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The U. S. Border Patrol is mon­i­tor­ing mil­lions of American dri­vers na­tion­wide in a se­cre­tive pro­gram to iden­tify and de­tain peo­ple whose travel pat­terns it deems sus­pi­cious, The Associated Press has found.

The pre­dic­tive in­tel­li­gence pro­gram has re­sulted in peo­ple be­ing stopped, searched and in some cases ar­rested. A net­work of cam­eras scans and records ve­hi­cle li­cense plate in­for­ma­tion, and an al­go­rithm flags ve­hi­cles deemed sus­pi­cious based on where they came from, where they were go­ing and which route they took. Federal agents in turn may then flag lo­cal law en­force­ment.

Suddenly, dri­vers find them­selves pulled over — of­ten for rea­sons cited such as speed­ing, fail­ure to sig­nal, the wrong win­dow tint or even a dan­gling air fresh­ener block­ing the view. They are then ag­gres­sively ques­tioned and searched, with no inkling that the roads they drove put them on law en­force­men­t’s radar.

Once lim­ited to polic­ing the na­tion’s bound­aries, the Border Patrol has built a sur­veil­lance sys­tem stretch­ing into the coun­try’s in­te­rior that can mon­i­tor or­di­nary Americans’ daily ac­tions and con­nec­tions for anom­alies in­stead of sim­ply tar­get­ing wanted sus­pects. Started about a decade ago to fight il­le­gal bor­der-re­lated ac­tiv­i­ties and the traf­fick­ing of both drugs and peo­ple, it has ex­panded over the past five years.

The Border Patrol has re­cently grown even more pow­er­ful through col­lab­o­ra­tions with other agen­cies, draw­ing in­for­ma­tion from li­cense plate read­ers na­tion­wide run by the Drug Enforcement Administration, pri­vate com­pa­nies and, in­creas­ingly, lo­cal law en­force­ment pro­grams funded through fed­eral grants. Texas law en­force­ment agen­cies have asked Border Patrol to use fa­cial recog­ni­tion to iden­tify dri­vers, doc­u­ments show.

This ac­tive role be­yond the bor­ders is part of the quiet trans­for­ma­tion of its par­ent agency, U. S. Customs and Border Protection, into some­thing more akin to a do­mes­tic in­tel­li­gence op­er­a­tion. Under the Trump ad­min­is­tra­tion’s height­ened im­mi­gra­tion en­force­ment ef­forts, CBP is now poised to get more than $2.7 bil­lion to build out bor­der sur­veil­lance sys­tems such as the li­cense plate reader pro­gram by lay­er­ing in ar­ti­fi­cial in­tel­li­gence and other emerg­ing tech­nolo­gies.

The re­sult is a mass sur­veil­lance net­work with a par­tic­u­larly American fo­cus: cars.

This in­ves­ti­ga­tion, the first to re­veal de­tails of how the pro­gram works on America’s roads, is based on in­ter­views with eight for­mer gov­ern­ment of­fi­cials with di­rect knowl­edge of the pro­gram who spoke on the con­di­tion of anonymity be­cause they weren’t au­tho­rized to speak to the me­dia, as well as dozens of fed­eral, state and lo­cal of­fi­cials, at­tor­neys and pri­vacy ex­perts. The AP also re­viewed thou­sands of pages of court and gov­ern­ment doc­u­ments, state grant and law en­force­ment data, and ar­rest re­ports.

The Border Patrol has for years hid­den de­tails of its li­cense plate reader pro­gram, try­ing to keep any men­tion of the pro­gram out of court doc­u­ments and po­lice re­ports, for­mer of­fi­cials say, even go­ing so far as to pro­pose drop­ping charges rather than risk re­veal­ing any de­tails about the place­ment and use of their covert li­cense plate read­ers. Readers are of­ten dis­guised along high­ways in traf­fic safety equip­ment like drums and bar­rels.

The Border Patrol has de­fined its own cri­te­ria for which dri­vers’ be­hav­ior should be deemed sus­pi­cious or tied to drug or hu­man traf­fick­ing, stop­ping peo­ple for any­thing from dri­ving on back­coun­try roads, be­ing in a rental car or mak­ing short trips to the bor­der re­gion. The agen­cy’s net­work of cam­eras now ex­tends along the south­ern bor­der in Texas, Arizona and California, and also mon­i­tors dri­vers trav­el­ing near the U. S.-Canada bor­der.

And it reaches far into the in­te­rior, im­pact­ing res­i­dents of big met­ro­pol­i­tan ar­eas and peo­ple dri­ving to and from large cities such as Chicago and Detroit, as well as from Los Angeles, San Antonio, and Houston to and from the Mexican bor­der re­gion. In one ex­am­ple, AP found the agency has placed at least four cam­eras in the greater Phoenix area over the years, one of which was more than 120 miles (193 kilo­me­ters) from the Mexican fron­tier, be­yond the agen­cy’s usual ju­ris­dic­tion of 100 miles (161 kilo­me­ters) from a land or sea bor­der. The AP also iden­ti­fied sev­eral cam­era lo­ca­tions in met­ro­pol­i­tan Detroit, as well as one placed near the Michigan-Indiana bor­der to cap­ture traf­fic headed to­wards Chicago or Gary, Indiana, or other nearby des­ti­na­tions.

Border Patrol’s par­ent agency, U. S. Customs and Border Protection, said they use li­cense plate read­ers to help iden­tify threats and dis­rupt crim­i­nal net­works and are “governed by a strin­gent, multi-lay­ered pol­icy frame­work, as well as fed­eral law and con­sti­tu­tional pro­tec­tions, to en­sure the tech­nol­ogy is ap­plied re­spon­si­bly and for clearly de­fined se­cu­rity pur­poses.”

“For na­tional se­cu­rity rea­sons, we do not de­tail the spe­cific op­er­a­tional ap­pli­ca­tions,” the agency said. While the U. S. Border Patrol pri­mar­ily op­er­ates within 100 miles of the bor­der, it is legally al­lowed “to op­er­ate any­where in the United States,” the agency added.

While col­lect­ing li­cense plates from cars on pub­lic roads has gen­er­ally been up­held by courts, some le­gal schol­ars see the growth of large dig­i­tal sur­veil­lance net­works such as Border Patrol’s as rais­ing con­sti­tu­tional ques­tions. Courts have started to rec­og­nize that “large-scale sur­veil­lance tech­nol­ogy that’s cap­tur­ing every­one and every­where at every time” might be un­con­sti­tu­tional un­der the Fourth Amendment, which pro­tects peo­ple from un­rea­son­able searches, said Andrew Ferguson, a law pro­fes­sor at George Washington University.

Today, pre­dic­tive sur­veil­lance is em­bed­ded into America’s road­ways. Mass sur­veil­lance tech­niques are also used in a range of other coun­tries, from au­thor­i­tar­ian gov­ern­ments such as China to, in­creas­ingly, democ­ra­cies in the U. K. and Europe in the name of na­tional se­cu­rity and pub­lic safety.

“They are col­lect­ing mass amounts of in­for­ma­tion about who peo­ple are, where they go, what they do, and who they know … en­gag­ing in drag­net sur­veil­lance of Americans on the streets, on the high­ways, in their cities, in their com­mu­ni­ties,” Nicole Ozer, the ex­ec­u­tive di­rec­tor of the Center for Constitutional Democracy at UC Law San Francisco, said in re­sponse to the AP’s find­ings. “These sur­veil­lance sys­tems do not make com­mu­ni­ties safer.”

In February, Lorenzo Gutierrez Lugo, a dri­ver for a small truck­ing com­pany that spe­cial­izes in trans­port­ing fur­ni­ture, cloth­ing and other be­long­ings to fam­i­lies in Mexico, was dri­ving south to the bor­der city of Brownsville, Texas, car­ry­ing pack­ages from im­mi­grant com­mu­ni­ties in South Carolina’s low coun­try.

Gutierrez Lugo was pulled over by a lo­cal po­lice of­fi­cer in Kingsville, a small Texas city near Corpus Christi that lies about 100 miles from the Mexican bor­der. The of­fi­cer, Richard Beltran, cited the truck’s speed of 50 mph (80 kph) in a 45 mph (72 kph) zone as the rea­son for the stop.

But speed­ing was a pre­text: Border Patrol had re­quested the stop and said the black Dodge pickup with a white trailer could con­tain con­tra­band, ac­cord­ing to po­lice and court records. U. S. Route 77 passes through Kingsville, a route that state and fed­eral au­thor­i­ties scru­ti­nize for traf­fick­ing of drugs, money and peo­ple.

Gutierrez Lugo, who through a lawyer de­clined to com­ment, was in­ter­ro­gated about the route he drove, based on li­cense plate reader data, per the po­lice re­port and court records. He con­sented to a search of his car by Beltran and Border Patrol agents, who even­tu­ally ar­rived to as­sist.

They un­earthed no con­tra­band. But Beltran ar­rested Gutierrez Lugo on sus­pi­cion of money laun­der­ing and en­gag­ing in or­ga­nized crim­i­nal ac­tiv­ity be­cause he was car­ry­ing thou­sands of dol­lars in cash — money his su­per­vi­sor said came di­rectly from cus­tomers in lo­cal Latino com­mu­ni­ties, who are ac­cus­tomed to pay­ing in cash. No crim­i­nal charges were ul­ti­mately brought against Gutierrez Lugo and an ef­fort by pros­e­cu­tors to seize the cash, ve­hi­cle and trailer as con­tra­band was even­tu­ally dropped.

Luis Barrios owns the truck­ing com­pany, Paquetería El Guero, that em­ployed the dri­ver. He told AP he hires peo­ple with work au­tho­riza­tion in the United States and was taken aback by the treat­ment of his em­ployee and his trailer.

“We did every­thing right and had noth­ing to hide, and that was ul­ti­mately what they found,” said Barrios, who es­ti­mates he spent $20,000 in le­gal fees to clear his dri­ver’s name and get the trailer out of im­pound.

Border Patrol agents and lo­cal po­lice have many names for these kinds of stops: “whisper,” “intel” or “wall” stops. Those stops are meant to con­ceal — or wall off — that the true rea­son for the stop is a tip from fed­eral agents sit­ting miles away, watch­ing data feeds show­ing who’s trav­el­ing on America’s roads and pre­dict­ing who is “suspicious,” ac­cord­ing to doc­u­ments and peo­ple in­ter­viewed by the AP.

In 2022, a man from Houston had his car searched from top to bot­tom by Texas sher­if­f’s deputies out­side San Antonio af­ter they got a sim­i­lar tipoff from Border Patrol agents about the dri­ver, Alek Schott.

