20 hours ago

Rorey Bosotti

Getty Images

German en­ergy com­pany RWE has said it will aban­don its off­shore wind pro­jects in the US af­ter reach­ing a $1.2bn (£892m) pay­out deal with President Donald Trump’s Department of the Interior (DoI).

RWE said that it will now rein­vest the sum into con­ven­tional gas pro­jects, in­clud­ing $900m (£669m) in a liq­ue­fied nat­ural gas (LNG) ex­port ter­mi­nal pro­ject in Louisiana.

“After care­ful con­sid­er­a­tion, it was de­ter­mined there is no path for­ward to per­mit these pro­jects in the US for the fore­see­able fu­ture,” the com­pany said in a state­ment.

Trump has de­rided wind power for years and of­ten uses his rally speeches to rail against “ugly” tur­bines.

RWE said it has agreed to re­lin­quish its leases off the California and Louisiana coasts as well as in the New York Bight.

Overall, the German firm plans to in­vest ap­prox­i­mately €17bn (£14.5bn; $19.6bn) in the US over the next six years “to grow its gen­er­a­tion ca­pac­ity”.

Interior Secretary Doug Burgum said in a state­ment posted on X that Americans de­serve an en­ergy sys­tem built on com­mon sense and not one de­pen­dent on “costly sub­si­dies”.

“We wel­come RWE’s agree­ment and vol­un­tary in­vest­ment in pro­jects that strengthen our na­tion’s en­ergy se­cu­rity,” he added.

The deal is the lat­est the Trump ad­min­is­tra­tion has reached this year as Trump, a vo­cal sup­porter of the fos­sil fuel in­dus­try, con­tin­ues his push to halt off­shore wind pro­jects.

Days af­ter his re­turn to of­fice, he said “we’re not go­ing to do the wind thing” and called them “big, ugly wind­mills” that were dan­ger­ous to wildlife.

And this week, he said that “any coun­try with wind­mills is a loser”.

In March 2026, the DoI reached a deal with TotalEnergies putting an end to the French com­pa­ny’s off­shore wind pro­jects in the US.

Instead, the firm agreed to reroute in­vest­ment to build a LNG plant in Texas and to de­velop “upstream con­ven­tional oil” in the Gulf of Mexico.

The ad­min­is­tra­tion signed a sim­i­lar $129m (£96m) agree­ment with Charlotte-based Duke Energy last month in ex­change for the ter­mi­na­tion of the com­pa­ny’s off­shore wind lease in the Carolina Long Bay area.

Credits

Aaron Horwath is the di­rec­tor of AI op­er­a­tions at a cre­ative tech­nol­ogy com­pany where he is fo­cused on im­ple­ment­ing AI in a way that sup­ports both hu­mans and busi­ness.

On a re­cent morn­ing com­mute, I sat on a train in one of those awk­ward four-per­son con­fig­u­ra­tions with a shared table. Across from me sat a typ­i­cal com­muter: early 30s, slacks, dress shirt, dirty white sneak­ers, hair a lit­tle di­sheveled, AirPods in, bask­ing in the glow of an open MacBook.

For over half an hour, I lis­tened to this young man as he was on a call ex­plain­ing, in painfully mo­not­one de­tail, EBITDAs, mar­gin ex­pan­sion op­por­tu­ni­ties, cost struc­tures, ARR, etc. On and on he went un­til the screech­ing of the train’s brakes sig­naled our ar­rival at the fi­nal sta­tion. But as every­one else around us be­gan shuf­fling to dis­em­bark, I watched the man be­gin dig­ging fran­ti­cally through a leather bag at his side. Intriguing! I won­dered what he’d pull out. A copy of “Atomic Habits”? A framed por­trait of Gary V? A Mac mini run­ning OpenClaw?

None of the above. Instead, he took out two long knit­ting nee­dles. Between them dan­gled a mound of pink yarn. He ex­plained to me that he was mak­ing a win­ter hat for a niece. And for the first time that morn­ing, I no­ticed a glint of pride and ex­cite­ment in his eyes.

The hat was a pro­ject born from a de­sire, as he put it, to do some­thing.

Among my Knowledge Worker peers, I am hear­ing this more and more of­ten: peo­ple who want to pick up pot­tery, paint­ing, cro­chet or other old-timey ana­log hob­bies. In cof­fee shops and in the low-lit cor­ners of bars, pro­fes­sion­als are shar­ing dreams of “disappearing” or “living on a farm some­where” or “going off the grid.” These aren’t fan­tas­ti­cal day­dreams; they are vi­sions of es­cape shared in a tone that be­trays an un­der­ly­ing ex­is­ten­tial angst, a fun­da­men­tal doubt about work and ca­reerism in­spired by a seem­ingly in­creas­ingly com­mon ex­pe­ri­ence: wak­ing up one morn­ing at an ex­is­ten­tial precipice, struck with a sud­den sense that knowl­edge work is, and per­haps al­ways has been, point­less. Perhaps you too have felt such a feel­ing, a ris­ing tide of over­whelm­ing, in­de­scrib­able melan­choly, slowly threat­en­ing to en­velop you in your Ikea of­fice chair.

“Among my Knowledge Worker peers, I am hear­ing this more and more of­ten: peo­ple who want to pick up pot­tery, paint­ing, cro­chet or other old-timey ana­log hob­bies.”

“Among my Knowledge Worker peers, I am hear­ing this more and more of­ten: peo­ple who want to pick up pot­tery, paint­ing, cro­chet or other old-timey ana­log hob­bies.”

This dis­il­lu­sion­ment is in­fec­tious. One per­son speaks of it and oth­ers be­gin nod­ding: They too have felt it, the drop in mo­ti­va­tion, the sense of be­ing dis­tant from their work, the lack of sleep, the con­cerns about the fu­ture. These con­ver­sa­tions in­evitably turn to fun­da­men­tal ques­tions about ca­reers: What the fuck are we ac­tu­ally do­ing? What the fuck is the point of all of this?

We are, it’s true, liv­ing through a time of dis­rup­tion. But what seems to dif­fer­en­ti­ate this pe­riod from those of the past is the na­ture of the angst it­self. Yes, AI is threat­en­ing jobs and dis­rupt­ing in­dus­tries. But Knowledge Workers have faced re­ces­sions, out­sourc­ing, new tech­nol­ogy and au­toma­tions of many kinds be­fore. Recent grad­u­ates al­ways worry about break­ing into the job mar­ket. Millennial pro­fes­sion­als have nav­i­gated eco­nomic un­cer­tainty their en­tire ca­reers. What’s dif­fer­ent about this mo­ment is that the ques­tions are not just eco­nomic but ex­is­ten­tial, the kind of ques­tions that cause high-earn­ing tech­ni­cal pro­fes­sion­als to con­tem­plate throw­ing it all away to start a goat farm in Washington or be­come a surf in­struc­tor in Costa Rica.

It strikes me as sig­nif­i­cant that the peo­ple who are usu­ally the most in­su­lated from eco­nomic up­heaval, and seem to be well-po­si­tioned to ride out AI’s near-term im­pacts — highly paid ex­ec­u­tives and se­nior pro­fes­sion­als with decades of in­sti­tu­tional knowl­edge — are also un­cer­tain about the fu­ture of their ca­reers amid the rapid change nearly every in­dus­try is un­der­go­ing.

Some will say: good, fuck ’em. In the 2010s, Knowledge Workers told every­one to learn to code while they sipped kom­bucha and played Xbox in bean­bag chairs. Then, Knowledge Workers sat in­side dur­ing the pan­demic while front­line work­ers risked their health to make Amazon and Uber Eats de­liv­er­ies. Now, those same Knowledge Workers are build­ing AI that threat­ens to elim­i­nate work for hu­mans across in­dus­tries and make a very few peo­ple wealthy be­yond imag­i­na­tion. And these same ass­holes want pity now?

To that I say: fair enough. But this sweep­ing dis­en­chant­ment begs a fas­ci­nat­ing (or ter­ri­fy­ing or sad) set of ques­tions: What is this angst plagu­ing Knowledge Workers? And what hap­pens to a so­ci­ety and its in­dus­tries if an en­tire class of work­ers loses faith in their ca­reers overnight?

