To help train AI mod­els, Meta and other tech com­pa­nies have down­loaded and shared pi­rated books via BitTorrent from Anna’s Archive and other shadow li­braries. In an on­go­ing law­suit, Meta now ar­gues that up­load­ing pi­rated books to strangers via BitTorrent qual­i­fies as fair use. The com­pany also stresses that the data helped es­tab­lish U. S. global lead­er­ship in AI.

To help train AI mod­els, Meta and other tech com­pa­nies have down­loaded and shared pi­rated books via BitTorrent from Anna’s Archive and other shadow li­braries. In an on­go­ing law­suit, Meta now ar­gues that up­load­ing pi­rated books to strangers via BitTorrent qual­i­fies as fair use. The com­pany also stresses that the data helped es­tab­lish U. S. global lead­er­ship in AI.

In the race to build the most ca­pa­ble LLM mod­els, sev­eral tech com­pa­nies sourced copy­righted con­tent for use as train­ing data, with­out ob­tain­ing per­mis­sion from con­tent own­ers.

Meta, the par­ent com­pany of Facebook and Instagram, was one of the com­pa­nies to get sued. In 2023, well-known book au­thors, in­clud­ing Richard Kadrey, Sarah Silverman, and Christopher Golden, filed a class-ac­tion law­suit against the com­pany.

Last sum­mer, Meta scored a key vic­tory in this case, as the court con­cluded that us­ing pi­rated books to train its Llama LLM qual­i­fied as fair use, based on the ar­gu­ments pre­sented in this case. This was a bit­ter­sweet vic­tory, how­ever, as Meta re­mained on the hook for down­load­ing and shar­ing the books via BitTorrent.

By down­load­ing books from shadow li­braries such as Anna’s Archive, Meta re­lied on BitTorrent trans­fers. In ad­di­tion to down­load­ing con­tent, these typ­i­cally up­load data to oth­ers as well. According to the au­thors, this means that Meta was en­gaged in wide­spread and di­rect copy­right in­fringe­ment.

In re­cent months, the law­suit con­tin­ued based on this re­main­ing di­rect copy­right in­fringe­ment claim. While both par­ties col­lected ad­di­tional ev­i­dence through the dis­cov­ery process, it re­mained un­clear what de­fense Meta would use. Until now.

Last week, Meta served a sup­ple­men­tal in­ter­roga­tory re­sponse at the California fed­eral court, which marks a new di­rec­tion in its de­fense. For the first time, the com­pany ar­gued that up­load­ing pi­rated books to other BitTorrent users dur­ing the tor­rent down­load process also qual­i­fies as fair use.

Meta’s rea­son­ing is straight­for­ward. Anyone who uses BitTorrent to trans­fer files au­to­mat­i­cally up­loads con­tent to other peo­ple, as it is in­her­ent to the pro­to­col. In other words, the up­load­ing was­n’t a choice, it was sim­ply how the tech­nol­ogy works.

Meta also ar­gued that the BitTorrent shar­ing was a ne­ces­sity to get the valu­able (but pi­rated) data. In the case of Anna’s Archive, Meta said, the datasets were only avail­able in bulk through tor­rent down­loads, mak­ing BitTorrent the only prac­ti­cal op­tion.

“Meta used BitTorrent be­cause it was a more ef­fi­cient and re­li­able means of ob­tain­ing the datasets, and in the case of Anna’s Archive, those datasets were only avail­able in bulk through tor­rent down­loads,” Meta’s at­tor­ney writes.

“Accordingly, to the ex­tent Plaintiffs can come forth with ev­i­dence that their works or por­tions thereof were the­o­ret­i­cally ‘made avail­able’ to oth­ers on the BitTorrent net­work dur­ing the tor­rent down­load process, this was part-and-par­cel of the down­load of Plaintiffs’ works in fur­ther­ance of Meta’s trans­for­ma­tive fair use pur­pose.”

In other words, ob­tain­ing the mil­lions of books that were needed to en­gage in the fair use train­ing of its LLM, re­quired the di­rect down­load­ing, which ul­ti­mately serves the same fair use pur­pose.

The au­thors were not happy with last week’s late Friday sub­mis­sion and the new de­fense. On Monday morn­ing, their lawyers filed a let­ter with Judge Vince Chhabria flag­ging the late-night fil­ing as an im­proper end-run around the dis­cov­ery dead­line.

They point out that Meta had been aware of the up­load­ing claims since November 2024, but that it never brought up this fair use de­fense in the past, not even when the court asked about it.

The let­ter specif­i­cally men­tions that while Meta has a “continuing duty” to sup­ple­ment dis­cov­ery un­der Rule 26(e), this rule does not cre­ate a “loophole” al­low­ing a party to add new de­fenses to its ad­van­tage af­ter a court dead­line has passed.

“Meta (for un­der­stand­able rea­sons) never once sug­gested it would as­sert a fair use de­fense to the up­load­ing-based claims, in­clud­ing af­ter this Court raised the is­sue with Meta last November,” the lawyers write.

Meta’s le­gal team fired back the fol­low­ing day, fil­ing their own let­ter with Judge Chhabria. This let­ter ex­plains that the fair use ar­gu­ment for the di­rect copy­right in­fringe­ment claim is not new at all.

Meta pointed to the par­ties’ joint December 2025 case man­age­ment state­ment, in which it had ex­plic­itly flagged the de­fense, and noted that the au­thor’s own at­tor­ney had ad­dressed it at a court hear­ing days later.

“In short, Plaintiffs’ as­ser­tion that Meta ‘never once sug­gested it would as­sert a fair use de­fense to the up­load­ing-based claims, in­clud­ing af­ter’ the November 2025 hear­ing, is false” Meta’s at­tor­ney writes in the let­ter.

Meanwhile, it’s worth not­ing that Meta’s in­ter­roga­tory re­sponse also cites de­po­si­tion tes­ti­mony from the au­thors them­selves, us­ing their own words to bol­ster its fair use de­fense.

The com­pany notes that every named au­thor has ad­mit­ted they are un­aware of any Meta model out­put that repli­cates con­tent from their books. Sarah Silverman, when asked whether it mat­tered if Meta’s mod­els never out­put lan­guage from her book, tes­ti­fied that “It does­n’t mat­ter at all.”

Meta ar­gues these ad­mis­sions un­der­cut any the­ory of mar­ket harm. If the au­thors them­selves can­not point to in­fring­ing out­put or lost sales, the law­suit is less about pro­tect­ing their books and more about chal­leng­ing the train­ing process it­self, which the court al­ready ruled was fair use.

These ad­mis­sions were cen­tral to Meta’s fair use de­fense on the train­ing claims, which Meta won last sum­mer. Whether they carry the same weight in the re­main­ing BitTorrent dis­tri­b­u­tion dis­pute has yet to be seen.

In its in­ter­roga­tory re­sponse, Meta added fur­ther weight by stress­ing that its in­vest­ment in AI has helped the U. S. to es­tab­lish U.S. global lead­er­ship, putting the coun­try ahead of geopo­lit­i­cal com­peti­tors. That’s a valu­able as­set worth trea­sur­ing, it in­di­rectly sug­gested.

As the case moves for­ward, Judge Chhabria will have to de­cide whether to al­low this “fair use by tech­ni­cal ne­ces­sity” de­fense. Needless to say, this will be of vi­tal im­por­tance to this and many other AI law­suits, where the use of shadow li­braries is at stake.

For now, the BitTorrent dis­tri­b­u­tion claims re­main the last live piece of a law­suit filed in 2023. Whether Judge Chhabria will al­low Meta’s new de­fense to pro­ceed has yet to be seen.

