California’s leg­is­la­ture has passed Assembly Bill 1856, ex­empt­ing open-source op­er­at­ing sys­tems from the State’s Digital Age Assurance Act months be­fore the law is due to take ef­fect on January 1, 2027. The Senate amended the Bill on August 21 be­fore pass­ing it on the 26th in a 39 – 0 vote, with the Assembly then ac­cept­ing these changes in a con­cur­rence vote the fol­low­ing day. The amend­ment ends al­most a year of un­cer­tainty sur­round­ing whether Linux dis­tri­b­u­tions and SteamOS would be forced to col­lect user age data dur­ing ac­count setup along­side Windows, ma­cOS, iOS, and Android. AB 1856 has now been sent to Governor Gavin Newsom, who signed the orig­i­nal act into law last October.

These amend­ments re­de­fine the term “operating sys­tem provider” to ex­clude any per­son or en­tity that dis­trib­utes an OS or ap­pli­ca­tion “under li­cense terms that per­mit a re­cip­i­ent to copy, re­dis­trib­ute, and mod­ify the soft­ware.” Any soft­ware dis­trib­uted un­der the GPL, MIT, BSD, and Apache li­censes sat­is­fies that test, which re­moves the likes of Debian, Fedora, Ubuntu, Arch, and the BSD fam­ily from AB 1856’s scope.

A sec­ond ex­clu­sion re­moves soft­ware com­po­nents that aren’t “offered to con­sumers as a stand-alone ex­e­cutable ap­pli­ca­tion through a cov­ered ap­pli­ca­tion store” from the law’s de­f­i­n­i­tion of an ap­pli­ca­tion, cov­er­ing li­braries and de­pen­den­cies dis­trib­uted through pack­age man­agers like apt and pac­man. AB 1856 does­n’t ex­plic­itly say that re­pos aren’t app stores, but a store’s main oblig­a­tion un­der the law is to re­quest an age sig­nal from the user’s OS provider and pass it to de­vel­op­ers; an ex­empt open-source OS pro­duces no sig­nal. A third carve-out ex­cludes store­fronts dis­trib­ut­ing ex­ten­sions or add-ons that run ex­clu­sively in­side a host ap­pli­ca­tion, which takes browser ex­ten­sion stores out of scope.

The amend­ments to AB 1856 also re­move the orig­i­nal de­f­i­n­i­tion of “user,” which read, “a child that is the pri­mary user of a de­vice,” and tech­ni­cally clas­si­fied every de­vice owner in California as a child. The law’s sig­nal­ing frame­work de­pends on adults de­clar­ing their age on ac­count setup, so their de­vices get flagged as 18 and over, but un­der that de­f­i­n­i­tion no­body could ever be flagged as an adult.

In ad­di­tion, law­mak­ers in­serted a new pro­vi­sion pro­hibit­ing any­one from re­quest­ing an age sig­nal from an OS provider or app store un­less re­quired by law. That closes off po­ten­tial abuse of the age API that could have led to it be­ing used as a gen­eral-pur­pose data col­lec­tion chan­nel even when age ver­i­fi­ca­tion was­n’t re­quired. Platforms and de­vel­op­ers also gain a good-faith safe har­bor against er­ro­neous sig­nals, pro­tect­ing them from li­a­bil­ity when age-gat­ing sig­nals are in­ac­cu­rate.

Windows, ma­cOS, iOS, and Android re­main fully in scope, with age col­lec­tion re­quired at ac­count setup from January 1, 2027. A later July 1, 2027, dead­line ap­plies to de­vices set up be­fore January 1. Whether SteamOS is in scope is­n’t yet clear: its Arch-based sys­tem com­po­nents are open source, but Valve dis­trib­utes the im­age along­side the pro­pri­etary Steam client. GrapheneOS, which in March said it would refuse to com­ply with age-ver­i­fi­ca­tion man­dates, is dis­trib­uted un­der open-source MIT and Apache li­censes and now falls out­side the law’s scope en­tirely, though Brazil’s Digital ECA still ap­plies to it.

Assemblymember Buffy Wicks, who wrote both the Digital Age Assurance Act and the AB 1856 amend­ment, in­tro­duced the ex­emp­tion back in February fol­low­ing crit­i­cism from Linux de­vel­op­ers and the Electronic Frontier Foundation.

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Luke James is a free­lance writer and jour­nal­ist.  Although his back­ground is in le­gal, he has a per­sonal in­ter­est in all things tech, es­pe­cially hard­ware and mi­cro­elec­tron­ics, and any­thing reg­u­la­tory.

I’m a kid of the 90s, and I still re­mem­ber the early in­ter­net. It was slow, ugly, un­re­li­able, and full of cranks, a strange world of wheez­ing dial-up modems, Usenet flame­wars, <marquee> tags, and danc­ing ba­bies. It was also stub­bornly alive and hu­man. People built web­sites about Babylon 5, model rock­ets, train timeta­bles, share­ware, and what­ever else had colonised their minds. Most of it had no busi­ness model. That was the lit­eral point. The web felt like a pub­lic square as­sem­bled by ob­ses­sive am­a­teurs.

None of this was en­tirely in­no­cent. There were scams, viruses, Nazis, pornog­ra­phy, and chain emails from de­posed Nigerian princes. But then pre­da­tion moved from the pe­riph­ery to the cen­tre. It used to be an abuse of the net­work. Now it is the net­work’s or­gan­is­ing prin­ci­ple. The scam­mer once had to find a vic­tim. The plat­form now finds one, pro­files the weak­ness, op­ti­mises the pitch, processes the pay­ment, and rec­om­mends the next scam. What was once an aber­ra­tion has be­come the norm.

The mod­ern in­ter­net is now a highly op­ti­mised ma­chine for de­tect­ing hu­man vul­ner­a­bil­ity, am­pli­fy­ing it, and plac­ing a pay­ment link be­side it. Any in­se­cu­rity can be­come a com­mer­cial niche, in­clud­ing the de­sire to es­cape com­mer­cial life it­self. There is al­ways a course, a newslet­ter, a pri­vate com­mu­nity, or a re­fer­ral code wait­ing at the end of the fun­nel.

The bleak part is not that grifters ex­ist. Every so­ci­ety has huck­sters. It is that much of the pop­u­la­tion has been con­scripted into the down­line. Ordinary peo­ple now spend their lives pro­mot­ing in­vest­ments they barely un­der­stand, prod­ucts that do not work, and po­lit­i­cal claims they have never ex­am­ined. Many earn noth­ing. They are un­paid dis­trib­u­tors for some­one far­ther up the pyra­mid. The con­sumer, sales­man, and prod­uct have col­lapsed into the same ex­hausted per­son.

People in­creas­ingly be­have like ad­dicts be­cause ad­dic­tion is the busi­ness model. The feed sup­plies al­ter­nat­ing doses of out­rage, fear, envy, lust, and hope. Each feel­ing ar­rives with some­thing to buy. People doom­scroll un­til they ac­quire the anx­i­ety that the next in­flu­encer will mon­e­tise. Then they pur­chase a bet, a coin, a sup­ple­ment, a course, or an en­emy. Finally, they re­post the pitch. Consumption be­comes dis­tri­b­u­tion. The mark be­comes the sales­man.

This is an in­dus­trial sys­tem for man­u­fac­tur­ing weak­ness at scale. A le­git­i­mate busi­ness can sur­vive a sat­is­fied cus­tomer. A grift can­not. It needs the cus­tomer fright­ened, ag­grieved, lonely, sick, or greedy for­ever.

When I started writ­ing about cryp­tocur­rency in 2020, I still car­ried a naive as­sump­tion about the size of this econ­omy. I thought peo­ple were gen­er­ally de­cent and the grifter class was a small pool of de­gen­er­ates with rot­ten moral char­ac­ter, prey­ing on those made vul­ner­a­ble by the ma­te­r­ial con­di­tions of our time.

I was very wrong. The grift econ­omy is mas­sive. More dis­turb­ing still, it is par­tic­i­pa­tory. A large and grow­ing share of the pop­u­la­tion now ap­pears will­ing to de­vote every wak­ing hour to fleec­ing their fel­low man as a ca­reer choice. They stream, post, re­cruit, pro­mote, re­fer, as­tro­turf, and close. They turn every friend­ship into a lead and every con­ver­sa­tion into a qual­i­fy­ing call. They do not clock out be­cause the mar­ket fol­lows them into bed. The smart­phone is a shop counter that sleeps be­side their head.

Obviously most of these peo­ple are not suc­ceed­ing. The maths sim­ply can never work out. That is part of the trick. The as­pir­ing in­flu­encer with forty-seven fol­low­ers is not an en­tre­pre­neur in any mean­ing­ful sense. He is free labour for the plat­form and cheap dis­tri­b­u­tion for the per­son sell­ing him the dream. The af­fil­i­ate mar­keter buys a course about af­fil­i­ate mar­ket­ing, then re­cov­ers the cost by sell­ing the same course to the next af­fil­i­ate mar­keter. The life coach coaches new life coaches. The drop­ship­per sells tu­to­ri­als to failed drop­ship­pers. The pyra­mid is so­cial be­fore it is fi­nan­cial. Everyone stands on some­one else while in­sist­ing they are about to es­cape.

This arrange­ment blurs the use­ful moral dis­tinc­tion be­tween preda­tor and prey. Many on­line grifters are them­selves marks. They be­lieve the rub­bish they sell be­cause be­lief makes the sell­ing bear­able. They have sunk money, time, iden­tity, and pub­lic dig­nity into the scheme. Admitting the prod­uct is worth­less would mean ad­mit­ting that years of their life were worth­less too. It is psy­cho­log­i­cally cheaper to re­cruit an­other vic­tim. The fraud sus­tains the faith, and the faith sus­tains the fraud.

A nor­mal trade ends when a need is sa­ti­ated. You need a chair. Someone sells you a chair. You sit down and stop think­ing about chairs. However, an on­line grift can never sa­ti­ate. It must pre­serve the need that feeds it. The griev­ance mer­chant can­not re­solve your griev­ance. The well­ness in­flu­encer can­not let you feel well. The trad­ing guru can­not let you be­come fi­nan­cially se­cure. The manos­phere pod­caster can­not let young men be­come calm, loved, and so­cially com­pe­tent. Satisfaction is churn. Misery is re­cur­ring rev­enue.

The plat­forms did not in­vent fear, greed, lone­li­ness, or sta­tus anx­i­ety. They in­dus­tri­alised their ex­trac­tion. Their rec­om­men­da­tion sys­tems are vast re­in­force­ment-learn­ing loops that con­tin­u­ously ex­per­i­ment on hu­man weak­ness. Each ob­jec­tive is a mov­ing com­pos­ite of high-di­men­sional sig­nals for at­ten­tion, re­ten­tion, and con­ver­sion, dis­persed across mod­els, met­rics, tests, and feed­back sys­tems. The sub­ject can­not see the ex­per­i­ment. The op­er­a­tor can­not fully ex­plain it. The reg­u­la­tor can barely com­pre­hend it. The loop knows only that one stim­u­lus keeps a per­son scrolling while an­other lets them leave. Calm ac­cu­racy loses. Threat, trans­gres­sion, hu­mil­i­a­tion, and im­pos­si­ble promises win. The re­sult­ing so­cial dam­age ap­pears nowhere in the ob­jec­tive func­tion. It ar­rives as an ex­ter­nal­ity.

This cre­ates a bru­tal se­lec­tion en­vi­ron­ment. The hon­est fi­nan­cial ad­viser ex­plains di­ver­si­fi­ca­tion and gets twelve views. The crypto lu­natic pre­dicts a thou­sand­fold re­turn and gets twelve mil­lion. The physi­cian says a chronic con­di­tion re­quires care­ful man­age­ment. The well­ness crank says seed oils are poi­son­ing your soul. The his­to­rian de­scribes an am­bigu­ous event with con­tin­gent causes. The po­lit­i­cal in­flu­encer iden­ti­fies a se­cret ca­bal and gives you the ad­dress of a pizza par­lour. One of these peo­ple has the bet­ter busi­ness model. It is not the one bur­dened by re­al­ity.

The sys­tem is dopamin­er­gic in the most ba­nal and me­chan­i­cal sense. It runs on an­tic­i­pa­tion, un­cer­tainty, and vari­able re­ward. The next re­fresh might bring ap­proval, out­rage, profit, or vin­di­ca­tion. Usually it brings noth­ing, which makes the next re­fresh more ur­gent. Social me­dia fused the Skinner box with the com­mis­sion struc­ture. The ad­dict is handed a re­fer­ral code and told he is now a small busi­ness owner.

Crypto has be­come the sub­ject of my ver­bal ire so of­ten be­cause it is the apoth­e­o­sis of the grift econ­omy. It takes alien­ation, pre­car­ity, gam­bling ad­dic­tion, tech­no­log­i­cal mys­ti­fi­ca­tion, and a thick slurry of lib­er­tar­ian derp, then syn­the­sises them into the ul­ti­mate preda­tory in­vest­ment prod­uct.

Crypto also per­fected the re­cur­sive struc­ture of the mod­ern on­line grift. Promotion cre­ates price move­ment. Price move­ment is pre­sented as proof of adop­tion. That proof re­cruits new buy­ers. Their money cre­ates more price move­ment. Every par­tic­i­pant has a di­rect fi­nan­cial in­cen­tive to be­come a pub­li­cist for his own po­si­tion. The as­set comes with its own vol­un­teer pro­pa­ganda net­work. It is a pyra­mid scheme with a pod­cast de­part­ment.

Much to my dis­may, the rest of the in­ter­net has learned the same les­son. The cheap­est prod­uct is empty promises un­teth­ered to re­al­ity. The most scal­able labour force is the ad­dict. The best mar­ket­ing con­ceals it­self in­side iden­tity. Sell peo­ple a world­view, and they will ad­ver­tise it for free be­cause crit­i­cism of the prod­uct now feels like crit­i­cism of the self.

Language mod­els will make this cheaper and worse. The cost of pro­duc­ing plau­si­ble lies has been dri­ven to pre­cisely zero. One per­son can gen­er­ate a land­fill of ar­ti­cles, videos, tes­ti­mo­ni­als, in­vest­ment analy­sis, and syn­thetic ex­perts be­fore break­fast. The grift no longer needs con­vic­tion, charisma, or even a pulse. It needs a lan­guage model, an af­fil­i­ate ac­count, and ac­cess to a pop­u­la­tion whose crit­i­cal fac­ul­ties have been sand­blasted by twenty years of al­go­rith­mic me­dia.

There is a temp­ta­tion to re­gard the peo­ple caught in this ma­chine with sim­ple con­tempt. Some de­serve it. A per­son who know­ingly ru­ins strangers for com­mis­sion has made a moral choice. But con­tempt is not an analy­sis. Precarity sup­plies the re­cruits. Alienation sup­plies the au­di­ence. The col­lapse of sta­ble work, af­ford­able hous­ing, lo­cal in­sti­tu­tions, and plau­si­ble routes to ma­te­r­ial se­cu­rity is what makes the pitch of the grift econ­omy so se­duc­tive. The grift of­fers agency where or­di­nary life of­fers de­lay. It of­fers com­mu­nity where so­ci­ety of­fers iso­la­tion. It of­fers a jack­pot where work of­fers a per­for­mance re­view and an­other year of rent in­creases.

Then it metabolises those in­juries into new in­juries. The lonely man buys a doc­trine that makes him in­tol­er­a­ble to women. The in­debted worker gam­bles his re­main­ing sav­ings on a crypto to­ken. The fright­ened pa­tient aban­dons med­i­cine for sup­ple­ments. The po­lit­i­cally pow­er­less per­son spends four­teen hours a day scream­ing at strangers while the peo­ple with power qui­etly cut his wages and pub­lic ser­vices. The promised es­cape re­pro­duces the con­di­tion that made es­cape de­sir­able.

