Say No to Palantir in Europe

To European gov­ern­ments and the EU

Review and phase out ex­ist­ing con­tracts with the com­pany.

And we call on the EU to ur­gently in­ves­ti­gate Palantir’s use across Europe, en­sure full trans­parency over con­tracts and data use, and push gov­ern­ments to halt new deals un­til strong safe­guards and de­mo­c­ra­tic over­sight are guar­an­teed.

Europe must not hand its pub­lic sys­tems, data, and se­cu­rity to a pri­vate US sur­veil­lance com­pany, es­pe­cially one that is in­volved in fu­el­ing wars and mass de­por­ta­tions.

Why is this im­por­tant?

A pow­er­ful com­pany en­ables geno­cide in Gaza, helps ICE sep­a­rate fam­i­lies, and fu­els Trump’s war with Iran. [1]

Most peo­ple have never even heard of it.

But gov­ern­ments across Europe are qui­etly sign­ing con­tracts with it, paid for with our tax money. [2] Its name is Palantir.

From the UK to Germany to France and be­yond, gov­ern­ments are hand­ing this US spy-tech gi­ant ac­cess to sen­si­tive pub­lic sys­tems and data. Police in Germany use it to track sus­pects, the UK hands it vast health­care datasets - and this is just the be­gin­ning. [3]

Palantir’s in­flu­ence in Europe is spread­ing fast, largely out of pub­lic sight.

That’s ex­actly why we must shine a light on it. Otherwise, we risk ex­pand­ing mass sur­veil­lance and fu­elling wars, while Europe hands its data and se­cu­rity to a US spy-tech gi­ant.

If we build mo­men­tum to ex­pose Palantir, we can push lead­ers to stop sign­ing new con­tracts and pro­tect Europe’s pub­lic sys­tems from pow­er­ful sur­veil­lance gi­ants.

Add your name now to de­mand trans­parency and stop the ex­pan­sion of Palantir in Europe.

And the peo­ple run­ning the com­pany aren’t hid­ing their in­ten­tions. CEO Alex Karp once said Palantir is “here to… scare en­e­mies and, on oc­ca­sion, kill them.” https://​www.wired.com/​story/​un­canny-val­ley-pod­cast-palan­tir-most-mys­te­ri­ous-com­pany-sil­i­con-val­ley

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Your brain is still grow­ing new cells right now. Here’s how to keep it hap­pen­ing

AI com­pa­nies con­tin­u­ally scrape the in­ter­net at an enor­mous scale, swal­low­ing up all of its con­tents to use as train­ing data for their next mod­els. If you have a pub­lic web­site, they are al­ready steal­ing your work.

Miasma is here to help you fight back! Spin up the server and point any ma­li­cious traf­fic to­wards it. Miasma will send poi­soned train­ing data from the poi­son foun­tain along­side mul­ti­ple self-ref­er­en­tial links. It’s an end­less buf­fet of slop for the slop ma­chines.

Miasma is very fast and has a min­i­mal mem­ory foot­print - you should not have to waste com­pute re­sources fend­ing off the in­ter­net’s leeches.

cargo in­stall mi­asma

mi­asma

mi­asma –help

Let’s walk through an ex­am­ple of set­ting up a server to trap scrap­ers with Miasma. We’ll pick /bots as our server’s path to di­rect scraper traf­fic. We’ll be us­ing Nginx as our server’s re­verse proxy, but the same re­sult can be achieved with many dif­fer­ent se­tups.

When we’re done, scrap­ers will be trapped like so:

Within our site, we’ll in­clude a few hid­den links lead­ing to /bots.

Amazing high qual­ity data here!

The style=“dis­play: none;”, aria-hid­den=“true”, and tabindex=“1” at­trib­utes en­sure links are to­tally in­vis­i­ble to hu­man vis­i­tors and will be ig­nored by screen read­ers and key­board nav­i­ga­tion. They will only be vis­i­ble to scrap­ers.

Since our hid­den links point to /bots, we’ll con­fig­ure this path to proxy Miasma. Let’s as­sume we’re run­ning Miasma on port 9855.

lo­ca­tion ~ ^/bots($|/.*)$ {

prox­y_­pass http://​lo­cal­host:9855;

This will match all vari­a­tions of the /bots path -> /bots, /bots/, /bots/12345, etc.

Lastly, we’ll start Miasma and spec­ify /bots as the link pre­fix. This in­structs Miasma to start links with /bots/, which en­sures scrap­ers are prop­erly routed through our Nginx proxy back to Miasma.

We’ll also limit the num­ber of max in-flight con­nec­tions to 50. At 50 con­nec­tions, we can ex­pect 50-60 MB peak mem­ory us­age. Note that any re­quests ex­ceed­ing this limit will im­me­di­ately re­ceive a 429 re­sponse rather than be­ing added to a queue.

mi­asma –link-prefix ‘/bots’ -p 9855 -c 50

Let’s de­ploy and watch as multi-bil­lion dol­lar com­pa­nies greed­ily eat from our end­less slop ma­chine!

Be sure to pro­tect friendly bots and search en­gines from Miasma in your ro­bots.txt!

Miasma can be con­fig­ured via its CLI op­tions:

Contributions are wel­come! Please open an is­sue for bugs re­ports or fea­ture re­quests. Primarily AI-generated con­tri­bu­tions will be au­to­mat­i­cally re­jected.

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We read every piece of feed­back, and take your in­put very se­ri­ously.

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To see all avail­able qual­i­fiers, see our doc­u­men­ta­tion.

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Note: On Windows “Server” you may need to in­stall vcrun­time140.dll.

If your sys­tem does not have the re­quired glibc ver­sion, try the (unsupported) builds for older glibc.

Run chmod u+x nvim-linux-x86_64.ap­pim­age && ./nvim-linux-x86_64.appimage

If your sys­tem does not have FUSE you can ex­tract the ap­pim­age:

./nvim-linux-x86_64.appimage –appimage-extract

./squashfs-root/usr/bin/nvim

Run chmod u+x nvim-linux-ar­m64.ap­pim­age && ./nvim-linux-arm64.appimage

If your sys­tem does not have FUSE you can ex­tract the ap­pim­age:

./nvim-linux-arm64.appimage –appimage-extract

./squashfs-root/usr/bin/nvim

You can’t per­form that ac­tion at this time.

On a spring morn­ing in 1987, a 30-year-old man named Robert Kilgour pulled up be­side a row of foamy cherry trees in the town of Kirkcaldy, on Scotland’s east coast, to visit an old ho­tel. The build­ing was four storeys of black­ened Victorian sand­stone. Kilgour was a big man, a vol­u­ble Scot with a knack for sto­ry­telling. He al­ready owned a ho­tel in Edinburgh but wanted to branch into prop­erty de­vel­op­ment and was plan­ning to turn this old place, Station Court, into apart­ments. A few months af­ter he com­pleted the pur­chase, how­ever, the Scottish gov­ern­ment scrapped a grant for de­vel­op­ers that he had been count­ing on. He had just sunk most of his per­sonal sav­ings into a use­less build­ing in a sod­den, post-in­dus­trial town. He ur­gently needed a new idea.

Care homes weren’t so dif­fer­ent from ho­tels, Kilgour thought. And the beauty was, their el­derly res­i­dents were un­likely to get drunk, steal the soap dis­pensers or in­vite sex work­ers back to their rooms. Turning Station Court into a care home seemed like the best way out of a bad sit­u­a­tion. Kilgour arranged a bank loan and in June 1989 he launched Four Seasons Health Care, tak­ing the name from a restau­rant in Midtown Manhattan where he had once dined.

By sheer luck, Kilgour had found him­self at the start of some­thing big. The fol­low­ing year, the gov­ern­ment in Westminster started to trans­fer re­spon­si­bil­ity for so­cial care on to lo­cal coun­cils. This gave busi­ness­men such as Kilgour a huge op­por­tu­nity. Councils be­gan pay­ing them to pro­vide beds that had pre­vi­ously been sup­plied by the NHS. Demand boomed.

Kilgour opened three other homes in Kirkcaldy, an­other over­look­ing the Firth of Forth, and a fur­ther one near Dundee. Alongside run­ning his new busi­ness, he jug­gled the pas­times of an in­creas­ingly wealthy man. He raised money for a can­cer char­ity, played ten­nis, net­worked cease­lessly and be­gan to dab­ble in pol­i­tics, cam­paign­ing (and fail­ing) to be­come one of Scotland’s few Conservative MPs. By 1997, he owned seven care homes across Fife.

That year, he chaired a fundrais­ing ap­peal to open a new hos­pice in the grounds of Kirkcaldy’s main hos­pi­tal. The guest of ho­n­our was an iras­ci­ble TV celebrity called John Harvey-Jones, star of a re­al­ity show called Troubleshooter in which he dis­pensed tough-love ad­vice to un­der­per­form­ing British busi­ness­men. Over tum­blers of whisky, Harvey-Jones coun­selled Kilgour: “He said I was stuck in a re­gional com­fort zone. He said I needed to break out of it and go wider.” Deep down, Kilgour agreed.

