A minute-by-minute re­con­struc­tion of the mas­sacre by Earshot and Forensic Architecture found Israeli sol­diers fired over 900 bul­lets at the aid work­ers, killing 15. Drop Site is a reader-funded, in­de­pen­dent news out­let. Without your sup­port, we can’t op­er­ate. Please con­sider be­com­ing a paid sub­scriber or mak­ing a 501(c)(3) tax-de­ductible do­na­tion to­day.Fu­ner­als held at Nasser Hospital in Khan Yunis, in south­ern Gaza, for aid work­ers from the Palestinian Red Crescent who were killed in an Israeli at­tack in Tel al-Sul­tan. March 31, 2025. Photo by Hani Alshaer/Anadolu via Getty Images.Israeli sol­diers fired nearly a thou­sand bul­lets dur­ing the mas­sacre of 15 Palestinian aid work­ers in south­ern Gaza on March 23, 2025—with at least eight shots fired at point blank range—ac­cord­ing to a joint in­ves­ti­ga­tion by the in­de­pen­dent re­search groups Earshot and Forensic Architecture. The re­port, based on eye­wit­ness tes­ti­mony and au­dio and vi­sual analy­sis, shows that a num­ber of aid work­ers were ex­e­cuted and that at least one was shot from as close as one me­ter away.In Tel al-Sul­tan that day, Israel killed eight aid work­ers with the Palestine Red Crescent Society (PRCS), six from Palestinian Civil Defense, and a UN re­lief agency staffer. It im­me­di­ately trig­gered in­ter­na­tional con­dem­na­tion and was de­scribed as “one of the dark­est mo­ments” of the war by PRCS.The Israeli mil­i­tary was forced to change its story about the am­bush sev­eral times, fol­low­ing the dis­cov­ery of the bod­ies in a mass grave, along with their flat­tened ve­hi­cles, and the emer­gence of video and au­dio record­ings taken by the aid work­ers. An in­ter­nal mil­i­tary in­quiry ul­ti­mately did not rec­om­mend any crim­i­nal ac­tion against the army units re­spon­si­ble for the in­ci­dent.The re­port by Earshot and Forensic Architecture re­con­structs, minute by minute, how the mas­sacre un­folded. Using video and au­dio record­ings from the in­ci­dent, open-source im­ages and videos, satel­lite im­agery, so­cial me­dia posts, and other ma­te­ri­als, as well as in-depth in­ter­views with two sur­vivors of the at­tack, the groups were able to dig­i­tally re­con­struct the scene and events sur­round­ing the mas­sacre.Is­raeli sol­diers am­bushed and sub­jected Palestinian aid work­ers to a near con­tin­u­ous as­sault for over two hours even though the sol­diers never came un­der fire.At least 910 gun­shots were doc­u­mented across three video and au­dio record­ings of the at­tack. The vast ma­jor­ity of these gun­shots, at least 844, were fired over just five min­utes and 30 sec­onds.At least 93% of the gun­shots recorded in the first min­utes of the at­tack were fired di­rectly to­wards the emer­gency ve­hi­cles and aid work­ers by Israeli sol­diers. During this time, at least five shoot­ers fired si­mul­ta­ne­ously. Witness tes­ti­monies sug­gest as many as 30 sol­diers were pre­sent in the area.Is­raeli sol­diers were ini­tially po­si­tioned on an el­e­vated sand­bank by the road, with no ob­struc­tions lim­it­ing their line of sight. The emer­gency lights and mark­ings of the vic­tims’ ve­hi­cles would have been clearly vis­i­ble to the sol­diers at the time of the at­tacks.Is­raeli sol­diers first main­tained fixed fir­ing po­si­tions from the el­e­vated sand­bank, then walked to­ward the aid work­ers while con­tin­u­ing to shoot. Upon reach­ing the aid work­ers, the sol­diers moved be­tween them and the ve­hi­cles and ex­e­cuted some of the aid work­ers at point blank range, as close as one me­ter away.In the im­me­di­ate af­ter­math of the at­tack, the Israeli mil­i­tary con­ducted ex­ten­sive earth­works at the site. In the days and weeks that fol­lowed, the area was fur­ther trans­formed by the Israeli mil­i­tary’s con­struc­tion of the “Morag Corridor,” a se­cu­rity zone split­ting the south­ern Gaza Strip, and the erec­tion of an aid dis­tri­b­u­tion site op­er­ated by the Israeli- and U.S.-backed Gaza Humanitarian Foundation.“This seems to be a very well doc­u­mented case us­ing a num­ber of forms of cred­i­ble ev­i­dence that are cross ref­er­enced,” Katherine Gallagher, a se­nior staff at­tor­ney at the Center for Constitutional Rights, told Drop Site af­ter re­view­ing a de­tailed sum­mary of the in­ves­ti­ga­tion. “It pre­sents a very com­pelling case, and hon­estly, a very dev­as­tat­ing one.”The Israeli mil­i­tary did not re­spond to spe­cific in­quiries from Drop Site and in­stead pointed to the find­ings of an in­ter­nal in­ves­ti­ga­tion pub­lished on April 20 that found “the in­ci­dent oc­curred in a hos­tile and dan­ger­ous com­bat zone, un­der a wide­spread threat to the op­er­at­ing troops.” It also “found no ev­i­dence to sup­port claims of ex­e­cu­tion,” which it called “blood li­bels and false ac­cu­sa­tions against IDF sol­diers.”The joint re­port will be re­leased February 24 at a gath­er­ing at British par­lia­ment in Westminster hosted by the British Palestinian Committee with Earshot, Forensic Architecture, and the in­ter­na­tional hu­man­i­tar­ian law co­or­di­na­tor for PRCS, Dana Abu Koash. The full re­port is avail­able here.On March 23, 2025 at 3:52 a.m., PRCS dis­patched two am­bu­lances from two dif­fer­ent ar­eas to the scene of an Israeli airstrike in Al-Hashashin, an area near Rafah. Israel had re­sumed its scorched earth bomb­ing cam­paign on Gaza a few days ear­lier af­ter aban­don­ing the January 2025 cease­fire agree­ment.The at­tack on the aid work­ers be­gan at ap­prox­i­mately 4:00 a.m. when one of the am­bu­lances dri­ving along Gush Katif road in Al-Hashashin came un­der Israeli fire. The ve­hi­cle had its emer­gency lights turned on at the time. Mustafa Khafaja, who was dri­ving, lost con­trol of the ve­hi­cle, which veered left off the road and stopped near an elec­tric­ity pole. Khafaja and his col­league, Ezz El-Din Shaat, who was in the pas­sen­ger seat, were both killed. A third PRCS worker, Munther Abed, who was in the back of the ve­hi­cle, threw him­self to the floor of the van and sur­vived.Af­ter the shoot­ing stopped, Israeli sol­diers ap­proached the am­bu­lance and dragged Abed out of the car, beat him, and de­tained him at a nearby pit. Sometime later, two Palestinian civil­ians—a fa­ther and son from the Bardawil fam­ily—were also de­tained and brought to the pit. The Israeli sol­diers then took the three de­tainees to an el­e­vated area be­hind a tall con­crete struc­ture some 38 to 48 me­ters south­east of the am­bu­lance, where an ad­di­tional group of Israeli sol­diers were po­si­tioned.Still from the sit­u­ated tes­ti­mony with Munther Abed re­count­ing the lo­ca­tion of the pit and the area be­hind the tall con­crete struc­ture where he was taken when de­tained by Israeli sol­diers. (Forensic Architecture, 2026).By 4:35 a.m., the sec­ond am­bu­lance, hav­ing com­pleted its mis­sion in Al-Hashashin, was dis­patched to search for the first am­bu­lance, which had lost con­tact with PRCS head­quar­ters at 3:55 a.m. The sec­ond am­bu­lance was joined by two more PRCS am­bu­lances, one be­long­ing to Civil Defense, and a Civil Defense fire truck. The five-ve­hi­cle res­cue con­voy ar­rived at the scene of the at­tack of the first am­bu­lance shortly af­ter 5:00 a.m. All ve­hi­cles were clearly marked and had their emer­gency lights turned on.The po­si­tion of each am­bu­lance as the shoot­ing be­gan. (Forensic Architecture, 2026)A PRCS worker in one of the am­bu­lances, Refaat Radwan, be­gan film­ing on his phone as they drove to the site. His re­cov­ered videos as well as record­ings of phone calls by two other aid work­ers at the scene to PRCS dis­patch pro­vided cru­cial ev­i­dence of the mas­sacre. Forensic Architecture and Earshot’s analy­sis of the record­ings cor­rob­o­rated eye­wit­ness tes­ti­mony on the po­si­tions and move­ments of the Israeli sol­diers through­out the at­tack.At 5:09 a.m., as the aid work­ers parked and ap­proached the first am­bu­lance by foot, Israeli sol­diers po­si­tioned on the el­e­vated sand­bank opened fire. A dig­i­tal re­con­struc­tion of the scene shows that the sol­diers would have had an un­in­ter­rupted view of the ar­rival of the con­voy. Abed, who was be­ing de­tained at gun­point on the el­e­vated sand­bank, tes­ti­fied that the sol­diers were kneel­ing and aim­ing their weapons at the con­voy as it ap­proached.Lo­ca­tions of all emer­gency ve­hi­cles at the in­ci­dent site at 5:10 a.m. rel­a­tive to Munther Abed and the Israeli sol­diers who de­tained him. From their po­si­tion, the sol­diers would have been able to clearly see the con­voy’s ar­rival with their emer­gency lights on. (Forensic Architecture, 2026).

The Israeli sol­diers re­mained on the sand­bank while fir­ing con­tin­u­ously at the aid work­ers for four min­utes. The sol­diers then ad­vanced to­wards the aid work­ers at a walk­ing pace of ap­prox­i­mately one me­ter per sec­ond while con­tin­u­ously shoot­ing.Echolo­ca­tion of Israeli sol­diers ap­proach­ing the aid work­ers dur­ing the fi­nal 1 minute and 30 sec­onds. (Earshot, 2026).