Federal agents ob­served that Schott had made an overnight trip from Houston to Carrizo Springs, Texas, and back, court records show. They knew he stayed overnight in a ho­tel about 80 miles (129 kilo­me­ters) from the U. S.-Mexico bor­der. They knew that in the morn­ing Schott met a fe­male col­league there be­fore they drove to­gether to a busi­ness meet­ing.

At Border Patrol’s re­quest, Schott was pulled over by Bexar County sher­if­f’s deputies. The deputies held Schott by the side of the road for more than an hour, searched his car and found noth­ing.

“The beau­ti­ful thing about the Texas Traffic Code is there’s thou­sands of things you can stop a ve­hi­cle for,” said Joel Babb, the sher­if­f’s deputy who stopped Schott’s car, in a de­po­si­tion in a law­suit Schott filed al­leg­ing vi­o­la­tions of his con­sti­tu­tional rights.

According to tes­ti­mony and doc­u­ments re­leased as part of Schott’s law­suit, Babb was on a group chat with fed­eral agents called Northwest Highway. Babb deleted the WhatsApp chat off his phone but Schott’s lawyers were able to re­cover some of the text mes­sages.

Through a pub­lic records act re­quest, the AP also ob­tained more than 70 pages of the Northwest Highway group chats from June and July of this year from a Texas county that had at least one sher­if­f’s deputy ac­tive in the chat. The AP was able to as­so­ci­ate nu­mer­ous phone num­bers in both sets of doc­u­ments with Border Patrol agents and Texas law en­force­ment of­fi­cials.

The chat logs show Border Patrol agents and Texas sher­iffs deputies trad­ing tips about ve­hi­cles’ travel pat­terns — based on sus­pi­cions about lit­tle more than some­one tak­ing a quick trip to the bor­der re­gion and back. The chats show how thor­oughly Texas high­ways are sur­veilled by this fed­eral-lo­cal part­ner­ship and how much de­tailed in­for­ma­tion is in­for­mally shared.

In one ex­change a law en­force­ment of­fi­cial in­cluded a photo of some­one’s dri­ver’s li­cense and told the group the per­son, who they iden­ti­fied us­ing an ab­bre­vi­a­tion for some­one in the coun­try il­le­gally, was headed west­bound. “Need BP?,” re­sponded a group mem­ber whose num­ber was la­beled “bp Intel.” “Yes sir,” the of­fi­cial an­swered, and a Border Patrol agent was en route.

Border Patrol agents and lo­cal law en­force­ment shared in­for­ma­tion about U. S. cit­i­zens’ so­cial me­dia pro­files and home ad­dresses with each other af­ter stop­ping them on the road. Chats show Border Patrol was also able to de­ter­mine whether ve­hi­cles were rentals and whether dri­vers worked for rideshare ser­vices.

In Schott’s case, Babb tes­ti­fied that fed­eral agents “actually watch travel pat­terns on the high­way” through li­cense plate scans and other sur­veil­lance tech­nolo­gies. He added: “I just know that they have a lot of toys over there on the fed­eral side.”

After find­ing noth­ing in Schott’s car, Babb said “nine times out of 10, this is what hap­pens,” a phrase Schott’s lawyers claimed in court fil­ings shows the sher­if­f’s de­part­ment finds noth­ing sus­pi­cious in most of its searches. Babb did not re­spond to mul­ti­ple re­quests for com­ment from AP.

The Bexar County sher­if­f’s of­fice de­clined to com­ment due to pend­ing lit­i­ga­tion and re­ferred all ques­tions about the Schott case to the coun­ty’s dis­trict at­tor­ney. The dis­trict at­tor­ney did not re­spond to a re­quest for com­ment.

The case is pend­ing in fed­eral court in Texas. Schott said in an in­ter­view with the AP: “I did­n’t know it was il­le­gal to drive in Texas.”

Today, the deserts, forests and moun­tains of the na­tion’s land bor­ders are dot­ted with check­points and in­creas­ingly, sur­veil­lance tow­ers, Predator drones, ther­mal cam­eras and li­cense plate read­ers, both covert and overt.

Border Patrol’s par­ent agency got au­tho­riza­tion to run a do­mes­tic li­cense plate reader pro­gram in 2017, ac­cord­ing to a Department of Homeland Security pol­icy doc­u­ment. At the time, the agency said that it might use hid­den li­cense plate read­ers ”for a set pe­riod of time while CBP is con­duct­ing an in­ves­ti­ga­tion of an area of in­ter­est or smug­gling route. Once the in­ves­ti­ga­tion is com­plete, or the il­licit ac­tiv­ity has stopped in that area, the covert cam­eras are re­moved,” the doc­u­ment states.

But that’s not how the pro­gram has op­er­ated in prac­tice, ac­cord­ing to in­ter­views, po­lice re­ports and court doc­u­ments. License plate read­ers have be­come a ma­jor — and in some places per­ma­nent — fix­ture of the bor­der re­gion.

In a bud­get re­quest to Congress in fis­cal year 2024, CBP said that its Conveyance Monitoring and Predictive Recognition System, or CMPRS, “collects li­cense plate im­ages and matches the processed im­ages against es­tab­lished hot lists to as­sist … in iden­ti­fy­ing travel pat­terns in­dica­tive of il­le­gal bor­der re­lated ac­tiv­i­ties.” Several new de­vel­oper jobs have been posted seek­ing ap­pli­cants to help mod­ern­ize its li­cense plate sur­veil­lance sys­tem in re­cent months. Numerous Border Patrol sec­tors now have spe­cial in­tel­li­gence units that can an­a­lyze li­cense plate reader data, and tie com­mer­cial li­cense plate read­ers to its na­tional net­work, ac­cord­ing to doc­u­ments and in­ter­views.

Border Patrol worked with other law en­force­ment agen­cies in Southern California about a decade ago to de­velop pat­tern recog­ni­tion, said a for­mer CBP of­fi­cial who spoke on the con­di­tion of anonymity for fear of reprisal. Over time, the agency learned to de­velop what it calls “patterns of life” of ve­hi­cle move­ments by sift­ing through the li­cense plate data and de­ter­min­ing “abnormal” routes, eval­u­at­ing if dri­vers were pur­posely avoid­ing of­fi­cial check­points. Some cam­eras can take pho­tos of a ve­hi­cle’s plates as well as its dri­ver’s face, the of­fi­cial said.

Another for­mer Border Patrol of­fi­cial com­pared it to a more tech­no­log­i­cally so­phis­ti­cated ver­sion of what agents used to do in the field — de­velop hunches based on ex­pe­ri­ence about which ve­hi­cles or routes smug­glers might use, find a le­gal ba­sis for the stop like speed­ing and pull dri­vers over for ques­tion­ing.

The cam­eras take pic­tures of ve­hi­cle li­cense plates. Then, the pho­tos are “read” by the sys­tem, which au­to­mat­i­cally de­tects and dis­tills the im­ages into num­bers and let­ters, tied to a ge­o­graphic lo­ca­tion, for­mer CBP of­fi­cials said. The AP could not de­ter­mine how pre­cisely the sys­tem’s al­go­rithm de­fines a quick turn­around or an odd route. Over time, the agency has amassed data­bases re­plete with im­ages of li­cense plates, and the sys­tem’s al­go­rithm can flag an un­usual “pattern of life” for hu­man in­spec­tion.

The Border Patrol also has ac­cess to a na­tion­wide net­work of plate read­ers run by the Drug Enforcement Administration, doc­u­ments show, and was au­tho­rized in 2020 to ac­cess li­cense plate reader sys­tems sold by pri­vate com­pa­nies. In doc­u­ments ob­tained by the AP, a Border Patrol of­fi­cial boasted about be­ing able to see that a ve­hi­cle that had trav­eled to “Dallas, Little Rock, Arkansas and Atlanta” be­fore end­ing up south of San Antonio.

Documents show that Border Patrol or CBP has in the past had ac­cess to data from at least three pri­vate sec­tor ven­dors: Rekor, Vigilant Solutions and Flock Safety.

Through Flock alone, Border Patrol for a time had ac­cess to at least 1,600 li­cense plate read­ers across 22 states, and some coun­ties have re­ported look­ing up li­cense plates on be­half of CBP even in states like California and Illinois that ban shar­ing data with fed­eral im­mi­gra­tion au­thor­i­ties, ac­cord­ing to an AP analy­sis of po­lice dis­clo­sures. A Flock spokesper­son told AP the com­pany “for now” had paused its pi­lot pro­grams with CBP and a sep­a­rate DHS agency, Homeland Security Investigations, and de­clined to dis­cuss the type or vol­ume of data shared with ei­ther fed­eral agency, other than to say agen­cies could search for ve­hi­cles wanted in con­junc­tion with a crime. No agen­cies cur­rently list Border Patrol as re­ceiv­ing Flock data. Vigilant and Rekor did not re­spond to re­quests for com­ment.

Where Border Patrol places its cam­eras is a closely guarded se­cret. However, through pub­lic records re­quests, the AP ob­tained dozens of per­mits the agency filed with Arizona and Michigan for per­mis­sion to place cam­eras on state-owned land. The per­mits show the agency fre­quently dis­guises its cam­eras by con­ceal­ing them in traf­fic equip­ment like the yel­low and or­ange bar­rels that dot American road­ways, or by la­bel­ing them as job­site equip­ment. An AP pho­tog­ra­pher in October vis­ited the lo­ca­tions iden­ti­fied in more than two dozen per­mit ap­pli­ca­tions in Arizona, find­ing that most of the Border Patrol’s hid­den equip­ment re­mains in place to­day. Spokespeople for the Arizona and Michigan de­part­ments of trans­porta­tion said they ap­prove per­mits based on whether they fol­low state and fed­eral rules and are not privy to de­tails on how li­cense plate read­ers are used.

Texas, California, and other bor­der states did not pro­vide doc­u­ments in re­sponse to the AP’s pub­lic records re­quests.

CBP’s at­tor­neys and per­son­nel in­structed lo­cal cities and coun­ties in both Arizona and Texas to with­hold records from the AP that might have re­vealed de­tails about the pro­gram’s op­er­a­tions, even though they were re­quested un­der state open records laws, ac­cord­ing to emails and le­gal briefs filed with state gov­ern­ments. For ex­am­ple, CBP claimed records re­quested by the AP in Texas “would per­mit pri­vate cit­i­zens to an­tic­i­pate weak­nesses in a po­lice de­part­ment, avoid de­tec­tion, jeop­ar­dize of­fi­cer safety, and gen­er­ally un­der­mine po­lice ef­forts.” Michigan redacted the ex­act lo­ca­tions of Border Patrol equip­ment, but the AP was able to de­ter­mine gen­eral lo­ca­tions from the name of the county.

One page of the group chats ob­tained by the AP shows that a par­tic­i­pant en­abled WhatsApp’s dis­ap­pear­ing mes­sages fea­ture to en­sure com­mu­ni­ca­tions were deleted au­to­mat­i­cally.

