Continuous Exploration

Automating dis­cov­ery to ac­cel­er­ate sci­ence and en­gi­neer­ing for the world.

Scientific dis­cov­ery is bot­tle­necked.

The sci­en­tific method is one of the great­est tools hu­man­ity has ever de­vised, yet ex­e­cu­tion en­tails repet­i­tive ex­per­i­men­tal loops that are hard to scale with to­day’s man­ual ef­forts: you pro­pose an ex­per­i­ment, im­ple­ment and run it, ex­am­ine the re­sults, then it­er­ate to re­fine your ap­proach.

Historically, sci­en­tific progress has re­lied on these se­quen­tial hu­man it­er­a­tions. In many do­mains, this process re­mains in­cred­i­bly slow and la­bor-in­ten­sive.

01 — The Approach

Automating the ex­per­i­men­tal loop.

At Discovery Loop, we are build­ing sys­tems to au­to­mate these en­tire ex­per­i­men­tal loops. By uti­liz­ing fron­tier AI mod­els and large-scale com­pu­ta­tional in­fra­struc­ture, our sys­tems will be able to rapidly pro­pose, run, and learn from eval­u­a­tions.

This ap­proach al­lows for the par­al­lel ex­e­cu­tion of thou­sands of ex­per­i­ments, dras­ti­cally com­press­ing it­er­a­tion time and dri­ving up the quan­tity and qual­ity of sci­en­tific and en­gi­neer­ing out­put.

Start with Machine Learning

We will ini­tially fo­cus on au­tomat­ing the process of ma­chine learn­ing re­search and en­gi­neer­ing.

Act as Our Own First Customer

We will use these au­to­mated ML ca­pa­bil­i­ties to rapidly op­ti­mize our own tech­nol­ogy stack be­fore ex­pand­ing to other do­mains.

Grand Challenges

We be­lieve our ap­proach will be able to solve any learn­ing loop with mea­sur­able out­comes within the do­mains of sci­ence and en­gi­neer­ing. Ultimately, we are build­ing sys­tems ca­pa­ble of tak­ing on National Academy of Engineering (NAE) Grand Challenges—such as en­gi­neer­ing bet­ter med­i­cines, ad­vanc­ing health in­for­mat­ics, mak­ing so­lar en­ergy eco­nom­i­cal, pro­vid­ing ac­cess to clean wa­ter, se­cur­ing cy­ber­space, and en­gi­neer­ing the tools of sci­en­tific dis­cov­ery.

02 — Mission

Our mis­sion is straight­for­ward: we are build­ing AI so­lu­tions that can au­to­mat­i­cally solve im­por­tant prob­lems in ma­chine learn­ing, sci­ence, and en­gi­neer­ing. By ad­vanc­ing the pace at which we con­duct en­gi­neer­ing and sci­en­tific dis­cov­ery, we can bring the ben­e­fits of sci­ence and tech­nol­ogy to the world much faster. Ultimately, our goal is to build AI sys­tems that act as a deeply pos­i­tive, em­pow­er­ing force for hu­man­ity, de­liv­er­ing tech­nol­ogy so­lu­tions that im­prove peo­ple’s lives on a global scale.

04 — The Team

The brain trust.

Our found­ing team — Jeff Dean, Sanjay Ghemawat, Quoc Le, and Oriol Vinyals — has a shared his­tory of deep friend­ship and decades of close and im­pact­ful col­lab­o­ra­tion.

From left Oriol Vinyals  ·  Sanjay Ghemawat  ·  Jeff Dean  ·  Quoc Le

Collectively, we rep­re­sent three of the most-cited re­searchers in ar­ti­fi­cial in­tel­li­gence and two of the most-cited re­searchers in dis­trib­uted sys­tems.

Between us, we have pi­o­neered mas­sive scale com­put­ing and led the cre­ation of crit­i­cal in­fra­struc­ture, prod­ucts, and foun­da­tional AI ad­vances that the world re­lies on, in­clud­ing mul­ti­ple gen­er­a­tions of Google Search, Google Ads, Google News, Google Translate, Google File System, MapReduce, BigTable, Spanner, TensorFlow, Pathways, TPUs, AlphaChip, AlphaStar, AlphaCode, AlphaFold, Gemini, model dis­til­la­tion, mix­ture-of-ex­perts model ar­chi­tec­tures, word2vec, se­quence-to-se­quence mod­els, chain of thought rea­son­ing, neural ar­chi­tec­ture search, and mul­ti­ple gen­er­a­tions of Large Language Models (LLMs) among oth­ers.

Our rel­a­tive ad­van­tage is­n’t just our tech­ni­cal abil­ity; it is the un­prece­dented scale of the sys­tems we have pre­vi­ously built. We pos­sess true full-stack depth that spans chips, hard­ware in­fra­struc­ture, soft­ware in­fra­struc­ture, ML mod­els, and prod­ucts.

04 — What’s Next

Imagine a fu­ture where a hand­ful of peo­ple can con­duct sci­en­tific re­search and en­gi­neer­ing tasks much more rapidly, and with higher qual­ity, than mas­sive teams of sci­en­tists and en­gi­neers do to­day. By au­tomat­ing the loops of dis­cov­ery, the world will be able to make much more rapid ad­vances across count­less fields of sci­ence.

We are build­ing a lean, in-per­son team to ex­e­cute this trans­for­ma­tive vi­sion.

Editor’s note: Today, Google and Alphabet CEO Sundar Pichai shared some changes with Google DeepMind teams, in­clud­ing new roles for Demis Hassabis and Koray Kavukcuoglu. Below are the mes­sages Sundar and Demis sent to em­ploy­ees.

Message from Sundar Pichai

We’ve made ex­tra­or­di­nary progress to de­liver on our full AI stack. We’ve got amaz­ing tal­ent, world-class com­pute, and prod­ucts that bring AI to more peo­ple than any other com­pany. And you saw the in­cred­i­ble mo­men­tum at earn­ings across all our busi­nesses, in­clud­ing Search, YouTube, and Cloud. Our Gemini mod­els are in high de­mand among de­vel­op­ers and busi­nesses, and the Gemini app reached 950M+ monthly users. Meanwhile, our AI re­search con­tin­ues to drive field-defin­ing break­throughs (like last week’s Gemini Robotics ad­vances).

We have to ac­cel­er­ate all this work and stay fo­cused on the AI fron­tier. At the same time, there’s never been a more im­por­tant mo­ment to shape the fu­ture of AGI and sci­ence. Today Demis, Koray and I are shar­ing a few changes to our Google DeepMind teams that will en­able us to do both.