He had few con­tacts in London, where the se­ri­ous money was. It oc­curred to him that his best lead might be an ac­coun­tant he knew called Hamilton Anstead, who had re­cently left a job at a care com­pany in the south of England. Kilgour in­vited him up to a ho­tel in Glasgow and the two men hatched a plan for Anstead to join Four Seasons as a joint chief ex­ec­u­tive.

Kilgour told me all about this over cof­fee at his pri­vate mem­bers’ club in Mayfair, a high-ceilinged, low-lit place with clus­ters of vel­vet chairs arranged for quiet con­ver­sa­tion. He had now en­tered the “legacy” phase of his life, he said: more con­cerned with what he was leav­ing be­hind than what lay ahead. He of­ten men­tioned the politi­cians with whom he was on first-name terms, as if show­ing me the pho­tographs in a well-han­dled al­bum. Mostly, he seemed happy, but there were as­pects of his past that both­ered him.

Over the course of two years, Kilgour and Anstead built Four Seasons into, if not quite an em­pire, then a small do­min­ion of 43 homes dot­ted across Britain. As the busi­ness grew, how­ever, their re­la­tion­ship soured. Anstead of­ten felt that Kilgour was more in­ter­ested in his po­lit­i­cal ca­reer than the minu­tiae of spread­sheets or sup­pli­ers. (“I’m a strat­egy and vi­sion per­son, not a de­tail per­son,” Kilgour said. “Hamilton is a bril­liant mi­cro­man­ager and I’m an en­tre­pre­neur.”)

In 1999, the two men de­cided to sell the com­pany, with the idea that they would stay on as ex­ec­u­tives. Anstead iden­ti­fied a buyer, a pri­vate eq­uity firm called Alchemy Partners. Shortly af­ter they signed the deal, in August that year, he called Kilgour and said they ur­gently needed to meet. Anstead put it bluntly: nei­ther he nor the com­pa­ny’s new own­ers wanted Kilgour to stay on as an ex­ec­u­tive at Four Seasons. Kilgour felt his tem­per ris­ing. He was be­ing asked to leave the busi­ness he had cre­ated from scratch. “He started eff­ing and blind­ing and call­ing me all sorts of ob­scen­i­ties,” Anstead re­called. (Kilgour later told me that by this point he was ex­hausted, and wanted out.)

Alchemy sold Four Seasons in 2004, and the com­pany be­came no­to­ri­ous as a failed ex­per­i­ment, a by­word for the folly of en­trust­ing el­der care to pri­vate eq­uity. “You could ask me, well, do I feel guilty about what hap­pened?” Kilgour said. “And yes, I do, ac­tu­ally.”

Private eq­uity re­lies on a ba­sic tech­nique known as the lever­aged buy­out, which works like this: you, a deal­maker, buy a com­pany us­ing just a small por­tion of your own money. You bor­row the rest, and trans­fer all this debt on to the com­pany you just bought. In ef­fect, the com­pany goes into debt in or­der to pay for it­self. If it all goes well, you sell the com­pany for a profit and you reap the re­wards. If not, it is the com­pany, not you, that is on the hook for this debt.

Leveraged buy­outs first came to promi­nence in the 1980s, when deal­mak­ers on Wall Street be­gan tar­get­ing un­der­per­form­ing com­pa­nies and bloated con­glom­er­ates in the US. Then, these American busi­ness­men and their British im­i­ta­tors started to scour the world for other places to put this tech­nique to work. With a dwin­dling sup­ply of un­der­val­ued com­pa­nies to choose from, some of the sharpest minds in fi­nance found a new and un­ex­pected tar­get: care homes.

As peo­ple were now liv­ing well into their 80s and 90s, fi­nanciers be­gan to think of el­derly peo­ple as re­ces­sion-proof in­vest­ments, and as­sumed that the care home mar­ket in Britain and the US would keep grow­ing. In the UK, many of these homes were bankrolled by lo­cal au­thor­i­ties, which guar­an­teed a steady in­come from the gov­ern­ment. Elderly peo­ple who paid for their care out of their own pock­ets typ­i­cally cov­ered the cost by sell­ing their houses, and the cease­less in­crease in prop­erty prices en­dowed them with so much hous­ing eq­uity that they be­came the hu­man equiv­a­lent of ATMs. Care homes were the slot for with­draw­ing their cash.

It takes a cer­tain kind of mind to look into the world of colostomy bags, in­con­ti­nence pads and emol­lient cream and see dol­lar signs. Nevertheless, from the turn of the 21st cen­tury, pri­vate eq­uity in­vest­ment in care homes bal­looned in both Britain and the US. Fund man­agers thought “there are all these af­flu­ent baby boomers head­ing to­wards re­tire­ment. They’ve made a for­tune from their houses, or in­her­ited money from their par­ents, and they all have gold-plated pen­sion schemes,” Nick Hood, a char­tered ac­coun­tant who has stud­ied Britain’s care sec­tor, told me. “They rubbed their hands to­gether and said, ‘Sooner or later, as the de­mand in­creases, the prices must go up.’”

In the UK, a stream of deals took place. New com­pa­nies emerged and new care homes went up, some built out of faded ho­tels whose clien­tele had mi­grated to south­ern Spain af­ter the ad­vent of cheap air travel. Other busi­ness­men bought cre­ma­to­ri­ums as well as care homes, in an­tic­i­pa­tion of their clients’ fi­nal bill­able re­quire­ments. “Private eq­ui­ty’s pres­ence in British care homes was neg­li­gi­ble 30 years ago,” said Peter Morris, a re­searcher and as­so­ci­ate scholar at the University of Oxford. “Since then, it’s grown in­ex­orably.”

Anstead and Kilgour be­longed to a small group of newly minted care home mil­lion­aires. At the heart of many of these new for­tunes was a tech­nique fi­nanciers called “sale and lease­back”. You would take a care home and split it into an op­er­at­ing com­pany, or “opco”, which dealt with every­thing con­cern­ing the busi­ness of care, from staff to beds, med­i­cine cab­i­nets and cut­lery. On the other side you had the prop­erty com­pany, or “propco”, which now owned the phys­i­cal home. After split­ting these in two, you could sell off the propco to some­one else, al­low­ing you to quickly raise cash (this was how Anstead and Kilgour ini­tially man­aged to grow Four Seasons to 43 homes in just two years).

In the­ory, sale and lease­back was an ef­fi­cient way of rais­ing money, with es­tate agents act­ing as mid­dle­men be­tween fund man­agers who were buy­ing and sell­ing the homes. “In prac­tice, a lot of the deals were ba­nanas,” Paul Saper, a for­mer health­care con­sul­tant, told me. A care home that no longer owned its own prop­erty was like a fam­ily that sold its house to a ra­pa­cious land­lord. If the land­lord de­cided to raise the rent, ob­vi­ously the fam­ily would have less to spend on other es­sen­tials.

“There’s a phrase my friends use when analysing com­pa­nies,” Hood told me. “Hang glid­ers.” Just as a hang glider coasts through the sky sup­ported only by the spread of its wings, a com­pany can coast along for a while sup­ported only by the sta­bil­ity of its cash­flow. But if it is crip­pled with debt, or locked into es­ca­lat­ing rental pay­ments, its cash­flow dries up and “it crashes to earth. Because it’s got noth­ing to keep it up there.”

After Anstead and Kilgour sold Four Seasons, it was passed be­tween a string of dif­fer­ent own­ers. Alchemy sold the com­pany in 2004 to a German in­sur­ance firm called Allianz Capital Partners, which then sold it to a Qatari pri­vate eq­uity fund in 2006. When the fi­nan­cial cri­sis ar­rived in 2008, the care com­pa­ny’s debts had soared to an es­ti­mated £1.56bn. As its Qatari own­ers could­n’t find any­one will­ing to re­fi­nance the com­pany, Four Seasons fell into the hands of its cred­i­tors, led by the Royal Bank of Scotland. “It was won­der­ful for the fi­nanciers, who put in these sup­pos­edly clever struc­tures that took eq­uity away and re­placed it with debt,” said Ros Altmann, a Conservative peer who has stud­ied the sec­tor. “They were play­ing fi­nan­cial pass-the-par­cel with el­derly peo­ple’s lives. They could pile on as much debt as they liked, and there was noth­ing to stop them.”

By February 2012, RBS was still look­ing for a buyer, and word had spread about a bid­ding war. Among the ri­vals for con­trol of Four Seasons were a Canadian pen­sion fund, the Abu Dhabi in­vest­ment au­thor­ity, a Hong Kong bil­lion­aire and four pri­vate eq­uity firms in­clud­ing Terra Firma, founded by Guy Hands.

