In September 2024, Amandla Thomas-Johnson was a Ph. D. candidate study­ing in the U.S. on a stu­dent visa when he briefly at­tended a pro-Pales­tin­ian protest. In April 2025, Immigration and Customs Enforcement (ICE) sent Google an ad­min­is­tra­tive sub­poena re­quest­ing his data. The next month, Google gave Thomas-Johnson’s information to ICE with­out giv­ing him the chance to chal­lenge the sub­poena, break­ing a nearly decade-long promise to no­tify users be­fore hand­ing their data to law en­force­ment.

Today, the Electronic Frontier Foundation sent com­plaints to the California and New York Attorneys General ask­ing them to in­ves­ti­gate Google for de­cep­tive trade prac­tices for break­ing that promise. You can read about the com­plaints here. Below is Thomas-Johnson’s ac­count of his or­deal.

I thought my or­deal with U. S. immigration au­thor­i­ties was over a year ago, when I left the coun­try, cross­ing into Canada at Ni­a­gara Falls.

By that point, the Trump ad­min­is­tra­tion had ef­fec­tively turned fed­eral power against in­ter­na­tional stu­dents like me. After I attended a pro-Palestine protest at Cornell University—for all of five min­utes—the ad­min­is­tra­tion’s rhetoric about crack­ing down on stu­dents protest­ing what we saw as geno­cide forced me into hid­ing for three months. Federal agents came to my home look­ing for me. A friend was de­tained at an air­port in Tampa and in­ter­ro­gated about my where­abouts.

I’m currently a Ph. D. stu­dent. Before that, I was a re­porter. I’m a dual British and Trinadad and Tobago cit­i­zen. I have not been ac­cused of any crime.

I be­lieved that once I left U. S. territory, I had also left the reach of its au­thor­i­ties. I was wrong.

Weeks later, in Geneva, Switzerland, I re­ceived what looked like a rou­tine email from Google. It in­formed me that the com­pany had al­ready handed over my ac­count data to the Department of Homeland Security.

At first, I wasn’t alarmed. I had seen some­thing sim­i­lar be­fore. An as­so­ci­ate of mine, Momodou Taal, had re­ceived ad­vance no­tice from Google and Facebook that his data had been re­quested. He was given ad­vanced no­tice of the sub­poe­nas, and law en­force­ment even­tu­ally with­drew them be­fore the com­pa­nies turned over his data.

Google had al­ready dis­closed my data with­out telling me.

I as­sumed I would be given the same op­por­tu­nity. But the lan­guage in my email was dif­fer­ent. It was fi­nal: “Google has re­ceived and re­sponded to le­gal process from a law en­force­ment au­thor­ity com­pelling the re­lease of in­for­ma­tion re­lated to your Google Account.”

Google had al­ready dis­closed my data with­out telling me. There was no op­por­tu­nity to con­test it.

To be clear, this should not have hap­pened this way. Google promises that it will no­tify users be­fore their data is handed over in re­sponse to le­gal processes, in­clud­ing ad­min­is­tra­tive sub­poe­nas. That no­tice is meant to pro­vide a chance to chal­lenge the re­quest. In my case, that safe­guard was by­passed. My data was handed over with­out warn­ing—at the re­quest of an ad­min­is­tra­tion tar­get­ing stu­dents en­gaged in pro­tected po­lit­i­cal speech.

Months later, my lawyer at the Electronic Frontier Foundation obtained the sub­poena it­self. On pa­per, the re­quest fo­cused largely on sub­scriber in­for­ma­tion: IP ad­dresses, phys­i­cal ad­dress, other iden­ti­fiers, and ses­sion times and du­ra­tions.

But taken to­gether, these frag­ments form some­thing far more pow­er­ful—a de­tailed sur­veil­lance pro­file. IP logs can be used to ap­prox­i­mate lo­ca­tion. Phys­i­cal ad­dresses show where you sleep. Ses­sion times would show when you were com­mu­ni­cat­ing with friends or fam­ily. Even with­out mes­sage con­tent, the pic­ture that emerges is in­ti­mate and in­va­sive.

What this ex­pe­ri­ence has made clear is that any­one can be tar­geted by law en­force­ment. And with their mas­sive stores of data, tech­nol­ogy com­pa­nies can fa­cil­i­tate those ar­bi­trary in­ves­ti­ga­tions. Together, they can com­bine state power, cor­po­rate data, and al­go­rith­mic in­fer­ence in ways that are dif­fi­cult to see—and even harder to chal­lenge.

The con­se­quences of what hap­pened to me are not ab­stract. I left the United States. But I do not feel that I have left its reach. Being in­ves­ti­gated by the fed­eral gov­ern­ment is in­tim­i­dat­ing. Questions run through your head. Am I now a marked in­di­vid­ual? Will I face height­ened scrutiny if I con­tinue my re­port­ing? Can I travel safely to see fam­ily in the Caribbean?

Who, ex­actly, can I hold ac­count­able?

I took this snap­shot, and the rest of them, at the Natural History Museum of Los Angeles County’s Unearthed: Raw Beauty ex­hi­bi­tion yes­ter­day. Enjoy.

This en­try was posted on March 3, 2026 at 7:47 pm and is filed un­der Uncategorized. You can fol­low any re­sponses to this en­try through the RSS 2.0 feed.

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It’s not about tech­nol­ogy for its own sake. It’s about be­ing able to im­ple­ment your ideas.

Imagine you don’t know any­thing about pro­gram­ming, and you want learn how to do it. You take a look at Amazon.com, and there’s a highly rec­om­mended set of books by Knute or some­thing with a promis­ing ti­tle, The Art of Computer Programming, so you buy them. Now imag­ine that it’s more than just a poor choice, but that all the books on pro­gram­ming are at writ­ten at that level.

That’s the sit­u­a­tion with books about writ­ing com­pil­ers.

It’s not that they’re bad books, they’re just too broadly scoped, and the au­thors pre­sent so much in­for­ma­tion that it’s hard to know where to be­gin. Some books are bet­ter than oth­ers, but there are still the thick chap­ters about con­vert­ing reg­u­lar ex­pres­sions into ex­e­cutable state ma­chines and dif­fer­ent types of gram­mars and so on. After slog­ging through it all you will have un­doubt­edly ex­panded your knowl­edge, but you’re no closer to ac­tu­ally writ­ing a work­ing com­piler.

Not sur­pris­ingly, the opaque­ness of these books has led to the myth that com­pil­ers are hard to write.