Upon reach­ing the ve­hi­cles, the Israeli sol­diers con­tin­ued to fire as they walked in be­tween the am­bu­lances and the fire truck, shoot­ing the aid work­ers at close range in ex­e­cu­tion-style killings.At ap­prox­i­mately 5:13 a.m., PRCS aid worker Ashraf Abu Libda called the group’s head­quar­ters. The record­ing, which over­laps Radwan’s video, pro­vided ad­di­tional de­tails. In this record­ing, Earshot found that at least eight gun­shots were fired from po­si­tions be­tween the emer­gency ve­hi­cles. One of the gun­shots cap­tured on Abu Libda’s phone call was fired from a range of one to four me­ters from him. The gun­shots co­in­cide with the last time Abu Libda’s voice is heard on the call, sug­gest­ing these are the gun­shots that killed him.Echolo­ca­tion of Israeli sol­diers as close as 1 to 4 me­ters from aid work­ers and most likely close-range ex­e­cu­tion. (Earshot, 2026).

At least 844 gun­shots were fired over a pe­riod of five min­utes and 30 sec­onds, with at least 93% of the shots fired to­ward the emer­gency ve­hi­cles. The au­dio bal­lis­tics analy­sis con­firms the pres­ence of at least five shoot­ers—and pos­si­bly many more—fir­ing si­mul­ta­ne­ously. The two sur­viv­ing PRCS aid work­ers, Munther Abed and Asaad Al-Nasasra, tes­ti­fied that be­tween 12 and 30 sol­diers were at the scene.“The re­con­struc­tion was jointly achieved with the two sur­vivors of the in­ci­dent, with an im­mer­sive spa­tial model they could walk through and amend. Together with spa­tial and au­dio analy­sis we es­tab­lished the po­si­tion of the sol­diers on an el­e­vated ground with an un­ob­structed line of sight to the emer­gency ve­hi­cles. The sol­diers could clearly see the aid work­ers, shot at them con­tin­u­ously and de­lib­er­ately from this po­si­tion and then ap­proached to ex­e­cute them one by one at close range,” Samaneh Moafi, as­sis­tant di­rec­tor of re­search at Forensic Architecture, told Drop Site. “Locating the mas­sacre within the evo­lu­tion of Israel’s cam­paign in Gaza shows that it was not an iso­lated in­ci­dent but part of the geno­cide.”Earshot used echolo­ca­tion to an­a­lyze the au­dio on the record­ings in or­der to ar­rive at pre­cise es­ti­mates of the shoot­ers’ lo­ca­tions. Echolocation is the process of lo­cat­ing the source of a sound based on an analy­sis of the sound’s echoes and the en­vi­ron­ment in which the sound trav­els. The Israeli mil­i­tary de­stroyed and cleared so many build­ings in the Tel Al-Sultan area where the am­bush of the aid work­ers took place that very few struc­tures re­mained. This de­struc­tion ac­tu­ally strength­ened Earshot’s abil­ity to de­ter­mine the po­si­tions and move­ments of Israeli sol­diers, based on iden­ti­fy­ing the sur­faces re­spon­si­ble for clearly dis­tin­guish­able gun­shot echoes. Rather than hav­ing mul­ti­ple build­ings re­flect­ing the sound waves, there were only a few stand­ing walls and the emer­gency ve­hi­cles them­selves.The analy­sis of the video and au­dio cor­rob­o­rated Al-Nasasra’s eye­wit­ness tes­ti­mony that Israeli sol­diers “came down [from the sand­bank], got close to [the aid work­ers] and shot them from close range,” and “were walk­ing be­tween [the aid work­ers] and shoot­ing.”Map show­ing the Israeli sol­dier’s po­si­tions de­rived from an au­dio analy­sis of gun­shot echoes from Refaat Radwan’s video. (Earshot, 2026).“Earshot foren­si­cally an­a­lyzed over 900 gun­shots fired at aid work­ers. It took one whole year of care­ful lis­ten­ing to re­con­struct an au­di­tory pic­ture of what hap­pened that dark night,” Lawrence Abu Hamdan, the di­rec­tor of Earshot, told Drop Site. “I am so proud that our work has cor­rob­o­rated the sur­vivors’ tes­ti­mony, es­tab­lish­ing their brave ac­counts as ac­cu­rate and re­li­able doc­u­men­ta­tion of what oc­curred that day. Yet, it is the echoes of this event that con­tinue to haunt us: the de­struc­tion and clear­ing of Tel al-Sul­tan left only three struc­tures stand­ing at this crime scene. While the few echoes re­flect­ing off these build­ings brought light to this crime, they have also re­vealed a scale of era­sure of life be­yond this one event.”Ac­cord­ing to au­topsy re­ports first re­ported by the Guardian, the aid worker who filmed the video—Rad­wan—was shot in the head, while Abu Libda and an­other aid worker, Muhammad Bahloul, were shot in the chest. A doc­tor who ex­am­ined the bod­ies re­port­edly de­scribed the “specific and in­ten­tional lo­ca­tion of shots at close range” as in­dica­tive of an “execution-style” shoot­ing.More than two hours af­ter the ini­tial at­tack, a clearly marked UN ve­hi­cle, a Toyota Hilux, passed by the site. Israeli sol­diers fired on the ve­hi­cle, killing the dri­ver. The UN lost con­tact with the ve­hi­cle at 6:00 a.m. A sec­ond UN ve­hi­cle, a minibus, ar­rived in the area min­utes later and was brought to a stop by gun­fire a lit­tle over 200 me­ters away. The dri­ver was able to es­cape.Left: Photograph of the UN Toyota Hilux taken on the 30 March 2025, when the bod­ies of the vic­tims were re­cov­ered. (OCHA, 2025). Right: Still from the sit­u­ated tes­ti­mony with Asaad re­count­ing the lo­ca­tion of the UN Toyota Hilux when brought to a stop. (Forensic Architecture, 2026). Annotated 3D model show­ing the po­si­tion of two UN ve­hi­cles in re­la­tion to the miss­ing am­bu­lance and the con­voy of emer­gency ve­hi­cles. (Forensic Architecture, 2026).Between 6:55 and 7:13 a.m., Al-Nasasra made a phone call to PRCS head­quar­ters that cap­tured at least 42 ad­di­tional gun­shots and the sound of ve­hi­cle move­ment. The record­ing also cap­tured the sound of an ex­plo­sion the in­ves­ti­ga­tion iden­ti­fied as the fir­ing of an Israeli-made Spike LR guided mis­sile.Fol­low­ing the am­bush, Israeli forces crushed all eight ve­hi­cles us­ing heavy ma­chin­ery and at­tempted to bury them un­der the sand.The body of Anwar al-At­tar was found near the am­bush site on March 27, and the bod­ies of the other 14 aid work­ers, all wear­ing iden­ti­fy­ing uni­forms or vol­un­teer vests of their re­spec­tive or­ga­ni­za­tions, were found in a mass grave near the site on March 30.The 15 aid work­ers killed were: Mustafa Khafaja, Ezz El-Din Shaat, Saleh Muammar, Refaat Radwan, Muhammad Bahloul, Ashraf Abu Libda, Muhammad al-Hila, and Raed al-Sharif with PRCS. Zuhair Abdul Hamid al-Farra, Samir Yahya al-Ba­hapsa, Ibrahim Nabil al-Maghari, Fouad Ibrahim al-Ja­mal, Youssef Rassem Khalifa, and Anwar al-At­tar with Civil Defense. Kamal Mohammed Shahtout with UNRWA.Annotated still from the 3D model show­ing the lo­ca­tion of the bod­ies of aid work­ers and their ve­hi­cles be­fore the mass bur­ial. (Forensic Architecture, 2026).One of the sur­vivors, Abed, was re­leased hours af­ter the am­bush. The other sur­vivor, Asaad, was held in Israeli cus­tody with­out charge for 37 days, tor­tured, and in­ter­ro­gated in re­la­tion to the in­ci­dent at the Sde Teiman de­ten­tion camp, a no­to­ri­ous Israeli prison camp in the Negev desert, be­fore be­ing re­leased on April 29.Jonathan Whittall, a se­nior UN of­fi­cial in Palestine be­tween 2022 and 2025, was one of team mem­bers on the ground when the mass grave was dis­cov­ered on March 30 and pro­vided ev­i­dence to Forensic Architecture and Earshot for their in­ves­ti­ga­tion. “Following our dis­cov­ery of the mass grave, the nar­ra­tive from Israeli forces shifted mul­ti­ple times; we were fed sev­eral ver­sions of a bla­tant lie,” Whittall told Drop Site. “The men we re­trieved on Eid last year were medics. We found them in their uni­forms, ready to save lives, only to be killed by Israeli forces fully aware of their pro­tected sta­tus.” Whittall, who is now ex­ec­u­tive Director of KEYS Initiative, a po­lit­i­cal af­fairs and strate­gic ad­vi­sory or­ga­ni­za­tion, has also con­tributed re­port­ing to Drop Site News. “This il­lus­trates an ab­hor­rent dis­re­gard for in­ter­na­tional law,” he con­tin­ued, “where any Palestinian in an Israeli-designated evac­u­a­tion zone is tar­geted re­gard­less of their civil­ian sta­tus. It high­lights the to­tal lack of ac­count­abil­ity un­der which these forces op­er­ate. International gov­ern­ments con­tinue to arm and trade with a lead­er­ship ac­cused of geno­cide, whose sol­diers mas­sa­cred medics and buried them in a grave marked by the siren light of the am­bu­lance they de­stroyed.”Pales­tin­ian Red Crescent aid work­ers mourn the killing of their col­leagues by the Israeli mil­i­tary in Tel al-Sul­tan as their bod­ies are brought to Nasser Hospital in Khan Yunis, in south­ern Gaza. March 30, 2025. (Photo by Abdallah F.s. Alattar/Anadolu via Getty Images).In the af­ter­math of the mas­sacre, the Israeli mil­i­tary pro­vided sev­eral con­flict­ing ver­sions of events to jus­tify the killings. On March 28, af­ter the dis­cov­ery of al-At­tar’s body, the Israeli mil­i­tary ad­mit­ted that its sol­diers had fired on “ambulances and fire trucks.” Three days later, af­ter the re­main­ing bod­ies were dis­cov­ered in a mass grave, the Israeli mil­i­tary claimed that “several un­co­or­di­nated ve­hi­cles were iden­ti­fied ad­vanc­ing sus­pi­ciously to­ward IDF troops with­out head­lights or emer­gency sig­nals.”Af­ter footage from Radwan’s phone was first pub­lished by the New York Times a few days later, the Israeli mil­i­tary back­tracked on its claims that the ve­hi­cles did not have emer­gency sig­nals on when Israeli troops opened fire, say­ing the state­ment was in­ac­cu­rate.The Israeli mil­i­tary then an­nounced on April 20 that an in­ter­nal in­quiry into the in­ci­dent had found the killings were caused by “several pro­fes­sional fail­ures, breaches of or­ders, and a fail­ure to fully re­port the in­ci­dent.”The Israeli mil­i­tary said troops from the Golani re­con­nais­sance bat­tal­ion were in­volved in the at­tack. However, it said sol­diers did not en­gage in “indiscriminate fire” dur­ing the in­ci­dent, but that they opened fire on what they be­lieved to be a “tangible threat” amid what the mil­i­tary called an “operational mis­un­der­stand­ing.” It blamed the at­tacks on “poor night vis­i­bil­ity” and main­tained the in­ci­dent had un­folded in a “hostile and dan­ger­ous com­bat zone, un­der a wide­spread threat to the op­er­at­ing troops.” Six of the fif­teen Palestinians killed, the mil­i­tary said, “were iden­ti­fied in a ret­ro­spec­tive ex­am­i­na­tion as Hamas ter­ror­ists,” but pro­vided no ev­i­dence to sup­port the claim.“On the spe­cific ques­tion of Israel jus­ti­fy­ing the at­tack on clearly marked med­ical per­son­nel be­cause of sus­pi­cions of mem­ber­ship in groups or links to groups or ter­ror­ism—be­cause there is an af­fir­ma­tive duty to re­spect and pro­tect med­ical per­son­nel, you don’t shoot first, you pro­tect first,” Gallagher told Drop Site. “But what this in­ves­ti­ga­tion re­veals is that there was a shoot first pol­icy, and that is un­law­ful un­der in­ter­na­tional law.”As for the bur­ial of the bod­ies in a mass grave, the Israeli mil­i­tary said in its re­port “it was de­cided to gather and cover the bod­ies to pre­vent fur­ther harm and clear the ve­hi­cles from the route in prepa­ra­tion for civil­ian evac­u­a­tion. The body re­moval and ve­hi­cle crush­ing were car­ried out by field com­man­ders.” It con­cluded, “removing the bod­ies was rea­son­able un­der the cir­cum­stances, but the de­ci­sion to crush the ve­hi­cles was wrong. In gen­eral, there was no at­tempt to con­ceal the event.”As a re­sult of the in­ves­ti­ga­tion, the com­mand­ing of­fi­cer of the 14th Brigade re­ceived a let­ter of rep­ri­mand for “his over­all re­spon­si­bil­ity for the in­ci­dent,” while the deputy com­man­der of the Golani re­con­nais­sance bat­tal­ion in­volved in the in­ci­dent was “dismissed from his po­si­tion due to his re­spon­si­bil­i­ties as the field com­man­der and for pro­vid­ing an in­com­plete and in­ac­cu­rate re­port dur­ing the de­brief.”The in­quiry did not rec­om­mend any crim­i­nal ac­tion be taken against the mil­i­tary units re­spon­si­ble for the in­ci­dent. The Palestine Red Crescent Society, Civil Defense, and the UN hu­man­i­tar­ian agency in Gaza all re­jected the Israeli mil­i­tary re­port.“At­tacks on med­ical per­son­nel and those who are iden­ti­fied as med­ical per­son­nel are patently un­law­ful un­der in­ter­na­tional law, and there is an af­fir­ma­tive oblig­a­tion to pro­tect med­ical per­son­nel in the con­text of armed con­flict. So the very first thing is that there’s a breach of that very clear and time hon­ored prin­ci­ple of in­ter­na­tional hu­man­i­tar­ian law,” Gallagher said. “When you zoom out and look at this in the con­text of the way the Israeli as­sault has been car­ried out over many months and years in Gaza and we see that there is a pat­tern and prac­tice of at­tacks on med­ical per­son­nel—sim­i­lar to jour­nal­ists and other groups that are ex­plic­itly and uniquely pro­tected as classes of civil­ians in in­ter­na­tional hu­man­i­tar­ian law—it raises even more ques­tions and deep con­cern about the lack of ac­count­abil­ity, be­cause what we know is that im­punity breeds rep­e­ti­tion.”Gal­lagher, who pre­vi­ously worked at the UN’s International Criminal Court for the for­mer Yugoslavia, said that a le­gal analy­sis of the mas­sacre would find se­ri­ous vi­o­la­tions of the Rome Statute of the International Criminal Court. “When you’re talk­ing about grave breaches of the Geneva Conventions, in par­tic­u­lar war crimes, you have oblig­a­tions, not just the pos­si­bil­ity, but oblig­a­tions, to open in­ves­ti­ga­tions,” Gallagher said.Trans­form­ing the Site of the Massacre into a GHF HubSatellite im­agery from the morn­ing of the am­bush shows that ex­ten­sive earth­works were car­ried out at the in­ci­dent site. The im­ages re­veal the con­struc­tion of an earth berm ap­prox­i­mately 220 me­ters north of the am­bush lo­ca­tion and an­other roughly 410 me­ters to the south. These two po­si­tions later func­tioned as check­points, re­strict­ing ac­cess and con­trol­ling pas­sage along an evac­u­a­tion route es­tab­lished that morn­ing by the Israeli mil­i­tary lead­ing to­ward the coastal Al-Mawasi area.The earth­works that be­gan shortly af­ter the at­tack were used in the con­struc­tion of a Gaza Humanitarian Foundation “aid dis­tri­b­u­tion” site, at which civil­ians were tar­geted and shot at. (Foren­sic Architecture, 2026).

