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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

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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.