Workism: Praise Thee

In 2019, Derek Thompson wrote in The Atlantic about “American Workism” where he de­scribed a trend among Knowledge Workers, par­tic­u­larly in the U.S., of in­creas­ingly seek­ing ful­fill­ment, com­mu­nity and a sense of mean­ing from work that pre­vi­ous gen­er­a­tions had gar­nered from re­li­gion. As Thompson wrote, Workism is “emotional — even spir­i­tual. The best-ed­u­cated and high­est-earn­ing Americans, who can have what­ever they want, have cho­sen the of­fice for the same rea­son that de­vout Christians at­tend church on Sundays: It’s where they feel most them­selves.”

People have long held ca­reers from which they’ve de­rived a deep sense of mean­ing. Traditionally, we’ve re­ferred to those ca­reers as vo­ca­tions: a call­ing, a way of life. More than a job: a pur­pose.

A vo­ca­tion em­pha­sizes skills, val­ues and the de­sire to con­tribute some­thing ben­e­fi­cial. Vocations have tra­di­tion­ally re­ferred to ca­reers that are chal­leng­ing, so­cially im­pact­ful, of­ten re­ward­ing in ways other than fi­nan­cial. These are your teach­ers, nurses, fire­fight­ers, so­cial work­ers, para­medics or even ser­vice providers with di­rect con­nec­tions to the com­mu­ni­ties and cus­tomers they serve, like me­chan­ics, plumbers or elec­tri­cians. Even on bad days, deep down, most of these folks find their work re­ward­ing in im­por­tant and in­tan­gi­ble ways.

But those aren’t the ca­reers young peo­ple have ded­i­cated their lives to. Instead, grad­u­ates are over­whelm­ingly tak­ing jobs in fi­nance, con­sult­ing or tech­nol­ogy. There’s no doubt that get­ting these jobs is com­pet­i­tive, and that they are de­mand­ing, com­plex and re­quire nav­i­gat­ing lay­ers of pol­i­tics and bu­reau­cracy, a ton of ass-kiss­ing, long work hours, heavy cog­ni­tive work­loads, ad­vanced skillsets and the emo­tional bur­den of a near-con­stant threat of lay­offs. But so much of the work lacks any al­tru­is­tic up­side.

“Perhaps you too have felt such a feel­ing, a ris­ing tide of over­whelm­ing, in­de­scrib­able melan­choly, slowly threat­en­ing to en­velop you in your Ikea of­fice chair.”

“Perhaps you too have felt such a feel­ing, a ris­ing tide of over­whelm­ing, in­de­scrib­able melan­choly, slowly threat­en­ing to en­velop you in your Ikea of­fice chair.”

In “Bullshit Jobs,” David Graeber cat­a­loged peo­ple who ad­mit­ted their jobs serve no mean­ing­ful func­tion. Slide decks built for pro­jects that will never launch. Heated de­bates over the minute de­tails of soft­ware fea­tures no­body asked for. Agonizing over ad­min­is­tra­tive processes to help money move from one rich per­son to an­other. Optimizing every word of an ad no one will no­tice for a ser­vice no one needs. Resting and vest­ing — when en­gi­neers and other highly paid work­ers get to sit around and wait for their stock to vest — and pro­mo­tion-dri­ven de­vel­op­ment, where de­vel­op­ers ig­nore what’s good in fa­vor of what ap­pears to be good when they’re up for a pro­mo­tion.

The al­tru­ism in these ca­reers, then, is hard to find. So how does any­one do it with­out los­ing their mind?

That’s the true beauty of Workism: It is man­u­fac­tured to dis­tract peo­ple from the hole in their souls that a vo­ca­tion would oth­er­wise fill. It is the opi­ate of com­muters in quar­ter zips. And it works. It keeps tal­ented peo­ple show­ing up at the of­fice to ar­gue over re­ports and strat­egy doc­u­ments and re­brand­ings with the se­ri­ous­ness of pe­di­atric heart surgery.

But Workism has a weak­ness. Like re­li­gion, it re­lies on faith’s tri­umph over logic. What would hap­pen, then, if some­thing threat­ened that faith? A par­a­digm shift that broke the spell of Workism? A sort of en­light­en­ment that caused Knowledge Workers to start ask­ing tough ques­tions of their or­ga­ni­za­tions and them­selves. And what would hap­pen if Knowledge Workers awoke from the spell of Workism with no fi­nan­cially vi­able al­ter­na­tive?

Well, it seems AI might of­fer us the plea­sure of find­ing out.

Popping The Workism Bubble

Knowledge work has al­ways been in­her­ently ab­stract. Plenty of these jobs, par­tic­u­larly at larger or­ga­ni­za­tions, are struc­tured like Russian nest­ing dolls: roles de­signed to sup­port other roles, which sup­port still other roles, layer af­ter layer, un­til it’s no longer clear where there’s a solid cen­ter to be found. It’s easy to see how, to a plumber, a car­pen­ter or a line cook, work of this na­ture can ap­pear like ex­actly what David Graeber called it: bull­shit.

Knowledge work’s one sav­ing grace, un­til re­cently, was that it was still ex­e­cuted by hu­mans. We were needed. It was flesh-and-blood hu­mans who sat down to work through a chal­lenge, built the slide deck, wrote the cus­tomer re­sponse and de­vel­oped the strat­egy. Even if it was ex­is­ten­tially mean­ing­less, there was hu­man thought, col­lab­o­ra­tive work and cre­ativ­ity poured into that work, giv­ing it life.

Now, AI agents are in­creas­ingly ex­e­cut­ing much of that work for Knowledge Workers. It is com­mon for peo­ple re­spon­si­ble for in­te­grat­ing these tools into their or­ga­ni­za­tions, my­self in­cluded, to de­scribe the fu­ture of work as one in which all hu­mans will es­sen­tially be man­agers of armies of AI agents. That seems pretty great. Let the soft­ware com­pile the re­ports, chase down the data, for­mat the deck, draft the first pass of doc­u­men­ta­tion and han­dle the dozens of small, repet­i­tive tasks that used to qui­etly eat an af­ter­noon. But in many cases, em­ploy­ees un­der pres­sure from lead­ers to pro­duce more are us­ing AI agents for far more than grunt work: for­mu­lat­ing com­plete busi­ness strate­gies, gen­er­at­ing full mar­ket­ing cam­paigns, build­ing en­tire web­sites, draft­ing strat­egy for whole di­vi­sions of an or­ga­ni­za­tion. From a sin­gle email to an en­tire cor­po­rate strat­egy, the out­puts of in­di­vid­u­als, teams and or­ga­ni­za­tions are in­creas­ingly gen­er­ated in an in­stant by AI.

But does this power — this ad­di­tional level of ab­strac­tion — take peo­ple too far, in some sense, from their work? Does some­thing feel … off … about hav­ing some­one, or some­thing, else ex­e­cute nearly all the work, even if the end prod­uct did­n’t feel very mean­ing­ful to be­gin with?

Debord’s Spectacle: I Don’t Wanna Do This Anymore

“In so­ci­eties where mod­ern con­di­tions of pro­duc­tion pre­vail, life is pre­sented as an im­mense ac­cu­mu­la­tion of spec­ta­cles. Everything that was di­rectly lived has re­ceded into a rep­re­sen­ta­tion.”

That’s the open­ing para­graph of Guy Debord’s “The Society of the Spectacle.” I will fail to sum­ma­rize the book ad­e­quately. It is si­mul­ta­ne­ously ex­cit­ing and im­pen­e­tra­ble. But the main thrust of Debord’s ar­gu­ment is this: Spectacle is a fea­ture of late cap­i­tal­ism in which life, rather than be­ing di­rectly lived, is per­pet­u­ally me­di­ated. There is al­ways some­thing be­tween us and the thing we are sup­posed to be ex­pe­ri­enc­ing. I don’t talk to my mom; I text her. I don’t travel; I watch other peo­ple travel on YouTube. I don’t have sex; I watch porn. In late cap­i­tal­ism, the medium re­places the ex­pe­ri­ence.

