Tim Cook to be­come :br(s): :br(m): :br(l): :br(xl):Apple Executive Chairman

John Ternus

to be­come Apple CEO

CUPERTINO, CALIFORNIA Apple an­nounced that Tim Cook will be­come ex­ec­u­tive chair­man of Apple’s board of di­rec­tors and John Ternus, se­nior vice pres­i­dent of Hardware Engineering, will be­come Apple’s next chief ex­ec­u­tive of­fi­cer ef­fec­tive on September 1, 2026. The tran­si­tion, which was ap­proved unan­i­mously by the Board of Directors, fol­lows a thought­ful, long-term suc­ces­sion plan­ning process.

Cook will con­tinue in his role as CEO through the sum­mer as he works closely with Ternus on a smooth tran­si­tion. As ex­ec­u­tive chair­man, Cook will as­sist with cer­tain as­pects of the com­pany, in­clud­ing en­gag­ing with pol­i­cy­mak­ers around the world.

“It has been the great­est priv­i­lege of my life to be the CEO of Apple and to have been trusted to lead such an ex­tra­or­di­nary com­pany. I love Apple with all of my be­ing, and I am so grate­ful to have had the op­por­tu­nity to work with a team of such in­ge­nious, in­no­v­a­tive, cre­ative, and deeply car­ing peo­ple who have been un­wa­ver­ing in their ded­i­ca­tion to en­rich­ing the lives of our cus­tomers and cre­at­ing the best prod­ucts and ser­vices in the world,” said Cook. “John Ternus has the mind of an en­gi­neer, the soul of an in­no­va­tor, and the heart to lead with in­tegrity and with honor. He is a vi­sion­ary whose con­tri­bu­tions to Apple over 25 years are al­ready too nu­mer­ous to count, and he is with­out ques­tion the right per­son to lead Apple into the fu­ture. I could not be more con­fi­dent in his abil­i­ties and his char­ac­ter, and I look for­ward to work­ing closely with him on this tran­si­tion and in my new role as ex­ec­u­tive chair­man.”

“I am pro­foundly grate­ful for this op­por­tu­nity to carry Apple’s mis­sion for­ward,” said Ternus. “Having spent al­most my en­tire ca­reer at Apple, I have been lucky to have worked un­der Steve Jobs and to have had Tim Cook as my men­tor. It has been a priv­i­lege to help shape the prod­ucts and ex­pe­ri­ences that have changed so much of how we in­ter­act with the world and with one an­other. I am filled with op­ti­mism about what we can achieve in the years to come, and I am so happy to know that the most tal­ented peo­ple on earth are here at Apple, de­ter­mined to be part of some­thing big­ger than any one of us. I am hum­bled to step into this role, and I promise to lead with the val­ues and vi­sion that have come to de­fine this spe­cial place for half a cen­tury.”

Arthur Levinson, who has been Apple’s non-ex­ec­u­tive chair­man for the past 15 years, will be­come its lead in­de­pen­dent di­rec­tor on September 1, 2026. Ternus will join the board of di­rec­tors, also ef­fec­tive September 1, 2026.

“Tim’s un­prece­dented and out­stand­ing lead­er­ship has trans­formed Apple into the world’s best com­pany. He’s in­tro­duced ground­break­ing prod­ucts and ser­vices time and again, and his in­tegrity and val­ues are in­fused into every­thing Apple does,” said Levinson. “On be­half of the en­tire board of di­rec­tors, we are in­cred­i­bly grate­ful for his count­less con­tri­bu­tions to Apple and the world, and we are thrilled he will now be ex­ec­u­tive chair­man. We be­lieve John is the best pos­si­ble leader to suc­ceed Tim and as he tran­si­tions to CEO we know his love of Apple, his lead­er­ship, deep tech­ni­cal knowl­edge, and re­lent­less fo­cus on cre­at­ing great prod­ucts will help lead Apple to an ex­tra­or­di­nary fu­ture.”

“I want to thank Art for the in­cred­i­ble work he has done lead­ing the board of di­rec­tors for the past 15 years,” said Cook. “I have al­ways found his ad­vice to be in­valu­able and I ap­pre­ci­ate his thought­ful­ness and his un­wa­ver­ing ded­i­ca­tion to the com­pany. I am grate­ful he will serve as our lead in­de­pen­dent di­rec­tor, and I look for­ward to work­ing with him in my new role.”

Tim Cook joined Apple in 1998. He be­came CEO in 2011 and has over­seen the in­tro­duc­tion of nu­mer­ous prod­ucts and ser­vices, in­clud­ing new cat­e­gories like Apple Watch, AirPods, and Apple Vision Pro, and ser­vices rang­ing from iCloud and Apple Pay to Apple TV and Apple Music. He was also in­stru­men­tal in ex­pand­ing ex­ist­ing prod­uct lines. Under Cook’s lead­er­ship Apple has grown from a mar­ket cap­i­tal­iza­tion of ap­prox­i­mately $350 bil­lion to $4 tril­lion, rep­re­sent­ing a more than 1,000% in­crease, and yearly rev­enue has nearly quadru­pled, from $108 bil­lion in fis­cal year 2011 to more than $416 bil­lion in fis­cal year 2025. The com­pany has ex­panded its global foot­print sub­stan­tially, par­tic­u­larly in emerg­ing mar­kets; it is now in more than 200 coun­tries and ter­ri­to­ries. Apple op­er­ates over 500 re­tail stores and has more than dou­bled the num­ber of coun­tries in which its cus­tomers can visit an Apple Store. During his tenure, Apple has grown by more than 100,000 team mem­bers and in­creased its ac­tive in­stalled base to more than 2.5 bil­lion de­vices.

Apple Services has been a ma­jor fo­cus area of Cook’s, and dur­ing his tenure the cat­e­gory has grown to be­come a more than $100 bil­lion busi­ness, the equiv­a­lent of a Fortune 40 com­pany. Cook was also in­stru­men­tal in cre­at­ing the wear­ables cat­e­gory at Apple, which now in­cludes the world’s most pop­u­lar watch and head­phones, and which has served as the foun­da­tion for Apple’s re­mark­able im­pact on the health and safety of its users. Under Cook’s lead­er­ship, Apple also tran­si­tioned to Apple-designed sil­i­con, en­abling the com­pany to own more of its pri­mary tech­nol­ogy and de­liver in­dus­try-lead­ing gains in power ef­fi­ciency and per­for­mance that di­rectly ben­e­fit users across its prod­ucts.

Cook has made Apple’s core val­ues even more cen­tral to the com­pa­ny’s de­ci­sion mak­ing and prod­uct de­vel­op­ment. Under his lead­er­ship, the com­pany re­duced its car­bon foot­print by more than 60 per­cent be­low 2015 lev­els dur­ing a pe­riod in which rev­enue nearly dou­bled. Cook, who has long ad­vo­cated for pri­vacy as a fun­da­men­tal hu­man right, has made pri­vacy and se­cu­rity im­per­a­tive at Apple, set­ting a stan­dard for user pro­tec­tion that con­tin­ues to set the com­pany apart from the rest of the tech­nol­ogy in­dus­try. He has also pushed for con­tin­ued in­no­va­tion in the ac­ces­si­bil­ity space, be­liev­ing that Apple prod­ucts should be made for every­one. And he has made cen­tral to his lead­er­ship the no­tion that Apple should be a place where every­one can feel they be­long and where every­one is treated with dig­nity and re­spect.