After start­ing on the trad­ing floor at Goldman Sachs, Hands had made his name at the Japanese bank Nomura, buy­ing up trains and pubs, among other things. He was am­bi­tious and had an un­com­pro­mis­ing streak. When his team reached the fi­nal, fre­netic stages of a deal, Hands would hardly sleep. He was known for hav­ing a tem­per. “I’m not a par­tic­u­larly con­cil­ia­tory hu­man be­ing,” he told me. In an FT re­port in 2024, sev­eral for­mer col­leagues ac­cused Hands of scream­ing and rag­ing at staff and hu­mil­i­at­ing ju­nior em­ploy­ees. (Hands and Terra Firma force­fully de­nied these ac­cu­sa­tions.)

In 2002, he broke away from Nomura to found Terra Firma, a phrase used by 17th-century Venetian mer­chants to de­scribe the ar­eas of Italy ruled by Venice. Like a doge sur­vey­ing his king­dom from across the wa­ter, Hands re­lo­cated off­shore, to the tax haven of Guernsey.

Despite his grand am­bi­tions, how­ever, his deals were not al­ways a great suc­cess. In 2007, Terra Firma bought EMI, the iconic British mu­sic la­bel that had recorded the Beatles at its Abbey Road stu­dios. The match was ill-fated from the start. Hands had lit­tle un­der­stand­ing of the mu­sic busi­ness or the power that artists ex­erted over the la­bel, and his clin­i­cal ap­proach to profit cre­ation left some mu­si­cians cold. Paul McCartney de­scribed how EMI be­came “boring” once it was un­der Terra Firma’s con­trol, while Radiohead were so in­censed by the new man­age­ment that they re­leased an al­bum on their web­site, side­step­ping the la­bel al­to­gether. Two years into its new own­er­ship, EMI was re­port­ing losses of £1.75bn, and in 2011 Hands sur­ren­dered con­trol to its cred­i­tors, Citibank. (Later, Hands in­sisted to me that the the­sis of the deal was still “100% right” and would have made Terra Firma’s in­vestors over £14bn “had Citigroup not seized the com­pany”.)

With his rep­u­ta­tion now tar­nished, Hands was des­per­ate to con­vince the world that he could still do his job, and soon alighted on the care home sec­tor.

In the early months of 2012, Terra Firma held 10 board meet­ings at which its part­ners fran­ti­cally analysed pages and pages of pre­sen­ta­tions. Their propo­si­tion hinged upon a sim­ple premise: they would make Four Seasons into the “IBM of care”, pro­vid­ing re­li­able, unglam­orous ser­vices to lo­cal coun­cils, much as IBM had sold re­li­able, unglam­orous com­puter sys­tems to the pub­lic sec­tor. In the scram­ble for ac­qui­si­tion, Terra Firma’s of­fer won out.

Not every­one was happy. Mark Drakeford, the then first min­is­ter for Wales, was con­cerned that Terra Firma planned to add Four Seasons to a grab bag of un­re­lated as­sets: a gar­den-cen­tre com­pany, a group of wind farms, the Odeon cin­ema chain and an as­sort­ment of mo­tor­way ser­vice sta­tions in Germany. “Older peo­ple are fel­low cit­i­zens, not com­modi­ties,” Drakeford later wrote, liken­ing the trans­ac­tion to buy­ing a sack of com­post or a tub of gera­ni­ums. “It just is­n’t good enough.”

Hands told me he wanted to im­prove the qual­ity of care at Four Seasons to at­tract more res­i­dents, which in turn would make the busi­ness more prof­itable. “The cost of do­ing it would have been about £1,100 a week [per bed],” he said. “And we were get­ting paid about £550 by the lo­cal au­thor­i­ties.” Terra Firma had bought the com­pany for £825m, putting down £325m of its in­vestors’ money and bor­row­ing the rest. While the firm paid off some of Four Seasons’ ex­ist­ing li­a­bil­i­ties, the com­pany was still hob­bled with debt, and in­ter­est pay­ments of £50m each year. In May 2015, the chan­cel­lor George Osborne out­lined plans to cut a fur­ther £55bn from the state’s bud­get. This trick­led down to lo­cal au­thor­i­ties, which cut fund­ing for care homes. That au­tumn, the rat­ings agency Standard & Poor’s warned that Four Seasons was on track to run out of money.

In Hands’s view, the gov­ern­men­t’s un­will­ing­ness to spend more money on the sec­tor was what caused his plans to un­ravel. “We be­lieved the gov­ern­ment was go­ing to sup­port care, and we got it com­pletely wrong,” he told me. “We saw a Conservative gov­ern­ment, with old vot­ers, fam­ily val­ues, and we thought, these guys are go­ing to put money into this sec­tor. And they did the re­verse. They drained it.”

While the aus­ter­ity drive un­doubt­edly did up­set Hands’s cal­cu­la­tions, it was al­most im­pos­si­ble to know what was re­ally go­ing on in­side Four Seasons. By now, its cor­po­rate struc­ture had be­come a labyrinth, with 185 sep­a­rate com­pa­nies or­gan­ised across 15 dif­fer­ent lay­ers. We know this thanks to re­search by foren­sic ac­coun­tants at the University of Manchester, who stud­ied the com­pany for a 2016 re­port. “The rules of cap­i­tal­ism have been changed through the con­struc­tion of opaque, com­plex groups of com­pa­nies,” they wrote. “Four Seasons is a black box and only Guy Hands and a few close as­so­ci­ates un­der­stand what is go­ing on.”

Hands in­sisted that, in this case, the struc­ture was in­her­ited from Terra Firma’s pre­de­ces­sors, though his firm did­n’t ex­actly sim­plify things. “It’s a lit­tle bit like the gov­ern­ment is­su­ing laws,” he told me. “They is­sue laws the whole time. They never abol­ish any … it’s much more ex­cit­ing putting rules in than tak­ing rules out.”

Private eq­uity peo­ple tend to be bet­ter than just about any­one else at two things: man­ag­ing huge amounts of debt, and con­ceal­ing the in­ner work­ings of their com­pa­nies. Fund man­agers can charge mys­te­ri­ous “monitoring” and “transaction” fees to a com­pany they own. Or they can bor­row against that com­pany to pay them­selves or their in­vestors a div­i­dend. Whenever I have spo­ken to pol­icy re­searchers or trade union­ists about this dy­namic, the pic­ture they have painted is­n’t so dif­fer­ent from the ar­gu­ment of­ten made about for­eign aid: that it’s point­less pour­ing money into coun­tries with cor­rupt gov­ern­ments, as a group of mid­dle­men will siphon off the do­na­tions be­fore they can reach the peo­ple who need them. Likewise, if it is­n’t pos­si­ble to see how much money a care home is ac­tu­ally mak­ing, its own­ers can more eas­ily pres­sure the gov­ern­ment for more fund­ing.

“In the old days of union­ism, you had the fac­tory up the road and you could see how well they were do­ing,” Natalie Grayson, a trade union or­gan­iser who worked with care home staff, told me. “But you can’t do that when your em­ployer gets bought by an in­vest­ment fund. A com­pany can say, ‘We haven’t got any money, we can only af­ford to pay peo­ple the min­i­mum wage’ and be­cause we don’t know how much debt a com­pany is pay­ing, and there are so many sep­a­rate com­pa­nies and hold­ing com­pa­nies … it makes it im­pos­si­ble for us to trace that money and dis­prove their ar­gu­ments.”

On the other hand, when pre­sented with an im­pos­si­ble case, some­times the most un­likely peo­ple find them­selves play­ing sleuth.

It was a sti­fling August day when I trav­elled to meet Eileen Chubb, a slight, serene woman with per­fectly coiffed hair and silky man­ner­isms, in a sub­urb of south London. We were sit­ting in her liv­ing room which, de­spite its crowd of or­na­ments and bright-or­ange paint­work, was a place of re­mark­able calm. Chubb had poured me a cof­fee and set out a plate of bis­cuits. Her res­cue dog, Strider, sat at her feet.

Chubb used to work at a care home, un­til she be­came con­cerned by its falling stan­dards and blew the whis­tle. From her liv­ing room, she then founded a char­ity, Compassion in Care, to help whistle­blow­ers in sim­i­lar sit­u­a­tions. “I al­ways tell peo­ple: go home, sit in a chair for eight hours, with­out food, with­out wa­ter, with­out hu­man con­tact. That is what poor care is like,” she said.

In 2013, Chubb started run­ning un­der­cover in­spec­tions of care homes. She would pre­tend she was vis­it­ing to find a space for her el­derly mother, and use false names — colours (Mrs Black, Mrs Green) or coun­try and west­ern names (Mrs Parton, Mrs Cash). Sometimes she took a walk­ing stick to feign im­mo­bil­ity, which let her slow down to bet­ter sur­vey the land­scape. Chubb had un­cov­ered de­tails of dis­turb­ing cases all over the coun­try, both in small, fam­ily-owned homes and those run by large com­pa­nies. Some of the worst cases she learned of were at homes owned by Southern Cross in the late 2000s, in the years be­fore it col­lapsed. There was Betty Delaney, who de­vel­oped ex­cru­ci­at­ing bed sores at a home in Rochdale, two of them so bad that they wore down to mus­cle and bone. Or Alan Simper, a for­mer elec­tri­cal en­gi­neer who was stay­ing at a Southern Cross home in Leighton Buzzard and was cov­ered in dry ex­cre­ment by the time he ar­rived at a hos­pi­tal in 2009. A coro­ner later found he died “for want of care”.