The Border Patrol’s li­cense plate reader pro­gram is just one part of a steady trans­for­ma­tion of its par­ent agency, CBP, in the years since 9/11 into an in­tel­li­gence op­er­a­tion whose reach ex­tends far be­yond bor­ders, ac­cord­ing to in­ter­views with for­mer of­fi­cials.

CBP has qui­etly amassed ac­cess to far more in­for­ma­tion from ports of en­try, air­ports and in­tel­li­gence cen­ters than other lo­cal, state and fed­eral law en­force­ment agen­cies. And like a do­mes­tic spy agency, CBP has mostly hid­den its role in the dis­sem­i­na­tion of in­tel­li­gence on purely do­mes­tic travel through its use of whis­per stops.

Border Patrol has also ex­tended the reach of its li­cense plate sur­veil­lance pro­gram by pay­ing for lo­cal law en­force­ment to run plate read­ers on their be­half.

A fed­eral grant pro­gram called Operation Stonegarden, which has ex­isted in some form for nearly two decades, has handed out hun­dreds of mil­lions of dol­lars to buy au­to­mated li­cense plate read­ers, cam­era-equipped drones and other sur­veil­lance gear for lo­cal po­lice and sher­iffs agen­cies. Stonegarden grant funds also pay for lo­cal law en­force­ment over­time, which dep­u­tizes lo­cal of­fi­cers to work on Border Patrol en­force­ment pri­or­i­ties. Under President Donald Trump, the Republican-led Congress this year al­lo­cated $450 mil­lion for Stonegarden to be handed out over the next four fis­cal years. In the pre­vi­ous four fis­cal years, the pro­gram gave out $342 mil­lion.

In Cochise County, Arizona, Sheriff Mark Dannels said Stonegarden grants, which have been used to buy plate read­ers and pay for over­time, have let his deputies merge their mis­sion with Border Patrol’s to pri­or­i­tize bor­der se­cu­rity.

“If we’re shar­ing our au­thor­i­ties, we can put some con­se­quences be­hind, or de­ter­rence be­hind, ‘Don’t come here,’” he said.

In 2021, the Ward County, Texas, sher­iff sought grant fund­ing from DHS to buy a “covert, mo­bile, License Plate Reader” to pipe data to Border Patrol’s Big Bend Sector Intelligence Unit. The sher­if­f’s de­part­ment did not re­spond to a re­quest for com­ment.

Other doc­u­ments AP ob­tained show that Border Patrol con­nects lo­cally owned and op­er­ated li­cense plate read­ers bought through Stonegarden grants to its com­puter sys­tems, vastly in­creas­ing the fed­eral agen­cy’s sur­veil­lance net­work.

How many peo­ple have been caught up in the Border Patrol’s drag­net is un­known. One for­mer Border Patrol agent who worked on the li­cense plate reader pat­tern de­tec­tion pro­gram in California said the pro­gram had an 85% suc­cess rate of dis­cov­er­ing con­tra­band once he learned to iden­tify pat­terns that looked sus­pi­cious. But an­other for­mer of­fi­cial in a dif­fer­ent Border Patrol sec­tor said he was un­aware of suc­cess­ful in­ter­dic­tions based solely on li­cense plate pat­terns.

In Trump’s sec­ond term, Border Patrol has ex­tended its reach and power as bor­der cross­ings have slowed to his­toric lows and freed up agents for op­er­a­tions in the heart­land. Border Patrol Sector Chief Gregory Bovino, for ex­am­ple, was tapped to di­rect hun­dreds of agents from mul­ti­ple DHS agen­cies in the ad­min­is­tra­tion’s im­mi­gra­tion sweeps across Los Angeles, more than 150 miles (241 kilo­me­ters) from his of­fice in El Centro, California. Bovino later was el­e­vated to lead the ag­gres­sive im­mi­gra­tion crack­down in Chicago. Numerous Border Patrol of­fi­cials have also been tapped to re­place ICE lead­er­ship.

The re­sult has been more en­coun­ters be­tween the agency and the gen­eral pub­lic than ever be­fore.

“We took Alek’s case be­cause it was a clear-cut ex­am­ple of an un­con­sti­tu­tional traf­fic stop,” said Christie Hebert, who works at the non­profit pub­lic in­ter­est law firm Institute for Justice and rep­re­sents Schott. ”What we found was some­thing much larger — a sys­tem of mass sur­veil­lance that threat­ens peo­ple’s free­dom of move­ment.”

AP found nu­mer­ous other ex­am­ples sim­i­lar to what Schott and the de­liv­ery dri­ver ex­pe­ri­enced in re­view­ing court records in bor­der com­mu­ni­ties and along known smug­gling routes in Texas and California. Several po­lice re­ports and court records the AP ex­am­ined cite “suspicious” travel pat­terns or vague tipoffs from the Border Patrol or other un­named law en­force­ment agen­cies. In an­other fed­eral court doc­u­ment filed in California, a Border Patrol agent ac­knowl­edged “conducting tar­geted analy­sis on ve­hi­cles ex­hibit­ing sus­pi­cious travel pat­terns” as the rea­son he sin­gled out a Nissan Altima trav­el­ing near San Diego.

In cases re­viewed by the AP, lo­cal law en­force­ment some­times tried to con­ceal the role the Border Patrol plays in pass­ing along in­tel­li­gence. Babb, the deputy who stopped Schott, tes­ti­fied he typ­i­cally uses the phrase “subsequent to prior knowl­edge” when de­scrib­ing whis­per stops in his po­lice re­ports to ac­knowl­edge that the tip came from an­other law en­force­ment agency with­out re­veal­ing too much in writ­ten doc­u­ments he writes memo­ri­al­iz­ing mo­torist en­coun­ters.

Once they pull over a ve­hi­cle deemed sus­pi­cious, of­fi­cers of­ten ag­gres­sively ques­tion dri­vers about their trav­els, their be­long­ings, their jobs, how they know the pas­sen­gers in the car, and much more, po­lice records and body­worn cam­era footage ob­tained by the AP show. One Texas of­fi­cer de­manded de­tails from a man about where he met his cur­rent sex­ual part­ner. Often dri­vers, such as the one work­ing for the South Carolina mov­ing com­pany, were ar­rested on sus­pi­cion of money laun­der­ing merely for car­ry­ing a few thou­sand dol­lars worth of cash, with no ap­par­ent con­nec­tion to il­le­gal ac­tiv­ity. Prosecutors filed law­suits to try to seize money or ve­hi­cles on the sus­pi­cion they were linked to traf­fick­ing.

Schott warns that for every suc­cess story touted by Border Patrol, there are far more in­no­cent peo­ple who don’t re­al­ize they’ve be­come en­snared in a tech­nol­ogy-dri­ven en­force­ment op­er­a­tion.

“I as­sume for every one per­son like me, who’s ac­tu­ally stand­ing up, there’s a thou­sand peo­ple who just don’t have the means or the time or, you know, they just leave frus­trated and an­gry. They don’t have the abil­ity to move for­ward and hold any­one ac­count­able,” Schott said. “I think there’s thou­sands of peo­ple get­ting treated this way.”

When it comes to shar­ing mo­ments be­tween fam­ily and friends, what de­vice you have should­n’t mat­ter — shar­ing should just work. But we’ve heard from many peo­ple that they want a sim­pler way to share files be­tween de­vices.

Today, we’re in­tro­duc­ing a way for Quick Share to work with AirDrop. This makes file trans­fer eas­ier be­tween iPhones and Android de­vices, and starts rolling out to­day to the Pixel 10 fam­ily.

We built this with se­cu­rity at its core, pro­tect­ing your data with strong safe­guards that were tested by in­de­pen­dent se­cu­rity ex­perts. It’s just one more way we’re bring­ing bet­ter com­pat­i­bil­ity that peo­ple are ask­ing for be­tween op­er­at­ing sys­tems, fol­low­ing our work on RCS and un­known tracker alerts.

We’re look­ing for­ward to im­prov­ing the ex­pe­ri­ence and ex­pand­ing it to more Android de­vices. See it in ac­tion on the Pixel 10 Pro in this video, and try it out for your­self!

Preserving code that shaped gen­er­a­tions: Zork I, II, and III go Open Source

A game that changed how we think about play

Today, we’re pre­serv­ing a cor­ner­stone of gam­ing his­tory that is near and dear to our hearts. Together, Microsoft’s Open Source Programs Office (OSPO), Team Xbox, and Activision are mak­ing Zork I, Zork II, and Zork III avail­able un­der the MIT License. Our goal is sim­ple: to place his­tor­i­cally im­por­tant code in the hands of stu­dents, teach­ers, and de­vel­op­ers so they can study it, learn from it, and, per­haps most im­por­tantly, play it.

A game that changed how we think about play

When Zork ar­rived, it did­n’t just ask play­ers to win; it asked them to imag­ine. There were no graph­ics, no joy­stick, and no sound­track, only words on a screen and the play­er’s cu­rios­ity. Yet those words built worlds more vivid than most games of their time. What made that pos­si­ble was­n’t just clever writ­ing, it was clever en­gi­neer­ing.

Beneath that world of words was some­thing qui­etly rev­o­lu­tion­ary: the Z-Machine, a cus­tom-built en­gine. Z-Machine is a spec­i­fi­ca­tion of a vir­tual ma­chine, and now there are many Z-Machine in­ter­preters that we used to­day that are soft­ware im­ple­men­ta­tions of that VM. The orig­i­nal main­frame ver­sion of Zork was too large for early home com­put­ers to han­dle, so the team at Infocom made a prac­ti­cal choice. They split it into three games ti­tled Zork I, Zork II, and Zork III, all pow­ered by the same un­der­ly­ing sys­tem. This also meant that in­stead of re­build­ing the game for each plat­form, they could use the Z-Machine to in­ter­pret the same story files on any com­puter. That de­sign made Zork one of the first games to be truly cross-plat­form, ap­pear­ing on Apple IIs, IBM PCs, and more.

Game preser­va­tion takes many forms, and it’s im­por­tant to con­sider re­search as well as play. The Zork source code de­serves to be pre­served and stud­ied. Rather than cre­at­ing new repos­i­to­ries, we’re con­tribut­ing di­rectly to his­tory. In col­lab­o­ra­tion with Jason Scott, the well-known dig­i­tal archivist of Internet Archive fame, we have of­fi­cially sub­mit­ted up­stream pull re­quests to the his­tor­i­cal source repos­i­to­ries of Zork I, Zork II, and Zork III. Those pull re­quests add a clear MIT LICENSE and for­mally doc­u­ment the open-source grant.

Accompanying doc­u­men­ta­tion where avail­able, such as build notes, com­ments, and his­tor­i­cally rel­e­vant files.

Clear li­cens­ing and at­tri­bu­tion, via MIT LICENSE.txt and repos­i­tory-level meta­data.

