Each of the cells wakes up and yells at your brain at a different volume, and that’s it. All your brain has available to work with to see color is how loud each of those cells are yelling, and has to reconstruct the whole rainbow from that alone.
A direct consequence of this is that any two spectra that make your cones all yell with the same pattern are indistinguishable to your brain. Even if the spectra contain entirely different wavelengths of light, to you they will look the same color. You don’t actually see light, not directly. You see how loud your cone cells yell.
Suppose color screens didn’t exist, and you were trying to design one for the very first time. The fact that we only have three different cones would seem very convenient. If you can figure out how to manipulate each of those three different cones independently, then your screen can make any human who looks at it see any color that a human can see. It doesn’t matter if it doesn’t show the real light spectra of real objects. All that matters is that the screen manipulates human cone cells, and can make them yell at human brains at different volumes. If you can do that, you’ve solved the whole problem. You might notice the suspicious coincidence between three cone cells and three primary colors. This is not a coincidence.
In 1931, CIE, (International Commission on Illumination) set out to characterize the whole space of human color vision. They produced this graph.
The outer rim of this graph shows every individual wavelength of light that humans can see. In the space enclosed by that rim are all the colors that can be produced with mixtures of those wavelengths. The points in this graph combine linearly, so if a color is in between two wavelengths, you can make that color by mixing those two wavelengths.
On this map they marked three wavelengths of light to be primary colors, and any color inside the triangle of those primary colors can be made by mixing them. The goal of these primary colors is to yank around your cone cells, and they picked these three because each of them yanks around one cone more than it yanks around the other two cones. This gives you pretty good control over a person’s eyes. You can almost make them see any color, but not quite.
Right away you see the problem. There’s a whole giant lobe of green/cyan/blue that can’t be made by mixing the primaries they chose. The green and blue primaries make one of your cones yell more than they’re supposed to. You can see this clearly on a chart of how to mix the primaries to make each wavelength. To make cyans that are cyan enough to be the most cyan thing we can see, you’d need to have negative red. Negative red doesn’t exist.
But wait, it gets worse. To make isolated pure wavelengths of light, CIE used prisms to scatter the light, followed by narrow slits to select a tiny band of a pure wavelength, a device called a monochromator. This is necessarily a big heavy bit of equipment that wastes most of its light, not something you would want to carry around in your pocket for a screen. When it came time to invent color TV, they didn’t use monochromators, they used phosphors. Phosphors don’t glow at pure wavelengths, so there was no physical way to push the primary colors on color TV to the edge of the chromaticity graph. Due to the limits of the phosphors they could make, we ended up with this.
That is, frankly, just not a lot of color. We have a much wider variety of light making technology available to us today. We have LEDs. We have lasers. We could do way better now. But CRT monitors displayed color with the same tech as color TVs, standards are standards, and most applications that use color are stuck inside that little window. This is called the sRGB gamut. Standard PC monitors, basically the whole internet, and mass market photography all live inside of sRGB. Critically for this article, matplotlib, the library I’m using to make graphs, only supports sRGB, so none of the colors outside of it will be represented in these graphs. Apple being Apple decided that wasn’t good enough so improved things a little bit.
This slightly wider triangle is standard now on essentially all smartphone screens regardless of manufacturer, all Macs, and most smartphone photos. Whether the content you’re viewing on the screens actually exercises the full color range is a different question, and is dependent on whether everything in the chain from the source to your eye preserved the colorspace.
It is not just our screens that are depriving us of cyans, it is also our lights. By unfortunate coincidence, the exact colors that screens can’t reproduce are also poorly reproduced by LED lighting. White LEDs are most commonly made with a blue LED and a yellow phosphor, and cyans fall right in the gap between the two. High CRI bulbs improve this by adding several different phosphors, but cyans are still the light they emit least.
It’s not enough to get off your screen, you’ll also have to go outside. Let me show you where.
Color Atlas
Natural Filters
When you look at a plant under normal light, its leaves are almost always within the sRGB triangle. Plants are green, but they aren’t that green. Their leaves absorb a lot of blue and red light, but not so much that it pushes us to the edge of the colorspace. The magic happens in a deciduous forest, when the light isn’t just reflected, it is transmitted. The transmittance curves of foliage are much more selective than their reflectance curves, so the color you see passing through a leaf is much more saturated than the color that bounces off of it. You’ve probably noticed this in person. A leaf lit by sunlight looks from the top to be relatively ordinary, but from underneath, it glows.
A single pass through a leaf knocks out all of the blues, and half of the reds, but the light then continues on, passing through other leaves, and bouncing off other leaves. These effects stack exponentially. The more times the light interacts with a leaf, the more it is purified to its spectral peak, generally around 550 nm. The colors you’ll see will be all the greens and yellows contained in the lobe traced out by the paths of repeated reflections and repeated transmissions. A green leaf lit by light that passes through another leaf one time is already outside of the gamut, greener than green.
When you’re standing in a maple forest at noon in the middle of summer, the intensity of the green is indescribable. Being in a fully lit and fully leafed deciduous forest is like being underwater if the water were green, which brings us to our next subject, water.
Water aggressively absorbs reds, slowly absorbs greens, and barely absorbs blues at all. This pattern pushes nearly any spectrum with blue and green in it out of the sRGB gamut almost immediately. When you look at sand in the shallow water near the coast, it traces a curve through colorspace as the depth of the water changes. The light from the sun is filtered once as it passes through the water on the way down, bounces off the sand, and filtered again as it comes back up to your eye. White or yellow sand will first shift to unrepresentable cyans, then to unrepresentable blues, and then finally converges close to the sRGB blue primary again once the water is very deep and dark.