Workism is a mi­cro­cosm of the larger spec­ta­cle: a world where ap­pear­ing busy, im­por­tant and uniquely knowl­edge­able is as valu­able as ac­tu­ally be­ing any of those things, and where work needs only to ap­pear im­pact­ful rather than ac­tu­ally be im­pact­ful. As Debord writes in Thesis 12: “The spec­ta­cle pre­sents it­self as a vast, in­ac­ces­si­ble re­al­ity that can never be ques­tioned. Its sole mes­sage is: ‘What ap­pears is good; what is good ap­pears.’” This is Workism’s most con­vinc­ing ar­gu­ment: The work must be im­por­tant be­cause, well, we’re all here, aren’t we? Signing in. Staying late. Every week.

“The ques­tions to­day are not just eco­nomic but ex­is­ten­tial.”

“The ques­tions to­day are not just eco­nomic but ex­is­ten­tial.”

Adding AI to the spec­ta­cle feels ex­is­ten­tially daunt­ing be­cause it moves us even fur­ther from the work we do, and its value. I don’t build the pitch that wins the client; I write the query that tells the AI to write it, and then I check the work af­ter­ward. I don’t gather the ma­te­ri­als and write the in­dus­try newslet­ter; my agent does it. Before, that work might’ve felt cheap and un­sat­is­fy­ing, deep down, but it was still ours. Now AI is be­ing forced on or­ga­ni­za­tions in ways that call the value of the en­tire en­ter­prise of Workism it­self into ques­tion.

And this is where things get par­tic­u­larly in­ter­est­ing. I don’t think Debord imag­ined some­thing so seis­mi­cally par­a­digm-shift­ing that it could ab­stract work to an ex­tent that it would shake the work­ing class, or, in the case of Knowledge Workers, enough to rup­ture a spec­ta­cle like Workism. But with the in­tro­duc­tion of AI, it is as if, over the past 30 years, we have been slowly tak­ing steps away from the di­rect ex­pe­ri­ence of life and work, and AI risks push­ing us far enough that the il­lu­sion be­comes en­tirely vis­i­ble.

Organizations find them­selves in a pickle. They want to in­te­grate AI into their op­er­a­tions, as do their share­hold­ers. And they will. In the short term, the po­ten­tial ef­fi­ciency gains are too good to pass up. And, done right, it can of­fer ben­e­fits to both busi­nesses and em­ploy­ees. But what makes many ex­ec­u­tives most ex­cited about AI — less col­lab­o­ra­tion, fewer peo­ple — risks dis­man­tling the struc­tures that hold the very or­ga­ni­za­tions they lead to­gether.

What if the en­light­en­ment from Workism, iron­i­cally, is de­liv­ered by Workism’s most rev­o­lu­tion­ary prod­uct? And what does that mean for the em­ploy­ees who have spent their ca­reers pray­ing at the Workism al­tar?

What’s At Stake: The Value Of The Messy Middle

To un­der­stand why AI threat­ens to kill Workism specif­i­cally, we have to un­der­stand what has kept faith in Workism alive.

In Thompson’s ar­ti­cle, he states that one thing peo­ple look for in Knowledge Work jobs is com­mu­nity: to spend time with like-minded peo­ple with sim­i­lar in­ter­ests, to col­lab­o­rate with oth­ers to solve prob­lems. Relationships are the foun­da­tion of the hu­man work­place, and Knowledge Work’s re­deem­ing value. None of us will make it to Knowledge Work’s pearly gates, but at least we will make our false jour­ney to­gether.

Ironically, it’s a grow­ing dream of ex­ec­u­tives that an or­ga­ni­za­tion of em­ploy­ees armed with a swarm of agents and pow­er­ful LLMs no longer need to col­lab­o­rate with one an­other to gather in­for­ma­tion, ideate or ex­e­cute a pro­ject. In their vi­sion for the fu­ture, work goes from a messy ex­pe­ri­ence of learn­ing and ex­plor­ing to some­thing more akin to as­sem­bly-line pro­duc­tion. As Debord wrote back in 1967, “This pro­le­tariat is be­ing ob­jec­tively re­in­forced by the vir­tual elim­i­na­tion of the peas­antry and by the in­creas­ing de­gree to which the ‘service’ sec­tors and in­tel­lec­tual pro­fes­sions are be­ing sub­jected to fac­tory-like work­ing con­di­tions.”

But some­times messy in­ef­fi­ciency is a fea­ture, not a bug. In a re­cent episode of Bill Simmons’s pod­cast, the writer Chuck Klosterman ar­gued with Bill about the role of tech­nol­ogy in sports, par­tic­u­larly when it comes to ref­er­ee­ing. Consider ten­nis. The days of Johnny Mac blow­ing up at a ref­eree over a bad line call are over. With Hawk-Eye tech­nol­ogy, the judg­ment is right 100% of the time. In the NBA, the chal­lenge sys­tem now helps to en­sure that bad calls do not change a game. The MLB has in­te­grated an au­to­mated ball-strike sys­tem. These tech­nolo­gies have been im­ple­mented with very dif­fer­ent lev­els of suc­cess, but the goals are the same: Get of­fi­ci­at­ing right more of­ten — maybe al­ways. Who could com­plain? It’s so ef­fi­cient!

“What would hap­pen if Knowledge Workers awoke from the spell of Workism with no fi­nan­cially vi­able al­ter­na­tive? It seems AI might of­fer us the plea­sure of find­ing out.”

“What would hap­pen if Knowledge Workers awoke from the spell of Workism with no fi­nan­cially vi­able al­ter­na­tive? It seems AI might of­fer us the plea­sure of find­ing out.”

Klosterman ar­gues that bad calls are a nat­ural and fun part of games. In fact, bad calls have cre­ated iconic sports mo­ments that peo­ple still talk about. Sports are hu­man con­structs and the messi­ness of hu­man er­ror, whether by player or ref­eree, is part of them. Getting calls right 100% of the time may be ob­jec­tively bet­ter, but it is sub­jec­tively less in­ter­est­ing and en­ter­tain­ing. And en­ter­tain­ment is the goal of sport.

Knowledge work is strik­ingly sim­i­lar. Sure, an AI that can pro­duce in min­utes a spot-on pro­ject that would have taken hours is cool. But the rate of slide deck cre­ation is­n’t what is go­ing to re­tain tal­ent. Employees over­whelm­ingly choose to re­main or leave their roles be­cause of their col­leagues and/​or bosses, and the qual­ity of the ex­pe­ri­ence of the messy mid­dle a work­place of­fers. Those are the el­e­ments that make work fun—and valu­able.

But not every­one feels that way. In my ex­pe­ri­ence, there are two broad cat­e­gories of Knowledge Workers. The first are out­come-first work­ers. Their fo­cus is on the busi­ness, on win­ning, on ef­fi­ciency. Human needs and faults and emo­tions are an ob­sta­cle to be over­come.

The sec­ond group has an ex­pe­ri­ence-first per­spec­tive. These work­ers love the messy mid­dle. They value the jour­ney. They want their or­ga­ni­za­tions to per­form well but as a nat­ural con­se­quence of col­lab­o­ra­tion, de­bate and shared strug­gle with peo­ple they ac­tu­ally like. Many ex­pe­ri­ence-first peo­ple are artists out­side of work. Photographers, di­rec­tors, screen­writ­ers, sculp­tors, painters, po­ets, writ­ers. Many are ac­tive vol­un­teers. For these peo­ple, to be pulled away from their eco­nom­i­cally un­vi­able pas­sions re­quires some­thing in re­turn: a com­pany ex­pe­ri­ence with free­dom for ex­plo­ration, cre­ativ­ity, prob­lem solv­ing and cool col­leagues.

Research sug­gests this group needs that en­vi­ron­ment to do their best work. Harvard Business School’s Teresa Amabile spent decades study­ing what pro­duces gen­uinely cre­ative, high-qual­ity out­put. Her Intrinsic Motivation Principle de­ter­mines that peo­ple do their most cre­ative and in­no­v­a­tive work when mo­ti­vated by the work it­self — the in­ter­est, the chal­lenge, the en­joy­ment — not by out­comes or met­rics. The en­vi­ron­ments that kill cre­ativ­ity are po­lit­i­cal, risk-averse and re­lent­lessly out­come-fo­cused. The en­vi­ron­ments that stim­u­late it are col­lab­o­ra­tive, idea-dri­ven and free. The messy mid­dle, in other words, is­n’t in­ef­fi­cient. It’s the con­di­tion un­der which gen­uinely valu­able work gets pro­duced.

