Passengers at Britain’s biggest air­port, Heathrow, can leave liq­uids in con­tain­ers up to two litres in their bags while go­ing through se­cu­rity, af­ter it fi­nally com­pleted the roll­out of new high-tech CT scan­ners. Electronics such as lap­tops can also be left in lug­gage, while clear plas­tic bags for liq­uids no longer have to be used.Heathrow now says it is the biggest air­port in the world to have the new equip­ment fully rolled out across all its ter­mi­nals.But while it has be­come the largest air­port to roll out the new high-tech scan­ners, it is far from the UK’s first, with Gatwick, Edinburgh and Birmingham air­ports hav­ing up­graded to them in re­cent years and in­creased to a two-litre limit.

At most UK air­ports, pas­sen­gers can keep liq­uid con­tain­ers of up to 100ml in their lug­gage, with­out hav­ing to re­move them and use clear plas­tic bags. Bristol and Belfast air­ports have also raised their liq­uid lim­its to two litres.How­ever, other air­ports that have the new scan­ners in­stalled are wait­ing for the green light from the Department for Transport (DfT) to raise the limit from 100ml.A re­cent re­port by con­sumer group Which? found that the sen­si­tiv­ity of the new scan­ners be­ing rolled out means that at some air­ports, more bag searches end up be­ing car­ried out by hand af­ter pass­ing through them. Heathrow said the scan­ners, which pro­vide bet­ter im­ages of cabin bags, could ser­vice “thousands of pas­sen­gers an hour with sig­nif­i­cantly greater ef­fi­ciency, while main­tain­ing high safety and se­cu­rity stan­dards”.The rule change only ap­plies to flights leav­ing Heathrow, and pas­sen­gers must check re­stric­tions on lug­gage at the air­ports they are re­turn­ing from be­fore board­ing flights to the UK. The roll­out of the new high-tech scan­ners across the UK has suf­fered a se­ries of set­backs over the past few years.Boris Johnson promised in 2019 that the rules about tak­ing liq­uids through se­cu­rity in con­tain­ers of no more than 100ml, in­side plas­tics bags, would be scrapped by the end of 2022. The pan­demic even­tu­ally put paid to that.In December 2022, the Conservative gov­ern­ment promised state-of-the-art scan­ning equip­ment would be in­stalled in se­cu­rity lanes by June 2024 in the “biggest shake-up of air­port se­cu­rity rules in decades”.

Then-Transport Secretary Mark Harper said the dom­i­nance of “tiny toi­letry” was nearly over. But, as it turned out, the June 2024 dead­line was not achiev­able for the biggest air­ports - al­though a num­ber of smaller ones, with fewer lanes to get sorted, did in­stall the scan­ners in place be­fore that date.Then, on the evening of Friday 13 June, 2024, the gov­ern­ment said those smaller air­ports who had al­ready in­tro­duced the new scan­ners and dropped their 100ml liq­uids rules, must re­in­state them. This trig­gered anger among air­port op­er­a­tors.The EU also an­nounced a re­ver­sion to the 100ml rule in July that year. There has since been a pe­riod of in­con­sis­tency. Last sum­mer, the Transport Secretary was telling pas­sen­gers to as­sume the 100ml rule still ap­plied.

Heathrow chief ex­ec­u­tive Thomas Woldbye said the £1bn pack­age of up­grades would mean pas­sen­gers could spend “less time prepar­ing for se­cu­rity and more time en­joy­ing their jour­ney”.Of the world’s busiest 10 air­ports, Heathrow is the only one to have scrapped the 100ml rule for liq­uid con­tain­ers on in­ter­na­tional flights. A DfT spokesper­son said: “Heathrow is the lat­est UK air­port to com­plete its roll­out of next-gen­er­a­tion se­cu­rity equip­ment for pas­sen­gers, help­ing en­sure se­cu­rity checks re­main ro­bust and can be com­pleted smoothly.“Air­ports are re­spon­si­ble for the in­stal­la­tion and op­er­a­tion of se­cu­rity equip­ment. “Passengers should con­tinue to check se­cu­rity re­quire­ments with air­ports be­fore they travel and come pre­pared with liq­uids in con­tain­ers no larger than 100ml in hand bag­gage un­less ad­vised oth­er­wise.“The Advantage Travel Partnership, a net­work of travel agents, said air­ports set­ting their own time­lines on the lift­ing of the 100ml cap has “led to con­fu­sion and frus­tra­tion” and pas­sen­gers have been “tripped up”.Chief ex­ec­u­tive Julia Lo Bue-Said said: “We would urge UK air­ports to work col­lec­tively with the gov­ern­ment to en­sure there is clear mes­sag­ing around the rules to avoid con­fu­sion and de­lays where pos­si­ble.”

FBI Director Kash Patel said Monday that he had opened an in­ves­ti­ga­tion into the Signal group text chats that Minnesota res­i­dents are us­ing to share in­for­ma­tion about fed­eral im­mi­gra­tion agents’ move­ments, launch­ing a new front in the Trump ad­min­is­tra­tion’s con­flict there with po­ten­tial free speech im­pli­ca­tions.

Patel said in an in­ter­view with con­ser­v­a­tive pod­caster Benny Johnson that he wanted to know whether any Minnesota res­i­dents had put fed­eral agents “in har­m’s way” with ac­tiv­i­ties such as shar­ing agents’ li­cense plate num­bers and lo­ca­tions.

“You can­not cre­ate a sce­nario that il­le­gally en­traps and puts law en­force­ment in har­m’s way,” he said in the in­ter­view, which was posted to YouTube.

The in­ves­ti­ga­tion quickly drew skep­ti­cism from free speech ad­vo­cates who said the First Amendment pro­tects mem­bers of the pub­lic who share legally ob­tained in­for­ma­tion, such as the names of fed­eral agents or where they are con­duct­ing en­force­ment op­er­a­tions.

“There are le­git­i­mate rea­sons to share such in­for­ma­tion, in­clud­ing en­abling mem­bers of the pub­lic to ob­serve and doc­u­ment law en­force­ment ac­tiv­ity and to hold of­fi­cials ac­count­able for mis­con­duct,” Aaron Terr, di­rec­tor of pub­lic ad­vo­cacy at the Foundation for Individual Rights and Expression, said in an email.