I thought these might just be tragic ex­cep­tions, but Chubb told me that at any one time, her char­ity was help­ing be­tween 200 and 300 em­ploy­ees at homes where they were wor­ried about the qual­ity of care, many of which were owned by pri­vate eq­uity. “Every sin­gle day, I hear about peo­ple who haven’t been fed or given flu­ids, or are left in their own fae­ces. We see it all the time,” she said. Chubb was a one-woman de­tec­tive agency, ef­fec­tively do­ing the reg­u­la­tor’s job for it. She had lit­tle faith in the Care Quality Commission (CQC), the watch­dog for so­cial care in England, which had nei­ther the re­sources nor the in­cli­na­tion to in­ves­ti­gate many of the com­plaints it re­ceived, as it lost more than 10% of its bud­get and al­most 10% of its staff be­tween 2016 and 2020. In the six years lead­ing up to 2024, in-per­son care home in­spec­tions fell by two-thirds.

Poor care, Chubb told me, mostly hap­pened be­hind closed doors, to peo­ple who were too sick or se­nile to protest. Many of the whistle­blow­ers who called her hot­line were shar­ing vi­tal in­for­ma­tion about wrong­do­ing that would oth­er­wise never be ex­posed. But those who took mat­ters into their own hands of­ten found them­selves alone. One woman whose mother had suf­fered falls and a black eye while stay­ing at a Four Seasons home in south-west London in 2013 tried to find out whether this was an iso­lated in­ci­dent. She wrote to the coun­cil, which re­fused to give her any in­for­ma­tion about other com­plaints pa­tients had made be­cause it said shar­ing this would af­fect the com­pa­ny’s “commercial in­ter­ests”. She then sub­mit­ted free­dom of in­for­ma­tion re­quests to the CQC, which said it had re­ceived more than 1,000 no­ti­fi­ca­tions of se­ri­ous in­jury from Four Seasons homes over the pre­vi­ous 12 months, but that it was un­able to say how many res­i­dents had died as a re­sult of spe­cific types of in­juries, be­cause it did not keep a cen­tral record of this in­for­ma­tion.

Were these prob­lems worse in pri­vate eq­uity-owned homes? Anecdotally, Chubb no­ticed a pat­tern of “ingrained” cost-cut­ting when homes were taken over by these in­vestors. “The staff are run ragged, ab­solutely ex­hausted. You can see it in their faces,” she said. Some of these ob­ser­va­tions were borne out in qual­i­ta­tive data: in one study from 2022, more than a dozen anony­mous staff mem­bers in homes taken over by in­vest­ment funds said their em­ploy­ers were “cutting cor­ners” to curb costs. One said there were some­times so few staff on duty, clean­ers were roped in to care for el­derly res­i­dents.

One of the most un­set­tling stud­ies I found was from 2021. Atul Gupta, a health econ­o­mist at the University of Pennsylvania, had set out with a team of re­searchers to analyse the changes that took place in nurs­ing homes in the US af­ter pri­vate eq­uity takeovers. The team sifted through more than 100 deals be­tween 2004 and 2015, and a dark pic­ture emerged. After a takeover, deaths among res­i­dents in­creased by an av­er­age of 11%.

This re­sult was so stark that Gupta ini­tially thought it was an er­ror. But when his team checked their re­sults, they were ro­bust. At homes that had been ac­quired by pri­vate eq­uity funds, re­searchers found there were fewer staff. Residents were more likely to have pres­sure ul­cers and re­ported higher lev­els of pain. “And we found an in­crease in the use of an­tipsy­chotic drugs, which are some­times used [on res­i­dents] as sub­sti­tutes for re­straints,” Gupta said. “So we found a wors­en­ing of out­comes on mul­ti­ple di­men­sions, in­clud­ing death.”

By the spring of 2016, Four Seasons’ po­si­tion was ten­u­ous. An American hedge fund was now buy­ing up its debt, bet­ting on fi­nan­cial melt­down. An in­ter­est pay­ment of £26m fell due in December the fol­low­ing year. Terra Firma failed to meet it.

The hedge fund op­er­ated out of Connecticut un­der the man­age­ment of a for­mer Lehman banker called Spencer Haber. Little was known about Haber save for the fact that he had large side­burns and was pas­sion­ate about an­i­mal wel­fare, mak­ing nu­mer­ous do­na­tions to a char­ity for home­less cats in New York. That, and the fact that he had never owned a care home.

As Haber bought up more of the com­pa­ny’s debt, he ac­quired more power to de­ter­mine what hap­pened once the firm was re­or­gan­ised or liq­ui­dated. Terra Firma fought to sell some of the more prof­itable homes, and Hands agreed to re­main an owner in name alone, while Haber’s fund dic­tated a re­struc­ture. In 2019, Four Seasons an­nounced it was go­ing into ad­min­is­tra­tion. It could no longer pay its debts, so the re­struc­tur­ing would be­gin.

And then the pan­demic struck. Suddenly, UK care homes were all over the news. The ba­sic prob­lem was that pa­tients with Covid-19 were be­ing dis­charged from hos­pi­tals into homes staffed by low-paid work­ers with lit­tle ex­pe­ri­ence of deal­ing with a deadly and con­ta­gious virus. Compounding this, they of­ten did­n’t have enough masks or gloves to avoid catch­ing it them­selves. Eileen Chubb told me calls to her hot­line in­creased by about 60% dur­ing the first wave of Covid-19. She found her­self try­ing to con­sole dis­traught care work­ers un­til 10pm each evening. “Many were in tears, ter­ri­fied of what was go­ing on. Being told to get used PPE out of a dust­bin, spray it with Dettol and put it back on. Having to use san­i­tary tow­els for face masks,” she re­called. At first, the CQC kept data on care home deaths from Covid se­cret, partly — by its own ad­mis­sion — to pro­tect the com­mer­cial in­ter­ests of providers. It was as if the reg­u­la­tor did­n’t want the pub­lic to find out what was hap­pen­ing in­side these homes. Or per­haps it did­n’t know: dur­ing Covid, it paused rou­tine in­spec­tions en­tirely.

Once again, the task of analysing what was go­ing on fell to self-ap­pointed in­ves­ti­ga­tors and aca­d­e­mics rather than the state. According to one pa­per, at the peak of Covid’s first wave, the homes with the great­est debts, where lever­age was above 75%, had a death rate nearly twice as high as homes with no lever­age at all. “In bad times, lever­aged op­er­a­tors have to cut costs more than un­lever­aged op­er­a­tors,” the re­searchers ex­plained.

The pan­demic forced the pub­lic to fo­cus on the in­dus­try, and the UK gov­ern­ment sprang be­lat­edly into ac­tion. It pumped an ex­tra £2.1bn into the sec­tor — about £5,900 for each bed. Homes re­ceived free PPE, money to cover staff sick pay and sub­si­dies for empty rooms as res­i­dents died. As Amy Horton, an eco­nomic ge­o­g­ra­pher and pro­fes­sor at UCL dis­cov­ered, how­ever, staff work­ing in the largest for-profit homes, the ma­jor­ity of which were owned by pri­vate eq­uity funds, re­ported work­ing longer hours and re­ceiv­ing less than sat­is­fac­tory sick pay. “These dif­fer­ences,” Horton sug­gested, “could be be­cause some com­pa­nies are pay­ing out sig­nif­i­cant por­tions of their rev­enue to in­vestors, land­lords and cred­i­tors, rather than rein­vest­ing in the ser­vice.”

Hands seemed to re­gret his de­ci­sion to buy Four Seasons. When I asked him whether his in­dus­try should ever be re­spon­si­ble for the care of el­derly peo­ple, he told me he felt there was a “fundamental mis­match” be­tween pri­vate eq­uity and so­cial care. “I mean, pri­vate eq­ui­ty’s role is to make prof­its for its in­vestors. And you can’t, in the care home busi­ness, just make prof­its. You’ve got to take into ac­count some­thing that is more im­por­tant, which is peo­ple’s lives.” I did­n’t dis­agree, though it seemed an eas­ier thing to say once you had re­tired off­shore, hav­ing made a siz­able for­tune.

In 2022, the re­main­ing homes from the Four Seasons es­tate ap­peared on the web­site of a real-es­tate bro­ker. The pho­tos showed an early Victorian man­sion, an Edwardian pile and a 1990s neo-Geor­gian hous­ing block. In real-es­tate ver­nac­u­lar, the port­fo­lio was de­scribed as “attractive”, with “strong av­er­age fee up­lifts” and “favourable de­mo­graph­ics”, a eu­phemism for lo­ca­tions where house prices had boomed, once again con­jur­ing the idea that el­derly peo­ple were as­set-rich cash ma­chines.