AGI and sci­ence: Demis has de­scribed us as stand­ing in the foothills of the sin­gu­lar­ity, and has been spend­ing a lot of his time en­gag­ing ex­ter­nally. He and I have been long dis­cussing a role that al­lows him to put his full at­ten­tion on ac­tively shap­ing the fu­ture of AGI. It’s work that is vi­tally im­por­tant to Alphabet and hu­man­ity, and I can’t imag­ine a bet­ter per­son than Demis to do it. So, mov­ing for­ward, Demis will be­come the Chair of GDM and Chief Scientist of Alphabet, while con­tin­u­ing to lead Isomorphic Labs. He’ll re­main closely con­nected to Koray, Josh, and our GDM teams, ad­vis­ing across mod­els and re­search. I’m so ex­cited for Demis — this is truly his life’s work and pur­pose. You can read Demis’s note to GDM be­low.

Google DeepMind: We are build­ing strong mo­men­tum: Flash is in high de­mand, our Cyber model is live, and Gemma mod­els have sur­passed 900M+ down­loads. We are com­mit­ted to be­ing at the fron­tier, and are su­per fo­cused on the ar­eas where we need to im­prove. I’m re­ally ex­cited for our up­com­ing model re­leases and the progress we’re see­ing. We have to con­tinue to move fast and with clear pur­pose here. Koray, the cur­rent Chief Technology Officer of GDM and our Chief AI Architect, will step up as SVP of Google DeepMind, re­port­ing to me. He will over­see Gemini model de­vel­op­ment, Frontier AI re­search, and the Gemini app and de­vel­oper teams. Koray has been at DeepMind since its early days, and over his 13 years there, he has started our deep learn­ing team and led the way on break­throughs like WaveNet and DQN. I look for­ward to see­ing him lead GDM into this next chap­ter.

Lastly, af­ter an in­cred­i­ble 27-year run, Jeff Dean is at a mo­ment where he wants to try some­thing new, and we’re ex­cited to sup­port him in that. Jeff and Google Senior Fellow Sanjay Ghemawat are launch­ing an in­de­pen­dent pub­lic ben­e­fit cor­po­ra­tion to ac­cel­er­ate dis­cov­er­ies in ML, sci­ence, and en­gi­neer­ing. Jeff and Sanjay helped to drive some of the most sig­nif­i­cant tech­nol­ogy tran­si­tions, from our early search in­fra­struc­ture to the neural net­works that helped cre­ate the mod­ern AI era. On a per­sonal note, it’s been a priv­i­lege to work along­side Jeff and Sanjay, and I wish them all the best! We’ll con­tinue to work with them as a found­ing in­vestor and Cloud part­ner, and col­lab­o­rate on a re­search frame­work for ML sys­tems and re­lated in­fra­struc­ture ad­vances.

We are at a dy­namic mo­ment with so much op­por­tu­nity ahead. With to­day’s changes we’re go­ing to keep dri­ving our mo­men­tum. Onwards!

-Sundar

Message from Demis Hassabis

Hi Team

We have ar­rived at a piv­otal mo­ment in hu­man his­tory. I’ve been work­ing to­wards AGI my whole life and now, like many of you, I feel it is close at hand. It’s crit­i­cal that we col­lec­tively get the next steps right to en­sure this all goes well for hu­man­ity and we usher in an in­cred­i­ble new age of dis­cov­ery and won­der.

With this back­drop, I’ve de­cided that now is the right time for me to hand over my day-to-day op­er­a­tional re­spon­si­bil­i­ties at GDM, so that I have the time and space to fo­cus on the big pic­ture and help in­flu­ence what is to come to the best of my abil­ity. I will be tak­ing on a new strate­gic role as Chair of GDM and Chief Scientist of Alphabet, and I’m ex­cited to an­nounce that Koray will be step­ping up to lead GDM as SVP of Google DeepMind, in ad­di­tion to his role as Chief AI Architect of Google.

Koray and I have been work­ing to­gether for over 13 years, since the early days of DeepMind. He is one of the world’s fore­most AI ex­perts and has been cham­pi­oning GDM’s mis­sion from day one. I have to­tal con­fi­dence in Koray, Josh, and the rest of the GDM exec team as they con­tinue to spear­head the lat­est AI de­vel­op­ments across Google. The Gemini mod­els are in good hands with Koray and the leads, as they have been for a while, and I’m ex­cited about the great progress we’re mak­ing with our new mod­els in­clud­ing Gemini 4.

In my new role, I will con­tinue to work closely with Sundar on strate­gic and global AGI mat­ters, and to ad­vise Koray, Josh, and the GDM leads, from our awe­some new London Platform 37 of­fices. As part of this tran­si­tion, I’ll also be lean­ing into my role at Isomorphic, where we are mak­ing ex­tremely rapid and promis­ing progress, to ac­cel­er­ate our mis­sion there even faster. As you’ve heard me say many times, I’ve al­ways be­lieved the No.1 ap­pli­ca­tion of AI should be to im­prove hu­man health. It’s time for AI to prove its un­equiv­o­cal value to the world, and what bet­ter way to demon­strate that than to help fi­nally cure dis­eases like can­cer.

We’ve built a unique cul­ture at GDM that has served us very well. I want to thank each and every one of you for your bril­liance, ded­i­ca­tion, and ef­fort that make GDM the huge suc­cess it is to­day. We should all be ex­tremely proud of the amaz­ing things we’ve achieved so far. We’ve be­come the AI en­gine room of Google, with Gemini de­liv­er­ing help­ful ex­pe­ri­ences every­where in­clud­ing AI Mode and AI Overviews, the Gemini App rock­et­ing to over 950M monthly users, and our fun­da­men­tal and sci­en­tific re­search con­tin­ues to lead the world. I’m very ex­cited for our next chap­ter and the best is yet to come!

As a busi­ness we are in an in­cred­i­bly strong po­si­tion. We are the only com­pany that has the full stack and we’re world-class at every layer from in­fra­struc­ture to cloud to fron­tier mod­els to AI-first ap­pli­ca­tions. We have all the in­gre­di­ents to lead from here, and I firmly be­lieve we will.

Best

Demis

Software is made be­tween com­mits

Be among the first to try DeltaDB, a ver­sion con­trol sys­tem that records the work as it un­folds and keeps every change con­nected to the con­ver­sa­tion that shaped it.

Rewind to any edit

DeltaDB cap­tures every op­er­a­tion in be­tween com­mits and gives each one a sta­ble iden­tity, so you can point to the code at any mo­ment in its evo­lu­tion.