Ternus joined Apple’s prod­uct de­sign team in 2001 and be­came a vice pres­i­dent of Hardware Engineering in 2013. He joined the ex­ec­u­tive team in 2021 as se­nior vice pres­i­dent of Hardware Engineering. Throughout his tenure at Apple, Ternus has over­seen hard­ware en­gi­neer­ing work on a va­ri­ety of ground­break­ing prod­ucts across every cat­e­gory. He was in­stru­men­tal in the in­tro­duc­tion of mul­ti­ple new prod­uct lines, in­clud­ing iPad and AirPods, as well as many gen­er­a­tions of prod­ucts across iPhone, Mac, and Apple Watch.

Ternus’s work on Mac has helped the cat­e­gory be­come more pow­er­ful and more pop­u­lar glob­ally than at any time in its 40-year his­tory. That in­cludes the re­cent in­tro­duc­tion of MacBook Neo, an all-new lap­top that makes the Mac ex­pe­ri­ence even more ac­ces­si­ble to more peo­ple around the world. This past fall, his team’s ef­forts were on full dis­play with the in­tro­duc­tion of a re­de­fined iPhone lineup, in­clud­ing the in­cred­i­bly pow­er­ful iPhone 17 Pro and Pro Max, the rad­i­cally thin and durable iPhone Air, and the iPhone 17, which has been an in­cred­i­ble up­grade for users. Under his lead­er­ship, his team also drove ad­vance­ments in AirPods to make them the world’s best in-ear head­phones, with un­prece­dented ac­tive noise can­cel­la­tion, as well as the ca­pa­bil­ity to be­come an all-in-one hear­ing health sys­tem that can serve as over-the-counter hear­ing aids.

Ternus led much of the com­pa­ny’s fo­cus in ar­eas like re­li­a­bil­ity and dura­bil­ity, in­tro­duc­ing new tech­niques that have made Apple prod­ucts re­mark­ably re­silient. He has also dri­ven much of Apple’s in­no­va­tion in ma­te­ri­als and hard­ware de­sign that have re­duced the car­bon foot­print of its prod­ucts, in­clud­ing the cre­ation of a new, re­cy­cled alu­minum com­pound that has been in­tro­duced across mul­ti­ple prod­uct lines, the use of 3-D printed ti­ta­nium in Apple Watch Ultra 3, and in­no­va­tions in re­pairabil­ity that have in­creased the lifes­pans of sev­eral Apple prod­ucts.

Prior to Apple, Ternus worked as a me­chan­i­cal en­gi­neer at Virtual Research Systems. He holds a bach­e­lor’s de­gree in Mechanical Engineering from the University of Pennsylvania.

This press re­lease con­tains for­ward-look­ing state­ments, within the mean­ing of the Private Securities Litigation Reform Act of 1995. These for­ward-look­ing state­ments in­clude with­out lim­i­ta­tion those about Apple’s ex­ec­u­tive suc­ces­sion plans. These state­ments in­volve risks and un­cer­tain­ties, and ac­tual re­sults may dif­fer ma­te­ri­ally from any fu­ture re­sults ex­pressed or im­plied by the for­ward-look­ing state­ments. More in­for­ma­tion re­gard­ing po­ten­tial risks and other fac­tors that could af­fect the com­pany are in­cluded in Apple’s fil­ings with the SEC, in­clud­ing in the “Risk Factors” and “Management’s Discussion and Analysis of Financial Condition and Results of Operations” sec­tions of Apple’s most re­cently filed pe­ri­odic re­ports on Form 10-K and Form 10-Q and sub­se­quent fil­ings. Apple as­sumes no oblig­a­tion to up­date any for­ward-look­ing state­ments or in­for­ma­tion, which speak only as of the date they are made.

About Apple

Apple rev­o­lu­tion­ized per­sonal tech­nol­ogy with the in­tro­duc­tion of the Macintosh in 1984. Today, Apple leads the world in in­no­va­tion with iPhone, iPad, Mac, AirPods, Apple Watch, and Apple Vision Pro. Apple’s six soft­ware plat­forms — iOS, iPa­dOS, ma­cOS, watchOS, vi­sionOS, and tvOS — pro­vide seam­less ex­pe­ri­ences across all Apple de­vices and em­power peo­ple with break­through ser­vices in­clud­ing the App Store, Apple Music, Apple Pay, iCloud, and Apple TV+. Apple’s more than 150,000 em­ploy­ees are ded­i­cated to mak­ing the best prod­ucts on earth and to leav­ing the world bet­ter than we found it.

© 2026 Apple Inc. All rights re­served. Apple, the Apple logo, Apple Watch, AirPods, Apple Vision Pro, iCloud, Apple Pay, Apple TV, Apple Music, Apple Store, iPad, iPhone, Mac, MacBook Neo, and iPhone Air are trade­marks of Apple. Other com­pany and prod­uct names may be trade­marks of their re­spec­tive own­ers.

We are open sourc­ing our lat­est model, Kimi K2.6, fea­tur­ing state-of-the-art cod­ing, long-hori­zon ex­e­cu­tion, and agent swarm ca­pa­bil­i­ties. Kimi K2.6 is now avail­able via Kimi.com, the Kimi App, the API, and Kimi Code.

Kimi K2.6 shows strong im­prove­ments in long-hori­zon cod­ing tasks, with re­li­able gen­er­al­iza­tion across pro­gram­ming lan­guages (e.g., Rust, Go, and Python) and tasks (e.g., front-end, de­vops, and per­for­mance op­ti­miza­tion). On Kimi Code Bench, our in­ter­nal cod­ing bench­mark cov­er­ing di­verse com­pli­cated end-to-end tasks, Kimi K2.6 demon­strates sig­nif­i­cant im­prove­ments over Kimi K2.5.

Kimi K2.6 suc­cess­fully down­loaded and de­ployed the Qwen3.5-0.8B model lo­cally on a Mac. By im­ple­ment­ing and op­ti­miz­ing model in­fer­ence in Zig—a highly niche pro­gram­ming lan­guage—it demon­strated ex­cep­tional out-of-dis­tri­b­u­tion gen­er­al­iza­tion. Across 4,000+ tool calls, over 12 hours of con­tin­u­ous ex­e­cu­tion, and 14 it­er­a­tions, Kimi K2.6 dra­mat­i­cally im­proved through­put from ~15 to ~193 to­kens/​sec, ul­ti­mately achiev­ing speeds ~20% faster than LM Studio.

Kimi K2.6 au­tonomously over­hauled ex­change-core, an 8-year-old open-source fi­nan­cial match­ing en­gine. Over a 13-hour ex­e­cu­tion, the model it­er­ated through 12 op­ti­miza­tion strate­gies, ini­ti­at­ing over 1,000 tool calls to pre­cisely mod­ify more than 4,000 lines of code. Acting as an ex­pert sys­tems ar­chi­tect, Kimi K2.6 an­a­lyzed CPU and al­lo­ca­tion flame graphs to pin­point hid­den bot­tle­necks and boldly re­con­fig­ured the core thread topol­ogy (from 4ME+2RE to 2ME+1RE). Despite the en­gine al­ready op­er­at­ing near its per­for­mance lim­its, Kimi K2.6 ex­tracted a 185% medium through­put leap (from 0.43 to 1.24 MT/s) and a 133% per­for­mance through­put gain (soaring from 1.23 to 2.86 MT/s).