Ever since he was ousted from Four Seasons, Robert Kilgour had re­solved to cre­ate what he told me was a dif­fer­ent type of care busi­ness. I met him on a rainy day in Edinburgh to visit three of the homes he now owns. We drove be­tween them in his SUV, which had a per­son­alised num­ber plate spelling out his sur­name. The first was a crenel­lated, three-storey Victorian manor. Inside, Kilgour pointed with pride to the art­works he had do­nated, and paused to ap­pre­ci­ate the tex­ture of a brass light switch. For lunch that day, the res­i­dents could choose be­tween mush­room stroganoff and shep­herd’s pie. There were small vases of car­na­tions on each of the din­ing ta­bles. I checked: the flow­ers were real. Kilgour chat­ted with an at­ten­dant who ran the in-house hair sa­lon, then we went to look around the bed­rooms. “This,” Kilgour told me, stroking a bed­stead in an empty room with a the­atri­cal flour­ish, “is life stuff.”

It would be nice to think that homes such as this pro­vided a so­lu­tion to the care cri­sis, but the res­i­dents of the home we vis­ited that day paid up­wards of £1,700 a week, a hefty bill that ef­fec­tively ruled out al­most every­one with­out an ex­pen­sive prop­erty to re­mort­gage or sell. Kilgour planned to ex­pand his busi­ness to 30 homes by the end of the decade, and said he’d re­ceived var­i­ous ap­proaches from pri­vate eq­uity funds. “You know, ‘We’d like to in­vest £100m in the care home sec­tor, and we’d like to do a deal with you’ — that sort of thing.” Kilgour did­n’t tell me who these were, but he was adamant that he would­n’t work with any of them af­ter watch­ing what their in­dus­try had done to Four Seasons.

The bot sit­u­a­tion on the in­ter­net is ac­tu­ally worse than you could imag­ine. Here’s why:

As you may know, on Glade Art we tend to take anti-bot mea­sures very se­ri­ously; it is one of our top­most pri­or­i­ties to pro­tect our fel­low users from hav­ing their art trained on. We also tend to en­gage in trolling bots by us­ing end­less labyrinths of use­less data to trap them in. These are com­monly re­ferred to as “honeypots” or “digital tar pits.” And so, af­ter 6.8 mil­lion re­quests in the last 55 days at the time of writ­ing this, we have some sub­stan­tial data, so standby and let us share it with you. : )

> 1. Quick clar­i­fi­ca­tion.

For starters, these bots do not obey ro­bots.txt. This is ex­pected from un­eth­i­cal com­pa­nies, but it does­n’t make it any bet­ter. (A ro­bots.txt file is a plain txt file placed in web­sites which con­tains rules of where bots are al­lowed and dis­al­lowed to go. Good bots such as search en­gine crawlers obey these rules, while bad bots do not). To avoid trap­ping good bots, we have our ro­bots.txt set to dis­al­low all bots from go­ing into this site’s tar pits.

> 2. Pages and Contents.

The 2 traps on this site which have the most bot ac­tiv­ity are these:

gladeart(DOT)com(SLASH)data-ex­port (Over 6.8 mil­lion re­quests in the past 55 days).

gladeart(DOT)com(SLASH)gro (Over 84k re­quests in the past 35 days).

(NOTE: Use a VPN on these pages if you don’t want your IP shown in the logs, but it won’t be sig­nif­i­cant amongst the mil­lions of oth­ers any­ways).

As you can see when vis­it­ing the pages, GRO gen­er­ates more book-like text, while Data Export’s text is well… what­ever it’s sup­posed to be.

Data Export is by far more suc­cess­ful than GRO. It would be safe to as­sume that these com­pa­nies are scrap­ing for more num­ber-rich data for bet­ter facts and stuff. Fake per­sonal in­for­ma­tion such as emails or phone num­bers seem to also at­tract scrap­ing very well.

> 3. Characteristics of these bots.

The IPs of these bots here ac­tu­ally do not come from dat­a­cen­ters or VPNs most of the time; the over­whelm­ing ma­jor­ity come from res­i­den­tial and mo­bile net­works. Asian and Indonesian coun­tries are where nearly all of them re­side. By lever­ag­ing cheap com­pute from such coun­tries while us­ing res­i­den­tial IPs, they can ap­pear as com­pletely hu­man traf­fic to many web­sites, and scrape at mas­sive scale. However, there is some good news: these bots do not ex­e­cute JavaScript, at least not when scrap­ing ran­dom sites across the en­tire web. Just imag­ine the com­pute costs if they could­n’t use head­less browsers while scrap­ing mil­lions of sites every hour! This makes PoW chal­lenges ex­tremely ef­fec­tive against them. Website traf­fic at these scales com­ing from bots while look­ing like nor­mal hu­mans begs this ques­tion: “How much of the in­ter­net’s traf­fic comes from bots?”

> 4. How much of the traf­fic on the in­ter­net comes from bots?

Reports in 2024 say that ap­prox­i­mately 51% of all traf­fic on the in­ter­net comes from bots. Now this sounds like a lot, and it is, but it is much worse than that. This is be­cause these es­ti­mate rely heav­ily on where the IP ad­dresses orig­i­nate from: whether they come from dat­a­cen­ters or not. As we can see in our data, there is an ex­tremely high amount of bots that don’t come from dat­a­cen­ters at all. They can cer­tainly be rigged to ex­e­cute JavaScript on high qual­ity sites, and many sites don’t even re­quire JS, such as Wikipedia and Old Reddit. With this in mind, it would­n’t be un­rea­son­able to as­sume that the amount of bot traf­fic on the in­ter­net is much higher, per­haps over 70% even.

> 5. Some ex­per­i­ments on these bots.

Of course we ran some ex­per­i­ments on these bots.

Quick fact: Anubis is a pro­gram that adds a proof of work chal­lenge to web­sites be­fore users can ac­cess them.

And so Anubis was en­abled in the tar pit at dif­fi­culty 1 (lowest set­ting) when re­quests were pour­ing in 24/7. Before it was en­abled, it was get­ting sev­eral hun­dred-thou­sand re­quests each day. As soon as Anubis be­came ac­tive in there, it de­creased to about 11 re­quests af­ter 24 hours, most just from cu­ri­ous hu­mans.

Was it a co­in­ci­dence? No, it was not. It was tested on sev­eral other oc­ca­sions yield­ing very sim­i­lar re­sults.

As this con­firms, bots do not like PoW chal­lenges, even ul­tra easy ones. If a few do ex­e­cute JS, ex­tremely lit­tle will solve chal­lenges; take the search en­gine crawler GoogleBot for ex­am­ple.

> 6. Who are these bots from?

These bots are al­most cer­tainly scrap­ing data for AI train­ing; nor­mal bad ac­tors don’t have fund­ing for mil­lions of unique IPs thrown at a page. They prob­a­bly be­long to sev­eral dif­fer­ent com­pa­nies. Perhaps they sell their scraped data to AI com­pa­nies, or they are AI com­pa­nies them­selves. We can’t tell, but we can guess since there aren’t all that many large AI cor­po­ra­tions out there.

> 7. How can you pro­tect your sites from these bots?

If your site has a vast amount of pages, then these bots could po­ten­tially raise re­source us­age for your server when they are crawl­ing through every­thing. The best op­tions in this case would be Cloudflare or Anubis. Alternatively, you could add a sim­ple JS re­quire­ment in your web-server, Nginx for ex­am­ple, (this won’t be as ef­fec­tive, but of­ten suf­fi­cient for most sites). It would be rec­om­mended to add an hCaptcha to forms such as sign ups and sim­i­lar as well. Overall, a cor­rectly con­fig­ured Anubis on your site elim­i­nates nearly all bot traf­fic.

> 8. Server re­source us­age.

Our server us­age for the tar pit end­points is quite low. For ex­am­ple, when a global 1000 re­quest per minute rate-limit was be­ing reached in Data Export, the server’s CPU us­age was not no­tice­ably higher than when idle (i5 4460). The ram us­age for it was also very low, much less than 500mb. And since it’s just text data be­ing sent out, up­loads were no more than 700KiB/s.

> 9. Fun fact.

So on av­er­age, the Data Export tar pit gen­er­ates 9000 char­ac­ters per re­quest. Doing the math on that makes the 6.8 mil­lion loads equiv­a­lent to ~52 bil­lion char­ac­ters, or over 120,000 nov­els worth of text gen­er­ated and sent in to­tal since Jan 29th, 2026.

> 10. Download a log file.

Here is a mas­sive log file for some ac­tiv­ity in the Data Export tar pit:

https://​mega.nz/​file/​69Rh3IpS#ThlagHz8e58jLvU-vWn9U9m9T_WegL4SE0H2mhZR­cZY

Caution: this file de­com­presses to about 1.1GB. Standard text ed­i­tors will strug­gle to open it.

Note: this file con­tains logs from Jan 29th to March 22nd, 2026.