But what happens if we combine water with a forest? Water in the wild isn’t just pure water, there are a lot of microscopic living things in it, and most of those little guys photosynthesize. They’re green just like leaves. Real water is like a mixture between pure water and a forest, and the density of phytoplankton in the water determines the path the spectra take as the water gets deeper.
When you are looking from above, the scattering of the light by the water itself and the particles in it begins to dominate the color of the sand. The depth of saturation the color can reach is limited, because mostly what you are seeing in deep water is light reflected back at you through just the top layers of water.
Just like in a forest, the real magic happens once you go inside of it, once you dive. If you are deep in the water itself, you are past the scattering, so the water and the plankton can repeatedly filter the light to their combined spectral peak before it arrives at your depth. You can fill nearly the whole gamut this way, but the BBC can’t show it to you on Blue Planet. It is more vivid than video can capture. Underwater photographers often use filters to block out blues, so that the whole scene doesn’t just clip against the limits of their sensor. These intensities of blues and greens are mostly unknown to the surface world, and beyond what we even have language to describe.
Note the commonalities of these processes. To get to the edge of the colorspace, they had to repeatedly filter light. Most natural materials are not so selective in their reflectance that their color includes none of the light on the opposite side of the color space, and that opposed light pulls the color in towards the center. It’s only by applying this process several times that the color is purified. There are however some processes in nature that are capable of this kind of filtering in one step, most commonly in birds.
Birds, Butterflies, and Structural Color
If I were writing this article for birds, It would be shorter to write about the inverse, the small set of bird colors that screens can show. Screens were designed for our mammal eyes, not for birds, and mammals, all mammals, can barely see color. We’re descended from tiny nocturnal scurrying things that lived during the Cretaceous. Our senses adapted accordingly. We have great noses, and good low light vision, but we lost most color vision. At night it’s just not worth differentiating the wavelength of a photon when there are barely any photons to go around. Better to indiscriminately absorb any scant few that make it into your eyes. Only primates have re-evolved the ability to tell reds from greens. Tigers are orange because deer, their primary prey, can’t tell the difference between tiger orange and grass green. Of the two colors, orange is easier for melanin to make, and to a deer, both orange and green are the color of grass.
Birds, however, are descended from big stomping dinosaurs that ruled the days, and have eyes perfectly adapted to the spectra of sunlight. The peak sensitivities of their cones are evenly spaced in the spectrum. They even have an independent cone for seeing ultraviolet light, which makes their fully saturated color space 3 dimensional. You cannot make a chromaticity diagram for a bird in a flat image. You’d have to make a chromaticity volume. A screen made for humans can’t even approximate the vision of birds. To them it would look like black and white with one added color.
The famous T-Rex Jurassic Park scene is totally implausible. It might be possible to salvage it by claiming that T-Rex had poor vision in low light, that is a competitive advantage of mammals, except that we also know that T-Rex’s eyeballs were some of the biggest in the animal kingdom, way bigger than an owl’s.
The quality of bird color vision has given birds much more reason than mammals to evolve vibrant colors for display. If mammals evolved vibrant colors, most other mammals couldn’t see them.
To make intense yellows, oranges, and reds, birds use the same chemicals, carotenoids, that make vegetables like tomatoes or carrots (eponymous) the same color. No animals can synthesize them themselves, so birds transfer them straight from their diets to their feathers, with sometimes just a little metabolism to shift their color on the way. To make blues and greens however, birds use an entirely different strategy, and this is the other reason for the intensity of bird coloration. Feathers also have a much wider variety of tools to make color.
We tend to think of light as a diffuse uniform field, or an abstraction, if we think about it at all, but real physical light has a length, and the length isn’t as small as you might think. The wavelengths of light you can see range from about ½ to ¾ of a micrometer, which is about 1/10th of the thickness of a strand of spider silk, or about 1/20th the thickness of plastic wrap. Light is small, definitely microscopic, but still similar to the size of real things.
The length of light determines where light can “fit.” Anything in nature that has patterns at around that scale can interact with light physically, not just chemically. You’ve seen this in the rainbows on a soap bubble, or in an oil slick. The liquid spreads out very thin, thin enough that it physically interacts with the length of light. Small variations in the thickness shift which colors it interacts with, which is how you get rainbows out of it. This is how birds make some of their most intense colors, especially those in the blue/green part of the spectrum.
Feathers are basically fractal hairs, as if a strand of hair grew hairs, and that hair also grew hairs, and then that hair also grew hairs. They have hairs on top of hairs, four levels deep. The first hair is the rachis, the shaft of the feather. The second hairs, the barbs, stick out laterally from the shaft, and are the smallest parts you can clearly see with the naked eye. Barbules extend sideways from the barbs, and zip together, using the fourth even smaller hairs, barbicels, as hooks that cling to each other when they are preened.
The barbs are too thick to do complicated things with light on their own, but the barbules are almost the right thickness. Birds that have flat omnidirectional color, like bluejays, make color inside their barbs by filling them with bubbles that are half the width of a wavelength. Birds that have iridescent colors, like hummingbirds or peacocks, make them in their barbules by stacking thin layers of dark brown absorbing melanin spaced half a wavelength apart. Light that is the right size can dodge the browns, but light that is bigger or smaller hits them and is absorbed.
Iridescent colors tend to be the most saturated structural colors. For a structure to be selective of the wavelengths that it reflects, light that hits it must always encounter gaps of the same width. It is difficult geometrically to do that in exactly the same way at every angle. From some directions the waves will fit nicely and reinforce each other. From others they will be askew, not fit, and be absorbed. Hence, iridescence.