Trace code to con­ver­sa­tion

Every change is linked to the agent con­ver­sa­tion that pro­duced it. From any line of code, find the con­ver­sa­tion. From any mes­sage, jump to the code it touched.

Branch at any mo­ment

DeltaDB vir­tu­al­izes the work­tree, so spin­ning up a new agent branch is ef­fec­tively free. Any point in his­tory is a valid branch point, in­clud­ing mid-run.

Share the thread, not the PR

A team­mate can join while the work is still hap­pen­ing, talk to the agent that did the work, and an­no­tate as they go, with­out wait­ing for you to com­mit and push first.

SO YOU WANT TO TEACH YOURSELF BOTANY…

I fre­quently get mes­sages from peo­ple who re­ally want to teach them­selves botany and learn ex­actly where the fuck to start iden­ti­fy­ing plants and learn­ing about them. The field is full of in­tim­i­dat­ing words (as well as some pow­dery stiffs, like much of Academia) and a con­fus­ing lex­i­con that can be a turn off to the layper­son. I’m telling you this though - don’t be in­tim­i­dated. With the in­ter­net, you have 24 hour ac­cess to the li­brary. Use it. Ask ques­tions. See a word you don’t un­der­stand? Look it up. Read about a con­cept that does­n’t make any sense to you (ie “what the shit does ‘monophyletic’ mean and why is it im­por­tant)? Figure out what about it is con­fus­ing and ask the damn google.

That said, there are some key con­cepts you should un­der­stand that will make things a lot eas­ier. They are:

Latin ter­mi­nol­ogy - why do we use Latin? Well, be­cause some dead guy named Linnaeus re­al­ized we need a uni­ver­sal sys­tem that sci­en­tists from mul­ti­ple dif­fer­ent cul­tures could use (at the time, he was prob­a­bly mostly think­ing of “white European cul­tures”, and while we can ac­knowl­edge how back­wards and fuck­ing goofy this is now, we can still ad­mit that Linnaeus’ ide­ol­ogy was sim­ply flawed like his time and NOT throw out the baby with the prover­bial bath wa­ter. The fucker cre­ated a beau­ti­ful sys­tem, and it works. And that’s why we still use it. I say this be­cause a few un­think­ing “performative left­ist” nitwits as of late have de­cided to at­tack the sci­ence of tax­on­omy it­self). Common names sim­ply don’t work on a large scale. One com­mon name (ie “cedar”) can re­fer to 8 dif­fer­ent to­tally un­re­lated plants, where as Cedrus refers specif­i­cally to the genus which con­tains the species C. libani, C. de­o­dara, and C. at­lantica. When botanic names (or any or­gan­is­m’s name) is writ­ten in sci­en­tific nomen­cla­ture, the genus name is cap­i­tal­ized and the species name is lower case, and the name it­self is usu­ally writ­ten in ital­ics. If you write a species name “Cedrus Atlantica” it is a dead give-away that you don’t know what the fuck you are do­ing. I was po­litely cor­rected on this point more than a decade ago, and I never hes­i­tate to po­litely cor­rect oth­ers. It’s like be­ing cour­te­ous enough to tell some­body that they have a booger on their face.

Taxonomy : IS THERE A METHOD TO THE MADNESS? WHAT GIVES? - In short, yes, there is, ab­solutely, and it is so FUCKING cool. Why is it cool? Because we now group things ac­cord­ing to how evo­lu­tion­ar­ily re­lated are, and how they evolved. Once you learn the key con­cepts and traits that unite a fam­ily or a genus or a tribe, you can now iden­tify mem­bers of that evo­lu­tion­ary group­ing that you have never seen be­fore. This is how I can see a plant that I have never en­coun­tered be­fore and au­to­mat­i­cally know what other plants its re­lated to, what fam­ily or genus it is in, and thus know what tax­o­nomic group to search for it un­der (on inat­u­ral­ist us­ing the “explore” fea­ture or with a key (flora), etc).

Plant Systematics by Michael Simpson

This is the sem­i­nal text­book to use if you are de­cid­ing to take the deeper dive into botany. Plant Systematics is the study of plant evo­lu­tion, and fur­ther­more is the study of plant iden­ti­fi­ca­tion as it re­lates to plant evo­lu­tion via an un­der­stand­ing of SYNAPOMORPHIES. This text­book by Dr. Michael Simpson lays out why botanists were able to tell how closely re­lated cer­tain plant fam­i­lies and or­ders were BEFORE the ad­vent of DNA analy­sis, as well as why some of those prior as­sump­tions were found to be wrong. This is a fam­ily-by-fam­ily, and or­der-by-or­der way to be­come fa­mil­iar with plant mor­phol­ogy and evo­lu­tion. This is also an ex­cel­lent way to one day be able to see new plants that you have never seen be­fore and au­to­mat­i­cally know what fam­i­lies or gen­era they might be re­lated to sim­ply by ob­serv­ing them. At pre­sent, the third edi­tion is the most cur­rent and it is a book that you will use as a ref­er­ence for the next ten years (at least) of your life.

Plant Systematics by Michael Simpson

This is the sem­i­nal text­book to use if you are de­cid­ing to take the deeper dive into botany. Plant Systematics is the study of plant evo­lu­tion, and fur­ther­more is the study of plant iden­ti­fi­ca­tion as it re­lates to plant evo­lu­tion via an un­der­stand­ing of SYNAPOMORPHIES. This text­book by Dr. Michael Simpson lays out why botanists were able to tell how closely re­lated cer­tain plant fam­i­lies and or­ders were BEFORE the ad­vent of DNA analy­sis, as well as why some of those prior as­sump­tions were found to be wrong. This is a fam­ily-by-fam­ily, and or­der-by-or­der way to be­come fa­mil­iar with plant mor­phol­ogy and evo­lu­tion. This is also an ex­cel­lent way to one day be able to see new plants that you have never seen be­fore and au­to­mat­i­cally know what fam­i­lies or gen­era they might be re­lated to sim­ply by ob­serv­ing them. At pre­sent, the third edi­tion is the most cur­rent and it is a book that you will use as a ref­er­ence for the next ten years (at least) of your life.