The best source for break­ing this myth is Jack Crenshaw’s se­ries, Let’s Build a Compiler!, which started in 1988. This is one of those gems of tech­ni­cal writ­ing where what’s as­sumed to be a com­plex topic ends up be­ing suit­able for a first year pro­gram­ming class. He fo­cuses on com­pil­ers of the Turbo Pascal class: sin­gle pass, pars­ing and code gen­er­a­tion are in­ter­min­gled, and only the most ba­sic of op­ti­miza­tions are ap­plied to the re­sult­ing code. The orig­i­nal tu­to­ri­als used Pascal as the im­ple­men­ta­tion lan­guage, but there’s a C ver­sion out there, too. If you’re truly ad­ven­tur­ous, Marcel Hendrix has done a Forth trans­la­tion (and as Forth is an in­ter­ac­tive lan­guage, it’s eas­ier to ex­per­i­ment with and un­der­stand than the C or Pascal sources).

As good as it is, Crenshaw’s se­ries has one ma­jor omis­sion: there’s no in­ter­nal rep­re­sen­ta­tion of the pro­gram at all. That is, no ab­stract syn­tax tree. It is in­deed pos­si­ble to by­pass this step if you’re will­ing to give up flex­i­bil­ity, but the main rea­son it’s not in the tu­to­ri­als is be­cause ma­nip­u­lat­ing trees in Pascal is out of sync with the sim­plic­ity of the rest of the code he pre­sents. If you’re work­ing in a higher level lan­guage–Python, Ruby, Erlang, Haskell, Lisp–then this worry goes away. It’s triv­ially easy to cre­ate and ma­nip­u­late tree-like rep­re­sen­ta­tions of data. Indeed, this is what Lisp, Erlang, and Haskell were de­signed for.

That brings me to A Nanopass Framework for Compiler Education [PDF] by Sarkar, Waddell, and Dybvig. The de­tails of this pa­per aren’t quite as im­por­tant as the gen­eral con­cept: a com­piler is noth­ing more than a se­ries of trans­for­ma­tions of the in­ter­nal rep­re­sen­ta­tion of a pro­gram. The au­thors pro­mote us­ing dozens or hun­dreds of com­piler passes, each be­ing as sim­ple as pos­si­ble. Don’t com­bine trans­for­ma­tions; keep them sep­a­rate. The frame­work men­tioned in the ti­tle is a way of spec­i­fy­ing the in­puts and out­puts for each pass. The code is in Scheme, which is dy­nam­i­cally typed, so data is val­i­dated at run­time.

After writ­ing a com­piler or two, then go ahead and plunk down the cash for the in­fa­mous Dragon Book or one of the al­ter­na­tives. Maybe. Or you might not need them at all.

Your Backpack Got Worse On PurposeIn 1986, a cor­po­ra­tion that made wom­en’s lin­gerie bought every back­pack brand you’ve ever trusted. VF Corporation started as Vanity Fair Mills. Bras and un­der­wear. They paid $762 mil­lion for a com­pany called Blue Bell and picked up JanSport in the deal. That ac­qui­si­tion made them the largest pub­licly traded cloth­ing com­pany in the world.Then they went shop­ping.In 2000, they bought The North Face. Same year, they bought Eastpak. In 2004, Kipling. In 2007, Eagle Creek. By the time they were done, VF Corporation con­trolled an es­ti­mated 55% of the US back­pack mar­ket.More than half. One com­pany.Every time you stood in a store in the 2010s and com­pared a JanSport to a North Face to an Eastpak, you were com­par­ing three la­bels owned by the same par­ent cor­po­ra­tion. Same earn­ings call. Same mar­gin tar­gets. Same quar­terly pres­sure. The sense that you were choos­ing be­tween com­peti­tors was a fic­tion that VF Corp had no in­cen­tive to cor­rect.Com­pe­ti­tion is what kept these brands hon­est when they were in­de­pen­dent. If JanSport built a shitty bag in 1985, you walked across the aisle and bought an Eastpak in­stead. That threat dis­ci­plined every ma­te­r­ial choice, every stitch count, every zip­per spec. Once they all re­port to the same par­ent, the dis­ci­pline evap­o­rates. Nobody needs to out­build any­body. The only pres­sure left is the one com­ing from above: hit the mar­gin tar­get.The eas­i­est way to hit a mar­gin tar­get is to make every­thing a lit­tle worse, across the board, all at once.De­nier count is the most mea­sur­able in­di­ca­tor of fab­ric dura­bil­ity. It mea­sures fiber thick­ness. A bag made with 1000-denier Cordura ny­lon can sur­vive years of daily use. Drop that to 600-denier poly­ester and you have a bag that looks iden­ti­cal on the shelf and lasts half as long.YKK makes the best zip­pers on earth. They’re Japanese, they cost more per unit, and brands that care about longevity use them be­cause a zip­per fail­ure kills a bag faster than fab­ric wear. On VF Corp’s lower-tier mod­els, YKK hard­ware got swapped for generic al­ter­na­tives. A few cents saved per unit across mil­lions of bags.Stitch­ing den­sity went down. More stitches per inch means stronger seams. Fewer stitches means faster pro­duc­tion. When you’re run­ning mil­lions of units through fac­to­ries in Vietnam, Bangladesh, and Cambodia, shav­ing sec­onds off each seam saves se­ri­ous money. It also cre­ates fail­ure points at every spot where the bag takes stress. Strap junc­tions. Zipper ter­mi­na­tions. The bot­tom panel.None of this shows up on the shelf. The col­ors are right. The lo­gos are crisp. The prod­uct pho­tog­ra­phy is ex­cel­lent. You dis­cover what you ac­tu­ally bought three months in, when the stitch­ing pulls apart at every stress point.Some­one in the in­dus­try pushed back on an ear­lier ver­sion of this piece with a fair point: VF Corp’s brands still op­er­ate with their own de­sign teams and their own head­quar­ters. The brands aren’t lit­er­ally merged. And the pre­mium tiers within North Face and JanSport still use qual­ity ma­te­ri­als. The Summit Series from TNF still has Cordura. You can still find a JanSport with YKK zip­pers if you know where to look.All of that is true. But it ac­tu­ally makes the ar­gu­ment worse, not bet­ter.The fact that VF Corp kept the pre­mium tiers in­tact while de­grad­ing the en­try-level and mid-range prod­ucts means this was a de­lib­er­ate seg­men­ta­tion strat­egy. They still make the good ver­sion. They just also sell a garbage ver­sion un­der the same trusted name, in the same stores, to the peo­ple who don’t know the dif­fer­ence. The brand rep­u­ta­tion built by decades of qual­ity prod­ucts is now be­ing used to move cheap prod­ucts to buy­ers who trust the logo.Wal­mart’s JanSport and REI’s JanSport are not the same bag. But they carry the same name, and that’s the point. The name is do­ing the sell­ing. The prod­uct does­n’t have to.The war­ranty is do­ing the same thing­JanS­port still ad­ver­tises a life­time war­ranty. It sounds like a com­pany that stands be­hind its prod­uct.Go try to use it.You ship the bag back at your own ex­pense. That runs $12 to $25 de­pend­ing on size and where you live. You wait three to six weeks. That’s the cur­rent turn­around per JanSport’s own war­ranty page. Then they eval­u­ate the dam­age.“Nor­mal wear and tear” is­n’t cov­ered. Only “defects in ma­te­ri­als and work­man­ship.” Think about what that means for a bag en­gi­neered to last two years. When it starts falling apart at eigh­teen months, that fail­ure can be clas­si­fied as the prod­uct reach­ing its ex­pected life­time, not as a de­fect. The war­ranty lan­guage is struc­turally de­signed to ex­clude the ex­act type of fail­ure the prod­uct is now built to have.Peo­ple who do get war­ranty re­place­ments re­port re­ceiv­ing bags that are worse than the one they sent in. Thinner fab­ric. Cheaper hard­ware. You mailed back a 2016 JanSport and got a 2025 JanSport, and those are fun­da­men­tally dif­fer­ent prod­ucts.The war­ranty used to be leg­endary. JanSport used to be the brand peo­ple cited when they talked about com­pa­nies that ac­tu­ally stood be­hind their stuff. That rep­u­ta­tion still ex­ists in peo­ple’s mem­o­ries. The war­ranty now runs on that left­over trust.One per­son told me they called about get­ting a zip­per re­placed on a JanSport from the late 90s. They were told it was nor­mal wear and tear. They tried tai­lors, got quoted $50 to $100 for a new zip­per. They looked at buy­ing a new JanSport and saw how far the qual­ity had fallen. They ended up buy­ing a used back­pack at a thrift store for four dol­lars.Ten to twenty used bags for the price of one new one that’ll fall apart. That’s where we’re at.The math that makes this in­ten­tion­al­Price of a bag di­vided by years it ac­tu­ally lasts. That’s your cost per year.A $35 JanSport that dies in eigh­teen months: $23 per year. Add the ship­ping cost when you try the war­ranty. Add the re­place­ment cost when the claim gets de­nied. Add your time.A $200 bag that lasts ten years: $20 per year. Already cheaper. At fif­teen years, which the well-built ones con­sis­tently do, you’re at $13 per year.The “expensive” bag costs less. But VF Corp does­n’t want you to do this math, be­cause the $35 bag cre­ates a re­peat cus­tomer every eigh­teen months. The $200 bag cre­ates one trans­ac­tion and zero fol­low-ups. From a share­hold­er’s per­spec­tive, the bag that falls apart is the bet­ter prod­uct.That’s the busi­ness model. Repeat fail­ure, re­peat pur­chase, re­peat rev­enue. The qual­ity de­cline is­n’t a side ef­fect. It’s the strat­egy.And then they tried to sell the whole thin­gIn 2021, VF Corp sold Eagle Creek to a for­mer em­ployee who ba­si­cally res­cued the brand from be­ing shut down.By 2023, VF Corp an­nounced it was ex­plor­ing “strategic al­ter­na­tives” for its en­tire re­main­ing back­pack di­vi­sion. JanSport. Eastpak. Kipling. All of them po­ten­tially up for sale be­cause they weren’t gen­er­at­ing enough profit.The brands your par­ents trusted went from in­de­pen­dent com­pa­nies to con­glom­er­ate as­sets to mar­gin op­ti­miza­tion tar­gets to po­ten­tial fire-sale can­di­dates. All in un­der forty years.And some­thing worth know­ing: VF Corporation sold its lin­gerie busi­ness (the one it was lit­er­ally founded on) back in 2007. Vanity Fair in­ti­mates went to Fruit of the Loom. The com­pany shed the thing it ac­tu­ally knew how to make so it could fo­cus on ex­tract­ing value from the brands it bought. They did­n’t build any of these out­door brands. They ac­quired them, op­ti­mized them, and when the op­ti­miza­tion stopped pro­duc­ing re­turns, started look­ing for the exit.This is the pat­tern. Acquisition. Cost op­ti­miza­tion. Quality de­cline. Warranty nar­row­ing. Brand eq­uity ex­trac­tion. And even­tu­ally, di­vesti­ture.It hap­pened to your back­pack. The same play­book is run­ning right now on your power tools, your boots, your sun­glasses, and about a dozen other prod­uct cat­e­gories where a com­pany you trusted qui­etly got ab­sorbed by a cor­po­ra­tion you’ve never heard of.I’ll be writ­ing about those next.

