There’s a gap be­tween giv­ing an agent your real ac­count and stop­ping every­thing to build a con­tainer or VM. jai fills that gap. One com­mand, no im­ages, no Dockerfiles — just a light-weight bound­ary for the work­flows you’re al­ready run­ning: quick cod­ing help, one-off lo­cal tasks, run­ning in­staller scripts you did­n’t write. Use AI agents with­out hand­ing over your whole ac­count. jai gives your work­ing di­rec­tory full ac­cess and keeps the rest of your home be­hind a copy-on-write over­lay — or hid­den en­tirely.One-line in­staller scripts, AI-generated shell com­mands, un­fa­mil­iar CLIs — stop run­ning them against your real home di­rec­tory. Drop jai in front and the worst case gets a lot smaller.No im­ages to build, no Dockerfiles to main­tain, no 40-flag bwrap in­vo­ca­tions. Just jai your-agent. If con­tain­ment is­n’t eas­ier than YOLO mode, no­body will bother.

Pick the level of iso­la­tion that fits your work­flow.

jai is free soft­ware, brought to you by the Stanford Secure Computer Systems re­search group and the Future of Digital Currency Initiative. The goal is to get peo­ple us­ing AI more safely.

jai is not try­ing to re­place con­tain­ers. It fills a dif­fer­ent niche. Great for re­pro­ducible, im­age-based en­vi­ron­ments. Heavier to set up for ad-hoc sand­box­ing of host tools. No over­lay-on-home work­flow.Pow­er­ful name­space sand­box. Requires ex­plic­itly as­sem­bling the filesys­tem view — of­ten turns into a long wrap­per script, which is the fric­tion jai re­moves.Not a se­cu­rity mech­a­nism. No mount iso­la­tion, no PID name­space, no cre­den­tial sep­a­ra­tion. Linux doc­u­ments it as not in­tended for sand­box­ing.

jai is not a promise of per­fect safety.jai is a ca­sual sand­box — it re­duces the blast ra­dius, but does not elim­i­nate all the ways AI agents can harm you or your sys­tem. Casual mode does not pro­tect con­fi­den­tial­ity. Even strict mode is not equiv­a­lent to a hard­ened con­tainer run­time or VM. When you need strong multi-ten­ant iso­la­tion or de­fense against a de­ter­mined ad­ver­sary, use a proper con­tainer or vir­tual ma­chine. Read the full se­cu­rity model →

Alongside the var­i­ous bugs you get, one of the is­sues of up­grad­ing to MacOS 26 is that it has one of the most no­to­ri­ous in­con­sis­tency is­sues in win­dow cor­ners. I’m not sure what ex­actly pushes prod­uct de­sign­ers to like the ex­ces­sive round­nes­sOne of the ugli­est round­ness ex­am­ples I’ve ever seen is the cur­rent one in the YouTube UI de­sign. I be­lieve that UI de­sign is the most in­flu­en­civethat’s to say, con­ta­gious form in­wards field ever since de­sign­ers just try to fol­low what­ever big com­pa­nies do (in fact I see this a lot in my work, when two de­sign­ers are hav­ing an ar­gu­ment, one of them would re­solve it to, let’s see how Apple draw that but­ton), which means that we are prob­a­bly go­ing to see this ugly ef­fect else­where very soon.

Anyway, re­cently I had to up­grade re­cently to MacOS 26. And I found the edges ugly, like every­one else did. However, what’s even uglier, is the in­con­sis­tency. Many peo­ple try to re­solve this by dis­abling MacOS sys­tem in­tegrity pro­tec­tion, which re­sults in mak­ing them pos­si­bly vul­ner­a­bleAr­guable, since you just lose se­cu­rity over /root, which is not a big deal if some­one al­ready gained ac­cess to your ma­chine, at least for me. Edit: I learnt that this is not the case from com­ments, how­ever, I still be­lieve that if you’re al­ready pwned, SIP can’t do much there.. The rea­son why you need to dis­able SIP, is that to edit the dy­namic li­braries that sys­tem apps like Safari (which has crazy bad cor­ners) use, you need to edit sys­tem li­braries that ex­ist the root. To me though, I don’t find the cor­ners so bad, but I find the in­con­sis­tency very an­noy­ing. So I think a bet­ter so­lu­tion to this is; in­stead of mak­ing every­thing round­less, make every­thing more rounded, which re­quires you to edit only user apps (i.e. no SIP dis­abling needed). I forked a so­lu­tion that makes things round­less to mod­ify it to have my ap­proach. It’s sim­ply as fol­lows:

You can have this plist too to load it in once your com­puter loads:

Now at least every­thing is con­sis­tently bad. #Programming

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Spanish leg­is­la­tion as a Git repo — every law is a Markdown file, every re­form a com­mit. 8,600+ laws.

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On the first morn­ing of Operation Epic Fury, 28 February 2026, American forces struck the Shajareh Tayyebeh pri­mary school in Minab, in south­ern Iran, hit­ting the build­ing at least two times dur­ing the morn­ing ses­sion. American forces killed be­tween 175 and 180 peo­ple, most of them girls be­tween the ages of seven and 12.

Within days, the ques­tion that or­gan­ised the cov­er­age was whether Claude, a chat­bot made by Anthropic, had se­lected the school as a tar­get. Congress wrote to the US sec­re­tary of de­fense, Pete Hegseth, about the ex­tent of AI use in the strikes. The New Yorker mag­a­zine asked whether Claude could be trusted to obey or­ders in com­bat, whether it might re­sort to black­mail as a self-preser­va­tion strat­egy, and whether the Pentagon’s chief con­cern should be that the chat­bot had a per­son­al­ity. Almost none of this had any re­la­tion­ship to re­al­ity. The tar­get­ing for Operation Epic Fury ran on a sys­tem called Maven. Nobody was ar­gu­ing about Maven.

Eight years ago, Maven was the most con­tested pro­ject in Silicon Valley. In 2018, more than 4,000 Google em­ploy­ees signed a let­ter op­pos­ing the com­pa­ny’s con­tract to build ar­ti­fi­cial in­tel­li­gence for the Pentagon’s tar­get­ing sys­tems. Workers or­gan­ised a walk out. Engineers quit. And Google ul­ti­mately aban­doned the con­tract. Palantir Technologies, a data an­a­lyt­ics com­pany and de­fence con­trac­tor co-founded by Peter Thiel, took it over and spent the next six years build­ing Maven into a tar­get­ing in­fra­struc­ture that pulls to­gether satel­lite im­agery, sig­nals in­tel­li­gence and sen­sor data to iden­tify tar­gets and carry them through every step from first de­tec­tion to the or­der to strike.