Raven’s Biology of Plants

This text­book cov­ers many of the ba­sics of plant bi­ol­ogy as well as get­ting into the nu­ances of plant evo­lu­tion, with ex­cel­lent ex­am­ples of some of the more charis­matic and cu­ri­ous plant species out there. It also does a great job of ex­plain­ing what botanists know so far about how plants evolve and how se­lec­tion pres­sures work to cause all the “variations on a theme” and “endless forms most beau­ti­ful” that got Darwin all horny. What is an eco­type you say? What is the Hardy-Weinberg the­o­rem? What are al­lele fre­quen­cies? What is con­ver­gent evo­lu­tion? All these con­cepts are ex­plained, in depth, in this ex­cel­lent text book.

Raven’s Biology of Plants

This text­book cov­ers many of the ba­sics of plant bi­ol­ogy as well as get­ting into the nu­ances of plant evo­lu­tion, with ex­cel­lent ex­am­ples of some of the more charis­matic and cu­ri­ous plant species out there. It also does a great job of ex­plain­ing what botanists know so far about how plants evolve and how se­lec­tion pres­sures work to cause all the “variations on a theme” and “endless forms most beau­ti­ful” that got Darwin all horny. What is an eco­type you say? What is the Hardy-Weinberg the­o­rem? What are al­lele fre­quen­cies? What is con­ver­gent evo­lu­tion? All these con­cepts are ex­plained, in depth, in this ex­cel­lent text book.

Additional Texts

It’s al­ways great to be able to sup­port au­thors by buy­ing their books, but some­times the cost of self-ed­u­ca­tion can pro­hib­i­tive. It is my firm be­lief that any of the au­thors listed be­low (unless they’re dicks) would not want any­one to be pro­hib­ited from read­ing their work. This is why sources like www.lib­gen.is and www.sci-hub.se and other book shar­ing web­sites ex­ist. In this case I sug­gest pur­chas­ing a cheap an­droid tablet and be­com­ing ac­quainted with the idea of read­ing text­books in pdf form. A half pound elec­tronic de­vice can store up­wards of half a mil­lion pages or more worth of text­books. Phylogeny and Evolution of the Angiosperms by Pam and Doug SoltisThe Tangled Tree by David QUammen - A Good pop-sci ex­pla­na­tion of mol­e­c­u­lar phy­lo­ge­net­ics and un­der­stand­ing evo­lu­tion­Plant Evolution : An Introduction to the History of Life by Karl NiklasBotany Illustrated by Janice Glimm-LacyAnnals of the Former World (geology) by John MacpheeBotany for Gardeners by Brian CaponThe Ecology of Plants by Jessica GurevitchA Botanist’s Vocabulary by Susan PellThe Rose’s Kiss by Peter BernhardtFlowering Plant Families by Wendy ZomleferHow the Earth Turned Green by Joseph ArmstrongThe Origin, Expansion, and Demise of Plant Species by Donald LevinThe Ecology of Seeds by Michael FennerThe Fungi by Sarah WatkinsonBiogeography : An Ecological and Evolutionary Approach by C. Barry CoxEvolution Making Sense of Life by Carl ZimmerCacti Biology and UsesAn Island Called California by Elna Baker (a great text due to it’s ex­pla­na­tion of eco­log­i­cal re­la­tion­ships even if you don’t live in California)Serpentine Geoecology of Western North America by Earl AlexanderA Natural History of California by Allan SchoenherrEcology of Desert Systems by WhitfordThe California Deserts by Bruce PavlikPlant and Animal Endemism in California by Susan Harrison (again - great ex­pla­na­tions even if you’re not into California. IT is a great case study)

Additional Texts

It’s al­ways great to be able to sup­port au­thors by buy­ing their books, but some­times the cost of self-ed­u­ca­tion can pro­hib­i­tive. It is my firm be­lief that any of the au­thors listed be­low (unless they’re dicks) would not want any­one to be pro­hib­ited from read­ing their work. This is why sources like www.lib­gen.is and www.sci-hub.se and other book shar­ing web­sites ex­ist. In this case I sug­gest pur­chas­ing a cheap an­droid tablet and be­com­ing ac­quainted with the idea of read­ing text­books in pdf form. A half pound elec­tronic de­vice can store up­wards of half a mil­lion pages or more worth of text­books.

Phylogeny and Evolution of the Angiosperms by Pam and Doug Soltis

The Tangled Tree by David QUammen - A Good pop-sci ex­pla­na­tion of mol­e­c­u­lar phy­lo­ge­net­ics and un­der­stand­ing evo­lu­tion

Plant Evolution : An Introduction to the History of Life by Karl Niklas

Botany Illustrated by Janice Glimm-Lacy

Annals of the Former World (geology) by John Macphee

Botany for Gardeners by Brian Capon

The Ecology of Plants by Jessica Gurevitch

A Botanist’s Vocabulary by Susan Pell

The Rose’s Kiss by Peter Bernhardt

Flowering Plant Families by Wendy Zomlefer

How the Earth Turned Green by Joseph Armstrong

The Origin, Expansion, and Demise of Plant Species by Donald Levin

The Ecology of Seeds by Michael Fenner

The Fungi by Sarah Watkinson

Biogeography : An Ecological and Evolutionary Approach by C. Barry Cox

Evolution Making Sense of Life by Carl Zimmer

Cacti Biology and Uses

An Island Called California by Elna Baker (a great text due to it’s ex­pla­na­tion of eco­log­i­cal re­la­tion­ships even if you don’t live in California)

Serpentine Geoecology of Western North America by Earl Alexander

A Natural History of California by Allan Schoenherr

Ecology of Desert Systems by Whitford

The California Deserts by Bruce Pavlik

Plant and Animal Endemism in California by Susan Harrison (again - great ex­pla­na­tions even if you’re not into California. IT is a great case study)

02 Aug, 2026

For the past months or even years, I’ve be­come more and more dis­sat­is­fied with the path Google has taken with the Android Open Source Project (AOSP). Be it the de­pen­dency and in­sane track­ing of Google Play Services, lock­ing down of de­vice trees to hin­der cus­tom ROM de­vel­op­ment, all the AI stuff be­ing put into the sys­tem, or most re­cently (and per­haps worst), re­mov­ing the abil­ity of in­stalling apps per your own wish­ing - it’s be­come sort of like a “death by a thou­sand pa­per­cuts” sit­u­a­tion. While the AOSP is­n’t ex­actly dead (yet), it kind of feels like it’s just a ques­tion of time. Therefore, I’ve de­cided to jump ship. I’m in­stalling linux on my phone.

