We’re up­grad­ing Claude Opus to a new ver­sion: Claude Opus 4.8. It builds on Opus 4.7 with im­prove­ments across bench­marks, and is a more ef­fec­tive col­lab­o­ra­tor. It’s avail­able to­day for the same price.

Opus 4.8 launches along­side sev­eral new fea­tures. Users on claude.ai now have con­trol over the amount of ef­fort Claude puts into a task. Claude Code has a new “dynamic work­flows” fea­ture that al­lows it to tackle very large-scale prob­lems. And fast mode for Opus 4.8—where the model can work at 2.5× the speed—is now three times cheaper than it was for pre­vi­ous mod­els.

Opus 4.8’s ca­pa­bil­i­ties

The table be­low shows how Opus 4.8 com­pares to its pre­de­ces­sor and to other mod­els on tests of cod­ing, agen­tic skills, rea­son­ing, and prac­ti­cal knowl­edge work tasks. More de­tails and a much wider range of ca­pa­bil­ity eval­u­a­tions are pro­vided in the Claude Opus 4.8 System Card.

Collaborating with Opus 4.8

Early testers have found Claude Opus 4.8 to be more re­li­able and sharper in its judge­ment when it’s per­form­ing agen­tic tasks. Below are quotes from many of these testers about their ex­pe­ri­ence col­lab­o­rat­ing with Opus 4.8:

Claude Opus 4.8 has no­tice­ably bet­ter judg­ment. In Claude Code, it asks the right ques­tions, catches its own mis­takes, pushes back when a plan is­n’t sound, and builds up con­fi­dence around com­plex, multi-ser­vice ex­plo­rations be­fore mak­ing big changes. It’s a great model to build with.

Claude Opus 4.8 has no­tice­ably bet­ter judg­ment. In Claude Code, it asks the right ques­tions, catches its own mis­takes, pushes back when a plan is­n’t sound, and builds up con­fi­dence around com­plex, multi-ser­vice ex­plo­rations be­fore mak­ing big changes. It’s a great model to build with.

On our Super-Agent bench­mark, Claude Opus 4.8 is the only model to com­plete every case end-to-end, beat­ing prior Opus mod­els and GPT-5.5 at par­ity on cost. For agent prod­ucts in trans­la­tion, deep re­search, slide-build­ing, and analy­sis, it de­liv­ers pow­er­ful re­li­a­bil­ity.

On our Super-Agent bench­mark, Claude Opus 4.8 is the only model to com­plete every case end-to-end, beat­ing prior Opus mod­els and GPT-5.5 at par­ity on cost. For agent prod­ucts in trans­la­tion, deep re­search, slide-build­ing, and analy­sis, it de­liv­ers pow­er­ful re­li­a­bil­ity.

On CursorBench, Claude Opus 4.8 ex­ceeds prior Opus mod­els across every ef­fort level. Tool call­ing is mean­ing­fully more ef­fi­cient, us­ing fewer steps for the same in­tel­li­gence, and it car­ries end-to-end tasks through.

On CursorBench, Claude Opus 4.8 ex­ceeds prior Opus mod­els across every ef­fort level. Tool call­ing is mean­ing­fully more ef­fi­cient, us­ing fewer steps for the same in­tel­li­gence, and it car­ries end-to-end tasks through.

Claude Opus 4.8 de­liv­ers the high­est score recorded on our Legal Agent Benchmark, and is the first model to break 10% over­all on the all-pass stan­dard. For sub­stan­tive le­gal work, that’s the kind of ac­cu­racy lift that trans­lates di­rectly into how much real at­tor­ney work our cus­tomers can hand off with con­fi­dence.

Claude Opus 4.8 de­liv­ers the high­est score recorded on our Legal Agent Benchmark, and is the first model to break 10% over­all on the all-pass stan­dard. For sub­stan­tive le­gal work, that’s the kind of ac­cu­racy lift that trans­lates di­rectly into how much real at­tor­ney work our cus­tomers can hand off with con­fi­dence.

Claude Opus 4.8 feels like a ma­jor qual­ity-of-life up­date over Opus 4.7: faster, eas­ier to col­lab­o­rate with, and bet­ter at car­ry­ing con­text and style di­rec­tion across a long ses­sion. Opus 4.8 is the model I kept trust­ing for work where voice, taste, and tech­ni­cal ex­e­cu­tion all have to hap­pen side-by-side.

Claude Opus 4.8 feels like a ma­jor qual­ity-of-life up­date over Opus 4.7: faster, eas­ier to col­lab­o­rate with, and bet­ter at car­ry­ing con­text and style di­rec­tion across a long ses­sion. Opus 4.8 is the model I kept trust­ing for work where voice, taste, and tech­ni­cal ex­e­cu­tion all have to hap­pen side-by-side.

Claude Opus 4.8 is the strongest com­puter-use and browser-agent model we’ve tested, scor­ing 84% on Online-Mind2Web, which is a mean­ing­ful jump over both Opus 4.7 and GPT-5.5. It stays re­flec­tive and on-task in the way our cus­tomers’ agent work­loads need to be re­li­able end-to-end.

Claude Opus 4.8 is the strongest com­puter-use and browser-agent model we’ve tested, scor­ing 84% on Online-Mind2Web, which is a mean­ing­ful jump over both Opus 4.7 and GPT-5.5. It stays re­flec­tive and on-task in the way our cus­tomers’ agent work­loads need to be re­li­able end-to-end.

Claude Opus 4.8 uses tools cleanly and fol­lows in­struc­tions with the con­sis­tency our au­tonomous en­gi­neer­ing work­loads need to keep run­ning un­at­tended. It im­proves on Opus 4.6 and fixes the com­ment-ver­bosity and tool-call­ing is­sues we saw with Opus 4.7. This re­lease from Anthropic trans­lates di­rectly into faster ca­pa­bil­ity gains for en­gi­neers build­ing on Devin.

Claude Opus 4.8 uses tools cleanly and fol­lows in­struc­tions with the con­sis­tency our au­tonomous en­gi­neer­ing work­loads need to keep run­ning un­at­tended. It im­proves on Opus 4.6 and fixes the com­ment-ver­bosity and tool-call­ing is­sues we saw with Opus 4.7. This re­lease from Anthropic trans­lates di­rectly into faster ca­pa­bil­ity gains for en­gi­neers build­ing on Devin.

On our long-run­ning evals, Claude Opus 4.8’s analy­sis was con­sis­tently higher qual­ity than prior Opus mod­els. It fin­ished faster and pro­duced richer, more in­for­ma­tion dense out­puts. Overall, a no­tice­ably bet­ter sig­nal to noise ra­tio. The biggest dif­fer­en­tia­tor was Opus 4.8’s ten­dency to proac­tively flag is­sues with the in­puts and out­puts of an analy­sis, some­thing other mod­els rou­tinely missed and left to the users to catch.

On our long-run­ning evals, Claude Opus 4.8’s analy­sis was con­sis­tently higher qual­ity than prior Opus mod­els. It fin­ished faster and pro­duced richer, more in­for­ma­tion dense out­puts. Overall, a no­tice­ably bet­ter sig­nal to noise ra­tio. The biggest dif­fer­en­tia­tor was Opus 4.8’s ten­dency to proac­tively flag is­sues with the in­puts and out­puts of an analy­sis, some­thing other mod­els rou­tinely missed and left to the users to catch.

Across CoCounsel Legal, Claude Opus 4.8 de­liv­ered mean­ing­ful im­prove­ments in con­sis­tency and rea­son­ing qual­ity com­pared to prior Opus mod­els. For the high-stakes pro­fes­sional work­flows our cus­tomers de­pend on, that re­li­a­bil­ity mat­ters. As we build fidu­ciary-grade AI sys­tems for le­gal and tax pro­fes­sion­als, ad­vances like these help raise the stan­dard for trusted AI per­for­mance in real-world work­flows.