Peacocks are a prototypical example. Using just the shape of the melanin layers in their barbules, peacocks can make half a dozen different colors. The blue on their chests and necks, and the cyan ringing the eyespots on their trains, are both outside the gamut, but all of these are made with the same dark brown pigments, spaced in layers to absorb all the photons whose length doesn’t let them weave between them. If you ground a peacock feather to powder, even if you were careful to only use regions of the same color, the result would be dark brown.
There are around 500 species of birds with colors outside the sRGB gamut, and around 100 outside Display-P3. (The dataset I used was not exhaustive. There are probably more.) Some birds, like the male golden-tailed sapphire, a hummingbird from the western Amazon, have practically the whole spectrum in one bird.
Browsing the outer edges of this graph was a delight, and while I can’t show you what they truly look like through a photo or a screen, I want to at least show you the highlights and a suggestive hint of their appearance, which is all a photo can be.
The quality of bird color vision didn’t just affect the coloration of other birds, it also affected the colors of their prey. Butterflies, in order to show off to birds that they are unpalatable or toxic, evolved iridescence dozens of separate times. The group of aptly named birdwing butterflies, with the largest wings of any butterfly, have the rare distinction of a species with an orange too orange for a Display-P3 screen. Ornithoptera Croesus has a color as rich as Croesus.
The scales on an iridescent butterfly’s wings are so complicated and varied that it is difficult to generalize between them, and even difficult to describe them as having a “color” rather than a range of colors in different circumstances. A single papilio palinurus butterfly can sweep across the colorspace from green to blue with different angles of view, or yellow to blue with different polarizations of light.
The morpho genus is perhaps the most famous, huge neotropical butterflies with a variety of intense blues and cyans between them and within them. I have a mounted specimen of morpho rhetenor. I can take a photograph of it, but in person it looks nothing like the photograph. I lack the words to describe how it differs from the photo except that it is somehow both more blue and more green.
Luminescence and Fluorescence
Deep in the ocean where there is no remaining light, animals have to make their own. The light they make could be any color, but water in the deeps still has the same absorbing properties that it does at the surface. If that glow is going to travel more than a short distance, it has to be blue or green. Creatures that glow cyan are abundant in the deep ocean, but sometimes the color comes to the surface. When the conditions are right, microscopic bioluminescent dinoflagellates bloom in surface waters in enormous numbers. If the night is dark enough to see it, they fill crashing ocean waves with the glow of the deep.
In some warm hypersaline lagoons, like on the island of Vieques in Puerto Rico, the conditions are always right, and anything dipped in the water at night, such as a kayak paddle, leaves a trail of cyan light behind it.
If you can’t catch a dinoflagellate bloom on the shore, or descend into the depths of the ocean, there are other species above water that glow with a similar color, but you still have to descend into the depths. In New Zealand caves, wherever the rocky ceilings stretch over water, they are speckled with cyan stars. The black pools of water below mirror the constellations above. These lights, despite looking like ocean bioluminescence, are made by glow worms, with an independent chemistry and evolutionary history. The worms make light to attract prey into their dangling mucus strands, which stretch up to two feet down from the ceiling, but are invisible in the dark. Better to keep the lights off.
There is another source of this color on land best seen in the dark. If you walk around any arid area at night with a black light flashlight, you may see things glowing cyan in the grass, things you may not have ever otherwise known were there, scorpions. Nearly every species of scorpion intensely fluoresces under UV light, with roughly the same teal as a dinoflagellate or glow worm glow.
No one knows for certain why. The primary theory is that it helps the scorpion see itself. Scorpions have photoreceptors in their tails, separate from their eyes. Scorpions also rely on hiding for their survival, lots of animals think of scorpions as a big tasty meal. It is hypothesized that a scorpion uses this fluorescence to tell whether any bit of its body is left exposed from its hiding place. Its tail “looks” down at its body, and if it sees its own fluorescence, it knows it is exposed to light, and in danger.
Man Made Color
“But Ryan,” you say, “I’m stuck at my desk right now. I can’t go to the beach, I can’t go to the woods, I can’t go out looking for tropical birds and butterflies, and I can’t go black light hunting for scorpions, as much as I would like to. Can’t you show me anything closer?”
You’re in luck. Today, on your way home, look at the “green” light on a traffic signal. It’s not green.
This may be the most acute Sapir-Whorf example I know of, that calling a “green” traffic light “green” was enough to make me ignore what my own eyes were telling me for my entire life. Green traffic lights are a beautiful indescribable turquoise, the most intense turquoise you’ve ever seen.
You’ll feel crazy once you see it, and want to run around telling everyone. Green traffic lights not only aren’t green, but they’re also exquisitely beautiful. My commute home the afternoon I learned about this was transcendent. I felt like my life suddenly had an entirely new sensation. How could I never have noticed? Green traffic lights are anti-memetic because you only stare at a traffic light when it’s red.
This is a good time to spare a thought for our red-green colorblind brethren. It is unlikely that any of them have read this far about a subject so alien to what they can experience, but it is to them that we owe the beautiful color of green traffic lights. The spectral requirements that make the green signals distinguishable from red in their eyes make them beautiful in ours.
The NIST standard for traffic lights has some tiny region of overlap with the display gamuts, but modern traffic lights are made with LEDs, and all LEDs (unless they have an added phosphor) make nearly pure spectral colors. This is probably the cheapest and most practical way to reproduce the whole colorspace. LEDs in spectral colors from one end to the other are readily available commercially.