“Given this ad­min­is­tra­tion’s poor track record of dis­tin­guish­ing pro­tected speech from crim­i­nal con­duct, any in­ves­ti­ga­tion like this de­serves very close scrutiny,” he said.

For months, dig­i­tal tools have been at the cen­ter of how peo­ple have pushed back against im­mi­gra­tion en­force­ment ef­forts in Minnesota and across the coun­try. The ad­min­is­tra­tion’s op­po­nents have used group text chats to track Immigration and Customs Enforcement op­er­a­tions, share pho­tos of sus­pected ICE ve­hi­cles and raise aware­ness for neigh­bors. In June, ad­min­is­tra­tion of­fi­cials crit­i­cized ICEBlock, an app de­signed to share in­for­ma­tion about ICE sight­ings. Apple re­moved the app from its app store in October, prompt­ing a law­suit from the ap­p’s de­vel­oper al­leg­ing the ad­min­is­tra­tion un­law­fully pres­sured Apple to re­move it.

In the past few days, the group text chats — es­pe­cially those on the en­crypted mes­sag­ing app Signal — have drawn at­ten­tion from right-wing me­dia. On Saturday, Cam Higby, a con­ser­v­a­tive jour­nal­ist based near Seattle, said in a thread on X that he had “infiltrated” Signal groups from around Minneapolis that he al­leged were ob­struct­ing law en­force­ment. His thread, which got 20 mil­lion views, fo­cused on how the groups share such in­for­ma­tion as the li­cense plate num­bers of sus­pected fed­eral ve­hi­cles. NBC News has not ver­i­fied Higby’s claims.

Patel said he got the idea for the in­ves­ti­ga­tion from Higby.

“As soon as Higby put that post out, I opened an in­ves­ti­ga­tion on it,” he said. “We im­me­di­ately opened up that in­ves­ti­ga­tion, be­cause that sort of Signal chat — be­ing co­or­di­nated with in­di­vid­u­als not just lo­cally in Minnesota, but maybe even around the coun­try — if that leads to a break in the fed­eral statute or a vi­o­la­tion of some law, then we are go­ing to ar­rest peo­ple.”

The Signal Foundation, the non­profit or­ga­ni­za­tion that op­er­ates the Signal app, did not im­me­di­ately re­spond to a re­quest for com­ment.

Signal, which is con­sid­ered one of the most se­cure chat apps, is a go-to re­source for peo­ple con­cerned about pri­vacy. It is per­haps best known as the app Defense Secretary Pete Hegseth used to share sen­si­tive mil­i­tary in­for­ma­tion last year in a group chat that ac­ci­den­tally in­cluded a jour­nal­ist.

In the Twin Cities, Signal group chats have been a stan­dard part of toolk­its — along with walkie-talkies and whis­tles — used by ac­tivists, par­ents and neigh­bor­hood-watch mem­bers who have or­ga­nized as vol­un­teers to warn fam­i­lies about im­mi­gra­tion en­force­ment ac­tiv­i­ties by re­lay­ing real-time in­for­ma­tion, es­pe­cially near schools. Patrol vol­un­teers have said that, with more than 3,000 fed­eral im­mi­gra­tion agents in Minnesota, they are mo­ti­vated by a de­sire to pro­tect par­ents, chil­dren and school staff mem­bers who are not U. S. cit­i­zens.

Patel did not say which laws he thought Minnesota res­i­dents may have vi­o­lated. An FBI spokesper­son said the bu­reau had no fur­ther in­for­ma­tion to pro­vide.

The an­nounce­ment seemed likely to have im­pli­ca­tions for the First Amendment’s guar­an­tee of free speech. Alex Abdo, lit­i­ga­tion di­rec­tor at the Knight First Amendment Institute at Columbia University, said the First Amendment pro­tects the right to record law en­force­ment of­fi­cers as they carry out their of­fi­cial re­spon­si­bil­i­ties.

“The abil­ity of every­day cit­i­zens to hold gov­ern­ment agents to ac­count, by ob­serv­ing them and ad­vo­cat­ing for change, is what has dis­tin­guished the American ex­per­i­ment with democ­racy from au­thor­i­tar­ian regimes around the world,” Abdo said in an email.

“Unless the FBI has ev­i­dence of a crime, and not just ev­i­dence of ac­tiv­ity the Constitution pro­tects, it should stand down,” he said.

Patel ac­knowl­edged in the in­ter­view with Johnson that an in­ves­ti­ga­tion into group text chats would raise free speech con­cerns and said the FBI would “balance” the rights guar­an­teed by the First and Second amend­ments with what he said were po­ten­tial vi­o­la­tions of fed­eral law.

“Now, we will bal­ance the First and Second amend­ment con­stantly, but we have to let the com­mu­nity know that we will not tol­er­ate acts of vi­o­lence and an es­ca­la­tion and a vi­o­la­tion of the fed­eral code,” he said. The Second Amendment could be at is­sue be­cause Alex Pretti, the nurse shot and killed by a fed­eral agent Saturday in Minneapolis, was per­mit­ted to carry a gun in pub­lic and had one with him.

Terr, of the Foundation for Individual Rights and Expression, said the gov­ern­ment does not get to “balance” the First Amendment against its other in­ter­ests.

“The Constitution takes prece­dence over any con­flict­ing state or fed­eral law, and over any of­fi­cial’s de­sire to sup­press speech they dis­like,” he said in his email.

He added: “There is a First Amendment ex­cep­tion for speech in­tended and likely to pro­voke im­mi­nent un­law­ful ac­tion, but that does­n’t ap­ply to just any speech the gov­ern­ment claims puts of­fi­cials in har­m’s way. By con­trast, if in­di­vid­u­als are threat­en­ing fed­eral agents or con­spir­ing to phys­i­cally harm them, that is il­le­gal. But con­spir­acy re­quires an agree­ment to com­mit a spe­cific crime and a sub­stan­tial step to­ward car­ry­ing it out.”

Patel also said the FBI had made “substantial progress” in an in­ves­ti­ga­tion into groups and peo­ple re­spon­si­ble for fund­ing re­sis­tance to im­mi­gra­tion en­force­ment. He al­leged that the protests and neigh­bor­hood mon­i­tor­ing are “not hap­pen­ing or­gan­i­cally” but did not im­me­di­ately pro­vide ev­i­dence.