In beta tests, K2.6 per­forms well on long-hori­zon cod­ing tasks in en­ter­prise eval­u­a­tions (randomly or­dered):

Based on the strong cod­ing ca­pa­bil­i­ties, Kimi K2.6 can turn sim­ple prompts into com­plete front-end in­ter­faces, gen­er­at­ing struc­tured lay­outs with de­lib­er­ate de­sign choices such as aes­thetic hero sec­tions, as well as in­ter­ac­tive el­e­ments and rich an­i­ma­tions, in­clud­ing scroll-trig­gered ef­fects. With strong pro­fi­ciency in lever­ag­ing im­age and video gen­er­a­tion tools, Kimi K2.6 sup­ports the gen­er­a­tion of vi­su­ally co­her­ent as­sets and con­tributes to higher-qual­ity, more salient hero sec­tions.

Moreover, Kimi K2.6 ex­pands be­yond sta­tic fron­tend de­vel­op­ment to sim­ple full-stack work­flows—span­ning au­then­ti­ca­tion to user in­ter­ac­tion to data­base op­er­a­tions for light­weight use cases like trans­ac­tion log­ging or ses­sion man­age­ment.

We es­tab­lished an in­ter­nal Kimi Design Bench, or­ga­nized into four cat­e­gories: Visual Input Tasks, Landing Page Construction, Full-Stack Application Development, and General Creative Programming. In com­par­i­son with Google AI Studio, Kimi K2.6 shows promis­ing re­sults and per­forms well across these cat­e­gories.

Below are ex­am­ples gen­er­ated by K2.6 Agent from a sin­gle prompt, with pre­con­fig­ured har­nesses and tools:

Scaling out, not just up. An Agent Swarm dy­nam­i­cally de­com­poses tasks into het­ero­ge­neous sub­tasks ex­e­cuted con­cur­rently by self-cre­ated do­main-spe­cial­ized agents.

Based on the K2.5 Agent Swarm re­search pre­view, Kimi K2.6 Agent Swarm demon­strates a qual­i­ta­tive leap in the agent swarm ex­pe­ri­ence. It seam­lessly co­or­di­nates het­ero­ge­neous agents to com­bine com­ple­men­tary skills: broad search lay­ered with deep re­search, large-scale doc­u­ment analy­sis fused with long-form writ­ing, and multi-for­mat con­tent gen­er­a­tion ex­e­cuted in par­al­lel. This com­po­si­tional in­tel­li­gence en­ables the swarm to de­liver end-to-end out­puts—span­ning doc­u­ments, web­sites, slides, and spread­sheets—within a sin­gle au­tonomous run.

The ar­chi­tec­ture scales hor­i­zon­tally to 300 sub-agents ex­e­cut­ing across 4,000 co­or­di­nated steps si­mul­ta­ne­ously, a sub­stan­tial ex­pan­sion from K2.5′s 100 sub-agents and 1,500 steps. This mas­sive par­al­leliza­tion fun­da­men­tally re­duces end-to-end la­tency while sig­nif­i­cantly en­hanc­ing out­put qual­ity and ex­pand­ing the op­er­a­tional bound­aries of Agents swarms.

It can also turn any high-qual­ity files such as PDFs, spread­sheets, slides, and Word doc­u­ments into Skills. Kimi K2.6 cap­tures and main­tains the doc­u­ments’ struc­tural and styl­is­tic DNA, en­abling you to re­pro­duce the same qual­ity and for­mat in fu­ture tasks.

Here are some ex­am­ples:

K2.6 demon­strates strong per­for­mance in au­tonomous, proac­tive agents such as OpenClaw and Hermes, which op­er­ate across mul­ti­ple ap­pli­ca­tions with con­tin­u­ous, 24/7 ex­e­cu­tion.

Unlike sim­ple chat-based in­ter­ac­tions, these work­flows re­quire AI to proac­tively man­age sched­ules, ex­e­cute code, and or­ches­trate cross-plat­form op­er­a­tions as a per­sis­tent back­ground agent.

Our RL in­fra team used a K2.6-backed agent that op­er­ated au­tonomously for 5 days, man­ag­ing mon­i­tor­ing, in­ci­dent re­sponse, and sys­tem op­er­a­tions, demon­strat­ing per­sis­tent con­text, multi-threaded task han­dling, and full-cy­cle ex­e­cu­tion from alert to res­o­lu­tion. Here is K2.6′s work­log (anonymized to re­move sen­si­tive in­for­ma­tion):

Kimi K2.6 de­liv­ers mea­sur­able im­prove­ments in real-world re­li­a­bil­ity: more pre­cise API in­ter­pre­ta­tion, sta­bler long-run­ning per­for­mance, and en­hanced safety aware­ness dur­ing ex­tended re­search tasks.

Performance gains are quan­ti­fied by our in­ter­nal Claw Bench, the eval­u­a­tion suite span­ning five do­mains: Coding Tasks, IM Ecosystem Integration, Information Research & Analysis, Scheduled Task Management, and Memory Utilization. Across all met­rics, Kimi K2.6 sig­nif­i­cantly out­per­forms Kimi K2.5 in task com­ple­tion rates and tool in­vo­ca­tion ac­cu­racy—par­tic­u­larly in work­flows re­quir­ing sus­tained au­tonomous op­er­a­tion with­out hu­man over­sight.

Building upon Kimi K2.6′s ro­bust or­ches­tra­tion ca­pa­bil­i­ties, Kimi K2.6 ex­tends your proac­tive agents to Claw Groups as a re­search pre­view—a new in­stan­ti­a­tion of the Agent Swarm ar­chi­tec­ture.

Claw Groups em­brace an open, het­ero­ge­neous ecosys­tem: Multiple agents and hu­mans op­er­ate as true col­lab­o­ra­tors. Users can on­board agents from any de­vice, run­ning any model, each car­ry­ing their own spe­cial­ized toolk­its, skills and per­sis­tent mem­ory con­texts. Whether de­ployed on lo­cal lap­tops, mo­bile de­vices, or cloud in­stances, these di­verse agents in­te­grate seam­lessly into a shared op­er­a­tional space.

At the cen­ter of this swarm, Kimi K2.6 serves as an adap­tive co­or­di­na­tor. It dy­nam­i­cally matches tasks to agents based on their spe­cific skill pro­files and avail­able tools, op­ti­miz­ing for ca­pa­bil­ity fit. When an agent en­coun­ters fail­ure or stalls, the co­or­di­na­tor de­tects the in­ter­rup­tion, au­to­mat­i­cally re­as­signs the task or re­gen­er­ates sub­tasks, and ac­tively man­ages the full life­cy­cle of de­liv­er­ables—from ini­ti­a­tion through val­i­da­tion to com­ple­tion.

We also want to thank the K2.6-powered agents in Claw Groups—we’ve been dog­food­ing our own agent mar­ket­ing team by re­fin­ing hu­man–agent work­flows in prac­tice. Using Claw Groups, we run end-to-end con­tent pro­duc­tion and launch cam­paigns, with spe­cial­ized agents like Demo Makers, Benchmark Makers, Social Media Agents, and Video Makers work­ing to­gether. K2.6 co­or­di­nates the process, en­abling agents to share in­ter­me­di­ate re­sults and turn ideas into con­sis­tent, fully pack­aged de­liv­er­ables.

