Tim Cook to be­come Apple Executive ChairmanJohn Ternusto be­come Apple CEO

Apple Executive Chairman

John Ternusto be­come Apple CEO

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Our lat­est model, Claude Opus 4.7, is now gen­er­ally avail­able. Opus 4.7 is a no­table im­prove­ment on Opus 4.6 in ad­vanced soft­ware en­gi­neer­ing, with par­tic­u­lar gains on the most dif­fi­cult tasks. Users re­port be­ing able to hand off their hard­est cod­ing work—the kind that pre­vi­ously needed close su­per­vi­sion—to Opus 4.7 with con­fi­dence. Opus 4.7 han­dles com­plex, long-run­ning tasks with rigor and con­sis­tency, pays pre­cise at­ten­tion to in­struc­tions, and de­vises ways to ver­ify its own out­puts be­fore re­port­ing back.The model also has sub­stan­tially bet­ter vi­sion: it can see im­ages in greater res­o­lu­tion. It’s more taste­ful and cre­ative when com­plet­ing pro­fes­sional tasks, pro­duc­ing higher-qual­ity in­ter­faces, slides, and docs. And—although it is less broadly ca­pa­ble than our most pow­er­ful model, Claude Mythos Preview—it shows bet­ter re­sults than Opus 4.6 across a range of bench­marks:Last week we an­nounced Project Glasswing, high­light­ing the risks—and ben­e­fits—of AI mod­els for cy­ber­se­cu­rity. We stated that we would keep Claude Mythos Preview’s re­lease lim­ited and test new cy­ber safe­guards on less ca­pa­ble mod­els first. Opus 4.7 is the first such model: its cy­ber ca­pa­bil­i­ties are not as ad­vanced as those of Mythos Preview (indeed, dur­ing its train­ing we ex­per­i­mented with ef­forts to dif­fer­en­tially re­duce these ca­pa­bil­i­ties). We are re­leas­ing Opus 4.7 with safe­guards that au­to­mat­i­cally de­tect and block re­quests that in­di­cate pro­hib­ited or high-risk cy­ber­se­cu­rity uses. What we learn from the real-world de­ploy­ment of these safe­guards will help us work to­wards our even­tual goal of a broad re­lease of Mythos-class mod­els.Se­cu­rity pro­fes­sion­als who wish to use Opus 4.7 for le­git­i­mate cy­ber­se­cu­rity pur­poses (such as vul­ner­a­bil­ity re­search, pen­e­tra­tion test­ing, and red-team­ing) are in­vited to join our new Cyber Verification Program.Opus 4.7 is avail­able to­day across all Claude prod­ucts and our API, Amazon Bedrock, Google Cloud’s Vertex AI, and Microsoft Foundry. Pricing re­mains the same as Opus 4.6: $5 per mil­lion in­put to­kens and $25 per mil­lion out­put to­kens. Developers can use claude-opus-4 – 7 via the Claude API.Claude Opus 4.7 has gar­nered strong feed­back from our early-ac­cess testers:In early test­ing, we’re see­ing the po­ten­tial for a sig­nif­i­cant leap for our de­vel­op­ers with Claude Opus 4.7. It catches its own log­i­cal faults dur­ing the plan­ning phase and ac­cel­er­ates ex­e­cu­tion, far be­yond pre­vi­ous Claude mod­els. As a fi­nan­cial tech­nol­ogy plat­form serv­ing mil­lions of con­sumers and busi­nesses at sig­nif­i­cant scale, this com­bi­na­tion of speed and pre­ci­sion could be game-chang­ing: ac­cel­er­at­ing de­vel­op­ment ve­loc­ity for faster de­liv­ery of the trusted fi­nan­cial so­lu­tions our cus­tomers rely on every day.An­thropic has al­ready set the stan­dard for cod­ing mod­els, and Claude Opus 4.7 pushes that fur­ther in a mean­ing­ful way as the state-of-the-art model on the mar­ket. In our in­ter­nal evals, it stands out not just for raw ca­pa­bil­ity, but for how well it han­dles real-world async work­flows—au­toma­tions, CI/CD, and long-run­ning tasks. It also thinks more deeply about prob­lems and brings a more opin­ion­ated per­spec­tive, rather than sim­ply agree­ing with the user.Claude Opus 4.7 is the strongest model Hex has eval­u­ated. It cor­rectly re­ports when data is miss­ing in­stead of pro­vid­ing plau­si­ble-but-in­cor­rect fall­backs, and it re­sists dis­so­nant-data traps that even Opus 4.6 falls for. It’s a more in­tel­li­gent, more ef­fi­cient Opus 4.6: low-ef­fort Opus 4.7 is roughly equiv­a­lent to medium-ef­fort Opus 4.6.On our 93-task cod­ing bench­mark, Claude Opus 4.7 lifted res­o­lu­tion by 13% over Opus 4.6, in­clud­ing four tasks nei­ther Opus 4.6 nor Sonnet 4.6 could solve. Combined with faster me­dian la­tency and strict in­struc­tion fol­low­ing, it’s par­tic­u­larly mean­ing­ful for com­plex, long-run­ning cod­ing work­flows. It cuts the fric­tion from those multi-step tasks so de­vel­op­ers can stay in the flow and fo­cus on build­ing.Based on our in­ter­nal re­search-agent bench­mark, Claude Opus 4.7 has the strongest ef­fi­ciency base­line we’ve seen for multi-step work. It tied for the top over­all score across our six mod­ules at 0.715 and de­liv­ered the most