Across CoCounsel Legal, Claude Opus 4.8 de­liv­ered mean­ing­ful im­prove­ments in con­sis­tency and rea­son­ing qual­ity com­pared to prior Opus mod­els. For the high-stakes pro­fes­sional work­flows our cus­tomers de­pend on, that re­li­a­bil­ity mat­ters. As we build fidu­ciary-grade AI sys­tems for le­gal and tax pro­fes­sion­als, ad­vances like these help raise the stan­dard for trusted AI per­for­mance in real-world work­flows.

Claude Opus 4.8 sets a new bar for en­ter­prise AI. In Genie, Databricks’ AI agent for data and knowl­edge work, the new Opus model un­locks a step change in agen­tic rea­son­ing, tack­ling deeper, mul­ti­step ques­tions faster than any prior Opus. Its mul­ti­modal strength also lets Genie rea­son di­rectly over PDFs, di­a­grams, and other un­struc­tured con­tent at 61% cheaper to­ken cost than Opus 4.7.

Claude Opus 4.8 sets a new bar for en­ter­prise AI. In Genie, Databricks’ AI agent for data and knowl­edge work, the new Opus model un­locks a step change in agen­tic rea­son­ing, tack­ling deeper, mul­ti­step ques­tions faster than any prior Opus. Its mul­ti­modal strength also lets Genie rea­son di­rectly over PDFs, di­a­grams, and other un­struc­tured con­tent at 61% cheaper to­ken cost than Opus 4.7.

For fi­nan­cial-doc­u­ment work­flows in Hebbia’s or­ches­tra­tor, Claude Opus 4.8 de­liv­ers the same strong qual­ity as Opus 4.7 with no­tice­ably bet­ter ci­ta­tion pre­ci­sion and more to­ken ef­fi­ciency on re­trieval, which works in­cred­i­bly well for the kinds of dense fil­ings our cus­tomers run every day.

For fi­nan­cial-doc­u­ment work­flows in Hebbia’s or­ches­tra­tor, Claude Opus 4.8 de­liv­ers the same strong qual­ity as Opus 4.7 with no­tice­ably bet­ter ci­ta­tion pre­ci­sion and more to­ken ef­fi­ciency on re­trieval, which works in­cred­i­bly well for the kinds of dense fil­ings our cus­tomers run every day.

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One of the most promi­nent im­prove­ments in Opus 4.8 is its hon­esty. We train all our mod­els to be hon­est—for in­stance, to avoid mak­ing claims that they can’t sup­port. But a gen­eral prob­lem with AI mod­els is that they some­times jump to con­clu­sions, con­fi­dently claim­ing to have made progress in their work de­spite the ev­i­dence be­ing thin. Early testers re­port that Opus 4.8 is more likely to flag un­cer­tain­ties about its work and less likely to make un­sup­ported claims. This is borne out in our eval­u­a­tions, which show that Opus 4.8 is around four times less likely than its pre­de­ces­sor to al­low flaws in code it has writ­ten to pass un­re­marked.

As al­ways, we ran a de­tailed align­ment as­sess­ment on the model be­fore re­lease. In terms of pos­i­tive traits, our Alignment team con­cluded that Opus 4.8 “reaches new highs on our mea­sures of proso­cial traits like sup­port­ing user au­ton­omy and act­ing in the user’s best in­ter­est.” The as­sess­ment also showed Opus 4.8 to have rates of mis­aligned be­hav­ior (such as de­cep­tion or co­op­er­a­tion with mis­use) that are sub­stan­tially lower than Opus 4.7, and sim­i­lar to our best-aligned model, Claude Mythos Preview. The full align­ment as­sess­ment, ac­com­pa­nied by a suite of pre-de­ploy­ment safety tests, is re­ported in the Claude Opus 4.8 System Card.

Also launch­ing to­day

In ad­di­tion to Claude Opus 4.8, we’re mak­ing the fol­low­ing up­dates:

Dynamic work­flows. This new fea­ture, avail­able in re­search pre­view, al­lows Claude to take on even big­ger tasks in Claude Code. Claude can plan the work and then run hun­dreds of par­al­lel sub­agents in a sin­gle ses­sion (and with Opus 4.8, the agents can run for even longer). It then ver­i­fies its out­puts be­fore re­port­ing back to the user. For ex­am­ple, Claude Code with Opus 4.8 can now carry out code­base-scale mi­gra­tions across hun­dreds of thou­sands of lines of code from kick­off to merge, with the ex­ist­ing test suite as its bar. You can read more about dy­namic work­flows—avail­able in Claude Code for Enterprise, Team, and Max plans—in this post.

Effort con­trol in claude.ai and Cowork. A new con­trol along­side the model se­lec­tor lets users choose how much ef­fort Claude puts into a re­sponse. On higher ef­fort set­tings, Claude will think more fre­quently and more deeply to give bet­ter re­sponses. On lower ef­fort set­tings, Claude will re­spond faster and use up a user’s rate lim­its more slowly. Users now have this choice—the ef­fort con­trol is avail­able on all plans.

The Messages API now ac­cepts sys­tem en­tries in­side the mes­sages ar­ray. Developers can up­date Claude’s in­struc­tions mid-task with­out break­ing the prompt cache or rout­ing the up­date through a user turn. This can be used in a given har­ness to up­date per­mis­sions, to­ken bud­gets, or en­vi­ron­ment con­text as an agent runs.

A note on ef­fort

Opus 4.8 de­faults to high ef­fort, which we judge to be the best over­all bal­ance of qual­ity and user ex­pe­ri­ence. On cod­ing tasks, this ef­fort level spends a sim­i­lar num­ber of to­kens as Opus 4.7’s de­fault, but with bet­ter per­for­mance. Users can choose “extra” (“xhigh” in Claude Code) or “max,” and the model will spend more to­kens to get bet­ter re­sults; we rec­om­mend us­ing “extra” for dif­fi­cult tasks and long-run­ning asyn­chro­nous work­flows. We have in­creased rate lim­its in Claude Code to ac­com­mo­date the higher to­ken us­age of higher ef­fort lev­els; users can se­lect whichever makes sense for their par­tic­u­lar pro­ject.

What’s next?

Users will find Opus 4.8 to be a mod­est but tan­gi­ble im­prove­ment on its pre­de­ces­sor. There’s still more to be done: we’re work­ing on de­vel­op­ing and re­leas­ing mod­els that pro­vide many of the same ca­pa­bil­i­ties as Opus at a lower cost.

Not only that, but we plan to re­lease a new class of model with even higher in­tel­li­gence than Opus. As part of Project Glasswing, a small num­ber of or­ga­ni­za­tions are cur­rently us­ing Claude Mythos Preview for cy­ber­se­cu­rity work. Models of this ca­pa­bil­ity level re­quire stronger cy­ber safe­guards be­fore they can be gen­er­ally re­leased. We’re mak­ing swift progress on de­vel­op­ing these safe­guards and ex­pect to be able to bring Mythos-class mod­els to all our cus­tomers in the com­ing weeks.

Availability

Claude Opus 4.8 is avail­able every­where to­day. Pricing for reg­u­lar us­age is un­changed from Opus 4.7: $5 per mil­lion in­put to­kens and $25 per mil­lion out­put to­kens. Pricing for fast mode is $10 per mil­lion in­put to­kens and $50 per mil­lion out­put to­kens. Developers can use claude-opus-4 – 8 via the Claude API.

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Anthropic opens Milan of­fice to sup­port Italian en­ter­prise, re­search, and de­vel­op­ers

We’re open­ing a new of­fice in Milan, our sixth in Europe.

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MyBrickLog is a free web­site for LEGO® col­lec­tors to track their sets, check prices, and man­age wish­lists. Browse over 20,000 LEGO sets across every theme ever re­leased. Please en­able JavaScript to use the full app.

Track which LEGO® sets you own and how many copies­Track minifig­ures for every set in your col­lec­tion­Browse every LEGO theme and sub­theme ever re­leased

Be sure to use AI when mak­ingy­our next, I don’t know, meal plan,for ex­am­ple. Definitely do not cal­ly­our friend who loves to cook and ask her­for her fa­vorite recipes or tips or ways to save time mak­ing meals, be­cause you will endup talk­ing for longer than you had hoped,hear­ing, per­haps, about her fa­ther’s can­cer di­ag­no­sis or how lonely she’s been or even­what she’s planted in her spring gar­den and then lost with the early frost.