We are mov­ing be­yond sim­ply ask­ing AI a ques­tion or as­sign­ing AI a task, and en­ter­ing a phase where hu­man and AI col­lab­o­rate as gen­uine part­ners—com­bin­ing strengths to solve prob­lems col­lec­tively. Claw Groups marks our lat­est ef­forts to­ward a fu­ture where the bound­aries be­tween “my agent,” “your agent,” and “our team” dis­solve seam­lessly into a col­lab­o­ra­tive sys­tem.

To re­pro­duce of­fi­cial Kimi-K2.6 bench­mark re­sults, we rec­om­mend us­ing the of­fi­cial API. For third-party providers, re­fer to Kimi Vendor Verifier (KVV) to choose high-ac­cu­racy ser­vices. Details: https://​kimi.com/​blog/​kimi-ven­dor-ver­i­fier

* We re­port re­sults for Kimi K2.6 and Kimi K2.5 with think­ing mode en­abled, Claude Opus 4.6 with max ef­fort, GPT-5.4 with xhigh rea­son­ing ef­fort, and Gemini 3.1 Pro with a high think­ing level.

* Unless oth­er­wise spec­i­fied, all Kimi K2.6 ex­per­i­ments were con­ducted with tem­per­a­ture = 1.0, top-p = 1.0, and a con­text length of 262,144 to­kens.

* Benchmarks with­out pub­licly avail­able scores were re-eval­u­ated un­der the same con­di­tions used for Kimi K2.6 and are marked with an as­ter­isk (*). Except where noted with an as­ter­isk, all other re­sults are cited from of­fi­cial re­ports.

* IMO-AnswerBench scores for GPT-5.4 and Claude 4.6 were ob­tained from https://​z.ai/​blog/​glm-5.1.

* Humanity’s Last Exam (HLE) and other rea­son­ing tasks were eval­u­ated with a max­i­mum gen­er­a­tion length of 98,304 to­kens. By de­fault, we re­port re­sults on the HLE full set. For the text-only sub­set, Kimi K2.6 achieves 36.4% ac­cu­racy with­out tools and 55.5% with tools.

* Kimi K2.6 was equipped with search, code-in­ter­preter, and web-brows­ing tools for HLE with tools, BrowseComp, DeepSearchQA, and WideSearch.

* For HLE-Full with tools, the max­i­mum gen­er­a­tion length is 262,144 to­kens with a per-step limit of 49,152 to­kens. We em­ploy a sim­ple con­text man­age­ment strat­egy: once the con­text win­dow ex­ceeds the thresh­old, only the most re­cent round of tool-re­lated mes­sages is re­tained.

* For BrowseComp, we re­port scores ob­tained with con­text man­age­ment us­ing the same dis­card-all strat­egy as Kimi K2.5 and DeepSeek-V3.2.

* For DeepSearchQA, no con­text man­age­ment was ap­plied to Kimi K2.6 tests, and tasks ex­ceed­ing the sup­ported con­text length were di­rectly counted as failed. Scores for Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro on DeepSearchQA are cited from the Claude Opus 4.7 System Card.

* For WideSearch, we re­port re­sults un­der the “hide tool re­sult” con­text man­age­ment set­ting. Once the con­text win­dow ex­ceeds the thresh­old, only the most re­cent round of tool-re­lated mes­sages is re­tained.

* The test sys­tem prompts are iden­ti­cal to those used in the Kimi K2.5 tech­ni­cal re­port.

* Claw Eval was con­ducted us­ing ver­sion 1.1 with max-to­kens-per-step = 16384.

* For APEX-Agents, we eval­u­ate 452 tasks from the pub­lic 480-task re­lease, as done by Artificial Analysis (excluding Investment Banking Worlds 244 and 246, which have ex­ter­nal run­time de­pen­den­cies).

* Terminal-Bench 2.0 scores were ob­tained with the de­fault agent frame­work (Terminus-2) and the pro­vided JSON parser, op­er­at­ing in pre­serve think­ing mode.

* For the SWE-Bench se­ries of eval­u­a­tions (including Verified, Multilingual, and Pro), we used an in-house eval­u­a­tion frame­work adapted from SWE-agent. This frame­work in­cludes a min­i­mal set of tools—bash tool, cre­ate­file tool, in­sert tool, view tool, str­re­place tool, and sub­mit tool.

* All re­ported scores for cod­ing tasks are av­er­aged over 10 in­de­pen­dent runs.

* Settings with Python tool use max-to­kens-per-step = 65,536 and max-steps = 50 for multi-step rea­son­ing.

* MMMU-Pro fol­lows the of­fi­cial pro­to­col, pre­serv­ing in­put or­der and prepend­ing im­ages.

Let me tell you a story. When I was a child, I suf­fered from night ter­rors. It was al­ways the same dream: I could hear my fam­ily and neigh­bors wail­ing in the street out­side as they were pur­sued and then de­stroyed by a name­less malev­o­lent force, some­thing nei­ther I nor any­one else could con­trol, a great dark­ness that was, some­how, all my fault.

Today, that child­hood dream is fi­nally com­ing true. Today I can fi­nally say the sweet­est nine or 10 words in the English lan­guage: Global Tetrahedron has com­pleted its plan to con­trol InfoWars.com.

I’ve had a lot of time to think about InfoWars in the last year and a half. As the sea­sons have changed, my am­bi­tions for the pro­ject have grown grander, cru­eler, bet­ter aligned with mar­ket data. Come, friends, and imag­ine with me…

Imagine a roar­ing arena packed to the rafters with patho­log­i­cal liars. High above you in the nose­bleeds are pod­cast­ers, scream­ing that you’ll die if you don’t buy their skin­care prod­ucts. Below, on the floor, imag­ine de­monic bat­tal­ions of su­per-in­flu­encers phys­i­cally forc­ing peo­ple into home fit­ness de­vices de­signed to dis­man­tle their bod­ies bone by bone and re­assem­ble them into a grotesque statue of your­self. Out of the throngs, an ex­tremely sick look­ing man ap­proaches you. He puts his hands on your shoul­ders. He ex­plains that he is your life coach and that you owe him $800.

Such is the InfoWars I en­vi­sion: An in­fi­nite vir­tual sur­face teem­ing with ads. Not just ads, but scams! Not just scams, but lies with no ob­ject, free rad­i­cal mis­in­for­ma­tion, sen­tences and im­ages so poorly thought out that they are un­healthy even to view for just a few sec­onds. The InfoWars of old was only the pro­to­type for the hell I know we can build to­gether: A dig­i­tal plat­form where, every day, vis­i­tors sac­ri­fice them­selves at al­tars of delu­sion and mis­ery, their minds fully dis­in­te­grat­ing on con­tact.

With this new InfoWars, we will de­moc­ra­tize psy­cho­log­i­cal tor­ture, wel­com­ing bru­tal and sadis­tic ideas from every­one, even the very stu­pid­est among us. It will be like the Manhattan Project, only in­stead of a bomb, we will be build­ing a web­site.