The build­ing in Minab had been clas­si­fied as a mil­i­tary fa­cil­ity in a Defense Intelligence Agency data­base that, ac­cord­ing to CNN, had not been up­dated to re­flect that the build­ing had been sep­a­rated from the ad­ja­cent Islamic Revolutionary Guard Corps com­pound and con­verted into a school, a change that satel­lite im­agery shows had oc­curred by 2016 at the lat­est. A chat­bot did not kill those chil­dren. People failed to up­date a data­base, and other peo­ple built a sys­tem fast enough to make that fail­ure lethal. By the start of the Iran war, Maven — the sys­tem that had en­abled that speed — had sunk into the plumb­ing, it had be­come part of the mil­i­tary’s in­fra­struc­ture, and the ar­gu­ment was all about Claude. This ob­ses­sion with Claude is a kind of AI psy­chosis, though not of the kind we nor­mally talk about, and it af­flicts crit­ics and op­po­nents of the tech­nol­ogy as fiercely as it does its boost­ers. You do not have to use a lan­guage model to let it or­gan­ise your at­ten­tion or dis­tort your think­ing.

In 2019, the scholar Morgan Ames pub­lished The Charisma Machine, a study of how cer­tain tech­nolo­gies draw at­ten­tion, re­sources and at­tri­bu­tion to­ward them­selves and away from every­thing else. The usual frame­work for un­der­stand­ing this dy­namic is “hype”, but hype only de­scribes what boost­ers do, and it as­signs crit­ics a priv­i­leged de­bunk­ing role that still leaves the tech­nol­ogy at the cen­tre of every ar­gu­ment. A charis­matic tech­nol­ogy shapes the whole field around it, the way a mag­net or­gan­ises iron fil­ings. LLMs may be the most pow­er­ful in­stance of this type in his­tory.

By the time the war be­gan, “AI safety” and “alignment” and “hallucination” and “stochastic par­rots” had be­come the terms of every ar­gu­ment about ar­ti­fi­cial in­tel­li­gence, struc­tur­ing and lim­it­ing what we could even say. Worse, “artificial in­tel­li­gence” it­self had come to be syn­ony­mous with LLMs. When the school was bombed, those were the terms peo­ple reached for, de­spite the fact that this crit­i­cal ap­pa­ra­tus of­fered a poor fit for the older, more ma­ture stack of tech­nolo­gies in­volved in tar­get­ing. The real ques­tion, the ques­tion al­most no­body was ask­ing, is not about Claude or any lan­guage model. It is a bu­reau­cratic ques­tion about what hap­pened to the kill chain, and the an­swer is Palantir.

As mil­i­tary jar­gon goes, “kill chain” is a re­mark­ably hon­est term. In essence, it refers to the bu­reau­cratic frame­work for or­gan­is­ing the steps be­tween de­tect­ing some­thing and de­stroy­ing it. The old­est ref­er­ence to the term it­self I can find is from the 1990s, but the idea is quite old — dat­ing at least to the 1760s, when French ar­tillery re­form­ers be­gan re­plac­ing the gun­ner’s ex­pe­ri­enced eye with bal­lis­tic ta­bles, el­e­va­tion screws and stan­dard­ised fir­ing pro­ce­dures. The steps in the kill chain are sub­ject to con­stant change, to keep pace with changes in tar­get­ing doc­trine, but also to in­cor­po­rate what­ever man­age­ment fads come to af­flict the mil­i­tary’s strate­gic thinkers. The US mil­i­tary has named and re­named the steps for 80 years. In the sec­ond world war the se­quence was find, fix, fight, fin­ish. By the 1990s the air force had stretched it to find, fix, track, tar­get, en­gage, as­sess, or F2T2EA. Every gen­er­a­tion of mil­i­tary tech­nol­ogy has been sold on the promise of mak­ing every­thing about kill chains shorter, ex­cept for the acronyms.

Palantir’s Maven Smart System is the lat­est it­er­a­tion of this com­pres­sion, and it grew out of a shift in strate­gic think­ing dur­ing Obama’s sec­ond term. In 2014, the sec­re­tary of de­fense, Chuck Hagel, and his deputy, Robert Work, an­nounced what they called the “third off­set strat­egy”. An “offset” in this line of think­ing is a bet that a tech­no­log­i­cal ad­van­tage can com­pen­sate for a strate­gic weak­ness the coun­try can­not fix di­rectly. The first two off­sets ad­dressed the same prob­lem: the United States could not match the Soviet Union in con­ven­tional forces. The think­ing was that the Red Army could just con­tinue to throw per­son­nel at a prob­lem, as they did at Stalingrad, or, to be anachro­nis­tic, as the con­tem­po­rary Russian army did at Bakhmut and Avdiivka. Nuclear weapons, the first off­set, made the per­son­nel ad­van­tage ir­rel­e­vant in the 1950s. When the Soviets reached nu­clear par­ity in the 1970s, pre­ci­sion-guided mu­ni­tions and stealth of­fered the promise that a smaller force could de­feat a larger one. By 2014, that ad­van­tage was erod­ing. China and Russia had spent two decades ac­quir­ing pre­ci­sion-guided mu­ni­tions and build­ing de­fence sys­tems de­signed to keep American forces out of range. Robert Work in­sisted that the third off­set was not about any par­tic­u­lar tech­nol­ogy but about us­ing tech­nol­ogy to re­or­gan­ise how the mil­i­tary op­er­ated, let­ting the US make de­ci­sions faster than China and Russia, over­whelm­ing and dis­ori­ent­ing the en­emy by main­tain­ing a faster op­er­a­tional tempo than they could match.