This re­lease fo­cuses purely on the code it­self. It does not in­clude com­mer­cial pack­ag­ing or mar­ket­ing ma­te­ri­als, and it does not grant rights to any trade­marks or brands, which re­main with their re­spec­tive own­ers. All as­sets out­side the scope of these ti­tles’ source code are in­ten­tion­ally ex­cluded to pre­serve his­tor­i­cal ac­cu­racy.

More than forty years later, Zork is still alive and eas­ier than ever to play. The games re­main com­mer­cially avail­able via The Zork Anthology on Good Old Games. For those who en­joy a more hands on ap­proach, the games can be com­piled and run lo­cally us­ing ZILF, the mod­ern Z-Machine in­ter­preter cre­ated by Tara McGrew. ZILF com­piles ZIL files into Z3s that can be run with Tara’s own ZLR which is a sen­tence I never thought I’d write, much less say out loud! There are a huge num­ber of won­der­ful Z-machine run­ners across all plat­forms for you to ex­plore.

Here’s how to get started run­ning Zork lo­cally with ZILF. From the com­mand line, com­pile and as­sem­bly the zork1.zil into a runnable z3 file.

Then run your Z3 file in a Zmachine run­ner. I’m us­ing Windows Frotz from David Kinder based on Stefan Jokisch’s Frotz core:

Or, if you’re of a cer­tain age as I am, you can ap­ply a CRT fil­ter to your Terminal and use a CLI im­ple­men­ta­tion of a Zmachine like Matthew Darby’s “Fic” writ­ten in Python:

We will use the ex­ist­ing his­tor­i­cal repos­i­to­ries as the canon­i­cal home for Zork’s source. Once the ini­tial pull re­quests land un­der the MIT License, con­tri­bu­tions are wel­come. We chose MIT for its sim­plic­ity and open­ness be­cause it makes the code easy to study, teach, and build upon. File is­sues, share in­sights, or sub­mit small, well-doc­u­mented im­prove­ments that help oth­ers learn from the orig­i­nal de­sign. The goal is not to mod­ern­ize Zork but to pre­serve it as a space for ex­plo­ration and ed­u­ca­tion.

Zork has al­ways been more than a game. It is a re­minder that imag­i­na­tion and en­gi­neer­ing can out­last gen­er­a­tions of hard­ware and play­ers. Bringing this code into the open is both a cel­e­bra­tion and a thank you to the orig­i­nal Infocom cre­ators for in­vent­ing a uni­verse we are still ex­plor­ing, to Jason Scott and the Internet Archive for decades of stew­ard­ship and part­ner­ship, and to col­leagues across Microsoft OSPO, Xbox, and Activision who helped make open source pos­si­ble.

In October, I re­ported two se­cu­rity is­sues to Okta’s au­th0/​nex­tjs-au­th0 pro­ject, here and here. The lat­ter bug, an oauth pa­ra­me­ter in­jec­tion, al­lows for a range of types of abuse, like scop­ing to­kens for un­in­tended ser­vices, set­ting redi­rec­t_uri and scope to ar­bi­trary val­ues to leak to­kens, and so on.

The patch was sim­ple enough, so I opened a PR:

All’s well that ends well, right? Obviously, no.

The PR, 3 weeks later, was closed by the main­tainer, an au­th0 (an Okta com­pany) em­ployee, with the fol­low­ing com­ment:

This change is su­per­seded by #2413. This was done to en­sure that com­mits are signed. Orignal con­tri­bu­tion his­tory has been pre­served. Hence clos­ing this PR now.

Hmm, let’s take a look at that PR:

Hmm. That patch looks fa­mil­iar. And who is Simen Olsen?

no it has­n’t. I don’t know who “Simen A. W. Olsen my@simen.io” is but it is­n’t me and my com­mit here does­n’t ref­er­ence that name or email ad­dress at all. Was it ai gen­er­ated or some­thing?

Of course, the an­swer was: yes. It was AI slop. Just like my pre­vi­ous post about gixy-ng (a fun read for any­body deal­ing with ng­inx), the de­vel­oper had used CoPilot to some­bow gen­er­ate their patches:

Hi @MegaManSec I sin­cerely apol­o­gize for this at­tri­bu­tion er­ror.

Can con­firm that an AI work­flow was used to cre­ated the re­based com­mit, which got con­fused with OP de­tails. I’ve added a cor­rec­tion to #2413, and will en­sure the changelog is up­dated.

Thank you for call­ing this out, we’ll make sure this does­n’t hap­pen again.

Not only did the main­tainer state the above, they also used AI to gen­er­ate the re­sponse! In a now-deleted com­ment, they clearly used some AI to re­spond to my com­plaint:

With the clas­sic ChatGPT “you are ab­solutely cor­rect”, it’s pretty frus­trat­ing that this de­vel­oper used AI to:

Take my re­port/​PR and com­mit it them­selves.

Used AI to com­mit it, re­mov­ing my at­tri­bu­tion.

Used AI to “apologise” for us­ing AI, then stated that “it won’t hap­pen again” — (yeah right; please pro­vide a de­tailed ex­pla­na­tion how you’re go­ing to en­sure that, when clearly a 1-line code change is too much for your AI to han­dle with­out break­ing).

Refused to fix the com­mit to re­move the in­valid / AI-generated-slop de­tails, and add back mine.

I would ap­pre­ci­ate force-push­ing a fix for the com­mit to prop­erly in­clude my in­for­ma­tion in the com­mit.

I was told that they can­not change it. That seems like a copy­right in­fringe­ment to me: tak­ing some­body else’s code, then chang­ing the au­thor’s name?

What I re­ally find the most in­ter­est­ing is re­ally how this AI slop even came to be. I can­not find any ref­er­ence to the email ad­dress “my@simen.io” any­where on­line. On GitHub, the only ref­er­ence to this email ad­dress is from the nex­tjs-au­th0 PR. Simen Olsen has never con­tributed to any of the nex­tjs-au­th0 repos­i­to­ries as far as I can tell (searching org:au­th0 au­thor:sime­nan­dre on GitHub), and that does­n’t even seem to be their real email ad­dress. so was this some type of ai hal­lu­ci­na­tion? And why? The code change was tiny. I just to­tally don’t get it: I have lit­er­ally never had any AI tool­ing fail like this and come up with some other per­son’s (fake) con­tact de­tails. It’s sim­ply ab­surd; are au­th0’s en­gi­neers us­ing some ex­tremely (extremely) low qual­ity lo­cal model or some­thing? If ChatGPT failed like this for me even once every thou­sand times, I would sim­ply never use it again.

In the end, at the time of writ­ing this, the au­th0/​nex­tjs-au­th0 main­tainer, Tushar Pandey, who made all of these mis­takes, has not fixed at­tri­bu­tion mis­take in the com­mit his­tory. In ad­di­tion to this, that first bug, which al­lows for ar­bi­trary ac­count hi­jack­ing in this soft­ware, has been fixed af­ter 3 weeks, with new ver­sions of the nex­tjs-au­th0 soft­ware re­leased, but Okta’s se­cu­rity peo­ple stat­ing that “unless you cre­ate a video abus­ing this vul­ner­a­bil­ity, we aren’t go­ing to ac­cept this as a se­cu­rity is­sue” — LMAO; “yeah, it’s a vul­ner­a­bil­ity, we fixed in the code, it can be used to takeover ac­counts, but you need to cre­ate a video”. Hilarious. That’s just an­other case to add to my list of hi­lar­i­ous prob­lems re­lated to re­port­ing se­cu­rity is­sue, that my next post will doc­u­ment.

The f32 is an ul­tra-com­pact ESP32 de­vel­op­ment board de­signed to mount di­rectly be­hind a USB-C re­cep­ta­cle. The PCB mea­sures just 9.85 mm x 8.45 mm. It’s pow­ered by the ESP32-C3FH4 mi­cro­con­troller and was cre­ated pri­mar­ily for re­search and as a bit of a stress test for the ESP32, since it in­ten­tion­ally ig­nores many stan­dard de­sign guide­lines. There’s only one ex­posed GPIO and it is con­nected to an on­board LED, so most of the de­vel­op­ment on here would be more catered for wifi/​web.

To test the f32 an ex­am­ple ap­pli­ca­tion was cre­ated that users can in­ter­act with. The ap­pli­ca­tion turns the f32 into a cap­tive por­tal so when it’s pow­ered on it will show up as an open ac­cess point that the user can se­lect from avail­able WiFi net­works. The user is then au­to­mat­i­cally sent to the f32′s con­trol page where they can in­ter­act with some of its ba­sic func­tion­al­ity such as turn­ing on an LED or scan­ning for sur­round­ing WiFi net­works. There’s also an “About” page that pro­vides a small overview of the de­vice. Below are some screen­shots and a gif of in­ter­act­ing with the de­vice.

Initially the f32 did­n’t seem to want to work. I could­n’t get it to con­nect to any net­works or broad­cast it’s own net­work. Im 100% sure this is due to the poor an­tenna cir­cuitry or lack of, but I did man­age to get it func­tional af­ter adding an ad­di­tional tiny an­tenna onto the chip an­tenna as seen in the pic­ture be­low. This was just a piece of bent wire sol­dered to the end lead and float­ing above the first lead.

Since I don’t have fancy sig­nal test­ing equip­ment I re­lied on some man­ual test­ing such as see­ing if I can still con­nect to the de­vice and con­trol the LED. In a clear line of sight test with the f32 placed about 3ft off the ground I was able to con­nect and per­form scans/​con­trol the LED at roughly 120ft! This can be seen in my highly nec­es­sary de­pic­tion be­low.

The PCB was de­signed us­ing DipTrace and man­u­fac­tured by PCBWay with a board thick­ness of 0.6mm, min hole size of 0.2mm, and min track/​spac­ing of 4/4mil. At the time of mak­ing this it only cost $10.75 for 5 boards shipped! That still blows my mind. PCBWay does also of­fer as­sem­bly ser­vices, but I chose to as­sem­ble this at home and suf­fer a bit. This took a bit of trial and er­ror with such small parts, but I de­cided the best way for me was to ditch the sten­cil and make flux my best friend.

* Send the ger­ber file f32_ger­ber.zip found in the hard­ware folder to PCBWay with the specs men­tioned above.

* Order the com­po­nents noted in f32_bom.pdf. These parts can be found on both DigiKey and Mouser ex­cept the an­tenna. I don’t re­mem­ber where I had orig­i­nally or­dered them, but I be­lieve they are CrossAir CA-C03.

** Tip: Always or­der more than you need, es­pe­cially with com­po­nents as small as these.

* ** Tip: Always or­der more than you need, es­pe­cially with com­po­nents as small as these.

* Clean the pcb re­ally well with 99% Alcohol.