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Today, we are in­tro­duc­ing Kimi K2.5, the most pow­er­ful open-source model to date. Kimi K2.5 builds on Kimi K2 with con­tin­ued pre­train­ing over ap­prox­i­mately 15T mixed vi­sual and text to­kens. Built as a na­tive mul­ti­modal model, K2.5 de­liv­ers state-of-the-art cod­ing and vi­sion ca­pa­bil­i­ties and a self-di­rected agent swarm par­a­digm.For com­plex tasks, Kimi K2.5 can self-di­rect an agent swarm with up to 100 sub-agents, ex­e­cut­ing par­al­lel work­flows across up to 1,500 tool calls. Compared with a sin­gle-agent setup, this re­duces ex­e­cu­tion time by up to 4.5x. The agent swarm is au­to­mat­i­cally cre­ated and or­ches­trated by Kimi K2.5 with­out any pre­de­fined sub­agents or work­flow.Kimi K2.5 is avail­able via Kimi.com, the Kimi App, the API, and Kimi Code. Kimi.com & Kimi App now sup­ports 4 modes: K2.5 Instant, K2.5 Thinking, K2.5 Agent, and K2.5 Agent Swarm (Beta). Agent Swarm is cur­rently in beta on Kimi.com, with free cred­its avail­able for high-tier paid users.Across three agen­tic bench­marks—HLE, BrowseComp, and SWE-Verified—Kimi K2.5 de­liv­ers strong per­for­mance at a frac­tion of the cost.Kimi K2.5 is the strongest open-source model to date for cod­ing, with par­tic­u­larly strong ca­pa­bil­i­ties in front-end de­vel­op­ment.K2.5 can turn sim­ple con­ver­sa­tions into com­plete front-end in­ter­faces, im­ple­ment­ing in­ter­ac­tive lay­outs and rich an­i­ma­tions such as scroll-trig­gered ef­fects. Below are ex­am­ples gen­er­ated by K2.5 from a sin­gle prompt with im­age-gen tool:Be­yond text prompts, K2.5 ex­cels at cod­ing with vi­sion. By rea­son­ing over im­ages and video, K2.5 im­proves im­age/​video-to-code gen­er­a­tion and vi­sual de­bug­ging, low­er­ing the bar­rier for users to ex­press in­tent vi­su­ally.Here is an ex­am­ple of K2.5 re­con­struct­ing a web­site from video:This ca­pa­bil­ity stems from mas­sive-scale vi­sion-text joint pre-train­ing. At scale, the trade-off be­tween vi­sion and text ca­pa­bil­i­ties dis­ap­pears — they im­prove in uni­son.Be­low is an ex­am­ple of K2.5 rea­son­ing over a puz­zle and mark­ing the short­est path us­ing code:K2.5 ex­cels in real-world soft­ware en­gi­neer­ing tasks. We eval­u­ate it us­ing Kimi Code Bench, our in­ter­nal cod­ing bench­mark cov­er­ing di­verse end-to-end tasks — from build­ing to de­bug­ging, refac­tor­ing, test­ing, and script­ing — across mul­ti­ple pro­gram­ming lan­guages. On this bench­mark, K2.5 shows con­sis­tent and mean­ing­ful im­prove­ments over K2 across task types.To try out K2.5′s agen­tic cod­ing ca­pa­bil­i­ties, K2.5 Agent of­fers a set of pre­con­fig­ured tools for im­me­di­ate, hands-on ex­pe­ri­ences. For soft­ware en­gi­neer­ing use cases, we rec­om­mend pair­ing Kimi K2.5 with our new cod­ing prod­uct, Kimi Code.Kimi Code works in your ter­mi­nal and can be in­te­grated with var­i­ous IDEs in­clud­ing VSCode, Cursor, Zed, etc. Kimi Code is open-sourced and sup­ports im­ages and videos as in­puts. It also au­to­mat­i­cally dis­cov­ers and mi­grates ex­ist­ing skills and MCPs into your work­ing en­vi­ron­ment in Kimi Code.Here’s an ex­am­ple us­ing Kimi Code to trans­late the aes­thetic of Matisse’s La Danse into the Kimi App. This demo high­lights a break­through in au­tonomous vi­sual de­bug­ging. Using vi­sual in­puts and doc­u­men­ta­tion lookup, K2.5 vi­su­ally in­spects its own out­put and it­er­ates on it au­tonomously. It cre­ates an art-in­spired web­page cre­ated end to end:Scal­ing Out, Not Just Up. We re­lease K2.5 Agent Swarm as a re­search pre­view, mark­ing a shift from sin­gle-agent scal­ing to self-di­rected, co­or­di­nated swarm-like ex­e­cu­tion.Trained with Parallel-Agent Reinforcement Learning (PARL), K2.5 learns to self-di­rect an agent swarm of up to 100 sub-agents, ex­e­cut­ing par­al­lel work­flows across up to 1,500 co­or­di­nated steps, with­out pre­de­fined roles or hand-crafted work­flows.PARL uses a train­able or­ches­tra­tor agent to de­com­pose tasks into par­al­leliz­able sub­tasks, each ex­e­cuted by dy­nam­i­cally in­stan­ti­ated, frozen sub­agents. Running these sub­tasks con­cur­rently sig­nif­i­cantly re­duces end-to-end la­tency com­pared to se­quen­tial agent ex­e­cu­tion.Train­ing a re­li­able par­al­lel or­ches­tra­tor is chal­leng­ing due to de­layed, sparse, and non-sta­tion­ary feed­back from in­de­pen­dently run­ning sub­agents. A com­mon fail­ure mode is se­r­ial col­lapse, where the or­ches­tra­tor de­faults to sin­gle-agent ex­e­cu­tion de­spite hav­ing par­al­lel ca­pac­ity. To ad­dress this, PARL em­ploys staged re­ward shap­ing that en­cour­ages par­al­lelism early in train­ing and grad­u­ally shifts fo­cus to­ward task suc­cess.We de­fine the re­ward aswhere an­neals from over train­ing. Early on, the aux­il­iary re­ward in­cen­tivizes sub­agent in­stan­ti­a­tion and con­cur­rent ex­e­cu­tion, pro­mot­ing ex­plo­ration of the par­al­lel sched­ul­ing space. As train­ing pro­gresses, op­ti­miza­tion shifts to­ward end-to-end task qual­ity  , pre­vent­ing de­gen­er­ate so­lu­tions where par­al­lelism is en­abled in name only.To fur­ther force par­al­lel strate­gies to emerge, we in­tro­duce a com­pu­ta­tional bot­tle­neck that makes se­quen­tial ex­e­cu­tion im­prac­ti­cal. Instead of count­ing to­tal steps, we eval­u­ate per­for­mance us­ing Critical Steps, a la­tency-ori­ented met­ric in­spired by the crit­i­cal path in par­al­lel com­pu­ta­tion:cap­tures or­ches­tra­tion over­head, while re­flects the slow­est sub­agent at each stage. Under this met­ric, spawn­ing more sub­tasks only helps if it short­ens the crit­i­cal path.An agent swarm has an or­ches­tra­tor that dy­nam­i­cally cre­ates spe­cial­ized sub­agents (e.g., AI Researcher, Physics Researcher, Fact Checker) and de­com­poses com­plex tasks into par­al­leliz­able sub­tasks for ef­fi­cient dis­trib­uted ex­e­cu­tion.In our par­al­lel-agent re­in­force­ment learn­ing en­vi­ron­ment, the re­ward in­creases smoothly as train­ing pro­gresses. At the same time, the level of par­al­lelism dur­ing train­ing also grad­u­ally in­creases.K2.5 Agent Swarm im­proves per­for­mance on com­plex tasks through par­al­lel, spe­cial­ized ex­e­cu­tion. In our in­ter­nal eval­u­a­tions, it leads to an 80% re­duc­tion in end-to-end run­time while en­abling more com­plex, long-hori­zon work­loads, as shown be­low.Agent Swarm re­duces the min­i­mum crit­i­cal steps re­quired to achieve tar­get per­for­mance by 3×–4.5× com­pared to sin­gle-agent ex­e­cu­tion in wide search sce­nario, with sav­ings scal­ing as tar­gets rise—trans­lat­ing to up to 4.5× wall-clock time re­duc­tion via par­al­leliza­tion.Here are rep­re­sen­ta­tive tra­jec­to­ries demon­strat­ing K2.5 Agent Swarm in ac­tion:K2.5 Agent can han­dle high-den­sity, large-scale of­fice work end to end. It rea­sons over large, high-den­sity in­puts, co­or­di­nates multi-step tool use, and de­liv­ers ex­pert-level out­puts: doc­u­ments, spread­sheets, PDFs, and slide decks—di­rectly through con­ver­sa­tion.With a fo­cus on real-world pro­fes­sional tasks, we de­sign two in­ter­nal ex­pert pro­duc­tiv­ity bench­marks. The AI Office Benchmark eval­u­ates end-to-end Office out­put qual­ity, while the General Agent Benchmark mea­sures multi-step, pro­duc­tion-grade work­flows against hu­man ex­pert per­for­mance. Across both bench­marks, K2.5 shows 59.3% and 24.3% im­prove­ments over K2 Thinking, re­flect­ing stronger end-to-end per­for­mance on real-world tasks.