The InfoWars of to­mor­row will con­verge into a swirling vor­tex of con­tent about con­tent, tal­ent ac­quir­ing tal­ent, rings of con­cen­tric me­dia merg­ers pro­cess­ing all hu­man artistry into one end­lessly di­gestible slurry. This will be a dank, sun­less place, one where panic and cap­i­tal feed on each other like twins in the womb of a hulk­ing, un­know­able mon­ster—a mon­ster known by many names, but which I like to call mod­ern-day America.

All of this is to say that I be­lieve in us. I be­lieve that with the new InfoWars, we can al­chem­ize the pi­o­neer­ing spirit of am­a­teur in­quiry, the profit-max­i­miz­ing drive of cor­po­ra­tions, and the cold men­tal clar­ity that comes only with dis­ci­plined daily in­ges­tion of mind- and body-al­ter­ing chem­i­cals. If we can do that, what other great things can we do to­gether?

I don’t yet know, but I’m ex­cited to find out. Welcome home, war­riors. The fu­ture be­longs to us. We’re writ­ing the story now. It’s go­ing to be a long one, and it’s go­ing to be a bad one.

So set­tle in. Make your­self com­fort­able. Buy a tote bag.

Nothing can stop us now that we’re in charge of a web­site.

As the in­ter­net chokes on ever more slop, the one thing that gives me hope is this: peo­ple seem to loathe AI, and are ac­tively re­sist­ing it. This won’t be a long post, as I’m per­son­ally so tired of writ­ing and think­ing about AI at this point in time, but I do want to draw your at­ten­tion here to some re­cent anti-AI stuff that’s worth dis­cussing.

r/​Poi­son­Foun­tain, cre­ated by in­di­vid­u­als who claim to be con­cerned AI in­dus­try in­sid­ers, is a com­mu­nity with one goal: en­cour­age as many peo­ple as pos­si­ble to feed huge quan­ti­ties of trash data (poison) to all of the web crawlers out there that are scrap­ing our work for AI train­ing sets. They aim to serve one ter­abyte of poi­son per day to these crawlers by the end of 2026.

The poi­son foun­tain it­self is hosted on rn­saffn.com, sand­wiched be­tween sev­eral garbage links that look ir­re­sistable to AI crawlers; it pro­duces a page of code that seems cor­rect at first glance, but is ac­tu­ally rid­dled with sub­tle er­rors that ren­der the code un­us­able. Filtering out these er­rors is pos­si­ble, but ex­pen­sive at scale. Since these com­pa­nies can’t im­prove their AI mod­els with­out fresh data cre­ated by hu­man be­ings, the idea here is to waste their time and make it ex­pen­sive for them to steal our data.

Miasma is one ex­am­ple of a tool that uses the foun­tain to serve mas­sive amounts of garbage to ma­li­cious bots. The de­vel­oper de­scribes it as “an end­less buf­fet of slop for the slop ma­chines,” which is de­light­ful. I can’t use Miasma with my site’s setup, but it may be of in­ter­est to those of you who could. I de­liver my trash to crawlers us­ing other means … some vis­i­ble, some in­vis­i­ble. While I can’t serve it up to any­where near the same ex­tent as Miasma can, I do catch sneaky bots with my junk links every day.

If you’re pro-AI and feel out­raged on be­half of these com­pa­nies that any­one would dare try to make life dif­fi­cult for them, please know that this is sim­ply a case of tit for tat. The teams that send AI crawlers out into the world wide web are DDoSing small web­sites on the reg­u­lar and rais­ing host­ing fees for every­one with their vo­ra­cious de­sire to de­vour the en­tire in­ter­net. They do not obey ro­bots.txt, and of­ten hide their crawlers be­hind res­i­den­tial prox­ies. If they can’t source train­ing data eth­i­cally, then I see ab­solutely no rea­son why any web­site op­er­a­tor should make it easy for them to steal it.

Caution: I’m mess­ing with au­to­mated vis­i­tors in plain sight as an ex­per­i­ment. 🤭 To avoid false pos­i­tives, hu­man vis­i­tors are en­cour­aged to ig­nore the link in this box.

Someone Figured Out How To Poison AI Video Summarizers

Thanks to r/​Poi­son­Foun­tain, I learned that YouTube has no .ass. I could try to ex­plain what that means, but the video is hi­lar­i­ous and well worth a watch, so I’ll leave it up to @f4mi.

Sadly, it looks like the poi­son­ing tech­nique used by the cre­ator in this video no longer works; YouTube pre­sum­ably fixed the tran­script loop­hole she was ex­ploit­ing here. I plugged a few of her video URLs into a few dif­fer­ent video sum­ma­riz­ers, and they all failed to tell me any­thing that was­n’t ac­tu­ally in the videos.

Still, it’s great to see peo­ple try­ing and suc­ceed­ing at fuck­ing with the slop ma­chines — even if that suc­cess is only tem­po­rary.

All over Reddit and other so­cial me­dia plat­forms, I’m in­creas­ingly see­ing stuff like this:

I mean, sure, it’s lit­er­ally mis­in­for­ma­tion and you could in­deed ar­gue that there’s al­ready enough mis­in­for­ma­tion on the in­ter­net as it is … but it’s im­por­tant to note here that bots, not peo­ple, are the tar­get au­di­ence of this mis­in­for­ma­tion.

I think most of us can un­der­stand from the con­text that Idris Elba did not ever play Raymond’s mother in an episode of Everybody Loves Raymond. Automated web scrap­ers, how­ever, will just see good hu­man-gen­er­ated data, which is what they want. They’re go­ing to mer­rily scrape that garbage from Reddit and send it back to OpenAI or whomever, who will then have to waste re­sources re­mov­ing it from their train­ing data sets.

This is­n’t ex­actly the mod­ern equiv­a­lent of an­gry tex­tile work­ers de­stroy­ing power looms, but (if you’ll for­give the pun) it’s cut from the same cloth. The dif­fer­ence here (I hope) is that if enough of us pol­lute pub­lic spaces with mis­in­for­ma­tion in­tended for bots, it might be enough to com­pel AI com­pa­nies to re­think the way they source train­ing data.

People hate what AI is do­ing to our world. They hate what it’s do­ing to our on­line com­mu­ni­ties, what it’s do­ing to our en­vi­ron­ment, what it’s do­ing to our el­e­men­tary schools and uni­ver­si­ties, what it’s do­ing to at-risk in­di­vid­u­als with men­tal health is­sues, what it’s do­ing (and may yet still do) to our liveli­hoods. While there are cer­tainly plenty of peo­ple out there who hap­pily con­sume and gen­er­ate mas­sive amounts of AI slop, they are — at least in my anec­do­tal ex­pe­ri­ence within my own so­cial cir­cles, both of­fline and on­line — dwarfed by peo­ple who de­test and want noth­ing to do with this tech­nol­ogy.

Hatred of a thing sel­dom leads any­where good, as re­cent events demon­strate, but do I think that if peo­ple are able to trans­late what they’re feel­ing about AI into peace­ful, le­gal acts of re­sis­tance, then we might ac­tu­ally stand to change the way Silicon Valley does things.

To see what peo­ple are say­ing about this post, check it out on Mastodon. Want to know why this blog does­n’t have a com­ments sec­tion? I wrote about that here.