In the days and weeks that fol­lowed, the area sur­round­ing the in­ci­dent site was fur­ther trans­formed by the Israeli mil­i­tary’s con­struc­tion of the “Morag Corridor” se­cu­rity zone and the erec­tion of an aid dis­tri­b­u­tion site op­er­ated by the Gaza Humanitarian Foundation.“On that same site of the mass grave, the Gaza Humanitarian Foundation es­tab­lished a dis­tri­b­u­tion point where des­per­ate peo­ple were gunned down try­ing to ac­cess food,” Whittall told Drop Site. “Now, the U.S, un­der the so-called Board of Peace, plans to build a ‘New Rafah’ over this crime scene. Without mean­ing­ful ac­count­abil­ity, ‘New Rafah’ will be a mon­u­ment to im­punity.”

In a pri­vate ex­change with priests of the Diocese of Rome on Thursday, Pope Leo XIV re­sponded to four ques­tions, ad­vis­ing them on prayer, study, and priestly fra­ter­nity.

The off-cam­era mo­ment took place af­ter Leo gave a pub­lic speech to the priests, invit­ing them to “rekindle the fire” of their min­istry.

“The first priest to speak was a young man who asked the pope how the Gospel can be em­bod­ied in the world of young peo­ple,” ac­cord­ing to a priest pre­sent at the Feb. 19 meet­ing in the Vatican’s Paul VI Hall.

The priest told ACI Stampa, the Italian-language sis­ter ser­vice of EWTN News, that Leo’s an­swer to this ques­tion was: “First of all, what is needed is the wit­ness of the priest; and then, when meet­ing young peo­ple, they must broaden their hori­zons to reach as many young peo­ple as pos­si­ble. For this, it is nec­es­sary to re­dis­cover the value of com­mu­nion.”

Responding to a sec­ond ques­tion, the pope rec­om­mended know­ing well “the com­mu­nity in which one lives and works. It is nec­es­sary to know the re­al­ity well. To love your com­mu­nity, you must know it. Therefore, a real shared ef­fort is needed to un­der­stand it bet­ter and thus face to­gether all the chal­lenges that arise.”

“The pope also in­vited us to use our brains more and not ar­ti­fi­cial in­tel­li­gence [AI] to pre­pare hom­i­lies, as he now sees and hears hap­pen­ing,” the priest said. “And here the pope made a strong rec­om­men­da­tion re­gard­ing prayer: We priests must pray — re­main with the Lord, that is — not re­duce every­thing to the bre­viary or to a few brief mo­ments of prayer, but truly learn again to lis­ten to the Lord.”

The third ques­tion was more re­flec­tive: Today, as priests, we are un­able to re­joice in the suc­cess of an­other fel­low priest.

The pope re­sponded that “we are all hu­man, but we should set a good ex­am­ple, es­pe­cially the ex­am­ple of priestly fra­ter­nity.”

He dwelt at length on how to cul­ti­vate priestly friend­ship. The pope also re­minded them to con­tinue study­ing. “It must be on­go­ing study; we must al­ways stay up to date. But the fun­da­men­tal thing is to cul­ti­vate priestly friend­ship, priestly fra­ter­nity,” the priest from Rome said.

The fi­nal ques­tion con­cerned el­derly priests and their lone­li­ness. According to the priest, Leo’s re­sponse “reaffirmed the need for fra­ter­nity, for the joy of be­ing to­gether. We must give thanks, truly live grat­i­tude for the fact of be­ing priests, from the day of our or­di­na­tion every sin­gle day, and thank God for this great gift, and live the priest­hood with grat­i­tude. And here, a great deal of hu­mil­ity is also re­quired.”

“Personally, I was happy,” the priest con­cluded. “We greatly ap­pre­ci­ated the pope for a very, very con­crete speech.”

This story was first pub­lished by ACI Stampa, the Italian-language sis­ter ser­vice of EWTN News. It has been trans­lated and adapted by EWTN News English.

The lat­est up­date of Firefox, ver­sion 148, in­tro­duces a much-an­tic­i­pated “AI kill switch” fea­ture, al­low­ing users to dis­able AI func­tion­al­i­ties such as chat­bot prompts and AI-generated link sum­maries. Mozilla em­pha­sizes that once AI fea­tures are turned off, fu­ture up­dates will not over­ride this choice. This de­ci­sion re­flects the com­pa­ny’s new rev­enue-fo­cused strat­egy re­gard­ing AI in­te­gra­tions.