And be sure to use AI when plan­ning that nextcamp­ing trip, the last one you will take­with this par­tic­u­lar child. Definitely do not text your friend who has fly-fished every river in Pennsylvania and biked every back­woods trail, be­cause you might end up tex­ting back and forth for the rest of the dayor even meet­ing up late for a beer and hear­ing­how he has ended each re­cent night black-out drunk, or per­haps you’ll hear how his­cousin is an id­iot on Facebook or maybe just­that he re­paired his own wash­ing ma­chine­and is pretty damn proud of that.

And be sure to use AI when your next child­gets mar­ried, so that you can write themthe per­fect toast or poem or speech or song­be­cause no one wants to hear your words, the ac­tual poorly writ­ten words of a par­ent (you) who changed­hun­dreds of di­a­pers for said child or fed them in the mid­dle of the night from your ac­tual body. Or cried when they were late home be­cause you were pos­i­tive they were dead. We don’t want those words—we’d pre­fer the ster­ile words of a ma­chine that never lived, never had an orig­i­nal thought, never felt the pain of mis­car­riage or bro­ken­re­la­tion­ships or the joy of a friend­ship re­store­dor of see­ing spring’s first robin danc­ing on frost.

And be sure to use AI when work­ing on your next­book or es­say or piece of art or pho­tog­ra­phy,and then smile or even laugh at your own­clev­er­ness when you see how good it is, and how easy, be­cause who the hell has timeto work at some­thing, to give time to craft, tocre­ate with their own minds, to spend years be­ing mediocre. Why do that when­mas­tery, or at least com­pe­tency is so sim­pleonly a good prompt away?

How mag­nif­i­cent the fu­neral song our chil­dren or con­tem­po­rarieswill write for us, a song they will make by tak­ing our obit­u­ary and Facebook posts,plus ran­dom quotes from our al­go­rithm,and feed­ing them into Chat or Gemini or Claude. The tears that will fall in the face of such­san­i­tary sweet­ness!

Be sure to use AI

and while you do I’ll be over here in my 50thyear, my youngest daugh­ter asleep on my chest,my arm falling asleep be­cause I dare not move­lest I scare away this mo­ment, ly­ing here melan­choly about my older chil­dren mov­ing out and my mid­dlechil­dren no longer need­ing me, at least­not like they used to, weary about this bodythat fails me now in ever in­creas­ing ways that will never be re­stored. Sighing over sto­ries I tried to write but never hit the page the way they felt in my mind.

But is­n’t that, my flesh-and-blood friend, the nat­ural or­der of things?

the long­ing for some­thing that could al­ways bea bit bet­ter

or the way that any­thing­worth do­ing feels a bit clumsy and painful, es­pe­cially at first

or hear­ing an­other hu­man voice and some­howre­al­iz­ing the beauty of life is found in all of the­sesub­tle im­per­fec­tions

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This post shares the story of the nice!nano; a wire­less, Pro Micro-compatible mi­cro­con­troller board I made in my fresh­man year of col­lege. The nice!nano pow­ers tens of thou­sands of key­boards, has in­spired many, and changed my life.

Over my first win­ter break in col­lege, I cre­ated what I called the Dissatisfaction65, a wire­less 65% key­board in­spired by the Satisfaction75. I don’t re­mem­ber ex­actly why, but I wanted to try mak­ing a DIY wire­less key­board af­ter hav­ing made a few wired ones. The Adafruit 32u4 Bluefruit LE mi­cro­con­troller was used to ac­com­plish wire­less since the open-source QMK key­board firmware sup­ported Bluetooth with this spe­cific board. The pro­ject looked great in the end, but its per­for­mance was aw­ful. The typ­ing la­tency was nearly un­us­able, and it only lasted a few days on bat­tery even with a huge bat­tery in­side.

Seeing all the low-la­tency, long bat­tery-life wire­less prod­ucts from com­pa­nies like Logitech and Apple, I knew that some­thing bet­ter was pos­si­ble. In the next two months I dove into the world of wire­less mi­cro­con­trollers and DIY key­boards. I quickly learned that Nordic mi­crochips were the hob­by­ist’s choice and the Pro Micro for­mat reigned as king for DIY key­boards. In my search I dis­cov­ered three mi­cro­con­trollers try­ing to fill the gap be­tween the two: the BlueMicro, the nRFMi­cro, and the BLE-Micro-Pro.1

The BlueMicro’s form fac­tor meant that I could­n’t build most Pro Micro key­boards since it would in­ter­fere. The BLE-Micro-Pro was pretty ex­pen­sive, locked down, and only sold in Japan. The nRFMi­cro was pretty close. At first, I de­cided to mod­ify the nRFMi­cro to fit my needs, but I soon re­al­ized my goals were a bit too am­bi­tious, so I restarted from scratch.

The nice!nano was born#

The week­end (yes, the whole thing was de­signed in a week­end) I cre­ated the nice!nano, I don’t think I left my desk for more than sleep­ing and get­ting food from the din­ing hall maybe three times. It was just me, KiCad, Nordic’s Infocenter2, nRFMi­cro wiki, and the Adafruit nR­F52840 Feather schematic. I put to­gether the schematic and BOM, laid out the PCB, and routed (and re-routed) the con­nec­tions. On the other side I came out with the thinnest Pro Micro com­pat­i­ble nR­F52840 based board.

Over the next week I cre­ated a name and found my PCB as­sem­bler. The name is based on my on­line user­name, “Nicell”. I wanted to con­tinue the spirit of met­ric nam­ing of the Pro Micro and came up with “nice!nano”. The styl­ized lower-case pixel font mark was cre­ated to sit atop the an­tenna. After reach­ing out to a few as­sem­blers, the cheap­est op­tion for pro­duc­ing five was about $100. That was a lot to spend on what could’ve eas­ily been a bro­ken de­sign, but af­ter a few days of metic­u­lously re-re­view­ing my de­signs, I paid.3

A few weeks later the boards showed up at my door. I was both ec­sta­tic and ter­ri­fied they would­n’t work. As I plugged in my first one I closed my eyes, tensed up, and peeked. To my sur­prise and re­lief, they worked! Over the next cou­ple of weeks I built a Lily58 with them and got a mod­i­fied ver­sion of QMK work­ing on it. In my test­ing I found the board could last a few weeks on a 110mAh bat­tery. When com­par­ing to the Dissatisfaction65 that lasted a few days on a 2,500mAh bat­tery, we were look­ing at over a 100x im­prove­ment in power ef­fi­ciency. I was elated, and I posted my fully wire­less Lily58 on Reddit and it got quite a bit of in­ter­est.

Over the next few weeks my tiny Discord grew into a siz­able com­mu­nity fo­cused on wire­less key­board in­no­va­tion. I launched an in­ter­est check for a group buy, made a few more re­fine­ments of the nice!nano, and then I was ready to launch the group buy in mid June.

Group buys are aw­ful#

As a col­lege stu­dent, I did­n’t have the money to bank roll a pur­chase of 1,000 nice!nanos, so I ran a group buy pre-pur­chase. At the time I had set a min­i­mum pur­chase amount of 200 pieces for the or­der to go through and a max­i­mum of 1,000 be­cause I did­n’t think I could han­dle more than that. I set the end date for a month later. In the end, it was­n’t even open for a day.

The sale went live on June 20th at 11am cen­tral. Within the first few min­utes I had met my min­i­mum pur­chase amount. I re­mem­ber sit­ting in my child­hood bed­room (thanks covid) on the Shopify dash­board watch­ing or­ders pour in. It was an in­cred­i­ble feel­ing. Within just seven hours all 1,000 nice!nanos had been sold, end­ing the group buy. In the next two months I got all the prod­uct in and shipped out the 400+ unique or­ders with the help of my fam­ily.