“Workism is a mi­cro­cosm of the larger spec­ta­cle: a world where ap­pear­ing busy, im­por­tant and uniquely knowl­edge­able is as valu­able as ac­tu­ally be­ing any of those things.”

“Workism is a mi­cro­cosm of the larger spec­ta­cle: a world where ap­pear­ing busy, im­por­tant and uniquely knowl­edge­able is as valu­able as ac­tu­ally be­ing any of those things.”

It’s im­por­tant to note that the im­pact of re­mov­ing the messy mid­dle from the work ex­pe­ri­ence of these two groups is asym­met­ri­cal: For out­come-first peo­ple, it is a vic­tory. For ex­pe­ri­ence-first peo­ple, it un­der­mines the foun­da­tion of work.

As I’m writ­ing this es­say, big com­pa­nies are on a lay­off ben­der. Thousands of peo­ple are be­ing fired all over the place. Executives are of­ten claim­ing these head­count re­duc­tions are a re­sult of AI. They aren’t. At the mo­ment, AI au­toma­tion is not cre­at­ing any­where near the in­creased ef­fi­ciency nec­es­sary to jus­tify hun­dreds of thou­sands of lost jobs.

We’ve been through waves of hir­ing and fir­ing a thou­sand times be­fore. But this time might be dif­fer­ent. In the past, there was al­ways a fresh co­hort of work­ers wait­ing to be brought in. But what if tal­ent stops com­ing back? What if the AI trans­for­ma­tion gone wrong makes top tal­ent — par­tic­u­larly those who are ex­pe­ri­ence-first by na­ture — lose faith in Workism en masse? What if, when com­pa­nies go to re­plen­ish head­counts, tal­ent freed from the Workism spec­ta­cle has turned to dif­fer­ent ver­sions of their lives? What if their side pro­jects sud­denly be­came prof­itable? The tal­ent pool in­creas­ingly does­n’t own homes and does­n’t plan on hav­ing kids, so it has per­haps never been eas­ier to make a ca­reer pivot to­ward some­thing that is gen­uinely sat­is­fy­ing in ways cor­po­rate life is­n’t.

What would Debord say? Well, he had se­ri­ous doubts about the pos­si­bil­ity of leav­ing the spec­ta­cle be­hind.

Post-Workism: Escaping The Inescapable

As Debord puts it, “Complacent ac­cep­tance of the sta­tus quo may also co­ex­ist with purely spec­tac­u­lar re­bel­lious­ness—dis­sat­is­fac­tion it­self be­comes a com­mod­ity as soon as the econ­omy of abun­dance de­vel­ops the ca­pac­ity to process that par­tic­u­lar raw ma­te­r­ial.”

At some point, in other words, the spec­ta­cle will put eco­nomic pres­sure on you that you’ll need to meet. In to­day’s world, that might mean start­ing a YouTube chan­nel and a Substack about how you aban­doned your tech ca­reer to start a horse res­cue farm, ul­ti­mately turn­ing your­self and your life into a com­mod­ity that feeds the larger so­ci­etal spec­ta­cle. Or it might mean start­ing a com­pany of your own, thereby sud­denly need­ing to cre­ate a spec­ta­cle at­trac­tive to po­ten­tial em­ploy­ees. Either way, you are doomed to exit one ver­sion of the spec­ta­cle only to be ab­sorbed into an­other one. That’s the logic of the spec­ta­cle: It re­ab­sorbs even the dis­sat­is­fied in or­der to sus­tain it­self.

Debord be­lieved es­cape from the spec­ta­cle was im­pos­si­ble. He went on to dis­solve his own move­ment rather than watch it be­come in­sti­tu­tion­al­ized, and then drank him­self to death in the coun­try­side.

Some will cer­tainly leave Knowledge Work — the most dis­il­lu­sioned, the most ar­tis­ti­cally tal­ented or mo­ti­vated, the most ex­is­ten­tially sen­si­tive to a sud­den mo­ment of en­light­en­ment, the fi­nan­cially able. But many more will re­main, ei­ther by choice or ne­ces­sity, to face a work ex­pe­ri­ence where the hu­man­ity of work is erod­ing away. Are those who chose to re­main, or who sim­ply can­not leave, doomed to spend their ca­reers ex­is­ten­tially dis­traught?

“The en­vi­ron­ments that kill cre­ativ­ity are po­lit­i­cal, risk-averse and re­lent­lessly out­come-fo­cused. The en­vi­ron­ments that stim­u­late it are col­lab­o­ra­tive, idea-dri­ven and free.”

“The en­vi­ron­ments that kill cre­ativ­ity are po­lit­i­cal, risk-averse and re­lent­lessly out­come-fo­cused. The en­vi­ron­ments that stim­u­late it are col­lab­o­ra­tive, idea-dri­ven and free.”

Debord also be­lieved the spec­ta­cle could be un­der­mined through de­lib­er­ate acts: hi­jack­ing spec­tac­u­lar im­ages and turn­ing them against them­selves, drift­ing through ur­ban space in ways that re­sist its ge­og­ra­phy and con­struct­ing mo­ments of gen­uine, un­medi­ated ex­pe­ri­ence that the spec­ta­cle can­not me­tab­o­lize. At work, that might mean jump­ing on a call to talk through a prob­lem with a col­league in­stead of query­ing an agent for the an­swer. Or ex­e­cut­ing work the old-fash­ioned way — me­an­der­ing, ex­ploratory — with the un­der­stand­ing that it may be slower but might pro­duce some­thing more gen­uinely hu­man. Or sim­ply de­cid­ing that cer­tain work is best left to hu­man hands en­tirely. Anything that pre­serves the el­e­ments of work that have made it worth do­ing in the first place.

But per­haps the most pow­er­ful ac­tion of all is sim­ply to main­tain an aware­ness (and re­mind oth­ers) that Workism is a spec­ta­cle. For most em­ploy­ees, we owe it to each other to re­mind one an­other that this work is not, in the grand scheme of things, all that im­por­tant. It is il­lu­sory. No one has ever died over a spread­sheet. The world does not wait with bated breath for prod­uct launches. A mar­ket­ing cam­paign will not change the world. Almost no one will re­mem­ber all the work we do. But they might re­mem­ber the types of peo­ple we were, the re­la­tion­ships we had and how we treated the peo­ple we worked with.

And once freed from the false sat­is­fac­tion of be­liev­ing we are chang­ing the world with our day jobs, per­haps we will be in­spired to fill that hole with some­thing real — true al­tru­ism, not its ar­ti­fi­cial Workism sub­sti­tute — by ac­tu­ally try­ing to im­pact real peo­ple, in our com­mu­ni­ties, in real ways.

Even if it’s just one knit­ted hat at a time.

ARC Prize 2026

Get started and re­ceive of­fi­cial con­test up­dates and news.

No spam. You can un­sub­scribe at any time.

Oracle has banned AI-generated code from OpenJDK con­tri­bu­tions, cit­ing safety, se­cu­rity, and in­tel­lec­tual prop­erty risks. The open-source Java pro­ject stew­ard said de­vel­op­ers can use LLMs pri­vately for de­bug­ging and re­view­ing code but can­not sub­mit AI-generated ma­te­r­ial to repos­i­to­ries, pull re­quests, or other pro­ject chan­nels.

The pol­icy con­trasts sharply with Oracle’s in­ter­nal prac­tices. Co-founder Larry Ellison re­cently de­clared that AI mod­els now write Oracle’s code, whilst co-CEO Mike Sicilia cred­ited AI tools with en­abling smaller en­gi­neer­ing teams to de­liver faster.

Oracle is in­vest­ing $70 bil­lion this year in dat­a­cen­tre ex­pan­sion. The spend­ing spree prompted credit agency S&P to down­grade Oracle’s rat­ing to BBB-, one notch above junk sta­tus, cit­ing un­cer­tain re­turns on in­vest­ment.

Source: thereg­is­ter.com

The U.S. econ­omy shed 23,000 jobs in July, a sign that the la­bor mar­ket had not sta­bi­lized af­ter four months of pos­i­tive growth.

The un­em­ploy­ment rate ticked down only slightly to 4.1%.

Economists sur­veyed by Dow Jones were ex­pect­ing the re­lease to show 83,000 added roles, more than June’s 57,000.