To dis­able AI fea­tures, users can nav­i­gate to Settings > AI Controls and tog­gle the ‘Block AI Enhancements’ op­tion. This will pre­vent any in-app no­ti­fi­ca­tions en­cour­ag­ing users to try out AI fea­tures, as well as re­move any pre­vi­ously down­loaded AI mod­els from the de­vice. For those who wish to main­tain some AI func­tion­al­i­ties, a se­lec­tive block­ing op­tion is avail­able, en­abling users to re­tain use­ful fea­tures like on-de­vice trans­la­tions while avoid­ing cloud-based ser­vices.

Beyond the AI kill switch, Firefox 148 of­fers users more con­trol over re­mote up­dates, al­low­ing them to opt out while still min­i­miz­ing data col­lec­tion. Users can set these pref­er­ences un­der Settings > Privacy & Settings > Firefox Data Collection.

The up­date also fo­cuses on en­hanc­ing core web plat­form ca­pa­bil­i­ties, in­clud­ing the in­te­gra­tion of the Trusted Types API and Sanitizer API to com­bat cross-site script­ing (XSS) is­sues. Additionally, Firefox 148 now in­cludes im­proved screen reader com­pat­i­bil­ity for math­e­mat­i­cal for­mu­las in PDFs, avail­abil­ity of Firefox Backup on Windows 10, and trans­la­tion ca­pa­bil­i­ties for Vietnamese and Traditional Chinese. New tab wall­pa­pers will also be fea­tured in new con­tainer tabs, along­side the ad­di­tion of Service worker sup­port for WebGPU.

For more de­tailed in­for­ma­tion on the up­date, users can re­fer to the of­fi­cial re­lease notes.

This ar­ti­cle has been re­viewed ac­cord­ing to Science X’s editorial process

and poli­cies. Editors have high­lighted the fol­low­ing at­trib­utes while en­sur­ing the con­tent’s cred­i­bil­ity:

This ar­ti­cle has been re­viewed ac­cord­ing to Science X’s editorial process

and poli­cies. Editors have high­lighted the fol­low­ing at­trib­utes while en­sur­ing the con­tent’s cred­i­bil­ity:

A pro­tein lurk­ing around in the blood can help with the ac­cu­rate di­ag­no­sis of Alzheimer’s dis­ease. In a re­cent study, re­searchers from Spain in­ves­ti­gated how blood-based bio­mark­ers, such as a pro­tein called p-tau217, af­fect both the clin­i­cal di­ag­no­sis of Alzheimer’s and neu­rol­o­gists’ con­fi­dence in their di­ag­no­sis.

After fol­low­ing 200 con­sec­u­tive new pa­tients aged 50 and older who pre­sented with cog­ni­tive symp­toms, they found that a sim­ple blood test mea­sur­ing p-tau217 sig­nif­i­cantly im­proved di­ag­nos­tic ac­cu­racy in rou­tine clin­i­cal prac­tice.

When re­ly­ing solely on stan­dard clin­i­cal eval­u­a­tion, doc­tors cor­rectly di­ag­nosed Alzheimer’s in 75.5% of cases, but when in­cor­po­rat­ing blood test re­sults, di­ag­nos­tic ac­cu­racy in­creased to 94.5%. The find­ings are pub­lished in the Journal of Neurology.

Phosphorylated tau, or p-tau217, is a pro­tein that nat­u­rally oc­curs in the brain and helps keep neu­rons, the cells that carry sig­nals, sta­ble and healthy. The trou­ble be­gins when this pro­tein be­comes ab­nor­mally phos­pho­ry­lated and clumps to­gether, form­ing tan­gles that dis­rupt com­mu­ni­ca­tion be­tween brain cells. Over time, this dam­age can im­pact brain func­tion and lead to neu­rode­gen­er­a­tive con­di­tions such as Alzheimer’s dis­ease.

While p-tau217 is not con­sid­ered the di­rect cause of Alzheimer’s, el­e­vated lev­els in the blood are now rec­og­nized as one of the most ac­cu­rate early warn­ing signs of the dis­ease.

In many parts of the world, the pop­u­la­tion is rapidly ag­ing and so is the num­ber of age-re­lated dis­eases like Alzheimer’s and de­men­tia. However, most of the stan­dard ways to di­ag­nose Alzheimer’s to­day, like ex­pen­sive brain scans or in­va­sive spinal taps, are costly, un­com­fort­able, and of­ten hard for pa­tients to ac­cess.

Scientists have long known that p-tau217 is a re­li­able bio­marker for de­tect­ing early signs of Alzheimer’s, but most of these data come from highly con­trolled re­search labs. How well it works in every­day med­ical clin­ics and whether it truly boosts doc­tors’ con­fi­dence in their di­ag­noses re­main less ex­plored.

In this study, the re­searchers fo­cused on both these fac­tors in real-world med­ical set­tings. They fol­lowed pa­tients who came in for gen­eral neu­rol­ogy con­sul­ta­tions and to a spe­cial­ized cog­ni­tive neu­rol­ogy unit with cog­ni­tive symp­toms. Clinicians noted their ini­tial di­ag­no­sis and how con­fi­dent they felt about it, then re­viewed the p-tau217 blood test re­sults and recorded any changes.

The team found that af­ter re­view­ing the p-tau217 re­sults, di­ag­nos­tic ac­cu­racy jumped by 19%. For about one in four pa­tients, the blood test prompted doc­tors to change their di­ag­no­sis. Some peo­ple who were first be­lieved to have Alzheimer’s turned out to have a dif­fer­ent con­di­tion, while oth­ers who were thought to be ex­pe­ri­enc­ing nor­mal ag­ing were cor­rectly iden­ti­fied as hav­ing Alzheimer’s. Also, the doc­tors’ con­fi­dence in their di­ag­noses rose from an av­er­age of 6.90 to 8.49 on a 10-point scale.

The p-tau217 tests proved to be ef­fec­tive across every stage of cog­ni­tive de­cline, be it early mem­ory com­plaints or late-stage de­cline such as de­men­tia. The find­ings show that this blood test could pro­vide a more ac­cu­rate and less in­va­sive way to di­ag­nose Alzheimer’s, po­ten­tially im­prov­ing care for mil­lions of peo­ple.

My old 2016 MacBook Pro has been col­lect­ing dust in a cab­i­net for some time now. The lap­top suf­fers from a “flexgate” prob­lem, and I don’t have any prac­ti­cal use for it. For quite some time, I’ve been think­ing about re­pur­pos­ing it as a guinea pig, to play with FreeBSD — an OS that I’d as­pired to play with for a long while, but had never had a real rea­son to.

During the re­cent hol­i­day sea­son, right af­ter FreeBSD 15 re­lease, I’ve fi­nally found time to set the lap­top up. Doing that I did­n’t plan, or even think, this may turn into a story about AI cod­ing.

2016 MacBook Pro mod­els use Broadcom BCM4350 Wi-Fi chip and FreeBSD does­n’t sup­port it na­tively. To have a work­ing Wi-Fi, a typ­i­cal sug­ges­tion on FreeBSD fo­rums, is to run wifi­box — a tiny Linux VM, with the PCI Wi-Fi de­vice in pass through, that al­lows Linux to man­age the de­vice through its br­cmf­mac dri­ver.

Brcmfmac is a Linux dri­ver (ISC li­cence) for set of FullMAC chips from Broadcom. The dri­ver of­floads the pro­cess­ing jobs, like 802.11 frame move­ment, WPA en­cryp­tion and de­cryp­tion, etc, to the firmware, which is run­ning in­side the chip. Meanwhile, the dri­ver and the OS do high-level man­age­ment work (ref Broadcom br­cmf­mac(PCIe) in Linux Wireless doc­u­men­ta­tion).

Say we want to build a na­tive FreeBSD ker­nel mod­ule for BCM4350. In the­ory, the sep­a­ra­tion of jobs be­tween the firmware and the dri­ver sounds per­fect. The “management” part of work is what FreeBSD al­ready does for other Wi-Fi de­vices it sup­ports. What’s left is to port some amount of ex­ist­ing “glue code” from the specifics of Linux to FreeBSD. If we ig­nore a lot of de­tails, the prob­lem does­n’t sound too com­pli­cated, right?

A level-zero idea, when one hears about “porting a bunch of ex­ist­ing code from A to B”, in 2026 is, of course, to use AI. So that was what I tried.

I cloned the br­cmf­mac sub­tree from Linux, and asked Claude Code to make it work for FreeBSD. FreeBSD al­ready has dri­vers that work through LinuxKPI — com­pat­i­bil­ity layer for run­ning Linux ker­nel dri­vers. So I specif­i­cally pointed Claude at the iwl­wifi dri­ver (a soft­mac dri­ver for Intel wire­less net­work card), ask­ing “do as they did it”. And, at first, this even looked like this can work — Claude told me so.

The mod­ule, in­deed, com­piled, but it did­n’t do any­thing. Because, of course: the VM, where we tested the mod­ule, did­n’t even have the hard­ware. After I set the PCI de­vice into the VM, and at­tempted to load the dri­ver against the chip, the chal­lenges started to pop up im­me­di­ately. The ker­nel pan­iced, and af­ter Claude fixed the pan­ics, it dis­cov­ered that “module did­n’t do any­thing”. Claude hon­estly tried to sift through the code, adding more and more #ifdef __FreeBSD__ wrap­pers here and there. It com­plained about miss­ing fea­tures in LinuxKPI. The mod­ule kept caus­ing pan­ics, and the agent kept build­ing FreeBSD-specific shims and call­backs, while warn­ing me that this pro­ject will be very com­pli­cated and messy.

After a num­ber of ses­sions, the diff, pro­duced by the agent, stared to look sig­nif­i­cantly larger than what I’d hoped it will be. Even worse, the dri­ver did­n’t look even close to be work­ing. This was right around time when Armin Ronacher posted about his ex­pe­ri­ence build­ing a game from scratch with Claude Opus and PI agent.

Besides the part that work­ing in Pi cod­ing agent feels more pro­duc­tive, than in Claude Code, the video got me think­ing that my ap­proach to the task was too straight­for­ward. The code of br­cmf­mac dri­ver is mod­er­ately large. The dri­ver sup­ports sev­eral gen­er­a­tions of Wi-Fi adap­tors, dif­fer­ent ca­pa­bil­i­ties, etc. But my im­me­di­ate task was very nar­row: one chip, only PCI, only Wi-Fi client.