My mom posted about the ful­fill­ment process on Facebook. It was a fam­ily ef­fort!

With the suc­cess of the group buy, you might won­der, what’s so aw­ful? Well, it was ex­tremely stress­ful hold­ing on to so many peo­ple’s money with­out a phys­i­cal prod­uct to back it yet. Along with PayPal hold­ing half the funds of the group buy for a while, it was a bit ter­ri­fy­ing. At the same time, group buys have caused the me­chan­i­cal key­board com­mu­nity a lot of strife with stolen funds and ex­tremely de­layed pro­jects. When I see well-es­tab­lished stores that should have cap­i­tal run­ning group buys, I can’t help but shake my head. I de­cided shortly af­ter this that I will never run a group buy again.

ZMK#

Rewinding back a cou­ple of months, as I was wait­ing for group buy prod­uct to come in, there was still a fairly ma­jor part of the ecosys­tem miss­ing: de­cent firmware. I bounced be­tween dif­fer­ent ex­ist­ing op­tions un­sat­is­fied with the re­sult. That was un­til I was con­nected with Pete Johanson who co­in­ci­den­tally had started work­ing on a wire­less key­board firmware pow­ered by the mod­ern Zephyr RTOS.

I quickly sent some pre-pro­duc­tion units to Pete to mess around with. Shortly af­ter, he got an early ver­sion of ZMK work­ing on the nice!nano, and we hit the ground run­ning build­ing a new wire­less-first firmware with a low-power fo­cus. By early 2021 a small com­mu­nity led by Pete had cre­ated an ex­tremely per­for­mant and fea­ture-full wire­less firmware.

Settling in#

In 2021 I re­ally set­tled in to my new small busi­ness. My ven­dor net­work was grow­ing around the world, nice!nanos were fly­ing off the shelves and hard to keep in stock, and the ZMK com­mu­nity con­tin­ued to grow and strengthen. Other pop­u­lar ZMK boards started to pop up around here with lots of them in­spired by the nice!nano, or they were at least us­ing my schematic, which I re­leased pub­licly.

Everything was look­ing great, but I no­ticed that most of my ven­dors did not carry all the parts needed for a wire­less build, or that their builds fo­cused on wired mi­cro­con­trollers. I fig­ured I could bring some­thing new to this area.

Becoming the ven­dor#

As a full time stu­dent, I knew I could­n’t run a whole ecom­merce store eas­ily by my­self. Luckily, my par­ents de­cided to re­tire at the end of 2021, and my dad was say­ing he needed some­thing to keep him­self busy. Together in 2022 we started Typeractive, a key­board store fo­cused on the wire­less key­board ex­pe­ri­ence.

I cre­ated a 3D in­ter­ac­tive con­fig­u­ra­tion tool for peo­ple to get all the parts they needed and kits spe­cially de­signed for wire­less boards. This low fric­tion ex­pe­ri­ence was a huge suc­cess, and now in 2025 we’re one of the largest split key­board stores. There’s a lot more that hap­pened with Typeractive, but I can tell that story an­other time.

Cloned, twice!#

In 2023 the nice!nano was cloned, not once, but twice. Two dif­fer­ent de­signed copies popped up on Taobao, and it was­n’t long be­fore they ended up on AliExpress and even on my ex­ist­ing ven­dors’ stores. I was a bit shocked by this, and in the end I found I could­n’t do much about this.

To be clear, these are clones. I think com­pe­ti­tion is fair, but both of these new boards that popped up are ad­ver­tised as nice!nanos and are shipped with the ex­act same firmware I use on the nice!nano, so when some­one plugs it in, it says it’s a nice!nano. If the man­u­fac­tur­ers would have just built their own firmware (it’s open source!) and not used the nice!nano in the ti­tle of their list­ings, I would say it’s fair game.

Seeing my prod­uct get cloned gave me mixed feel­ings. As every­one knows, im­i­ta­tion is the great­est form of flat­tery, but see­ing them ride the coat­tails of my work was frus­trat­ing. At the end of the day though, their prod­uct is sub­par, and nice!nanos con­tinue to sell at a con­sis­tent rate. Some of that is likely due to the largest DIY wire­less key­board store not stock­ing them. Thanks, Typeractive!

The mil­lion dol­lar prod­uct#

Ok, so the ti­tle is a bit of click-bait, but if you made it this far, I sup­pose it worked. The nice!nano might have been de­signed in my dorm room, but this was a multi-year jour­ney. To date, over 50,000 nice!nanos have been sold at var­i­ous on­line re­tail­ers around the world rep­re­sent­ing over a mil­lion dol­lars in sales. It’s hard to wrap my head around still, and I’m ex­tremely grate­ful. While I put in a lot of hard work, I also rec­og­nize that tim­ing and luck played a sig­nif­i­cant role. The grow­ing in­ter­est in wire­less key­boards and the lack of avail­able op­tions in the DIY space cre­ated the per­fect en­vi­ron­ment for the nice!nano to thrive.

Creating this post has been an in­cred­i­ble trip down mem­ory lane. The nice!nano has had im­mea­sur­able im­pact on my life, and it only hap­pened thanks to so many peo­ple that helped along the way. In semi-chrono­log­i­cal or­der, I’d like to shout out in­di­vid­u­als that helped me im­mensely:

Joric (creator of the nRFMi­cro)

Pierre Constantineau (creator of the BlueMicro board and firmware)

Pete Johanson (creator of ZMK)

Mike and Pam (my par­ents)

Thank you. It’s been in­cred­i­bly re­ward­ing to see all the cus­tom key­boards built with or de­rived from the nice!nano. The com­mu­nity is still grow­ing, and I’m glad that the nice!nano gets to be a big part of it.

Footnotes#

I’ve pur­pose­fully left links to each repos­i­tory in the state I would have been read­ing them back in early 2020. ↩

I’ve pur­pose­fully left links to each repos­i­tory in the state I would have been read­ing them back in early 2020. ↩

As I wrote this, I found out that Nordic’s Infocenter was shut down, RIP. ↩

As I wrote this, I found out that Nordic’s Infocenter was shut down, RIP. ↩

I some­times laugh at how scary that $100 pur­chase was for me at the time. All things con­sid­ered, this is an ex­tremely cheap R&D in­vest­ment. ↩

I some­times laugh at how scary that $100 pur­chase was for me at the time. All things con­sid­ered, this is an ex­tremely cheap R&D in­vest­ment. ↩

There’s been some drama un­fold­ing lately in the Windows se­cu­rity world, and to­day’s episode comes from yet an­other ap­par­ent run-in of re­searcher Nightmare-Eclipse (aka Chaotic Eclipse) against Microsoft. The com­pany saw fit to ban Eclipse’s GitHub ac­count for as-of-yet un­spec­i­fied rea­sons, forc­ing them to pack up and move shop to GitLab in­stead. Additionally, the Redmond firm had al­legedly al­ready deleted the Microsoft ac­count Eclipse used for re­port­ing the bugs.

In a blog post, Eclipse claims this ac­tion was vin­dic­tive, stat­ing once again that Microsoft re­fused com­mu­ni­ca­tion at­tempts and that they “got zero pen­nies from do­ing so”, a likely al­lu­sion to un­paid bug boun­ties from the MSRC pro­gram. The ini­tia­tive pays out up to $30,000 to $100,000 for per end-point zero-day de­pend­ing on con­di­tions, and a cool $250,000 if you can crack open Hyper-V. Already hav­ing six zero-day ex­ploits un­der their belt, Eclipse claims that July 14 will bring a reck­on­ing of sorts for the com­pany, hy­po­thet­i­cally in the form of more zero-day ex­ploits be­ing pub­lished.