While the band gap of LEDs admits only a very narrow range of wavelengths, there are some sources that are even more pure. Lasers are basically light duplicating machines. By energizing certain materials, a laser creates the conditions where one photon passing near an atom can cause an exact duplicate of the photon to be emitted. That duplicate goes on to create new duplicates by passing close to other atoms, in a chain reaction. Even if light enters the medium with a mixture of wavelengths, one wavelength will win out through this repeated duplication, and by the time the photons reach the other side, they are all exactly the same. So if you want to be absolutely certain you are seeing the purest most intense colors that it is possible to see, use lasers.
In all of my hunting, there was one region of the colorspace I was never able to fill with a naturally occurring color, my blue-green white whale. From what I’ve been able to find, no natural process emits 520 nm light at sufficient purity to make it close to the very top of the colorspace. Bioluminescent fungus peaks at around that wavelength, but the mixture of other wavelengths it emits causes its color to register far below.
This confused me for a long time until I realized it had a geometric explanation. At most of the positions at the edge of the color space, the spectral curve boundary is close to straight. An average of two points on a line always produces another point on the line, so in those regions a wider band of wavelengths doesn’t pull the color away from the edge, as long as it isn’t too wide. It is only when that band of frequencies passes the top of the curve at 520 nm and begins creeping down the other side that it pulls the chromaticity towards the center of the diagram. Extending far past 400 nm on one side or 700 nm on the other doesn’t desaturate the color, only crossing 520 nm in the center. This makes a color equivalent to the 520 nm point difficult for natural objects to produce. If the spectrum of an object is centered on 520 nm, any symmetric deviation from the peak immediately pulls the color away from the 520 nm point, and down into the center.
From this we can conclude what science fiction movies have understood intuitively all along. The most artificial color in the world, the clearest visual indication that you are interacting with advanced technology, is a green laser beam.
Qualia
At the end of all of this you might be wondering, “if I saw one of these, would I really notice? Is the difference actually apparent? Is this a genuinely new sensation, or maybe just a brighter version of what I am already familiar with?”
I can only speak for myself, but I noticed a very consistent pattern in my own sensations as I was searching for and studying color. I didn’t actually notice them, until I knew, and once I knew I couldn’t believe that I hadn’t noticed before. When you know what to look for, you attend to the sensations more closely, and they rise higher in your awareness than they otherwise would have. This is perhaps akin to what meditators report about their experience of their own self. When you ruminate on something you experience more of it.
The way we see the world isn’t just intermediated by screens. It is also intermediated by our own thoughts, what we notice and don’t, and what we think is important. In the same way that the designers of color standards had to make decisions about what sensations to reproduce and what to leave out, we are ourselves constantly triaging which of the demands on our attention are most important. The intensity of a color may not make the cut.
I can’t show you these colors, but by telling you about them I can help you notice them. When you notice, you may be astonished to find that they were there all along, and that your screens are duller than you thought they were. When you drive home today and see a green traffic light, notice it. Try to see it as bright and as beautiful as it really is.
But don’t bother taking a picture. It won’t work. Everyone else will have to see it for themselves.
Methodology and Acknowledgements
All colors of objects were rendered under the D65 standard illuminant using measured reflectance data. For data I could find in a repository, I used it directly. For data only present in a figure in a paper, I had Gemini 3.1 Pro extract it from the figure at 10 nm intervals, then plotted the extracted data to make sure it matched the original source without any gross errors. To find examples I started with hypotheses and then found spectral data to support them. There are likely many examples I didn’t find. In particular, I did not explore flowers, and did not explore synthetic pigments. (If anyone has a good data set to start from, I may add it later, or as part 2.)
The physical simulations of leaves and water I aimed to make naturalistic enough to be sure I was not misleading anyone about the intensity of colors that could be seen, without worrying too much about the exact physical circumstances where they could be seen. You might have to go deeper, or shallower, or in clearer or more fertile water than these graphs would indicate to achieve the colors depicted, but I tried to make sure I included all the important terms that might desaturate the color.
I would like to especially thank the colour python package for making this investigation possible, and the Bird Color Database for its wonderful collection which convinced me this project was doable and worth doing. And finally, I’d like to thank my family for putting up with me talking about color in every spare moment while on vacation.
References
Anderson, M., Motta, R., Chandrasekar, S., & Stokes, M. (1996). Proposal for a standard default color space for the Internet — sRGB. Proceedings of the IS&T/SID Fourth Color Imaging Conference, 238 – 245.
Mansencal, T., Mauderer, M., Parsons, M., Shaw, N., Wheatley, K., Cooper, S., Vandenberg, J. D., Canavan, L., Crowson, K., Lev, O., Leinweber, K., Sharma, S., Sobotka, T. J., Moritz, D., Pppp, M., Rane, C., Eswaramoorthy, P., Mertic, J., Pearlstine, B., … Schmidt, L. (2025). Colour 0.4.7. Zenodo.
Shawn Serbin. 2014. Fresh Leaf Spectra to Estimate Leaf Morphology and Biochemistry for Northern Temperate Forests. Ecological Spectral Information System (EcoSIS).
Serbin, S., Meng, R., Wu, J., & Ely, K. (2019). NGEE Tropics GLiHT Puerto Rico Campaign: Leaf Spectral Reflectance and Transmittance, March 2017. Ecological Spectral Information System (EcoSIS).
Pope, R. M., & Fry, E. S. (1997). Absorption spectrum (380 – 700 nm) of pure water. Applied Optics, 36(33), 8710 – 8723.
Ong, Cindy; & Daniels, Paul (2019): Reflectance Spectral Data of Australian Beach Sands. v1. CSIRO. Data Collection.
Bricaud, A., Babin, M., Morel, A., & Claustre, H. (1995). Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization. Journal of Geophysical Research: Oceans, 100(C7), 13321 – 13332.
Mobley, C. D. (1994). Light and Water: Radiative Transfer in Natural Waters / The Oceanic Optics Book. Ocean Optics Web Book.