The mo­bile linux space is­n’t ex­actly look­ing good right now, but it holds some promise. Ubuntu Touch man­aged to sur­vive be­ing aban­doned by Canonical, and post­mar­ket is still see­ing good de­vel­op­ment even if not hav­ing great hard­ware ada­p­a­tion at the mo­ment. SailfishOS (even if it has pro­pri­etary parts) has also made great progress with strong sup­port for an­droid apps on of­fi­cial de­vices and what seems to be a real good launch of their Jolla 2.

Personally I’m lucky enough to own a Fairphone 4 that just so hap­pens to sup­port all of these. After a bit back and forth I’ve ul­ti­mately ended up with SailfishOS. Overall it’s a good sys­tem - I like the ges­ture based nav­i­ga­tion and its ap­pli­ca­tion frame­work is beau­ti­ful. It’s also cool how it’s very linux. If I have an is­sue or some­thing I want to tin­ker with I can just ssh over from my PC ei­ther wire­lessly or via USB. It’s not with­out it’s is­sues though. For some rea­son it ships hor­ri­bly out­dated ver­sions of python and glibc mak­ing some things more dif­fi­cult than needed, and way­droid and GPS is bro­ken on the fair­phone port - worth not­ing an un­of­fi­cial port. Many of the com­mu­nity-built apps are also ei­ther in-part or com­pletely slop-coded, such as a what­sapp client that I (sadly) am de­pen­dant on.

Ubuntu Touch is also an op­tion, but is­n’t with­out it’s own bag of is­sues. Waydroid runs there which is a huge plus, but no­ti­fi­ca­tions and clip­board does­n’t sync across mak­ing it re­ally hard to use ie. Bitwarden (Sailfish has an un­of­fi­cial port which is al­most flaw­less). The de­fault and na­tive apps are pretty lack­lus­ter com­pared to both Android and Sailfish. Among other things I was un­able to fig­ure out how to block phone num­bers, a highly needed fea­ture for any­one that has VIVO as their cel­lu­lar provider. I’m also not a big fan of how the UI works with app nav­i­ga­tion and with the “top bar”/“​drop down menu”. If you want to know more about it, The Linux Experiments has some good videos on both youtube and peer­tube.

Sadly, I won’t be able to aban­don an­droid com­pletely yet. The no way­droid means that I can’t ac­cess some apps that I need be it for on­line ver­i­fi­ca­tion for log­ging onto bank and gov­ern­ment ser­vices in Norway or apps I need for my own se­cu­rity here in Brazil such as Uber. Luckily I’ve had a Galaxy A17 ly­ing around as a backup phone for half a year now which I can carry around for these ex­act ser­vices. I just open up a wifi hotspot from my FP4, do the ex­act tasks, and close it off again. Other than that, I will try to avoid us­ing it as much as phys­i­cally pos­si­ble.

As this pro­gresses I will take note of what works and what does­n’t be­fore mak­ing ei­ther a proper writeup or video in a while. It’s not gonna be easy, but hope­fully it will be worth it.

Oh, and slight spoil­ers for what my ex­pe­ri­ence with Sailfish is af­ter some us­age, but I might go ahead and buy a Jolla Phone 2 when I re­turn to Norway.

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#free soft­ware

#tech

“Most teams’ best train­ing data is just sit­ting in their data­bases. The prob­lem is that turn­ing raw data into some­thing us­able is hard, and let­ting agents read, search, and mu­tate data cheaply at scale re­quires ad­vanced in­fra. Pointing Castform at Neon skips both.”Ying Hang Seah, co­founder, Castform

“Most teams’ best train­ing data is just sit­ting in their data­bases. The prob­lem is that turn­ing raw data into some­thing us­able is hard, and let­ting agents read, search, and mu­tate data cheaply at scale re­quires ad­vanced in­fra. Pointing Castform at Neon skips both.”

A “good agent” needs to be strong in 2 ar­eas:

Context: can we pro­vide the tools to find the right data?

Model: can the model de­cide what to search for?

Neon (Lakebase Postgres) and their new Search ex­ten­sions solve the first; Castform solves the sec­ond.

In ~2022, the in­dus­try was go­ing all in on em­bed­ding search. Every data­base provider added one, and pgvec­tor was Neon’s most down­loaded ex­ten­sion. To pro­vide con­text to LLMs, en­gi­neers hand­crafted RAG pipelines, which in essence, is some form of em­bed­ding sim­i­lar­ity search.

In ~2025, agents started to gain more trac­tion. Developers started cre­at­ing multi-hop search work­flows, de­com­pos­ing big prob­lems into smaller ones. Retrieval has shifted from the one-shot search sys­tems to agen­tic re­trieval. Instead of is­su­ing a sin­gle query, mod­els plan and search mul­ti­ple times in a loop. Every loop it­er­a­tion meant an­other call to the fron­tier model, in­creas­ing the over­all cost and la­tency per user re­quest.

Concretely, a typ­i­cal multi-turn search re­quest with gpt-5.6-sol takes >10s and costs ~$0.03 end-to-end, mak­ing it pro­hib­i­tively slow and ex­pen­sive.

Meanwhile, small open-weights mod­els are 100x cheaper. But, out of the box, their ca­pa­bil­i­ties lag be­hind closed api mod­els. RL post-train­ing helps bridge this gap. On spe­cific tasks like search, post-trained open-source mod­els can match & beat fron­tier mod­els while cost­ing or­ders of mag­ni­tude less per re­quest.

That is why we built Castform: to en­able de­vel­op­ers to RL post-train mod­els with­out hav­ing to deal with ma­chine learn­ing & gpu in­ter­nals. The goal’s to make post-train­ing as ap­proach­able as prompt en­gi­neer­ing.

Castform’s pipeline runs against Neon via Lakebase Search:

To per­form RL post-train­ing ef­fec­tively, you need a task (e.g. an­swer a user’s ques­tion), the en­vi­ron­ment for the agent to run in (e.g. a search tool for your cor­pus) and a re­ward func­tion (e.g. is the an­swer cor­rect?).

With all 3 pieces in place, the RL post-train­ing is a loop of trial and er­ror: the model at­tempts the task given the tools, the re­ward func­tion scores the at­tempt, and the feed­back sig­nal guides the model on how to hill-climb its way to op­ti­mal per­for­mance.

Yet, most com­pa­nies do not have a clean dataset of tasks and re­ward func­tions ready for post-train­ing.

Enterprises do have a large set of pro­pri­etary data:

in­ter­nal doc­u­men­ta­tion

prod­uct records

sup­port ar­ti­cles

cus­tomer in­ter­ac­tions

wikis

op­er­a­tional data­bases

This data con­tains the knowl­edge an agent needs, but turn­ing it into an ef­fec­tive train­ing dataset nor­mally re­quires sub­stan­tial data en­gi­neer­ing and man­ual la­bel­ing.