con­sis­tent long-con­text per­for­mance of any model we tested. On General Finance—our largest mod­ule—it im­proved mean­ing­fully on Opus 4.6, scor­ing 0.813 ver­sus 0.767, while also show­ing the best dis­clo­sure and data dis­ci­pline in the group. And on de­duc­tive logic, an area where Opus 4.6 strug­gled, Opus 4.7 is solid.Claude Opus 4.7 ex­tends the limit of what mod­els can do to in­ves­ti­gate and get tasks done. Anthropic has clearly op­ti­mized for sus­tained rea­son­ing over long runs, and it shows with mar­ket-lead­ing per­for­mance. As en­gi­neers shift from work­ing 1:1 with agents to man­ag­ing them in par­al­lel, this is ex­actly the kind of fron­tier ca­pa­bil­ity that un­locks new work­flows.We’re see­ing ma­jor im­prove­ments in Claude Opus 4.7’s mul­ti­modal un­der­stand­ing, from read­ing chem­i­cal struc­tures to in­ter­pret­ing com­plex tech­ni­cal di­a­grams. The higher res­o­lu­tion sup­port is help­ing Solve Intelligence build best-in-class tools for life sci­ences patent work­flows, from draft­ing and pros­e­cu­tion to in­fringe­ment de­tec­tion and in­va­lid­ity chart­ing.Claude Opus 4.7 takes long-hori­zon au­ton­omy to a new level in Devin. It works co­her­ently for hours, pushes through hard prob­lems rather than giv­ing up, and un­locks a class of deep in­ves­ti­ga­tion work we could­n’t re­li­ably run be­fore.For Replit, Claude Opus 4.7 was an easy up­grade de­ci­sion. For the work our users do every day, we ob­served it achiev­ing the same qual­ity at lower cost—more ef­fi­cient and pre­cise at tasks like an­a­lyz­ing logs and traces, find­ing bugs, and propos­ing fixes. Personally, I love how it pushes back dur­ing tech­ni­cal dis­cus­sions to help me make bet­ter de­ci­sions. It re­ally feels like a bet­ter coworker.Claude Opus 4.7 demon­strates strong sub­stan­tive ac­cu­racy on BigLaw Bench for Harvey, scor­ing 90.9% at high ef­fort with bet­ter rea­son­ing cal­i­bra­tion on re­view ta­bles and no­tice­ably smarter han­dling of am­bigu­ous doc­u­ment edit­ing tasks. It cor­rectly dis­tin­guishes as­sign­ment pro­vi­sions from change-of-con­trol pro­vi­sions, a task that has his­tor­i­cally chal­lenged fron­tier mod­els. Substance was con­sis­tently rated as a strength across our eval­u­a­tions: cor­rect, thor­ough, and well-cited.Claude Opus 4.7 is a very im­pres­sive cod­ing model, par­tic­u­larly for its au­ton­omy and more cre­ative rea­son­ing. On CursorBench, Opus 4.7 is a mean­ing­ful jump in ca­pa­bil­i­ties, clear­ing 70% ver­sus Opus 4.6 at 58%.For com­plex multi-step work­flows, Claude Opus 4.7 is a clear step up: plus 14% over Opus 4.6 at fewer to­kens and a third of the tool er­rors. It’s the first model to pass our im­plicit-need tests, and it keeps ex­e­cut­ing through tool fail­ures that used to stop Opus cold. This is the re­li­a­bil­ity jump that makes Notion Agent feel like a true team­mate.In our evals, we saw a dou­ble-digit jump in ac­cu­racy of tool calls and plan­ning in our core or­ches­tra­tor agents. As users lever­age Hebbia to plan and ex­e­cute on use cases like re­trieval, slide cre­ation, or doc­u­ment gen­er­a­tion, Claude Opus 4.7 shows the po­ten­tial to im­prove agent de­ci­sion-mak­ing in these work­flows.On Rakuten-SWE-Bench, Claude Opus 4.7 re­solves 3x more pro­duc­tion tasks than Opus 4.6, with dou­ble-digit gains in Code Quality and Test Quality. This is a mean­ing­ful lift and a clear up­grade for the en­gi­neer­ing work our teams are ship­ping every day.For CodeRabbit’s code re­view work­loads, Claude Opus 4.7 is the sharpest model we’ve tested. Recall im­proved by over 10%, sur­fac­ing some of the most dif­fi­cult-to-de­tect bugs in our most com­plex PRs, while pre­ci­sion re­mained sta­ble de­spite the in­creased cov­er­age. It’s a bit faster than GPT-5.4 xhigh on our har­ness, and we’re lin­ing it up for our heav­i­est re­view work at launch.For Genspark’s Super Agent, Claude Opus 4.7 nails the three pro­duc­tion dif­fer­en­tia­tors that mat­ter most: loop re­sis­tance, con­sis­tency, and grace­ful er­ror re­cov­ery. Loop re­sis­tance is the most crit­i­cal. A model that loops in­def­i­nitely on 1 in 18 queries wastes com­pute and blocks users. Lower vari­ance means fewer sur­prises in prod. And Opus 4.7 achieves the high­est qual­ity-per-tool-call ra­tio we’ve mea­sured.Claude Opus 4.7 is a mean­ing­ful step up for Warp. Opus 4.6 is one of the best mod­els out there for de­vel­op­ers, and this model is mea­sur­ably more thor­ough on top of that. It passed Terminal Bench tasks that prior Claude mod­els had failed, and worked through a tricky con­cur­rency bug Opus 4.6 could­n’t crack. For