K2.5 agent sup­ports ad­vanced tasks such as adding an­no­ta­tions in Word, con­struct­ing fi­nan­cial mod­els with Pivot Tables, and writ­ing LaTeX equa­tions in PDFs, while scal­ing to long-form out­puts like 10,000-word pa­pers or 100-page doc­u­ments.Tasks that once took hours or days now com­plete in min­utes. Here are some ex­am­ples:Grounded in ad­vances in cod­ing with vi­sion, agent swarms, and of­fice pro­duc­tiv­ity, Kimi K2.5 rep­re­sents a mean­ing­ful step to­ward AGI for the open-source com­mu­nity, demon­strat­ing strong ca­pa­bil­ity on real-world tasks un­der real-world con­straints. Looking ahead, we will push fur­ther into the fron­tier of agen­tic in­tel­li­gence, re­defin­ing the bound­aries of AI in knowl­edge work.To re­pro­duce of­fi­cial Kimi-K2.5 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.htmlWe re­port re­sults for Kimi K2.5 and DeepSeek-V3.2 with think­ing mode en­abled, Claude Opus 4.5 with ex­tended think­ing mode, GPT-5.2 with xhigh rea­son­ing ef­fort, and Gemini 3 Pro with a high think­ing level. For vi­sion bench­marks, we ad­di­tion­ally re­port re­sults for Qwen3-VL-235B-A22B-Thinking.Unless oth­er­wise spec­i­fied, all Kimi K2.5 ex­per­i­ments were con­ducted with tem­per­a­ture = 1.0, top-p = 0.95, and a con­text length of 256k to­kens.Bench­marks with­out pub­licly avail­able scores were re-eval­u­ated un­der the same con­di­tions used for Kimi K2.5 and are marked with an as­ter­isk (*).We could not eval­u­ate GPT-5.2 xhigh on all bench­marks due to ser­vice sta­bil­ity is­sues. For bench­marks that were not tested, we mark them as “-”.HLE, AIME 2025, HMMT 2025 (Feb), GPQA-Diamond and IMO-AnswerBench were eval­u­ated with a max­i­mum com­ple­tion bud­get of 96k to­kens.Re­sults for AIME and HMMT are av­er­aged over 32 runs (avg@32); GPQA-Diamond over 8 runs (avg@8).For HLE, we re­port scores on the full set (text & im­age). Kimi K2.5 scores 31.5 (text) and 21.3 (image) with­out tools, and 51.8 (text) and 39.8 (image) with tools. The DeepSeek-V3.2 score cor­re­sponds to its text-only sub­set (marked with †) . Hugging Face ac­cess was blocked to pre­vent po­ten­tial data leak­age. HLE with tools uses sim­ple con­text man­age­ment: once the con­text ex­ceeds a thresh­old, only the lat­est round of tool mes­sages is re­tained.Kimi K2.5 was equipped with search, code-in­ter­preter, and web-brows­ing tools for HLE with tools and all agen­tic search bench­marks.Ex­cept for BrowseComp (where K2.5 and DeepSeek-V3.2 used the dis­card-all strat­egy), no con­text man­age­ment was ap­plied, and tasks ex­ceed­ing the sup­ported con­text length were di­rectly counted as failed.The test sys­tem prompts em­pha­size deep and proac­tive tool use, in­struct­ing mod­els to rea­son care­fully, lever­age tools, and ver­ify un­cer­tain in­for­ma­tion. Full prompts will be pro­vided in the tech­ni­cal re­port.Re­sults for Seal-0 and WideSearch are av­er­aged over four runs (avg@4).ZeroBench (w/ tools) uses max-to­kens-per-step = 24k and max-steps = 30 for multi-step rea­son­ing.MMMU-Pro fol­lows the of­fi­cial pro­to­col, pre­serv­ing in­put or­der and prepend­ing im­ages.GPT-5.2-xhigh had ~10% fail­ure rate (no out­put de­spite 3 re­tries), treated as in­cor­rect; re­ported scores likely un­der­es­ti­mate true per­for­mance.Om­niDocBench Score is com­puted as (1 − nor­mal­ized Levenshtein dis­tance) × 100, where a higher score de­notes su­pe­rior ac­cu­racy.Ter­mi­nal-Bench 2.0 scores were ob­tained with the de­fault agent frame­work (Terminus-2) and the pro­vided JSON parser. In our im­ple­men­ta­tion, we eval­u­ated Terminal-Bench 2.0 un­der non-think­ing mode. This choice was made be­cause our cur­rent con­text man­age­ment strat­egy for the think­ing mode is in­com­pat­i­ble with Terminus-2.For the SWE-Bench se­ries of eval­u­a­tions (including ver­i­fied, mul­ti­lin­gual, and pro), we used an in­ter­nally de­vel­oped eval­u­a­tion frame­work. This frame­work in­cludes a min­i­mal set of tools—bash tool, cre­ate­file tool, in­sert tool, view tool, str­re­place tool, and sub­mit tool—along with tai­lored sys­tem prompts de­signed for the tasks. The high­est scores were achieved un­der non-think­ing mode.The score of Claude Opus 4.5 on CyberGym is re­ported un­der the non-think­ing set­ting.All re­ported scores of cod­ing tasks are av­er­aged over 5 in­de­pen­dent runs.