In April 2017, early in the first Trump ad­min­is­tra­tion, Work helped es­tab­lish the Algorithmic Warfare Cross-Functional Team, des­ig­nated Project Maven. One of the gen­er­als over­see­ing Maven, Lt Gen Jack Shanahan, put the prob­lem plainly: thou­sands of in­tel­li­gence an­a­lysts were spend­ing 80% of their time on mun­dane tasks, drown­ing in footage from sur­veil­lance drones that no one had time to watch. A sin­gle Predator drone mis­sion could gen­er­ate hun­dreds of hours of video, and the an­a­lysts tasked with un­der­stand­ing this were faced with an in­for­ma­tion over­load prob­lem. “We’re not go­ing to solve it by throw­ing more peo­ple at the prob­lem,” Shanahan said. “That’s the last thing that we ac­tu­ally want to do.” The core con­ceit of the pro­ject was that the ma­chine could watch so that the an­a­lyst could think.

The Pentagon needed some­one to build it. Google took the con­tract, and what hap­pened next be­came the most vis­i­ble labour ac­tion in the his­tory of Silicon Valley.

After Google aban­doned the Maven con­tract, Palantir took it over in 2019. The XVIII Airborne Corps be­gan test­ing the sys­tem in an ex­er­cise called Scarlet Dragon, which started in 2020 as a table­top wargam­ing ex­er­cise in a win­dow­less base­ment at Fort Bragg. Its com­man­der, Lt Gen Michael Erik Kurilla, wanted to build what he called the first “AI-enabled corps” in the army. The goal was to test whether the sys­tem could give a small team the tar­get­ing ca­pac­ity that had pre­vi­ously re­quired thou­sands of peo­ple.

Over the next five years, Scarlet Dragon grew into a mil­i­tary ex­er­cise us­ing live am­mu­ni­tion, span­ning mul­ti­ple states and branches of the armed forces, with “forward-deployed en­gi­neers” from Palantir and other con­trac­tors em­bed­ded along­side sol­diers. Each time the ex­er­cise was run, it was meant to an­swer the same ques­tion: how fast could the sys­tem move from de­tec­tion to de­ci­sion? The bench­mark was the 2003 in­va­sion of Iraq, where roughly 2,000 peo­ple worked the tar­get­ing process for the en­tire war. During Scarlet Dragon, 20 sol­diers us­ing Maven han­dled the same vol­ume of work. By 2024, the stated goal was 1,000 tar­get­ing de­ci­sions in an hour. That is 3.6 sec­onds per de­ci­sion, or from the in­di­vid­ual “targeteer’s” per­spec­tive, one de­ci­sion every 72 sec­onds.

The Maven Smart System is the plat­form that came out of those ex­er­cises, and it, not Claude, is what is be­ing used to pro­duce “target pack­ages” in Iran. There are real lim­its to what a civil­ian such as my­self can know about this sys­tem, and what fol­lows is based on pub­licly avail­able in­for­ma­tion, as­sem­bled from Palantir prod­uct demos, con­fer­ences, as well as in­struc­tional ma­te­r­ial pro­duced for mil­i­tary users. But we can know quite a bit.

The Maven in­ter­face looks like a mil­i­tary-skinned ver­sion of cor­po­rate pro­ject man­age­ment soft­ware crossed with a map­ping ap­pli­ca­tion. What the mil­i­tary an­a­lyst build­ing the tar­get list sees is ei­ther a map lay­ered with in­tel­li­gence data or a screen or­gan­ised into columns, each rep­re­sent­ing a stage of the tar­get­ing process. Individual tar­gets move across the columns from left to right as they progress through each stage, a for­mat bor­rowed from Kanban, a “lean man­u­fac­tur­ing” work­flow sys­tem de­vel­oped at Toyota, and now widely used in soft­ware de­vel­op­ment.

Before Maven, op­er­a­tors worked across eight or nine sep­a­rate sys­tems si­mul­ta­ne­ously, pulling data from one, cross-ref­er­enc­ing in an­other, man­u­ally mov­ing de­tec­tions be­tween plat­forms to as­sem­ble the in­tel­li­gence and ap­provals needed for each strike. Maven con­sol­i­dated all of these be­hind a sin­gle in­ter­face. Cameron Stanley, the Pentagon’s chief dig­i­tal and AI of­fi­cer, called it an “abstraction layer”, a com­mon term in soft­ware en­gi­neer­ing, mean­ing a sys­tem that hides the com­plex­ity un­der­neath it. Humans run the tar­get­ing. Underneath the in­ter­face, ma­chine-learn­ing sys­tems analyse satel­lite im­agery and sen­sor data to de­tect and clas­sify ob­jects, scor­ing each iden­ti­fi­ca­tion by how con­fi­dent the sys­tem is that it got it right. Three clicks con­vert a data point on the map into a for­mal de­tec­tion and move it into a tar­get­ing pipeline. These tar­gets then move through columns rep­re­sent­ing dif­fer­ent de­ci­sion-mak­ing processes and rules of en­gage­ment. The sys­tem rec­om­mends how to strike each tar­get — which air­craft, drone or mis­sile to use, which weapon to pair with it — what the mil­i­tary calls a “course of ac­tion”. The of­fi­cer se­lects from the ranked op­tions, and the sys­tem, de­pend­ing on who is us­ing it, ei­ther sends the tar­get pack­age to an of­fi­cer for ap­proval or moves it to ex­e­cu­tion.

The AI un­der­neath the in­ter­face is not a lan­guage model, or at least the AI that counts is not. The core tech­nolo­gies are the same ba­sic sys­tems that recog­nise your cat in a photo li­brary or let a self-dri­ving car com­bine its cam­era, radar and li­dar into a sin­gle pic­ture of the road, ap­plied here to drone footage, radar and satel­lite im­agery of mil­i­tary tar­gets. They pre­date large lan­guage mod­els by years. Neither Claude nor any other LLMs de­tects tar­gets, processes radar, fuses sen­sor data or pairs weapons to tar­gets. LLMs are late ad­di­tions to Palantir’s ecosys­tem. In late 2024, years af­ter the core sys­tem was op­er­a­tional, Palantir added an LLM layer — this is where Claude sits — that lets an­a­lysts search and sum­marise in­tel­li­gence re­ports in plain English. But the lan­guage model was never what mat­tered about this sys­tem. What mat­tered was what Maven did to the tar­get­ing process: it con­sol­i­dated the sys­tems, com­pressed the time and re­duced the peo­ple. That is not a new idea. The US mil­i­tary has been try­ing to close the gap be­tween see­ing some­thing and de­stroy­ing it for as long as that gap has ex­isted, and every at­tempt has pro­duced the same fail­ure. Maven may not even be the most ex­treme case.