It is a des­per­ately sad way to live. There is no craft in it, no sol­i­dar­ity, and no com­ple­tion. No com­pas­sion or joy. Every re­la­tion­ship be­comes an au­di­ence. Every in­ter­est just be­comes grist for the con­tent mill. Every con­vic­tion be­comes a con­tent strat­egy. The grifter can never rest be­cause ab­sence kills en­gage­ment. The mark can never rest be­cause the next post might con­tain the se­cret. Both wake to the same no­ti­fi­ca­tions, trapped on a dopamine tread­mill dri­ven by opaque al­go­rithms that can never slow down.

The worst ad­vice from the 90s, “just say no,” starts to look less stu­pid when our great­est tech­ni­cal in­no­va­tion learns to turn dis­tress into in­ven­tory. Disconnection is not Luddism in that en­vi­ron­ment. It is the re­fusal to mis­take a preda­tory sys­tem for a so­cial world.

Complete dis­con­nec­tion is nearly im­pos­si­ble. Modern life no longer per­mits it. But an ap­pli­ance is used for a bounded pur­pose and then put away. Emails, train times, ar­ti­cles, and files all have end­points. Infinite feeds of dri­vel do not. They carry the casino into bed and let an opaque RL loop se­lect the emo­tions that ar­rive be­fore break­fast.

The in­ter­net is in­dis­putably an in­hu­man place. Not be­cause it con­tains no hu­mans. Billions of us are in here, scream­ing fran­ti­cally at each other while feel­ing ut­terly alone. It is in­hu­man be­cause the sys­tems gov­ern­ing it are ut­terly alien al­go­rithms that can­not recog­nise hu­man ends. They recog­nise en­gage­ment, con­ver­sion, re­ten­tion, and growth. Grief is a mar­ket seg­ment. Loneliness is a tar­get­ing sig­nal. Friendship is a re­ten­tion mech­a­nism. Political con­vic­tion is ad in­ven­tory. Nothing can sim­ply mat­ter. It must per­form.

Life in­side this en­vi­ron­ment means adopt­ing its cat­e­gories. Thoughts are as­sessed by their reach, ex­pe­ri­ences by their share­abil­ity, and peo­ple by their use­ful­ness to an iden­tity. A per­son be­comes leg­i­ble to the ma­chine by be­com­ing less leg­i­ble to him­self. Eventually the sys­tem no longer needs to im­pose its val­ues. Its sub­jects carry them in their pock­ets and en­force them on their own minds.

The phys­i­cal world is not pure. It con­tains sales­men, casi­nos, dem­a­gogues, fa­nat­ics, and bores. It also con­tains stub­born lim­its. A con­ver­sa­tion ends. A pub closes. A book runs out of pages. Your friend gets tired of hear­ing you talk and tells you to shut up. Reality sup­plies fric­tion, and fric­tion is one of the few re­main­ing de­fences against ap­petite with­out limit.

We are not go­ing back to the early in­ter­net. Nor should we ro­man­ti­cise it. The old web had plenty of sewage. What it also had was space be­yond the mar­ket. A per­son could make some­thing with­out be­com­ing a brand. A con­ver­sa­tion could end with­out a con­ver­sion. A com­mu­nity could ex­ist with­out turn­ing its mem­bers into marks for an in­vest­ment scheme.

The ques­tion is not whether the in­ter­net con­tains use­ful things. It does. The ques­tion is whether hu­man ex­is­tence should be or­gan­ised around alien and in­hu­man ob­jec­tive func­tions no hu­man chose and no­body can in­spect or un­der­stand. An RL loop can op­ti­mise en­gage­ment, re­ten­tion, and con­ver­sion. It can­not tell us what a hu­man life is for. The fi­nal grift is let­ting the loop de­cide what your life should be.

The Trump ad­min­is­tra­tion has been de­ploy­ing an ob­scure le­gal ma­neu­ver to try to ob­tain pri­vate in­for­ma­tion on jour­nal­ists, non-prof­its and unions, rais­ing alarm over a power the gov­ern­ment has as­serted with­out ju­di­cial over­sight.

In one in­stance, the gov­ern­ment ob­tained six months of tele­phone records for Georgia Fort, a Minneapolis jour­nal­ist. Fort was not no­ti­fied of the re­quest for her in­for­ma­tion, nor was she given a chance to con­test the gov­ern­men­t’s ef­fort to ob­tain them, her lawyers said in court pa­pers.

In February of this year, fed­eral pros­e­cu­tors twice sought search war­rants for ac­count in­for­ma­tion for the YouTube chan­nel of Fort and the jour­nal­ist Don Lemon, both of whom have pleaded not guilty to crim­i­nal charges in con­nec­tion to a protest at a Minneapolis church in January that they were cov­er­ing. A judge twice re­jected the re­quest, writ­ing that the gov­ern­ment had failed to es­tab­lish prob­a­ble cause of a crime and that he wanted Lemon and Fort to be in­formed of the re­quest so they could have a chance to chal­lenge it. About a month af­ter the judge’s rul­ing in late February, the gov­ern­ment said it was with­draw­ing the re­quest.

But of­fi­cials had­n’t given up on get­ting the data.

Less than a month later, the DHS served Google with a dif­fer­ent re­quest for the YouTube in­for­ma­tion. This time, DHS uti­lized a dif­fer­ent method that did­n’t re­quire ap­proval from a judge, only a sign-off from a DHS of­fi­cial. It served Google an ad­min­is­tra­tive sum­mons cit­ing an ar­cane pro­vi­sion of fed­eral law — 19 USC 1509 — deal­ing with cus­toms im­ports. The pro­vi­sion gives the DHS broad power to in­spect records in or­der to de­ter­mine whether du­ties and taxes are be­ing cor­rectly levied on im­ported items. It also in­structed the re­cip­i­ents of the sum­mons to keep it se­cret.

The DHS sum­mons was is­sued un­der a statute that does give the agency broad power to de­mand records, but only in the lim­ited cir­cum­stance of there be­ing a need to in­ves­ti­gate a cus­toms is­sue, said Chris Duncan, a for­mer lawyer at the Department of Homeland Security. “These laws have ab­solutely noth­ing to do with a do­mes­tic sit­u­a­tion at a church, a so­cial me­dia post, even an im­mi­gra­tion mat­ter,” he said.

“It’s out­ra­geous con­duct on so many lev­els. It’s hard to know where to be­gin,” said John Roth, who served as the in­spec­tor gen­eral for the Department of Homeland Security from 2014 to 2017. “This is an im­proper use of the sub­poena un­der any cir­cum­stances. This is not a cus­toms case; it is not a cus­toms vi­o­la­tion. They are not in­ves­ti­gat­ing a cus­toms vi­o­la­tion.”

The episode in Minnesota was par­tic­u­larly alarm­ing be­cause it ap­peared to be an end run around a judge who was skep­ti­cal of the gov­ern­men­t’s need for the in­for­ma­tion.

“There is no judge in the loop. You don’t have that in­de­pen­dent au­thor­ity to scru­ti­nize the de­mand and to say whether or not it’s le­git­i­mate,” said Caitlin Vogus, a se­nior ad­viser at the Freedom of the Press Foundation.

The DHS also sought and ob­tained six months of phone records for Fort from T-Mobile, which in­cluded records for more than 10,000 calls and text mes­sages. Fort was not no­ti­fied the gov­ern­ment was seek­ing the records un­til mid-July, when gov­ern­ment lawyers pro­duced them to her lawyers. Fort’s lawyers wrote in a fil­ing this week they were “stunned” to see the gov­ern­ment had uni­lat­er­ally been able to ob­tain a log of her com­mu­ni­ca­tions af­ter a judge had warned them about ob­tain­ing records about a jour­nal­ist.

“That’s very con­cern­ing be­cause the in­for­ma­tion de­manded can help the gov­ern­ment un­cover a jour­nal­ist’s con­fi­den­tial sources,” Vogus said.

In a state­ment, T-Mobile did not ad­dress why it turned over the in­for­ma­tion.

“We take our re­spon­si­bil­ity to pro­tect cus­tomers’ pri­vacy and per­sonal in­for­ma­tion very se­ri­ously. Our team care­fully re­views gov­ern­ment de­mands for cus­tomer in­for­ma­tion and re­sponds in ac­cor­dance with the law. We don’t com­ment on spe­cific law en­force­ment de­mands,” the com­pany said.

The Department of Justice and the Department of Homeland Security both de­clined to com­ment on the use of the sum­mons.

In ad­di­tion to Fort and Lemon, the DHS also sought in­for­ma­tion on the YouTube ac­counts for the left-lean­ing out­let Democracy Now, con­ser­v­a­tive pod­caster Megyn Kelly, the Milwaukee Journal-Sentinel and an in­de­pen­dent jour­nal­ist named Brendan Gutenschwager. Some of the videos they cited in the sum­mons were livestreams of the protest, but not all of them. The video cited as part of the re­quest for in­for­ma­tion on Democracy Now in­volved a news re­port on the protest and an in­ter­view with Nekima Levy Armstrong, who led the protest. The video cited on the re­quest for Kelly’s show in­cluded an in­ter­view with Jonathan Parnell, the pas­tor at the church.

It’s un­clear why ex­actly the DHS wanted the YouTube ac­count in­for­ma­tion, which in­cludes things like a user’s IP ad­dress, and in­for­ma­tion about when a user was log­ging in.

“It’s still con­cern­ing that the gov­ern­ment sought sub­scriber in­for­ma­tion for Lemon and Fort be­cause there’s no rea­son it would need this in­for­ma­tion for the crim­i­nal charges that it’s brought against them,” Vogus said. “It’s not a crime to post a YouTube video, and it’s not at all clear why the gov­ern­ment is de­mand­ing this in­for­ma­tion about Lemon and Fort’s YouTube ac­counts.”

The episode was the most re­cent ex­am­ple of an alarm­ing pat­tern in re­cent months in which the DHS avoided ju­di­cial scrutiny and de­ployed a sum­mons re­lated to cus­toms en­force­ment to pres­sure com­pa­nies into turn­ing over in­for­ma­tion on Americans. The US con­sti­tu­tion’s fourth amend­ment pro­tects against un­rea­son­able searches and seizures and law en­force­ment gen­er­ally must show a judge or a grand jury they have prob­a­ble cause to be­lieve the ma­te­ri­als they want to search will pro­duce ev­i­dence of a crime.

But over the last few months, the Department of Homeland Security has un­der­taken a brazen ef­fort to get around that fun­da­men­tal safe­guard. In ad­di­tion to ef­forts to ob­tain records on Minnesota jour­nal­ists, the DHS has used 1509 sum­monses to pres­sure so­cial me­dia com­pa­nies to un­mask the iden­ti­ties of peo­ple who have crit­i­cized ICE of­fi­cers and to ob­tain fi­nan­cial in­for­ma­tion on a host of unions and left-lean­ing non-profit or­ga­ni­za­tions in Minneapolis.

In a sep­a­rate case in which 15 ac­tivists face crim­i­nal con­spir­acy charges, DHS suc­cess­fully ob­tained the fi­nan­cial records of the Sunrise Movement, the Service Employees International Union (SEIU) and the Communications Workers of America, as well as Venmo records for a non-profit or­ga­ni­za­tion called Voices for Racial Justice. None of the or­ga­ni­za­tions is charged with crimes and the DHS did not of­fer an ex­pla­na­tion for why it needed the records. PayPal, Venmo’s par­ent com­pany, de­clined to com­ment.

“There’s a long his­tory of DHS abus­ing this sum­mons au­thor­ity in par­tic­u­lar, and us­ing it to seek both records that are clearly out­side of its scope in gen­eral, and more par­tic­u­larly to try to go af­ter peo­ple whose speech DHS is some­how irked by — but whose speech is pro­tected by the first amend­ment,” said Nathan Freed Wessler, a lawyer at the American Civil Liberties Union who spe­cial­izes in pri­vacy is­sues.

It is dif­fi­cult to de­ter­mine the fre­quency with which the DHS is serv­ing the 1509 sum­monses and how of­ten they are suc­cess­ful in ob­tain­ing in­for­ma­tion. The sum­monses of­ten re­main hid­den from pub­lic view un­less the com­pany be­ing served, or the user, chal­lenges them. Companies are not re­quired to no­tify users that they have been served with a 1509 sum­mons for in­for­ma­tion, though some do. The New York Times re­ported in February that the DHS had served hun­dreds of ad­min­is­tra­tive sub­poe­nas on so­cial me­dia com­pa­nies for in­for­ma­tion on users.

“Without know­ing how many of these sub­poe­nas there are and what they’re be­ing used for, there’s no way for courts or law­mak­ers or the pub­lic to put checks on ex­ec­u­tive branch abuses,” said Wessler, the ACLU at­tor­ney.

In the Minneapolis church case in­volv­ing Lemon and Fort, the Trump ad­min­is­tra­tion has ar­gued it had the power to use the cus­toms-re­lated sum­mons to ob­tain in­for­ma­tion even though the crimes the de­fen­dants were charged with have noth­ing to do with cus­toms. The pro­test­ers en­tered the church be­cause a lo­cal ICE of­fi­cial was a pas­tor there, and could have po­ten­tially as­saulted him or in­ter­fered with his du­ties, jus­tice de­part­ment lawyers wrote in a fil­ing ear­lier this year. Even though the man does not ap­pear to have been there, and there were no DHS of­fi­cials at the church or in­volved in the protest, lawyers said the DHS was en­ti­tled to is­sue the sum­mons be­cause it was in­ves­ti­gat­ing a po­ten­tial as­sault on a law en­force­ment of­fi­cer.

In court fil­ings, the Trump ad­min­is­tra­tion has ar­gued the Department of Homeland Security has the power to de­mand such records with­out ju­di­cial over­sight.

“Although § 1509 ref­er­ences ‘duties, fees, and taxes,’ the plain lan­guage of the statute does not limit DHS’s in­ves­tiga­tive au­thor­ity to those sub­jects; in­stead, DHS is au­tho­rized to in­ves­ti­gate po­ten­tial crimes to en­sure ‘compliance with the laws of the United States ad­min­is­tered by the United States Customs Service,’” which has been folded into the Department of Homeland Security, a lawyer wrote in a December court fil­ing last year.

That is an over­broad mis­read­ing of the statute, one ex­pert said.

“I don’t buy that,” said Duncan, the for­mer DHS lawyer. “It’s not a free-for-all that was thrown in there. Congress does not op­er­ate that way. Given these pro­vi­sions were specif­i­cally in­cor­po­rated into ti­tle 19, the cus­toms statute, Congress ob­vi­ously in­tended to au­tho­rize only records, de­mands and in­ter­views in fur­ther­ance of in­ves­ti­ga­tions into cus­toms vi­o­la­tions, not wild goose chases into pos­si­ble vi­o­la­tions of any fed­eral law with­out any ju­di­cial over­sight.”

In Fort’s case, Google did not com­ply with the sum­mons for any of the ac­counts. The com­pany re­sponded to the DHS by say­ing it had not of­fered ev­i­dence of how it was re­lated to a cus­toms in­ves­ti­ga­tion.

A Google spokesper­son said the com­pany re­views each re­quest for data it gets to en­sure it is le­gal and pushes back when it is too broad or does­n’t fol­low the cor­rect process.

Many so­cial me­dia com­pa­nies say they no­tify users when law en­force­ment makes a re­quest for their in­for­ma­tion and give them a chance to con­test the sum­mons. It’s not al­ways clear what the com­pa­nies will do if the user does­n’t re­spond or won’t con­test the re­quest them­selves. But pri­vacy ex­perts ques­tion whether that no­tice is ad­e­quate, say­ing many users are con­fused when they are con­tacted and do not have the re­sources to get a lawyer to con­test the de­mand in court.

“They want peo­ple to think that they are go­ing to stand up for peo­ple’s pri­vacy, but they re­ally shifted the bur­den com­pletely onto the user,” said Lauren Regan, ex­ec­u­tive di­rec­tor of the Civil Liberties Defense Center, which rep­re­sented a Reddit user who chal­lenged the gov­ern­men­t’s ef­forts to get Reddit to re­veal their iden­tity through a 1509 sum­mons.