A copy of Meta’s sup­ple­men­tal in­ter­roga­tory re­sponse is avail­able here (pdf). The au­thors’ let­ter to Judge Chhabria can be found here (pdf). Meta’s re­sponse to that let­ter is avail­able here (pdf).

Bridge the gap be­tween cod­ing in­tent and ac­tion: ma­nip­u­late syn­tax struc­tures di­rectly, avoid­ing mouse or key­board gym­nas­tics. Amplify your cod­ing ef­fi­ciency: wield mul­ti­ple cur­sors for par­al­lel syn­tax node op­er­a­tions, rev­o­lu­tion­iz­ing bulk ed­its and refac­tor­ing.Se­lec­tion Modes stan­dard­ize move­ments across words, lines, syn­tax nodes, and more, of­fer­ing un­prece­dented flex­i­bil­ity and con­sis­tency.

A US ZIP code is 5 char­ac­ters. From those 5 char­ac­ters you can de­ter­mine the city, the state, and the coun­try. That’s 3 fields. Autofilled. From one in­put.

But you don’t do that, do you? No. You make me type my street ad­dress, then my city, then scroll through a drop­down of 50 states to find Illinois wedged be­tween Idaho and Indiana, then type my ZIP, then — the pièce de ré­sis­tance — scroll through 200+ coun­tries to find United States, which half the time is filed un­der “T” be­cause some dip­shit thought “The United States of America” was the cor­rect sort key.

It’s 2026. What the fuck are we do­ing.

I type 90210. You now know I’m in Beverly Hills, California, United States. You did­n’t need me to tell you that. You did­n’t need a drop­down. You did­n’t need me to scroll past Turkmenistan. You had the an­swer the en­tire time, in 5 dig­its, and you just… did­n’t use it.

And here’s the bonus: once you know the ZIP, your street ad­dress au­to­com­plete is search­ing a few thou­sand ad­dresses in­stead of 160 mil­lion. It’s faster. It’s more ac­cu­rate. I type less. You get cleaner data. Everyone wins.

This is not new tech­nol­ogy. Free APIs ex­ist. It’s like 4 lines of code. Look:

const res = await fetch(`https://​api.zip­popotam.us/​us/${​zip}`)

const data = await res.json()

city.value = data.places[0][“place name”]

state.value = data.places[0][“state”]

coun­try.value = “United States”

That’s it. That’s the whole thing. You could have shipped this in­stead of read­ing this web­site.

See how that works? See how you typed 5 num­bers and 3 fields filled them­selves in? See how you’re now typ­ing your street ad­dress and it al­ready knows what city you’re in? That’s not magic. That’s a lookup table. We’ve had those since the 1960s.

Tier 1: ZIP at the bot­tom. Street, city, state, ZIP, coun­try. You had the data to aut­ofill 3 fields and you just… put it last. Amazon does this. Target does this. Walmart does this. Basically every­one does this. Billions of col­lec­tive hours of hu­man life, spent scrolling for “Illinois.”

Tier 2: No aut­ofill at all. You col­lect the ZIP. You have the ZIP. You do noth­ing with it. The ZIP just sits there in your data­base, in­ert, like a fire ex­tin­guisher in a glass case that says “do not break.” What are you sav­ing it for.

Tier 3: The scrol­lable coun­try drop­down. 240 coun­tries. No search. No type-ahead. Just pure, un­fil­tered, al­pha­bet­i­cal scrolling. Bonus points if the US is un­der “T.” Extra bonus points if it’s not even al­pha­bet­i­cal. You ab­solute psy­chopaths.

Tier 4: The form that re­sets when you hit back. I filled out 14 fields. Your pay­ment proces­sor failed. I hit back. Everything is gone. My street. My city. My state. My will to live. All of it. Returned to the void. The de­vel­oper re­spon­si­ble for this sleeps eight hours a night. That’s the part that haunts me.

While we’re here:

Invoke the right key­board. If you’re ask­ing for a ZIP code, use in­put­mode=“nu­meric”. It’s one HTML at­tribute. On mo­bile, I should see a num­ber pad, not a full QWERTY key­board. This ap­plies to phone num­bers, credit cards, and any­thing else that’s ob­vi­ously just dig­its. You al­ready know the in­put type. Tell the phone.

Work with aut­ofill, not against it. Browsers have had aut­ofill for over a decade. Use the right au­to­com­plete at­trib­utes — postal-code, ad­dress-line1, coun­try. If your form fights the browser’s aut­ofill, your form is wrong. The browser is try­ing to save your user 45 sec­onds. Let it.

Fine, maybe coun­try first. The purists in the com­ments are tech­ni­cally cor­rect — postal codes aren’t glob­ally unique. You could do coun­try first (pre-filled via IP), then postal code, then let the magic hap­pen. The point was never “skip the coun­try field.” The point is: stop mak­ing me type things you al­ready know.

Found a site that puts the ZIP code last? A coun­try drop­down sorted by vibes? A form that makes you cry?

Send it to us →

Put the ZIP code first. Autofill the city. Autofill the state. Autofill the coun­try. Let the user type their street ad­dress last, with au­to­com­plete scoped to their ZIP.

It is a solved prob­lem. The API is free. The code is 5 lines. There is gen­uinely no rea­son not to do this other than the mass in­sti­tu­tional in­er­tia of a mil­lion prod­uct man­agers copy-past­ing the same ad­dress form tem­plate from 2009 and never once ask­ing “wait, why is the ZIP code at the bot­tom?”

Why is the ZIP code at the bot­tom?

Put it first, you an­i­mals.

Tweet this ·

Post to HN ·

Copy link

Share this be­fore you have to fill out an­other ad­dress form.

Effort comes af­ter re­ports of in­di­vid­u­als sus­pi­ciously earn­ing mas­sive pay­outs be­fore Iran Strikes, Venezuela Military Actions

Washington, D. C. — Today, Oregon’s U.S. Senator Jeff Merkley and Minnesota’s U.S. Senator Amy Klobuchar launched a new ef­fort to pre­vent gov­ern­ment of­fi­cials at the high­est lev­els from en­gag­ing in pre­dic­tion mar­kets, crack­ing down on the po­ten­tial for any in­sider trad­ing.

Following mul­ti­ple pub­lic re­ports on the grow­ing in­flu­ence of pre­dic­tion mar­kets and their po­ten­tial for cor­rup­tion, Merkley and Klobuchar in­tro­duced the End Prediction Market Corruption Act—a new bill to ban the President, Vice President, Members of Congress, and other pub­lic of­fi­cials from trad­ing event con­tracts. The bill will en­sure that fed­eral elected of­fi­cials main­tain their oath of of­fice to serve the peo­ple by pre­vent­ing them from trad­ing on in­for­ma­tion that they gained through their role.

“When pub­lic of­fi­cials use non-pub­lic in­for­ma­tion to win a bet, you have the per­fect recipe to un­der­mine the pub­lic’s be­lief that gov­ern­ment of­fi­cials are work­ing for the pub­lic good, not for their own per­sonal prof­its,” said Merkley. “Perfectly timed bets on pre­dic­tion mar­kets have the un­mis­tak­able stench of cor­rup­tion. To pro­tect the pub­lic in­ter­est, Congress must step up and pass my End Prediction Market Corruption Act to crack down on this bad bet for democ­racy.”