That leads many teams to dis­miss post-train­ing for one of two rea­sons:

“We don’t have the train­ing data.”

“Fine-tuning is too dif­fi­cult and re­quires in­fra­struc­ture we don’t have.”

Castform ad­dresses both. It turns an ex­ist­ing cor­pus into train­ing tasks, then man­ages the RL loop needed to teach an open-source model how to use that data ef­fec­tively.

With Castform, you can turn your com­pany knowl­edge base into a model:

Document (from your data): Trains booked through Navan will be paid by GitLab travel card. Train rides must be stan­dard cabin class with 14 day book­ing lead time

Ground truth (inferred from your data): Train rides must be stan­dard cabin class with a 14 day book­ing lead time.

Question (synthetically gen­er­ated): When book­ing a rail trip in Navan, what are the rules for how early I need to re­serve it and which seat­ing level I’m ex­pected to choose?

With the gen­er­ated ques­tion-an­swer dataset, Castform lets you scaf­fold the train­ing run by spec­i­fy­ing the tools the agent has ac­cess to and a re­ward func­tion.

The re­ward func­tion spec­i­fies what you want your model to get good at. In our case, we want it to re­trieve the cor­rect chunks, cite the right sources along with pro­vid­ing the right fi­nal an­swer.

def run_­tool(tool, tool_args): “”″Single tool: hy­brid search over Lakebase.“”″ if tool == “search”: query = tool_args[“query”] bm25 = neon.lake­base_­text(query, k) vec­tor = neon.lake­base_vec­tor(query, k) re­turn rrf_merge(bm25, vec­tor, k)

def re­ward(trace, ground_truth): “”″Grade a trace against the ground-truth an­swer.“”″ an­swer = parse_­trace(trace) re­trieval = … # did it re­trieve the right source ci­ta­tion = … # did it cite the right chunk cor­rect­ness = … # did it land on the right an­swer re­turn re­trieval + ci­ta­tion + cor­rect­ness

See a com­pre­hen­sive code ex­am­ple here.

Castform gives you full ob­serv­abil­ity into your RL run. You can mon­i­tor your re­ward climb with each step, but more im­por­tantly you can drop into in­di­vid­ual tasks/​prompts to watch how the model per­forms qual­i­ta­tively, al­low­ing you to de­bug prob­lems such as bro­ken tools or re­ward hack­ing.

For more de­tails on how to mon­i­tor your train­ing runs, you can check out the Castform blog here. You can also check out our ex­am­ple train­ing run here.

During train­ing, the agent re­peat­edly calls Lakebase Search un­til it has enough con­text to an­swer. Across thou­sands of par­al­lel roll­outs, each po­ten­tially mak­ing dozens of calls, this cre­ates a highly bursty work­load.

Neon’s dy­namic com­pute scal­ing ab­sorbs these peaks with­out re­quir­ing Castform to pro­vi­sion for max­i­mum ca­pac­ity around the clock. Training runs get low-la­tency search when de­mand spikes, while com­pute scales down dur­ing idle pe­ri­ods.

This in­fra­struc­ture be­comes even more valu­able as agents move be­yond search and be­gin mod­i­fy­ing data. Training state­ful agents re­quires iso­lated en­vi­ron­ments that can be cre­ated and re­set cheaply, pre­vent­ing one roll­out’s ac­tions from af­fect­ing an­other or touch­ing pro­duc­tion.

Neon branch­ing can give each roll­out an iso­lated data­base state, while time-travel queries make it pos­si­ble to re­con­struct and in­spect the state an agent en­coun­tered. Combined with au­toscal­ing and scale-to-zero, this cre­ates a path to­ward train­ing thou­sands of state­ful agent roll­outs with­out main­tain­ing thou­sands of con­tin­u­ously run­ning en­vi­ron­ments.

Castform makes it easy for any de­vel­oper to post-train open-source mod­els to be cheaper, faster, bet­ter than the fron­tier. Post-train your first model to­day at cast­form.com.

Appreciating ty­pog­ra­phy is a study in para­dox. The pri­mary goal of well-de­signed ty­pog­ra­phy is to help you read the words, not ap­pre­ci­ate how the words are com­posed of let­ters and how each of those in­di­vid­ual let­ters has been de­signed to con­vey a small bit of mean­ing. If you see the ty­pog­ra­phy rather than read the words, the ty­pog­ra­phy has failed in its job, right? You should be read­ing, not star­ing at the shape of that up­per­case A.

The goal with ty­pog­ra­phy is func­tional, right? To con­vey the mean­ing, not the feel­ing?

Wrong.

Feeling is al­ways con­veyed. The ques­tion is, de­pend­ing on the pro­ject, “How much feel­ing is re­quired?”

Fixed Width

I’ve cre­ated a full-time job for my­self talk­ing to Claude Code. This is Claude run­ning from the com­mand line of ma­cOS, which gives me un­hin­dered ac­cess to my data. I’m us­ing Ghostty. I’m star­ing at a fixed-width type­face all day. Apple pro­vides a func­tional and gor­geous fixed-width vari­ant of their San Francisco type­face called SF Mono, which I’ve been us­ing for months, but, well, I have a short at­ten­tion span — ty­po­graph­i­cally speak­ing.

In the past month, I’ve eval­u­ated many ad­di­tional fixed-width type­faces fa­vored by the nerd­core. Here are the six that have made my cut:1

The ques­tion is: how do they make you feel? For a type­face de­signed for cod­ing, you first want fixed-width. Every let­ter and sym­bol is the same width, giv­ing you a pre­dictable and read­able grid. But how wide? And how tall? Also, how much in­for­ma­tion do you want to be able to see on a screen?

Your brain builds a very per­sonal and emo­tional im­pres­sion of the col­lec­tion of let­ters that make words that con­vey mean­ing. In the case of ter­mi­nal or cod­ing type­faces, the de­sign goal is cer­tainly to pro­vide struc­tural func­tion and not feel­ing, but here’s the deal:

They do.

Isn’t this about the Blade Runner Title Sequence?

Flight to New York. My board­ing pro­ce­dure for mod­er­ate to long flights is: sit down, find a movie I’ve seen a dozen times, and hit play. No sound. This is vi­sual back­ground noise while I sort wifi, pre­pare for a meal, and find a pro­ject. On this flight, I picked Blade Runner. Fun fact: I can re­cite 50% of the di­a­logue from this movie from mem­ory.