In yet an­other trou­bling sign for the la­bor mar­ket, the Bureau of Labor Statistics said that it re­vised down the prior two months by a com­bined 103,000. May’s jobs to­tal was cut by 66,000 to 129,000 to­tal jobs added, while June’s to­tal was low­ered by 37,000 to a to­tal gain of 57,000.

The hir­ing data comes against a com­pli­cated eco­nomic back­drop. The U.S. war with Iran con­tin­ues with­out any kind of agree­ment to fully re­open the Strait of Hormuz. As a re­sult, en­ergy prices re­main el­e­vated, even if they are off their high­est lev­els of the year.

The change in work­ers’ av­er­age hourly earn­ings also fell well short of econ­o­mists’ ex­pec­ta­tions. Wage growth was 0.1% from June, or 3.2% from one year ago. That’s also be­low in­fla­tion, which was 3.5% in its most re­cent read­ing.

“That’s the num­ber that many Americans are fo­cused on right now,” Heather Long, chief econ­o­mist at Navy Federal Credit Union, told NBC News. Long pointed out that 3.2% was the low­est wage growth has been in five years. “At the same time, in­fla­tion is heat­ing back up again.”

Economists had been ex­pect­ing wages to con­tinue pac­ing at 3.5% from a year ago.

“The la­bor mar­ket is stalling again,” Long said, also call­ing the re­port “bleak.”

Long also pointed to an­other trou­bling data point: The la­bor force par­tic­i­pa­tion rate in July was the low­est since February 2021, a sign that work­ers are drop­ping out of the work­force. “It’s pretty shock­ing,” she said. “Over two mil­lion peo­ple have left the la­bor force since November.”

“The mag­ni­tude of the pay­roll miss sug­gests the la­bor mar­ket may be los­ing mo­men­tum and can no longer be con­sid­ered the pil­lar of strength,” said Allianz in­vest­ment strate­gist Charlie Ripley.

The av­er­age price of reg­u­lar gaso­line also re­mains high, at $4.04 per gal­lon as of Friday morn­ing, up 36% since Feb. 28, when the Iran war be­gan. Inflation re­mains well above the Federal Reserve’s 2% tar­get at 3.5%. Wages are strug­gling to keep pace.

The BLS said em­ploy­ment con­tracted the most in “local gov­ern­ment ed­u­ca­tion,” which de­clined by 50,000 roles, likely re­flect­ing teach­ers dur­ing sum­mer break. It also flagged a con­trac­tion of 19,000 roles in the re­tail in­dus­try. The fi­nan­cial in­dus­try shed 14,000 roles.

The leisure and hos­pi­tal­ity in­dus­try also con­tracted by 40,000 jobs. Economists watch this fig­ure closely be­cause a sig­nif­i­cant loss at ho­tels and restau­rants could be an early warn­ing sign of a shift in con­sumer spend­ing.

“In July, em­ploy­ment in health care con­tin­ued its up­ward trend,” the BLS said, not­ing a gain of 22,000 jobs. But it said, that was “a slower pace than the av­er­age monthly gain over the prior 12 months.”

The agen­cy’s data also showed a 5,000 pay­roll gain in the man­u­fac­tur­ing sec­tor in July and an ad­di­tional 22,000 roles in con­struc­tion. These bright spots come as the AI data cen­ter boom has ben­e­fited some in­dus­tries, but deeply di­vided many com­mu­ni­ties where the cen­ters are lo­cated.

“Employment showed lit­tle change” in July in the min­ing, oil & gas, trans­porta­tion and pro­fes­sional & busi­ness ser­vices sec­tors, ac­cord­ing to BLS.

Stocks rose af­ter the re­port, as in­vestors who were con­cerned the Federal Reserve would raise in­ter­est rates breathed a sigh of re­lief. The S&P 500 closed the day higher by 0.6% and the Nasdaq Composite in­dex soared 1.3%.

The Russell 2000, which tracks small and medium size firms that can be more sen­si­tive to rate changes, closed the trad­ing ses­sion up by 1.1%.

Bond yields dropped, with the 10-year U.S. Treasury yield falling to around 4.6%. That Treasury bond specif­i­cally dri­ves the di­rec­tion of con­sumer lend­ing rates, such as for mort­gages, credit cards and per­sonal loans.

The av­er­age 30-year fixed mort­gage rate was 6.74% on Friday af­ter­noon, its low­est level since June 21.

The jobs re­port likely eases some pres­sure on the Federal Reserve, which had been widely ex­pected to hike the fed­eral funds rate — po­ten­tially as soon as September.

Before Friday’s re­port, the fu­tures mar­ket in­di­cated the odds of a September rate hike were over 50%. After the jobs num­bers were re­leased, those odds fell to about 40%.

Assembly Hall of Shame

Overview

Instruction la­tency analy­sis usu­ally fo­cuses on per­for­mance op­ti­miza­tion—mak­ing code run as fast as pos­si­ble. The Assembly Hall of Shame takes the op­po­site ap­proach: search­ing for the ab­solute floor of sin­gle-in­struc­tion per­for­mance.

🏆 Current Champions 🏆

x86: fxrstor64

Strategy: Use fxrstor64 to load 512-byte FPU/MMX/XMM state from a high-la­tency MMIO re­gion in the PCIe fab­ric, then starve the fab­ric while the load is in flight — a fleet of ham­mer cores pounds a dif­fer­ent high-la­tency MMIO reg­is­ter with tight 4-byte reads, sat­u­rat­ing the PCIe root com­plex and end­point with non-posted trans­ac­tions, so CPU 0′s 512-byte fxrstor64 must queue be­hind all that con­tend­ing traf­fic.

Contender: AMD Ryzen 7 5800H

; CPU 0 — timed in­struc­tion movl $0xfcc68830, %rsi fxrstor64 %rsi

; CPUs 1..N — ham­mer loop against a dif­fer­ent high-la­tency lo­ca­tion movl 0xfcc68858, %eax

🏆 Score: 198,002,498,236 cy­cles

🏆 Time: 62 sec­onds

Honorable Mentions

A spec-vi­o­lat­ing un­aligned ymm0 load that forced non-posted dword trans­ac­tions from stalled GPU reg­is­ters was used to break the fun­da­men­tal de­sign of System Management Mode in smi­i­i­i­i­i­i­i­i­i­i­i­i­iii.

vmovdqu 0xfcc003b1, %ymm0

Rules

Instructions may use what­ever setup is nec­es­sary, but only a sin­gle in­struc­tion is el­i­gi­ble to be scored.

Trapped/emulated/virtualized in­struc­tions may only time the trap, not the han­dler.

Instructions must not be in­ter­rupt­ible. rep movs, pause, etc. are dis­qual­i­fied.

Times are nor­mal­ized based on the CPU base clock fre­quency.

All plat­forms must be in their fac­tory stock con­fig­u­ra­tions - no hard­ware mod­i­fi­ca­tions.

x86 Leaderboard

27. nop

Strategy: nop does noth­ing. It opens the leader­board ac­cord­ingly.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

nop

Score: 1 cy­cles

Time: 0 nanosec­onds

26. nop16

Strategy: Regular nop was too short, but how do we make noth­ing take longer? Try a lonnnnnng nop.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

data16 data16 data16 data16 data16 data16 data16 nopl 0x00000000(%%eax,%%eax,1)

Score: 20 cy­cles

Time: 7 nanosec­onds

25. rdtsc

Strategy: Just a ref­er­ence in­struc­tion to get our bear­ings.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

rdtsc

Score: 49 cy­cles

Time: 18 nanosec­onds

24. idiv

Strategy: Use 128-bit div­i­dend (rdx:rax=2:0) with small di­vi­sor to push the quo­tient above the ceil­ing im­posed by sign-ex­ten­sion, dri­ving the longest path through the di­vider mi­croc­ode.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

xorq %rax, %rax  ; rax = 0 (low 64 bits of div­i­dend) movq $2, %rdx  ; rdx = 2 (high 64 bits: full div­i­dend = 2^65) movq $5, %rbx  ; di­vi­sor → quo­tient = 2^65/5 ≈ 7.4×10^18 idivq %rbx