Eclipse’s dra­matic dis­pute with Microsoft has been on­go­ing since early April, when they pub­lished the BlueHammer zero-day with­out warn­ing. The lan­guage in their blog posts is un­clear and pas­sion­ate, di­rect­ing cargo tanks of vit­riol at Microsoft/MSRC. As a broad sum­mary, Eclipse im­plies that Microsoft ig­nored or re­fused their zero-day re­ports and/​or did not pay out boun­ties as re­quested, some­how caus­ing fi­nan­cial harm in the process. Among other state­ments, Eclipse says “[they were] told per­son­ally by [Microsoft] that they will ruin my life and they did”, that there’s a dead-man switch of some sort, and that they “will make sure [Microsoft’s] bones are shat­tered.”

The saga has drawn spec­u­la­tion from other ex­perts, like William Dormann from Tharros, who said that “MSRC used to be quite ex­cel­lent to work with. But to save money, Microsoft fired the skilled peo­ple, leav­ing flow­chart fol­low­ers. I would­n’t be sur­prised if Microsoft closed the case af­ter the re­porter re­fused to sub­mit a video of the ex­ploit, since that’s ap­par­ently an MSRC re­quire­ment now.”

Microsoft has been mum on any de­tails about these mat­ters, so it’s hard to tell if the sit­u­a­tion is about an un­co­op­er­a­tive re­searcher who does­n’t fol­low stan­dard dis­clo­sure rules or a com­pany be­ing dif­fi­cult about se­cu­rity re­ports. Regardless, the move to ban Eclipse’s GitHub ac­count makes for poor op­tics, as it is be­ing heav­ily crit­i­cized, and ul­ti­mately achieves noth­ing for se­cu­rity, since the code is out there any­way.

In this day and age, when AI-powered se­cu­rity re­search has ar­guably made the stan­dard 90-day dis­clo­sure-to-patch win­dow com­pletely ob­so­lete, and both time-un­til-ex­ploit and un­used ex­ploits are both near­ing zero, Microsoft and other soft­ware play­ers would do well to ad­just their poli­cies.

Eclipse’s tech­ni­cal track record is im­pres­sive. They pub­lished a string of zero-day ex­ploits for Windows: BlueHammer gets ac­cess to the SYSTEM user via Defender, and RedSun does the same; UnDefend knocks Defender of­fline; GreenPlasma gets SYSTEM ac­cess via the CTFMon ser­vice, while MiniPlasma grants sim­i­lar ac­cess via a flaw in the Windows Cloud Filter dri­ver. Finally, there’s YellowKey, a vul­ner­a­bil­ity in BitLocker that lets an at­tacker open up en­crypted dri­ves with next to no ef­fort — pre­cisely the ac­tion the tech­nol­ogy was de­signed to pre­vent.

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BlueHammer, RedSun, and UnDefend have all been con­firmed to be un­der­go­ing ac­tive ex­ploita­tion in the wild, and it’s not hard to imag­ine the oth­ers are as well, as Eclipse’s pub­li­ca­tions of full or par­tial proof-of-con­cept code made it triv­ial for an in­ter­ested party to use them.

Follow Tom’s Hardware on Google News, or add us as a pre­ferred source, to get our lat­est news, analy­sis, & re­views in your feeds.

Bruno Ferreira is a con­tribut­ing writer for Tom’s Hardware. He has decades of ex­pe­ri­ence with PC hard­ware and as­sorted sun­dries, along­side a ca­reer as a de­vel­oper. He’s ob­sessed with de­tail and has a ten­dency to ram­ble on the top­ics he loves. When not do­ing that, he’s usu­ally play­ing games, or at live mu­sic shows and fes­ti­vals.

13 May 2026

Thomas Germain

Serenity Strull/ Getty Images

From your weight and fa­cial ex­pres­sions to your des­ti­na­tion, cars col­lect a star­tling amount of data about you. Some of it may even raise your in­sur­ance costs. But you can take some sim­ple steps to limit what they know about you.

Cars used to mean free­dom. When I first got the keys to the old fam­ily Toyota it was a rite of pas­sage, a sign I was old enough to step away from the watch­ful eyes of my par­ents and en­ter a world where time and de­ci­sions were mine alone. Things change.

Modern cars are com­put­ers on wheels, and gi­ant cor­po­ra­tions are us­ing them to suck up in­ti­mate de­tails about your life and make more money. If you think dri­ving to­day is a chance for soli­tude and in­de­pen­dence, think again. And it looks like it’s about to get a lot worse.

Car com­pa­nies will tell you them­selves if you wade through their pri­vacy poli­cies. The in­for­ma­tion they har­vest can in­clude pre­cise lo­ca­tion data about every­where you go, who’s in the car with you, what’s on the ra­dio and whether you buckle your seat­belt, drive too fast or brake too hard. Some can gather de­tails you might not ex­pect like your weight, age, race and fa­cial ex­pres­sions. Do you pick your nose? Some cars have cam­eras on the in­side pointed at the dri­ver’s seat. And most come with in­ter­net con­nec­tions that can ship off that data as you drive in bliss­ful ig­no­rance.

This is a pri­vacy prob­lem that can cost you money. Among the biggest cus­tomers for car data are in­sur­ance com­pa­nies, and they’re us­ing it to charge some peo­ple higher prices. But there’s no telling where your in­for­ma­tion is go­ing. Some car com­pa­nies ad­mit they sell your data, but they don’t have to say who’s buy­ing. That’s to say noth­ing of the fact that you might find it a lit­tle creepy. Most con­sumers, ex­perts say, have no idea it’s even hap­pen­ing.

“People would be shocked at the num­ber of data points that their car col­lects and trans­mits to other peo­ple, ei­ther the man­u­fac­turer or third-party ap­pli­ca­tions,” says Darrell West, a se­nior fel­low in the Center for Technology Innovation at the Brookings Institute in Washington DC. “It ba­si­cally means your life can be recre­ated al­most on a sec­ond-by-sec­ond ba­sis.”

Feeling un­com­fort­able yet? A fed­eral law is about to in­crease the amount of data your car can gather about you. It will soon re­quire American car com­pa­nies to in­stall in­frared bio­met­ric cam­eras and other sys­tems to scan your body lan­guage, track your eyes or other as­pects of your be­havoiur to de­tect whether you’re too drunk or tired to drive. But it will also open up a whole new trove of data about your health and your habits. There are no rules lim­it­ing what the car com­pa­nies can do with that in­for­ma­tion.

With au­tomak­ers set to ex­pand their data em­pires, this is a crit­i­cal mo­ment to un­der­stand what’s hap­pen­ing un­der the hood and how it af­fects you

Of course, there are ben­e­fits too. Internet-connected cars can be more con­ve­nient. The sen­sors they bris­tle with can make dri­ving safer and more com­fort­able. Insurance com­pa­nies could de­cide to charge you less be­cause you’re such a good dri­ver.

But with au­tomak­ers set to ex­pand their data em­pires, this is a crit­i­cal mo­ment to un­der­stand what’s hap­pen­ing un­der the hood and how it af­fects you.

The data su­per­high­way

If your car is even rel­a­tively new, it’s prob­a­bly in­volved. The con­sult­ing firm McKinsey found 50% of cars on the road in 2021 had in­ter­net con­nec­tions and pre­dicted the num­ber will rise to 95% by 2030. If your car is hooked up to the in­ter­net, pri­vacy is al­most cer­tainly an is­sue you need to care about.

Car com­pa­nies can also snoop when you hook your phone up to the in­fo­tain­ment sys­tem, or if you use cer­tain apps made for dri­ving. Some dri­vers also use in­sur­ance com­pa­nies’ tele­met­rics sys­tem, which mon­i­tor you in ex­change for po­ten­tial dis­counts.

A 2023 analysis by Mozilla, the maker of the Firefox browser, ex­am­ined the pri­vacy poli­cies of 25 car brands. Every one failed to meet the pri­vacy and se­cu­rity stan­dards that Mozilla uses to com­pare brands. Mozilla said cars were “the worst prod­uct cat­e­gory we have ever re­viewed for pri­vacy”.