I have for years been in­ter­ested in sleep re­search due to my pro­fes­sional in­volve­ment in mem­ory and learn­ing. This ar­ti­cle at­tempts to pro­duce a syn­the­sis of what is known about sleep with a view to prac­ti­cal ap­pli­ca­tions, esp. in peo­ple who need top-qual­ity sleep for their learn­ing or cre­ative achieve­ments. Neurophysiology of sleep is an ex­plo­sively grow­ing branch of sci­ence. Many the­o­ries that are cur­rently con­tested will soon be for­got­ten as a re­sult of new find­ings. Consequently, this text is likely to grow old very quickly (compare the old ver­sion from the year 2000 here). Still, some ba­sic truths about sleep are well-es­tab­lished, and prac­ti­cal con­clu­sions can be drawn with the ben­e­fit to hu­man cre­ativ­ity and in­tel­lec­tual ac­com­plish­ment. In this text, I pro­vide some links to re­search pa­pers and pop­u­lar-sci­en­tific ar­ti­cles that ad­vo­cate dis­parate and con­tra­dic­tory the­o­ries. Please con­sult other sources to be cer­tain you do not to get a one-sided view! This ar­ti­cle in­cludes some in­di­ca­tions on how to use free run­ning sleep in the treat­ment of in­som­nia, ad­vanced and de­layed phase shift syn­dromes, and some other sleep dis­or­ders. If your own ex­pe­ri­ence can con­tribute to the ideas pre­sented herein, I will gladly hear from you (esp. in the con­text of learn­ing and cre­ativ­ity).