Clawdbot is a per­sonal AI as­sis­tant you run on your own de­vices. It an­swers you on the chan­nels you al­ready use (WhatsApp, Telegram, Slack, Discord, Google Chat, Signal, iMes­sage, Microsoft Teams, WebChat), plus ex­ten­sion chan­nels like BlueBubbles, Matrix, Zalo, and Zalo Personal. It can speak and lis­ten on ma­cOS/​iOS/​An­droid, and can ren­der a live Canvas you con­trol. The Gateway is just the con­trol plane — the prod­uct is the as­sis­tant.

If you want a per­sonal, sin­gle-user as­sis­tant that feels lo­cal, fast, and al­ways-on, this is it.

Preferred setup: run the on­board­ing wiz­ard (clawdbot on­board). It walks through gate­way, work­space, chan­nels, and skills. The CLI wiz­ard is the rec­om­mended path and works on ma­cOS, Linux, and Windows (via WSL2; strongly rec­om­mended). Works with npm, pnpm, or bun. New in­stall? Start here: Getting started

Model note: while any model is sup­ported, I strongly rec­om­mend Anthropic Pro/Max (100/200) + Opus 4.5 for long‑con­text strength and bet­ter prompt‑in­jec­tion re­sis­tance. See Onboarding.

npm in­stall -g clawd­bot@lat­est

# or: pnpm add -g clawd­bot@lat­est

clawd­bot on­board –install-daemon

The wiz­ard in­stalls the Gateway dae­mon (launchd/systemd user ser­vice) so it stays run­ning.

clawd­bot on­board –install-daemon

clawd­bot gate­way –port 18789 –verbose

# Send a mes­sage

clawd­bot mes­sage send –to +1234567890 –message “Hello from Clawdbot”

# Talk to the as­sis­tant (optionally de­liver back to any con­nected chan­nel: WhatsApp/Telegram/Slack/Discord/Google Chat/Signal/iMessage/BlueBubbles/Microsoft Teams/Matrix/Zalo/Zalo Personal/WebChat)

clawd­bot agent –message “Ship check­list” –thinking high

Prefer pnpm for builds from source. Bun is op­tional for run­ning TypeScript di­rectly.

git clone https://​github.com/​clawd­bot/​clawd­bot.git

cd clawd­bot

pnpm in­stall

pnpm ui:build # auto-in­stalls UI deps on first run

pnpm build

pnpm clawd­bot on­board –install-daemon

# Dev loop (auto-reload on TS changes)

pnpm gate­way:watch

Note: pnpm clawd­bot … runs TypeScript di­rectly (via tsx). pnpm build pro­duces dist/ for run­ning via Node / the pack­aged clawd­bot bi­nary.

* DM pair­ing (dmPolicy=“pairing” / chan­nels.dis­cord.dm.pol­icy=“pair­ing” / chan­nels.slack.dm.pol­icy=“pair­ing”): un­known senders re­ceive a short pair­ing code and the bot does not process their mes­sage.

* Approve with: clawd­bot pair­ing ap­prove (then the sender is added to a lo­cal al­lowlist store).

* Public in­bound DMs re­quire an ex­plicit opt-in: set dm­Pol­icy=“open” and in­clude “*” in the chan­nel al­lowlist (allowFrom / chan­nels.dis­cord.dm.al­lowFrom / chan­nels.slack.dm.al­lowFrom).

Clawdbot can auto-con­fig­ure Tailscale Serve (tailnet-only) or Funnel (public) while the Gateway stays bound to loop­back. Configure gate­way.tailscale.mode:

* serve: tail­net-only HTTPS via tailscale serve (uses Tailscale iden­tity head­ers by de­fault).

* gate­way.bind must stay loop­back when Serve/Funnel is en­abled (Clawdbot en­forces this).

* Serve can be forced to re­quire a pass­word by set­ting gate­way.auth.mode: “password” or gate­way.auth.al­low­Tailscale: false.

* Funnel re­fuses to start un­less gate­way.auth.mode: “password” is set.

It’s per­fectly fine to run the Gateway on a small Linux in­stance. Clients (macOS app, CLI, WebChat) can con­nect over Tailscale Serve/Funnel or SSH tun­nels, and you can still pair de­vice nodes (macOS/iOS/Android) to ex­e­cute de­vice‑lo­cal ac­tions when needed.

* Gateway host runs the exec tool and chan­nel con­nec­tions by de­fault.