In the late 1960s, the US faced a ver­sion of the same prob­lem in Vietnam. Supplies were mov­ing south along the Ho Chi Minh trail through jun­gle the mil­i­tary could not see into. The so­lu­tion was Operation Igloo White, a $1bn-a-year pro­gramme that scat­tered 20,000 acoustic and seis­mic sen­sors along the trail. These sen­sors trans­mit­ted data to re­lay air­craft over­head, which fed the sig­nals to IBM 360 com­put­ers at Nakhon Phanom air­base in Thailand. The com­put­ers analysed the sen­sor data and pre­dicted where con­voys would be, and strike air­craft were di­rected to those co­or­di­nates.

The sys­tem could sense but it could not see. It could de­tect a vi­bra­tion but it could not tell a truck from an ox cart. The North Vietnamese fig­ured this out. They played record­ings of truck en­gines, herded an­i­mals near the sen­sors to trig­ger vi­bra­tion de­tec­tion, and hung buck­ets of urine in trees to set off the chem­i­cal de­tec­tors. The sys­tem could be fooled be­cause no­body in the process could look at what it was sens­ing. The air force claimed 46,000 trucks were de­stroyed or dam­aged over the course of the cam­paign. The CIA re­ported that the claims for a sin­gle year ex­ceeded the to­tal num­ber of trucks be­lieved to ex­ist in all of North Vietnam. The sys­tem’s own out­put was the only mea­sure of its per­for­mance, and no­body out­side the sys­tem had stand­ing to chal­lenge it. Air force his­to­rian Bernard Nalty later called the ser­vice’s ca­su­alty com­pu­ta­tions “an ex­er­cise in meta­physics rather than math­e­mat­ics” and his col­league Earl Tilford con­cluded that “the air force suc­ceeded only in fool­ing it­self”. When day­time re­con­nais­sance flights failed to find the wreck­age of all those trucks, air force per­son­nel in­vented a crea­ture to ex­plain the ab­sence. They called it the “great Laotian truck eater”.

The pat­tern that played out in Vietnam — a tar­get­ing sys­tem that could only mea­sure its own per­for­mance and ended up be­liev­ing its own out­put — is ac­tu­ally older than dig­i­tal com­put­ing. Michael Sherry’s 1987 book The Rise of American Air Power traces it to the found­ing doc­trine of pre­ci­sion bomb­ing, whose con­fi­dence in its own meth­ods made ex­am­in­ing what those meth­ods pro­duced un­nec­es­sary. “Belief in suc­cess,” Sherry wrote, “encouraged im­pre­ci­sion about how to achieve it.” By 1944, op­er­a­tions an­a­lysts on both sides of the Atlantic were mea­sur­ing bomb­ing in a shared lan­guage of in­dus­trial op­ti­mi­sa­tion. Civilians bombed out of their homes were recorded as “dehoused”. For every tonne of bombs dropped, an­a­lysts cal­cu­lated how many hours of en­emy labour it de­stroyed. One British eval­u­a­tion treated the bomber it­self as a cap­i­tal as­set: a sin­gle sor­tie against a German city wiped off the cost of build­ing the air­craft, and every­thing af­ter that was “clear profit”. Sherry called the re­sult­ing mind­set “technological fa­nati­cism”.

Sherry’s point was not that any­one chose de­struc­tion. It was that the peo­ple re­fin­ing the tech­nique of bomb­ing stopped ask­ing what the bomb­ing was for. But even by the time the op­er­a­tions re­searchers had got their hands on tar­get­ing, this logic was al­ready tak­ing shape. As the his­to­rian of sci­ence William Thomas has ar­gued, the op­er­a­tions an­a­lysts did not im­pose this logic on the mil­i­tary; the mil­i­tary was al­ready con­vert­ing op­er­a­tional ex­pe­ri­ence into sys­tem­atic pro­ce­dure, and had been for decades. Nobody stopped mak­ing judg­ments. But the judg­ments were no longer about whether the bomb­ing served a strate­gic pur­pose. They were about how to mea­sure it and how to op­ti­mise around those mea­sure­ments.

Carl von Clausewitz, the 19th-century Prussian gen­eral whose writ­ings re­main the foun­da­tion of west­ern mil­i­tary thought, had a word for every­thing the op­ti­mi­sa­tion leaves out. He called it “friction”, the ac­cu­mu­la­tion of un­cer­tainty, er­ror and con­tra­dic­tion that en­sures no op­er­a­tion goes as planned. But fric­tion is also where judg­ment forms. Clausewitz ob­served that most in­tel­li­gence is false, that re­ports con­tra­dict each other. The com­man­der who has worked through this learns to see the way an eye ad­justs to dark­ness, not by get­ting bet­ter light but by stay­ing long enough to use what light there is. This “staying” is what takes time. Compress the time and the fric­tion does not dis­ap­pear. You just stop notic­ing it. Clausewitz called this kind of plan­ning a “war on pa­per”. The plan pro­ceeds with­out re­sis­tance, not be­cause there is none, but be­cause every­thing con­nect­ing the plan to the real world has been stripped out.

Air power is uniquely vul­ner­a­ble to this. The pi­lot never sees what the bomb hits. The an­a­lyst works from im­agery, co­or­di­nates and data­bases. The en­tire en­ter­prise is me­di­ated by rep­re­sen­ta­tions of the tar­get, not the tar­get it­self, which means the gap be­tween the pack­age and the world can widen with­out any­one in the process feel­ing it. The 2003 in­va­sion of Iraq, the op­er­a­tion that Scarlet Dragon would later use as its bench­mark, was a case in point. Marc Garlasco, the Pentagon’s chief of high-value tar­get­ing dur­ing the in­va­sion, ran the fastest tar­get­ing cy­cle the US had op­er­ated to that point. He rec­om­mended 50 strikes on se­nior Iraqi lead­er­ship. The bombs were pre­cise — they hit ex­actly where they were aimed — but the in­tel­li­gence be­hind them was not. None of the 50 killed its in­tended tar­get. Two weeks af­ter the in­va­sion, Garlasco left the Pentagon for Human Rights Watch, went to Iraq, and stood in the crater of a strike he had tar­geted him­self. “These aren’t just name­less, face­less tar­gets,” he said later. “This is a place where peo­ple are go­ing to feel ram­i­fi­ca­tions for a long time.” The tar­get­ing cy­cle had been fast enough to hit 50 build­ings and too fast to dis­cover it was hit­ting the wrong ones.