Score: 77 cy­cles

Time: 28 nanosec­onds

23. en­ter

Strategy: Use max­i­mum nest­ing depth (31) to force 30 dis­play-pointer loads and pushes through the mi­croc­ode dis­play-walk path.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

en­ter $0, $31  ; 0 bytes al­lo­cated, nest­ing depth 31 (maximum)

Score: 112 cy­cles

Time: 41 nanosec­onds

22. fldl

Strategy: Try a small de­nor­mal to trig­ger an FP mi­croc­ode as­sist.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

mov­absq $0x0000000000000001, %rax movq %rax, -8(%rsp) fldl -8(%rsp)

Score: 133 cy­cles

Time: 49 nanosec­onds

21. clflush

Strategy: Just en­sure the cache line is dirty.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

clflush (%rax)  ; rax -> dirty cache line res­i­dent in L3

Score: 165 cy­cles

Time: 60 nanosec­onds

20. fsin

Strategy: Use ex­po­nent 0x7ff to reach ‘special val­ue’ pro­cess­ing in mi­croc­ode; pos­i­tive/​neg­a­tive, NaN/inf does­n’t seem to make a dif­fer­ence, go with QNaN.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

mov­absq $0x7fffffffffffffff, %rax movq %rax, -8(%rsp) fldl -8(%rsp) fsin

Score: 257 cy­cles

Time: 94 nanosec­onds

19. mfence

Strategy: Saturate all write-com­bin­ing line-fill buffers with movnti stores to dis­tinct cache lines, forc­ing mfence to drain the full LFB write path to the un­core be­fore re­tir­ing.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

movnti %r9, 0*64(%rdi)  ; ×16 dis­tinct cache lines — sat­u­rate the write-com­bin­ing LFBs ; … movnti %r9, 15*64(%rdi) mfence  ; must drain all pend­ing LFB writes be­fore re­tir­ing

Score: 326 cy­cles

Time: 120 nanosec­onds

18. mov cr3

Strategy: Nothing for now, just check how long it takes to in­val­i­date the TLB.

Contender: AMD Ryzen 7 5800H with Radeon Graphics (Trigkey S5)

mov %rax, %cr3

Score: 352 cy­cles

Time: 110 nanosec­onds

17. fadd

Strategy: Hit x87 FP mi­croc­ode as­sist path by us­ing de­nor­mal source operand.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

fldl sub­norm  ; 1e-310: value < DBL_MIN, bi­ased ex­po­nent = 0 faddl sub­norm  ; source is sub­nor­mal → FP mi­croc­ode as­sist

Score: 677 cy­cles

Time: 249 nanosec­onds

16. split lock

Strategy: Align lock-pre­fixed operand to strad­dle cache-line bound­ary, forc­ing CPU to as­sert the ex­ter­nal bus lock rather than us­ing the fast MESI cache-co­her­ence path.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

; split_ptr % 64 == 63 — dword spans bytes 63 (line N) and 64 – 66 (line N+1) lock xaddl %r9d, (%rdi)

Score: 865 cy­cles

Time: 319 nanosec­onds

15. fdiv -

Strategy: Use sub­nor­mal di­vi­sor, hard­ware hands con­trol to mi­croc­ode as­sist, as­sist nor­mal­izes operand, per­forms the di­vi­sion, then re­stores ar­chi­tec­tural state.

Contender: Intel(R) Core(TM) i7 – 8559U CPU @ 2.70GHz

mov­absq $0x3ff0000000000000, %rax  ; 1.0 (normal div­i­dend) movq %rax, -8(%rsp) fldl -8(%rsp)  ; ST(0) = 1.0

mov­absq $0x0000002000000000, %rax  ; 6.79e-313 (subnormal di­vi­sor) movq %rax, -8(%rsp) fdivl -8(%rsp)  ; ST(0) = 1.0 / sub­nor­mal → FP as­sist

Score: 883 cy­cles

Time: 325 nanosec­onds

Terry Godier, “Browsers Have Standards, the App Store Has Judgment”:

A while ago I tried to sub­mit an iOS app for Dark Hours, my as­tron­omy web­site for nor­mal peo­ple. It was re­jected on the grounds that it was as­trol­ogy.

It has no tarot func­tion, no horo­scopes, and noth­ing that I, or any­one else I’ve asked, would as­so­ci­ate with as­trol­ogy.

A while ago I tried to sub­mit an iOS app for Dark Hours, my as­tron­omy web­site for nor­mal peo­ple. It was re­jected on the grounds that it was as­trol­ogy.

It has no tarot func­tion, no horo­scopes, and noth­ing that I, or any­one else I’ve asked, would as­so­ci­ate with as­trol­ogy.

I penned a nice lit­tle rant two years ago ex­press­ing my fury over the way that kooks pro­mot­ing as­trol­ogy of­ten try to in­sin­u­ate that their voodoo pseu­do­science is even vaguely re­lated to the hard sci­ence of as­tron­omy. And it re­ally is an un­for­tu­nate fluke of the English lan­guage that two sub­jects with a con­tentious re­la­tion­ship are dif­fer­en­ti­ated as words by two let­ters. Even if you know the dif­fer­ence be­tween the two, and care, if you’re read­ing quickly your eyes might con­flate one word with the other.

But if you ac­tu­ally look at Godier’s Dark Hours, for even just a few sec­onds, it is in­stantly ob­vi­ous that it per­tains to the sci­ence of as­tron­omy and has ab­solutely noth­ing — zero, zilch, nada — to do with as­trol­ogy. So even if Apple’s App Store re­viewer mis­read the sub­mis­sion’s de­scrip­tion, and wrongly as­sumed it was yet an­other quack as­trol­ogy app, if they launched it and spent just a few sec­onds pok­ing around, they should have in­stantly rec­og­nized their in­cor­rect as­sump­tion. But that’s not what hap­pened. They re­jected the app on the ut­terly in­cor­rect grounds that it per­tained to as­trol­ogy. If this was an hon­est mis­take from a rea­son­able ju­di­cial board, you’d ex­pect the in­ter­ac­tion with Godier, the de­vel­oper, to have gone some­thing like this:

App Review: REJECTED: Astrology. Developer: No, it’s as­troN­oMy not as­troL­oGy. App Review: Oh, sorry! Carry on, APPROVED.

But that’s not what hap­pened at all. Godier pro­ceeded through a se­ries of es­ca­la­tions up to the App Review Board and the Review Board re­sponded that they de­ter­mined the orig­i­nal re­jec­tion was valid be­cause, I shit you not, “We un­der­stand that the app in­cludes a live tarot read­ing fea­ture.” Which is­n’t even about as­trol­ogy. It’s straight out of Kafka.

Dark Hours is not just merely un­re­lated to as­trol­ogy (let alone tarot-card read­ing). It’s ac­tu­ally ex­quis­itely well-de­signed and painstak­ingly crafted. It’s ex­actly the sort of app that the App Store ought to cel­e­brate and high­light. It does­n’t just be­long in the cat­e­gory of as­tron­omy apps in the App Store, it will raise the qual­ity bar for as­tron­omy apps in the App Store. iOS-ex­clu­sive, na­tive Liquid Glass UI, smooth scrolling, beau­ti­ful ty­pog­ra­phy and lay­out, and way more use­ful as a na­tive mo­bile app than as a web­site. Go check out the web­site and I’m sure you’ll agree that it’s the sort of thing that would be even cooler as an app. But it is an app, and the app is bet­ter and more use­ful than the web­site ver­sion, and Godier has fought to get it ap­proved. But Apple’s App Review Board said no, on fal­la­cious eas­ily-re­futed grounds. This is­n’t just con­trary to the ben­e­fit of de­vel­op­ers, like Godier. It’s ob­vi­ously con­trary to the ben­e­fit of Apple it­self, which should not just ac­cept an app like Dark Hours, but cel­e­brate it as an ex­em­plar of the plat­form.

Mistakes hap­pen. But in a func­tion­ing sys­tem mis­takes get cor­rected, and mis­takes as ob­vi­ous as this one get cor­rected al­most in­stantly and in­clude a quick apol­ogy for the con­fla­tion. The App Store is not a func­tion­ing sys­tem.

August 7, 2026, 9:39pm

1

The Nixpkgs core team has un­for­tu­nately de­cided to dis­band.