2 Importance of sleep

2.2 Why do we sleep?

2.4 If you do not sleep, you die!

2.4.4 Why do we die with­out sleep?

2.5 Two com­po­nents of sleep

2.5.4 The fun­da­men­tal the­o­rem of good sleep

2.5.4.1 When good sleep might not come?

3 Formula for good sleep

3.2 Should we free run our sleep?

3.4 Optimizing the tim­ing of brain­work

3.6 Kill the alarm clock!

3.7 Sleep in­er­tia

3.7.3 How can I re­cover from sleep in­er­tia?

4 Sleep habits

4.4 Charting sleep with SleepChart

4.7 Biphasic na­ture of hu­man sleep

4.8 Segmented sleep

4.8.4 Examples of seg­mented sleep

4.9 Delayed Sleep Phase Syndrome (DSPS)

4.9.4 Asynchronous DSPS

4.9.6 28 hour day sched­ule

4.9.6.1 28 hour day in DSPS

4.9.7 Curing DSPS and in­som­nia

4.10 Advanced Sleep Phase Syndrome (ASPS)

4.13 Baby sleep

4.13.1 How to make ba­bies sleep well?

4.13.7 What about the mom?

4.13.8 Why ba­bies sleep so much?

4.14 Insomnia

5 Napping

5.1 Napping is good

5.1.3 To nap or not to nap? Nap!

5.2 Napping myths

5.2.3 Myth #3: Make sure you wake up from Stage 2 NREM

5.2.4 Myth #4: The cir­ca­dian cy­cle can be ig­nored or abol­ished

5.4 One nap per day is enough

5.5 Polyphasic sleep

5.5.4 To sleep or not to sleep polypha­si­cally

5.5.7 Sleep and cre­ativ­ity: Less is more

5.5.11 Polyphasic sleep: sci­en­tific chal­lenge

5.5.11.1 Are early ris­ers bet­ter at polypha­sic adap­ta­tion?

5.5.11.2 Why so lit­tle re­search into polypha­sic sleep?

5.5.12 Charting polypha­sic sleep

5.5.17 Sustainability of polypha­sic sleep

5.5.17.2 The lim­its of the body clock train­ing

6 Factors that af­fect sleep

6.7 Exercise

6.7.2 What is the best time to ex­er­cise?

6.12 Learning

6.12.1 Learning should help you sleep

7 Sleep and learn­ing

7.1 Sleep length

7.1.1 Optimum length of sleep

7.1.1.5 Length of sleep among users of SuperMemo

7.1.3 Jim Horne and Daniel Kripke

7.1.4 Effects of sleep du­ra­tion and sleep phase on learn­ing

7.2 How sleep af­fects learn­ing?

7.2.1 Why is sleep im­por­tant for learn­ing?

7.3 Studying sleep and learn­ing with SuperMemo

7.3.6 Recall vs. Consolidation

7.4 How learn­ing af­fects sleep?

7.5 Sleep and school

8 Physiology of sleep

8.1 Why do we fall asleep?

8.1.1 Initiation of sleep

8.1.4 Phase re­sponse curve (PRC)

8.1.4.1 Changing the length of the cir­ca­dian pe­riod

8.1.5 Recursive phase re­sponse curve (rPRC)

8.2 NREM and REM sleep

8.3 Why do we need sleep?

8.3.2 Sleep the­o­ries

8.3.3 Sleep and mem­ory

8.3.3.1 NREM and mem­ory

8.3.3.2 REM and mem­ory

8.3.5 Neural op­ti­miza­tion in sleep

8.3.7 Robert Vertes and Jerome Siegel

8.3.7.1 1. Sleep does not serve a role in de­clar­a­tive mem­ory?

8.3.7.2 2. REM sleep de­pri­va­tion does not lead to cog­ni­tive im­pair­ment?

8.3.7.3 3. Sleep-dependent en­hance­ment of pro­ce­dural learn­ing has not been proven?

8.3.7.4 4. Learning in wak­ing is far more sig­nif­i­cant than overnight en­hance­ments?

8.3.7.6 How can ran­dom im­pul­sa­tions in REM make a sense in dreams?

9 Myths and facts

13 Summary

The good ed­u­ca­tor in­sists on ex­er­cise, play, and plen­ti­ful sleep: “the great cor­dial of na­ture.”

It is every­one’s dream to wake up fresh, happy, and ready for ac­tion on a daily ba­sis. Sadly, in the mod­ern world, only a small mi­nor­ity lives that dream. Yet the dream is within reach for most healthy peo­ple given:

I hope that this ar­ti­cle com­piles all the ba­sic in­gre­di­ents of knowl­edge that are help­ful in ac­com­plish­ing re­fresh­ing sleep. As for the sac­ri­fice, it is im­por­tant to be­gin with the un­der­stand­ing that one can­not eat one’s cake and have it too. Healthy sleep may be in­com­pat­i­ble with some mod­ern habits, some crav­ings, or some lifestyle choices. At worst, re­fresh­ing sleep may be in­com­pat­i­ble with one’s job or even long-term goals. Due to the lat­ter fact, this ar­ti­cle can­not pro­vide a so­lu­tion for every­one. Moreover, hav­ing a happy and fresh mind on a daily ba­sis is a dif­fi­cult thing to ac­com­plish even with an ar­se­nal of knowl­edge and full fo­cus on good sleep. However, let me state it em­phat­i­cally, good sleep on most nights is fea­si­ble for most peo­ple!

This ar­ti­cle was orig­i­nally writ­ten a decade ago. I have al­ways been in­ter­ested in mem­ory, learn­ing, and sleep. In ad­di­tion, in my job, sleep is as im­por­tant as oxy­gen. As we all move deeper into the Information Age and Knowledge Economy, the is­sues dis­cussed herein will be­come more and more im­por­tant for each of us. After writ­ing the orig­i­nal ar­ti­cle, I had the great plea­sure of get­ting in touch with hun­dreds of peo­ple ex­pe­ri­enc­ing var­i­ous sleep prob­lems. I came to see first hand how knowl­edge of sleep helps solve their prob­lems. I could also see how the in­dus­tri­al­ized age lays ob­sta­cles in one’s quest for good sleep and high pro­duc­tiv­ity. I have wit­nessed a true epi­demic of sleep phase dis­or­ders, an ex­plo­sion of in­ter­est in polypha­sic sleep, and an ex­po­nen­tial in­crease in in­ter­est in the mat­ters of sleep in gen­eral. Despite my pleas, many peo­ple just can­not avoid us­ing an alarm clock, run­ning all-nighters be­fore ex­ams, wak­ing their kids cranky for school, pop­ping pills be­fore sleep, leav­ing ba­bies in their cots to cry it out for sleep, etc. The pic­ture would be pretty sad and alarm­ing were it not for the fact that there is hope in knowl­edge. With a de­gree of de­ter­mi­na­tion, every­one can im­prove his, her, or their kids’ sleep.

This ar­ti­cle is a com­pi­la­tion of the most im­por­tant and the most in­ter­est­ing things about the bi­ol­ogy of sleep. It is sup­posed to help you gain knowl­edge needed to achieve high qual­ity re­fresh­ing sleep that will boost your men­tal pow­ers. The ar­ti­cle ex­plains why sleep is vi­tally im­por­tant for health and for the brain. It ar­gues that sleep de­serves high­est re­spect, and that most peo­ple could get ex­cel­lent sleep if they only fol­lowed the pre­scribed rules.