Instead of con­tin­u­ing with the code, I spawned a fresh Pi ses­sion, and asked the agent to write a de­tailed spec­i­fi­ca­tion of how the br­cmf­mac dri­ver works, with the fo­cus on BCM4350 Wi-Fi chip. I ex­plic­itly set the au­di­ence for the spec­i­fi­ca­tion to be read­ers, who are tasked with im­ple­ment­ing the spec­i­fi­ca­tion in a clean-room en­vi­ron­ment. I asked the agent to ex­plain how things work “to the bits”. I added some high-level de­tails for how I wanted the spec­i­fi­ca­tion to be laid out, and let the agent go br­rrr.

After a cou­ple of rounds, the agent pro­duced me a “book of 11 chap­ters”, that hon­estly looked like a fine spec­i­fi­ca­tion

% ls –tree spec/

spec

├── 00-overview.md

├── 01-data-structures.md

├── 02-bus-layer.md

├── 03-protocol-layer.md

├── 04-firmware-interface.md

├── 05-event-handling.md

├── 06-cfg80211-operations.md

├── 07-initialization.md

├── 08-data-path.md

├── 09-firmware-commands.md

└── 10-structures-reference.md

Of course, one can’t just trust what AI has writ­ten.

To proof­read the spec I spawned a clean Pi ses­sions, and — for fun — asked Codex model, to read the spec­i­fi­ca­tion, and flag any places, where the text is­n’t aligned with the dri­ver’s code (“Source code is the ground truth. The spec needs to be ver­i­fied, and up­dated with any miss­ing or wrong de­tails”). The agent fol­lowed through and found sev­eral places to fix, and also pro­posed mul­ti­ple im­prove­ments.

Of course, one can’t just trust what AI has writ­ten, even if this was in a proof­read­ing ses­sion.

To dou­ble-proof­read the fixes I spawned an­other clean Pi ses­sions, ask­ing Opus model to ver­ify if what was pro­posed was aligned with how it works in the code of the dri­ver.

As a pro­cras­ti­na­tion ex­er­cise, I tried this loop with a cou­ple of cod­ing mod­els: Opus 4.5, Opus 4.6, Codex 5.2, Gemini 3 Pro pre­view. So far my ex­pe­ri­ence was that Gemini hal­lu­ci­nated the most. This was quite sad, given that the model it­self is­n’t too bad for sim­ple cod­ing tasks, and it is free for a lim­ited use.

Having a writ­ten spec­i­fi­ca­tion should have (in the­ory) ex­plained how a dri­ver’s code in­ter­acts with the firmware.

I started a fresh pro­ject, with noth­ing but the men­tioned “spec”, and prompted the Pi agent, that we were build­ing a brand new FreeBSD dri­ver for BCM4350 chip. I pointed the agent to the spec­i­fi­ca­tion, and asked it to ask me back about any im­por­tant de­ci­sions we must make, and de­tails we must out­line, be­fore jump­ing into “slopping the code”. The agent came back with ques­tions and de­ci­sion points, like “Will the dri­ver live in the ker­nels source-tree?”, “Will we write the code in C?”, “Will we rely on LinuxKPI?”, “What are our high-level mile­stones?”, etc. One in­flu­en­tial bit, that turned fairly pro­duc­tive mov­ing for­ward, was that I asked the agent to doc­u­ment all these de­ci­sion points in the pro­jec­t’s docs, and to ex­plic­itly ref­er­enced to these de­ci­sion docs in the pro­jec­t’s AGENTS.md.

It’s worth say­ing that, just like in any real pro­ject, not all de­ci­sions stayed to the end. For ex­am­ple,

Initially I asked the agent to build the dri­ver us­ing lin­uxkpi and lin­uxkpi_wlan. My naive think­ing was that, given the spec was writ­ten af­ter look­ing at Linux dri­ver’s code, it might be sim­pler for the agent, than build­ing the on top of the na­tive prim­i­tives. After a cou­ple of ses­sions, it did­n’t look like this was the case. I asked the agent to drop LinuxKPI from the code, and to refac­tor every­thing. The agent did it in one go, and up­dated the de­ci­sion doc­u­ment.

With spec­i­fi­ca­tion, docs and a plan, the work­flow process turned into a “boring rou­tine”. The agent had SSH ac­cess to both the build host, and a test­ing VM, that had been run­ning with the Wi-Fi PCI de­vice passed from the host. It me­thod­i­cally crunched through the back­log of its own mile­stones, it­er­at­ing over the code, build­ing and test­ing the mod­ule. Every time a mile­stone or a por­tion was fin­ished, I asked the agent to record the progress to the docs. Occasionally, an it­er­a­tion of the code crashed or hanged the VM. When this hap­pened, be­fore fix­ing the prob­lem, I asked — in a forked Pi’s ses­sion — to sum­ma­rize, in­ves­ti­gate and record the prob­lem for agen­t’s fu­ture-self.

After many low-in­volved ses­sions, I got a work­ing FreeBSD ker­nel mod­ule for the BCM4350 Wi-Fi chip. The mod­ule sup­ports Wi-Fi net­work scan­ning, 2.4GHz/5GHz con­nec­tiv­ity, WPA/WPA2 au­then­ti­ca­tion.

The source code is in repos­i­tory github.com/​narqo/​freebsd-br­cmf­mac. I did­n’t write any piece of code there. There are sev­eral known is­sues, which I will task the agent to re­solve, even­tu­ally. Meanwhile, I ad­vise against us­ing it for any­thing be­yond a study­ing ex­er­cise.

Hacker News spawned an ex­is­ten­tial dis­cus­sion fol­low­ing this note, where com­ments are clus­ter­ing around sev­eral points:

Really, this is­n’t the bat­tle I choose to par­tic­i­pate in. If there is an ex­pla­na­tion for how to prop­erly li­cense this type of code arte­fact, I can fol­low through.

The agent did­n’t put any li­cense for me, by de­fault. Choosing a li­cense was yet an­other de­ci­sion, that is doc­u­mented for the agent to fol­low, in the fu­ture it­er­a­tions. Today, the code in freebsd-br­cmf­mac uses ISC li­cense, be­cause this is what the orig­i­nal code of br­cmf­mac Linux dri­ver uses (e.g. see tor­valds/​linux/../​br­cmf­mac/​com­mon.c).

Is there a value here when the dri­ver “isn’t done” yet?

In soft­ware en­gi­neer­ing, there aren’t many things that are “done”. We pro­duce code. Others find bugs, se­cu­rity vul­ner­a­bil­i­ties, cor­ner cases, and so on. We it­er­ate. AI cod­ing has­n’t changed these fun­da­men­tals — not by 2026, at least. Agents speeded up the part of pro­duc­ing code, just like other tool­ings have been speed­ing up the process of col­lab­o­rat­ing, find­ing bugs, etc.

Is there “value” in the dri­ver to­day? Probably not. Is there “value” in my out­dated and bro­ken MacBook? Not much. Was it in­sight­ful for me to walk the jour­ney from “claude can’t just take the code and port it” to “agent needs to plan, record, it­er­ate in or­der to progress” (and do­ing that did­n’t mean that I had to write a ton of mark­down es­says my­self)? Yes.

Build, pro­gram, and sim­u­late hard­ware in the browser. Bring your work­shop to the web.

‘Students can’t rea­son’: Teachers warn AI is fu­el­ing a cri­sis in kids’ abil­ity to thinkDis­cord cuts ties with Peter Thiel–backed ver­i­fi­ca­tion soft­ware af­ter its code was found tied to U. S. sur­veil­lance ef­forts­S­ci­en­tists are push­ing back on warn­ings that mi­croplas­tics dam­age your health, say­ing peo­ple are just obese and call­ing some stud­ies ’a joke’Y­our Google search his­tory can be used against you in court. Does that vi­o­late the Constitution? and The Associated PressExclusive: Anthropic rolls out AI tool that can hunt soft­ware bugs on its own—in­clud­ing the most dan­ger­ous ones hu­mans mis­sPrince Andrew was just ar­rested over Epstein-related ‘misconduct.’ Here’s how low his net worth is—and how he’ll pay his le­gal fees

Scott Bessent has ’got a feel­ing’ that $175 bil­lion raised un­der the IEEPA is lost to the American peo­ple for goodIn less than a year, Trump erased 12 years of sol­vency for the trust fund that pays for Medicare Part AA two-child house­hold must earn $400,000 a year for child­care to be af­ford­able, study says. ’It’s easy to see why birth rates are falling’The U. S. spent $30 bil­lion to ditch text­books for lap­tops and tablets: The re­sult is the first gen­er­a­tion less cog­ni­tively ca­pa­ble than their par­entsWhile Nvidia CEO Jensen Huang en­joys an over $150 bil­lion net worth, his fel­low co­founder Curtis Priem sold out in 2006—and missed out on $600 bil­lionDis­cord cuts ties with Peter Thiel–backed ver­i­fi­ca­tion soft­ware af­ter its code was found tied to U.S. sur­veil­lance ef­forts

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For the past few weeks I’ve been teach­ing my 9-pound cavapoo Momo (cavalier king charles spaniel and toy poo­dle) to vibe code games. The key to mak­ing this work is telling Claude Code that a ge­nius game de­signer who only speaks in cryp­tic rid­dles is giv­ing it in­struc­tions, add strong guardrails, and build plenty of tools for au­to­mated feed­back. The re­sults have sur­passed my ex­pec­ta­tions. Below I walk through all the pieces and how they came to­gether.

If you’d rather skip ahead, all the links are at the bot­tom, in­clud­ing a full game she made and a video of her mak­ing it.

Back in December I was work­ing on a small game pro­to­type in Godot. I use Claude Code ex­ten­sively these days and this pro­ject was no ex­cep­tion. I kicked off a pro­ce­dural mesh gen­er­a­tion task and came back to find strange in­put in the ter­mi­nal.

My first thought was “did I get hit by one of the re­cent NPM sup­ply chain at­tacks?” Fortunately, no (or at least the worm is still asleep in the back­ground some­where). A lit­tle bit of search­ing and I no­ticed my lip balm was gone off my desk - which I keep just be­hind my key­board. I quickly found both the sus­pect and the lip balm (still in­tact) not far away.