* Starting with the top side (Antenna side) ap­ply a thin layer of sol­der­ing flux across the en­tire board us­ing a tooth pick.

* Using a sol­der­ing iron with a fine tip ap­ply some sol­der to the tip and then go across all the ex­posed pads.

* Clean the board again with 99% al­co­hol and ver­ify all the pads on this side have some sol­der on them.

* Apply an­other thin layer of flux to the same side.

* Using tweez­ers and a mi­cro­scope/​loupe start plac­ing the top com­po­nents fol­low­ing the ref­er­ence guide f32_ref­er­ence.pdf.

* Gently move the board onto the sol­der­ing hot­plate or use the re­work sta­tion to heat the sol­der back up and watch the com­po­nents wig­gle into place.

* Repeat with Bottom side.

Bottom side must be done us­ing a re­work hot air gun, not pos­si­ble with hot­plate.

* Bottom side must be done us­ing a re­work hot air gun, not pos­si­ble with hot­plate.

After as­sem­bly you can use ESP-IDF VSCode ex­ten­sion or Arduino and up­load what­ever you’d like to the board or you can up­load my ex­am­ple ap­pli­ca­tion us­ing the steps be­low.

* Make sure you are in the base di­rec­tory of this repo and have ac­cess to es­p­tool.py.

* Make sure your es­p­tool ver­sion is at v4+

* Run the fol­low­ing com­mand re­plac­ing with whichever port the de­vice is con­nected to i.e. on Windows typ­i­cally some­thing like COM5 or on Linux /dev/ttyACM0

Well that’s up to you to de­cide. I started this pro­ject for some per­sonal re­search and also a fun learn­ing ex­pe­ri­ence. I had al­ways wanted a pro­ject that used 01005 com­po­nents ever since I had ac­ci­den­tally or­dered some years ago. Whatever you choose to use it for, please note that this de­sign in­ten­tion­ally ne­glects sev­eral fun­da­men­tal com­po­nents such as proper de­cou­pling ca­pac­i­tors, an an­tenna match­ing cir­cuit, USB ter­mi­na­tion re­sis­tors, and likely more. It does func­tion, but it’s in­ten­tion­ally bare.

* Expose more GPIOs on the sides of the PCB to make it a mount­able PCB.

Lastly, fun co­in­ci­dence, the ESP32 chip, the an­tenna, and the LDO all are “C3” mod­els!

ravynOS is a new open source OS pro­ject that aims to pro­vide a sim­i­lar ex­pe­ri­ence and some com­pat­i­bil­ity with ma­cOS on x86-64 (and even­tu­ally ARM) sys­tems. It builds on the solid foun­da­tions of FreeBSD, ex­ist­ing open source pack­ages in the same space, and new code to fill the gaps.

* Source com­pat­i­bil­ity with ma­cOS ap­pli­ca­tions (i.e. you could com­pile a Mac ap­pli­ca­tion on ravynOS and run it)

* Similar GUI metaphors and fa­mil­iar UX (file man­ager, ap­pli­ca­tion launcher, top menu bar that re­flects the open ap­pli­ca­tion, etc)

* Compatible with ma­cOS folder lay­outs (/Library, /System, /Users, /Volumes, etc) and per­haps filesys­tems (HFS+, APFS) as well as fully sup­port­ing ZFS

* Self-contained ap­pli­ca­tions in App Bundles, AppDirs, and AppImage files - an in­staller-less ex­pe­ri­ence for /Applications

* Mostly main­tain com­pat­i­bil­ity with the FreeBSD base sys­tem and X11 - a stan­dard Unix en­vi­ron­ment un­der the hood

* Pleasant to use, se­cure, sta­ble, and per­for­mant

Please visit ravynos.com for more info: Release Notes | Screenshots | FAQ

* Can you help build the dream? See the cur­rent pro­jects/​needs in CONTRIBUTING.md!

This is the top level of the FreeBSD source di­rec­tory.

FreeBSD is an op­er­at­ing sys­tem used to power mod­ern servers, desk­tops, and em­bed­ded plat­forms. A large com­mu­nity has con­tin­u­ally de­vel­oped it for more than thirty years. Its ad­vanced net­work­ing, se­cu­rity, and stor­age fea­tures have made FreeBSD the plat­form of choice for many of the busiest web sites and most per­va­sive em­bed­ded net­work­ing and stor­age de­vices.

For copy­right in­for­ma­tion, please see the file COPYRIGHT in this di­rec­tory. Additional copy­right in­for­ma­tion also ex­ists for some sources in this tree - please see the spe­cific source di­rec­to­ries for more in­for­ma­tion.

The Makefile in this di­rec­tory sup­ports a num­ber of tar­gets for build­ing com­po­nents (or all) of the FreeBSD source tree. See build(7), con­fig(8), FreeBSD hand­book on build­ing user­land, and Handbook for ker­nels for more in­for­ma­tion, in­clud­ing set­ting make(1) vari­ables.

For in­for­ma­tion on the CPU ar­chi­tec­tures and plat­forms sup­ported by FreeBSD, see the FreeBSD

web­site’s Platforms page.

For of­fi­cial FreeBSD bootable im­ages, see the re­lease page.

For in­for­ma­tion on syn­chro­niz­ing your source tree with one or more of the FreeBSD Project’s de­vel­op­ment branches, please see FreeBSD Handbook.

After build­ing a soft­ware com­pany to a multi-bil­lion dol­lar exit, I made the jump to hard­ware. Now I’m work­ing on car­bon re­moval + steel at Charm Industrial, and elec­tric long-haul truck­ing with Revoy. It’s epi­cally fun to be build­ing in the real world, but lit­tle did I ex­pect that more than half the cost of build­ing a hard­ware com­pany would come from reg­u­la­tory bot­tle­necks. Despite a huge push for cli­mate fixes and the bi­par­ti­san geopo­lit­i­cal de­sire to bring in­dus­try back to the USA, I’ve been shocked to find that the sin­gle biggest bar­rier—by far—is over-reg­u­la­tion from the mas­sive depth of bu­reau­cracy.

Hardtech com­pa­nies of all fla­vors are be­ing forced to burn through lim­ited cap­i­tal while they wait for reg­u­la­tory clar­ity and/​or per­mits. This cre­ates a con­stant cy­cle of cost in­creases that ul­ti­mately flows to con­sumers, it low­ers in­vest­ment in the US man­u­fac­tur­ing and in­dus­trial base, it de­lays in­no­v­a­tive new hard­ware get­ting into the hands of con­sumers and busi­nesses, and at the end of the day, it leaves us all worse off, stuck with a qual­ity of life pegged to tech­nol­ogy de­vel­oped decades ago.

Regulatory de­lays and bot­tle­necks have added mil­lions of pounds of pol­lu­tants like PM2.5, NOₓ and CO₂ to our air from the con­tin­u­a­tion of busi­ness as usual, in­stead of the de­ploy­ment of clean tech­nolo­gies from my two hardtech ef­forts alone. While CO₂ is a long-term cli­mate is­sue, PM2.5 and NOₓ are im­me­di­ate ma­jor dri­vers of asthma and ex­cess mor­bid­ity. Both op­er­a­tions have high bi­par­ti­san ap­peal—and we’ve never been de­nied a per­mit—be­cause we’re fun­da­men­tally clean­ing up things that mat­ter to every­one: dirty air, wild­fires, or­phaned oil wells. Revoy is also help­ing de­flate the cost of long-haul freight. But none of that has made get­ting free­dom to op­er­ate easy. For cre­ative new tech­nolo­gies the de­fault an­swer is “no” be­cause there is­n’t a clear path to per­mit­ting at all, and fig­ur­ing out that path it­self takes years — time that star­tups can’t af­ford to wait.

Regulation ob­vi­ously has a crit­i­cal role in pro­tect­ing peo­ple and the en­vi­ron­ment, but the sheer vol­ume, over-speci­ficity and some­times am­bi­gu­ity of those same reg­u­la­tions is now ac­tively work­ing against those goals! We’re un­in­ten­tion­ally block­ing the very things that would im­prove our en­vi­ron­ment. We’ve be­come a so­ci­ety that blocks all things, and we need to be a so­ci­ety that builds great things every day. The rest of this ar­ti­cle gets very spe­cific about the as­tro­nom­i­cal costs reg­u­la­tions are im­pos­ing on us as a so­ci­ety, and the mas­sive pos­i­tive im­pact that could be un­leashed by cut­ting back reg­u­la­tion that is work­ing against new, cost-sav­ing, cre­ative tech­nol­ogy that could also be mak­ing peo­ple and the en­vi­ron­ment healthy again.

To make it con­crete: both Charm and Revoy are cap­i­tal-ef­fi­cient hardtech com­pa­nies, but Charm will spend low hun­dreds of mil­lions to get to breakeven, and Revoy will spend tens of mil­lions. In both cases, more than half of the to­tal cost of build­ing each com­pany has gone to coun­ter­pro­duc­tive reg­u­la­tory bur­den. I’m hell­bent on push­ing through these bar­ri­ers, but the un­spo­ken re­al­ity is that our reg­u­la­tory morass is the deathbed of thou­sands of hardtech com­pa­nies that could be dras­ti­cally im­prov­ing our lives. We must un­leash them.

$300M in Societal Cost & $125M in Burden for Charm

Charm pro­duces and de­liv­ers ver­i­fied car­bon re­moval to com­pa­nies like Google, Microsoft and JPMorgan. Charm’s break­through was re­al­iz­ing that you could take CO₂ cap­tured in farm & forestry plant residues, con­vert it into a car­bon-rich, BBQ sauce-like liq­uid (it’s lit­er­ally the smoke fla­vor in BBQ sauce), and in­ject it into old oil wells to per­ma­nently re­move car­bon from the at­mos­phere. This has all kinds of co-ben­e­fits like re­duc­ing the mas­sive over­bur­den of wild­fire fu­els, clean­ing up & plug­ging nasty or­phaned oil wells, and im­prov­ing PM2.5 and NOₓ air qual­ity by avoid­ing that bio­mass be­ing burned in­stead.

And yet… there was a hangup: what kind of in­jec­tion well is this? Should it be per­mit­ted as a Class I dis­posal, Class II oil­field dis­posal, or Class V ex­per­i­men­tal? This ques­tion on per­mit­ting path took four years to an­swer. Four years to de­cide which path to use, not even the ac­tual per­mit! It took this long be­cause reg­u­la­tors are struc­turally faced with no up­side, only down­side le­gal risk in tak­ing a for­mal po­si­tion on some­thing new. Even when we’d done an enor­mous amount of lab and field work with bio-oil to un­der­stand its safety and be­hav­ior at sur­face and sub­sur­face con­di­tions. A reg­u­la­tor faces lit­tle cost to mov­ing in­cred­i­bly cau­tiously, but a ma­jor cost if they ap­prove some­thing that trig­gers ac­tivist push­back.