“At the same time that pre­dic­tion mar­kets have seen huge growth, we have seen in­creas­ing re­ports of mis­con­duct. This leg­is­la­tion strength­ens the Commodity Futures Trading Commission’s abil­ity to go af­ter bad ac­tors and pro­vides rules of the road to pre­vent those with con­fi­den­tial gov­ern­ment or pol­icy in­for­ma­tion from ex­ploit­ing their ac­cess for fi­nan­cial gain,” said Klobuchar.

Merkley and Klobuchar’s End Prediction Market Corruption Act is cosponsored by U. S. Senators Chris Van Hollen (D-MD), Adam Schiff (D-CA), and Kirsten Gillibrand (D-NY).

Their bill is sup­ported by Public Citizen, Citizens for Responsibility and Ethics in Washington (CREW), and Project On Government Oversight (POGO).

“The American peo­ple de­serve un­wa­ver­ing eth­i­cal stan­dards from their gov­ern­ment of­fi­cials. Officials have a re­spon­si­bil­ity to avoid not only ac­tual con­flicts of in­ter­est but even the ap­pear­ance of im­pro­pri­ety. POGO is pleased to en­dorse the End Prediction Market Corruption Act, which will fur­ther pro­hibit cov­ered gov­ern­ment of­fi­cials from ex­ploit­ing non­pub­lic in­for­ma­tion for per­sonal gain in pre­dic­tion mar­kets,” said Janice Luong, Policy Associate for the Project On Government Oversight (POGO).

“It is now more im­por­tant than ever that pre­dic­tion mar­kets be gov­erned by eth­i­cal con­straints, es­pe­cially when it comes to bets placed by gov­ern­men­tal of­fi­cials. Sen. Merkley’s leg­is­la­tion would ap­pro­pri­ately pro­hibit key gov­ern­ment of­fi­cials from buy­ing or sell­ing on the pre­dic­tion mar­kets con­tracts in which they could have in­sider in­for­ma­tion on changes in the mar­ket. Public Citizen heartily en­dorses this bill,” said Craig Holman, Ph. D., Public Citizen.

“The rapid rise of re­tail pre­dic­tion mar­kets cre­ates the risk that of­fi­cials across the gov­ern­ment could use non­pub­lic in­for­ma­tion to trade on and profit off event con­tracts,” said Debra Perlin, Vice President of Policy of Citizens for Responsibility and Ethics in Washington (CREW). “The American people must be able to trust that their gov­ern­ment of­fi­cials are work­ing on their be­half rather than for per­sonal gain. Senator Merkley’s leg­is­la­tion rep­re­sents a vi­tal step for­ward to en­sure that those in po­si­tions of power, in­clud­ing se­nior ex­ec­u­tive branch of­fi­cials and mem­bers of Congress, can­not abuse their ac­cess to non­pub­lic in­for­ma­tion in or­der to profit.”

Merkley has been a long-time leader in the push to end pub­lic cor­rup­tion. He has led the charge to crack down on elec­tion gam­bling and dark money in pol­i­tics, pre­vent law­mak­ers from trad­ing stocks, and ban cryp­tocur­rency-re­lated cor­rup­tion by elected of­fi­cials at the high­est lev­els of the fed­eral gov­ern­ment.

Full text of the End Prediction Market Corruption Act can be found by click­ing here.

CasNum (Compass and straight­edge Number) is a li­brary that im­ple­ments ar­bi­trary pre­ci­sion arith­metic us­ing

com­pass and straight­edge con­struc­tions. Arbitrary pre­ci­sion arith­metic, now with 100% more Euclid. Featuring a func­tional mod­i­fied Game Boy em­u­la­tor where every ALU op­code is im­ple­mented en­tirely through geo­met­ric con­struc­tions.

This pro­ject be­gan with a sim­ple com­pass-and-straight­edge ‘engine’, which can be found un­der the di­rec­tory cas/. In com­pass-and-straight­edge con­struc­tions, one start with just two points: The ori­gin, and a unit. Exactly as God in­tended. The en­gine then al­lows us to do what the an­cients did:

* Construct the line through two points

* Construct the cir­cle that con­tains one point and has a cen­ter at an­other point

* Construct the point at the in­ter­sec­tion of two (non-parallel) lines

* Construct the one or two points in the in­ter­sec­tion of a line and a cir­cle (if they in­ter­sect)

* Construct the one point or two points in the in­ter­sec­tion of two cir­cles (if they in­ter­sect) (Which, by the way turns out to be a nasty 4th de­gree equa­tion. Check out the for­mula in cir­cle.py, over 3600 char­ac­ters, yikes. Good thing we have WolframAlpha).

These five con­struc­tions are con­sid­ered the ba­sic com­pass and straight­edge con­struc­tions. Think of these as your ISA.

On top of the com­pass-and-straight­edge en­gine, we have the CasNum class. In CasNum, a num­ber x is rep­re­sented as the point (x,0) in the plane. Now, the fun part: im­ple­ment­ing all arith­metic and log­i­cal op­er­a­tions. We can con­struct the ad­di­tion of two points by find­ing the mid­point be­tween them and dou­bling it, which are both stan­dard com­pass-and-straight­edge con­struc­tions. Then, we can build the prod­uct and quo­tient of num­bers us­ing tri­an­gle sim­i­lar­ity. The log­i­cal op­er­a­tions (AND, OR, XOR) are a lit­tle uglier, since they are not a “clean al­ge­braic op­er­a­tion” in the rel­e­vant sense, but, hey, it works right?

What I thought was pretty neat is that im­ple­ment­ing all this from scratch leaves a lot of room for op­ti­miza­tion. For ex­am­ple, mul­ti­pli­ca­tion by 2 can be im­ple­mented much more ef­fi­ciently than the generic al­go­rithm for mul­ti­pli­ca­tion us­ing tri­an­gle sim­i­lar­ity. Then, im­ple­ment­ing mod­ulo by first re­mov­ing the high­est power of two times the mod­u­lus from the div­i­dend yielded much bet­ter re­sults than the naive im­ple­men­ta­tion.

* Integrate into the ALU of a Game Boy em­u­la­tor, thus ob­tain­ing a Game Boy that arith­meti­cally and log­i­cally runs solely on com­pass and straight­edge con­struc­tions

The first two ex­am­ples were ac­tu­ally im­ple­mented and can be found un­der the ex­am­ples/ di­rec­tory. So ap­par­ently one can­not square the cir­cle

us­ing a com­pass and a straight­edge, but at least one can run Pokémon Red. Man, I’m sure the an­cient Greeks would have loved to see this.

Thanks to the great code writ­ten by PyBoy, in­te­grat­ing CasNum within it was pretty seam­less. The only file I needed to edit was op­codes_­gen.py, and the edit was pretty min­i­mal.

As al­ways, please save any im­por­tant work be­fore run­ning any­thing I ever write.

To clone the repo, and in­stall re­quire­ments:

git clone –recursive git@github.com:0x0mer/CasNum.git

cd CasNum

pip in­stall -r re­quire­ments.txt

You can run the rsa and ba­sic ex­am­ples from the re­po’s root di­rec­tory like so:

python3 -m ex­am­ples.ba­sic

python3 -m ex­am­ples.rsa

The li­brary comes with a viewer (casnum/cas/viewer.py) that shows the com­pass and straight­edge con­struc­tions. It has an au­to­matic zoom that kinda works, but it goes crazy in the rsa ex­am­ple, so you may want to use man­ual zoom there.