Gluckman, T., Endler, J. (2017) Bird Color Base: Avian Coloration Database. GitHub.
Armenta, J. K., P. O. Dunn, and L. A. Whittingham. 2008. Quantifying avian sexual dichromatism: a comparison of methods. J Experimental Biology 211:2423 – 2430.
Cardoso, G. C., and P. G. Mota. 2008. Speciational evolution of coloration in the genus Carduelis. Evolution 62:753 – 762.
Doutrelant, C., M. Paquet, J. P. Renoult, A. Gregoire, P. A. Crochet, and R. Covas. 2016. Worldwide patterns of bird colouration on islands. Ecology Letters 19:537 – 545.
Dunn, P. O., J. K. Armenta, and L. A. Whittingham. 2015. Natural and sexual selection act on different axes of variation in avian plumage color. Science Advances 1:10.1126/sciadv.1400155.
Dunning, J., C. Sheard, and J. A. Endler. 2025. Viewing conditions predict evolutionary diversity in avian plumage colour. Proceedings of the Royal Society B: Biological Sciences 292:20241728.
Eaton, M. D. 2005. Human vision fails to distinguish widespread sexual dichromatism among sexually “monochromatic” birds. Proceedings of the National Academy of Sciences, USA 102:10942 – 10946.
Fargevieille, A., A. Grégoire, D. Gomez, and C. Doutrelant. 2023. Evolution of female colours in birds: The role of female cost of reproduction and paternal care. Journal of Evolutionary Biology 36:579 – 588.
Gomez, D., and M. Théry. 2007. Simultaneous crypsis and conspicuousness in color patterns: comparative analysis of a neotropical rainforest bird community. American Naturalist 169:S42-S61.
Maia, R., D. R. Rubenstein, and M. D. Shawkey. 2016. Selection, constraint, and the evolution of coloration in African starlings. Evolution 70:1064 – 1079.
Shultz, A. J., and K. J. Burns. 2017. The role of sexual and natural selection in shaping patterns of sexual dichromatism in the largest family of songbirds (Aves: Thraupidae). Evolution 71:1061 – 1074.
Stoddard, M. C., and R. O. Prum. 2011. How colorful are birds? Evolution of the avian plumage color gamut. Behavioral Ecology 22:1042 – 1052.
Armenta, J. K., P. O. Dunn, and L. A. Whittingham. 2008. Quantifying avian sexual dichromatism: a comparison of methods. J Experimental Biology 211:2423 – 2430.
Cardoso, G. C., and P. G. Mota. 2008. Speciational evolution of coloration in the genus Carduelis. Evolution 62:753 – 762.
Doutrelant, C., M. Paquet, J. P. Renoult, A. Gregoire, P. A. Crochet, and R. Covas. 2016. Worldwide patterns of bird colouration on islands. Ecology Letters 19:537 – 545.
Dunn, P. O., J. K. Armenta, and L. A. Whittingham. 2015. Natural and sexual selection act on different axes of variation in avian plumage color. Science Advances 1:10.1126/sciadv.1400155.
Dunning, J., C. Sheard, and J. A. Endler. 2025. Viewing conditions predict evolutionary diversity in avian plumage colour. Proceedings of the Royal Society B: Biological Sciences 292:20241728.
Eaton, M. D. 2005. Human vision fails to distinguish widespread sexual dichromatism among sexually “monochromatic” birds. Proceedings of the National Academy of Sciences, USA 102:10942 – 10946.
Fargevieille, A., A. Grégoire, D. Gomez, and C. Doutrelant. 2023. Evolution of female colours in birds: The role of female cost of reproduction and paternal care. Journal of Evolutionary Biology 36:579 – 588.
Gomez, D., and M. Théry. 2007. Simultaneous crypsis and conspicuousness in color patterns: comparative analysis of a neotropical rainforest bird community. American Naturalist 169:S42-S61.
Me again! I’m so happy you’re all here. Thanks for letting me nerd out in your inbox week after week.
💙 Amanda
Look out your window. Can you see three trees?
That’s the first question of the 3 – 30-300 test — a standard that has become the go-to for solving a universal urban problem: Does this city have enough trees, and are they in the right place?
The 3 – 30-300 test is simple. Every home, school and office should have a view of at least three trees, be in a neighbourhood with 30% tree cover, and be within 300 metres of a park.
Proposed just a few years ago by Cecil Konijnendijk, the rule has spread quickly. The Italian city of Florence committed to planting 50,000 trees by 2030 under the framework. Fort Collins, Colorado made it a formal planning target. Cities from Haarlem, Netherlands to Saanich, British Columbia have followed suit.
Its popularity makes sense: 3 – 30-300 is a catchy, straightforward test that sets a clear benchmark for measuring equal access to nature.
But is it achievable?
Having greenery in sight, not just nearby, is good for your head. People who can see at least three trees from their window have better mental health than those who can’t.
It seems like the easiest of the three goals to achieve, but a study assessing the 3 – 30-300 rule in 862 European cities found that only about half the population has a three-tree view.
There are fewer tree-lined views for southern Europeans
Population, by city, that achieves the three-trees rule.
When it comes to seeing green, Europe is roughly split down the middle. In half its cities, most residents have three trees in view; in the other half, the majority don’t. Cities with the poorest tree visibility tend to be in southern Europe. Valencia, in Spain, has one of the worst records: Only one in ten residents can see three trees.
How do I compare?I can only see two trees from where I’m working today. That’s one tree too few.
How do you compare?This one’s easy to assess. Just look out the window!
Is 30% of your neighbourhood covered by trees?