According to the re­port, car com­pa­nies re­serve the right to col­lect de­tails in­clud­ing your name, age, race, weight, fi­nan­cial de­tails, fa­cial ex­pres­sions, psy­cho­log­i­cal trends and more. Kia’s pri­vacy pol­icy, for ex­am­ple, sug­gests the com­pany may even col­lect de­tails about your “sex life” and gen­eral health.

Kia spokesper­son James Bell says the com­pany has never ac­tu­ally col­lected data on dri­vers’ sex lives or health. These de­tails only ap­pear in Kia’s pri­vacy pol­icy be­cause the com­pany is list­ing California’s de­f­i­n­i­tion of “sensitive data”, he says. Bell says Kia’s pri­vacy prac­tices are trans­par­ent and the com­pany only shares data with in­sur­ance com­pa­nies if dri­vers opt in. The com­pany did not ex­plain what kinds of “sensitive data” it does col­lect, how­ever.

Serenity Strull/ Getty Images

Some of that might be hard to pic­ture, but cars are lit­tered with sen­sors: in the seats, the dash­board, the en­gine, the steer­ing wheel, you name it. Many cars, for ex­am­ple, have cam­eras in­side and out. If you’re do­ing some­thing in a mod­ern car, chances are there’s a way for com­pa­nies to learn about it.

Mozilla found 19 of the car com­pa­nies said they might sell your data, and that’s ex­actly what’s hap­pen­ing. For ex­am­ple, both state and fed­eral agen­cies in the US took ac­tion against General Motors (GM) for al­legedly sell­ing car lo­ca­tion data with­out con­sent. US Senators have ac­cused Honda and Hyundai of sim­i­lar prac­tices — and these are just the ex­am­ples the pub­lic knows about.

“They’re tak­ing all the in­for­ma­tion they col­lect on you, which is a lot, and us­ing it to make in­fer­ences about who you are, how in­tel­li­gent you are, what your psy­cho­log­i­cal pro­file is, what your po­lit­i­cal be­liefs are,” says Jen Caltrider, a pri­vacy an­a­lyst who led Mozilla’s car re­search. “That’s the stuff peo­ple don’t nec­es­sar­ily think about.”

There are ba­si­cally no rules about who can buy this data or what its used for, Caltrider says. It can be used to mar­ket things to you. Companies could used it in hir­ing de­ci­sions. Law en­force­ment can buy car data when they can’t get a search war­rant. Once it leaves your dash­board, you have no con­trol over where it ends up.

It may be get­ting worse

This is about more than com­pa­nies snoop­ing on your pri­vate life. For ex­am­ple, General Motors sold dri­ver in­for­ma­tion to a com­pany called LexisNexis, a data bro­ker that buys and sells de­tails about con­sumers. A dri­ver who got a copy of that data re­port­edly found LexisNexis had 130 pages of in­for­ma­tion, de­tail­ing every trip he and his wife took over six months. He told the New York Times that af­ter his in­sur­ance costs jumped 21%, an in­sur­ance agent told him the data was a fac­tor. LexisNexis did not re­spond to a re­quest for com­ment.

The US Federal Trade Commission took ac­tion, and GM is now barred from sell­ing ve­hi­cle data for five years — but it’s free to re­sume the prac­tice af­ter­wards so long as it ob­tains ex­press con­sent from dri­vers and fol­lows other con­di­tions. Meanwhile, LexisNexis and other com­pa­nies are still sell­ing ve­hi­cle data they get from other car man­u­fac­tures and apps the peo­ple use while dri­ving. GM and LexisNexis did not re­spond to re­quests for com­ment.

“Insurance com­pa­nies have been col­lect­ing vast amounts of con­sumer data, es­pe­cially on con­sumer dri­ving data, and us­ing it to try and charge peo­ple higher pre­mi­ums, deny cov­er­age or slice and dice con­sumers into var­i­ous cat­e­gories,” says Michael DeLong, a re­search and ad­vo­cacy ad­vo­cate who cov­ers auto in­sur­ance for the Consumer Federation of America, a US-based non-profit.

Keeping Tabs

Thomas Germain is a se­nior tech­nol­ogy jour­nal­ist at the BBC. He writes the col­umn Keeping Tabs and co-hosts the pod­cast The Interface. His work un­cov­ers the hid­den sys­tems that run your dig­i­tal life, and how you can live bet­ter in­side them.

Car com­pa­nies say they get their per­mis­sion be­fore track­ing you. In prac­tice, that usu­ally means agree­ing to forms and pri­vacy poli­cies when you set up the in­fo­tain­ment sys­tem or apps con­nected to your car. In some ve­hi­cles they pop up every time you start the en­gine. Did you read them? Of course not.

In the US, there is no pri­vacy law at the na­tional level. Protections in in­di­vid­ual states are piece­meal, and ac­cord­ing to some pri­vacy ex­perts, they don’t go far enough. The pic­ture is a lit­tle bet­ter in Europe, in­clud­ing the UK, where there are spe­cial pro­tec­tions for cer­tain sen­si­tive cat­e­gories of in­for­ma­tion and con­sumers have some rights that let them ac­cess their data and tell com­pa­nies to delete it. But it’s not a solved prob­lem in Europe ei­ther.

“Europeans are still be­holden to pri­vacy poli­cies,” Caltrider says. “And you have to count on the reg­u­la­tions to be fol­lowed and en­forced, and that’s some­thing that’s not al­ways hap­pen­ing, with cars es­pe­cially.”

The prob­lem is­n’t new, but there are rea­sons to think it’s ac­cel­er­at­ing. US law man­dates that car man­u­fac­tures will soon need to in­stall “advanced im­paired-dri­ving pre­ven­tion tech­nol­ogy” in new pas­sen­ger ve­hi­cles within the next few years. The tech­nol­ogy is meant to stop peo­ple from dri­ving if they’re drunk, tired or un­fit to drive us­ing in­frared cam­eras or other sys­tems.

The prob­lem, Caltrider and oth­ers say, is the law in­cludes zero pro­vi­sions that ad­dress what hap­pens to the data these sys­tems cre­ate.

A spokesper­son for the US National Highway Traffic Safety Administration (NHTSA) — which is charged with en­forc­ing the rule — says “NHTSA is com­mit­ted to re­duc­ing im­paired dri­ving fa­tal­i­ties us­ing every tool at our dis­posal”, and it “continues to ad­dress crit­i­cal and com­plex top­ics” such as pri­vacy con­cerns. It’s likely the im­plan­ta­tion of this law will be de­layed be­cause the tech­nol­ogy is­n’t ready, but pri­vacy ad­vo­cates are sound­ing the alarm.

“We need to keep drunk dri­vers off the road, and it would be great if there was a guar­an­tee that the data won’t be used for other pur­poses, but that’s not what’s hap­pen­ing,” says Caltrider. “So many of the data col­lect­ing ad­vances we see in cars are done un­der the guise of safety.” It could hand the auto in­dus­try a trove of what amounts to med­ical in­for­ma­tion with no safe­guards in place.

More like this:

Like so many pri­vacy prob­lems, the car data prob­lem is­n’t one you can solve en­tirely, but there are steps you can take.

For one, “do not en­rol in the in­sur­ance telem­at­ics pro­gramme if you’ve got any con­cerns about pri­vacy”, DeLong says. The pri­vacy risks are sig­nif­i­cant and the pay­off is­n’t a guar­an­tee. An analy­sis from the state of Maryland found 31% of dri­vers saw their in­sur­ance rates drop, but prices went up for 24% of dri­vers and 45% found no change.

Some car man­u­fac­tures of­fer pri­vacy set­tings you can ad­just that may limit the shar­ing and col­lec­tion of data. Look for op­tions in the set­tings of your car’s in­fo­tain­ment sys­tem and any ac­com­pa­ny­ing app that works with your car. Consumer Reports (where I used to work) has a de­tailed guide you can use with more in­for­ma­tion.

Steps like these can help, Caltrider says, but it should­n’t be your re­spon­si­bil­ity to do a bunch of work to stop com­pa­nies from vi­o­lat­ing your pri­vacy. “Until the whole game changes, un­til we own our data and we con­trol our data, and com­pa­nies have to ask us for per­mis­sion to use it, I think this is­sue is just go­ing to keep get­ting worse and worse.”