In the end, we’re grate­ful that—even­tu­ally—a state reg­u­la­tor took the reins and re­viewed, man­aged, and is­sued the first-ever Class V bio-oil se­ques­tra­tion per­mit, through what was still an in­cred­i­bly com­plex and de­tailed 14-month re­view process.

Now imag­ine that, in­stead of the 5.5 years from first con­tact to is­sued per­mit, it had only taken the 6 months it ac­tu­ally re­quired to get every­one across the reg­u­la­tory es­tab­lish­ment to agree on a Class V path­way, we would have had 5 ad­di­tional years op­er­at­ing the well. That’s the equiv­a­lent, from our real sup­ply chain, of sink­ing at least 30,000 tonnes of car­bon per year at $600/tonne. Looking only at this one as­pect, this de­lay came with a $90M price tag for Charm. We’ve also spent un­told mil­lions on reg­u­la­tory af­fairs at all lev­els of gov­ern­ment, not to men­tion the missed ac­cel­er­a­tion in sales, and other di­rect hard costs spent in R&D and pro­cess­ing bio-oil for in­ef­fi­cient and ex­pen­sive in­jec­tion into salt cav­erns in­stead.

But the pub­lic health bur­den cre­ated by this reg­u­la­tory slow­ness is where it gets re­ally crazy. This one reg­u­la­tory de­lay meant we all got sub­jected to de­creased air qual­ity from an ad­di­tional 30,000 tonnes per year of pile burn­ing. The re­sult­ing par­tic­u­late emis­sions alone are es­ti­mated to have caused a mind­blow­ing $40m/year in health­care costs. This is $200M in ad­di­tional health­care bur­den over those five years, mostly borne by Medicare and Medicaid. There are ad­di­tional costs to NOₓ emis­sions and more that take it to $300M.

In to­tal, the to­tal cost to so­ci­ety of this sin­gle reg­u­la­tory de­lay will be about $400M: $120-150M of un­nec­es­sary cost to Charm, and the bulk of it—$300M or so—borne by the pub­lic in health­care costs. I’m not shar­ing these num­bers to com­plain or make ex­cuses; Charm is still on the path to hav­ing a huge im­pact and we’re among the lucky few that can sur­vive these de­lays. What pains me most is the 5 years of lost car­bon re­moval and pol­lu­tant re­duc­tion, and the com­pound­ing ef­fect that has on all our health and health­care costs. Over-regulation is now work­ing against the very things it’s in­tended to pro­tect.

Regulators do their ab­solute best with the sys­tem they have, but the com­bined ef­fects of: (1) ex­tremely de­tailed and com­plex reg­u­la­tion, (2) chaotic bud­gets and un­der­staffing that dis­rupt an ef­fi­cient process, and (3) end­less law­suits against reg­u­la­tors since 1970s-era Naderism have cre­ated an at­mos­phere of fear. If we want to solve the cli­mate cri­sis, build abun­dance, lower costs, and gen­er­ate wealth for all, this has to change. We need to delete and sim­plify reams of reg­u­la­tions. We need to pay reg­u­la­tors well, and we need to trust our reg­u­la­tors to op­er­ate quickly and de­ci­sively by putting rea­son­able lim­its on end­less ac­tivist le­gal chal­lenges.

Revoy’s break­through was re­al­iz­ing that you could lower long-haul freight costs and elec­trify long-haul semi trucks by leav­ing the diesel trac­tor in place and drop­ping an elec­tric pow­er­train onto the back of the semi. Today, we boost semis from 7 mpg to 120 mpg, dri­ving a 94% re­duc­tion in fuel con­sump­tion. This slashes emis­sions that neg­a­tively im­pact both air qual­ity and cli­mate.

And yet again… a hangup: what ex­actly is this elec­tric doohickey? Is it a truck? A trailer? Something else? It was clear from the reg­u­la­tions that it was a “converter dolly”. But get­ting com­plete align­ment on that sim­ple fact across an al­pha­bet soup of gov­ern­ment agen­cies span­ning both fed­eral and state—NHTSA, FMCSA, FHWA, state tran­sit au­thor­i­ties, air qual­ity man­age­ment dis­tricts, state DMVs, high­way pa­trols and more—took years.

A “powered con­verter dolly” is­n’t even a new thing! Here’s one from the six­ties that ran on diesel to help trucks get over moun­tain passes:

There were some bright spots. The Federal Motor Carrier Safety Administration (FMCSA) and the National Highway Transportation Safety Administration (NHTSA) quickly con­verged on in­for­mal de­f­i­n­i­tional clar­ity, and then even­tu­ally a Highway Patrol Captain who was ea­ger to get in­no­v­a­tive elec­tric ve­hi­cles on the road pushed it through with a state DMV to reg­is­ter the first four Revoys. But bring­ing along the rest of the agen­cies, and the rest of the states, was not fast. It de­layed de­ploy­ments, soaked up hun­dreds of thou­sands of dol­lars of le­gal and lob­by­ist time (not to men­tion all the cor­re­spond­ing time on the gov­ern­ment side that all of us tax­pay­ers have to bear), and maybe most im­por­tantly… even with a for­mal memo from the Federal DOT, it is still not 100% re­solved in some states.

As one ex­am­ple, one state agency has asked Revoy to do cer­ti­fied en­gine test­ing to prove that the Revoy does­n’t in­crease emis­sions of semi trucks. And that Revoy must do this cer­ti­fi­ca­tion across every sin­gle truck en­gine fam­ily. It costs $100,000 per cer­ti­fi­ca­tion and there are more than 270 en­gine fam­i­lies for the 9 en­gines that our ini­tial part­ners use. That’s $27,000,000 for this one reg­u­la­tory item. And keep in mind that this is to cer­tify that a de­vice—whose sole rea­son for ex­is­tence is to cut pol­lu­tion by >90%, and which has demon­stra­bly done so across nearly 100,000 miles of test­ing and op­er­a­tions—is not in­creas­ing the emis­sions of the truck. It’s a com­plete waste of money for every­one.

And that $27M dol­lar cost does­n’t in­clude the cost to so­ci­ety. This over-reg­u­la­tion will de­lay de­ploy­ment of EV trucks by years, in­creas­ing NOₓ and PM 2.5 air pol­lu­tion ex­po­sure for many of so­ci­ety’s least well-off who live near free­ways. The de­layed de­ploy­ment will also in­crease CO₂ emis­sions that threaten the cli­mate and en­vi­ron­ment. Revoy’s Founder (Ian Rust) and I ac­tu­ally dis­agree on what ex­actly it is about the reg­u­la­tory en­vi­ron­ment that needs to change, but we agree it’s com­pletely bro­ken and hurt­ing both peo­ple and the planet.

In every in­ter­ac­tion I have with reg­u­la­tors, I’m re­minded that they’re good peo­ple do­ing god’s work op­er­at­ing in a fun­da­men­tally bro­ken sys­tem. A reg­u­la­tory sys­tem that struc­turally in­sists on le­gal­is­tic, ul­tra-ex­treme cau­tion is bound to gen­er­ate a mas­sive neg­a­tive re­turn for so­ci­ety.

If we had a reg­u­la­tory sys­tem that could move fast to ex­per­i­ment with cre­ative new tech­nolo­gies, we’d live in a world where our en­vi­ron­ment gets cleaned up faster, where awe­some new hard­ware was con­stantly im­prov­ing our lives by mak­ing things bet­ter and cheaper, and where large-scale hardtech in­no­va­tion hap­pened here at home in the USA, not in China.

As we col­lec­tively work to build more man­u­fac­tur­ing ca­pac­ity at home and build the next wave of tech­nolo­gies to power the econ­omy, we need to grap­ple with the real bot­tle­necks hold­ing us back. I hope other hardtech founders will pub­licly share more of their sto­ries as well (the sto­ries I’ve heard in pri­vate would shock you). Props to Blake Scholl for do­ing so.

We need a come-to-je­sus about reg­u­la­tory lim­its, time­lines, and scope. Yes, we need ba­sic and strong pro­tec­tions for clear harms, but we need to un­leash every hard­work­ing American, not just a few com­pa­nies with mas­sive fund­ing, to in­vent and build hard­ware again. We need to com­bine many ap­proaches to get there: ex­pe­dited re­views for new tech­nol­ogy, free­dom to op­er­ate by de­fault, per­mits by right-not-process, delet­ing as many reg­u­la­tory steps as pos­si­ble, and more. CA YIMBY’s suc­cess­ful push to pass a del­uge of hous­ing ac­cel­er­a­tion laws in the past two years could serve as a model. America build­ing things again is the foun­da­tion of a pros­per­ous, pow­er­ful, and clean America.

Choose your pref­er­ences above and click Generate to view boards

FEX al­lows you to run x86 ap­pli­ca­tions on ARM64 Linux de­vices, sim­i­lar to qemu-user and box64. It of­fers broad com­pat­i­bil­ity with both 32-bit and 64-bit bi­na­ries, and it can be used along­side Wine/Proton to play Windows games.

It sup­ports for­ward­ing API calls to host sys­tem li­braries like OpenGL or Vulkan to re­duce em­u­la­tion over­head. An ex­per­i­men­tal code cache helps min­i­mize in-game stut­ter­ing as much as pos­si­ble. Furthermore, a per-app con­fig­u­ra­tion sys­tem al­lows tweak­ing per­for­mance per game, e.g. by skip­ping costly mem­ory model em­u­la­tion. We also pro­vide a user-friendly FEXConfig GUI to ex­plore and change these set­tings.

On the tech­ni­cal side, FEX fea­tures an ad­vanced bi­nary re­com­piler that sup­ports all mod­ern ex­ten­sions of the x86(-64) in­struc­tion set, in­clud­ing AVX/AVX2. The heart of this re­com­piler is a cus­tom IR that al­lows us to gen­er­ate more op­ti­mized code than a tra­di­tional splat­ter JIT. A com­pre­hen­sive sys­tem call trans­la­tion layer takes care of dif­fer­ences be­tween the em­u­lated and host op­er­at­ing sys­tems and im­ple­ments even niche fea­tures like sec­comp. A mod­u­lar core en­ables FEX to be used as a WoW64/ARM64EC back­end in Wine.

Try it out

You would think do­ing this month af­ter month we would even­tu­ally run out of things to work on, but in true em­u­la­tor fash­ion the work never ends. Let’s jump in to what has changed for the re­lease this month!

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We’re just gonna kick out this lit­tle re­lease and be on our way. There might be some in­ter­est­ing things this month, read and find out!

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After last mon­th’s enor­mous im­prove­ments, this re­lease will look quite tame in com­par­i­son. Although we still did a bunch of work, so let’s dive in.