Viewed from above, one third of your neighbourhood should be covered by trees. As our planet warms, the consequences of not meeting that standard are measurable: Hitting the 30% goal across all European cities could prevent 2,644 heat deaths each summer, found a Lancet study. And that’s the bare minimum. Researchers in Madison, Wisconsin concluded that meaningful cooling really only kicks in at 40% tree cover.
Unfortunately, that study of 862 European cities found the tree-cover standard is rarely met.
One in three Europeans live in an area with at least 30% tree cover
Population, by city, that achieves the 30% rule.
In seventeen cities, at least three quarters of residents live in an area that meets the 30% tree cover requirement. However, these are all relatively small places, with populations of just a few hundred thousand. Interestingly, ten of these cities are tightly clustered together in Western Germany, near the Dutch border.
How do I compare?Apparently the tree cover in my area is only 17%, which I worked out using Tree Equity Score. That’s disappointing.
How do you compare?US and UK readers: You can use Tree Equity Score to find canopy cover percentages for your neighbourhood. Everyone else: Consider getting a rough estimate by using Google Maps to look at your neighbourhood from a bird’s-eye view.
Do you live 300m from a park?
Of the three criteria, this is the most-often met. Regular use of parks and green spaces is associated with lower rates of obesity, improved cardiovascular health, reduced stress and better mental health. But these spaces need to be close enough; park use drops sharply when it’s beyond a 300-metre walking distance (roughly a five minute stroll, or the length of about three American football fields).
Again, northern countries fare better. Nearly all cities with the best park access are in northern Europe.
Almost 60% of Europeans live within 300m of a park
Population, by city, that achieves the 300m rule.
How do I compare?I was absolutely convinced I would pass this last rule! But using Google Maps, I found that my closest park isn’t 300 metres away, it’s 400 metres. That’s close, but a fail.
How do you compare?Open Google Maps, drop a pin on your home and draw a 300m radius. Do you see a park? Or use the navigation feature to get walking directions to your nearest green space. It should note the distance.
An ambitious target
The 3 – 30-300 rule is simple, but that doesn’t mean it’s easy to achieve. In fact, only 14% of Europeans live in an area that meets all three criteria. And 21% live somewhere that doesn’t meet a single one.
Most Europeans don’t live in an area that passes the 3 – 30-300 test
Portion of European population living in areas that meet 1,2,3 or none of the requirements.
There are only two European cities where more than half of residents satisfy the rule: Espoo in Finland and Varese in Italy. There are also only about 20 cities where this percentage is above 40%, most of which are located in Scandinavia, Germany and Poland. These low percentages are primarily due to the lack of places meeting the 30% tree cover requirement.
The global picture is equally sobering. Testing the rule across eight major cities, different researchers found that only Singapore met the standard.
Of eight global cities, Singapore alone passes the 3 – 30-300 test
Percentage of buildings in each city that passes each part of the 3 – 30-300 test.
It doesn’t seem like too much to ask: Trees in your eyeline, shade over your street, a park down the road. And not just in the best parts of town. (As I wrote back in March: Shade, like so much else, is often a privilege of the wealthy.) These are meant to be minimum standards, not aspirations. But the findings of these two studies show that cities across the world aren’t meeting them.
And the 3 – 30-300 rule isn’t just for making nice places to live; it has measurable health consequences. People living in areas that achieve the rule have better mental health and use fewer medications. And as summer heat grows more dangerous, adequate tree cover is increasingly vital.
If you tested the 3 – 30-300 rule yourself, how did it turn out? I live in a beautiful, leafy city with lots of parks. So, I was confident about passing at least two of the three measures. But I was wrong! The data show I’m certainly not alone. And it’s likely you were surprised by your results, too.
So how can the 3 – 30-300 rule actually be implemented? I like what the researchers behind the eight-city study concluded. A simple but powerful call to action:
Tear up the asphalt; plant trees.
I DIDN’T DO IT
I absolutely did not employ the pun ‘tree-o’ in reference to the three tree-based metrics in this piece. Not once. Not. Even. Once.
THE 10 – 90 RULE
The 3 – 30-300 rule exists to make nature accessible to all, regardless of income or neighbourhood. Not-Ship runs on a similar principle: 10% of readers pay so the other 90% don’t have to. Data, like trees, should be available to everyone. If you agree, it’s only $9/month ($90/year).
FROM ELSEWHERE
Here’s what I found interesting, important or delightful this week:
The handmade web. The Tiny Awards celebrate the Internet’s small things. I particularly like the 2024 winner, One Minute Park, which lets you spend time in a public green space somewhere in the world. After 60 seconds, it shifts to a new one.
What’s normal? For the Pudding, Alvin Chang tracks 1,000 people through the ups and downs of their relationships. I love how the charts come alive with the small people icons.
MORE NOT-SHIP
People don’t linger like they used to. It’s a problem.
In public: Walking speed is up, group gatherings are down, and we’ve lost the art of lingering.
Not-ShipAmanda Shendruk
Looking for the rich? Check in the shadows.
Across the world, shade is a privilege of the wealthy.
Not-ShipAmanda Shendruk
Huh. Apparently cars don’t have to kill people.
For a fast way to reduce traffic deaths: Just slow down.
Not-ShipAmanda Shendruk
A while ago I wrote about storing two bytes inside my mouse’s DPI register. It wasn’t useful. It wasn’t practical. But it did something unfortunate to my brain. Once you’ve successfully hidden data somewhere it doesn’t belong, you start looking at everything as potential storage.
A monitor is storage.
A keyboard is storage.
A BIOS splash screen is (maybe) storage.
A favicon is storage.
And yes, here we are.
Every website has a favicon. It’s that little icon in your browser tab. Usually you upload it once and then never think about it again. But. A favicon is just an image. An image is just pixels. And pixels are just bytes.