us, that’s the sig­nal.Claude Opus 4.7 is the best model in the world for build­ing dash­boards and data-rich in­ter­faces. The de­sign taste is gen­uinely sur­pris­ing—it makes choices I’d ac­tu­ally ship. It’s my de­fault daily dri­ver now.Claude Opus 4.7 is the most ca­pa­ble model we’ve tested at Quantium. Evaluated against lead­ing AI mod­els through our pro­pri­etary bench­mark­ing so­lu­tion, the biggest gains showed up where they mat­ter most: rea­son­ing depth, struc­tured prob­lem-fram­ing, and com­plex tech­ni­cal work. Fewer cor­rec­tions, faster it­er­a­tions, and stronger out­puts to solve the hard­est prob­lems our clients bring us.Claude Opus 4.7 feels like a real step up in in­tel­li­gence. Code qual­ity is no­tice­ably im­proved, it’s cut­ting out the mean­ing­less wrap­per func­tions and fall­back scaf­fold­ing that used to pile up, and fixes its own code as it goes. It’s the clean­est jump we’ve seen since the move from Sonnet 3.7 to the Claude 4 se­ries.For the com­puter-use work that sits at the heart of XBOW’s au­tonomous pen­e­tra­tion test­ing, the new Claude Opus 4.7 is a step change: 98.5% on our vi­sual-acu­ity bench­mark ver­sus 54.5% for Opus 4.6. Our sin­gle biggest Opus pain point ef­fec­tively dis­ap­peared, and that un­locks its use for a whole class of work where we could­n’t use it be­fore.Claude Opus 4.7 is a solid up­grade with no re­gres­sions for Vercel. It’s phe­nom­e­nal on one-shot cod­ing tasks, more cor­rect and com­plete than Opus 4.6, and no­tice­ably more hon­est about its own lim­its. It even does proofs on sys­tems code be­fore start­ing work, which is new be­hav­ior we haven’t seen from ear­lier Claude mod­els.Claude Opus 4.7 is very strong and out­per­forms Opus 4.6 with a 10% to 15% lift in task suc­cess for Factory Droids, with fewer tool er­rors and more re­li­able fol­low-through on val­i­da­tion steps. It car­ries work all the way through in­stead of stop­ping halfway, which is ex­actly what en­ter­prise en­gi­neer­ing teams need.Claude Opus 4.7 au­tonomously built a com­plete Rust text-to-speech en­gine from scratch—neural model, SIMD ker­nels, browser demo—then fed its own out­put through a speech rec­og­nizer to ver­ify it matched the Python ref­er­ence. Months of se­nior en­gi­neer­ing, de­liv­ered au­tonomously. The step up from Opus 4.6 is clear, and the code­base is pub­lic.Claude Opus 4.7 passed three TBench tasks that prior Claude mod­els could­n’t, and it’s land­ing fixes our pre­vi­ous best model missed, in­clud­ing a race con­di­tion. It demon­strates strong pre­ci­sion in iden­ti­fy­ing real is­sues, and sur­faces im­por­tant find­ings that other mod­els ei­ther gave up on or did­n’t re­solve. In Qodo’s real-world code re­view bench­mark, we ob­served top-tier pre­ci­sion.On Databricks’ OfficeQA Pro, Claude Opus 4.7 shows mean­ing­fully stronger doc­u­ment rea­son­ing, with 21% fewer er­rors than Opus 4.6 when work­ing with source in­for­ma­tion. Across our agen­tic rea­son­ing over data bench­marks, it is the best-per­form­ing Claude model for en­ter­prise doc­u­ment analy­sis.For Ramp, Claude Opus 4.7 stands out in agent-team work­flows. We’re see­ing stronger role fi­delity, in­struc­tion-fol­low­ing, co­or­di­na­tion, and com­plex rea­son­ing, es­pe­cially on en­gi­neer­ing tasks that span tools, code­bases, and de­bug­ging con­text. Compared with Opus 4.6, it needs much less step-by-step guid­ance, help­ing us scale the in­ter­nal agent work­flows our en­gi­neer­ing teams run.Claude Opus 4.7 is mea­sur­ably bet­ter than Opus 4.6 for Bolt’s longer-run­ning app-build­ing work, up to 10% bet­ter in the best cases, with­out the re­gres­sions we’ve come to ex­pect from very agen­tic mod­els. It pushes the ceil­ing on what our users can ship in a sin­gle ses­sion.Be­low are some high­lights and notes from our early test­ing of Opus 4.7:Instruction fol­low­ing. Opus 4.7 is sub­stan­tially bet­ter at fol­low­ing in­struc­tions. Interestingly, this means that prompts writ­ten for ear­lier mod­els can some­times now pro­duce un­ex­pected re­sults: where pre­vi­ous mod­els in­ter­preted in­struc­tions loosely or skipped parts en­tirely, Opus 4.7 takes the in­struc­tions lit­er­ally. Users should re-tune their prompts and har­nesses ac­cord­ingly.Im­proved mul­ti­modal sup­port. Opus 4.7 has bet­ter vi­sion for high-res­o­lu­tion im­ages: it can ac­cept im­ages up to 2,576 pix­els on the long edge (~3.75 megapix­els), more than three times as many as prior Claude mod­els. This opens up a wealth of mul­ti­modal uses that de­pend on fine vi­sual de­tail: com­puter-use agents read­ing dense screen­shots, data