* Device nodes run de­vice‑lo­cal ac­tions (system.run, cam­era, screen record­ing, no­ti­fi­ca­tions) via node.in­voke.

In short: exec runs where the Gateway lives; de­vice ac­tions run where the de­vice lives.

The ma­cOS app can run in node mode and ad­ver­tises its ca­pa­bil­i­ties + per­mis­sion map over the Gateway WebSocket (node.list / node.de­scribe). Clients can then ex­e­cute lo­cal ac­tions via node.in­voke:

* sys­tem.run runs a lo­cal com­mand and re­turns std­out/​stderr/​exit code; set needsS­creen­Record­ing: true to re­quire screen-record­ing per­mis­sion (otherwise you’ll get PERMISSION_MISSING).

* sys­tem.no­tify posts a user no­ti­fi­ca­tion and fails if no­ti­fi­ca­tions are de­nied.

* can­vas.*, cam­era.*, screen.record, and lo­ca­tion.get are also routed via node.in­voke and fol­low TCC per­mis­sion sta­tus.

* Use /elevated on|off to tog­gle per‑ses­sion el­e­vated ac­cess when en­abled + al­lowlisted.

* Gateway per­sists the per‑ses­sion tog­gle via ses­sions.patch (WS method) along­side think­ingLevel, ver­bose­Level, model, send­Pol­icy, and groupActi­va­tion.

* Use these to co­or­di­nate work across ses­sions with­out jump­ing be­tween chat sur­faces.

ClawdHub is a min­i­mal skill reg­istry. With ClawdHub en­abled, the agent can search for skills au­to­mat­i­cally and pull in new ones as needed.

Send these in WhatsApp/Telegram/Slack/Google Chat/Microsoft Teams/WebChat (group com­mands are owner-only):

* /new or /reset — re­set the ses­sion

The Gateway alone de­liv­ers a great ex­pe­ri­ence. All apps are op­tional and add ex­tra fea­tures.

If you plan to build/​run com­pan­ion apps, fol­low the plat­form run­books be­low.

* Menu bar con­trol for the Gateway and health.

Note: signed builds re­quired for ma­cOS per­mis­sions to stick across re­builds (see docs/​mac/​per­mis­sions.md).

* Pairs as a node via the Bridge.

* Pairs via the same Bridge + pair­ing flow as iOS.

agent: {

model: “anthropic/claude-opus-4-5”

* Default: tools run on the host for the main ses­sion, so the agent has full ac­cess when it’s just you.

* Group/channel safety: set agents.de­faults.sand­box.mode: “non-main” to run non‑main ses­sions (groups/channels) in­side per‑ses­sion Docker sand­boxes; bash then runs in Docker for those ses­sions.

* Allowlist who can talk to the as­sis­tant via chan­nels.what­sapp.al­lowFrom.

* If chan­nels.what­sapp.groups is set, it be­comes a group al­lowlist; in­clude “*” to al­low all.

* Optional: set chan­nels.telegram.groups (with chan­nels.telegram.groups.“*”.re­quire­Men­tion); when set, it is a group al­lowlist (include “*” to al­low all). Also chan­nels.telegram.al­lowFrom or chan­nels.telegram.web­hookUrl as needed.

chan­nels: {

telegram: {

bot­To­ken: “123456:ABCDEF”

* Optional: set com­mands.na­tive, com­mands.text, or com­mands.use­Ac­cess­Groups, plus chan­nels.dis­cord.dm.al­lowFrom, chan­nels.dis­cord.guilds, or chan­nels.dis­cord.me­dia­MaxMb as needed.

chan­nels: {

dis­cord: {

to­ken: “1234abcd”

* ma­cOS only; Messages must be signed in.

* If chan­nels.imes­sage.groups is set, it be­comes a group al­lowlist; in­clude “*” to al­low all.

* Allowlist who can talk via msteams.al­lowFrom; group ac­cess via msteams.groupAl­lowFrom or msteams.group­Pol­icy: “open”.

browser: {

en­abled: true,

color: “#FF4500”

Use these when you’re past the on­board­ing flow and want the deeper ref­er­ence.

Clawdbot was built for Clawd, a space lob­ster AI as­sis­tant. 🦞 by Peter Steinberger and the com­mu­nity.

See CONTRIBUTING.md for guide­lines, main­tain­ers, and how to sub­mit PRs. AI/vibe-coded PRs wel­come! 🤖

Special thanks to Mario Zechner for his sup­port and for

pi-mono.

Thanks to all clawtrib­u­tors:

Moltbot is a per­sonal AI as­sis­tant you run on your own de­vices. It an­swers you on the chan­nels you al­ready use (WhatsApp, Telegram, Slack, Discord, Google Chat, Signal, iMes­sage, Microsoft Teams, WebChat), plus ex­ten­sion chan­nels like BlueBubbles, Matrix, Zalo, and Zalo Personal. It can speak and lis­ten on ma­cOS/​iOS/​An­droid, and can ren­der a live Canvas you con­trol. The Gateway is just the con­trol plane — the prod­uct is the as­sis­tant.

If you want a per­sonal, sin­gle-user as­sis­tant that feels lo­cal, fast, and al­ways-on, this is it.

Preferred setup: run the on­board­ing wiz­ard (moltbot on­board). It walks through gate­way, work­space, chan­nels, and skills. The CLI wiz­ard is the rec­om­mended path and works on ma­cOS, Linux, and Windows (via WSL2; strongly rec­om­mended). Works with npm, pnpm, or bun. New in­stall? Start here: Getting started

Model note: while any model is sup­ported, I strongly rec­om­mend Anthropic Pro/Max (100/200) + Opus 4.5 for long‑con­text strength and bet­ter prompt‑in­jec­tion re­sis­tance. See Onboarding.

npm in­stall -g molt­bot@lat­est

# or: pnpm add -g molt­bot@lat­est

molt­bot on­board –install-daemon

The wiz­ard in­stalls the Gateway dae­mon (launchd/systemd user ser­vice) so it stays run­ning. Legacy note: clawd­bot re­mains avail­able as a com­pat­i­bil­ity shim.

molt­bot on­board –install-daemon

molt­bot gate­way –port 18789 –verbose

# Send a mes­sage

molt­bot mes­sage send –to +1234567890 –message “Hello from Moltbot”

# Talk to the as­sis­tant (optionally de­liver back to any con­nected chan­nel: WhatsApp/Telegram/Slack/Discord/Google Chat/Signal/iMessage/BlueBubbles/Microsoft Teams/Matrix/Zalo/Zalo Personal/WebChat)

molt­bot agent –message “Ship check­list” –thinking high

Prefer pnpm for builds from source. Bun is op­tional for run­ning TypeScript di­rectly.

git clone https://​github.com/​molt­bot/​molt­bot.git

cd molt­bot

pnpm in­stall

pnpm ui:build # auto-in­stalls UI deps on first run

pnpm build

pnpm molt­bot on­board –install-daemon

# Dev loop (auto-reload on TS changes)

pnpm gate­way:watch

Note: pnpm molt­bot … runs TypeScript di­rectly (via tsx). pnpm build pro­duces dist/ for run­ning via Node / the pack­aged molt­bot bi­nary.