So of course I wondered if I could store something inside one.
The idea
My first thought was steganography.
Steganography is basically about hiding data in an image without making it obvious. You take a perfect normal photograph and modify a few bits so it secretly contains a message.
The favicon itself (at least in my demo) doesn’t need to look like an icon. It could become pure storage.
Every pixel has red, green and blue values. That’s three bytes. If I wanted to store text, I could just take the UTF-8 bytes of the text and write them directly into the RGB channels.
The browser doesn’t care what those bytes represent. To the browser they’re colors. To me in this case they’re HTML.
Building a favicon website
I started with a tiny HTML payload:
<h1>Website in a Favicon</h1> <p> Everything you’re reading right now was decoded from favicon pixels. </p>
The process is pretty straightforward.
First I convert the HTML into bytes using TextEncoder.
Then I prepend four bytes containing the payload length.
The length header is important because the image itself may contain unused pixels at the end. If there’s no length value, there’s no way to know where the real payload stops.
Then I just start filling pixels: the first byte becomes the red channel of the first pixel, the second becomes the green, the third becomes blue, and then the next pixel, and the next, and the next, until the whole HTML document exists as colored pixels. The result looks like visual noise.
Very small
What surprised me most wasn’t that it worked, to be honest. It was how small the resulting image was.
The payload ended up being 208 bytes.
Adding the 4-byte header brings the total to 212 bytes.
Since every pixel stores three bytes, I needed:
212 bytes total
71 pixels
A square image large enough to contain them
The smallest square that works is 9x9 pixels.
That’s only 81 pixels.
The final stats looked like this:
Payload: 208 bytes
Image size: 9x9 pixels
Capacity: 239 bytes
Used: 87%
Somehow a whole little website (okayy, html with some styling) fits inside an image that’s smaller than the usual favicon.
Reading the website back out
Storing data is only half the problem. The other half is getting it back.
Browsers already have everything needed for this.
The favicon gets loaded as image.
The image gets drawn onto a canvas.
The canvas API lets JavaScript read every pixel.
Once I have the pixel data, I simply reverse the process.
Read the RGB values.
Reconstruct the byte array.
Read the first four bytes to determine the payload length.
Extract the payload.
Decode the UTF-8 text.
At that point I have the original HTML again.
The browser read a website out of its own favicon.
The important catch
The favicon doesn’t actually contain the whole website itself.
It contains the content of a website.
You still need a tiny bootstrap loader to decode the image.
Without the JavaScript the favicon is just a PNG (which contains your website content).
For showing this scenario the site includes a “Render Website” button. It reads the favicon, decodes the HTML, and replaces the page with the reconstructed content.
Is this useful?
No, of course not.
The amount of data you can store is tiny. The page needs JavaScript to bootstrap itself. There are dozens of better ways to distribute a small HTML document.
But at the end its about testing the boundaries, right?
A favicon feels like a very specific thing. It’s supposed to be an icon.
But at the end it can just be a PNG.
And a PNG file is basically just bytes.
And this is probably the smallest website I’ve built…
Alternative approaches
Store markup directly in SVG favicon and read it on page load.
Use PNG comment chunks like tEXt, zTXt and iTXt.
Use the ico file format since it allows multiple icons with different resolutions.
Here is the link to the site: https://www.timwehrle.de/labs/favicon-site/
And if you want to see how it works: https://github.com/timwehrle/favicon
Elon Musk became the world’s first trillionaire last week after SpaceX debuted on the stock market with a valuation of $1.77tn.
Millions of Americans could soon become indirect investors in SpaceX and other emerging AI-focused companies as US markets increasingly shift toward AI-driven investments.
Many Americans’ retirement savings are heavily tied to the US stock market through private 401(k) retirement savings plans. Those plans are heavily invested in index funds that track the major stock market indices. So even those who do not invest directly in these new tech giants may still end up owning them.
Musk pushed for a rule change to allow SpaceX shares into index funds earlier than is typical, many Americans could find their retirement savings and pensions increasingly tied to the company and other AI firms.
“We’ve all been forced into a giant casino,” said Tim, a 62-year-old engineer based in Alameda, California.
The Guardian asked people in the US their views on the SpaceX initial public offering (IPO) and how it might affect them. More than 150 responded, overwhelmingly to express concern about having their savings tied to major technology firms, citing fears over widening inequality, market instability, and the long-term sustainability of the AI boom.
For Tim, a 62-year-old engineer based in Alameda, California, investing in SpaceX is less a choice than a necessity.
“I’ve never wanted to participate in the so-called AI bubble,” Tim continued. “Basically my entire retirement is in the S&P 500. Not out of choice, but if you don’t have investments in the stock market, you’re losing ground compared to everybody who does. That’s the pernicious thing about it. There’s really no way for the average person to diversify.”
Stephen, a 33-year-old engineer from Michigan, shares his unease and describes his disgust over the growing influence of tech companies over retirement savings.
“I think that the amount is absolutely ridiculous and untethered to the company’s actual value,” he said. “I think it’s abhorrent that my savings and retirement funds are tied so intricately to these tech companies, especially when they cannot be held accountable by investors.”
Similar concerns were raised by Matt Reynolds, a 57-year-old professor based in eastern Washington, who worries both about his financial future and the influence of tech moguls.
“As someone looking to retire in the next five to 10 years, I’m alarmed at big tech’s market consolidation and its impact on my savings and investments. As a human being, I’m distraught that these companies all seem to be run by people with little accountability or moral compass,” he said. “How and why do my finances have to be bound to a racist, narcissistic, baby man who does not seem to care about other human beings? Everything about this is wrong.”