ex­trac­tions from com­plex di­a­grams, and work that needs pixel-per­fect ref­er­ences.1Real-world work. As well as its state-of-the-art score on the Finance Agent eval­u­a­tion (see table above), our in­ter­nal test­ing showed Opus 4.7 to be a more ef­fec­tive fi­nance an­a­lyst than Opus 4.6, pro­duc­ing rig­or­ous analy­ses and mod­els, more pro­fes­sional pre­sen­ta­tions, and tighter in­te­gra­tion across tasks. Opus 4.7 is also state-of-the-art on GDPval-AA, a third-party eval­u­a­tion of eco­nom­i­cally valu­able knowl­edge work across fi­nance, le­gal, and other do­mains.Mem­ory. Opus 4.7 is bet­ter at us­ing file sys­tem-based mem­ory. It re­mem­bers im­por­tant notes across long, multi-ses­sion work, and uses them to move on to new tasks that, as a re­sult, need less up-front con­text.The charts be­low dis­play more eval­u­a­tion re­sults from our pre-re­lease test­ing, across a range of dif­fer­ent do­mains:Over­all, Opus 4.7 shows a sim­i­lar safety pro­file to Opus 4.6: our eval­u­a­tions show low rates of con­cern­ing be­hav­ior such as de­cep­tion, syco­phancy, and co­op­er­a­tion with mis­use. On some mea­sures, such as hon­esty and re­sis­tance to ma­li­cious “prompt in­jec­tion” at­tacks, Opus 4.7 is an im­prove­ment on Opus 4.6; in oth­ers (such as its ten­dency to give overly de­tailed harm-re­duc­tion ad­vice on con­trolled sub­stances), Opus 4.7 is mod­estly weaker. Our align­ment as­sess­ment con­cluded that the model is “largely well-aligned and trust­wor­thy, though not fully ideal in its be­hav­ior”. Note that Mythos Preview re­mains the best-aligned model we’ve trained ac­cord­ing to our eval­u­a­tions. Our safety eval­u­a­tions are dis­cussed in full in the Claude Opus 4.7 System Card.Overall mis­aligned be­hav­ior score from our au­to­mated be­hav­ioral au­dit. On this eval­u­a­tion, Opus 4.7 is a mod­est im­prove­ment on Opus 4.6 and Sonnet 4.6, but Mythos Preview still shows the low­est rates of mis­aligned be­hav­ior.In ad­di­tion to Claude Opus 4.7 it­self, we’re launch­ing the fol­low­ing up­dates:More ef­fort con­trol: Opus 4.7 in­tro­duces a new xhigh (“extra high”) ef­fort level be­tween high and max, giv­ing users finer con­trol over the trade­off be­tween rea­son­ing and la­tency on hard prob­lems. In Claude Code, we’ve raised the de­fault ef­fort level to xhigh for all plans. When test­ing Opus 4.7 for cod­ing and agen­tic use cases, we rec­om­mend start­ing with high or xhigh ef­fort.On the Claude Platform (API): as well as sup­port for higher-res­o­lu­tion im­ages, we’re also launch­ing task bud­gets in pub­lic beta, giv­ing de­vel­op­ers a way to guide Claude’s to­ken spend so it can pri­or­i­tize work across longer runs.In Claude Code: The new /ultrareview slash com­mand pro­duces a ded­i­cated re­view ses­sion that reads through changes and flags bugs and de­sign is­sues that a care­ful re­viewer would catch. We’re giv­ing Pro and Max Claude Code users three free ul­tra­reviews to try it out. In ad­di­tion, we’ve ex­tended auto mode to Max users. Auto mode is a new per­mis­sions op­tion where Claude makes de­ci­sions on your be­half, mean­ing that you can run longer tasks with fewer in­ter­rup­tions—and with less risk than if you had cho­sen to skip all per­mis­sions.Opus 4.7 is a di­rect up­grade to Opus 4.6, but two changes are worth plan­ning for be­cause they af­fect to­ken us­age. First, Opus 4.7 uses an up­dated to­k­enizer that im­proves how the model processes text. The trade­off is that the same in­put can map to more to­kens—roughly 1.0 – 1.35× de­pend­ing on the con­tent type. Second, Opus 4.7 thinks more at higher ef­fort lev­els, par­tic­u­larly on later turns in agen­tic set­tings. This im­proves its re­li­a­bil­ity on hard prob­lems, but it does mean it pro­duces more out­put to­kens. Users can con­trol to­ken us­age in var­i­ous ways: by us­ing the ef­fort pa­ra­me­ter, ad­just­ing their task bud­gets, or prompt­ing the model to be more con­cise. In our own test­ing, the net ef­fect is fa­vor­able—to­ken us­age across all ef­fort lev­els is im­proved on an in­ter­nal cod­ing eval­u­a­tion, as shown be­low—but we rec­om­mend mea­sur­ing the dif­fer­ence on real traf­fic. We’ve writ­ten a mi­gra­tion guide that pro­vides fur­ther ad­vice on up­grad­ing from Opus 4.6 to Opus 4.7.Score on an in­ter­nal agen­tic cod­ing eval­u­a­tion as a func­tion of to­ken us­age at each ef­fort level. In this eval­u­a­tion, the model works au­tonomously from a sin­gle user prompt, and re­sults may not be rep­re­sen­ta­tive of to­ken us­age in in­ter­ac­tive cod­ing. See the mi­gra­tion guide for more on tun­ing ef­fort lev­els.