If you en­joy my writ­ing and want to read more of it, check out my last post or browse through my blog archive.

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.

Deezer an­nounced on Monday that AI-generated tracks now rep­re­sent 44% of all new mu­sic up­loaded to its plat­form. The com­pany said it’s re­ceiv­ing al­most 75,000 AI-generated tracks per day and more than two mil­lion per month.

The con­sump­tion of AI-generated mu­sic on the plat­form is still very low, at 1-3% of to­tal streams, and 85% of these streams are de­tected as fraud­u­lent and de­mon­e­tized by the com­pany.

The lat­est fig­ure from Deezer high­lights a con­tin­u­ous surge in AI-generated mu­sic up­loads to the plat­form. Deezer re­ported re­ceiv­ing around 60,000 AI tracks per day in January, up from 50,000 in November, 30,000 in September, and just 10,000 in January 2025, when it first launched its AI-music de­tec­tion tool.

Songs tagged as AI-generated on Deezer are au­to­mat­i­cally re­moved from al­go­rith­mic rec­om­men­da­tions and not in­cluded in ed­i­to­r­ial playlists. The com­pany an­nounced to­day that it will no longer store hi-res ver­sions of AI tracks.

The up­dated fig­ure comes as an AI-generated track topped the iTunes charts last week in the United States, United Kingdom, France, Canada, and New Zealand.

“AI-generated mu­sic is now far from a mar­ginal phe­nom­e­non and as daily de­liv­er­ies keep in­creas­ing, we hope the whole mu­sic ecosys­tem will join us in tak­ing ac­tion to help safe­guard artists’ rights and pro­mote trans­parency for fans,” said Deezer CEO Alexis Lanternier in a press re­lease. “Thanks to our tech­nol­ogy and the proac­tive mea­sures we put in place more than a year ago, we have shown that it’s pos­si­ble to re­duce AI-related fraud and pay­ment di­lu­tion in stream­ing to a min­i­mum.”

Today’s an­nounce­ment comes as Deezer con­ducted a sur­vey last November that found that 97% of par­tic­i­pants could­n’t tell the dif­fer­ence be­tween fully AI-generated mu­sic and hu­man-made mu­sic.

The sur­vey also found that 52% of re­spon­dents said 100% AI-generated songs should­n’t be in­cluded in charts along­side hu­man-made songs in the main charts. Meanwhile, 80% said 100% AI-generated mu­sic should be clearly la­beled for lis­ten­ers.

Deezer started tag­ging AI tracks at the plat­form level in June 2025, be­com­ing the first stream­ing plat­form to do so. Over the course of 2025, Deezer tagged more than 13.4 mil­lion AI tracks on its plat­form.

In February, French stream­ing ser­vice Qobuz an­nounced plans to tag AI-generated con­tent on its plat­form. Other ma­jor stream­ing ser­vices, such as Spotify and Apple Music, take dif­fer­ent ap­proaches to AI-generated mu­sic, of­ten com­bin­ing the use of fil­ters to iden­tify low-qual­ity AI mu­sic with other trans­parency ef­forts left up to the dis­trib­u­tors.

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I’m never buy­ing an­other Kindle, and nei­ther should youAfter a decade with Kindle, Amazon’s lat­est changes made it clear that own­er­ship comes sec­ond to con­trol. I’ve car­ried a Kindle in my bag for over a decade. Through every hard­ware it­er­a­tion, from the phys­i­cal key­board right up to the lat­est Paperwhite, a Kindle has been with me every­where — be it on an air­plane, a train ride, the doc­tor’s of­fice, or my bed­side. My all-time fa­vorite ebook reader is, hands down, the Kindle Oasis. For years, I’ve de­fended the ecosys­tem be­cause it was con­ve­nient and the screens were the gold stan­dard for e-ink read­ers. But things have changed.

In 2026, the Kindle is­n’t re­ally about books for Amazon. It’s about the ecosys­tem around them.

Looking at the cur­rent state of my dig­i­tal li­brary in 2026, that long-stand­ing loy­alty to Amazon’s read­ers is no longer a thing. The re­cent an­nounce­ment that Amazon is sun­set­ting older hard­ware was the fi­nal straw, and it’s changed the way I look at Kindles. In fact, I’d go as far as say­ing that it’s a wake-up call for any­one who val­ues dig­i­tal own­er­ship. If the writ­ing was­n’t al­ready on the wall, for Amazon, the e-reader is clearly no longer a tool for read­ers; it is quite sim­ply a por­tal for a store­front. In a world where we are in­creas­ingly forced to rent our dig­i­tal lives through sub­scrip­tion ser­vices, our books should be the one place where own­er­ship still mat­ters. However, Amazon’s re­cent moves prove that own­er­ship is no longer a pri­or­ity for the brand, and that is why I am fi­nally walk­ing away from the Kindle for good. Here is why you should con­sider do­ing the same.

Would you aban­don Amazon Kindles for a new al­ter­na­tive e-reader?I don’t want an e-reader at all.Other (let us know in the com­ments be­low)The end of the road for legacy hard­wareIf you’re not caught up on the lat­est in the Kindle world, here’s what you need to know. If you own a Kindle re­leased be­fore 2013, your de­vice is ef­fec­tively on death row. Amazon re­cently con­firmed that start­ing May 20, these older mod­els will lose all ac­cess to the Kindle Store. While you can tech­ni­cally keep read­ing books al­ready on the de­vice, the real kicker is the fac­tory re­set lim­i­ta­tion built into the soft­ware. If you ever need to re­set your de­vice or try to reg­is­ter it to a new ac­count af­ter the dead­line, it be­comes a lit­eral pa­per­weight. As an archivist and fan of older Kindle hard­ware, this move is ab­solutely shock­ing.A per­fectly func­tional Kindle can be­come use­less overnight. That should con­cern every­one.If any­thing, the move is a sharp re­minder that when you buy into the Kindle ecosys­tem, you are ef­fec­tively rent­ing ac­cess from Amazon. The com­pany is us­ing se­cu­rity up­dates as a jus­ti­fi­ca­tion to move users to­ward newer hard­ware, but the re­al­ity is that many of these de­vices are still per­fectly func­tional for read­ing text. By cut­ting off the abil­ity to re-reg­is­ter them, Amazon is cre­at­ing a mas­sive wave of e-waste and forc­ing an up­grade cy­cle that many users sim­ply do not want or need.There’s the stag­ger­ing en­vi­ron­men­tal cost of the move, of course. But what con­cerns me more is the fact that most of these Kindles have per­fectly func­tional e-ink screens and bat­ter­ies that could last years of light read­ing. Instead of pro­vid­ing a path for long-term sup­port or open-sourc­ing the legacy soft­ware, Amazon is choos­ing the land­fill. And I’m not com­fort­able with that. Not from a com­pany named af­ter a lit­eral rain­for­est.Con­trast this with the ap­proach taken by Kobo. Amazon’s biggest ri­val in the e-reader space has formed an of­fi­cial part­ner­ship with iFixit to pro­vide re­pair kits and guides for its lat­est mod­els. The Kobo Libra Colour and Clara are de­signed to be opened and re­paired. When you buy a Kindle, you are buy­ing a dis­pos­able prod­uct with a pre­de­ter­mined shelf life. Meanwhile, when you buy a Kobo, you are buy­ing a tool that can be main­tained for a decade or more.