Companies are not re­quired to re­spond to a 1509 sum­mons and can ig­nore the re­quest if they think it is un­law­ful, forc­ing the gov­ern­ment to go to court to try to en­force the sum­mons. The Guardian was un­able to iden­tify any cases where the gov­ern­ment at­tempted to get a court or­der to en­force a 1509 sum­mons out­side of the tra­di­tional cus­toms con­text. Companies can also file their own mo­tions to try to quash the sum­monses.

“If a user ac­tu­ally hired a lawyer, it would cost tens of thou­sands of dol­lars to fight one of these,” said F Mario Trujillo, a lawyer at the Electronic Frontier Foundation, a pri­vacy watch­dog. “They are not shoul­der­ing that bur­den; they’re push­ing that cost onto users and onto non-profit groups when they could eas­ily get their high-pow­ered lawyers who are be­ing paid $500 to $1,000 an hour to fight these.”

In 2017, Twitter filed a law­suit chal­leng­ing a Department of Homeland Security 1509 sum­mons seek­ing to un­mask an ac­count, @alt_uscis, that was crit­i­cal of the DHS. The de­part­ment ul­ti­mately with­drew the sum­mons.

In sev­eral cases, the DHS has with­drawn a 1509 sum­mons af­ter it was chal­lenged in court and be­fore a judge could rule on its le­gal­ity. That may be a de­lib­er­ate strat­egy to avoid hav­ing a judge rule on the le­gal­ity of the sum­mons.

In one in­stance last year, the DHS served a 1509 sum­mons on Meta to un­mask the user be­hind an Instagram and Facebook ac­count that mon­i­tored ICE agent ac­tiv­i­ties in the Philadelphia sub­urbs. The user chal­lenged the sum­mons in court, say­ing it was clearly not au­tho­rized un­der the law. Lawyers for the DHS de­fended the sum­mons, say­ing it fell within the scope of laws the DHS en­forced. Both sides pre­sented ar­gu­ments be­fore a judge on 15 January and DHS with­drew the sum­mons the next day.

“They don’t want a judge to take away this scary tool be­cause they are get­ting stuff out of it,” Regan said. “Once a court rul­ing says ‘thou shalt not use this statute’, it does not ap­ply.”

In 2017, the DHS in­spec­tor gen­eral is­sued a re­port find­ing “inconsistent — and, in some cases, im­proper” — use of the 1509 sum­monses af­ter the @alt_uscis case.

The of­fice of the in­spec­tor gen­eral re­view found that of­fi­cials in Customs and Border Protection’s of­fice of pro­fes­sional re­spon­si­bil­ity were reg­u­larly mis­us­ing the sub­poena and rec­om­mended a se­ries of re­forms to en­sure more over­sight over those that were used. The of­fice agreed to the re­forms.

Audio record­ing is au­to­mated for ac­ces­si­bil­ity. Humans wrote and edited the story. See our AI pol­icy, and give us feed­back.

In 2023, the Texas Legislature unan­i­mously passed a law rais­ing auto in­sur­ance costs for Texans by $1 to com­bat ram­pant cat­alytic con­verter theft.

Three years later, a lit­tle-known state agency has de­voted at least $30 mil­lion of that fee to­ward su­per­charg­ing the state’s Flock sur­veil­lance net­work, plac­ing cam­eras along high­ways and streets from El Paso to the Louisiana bor­der, an analy­sis by The Texas Tribune found.

The Motor Vehicle Crime Prevention Authority, led by a board mostly ap­pointed by Gov. Greg Abbott, has turned the $1 fee hike into at least 3,200 Flock cam­eras.

The agency has awarded no fewer than 95 grants to help law en­force­ment agen­cies pur­chase and main­tain about 2,000 Flock cam­eras. Another $15.9 mil­lion is help­ing the Texas Department of Public Safety add al­most 1,200 more.

The ef­fort is far from over. In early August, the agency ap­proved an­other $3 mil­lion to help DPS in­stall 583 more cam­eras along Texas toll­ways over the next year.

Miguel Rodriguez, chair of the Motor Vehicle Crime Prevention Authority, said dur­ing an August 2023 meet­ing that he hoped to use pro­ceeds from the fee in­crease to “cover the en­tire state” with cam­eras.

Rodriguez, who is also the Laredo Police Chief, sees the cam­eras as a pow­er­ful law en­force­ment tool, par­tic­u­larly to com­bat crim­i­nal or­ga­ni­za­tions.

“That kind of ca­pa­bil­ity di­rectly dis­rupts the op­er­a­tional ad­van­tage these transna­tional crim­i­nal or­ga­ni­za­tions rely on, and it strength­ens our abil­ity to pro­tect both Texas com­mu­ni­tiesand the broader re­gion,” he said in an email.

But on Friday evening, af­ter mul­ti­ple re­quests for com­ment from the Tribune about its find­ings, Abbott’s of­fice said the gov­er­nor was paus­ing all state fund­ing for lo­cal grants to be used for Flock cam­eras.

“To the ex­tent that cities get any fund­ing for those cam­eras, most of it comes from the fed­eral gov­ern­ment. To the ex­tent any fund­ing comes from Texas agen­cies, those agen­cies are clar­i­fy­ing that those funds can­not be used for Flock cam­eras,” Abbott spokesper­son Andrew Mahaleris said in a state­ment shared first with the Tribune.

The $1 per year fee in­crease has raised an es­ti­mated $81 mil­lion, al­low­ing the au­thor­ity to fun­nel $50.8 mil­lion into 234 grants that have helped re­im­burse po­lice de­part­ments for of­fi­cers, crime an­a­lysts and at­tor­neys to pros­e­cute ve­hic­u­lar crimes, as well as drones and other sur­veil­lance de­vices.

The Tribune found agency grants to po­lice de­part­ments ranged from $7,000 for two Flock cam­eras in Bellmead, near Waco, to al­most $1.7 mil­lion for 201 cam­eras in Dallas. Some city net­works — like the 165 cam­eras in Laredo and 150 in El Paso — were en­tirely sub­si­dized by the grants.

The state agency does not de­tail how much of its grant money went to Flock cam­eras. Instead, the Tribune tracked the grants by re­view­ing the ve­hi­cle au­thor­i­ty’s meet­ing records, as well as doc­u­ments, agen­das and dis­cus­sions from 101 city coun­cils and county com­mis­sions that ap­proved or dis­cussed Flock-related grants.

It is likely the au­thor­ity has paid for more Flock cam­eras than the Tribune analy­sis found be­cause 124 grants lack clear pub­lic doc­u­men­ta­tion about what was pur­chased.

The statewide ef­fort to pro­lif­er­ate Flock cam­eras comes as back­lash is mount­ing over the sur­veil­lance, in­clud­ing from some mem­bers of the Legislature, where the $1 fee was ap­proved with­out op­po­si­tion.

“The sheer vol­ume of in­for­ma­tion cap­tured is not some­thing that is en­ter­tained, in my view, by the Fourth Amendment,” said Rep. Mitch Little, R-Lewisville.

State Sen. Carol Alvarado and Rep. Jeff Leach, the bil­l’s au­thor and House spon­sor, said sur­veil­lance cam­eras were never dis­cussed when the bill was con­sid­ered. Alvarado said she was sur­prised to learn from the Tribune that the in­sur­ance fee was fund­ing AI-supported li­cense plate read­ers.

“I did not have that in mind when we passed the bill,” said Alvarado, D-Houston. “When I think of com­bat­ing crime, I’m think­ing … more boots on the ground, hir­ing more of­fi­cers to tackle the crime or some type of un­der­cover work.”

Alvarado said there is a “fine line” be­tween pro­tect­ing the pub­lic from crime and pro­tect­ing peo­ple’s pri­vacy, but said she did not plan to file leg­is­la­tion to shift the grant re­quire­ments.

Flock cam­eras, the na­tion’s most com­monly used li­cense plate reader, cre­ate a “vehicle fin­ger­print” with the use of ar­ti­fi­cial in­tel­li­gence — stor­ing each ve­hi­cle’s li­cense plate, make, model, color and de­tails such as dents and bumper stick­ers in a data­base ac­ces­si­ble by law en­force­ment across the coun­try with­out hav­ing to ob­tain a search war­rant.

The ex­act num­ber of Flock cam­eras in Texas is un­clear. DeFlock, an anti-sur­veil­lance watch­dog that has mapped the lo­ca­tions of Flock cam­eras us­ing ver­i­fied crowd­sourc­ing, has iden­ti­fied about 13,000 in the state. By that count, the in­sur­ance fee in­crease has paid for one in four Texas cam­eras.

That also rep­re­sents a sharp in­crease in Flock cam­eras in the state since December 2023, when a com­pany spokesper­son told the ve­hi­cle au­thor­i­ty’s board that there were about 7,500 cam­eras in the state.

Flock does not dis­close how many cam­eras it has in the field, but Texas is es­ti­mated to have the sec­ond most in the na­tion, af­ter California. Nationally, Flock of­fi­cials say, about 7,000 law en­force­ment agen­cies use a to­tal of 120,000 cam­eras and other sur­veil­lance prod­ucts.

In Texas, state agen­cies don’t rely solely on ve­hi­cle au­thor­ity grants to add sur­veil­lance de­vices, and Abbott’s of­fice pointed to fed­eral grants for fund­ing the cam­eras. DPS, for ex­am­ple, has a $28.5 mil­lion con­tract for Flock cam­eras. But the grants have helped get the cam­eras into the hands of the state’s smaller po­lice de­part­ments that may have strug­gled with the cost of the equip­ment.

Departments that opt in to Flock’s na­tional lookup pro­gram can search each oth­er’s data from any­where in the coun­try, al­low­ing ve­hi­cles to be tracked with un­prece­dented ef­fi­ciency.

The abil­ity to share data “has been one of the most ef­fec­tive ways Flock has been able to help find, just last year, over 10,000 miss­ing per­sons,” Flock spokesper­son Trevor Chandler said in an in­ter­view.

But for a rapidly grow­ing coali­tion of Texans op­posed to the cam­eras, the Flock net­work is a dan­ger­ous com­bi­na­tion of in­va­sive sur­veil­lance and lim­ited over­sight that un­der­mines pri­vacy rights.

Kenneth Feagins, an or­ga­nizer with DFW DeFlock, one of sev­eral new grass­roots anti-sur­veil­lance groups in the state, said he sees an “alarming trend” of po­lice im­prop­erly ac­cess­ing a net­work that can amass data on where peo­ple live, shop, wor­ship and work.

“For me, it’s al­ways been the ques­tion of, well, how much lib­erty are we will­ing to trade for safety?” Feagins said.

Recently re­vealed ex­am­ples of mis­use — in­clud­ing po­lice of­fi­cers us­ing Flock data to stalk ex-part­ners and co-work­ers — have sharp­ened those con­cerns.

A Lufkin of­fi­cer was in­dicted Aug. 24 on 100 counts of mis­us­ing of­fi­cial in­for­ma­tion, which Abbott cited as a con­cern­ing de­vel­op­ment dur­ing a Friday ra­dio in­ter­view. Officers in Baytown, Harris County, Fort Bend County, Temple and Pasadena also have been ar­rested, dis­ci­plined or in­ves­ti­gated.

“There’s a lot of ma­li­cious things that can be done with this data, and those things are no longer hy­po­thet­i­cal,” Feagins said.

Cameras “changed the game” for po­lice

The Motor Vehicle Crime Prevention Authority was es­tab­lished by the Legislature in 1991 to com­bat au­to­mo­bile theft.

The au­thor­ity is led by a DPS of­fi­cial and six gov­er­nor-ap­pointed board mem­bers — two from law en­force­ment, two from the in­sur­ance in­dus­try and two con­sumer rep­re­sen­ta­tives.

The ve­hi­cle au­thor­ity pri­mar­ily flexes its mus­cle via grants funded by fees added to an­nual auto in­sur­ance pre­mi­ums — $1 ini­tially, ris­ing to $2 in 2011 and $4 in 2019 — that largely went to fund task force ef­forts for po­lice de­part­ments.

In 2023, with cat­alytic con­verter theft spik­ing across Texas, law­mak­ers ap­proved adding an­other $1 to the in­sur­ance fee in a bill named for Harris County Deputy Darren Almendarez, who was shot to death af­ter in­ter­rupt­ing cat­alytic con­verter thieves in a gro­cery store park­ing lot.

The leg­is­la­tion made no ref­er­ence to li­cense plate read­ers, and Rep. Brian Harrison, who voted for the bill, said he was­n’t aware of any con­ver­sa­tions about us­ing the fee in­crease that way. The Midlothian Republican filed bills that year and in 2025 to re­quire a war­rant be­fore po­lice could ac­cess li­cense plate reader data.

“In a mil­lion years, I never could have even con­tem­plated that this would be used to fund what is ef­fec­tively war­rant­less sur­veil­lance,” Harrison said. “Otherwise, I can’t imag­ine it would have got­ten unan­i­mous sup­port. I sure as hell would­n’t have voted for it if I knew some bu­reau­crat was go­ing to redi­rect the money to Flock cam­eras.”

Anticipating mil­lions from the $1 in­sur­ance fee hike, the ve­hi­cle au­thor­ity in 2023 asked law en­force­ment for ad­vice on how best to spend the money. Automatic li­cense plate read­ers like Flock cam­eras were by far the top choice for com­bat­ting cat­alytic con­verter theft, beat­ing out over­time for in­ves­ti­ga­tors and ad­di­tional train­ing.

The cam­eras, Rodriguez said, are par­tic­u­larly help­ful for ad­dress­ing cat­alytic con­verter theft, a “mobile, high-vol­ume, low-wit­ness crime” where the ve­hi­cle used is of­ten the only lead.

Without tech­no­log­i­cal help, de­part­ments were left work­ing with par­tial de­scrip­tions taken from grainy sur­veil­lance footage of sus­pect ve­hi­cles, Rodriguez said.

Data from the li­cense plate read­ers, known in law en­force­ment as LPRs, was searched 62,000 times in 2025, lead­ing to about 1,660 cleared cat­alytic con­verter theft cases, a re­port from the au­thor­ity said.

Pasadena Police Sgt. Douglas Buckert said the cam­eras “changed the game” for cat­alytic con­verter theft in­ves­ti­ga­tions.

“The num­ber of leads we’ve got­ten since our de­part­ment has de­ployed Flock cam­eras is out­ra­geous,” Buckert told the au­thor­ity in early 2024. “I could have six more in­ves­ti­ga­tors and not get it all done.”

Grant-funded cam­eras, much more than other tech­nol­ogy, have also ex­panded the reach of po­lice in in­ves­ti­ga­tions far be­yond cat­alytic con­verter cases, de­part­ment of­fi­cials say.

Dallas Police Sgt. Bryan Roden told board mem­bers dur­ing a January meet­ing that an au­thor­ity grant let his de­part­ment in­crease its net­work from 100 to 300 cam­eras, help­ing to bust a mil­lion-dol­lar tire theft ring and solve a hit and run. Working with the Department of Homeland Security, Dallas po­lice lo­cated a fugi­tive wanted for co­caine man­u­fac­tur­ing by us­ing Flock cam­eras he reg­u­larly passed to build a “pattern of life as­sess­ment,” Roden said.

Temple Police crime an­a­lyst Mike Treehern told the Tribune that the cam­eras helped de­crease the num­ber of stolen ve­hi­cles in his city, where 84% of their Flock cam­eras are funded by the state grant.

“It’s ab­solutely helped us, and we would not have any­where near the amount of cam­eras that we do with­out [vehicle au­thor­ity] funds,” Treehern said.

The grant has also been a force mul­ti­plier for smaller de­part­ments. In Cibolo, a city north of San Antonio with a pop­u­la­tion of 36,000, an au­thor­ity grant mul­ti­plied the num­ber of Flock cam­eras from 11 to 52.