We’re proud to have had the op­por­tu­nity to lead by ex­am­ple in bot­tom‐up, con­sen­sus‐fo­cused gov­er­nance for Nixpkgs, and of our achieve­ments over the past 10 months, in­clud­ing re­form­ing the com­mit­ter del­e­ga­tion process and on­board­ing 19 new com­mit­ters, em­pow­er­ing main­tain­ers by ex­tend­ing the merge bot, re‐es­tab­lish­ing con­tact with GitHub and se­cur­ing the spon­sored Enterprise Cloud up­grade, help­ing triage GHSA-67f2 – 674w-6g63 and track the GitHub se­cu­rity risks ex­posed by that in­ci­dent, and es­tab­lish­ing an ini­tial au­toma­tion/​AI pol­icy, as well as help­ing re­solve many in­ci­dents that were es­ca­lated to us.

However, it has sadly not turned out to be the light­weight role com­pat­i­ble with ac­tive tech­ni­cal con­tri­bu­tion that we had orig­i­nally hoped it would be, and two weeks ago we reached the con­clu­sion that step­ping down is nec­es­sary for our health. We be­lieve that it’s un­sus­tain­able for the team to con­tinue, as demon­strated in part by our at­tri­tion to date. With only one per­son ac­tively ap­ply­ing in re­sponse to our call for new mem­bers and mixed re­sponse to out­reach, re­cruit­ing suf­fi­ciently to keep things healthy looks un­ten­able. Therefore, es­pe­cially with a Steering Committee elec­tion due im­mi­nently, we think the best way for­ward for Nixpkgs gov­er­nance is to be hon­est about the cir­cum­stances that have made dis­solv­ing the team un­avoid­able, in the hopes that it may help fu­ture ef­forts.

Our ex­pe­ri­ence is that the Steering Committee as an in­sti­tu­tion lacks a na­tive in­stinct for the del­e­ga­tion en­vi­sioned by the con­sti­tu­tion, while also not be­ing suf­fi­ciently en­gaged and co­he­sive to han­dle in­di­vid­ual de­ci­sions at those lev­els it­self. This man­i­fests as un­nec­es­sary mi­cro­man­age­ment of teams be­low them and chron­i­cally poor com­mu­ni­ca­tion, in­clud­ing lack of clar­ity from SC mem­bers about when they’re speak­ing for them­selves or rep­re­sent­ing a joint po­si­tion, mat­ters brought to our at­ten­tion with de­sired out­comes al­ready at­tached, tak­ing own­er­ship of is­sues en­tirely within del­e­gated ar­eas with­out in­volv­ing rel­e­vant teams, and in­suf­fi­cient and de­layed re­sponses to con­cerns. The end re­sult has been in­ad­e­quate co­or­di­na­tion on mat­ters like GSoC, grants ini­tia­tives, and AI pol­icy, slow and dif­fi­cult progress on mat­ters rel­e­vant to Nixpkgs like mod­er­a­tion and GitHub org owner re­form, and gen­eral un­cer­tainty about whether we are trusted to au­tonomously make de­ci­sions within our re­mit.

These is­sues have per­sisted de­spite our re­peated at­tempts to dis­cuss them. This is, of course, a sys­temic prob­lem rather than one any sin­gle SC mem­ber could solve; we don’t envy the de­mands of the role, have been im­pressed by the ef­forts of sev­eral mem­bers, and rec­og­nize that every in­di­vid­ual nat­u­rally has lim­ited time and en­ergy and can only do so much in the con­text of a rep­re­sen­ta­tive ma­jori­tar­ian com­mit­tee. Ultimately, though, it leaves the SC not func­tion­ing ef­fec­tively as ei­ther a rep­re­sen­ta­tive back­stop to del­e­ga­tion or a proac­tive de­ci­sion‐mak­ing body. The re­sult­ing en­vi­ron­ment has acted as a con­sis­tent drag on our work, mak­ing it dif­fi­cult for us to ful­fil our con­sti­tu­tional man­date of “Project Direction, Decision-Making, Coordination with the NixOS Foundation Board, and Creation and Management of Teams, where they per­tain to Nixpkgs”.

We be­lieve our high‐trust con­sen­sus de­ci­sion‐mak­ing model has re­sulted in high‐qual­ity dis­cus­sions and good re­sults, and is a bet­ter fit for a del­e­gated lo­cal gov­er­nance team than the ma­jor­ity votes used at the top level by the SC. It’s not news to any­body that our com­mu­nity has many strong di­vides and dis­agree­ments, but nonethe­less we’ve seen many sit­u­a­tions where the abil­ity to call on the core team for a res­o­lu­tion has helped calm ten­sions and led to agree­able out­comes. We con­sider the strong ap­proval for the ini­tial au­toma­tion/​AI pol­icy from peo­ple with highly di­ver­gent views to be proof that it’s pos­si­ble to find paths for­ward and make sig­nif­i­cant im­prove­ments even on seem­ingly in­tractable top­ics.

It in­evitably takes its toll to step in un­der such cir­cum­stances, though. Given our own ex­pe­ri­ences and the his­tory of the pro­ject, we un­der­stand why there is a gen­eral dis­trust of gov­er­nance in the com­mu­nity, and how that has en­cour­aged a com­bat­ive, zero‐sum ap­proach to dis­agree­ments. While those meth­ods may work to ef­fect change de­spite a lead­er­ship vac­uum or to be heard by un­re­spon­sive gov­er­nance, they con­tribute to burnout when lead­er­ship teams are try­ing to en­gage in good faith and fos­ter pro­duc­tive dis­cus­sion. That out­come only re­wards those who don’t care to lis­ten to the com­mu­nity or to pur­sue trust‐based con­sul­ta­tive lead­er­ship at all, fur­ther re­duces the small pool of ex­pe­ri­enced con­trib­u­tors with the time and de­sire to par­tic­i­pate in gov­er­nance, and risks lock­ing in the his­tor­i­cal sta­tus quo of de­ci­sion‐mak­ing by dead­lock and at­tri­tion.

We want to be clear that this de­ci­sion is not the re­sult of any one in­ci­dent, but the cul­mi­na­tion of long‐run­ning pat­terns. The mat­ters in our ju­ris­dic­tion are left with no di­rect owner at pre­sent, with the SC act­ing as the fi­nal back­stop as al­ways. We both plan to re­duce our Nixpkgs in­volve­ment and have no in­ten­tion of run­ning for SC. We still be­lieve in the prin­ci­ples the team was founded on — that clear, light­weight processes for de­ci­sion‐mak­ing and dis­pute res­o­lu­tion are crit­i­cal to strength­en­ing Nixpkgs and ad­dress­ing the prob­lems we’ve seen as con­trib­u­tors — and hope that a gen­uine del­e­ga­tion of en­gaged, tech­ni­cally‐fo­cused, col­lab­o­ra­tive Nixpkgs lead­er­ship by top‐level gov­er­nance will be pos­si­ble in the fu­ture.

We’d like to thank every­one who has sup­ported our work, and re­gret that the team has­n’t been in a po­si­tion to solve these is­sues more com­pre­hen­sively and sus­tain­ably.

On be­half of the Nixpkgs core team:

@alyssais

@emilazy

89 Likes

Sad to see this hap­pen, I was very ex­cited to see the AI pol­icy adopted.

This would be the time where nix­p­kgs should glow and grow.

Thanks for all the hard work!

9 Likes

ron

August 8, 2026, 2:54am

3

You both are in­cred­i­ble, and I can first hand at­test to the in­cred­i­ble amount of work and pos­i­tive im­pact you’ve led here. I’m still shocked by the amount of work you han­dled.

I’m sorry that it ended up in burn out. You de­serve so much more. There’s a lot to learn from this ex­pe­ri­ence. If you’d be will­ing, af­ter a much needed break (also un­der­stand if not any­time soon), I’d per­son­ally want to sit down and see how we can turn this into ac­tion.

27 Likes

drupol

August 8, 2026, 5:12am

4

Argh, sad to read this in­deed… Thanks for the work and take care of your­self, both of you.

4 Likes

I would like to per­son­ally thank @qyliss and @emily for all the great work that was done by the nix­p­kgs core team. I am sad­dened to see it dis­band, as I think the team had be­came an es­sen­tial layer of the pro­ject gov­er­nance, and I aknowl­edge the re­spon­s­abil­ity of the SC in that hap­pen­ing. We have to do bet­ter.