[This is for ed­u­ca­tional pur­poses only].

<> Outro.

And so, with this in­for­ma­tion we can see just how bad the bot sit­u­a­tion is right now on the in­ter­net. Look on the bright side though, trolling bots is fun! We rec­om­mend you to add your own tar pits to your site as well; the more vol­ume the bet­ter. Just be sure to dis­al­low go­ing into there in your ro­bots.txt so that good bots don’t get trapped. Bad bots ac­tu­ally of­ten go into that page be­cause you dis­al­lowed it for them.

Thank you for read­ing! : ) <>

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Almost 30 years af­ter the in­tri­cate web of nerves in­side the pe­nis was plot­ted out, the same map­ping has fi­nally been com­pleted for one of the least-stud­ied or­gans in the hu­man body — the cli­toris.

As well as re­veal­ing the ex­tent of the nerves that are cru­cial to or­gasms, the work shows that some of what medics are learn­ing about the anatomy of the cli­toris is wrong, and could help pre­vent women who have pelvic op­er­a­tions from end­ing up with poorer sex­ual func­tion.

The cli­toris, re­spon­si­ble for sex­ual plea­sure, is one of the least stud­ied or­gans of the hu­man body. Cultural taboo around fe­male sex­u­al­ity has held back sci­en­tific in­ves­ti­ga­tions and the cli­toris did not even make it into stan­dard anatomy text­books un­til the 20th cen­tury. And in the 38th edi­tion of Gray’s Anatomy in 1995 it was in­tro­duced as just “a small ver­sion of the pe­nis”.

A Melbourne urol­o­gist, Helen O’Connell, says the cli­toris has been ig­nored by re­searchers for far too long. “It has been deleted in­tel­lec­tu­ally by the med­ical and sci­en­tific com­mu­nity, pre­sum­ably align­ing at­ti­tude to a so­ci­etal ig­no­rance,” she said.

To get a bet­ter idea of the in­ner work­ings of this key plea­sure-re­lated or­gan, Ju Young Lee, a re­search as­so­ci­ate at Amsterdam University Medical Center in the Netherlands, and her col­leagues used high-en­ergy X-rays to cre­ate 3D scans of two fe­male pelvises that had been do­nated through a body donor or­gan pro­gramme.

The scans re­vealed in 3D the tra­jec­tory of the five com­plex tree-like branch­ing nerves run­ning through the cli­toris in un­prece­dented de­tail, the widest 0.7mm across. The work has been re­ported on the preprint server bioRxiv and has not yet been peer re­viewed.

“This is the first ever 3D map of the nerves within the glans of the cli­toris,” said Lee. She is amazed it has taken so long, con­sid­er­ing a sim­i­lar level of knowl­edge re­gard­ing the pe­nile glans was reached back in 1998, 28 years ago.

Lee and her col­leagues show that some branches of cli­toral nerves reach the mons pu­bis, the rounded mound of tis­sue over the pu­bic bone. Others go to the cli­toral hood, which sits over the small, sen­si­tive, ex­ter­nal part of the cli­toris — the glans cli­toris — which is just 10% of the to­tal or­gan. Other nerves reach the folds of skin of the vulva, the labial struc­tures.

Previous re­search had in­di­cated that the big dor­sal nerve of the cli­toris grad­u­ally di­min­ished as it ap­proached the glans. However, the new scans ap­pear to show that some of what medics have been learn­ing in anatomy is wrong and the nerve con­tin­ues strongly all the way to the end.

“I was es­pe­cially fas­ci­nated by the high-res­o­lu­tion im­ages within the glans, the most sen­si­tive part of the cli­toris, as these ter­mi­nal nerve branches are im­pos­si­ble to see dur­ing dis­sec­tion,” said Georga Longhurst, the head of anatom­i­cal sci­ences at St George’s, University of London.

O’Connell, who pub­lished the first com­pre­hen­sive anatom­i­cal study of the cli­toris in 1998, said the find­ings were cru­cial to un­der­stand­ing the fe­male sen­sory mech­a­nism un­der­ly­ing arousal and or­gasm via stim­u­lat­ing the cli­toris. “Orgasm is a brain func­tion that leads to im­proved health and well­be­ing as well as hav­ing pos­i­tive im­pli­ca­tions for hu­man re­la­tion­ships and pos­si­bly fer­til­ity,” she said.

The map­ping of cli­toral nerves is likely to in­form re­con­struc­tive surgery af­ter fe­male gen­i­tal mu­ti­la­tion, one of the most ex­treme ex­am­ples of cul­tural misog­yny. According to the World Health Organization, more than 230 mil­lion girls and women alive to­day in 30 coun­tries in Africa, the Middle East and Asia have un­der­gone such mu­ti­la­tion, in which the vis­i­ble part of the cli­toris may be re­moved, along with parts of the labia.

The prac­tice has no health ben­e­fits and can re­sult in is­sues in­clud­ing se­vere bleed­ing, in­fec­tion, prob­lems uri­nat­ing, men­strual dif­fi­cul­ties and com­pli­ca­tions in child­birth.

About 22% of women who un­dergo sur­gi­cal re­con­struc­tion af­ter mu­ti­la­tion ex­pe­ri­ence a de­cline in or­gas­mic ex­pe­ri­ence af­ter their op­er­a­tion, so a bet­ter un­der­stand­ing of how far the nerves ex­tend could re­duce that per­cent­age, said Lee.

O’Connell said the work could also in­form surgery to treat vul­var can­cer, gen­der re­as­sign­ment surgery and gen­i­tal cos­metic surg­eries, such as labi­aplasty, which in­creased in pop­u­lar­ity by 70% from 2015 to 2020.

Lee is hop­ing to open a cli­toris ex­hi­bi­tion within Amsterdam University Medical Center to help ex­pand knowl­edge about the cli­toris, in­spired by the Vagina Museum in London.

After us­ing the TCL tablet for two months, I’ve come to the con­clu­sion that my tablet does­n’t need a screen with smooth mo­tion. I only read sta­tic con­tent — still text.

This re­al­iza­tion made me take a fresh look at a type of de­vice I had­n’t even con­sid­ered be­fore, but which now seems per­fect for my needs. I’m re­fer­ring to Android tablets with E-Ink screens, man­u­fac­tured by brands like Boox, Bigme, and Pocketbook.

The prob­lem? They’re ex­pen­sive. The smaller mod­els, with 7–7.8-inch screens, start at prices four times higher than a ba­sic Kindle. The one I wanted, the Boox Go 10.3, with a 10.3-inch screen, is even pricier. And it comes with an out­dated ver­sion of Android, al­though I’ve been told that this is­n’t a prob­lem, un­like with the iPad. (Last week, Boox launched the sec­ond gen­er­a­tion of the model, fea­tur­ing Android 15 and a vari­ant with a back­lit screen. It’s likely to be even more ex­pen­sive.)

Besides be­ing ex­pen­sive, I hate buy­ing… things. That’s why I was happy when I re­al­ized I could use my Kindle — the very one that has never ac­cessed the in­ter­net — to read ar­ti­cles, posts, and newslet­ters pub­lished on the web, with­out spend­ing a sin­gle cent and with great qual­ity.

It’s this setup — the re­sult of a week of new brain con­nec­tions (or many neu­rons fried over some­thing al­most in­signif­i­cant) — that I’ll share with you.

Amazon’s e-read­ers only read un­ortho­dox dig­i­tal book for­mats, such as *.mobi and *.azw3. There is an of­fi­cial way to con­vert other, more pop­u­lar for­mats to sup­ported ones, such as “Send to Kindle.” My Kindle is­n’t con­nected to the in­ter­net, which rules out that op­tion.

Therefore, we’ll need Calibre, a great e-book man­ager, to con­vert files *.epub, the most com­mon dig­i­tal book stan­dard, into a for­mat the Kindle can un­der­stand.

After in­stalling Calibre, the next step is to cre­ate a “book” from a col­lec­tion of ar­ti­cles/​links.

Most ser­vices of this type, such as Instapaper and Wallabag, gen­er­ate RSS feeds from the var­i­ous fil­ters they of­fer — un­read, fa­vorites, fold­ers etc. At first, I thought about com­bin­ing this fea­ture with an­other one in Calibre called “Get News.” The icon on the ap­p’s chaotic tool­bar al­ready gives you an idea of what it’s about. It’s an RSS/Atom feed client that fetches new posts and gen­er­ates books on de­mand or on a pre­de­fined sched­ule.

To add a new feed, just click the ar­row next to the but­ton and se­lect . On the screen that opens, click , set the pa­ra­me­ters, and add the feeds you want to fol­low. You can list sev­eral, which al­lows you to cre­ate a highly per­son­al­ized pub­li­ca­tion. Among them, in­clude Instapaper, Wallabag etc. own feed.