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Older Posts

Language mod­els are of­ten treated as snap­shots—brief cap­tures of a long and care­fully cu­rated de­vel­op­ment process. But shar­ing only the end re­sult ob­scures the rich con­text needed to mod­ify, adapt, and ex­tend a mod­el’s ca­pa­bil­i­ties. Many mean­ing­ful ad­just­ments re­quire in­te­grat­ing do­main-spe­cific knowl­edge deep within the de­vel­op­ment pipeline, not merely at the fi­nal stage. To truly ad­vance open AI de­vel­op­ment and re­search, the en­tire model flow — not just its end­point — should be ac­ces­si­ble and cus­tomiz­able. The model flow is the full life­cy­cle of an LM: every stage, check­point, dataset, and de­pen­dency re­quired to cre­ate and mod­ify it. By ex­pos­ing this com­plete process, the goal is to en­gen­der greater trust and en­able more ef­fec­tive adap­ta­tion, col­lab­o­ra­tion, and in­no­va­tion.

With to­day’s re­lease of Olmo 3, we’re em­pow­er­ing the open source com­mu­nity with not only state-of-the-art open mod­els, but the en­tire model flow and full trace­abil­ity back to train­ing data.

At its cen­ter is Olmo 3-Think (32B), the best fully open 32B-scale think­ing model that for the first time lets you in­spect in­ter­me­di­ate rea­son­ing traces and trace those be­hav­iors back to the data and train­ing de­ci­sions that pro­duced them. Olmo 3 is a fam­ily of com­pact, dense mod­els at 7 bil­lion and 32 bil­lion pa­ra­me­ters that can run on every­thing from lap­tops to re­search clus­ters.

Olmo 3-Base (7B, 32B) is our most pow­er­ful base model yet. When eval­u­ated on our ex­panded, di­verse eval­u­a­tion suite, Olmo 3-Base de­liv­ers the strongest per­for­mance among fully open base mod­els — where train­ing data, code, and weights are all pub­licly avail­able, like Stanford’s Marin and Swiss AI’s Apertus — and achieves com­pet­i­tive per­for­mance with some of the best open-weights base mod­els of com­pa­ra­ble size and ar­chi­tec­ture, in­clud­ing Qwen 2.5 and Gemma 3. Achieving strong re­sults in pro­gram­ming, read­ing com­pre­hen­sion, and math prob­lem solv­ing, Olmo 3-Base main­tains per­for­mance at ex­tended con­text lengths (~up to 65K to­kens)—pro­vid­ing a ver­sa­tile foun­da­tion for con­tin­ued pre­train­ing, tar­geted fine-tun­ing, and re­in­force­ment learn­ing and mak­ing it easy to build in spe­cial­ized ca­pa­bil­i­ties like rea­son­ing, tool use (function call­ing), and in­struc­tion fol­low­ing through post-train­ing. Olmo 3-Think (7B, 32B) is our flag­ship post-trained rea­son­ing set built on Olmo 3-Base. At a time when few or­ga­ni­za­tions are re­leas­ing truly open mod­els at this scale, Olmo 3-Think (32B) serves as a work­horse for RL re­search, long-hori­zon rea­son­ing, and other ad­vanced ex­per­i­ments that re­quire sub­stan­tial com­pute. On our suite of rea­son­ing bench­marks (discussed be­low), it’s the strongest fully open think­ing model we’re aware of, nar­row­ing the gap to the best open-weight mod­els of sim­i­lar scale — such as Qwen 3 32B — while train­ing on roughly 6x fewer to­kens. Olmo 3-Think (7B) brings the same de­sign and train­ing ap­proach to an even more ef­fi­cient form fac­tor, sur­fac­ing in­ter­me­di­ate think­ing steps for com­plex prompts while mak­ing open, in­spectable rea­son­ing ac­ces­si­ble on more mod­est hard­ware.Olmo 3-Instruct (7B) is a chat and quick-re­sponse fo­cused post-train of Olmo 3-Base that han­dles multi-turn, in­struc­tion-fol­low­ing, tool use, and more. In our eval­u­a­tions, it matches or out­per­forms open-weight mod­els in­clud­ing Qwen 2.5, Gemma 3, and Llama 3.1, and nar­rows the gap with Qwen 3 model fam­i­lies at a sim­i­lar scale—de­liv­er­ing a strong, fully open al­ter­na­tive for high-qual­ity con­ver­sa­tional and tool-us­ing agents.Olmo 3-RL Zero (7B), is a fully open re­in­force­ment learn­ing path­way built on Olmo 3-Base, de­signed to boot­strap com­plex rea­son­ing be­hav­iors and en­able clear bench­mark­ing of RL al­go­rithms. We re­lease four se­ries of check­points from do­main-fo­cused train­ing on math, code, in­struc­tion fol­low­ing, and gen­eral chat, en­abling care­ful study of re­in­force­ment learn­ing with ver­i­fi­able re­wards (RLVR).

Instead of a sin­gle set of frozen weights, Olmo 3 of­fers mul­ti­ple, fully doc­u­mented paths through de­vel­op­ment: the Instruct path for every­day chat and tool use, the RL Zero path for RL ex­per­i­men­ta­tion from base mod­els, and the Think/reasoning path for mod­els that lever­age in­fer­ence-time scal­ing to un­lock com­plex rea­son­ing and agen­tic be­hav­iors. Each path is a con­crete ex­am­ple of how to shape be­hav­ior from the same base model, and you’re free to fork or remix them—start with Olmo 3-Base, ex­plore your own su­per­vised fine-tun­ing (SFT) or di­rect pref­er­ence op­ti­miza­tion (DPO) recipe for in­struct-style use cases, or plug in a new RL ob­jec­tive to probe dif­fer­ent trade­offs. The flow it­self be­comes a rich, reusable ob­ject—not just a record of how we built Olmo 3, but a scaf­fold for how you can build your own sys­tems.

Click on any stage to learn more about it and down­load ar­ti­facts.

The Olmo 3 check­points we’re re­leas­ing rep­re­sent our ini­tial paths tar­get­ing our goals around rea­son­ing, tool use, and gen­eral ca­pa­bil­i­ties — we have ex­cit­ing plans for other ways to lever­age Olmo 3-Base 32B. But be­cause we’re re­leas­ing the en­tire flow, you can in­ter­vene at any point: swap in do­main-spe­cific data dur­ing mid-train­ing, ad­just post-train­ing for your use case, or build on an ear­lier check­point that bet­ter suits your needs.

As with Olmo and Olmo 2, we’re re­leas­ing all com­po­nents of the Olmo 3 flow — data, code, model weights, and check­points — un­der per­mis­sive open source li­censes.

Try Olmo 3 | Download the mod­els & data | Read the re­port

We run the Olmo 3 check­points through a broad, up­dated bench­mark suite, group­ing dozens of in­dus­try-stan­dard tasks (plus a few new ones we in­tro­duce) into sev­eral ca­pa­bil­ity clus­ters. Together, the clus­tered suite and these held-out tasks give us a ca­pa­bil­ity pro­file of Olmo 3—a clear pic­ture of how well it solves math prob­lems, codes, uses tools, an­swers gen­eral-knowl­edge ques­tions, and more.

At a high level, the Olmo 3 fam­ily de­liv­ers the strongest fully open base and think­ing mod­els we’re aware of. Olmo 3-Base 32B out­per­forms other fully open base mod­els, and Olmo 3-Think 32B emerges as the strongest fully open think­ing model.

Our re­sults were made pos­si­ble by rig­or­ous data cu­ra­tion at every stage of train­ing, a care­fully de­signed train­ing recipe for each model, and a set of new al­go­rith­mic and in­fra­struc­ture ad­vances across data pro­cess­ing, train­ing, and re­in­force­ment learn­ing. We also in­tro­duce an en­hanced re­in­force­ment learn­ing frame­work that guides the de­vel­op­ment of our mod­els and is par­tic­u­larly es­sen­tial for our think­ing mod­els. To de­sign the train­ing recipe and co­or­di­nate tar­geted im­prove­ments across a wide range of ca­pa­bil­i­ties at each stage of the model train­ing pipeline, our de­vel­op­ment frame­work bal­ances dis­trib­uted in­no­va­tion with cen­tral­ized eval­u­a­tion.

Olmo 3-Base, with a train­ing pipeline that first fo­cuses on broad cov­er­age over di­verse text, code, and math, then con­cen­trates on harder dis­tri­b­u­tions to sharpen pro­gram­ming, quan­ti­ta­tive rea­son­ing, and read­ing com­pre­hen­sion, is clearly the strongest set of fully open base mod­els in our eval­u­a­tions. It’s also ar­guably the best 32B model in the en­tire ecosys­tem of mod­els with open weights, per­form­ing im­pres­sively in pro­gram­ming, read­ing com­pre­hen­sion, math prob­lem solv­ing, and long-con­text bench­marks like RULER, which tests in­for­ma­tion re­trieval from lengthy texts. Olmo 3-Base (7B) and Olmo 3-Base (32) main­tain qual­ity at ex­tended con­text lengths and in­te­grate cleanly with RL work­flows, pro­vid­ing a ro­bust foun­da­tion for con­tin­ued pre­train­ing and post-train­ing.

Olmo 3-Think, which turns the Base into a rea­son­ing model by train­ing on multi-step prob­lems span­ning math, code, and gen­eral prob­lem solv­ing, then run­ning the think­ing SFT → think­ing DPO → RLVR model flow to elicit high-qual­ity rea­son­ing traces, com­petes with or ex­ceeds sev­eral open-weight rea­son­ing mod­els of sim­i­lar sizes. On math bench­marks, Olmo 3-Think (7B) matches Qwen 3 8B on MATH and comes within a few points on AIME 2024 and 2025, and also leads all com­par­i­son mod­els on HumanEvalPlus for cod­ing—per­form­ing strongly on MBPP and LiveCodeBench to demon­strate par­tic­u­lar strength in code-in­ten­sive rea­son­ing. On broader rea­son­ing tasks like BigBench Hard and AGI Eval English, Olmo 3-Think (7B) re­mains com­pet­i­tive with Qwen 3 8B rea­son­ing and Qwen 3 VL 8B Thinker while stay­ing fully open and slightly smaller.

For the 32B model, Olmo 3-Think scales these trends up and be­comes one of the strongest fully open rea­son­ing mod­els in its class. Olmo 3-Think (32B) ei­ther wins or sits within roughly two points of the best open-weight model on MATH, OMEGA, BigBenchHard, HumanEvalPlus, PopQA, and IFEval. It ties Qwen 3 VL 32B Thinking for the top score on the OMEGA suite while stay­ing clearly ahead of Gemma 3 27B Instruct and com­pet­i­tive with DeepSeek R1 Distill 32B on math and rea­son­ing. On broader knowl­edge and QA, Olmo 3-Think (32B) is ef­fec­tively neck-and-neck with the Qwen 3 mod­els on PopQA. And in in­struc­tion fol­low­ing, Olmo 3-Think (32B) tops this sub­set on IFEval and re­mains solid on IFBench and AlpacaEval 2 LC—offering a strong de­fault for rea­son­ing work­loads at the 32B scale.