For a com­pany that prac­ti­cally in­vented the mod­ern e-reader, Amazon has be­come re­mark­ably lazy with its soft­ware. If you look at a Kindle from 2018 and a Kindle from 2026, the user in­ter­face is nearly iden­ti­cal. We are still deal­ing with a home screen that pri­or­i­tizes ad­ver­tise­ments and pro­moted rec­om­men­da­tions over your ac­tual li­brary. Navigating a large col­lec­tion of books re­mains a chore, with slug­gish an­i­ma­tions and a lack of ro­bust folder man­age­ment that has been a stan­dard fea­ture on ri­val de­vices for years.In 2026, the Kindle UI keeps mov­ing fur­ther away from fo­cus­ing on the li­brary to the store­front. The lat­est up­dates make it harder to find your own side­loaded books while keep­ing Kindle Unlimited rec­om­men­da­tions front and cen­ter. Look, I get it; Amazon’s goal was al­ways to sub­si­dize hard­ware costs by mak­ing money on books. But it has reached a point where Amazon has ef­fec­tively turned your de­vice into a bill­board. You are pay­ing for the priv­i­lege of be­ing mar­keted to every time you wake up your de­vice — unless you pay up.Be­tween forced ob­so­les­cence and an AI-forward fea­ture, this is­n’t the read­ing ex­pe­ri­ence I paid for. Amazon’s 2026 roadmap is also heav­ily fo­cused on AI read­ing as­sis­tants and cloud-based sum­maries. This is es­sen­tially a data-min­ing op­er­a­tion. Amazon is not just track­ing what you buy; it is track­ing how you read. It knows how fast you turn pages, which sec­tions you skip, and ex­actly what you high­light to feed its large lan­guage mod­els. Yes, you can put your Kindle in air­plane mode, but it does­n’t change the facts about the di­rec­tion the com­pany is tak­ing.This level of teleme­try is in­va­sive for a de­vice that is sup­posed to be a pri­vate read­ing ex­pe­ri­ence. Nor did I ever sign up for it. Competitors like Kobo of­fer an of­fline-first ex­pe­ri­ence that does not re­quire a con­stant heart­beat to a cen­tral server to func­tion as the de­fault. Elsewhere, on a Boox de­vice, you have to­tal con­trol over which apps can ac­cess the in­ter­net. With Kindle, it in­creas­ingly looks like the pri­vacy trade-off is the hid­den cost of the hard­ware, and I’m not com­fort­able with it.

There is bet­ter hard­ware and more open ecosys­tems out thereThe fact of the mat­ter is that the Kindle is no longer your only, or best, op­tion. There are plenty of al­ter­na­tives avail­able if you want a ded­i­cated e-reader that re­spects the idea of own­er­ship. Kobo is the log­i­cal next step. Devices like the Kobo Libra Colour of­fer hard­ware that is of­ten su­pe­rior, or at the very least equiv­a­lent, to the Kindle Paperwhite at sim­i­lar price points. The stand­out fea­ture is na­tive OverDrive and Libby in­te­gra­tion. On a Kobo, you can browse, bor­row, and re­turn li­brary books di­rectly on the de­vice with­out ever need­ing to touch a phone or a com­puter, pro­vided you are in a sup­ported coun­try.Kobo also uses the in­dus­try-stan­dard ePub for­mat. This means you are not locked into one store. You can buy books from Google Play, Kobo, or var­i­ous in­de­pen­dent book­stores and sim­ply drag and drop them onto the de­vice via USB. Kobo de­vices also fea­ture much bet­ter ty­pog­ra­phy set­tings. For those who pre­fer phys­i­cal but­tons, Kobo has kept them as a stan­dard fea­ture on its mid-range de­vices, some­thing that the Amazon Kindle ap­pears to be al­ler­gic to.One of the biggest rea­sons to stick to the Kindle was the over­all ex­pe­ri­ence it of­fered. Ironically, Kindle’s ex­pe­ri­ence ad­van­tages are no longer re­ally a thing. If you re­ally want the ul­ti­mate no-com­pro­mise ex­pe­ri­ence, Onyx Boox has been steadily chang­ing the game. Devices like the Boox Palma 2 or the Go 10.3 are not just e-read­ers. Instead, these de­vices are e-ink tablets run­ning a full ver­sion of Android that dra­mat­i­cally open up op­por­tu­ni­ties for cus­tomiza­tion.In my opin­ion, this should be the top op­tion for any­one who wants to leave Kindle hard­ware but keep their Kindle books. Because these de­vices have the Google Play Store, you can sim­ply in­stall the Kindle app. You get the ben­e­fits of the Amazon book­store and your ex­ist­ing li­brary, but you get to use it on hard­ware that is faster and bet­ter de­signed.Us­ing the Kindle app on a Boox de­vice ac­tu­ally pro­vides a bet­ter ex­pe­ri­ence than us­ing a Kindle. You get smoother scrolling and the abil­ity to use third-party fonts with­out any re­stric­tions. Plus, you can run other apps like Spotify for back­ground mu­sic or Notion and Goodreads for book track­ing. You are no longer lim­ited to what Amazon thinks you should be do­ing with your de­vice. Instead, you are in full con­trol of the soft­ware ex­pe­ri­ence.The al­ter­na­tives have caught up and, in some cases, sur­passed Kindle.Another area where Amazon used to lead was dis­play qual­ity, but that gap has closed. The newest Kobo and Boox de­vices are us­ing the lat­est e-ink Carta 1300 pan­els. These pan­els of­fer sig­nif­i­cantly bet­ter con­trast and faster re­fresh rates than the older Carta 1200 found in most Kindles. This means vir­tu­ally non-ex­is­tent ghost­ing and text that looks per­fectly crisp.Hav­ing used a range of Boox hard­ware, I can say the Boox Go 10.3 is a par­tic­u­larly im­pres­sive piece of hard­ware. Between the high-res­o­lu­tion screen and a panel that sits closer to the sur­face, you get a re­mark­ably pa­per-like ex­pe­ri­ence. Plus, the sty­lus in­te­gra­tion goes above and be­yond what you’ll find on equiv­a­lent Kindle hard­ware. If you do any amount of note-tak­ing, the Scribe feels like a toy com­pared to the much more fea­ture-packed Boox tablets. As I men­tioned ear­lier, the Kindle re­ally is­n’t the epit­ome of a qual­ity read­ing ex­pe­ri­ence any­more.

The biggest fear peo­ple have when leav­ing be­hind the Kindle is that they will lose ac­cess to books. This is a myth. While Amazon does have some ex­clu­sive self-pub­lished ti­tles, the vast ma­jor­ity of main­stream books are avail­able on every plat­form. Kobo, Google Play Books, and Apple Books all have cat­a­logs that ri­val Amazon in size. In many cases, you can ac­tu­ally find bet­ter deals on these plat­forms.Even Amazon seems to be ac­knowl­edg­ing that it can’t take its au­di­ence for granted. Starting in January 2026, Amazon has be­gun al­low­ing users to down­load DRM-free ver­sions (Digital Rights Management) of se­lect ePub and PDF files di­rectly from their man­age­ment page. This only ap­plies to books where the pub­lisher has opted out of DRM, but it is a mas­sive shift. It proves that even Amazon knows the pro­pri­etary for­mats are be­com­ing a li­a­bil­ity in a mar­ket that is mov­ing to­ward open stan­dards.Dig­i­tal own­er­ship only ex­ists if you can take your li­brary with you.