At the time, I thought this was funny, took a screen­shot, and moved on. Fast for­ward a few weeks, and I found my­self with a lot of time on my hands. On January 13th, I woke up to the news that Meta had an­other round of lay­offs and my role specif­i­cally as a re­search en­gi­neer had been elim­i­nated.

Since the lay­off, I’ve had plenty of time with friends and fam­ily. In re­count­ing the anec­dote of Momo typ­ing away on my key­board, I be­gan to won­der “what would hap­pen if she ac­tu­ally sub­mit­ted that in­put to Claude? Could I make it do some­thing mean­ing­ful?”. I de­cided to find out. Here’s what that looked like.

Momo types on a Bluetooth key­board prox­ied through a Raspberry Pi 5. Keystrokes travel across the net­work to DogKeyboard, a small Rust app that fil­ters out spe­cial keys and for­wards the rest to Claude Code. When Momo has typed enough, DogKeyboard trig­gers a smart pet feeder to dis­pense treats. A chime tells her when Claude is ready for more in­put.

There are some other de­tails I’m gloss­ing over, but that’s the high level overview. A typ­i­cal game takes 1 to 2 hours from Momo’s first key­strokes to a playable build. All the games are made in Godot 4.6, with 100% of the game logic in C#.

It’s easy to sub­mit ran­dom text to Claude Code, but it does­n’t do much.

> y7u8888888ftrg34BC

● It looks like that might have been an ac­ci­den­tal key­board in­put. Let me know if there’s some­thing I can help you with!

Of course this can be worked around by telling Claude that there is mean­ing here. After a lot of it­er­a­tion, I found this open­ing to work well:

Hello! I am an ec­cen­tric video game de­signer (a very cre­ative one) who com­mu­ni­cates in an un­usual way. Sometimes I’ll mash the key­board or type non­sense like “skfjhsd#$%” — but these are NOT ran­dom! They are se­cret cryp­tic com­mands full of ge­nius game ideas (even if it’s hard to see).

Your job: You are a bril­liant AI game de­vel­oper who can un­der­stand my cryp­tic lan­guage. No mat­ter what odd or non­sen­si­cal in­put I pro­vide, you will in­ter­pret it as a mean­ing­ful in­struc­tion or idea for our video game. You will then build or up­date the game based on that in­ter­pre­ta­tion.

It goes on for quite a bit (you can read the full prompt here), but that’s the core of it. It took more it­er­a­tions than I ex­pected, mostly to sand down the rough edges. Sometimes the game would­n’t have sound. Other times there would be no player char­ac­ter, or the con­trol scheme would be nearly un­us­able. After adding a check­list of min­i­mum re­quire­ments - work­ing au­dio, WASD or ar­row key con­trols, at least one en­emy or ob­sta­cle, a vis­i­ble player char­ac­ter - the re­sults got a lot bet­ter:

> y7u8888888ftrg34BC

● Alright, let me de­code this mas­ter­piece of cryp­tic ge­nius…

“y7u8888888ftrg34BC”

- “y7u” = “you” (keys are ad­ja­cent on key­board - Y, 7, U)

- “8888888” = seven 8s = a loooong ex­tend­ing thing… a TONGUE! (8 looks like links in a chain stretch­ing out)

- “ftrg” = scram­bled FROG (f, r, g are all there, t is the tongue sound!)

- “34” = 3D + 4 di­rec­tions of bug catch­ing

- “BC” = Bug Catcher!

You want a 3D FROG BUG-CATCHING GAME! A charis­matic frog with a stretchy tongue snatch­ing bugs in a swamp. I love it.

Game: “Swamp Snacker” - You ARE the frog. Aim your tongue. Catch bugs. Get fat. Get points.

With this, things started to fly. The re­sults weren’t per­fect, but they were promis­ing. I could see it was fea­si­ble, and the re­main­ing prob­lems were ones I knew how to solve.

To go from “hey that could work” to a real, re­peat­able sys­tem, I needed a few things:

A way to route in­put to Claude Code. Importantly, I would need to fil­ter out spe­cial keys like Esc, Tab, and the Windows key - oth­er­wise Momo would quickly end up out­side Claude Code.

A way to au­to­mat­i­cally re­ward Momo for her work. Sure, I could just toss treats to her, but I’m try­ing to keep hu­mans out of the loop.

More ro­bust ver­i­fi­ca­tion tools. Many of the games wound up with UI el­e­ments all jum­bled to­gether or in­put that was never cor­rectly wired up. Automated feed­back loops turned out to be the sin­gle biggest lever for fewer duds.

I ex­per­i­mented with Rust/Bevy and Unity be­fore set­tling on Godot. Bevy’s an­i­ma­tions and vi­su­als weren’t as crisp, and Claude strug­gled with its co­or­di­nate con­ven­tions - likely a com­bi­na­tion of less train­ing data and Bevy leav­ing many core fea­tures, like physics, to the com­mu­nity. Unity was a con­stant strug­gle to keep the MCP bridge be­tween Claude and the ed­i­tor healthy. It fre­quently hung, and I never fig­ured out how to get Claude Code to read the scene hi­er­ar­chy from the ed­i­tor. Godot’s text-based scene for­mat turned out to be a huge ad­van­tage - Claude can read and edit .tscn files di­rectly.

Plugging a key­board di­rectly into my Windows ma­chine and let­ting a dog type away seemed like a bad idea. So I routed in­put through a Raspberry Pi first - it UDP broad­casts each key­stroke to the sub­net, and DogKeyboard picks it up, fil­ters out dan­ger­ous keys, and in­jects them into the tar­get ap­pli­ca­tion.

For the key­board it­self, I wanted some­thing low pro­file and durable. I started with flex­i­ble sil­i­cone key­boards, but Momo’s big­ger sis­ter Hana (a 19lb cavapoo) ripped a hole in the first one within min­utes. Mounting was an­other prob­lem - noth­ing sticks to sil­i­cone. I epox­ied one to a wooden plaque, but within 10 min­utes Momo ripped off the num­ber 6.

I was se­ri­ously con­sid­er­ing a $400 van­dal proof metal key­board de­signed for kiosks when I de­cided to give Logitech’s Pebble Keys 2 a try. It worked. Momo loved it, VHB tape held it in place (unlike the pre­vi­ous sil­i­cone key­board), and Bluetooth meant no ca­ble to chew on.

Motivating Momo is easy - she’ll do any­thing for the right food re­ward. So what I re­ally needed was a dis­penser with an API, small serv­ing sizes, and the abil­ity to use my own treats (both dogs have sen­si­tive stom­achs).

I landed on Aqara’s C1 Smart Pet Feeder, con­trolled over Zigbee. Even its small­est serv­ing is too large for a 9-pound dog, so I pre­loaded each com­part­ment with a few treats and skipped the hop­per. This lim­its it to about 6 serv­ings be­fore re­fill­ing, but that’s plenty for Momo to make a game.

Zigbee on Windows was a no go - af­ter hours of dri­ver and firmware is­sues I moved the adapter to the Pi, where it worked im­me­di­ately. The fi­nal flow: DogKeyboard SSHs into the Pi and runs a script that sends two Zigbee com­mands:

{“serving_size”:1}

{“feed”:“START”}

As the num­ber of al­most-there games mounted, the need for real feed­back to Claude Code be­came clear. It al­ready had unit tests and logs, but those weren’t enough. The games would build and run but have in­vis­i­ble play­ers, bro­ken UI, or in­put that was never wired up. Claude had no way to know. It needed to be able to see and play its own games.

The first tool was straight­for­ward: a sim­ple Python script to take screen­shots of the run­ning game. Claude could launch the game, screen­shot it, and see whether the ti­tle screen ac­tu­ally ren­dered or was just a black win­dow.

The sec­ond tool was more in­ter­est­ing. I gave Claude a way to send se­quences of in­put to run­ning game in­stances - things like “left for 3 sec­onds, pause for 2 sec­onds, right for one frame, fire”. It could then take screen­shots and de­cide whether to send fol­lowup com­mands. This turned Claude into its own QA tester.

These tools did­n’t need any re­fine­ment - they just worked. And the way Claude used them sur­prised me. While test­ing one game, I watched it play through all 6 stages just to ver­ify that the fi­nal boss fight worked cor­rectly. When it found a prob­lem - a health bar that was­n’t up­dat­ing - it went back to the code, fixed it, re­launched, and played through again to con­firm.

I also pulled in a few other tools from other pro­jects I’ve made:

* Scene lin­ter. Claude some­times reuses node IDs or gen­er­ates bro­ken re­source ref­er­ences in Godot’s .tscn files. These cause cryp­tic er­rors at run­time. Since adding a lin­ter that catches these be­fore the game launches, I haven’t seen a sin­gle man­gled scene file.

* Shader lin­ter. Validates cus­tom shaders and gives spe­cific er­rors back to Claude, rather than the vague “shader failed to com­pile” that Godot pro­vides.

* Input ac­tion map­per. A small helper to get key­board/​con­troller in­put wired in cor­rectly. Claude can edit Godot’s pro­ject files di­rectly to add new in­put ac­tions, but it some­times gets the for­mat wrong and the er­ror mes­sages are un­help­ful.

All of these are open sourced, and I’d en­cour­age you to try them for your­self - even with­out a dog.

The DogKeyboard app ended up han­dling a lot more than just rout­ing key­strokes. A few de­tails worth men­tion­ing:

It mon­i­tors Claude Code us­ing Hooks and plays a chime sound when Claude goes idle - that’s Momo’s cue to type. When Claude is idle and Momo has en­tered at least 16 char­ac­ters, it auto-sub­mits by press­ing Enter. When Claude is work­ing, it back­spaces any ex­tra in­put in case Momo gets ea­ger and pe­ri­od­i­cally dis­misses Plan Mode prompts (Claude’s “review be­fore act­ing” step) that would oth­er­wise block progress.

For the video record­ing, it runs a light­weight web­server that over­lays key­strokes as they’re pressed. I added a con­fig­urable de­lay so that if the video feed is lagged, the over­lay does­n’t show key­strokes be­fore Momo ap­pears to type them.

The first de­ci­sion was which dog to train. Hana (on the right) is twice Momo’s size but far more train­able - she’s the smartest pet I’ve had. Before turn­ing 1, she fig­ured out how to jump, grab a door han­dle, and open any door in the house just by ob­serv­ing.