In or­der to run PyBoy, first you need a ROM. In or­der to avoid copy­right in­fringe­ment, I in­cluded the ROM for 2048, free to dis­trib­ute un­der the zlib li­cense. But if, for ex­am­ple, the ROM you have is ‘Pokemon.gb’, then you can place it in ex­am­ples/​Py­boy and run:

cd ex­am­ples/​Py­Boy

pip in­stall -r re­quire­ments.txt

PYTHONPATH=../.. python

Then, once in python, run:

from py­boy im­port PyBoy

from cas­num im­port viewer

viewer.start()

py­boy = PyBoy(‘2048.gb’) # Or what­ever ROM you have

while py­boy.tick():

pass

py­boy.stop()

the viewer.start() just dis­plays the com­pass-and-straight­edge con­struc­tions, it is not strictly needed, but it is fun.

Notice how­ever that the first run of Pokemon on the Game Boy em­u­la­tor takes ap­prox­i­mately 15 min­utes to boot, so play­ing it may re­quire some­what in­creased pa­tience. You see, Euclid would­n’t have op­ti­mized the Game Boy boot screen. He would have spent those 15 min­utes in silent ap­pre­ci­a­tion, think­ing, “Yeah. That’s about how long that should take.”

After run­ning it once, most cal­cu­la­tions should al­ready be cached if you run it from the same python in­ter­preter in­stance, so on the sec­ond run you should be able to get a de­cent 0.5~1 FPS, which is to­tally al­most playable.

Most mod­ern de­vel­op­ers are con­tent with a + b. They don’t want to work for it. They don’t want to see the mid­point be­ing birthed from the in­ter­sec­tion of two cir­cles.

CasNum is for the de­vel­oper who be­lieves that if you did­n’t have to solve a 4th-degree poly­no­mial just to in­cre­ment a loop counter, you did­n’t re­ally in­cre­ment it.

Python’s lru_­cache is used to cache al­most any cal­cu­la­tion done in the li­brary, as every­thing is so ex­pen­sive. Memory us­age may blow up, run at your own risk.

* py­glet (optional but highly rec­om­mended. Only needed if you want to dis­play the

com­pass-and-straight­edge con­struc­tions)

* pytest-lazy-fix­tures (Only needed in or­der to run the tests)

* py­cryptodome (Only needed if you want to run the rsa ex­am­ple)

A: It can’t re­ally “run” any­thing, its a num­ber.

A: Define “fast”. If you mean “faster than copy­ing Euclid by hand”, then yes, dra­mat­i­cally.

Q: Why did you make this?

A: I wanted ar­bi­trary pre­ci­sion arith­metic, but I also wanted to feel some­thing.

The code in the root of this repos­i­tory is li­censed un­der the MIT License.

This pro­ject in­cor­po­rates the fol­low­ing third-party ma­te­ri­als:

PyBoy (Modified): Located in ./examples/PyBoy/. Distributed un­der the GNU Lesser General Public License (LGPL) v3.0.

Notice of Modification: This ver­sion of PyBoy has been mod­i­fied from the orig­i­nal source code to use the CasNum li­brary in­stead of Python’s int.

The orig­i­nal, un­mod­i­fied source code for PyBoy can be found at: https://​github.com/​Baekalfen/​Py­Boy.

The full LGPL li­cense text is avail­able in ./examples/PyBoy/License.md.

* Notice of Modification: This ver­sion of PyBoy has been mod­i­fied from the orig­i­nal source code to use the CasNum li­brary in­stead of Python’s int.

* The orig­i­nal, un­mod­i­fied source code for PyBoy can be found at: https://​github.com/​Baekalfen/​Py­Boy.

* The full LGPL li­cense text is avail­able in ./examples/PyBoy/License.md.

2048.gb: This Game Boy ROM bi­nary is dis­trib­uted un­der the zlib License.

Disclaimer: This soft­ware is pro­vided ‘as-is’, with­out any ex­press or im­plied war­ranty. In no event will the au­thors be held li­able for any dam­ages aris­ing from the use of this soft­ware.

* Disclaimer: This soft­ware is pro­vided ‘as-is’, with­out any ex­press or im­plied war­ranty. In no event will the au­thors be held li­able for any dam­ages aris­ing from the use of this soft­ware.

As lone­li­ness deep­ens in one of the world’s fastest-age­ing na­tions, a net­work of women de­liv­er­ing pro­bi­otic milk drinks has be­come a vi­tal source of rou­tine, con­nec­tion and care.

A woman in a neat navy suit and pow­der-blue shirt cy­cles pur­pose­fully down a quiet res­i­den­tial street in Tokyo. It’s 08:30 but al­ready balmy, and she’s grate­ful for the match­ing vi­sor that shields her eyes from the sum­mer sun.

She ar­rives at her first stop, parks her bike and knocks on the door of a small wooden house with pot­ted plants flank­ing the en­trance. Inside, an el­derly woman waits. Her face breaks into a broad smile as she opens the door — she has been ex­pect­ing this visit.

Japan is the world’s most rapidly age­ing ma­jor econ­omy. Nearly 30% of its pop­u­la­tion is now over 65, and the num­ber of el­derly peo­ple liv­ing alone con­tin­ues to rise. As fam­i­lies shrink and tra­di­tional multi-gen­er­a­tional house­holds de­cline, iso­la­tion has be­come one of the coun­try’s most press­ing so­cial chal­lenges.

The suited woman is a Yakult Lady — one of tens of thou­sands across Japan who de­liver the epony­mous pro­bi­otic drinks di­rectly to peo­ple’s homes. On pa­per they’re de­liv­ery work­ers, but in prac­tice they’re part of the coun­try’s in­for­mal so­cial safety net. In a coun­try grap­pling with a rapidly age­ing pop­u­la­tion and a deep­en­ing lone­li­ness cri­sis, Yakult Ladies have be­come an un­likely source of com­mu­nity, help­ing to re­duce the prob­lem of iso­la­tion one drop-off at a time.

With their dis­tinc­tive squat plas­tic bot­tles and shiny red caps, Yakult pi­o­neered a genre. The pro­bi­otic drink was launched in Japan 90 years ago — long be­fore “microbiome” be­came com­mon par­lance. But to­day, the women who de­liver them are as im­por­tant to the brand’s iden­tity as the prod­uct it­self.

Ayatollah Ali Khamenei was not, it’s safe to as­sume, a de­voted Polymarket user. If he had been, the Iranian leader might still be alive. Hours be­fore Khamenei’s com­pound in Tehran was re­duced to rub­ble last week, an ac­count un­der the user­name “magamyman” bet about $20,000 that the supreme leader would no longer be in power by the end of March. Polymarket placed the odds at just 14 per­cent, net­ting “magamyman” a profit of more than $120,000.

Everyone knew that an at­tack might be in the works—some American air­craft car­ri­ers had al­ready been de­ployed to the Middle East weeks ago—but the Iranian gov­ern­ment was caught off guard by the tim­ing. Although the ay­a­tol­lah surely was aware of the risks to his life, he pre­sum­ably did not know that he would be tar­geted on this par­tic­u­lar Saturday morn­ing. Yet on Polymarket, plenty of warn­ing signs pointed to an im­pend­ing at­tack. The day be­fore, 150 users bet at least $1,000 that the United States would strike Iran within the next 24 hours, ac­cord­ing to a New York Times analy­sis. Until then, few peo­ple on the plat­form were bet­ting that kind of money on an im­me­di­ate at­tack.