Since writ­ing the orig­i­nal Good sleep, good learn­ing, good life, tremen­dous progress has been made in the sci­ence of sleep. My own work with tools such as SleepChart and SuperMemo has shed some in­ter­est­ing light on the con­nec­tion be­tween sleep and learn­ing. As I kept ad­dress­ing the progress in sleep sci­ence in mi­nor ar­ti­cles and FAQs, some vis­i­tors to su­per­memo.com com­plained that valu­able nuggets of in­for­ma­tion are dis­persed through­out the site in­stead of be­ing or­ga­nized in a more en­cy­clo­pe­dic man­ner in a sin­gle ar­ti­cle. Here then comes a com­pre­hen­sive com­pi­la­tion, in which I would like to re­tain the fo­cus on prac­ti­cal knowl­edge that is help­ful in achiev­ing good sleep. However, I would still like to smug­gle in some lesser known re­search find­ings that might be in­spir­ing for an av­er­age reader and/​or a sci­en­tist work­ing in the fields of sleep, mem­ory, and learn­ing. If you be­lieve I left out any­thing im­por­tant that oth­ers should know, please let me know.

As the ar­ti­cle grew to be in­sanely long, you may wish to be­gin with the sum­mary at the bot­tom of the ar­ti­cle. And if even that is too long, here are the high­lights:

re­spect sleep as your tool for high IQ and good learn­ing

free run­ning sleep can help you re­solve many sleep prob­lems

bipha­sic sleep sched­ule is prob­a­bly the health­i­est sched­ule for cre­ative peo­ple

do not wake up kids for school; if they can­not wake up in time, let them skip a class or two, or con­sider home­school­ing

let ba­bies and young chil­dren sleep on de­mand, co-sleep­ing is a great idea (even if many pe­di­a­tri­cians will tell you oth­er­wise)

ex­er­cise, learn­ing, and sleep are your best tools for brain growth!

avoid reg­u­lat­ing sleep and alert­ness with sub­stances, esp. sleep­ing pills, al­co­hol, il­le­gal drugs, nico­tine, and caf­feine

Incremental writ­ing: Due to the size of the ma­te­r­ial, this ar­ti­cle was writ­ten us­ing a tech­nique called in­cre­men­tal writ­ing. Incremental writ­ing is help­ful in or­ga­niz­ing a large body of ear­lier writ­ings into a sin­gle lin­ear piece. The main ad­van­tage of in­cre­men­tal writ­ing is a rea­son­able de­gree of co­her­ence de­spite speedy pro­cess­ing of ma­te­ri­als taken from dis­parate sources. Texts pro­duced with in­cre­men­tal writ­ing are par­tic­u­larly suit­able for learn­ing with the help of in­cre­men­tal read­ing as they pro­duce small in­de­pen­dent Wikipedia-style sub-ar­ti­cles. For a lin­ear reader, how­ever, this may mean a de­gree of bloat­ed­ness and an an­noy­ing repet­i­tive­ness of the main themes for which I apol­o­gize. If the size of the ar­ti­cle is in­tim­i­dat­ing, you could try read­ing it in­cre­men­tally (e.g. with SuperMemo 2004 Freeware)?

References: Due to the vol­ume of the ma­te­r­ial, I was not able to pro­vide ref­er­ences for all state­ments in­cluded in the text. Some of these are com­mon sense, some are com­mon knowl­edge, oth­ers I took from mem­ory or from SuperMemo with­out dig­ging deep to the di­rect source. If you can­not find a ref­er­ence for a par­tic­u­lar claim, please let me know

Spotify and sev­eral ma­jor record la­bels, in­clud­ing UMG, Sony, and Warner, se­cured a $322 mil­lion de­fault judg­ment against the un­known op­er­a­tors of Anna’s Archive. The shadow li­brary failed to ap­pear in court and briefly re­leased mil­lions of tracks that were scraped from Spotify via BitTorrent. In ad­di­tion to the mon­e­tary penalty, a per­ma­nent in­junc­tion re­quired do­main reg­is­trars and other par­ties to sus­pend the site’s do­main names.

Spotify and sev­eral ma­jor record la­bels, in­clud­ing UMG, Sony, and Warner, se­cured a $322 mil­lion de­fault judg­ment against the un­known op­er­a­tors of Anna’s Archive. The shadow li­brary failed to ap­pear in court and briefly re­leased mil­lions of tracks that were scraped from Spotify via BitTorrent. In ad­di­tion to the mon­e­tary penalty, a per­ma­nent in­junc­tion re­quired do­main reg­is­trars and other par­ties to sus­pend the site’s do­main names.

Anna’s Archive is gen­er­ally known as a meta-search en­gine for shadow li­braries, help­ing users find pi­rated books and other re­lated re­sources.

However, last December, the site an­nounced that it had also backed up Spotify, which came as a shock to the mu­sic in­dus­try.

Anna’s Archive ini­tially re­leased only Spotify meta­data, and no ac­tual mu­sic, but that put the mu­sic in­dus­try on high alert. Together with the likes of Universal, Warner, and Sony, Spotify filed a law­suit days later, hop­ing to shut the site down.

Through a pre­lim­i­nary in­junc­tion tar­get­ing do­main reg­is­trars and reg­istries, the shadow li­brary lost sev­eral do­main names. However, not all were taken down, and the site reg­is­tered var­i­ous new do­main names as back­ups.

The le­gal pres­sure also ap­peared to pay off in other ways. Not long af­ter the law­suit was filed, the shadow li­brary re­moved the Spotify list­ing for their tor­rents page. The same ap­plies to the first batch of mu­sic files that was ac­ci­den­tally re­leased in February.

The site’s op­er­a­tor, Anna’s Archivist, hoped that these re­movals would mo­ti­vate the mu­sic in­dus­try to back down, but that was­n’t the case. Instead, they re­turned to court re­quest­ing a $322 mil­lion de­fault judg­ment af­ter the de­fen­dant failed to show up in court.

Yesterday, Judge Jed Rakoff of the Southern District of New York en­tered a de­fault judg­ment against the site’s un­known op­er­a­tors, award­ing Spotify and the ma­jor la­bels the re­quested $322 mil­lion dam­ages award in full.

The mu­sic la­bels get the statu­tory max­i­mum of $150,000 in dam­ages for around 50 works. Spotify adds a DMCA cir­cum­ven­tion claim of $2,500 for 120,000 mu­sic files, bring­ing the to­tal to more than $322 mil­lion.

The plain­tiff pre­vi­ously de­scribed their dam­ages re­quest as “extremely con­ser­v­a­tive.” The DMCA claim is based only on the 120,000 files, not the full 2.8 mil­lion that were re­leased. Had they ap­plied the $2,500 rate to all re­leased files, the dam­ages fig­ure would ex­ceed $7 bil­lion.

Anna’s Archive did not show up in court, and the op­er­a­tors of the site re­main uniden­ti­fied. The judg­ment at­tempts to ad­dress this di­rectly, by or­der­ing Anna’s Archive to file a com­pli­ance re­port within ten busi­ness days, un­der penalty of per­jury, that in­cludes valid con­tact in­for­ma­tion for the site and its man­ag­ing agents.

Whether the site will com­ply with this or­der is highly un­cer­tain.