“Honestly, a lot of our sur­round­ing com­mu­ni­ties started look­ing at them, specif­i­cally through the [Motor Vehicle Crime Prevention Authority],” Cibolo Police Lt. John Wells told board mem­bers in a January meet­ing. “Seguin was look­ing at them, Guadalupe County, the New Braunfels Police Department, all of our neigh­bors, so we started look­ing as well.”

Hannah Foust, founder of DeFlock Carrollton, said the cam­eras give po­lice sur­veil­lance power well in ex­cess of what’s needed to stop car thieves.

“I do think that mo­tor ve­hi­cle theft is a con­cern, it’s an is­sue,” Foust said. “[But] I do think that this grant pro­gram, and the way it’s been used … it re­ally shifts the fo­cus to a broader sur­veil­lance pro­gram, as op­posed to fo­cus­ing on cat­alytic con­verter pre­ven­tion.”

$1 fee hike helped DPS ex­pand its Flock net­work

The au­thor­i­ty’s most sig­nif­i­cant in­vest­ment in Flock came in 2025 when it signed a three-year, $15.9 mil­lion con­tract with DPS to in­stall 1,183 cam­eras in a pro­ject largely over­seen by DPS Major Sharon Jones, the board’s self-de­scribed “pro Flock” mem­ber who left the po­si­tion Aug. 1.

The con­tract aims to bol­ster DPS’ net­work of cam­eras and make it ac­ces­si­ble to lo­cal law en­force­ment agen­cies in places that oth­er­wise could not be eas­ily reached — in­clud­ing lo­cal mu­nic­i­pal­i­ties that are re­sis­tant to the sur­veil­lance.

Patrick McBroom, po­lice com­man­der for the Panhandle Auto Burglary and Theft Unit, said DPS cam­eras help his task force mon­i­tor in­ter­state traf­fic at the Oklahoma and New Mexico bor­ders — ar­eas of Texas be­yond the view of 138 grant-funded cam­eras his team mon­i­tors.

“All those roads lead­ing out and into Texas have DPS cam­eras on them, so if we have stolen items that may be go­ing out of state, we’re able to look at those cam­eras to see if those ve­hi­cles have left the state,” McBroom said.

During an April con­ver­sa­tion about the DPS con­tract, Rodriguez noted the state po­lice force’s cam­eras could im­prove sur­veil­lance in ar­eas where lo­cals are un­will­ing to in­stall their own cam­eras. A grow­ing num­ber of cities and coun­ties, in­clud­ing Austin, have can­celed their Flock con­tracts in the face of res­i­dents’ pri­vacy con­cerns.

“I think that if for what­ever rea­son you are within those ju­ris­dic­tions that do not want Flock, let’s get to­gether with DPS, [so] that, you know, we can put those in state right-of-way. And there’s noth­ing that they can tell us,” Rodriguez said to Jones.

Rodriguez told the Tribune that the DPS net­work pro­vides a needed crime-fight­ing tool in ar­eas hos­tile to Flock cam­eras and sim­i­lar de­vices.

Almost 100 mu­nic­i­pal­i­ties in the U.S. have ended their Flock con­tracts in re­sponse to pub­lic out­cry, in­clud­ing sev­eral in Texas, such as Bandera and Hood County. Both had re­ceived grants for their cam­eras but ended their con­tracts af­ter is­sues with Flock in­stal­la­tion and in re­sponse to pub­lic up­roar over their use.

As crit­i­cism over the cam­eras has ex­ploded — in­clud­ing de­vices that were cut down or van­dal­ized as acts of protest — agency board mem­bers have ex­pressed frus­tra­tion at what they see as mis­in­for­ma­tion that clouds the pos­i­tive im­pact from the cam­eras.

“We’ll have a larger con­ver­sa­tion re­gard­ing the need to ed­u­cate the pub­lic, and we must put a stamp on those who are spread­ing false in­for­ma­tion on li­cense plate read­ers,” Jones said in a July grant meet­ing.

“A con­cern for our pri­vacy”

Foust started Carrollton’s DeFlock group af­ter her neigh­bors ex­pressed frus­tra­tion at the cam­eras’ rapid spread. Spotting a cam­era along the route her chil­dren walk to school gave her pause; see­ing one go up in front of her com­mu­nity recre­ation cen­ter made her act.

“You can’t en­ter or exit that com­plex with­out pass­ing a Flock cam­era, and that’s also my polling place, so that re­ally gave me a strong re­ac­tion,” Foust said. “They were in places that we nor­mally feel very safe at, and there’s no con­cerns for our safety, for our chil­dren’s well-be­ing, but there sud­denly was a con­cern for our pri­vacy.”

Foust is open to dis­cuss a va­ri­ety of so­lu­tions to her con­cerns, in­clud­ing ac­tion by the Texas Legislature, but said the first step is get­ting city of­fi­cials to be trans­par­ent about their use.

“I think be­fore we can have a true and hon­est con­ver­sa­tion about what leg­is­la­tion might look like or what guardrails or safe­guards could be put in place, I think we need to start on a level play­ing field of un­der­stand­ing,” Foust said. “What is the sys­tem, what is it ca­pa­ble of, and how could it be set up in a way that’s sup­pos­edly safe?”

Harrison and Little said they in­tend to file bills next leg­isla­tive ses­sion ban­ning the de­vices be­cause they be­lieve the cam­eras vi­o­late the Fourth Amendment’s pro­tec­tion against un­rea­son­able searches. Little said he’s par­tic­u­larly con­cerned about whether vast amounts of per­sonal data is se­curely stored and whether Flock, a pri­vate com­pany, should be able to ac­cess it.

“The peo­ple in Lewisville, Texas have a rea­son­able ex­pec­ta­tion of pri­vacy from po­lice of­fi­cers in Pampa, Texas, and yet they can ob­serve all that data,” Little said. “So the shar­ing of it across state lines, across ju­ris­dic­tional lines, to me is highly prob­lem­atic.”

Harrison said he was “shocked and dis­mayed” that so few Republicans had spo­ken out against Flock cam­eras and what he calls bla­tant con­sti­tu­tional vi­o­la­tions. He also said state of­fi­cials should take more im­me­di­ate ac­tion to “shut off” Flock grants be­cause leg­is­la­tors never in­tended to use the in­sur­ance fee in­crease for cam­eras.

“I think the Legislature should­n’t take this sit­ting down. I think the gov­er­nor should act on this,” Harrison said. “If that’s hap­pen­ing, what that means is there’s clearly no ex­plicit leg­isla­tive in­tent or di­rec­tion for that to be hap­pen­ing.”

Ranked among the top tier of open-source mod­els, Hy4 pre­view is built for real-world pro­duc­tiv­ity tasks, de­liv­er­ing out­stand­ing per­for­mance across cod­ing, of­fice work, and sci­en­tific re­search

Tencent has re­leased and open-sourced Tencent Hy4 pre­view, a next-gen­er­a­tion large lan­guage model with 770B to­tal pa­ra­me­ters and 49B ac­tive pa­ra­me­ters, and a con­text win­dow ex­ceed­ing 1M to­kens. It demon­strates out­stand­ing ca­pa­bil­i­ties on real-world pro­duc­tiv­ity tasks span­ning cod­ing, of­fice work, and sci­en­tific re­search.

Hy4 pre­view is now avail­able as an open-source model and can also be ac­cessed glob­ally through WorkBuddy and CodeBuddy, as well as Yuanbao, ima and other Tencent prod­ucts. Users can try the model di­rectly through these ap­pli­ca­tions, or con­nect to it via API through Tencent Cloud TokenHub and OpenRouter.

Upon launch, Hy4 pre­view will be avail­able for free on WorkBuddy and CodeBuddy for two weeks. Free ac­cess to Hy3 on both plat­forms has also been ex­tended un­til September 30.

Hy4 pre­view was ex­panded sig­nif­i­cantly in model size, con­text length, and data vol­ume, and  the ad­vances in both pre-train­ing and post-train­ing have led to a ma­jor leap in over­all in­tel­li­gence, plac­ing the model among the top tier of open-source mod­els.

Hunyuan con­tin­u­ously works in deep co-de­sign with prod­ucts such as CodeBuddy and WorkBuddy, op­ti­miz­ing the real-world user ex­pe­ri­ence across pro­duc­tiv­ity sce­nar­ios. In a blind eval­u­a­tion con­ducted in­ter­nally by Tencent in­volv­ing 163 ex­perts and 203 en­gi­neer­ing tasks, Hy4 pre­view scored an av­er­age of 2.99 out of 4.00, slightly ahead of GLM-5.3 (2.92/4.00) and Kimi K3 (2.94/4.00).

Designed for pro­duc­tiv­ity, Hy4 pre­view was de­vel­oped us­ing high-qual­ity train­ing data co-cre­ated with Tencent ex­perts across soft­ware en­gi­neer­ing, gam­ing, fi­nance, se­cu­rity, and other do­mains, as well as through deep co-de­sign with prod­ucts such as WorkBuddy. This has helped drive sig­nif­i­cant im­prove­ments across a wide range of real-world pro­duc­tiv­ity tasks.

In soft­ware en­gi­neer­ing, Hy4 pre­view de­liv­ers stronger un­der­stand­ing, plan­ning, de­bug­ging, and val­i­da­tion ca­pa­bil­i­ties for long-con­text de­vel­op­ment tasks, while also en­hanc­ing the vi­sual qual­ity and in­ter­ac­tion ex­pe­ri­ence of front-end de­vel­op­ment.

In of­fice pro­duc­tiv­ity and an­a­lyt­i­cal sce­nar­ios, the model demon­strates a sig­nif­i­cantly stronger un­der­stand­ing of com­plex work­ing en­vi­ron­ments and en­hanced fi­nan­cial analy­sis ca­pa­bil­i­ties. It has also been op­ti­mized for data analy­sis and cross-doc­u­ment col­lab­o­ra­tion, sup­port­ing the full work­flow from in­for­ma­tion pro­cess­ing through to the cre­ation of doc­u­ments, spread­sheets, and pre­sen­ta­tions.

In game de­vel­op­ment, Hy4 pre­view can gen­er­ate a playable pro­to­type from a sin­gle nat­ural-lan­guage re­quest, and work ef­fec­tively with game en­gines. Developers can then con­tinue re­fin­ing com­plex game pro­jects through multi-turn in­ter­ac­tions.

In sci­en­tific re­search, Hy4 pre­view demon­strates stronger ca­pa­bil­i­ties in un­der­stand­ing, rea­son­ing through and solv­ing com­plex re­search prob­lems, with no­table im­prove­ments across ar­eas in­clud­ing AI re­search and de­vel­op­ment, mol­e­c­u­lar dy­nam­ics sim­u­la­tion, con­densed-mat­ter physics and fun­da­men­tal math­e­mat­ics.

Notably, Hy4 pre­view also con­tributed to its own de­vel­op­ment process, par­tic­i­pat­ing for the first time in the au­to­mated op­ti­miza­tion of train­ing meth­ods, data strate­gies, eval­u­a­tion frame­works, and low-level op­er­a­tors. The model pro­posed ap­proaches, ran ex­per­i­ments, and it­er­ated based on the re­sults, with the re­sult­ing code, logs, and feed­back feed­ing into sub­se­quent rounds of ex­plo­ration. This es­tab­lished an early-stage re­cur­sive self-im­prove­ment loop.

Hy4 pre­view has also au­tonomously an­a­lyzed bot­tle­necks in its in­fer­ence sys­tem  and car­ried out mul­ti­ple rounds of op­ti­miza­tion on ar­eas such as op­er­a­tor fu­sion and com­mu­ni­ca­tion op­ti­miza­tion. These im­prove­ments in­creased end-to-end through­put by 31.8% com­pared with the base­line, with con­sis­tent gains across dif­fer­ent con­text lengths and con­cur­rency lev­els. This demon­strates the mod­el’s abil­ity to au­tonomously op­ti­mize its own in­fer­ence in­fra­struc­ture.

Hy4 pre­view con­tin­ues to of­fer cost ef­fi­ciency, help­ing make ad­vanced AI more widely ac­ces­si­ble. API pric­ing is set at USD 0.834 per mil­lion in­put to­kens, USD 2.501 per mil­lion out­put to­kens and USD 0.042 per mil­lion to­kens for cache hits.

Through a pre­view-first ap­proach, fol­lowed by of­fi­cial re­leases, Hunyuan con­tin­u­ously in­cor­po­rates real-world feed­back into its re­search and de­vel­op­ment process, en­abling its mod­els to im­prove by solv­ing real-world prob­lems. The next batch of mod­els in the Hy4 se­ries is ex­pected to roll out soon.

I used to won­der why I see so many more bugs than most peo­ple. I eas­ily ob­serve hun­dreds to thou­sands of bugs per week and noth­ing seems to work, but most peo­ple I talk to don’t see any­thing like this. For a long time, I thought this had some­thing to do with how I use com­put­ers but, over time, I’ve re­al­ized that it’s mostly that peo­ple are hit­ting the same bugs and don’t no­tice.

If you’re not a pro­gram­mer, that’s prob­a­bly a bet­ter way to see the world, but I think cur­ing qual­ity/​bug blind­ness is help­ful for pro­gram­mers. I’ve done this with a lot of friends and ac­quain­tances (just by point­ing out bugs). After a few weeks, peo­ple who are so in­clined tend to start notic­ing bugs as well.

Because I no­tice these kinds of things, I’ve had mul­ti­ple jobs where di­rec­tors/​VPs/​ex­ecs/​etc. some­times ask me to eval­u­ate some­thing when they want an ac­tual opin­ion from some­one who is rel­a­tively likely to no­tice is­sues (and fix them or drive fixes for them if nec­es­sary). Sometimes I won’t find any is­sues (there are likely is­sues that just aren’t the kind I no­tice). More of­ten, I find is­sues that fall some­where from “mild” to “moderate”. And, some­times, the is­sues are se­vere, to the point where one might even say the thing ac­tu­ally does­n’t work.

I find this last cat­e­gory a bit mys­te­ri­ous, as when I look up dis­cus­sions on how the thing got into this state, there’s usu­ally a stream of in­ter­nal com­ments in­di­cat­ing that the thing is great, it works well, etc., but when I open up the thing and try it, it’s in a state where the thing only works if you do quite a few non-in­tu­itive workarounds. More likely than not, not only would a nor­mal user not be able to use the thing, they’d have such a hi­lar­i­ously/​in­fu­ri­at­ingly bad ex­pe­ri­ence that they’d tell their friends.

I’ve had this post in mind for maybe a decade or so, but I was hes­i­tant to write it up be­cause, in the back of my mind, I al­ways won­dered if I’m some­how trig­ger­ing weird cor­ner case be­hav­ior most users don’t hit with­out re­al­iz­ing it. But af­ter see­ing more and more cases where the prod­uct launches and falls flat on its face be­cause users run into the ex­act same is­sues I saw, I don’t think that, in gen­eral, I’m hit­ting bugs be­cause I’m do­ing un­usual things a nor­mal user would­n’t do. If a prod­uct seems se­verely flawed when I use it, it prob­a­bly is. And with the magic of LLMs, nowa­days, I can even have LLMs act like nor­mal users in a lot of ways and show that the is­sues re­pro­duce across many dif­fer­ent sce­nar­ios.

A few ex­am­ples

I don’t want to give any spe­cific ex­am­ples where it was my job to see how well the thing worked be­cause, even if the in­ter­nal ex­am­ples are meant in a con­struc­tive, blame­less, way, they may not al­ways read that way when re-posted ex­ter­nally, so I’ll give a few less in­ter­est­ing and less well sup­ported “random” ex­am­ples.