Maybe all of this sounds eerily fa­mil­iar. In January, some­one on Polymarket made a se­ries of sus­pi­ciously well-timed bets right be­fore the U. S. at­tacked a for­eign coun­try and de­posed its leader. By the time Nicolás Maduro was ex­tracted from Venezuela and flown to New York, the user had pock­eted more than $400,000. Perhaps this trader and the Iran bet­tors who are now flush with cash sim­ply had the luck of a life­time—the gam­bling equiv­a­lent of mak­ing a half-court shot. Or maybe they knew what was hap­pen­ing ahead of time and flipped it for easy money. We sim­ply do not know.

Polymarket traders swap crypto, not cash, and con­ceal their iden­ti­ties through the blockchain. Even so, in­ves­ti­ga­tions into in­sider trad­ing are al­ready un­der­way: Last month, Israel charged a mil­i­tary re­servist for al­legedly us­ing clas­si­fied in­for­ma­tion to make un­spec­i­fied bets on Polymarket.

The plat­form for­bids il­le­gal ac­tiv­ity, which in­cludes in­sider trad­ing in the U. S. But with a few taps on a smart­phone, any­one with priv­i­leged knowl­edge can now make a quick buck (or a hun­dred thou­sand). Polymarket and other pre­dic­tion mar­kets—the san­i­tized, in­dus­try-fa­vored term for sites that let you wa­ger on just about any­thing—have been dogged by ac­cu­sa­tions of in­sider trad­ing in mar­kets of all fla­vors. How did a Polymarket user know that Lady Gaga, Cardi B, and Ricky Martin would make sur­prise ap­pear­ances dur­ing the Super Bowl half­time show, but that Drake and Travis Scott would­n’t? Shady bets on war are even stranger and more dis­turb­ing. They risk un­leash­ing an en­tirely new kind of na­tional-se­cu­rity threat. The U.S. caught a break: The Venezuela and Iran strikes were not thwarted by in­sider traders whose bets could have prompted swift re­tal­i­a­tion. The next time, we may not be so lucky.

The at­tacks in Venezuela and Iran—like so many mil­i­tary cam­paigns—were con­ducted un­der the guise of se­crecy. You don’t swoop in on an ad­ver­sary when they know you are com­ing. The Venezuela raid was re­port­edly so con­fi­den­tial that Pentagon of­fi­cials did not know about its ex­act tim­ing un­til a few hours be­fore President Trump gave the or­ders.

Any in­sid­ers who put money down on im­pend­ing war may not have thought that they were giv­ing any­thing away. An anony­mous bet that reeks of in­sider trad­ing is not al­ways easy to spot in the mo­ment. After the sus­pi­cious Polymarket bets on the Venezuela raid, the site’s fore­cast placed the odds that Maduro would be ousted at roughly 10 per­cent. Even if Maduro and his team had been glued to Polymarket, it’s hard to imag­ine that such long odds would have com­pelled him to flee in the mid­dle of the night. And even with so many peo­ple bet­ting last Friday on an im­mi­nent strike in Iran, Polymarket fore­casted only a 26 per­cent chance, at most, of an at­tack the next day. What’s the sig­nal, and what’s the noise?

In both cases, some­one adept at pars­ing pre­dic­tion mar­kets could have known that some­thing was up. “It’s pos­si­ble to spot these bets ahead of time,” Rajiv Sethi, a Barnard College econ­o­mist who stud­ies pre­dic­tion mar­kets, told me. There are some tell­tale be­hav­iors that could help dis­tin­guish a mil­i­tary con­trac­tor bet­ting off a state se­cret from a col­lege stu­dent mind­lessly scrolling on his phone af­ter one too many cans of Celsius. Someone who’s us­ing a newly cre­ated ac­count to wa­ger a lot of money against the con­ven­tional wis­dom is prob­a­bly the for­mer, not the lat­ter. And spot­ting these kinds of sus­pi­cious bet­tors is only get­ting eas­ier. The pre­dic­tion-mar­ket boom has cre­ated a cot­tage in­dus­try of tools that in­stan­ta­neously flag po­ten­tial in­sider trad­ing—not for le­gal pur­poses but so that you, too, can profit off of what the se­lect few al­ready know.

Unlike Kalshi, the other big pre­dic­tion-mar­ket plat­form, Polymarket can be used in the U. S only through a vir­tual pri­vate net­work, or VPN. In ef­fect, the site is able to skirt reg­u­la­tions that re­quire track­ing the iden­ti­ties of its cus­tomers and re­port­ing shady bets to the gov­ern­ment. In some ways, in­sider trad­ing seems to be the whole point: “What’s cool about Polymarket is that it cre­ates this fi­nan­cial in­cen­tive for peo­ple to go and di­vulge the in­for­ma­tion to the mar­ket,” Shayne Coplan, the com­pa­ny’s 27-year-old CEO, said in an in­ter­view last year. (Polymarket did not re­spond to a re­quest for com­ment.)

Consider if the Islamic Revolutionary Guard Corps had paid the monthly fee for a ser­vice that flagged rel­e­vant ac­tiv­ity on Polymarket two hours be­fore the strike. The supreme leader might not have hosted in-per­son meet­ings with his top ad­vis­ers where they were easy tar­gets for mis­siles. Perhaps Iran would have launched its own pre­emp­tive strikes, tar­get­ing mil­i­tary bases across the Middle East. Six American ser­vice mem­bers have al­ready died from Iran’s drone at­tacks in the re­gion; the death toll could have been higher if Iran had struck first. In other words, some­one’s idea of a get-rich-quick scheme may have ended with a mil­i­tary raid gone hor­ri­bly awry. (The Department of Defense did not re­spond to a re­quest for com­ment.)

Maybe this all sounds far-fetched, but it should­n’t. “Any ad­vance no­tice to an ad­ver­sary is prob­lem­atic,” Alex Goldenberg, a fel­low at the Rutgers Miller Center who has writ­ten about war mar­kets, told me. “And these pre­dic­tive mar­kets, as they stand, are de­signed to leak out this in­for­ma­tion.” In all like­li­hood, he added, in­tel­li­gence agen­cies across the world are al­ready pay­ing at­ten­tion to Polymarket. Last year, the mil­i­tary’s bul­letin for in­tel­li­gence pro­fes­sion­als pub­lished an ar­ti­cle ad­vo­cat­ing for the armed forces to in­te­grate data from Polymarket to “more fully an­tic­i­pate na­tional se­cu­rity threats.” After all, the Pentagon al­ready has some ex­pe­ri­ence with pre­dic­tion mar­kets. During the War on Terror, DARPA toyed with cre­at­ing what it billed the “Policy Analysis Market,” a site that would let anony­mous traders bet on world events to fore­cast ter­ror­ist at­tacks and coups. (Democrats in Congress re­volted, and the site was quickly canned.)

Now every ad­ver­sary and ter­ror­ist group in the world can eas­ily ac­cess war mar­kets that are far more ad­vanced than what the DOD ginned up two decades ago. What makes Polymarket’s en­trance into war­fare so trou­bling is not just po­ten­tial in­sider trad­ing from users like “magamyman.” If gov­ern­ments are eye­ing Polymarket for signs of an im­pend­ing at­tack, they can also be led astray. A gov­ern­ment or an­other so­phis­ti­cated ac­tor would­n’t need to spend much money to mas­sively swing the Polymarket odds on whether a Gulf state will im­mi­nently strike Iran—breeding panic and para­noia. More fun­da­men­tally, pre­dic­tion mar­kets risk warp­ing the ba­sic in­cen­tives of war, Goldenberg said. He gave the ex­am­ple of a Ukrainian mil­i­tary com­man­der mak­ing less than $1,000 a month, who could place bets that go against his own mil­i­tary’s ob­jec­tive. “Maybe you choose to re­treat a day early be­cause you can dou­ble, triple, or quadru­ple your money and then send that back to your fam­ily,” he said.