For now, the mon­e­tary judg­ment is mostly a vic­tory on pa­per, as re­coup­ing money from an un­known en­tity is im­pos­si­ble. For this rea­son, the mu­sic com­pa­nies also re­quested a per­ma­nent in­junc­tion.

In ad­di­tion to the dam­ages award, Rakoff en­tered a per­ma­nent world­wide in­junc­tion cov­er­ing ten Anna’s Archive do­mains: an­nas-archive.org, .li, .se, .in, .pm, .gl, .ch, .pk, .gd, and .vg.

Domain reg­istries and reg­is­trars of record, along with host­ing and in­ter­net ser­vice providers, are or­dered to per­ma­nently dis­able ac­cess to those do­mains, dis­able au­thor­i­ta­tive name­servers, cease host­ing ser­vices, and pre­serve ev­i­dence that could iden­tify the site’s op­er­a­tors.

The judg­ment names spe­cific third par­ties bound by those oblig­a­tions, in­clud­ing Public Interest Registry, Cloudflare, Switch Foundation, The Swedish Internet Foundation, Njalla SRL, IQWeb FZ-LLC, Immaterialism Ltd., Hosting Concepts B. V., Tucows Domains Inc., and OwnRegistrar, Inc.

Anna’s Archive is also or­dered to de­stroy all copies of works scraped from Spotify and to file a com­pli­ance re­port within ten busi­ness days, un­der penalty of per­jury, in­clud­ing valid con­tact in­for­ma­tion for the site and its man­ag­ing agents. That last re­quire­ment could prove sig­nif­i­cant, given that the iden­tity of the site’s op­er­a­tors re­mains un­known.

In the­ory, Anna’s Archive has the op­tion to pre­vent the do­main sus­pen­sion. The per­ma­nent in­junc­tion al­lows the site to seek re­lief from this mea­sure, af­ter show­ing that it has paid the full $322 mil­lion dam­ages award and com­plied with all in­junc­tive oblig­a­tions.

That’s an un­likely op­tion, to say the least. At the same time, how­ever, it is not guar­an­teed that the site’s do­main names will be sus­pended.

As re­ported pre­vi­ously, sev­eral do­main names, in­clud­ing the Greenland-based .gl ver­sion, are linked to reg­istries and reg­is­trars out­side the ju­ris­dic­tion of the U. S. court. As such, they pre­vi­ously did not com­ply to the pre­lim­i­nary in­junc­tion, and it is un­known whether the lat­est or­der changes that.

A copy of the de­fault judg­ment en­tered by Judge Rakoff is avail­able here (pdf).

Last week we learned about Anthropic’s Mythos, a new LLM so “strikingly ca­pa­ble at com­puter se­cu­rity tasks” that Anthropic did­n’t re­lease it pub­licly. Instead, only crit­i­cal soft­ware mak­ers have been granted ac­cess, pro­vid­ing them time to harden their sys­tems.

We quickly blew through our stan­dard stages of pro­cess­ing big AI claims: shock, ex­is­ten­tial fear, hype, skep­ti­cism, crit­i­cism, and (finally) mov­ing onto the next thing. I en­cour­aged peo­ple to take a wait-and-see ap­proach, as se­cu­rity ca­pa­bil­i­ties are tai­lor-made for im­pres­sive demos. Finding ex­ploits is a clearly de­fined, ver­i­fi­able search prob­lem. You’re not build­ing a com­plex sys­tem, but pok­ing at one that ex­ists. A prob­lem well suited to throw­ing mil­lions of to­kens at.

Yesterday, the first 3rd party analy­sis landed, from the AI Security Institute (AISI), largely sup­port­ing Anthropic’s claims. Mythos is re­ally good, “a step up over pre­vi­ous fron­tier mod­els in a land­scape where cy­ber per­for­mance was al­ready rapidly im­prov­ing.”

The en­tire re­port is worth read­ing, but I want to fo­cus on the fol­low­ing chart, de­tail­ing the abil­ity of dif­fer­ent mod­els to suc­cess­fully com­plete a sim­u­lated, com­plex cor­po­rate net­work at­tack:

“The Last Ones” is, “a 32-step cor­po­rate net­work at­tack sim­u­la­tion span­ning ini­tial re­con­nais­sance through to full net­work takeover, which AISI es­ti­mates to re­quire hu­mans 20 hours to com­plete.” The lines are the av­er­age per­for­mance across mul­ti­ple runs (10 runs for Mythos, Opus 4.6, and GPT-5.4), with the “max” lines rep­re­sent­ing the best of each batch. Mythos was the only model to com­plete the task, in 3 out of its 10 at­tempts.

This chart sug­gests an in­ter­est­ing se­cu­rity econ­omy: to harden a sys­tem we need to spend more to­kens dis­cov­er­ing ex­ploits than at­tack­ers spend ex­ploit­ing them.

AISI bud­geted 100M to­kens for each at­tempt. That’s $12,500 per Mythos at­tempt, $125k for all ten runs. Worryingly, none of the mod­els given a 100M bud­get showed signs of di­min­ish­ing re­turns. “Models con­tinue mak­ing progress with in­creased to­ken bud­gets across the to­ken bud­gets tested,” AISI notes.

If Mythos con­tin­ues to find ex­ploits so long as you keep throw­ing money at it, se­cu­rity is re­duced to a bru­tally sim­ple equa­tion: to harden a sys­tem you need to spend more to­kens dis­cov­er­ing ex­ploits than at­tack­ers will spend ex­ploit­ing them.

You don’t get points for be­ing clever. You win by pay­ing more. It is a sys­tem that echoes cryp­tocur­ren­cy’s proof of work sys­tem, where suc­cess is tied to raw com­pu­ta­tional work. It’s a low tem­per­a­ture lot­tery: buy the to­kens, maybe you find an ex­ploit. Hopefully you keep try­ing longer than your at­tack­ers.

This cal­cu­lus has a few im­me­di­ate take­aways:

For those of you who aren’t ex­posed to AI max­i­mal­ists, this state­ment feels ab­surd. But lately, af­ter the LiteLLM and Axios sup­ply chain scares, many have ar­gued for reim­ple­ment­ing de­pen­dency func­tion­al­ity us­ing cod­ing agents.

Classical soft­ware en­gi­neer­ing would have you be­lieve that de­pen­den­cies are good (we’re build­ing pyra­mids from bricks), but imo this has to be re-eval­u­ated, and it’s why I’ve been so grow­ingly averse to them, pre­fer­ring to use LLMs to “yoink” func­tion­al­ity when it’s sim­ple enough and pos­si­ble.