In September 2024, Amandla Thomas-Johnson was a Ph. D. candidate study­ing in the U.S. on a stu­dent visa when he briefly at­tended a pro-Pales­tin­ian protest. In April 2025, Immigration and Customs Enforcement (ICE) sent Google an ad­min­is­tra­tive sub­poena re­quest­ing his data. The next month, Google gave Thomas-Johnson’s information to ICE with­out giv­ing him the chance to chal­lenge the sub­poena, break­ing a nearly decade-long promise to no­tify users be­fore hand­ing their data to law en­force­ment.

Google names a hand­ful of ex­cep­tions to this promise (such as if Google re­ceives a gag or­der from a court) that do not ap­ply to Thomas-Johnson’s case. While ICE “requested” that Google not no­tify Thomas-Johnson, the re­quest was not en­force­able or man­dated by a court. Today, the Electronic Frontier Foundation sent com­plaints to the California and New York Attorneys General ask­ing them to in­ves­ti­gate Google for de­cep­tive trade prac­tices for break­ing that promise. You can read about the com­plaints here. Below is Thomas-Johnson’s ac­count of his or­deal.

I thought my or­deal with U. S. immigration au­thor­i­ties was over a year ago, when I left the coun­try, cross­ing into Canada at Ni­a­gara Falls.

By that point, the Trump ad­min­is­tra­tion had ef­fec­tively turned fed­eral power against in­ter­na­tional stu­dents like me. After I attended a pro-Palestine protest at Cornell University—for all of five min­utes—the ad­min­is­tra­tion’s rhetoric about crack­ing down on stu­dents protest­ing what we saw as geno­cide forced me into hid­ing for three months. Federal agents came to my home look­ing for me. A friend was de­tained at an air­port in Tampa and in­ter­ro­gated about my where­abouts.

I’m currently a Ph. D. stu­dent. Before that, I was a re­porter. I’m a dual British and Trinadad and Tobago cit­i­zen. I have not been ac­cused of any crime.

I be­lieved that once I left U. S. territory, I had also left the reach of its au­thor­i­ties. I was wrong.

Weeks later, in Geneva, Switzerland, I re­ceived what looked like a rou­tine email from Google. It in­formed me that the com­pany had al­ready handed over my ac­count data to the Department of Homeland Security.

At first, I wasn’t alarmed. I had seen some­thing sim­i­lar be­fore. An as­so­ci­ate of mine, Momodou Taal, had re­ceived ad­vance no­tice from Google and Facebook that his data had been re­quested. He was given ad­vanced no­tice of the sub­poe­nas, and law en­force­ment even­tu­ally with­drew them be­fore the com­pa­nies turned over his data.

Google had al­ready dis­closed my data with­out telling me.

I as­sumed I would be given the same op­por­tu­nity. But the lan­guage in my email was dif­fer­ent. It was fi­nal: “Google has re­ceived and re­sponded to le­gal process from a law en­force­ment au­thor­ity com­pelling the re­lease of in­for­ma­tion re­lated to your Google Account.”

Google had al­ready dis­closed my data with­out telling me. There was no op­por­tu­nity to con­test it.

To be clear, this should not have hap­pened this way. Google promises that it will no­tify users be­fore their data is handed over in re­sponse to le­gal processes, in­clud­ing ad­min­is­tra­tive sub­poe­nas. That no­tice is meant to pro­vide a chance to chal­lenge the re­quest. In my case, that safe­guard was by­passed. My data was handed over with­out warn­ing—at the re­quest of an ad­min­is­tra­tion tar­get­ing stu­dents en­gaged in pro­tected po­lit­i­cal speech.

Months later, my lawyer at the Electronic Frontier Foundation obtained the sub­poena it­self. On pa­per, the re­quest fo­cused largely on sub­scriber in­for­ma­tion: IP ad­dresses, phys­i­cal ad­dress, other iden­ti­fiers, and ses­sion times and du­ra­tions.

But taken to­gether, these frag­ments form some­thing far more pow­er­ful—a de­tailed sur­veil­lance pro­file. IP logs can be used to ap­prox­i­mate lo­ca­tion. Phys­i­cal ad­dresses show where you sleep. Ses­sion times would show when you were com­mu­ni­cat­ing with friends or fam­ily. Even with­out mes­sage con­tent, the pic­ture that emerges is in­ti­mate and in­va­sive.

What this ex­pe­ri­ence has made clear is that any­one can be tar­geted by law en­force­ment. And with their mas­sive stores of data, tech­nol­ogy com­pa­nies can fa­cil­i­tate those ar­bi­trary in­ves­ti­ga­tions. Together, they can com­bine state power, cor­po­rate data, and al­go­rith­mic in­fer­ence in ways that are dif­fi­cult to see—and even harder to chal­lenge.

The con­se­quences of what hap­pened to me are not ab­stract. I left the United States. But I do not feel that I have left its reach. Being in­ves­ti­gated by the fed­eral gov­ern­ment is in­tim­i­dat­ing. Questions run through your head. Am I now a marked in­di­vid­ual? Will I face height­ened scrutiny if I con­tinue my re­port­ing? Can I travel safely to see fam­ily in the Caribbean?

Who, ex­actly, can I hold ac­count­able?

Update: This post has been up­dated to in­clude more in­for­ma­tion about Google’s ex­cep­tions to their no­ti­fi­ca­tion pol­icy, none of which ap­plied to the sub­poena tar­get­ing Thomas-Johnson.

We’re check­ing if you’re a real per­son and not an au­to­mated bad bot. Usually, the captcha be­low will com­plete it­self. If it does­n’t, sim­ply click the check­box in the captcha to ver­ify. Once ver­i­fied, you’ll be taken to the page you wanted to visit.