* DM pair­ing (dmPolicy=“pairing” / chan­nels.dis­cord.dm.pol­icy=“pair­ing” / chan­nels.slack.dm.pol­icy=“pair­ing”): un­known senders re­ceive a short pair­ing code and the bot does not process their mes­sage.

* Approve with: molt­bot pair­ing ap­prove (then the sender is added to a lo­cal al­lowlist store).

* Public in­bound DMs re­quire an ex­plicit opt-in: set dm­Pol­icy=“open” and in­clude “*” in the chan­nel al­lowlist (allowFrom / chan­nels.dis­cord.dm.al­lowFrom / chan­nels.slack.dm.al­lowFrom).

Moltbot can auto-con­fig­ure Tailscale Serve (tailnet-only) or Funnel (public) while the Gateway stays bound to loop­back. Configure gate­way.tailscale.mode:

* serve: tail­net-only HTTPS via tailscale serve (uses Tailscale iden­tity head­ers by de­fault).

* gate­way.bind must stay loop­back when Serve/Funnel is en­abled (Moltbot en­forces this).

* Serve can be forced to re­quire a pass­word by set­ting gate­way.auth.mode: “password” or gate­way.auth.al­low­Tailscale: false.

* Funnel re­fuses to start un­less gate­way.auth.mode: “password” is set.

It’s per­fectly fine to run the Gateway on a small Linux in­stance. Clients (macOS app, CLI, WebChat) can con­nect over Tailscale Serve/Funnel or SSH tun­nels, and you can still pair de­vice nodes (macOS/iOS/Android) to ex­e­cute de­vice‑lo­cal ac­tions when needed.

* Gateway host runs the exec tool and chan­nel con­nec­tions by de­fault.

* Device nodes run de­vice‑lo­cal ac­tions (system.run, cam­era, screen record­ing, no­ti­fi­ca­tions) via node.in­voke.

In short: exec runs where the Gateway lives; de­vice ac­tions run where the de­vice lives.

The ma­cOS app can run in node mode and ad­ver­tises its ca­pa­bil­i­ties + per­mis­sion map over the Gateway WebSocket (node.list / node.de­scribe). Clients can then ex­e­cute lo­cal ac­tions via node.in­voke:

* sys­tem.run runs a lo­cal com­mand and re­turns std­out/​stderr/​exit code; set needsS­creen­Record­ing: true to re­quire screen-record­ing per­mis­sion (otherwise you’ll get PERMISSION_MISSING).

* sys­tem.no­tify posts a user no­ti­fi­ca­tion and fails if no­ti­fi­ca­tions are de­nied.

* can­vas.*, cam­era.*, screen.record, and lo­ca­tion.get are also routed via node.in­voke and fol­low TCC per­mis­sion sta­tus.

* Use /elevated on|off to tog­gle per‑ses­sion el­e­vated ac­cess when en­abled + al­lowlisted.

* Gateway per­sists the per‑ses­sion tog­gle via ses­sions.patch (WS method) along­side think­ingLevel, ver­bose­Level, model, send­Pol­icy, and groupActi­va­tion.

* Use these to co­or­di­nate work across ses­sions with­out jump­ing be­tween chat sur­faces.

ClawdHub is a min­i­mal skill reg­istry. With ClawdHub en­abled, the agent can search for skills au­to­mat­i­cally and pull in new ones as needed.

Send these in WhatsApp/Telegram/Slack/Google Chat/Microsoft Teams/WebChat (group com­mands are owner-only):

* /new or /reset — re­set the ses­sion

The Gateway alone de­liv­ers a great ex­pe­ri­ence. All apps are op­tional and add ex­tra fea­tures.

If you plan to build/​run com­pan­ion apps, fol­low the plat­form run­books be­low.

* Menu bar con­trol for the Gateway and health.

Note: signed builds re­quired for ma­cOS per­mis­sions to stick across re­builds (see docs/​mac/​per­mis­sions.md).

* Pairs as a node via the Bridge.

* Pairs via the same Bridge + pair­ing flow as iOS.

agent: {

model: “anthropic/claude-opus-4-5”

* Default: tools run on the host for the main ses­sion, so the agent has full ac­cess when it’s just you.

* Group/channel safety: set agents.de­faults.sand­box.mode: “non-main” to run non‑main ses­sions (groups/channels) in­side per‑ses­sion Docker sand­boxes; bash then runs in Docker for those ses­sions.

* Allowlist who can talk to the as­sis­tant via chan­nels.what­sapp.al­lowFrom.

* If chan­nels.what­sapp.groups is set, it be­comes a group al­lowlist; in­clude “*” to al­low all.

* Optional: set chan­nels.telegram.groups (with chan­nels.telegram.groups.“*”.re­quire­Men­tion); when set, it is a group al­lowlist (include “*” to al­low all). Also chan­nels.telegram.al­lowFrom or chan­nels.telegram.web­hookUrl as needed.

chan­nels: {

telegram: {

bot­To­ken: “123456:ABCDEF”

* Optional: set com­mands.na­tive, com­mands.text, or com­mands.use­Ac­cess­Groups, plus chan­nels.dis­cord.dm.al­lowFrom, chan­nels.dis­cord.guilds, or chan­nels.dis­cord.me­dia­MaxMb as needed.

chan­nels: {

dis­cord: {

to­ken: “1234abcd”

* ma­cOS only; Messages must be signed in.

* If chan­nels.imes­sage.groups is set, it be­comes a group al­lowlist; in­clude “*” to al­low all.