Again, we don’t know for sure whether any of this is hap­pen­ing. That may be the scari­est part. As long as Polymarket lets any­one bet on war anony­mously, we may never know. Last Saturday, the day of the ini­tial Iran at­tack, Polymarket processed a record $478 mil­lion in bets, ac­cord­ing to one analy­sis. All the while, Polymarket con­tin­ues to wedge it­self into the main­stream. Substack re­cently struck a part­ner­ship with Polymarket to in­cor­po­rate the plat­for­m’s fore­casts into its newslet­ters. (“Journalism is bet­ter when it’s backed by live mar­kets,” Polymarket posted on X in an­nounc­ing the deal.) All of this makes the site even more valu­able as an in­tel­li­gence as­set, and even more de­struc­tive for the rest of us. Polymarket keeps launch­ing more war mar­kets: Will the U. S. strike Iraq? Will Israel strike Beirut? Will Iran strike Cyprus? Somewhere out there, some­one likely al­ready knows the an­swers.

WorldIran warns it will hit US bases across re­gion hours af­ter pres­i­den­t’s apol­ogy In the cen­tre of the storm: what does the Iran war mean for Dubai?Trump’s war on Iran is spread­ing. Where does it stop?USIran warns it will hit US bases across re­gion hours af­ter pres­i­den­t’s apol­ogy Trump’s war on Iran is spread­ing. Where does it stop?Don­ald Trump calls for more US mil­i­tary ac­tion in Latin AmericaUS faced with few good op­tions to tamp down surg­ing oil prices CompaniesIn the cen­tre of the storm: what does the Iran war mean for Dubai?Is the night­mare sce­nario for global en­ergy here?The last of Hong Kong’s colo­nial-era trainee schemes­Ships in Gulf de­clare them­selves Chinese to dodge at­tack­TechUS draws up strict new AI guide­lines amid Anthropic clash­Google gives CEO Sundar Pichai new pay deal worth up to $692mnMarketsIs the night­mare sce­nario for global en­ergy here?US faced with few good op­tions to tamp down surg­ing oil prices Britain is now the home of the Middle ManInvestors are not ready for a true shockA week of war in charts: the im­pact on the USOpinionIs the night­mare sce­nario for global en­ergy here?Britain is now the home of the Middle ManWork & CareersGoogle gives CEO Sundar Pichai new pay deal worth up to $692mnPapier founder: ’I don’t own stocks or shares — it’s too much risk’Are you fi­nan­cially ‘prepped’ for higher in­fla­tion? After years of pay gap re­port­ing, what do we know? Life & ArtsTrump’s war on Iran is spread­ing. Where does it stop?Marinelli: my 15-year quest to ski the biggest face in the Alps How To Spend It

US econ­omy sheds 92,000 jobs in February in sharp slide per month. Complete dig­i­tal ac­cess to qual­ity FT jour­nal­ism on any de­vice. Cancel any­time dur­ing your trial. Essential dig­i­tal ac­cess to qual­ity FT jour­nal­ism on any de­vice. Pay a year up­front and save 20%.Complete dig­i­tal ac­cess to qual­ity FT jour­nal­ism with ex­pert analy­sis from in­dus­try lead­ers. Pay a year up­front and save 20%.Check whether you al­ready have ac­cess via your uni­ver­sity or or­gan­i­sa­tion.Dis­cover all the plans cur­rently avail­able in your coun­try­See why over a mil­lion read­ers pay to read the Financial Times.Find out why

How To Spend It

I used to work at a vec­tor data­base com­pany. My en­tire job was help­ing peo­ple un­der­stand why they needed a data­base pur­pose-built for AI; em­bed­dings, se­man­tic search, the whole thing. So it’s a lit­tle funny that I’m writ­ing this. But here I am, watch­ing every­one in the AI ecosys­tem sud­denly re­dis­cover the hum­ble filesys­tem, and I think they might be onto some­thing big­ger than most peo­ple re­al­ize.

Not big­ger than data­bases. Different from data­bases. I need to say that up­front be­cause I al­ready know some­one is go­ing to read this and think I’m say­ing “files good, data­bases bad.” I’m not. Stay with me.

If you’ve been pay­ing any at­ten­tion to the AI agent space over the last few months, you’ve no­ticed some­thing strange. LlamaIndex pub­lished “Files Are All You Need.” LangChain wrote about how agents can use filesys­tems for con­text en­gi­neer­ing. Oracle, yes Oracle (who is cook­ing btw), put out a piece com­par­ing filesys­tems and data­bases for agent mem­ory. Dan Abramov wrote about a so­cial filesys­tem built on the AT Protocol. Archil is build­ing cloud vol­umes specif­i­cally be­cause agents want POSIX file sys­tems.

Jerry Liu from LlamaIndex put it bluntly: in­stead of one agent with hun­dreds of tools, we’re mov­ing to­ward a world where the agent has ac­cess to a filesys­tem and maybe 5-10 tools. That’s it. Filesystem, code in­ter­preter, web ac­cess. And that’s as gen­eral, if not more gen­eral than an agent with 100+ MCP tools.

Karpathy made the ad­ja­cent ob­ser­va­tion that stuck with me. He pointed out that Claude Code works be­cause it runs on your com­puter, with your en­vi­ron­ment, your data, your con­text. It’s not a web­site you go to — it’s a lit­tle spirit that lives on your ma­chine. OpenAI got this wrong, he ar­gued, by fo­cus­ing on cloud de­ploy­ments in con­tain­ers or­ches­trated from ChatGPT in­stead of sim­ply run­ning on lo­cal­host.

And here’s the thing that makes all of this mat­ter com­mer­cially: cod­ing agents make up the ma­jor­ity of ac­tual AI use cases right now. Anthropic is re­port­edly ap­proach­ing prof­itabil­ity, and a huge chunk of that is dri­ven by Claude Code, a CLI tool. Not a chat­bot. A tool that reads and writes files on your filesys­tem.

Here’s where I think most of the dis­course misses the deeper point.

Memory; in the hu­man, psy­cho­log­i­cal sense is fun­da­men­tal to how we func­tion. We don’t re-read our en­tire life story every time we make a de­ci­sion. We have long-term stor­age, se­lec­tive re­call, the abil­ity to for­get things that don’t mat­ter and sur­face things that do. Context win­dows in LLMs are none of that. They’re more like a white­board that some­one keeps eras­ing.

If you’ve used Claude Code for any real pro­ject, you know the dread of watch­ing that “context left un­til auto-com­pact” no­ti­fi­ca­tion creep closer. Your en­tire con­ver­sa­tion, all the con­text the agent has built up about your code­base, your pref­er­ences, your de­ci­sions about to be com­pressed or lost.

Filesystems solve this in the most bor­ing, ob­vi­ous way pos­si­ble. Write things down. Put them in files. Read them back when you need them. Claude’s CLAUDE.md file gives the agent per­sis­tent con­text about your pro­ject. Cursor stores past chat his­tory as search­able files. People are writ­ing aboutme.md files that act as portable iden­tity de­scrip­tors any agent can read i.e. your pref­er­ences, your skills, your work­ing style, all in a file that moves be­tween ap­pli­ca­tions with­out any­one need­ing to co­or­di­nate an API.

Except! It might not be quite that sim­ple.