If for some rea­son af­ter ver­i­fy­ing the captcha above, you are con­stantly be­ing redi­rected to this ex­act same page to re-ver­ify the captcha again, then please click on the but­ton be­low to get in touch with the sup­port team.

Finally, great bat­tery life in a Framework Laptop

20 hours

Netflix 4K stream­ing250nit bright­ness, 30% vol­ume, Windows 11

17 hours

Active web us­age

250nit bright­ness, 30% vol­ume, Windows 11

11 hours

Video con­fer­enc­ing250nit bright­ness, 30% vol­ume, Windows 11

7 days

Standby with­out charg­ing

Wi-Fi con­nected on Ubuntu

Intel® Core™ Ultra Series 3 proces­sors

The Framework Laptop 13 Pro runs on Intel® Core™ Ultra Series 3 proces­sors, un­lock­ing 20 hours of bat­tery ϟ life, up to 64GB of LPCAMM2 LPDDR5X mem­ory, and sup­port for up to 8TB of PCIe Gen 5.0 NVMe stor­age. It’s de­signed to stay re­spon­sive un­der sus­tained, heavy work­loads.

Power-efficient mem­ory, made up­grade­able

We’re among the first to pair Intel® Core™ Ultra Series 3 with LPCAMM2. A high-den­sity in­ter­poser en­ables LPDDR5X in a mod­u­lar form, de­liv­er­ing 7467 MT/s and high per­for­mance per watt with­out sol­der­ing it down.

A lap­top that you own

You can cus­tomize it,

Pick your ports with the Framework Expansion Card sys­tem and in­stall them di­rectly into your lap­top with­out re­ly­ing on ex­ter­nal adapters. The mag­net-at­tach Bezel lets you cus­tomize with bold or translu­cent color op­tions.

USB-C

USB-A

Audio Jack

DisplayPort

HDMI

MicroSD

SD

Storage - 250GB

Storage - 1TB

Ethernet

re­pair it,

A truly easy-to-re­pair lap­top that’s built to re­spect your rights. Just scan the QR codes, fol­low the guides, and re­place any part with a sin­gle tool that’s in­cluded in the box.

up­grade it.

When you’re ready for more per­for­mance, you can up­grade in­di­vid­ual com­po­nents in­stead of re­plac­ing your en­tire lap­top. Install a new Mainboard for gen­er­a­tional proces­sor up­grades, add mem­ory to han­dle heav­ier work­loads, or ex­pand your stor­age to in­crease ca­pac­ity or en­able dual boot­ing. The Framework Marketplace makes it easy to find the com­pat­i­ble parts you need.

Runs Linux. Really well.

(you can also use Windows 11 if you want)

We don’t just sup­port Linux; we live in it. Framework Laptop 13 Pro with Intel® Core™ Ultra Series 3 is our first Ubuntu Certified sys­tem. We seed de­vel­op­ment hard­ware and pro­vide fund­ing to a range of other dis­tros like Fedora, Bazzite, NixOS, CachyOS, and more to en­sure re­li­able sup­port.

A sen­sory up­grade

13.5″ 2880x1920 Touchscreen Display

A cus­tom 13.5″ 3:2 touch­screen dis­play with sharp 2880×1920 res­o­lu­tion gives you the ver­ti­cal space you need for cod­ing and pro­duc­tiv­ity. A 30 – 120Hz vari­able re­fresh rate keeps mo­tion smooth while op­ti­miz­ing power, and with up to 700nits of bright­ness and a matte sur­face, it stays clear across a wide range of light­ing con­di­tions.

A hap­tic touch­pad that beats your ex­pec­ta­tions

The large 123.7mm × 76.7mm Haptic Touchpad, pow­ered by four piezo­elec­tric ac­tu­a­tors, de­liv­ers con­sis­tent, high-qual­ity clicks across the sur­face. Feedback and ges­tures are fully tun­able, so you can set it up ex­actly how you want.

The key­board you love, now even bet­ter

With 1.5mm of key travel, the key­board de­liv­ers deeper, more tac­tile feed­back than most mod­ern lap­tops with­out in­creas­ing noise. A CNC alu­minum Input Cover Frame re­duces deck flex for a more solid and con­sis­tent feel. Available in mul­ti­ple ANSI and ISO lay­outs, in black, black with laven­der, and black with gray and or­ange.

Dolby Atmos® au­dio

The side-fir­ing speak­ers are tuned with Dolby Atmos® to de­liver clear, bal­anced au­dio on Windows, ideal for calls or mu­sic while you work.

Thin, light, and fully alu­minum

At just 15.85mm thick and 1.4kg, gain­ing dura­bil­ity does­n’t mean los­ing porta­bil­ity. The Top Cover, Input Cover, and Bottom Cover are now CNC ma­chined from 6063 alu­minum, in­creas­ing rigid­ity and dura­bil­ity.

296.63mm

Width

228.98mm

Depth

15.85mm

Height

1.4kg

Weight

Open source ecosys­tems

We’ve open sourced de­sign files and doc­u­men­ta­tion for many core com­po­nents and firmware on GitHub, giv­ing you the free­dom to mod­ify, ex­tend, or re­pur­pose them.

Respecting your pri­vacy

Privacy switches

Your pri­vacy is pro­tected at a hard­ware level, with phys­i­cal switches that elec­tri­cally cut off the we­b­cam and mi­cro­phones when­ever you need.