The air force’s own tar­get­ing guide, in ef­fect dur­ing the Iraq war, said this was never sup­posed to hap­pen. Published in 1998, it de­scribed the six func­tions of tar­get­ing as “intertwined”, with the tar­ge­teer mov­ing “back” to re­fine ob­jec­tives and “forward” to as­sess fea­si­bil­ity. “The best analy­sis,” the man­ual stated, “is rea­soned thought with facts and con­clu­sions, not a check­list.” But Jon Lindsay, who served as a navy in­tel­li­gence of­fi­cer in Kosovo and later stud­ied spe­cial op­er­a­tions tar­get­ing in Iraq, found some­thing dif­fer­ent. Once a tar­get was rei­fied on a PowerPoint slide — the tar­get in­tel­li­gence pack­age, or TIP — it be­came a black box. Questioning the as­sump­tions be­hind it got harder as the hunt gained mo­men­tum, as the folder thick­ened with what Lindsay calls “representational residua”. There was more ma­chin­ery for build­ing up a tar­get than for in­spect­ing the qual­ity of its con­struc­tion. Personnel be­came dis­in­clined to ask whether some tar­gets were po­ten­tial al­lies, or not ac­tu­ally bad guys at all, be­cause pro­duc­ing tar­gets meant par­tic­i­pat­ing in the hunt. The tar­get­ing guide had warned about this too. “If tar­ge­teers don’t pro­vide full tar­get­ing ser­vice,” it read, “then other well mean­ing but un­der­trained and ill-ex­pe­ri­enced groups will step in.” Maven even­tu­ally would.

Lindsay’s book Information Technology and Military Power is the most care­ful study I’ve found of how tar­get­ing ac­tu­ally works, at least par­tially be­cause it was writ­ten by some­one who ac­tu­ally did it. During the Kosovo air war, Gen Wesley Clark de­manded 2,000 tar­gets, which made it easy to jus­tify any tar­get’s con­nec­tion to the Milošević gov­ern­ment. The CIA nom­i­nated just one tar­get dur­ing the en­tire war: the fed­eral di­rec­torate of sup­ply and pro­cure­ment. Analysts had a street ad­dress but not co­or­di­nates, so they tried to re­verse-en­gi­neer a lo­ca­tion from three out­dated maps. They ended up hit­ting the Chinese em­bassy — which had re­cently re­lo­cated — 300 me­tres from the build­ing they were aim­ing for. The state de­part­ment knew that the em­bassy had moved. The mil­i­tary’s fa­cil­i­ties data­base did not. Target re­views failed to no­tice, be­cause each val­i­da­tion re­lied on the last. Lindsay calls this “circular re­port­ing”: an ac­cu­mu­la­tion of sup­port­ing doc­u­ments that “created the il­lu­sion of mul­ti­ple val­i­da­tions” while am­pli­fy­ing a sin­gle er­ror. The PowerPoint slide looked as well vet­ted as the hun­dreds of oth­ers that Nato struck with­out in­ci­dent. On the night of the strike, an in­tel­li­gence an­a­lyst phoned head­quar­ters to ex­press doubts. Asked specif­i­cally about col­lat­eral dam­age, he could not ar­tic­u­late a con­cern. The strike pro­ceeded. It killed three Chinese jour­nal­ists. Lindsay, writ­ing in his jour­nal at the time, called the re­sult “an im­mense er­ror, per­fectly pack­aged”.

In 2005, Lt Col John Fyfe of the US air force pub­lished a study of time-sen­si­tive tar­get­ing dur­ing the 2003 in­va­sion. Fyfe high­lighted the dif­fer­ent ways UK and US forces ap­proached this chal­lenge. In the Combined Air Operations Center, RAF of­fi­cers served in key lead­er­ship po­si­tions along­side their American coun­ter­parts. They op­er­ated un­der more re­stricted rules of en­gage­ment. Fyfe noted that their “more re­served, con­ser­v­a­tive per­son­al­i­ties” pro­duced what he called “a very pos­i­tive damp­en­ing ef­fect on the some­times har­ried, chaotic pace of of­fen­sive op­er­a­tions”. The con­trast be­tween shifts was vis­i­ble: American lead­ers pressed ahead full bore, while British of­fi­cers me­thod­i­cally re­con­sid­ered risk and cost-ben­e­fit trade-offs be­fore ap­prov­ing ex­e­cu­tion. On UK-led shifts, there were no friendly fire in­ci­dents and no sig­nif­i­cant col­lat­eral dam­age. On nu­mer­ous oc­ca­sions, Fyfe notes, the British of­fi­cer in charge pre­vented the op­er­a­tion from get­ting ahead of it­self. What the next gen­er­a­tion of re­form­ers would mea­sure as la­tency — the de­lay be­tween iden­ti­fy­ing a tar­get and strik­ing it — was the win­dow in which mis­takes could be caught.

From in­side the ef­fi­ciency frame, every fea­ture Fyfe de­scribes reg­is­tered as a de­fect. The UK shifts were slower. The re­stricted rules of en­gage­ment added con­straints. The damp­en­ing ef­fect added time. Speed saves lives, the ar­gu­ment goes, but the fastest tar­get­ing cy­cle be­fore Maven was Garlasco’s, and it struck 50 build­ings with­out hit­ting a sin­gle in­tended tar­get. Scarlet Dragon elim­i­nated all of it. The dis­agree­ments about tar­get­ing stopped. So did the de­lib­er­a­tion, the hes­i­ta­tion and the mo­ments when some­one had time to ob­ject or no­tice some­thing was off.

Organisations that run on for­mal pro­ce­dure need some­one in­side the process to in­ter­pret rules, no­tice ex­cep­tions, recog­nise when the cat­e­gories no longer fit the case. If the or­gan­i­sa­tion con­cedes that its out­comes de­pend on the dis­cre­tion of the peo­ple ex­e­cut­ing it, then the pro­ce­dure is not a pro­ce­dure but a sug­ges­tion, and the au­thor­ity the or­gan­i­sa­tion de­rives from ap­pear­ing rule-gov­erned col­lapses. So the judg­ment has to hap­pen, and it has to look like some­thing else. It has to look like fol­low­ing the pro­ce­dure rather than in­ter­pret­ing it.