If se­cu­rity is purely a mat­ter of throw­ing to­kens at a sys­tem, Linus’s law that, “given enough eye­balls, all bugs are shal­low,” ex­pands to in­clude to­kens. If cor­po­ra­tions that rely on OSS li­braries spend to se­cure them with to­kens, it’s likely go­ing to be more se­cure than your bud­get al­lows. Certainly, this has com­plex­i­ties: crack­ing a widely used OSS pack­age is in­her­ently more valu­able than hack­ing a one-off im­ple­men­ta­tion, which in­cen­tivizes at­tack­ers to spend more on OSS tar­gets.

Second, hard­en­ing will be an ad­di­tional phase for agen­tic coders.

We’ve al­ready been see­ing de­vel­op­ers break their process into two steps, de­vel­op­ment and code re­view, of­ten us­ing dif­fer­ent mod­els for each phase. As this ma­tures, we’re see­ing pur­pose-built tool­ing meet­ing this pat­tern. Anthropic launched a code re­view prod­uct that costs $15-20 per re­view.

If the above Mythos claims hold, I sus­pect we’ll see a three phase cy­cle: de­vel­op­ment, re­view, and hard­en­ing.

Review: Document, refac­tor, and other gar­den­ing tasks, async, ap­ply­ing best prac­tices with each PR.

Hardening: Identify ex­ploits, au­tonomously, un­til the bud­get runs out.

Critically, hu­man in­put is the lim­iter for the first phase and money is the lim­iter for the last. This qual­ity in­her­ently makes them sep­a­rate stages (why spend to harden be­fore you have some­thing?). Previously, se­cu­rity au­dits were rare, dis­crete, and in­con­sis­tent. Now we can ap­ply them con­stantly, within an op­ti­mal (we hope!) bud­get.

Code re­mains cheap, un­less it needs to be se­cure. Even if costs go down as in­fer­ence op­ti­miza­tions, un­less mod­els reach the point of di­min­ish­ing se­cu­rity re­turns, you still need to buy more to­kens than at­tack­ers do. The cost is fixed by the mar­ket value of an ex­ploit.

The ed­i­tor in chief of this blog was born in 2004. She uses the 1997 win­dow man­ager, Enlightenment E16, daily. In this ar­ti­cle, I de­scribe the process of fix­ing a show-stop­ping, rare bug that dates back to 2006 in the code­base. Surprisingly, the is­sue has roots in a faulty im­ple­men­ta­tion of Newton’s al­go­rithm.

Some may find it weird, but I ac­tu­ally greatly en­joy us­ing Enlightenment E16 as my win­dow man­ager. It’s themable, hack­able, light­weight (24MB peak RSS!), amenable to heavy key­board users like my­self, and most im­por­tantly - it looks gore­gous:

E16 first came to be in 1997, thanks to Carsten Haitzler, and it has been in de­vel­op­ment ever since. Most have moved to E17 and other newer ver­sions; a com­mu­nity of hard­core en­thu­si­asts still uses E16, and I am one of them. The code­base is quite old, and it has ac­cu­mu­lated a lot of tech­ni­cal debt over the years.

Bugs al­ways come out of the wood­works in a time scram­ble and this one likely sensed a prime op­por­tu­nity: I was do­ing a lot of last-minute work on a cou­ple of slides for a course that I will be teach­ing. I had a cou­ple of PDFs with lec­ture slides and an ex­er­cise sheet type­set in LaTeX. At some point, I opened one of them in Atril, and the en­tire desk­top froze.

I killed the X11 ses­sion from a TTY. Sadly, the hang was de­ter­min­is­tic: every time I opened that spe­cific PDF.

Attaching gdb to the live process showed every sam­ple parked in im­lib2’s font cache, un­der the same e16 caller:

#0 __strcmp_evex ()

#1 __imlib_hash_find (hash=0x55bc9c111420, key=“\001\001\001\001\001”) ob­ject.c:172

#2 __imlib_font_cache_glyph_get (fn=…, in­dex=0) font_­draw.c:30

#3 __imlib_font_get_next_glyph (… utf8=“Kick­off.pdf — Introduction…“) font_­main.c:218

#4 __imlib_font_query_advance (…) font_­query.c:89

#5 im­lib_get_­tex­t_ad­vance (…) api_­text.c:231

#6 Efont_extents (…) tex­t_ift.c:87

#7 _ift_TextSize (…) tex­t_ift.c:156

#8 TextstateTextFitMB (ts=…, tex­twidth_limit=291) text.c:350

#9 TextstateTextFit (…) text.c:559

#10 TextstateTextDraw (… text=“Kick­off.pdf — Introduction…“) text.c:638

#11 ITApply (…) iclass.c:930

#12 ITApply (…) iclass.c:884

#13 _BorderWinpartITclassApply (ewin=…, i=2, force=1) bor­ders.c:179

#14 EwinBorderUpdateInfo (ewin=…) bor­ders.c:300

#15 EwinChangesProcess (…) ewins.c:2141

#16 EwinEventPropertyNotify (ewin=…, ev=…) ewins.c:1438

#21 main (…) main.c:320

Re-attaching re­peat­edly showed the pro­gram was not dead­locked. __imlib_font_cache_glyph_get was be­ing called with dif­fer­ent glyph in­dices (0, 20, 73, 81, 82, 87, 88, …) each time. So the in­ner font-mea­sure­ment was mak­ing progress; the loop was some­where out­side it.

After some fudg­ing, I found out that Frame 8 (TextstateTextFitMB at text.c:350) was the con­stant. That’s a ts->ops->Text­Size(ts, new_­line, 0, pw, &hh, &ascent); call in­side the mid­dle-el­lip­sis trun­ca­tion loop that tries to fit a string into tex­twidth_limit = 291 pix­els by nuk­ing char­ac­ters out of the mid­dle - used when ren­der­ing the ti­tle of the PDF, that hap­pened to also be the ti­tle of the win­dow, too long for the dec­o­ra­tion to con­tain.

Dumping the frame’s lo­cals across many sam­ples re­vealed a clean two-state os­cil­la­tion:

nuke_­count = 8 nc2 = 36 wc_len = 81 len_n = 76

nuke_­count = 11 nc2 = 35 wc_len = 81 len_n = 73

nuke_­count = 8 nc2 = 36 wc_len = 81 len_n = 76

I al­ways saw two trial trun­ca­tions, for­ever, same text each time.

We start at the low­est com­mon de­nom­i­na­tor - there is likely a logic bug here.

The loop is of patic­u­lar in­ter­est to us. Abridged:

This is a Newton-style search that es­ti­mates how many more/​fewer wchars to nuke based on how far off width is from tex­twidth_limit, us­ing cw = width / len_n as the de­riv­a­tive (average pix­els per char). Seeing clever and crafty so­lu­tions like this is de­light­ful. But to any­one who has ever im­ple­mented Newton’s method, this code screams some­thing ob­vi­ous: “Where is your it­er­a­tion limit?!”. Newton’s method can fail to con­verge, and it can also over­shoot and di­verge - all de­pend­ing on the start­ing point, the na­ture of the func­tion, and the qual­ity of the de­riv­a­tive es­ti­mate. In this case, the method was os­cil­lat­ing be­tween two points for­ever.