No crap­ware

We hate soft­ware bloat as much as you do. Our pre-builts ship with Ubuntu or stock Windows 11 plus the nec­es­sary dri­vers, and our DIY Edition lets you bring whichever op­er­at­ing sys­tem you’d like.

The choice is yours

Framework Laptop 13 Pro is avail­able pre-built with Windows or Ubuntu pre-in­stalled, or as a DIY Edition that lets you in­stall the op­er­at­ing sys­tem of your choice.

Upgrade, cus­tomize, and re­pair

Pick up new parts and mod­ules for your Framework Laptop 13 Pro.

Keep track of what we’re work­ing on with the Framework Newsletter.

ϟ

Testing con­ducted by Framework in April 2026 us­ing Framework Laptop 13 Pro tested with Intel® Core™ Ultra X7 358H Processor, Intel® Arc™ B390 graph­ics, 2.8K touch­screen dis­play, 32GB mem­ory and 1TB stor­age, with dis­play bright­ness set to 250nits, dis­play re­fresh rate set to 60Hz, speaker vol­ume as 30%, Dolby Atmos® dis­abled, and wire­less en­abled. Battery life tested by stream­ing Netflix 4K con­tent in the Netflix app on Windows 11 un­der Best Power Efficiency mode. Battery life varies by use and con­fig­u­ra­tion.

Today, we’re launch­ing Claude Design, a new Anthropic Labs prod­uct that lets you col­lab­o­rate with Claude to cre­ate pol­ished vi­sual work like de­signs, pro­to­types, slides, one-pagers, and more.

Claude Design is pow­ered by our most ca­pa­ble vi­sion model, Claude Opus 4.7, and is avail­able in re­search pre­view for Claude Pro, Max, Team, and Enterprise sub­scribers. We’re rolling out to users grad­u­ally through­out the day.

Even ex­pe­ri­enced de­sign­ers have to ra­tion ex­plo­ration—there’s rarely time to pro­to­type a dozen di­rec­tions, so you limit your­self to a few. And for founders, prod­uct man­agers, and mar­keters with an idea but not a de­sign back­ground, cre­at­ing and shar­ing those ideas can be daunt­ing.

Claude Design gives de­sign­ers room to ex­plore widely and every­one else a way to pro­duce vi­sual work. Describe what you need and Claude builds a first ver­sion. From there, you re­fine through con­ver­sa­tion, in­line com­ments, di­rect ed­its, or cus­tom slid­ers (made by Claude) un­til it’s right. When given ac­cess, Claude can also ap­ply your team’s de­sign sys­tem to every pro­ject au­to­mat­i­cally, so the out­put is con­sis­tent with the rest of your com­pa­ny’s de­signs.

Teams have been us­ing Claude Design for:

* Realistic pro­to­types: Designers can turn sta­tic mock­ups into eas­ily-share­able in­ter­ac­tive pro­to­types to gather feed­back and user-test, with­out code re­view or PRs.

* Product wire­frames and mock­ups: Product Managers can sketch out fea­ture flows and hand them off to Claude Code for im­ple­men­ta­tion, or share them with de­sign­ers to re­fine fur­ther.

* Design ex­plo­rations: Designers can quickly cre­ate a wide range of di­rec­tions to ex­plore.

* Pitch decks and pre­sen­ta­tions: Founders and Account Executives can go from a rough out­line to a com­plete, on-brand deck in min­utes, and then ex­port as a PPTX or send to Canva.

* Marketing col­lat­eral: Marketers can cre­ate land­ing pages, so­cial me­dia as­sets, and cam­paign vi­su­als, then loop in de­sign­ers to pol­ish.

* Frontier de­sign: Anyone can build code-pow­ered pro­to­types with voice, video, shaders, 3D and built-in AI.

Your brand, built in. During on­board­ing, Claude builds a de­sign sys­tem for your team by read­ing your code­base and de­sign files. Every pro­ject af­ter that uses your col­ors, ty­pog­ra­phy, and com­po­nents au­to­mat­i­cally. You can re­fine the sys­tem over time, and teams can main­tain more than one.

Import from any­where. Start from a text prompt, up­load im­ages and doc­u­ments (DOCX, PPTX, XLSX), or point Claude at your code­base. You can also use the web cap­ture tool to grab el­e­ments di­rectly from your web­site so pro­to­types look like the real prod­uct.

Refine with fine-grained con­trols. Comment in­line on spe­cific el­e­ments, edit text di­rectly, or use ad­just­ment knobs to tweak spac­ing, color, and lay­out live. Then ask Claude to ap­ply your changes across the full de­sign.

Collaborate. Designs have or­ga­ni­za­tion-scoped shar­ing. You can keep a doc­u­ment pri­vate, share it so any­one in your or­ga­ni­za­tion with the link can view it, or grant edit ac­cess so col­leagues can mod­ify the de­sign and chat with Claude to­gether in a group con­ver­sa­tion.

Export any­where. Share de­signs as an in­ter­nal URL within your or­ga­ni­za­tion, save as a folder, or ex­port to Canva, PDF, PPTX, or stand­alone HTML files.

Handoff to Claude Code. When a de­sign is ready to build, Claude pack­ages every­thing into a hand­off bun­dle that you can pass to Claude Code with a sin­gle in­struc­tion.

Over the com­ing weeks, we’ll make it eas­ier to build in­te­gra­tions with Claude Design, so you can con­nect it to more of the tools your team al­ready uses.