As I’ve been re­search­ing ty­pog­ra­phy for Ghostty, novel ty­pog­ra­phy tends to jump out at me. Like when you buy a car, and all you see is your new car on the road. Except it’s ty­pog­ra­phy.

Here are clips from the ti­tle se­quence for Blade Runner:

There’s a lot to dis­sect in this se­quence, but let’s start with the punch­line. This is a sin­gle type­face. It’s Goudy Oldstyle — that’s it. However, this is how they used ty­pog­ra­phy:

ALL CAPS for names, proper names, the ti­tle2, and a slightly larger ver­sion for the in­tro­duc­tion of Los Angeles, November, 20193

A smaller ALL CAPS for in­tros and other small im­por­tant words

For the ex­po­si­tion crawl, they use stan­dard cap­i­tal­iza­tion ex­cept for three vari­ants: small caps for proper names (like The Tyrell Corporation), a cap­i­tal R on Robot (not a proper noun, but this world treats it like one), or (spoiler alert) a red ver­sion of the text for the word Replicant — which is ital­i­cized every time it ap­pears, but only the de­but gets the red — and (spoiler alert) it’s the same red as the ti­tle of the movie4

Keep look­ing. The crawl is set like a book, not a movie — first-line in­dents, gen­er­ous word spac­ing — and Frederic Goudy would ap­prove: they let­ter­space the caps, never the low­er­case, obey­ing his fa­mous dic­tum that “anyone who would let­ter­space low­er­case would steal sheep.”5 Also, what’s up with that chonky em dash? It’s prob­a­bly been years since you’ve seen this, so here it is again:

Unlike our func­tional fixed-width type­faces, the role of ty­pog­ra­phy in this ti­tle se­quence is partly func­tional — to set the story — but the pri­mary pur­pose is es­tab­lish­ing mood. Director Ridley Scott ex­pertly drops us into the mid­dle of a dystopian fu­ture where we’ve en­slaved the ro­bots and, duh, they are re­belling.

How Much Feeling is Required?

Chances are, you never think about ty­pog­ra­phy. You hap­pily scribe your Messages (SF Pro), Mails (Helvetica), and Slacks (Lato), think­ing noth­ing of ser­ifs, lig­a­tures, or kern­ing. You are con­tent trust­ing that a some­one else has cho­sen a proper type­face for your cur­rent task. Maybe you bold, you un­der­line, and you ital­i­cize to slightly ad­just mean­ing. No is­sue. Respect.

However, we now live in a world where every­one is ca­pa­ble of build­ing what­ever they want thanks to the ro­bots. They’re do­ing it — right now. They find im­mense joy in typ­ing in a cou­ple of sen­tences and watch­ing the ro­bots mer­rily build what­ever they ask. See? I don’t need to be an en­gi­neer to build an app. And they are cor­rect. Sorta.

With op­ti­mism in my heart and a firm be­lief that the ro­bots can le­git­i­mately help many hu­mans, I can con­firm that the ma­jor­ity of con­sumer-fac­ing things be­ing built by hu­mans who’ve never built a thing… are garbage. Building a tool for your­self? A quick script to read your feeds and gen­er­ate a pleas­ant-to-read out­put? A+. Robots crush that… for you. Building a feed reader any­one on the planet can eas­ily use to read any num­ber of feeds? No. No, you aren’t; you can say you are, but un­til you’ve built a thing for every­one, you will not ap­pre­ci­ate that the last 10% of the work:

Takes most of the time.

Contains an end­less list of small de­ci­sions that feel unim­por­tant, but col­lec­tively make the dif­fer­ence be­tween ac­cept­able and fuck­ing amaz­ing.

Don’t be­lieve me? Here’s the first ver­sion of the ti­tle se­quence for Blade Runner’s work print, the close-to-com­plete cut be­fore the the­atri­cal re­lease:

That ti­tle type­face? Impact. A fine type­face, but a clumsy path-of-least-re­sis­tance slap-to-the-face choice to set the tone of a fu­ture sci­ence fic­tion mas­ter­piece. Garbage6.

I’ve never filmed a movie, but I have built quite a few prod­ucts that you are us­ing right now. In all the de­sign de­bates, we never ex­plic­itly de­bate feel­ing: we ob­sess over the de­tails. That ob­ses­sion is what you feel when you use our prod­ucts. It’s the col­lec­tive voice of every sin­gle hu­man who con­tributed small and large de­ci­sions to the prod­uct.

A good prod­uct sounds like the hu­mans who cre­ated it, and that’s what you will feel.

As I type this: IBM Plex Mono. By the time you read this: any­one’s guess. See: short at­ten­tion span. ↩︎

Type nerds, yes, I know the ti­tle of the movie is hand drawn, but it’s cer­tainly in­spired by Goudy Oldstyle ↩︎

Hey LA, I might rip on you be­cause you steal our wa­ter, but you’re do­ing bet­ter than Ridley Scott pre­dicted. Good job. ↩︎

It’s been there all along, folks, why did we ar­gue for all those years? ↩︎

Type nerds: Goudy’s orig­i­nal griev­ance was re­port­edly about black­let­ter, not low­er­case. The low­er­case ver­sion is the mis­quote that stuck — Erik Spiekermann liked it enough to name a book af­ter it. The rule holds ei­ther way. ↩︎

Yes, it does­n’t help that there is no Vangelis sound­track, yet. ↩︎

I came across a GH thread re­lated to chess en­gine de­vel­op­ment that made me think of why hobby pro­gram­ming com­mu­ni­ties are in­creas­ingly hos­tile to­ward LLM de­vel­op­ment. While the thread does­n’t give a lot of in­sight into an­swer­ing the ques­tion, it prompted me to think about it a bit. I’ve seen sim­i­lar sen­ti­ments ex­pressed in other niche hobby pro­gram­ming com­mu­ni­ties like OSDev, LangDev, TxtDev, EmuDev, RLDev, the demoscene, and code golfers. The gen­eral con­sen­sus seems to be that the knowl­edge that these com­mu­ni­ties work in is hard-fought and the use of LLMs is a form of miss­ing the point en­tirely. In these com­mu­ni­ties (keep in mind that there is an im­plicit “not all…” through­out) the process of mas­ter­ing a dif­fi­cult field it­self is the prod­uct, and some­thing that runs is gen­er­ally a nice-to-have.