A while ago, I wrote up the re­sults of some web search queries and found poor re­sults from Google, Bing and Kagi. In gen­eral, the ma­jor search en­gines failed to re­turn good re­sults for the queries and re­turned pages full of low-qual­ity SEO spam as well as some sites that were ac­tu­ally scams. BTW, on the scale men­tioned above, I would con­sider this “moderate” and not “severe” (severe would be some­thing like, the search en­gine re­turns 500 er­rors half the time, the ma­jor­ity of re­sults are scams, etc; my bar for se­vere is that a nor­mal user likely won’t be able to use the thing at all, not that they have a bad ex­pe­ri­ence). Almost no­body1 ob­jected to my char­ac­ter­i­za­tion of Google and Bing search re­sults, but peo­ple told me that I was wrong about Kagi. In some cases, peo­ple sent me their ac­tual search re­sults. In every such case, the search re­sults did not con­tain a good re­sult that I could see (e.g., for the sea­sonal fore­cast query, the search failed to re­turn an up-to-date sea­sonal fore­cast) and was full of SEO spam. In one case, a per­son passed me both their list of Kagi fil­ters as well as the search re­sults they got with­out mak­ing claims that the re­sults were good or bad, but peo­ple gen­er­ally in­sisted the re­sults were good even though the re­sults both failed to link to a use­ful re­sult and were full of spam ex­cept in cases where the user did some­thing like pin GitHub to the top of their re­sults, which worked for the queries where the goal was to down­load soft­ware that’s hosted on GitHub, but of course com­pletely fails for the other queries from the post.

In the ab­stract, I get that peo­ple who are fans of things tend to be blind to the thing’s faults. For ex­am­ple, since I bought a Volvo af­ter see­ing how they do in out-of-sam­ple crash tests, I some­times search for an­swers to my ques­tions on Volvo car fo­rums. For well over a decade, the re­li­a­bil­ity data that ex­ists (and I think this is backed up by the anec­do­tal ex­pe­ri­ence that me­chan­ics who work on Volvos have) is that Volvo re­li­a­bil­ity is mediocre to poor, but of course Volvo fo­rums are full of peo­ple who in­sist that Volvos are among the most re­li­able cars and that the data are all wrong.

An ex­am­ple that might be more cen­tral to the topic is Blackboard (the course man­age­ment soft­ware). Back when it was the most widely used soft­ware by uni­ver­si­ties for course­work, the soft­ware was widely dis­liked by both stu­dents and pro­fes­sors. I think it would be fair to say that it was the most widely dis­liked soft­ware in my so­cial cir­cles (there was more strongly dis­liked soft­ware, like Visual Source Safe, but any more strongly dis­liked soft­ware was­n’t widely used enough to be the most widely dis­liked over­all). The Wikipedia page notes

Blackboard had be­come “one of the most dis­liked — even de­tested — com­pa­nies in ed­u­ca­tion.”

Blackboard had be­come “one of the most dis­liked — even de­tested — com­pa­nies in ed­u­ca­tion.”

as well as

In December 2011, Fast Company re­ported that 93% of re­spon­dents to the Amplicate cus­tomer opin­ion sur­vey “hate” the com­pany.

In December 2011, Fast Company re­ported that 93% of re­spon­dents to the Amplicate cus­tomer opin­ion sur­vey “hate” the com­pany.

Back when I was much younger and had less of a fil­ter, I ran into some­one who worked at Blackboard and, with­out think­ing, I stu­pidly blurted out some­thing like “what’s it like to work on this soft­ware that so many peo­ple dis­like?”. Luckily, the per­son I was talk­ing to was­n’t of­fended at all and, in­stead, they were ac­tu­ally con­fused be­cause they thought it was widely loved soft­ware that users re­ally liked. They did­n’t re­ally be­lieve what I said could be true and I made some com­ment in­di­cat­ing that it was just con­fu­sion on my part and then the con­ver­sa­tion con­tin­ued in a dif­fer­ent di­rec­tion. At the time, as some­one much younger and more naive, I was re­ally sur­prised to hear that the soft­ware that was prob­a­bly the most widely dis­liked soft­ware in my so­cial cir­cles was thought to be re­ally well-liked soft­ware by the one em­ployee from the com­pany I met (and, pre­sum­ably other em­ploy­ees as well).

I can un­der­stand how the Volvo fo­rums get to be how they are, in that cars are re­li­able enough in gen­eral now that peo­ple gen­er­ally don’t ex­pe­ri­ence car break­downs, so it’s easy for some­one to think some­thing like “the data can’t be right; af­ter all, my car has never bro­ken down”. It’s more of a mys­tery to me how some­body can look at a set of search re­sults that are full of spam and then dash off a mes­sage ex­plain­ing how great the re­sults are, even if they’re a fan of a par­tic­u­lar search en­gine or how some­one can think that users gen­er­ally love soft­ware that’s fa­mous for be­ing dis­liked, to the point that every sin­gle per­son I talk to about it tells me how bad it is (often in un­prompted com­plaints), there are news ar­ti­cles that dis­cuss how much peo­ple dis­like the soft­ware, and the near-uni­ver­sal dis­like for the soft­ware is men­tioned on its Wikipedia page. Another Blackboard-like ex­am­ple might be Discourse (forum soft­ware) web per­for­mance, where one of the in­spi­ra­tions for this post was dis­cus­sions with Discourse em­ploy­ees who thought that Discourse had great per­for­mance. I found that one in­ter­est­ing be­cause Discourse ac­tu­ally had code in it that slowed down ac­tual page loads in or­der to cheat on web per­for­mance met­rics like LCP. That went well be­yond just op­ti­miz­ing for a bench­mark and rose to the level of ac­tual cheat­ing that not only had no ben­e­fit to the user, it ac­tu­ally harmed the user. At some level, the pro­gram­mers im­ple­ment­ing that sort of cheat­ing and ad­vis­ing users on how to not ac­ci­den­tally sub­vert the cheat­ing must know that the ac­tual per­for­mance of their app is poor, but it’s very easy for peo­ple to put up men­tal bar­ri­ers around this kind of thing.

By now, I would­n’t say that I’m sur­prised be­cause I’ve seen this kind of thing enough that I would ac­tu­ally con­sider it sur­pris­ing if it did­n’t hap­pen, but I still won­der what’s go­ing on in­side some­one’s head when some­thing like this hap­pens.

For a non-pro­gram­ming ex­am­ple, we pre­vi­ously noted in this post on how peo­ple have dif­fer­ent per­spec­tives on “obvious” facts, there’s a bas­ket­ball player who, sub­jec­tively, is gen­er­ally con­sid­ered to be the dirt­i­est player of his era. The NBA does­n’t track ob­jec­tive mea­sures of player dirt­i­ness, but he seems dom­i­nant on a wide va­ri­ety of mea­sures. For ex­ampe, al­though, like re­bounds be­fore 1950, gen­i­tal strikes aren’t an of­fi­cially tracked stat, he surely holds the record for punch­ing, kick­ing, knee­ing, or oth­er­wise strik­ing play­ers in the gen­i­tals this cen­tury (he should also hold the record for era-ad­justed num­bers, but it’s pos­si­ble that he does­n’t have the all-time record due to play be­ing much dirt­ier over­all in the 80s and 90s). In dis­cus­sions, most fans of his team don’t seem to no­tice this and the phrases “natural re­bound­ing mo­tion” and “natural shoot­ing mo­tion” have be­come run­ning jokes from how obliv­i­ous the team’s fans are when they jus­tify this play­er’s con­tor­tions when he strikes other play­ers in the gen­i­tals.

On av­er­age, hu­mans have a high abil­ity to ig­nore neg­a­tives in things they’re a fan of, in­clud­ing (and of­ten es­pe­cially) their own work or work their com­pany does. For bet­ter or for worse, I seem to have the op­po­site of this and my thoughts im­me­di­ately go to the flaws in my­self and my work. A num­ber of times, as a re­sult of a blog post, some­one has mes­saged me with some­thing like “how would you like it if some­one crit­i­cized your work?” or “how would you like it if some­one said your work is­n’t good?” To the for­mer, my thought is that I go to great lengths to get crit­i­cism from peo­ple who can poke holes in my rea­son­ing, so it’s pretty awe­some if some­one has re­motely rea­son­able crit­i­cism of my work. And to the lat­ter, I gen­er­ally think my work is full of ma­jor flaws, so, uhh, yeah, it seems pretty rea­son­able to say it is­n’t good. There are par­tic­u­lar as­pects of my work that I think are in­ter­est­ing or good but, over­all, I don’t know that I’d rate any­thing I’ve done as good. I’m not say­ing I don’t have blind spots, but I think I’m a bit less prone to this par­tic­u­lar one than most peo­ple2.

Habitual mit­i­ga­tions

If I think about anal­o­gous blind spots I’ve had, one that jumps out at me is from when I was a lit­tle kid and a friend of mine used my com­puter. For this story to make sense, you have to know that this was in the me­chan­i­cal mouse era. Over time, de­tri­tus would get stuck to your mouse ball and cause it to track er­rat­i­cally un­less you cleaned it out.

When my friend tried to use my com­puter he found it im­pos­si­ble to use the mouse be­cause mouse pointer move­ment seemed al­most ran­dom. When I sat down at the com­puter again and used the mouse I did­n’t have any prob­lem us­ing it at all, but on look­ing at what I was do­ing with my hand to smoothly move the pointer in a straight line, I was vi­o­lently throw­ing my hand all over the place. I re­al­ized I must’ve ad­justed to the de­tri­tus on the mouse ball over time as it ac­cu­mu­lated and I was some­how com­pen­sat­ing for the mouse’s ex­tremely er­ratic track­ing by mak­ing coun­ter­vail­ing er­ratic move­ments3. I thought it was pretty amaz­ing that I could not no­tice that I was do­ing this and I al­ways won­der if I’m do­ing some equiv­a­lent thing to­day.

I some­times think about all of the mit­i­ga­tions I’ve de­vel­oped to work around bugs. For ex­am­ple, when open­ing a new Google Doc, I used to im­me­di­ately put the ti­tle I wanted into the doc. At some point, maybe ten years ago or so, Google Docs added some kind of de­lay such that the typ­ing you do into the ti­tle box right af­ter you open the doc gets over­writ­ten, so I now have this habit where, af­ter open­ing a Google Doc, I do some­thing else and then I change the ti­tle. Over time, as Google Docs has had more and more fea­tures added, I’ve de­vel­oped a se­ries of habits that avoid all sorts of pit­falls (such as try­ing to search at the “wrong” time and get­ting the use­less na­tive browser search in­stead of the Google Docs search).

My feel­ing is that a large frac­tion of com­puter lit­er­acy and soft­ware lit­er­acy is de­vel­op­ing a large li­brary of these habits that you just do at a non-con­scious level. These are of­ten quite spe­cific to the sit­u­a­tion, such as a habit I de­vel­oped when I worked at Microsoft of flip­ping my lap­top’s WiFi switch to off be­fore log­ging in (which I no­ticed other peo­ple do­ing as well). This was be­cause there was some ser­vice, which would of­ten fail your lo­gin with “There are cur­rently no lo­gon servers avail­able to ser­vice the lo­gon re­quest”. But if that ser­vice could­n’t con­nect at all, the check would be by­passed and you could just log in.

Quality blind­ness

We could fill a post up with ex­am­ples like that, but back to the main topic of the post, one com­monly sug­gested way to try to over­come qual­ity blind­ness is to have peo­ple dog­food their own soft­ware. On av­er­age, this is a lot bet­ter than not dog­food­ing, but it only works to the ex­tent that peo­ple don’t fig­ure out (and then for­get about) habits that work around what­ever is­sues the soft­ware has. On av­er­age, pro­gram­mers are pretty good at work­ing around soft­ware foibles (you had to be in or­der to be an ef­fec­tive pro­gram­mer pre-LLM), so it’s very easy for pro­gram­mers to not no­tice these kinds of is­sues if they’re not pay­ing at­ten­tion.

On the flip side, a large part of mak­ing an app easy for peo­ple to use seems to mean mak­ing weird habits like these un­nec­es­sary. Although this sounds like it should be easy to do, from hav­ing seen peo­ple try to give feed­back about this kind of thing, the re­flex­ive re­ac­tion of most de­vel­op­ers seems to be “huh? It’s easy to do X, just do [complex se­quence of things that no nor­mal per­son would think of if they had­n’t used the app many times be­fore un­less it was specif­i­cally ex­plained to them or they saw some­one else do it]” or “huh? Didn’t you see that the in­struc­tions for this are clearly laid out in page 43 of the man­ual af­ter you ex­e­cute the steps in Appendix B on page 261?”.

That be­ing said, I think cur­ing peo­ple of qual­ity blind­ness is do-able be­cause I’ve done it quite a few times. I think this only re­ally works when the per­son is re­cep­tive, as peo­ple have in­fi­nite ca­pac­ity for will­ful blind­ness but, in cases where peo­ple are re­cep­tive, just point­ing out is­sues they did­n’t no­tice seems to work. Years or even a decade later, peo­ple will some­times tell me they see bugs every­where now.

The rea­son I think this is worth do­ing is that I’ve seen peo­ple and teams with a high de­gree of qual­ity blind­ness ship things that have re­duced or even no chance of suc­cess be­cause of prod­uct qual­ity is­sues4. It’s one thing to know­ingly and de­lib­er­ately trade off qual­ity for speed5, but when I’ve seen this hap­pen there’s al­ways been a kind of qual­ity blind­ness where every­one in­volved with the pro­ject thinks they’re ship­ping some­thing very high qual­ity when that’s not the case.

This has never been unim­por­tant, but it’s got­ten more im­por­tant with cod­ing agents be­cause, while it’s eas­ier than ever to churn out low qual­ity soft­ware, it’s also eas­ier than ever to im­prove qual­ity, whether that’s bet­ter per­for­mance, fewer bugs, etc.

But, to do this, you have to ac­tu­ally no­tice that this is pos­si­ble, that qual­ity can be im­proved.

Thanks to Yossi Kreinin, Dennis Snell, Michael Malis, Emu Chu, Gary Bernhardt, Jon Surrell, and Matt Mullenweg for com­ments/​cor­rec­tions/​dis­cus­sion.

Naturally, Gary Bernhardt ran into a Google Docs bug while read­ing a draft of this post.

P.S. Like I’ve men­tioned in the last four posts, I’ve been try­ing to write posts more quickly be­cause, with LLMs, it’s so much eas­ier to look at data and fig­ure things out but, since I’m not writ­ingn with LLMs, the time it takes to write some­thing up has­n’t fun­da­men­tally changed, un­less I want to move to a dif­fer­ent point in the qual­ity-ve­loc­ity trade-off space. The prior re­sult was that I would run some ex­per­i­ments and tell a few friends and then never write any­thing up be­cause, due to Amdahl’s law, writ­ing any­thing up would ef­fec­tively con­sume all of my band­width for run­ning ex­per­i­ments. In fact, de­spite try­ing to do this (my goal is to spend 30 min­utes per post on the write-up), since writ­ing my last post, I have three re­sults that I think could make a to­tally fine blog post that I haven’t had time to write up (not in­clud­ing things done for work, which would add a few more things). Without hav­ing LLMs write for me, I don’t see a rea­son­able way to get the time per post sig­nif­i­cantly be­low 30 min­utes (and I think I of­ten miss my goal and take more than 30 min­utes), so the non-LLM op­tions here are some posts that are much slop­pier than my nor­mal posts (in a hu­man slop kind of way), or al­most no posts.

Anyway, if you have opin­ions on these quick (and surely more wrong) write­ups, let me know what you think (X Bsky Mastodon)!