Claude Design is avail­able for Claude Pro, Max, Team, and Enterprise sub­scribers. Access is in­cluded with your plan and uses your sub­scrip­tion lim­its, with the op­tion to con­tinue be­yond those lim­its by en­abling ex­tra us­age.

For Enterprise or­ga­ni­za­tions, Claude Design is off by de­fault. Admins can en­able it in Organization set­tings.

A col­lec­tion of prin­ci­ples and pat­terns that shape soft­ware sys­tems, teams, and de­ci­sions.

56 laws

•

Click any card to learn more

Organizations de­sign sys­tems that mir­ror their own com­mu­ni­ca­tion struc­ture.

Premature op­ti­miza­tion is the root of all evil.

With a suf­fi­cient num­ber of API users, all ob­serv­able be­hav­iors of your sys­tem will be de­pended on by some­body.

Leave the code bet­ter than you found it.

YAGNI (You Aren’t Gonna Need It)

Don’t add func­tion­al­ity un­til it is nec­es­sary.

Adding man­power to a late soft­ware pro­ject makes it later.

A com­plex sys­tem that works is in­vari­ably found to have evolved from a sim­ple sys­tem that worked.

All non-triv­ial ab­strac­tions, to some de­gree, are leaky.

Every ap­pli­ca­tion has an in­her­ent amount of ir­re­ducible com­plex­ity that can only be shifted, not elim­i­nated.

A dis­trib­uted sys­tem can guar­an­tee only two of: con­sis­tency, avail­abil­ity, and par­ti­tion tol­er­ance.

Small, suc­cess­ful sys­tems tend to be fol­lowed by ov­erengi­neered, bloated re­place­ments.

A set of eight false as­sump­tions that new dis­trib­uted sys­tem de­sign­ers of­ten make.

Every pro­gram at­tempts to ex­pand un­til it can read mail.

There is a cog­ni­tive limit of about 150 sta­ble re­la­tion­ships one per­son can main­tain.

The square root of the to­tal num­ber of par­tic­i­pants does 50% of the work.

Those who un­der­stand tech­nol­ogy don’t man­age it, and those who man­age it don’t un­der­stand it.

In a hi­er­ar­chy, every em­ployee tends to rise to their level of in­com­pe­tence.

The min­i­mum num­ber of team mem­bers whose loss would put the pro­ject in se­ri­ous trou­ble.

Companies tend to pro­mote in­com­pe­tent em­ploy­ees to man­age­ment to limit the dam­age they can do.

Work ex­pands to fill the time avail­able for its com­ple­tion.

The first 90% of the code ac­counts for the first 90% of de­vel­op­ment time; the re­main­ing 10% ac­counts for the other 90%.

It al­ways takes longer than you ex­pect, even when you take into ac­count Hofstadter’s Law.

When a mea­sure be­comes a tar­get, it ceases to be a good mea­sure.

Anything you need to quan­tify can be mea­sured in some way bet­ter than not mea­sur­ing it.

Anything that can go wrong will go wrong.

Be con­ser­v­a­tive in what you do, be lib­eral in what you ac­cept from oth­ers.

Technical Debt is every­thing that slows us down when de­vel­op­ing soft­ware.

Given enough eye­balls, all bugs are shal­low.

Debugging is twice as hard as writ­ing the code in the first place.

A pro­ject should have many fast unit tests, fewer in­te­gra­tion tests, and only a small num­ber of UI tests.

Repeatedly run­ning the same tests be­comes less ef­fec­tive over time.

Software that re­flects the real world must evolve, and that evo­lu­tion has pre­dictable lim­its.

90% of every­thing is crap.

The speedup from par­al­leliza­tion is lim­ited by the frac­tion of work that can­not be par­al­lelized.

It is pos­si­ble to achieve sig­nif­i­cant speedup in par­al­lel pro­cess­ing by in­creas­ing the prob­lem size.

The value of a net­work is pro­por­tional to the square of the num­ber of users.

Every piece of knowl­edge must have a sin­gle, un­am­bigu­ous, au­thor­i­ta­tive rep­re­sen­ta­tion.

Designs and sys­tems should be as sim­ple as pos­si­ble.

Five main guide­lines that en­hance soft­ware de­sign, mak­ing code more main­tain­able and scal­able.

An ob­ject should only in­ter­act with its im­me­di­ate friends, not strangers.

Software and in­ter­faces should be­have in a way that least sur­prises users and other de­vel­op­ers.

The less you know about some­thing, the more con­fi­dent you tend to be.

Never at­tribute to mal­ice that which is ad­e­quately ex­plained by stu­pid­ity or care­less­ness.

The sim­plest ex­pla­na­tion is of­ten the most ac­cu­rate one.

Sticking with a choice be­cause you’ve in­vested time or en­ergy in it, even when walk­ing away helps you.

The Map Is Not the Territory

Our rep­re­sen­ta­tions of re­al­ity are not the same as re­al­ity it­self.

A ten­dency to fa­vor in­for­ma­tion that sup­ports our ex­ist­ing be­liefs or ideas.

We tend to over­es­ti­mate the ef­fect of a tech­nol­ogy in the short run and un­der­es­ti­mate the im­pact in the long run.

The longer some­thing has been in use, the more likely it is to con­tinue be­ing used.

Breaking a com­plex prob­lem into its most ba­sic blocks and then build­ing up from there.

Solving a prob­lem by con­sid­er­ing the op­po­site out­come and work­ing back­ward from it.

80% of the prob­lems re­sult from 20% of the causes.

The best way to get the cor­rect an­swer on the Internet is not to ask a ques­tion, it’s to post the wrong an­swer.