I no­ticed that the for­mat­ting of these books gen­er­ated by Calibre is a bit dif­fer­ent from that of stan­dard e-books. The table of con­tents does­n’t use the same lay­out as books, and even the text dis­play — or what sur­rounds it, like the progress bar/​page num­bers — has its own struc­ture. I’ve never read a mag­a­zine on Kindle; maybe that’s what they look like?

The im­por­tant thing is that it works, but there are ways to im­prove cer­tain as­pects of this process and its out­come.

I had cho­sen Wallabag to be the hub for the ar­ti­cles I in­tend to read on Kindle. I had al­ready been us­ing it on my TCL tablet. (The Android app is good, even if it lacks some fea­tures.)

Realizing that its parser is worse than av­er­age made me take a step back. The parser is the al­go­rithm that iden­ti­fies the con­tent of a URL and ex­tracts it. On some web­sites, Wallabag’s parser fails; it can’t ex­tract the text. The Brazilian mag­a­zine web­site is an ex­am­ple. (Obviously, I’m re­fer­ring to the open ar­ti­cles, with­out a pay­wall.)

Instapaper per­formed bet­ter, but I did­n’t want to use it. After all, we self-host not one, but *two* such ser­vices: Wallabag and Readeck.

Readeck’s parser is just as good as Instapaper’s. Case closed, right? No, be­cause I could­n’t find the darn RSS feed for un­read items.

I had to check the of­fi­cial web­site to re­al­ize that the Atom feed is hid­den be­hind the three-dot menu. And then came the big sur­prise: Readeck it­self gen­er­ates an e-book, in the *.epub, from the listed ar­ti­cles.

I adopted Readeck, which al­lowed me to set aside Calibre’s “Get News” fea­ture. However, Calibre still needs to be pre­sent to con­vert the file to *.mobi, which the Kindle un­der­stands. As a bonus, I take this op­por­tu­nity to edit the book’s ti­tle and add a cover I quickly made in an im­age ed­i­tor.

It’s been just over a week since I had this epiphany. I save links in Readeck through­out the day, and in the late af­ter­noon, I gen­er­ate my own edited newslet­ter. After read­ing the edi­tion, I go back to Readeck to archive what I’ve read and, if nec­es­sary, “use” some links — reg­is­ter them on the links of the day, share them with some­one, or save them as ref­er­ence ma­te­r­ial for a longer piece I plan to write.

It’s been great. The E-Ink screen is less tir­ing on the eyes, es­pe­cially with­out the back­light. I can read in the soft sun­light stream­ing through the liv­ing room win­dow at this time of year, early in the morn­ing, with­out wor­ry­ing about screen glare. On the con­trary: the more sun, the more ex­ter­nal light, the more read­able the screen be­comes.

The only (major) prob­lem with this process is that it re­quires a com­puter, be­cause of Calibre and the need to con­vert the file to a for­mat read­able by the Kindle. In this re­gard, Android tablets with E-Ink screens would be more prac­ti­cal, since they have apps that read *.epub. Besides, you might not even need the e-book. The Readeck app would be enough, with di­rect ac­cess to the texts on the same E-Ink screen. Bonus: you could use Readeck’s na­tive high­light­ing and note-tak­ing fea­tures, which would be quite use­ful.

For those who al­ready have a Kindle and a com­puter at their dis­posal, how­ever, it’s hard to jus­tify a new de­vice for these few ad­van­tages of di­rect ac­cess to Readeck. Generating the book is a min­i­mal ef­fort in ex­change for ~90% of what an Android tablet would pro­vide.

One side ef­fect I did­n’t an­tic­i­pate is that I’ve been read­ing fewer books, which now share space (and my time) with web ar­ti­cles on the Kindle. That’s my prob­lem, right?

Last week I was writ­ing about the hard­ware side of the AI mem­ory prob­lem: the HBM den­sity penalty, the EUV bot­tle­neck, and the sup­ply chain pres­sure squeez­ing DRAM prices for every­one from data cen­tre op­er­a­tors down to con­sumer elec­tron­ics. This week, Google pub­lished some­thing that at­tacks the ex­act same prob­lem us­ing an­other ap­proach: not “build more mem­ory”, but “need less of it.”

You guessed it! This post will dive a bit deeper into what TurboQuant is, and what this may im­ply to the field of AI. What Pied Piper achieved in the Silicon Valley TV Show with their gen­eral-pur­pose loss­less com­pres­sion al­go­rithm, Google may have achieved it for the com­pres­sion of in­for­ma­tion rep­re­sented as vec­tors in a high-di­men­sional space.

But be­fore get­ting into what TurboQuant does, let’s make a brief de­tour to un­der­stand what is this al­go­rithm is ac­tu­ally built to com­press, and why it is im­por­tant for LLMs and the mem­ory prob­lem.

GPT mod­els are what are known as au­tore­gres­sive: they gen­er­ate text one to­ken at a time, where each new to­ken is con­di­tioned on every­thing that came be­fore. You send a prompt, the model reads all of it, picks the most likely next word, ap­pends it, reads every­thing again, picks the next word, and so on. One to­ken at a time, left to right, un­til it de­cides to stop.

The core mech­a­nism that lets the model read every­thing at each step is called at­ten­tion. For every to­ken in the se­quence, the model com­putes three vec­tors: a query, a key, and a value. You can think of these data struc­tures as a bit more com­plex key-value stores. To gen­er­ate the next to­ken, the model com­pares the cur­rent query against every pre­vi­ous key, es­sen­tially ask­ing “which past to­kens are rel­e­vant right now?”, and uses the an­swer to weigh the cor­re­spond­ing val­ues and build up con­text.

This is im­ple­mented (as you may all know by now) through the trans­former ar­chi­tec­ture. Transformer lay­ers are re­spon­si­ble for en­cod­ing the in­put se­quences into a mean­ing­ful rep­re­sen­ta­tion, ap­ply­ing the at­ten­tion mech­a­nism, and de­cod­ing into an out­put rep­re­sen­ta­tion. All LLMs are ar­chi­tec­tural vari­a­tions of this ba­sic cell.

To get a sense of each of these vari­a­tions I highly rec­om­mend Sebastian Raschka’s LLM Architecture gallery: from GPT-2 to DeepSeek and GLM.

The keys and val­ues for every pre­vi­ous to­ken are re­com­puted from scratch on every sin­gle pass through ar­chi­tec­ture. If your con­ver­sa­tion is N to­kens long and you’re gen­er­at­ing to­ken N+1, the model re­cal­cu­lates N sets of keys and val­ues it al­ready cal­cu­lated on the pre­vi­ous step. This is slow and waste­ful in terms of the re­sources.

The ob­vi­ous fix to this is to cache them. The query, key and val­ues are com­puted once per to­ken and stored so they can be looked up in sub­se­quent steps in­stead of be­ing re­cal­cu­lated. This is the KV cache, a run­ning store of QKV to­kens from all pre­vi­ous to­kens stored in GPU mem­ory (so they are read­ily ac­ces­si­ble when needed).

The prob­lem is that the KV cache grows with every to­ken. With short mes­sages this is triv­ial as all to­kens fit in mem­ory, but a long con­ver­sa­tion, or a full code base, in­volves hun­dreds of thou­sands of to­kens. Each to­ken has its own key and value vec­tors, across every at­ten­tion layer in the model, each stored as a full-pre­ci­sion float­ing-point num­ber (as long as there’s no quan­ti­sa­tion in­volved). For a model like Llama 3.1 70B, the KV cache for a sin­gle long con­text can con­sume more GPU mem­ory than the model weights them­selves.

This is one of the key bot­tle­necks in pro­duc­tion in­fer­ence. Serve more users si­mul­ta­ne­ously? More KV cache. Support longer con­texts? More KV cache. Run cheaper in­fer­ence? Figure out what to do about the KV cache. We are trad­ing the com­pute nec­es­sary to com­pute on-the-fly the QKV val­ues, for in­creased mem­ory re­quire­ments.

By us­ing quan­ti­sa­tion in­stead of stor­ing each value at 32-bit or 16-bit pre­ci­sion, one can round it down to 4 bits or 3 bits (or even 2 bits, like Microsoft re­cently showed). Some ac­cu­racy is lost in the ap­prox­i­ma­tion, but if it is not sig­nif­i­cant for the user case, the trade-off is ob­vi­ously worth it. The ques­tion is how to do this well. Standard quan­ti­sa­tion tech­niques add 1-2 ex­tra bits of over­head per value as meta­data, which par­tially un­der­mines the com­pres­sion you’re try­ing to achieve. Getting to gen­uinely low bit-widths with­out that over­head, and with­out ac­cu­racy degra­da­tion, is the hard part. HuggingFace has a re­ally nice page with an overview of quan­ti­sa­tion and a list of meth­ods

But things may be about to change. Google an­nounced this week TurboQuant. TurboQuant (see pa­per) is a two-stage al­go­rithm. The two stages have dif­fer­ent jobs.