I’ve come to think of this as the “bureaucratic dou­ble bind” — the or­gan­i­sa­tion can­not func­tion with­out the judg­ment, and it can­not ac­knowl­edge the judg­ment with­out un­der­min­ing it­self and be­ing seen as “political”. One so­lu­tion to this prob­lem is to re­place the judg­ment with a num­ber. In his 1995 book Trust in Numbers, the his­to­rian of sci­ence Theodore Porter ar­gued that or­gan­i­sa­tions adopt quan­ti­ta­tive rules not be­cause num­bers are more ac­cu­rate but be­cause they are more de­fen­si­ble. Judgment is po­lit­i­cally vul­ner­a­ble. Rules are not. The pro­ce­dure ex­ists to make dis­cre­tion dis­ap­pear, or seem to. The sys­tem’s ac­tual flex­i­bil­ity lives en­tirely in this un­ac­knowl­edged in­ter­pre­tive work, which means it can be re­moved by any­one who mis­takes it for in­ef­fi­ciency.

In 1984, the his­to­rian David Noble showed that when the US mil­i­tary and American man­u­fac­tur­ers au­to­mated their fac­tory floors, they con­sis­tently chose sys­tems that were slower and more ex­pen­sive but which moved de­ci­sion-mak­ing away from work­ers and into man­age­ment. The point was not ef­fi­ciency — it was fre­quently ex­tremely waste­ful — but con­trol. A worker who un­der­stands what they are do­ing can ex­er­cise judg­ment the in­sti­tu­tion can­not gov­ern. Move that un­der­stand­ing into the sys­tem, and the worker has noth­ing left to do but fol­low in­struc­tions. Alex Karp, the CEO of Palantir, de­scribes ex­actly this achieve­ment in his 2025 book, The Technological Republic. “Software is now at the helm,” he writes, with hard­ware “serving as the means by which the rec­om­men­da­tions of AI are im­ple­mented in the world.” His model for what this should look like comes from na­ture: bee swarms and the mur­mu­ra­tions of star­lings. “There is no me­di­a­tion of the in­for­ma­tion cap­tured by the scouts once they re­turn to the hive,” Karp writes. The star­lings need no per­mis­sion from above, they re­quire “no weekly re­ports to mid­dle man­age­ment, no pre­sen­ta­tions to more se­nior lead­ers, no meet­ings or con­fer­ence calls to pre­pare for other meet­ings”. This sounds lib­er­at­ing, even utopian. But the sig­nal that passes with­out me­di­a­tion is also the sig­nal that no­body can ques­tion.

Karp thinks he is de­stroy­ing bu­reau­cracy. He is en­cod­ing it. The con­tempt for meet­ings and weekly re­ports and pre­sen­ta­tions to se­nior lead­ers; he treats these as the bu­reau­cratic process it­self. They are not. They were where peo­ple in­ter­preted pro­ce­dure, the place where some­one could no­tice when cat­e­gories no longer fit the case. The tar­get­ing doc­trine is still there. They are columns on a work­flow board now, stages a tar­get passes through on its way to be­ing struck. What Karp elim­i­nated was the dis­cre­tion the in­sti­tu­tion could never ad­mit it de­pended on. What re­mains is a bu­reau­cracy that can ex­e­cute its rules but with no one left to in­ter­pret them. Bureaucracy en­coded in soft­ware does not bend. It shat­ters.

The tar­get pack­age for the Shajareh Tayyebeh school pre­sented a mil­i­tary fa­cil­ity. Lucy Suchman, whose 1987 book Plans and Situated Actions re­mains the sharpest ac­count of how for­mal pro­ce­dures ob­scure the work that ac­tu­ally pro­duces their out­comes, would not have been sur­prised. Plans al­ways look com­plete af­ter­ward. They achieve com­plete­ness by fil­ter­ing out every­thing that was­n’t leg­i­ble to their cat­e­gories. This pack­age looked like every other pack­age in the queue. But out­side the pack­age, the school ap­peared in Iranian busi­ness list­ings. It was vis­i­ble on Google Maps. A search en­gine could have found it. Nobody searched. At 1,000 de­ci­sions an hour, no­body was go­ing to. A for­mer se­nior gov­ern­ment of­fi­cial asked the ob­vi­ous ques­tion: “The build­ing was on a tar­get list for years. Yet this was missed, and the ques­tion is how.” How in­deed.

Congress did not au­tho­rise this war. In two weeks, American forces struck 6,000 tar­gets. The school was one of them. American forces killed al­most 200 peo­ple, and the re­port­ing reached for “AI er­ror”, which do­mes­ti­cated the event into some­thing a bet­ter al­go­rithm or bet­ter guardrails could have pre­vented.

In the days af­ter the strike, the charisma of AI or­gan­ised the en­tire po­lit­i­cal con­ver­sa­tion around the tech­nol­ogy: whether Claude hal­lu­ci­nated, whether the model was aligned, whether Anthropic bore re­spon­si­bil­ity for its de­ploy­ment. The con­sti­tu­tional ques­tion of who au­tho­rised this war and the le­gal ques­tion of whether this strike con­sti­tutes a war crime were dis­placed by a tech­ni­cal ques­tion that is eas­ier to ask and im­pos­si­ble to an­swer in the terms it set. The Claude de­bate ab­sorbed the en­ergy. That is what charisma does.

It has also oc­cluded some­thing deeper: the hu­man de­ci­sions that led to the killing of be­tween 175 and 180 peo­ple, most of them girls be­tween the ages of seven and 12. Someone de­cided to com­press the kill chain. Someone de­cided that de­lib­er­a­tion was la­tency. Someone de­cided to build a sys­tem that pro­duces 1,000 tar­get­ing de­ci­sions an hour and call them high-qual­ity. Someone de­cided to start this war. Several hun­dred peo­ple are sit­ting on Capitol Hill, re­fus­ing to stop it. Calling it an “AI prob­lem” gives those de­ci­sions, and those peo­ple, a place to hide.

An ear­lier ver­sion of this ar­ti­cle ap­peared on Artificial Bureaucracy, Kevin T Baker’s Substack

The International Olympic Committee has barred trans­gen­der ath­letes from com­pet­ing in the wom­en’s cat­e­gory of the Olympics and said that all par­tic­i­pants in those events must un­dergo ge­netic test­ing.