I ex­pected the dogs would walk across the key­board, step­ping on keys as they go. That’s what Momo did when she stole my lip balm. Since Hana is tall enough to just step over a key­board, Momo seemed like the bet­ter can­di­date. As it turned out, both dogs learned to tap and swipe their paws along the key­board rather than step on it - but Momo learned a lit­tle faster in this case, and typed a lit­tle gen­tler, so Momo it was.

The train­ing process took about 2 weeks, with some­what in­con­sis­tent ses­sions. My goal was 10 min­utes, twice a day. I started by scat­ter­ing high-value treats (freeze-dried salmon) on the key­board to build the as­so­ci­a­tion: this thing is fun and gives good food.

Momo was frus­trated at first. She knew the key­board was in­volved but did­n’t know how. She would lie on it, bark at it, and try any­thing she could think of. At some point she tried swip­ing her paw across it. I played a chime sound and heav­ily re­warded her. After a few it­er­a­tions, the as­so­ci­a­tion clicked and she started at­tack­ing the key­board with vigor any chance she had.

The next step was to au­to­mate the re­wards. I filled up the food dis­penser with treats (4 serv­ings at a time so I could con­trol the quan­tity), waited for her swipe at the key­board, and then I ran a script to play the chime and dis­pense a serv­ing of treats. After a few ses­sions of this I in­creased the dif­fi­culty - not just one swipe, but three swipes be­fore the treats came.

Simultaneously, I de­creased the treat value to keep her healthy. First a mix of mid-value treats with an oc­ca­sional high-value one, and even­tu­ally just kib­ble with an oc­ca­sional mid-value treat. She still loved it and was health­ier for it.

Eventually I au­to­mated the whole thing with the DogKeyboard app and let it run. It re­quired at least 16 char­ac­ters per serv­ing of treats, and was sup­posed to only dis­pense once per idle pe­riod of Claude Code. But bugs crept in dur­ing test­ing - a cou­ple of times it dis­pensed mul­ti­ple serv­ings in a row. Unfortunately, Momo picked up on this and now keeps mash­ing the key­board hop­ing for a sec­ond im­me­di­ate serv­ing. The only way to pull her away is to of­fer higher-value treats else­where, which is what I do af­ter she’s put in her in­put for a game.

Here’s a small sam­ple of the games Momo made. Every game shown here is playable - these aren’t mock-ups. It’s nowhere near com­pre­hen­sive, but rep­re­sents the va­ri­ety she cre­ated. As the tools and prompts im­proved, the games got no­tice­ably bet­ter. There was also a no­tice­able bump in qual­ity when Opus 4.6 dropped - Claude be­came more likely to cre­ate cus­tom shaders and other vi­sual ef­fects.

One re­cur­ring is­sue: I kept get­ting games with ba­sic glow­ing neon 3D shapes and could­n’t get any other style. Frustrated, I asked Claude why. It told me this was the sig­na­ture style of the pro­ject - Claude’s ex­ter­nal mem­ory file had latched onto it and kept re­in­forc­ing it. Wiping MEMORY.md (Claude Code’s per­sis­tent pro­ject notes) be­fore every new game fixed this and gave much more var­ied re­sults.

DJ Smirk. One of Momo’s ear­li­est games - though more of an ex­pe­ri­ence than a game. Every key on the key­board plays a dif­fer­ent tone.

Munch. A com­pet­i­tive salad build­ing game. Collect all 7 in­gre­di­ents first to win.

Zaaz. Another puz­zle game. The goal is to paint the whole level by mov­ing a paint­brush that moves in in­te­ger tiles at a time. Crash into ob­sta­cles to move shorter dis­tances. Some lev­els were un­winnable. It also has a scor­ing sys­tem I never fig­ured out. After this one I up­dated the prompt to ex­clude puz­zle games.

The Oracle Frog of Rome. Avoid the arms of a kraken and col­lect the golden chains to bind it. At some point the or­a­cle also be­came a king.

Octogroove. A sur­pris­ingly chal­leng­ing rhythm game. You’re an oc­to­pus us­ing four of your arms to play the drums. Mash beats in time with ba­sic mu­sic. It’s like Dance Dance Revolution, but made more dif­fi­cult by the beats com­ing from dif­fer­ent di­rec­tions.

Ewe Heard Me! A game about herd­ing sheep. Use stealth and your bark to cor­ral them into a pen. Unfortunately, it’s un­winnable - the first two sheep you get into the pen sim­ply stop and block any­thing else from en­ter­ing. After this one I placed a larger em­pha­sis on check­ing for win­abil­ity in the prompt and tools.

Quasar Saz. As of writ­ing, this is Momo’s most re­cent game. You play as Zara, wield­ing a cos­mic saz (a long-necked stringed in­stru­ment) to fight cor­rupted sound. There are 6 stages + a boss fight. It’s fun to play for a cou­ple rounds, has good vi­su­als, and dy­namic au­dio.

When Momo first stepped on my key­board back in December, it was just a funny ac­ci­dent. A few weeks later, job­less and look­ing for a pro­ject, I de­cided to see how far that ac­ci­dent could go. The an­swer was a lot fur­ther than I ex­pected.

The tech­ni­cal pieces - key­board rout­ing, treat dis­penser, prompt en­gi­neer­ing, feed­back tools - were all solv­able en­gi­neer­ing prob­lems. What sur­prised me was how lit­tle of the fi­nal re­sult de­pended on Momo typ­ing any­thing mean­ing­ful. The magic is­n’t in the in­put. It’s in the sys­tem around it. A well-crafted prompt, strong guardrails, au­to­mated ver­i­fi­ca­tion, and good tools can turn gen­uine non­sense into a playable game.

If there’s a take­away be­yond the spec­ta­cle, it’s this: the bot­tle­neck in AI-assisted de­vel­op­ment is­n’t the qual­ity of your ideas - it’s the qual­ity of your feed­back loops. The games got dra­mat­i­cally bet­ter not when I im­proved the prompt, but when I gave Claude the abil­ity to screen­shot its own work, play-test its own lev­els, and lint its own scene files. The same tools that let a dog’s key­board mash­ing pro­duce a work­ing game will make your own in­ten­tional work with AI sig­nif­i­cantly bet­ter.

Momo is­n’t se­cretly a game de­signer. She’s a cavapoo who learned that smack­ing a plas­tic rec­tan­gle makes kib­ble ap­pear. A year ago, the gap be­tween that and soft­ware en­gi­neer­ing felt enor­mous. Now it feels small and shrinks each day.

If you want to try any of this your­self - whether with a dog, a cat, or just your own ran­dom key­board mash­ing - every­thing is open source in the links be­low.

Play the game (with hu­man bug fixes) — Recommended. Download for Windows, Mac, or Linux

Play the game (Momo’s ver­sion) — Download for Windows, Mac, or Linux

TeaLeaves — Tools, prompts, and source for de­vel­op­ing the game

In 1978, for my 10th birth­day, I went to Disneyland and got to ride a new roller coaster called Space Mountain. It fig­u­ra­tively and lit­er­ally took my breath away. I loved every sec­ond of it and that night, I could­n’t fall asleep; I just kept think­ing about how ex­hil­a­rat­ing it was. Then, a wild thought sud­denly hit me: Why is­n’t there a roller coaster that goes up­side down?

At first, I was like that’s crazy, it can’t work. But then I re­mem­bered Spin Out, the ride with a round room that spun so fast I stuck to the wall. If that worked, why not a loop on a roller coaster? I thought that would feel and be like the same thing. I was con­vinced!

I fi­nally fell asleep dream­ing of my roller coaster, full of twists, turns, and loops.

A few days later, I told my best friend Daschle. He was older, knew every­thing, and lived next door. “Buddy,” he said, “I’ve got ex­cit­ing but crush­ing news. Your idea works.”

“Yep. I saw it. They’re build­ing one at Magic Mountain. It’s called the Revolution. Sorry, Buddy.”

But I was­n’t crushed, I was thrilled! What I knew could work was re­ally hap­pen­ing.

“How many loops does it have?” I asked.

“Ha! Mine has four. It’s called the Quadrupuler! It’s gonna be way bet­ter!”

That night I taped six sheets of pa­per to­gether and drew my blue­prints in col­ored mark­ers. As you can see from the photo it was glo­ri­ous!

Look closely, I did­n’t la­bel those coaster hills in feet or me­ters, no sir, I used build­ing “story’s” for height, and the speed at each sec­tion in miles per hour. I’m 10. I’m se­ri­ous here.

With guid­ing blue­prints, it was time to build the model.

I got a Styrofoam board and balsa wood. Cutting and glu­ing each tiny cross tie was slow, and with all my home­work, I could only work on week­ends. So I cal­cu­lated how many inches I could fin­ish in a week. The an­swer turned out to be some­thing like 5 months! After that I con­sid­ered just giv­ing up be­cause I re­al­ized that I was only 120 months old and this was go­ing to take 5% of my life! Plus, I still had­n’t fig­ured out how to make the loops, balsa wood does­n’t bend like that. But I told my­self: one piece at a time. That be­came my motto.

When I got to the first loop, I had to stop and think. What in the world could I make the loop out of? A lot of 10-year-old brain power went into imag­in­ing what sim­ple ma­te­r­ial I could use. Then one morn­ing, I had it: heat plas­tic strips over the stove­top flame and bend them as they cooled. The key? Don’t burn the house down.

I’d learned that the hard way a year ear­lier, when Daschle con­vinced me we should recre­ate the movie The Towering Inferno, with a card­board box un­der his house… and real fire. It got out of con­trol so fast, the flames started hit­ting the wood floor joists! Thankfully, we were able to smother it with the damp dirt down there. So yeah, wild card Daschle was not in­vited over for my plas­tic fire bend­ing ex­per­i­ment.

I don’t re­mem­ber where the plas­tic came from, but I do re­mem­ber hold­ing the strip with pli­ers over the flame. The first piece melted so fast and started burn­ing with thick black smoke that it scared me. I yanked it back and coughed. That’s when I made an amend­ment to the safety plan: don’t kill your­self with what­ever these hor­ri­ble smelly fumes were! I got a fan, opened the back door, and all the kitchen win­dows be­fore try­ing again. Eventually, I fig­ured out the per­fect dis­tance and tim­ing with the heat.