Appendix: ad­ver­tis­ing blind­ness

Michael Malis (founder and for­mer CEO of Freshpaint) noted (in mes­sages, hence the mes­sage-like for­mat)

For a sim­i­lar but dif­fer­ent data point - I’ve seen sim­i­lar blind­ness when it comes to ad­ver­tis­ing. When I would ex­plain Freshpaint to peo­ple, I would tell them that we help hos­pi­tals with mar­ket­ing A com­mon ques­tion I get is why do hos­pi­tals do mar­ket­ing. The weird thing is if you pay at­ten­tion, hos­pi­tals do a ton of mar­ket­ing In SF there’s tons of bus ads and bill­boards for ucsf/​sut­ter health/​stan­ford and var­i­ous treat­ments

For a sim­i­lar but dif­fer­ent data point - I’ve seen sim­i­lar blind­ness when it comes to ad­ver­tis­ing. When I would ex­plain Freshpaint to peo­ple, I would tell them that we help hos­pi­tals with mar­ket­ing

A com­mon ques­tion I get is why do hos­pi­tals do mar­ket­ing. The weird thing is if you pay at­ten­tion, hos­pi­tals do a ton of mar­ket­ing

In SF there’s tons of bus ads and bill­boards for ucsf/​sut­ter health/​stan­ford and var­i­ous treat­ments

This is a dif­fer­ent topic from both Michael’s com­ments and the post, but I’ll say that I’ve talked to quite a few peo­ple who don’t be­lieve ads work at all, but I talked to some­one whose data method­ol­ogy and judge­ment I trust about ads A/B test­ing at one big com­pany I worked for and looked at the data my­self at an­other com­pany and I thought the causal ev­i­dence for ads pro­vid­ing real lift (well be­yond the cost of the ad) was strong in those cases. In the case where I looked at it, they did a geo-seg­mented A/B test where they bought ads in some geos but not oth­ers (this was done world­wide, with the re­gions be­ing things like U.S. states, Canadian provinces, etc.). This kind of geo-seg­men­ta­tion was done be­cause, even with cross-de­vice track­ing, it’s not 100% clear if some­one has been ex­posed to an ad or not (of course this is still the case with this kind of seg­men­ta­tion and I would pre­fer seg­men­ta­tion that was more clus­tered to pop­u­la­tion ar­eas and did­n’t have splits where peo­ple are rel­a­tively likely to, for ex­am­ple, com­mute from one side of a bound­ary to the other, but this kind of con­t­a­m­i­na­tion gen­er­ally makes the likely true lift higher than the es­ti­mated lift), so peo­ple some­times do these geo-seg­mented A/B tests.

Anyway, in these A/B tests, re­turn on ad spend was quite good just on di­rect rev­enue gain, and there was also a gain in users which seems likely to re­sult in more rev­enue down the road (the later rev­enue was­n’t an­a­lyzed). I don’t know about ad ef­fec­tive­ness in gen­eral or if your par­tic­u­lar ads are ef­fec­tive, but the com­monly re­peated idea that ads don’t work in gen­eral seems wrong to me.

On the topic of Michael’s com­ment, I think it’s easy for pro­gram­mers to not no­tice ads. Almost all pro­gram­mers I know use an ad blocker and, in real life, their eyes seem to just skim over ads and not no­tice them. I can see how this would feed into the idea that ads don’t work. Who the heck would look at these things? But from my in­ter­ac­tions with “normal” peo­ple as well as the data I’m fa­mil­iar with from my time at Google, many or per­haps most peo­ple don’t even re­al­ize that a lot of ads are ads. When they do a Google search and they click on the top re­sut, they of­ten have no idea they’re not look­ing at what Google “thinks” is the best link, they’re look­ing at at a link from who­ever paid Google the most to buy that ad slot.

Em Chu, on a ha­bit­ual bug mit­i­ga­tion:

I’m sure you can col­lect in­fi­nite ex­am­ples for this sec­tion, but I just want to Complain: when wak­ing up and un­lock­ing my lap­top (mac), it’s very easy to get it in a state where it’s “awake” but un­us­able (black screen with cur­sor or sim­i­lar) which can only be fixed by phys­i­cally clos­ing the lid and re-open­ing it. To work around this, I think I usu­ally wait a sec­ond af­ter the screen turns on, in­ter­act with the track­pad, and then un­lock it, though hon­estly that hap­pens mostly sub­con­sciously, and I clearly need prac­tice given that I still hit the bug a few times a month.

I’m sure you can col­lect in­fi­nite ex­am­ples for this sec­tion, but I just want to Complain: when wak­ing up and un­lock­ing my lap­top (mac), it’s very easy to get it in a state where it’s “awake” but un­us­able (black screen with cur­sor or sim­i­lar) which can only be fixed by phys­i­cally clos­ing the lid and re-open­ing it. To work around this, I think I usu­ally wait a sec­ond af­ter the screen turns on, in­ter­act with the track­pad, and then un­lock it, though hon­estly that hap­pens mostly sub­con­sciously, and I clearly need prac­tice given that I still hit the bug a few times a month.

On read­ing this, I ex­am­ined how I open my lap­top and re­al­ized that I have some funny habits as a re­sult of work­ing around other lap­top bugs. The spe­cific bug men­tioned here does­n’t re­pro­duce on my lap­top and it seems that I can stop the ha­bit­ual mit­i­ga­tion I put into place for some prior lap­top.

Gary Bernhardt, on his ex­pe­ri­ence read­ing a draft of this post

While read­ing it, Google Docs’ UI seems to have bro­ken, mak­ing it im­pos­si­ble to scroll up to read some com­ments (see screen­shot [not shown in post]).

While read­ing it, Google Docs’ UI seems to have bro­ken, mak­ing it im­pos­si­ble to scroll up to read some com­ments (see screen­shot [not shown in post]).

From look­ing at the screen­shot, I’ve seen the ex­act same bug and have some mit­i­ga­tions for it (different ones de­pend­ing on the con­text). I would per­son­ally rate Google Docs as far above av­er­age in terms of soft­ware qual­ity: I find it much less buggy and janky than the ma­jor al­ter­na­tives (Microsoft Word, Open Office, var­i­ous old ed­i­tors that are long gone like StarOffice, Lotus, etc.). And yet, I could eas­ily sit down and write a 10k word post on Google Docs bugs and the workarounds I have for them.

At times, I’ve tried to see if I can get a job some­where where I just fix qual­ity is­sues all day. This has never panned out, due to some com­bi­na­tion of this not be­ing a very high pri­or­ity and it also not be­ing a nor­mal role that com­pa­nies have a role for. I some­times day­dream about join­ing com­pa­nies as an in­tern and just fix­ing qual­ity is­sues for a few months and then leav­ing. In prac­tice, I think if I got such a job, a lot of the fixes would get blocked and it would be very dif­fi­cult to ac­tu­ally drive change as an in­tern for three months, so it would have to be some mostly aban­doned pro­ject where no­body cares what I do (and cor­po­rate pri­or­ties aren’t so fo­cused on ship­ping fea­tures that fixes get im­me­di­ately re-bro­ken).

@IncidentNoodle

Unintentionally on topic: the <abbr> tags worked on mo­bile ~last week, but are no longer work­ing across any iOS browser (safari/chrome/firefox), and I had a hard time fig­ur­ing out that hover showed them on ma­cOS browsers (all three) due to the long de­lay

Unintentionally on topic: the <abbr> tags worked on mo­bile ~last week, but are no longer work­ing across any iOS browser (safari/chrome/firefox), and I had a hard time fig­ur­ing out that hover showed them on ma­cOS browsers (all three) due to the long de­lay

@gunch­leoc@mastodon.scot:

Germans have a word for that - Betriebsblindheit

Germans have a word for that - Betriebsblindheit

@oulipien.bsky.social:

Crazy anec­dote from @danluu.com here and I wish he’d been even blunter at the time and asked this per­son where they’d got­ten this be­lief about Blackboard be­ing liked by any­one at all. User sur­veys? Principal (as in, not agent) sur­veys? Inner con­vic­tion??? [screenshot of Blackboard anecodote] [Some vari­ant of, peo­ple are forced to say that they don’t see bugs by their bosses]

Crazy anec­dote from @danluu.com here and I wish he’d been even blunter at the time and asked this per­son where they’d got­ten this be­lief about Blackboard be­ing liked by any­one at all. User sur­veys? Principal (as in, not agent) sur­veys? Inner con­vic­tion??? [screenshot of Blackboard anecodote]

[Some vari­ant of, peo­ple are forced to say that they don’t see bugs by their bosses]

I don’t thnk this is con­sis­tent with any of the ma­jor ex­am­ples in the post, let alone all of them. Consider the Blackboard ex­am­ple men­tioned above. It’s un­likely that I and other peo­ple this Blackboard em­ployee are “secret shop­pers” who are check­ing in on em­ploy­ees, and the em­ploy­ee’s re­ac­tion is clearly ab­surd to any­one who is­n’t such a hy­po­thet­i­cal (and in re­al­ity, non-ex­is­tent) se­cret shop­per, so re­act­ing like this just makes them look a bit silly in the eyes of a large frac­tion of the peo­ple they meet for no ben­e­fit (for ex­am­ple, see the pre­vi­ous quote, which seems like a typ­i­cal in­ter­nal re­ac­tion). Perhaps a few very para­noid em­ploy­ees would main­tain this front on the off chance they run into some friend or rel­a­tive of the boss who knows that they work for the com­pany who re­lays the story back and they have a boss who would care about this, but it’s just not plau­si­ble that this is (for ex­am­ple) the case for every Discourse em­ployee who reached out to me to ex­plain to me that Discource per­for­mance is re­ally good.

If we look at the bas­ket­ball ex­am­ple, this is even more ab­surd. You could pos­si­bly come up with some kind of rea­son­ing like, other fans would shun you if you did­n’t be­lieve or pre­tend to be­lieve the most ab­surd ra­tio­nal­iza­tion, but as some­one who has spent a lot of time around sports fans, I’ve gen­er­ally not found this to be the case. And, to the min­i­mal ex­tent to which this is kinda sorta the case, it’s more an is­sue of self se­lec­tion, where fans who are into the most ex­treme ra­tio­nal­iza­tions will spend more time around fans who are into the most ex­terme ra­tio­nal­iza­tions and fans who are less into these ex­treme ra­tio­nal­izaitons will tend to spend more time with fans who are less into them.

Also, just look­ing at the ca­reer path of peo­ple who don’t buy into these things and fix them, notic­ing these is­sues and fix­ing them has gone very well for those peo­ple. Pretending these is­sues don’t ex­ist (whether that’s at a con­cious level or not) also seems to work well, so I don’t know that fix­ing these is­sues is ac­tu­ally a bet­ter ca­reer path, but it’s cer­tainly not so bad that, in gen­eral, there’s mean­ing­ful ca­reer pres­sure to pre­tend these things don’t ex­ist over­all even if there are some in­di­vid­ual po­si­tions where there’s some di­rect pres­sure to pre­tend these is­sues aren’t real.

Daniel Gibson:

Who else uses the Shift key to end the screen­saver, be­cause in case the event goes through to an ac­tual pro­gram it’s least likely to do have un­in­tended ef­fects?

Who else uses the Shift key to end the screen­saver, be­cause in case the event goes through to an ac­tual pro­gram it’s least likely to do have un­in­tended ef­fects?

This re­minds me of how, when I want to send a queued mes­sage to codex im­me­di­ately and in­ter­rupt the cur­rent tool call, I put my fin­ger on the key and the press as quickly as pos­si­ble to re­duce the win­dow of time where the tool call will fin­ish and the es­cape key will stop codex en­tirely in­stead of caus­ing the mes­sage to send. I should prob­a­bly just run a patched ver­sion of codex that has fixes for this and a few other is­sues I’ve run into, but I’m al­ready do­ing things like try­ing out some weird work­load-spe­cific op­ti­mized ver­sion of rip­grep that also has an added na­tive code com­piler which com­piles match­ing ex­pres­sions in an­other thread while the search starts and then cuts over af­ter com­pi­la­tion com­pletes, so it’s not like I’m against cre­at­ing weird patches to im­prove my work­flow and it’s more of an is­sue of over­all band­width (no doubt, on writ­ing this, some­one will tell me that I could just hit an­other key in­stead and could’ve found this out by ask­ing codex about the key in the time it took me to write this com­ment). Just like with Google Docs, I con­sider codex above av­er­age in terms of soft­ware qual­ity in the space, but even though I haven’t been us­ing it for a year, I could eas­ily write 10k words on all the workarounds I’ve im­ple­mented (either by habit or, in some cases, with ac­tual scripts that mon­i­tor for bro­ken be­hav­ior and then cor­rect it).

some­one told me the re­sults did­n’t re­pro­duce on Google when they tried it some num­ber of weeks later. Of course it did­n’t, which I dis­cussed here in more de­tail, but for the short of it, here’s this post about scams and other bad re­sults on Google that was #1 on HN for a while. Of course some­body fixed that! And, also, ad re­sults are non-de­ter­min­is­tic and, while there are a lot of bad ads, it’s not like the ma­jor­ity are scams, so you would­n’t ex­pect to get scam ads at the top re­sults even if some­one else did for the same query. [return]

For ex­am­ple, any­one fa­mil­iar with my code at Twitter will re­call the huge com­ments I had at the top of the main files for the things I owned, which de­scribed the var­i­ous ways in which the thing is re­ally flawed. They were all things that, for one rea­son or an­other, I thought weren’t worth the time to fix, but they were still se­ri­ous prob­lems that any­one in­ter­act­ing with the code ought to know about. For this met­rics pro­ject, I even had a long doc that de­scribed the is­sues in great de­tail (IIRC, in a lot of cases, the rough shape of the fix was de­scribed; maybe to­day an LLM could take that and fix it). I have the same feel­ing about my writ­ing. While a huge num­ber of bugs sneak through my writ­ing (like spelling and gram­mat­i­cal er­rors), most of those are things I sort of don’t care about and will skim past in other peo­ple’s writ­ing as well. When I say don’t care, it’s not that I don’t want things to be bet­ter (when peo­ple send me cor­rec­tions I gen­er­ally fix things), it’s just that my brain does­n’t nat­u­rally pay at­ten­tion to those things no mat­ter whose writ­ing it is, so I don’t seem to have a par­tic­u­lar blind spot in my writ­ing with re­spect to these kinds of bugs. For the things I do care about, I could edit posts end­lessly be­cause, no mat­ter how much I edit, the post still seems pretty bad to me. I used to of­ten (and still some­times) send a post to some­one and ask them if it makes any sense to pub­lish it at all be­cause I gen­er­ally don’t like my out­put and, if I’m just look­ing at my own writ­ing, I don’t think it’s worth pub­lish­ing. At this point, I’ve done this enough that I’ll of­ten just pub­lish even though I don’t like what I wrote, but if some­one says “how would you like it if some­one told you your work was­n’t good?” as a kind of “gotcha”, boy, they re­ally have no idea how I think about my work. There are var­i­ous tricks I’ve used to get around this (not ex­plic­itly to get around this, but they do so as a side ef­fect). As dis­cussed in this old post on writ­ing, for a while, I hired a pro­fes­sional ed­i­tor and had a process goal of do­ing one pass on each post and then try­ing to im­prove the next post. And as noted in the post­script to re­cent posts, now I’m try­ing to write with ex­tremely min­i­mal cleanup and edit­ing and push posts out in half an hour re­gard­less of the state of the data I’m look­ing at or the post (which I’m gen­er­ally fail­ing to do; I thought I might suc­ceed on this one be­cause it does­n’t have any data analy­sis, but some­one made a com­ment on the draft post that got me to re-write the whole thing, and just on num­ber of words in the post, half an hour would re­ally be push­ing it on the orig­i­nal and then it in­creased in length). Of course a post that’s writ­ten as quickly as pos­si­ble with lit­tle to no re­gard for clean­ing things up is go­ing to be ter­ri­ble in all kinds of ways, so all flaws I see in the post don’t stop me from pub­lish­ing it. Have my re­cent posts been good? Of course not; for any of the ex­per­i­men­tal/​data posts, I could prob­a­bly name ten things that should be fixed about each of them off the top of my head. For this post, I’d have to re-read it to come up with ten things, but I’m sure if I did re-read it I’d want to re-write the whole thing be­cause of the is­sues it has. [return]

For ex­am­ple, any­one fa­mil­iar with my code at Twitter will re­call the huge com­ments I had at the top of the main files for the things I owned, which de­scribed the var­i­ous ways in which the thing is re­ally flawed. They were all things that, for one rea­son or an­other, I thought weren’t worth the time to fix, but they were still se­ri­ous prob­lems that any­one in­ter­act­ing with the code ought to know about. For this met­rics pro­ject, I even had a long doc that de­scribed the is­sues in great de­tail (IIRC, in a lot of cases, the rough shape of the fix was de­scribed; maybe to­day an LLM could take that and fix it).