Olmo 3-Instruct, which pro­duces shorter se­quences than the cor­re­spond­ing Olmo 3-Think mod­els to im­prove in­fer­ence ef­fi­ciency and is de­signed to fo­cus on gen­eral chat, tool use, and syn­thetic data gen­er­a­tion, out­per­forms com­pa­ra­bly-sized open-weight mod­els. Olmo 3-Instruct ties or sur­passes Qwen 2.5, Gemma 3, and Llama 3.1 in our eval­u­a­tions, and com­petes with the Qwen 3 fam­ily at sim­i­lar scale, de­liv­er­ing strong func­tion call­ing per­for­mance and in­struc­tion-fol­low­ing ca­pa­bil­i­ties in a fully open 7B model.

Olmo 3 uses a de­coder-only trans­former ar­chi­tec­ture and multi-stage train­ing pipeline. Pretraining runs in three stages—an ini­tial large-scale train­ing run that builds broad ca­pa­bil­i­ties; a mid-train­ing phase that fo­cuses on harder ma­te­r­ial like math, code, and read­ing com­pre­hen­sion; and a fi­nal long-con­text ex­ten­sion stage that trains the model on very long doc­u­ments. Together with ar­chi­tec­tural en­hance­ments, this yields a more ca­pa­ble, ef­fi­cient base for the Olmo 3 fam­ily.

Post-training then spe­cial­izes the pre­trained model for dif­fer­ent use cases. Building on Olmo 2, each path­way fol­lows a three-stage recipe — SFT, pref­er­ence tun­ing with DPO, and RLVR — but in Olmo 3, we ex­pose this as a fully doc­u­mented model flow with com­plete cus­tomiza­tion over each train­ing stage and dataset mix.

Instead of re­leas­ing only the fi­nal weights, we pro­vide check­points from each ma­jor train­ing mile­stone: the base pre­trained model, the mid-trained model af­ter tar­geted skill en­hance­ment, the long-con­text-ex­tended ver­sion, plus post-train­ing check­points for the Olmo 3-Think, Olmo 3-Instruct, and Olmo 3-RL Zero flows. You can study how ca­pa­bil­i­ties emerge over time, run ab­la­tions on spe­cific stages, and fork the model at what­ever point best fits your data, com­pute, and goals.

Compared to Olmo 2, we scaled data col­lec­tion and sig­nif­i­cantly strength­ened our dataset cu­ra­tion meth­ods. Continuing our com­mit­ment to full trans­parency, we’re re­leas­ing sev­eral new, higher-qual­ity datasets that cover every stage of base model train­ing and post-train­ing—from ini­tial learn­ing to spe­cial­ized skills like com­plex rea­son­ing and long-con­text un­der­stand­ing. This means any­one can see ex­actly what data shaped the mod­el’s ca­pa­bil­i­ties, re­pro­duce our re­sults, and reuse these datasets to train their own AI sys­tems.

Olmo 3 is pre­trained on Dolma 3, a new ~9.3-trillion-token cor­pus drawn from web pages, sci­ence PDFs processed with olmOCR, code­bases, math prob­lems and so­lu­tions, and en­cy­clo­pe­dic text. From this pool, we con­struct Dolma 3 Mix, a 5.9-trillion-token (~6T) pre­train­ing mix with a higher pro­por­tion of cod­ing and math­e­mat­i­cal data than ear­lier Dolma re­leases, plus much stronger de­con­t­a­m­i­na­tion via ex­ten­sive dedu­pli­ca­tion, qual­ity fil­ter­ing, and care­ful con­trol over data mix­ing. We fol­low es­tab­lished web stan­dards in col­lect­ing train­ing data and don’t col­lect from sites that ex­plic­itly dis­al­low it, in­clud­ing pay­walled con­tent.

On top of this, we in­tro­duce two Dolma 3-based mixes for later stages of base model train­ing. Dolma 3 Dolmino is our mid-train­ing mix: 100B train­ing to­kens sam­pled from a ~2.2T-token pool of high-qual­ity math, sci­ence, code, in­struc­tion-fol­low­ing, and read­ing-com­pre­hen­sion data, in­clud­ing rea­son­ing traces that also en­able RL di­rectly on the base model. Dolma 3 Longmino is our long-con­text mix: ~50B train­ing to­kens drawn from a 639B-token pool of long doc­u­ments com­bined with mid-train­ing data to teach Olmo 3 to track in­for­ma­tion over very long in­puts (like re­ports, logs, and multi-chap­ter doc­u­ments).

We also in­tro­duce Dolci, a new post-train­ing data suite tai­lored specif­i­cally for rea­son­ing, tool use, and in­struc­tion fol­low­ing. Dolci pro­vides sep­a­rate mixes for each stage of post-train­ing: SFT, DPO, and RLVR. For SFT, Dolci ag­gre­gates state-of-the-art datasets that ad­vance step-by-step rea­son­ing, tool use, and high-qual­ity con­ver­sa­tional be­hav­ior; for DPO, it sup­plies high-qual­ity con­trastive pref­er­ence data; and for RL, it in­cludes hard, di­verse prompts across math, cod­ing, in­struc­tion fol­low­ing, and gen­eral chat.

Together, Dolma 3 and Dolci give Olmo 3 a fully open data cur­ricu­lum from first to­ken to fi­nal post-trained check­point.

We pre­trained Olmo 3 on a clus­ter of up to 1,024 H100 GPUs; we achieved train­ing through­put of 7.7K to­kens per de­vice per sec­ond for Olmo 3-Base (7B). We mid-trained on 128 H100 GPUs, and post-trained on a set of 256 H100s.

For Olmo 3, build­ing on the work we did for Olmo 2, we were able to sig­nif­i­cantly im­prove the ef­fi­ciency of our post-train­ing code. By mov­ing SFT from Open Instruct (our post-train­ing code­base, pri­or­i­tiz­ing flex­i­bil­ity) to Olmo Core (our pre­train­ing code­base, de­signed to max­i­mize ef­fi­ciency), we in­creased through­put (tokens/second) by 8x. Similarly, by in­cor­po­rat­ing in-flight weight up­dates, con­tin­u­ous batch­ing, and a lot of thread­ing im­prove­ments, we made our RL train­ing 4x more ef­fi­cient—re­sult­ing in train­ing runs that are sig­nif­i­cantly cheaper and faster.

A note on our 32B mod­els: We be­lieve 32B sits in a sweet spot for re­search and tin­ker­ing. 32B mod­els are big enough to sup­port strong, com­pet­i­tive per­for­mance, but still small enough that a wide au­di­ence can fine-tune and de­ploy them on ac­ces­si­ble hard­ware.

For more de­tails, in­clud­ing ab­la­tions, please read our tech­ni­cal re­port.

A core goal of Olmo 3 is not just to open the model flow, but to make it ac­tion­able for peo­ple who want to un­der­stand and im­prove model be­hav­ior. Olmo 3 in­te­grates with OlmoTrace, our tool for trac­ing model out­puts back to train­ing data in real time.

For ex­am­ple, in the Ai2 Playground, you can ask Olmo 3-Think (32B) to an­swer a gen­eral-knowl­edge ques­tion, then use OlmoTrace to in­spect where and how the model may have learned to gen­er­ate parts of its re­sponse. This closes the gap be­tween train­ing data and model be­hav­ior: you can see not only what the model is do­ing, but why—and ad­just data or train­ing de­ci­sions ac­cord­ingly.

To fur­ther pro­mote trans­parency and ex­plain­abil­ity, we’re mak­ing every train­ing and fine-tun­ing dataset avail­able for down­load, all un­der a per­mis­sive li­cense that al­lows for cus­tom de­ploy­ment and reuse. The datasets come in a range of mixes to ac­com­mo­date dif­fer­ent stor­age and hard­ware con­straints, from sev­eral bil­lion to­kens all the way up to 6 tril­lion.

Our new tool­ing for data pro­cess­ing al­lows you to de-con­t­a­m­i­nate, to­k­enize, and de-du­pli­cate data in the same way we did for Olmo 3’s cor­pora. All the tool­ing is open source, en­abling you to repli­cate our train­ing curves or run con­trolled ab­la­tions across data mixes and ob­jec­tives.

Our Olmo util­i­ties and soft­ware cover the whole de­vel­op­ment cy­cle:

is a toolkit for re­pro­ducible evals. It in­cludes our brand-new eval col­lec­tion OlmoBaseEval, which we used for Olmo 3 base model de­vel­op­ment.

Importantly, our tool­ing al­lows you to in­stru­ment com­plex tasks and an­a­lyze in­ter­me­di­ate traces to un­der­stand where the mod­els suc­ceed—or strug­gle. Because the Olmo 3 data recipes, train­ing pipeline, and check­points are open, in­de­pen­dent teams can con­nect model be­hav­ior back to mea­sur­able prop­er­ties.

Ready to de­ploy and use

Together, the Olmo 3 fam­ily makes it eas­ier to build trust­wor­thy fea­tures quickly, whether for re­search, ed­u­ca­tion, or ap­pli­ca­tions. By mak­ing every de­vel­op­ment step avail­able and in­spectable, we’re en­abling en­tirely new cat­e­gories of re­search. You can run ex­per­i­ments on any train­ing phase, un­der­stand ex­actly how dif­fer­ent tech­niques con­tribute to model ca­pa­bil­i­ties, and build on our work at what­ever stage makes sense for your pro­ject.

For sci­en­tists, the fully open flow ex­poses the mod­el’s in­ner work­ings, so you can in­stru­ment ex­per­i­ments across cod­ing, rea­son­ing, RL, and tool use.

If you care about AI you can study, au­dit, and im­prove, Olmo 3 is for you. Try the demos in the Ai2 Playground, ex­plore the doc­u­men­ta­tion, and build on the re­leased weights and check­points. Then tell us what you dis­cover—we in­vite the com­mu­nity to val­i­date, cri­tique, and ex­tend our find­ings.

True open­ness in AI is­n’t just about ac­cess—it’s about trust, ac­count­abil­ity, and shared progress. We be­lieve the mod­els shap­ing our fu­ture should be fully in­spectable, not black boxes. Olmo 3 rep­re­sents a dif­fer­ent path: one where any­one can un­der­stand, ver­ify, and build upon the AI sys­tems that in­creas­ingly in­flu­ence our world. This is what open-first means—not just re­leas­ing weights, but shar­ing the com­plete knowl­edge needed to ad­vance AI re­spon­si­bly: the flow.

Try Olmo 3 | Download the mod­els & data | Read the re­port