The de­ci­sion, the most con­se­quen­tial since Kirsty Coventry was elected last year as the first woman to serve as pres­i­dent of the I. O.C., fol­lowed a board meet­ing and months of spec­u­la­tion over the or­ga­ni­za­tion’s pol­icy on one of the most con­tentious is­sues fac­ing global sports. The rules will be ap­plic­a­ble start­ing at the next Olympics, in Los Angeles in 2028.

Under the new pol­icy el­i­gi­bil­ity will be de­ter­mined by a one-time gene test, ac­cord­ing to the I. O.C. The test, which is al­ready be­ing used in track and field, re­quires screen­ing via saliva, a cheek swab or a blood sam­ple.

When Ms. Coventry, a for­mer Olympic cham­pion swim­mer from Zimbabwe, cam­paigned to lead the or­ga­ni­za­tion, she fre­quently said how im­por­tant it was to pro­tect the wom­en’s cat­e­gory amid broader — and of­ten bit­ter — de­bates about the par­tic­i­pa­tion of trans­gen­der ath­letes in sport­ing com­pe­ti­tions.

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LG’s new 1Hz dis­play is the se­cret be­hind a new lap­top’s bat­tery life

LG’s Oxide 1Hz panel can han­dle re­fresh rates of be­tween 1Hz and 120Hz.

PCWorld re­ports that LG Display’s new ‘Oxide 1Hz’ tech­nol­ogy en­ables lap­top dis­plays to vary re­fresh rates from 1Hz to 120Hz, po­ten­tially sav­ing up to 48% bat­tery life. Dell has al­ready adopted this in­no­v­a­tive dis­play as the de­fault op­tion in its XPS lap­top lineup, demon­strat­ing early mar­ket adop­tion.LG plans mass pro­duc­tion of 1Hz OLED pan­els fea­tur­ing this bat­tery-ex­tend­ing tech­nol­ogy by 2027, sig­nal­ing broader avail­abil­ity for fu­ture lap­tops.

Traditionally, a lap­top’s bat­tery life tends to be gov­erned not by the mi­cro­proces­sor in­side it, but by the dis­play that you’re star­ing at for hours on end. That’s why a new “Oxide 1Hz” dis­play tech­nol­ogy from LG Display is so in­ter­est­ing.

As a com­po­nent maker, LG will ship the panel to var­i­ous cus­tomers: lap­top mak­ers, man­u­fac­tur­ers of ex­ter­nal dis­plays, and so on. It’s the re­fresh rate that’s of in­ter­est, though: from 1Hz all the way to 120Hz. That will help save enor­mous amounts of power: up to 48 per­cent on a sin­gle charge, LG claims.

Why? Laptop pan­els “refresh,” or up­date, at var­i­ous rates. Traditional TVs used pan­els that re­freshed 60 times per sec­ond, or 60Hz. Most older lap­tops did too. As games be­came more of a fo­cus for lap­tops and TVs, man­u­fac­tur­ers turned to pan­els with higher re­fresh rates, of­fer­ing smoother game­play that matched the graph­ics ca­pa­bil­i­ties of con­soles and gam­ing lap­tops. Productivity ma­chines also saw a boost, too: Higher re­fresh rates mean smoother scrolling and mou­s­ing.

There’s a trade­off, how­ever, for higher re­fresh rates: bat­tery life. Refresh the screen more quickly, and the dis­play chews through more power. Previously, the an­swer was to dy­nam­i­cally ad­just the re­fresh rate, leav­ing it at 60Hz un­til the lap­top saw a need for the higher re­fresh rate and ad­justed it. More re­cently, lap­tops have taken this in the other di­rec­tion, ad­just­ing re­fresh rates down­ward to save power. Personally, 30Hz is as low I’ve seen a lap­top’s re­fresh rate go.

LG is cov­er­ing pretty much all of the bases with its Oxide 1Hz tech­nol­ogy, of­fer­ing re­fresh rates that can sip power at 1Hz, then dy­nam­i­cally sup­port up to 120Hz when needed.

LG’s press re­lease leaves sev­eral ques­tions unan­swered, in­clud­ing the source of the “Oxide” name. One ques­tion, how­ever, has been par­tially solved: Which lap­top maker will use it. LG is com­ing out and say­ing that it has al­ready shipped the Oxide 1Hz panel to Dell, as part of the XPS lineup it showed off in January. (Dell shipped us a Dell XPS 14 for re­view, which in­cluded an OLED panel, un­for­tu­nately.) LG Display is also prepar­ing to be­gin mass pro­duc­tion of a 1Hz OLED panel in­cor­po­rat­ing the same tech­nol­ogy in 2027.

Dell does­n’t of­fer LG’s 1Hz dis­play at a price pre­mium. Instead, it’s the de­fault op­tion.

A 1Hz panel is al­most, but not quite, on the level of an e-ink panel, which is­n’t the pret­ti­est to look at. LG’s panel also uses LED tech­nol­ogy, the main­stream panel tech­nol­ogy that’s be­ing over­taken at the high end by OLED pan­els with es­sen­tially per­fect con­trast. How fast the Oxide 1Hz panel leaps into faster re­fresh rates, and whether there are any vi­sual ar­ti­facts re­mains to be seen. As the screen­shot above in­di­cates, how­ever, price does­n’t seem to be an is­sue; the 1Hz dis­play is the de­fault op­tion.

Laptop mak­ers have more and more op­tions when it comes to ex­tend­ing bat­tery life, in­clud­ing new Panther Lake proces­sors from Intel and the up­com­ing Snapdragon X2 Elite from Qualcomm. Add LG’s dis­play to the mix, and you’ll be able to work on pre­sen­ta­tions, then watch movies on the same lap­top well into the evening.

Mark has writ­ten for PCWorld for the last decade, with 30 years of ex­pe­ri­ence cov­er­ing tech­nol­ogy. He has au­thored over 3,500 ar­ti­cles for PCWorld alone, cov­er­ing PC mi­cro­proces­sors, pe­riph­er­als, and Microsoft Windows, among other top­ics. Mark has writ­ten for pub­li­ca­tions in­clud­ing PC Magazine, Byte, eWEEK, Popular Science and Electronic Buyers’ News, where he shared a Jesse H. Neal Award for break­ing news. He re­cently handed over a col­lec­tion of sev­eral dozen Thunderbolt docks and USB-C hubs be­cause his of­fice sim­ply has no more room.