–

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Anthropic has raised $65 bil­lion in Series H fund­ing led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital, valu­ing the com­pany at $965 bil­lion post-money.

Global en­ter­prises across in­dus­tries are de­ploy­ing Claude in their core op­er­a­tions, and a grow­ing num­ber of peo­ple around the world use it for their every­day work. Since our Series G in February, adop­tion has con­tin­ued to grow across global en­ter­prise cus­tomers, and our run-rate rev­enue crossed $47 bil­lion ear­lier this month. This lat­est fund­ing is ex­pected to ad­vance our safety and in­ter­pretabil­ity re­search, ex­pand com­pute to meet grow­ing de­mand for Claude, and scale the prod­ucts and part­ner­ships our cus­tomers rely on.

“Claude is in­creas­ingly in­dis­pens­able to our grow­ing global com­mu­nity of cus­tomers, and we work tire­lessly to make tools like Claude Code and Cowork more help­ful, more pow­er­ful, and more adapt­able to their needs,” said Krishna Rao, Chief Financial Officer of Anthropic. “This fund­ing will help us serve the his­toric de­mand we are ex­pe­ri­enc­ing, stay at the re­search fron­tier, and bring Claude to more of the places where work hap­pens.”

The round was co-led by Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ, and XN. Significant in­vestors in this round in­clude AMP PBC, Baillie Gifford, Blackstone, Brookfield, D.E. Shaw Ventures, DST Global, Fidelity Management & Research Company, General Catalyst, Insight Partners, Jane Street, Lightspeed Venture Partners, MGX, NTTVC, NX1 Capital, Situational Awareness LP, T. Rowe Price Associates, Inc., T. Rowe Price Investment Management, Inc., and Temasek. It also in­cludes $15 bil­lion of pre­vi­ously com­mit­ted in­vest­ments from hy­per­scalers, in­clud­ing $5 bil­lion from Amazon.

Joining them are strate­gic in­fra­struc­ture part­ners—Mi­cron, Samsung, and SK hynix—whose tech­nolo­gies play a crit­i­cal role in the world’s sup­ply of mem­ory, stor­age, and logic chips. As de­mand for Claude con­tin­ues to grow, these re­la­tion­ships will help us scale our com­pute re­li­ably at the pace our cus­tomers need.

We have sig­nif­i­cantly ex­panded our com­pute ca­pac­ity in re­cent weeks. We signed agree­ments with Amazon for up to five gi­gawatts of new ca­pac­ity, with Google and Broadcom for five gi­gawatts of next-gen­er­a­tion TPU ca­pac­ity, and with SpaceX for ac­cess to GPU ca­pac­ity in Colossus 1 and Colossus 2. Claude is the first fron­tier model avail­able on all three of the world’s largest cloud plat­forms: Amazon Web Services, Google Cloud, and Microsoft Azure. AWS re­mains our pri­mary cloud provider and train­ing part­ner.

“Claude’s lat­est ad­vance­ments have dri­ven large-scale adop­tion among the world’s most de­mand­ing or­ga­ni­za­tions. This mo­men­tum po­si­tions Anthropic to lead the next phase of AI in­no­va­tion and cap­ture the enor­mous op­por­tu­nity ahead,” said Brad Gerstner, Founder and CEO of Altimeter Capital.

“Dragoneer has long part­nered with com­pa­nies build­ing the tech­nol­ogy that will shape our fu­ture. Anthropic is help­ing pull for­ward this fu­ture, as in­tel­li­gence be­comes an in­creas­ingly crit­i­cal in­gre­di­ent to the way busi­nesses op­er­ate and how their prod­ucts show up in the world,” said Marc Stad, Managing Partner at Dragoneer. “The tech­no­log­i­cal progress we are see­ing right now is breath­tak­ing. And we be­lieve that we are still in the ear­li­est days of both the de­vel­op­ment and com­mer­cial­iza­tion of this tech­nol­ogy.”

“Anthropic has built an or­ga­ni­za­tion in which the world’s best re­searchers and en­gi­neers op­er­ate with un­matched clar­ity of pur­pose, be­cause they be­lieve this is the most im­por­tant work they will ever do,” said Neil Mehta, Founder and Managing Partner at Greenoaks. “Rarely has a com­pa­ny’s cul­ture, mis­sion, and com­mer­cial mo­men­tum re­in­forced each other so com­pletely. We are hon­ored to deepen our part­ner­ship.”

“Startups and Global 5000 com­pa­nies alike are de­ploy­ing Claude to han­dle com­plex work­flows, and in do­ing so, Claude is learn­ing how busi­nesses ac­tu­ally op­er­ate: the con­text, the processes, the judg­ment,” said Alfred Lin, Partner at Sequoia Capital. “Anthropic is build­ing the bridge be­tween where en­ter­prise AI stands to­day and where it’s headed.”

We are grate­ful for the sup­port of our in­vestors and part­ners as we con­tinue build­ing Claude for peo­ple and or­ga­ni­za­tions around the world.

Related con­tent

Introducing Claude Opus 4.8

An up­grade to our Opus class of mod­els, with stronger per­for­mance across cod­ing, agen­tic tasks, and pro­fes­sional work, and the con­sis­tency to han­dle long-run­ning work.

Read more

Anthropic opens Milan of­fice to sup­port Italian en­ter­prise, re­search, and de­vel­op­ers

We’re open­ing a new of­fice in Milan, our sixth in Europe.

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Anthropic ap­points KiYoung Choi as Representative Director of Korea ahead of Seoul of­fice open­ing

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Shiv After Dark

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28 May, 2026

Looks like this ended up on the HN front-page: HN Thread

Late last year I started writ­ing a math blog and de­cided to use LLMs to pol­ish/​en­hance my writ­ing. The LLM gen­er­ated writ­ing ob­vi­ously felt sig­nif­i­cantly bet­ter than my own writ­ing. It had bet­ter vo­cab­u­lary, in­ter­est­ing sen­tence struc­tures etc etc. I swear it did not seem like AI-slop to me at the time. Then about 3 months later, I see the ex­act sen­tence struc­tures ap­pear­ing ACROSS THE ENTIRE F***** INTERNET. And what is fas­ci­nat­ing to me is that ai-smell seems like an ar­ti­fact that emerges across var­i­ous AI as­sisted tasks that you can now eas­ily rec­og­nize. A few ex­am­ples that I’ve col­lected so far to show the “ai-smells” across two do­mains:

1. LLM writ­ing (beyond the ob­vi­ous em-dashes):

Some picks from my math blog (now deleted) and the drafts that ac­com­pa­nied it

Way too many punch­lines

“Humans trust sym­me­try be­cause it feels like in­tel­li­gence made vis­i­ble.”

“The Tiger fit the story. Jin-yong fit the physics.”

“Symmetry be­comes a trap.”

Consecutive short sen­tences

“Yet the tilt is not an ac­ci­dent. It is the shape of the op­ti­mum.”

“Then AlphaEvolve ar­rived. It had no pref­er­ence for sym­me­try. No aes­thetic prior. No in­stinct to pre­serve har­mony.”

“These ex­am­ples are not dec­o­ra­tive. They form a dis­trib­uted ar­gu­ment.”

“X is the Y of Z”

“Cringe is the vis­i­ble sig­na­ture of mov­ing along a gra­di­ent you chose.”

“ist not just X, its Y”

“solutions that do not merely sat­isfy the con­straint but sat­isfy the aes­thetic in­stincts”

2. AI gen­er­ated web­sites

The “JetBrains Mono” font

The “step” and bul­lets on every web­page with this ex­act font:

Exactly these but­ton

These cards

This blink­ing-dot in a badge com­po­nent

Footnotes

I’m not against LLM/AI us­age for cre­ative tasks. This is just me notic­ing things.