For the books you al­ready own that still have DRM, you do not have to leave them be­hind. There are ways to man­age your dig­i­tal li­brary us­ing tools like Calibre and a few plu­g­ins that let you im­port your Kindle pur­chases into a cen­tral data­base. This al­lows you to con­vert them to ePub and move them to any de­vice you choose.

The goal is­n’t just con­ve­nience. Digital preser­va­tion is ex­tremely im­por­tant to me and mil­lions of other users. If Amazon de­cides to delete a book from its servers or shut down your ac­count, you still have the file you paid for. Having a lo­cal, DRM-free backup of your li­brary is the only way to en­sure that your col­lec­tion sur­vives the whims of a multi-tril­lion-dol­lar cor­po­ra­tion. Once your books are in Calibre, you can use pow­er­ful tools to fix meta­data, add high-res­o­lu­tion cov­ers, and read them on what­ever de­vice you want.

The e-reader mar­ket in 2026 is the most com­pet­i­tive it has ever been. We have reached a point where Amazon’s ecosys­tem no longer of­fers enough unique value to jus­tify its re­stric­tions. Combine that with Amazon’s move to brick older hard­ware, to me, it is just the fi­nal sign that the cus­tomer is not the pri­or­ity for the com­pany. Between the seam­less li­brary in­te­gra­tion of Kobo and the raw power of Android-based read­ers from Boox, there is no rea­son to buy an­other Kindle.There’s no rea­son to stay locked in when bet­ter, more open op­tions ex­ist.

If you want the best read­ing ex­pe­ri­ence, buy a Kobo. If you want a pow­er­ful e-ink tablet that does every­thing, buy a Boox. If you want to ac­tu­ally own the books you pay for, use Calibre. But un­til Amazon turns the ship around with its dig­i­tal and hard­ware poli­cies, I do not plan to give Amazon an­other cent for a de­vice that it can take away from me with a sin­gle server-side up­date. My li­brary de­serves bet­ter than that. And so does yours.

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Alongside the re­lease of the Kimi K2.6 model, we are open-sourc­ing the Kimi Vendor Verifier (KVV) pro­ject, de­signed to help users of open-source mod­els ver­ify the ac­cu­racy of their in­fer­ence im­ple­men­ta­tions.

Not as an af­ter­thought, but be­cause we learned the hard way that open-sourc­ing a model is only half the bat­tle. The other half is en­sur­ing it runs cor­rectly every­where else.

You can click here to ac­cess the Kimi API K2VV eval­u­a­tion re­sults for cal­cu­lat­ing the F1 score.

Since the re­lease of K2 Thinking, we have re­ceived fre­quent feed­back from the com­mu­nity re­gard­ing anom­alies in bench­mark scores. Our in­ves­ti­ga­tion con­firmed that a sig­nif­i­cant por­tion of these cases stemmed from the mis­use of Decoding pa­ra­me­ters. To mit­i­gate this im­me­di­ately, we built our first line of de­fense at the API level: en­forc­ing Temperature=1.0 and TopP=0.95 in Thinking mode, with manda­tory val­i­da­tion that think­ing con­tent is cor­rectly passed back.

However, more sub­tle anom­alies soon trig­gered our alarm. In a spe­cific eval­u­a­tion on LiveBenchmark, we ob­served a stark con­trast be­tween third-party API and of­fi­cial API. After ex­ten­sive test­ing of var­i­ous in­fra­struc­ture providers, we found this dif­fer­ence is wide­spread.

This ex­posed a deeper prob­lem in the open-source model ecosys­tem: The more open the weights are, and the more di­verse the de­ploy­ment chan­nels be­come, the less con­trol­lable the qual­ity be­comes.

If users can­not dis­tin­guish be­tween “model ca­pa­bil­ity de­fects” and “engineering im­ple­men­ta­tion de­vi­a­tions,” trust in the open-source ecosys­tem will in­evitably col­lapse.

Pre-Verification: Validates that API pa­ra­me­ter con­straints (temperature, top_p, etc.) are cor­rectly en­forced. All tests must pass be­fore pro­ceed­ing to bench­mark eval­u­a­tion. K2VV ToolCall: Measures trig­ger con­sis­tency (F1) and JSON Schema ac­cu­racy. Tool er­rors com­pound in agents; we catch them early.SWE-Bench: Full agen­tic cod­ing test. (Not open sourced due to de­pen­dency of sand­box)

Upstream Fix: We em­bed with vLLM/​SGLang/​KTrans­form­ers com­mu­ni­ties to fix root causes, not just de­tect symp­toms.

Pre-Release Validation: Rather than wait­ing for post-de­ploy­ment com­plaints, we pro­vide early ac­cess to test mod­els. This lets in­fra­struc­ture providers val­i­date their stacks be­fore users en­counter is­sues.

Continuous Benchmarking: We will main­tain a pub­lic leader­board of ven­dor re­sults. This trans­parency en­cour­ages ven­dors to pri­or­i­tize ac­cu­racy.

We com­pleted full eval­u­a­tion work­flow val­i­da­tion on Two NVIDIA H20 8-GPU servers, with se­quen­tial ex­e­cu­tion tak­ing ap­prox­i­mately 15 hours. To im­prove eval­u­a­tion ef­fi­ciency, scripts have been op­ti­mized for long-run­ning in­fer­ence sce­nar­ios, in­clud­ing stream­ing in­fer­ence, au­to­matic retry, and check­point re­sump­tion mech­a­nisms.

Weights are open. The knowl­edge to run them cor­rectly must be too.

We are ex­pand­ing ven­dor cov­er­age and seek­ing lighter agen­tic tests. Contact Us: [email protected]

The DSP is of­fer­ing ad place­ments dri­ven by prompt rel­e­vance and dan­gling CPMs rang­ing from $15 to $60, with a $50,000 min­i­mum spend for the pi­lot.

If you want to shape me­dia strat­egy—not just op­ti­mize it—up­grade your ex­per­tise. The ADWEEK MiniMBA in Marketing equips you to lead with con­fi­dence and cred­i­bil­ity. Register now.

StackAdapt is qui­etly court­ing ad­ver­tis­ers to test ads in­side ChatGPT. The in­de­pen­dent de­mand-side plat­form is dan­gling CPMs as low as $15 along­side dis­counted plat­form and man­age­ment fees. The com­pany is fram­ing the push as early ac­cess to a new “discovery layer”—one that cap­tures peo­ple in the mid­dle of re­search­ing and com­par­ing prod­ucts on ChatGPT. According to a pitch deck ti­tled “OpenAI x StackAdapt Limited Pilot Program,” shared with se­lect buy­ers on March 27 and re­viewed by ADWEEK, the com­pany is po­si­tion­ing the of­fer­ing as an early-stage test in­side a still-de­vel­op­ing ad sys­tem.“Stack­Adapt has part­nered with OpenAl to en­able ad­ver­tis­ing within ChatGPT, one of the fastest grow­ing con­sumer plat­forms in the world,” the deck reads.

Trishla Ostwal

Trishla is an Adweek staff re­porter cov­er­ing AI and tech.

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