I have the same feel­ing about my writ­ing. While a huge num­ber of bugs sneak through my writ­ing (like spelling and gram­mat­i­cal er­rors), most of those are things I sort of don’t care about and will skim past in other peo­ple’s writ­ing as well. When I say don’t care, it’s not that I don’t want things to be bet­ter (when peo­ple send me cor­rec­tions I gen­er­ally fix things), it’s just that my brain does­n’t nat­u­rally pay at­ten­tion to those things no mat­ter whose writ­ing it is, so I don’t seem to have a par­tic­u­lar blind spot in my writ­ing with re­spect to these kinds of bugs. For the things I do care about, I could edit posts end­lessly be­cause, no mat­ter how much I edit, the post still seems pretty bad to me.

I used to of­ten (and still some­times) send a post to some­one and ask them if it makes any sense to pub­lish it at all be­cause I gen­er­ally don’t like my out­put and, if I’m just look­ing at my own writ­ing, I don’t think it’s worth pub­lish­ing. At this point, I’ve done this enough that I’ll of­ten just pub­lish even though I don’t like what I wrote, but if some­one says “how would you like it if some­one told you your work was­n’t good?” as a kind of “gotcha”, boy, they re­ally have no idea how I think about my work.

There are var­i­ous tricks I’ve used to get around this (not ex­plic­itly to get around this, but they do so as a side ef­fect). As dis­cussed in this old post on writ­ing, for a while, I hired a pro­fes­sional ed­i­tor and had a process goal of do­ing one pass on each post and then try­ing to im­prove the next post. And as noted in the post­script to re­cent posts, now I’m try­ing to write with ex­tremely min­i­mal cleanup and edit­ing and push posts out in half an hour re­gard­less of the state of the data I’m look­ing at or the post (which I’m gen­er­ally fail­ing to do; I thought I might suc­ceed on this one be­cause it does­n’t have any data analy­sis, but some­one made a com­ment on the draft post that got me to re-write the whole thing, and just on num­ber of words in the post, half an hour would re­ally be push­ing it on the orig­i­nal and then it in­creased in length). Of course a post that’s writ­ten as quickly as pos­si­ble with lit­tle to no re­gard for clean­ing things up is go­ing to be ter­ri­ble in all kinds of ways, so all flaws I see in the post don’t stop me from pub­lish­ing it. Have my re­cent posts been good? Of course not; for any of the ex­per­i­men­tal/​data posts, I could prob­a­bly name ten things that should be fixed about each of them off the top of my head. For this post, I’d have to re-read it to come up with ten things, but I’m sure if I did re-read it I’d want to re-write the whole thing be­cause of the is­sues it has.

This was, in­ad­ver­tently, a kind of re­venge on my friend for when I tried to open his door for the first time to leave his place. Since the door clearly opened to the out­side, I tried push­ing on the door, which did­n’t work, so I checked if there was a latch that was stuck, if the door was still locked, if I needed to push harder, etc., none of which worked. When he saw that I could­n’t open the door I asked him what the trick was he said, in a tone of voice that made it sound like this was ob­vi­ously some­thing every­one should know, you need to pull the door be­fore push­ing it. The door was wedged such that the eas­i­est way to open the door was to pull the door as tightly shut as pos­si­ble and then im­me­di­ately shove the door open. This friend, since he grew up in that house, thought this was ob­vi­ous, ap­par­ently not re­al­iz­ing that it’s not nor­mal to have to try to close a door ex­tra hard to open it. [return]

a re­sponse I’ve heard to this kind of thing re­cently is that Anthropic had the best growth num­bers in his­tory while Claude was very buggy. If you have the best cod­ing model and agent in the world, you can get away with a lot, but even they seem to have spent a fair amount of ef­fort im­prov­ing qual­ity. Maybe you can also get away with it if you have a prod­uct that suc­ceeds due to bundling, the strength of your en­ter­prise sales team, net­work ef­fects, mo­nop­oly power, etc.; all but one of the cases I’m think­ing of are places where the team did­n’t have these things on their side. I ac­tu­ally thought the one other case I was think­ing of would be some­thing like Blackboard, but (if the Google re­sults are ac­cu­rate) I see that the soft­ware has de­clined from be­ing #1 in the mar­ket to be­ing a mi­nor­ity player, so maybe they could­n’t get away with it ei­ther (I did­n’t look into the rea­sons for the de­cline; per­haps it’s a co­in­ci­dence). As noted above, Blackboard is an ex­am­ple where you could ar­gue that the soft­ware qual­ity did­n’t mat­ter and peo­ple might as well just be­lieve what­ever makes them happy; if think­ing that users love the soft­ware, then why not think that? But most of the rest of the ex­am­ples that come to mind for me aren’t cases like that. I don’t think this is the best ex­am­ple, but it comes to mind be­cause the com­ment be­low is the last time I was re­minded of the Blackboard ex­am­ple. There was a com­ment from a Tumblr em­ployee who said that they’d solved the mod­er­a­tion (abuse / spam / tox­i­c­ity / etc.) prob­lem me­chan­i­cally at Tumblr via the way re­blogs worked and that the me­chan­ics Tumblr pro­vided to users were good enough that the com­mu­nity could self-po­lice bad be­hav­ior and that other so­cial me­dia sites would do well to learn from Tumblr. This was re­fer­ring to Tumblr back in its hey­day (maybe 2009 – 2014). I never re­ally read much on Tumblr so I don’t per­son­ally have an opin­ion, but back when it was a ma­jor so­cial me­dia plat­form, the rep­u­ta­tion among folks I know was that it was heavy on bad be­hav­ior, par­tic­u­larly pile-ons caused by peo­ple tak­ing out of con­text quotes and turn­ing them into rage­bait (not to say this does­n’t hap­pen on other plat­forms, but the be­lief was that the way Tumblr was struc­tured and/​or the com­mu­ni­ties in­volved made this worse on Tumblr). I’m not sure I know any­one who used Tumblr at the time who would say that the com­mu­nity was good at self-polic­ing. In fact, when Scott Alexander wrote one of his most fa­mous pieces, Toxoplasma Of Rage, he ded­i­cated an en­tire sec­tion to how Tumblr’s re­blog sys­tem is par­tic­u­larly bad and is guar­an­teed to re­sult in bad be­hav­ior. He ac­tu­ally says that who­ever de­signed the sys­tem ei­ther did­n’t un­der­stand what they were do­ing or they un­der­stood all too well and de­lib­er­ately made the most rage­bait-in­duc­ing sys­tem pos­si­ble. This was writ­ten dur­ing the time when this em­ployee said that Tumblr had solved the mod­er­a­tion prob­lem and uses ex­am­ples from that time. Moderation at scale is an im­pos­si­bly hard prob­lem, so as a non-Tum­blr user, I’m not even sure that Tumblr did worse than other plat­forms given its size and growth rate, but I think you’d need some qual­ity blind­ness to think that Tumblr had solved the mod­er­a­tion prob­lem. I think the strongest pos­i­tive case you could plau­si­bly make would be some­thing like “Tumblr was bet­ter than av­er­age, but many peo­ple had a worse than av­er­age ex­pe­ri­ence due to the com­mu­ni­ties they were in and some of these com­mu­ni­ties were un­usu­ally widely read and Tumblr there­fore un­fairly gained a rep­u­ta­tion as be­ing a par­tic­u­larly bad plat­form”. I don’t know if that’s true or not, but it does­n’t seem im­pos­si­ble that it could be true; it does seem im­pos­si­ble that Tumblr solved the mod­er­a­tion prob­lem. [return]

a re­sponse I’ve heard to this kind of thing re­cently is that Anthropic had the best growth num­bers in his­tory while Claude was very buggy. If you have the best cod­ing model and agent in the world, you can get away with a lot, but even they seem to have spent a fair amount of ef­fort im­prov­ing qual­ity.

Maybe you can also get away with it if you have a prod­uct that suc­ceeds due to bundling, the strength of your en­ter­prise sales team, net­work ef­fects, mo­nop­oly power, etc.; all but one of the cases I’m think­ing of are places where the team did­n’t have these things on their side. I ac­tu­ally thought the one other case I was think­ing of would be some­thing like Blackboard, but (if the Google re­sults are ac­cu­rate) I see that the soft­ware has de­clined from be­ing #1 in the mar­ket to be­ing a mi­nor­ity player, so maybe they could­n’t get away with it ei­ther (I did­n’t look into the rea­sons for the de­cline; per­haps it’s a co­in­ci­dence).

As noted above, Blackboard is an ex­am­ple where you could ar­gue that the soft­ware qual­ity did­n’t mat­ter and peo­ple might as well just be­lieve what­ever makes them happy; if think­ing that users love the soft­ware, then why not think that? But most of the rest of the ex­am­ples that come to mind for me aren’t cases like that. I don’t think this is the best ex­am­ple, but it comes to mind be­cause the com­ment be­low is the last time I was re­minded of the Blackboard ex­am­ple. There was a com­ment from a Tumblr em­ployee who said that they’d solved the mod­er­a­tion (abuse / spam / tox­i­c­ity / etc.) prob­lem me­chan­i­cally at Tumblr via the way re­blogs worked and that the me­chan­ics Tumblr pro­vided to users were good enough that the com­mu­nity could self-po­lice bad be­hav­ior and that other so­cial me­dia sites would do well to learn from Tumblr. This was re­fer­ring to Tumblr back in its hey­day (maybe 2009 – 2014). I never re­ally read much on Tumblr so I don’t per­son­ally have an opin­ion, but back when it was a ma­jor so­cial me­dia plat­form, the rep­u­ta­tion among folks I know was that it was heavy on bad be­hav­ior, par­tic­u­larly pile-ons caused by peo­ple tak­ing out of con­text quotes and turn­ing them into rage­bait (not to say this does­n’t hap­pen on other plat­forms, but the be­lief was that the way Tumblr was struc­tured and/​or the com­mu­ni­ties in­volved made this worse on Tumblr). I’m not sure I know any­one who used Tumblr at the time who would say that the com­mu­nity was good at self-polic­ing. In fact, when Scott Alexander wrote one of his most fa­mous pieces, Toxoplasma Of Rage, he ded­i­cated an en­tire sec­tion to how Tumblr’s re­blog sys­tem is par­tic­u­larly bad and is guar­an­teed to re­sult in bad be­hav­ior. He ac­tu­ally says that who­ever de­signed the sys­tem ei­ther did­n’t un­der­stand what they were do­ing or they un­der­stood all too well and de­lib­er­ately made the most rage­bait-in­duc­ing sys­tem pos­si­ble. This was writ­ten dur­ing the time when this em­ployee said that Tumblr had solved the mod­er­a­tion prob­lem and uses ex­am­ples from that time.

Moderation at scale is an im­pos­si­bly hard prob­lem, so as a non-Tum­blr user, I’m not even sure that Tumblr did worse than other plat­forms given its size and growth rate, but I think you’d need some qual­ity blind­ness to think that Tumblr had solved the mod­er­a­tion prob­lem. I think the strongest pos­i­tive case you could plau­si­bly make would be some­thing like “Tumblr was bet­ter than av­er­age, but many peo­ple had a worse than av­er­age ex­pe­ri­ence due to the com­mu­ni­ties they were in and some of these com­mu­ni­ties were un­usu­ally widely read and Tumblr there­fore un­fairly gained a rep­u­ta­tion as be­ing a par­tic­u­larly bad plat­form”. I don’t know if that’s true or not, but it does­n’t seem im­pos­si­ble that it could be true; it does seem im­pos­si­ble that Tumblr solved the mod­er­a­tion prob­lem.

most of my pro­jects are de­lib­er­ately low qual­ity; what I try to do is do the high­est ROI test­ing, not test to the point the qual­ity is what I would ac­tu­ally con­sider good, and this also goes for things like mak­ing in­ter­faces very nice, etc. [return]

You’ve prob­a­bly heard me com­plain about the “AI crawlers” be­fore, but now I ac­tu­ally have some hard num­bers I can put up to show their im­pact. In a few words, it’s bad enough to cre­ate a con­stant “background ra­di­a­tion” of sys­tem load, per­ma­nently ty­ing up a chunk of ca­pac­ity spent on pro­duc­ing out­put that is only use­ful for a sin­gle pur­pose — feed­ing a learn­ing model.

TL;DR: we spend more CPU cy­cles ren­der­ing com­mits for scrap­ers than we spend on all other kinds of le­git­i­mate ac­cess, in­clud­ing git clones. At any one time, across 5 geo-dis­trib­uted nodes, there are 14 CPU cores do­ing noth­ing but ren­der­ing git com­mits as html.

Why is git.ker­nel.org “interesting” to crawlers

Linux de­vel­op­ment hap­pens in the open — from git repos­i­to­ries you can clone, to dis­cus­sion archives you can fol­low in real time. To a large lan­guage model, this is a gold­mine of learn­ing data, be­cause all of this is not only im­me­di­ately avail­able, but is easy to fil­ter in or­der to guar­an­tee pure unadul­ter­ated pre-AI con­tent. Training an LLM on con­tent pro­duced by the LLM gives it the equiv­a­lent of a dig­i­tal prion dis­ease, so when a source is guar­an­teed to be LLM-free, like the en­tire his­tory of ker­nel com­mits, it’s worth its weight in gold as a source of train­ing data.

The stu­pid­est way of do­ing it

We make al­most every­thing clon­able, be­cause hey — we may not be around for­ever, so here — clone the re­pos. Also, clone the archives. Grab a copy just so we’re not the only ones who have it all. Seriously, it’s just a “git clone” away — and then you’ll have the whole his­tory.

For ex­am­ple, did you know you can clone the en­tirety of LKML and then do what­ever you want with it? It’s just git re­pos all the way down.

So, you’d think that some­thing that pre­tends to be “Artificial Intelligence” would use the most ef­fi­cient way of us­ing our data for train­ing pur­poses, right? Clone the re­pos, walk every com­mit. Done.

But no, let’s in fact choose the stu­pid­est pos­si­ble way of do­ing it — by ren­der­ing every­thing as HTML com­mit by com­mit and then pars­ing it.

At the time of writ­ing, linux.git is about 1.48 mil­lion com­mits. Oh, and we have about 922 forks of it on git.ker­nel.org — but don’t worry, it’s ac­tu­ally ex­tremely ef­fi­cient on the back­end, since it’s mostly the same ob­jects in every fork.

Unless, of course, you’re a scraper, in which case you have, oh, sev­eral BILLION valid URLs you can scrape, only to get 922 du­pli­cates of the same 1.48 mil­lion com­mits — which is ex­actly what the scrap­ers are do­ing.

But wait, it’s not just com­mits it­self. You can also ask for patches, plain ren­ders, diffs be­tween ar­bi­trary com­mits — cgit is happy to let you, which was per­fect for the times when the Internet was for hu­mans or crawlers who obeyed ro­bots.txt, and is AWFUL right about now, be­cause we can gen­er­ate 1.2 METRIC BAJILLION valid URLs just for a sin­gle fork of linux.git.

Block them

Initially, this was the so­lu­tion — look through the logs, find out which IPs are ob­vi­ous scraper bots, and fail2ban them. At first, this was easy, be­cause the bots help­fully told you who they were via their user-agent. Then, they wised up and started pre­tend­ing that they were ran­dom vanilla browsers.