Durable work­flows are a sim­ple but pow­er­ful tool for build­ing re­li­able pro­grams. The idea is that as your pro­gram runs, you reg­u­larly check­point its progress to a data­base. That way, if your pro­gram ever crashes or fails, you can re­load from the last check­point to re­cover it from its last com­pleted step. You can think of this like sav­ing in a video game: you reg­u­larly “save” your pro­gram’s progress so that if it crashes, you can “reload” it from its last check­point.

Most com­monly, durable work­flows are im­ple­mented via ex­ter­nal or­ches­tra­tion. This is the pat­tern used by sys­tems like Temporal, Airflow, and AWS Step Functions. In this model, durable pro­grams are writ­ten as work­flows of steps whose ex­e­cu­tion is co­or­di­nated by a cen­tral or­ches­tra­tor.

When a client sub­mits a work­flow, the or­ches­tra­tor cre­ates a record for it in a data store then dis­patches it to a worker for ex­e­cu­tion. Each time a worker com­pletes a step, it sends the step’s out­come back to the or­ches­tra­tor. The or­ches­tra­tor check­points the out­put in its data store, then dis­patches the next step. If a worker crashes or fails, the or­ches­tra­tor dis­patches its work­flows to an­other worker, start­ing them from their last check­pointed step.

In this blog post, we’ll ar­gue that ex­ter­nal or­ches­tra­tion is fun­da­men­tally over­com­pli­cated. The core idea of durable work­flows is to check­point pro­gram state in a data­base. But if durable work­flows are about data­bases, then there’s no rea­son to have a sep­a­rate or­ches­tra­tor server. Instead, it’s sim­pler and more ef­fi­cient to use the data­base it­self as an or­ches­tra­tor. To make this more con­crete, we’ll fo­cus specif­i­cally on build­ing durable work­flows on Postgres, be­cause its pop­u­lar­ity, scal­a­bil­ity, and rich ecosys­tem make it an ideal choice.

In a Postgres-backed durable work­flows sys­tem, ap­pli­ca­tion servers di­rectly com­mu­ni­cate with Postgres to ex­e­cute work­flows in­stead of go­ing through a cen­tral or­ches­tra­tor. A client sub­mits a work­flow for ex­e­cu­tion by cre­at­ing an en­try for it in a Postgres work­flows table. Application servers poll the table for work­flows to de­queue and ex­e­cute. As a server ex­e­cutes a work­flow, it check­points the out­put of each step to Postgres. If a server ex­e­cut­ing work­flows crashes or fails, an­other server can re­cover its work­flows from their check­points.

This de­sign ren­ders a cen­tral or­ches­tra­tor un­nec­es­sary be­cause ap­pli­ca­tion servers can co­or­di­nate through Postgres. Instead of re­ly­ing on a cen­tral or­ches­tra­tor to dis­patch work­flows to work­ers, servers co­op­er­a­tively de­queue work­flows from a Postgres table, us­ing mech­a­nisms such as lock­ing clauses to en­sure each work­flow is de­queued by ex­actly one worker. Instead of re­ly­ing on an or­ches­tra­tor to check­point step out­puts, work­ers check­point steps to Postgres them­selves. If mul­ti­ple work­ers try to ex­e­cute the same work­flow si­mul­ta­ne­ously, Postgres data­base in­tegrity con­straints let them de­tect the du­pli­cate work on check­point and back off.

Replacing a cen­tral or­ches­tra­tor with Postgres (or an­other data­base) makes durable work­flows fun­da­men­tally sim­pler. In par­tic­u­lar, it means hard prob­lems such as scal­a­bil­ity, avail­abil­ity, ob­serv­abil­ity, and se­cu­rity can be ad­dressed us­ing well-un­der­stood Postgres-native so­lu­tions.

Scalability and Availability

The scal­a­bil­ity and avail­abil­ity of a data­base-backed durable work­flows sys­tem are fun­da­men­tally de­ter­mined by the un­der­ly­ing data­base. The sys­tem can scale hor­i­zon­tally by adding more worker servers, so its max­i­mum ca­pac­ity is de­ter­mined by how quickly the data­base can process work­flows. Similarly, work­ers are fun­gi­ble and can freely re­cover each oth­er’s state, so the sys­tem is avail­able as long as the un­der­ly­ing data­base is avail­able.

When us­ing Postgres specif­i­cally, this is ben­e­fi­cial be­cause Postgres scal­a­bil­ity and avail­abil­ity are well-stud­ied prob­lems with ro­bust so­lu­tions. For scal­a­bil­ity, a sin­gle Postgres server can ver­ti­cally scale to han­dle tens of thou­sands of work­flows per sec­ond, and fur­ther scal­ing can be achieved by us­ing dis­trib­uted (e.g., CockroachDB) or sharded Postgres. For avail­abil­ity, Postgres sup­ports stream­ing repli­ca­tion with au­to­matic failover and man­aged of­fer­ings pro­vide multi-AZ de­ploy­ments with high-avail­abil­ity SLAs out of the box. As a re­sult, the decades of en­gi­neer­ing work and re­search that have gone into op­er­at­ing Postgres at scale can trans­late di­rectly to op­er­at­ing durable work­flows.

Observability

When us­ing Postgres-backed durable ex­e­cu­tion, work­flows and their steps are check­pointed to Postgres ta­bles. This means ob­serv­abil­ity is built-in: you can scan those check­points to mon­i­tor work­flows in real time and vi­su­al­ize work­flow ex­e­cu­tion.

Postgres ex­cels at this be­cause vir­tu­ally any work­flow ob­serv­abil­ity query can be ex­pressed in SQL. For ex­am­ple, here’s a query to find all work­flows that er­rored in the last month:

A query like this might seem ob­vi­ous, but it’s hard to over­state how pow­er­ful this is. It’s only pos­si­ble be­cause Postgres’s re­la­tional model lets you ex­press com­plex fil­ter­ing and an­a­lyt­i­cal op­er­a­tions de­clar­a­tively in SQL, lever­ag­ing decades of query op­ti­miza­tion re­search. Many sys­tems with sim­pler data mod­els, such as the key-value stores used by pop­u­lar ex­ter­nal or­ches­tra­tors, have no such sup­port. By stor­ing work­flow and step data in Postgres ta­bles and aug­ment­ing them with sec­ondary in­dexes for fast an­a­lyt­i­cal queries, you get ef­fi­cient ob­serv­abil­ity from your durable ex­e­cu­tion “for free.”

Reliability and Security

When us­ing an ex­ter­nal or­ches­tra­tor for durable ex­e­cu­tion, both the or­ches­tra­tor and its data store are sin­gle points of fail­ure. Because they di­rectly co­or­di­nate work­flow ex­e­cu­tion, if ei­ther has down­time, the en­tire ap­pli­ca­tion be­comes un­avail­able. Moreover, be­cause they process and store work­flow and step check­points, they likely have ac­cess to sen­si­tive ap­pli­ca­tion data, mean­ing they must be hard­ened, ac­cess-con­trolled, and au­dited like any other piece of sen­si­tive in­fra­struc­ture.

By con­trast, the only point of fail­ure in Postgres-backed durable ex­e­cu­tion is Postgres it­self, and all work­flow data is stored di­rectly in Postgres and never tran­sits any other sys­tem. If an ap­pli­ca­tion al­ready de­pends on Postgres, adopt­ing durable ex­e­cu­tion does not add any new points of fail­ure to the sys­tem nor in­tro­duce new sur­face area to se­cure. Databases are al­ready crit­i­cal in­fra­struc­ture, so it makes more sense to reuse them for or­ches­tra­tion than to add new crit­i­cal in­fra­struc­ture for it.

Learn More

If you like build­ing scal­able, re­li­able sys­tems, we’d love to hear from you. At DBOS, our goal is to make Postgres-backed durable ex­e­cu­tion as sim­ple and per­for­mant as pos­si­ble. Check it out:

Quickstart: https://​docs.dbos.dev/​quick­start

GitHub: https://​github.com/​dbos-inc

Discord com­mu­nity: https://​dis­cord.gg/​eMUHrvbu67