* Allowlist who can talk via msteams.al­lowFrom; group ac­cess via msteams.groupAl­lowFrom or msteams.group­Pol­icy: “open”.

browser: {

en­abled: true,

color: “#FF4500”

Use these when you’re past the on­board­ing flow and want the deeper ref­er­ence.

Moltbot was built for Molty, a space lob­ster AI as­sis­tant. 🦞 by Peter Steinberger and the com­mu­nity.

See CONTRIBUTING.md for guide­lines, main­tain­ers, and how to sub­mit PRs. AI/vibe-coded PRs wel­come! 🤖

Special thanks to Mario Zechner for his sup­port and for

pi-mono.

Thanks to all clawtrib­u­tors:

Researchers work­ing in south­ern Greece have iden­ti­fied the old­est known hand­held wooden tools, dated to about 430,000 years ago. The ob­jects came from Marathousa 1, a site in the Megalopolis Basin in the cen­tral Peloponnese. The area once held a lakeshore dur­ing the Middle Pleistocene, a pe­riod be­tween about 774,000 and 129,000 years ago.

Excavations at Marathousa 1 have pro­duced stone flakes, an­i­mal bones with cut marks, and the re­mains of a straight-tusked ele­phant. Archaeologists link these finds to re­peated vis­its by early hu­mans who processed large car­casses near wa­ter. Waterlogged sed­i­ments at the site cre­ated low oxy­gen con­di­tions. Such con­di­tions slowed de­cay and pre­served pieces of wood that usu­ally rot away over long spans of time.

Researchers ex­am­ined dozens of wood frag­ments un­der mi­cro­scopes. They stud­ied sur­face marks, in­ter­nal struc­ture, and wood species. This work helped the team sep­a­rate hu­man mod­i­fi­ca­tion from dam­age caused by roots, sed­i­ment pres­sure, or an­i­mals. Two frag­ments showed clear signs of shap­ing and use.

One piece comes from alder. The sur­face shows cut marks from stone tools and rounded ar­eas formed through re­peated con­tact with soil. The shape and wear fit use as a dig­ging stick near the lakeshore. Such a tool would have helped with loos­en­ing wet ground or ex­tract­ing plant foods. The sec­ond ar­ti­fact, a very small frag­ment from wil­low or poplar, shows carved edges and smooth­ing from han­dling. The size points to a fin­ger held tool. Researchers link this piece to fine tasks, such as ad­just­ing stone flakes dur­ing tool pro­duc­tion.

A third alder frag­ment drew at­ten­tion dur­ing sort­ing. Deep par­al­lel grooves run across the sur­face, with crushed fibers along the edges. Microscopic study matched these marks to claw dam­age from a large car­ni­vore, likely a bear. This ev­i­dence places large preda­tors at the same lo­ca­tion where hu­mans butchered ele­phants. Both groups used the lakeshore and may have com­peted for ac­cess to car­casses.

Before this work, the old­est known hand­held wooden tools came from sites in Africa, Europe, and Asia, all younger than 430,000 years. One older wooden struc­ture from Kalambo Falls in Zambia dates to about 476,000 years ago. Researchers in­ter­pret wood as part of built fea­tures rather than a hand­held im­ple­ment. The Marathousa finds push the record for shaped wooden tools back by at least 40,000 years and pro­vide the first such ev­i­dence from south­east­ern Europe.

The tools show care­ful se­lec­tion of lo­cal trees that grow in wet set­tings, in­clud­ing alder, wil­low, and poplar. Alongside stone and bone ar­ti­facts from the same lay­ers, the wooden pieces show broad knowl­edge of nat­ural ma­te­ri­als and var­ied tech­ni­cal skill dur­ing the Middle Pleistocene.

The text based in­ter­net can be ex­cit­ing, in­for­ma­tive, and fun. Using tel­net, you can ac­cess a va­ri­ety of these re­sources on the in­ter­net. Below you’ll find lists of a few places to get you started.

If you have an in­ter­est­ing item to add, just send an email to us:

Rainmaker was pretty great, and it lasted at least as far as 2018. I don’t re­call what hap­pened to it.

* nyan­cat.dakko.us

ANSI art an­i­ma­tion of “poptart cat”, with sup­port for many dif­fer­ent ter­mi­nals (cool screen­shots!)

The tel­net server is of­fline, but the web­site is still up for this one!

Both are of­fline at the time of this up­date.

http://​tel­netbb­s­guide.com

A large ac­tive list­ing of Dial-Up and Telnet ac­ces­si­ble Bulletin Board Systems on the Internet:

http://​www.jump­jet.info/​Off­beat-In­ter­net/​Pub­lic/​Tel­Net/​url.htm

Jumpjet has a nice list of tel­net lo­ca­tions or­ga­nized by cat­e­gory:

http://​www.mud­con­nect.com/

Mudconnect keeps a good list of muds and moos:

http://​www.lights.ca/​hytel­net/

Hytelnet is an old (an now un­main­tained) di­rec­tory:

Thinking about do­ing the thing is not do­ing the thing.

Dreaming about do­ing the thing is not do­ing the thing.

Visualizing suc­cess from do­ing the thing is not do­ing the thing.

Waiting to feel ready to do the thing is not do­ing the thing.

Talking about do­ing the thing is not do­ing the thing.

Explaining the thing to oth­ers is not do­ing the thing.

Arguing on­line about the thing is not do­ing the thing.

Announcing that you’ll start the thing is not do­ing the thing.

Listening to pod­casts about do­ing the thing is not do­ing the thing.

Watching tu­to­ri­als about do­ing the thing is not do­ing the thing.

Reading threads about how oth­ers did the thing is not do­ing the thing.

Planning the per­fect sys­tem for the thing is not do­ing the thing.

Buying tools for the thing is not do­ing the thing.

Reorganizing your work­space for the thing is not do­ing the thing.

Feeling guilty about not do­ing the thing is not do­ing the thing.

Being “busy” in­stead of do­ing the thing is not do­ing the thing.

Telling your­self you’ll start to­mor­row is not do­ing the thing.

Failing while do­ing the thing is do­ing the thing.

Doing it badly is do­ing the thing.

Doing it timidly is do­ing the thing.

Doing a small part of the thing is do­ing the thing.

Writing a blog about do­ing the thing is not do­ing the thing.

I should prob­a­bly get back to work.