Further, I’ve no­ticed that even in the in­stances where there was earnest early en­gage­ment with LLMs in some of these niche com­mu­ni­ties, the well was quickly poi­soned by a com­bi­na­tion of a lack of a deep un­der­stand­ing by the LLM prac­ti­tion­ers, and a vit­ri­olic sub­set of those com­mu­ni­ties that view the LLM en­ter­prise as a form of cheat­ing. Granted these com­mu­ni­ties have, in gen­eral, his­tor­i­cally been char­ac­ter­ized by fever­ish gate­keep­ing and painstak­ingly slow progress, so it makes sense that there might be a de­sire to grab some easy ca­chet by burst­ing onto the scene like the Kool-Aid man. OH YEAH…. OH NO!

In tra­di­tional niche dev cir­cles, re­spect is earned slowly through years of ac­tiv­ity in their re­spec­tive fora, shar­ing el­e­gant code, dis­plays of gen­uine cu­rios­ity, and through shar­ing deep do­main knowl­edge along the way. At the end of the day, these com­mu­ni­ties don’t care if your code works at all, but in­stead care that you know why and how it works. To me, an LLM func­tions best as a force mul­ti­plier, not a sur­ro­gate. In the hands of an ex­pert who al­ready un­der­stands a do­main deeply, it could act like a lever.1 But in these niche com­mu­ni­ties, the en­tire ex­er­cise is in the learn­ing. Using an LLM to gen­er­ate the fin­ished piece does­n’t make us crafts­men; it just robs us of the craft.

:F

This is the lat­est in my evolv­ing thoughts on LLMs. Also see: LLMe and Mind the van Emden Gap

That said, ex­per­tise of­fers no nat­ural im­mu­nity against be­ing fooled by LLMs.↩︎

That said, ex­per­tise of­fers no nat­ural im­mu­nity against be­ing fooled by LLMs.↩︎

Editor’s note: This ar­ti­cle con­tains de­scrip­tions of im­agery de­pict­ing child sex­ual abuse. Reader dis­cre­tion is strongly ad­vised.

Over the last nine months, Mark Zuckerberg’s Meta has run dozens of paid ads that in­clude ex­plicit AI-generated child sex­ual abuse ma­te­r­ial (CSAM) and im­ages of mi­nors along­side sex­u­ally sug­ges­tive state­ments, ac­cord­ing to de­tails of the ads shared with WIRED. The ads, which in some cases reached sev­eral thou­sand ac­counts, were tar­geted at peo­ple liv­ing in the United States, United Kingdom, and more than a dozen European coun­tries.

More than 50 im­age and video ads con­tain­ing abu­sive con­tent were re­cently dis­cov­ered in Meta’s ad li­brary by re­searchers at the Tech Transparency Project (TTP), with the list­ings show­ing that some ads linked out to so-called nud­ify or un­dress­ing apps. Meta’s ad li­brary—a trans­parency tool that cat­a­logues ads dis­played on the com­pa­ny’s plat­forms—shows the ads run­ning across Facebook, Instagram, Messenger, and/​or Threads. The find­ings are the sec­ond time in re­cent weeks that paid ads linked to child sex­ual abuse ma­te­r­ial have been found on Meta’s plat­forms.

“These ads made no ef­fort to mask the im­ages or hide what they were pro­mot­ing,” Katie Paul, the di­rec­tor of the TTP, an in­de­pen­dent watch­dog group, tells WIRED. “It’s im­por­tant to point out that this is­n’t con­tent posted by third par­ties on Facebook or Instagram, these are ads that were re­viewed, ap­proved, and al­lowed to run by Meta, never en­coun­ter­ing in­ter­fer­ence while the com­pany col­lected the ad dol­lars.”

The ads dis­cov­ered by the re­searchers—which have now been re­moved by Meta for vi­o­lat­ing its poli­cies on child sex­ual abuse and ex­ploita­tion ma­te­r­ial, and adult sex­ual so­lic­i­ta­tion and nu­dity—were all pub­lished be­tween November last year and the start of August. The ads, ac­cord­ing to data in Meta’s ad trans­parency li­brary, of­ten ran over a pe­riod of sev­eral days and some only tar­geted men.

One video ad, the TTP re­searchers say, used a thumb­nail im­age of a child sit­ting on the floor with text ap­pear­ing on top of it say­ing: “Realizing Deep Fantasies with Generation AI [sic]. There is so much more than what is shown, use your imag­i­na­tion.” The re­searchers say that when the ad was clicked, it played video clips of adults in­volved in sex­ual acts and then added the child’s face from the thumb­nail to a sex act.

Another ad the re­searchers say had been re­peat­edly posted showed an im­age of a young girl lay­ing back with her legs spread in a sex­ual po­si­tion with text above it say­ing: “I can show you more.”

Most of the ads were not ac­tively be­ing shown to users when re­searchers found them, but they con­tin­ued to be avail­able, ap­par­ently un­de­tected, in Meta’s ad li­brary for months. Although many of the ads, ac­cord­ing to Meta’s data, only “reached” a hand­ful of ac­counts, at least one reached 2,563 ac­counts in Europe—including in France, Germany, Ireland, Italy, the Netherlands, Spain, Sweden, and the United Kingdom. Meta’s ad li­brary data­base does not in­clude de­tails about ad per­for­mance in the US or all coun­tries, so the over­all reach may be higher.

Paul says TTP first dis­cov­ered the ads last week as its re­searchers con­tin­ued to in­ves­ti­gate nud­ify app ads on Meta, af­ter pub­lish­ing a re­port about a Chinese ad­ver­tis­ing part­ner post­ing them. Paul says the or­ga­ni­za­tion im­me­di­ately re­ported the find­ings to National Center for Missing and Exploited Children’s CyberTipline, which al­lows peo­ple to re­port on­line child sex­ual abuse ma­te­r­ial. A NCMEC spokesper­son says it does not com­ment on re­ports it re­ceives.

After WIRED reached out to Meta, the com­pany re­moved the ads from its ad­ver­tis­ing li­brary. “Sexual ex­ploita­tion is hor­rific, and we work ag­gres­sively to keep it off our plat­form,” a Meta spokesper­son says. “The ma­jor­ity of these ads had min­i­mal reach and many were dis­abled be­fore WIRED shared them. Many of these ads also pre­date new AI tech­nol­ogy we launched re­cently to bet­ter de­tect and block vi­o­lat­ing ads at up­load—and we’re con­stantly im­prov­ing these sys­tems. From re­mov­ing over 36 mil­lion pieces of child sex­ual ex­ploita­tion con­tent last year to tak­ing le­gal ac­tion against nud­ify app de­vel­op­ers, we will con­tinue to re­lent­lessly fight this abuse.”