To make mat­ters worse, the exit tol­er­ance () is tight - ac­cept only nc2 be­tween [0, 3*cw). This also ex­plains why or­di­nary short ti­tles never tripped it - on shorter strings or with wider cw, the branch kicks in and the step be­comes 1, which con­verges.

I have made three de­fen­sive changes, ap­plied sym­met­ri­cally to both the multi-byte and ASCII loops:

* Capped it­er­a­tion counts at 32. Past the cap, if the cur­rent trial fits nc2 >= 0 we just ac­cept it; oth­er­wise bump nuke_­count by 1 and retry. This guar­an­tees ter­mi­na­tion in bounded time and picks the first fit­ting trial once the Newton step has been shown to os­cil­late.

* We now floor nuke_­count at 1 in­side the loop, so a neg­a­tive cor­rec­tion can never pro­duce the de­gen­er­ate tail-over­laps-head string.

* Floor cw at 1, so a patho­log­i­cal zero-width mea­sure­ment can­not turn the step for­mu­las into a di­vide-by-zero.

Any win­dow whose WM_NAME is long enough that the mid­dle-el­lip­sis search falls into the over­shoot regime re­pro­duces this. The one in the wild:

Kickoff.pdf — Introduction to Information Theory Session 1: kick­off & first topic

Newer is not nec­es­sar­ily bet­ter. Fresh soft­ware car­ries brand new bugs for you and the main­tain­ers to en­joy, now em­pow­ered by the bar­rier to con­tribute be­ing much lower thanks to Large Language Models. But some­times sta­ble main­tain­ers do ab­surdly dumb things too:

On the 3rd of April 2026, I re­marked that fgetx­attr(54321, NULL, NULL, 0); ap­par­ently crashes yes­ter­day’s 6.6.y lts ker­nel. A call that should just re­turn -1 and set er­rno to EINVAL be­cause the path is in­valid, but a sta­ble main­tainer patched it out whole­sale.

Then, the aw­ful com­mit was re­verted, on the 8th of April. No CVE has been as­signed de­spite an ob­vi­ous Denial-Of-Service at­tack vec­tor be­ing in­tro­duced.

If this is what hap­pens by mis­take on a daily ba­sis, what hap­pens when the sup­ply chain is com­pro­mised and a ma­li­cious ac­tor in­ten­tion­ally in­tro­duces a bug? The mind bog­gles. Back when the XZ back­door was in­tro­duced, I was scrolling through news on my Debian Sid lap­top with some code com­pil­ing in the back­ground. I learned of a back­door in XZ Utils, po­ten­tially in­tro­duced by a state ac­tor in ver­sion v5.6.0. Thinking back to the fact that I do, in­deed, run a bleed­ing edge dis­tro and up­date of­ten, I im­me­di­ately ran apt list –upgradable | grep xz-utils. Sure enough, the stains on my lap­top from the cof­fee I spat out through the nose were pretty tough to deal with.

On the other hand, the amount of bugs in pri­vate check­outs of crusty old soft­ware main­tained by com­pe­tent de­vel­op­ers will mo­not­o­n­i­cally de­crease. If I need a fea­ture, I will im­ple­ment it. If there is a prob­lem, I only have my­self to blame. There is no sup­ply chain to com­pro­mise, and if a de­ter­mined, tar­get­ted state ac­tor wants sudo priv­i­leges on my ma­chine - they will find a way to get it any­way. Oh, also, eI prob­a­bly was­n’t go­ing to use what­ever fea­tures that my XFWM up­dates (the WM I used to use be­fore!) were go­ing to bring.

*Menu prices may dif­fer at spe­cial lo­ca­tion restau­rants, se­lected restau­rants and for de­liv­ery.

English menu is avail­able for your con­ve­nience

McDonald’s menu and al­ler­gen/​nu­tri­tion in­for­ma­tion is avail­able in English for the con­ve­nience of our cus­tomers, ex­cept for the in­for­ma­tion listed be­low, which is cur­rently avail­able only in Japanese in McDonald’s Japan web­site.

Information and notes on prod­ucts and avail­abil­ity

*McDonald’s Japan’s al­ler­gen in­for­ma­tion only cov­ers 8 in­gre­di­ents which must be in­di­cated on the la­bel and 20 which are rec­om­mended by Japanese Food Labeling Standard (Food Labeling Act) as of September 2024. You can also place an or­der in English on our of­fi­cial app. Several restau­rants also have English menus on hand, so please ask our crew if you are look­ing for an English menu.

※Click the im­age or prod­uct name to learn more about al­ler­gen/​nu­tri­tion in­for­ma­tion, and other de­tails.

※All dis­played prices are tax in­cluded and a sin­gle, tax-in­clu­sive price ap­plies for both eat-in and take­out (inc. drive-thru) or­ders (tax-exclusive price may dif­fer).

※Menu prices may dif­fer at spe­cial lo­ca­tion restau­rants and se­lected restau­rants.

※Some prod­ucts are not avail­able at all restau­rants.

※“Bai Burger” menu is avail­able for all reg­u­lar burg­ers ex­cept for “Roasted Soy Sauce Double Thick Beef” and “Roasted Soy Sauce Egg Bacon Thick Beef”.

※Breakfast is avail­able un­til 10:30am, Regular Menu is avail­able from 10:30am and Yoru Mac menu is avail­able from 5:00pm

※Asa Mac or­ders are ac­cepted un­til 10:20am for Mobile Order & Pay and McDelivery

※HiruMac is avail­able be­tween 10:30am and 2:00pm on week­days

※McShake®, McFloat®, Soft Twist, McFlurry® are avail­able be­tween 10:30am and 1:00 am the next day

※McShake® may be mixed with other fla­vors due to the na­ture of the ma­chine. For this rea­son, the al­lergy in­for­ma­tion may dif­fer from the usual in­for­ma­tion dur­ing lim­ited-time prod­uct sales. Please check the lat­est in­for­ma­tion each time you or­der.

※For cus­tomized prod­ucts, ex­act in­for­ma­tion may vary. Please be aware that cus­tomiza­tion is not a ser­vice that com­pletely elim­i­nates al­ler­gens.

※Oreo and the de­sign of the Oreo cookie are trade­marks li­censed by the Mondelez International Group.

※ Coke is a reg­is­tered trade­marks of The Coca-Cola Company.

※McCafé® menu at McCafé by Barista stores avail­abil­ity is sub­ject to McCafé by Barista counter busi­ness hours.

※McCafé® menu is not avail­able for pur­chase at the drive-thru at some McCafé by Barista stores.

※Images are for il­lus­tra­tive pur­poses only.

※Coupons for share­hold­ers are not re­deemable for Shaka Shaka Potato® Buttered Potato Flavor.