A re­cent pa­per from ETH Zürich eval­u­ated whether these repos­i­tory-level con­text files ac­tu­ally help cod­ing agents com­plete tasks. The find­ing was coun­ter­in­tu­itive: across mul­ti­ple agents and mod­els, con­text files tended to re­duce task suc­cess rates while in­creas­ing in­fer­ence cost by over 20%. Agents given con­text files ex­plored more broadly, ran more tests, tra­versed more files — but all that thor­ough­ness de­layed them from ac­tu­ally reach­ing the code that needed fix­ing. The files acted like a check­list that agents took too se­ri­ously.

This sounds like it un­der­mines the whole premise. But I think it ac­tu­ally sharp­ens it. The pa­per’s con­clu­sion was­n’t “don’t use con­text files.” It was that un­nec­es­sary re­quire­ments make tasks harder, and con­text files should de­scribe only min­i­mal re­quire­ments. The prob­lem is­n’t the filesys­tem as a per­sis­tence layer. The prob­lem is peo­ple treat­ing CLAUDE.md like a 2,000-word on­board­ing doc­u­ment in­stead of a con­cise set of con­straints. Which brings us to the ques­tion of stan­dards.

Right now we have CLAUDE.md, AGENTS.md, copi­lot-in­struc­tions.md, .cursorrules, and prob­a­bly five more by the time you read this. Everyone agrees that agents need per­sis­tent filesys­tem-based con­text. Nobody agrees on what the file should be called or what should go in it. I see ef­forts to con­sol­i­date, this is good.

Dan Abramov’s piece on a so­cial filesys­tem crys­tal­lized some­thing im­por­tant here. He de­scribes how the AT Protocol treats user data as files in a per­sonal repos­i­tory; struc­tured, owned by the user, read­able by any app that speaks the for­mat. The crit­i­cal de­sign choice is that dif­fer­ent apps don’t need to agree on what a “post” is. They just need to name­space their for­mats (using do­main names, like Java pack­ages) so they don’t col­lide. Apps are re­ac­tive to files. Every ap­p’s data­base be­comes de­rived data i.e. a cached ma­te­ri­al­ized view of every­body’s fold­ers.

The same ten­sion ex­ists in the agent con­text file space. We don’t need CLAUDE.md and AGENTS.md and copi­lot-in­struc­tions.md to con­verge into one file. We need them to co­ex­ist with­out col­li­sion. And to be fair, some con­ver­gence is hap­pen­ing. Anthropic re­leased Agent Skills as an open stan­dard, a SKILL.md for­mat that Microsoft, OpenAI, Atlassian, GitHub, and Cursor have all adopted. A skill you write for Claude Code works in Codex, works in Copilot. The file for­mat is the API.

NanoClaw, a light­weight per­sonal AI as­sis­tant frame­work, takes this to its log­i­cal con­clu­sion. Instead of build­ing an ever-ex­pand­ing fea­ture set, it uses a “skills over fea­tures” model. Want Telegram sup­port? There’s no Telegram mod­ule. There’s a /add-telegram skill, es­sen­tially a mark­down file that teaches Claude Code how to rewrite your in­stal­la­tion to add the in­te­gra­tion. Skills are just files. They’re portable, au­ditable, and com­pos­able. No MCP server re­quired. No plu­gin mar­ket­place to browse. Just a folder with a SKILL.md in it.

This is in­ter­op­er­abil­ity with­out co­or­di­na­tion. And I want to be spe­cific about what I mean by that, be­cause it’s a strong claim. In tech, get­ting two com­pet­ing prod­ucts to work to­gether usu­ally re­quires ei­ther a for­mal stan­dard that takes years to rat­ify, or a dom­i­nant plat­form that forces com­pat­i­bil­ity. Files side­step both. If two apps can read mark­down, they can share con­text. If they both un­der­stand the SKILL.md for­mat, they can share ca­pa­bil­i­ties. Nobody had to sign a part­ner­ship agree­ment. Nobody had to at­tend a stan­dards body meet­ing. The file for­mat does the co­or­di­nat­ing.

There’s a use­ful anal­ogy from in­fra­struc­ture. Traditional data ar­chi­tec­tures were de­signed around the as­sump­tion that stor­age was the bot­tle­neck. The CPU waited for data from mem­ory or disk, and com­pu­ta­tion was es­sen­tially re­ac­tive to what­ever stor­age made avail­able. But as pro­cess­ing power out­paced stor­age I/O, the par­a­digm shifted. The in­dus­try moved to­ward de­cou­pling stor­age and com­pute, let­ting each scale in­de­pen­dently, which is how we ended up with ar­chi­tec­tures like S3 plus ephemeral com­pute clus­ters. The bot­tle­neck moved, and every­thing re­or­ga­nized around the new con­straint.

Something sim­i­lar is hap­pen­ing with AI agents. The bot­tle­neck is­n’t model ca­pa­bil­ity or com­pute. It’s con­text. Models are smart enough. They’re just for­get­ful. And filesys­tems, for all their sim­plic­ity, are an in­cred­i­bly ef­fec­tive way to man­age per­sis­tent con­text at the ex­act point where the agent runs — on the de­vel­op­er’s ma­chine, in their en­vi­ron­ment, with their data al­ready there.

Now, I’d be a frawd if I did­n’t ac­knowl­edge the ten­sion here. Someone on Twitter joked that “all of you say­ing you don’t need a graph for agents while us­ing the filesys­tem are just in de­nial about us­ing a graph.” And… they’re not wrong. A filesys­tem is a tree struc­ture. Directories, sub­di­rec­to­ries, files i.e. a di­rected acyclic graph. When your agent runs ls, grep, reads a file, fol­lows a ref­er­ence to an­other file, it’s tra­vers­ing a graph.

Richmond in Oracle’s piece made the sharpest dis­tinc­tion I’ve seen: filesys­tems are win­ning as an in­ter­face, data­bases are win­ning as a sub­strate. The mo­ment you want con­cur­rent ac­cess, se­man­tic search at scale, dedu­pli­ca­tion, re­cency weight­ing — you end up build­ing your own in­dexes. Which is, let’s be hon­est, ba­si­cally a data­base.

Having worked at Weaviate, I can tell you that this is­n’t an ei­ther/​or sit­u­a­tion. The file in­ter­face is pow­er­ful be­cause it’s uni­ver­sal and LLMs al­ready un­der­stand it. The data­base sub­strate is pow­er­ful be­cause it pro­vides the guar­an­tees you need when things get real. The in­ter­est­ing fu­ture is­n’t files ver­sus data­bases. It’s files as the in­ter­face hu­mans and agents in­ter­act with, backed by what­ever sub­strate makes sense for the use case.

Here’s my ac­tual take on all of this, the thing I think peo­ple are danc­ing around but not say­ing di­rectly.

Filesystems can re­de­fine what per­sonal com­put­ing means in the age of AI.

Not in the “everything runs lo­cally” sense (but maybe?). In the sense that your data, your con­text, your pref­er­ences, your skills, your mem­ory — lives in a for­mat you own, that any agent can read, that is­n’t locked in­side a spe­cific ap­pli­ca­tion. Your aboutme.md works with your flavour of OpenClaw/NanoClaw to­day and what­ever comes to­mor­row. Your skills files are portable. Your pro­ject con­text per­sists across tools.

This is what per­sonal com­put­ing was sup­posed to be be­fore every­thing moved into walled-gar­den SaaS apps and pro­pri­etary data­bases. Files are the orig­i­nal open pro­to­col. And now that AI agents are be­com­ing the pri­mary in­ter­face to com­put­ing, files are be­com­ing the in­ter­op­er­abil­ity layer that makes it pos­si­ble to switch tools, com­pose work­flows, and main­tain con­ti­nu­ity across ap­pli­ca­tions, all with­out any­one’s per­mis­sion.