So, we started ban­ning them by IP — af­ter all, it’s easy to fig­ure out that an IP that is try­ing to grab every pos­si­ble com­mit in a 8-year-old aban­doned fork of linux is not re­ally some lone Chrome on Windows user who is just fu­ri­ously click­ing every link that comes across their screen.

The bots then started fan­ning out to en­tire sub­nets, but this was still meh, be­cause ob­vi­ously an IP com­ing from Google Compute is just pre­tend­ing to be a Firefox user. Banning the whole ASN was jus­ti­fied, even if this oc­ca­sion­ally caught a ran­dom le­git­i­mate in­stance try­ing to au­to­mate link check­ing in com­mits.

Enter… your TV?

And… that’s when things turned re­ally, re­ally ugly. Suddenly, the crawlers were com­ing from mil­lions of ran­dom res­i­den­tial or mo­bile IPs, all pre­tend­ing to be ran­dom mod­ern browsers. An IP like that would make 4 – 5 re­quests and then never show up in the logs again. There was no point in ban­ning them, be­cause by the time you fig­ured out that they were bots, they were al­ready done with you. You just need­lessly bal­looned your fire­wall rule­set by adding IPs that would never be back.

They de­scended like swarms of lo­cust, hit hard and fast un­til the sys­tem fell over and then moved on to the next tar­get un­til you re­cov­ered. Then, they re­turned. Rinse. Repeat.

They still do that — wel­come to the won­der­ful world of “proxy SDK mon­e­ti­za­tion.” It’s big busi­ness, and your TV is prob­a­bly do­ing it.

Make them pay

When this first be­came a prob­lem, oh, about a year ago, we naively thought that there was a way to make it stop. Just make the bots per­form a task that would flip the econ­omy of the whole thing up­side-down by mak­ing them burn some cy­cles do­ing throw­away math. Like, cal­cu­late what string, when com­bined with their own IP and a se­cret we pro­vide, would gen­er­ate a sha256 sum with 4 lead­ing ze­roes.

In other words, we put Anubis in front of every­thing.

It was im­me­di­ately ex­tremely ef­fec­tive — the bots just gave up. For a few months, it was bliss: bots were blocked at the perime­ter and gave up, mov­ing on to eas­ier tar­gets; the users were mildly an­noyed but tol­er­ated it, and the Anubis stack was easy enough to de­ploy every­where.

A few months later, the bots were back, solv­ing dif­fi­culty 4. No prob­lem, we said, let’s raise dif­fi­culty to 5.

The le­git­i­mate users were more an­noyed now. Difficulty 5 takes a few sec­onds to solve on a mo­bile de­vice, and the phone gets un­com­fort­ably warm as it’s do­ing the num­ber crunch­ing. However, it was ef­fec­tive and bought us a few more months of peace.

Then… the bots started solv­ing dif­fi­culty 5.

Where we are now

Today, git.ker­nel.org re­ceives about 6M daily re­quests de­mand­ing to see ran­dom com­mits. Of these, 66% are still im­me­di­ately bat­ted away with the Anubis chal­lenge, but 33% are now solv­ing the math and get­ting through to the main site — be­cause ap­par­ently what we have to of­fer is worth spend­ing a ton of cy­cles to cal­cu­late the Anubis chal­lenge.

It’s im­pos­si­ble to tell with cer­tainty which of these are bots and which are real hu­mans — but chances are, if it’s ask­ing for an old com­mit in a ran­dom old fork, it’s prob­a­bly not a real de­vel­oper try­ing to do their work.

With a bunch of gen­er­ous as­sump­tions, le­git­i­mate re­quests are only about 2% of git.ker­nel.org traf­fic — every­thing else are scrap­ers.

How bad is it?

At this point, we’re not quite over­whelmed — if you visit git.ker­nel.org, it will likely be snappy and re­spon­sive. The thing that usu­ally takes us down are not scraper bots, but poorly de­signed CI sys­tems that try to do some­thing stu­pid like shal­low-clone sta­ble.git from 20 dif­fer­ent nodes, all at the same time. (Shallow clones are aw­ful. Run your own damn mir­ror if you’re go­ing to do some­thing nasty like that.)

However, you should know that out of the to­tal of the 90 cores across 5 geo-dis­trib­uted nodes, there are 14 – 16 cores that are con­stantly do­ing noth­ing but ren­der­ing com­mits for scrap­ers. On av­er­age, that’s 20% of our en­tire ca­pac­ity — ex­cept the swarms de­scend in waves and the ac­tual graph is a lot more spiky than a 20% flat­line.

Where does that leave us?

Unclear. Maybe the AI bub­ble bursts and we sud­denly have a lot fewer en­ti­ties out there try­ing to train their mod­els. Alternatively, maybe they smarten up and stop con­sum­ing our data in the dumb­est way pos­si­ble.

In terms of what we’re do­ing, we’re turn­ing off fea­tures to re­duce the num­ber of crawlable URLs and to gate off ac­tions that are ex­pen­sive for us to run. Expect to lose some func­tion­al­ity, at least when ac­cess­ing our re­sources anony­mously. Trust me, we hate it just as much as you, but at this point it’s a ne­ces­sity.

Worst of all, there are no sim­ple so­lu­tions to the prob­lem. Companies of­fer­ing cus­tom “AI” mod­els still pop up daily, all of them hun­gry for train­ing data. App mak­ers are still look­ing for ways to turn a profit, so they will con­tinue to turn your house­hold ap­pli­ances into at­tack vec­tors.

That said, we promise to still of­fer all of our data for down­load to any­one who asks. You just may have to jump through more hoops to get it.

Sorry. (Oblig. Canadian thing to say.)

se­cu­rity

Polling finds two-thirds don’t trust this gov­ern­ment, or any fu­ture one, with ac­cess to their en­crypted chats

Brits have de­liv­ered a fairly un­am­bigu­ous ver­dict on giv­ing the gov­ern­ment ac­cess to their en­crypted mes­sages: no, thanks.

New polling com­mis­sioned by the Center for Democracy & Technology (CDT) found that 93 per­cent of British adults be­lieve they have a right to pri­vate con­ver­sa­tions on­line, while 89 percent think no­body should be able to ac­cess their per­sonal mes­sages with­out a court or­der.

Perhaps more awk­wardly for Westminster, two-thirds said they would not trust ei­ther the cur­rent gov­ern­ment or any fu­ture one with the power to ac­cess en­crypted mes­sages.

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That dis­trust crosses po­lit­i­cal lines. Among peo­ple who voted in the 2024 gen­eral elec­tion, 58 percent of Labour vot­ers said they would­n’t trust any gov­ern­ment with the power, along­side 59 percent of Conservatives, 56 percent of Liberal Democrats, 69 percent of Greens and 75 percent of Reform vot­ers.

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Public First did the ask­ing, polling 2,000 British adults for CDT in April and weight­ing the re­sults to re­flect the wider pop­u­la­tion. The mar­gin of er­ror is 2.2 per­cent­age points.

The find­ings land as the UK gov­ern­men­t’s ap­petite for slurp­ing en­crypted data con­tin­ues to col­lide with the tech in­dus­try’s in­sis­tence that en­cryp­tion works best when no­body has a spare key ly­ing around.

That fight be­came par­tic­u­larly pub­lic when Apple with­drew Advanced Data Protection from UK users af­ter re­ceiv­ing a se­cret Technical Capability Notice (TCN) un­der the Investigatory Powers Act. Apple chal­lenged the or­der, and while the US gov­ern­ment later said Britain had with­drawn its de­mand for ac­cess to Americans’ en­crypted data, re­ports have since sug­gested an­other TCN was is­sued fo­cus­ing on British users.

Despite the in­ter­na­tional row, 55 percent of those polled had­n’t heard about the Apple no­tice at all. Once pre­sented with the idea, en­thu­si­asm re­mained thin.

Just 12 percent backed the gov­ern­ment be­ing able to se­cretly or­der com­pa­nies to pro­vide ac­cess to users’ in­for­ma­tion while pre­vent­ing those com­pa­nies from re­veal­ing the or­der. A third said the gov­ern­ment should­n’t have that power at all, while an­other 41 percent wanted greater trans­parency or par­lia­men­tary over­sight.

Nor were re­spon­dents par­tic­u­larly sold on sac­ri­fic­ing se­cu­rity for law en­force­ment. 53 percent said the se­cu­rity risks of ac­cess­ing en­crypted mes­sages out­weighed the ben­e­fits, com­pared with 28 percent who thought the ben­e­fits came out on top.

The rea­sons will sound fa­mil­iar to any­one who has fol­lowed the en­cryp­tion de­bate for more than five min­utes. 84 percent wor­ried that mech­a­nisms al­low­ing ac­cess to en­crypted mes­sages could in­tro­duce vul­ner­a­bil­i­ties for hack­ers and crim­i­nals, while 82 percent were con­cerned the pow­ers could be abused.

Knowing some­one might be watch­ing could also change how peo­ple be­have. 65 percent said they’d be­come more cau­tious about what they liked, shared or com­mented on, while 41 percent said they’d self-cen­sor crit­i­cism of pub­lic in­sti­tu­tions or gov­ern­ment of­fi­cials.

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CDT is not a dis­in­ter­ested ob­server: the dig­i­tal rights group cam­paigns for strong en­cryp­tion and com­mis­sioned the re­search as part of that work. The polling it­self, how­ever, was car­ried out in­de­pen­dently.

Commenting on the re­search, Jim Killock, ex­ec­u­tive di­rec­tor of Open Rights Group, said: “The British pub­lic in­stinc­tively know that be­ing able to com­mu­ni­cate pri­vately is cru­cial to our in­di­vid­u­al­ity and to the sur­vival of a free and open so­ci­ety.

“The gov­ern­ment per­sists with the myth that it can weaken en­cryp­tion to tar­get the bad guys only. Attacks on the se­cu­rity of our phones, se­cu­rity tools and mes­sag­ing apps harm us all and make our democ­racy weaker.”

None of this is likely to end Westminster’s long-run­ning pur­suit of en­crypted com­mu­ni­ca­tions. But if min­is­ters were hop­ing the pub­lic was en­thu­si­as­ti­cally be­hind them, the num­bers sug­gest oth­er­wise. ®

Over the last few months, I’ve been work­ing on im­prov­ing CPython’s sup­port for the RISC-V ar­chi­tec­ture, and I’m thrilled to an­nounce that RISC-V is now of­fi­cially sup­ported by CPython as a tier 3 plat­form! 🎉 🚀

What is RISC-V?

RISC-V is an open in­struc­tion set ar­chi­tec­ture (ISA). Importantly, un­like pro­pri­etary in­struc­tion sets (such as x86 and ARM), it is de­vel­oped as an open stan­dard and can be im­ple­mented by any­one.

Its ecosys­tem has grown con­sid­er­ably in re­cent years and is pro­jected to quadru­ple by 2032. With that growth, it’s in­creas­ingly im­por­tant that Python works re­li­ably on these plat­forms.

Getting here

This would not have been pos­si­ble with­out com­mu­nity con­tri­bu­tions. RISC-V sup­port in CPython has de­vel­oped over time with peo­ple test­ing on real hard­ware, fix­ing ar­chi­tec­ture-spe­cific is­sues, im­prov­ing build sup­port, re­port­ing bugs, and re­view­ing patches. That work is what has brought the plat­form to the point where it could be added to PEP 11.

A par­tic­u­larly im­por­tant part of this has been hav­ing re­li­able, on­go­ing test­ing on real RISC-V hard­ware. I’d like to thank the RISE Project for their sup­port. RISE has kindly pro­vided sev­eral RISC-V ma­chines for CPython, giv­ing us build­bots for test­ing as well as de­bug­ging ar­chi­tec­ture-spe­cific is­sues.

I’d es­pe­cially like to thank Ludovic Henry from the RISE Project, Furkan Onder, and Emma Smith, along with the many oth­ers who have con­tributed. Additionally, I’m per­son­ally grate­ful for the Sovereign Tech Agency, which through their amaz­ing fel­low­ship sup­ported my work on this.

What’s next?

While tier 3 sup­port is an im­por­tant mile­stone, there’s plenty more to do. We are cur­rently in­ves­ti­gat­ing how to im­prove our test­ing fur­ther by bring­ing RISC-V di­rectly into CPython’s CI, again kindly sup­ported by RISE with their RISE RISC-V Runners ini­tia­tive. This should give con­trib­u­tors faster feed­back than the build­bots (which usu­ally run af­ter a patch is merged) and would al­low us to catch RISC-V-specific prob­lems ear­lier.

In the long term, I’d also love to work to­wards pro­mot­ing RISC-V to tier 2 sup­port.

There are also op­por­tu­ni­ties to move be­yond sim­ply mak­ing CPython work on RISC-V. I’d like to ex­plore ar­chi­tec­ture-spe­cific op­ti­miza­tions to take bet­ter ad­van­tage of RISC-V ca­pa­bil­i­ties where do­ing so can im­prove CPython’s per­for­mance.

Importantly, we need peo­ple to use it and give us feed­back. If you have ac­cess to RISC-V hard­ware, please try build­ing and run­ning CPython, run your work­loads and test suites, and please let us know what breaks. Testing across dif­fer­ent RISC-V en­vi­ron­ments will help us make sup­port bet­ter for every­one!

CPython is also only one part of the Python ecosys­tem. Continued com­mu­nity work across pack­ages, com­pil­ers, tool­ing, and in­fra­struc­ture will be im­por­tant in mak­ing RISC-V an in­creas­ingly well-sup­ported plat­form for Python as a whole.

There’s a lot still to do, and I’m look­ing for­ward to con­tin­u­ing that work!

Why?

Study af­ter study shows that us­ing LLMs can cause you to ac­cu­mu­late cog­ni­tive debt, make you less en­gaged with your work, neg­a­tively im­pact your crit­i­cal think­ing abil­i­ties, and ham­per your skill for­ma­tion.

Constant use of AI cre­ates blind spots. When we of­fload de­ci­sion-mak­ing, we be­come un­aware of the trade-offs. You can use No AI Fridays to as­sess what’s ac­tu­ally hap­pen­ing and ret­ro­spect on the choices the AI made for you. Make sure the di­rec­tion it’s steer­ing you to­wards is still aligned with your per­sonal pref­er­ences and style.

If the pro­duc­tiv­ity gains from AI are so big, spend­ing one day a week to min­i­mize its down­sides should­n’t be a dif­fi­cult trade-off.

By de­fault­ing to AI we miss op­por­tu­ni­ties for good old au­toma­tion. One day a week with­out to­kens can lead to sig­nif­i­cant to­ken us­age re­duc­tion over the long term.

If that’s still not enough for you, try to re­mem­ber when cod­ing was fun—how it felt to en­ter a flow state and de­liver cool stuff you were gen­uinely con­nected to and proud of.

How?

Just send this link to who­ever is the boss at your shop and ask them if you can do it. The ba­sic setup is sim­ple: turn off your AI as­sis­tants for the day, write code with your own hands, read the doc­u­men­ta­tion, and think things through your­self.

Who?

For now, as the CEO of HTMX, I’ve man­dated No AI Fridays.

htmx

If you want your com­pany to be added to the list, just ping me at @lazilyevaluated and I will add you.

FAQ

How do I con­vince my boss to do No AI Fridays at my shop?

Just send them a link to this web­site.

Can I still use grep­tile, pre­lint, etc.?

These are ac­tu­ally awe­some when used with hand­crafted code—they give you feed­back that you can learn from. Your LLM is not learn­ing shit from their feed­back, but you can.

Can I do more than one day per week?

Sure, de­pend­ing on how and what you do, you may find your bal­ance with up to 7 days with­out AI.

Can I do less than one day per week?

You are push­ing it, mate.

Can I just take a peek or two?

A cou­ple of shots of Claude or a pint of Codex is al­ways best when you try to quit, right?

Can I quit af­ter a cou­ple of weeks?

If quit­ting is what you like.

I have a ques­tion

Just send it to @lazilyevaluated.