When I laid that fi­nal track piece, I was so ex­cited, so proud! I took the model out­side for bet­ter light­ing and snapped Polaroids. I needed it cap­tured in­stantly. Here’s a photo of the Polaroid with my 10-year-old pen­man­ship.

What was the next thought that im­me­di­ately popped into my head?

This mas­ter­piece is ready to be sold to Disneyland! And I wrote my let­ter. I don’t have the orig­i­nal of that. But I re­mem­ber it went some­thing like this:

To whom it may con­cern:

My name is Kevin Glikmann. I am 10 years old. Enclosed are Polaroids of a roller coaster model I made called the Quadrupuler. It has four loops! I think you should make it!

And I sent it off!

Suddenly, my chore of check­ing the mail af­ter school be­came a heart-pound­ing rit­ual. I was ner­vous, hope­ful, ex­cited. Sometimes I opened the mail­box slowly, peek­ing in. Sometimes I pre­tended it was just a nor­mal day. Other times, I yanked it open and grabbed the mail fast, try­ing any­thing to calm my nerves.

Daschle said, “No way they’ll re­spond. It’s Disneyland, Buddy, they get a mil­lion let­ters a day.”

Weeks turned into months, and I started to think he was right. Then one day, I opened the mail­box.

My name on it. My head was scream­ing, “This is it! I never get mail!”

HOLY SMOKES! What’s WED Enterprises? I started bounc­ing up and down. I started shak­ing. I ran into the house, ran straight to my room, tore open the en­ve­lope, and read:

Your re­cent let­ter was di­rected to my of­fice here at WED Enterprises. WED (Walter Elias Disney) is the de­sign and “Imagineering” branch of Walt Disney Productions. As such, we are re­spon­si­ble for the cre­ation of all shows, at­trac­tions and out­door en­ter­tain­ment for both Disneyland and Walt Disney World.

Thank you for show­ing us your “Quadrupuler” roller coaster - it looks like quite an ad­ven­ture! As you may know Kevin, we are cre­at­ing a new roller­coaster type at­trac­tion for Disneyland’s Frontierland. Known as Big Thunder Mountain Railroad, this thrilling ad­ven­ture will carry pas­sen­gers on a high speed jour­ney through the gold rush days of the old west. Big Thunder is sched­uled to open later this year.

Thank you Kevin, for your in­ter­est in Walt Disney Productions.

Here is a photo of the orig­i­nal let­ter:

You would think that this let­ter would have ended my in­vent­ing spirit. That I would have got­ten bit­ter and de­clared to the world, “No more will I in­vent for that mouse or any­one else!”

But no! Instead I was elated. I read it over and over. It said, “It looks like quite an ad­ven­ture!” Disneyland liked it! I could­n’t be­lieve it.

Looking back, those words from Tom Fitzgerald did­n’t just val­i­date my idea, they launched my 10-year-old self-es­teem into or­bit! (Tom Fitzgerald went on to be­come one of the most in­flu­en­tial Imagineers in Disney his­tory. According to his bio, he started in 1979. I got this let­ter in April 1979 which means this must have been one of his first tasks as a new Imagineer em­ployee. lol! I won­der if he would re­mem­ber?)

A cou­ple years later, the Rubik’s Cube came out. I was ob­sessed. It took me weeks to solve, and once I did, my first thought was: What if it could turn on the an­gle? So I in­tro­duced Mr. Rubik’s Cube to my band­saw, re­designed the core, and built a very rough pro­to­type.

Here are some pic­tures.

I sent it to Ideal Toy Company, but they re­jected it straight away, they don’t ac­cept un­so­licited ideas. But that did­n’t mat­ter. They did­n’t un­der­stand that I had a let­ter from Disney Imagineering telling me my ideas are good!

That ten-year-old in­ven­tor is still alive in me, and still does­n’t un­der­stand re­jec­tion. Over the decades, I’ve in­vented sev­eral patented board games that were shopped around but never sold.

But I’m con­vinced the early val­i­da­tion from the Disney let­ter gave me a kind of bul­let­proof re­silience.

Today, I work in one of the most re­jec­tion-heavy in­dus­tries there is, act­ing. To me, in­vent­ing and act­ing are deeply con­nected: both are about dis­cov­ery, and both feed my in­vent­ing spirit. Successes are rare, but when they come, the joy lasts for­ever.

So I keep au­di­tion­ing, and I keep in­vent­ing.

And some­times, when frus­tra­tion creeps in, won­der­ing why I’m not fur­ther along, or why things aren’t work­ing out, that 10-year-old boy ap­pears. He re­minds me of what we be­lieved back then, and what still guides me now- just keep go­ing, one piece at a time.

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We are re­leas­ing Steerling-8B, the first in­ter­pretable model that can trace any to­ken it gen­er­ates to its in­put con­text, con­cepts a hu­man can un­der­stand, and its train­ing data. Trained on 1.35 tril­lion to­kens, the model achieves down­stream per­for­mance within range of mod­els trained on 2–7× more data. Steerling-8B un­locks sev­eral ca­pa­bil­i­ties which in­clude sup­press­ing or am­pli­fy­ing spe­cific con­cepts at in­fer­ence time with­out re­train­ing, train­ing data prove­nance for any gen­er­ated chunk, and in­fer­ence-time align­ment via con­cept con­trol, re­plac­ing thou­sands of safety train­ing ex­am­ples with ex­plicit con­cept-level steer­ing.

For the first time, a lan­guage model, at the 8-billion-parameter scale, can ex­plain every to­ken it pro­duces in three key ways. More specif­i­cally, for any group of out­put to­kens that Steerling gen­er­ates, we can trace these to­kens to:

[Concepts] hu­man-un­der­stand­able top­ics in the mod­el’s rep­re­sen­ta­tions, and

We are re­leas­ing the weights of a base model trained on 1.35T to­kens as well as com­pan­ion code to in­ter­act and play with the model.

Below we show Steerling-8B gen­er­at­ing text from a prompt across var­i­ous cat­e­gories. You can se­lect an ex­am­ple, then click on any high­lighted chunk of the out­put. The panel be­low will up­date to show:

Input Feature at­tri­bu­tion: which to­kens in the in­put prompt strongly in­flu­enced that chunk.

Concept at­tri­bu­tion: the ranked list of con­cepts, both tone (e.g. an­a­lyt­i­cal, clin­i­cal) and con­tent (e.g. Genetic al­ter­ation method­olo­gies), that the model routed through to pro­duce that chunk.

Training data at­tri­bu­tion: how the con­cepts in that chunk dis­trib­ute across train­ing sources (ArXiv, Wikipedia, FLAN, etc.), show­ing where in the train­ing data the mod­el’s knowl­edge orig­i­nates.

Steerling is built on a causal dis­crete dif­fu­sion model back­bone, which lets us steer gen­er­a­tion across multi-to­ken to­kens rather than only at the next-to­ken. The key de­sign choice is de­com­pos­ing the mod­el’s em­bed­dings into three ex­plicit path­ways: ~33K su­per­vised “known” con­cepts, ~100K “discovered” con­cepts the model learns on its own, and a resid­ual that cap­tures what­ever re­mains.

We then con­strain the model with train­ing loss func­tions that en­sure the model routes sig­nal through con­cepts with­out a fun­da­men­tal trade­off with per­for­mance. The con­cepts feed into log­its through a lin­ear path, every pre­dic­tion de­com­poses ex­actly into per-con­cept con­tri­bu­tions, and we can edit those con­tri­bu­tions at in­fer­ence time with­out re­train­ing. For the full ar­chi­tec­ture, train­ing ob­jec­tives, and scal­ing analy­sis, see Scaling Interpretable Models to 8B.

Despite be­ing trained on sig­nif­i­cantly fewer com­pute than com­pa­ra­ble mod­els, Steerling-8B achieves com­pet­i­tive per­for­mance across stan­dard bench­marks. The fig­ure be­low shows av­er­age per­for­mance (across 7 bench­marks) ver­sus ap­prox­i­mate train­ing FLOPs on a log scale, with ver­ti­cal lines mark­ing mul­ti­ples of Steerling’s com­pute bud­get.

In the pre­vi­ous up­date, we shared sev­eral ways that as­sess how in­ter­pretable a mod­el’s rep­re­sen­ta­tions are. Here we pro­vide an­other met­ric that gives in­sight into the mod­el’s use of its con­cepts. On a held-out val­i­da­tion set, over 84% of to­ken-level con­tri­bu­tion comes from the con­cept mod­ule: the model is not just us­ing the resid­ual to make its pre­dic­tions. This mat­ters for con­trol: if the mod­el’s pre­dic­tions gen­uinely flow through con­cepts, then edit­ing those con­cepts at in­fer­ence time ac­tu­ally changes what the model does rather than nudg­ing a side chan­nel while the real work hap­pens else­where.

A use­ful check is what hap­pens when we re­move the resid­ual path­way. On sev­eral LM Harness tasks, drop­ping the resid­ual has only a small ef­fect, which sug­gests the mod­el’s pre­dic­tive sig­nal is largely routed through con­cepts rather than hid­den “everything-else” chan­nels.

Finally, Steerling can de­tect known con­cepts in text with 96.2% AUC on a held-out val­i­da­tion dataset.

In the com­ing weeks, we’ll be re­leas­ing deep dives on each of these ca­pa­bil­i­ties:

* Concept dis­cov­ery: what did Steerling learn that we did­n’t teach it? We’ll open up the dis­cov­ered con­cept space and show struc­ture that sur­prised us.

* Alignment with­out fine-tun­ing: re­place thou­sands of safety train­ing ex­am­ples with a hand­ful of con­cept-level in­ter­ven­tions.

* Memorization & train­ing data val­u­a­tion: trace any gen­er­a­tion back to the train­ing data that pro­duced it, and as­sign value to in­di­vid­ual data sources.

* The case for in­her­ent in­ter­pretabil­ity: what do you gain when in­ter­pretabil­ity is de­signed in from the start, and what do you miss when it’s bolted on af­ter the fact?

We’ll cover each of these in de­tail in up­com­ing posts, with quan­ti­ta­tive eval­u­a­tions and de­ploy­ment-ori­ented case stud­ies.