Intel’s new gam­ing lap­top chips won’t ar­rive all at once

Windows 11 paves the way for eye-melt­ing 1,000Hz mon­i­tors

Under mi­cro­scopic mag­ni­fi­ca­tion, the unique beauty and in­di­vid­ual char­ac­ter of sand grains re­veal a di­verse ori­gin re­flect­ing ge­o­log­i­cal his­tory and ma­rine life bio­di­ver­sity. Sand is every­where on earth — on our beaches, in our deserts, and on the bot­toms of lakes, rivers and oceans. Sand par­ti­cles are coarser than silt but finer than gravel, rang­ing in size from 0.02 to 2 mm. They are cre­ated when weather and chem­i­cals break down ter­res­trial rocks, min­er­als, ma­rine bi­valves, corals, mol­lusks, bry­ozoans, and foraminifera.

Iran-linked hack­ers suc­cess­fully broke into FBI Director Kash Patel’s per­sonal email, the Department of Justice con­firmed to Reuters on Friday.

Reuters could not au­then­ti­cate the leaked emails them­selves but noted that the Gmail ad­dress matched an email ac­count “linked to Patel in pre­vi­ous data breaches ⁠preserved by the dark web in­tel­li­gence firm District 4 Labs.” The DOJ sug­gested the emails ap­peared to be au­then­tic.

On their web­site, the Handala Hack Team boasted that Patel “will now find his name among the list of suc­cess­fully hacked vic­tims.” The hacker group taunted Patel by shar­ing pho­tos of him sniff­ing cig­ars and hold­ing up a jug of rum, along with other doc­u­ments that Reuters re­ported were from 2010 to 2019.

“Soon you will re­al­ize that the FBI’s se­cu­rity was noth­ing more than a joke,” the group posted, as doc­u­mented in screen­shots from the web­site shared widely on X.

The hack came af­ter the DOJ dis­rupted some of the hacker group’s web­sites ear­lier this month. In a press re­lease, Patel threat­ened to “hunt” down the group, which Reuters re­ported “calls it­self a group of pro-Pales­tin­ian vig­i­lante hack­ers.” After de­tail­ing four at­tacks this month that the group had taken credit for, Patel of­fered re­wards of up to $10 mil­lion for in­for­ma­tion on its mem­bers.

“Iran thought they could hide be­hind fake web­sites and key­board threats to ter­ror­ize Americans and si­lence dis­si­dents,” Patel said. “We took down four of their op­er­a­tion’s pil­lars and we’re not done. This FBI will hunt down every ac­tor be­hind these cow­ardly death threats and cy­ber­at­tacks and will bring the full force of American law en­force­ment down on them.”

Both of the chip’s CPU dies will in­clude 64MB of ex­tra cache stacked be­neath.

For about four years now, AMD has of­fered spe­cial “X3D” vari­ants of its high-end desk­top proces­sors with an ex­tra 64MB of L3 cache at­tached, an ad­di­tion that dis­pro­por­tion­ately ben­e­fits games. AMD calls this “3D V-Cache” be­cause it stacks the cache di­rectly on top of (for Ryzen 5000 and 7000) or be­neath (for Ryzen 9000) the CPU die.

The 12- and 16-core Ryzen chips have their CPU cores split be­tween two sil­i­con chiplets, which has his­tor­i­cally made the 7900X3D, 7950X3D, 9900X3D, and 9950X3D a bit weird. One of their two CPU chiplets has the 64MB of 3D V-Cache at­tached, and one does not. AMD re­lies on its dri­ver soft­ware to make sure that soft­ware that ben­e­fits from the ex­tra cache is run on the V-Cache-enabled CPU cores, which usu­ally works well but is oc­ca­sion­ally er­ror-prone.

Enter the Ryzen 9 9950X3D2 Dual Edition, a mouth­ful of a chip that in­cludes 64MB of 3D V-Cache on both proces­sor dies, with­out the hy­brid arrange­ment that has de­fined the other chips up un­til now. This gives the chip a grand to­tal of 208MB of cache—16MB of L2 cache, the 32MB of L3 cache built into each of the two CPU dies (for a to­tal of 64MB), and then an­other 64MB chunk of 3D V-Cache per die. In to­tal, AMD says the new chip should be as much as 10 per­cent faster than the 9950X3D in games and other apps that ben­e­fit from the ex­tra cache.

The ex­tra cache does have mild down­sides. The 9950X3D2’s peak clock speed is 5.6 GHz, down very slightly from 5.7GHz for the 9950X and 9950X3D, and its de­fault TDP is 200 W in­stead of 170 W. Higher power con­sump­tion typ­i­cally comes with higher cool­ing re­quire­ments. And AMD’s ini­tial an­nounce­ment video did­n’t in­clude pric­ing; the vanilla 9950X3D cur­rently re­tails for around $675, not far from its $699 launch price. We’d ex­pect the 9950X3D2 to run at least a cou­ple hun­dred dol­lars more.

But the Ryzen 9000 se­ries has steadily eroded the down­sides of older Ryzen 5000 and Ryzen 7000 X3D chips. The 9950X3D2 will be fully over­clock­able and tun­able via AMD’s Precision Boost Overdrive/Curve Optimizer/Ryzen Master fea­tures; stack­ing the cache be­neath the CPU makes it eas­ier to keep those CPU cores cool; and there’s no longer a sev­eral-hun­dred-mega­hertz-large gap be­tween the base and boost clock speeds. The pe­ri­odic “core park­ing” is­sues with the hy­brid X3D chips was one of their last down­sides; the 9950X3D2 may be an ex­pen­sive so­lu­tion to the prob­lem, but it’s also a fool­proof fix that won’t re­quire OS re­in­stalls or dri­ver up­dates, which might make it worth it to a cer­tain type of high-end PC user.

The Ryzen 9950X3D2 Dual Edition will be avail­able start­ing April 22.

Andrew is a Senior Technology Reporter at Ars Technica, with a fo­cus on con­sumer tech in­clud­ing com­puter hard­ware and in-depth re­views of op­er­at­ing sys­tems like Windows and ma­cOS. Andrew lives in Philadelphia and co-hosts a weekly book pod­cast called Overdue.

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