I’ll ad­mit this is a bit ide­al­is­tic. The his­tory of open for­mats is lit­tered with stan­dards that won on pa­per and lost in prac­tice. Companies have strong in­cen­tives to make their con­text files just dif­fer­ent enough that switch­ing costs re­main high. The fact that we al­ready have CLAUDE.md and AGENTS.md and .cursorrules co­ex­ist­ing rather than one uni­ver­sal for­mat, is ev­i­dence that frag­men­ta­tion is the de­fault, not the ex­cep­tion. And the ETH Zürich pa­per is a re­minder that even when the for­mat ex­ists, writ­ing good con­text files is harder than it sounds. Most peo­ple will write bad ones, and bad con­text files are ap­par­ently worse than none at all.

But I keep com­ing back to some­thing Dan Abramov wrote: our mem­o­ries, our thoughts, our de­signs should out­live the soft­ware we used to cre­ate them. That’s not a tech­ni­cal ar­gu­ment. It’s a val­ues ar­gu­ment. And it’s one that the filesys­tem, for all its age and sim­plic­ity, is uniquely po­si­tioned to serve. Not be­cause it’s the best tech­nol­ogy. But be­cause it’s the one tech­nol­ogy that al­ready be­longs to you.

Those who have never en­dured the re­lent­less ring­ing of tin­ni­tus can only dream of the tor­ment. In fact, a bad dream may be the clos­est some get to ex­pe­ri­enc­ing any­thing like it.

The sub­jec­tive sound, which can also be a hiss­ing, buzzing, or click­ing, is heard by no one else, and it may be pre­sent con­stantly, or may come and go.

Neuroscientists at the University of Oxford now sus­pect that sleep and tin­ni­tus are closely in­ter­twined in the brain.

Their find­ings hint at a fun­da­men­tal re­la­tion­ship be­tween the two con­di­tions — one that has, sur­pris­ingly, been over­looked in the brain un­til very re­cently.

“What first made me and my col­leagues cu­ri­ous were the re­mark­able par­al­lels be­tween tin­ni­tus and sleep,” neu­ro­sci­en­tist Linus Milinski at Oxford’s Sleep and Circadian Neuroscience Institute told ScienceAlert.

“Tinnitus is a de­bil­i­tat­ing med­ical con­di­tion, whereas sleep is a nat­ural state we en­ter reg­u­larly, yet both ap­pear to rely on spon­ta­neous brain ac­tiv­ity. Because there is still no ef­fec­tive treat­ment for sub­jec­tive tin­ni­tus, I be­lieve that ex­plor­ing these sim­i­lar­i­ties might of­fer new ways to un­der­stand and even­tu­ally treat phan­tom per­cepts.”

Watch the video be­low for a sum­mary of the study:

A ‘phantom per­cept’ is when our brains fool us into think­ing we are see­ing, hear­ing, feel­ing, or smelling some­thing that is not there, phys­i­cally speak­ing.

Many peo­ple ex­pe­ri­ence phan­tom per­cepts only dur­ing sleep, but for about 15 per­cent of the world’s pop­u­la­tion, an in­escapable noise rings in their ears dur­ing wak­ing hours, too.

Tinnitus is the world’s most com­mon phan­tom per­cept, and yet there is no known cause or cure, de­spite a long list of hy­pothe­ses.

While many in­di­vid­u­als with tin­ni­tus re­port poor sleep and show poor sleep pat­terns, the po­ten­tial con­nec­tion to this cru­cial bod­ily func­tion has only re­cently come to light.

In 2022, Milinski led a re­view, which the au­thors claim is the first to con­sider, at a func­tional level, how sleep might im­pact tin­ni­tus, and vice versa.

The Oxford re­searchers pro­posed that the large spon­ta­neous waves of brain ac­tiv­ity that oc­cur dur­ing deep sleep, or non-rapid eye move­ment sleep (non-REM), might sup­press the brain ac­tiv­ity that leads to tin­ni­tus.

To test that idea, the team turned to fer­rets, which have a sim­i­lar au­di­tory sys­tem to hu­mans. In ex­per­i­ments pub­lished in 2024, re­searchers found that fer­rets that de­vel­oped more se­vere tin­ni­tus also showed dis­rupted sleep.

“We could ac­tu­ally see these sleep prob­lems ap­pear at the same time as tin­ni­tus af­ter noise ex­po­sure,” Milinski told ScienceAlert. “This sug­gested, for the first time, a clear link be­tween de­vel­op­ing tin­ni­tus and dis­rupted sleep.”

Crucially, the fer­rets that de­vel­oped tin­ni­tus showed overly re­spon­sive brain ac­tiv­ity to sound. When the fer­rets fi­nally did man­age to slip into non-REM sleep, that hy­per­ac­tiv­ity was damp­ened.

This sug­gests that sleep may tem­porar­ily mask the ef­fects of tin­ni­tus by en­gag­ing the same brain cir­cuits.

“Our find­ings in­di­cate that deep sleep may in­deed help mit­i­gate tin­ni­tus and could re­veal nat­ural brain mech­a­nisms for mod­u­lat­ing ab­nor­mal ac­tiv­ity,” said Milinski.

Research on non-hu­man an­i­mals has its ob­vi­ous lim­i­ta­tions, but the same sort of brain ac­tiv­ity pat­terns may ex­ist in hu­mans, too.

Since their 2022 re­view, Milinski says the field has rapidly ex­panded, with a grow­ing num­ber of large-scale stud­ies in­ves­ti­gat­ing how sleep, the en­vi­ron­ment, and tin­ni­tus in­ter­act — and not just in fer­rets.

“I hope this re­search will lead to greater aware­ness of tin­ni­tus and open new ways of ex­plor­ing treat­ments,” Milinski told ScienceAlert.

“Acknowledging the im­pact of tin­ni­tus, es­pe­cially in older adults, where hear­ing loss and tin­ni­tus can in­crease iso­la­tion and con­tribute to men­tal health prob­lems, is in­cred­i­bly im­por­tant.”

Just last year, a study from China found that in­di­vid­u­als with tin­ni­tus were less able to sup­press the hy­per­ac­tiv­ity of their awake brains as they tran­si­tioned into a sleep state.

During deep sleep, how­ever, the hy­per­ac­tiv­ity linked to tin­ni­tus was sup­pressed.

“This study es­tab­lishes sleep as a crit­i­cal ther­a­peu­tic tar­get to in­ter­rupt the 24-hour dys­func­tional cy­cle of tin­ni­tus,” the au­thors con­clude, led by Xiaoyu Bao of South China University of Technology.

At Oxford, Milinski and his col­leagues are now fo­cus­ing on how sleep may af­fect the de­vel­op­ment of tin­ni­tus.

“Tinnitus can make sleep worse, and poor sleep may, in turn, make tin­ni­tus worse. It may be a kind of vi­cious cir­cle, al­though I do not be­lieve it is un­break­able,” spec­u­lated Milinski.

“When we do not sleep well, we be­come more vul­ner­a­ble to stress, and stress is one of the strongest fac­tors known to worsen tin­ni­tus. Stress can even trig­ger tin­ni­tus to be­gin with.”

Further re­search could not only lead to ef­fec­tive tin­ni­tus treat­ments but also help sci­en­tists bet­ter un­der­stand the mys­ter­ies of sleep it­self.

The 2022 re­view was pub­lished in Brain Communications.

An ear­lier ver­sion of this ar­ti­cle was pub­lished in November 2025.