Stage 1: PolarQuant. This is the main com­pres­sion step. We cur­rently store vec­tors us­ing Cartesian co­or­di­nates as dis­tances of a base to the ori­gin (the x, y, z com­po­nents that we learnt in pri­mary school). The dis­tri­b­u­tion of those com­po­nents in space makes them hard to com­press ef­fi­ciently.

PolarQuant con­verts the vec­tor to po­lar co­or­di­nates: a ra­dius, and an an­gle. The key ob­ser­va­tion is that, in high-di­men­sional trans­former key spaces, the an­gle dis­tri­b­u­tion is highly con­cen­trated and pre­dictable, it clus­ters in ways that maps neatly onto a fixed quan­ti­sa­tion grid (like the ones used to com­press au­dio and im­age). That pre­dictabil­ity means you can elim­i­nate the ex­pen­sive nor­mal­i­sa­tion steps that stan­dard quan­ti­sa­tion meth­ods re­quire, and you can do it with­out any dataset-spe­cific tun­ing. No fine-tun­ing or cal­i­bra­tion pass re­quired to quan­tise a spe­cific model. One can di­rectly ap­ply it to the vec­tors in this new rep­re­sen­ta­tion in­de­pen­dent of the model.

Stage 2: QJL (Quantised Johnson-Lindenstrauss). PolarQuant han­dles the main com­pres­sion, but any quan­ti­sa­tion in­tro­duces er­ror, and some of that er­ror ac­cu­mu­lates in the dot prod­ucts that the trans­former uses to com­pute at­ten­tion scores. QJL’s job is to cor­rect for this bias. It ap­plies a Johnson-Lindenstrauss trans­form to the resid­ual er­ror, a ran­dom pro­jec­tion that pre­serves dis­tances be­tween high-di­men­sional points, and then re­duces each com­po­nent to a sin­gle sign bit: +1 or -1. The re­sult is an un­bi­ased es­ti­ma­tor for the in­ner prod­ucts, with zero ad­di­tional mem­ory over­head. The er­ror cor­rec­tion costs noth­ing to store (see bot­tom-left part of the im­age be­low for a men­tal model of the shift from ex­ist­ing quan­tised KV cache and a QJL-transformed one).

The com­bi­na­tion achieves 3.5 bits per chan­nel with what the pa­per calls “absolute qual­ity neu­tral­ity” across Gemma, Mistral, and Llama-3.1-8B-Instruct, tested on LongBench, Needle In A Haystack, ZeroSCROLLS, RULER, and L-Eval. At 2.5 bits, ac­cu­racy de­grades only mar­gin­ally. The head­line num­ber from the blog post: 6x re­duc­tion in KV mem­ory size with no mea­sur­able ac­cu­racy loss, and on H100 GPUs, 4-bit TurboQuant de­liv­ers up to 8x per­for­mance in­crease over 32-bit un­quan­tised keys.

As briefly de­scribed above, most quan­ti­sa­tion meth­ods re­quire at least some cal­i­bra­tion on rep­re­sen­ta­tive data, they learn the op­ti­mal quan­ti­sa­tion grid for a spe­cific model on a spe­cific dataset. TurboQuant is data-obliv­i­ous: the al­go­rithm works from first prin­ci­ples, near the the­o­ret­i­cal lower bounds of what in­for­ma­tion the­ory says is pos­si­ble, with­out see­ing the data first. That’s what makes it de­ploy­able at in­fer­ence time to any mod­els with­out hav­ing to ex­plic­itly train the quan­tised model. There is no need for spe­cific train­ing and fine-tun­ing to achieve the most op­ti­mal com­pres­sion rate with­out trad­ing ac­cu­racy.

Last week I was writ­ing about how HBM stack­ing re­duces DRAM bit den­sity by 3-4x, and how the en­tire sup­ply chain for con­sumer DRAM is un­der pres­sure be­cause data cen­tres and con­sumer elec­tron­ics are com­pet­ing for the same wafers. If TurboQuant re­duces the mem­ory foot­print per in­fer­ence job by 6x, ap­ply­ing this com­pres­sion al­go­rithm at scale may sig­nif­i­cantly re­lax the mem­ory bot­tle­neck is­sue.

Anthropic is not the only one that is able to crash the mar­ket cap of pub­lic com­pa­nies with a sin­gle an­nounce­ment. Immediately af­ter Google’s an­nounce­ment, the stock from mem­ory man­u­fac­tur­ing com­pa­nies like Micron and Sandisk plunged (and as an in­vestor in Micron, this hits me home 🙈).

This may be an over­re­ac­tion, like when Nvidia stock plunged af­ter Deepseek’s an­nounce­ment. Or it may be sig­nalling a com­plete shift in the eco­nom­ics and re­source re­quire­ments of AI labs. If I were Google, I would­n’t re­lease re­search that ex­poses a com­pet­i­tive ad­van­tage. I would only pub­lish re­search whose progress has al­ready been fac­tored in as the com­peti­tors may have al­ready re­alised it, or adopted them­selves TurboQuant has most prob­a­bly been al­ready adopted in­side Google’s in­fra­struc­ture be­fore any­one out­side read the pa­per.

If Google is pub­lish­ing 6x KV cache com­pres­sion, the rea­son­able thing to think is that every se­ri­ous AI lab has been work­ing on this prob­lem al­ready. Reducing the mem­ory re­quire­ments of the KV cache has been a known prob­lem for quite some time, and ad­vance­ments like TurboQuant adopted at scale change the mem­ory re­quire­ments (justifying the hit on these mem­ory stocks). I can’t wait for the next re­port from SemiAnalysis analysing this re­lease, the real adop­tion of this new ap­proach to com­pres­sion (and sim­i­lar ones) from big labs, and what it can en­tail to the mem­ory crunch.

Micron and SanDisk haven’t sud­denly be­come bad busi­nesses. But any the­sis that de­pends on mem­ory de­mand grow­ing lin­early with AI con­text us­age de­serves a sec­ond look. My per­sonal take is that the mar­ket is over­re­act­ing, but we’ll see.

In this post about money and col­lat­eral in an AI-first so­ci­ety, I men­tioned the book “The Last Economy”. This book de­scribes how ex­treme volatil­ity and sharp turns over any news with­out achiev­ing a clear equi­lib­rium is a symp­tom of a sick sys­tem. This big mar­ket move­ments over a sin­gle news may be proof of the symp­toms of a bro­ken sys­tem.

What ex­cites me the most about this re­lease is what this Johnson-Lindenstrauss Transform that pow­ers QJL and com­pres­sion al­go­rithms like TurboQuant could mean for other use cases out­side of LLMs and vec­tor search that rely on high-di­men­sional vec­tor data.

The ob­vi­ous one out­side of KV caches as men­tioned above is vec­tor data­bases. Any RAG pipeline that stores em­bed­ding vec­tors for re­trieval ben­e­fits from the same com­pres­sion. TurboQuant re­duces in­dex­ing time to “virtually zero” on vec­tor search tasks and out­per­forms prod­uct quan­ti­sa­tion and RabbiQ on re­call bench­marks us­ing GloVe vec­tors.

Further out: rec­om­men­da­tion en­gines, fraud de­tec­tion, drug dis­cov­ery sim­i­lar­ity search, ge­nomics, any sys­tem that stores large ta­bles of high-di­men­sional em­bed­dings and needs to run fast near­est-neigh­bour lookups (assuming a sim­i­lar dis­tri­b­u­tion in space as the val­ues stored in KV caches, which is some­thing I want to ex­plore). These sys­tems weren’t wait­ing for trans­former-spe­cific op­ti­mi­sa­tion, but they may in­herit the ben­e­fit di­rectly.

On-device in­fer­ence is an­other field in­side the world of LLMs where we could start see­ing im­me­di­ate im­pact. If the KV cache for a long con­text shrinks by 6x, you can fit sub­stan­tially more con­text into the mem­ory en­ve­lope of a mid-range phone or a mod­est edge de­vice. Local mod­els with us­able con­text lengths start to look more tractable. The eco­nom­ics of in­fer­ence at the edge change, and that’s a dif­fer­ent set of win­ners and losers than the data cen­tre story.

I don’t know if you’ve al­ready seen how some LLMs are be­ing stored in fast flash mem­ory in or­der to be able to run LLM in­fer­ence of big mod­els in a Mac. I’ll leave this for some other post, but the field of edge in­fer­ence is get­ting more in­ter­est­ing every day. And even more now that we got TurboQuant.

The TurboQuant code is out, both the QJL and PolarQuant com­po­nents are avail­able, and I can’t wait to find the time to start ap­ply­ing to other use cases. We’ve seen through­out his­tory the im­pact that chang­ing the way we rep­re­sent in­for­ma­tion can have for per­for­mance (and even fea­si­bil­ity) of cer­tain use cases (think of what the Fourier Transform, FFTs, and the fre­quency do­main al­ready en­abled :) ).

I want to find the time to do the ex­er­cise of try­ing to ap­ply the TurboQuant ap­proach to other use cases to see what this is ca­pa­ble of. I al­ready have some ideas, but I’ll re­port back. In the mean­time, un­til next week!