For Kendra Ford, a 54-year-old mother and climate activist based in Portsmouth, New Hampshire, the issue is both financial and moral.
“It is heartbreaking and enraging that Elon Musk can use the system to enrich himself while most people are not being paid fairly and so can’t afford food and healthcare. It’s a profound moral failing of our economic system and our society. I do think this brings us closer to profound social upheaval when the folks who are being exploited and hurt the most are going to refuse to participate,” she said.
Mia, a 58-year-old writer based in Washington DC, has taken a different approach, choosing not to invest in the stock market at all rather than prop up Musk’s plans for planetary colonization.
“I have intentionally not invested in the stock market, it’s a money game for rich people and I think it’s crazy that American taxpayers have allowed their life savings to be gambled in 401(k) accounts,” she said.
“It would be much easier to take that much money and clean up our planet than try to get to Mars and make that planet habitable for humanity. It’s a ridiculous scam,” Mia added.
Pedro, a retired businessman based in Denver, Colorado, has divested from index funds altogether.
“If we all were to do that, it would drive those stocks back to reality and send a message to the heads of those corporations who think they rule the world,” he said.
Jeffrey Munsie, a 57-year-old architect in Middletown, Connecticut, is trying to protect his savings by spreading his assets around.
“This IPO is far too large for any one entity or person to control or benefit from. That is an understatement. I am not fond of my savings and financial future being tied to the success of such large and narrowly focused companies, so I intend to now keep my investments well-diversified more actively,” he said.
But not everyone sees SpaceX’s eye-popping valuation so negatively. Some admire the company’s technological advances while still expressing concern about the concentration of wealth and power.
“I have mixed feelings about the SpaceX IPO. It is hard not to admire what the company has achieved. SpaceX has transformed the space industry, and the same can be said for some of the advances we are seeing in artificial intelligence,” said Dimitris Eleas, a 52-year-old political scientist based in Brooklyn, New York. “At the same time, I am very uneasy about the growing concentration of wealth and power in the hands of a small number of technology companies and their greedy founders.”
Steven, the engineer from Michigan, agrees.
“There is a palpable sense of unfairness and anger that our lives are inextricably tied to the choices of the few,” he said. “CEOs receive lavish sums of money even when they fail while our retirement funds and employment are married to the companies they run.”
Most of us think of espresso as a hot, high-pressure ritual. Finely ground coffee goes into a machine, boiling water is forced through it, and in about 30 seconds we get a concentrated shot with crema, aroma, bitterness, body and caffeine.
As someone from Colombia, I like to think coffee is in my blood — and I’m proud to come from a country known for producing some of the best coffee beans in the world.
So perhaps that’s why I have spent a lot of time in my laboratory with my team asking a simple question: does espresso really need hot water?
Our new research suggests the answer may be no.
Low energy, full strength
We have developed what we call an ultrasonic espresso: a room-temperature brewing process that uses high-frequency sound waves to extract the flavour, oils, aroma and caffeine from coffee grounds. The result is an espresso-strength coffee made in under three minutes, but needing far less energy than the conventional method.
Saving up to 75% of energy by not heating the water is a minor benefit for home users or small coffee shops. But for companies making ready-to-drink coffee products at industrial scale, it could be very significant indeed.
A concentrated room-temperature coffee could be used directly in bottled drinks, milk-based beverages or cold coffee products. It can also be shipped as a concentrate and diluted later. This would reduce not only energy use, but potentially processing time as well.
Ultrasound replaces heat
The key to the new process is ultrasound. These are sound waves above the range of human hearing.
In our system, a small metal device called a transducer presses against the side of a traditional espresso basket and makes it vibrate rapidly. Those vibrations move through the water and coffee grounds.
This creates a phenomenon known as acoustic cavitation. Tiny bubbles form and collapse in the liquid.
When these bubbles collapse near coffee particles, they produce microscopic jets and forces that act a little like scrubbing brushes. They pit and fracture the surface of the coffee grounds, helping flavour compounds, oils and caffeine move into the water much faster than they normally would at room temperature.
In other words, ultrasound helps us replace heat with mechanical energy.
Water, grind and time
This is not the same as cold brew. Cold brew is usually made by steeping coffee in cold water for 12 to 24 hours. It tends to be smooth, mellow and much less concentrated than espresso. In earlier work, we used ultrasound to speed up cold brew dramatically.
But the challenge in this project was different: could we produce something with the strength, body and intensity of espresso, without heating the water?
To do that, we adjusted several variables. Brew ratio was one of the most important: how much water we used for each gram of coffee. Too much water and the drink becomes diluted; too little and extraction becomes difficult.
Grind size also mattered. Finer grounds allowed us to extract flavour more rapidly. Finally, we tested how long the ultrasound should be applied. We found the sweet spot was about two-and-a-half to three minutes.
The taste test
Of course, making a concentrated coffee in the laboratory is one thing. The real test is whether people want to drink it.
So we ran a blind evaluation with around 100 regular coffee drinkers. They were not trained judges; they were everyday consumers who drink coffee at least once a week.
We served them four coffees in identical cups: traditional espresso, ultrasound-brewed espresso, traditional filter coffee and ultrasound-brewed filter coffee. All were freshly prepared, cooled to the same temperature and presented in random order.
For the espresso samples, participants could not reliably tell the traditional and ultrasonic versions apart. There were no significant differences in aroma, flavour, bitterness or overall liking. For filter coffee, the ultrasound version was actually preferred overall, with participants rating its bitterness more pleasantly.
Those results show espresso may not need to begin with hot water after all. By using sound waves to shake the coffee grounds, we were able to create the same richness, body and intensity, but with far less energy.
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