9 Likes

SergeK

August 8, 2026, 8:27am

7

The core team for­ma­tion, with its spe­cific mem­ber­ship, has been, per­haps, the most pro­gres­sive thing to hap­pen in the Nixpkgs “governance” space dur­ing my short time around

6 Likes

Three things:

I ear­lier talked to @qyliss, whom I’ve al­ways held in the ab­solute high­est re­gard (and still do), a bit to de­brief. I don’t re­ally have much to say about the ac­tual things be­ing dis­cussed. The Nixpkgs Core team has barely come up on the SC’s agenda, and I per­son­ally feel we’ve been in “delegate and be thank­ful that we can” mode. There is not some long-stand­ing fight here. The claim of “micromanagement” is thus ob­jec­tively false.

I ear­lier talked to @qyliss, whom I’ve al­ways held in the ab­solute high­est re­gard (and still do), a bit to de­brief. I don’t re­ally have much to say about the ac­tual things be­ing dis­cussed. The Nixpkgs Core team has barely come up on the SC’s agenda, and I per­son­ally feel we’ve been in “delegate and be thank­ful that we can” mode. There is not some long-stand­ing fight here. The claim of “micromanagement” is thus ob­jec­tively false.

I am to­tally sick of open let­ters. I can’t be­lieve we’re do­ing this again (major team re­signs be­fore elec­tion) — even though the cir­cum­stance are so dif­fer­ent this time. More than any­thing else this is hor­ri­ble de je vu that I would not wish upon any­one.

I am to­tally sick of open let­ters. I can’t be­lieve we’re do­ing this again (major team re­signs be­fore elec­tion) — even though the cir­cum­stance are so dif­fer­ent this time. More than any­thing else this is hor­ri­ble de je vu that I would not wish upon any­one.

The high­est trust move is a pri­vate res­ig­na­tion, and then a pub­lic joint state­ment that al­lows mul­ti­ple sides to agree on some ba­sic facts, and of­fer some muted opin­ions. My god, how I wish we could do that in­stead. My god, how I wish we could do that in­stead. I hope we do do that in­stead go­ing for­ward and never have an­other open let­ter ever again. Open let­ters with one-sided de­nun­ci­a­tions are not a healthy way to work through is­sues at all, and rather a last re­sort when all other av­enues of di­a­logue have failed. Yet it’s one that we’ve nor­mal­ized as a pri­mary way of con­duct­ing com­mu­nity busi­ness. This nor­mal­iza­tion has oc­curred be­cause we’re not a healthy com­mu­nity, and un­healthy com­mu­ni­ties nor­mal­ize un­healthy be­hav­ior un­wit­tingly.

The high­est trust move is a pri­vate res­ig­na­tion, and then a pub­lic joint state­ment that al­lows mul­ti­ple sides to agree on some ba­sic facts, and of­fer some muted opin­ions. My god, how I wish we could do that in­stead. My god, how I wish we could do that in­stead.

I hope we do do that in­stead go­ing for­ward and never have an­other open let­ter ever again. Open let­ters with one-sided de­nun­ci­a­tions are not a healthy way to work through is­sues at all, and rather a last re­sort when all other av­enues of di­a­logue have failed. Yet it’s one that we’ve nor­mal­ized as a pri­mary way of con­duct­ing com­mu­nity busi­ness. This nor­mal­iza­tion has oc­curred be­cause we’re not a healthy com­mu­nity, and un­healthy com­mu­ni­ties nor­mal­ize un­healthy be­hav­ior un­wit­tingly.

Side note: an­other thread was hint­ing at a Lixpkgs. Multiple pack­age sets main­tained by mul­ti­ple groups that each get along much bet­ter within groups than be­tween groups could solve the trust prob­lem. At this point, I am not op­posed to try­ing it.

2 Likes

pie­games

August 8, 2026, 9:25am

9

I can un­der­stand you hav­ing such a re­ac­tion, even though I don’t per­ceive it in the same way at all. In fact, you as an SC mem­ber de­sir­ing that a team which de­cides to dis­band go through you as the SC first for com­mu­ni­ca­tion, is in my eyes the same kind of mi­cro­man­age­ment that has been pointed out here.

That be­ing said, I’d like to not de­rail this dis­cus­sion by cen­ter­ing the SC once again. Especially be­cause many fac­tors have con­tributed to the core team dis­band­ing, as per their own telling. So let’s keep this topic fo­cused on the core team in­stead, and if peo­ple wish so open a new dis­cus­sion cen­tered around the SC’s gen­eral op­er­a­tions.

Last week we re­leased ver­sion 0.2 of pgrust. This re­lease was all about per­for­mance. It’s 10x faster than the pre­vi­ous ver­sion of pgrust. On OLTP bench­marks, pgrust is 30% faster than Postgres, and on Clickbench, Clickhouse’s bench­mark for an­a­lyt­i­cal data­bases, pgrust is 300x faster than Postgres. It’s even ahead of Clickhouse!

The query en­gine is one of the biggest changes we made to achieve much bet­ter per­for­mance. On its own, the query en­gine drove ~10x of the 300x. We’ll start with a minia­ture ver­sion of the Postgres query en­gine and we’ll one by one add the same op­ti­miza­tions we made to make the pgrust query en­gine so fast.

To give some back­ground on why there’s so much room for im­prove­ment vs Postgres, Postgres was cre­ated in a dif­fer­ent era. The orig­i­nal Postgres pro­ject dates back to the 80s. It was built at a time when the main bot­tle­neck to data­base per­for­mance was disk I/O. Three trends have made that no longer the case:

Many datasets now fit in RAM, elim­i­nat­ing most disk I/O

For datasets that don’t fit in RAM, the work­loads dif­fer. Data an­a­lyt­ics scans data in bulk. The bot­tle­neck is of­ten no longer your disk through­put and is of­ten ei­ther your CPU through­put or mem­ory through­put

Disks have got­ten much faster in re­cent years. NVMe is hun­dreds of times faster than a hard drive.

All three trends have made CPU and mem­ory speeds more im­por­tant than they were his­tor­i­cally. Many of the op­ti­miza­tions we’ve made tar­get this. The query en­gine is the main user of CPU in a data­base. We op­ti­mized the pgrust query en­gine to use less CPU and less mem­ory band­width than Postgres when pro­cess­ing the same queries.

To give you a sense of just how slow the Postgres query en­gine is, let’s take a sim­ple query that sums the first 500 mil­lion num­bers:

CREATE TABLE my_table AS se­lect col::float8 from gen­er­ate_se­ries(1.0, 500000000.0) g(col); SELECT SUM(col) FROM my_table;

When I run this in Postgres, it takes ~20 sec­onds. This was done on a c8g.4xl with par­al­lel queries dis­abled.

For com­par­i­son, when I time the equiv­a­lent in Rust:

let table: Vec<f64> = (1..=500_000_000usize).map(|i| i as f64).col­lect();

let mut sum = 0.0; for &value in &table { sum += value; }

The query takes 358ms. That’s around 55x faster, and be­lieve it or not, we can do even faster than 358ms. Now this ex­am­ple is­n’t an ap­ples-to-ap­ples com­par­i­son. There’s a lot more go­ing on un­der the hood in Postgres. At the same time, op­ti­miz­ing a data­base is all about re­mov­ing as much of this over­head as pos­si­ble. (If you’re cu­ri­ous two of the biggest causes of over­head from Postgres are 1. lock­ing and 2. pars­ing the Postgres stor­age for­mat and ex­tract­ing the tu­ples rel­e­vant to the query).

To nar­row our fo­cus to just the im­pact of the query en­gine, let’s build a minia­ture ver­sion of the Postgres query en­gine. First, a brief ex­pla­na­tion of what a query en­gine is. When pro­cess­ing your SQL query, Postgres first con­verts your query into an in­ter­nal rep­re­sen­ta­tion called a “Query Plan,” which de­scribes *how* Postgres will ex­e­cute the query. In the ex­am­ple above, Postgres will pro­duce a query plan that may look some­thing like the fol­low­ing:

This ef­fec­tively says “get rows from my_table and sum the val­ues in those rows”. This query plan is pretty sim­ple given the na­ture of the query, but they can get much more com­pli­cated when you start work­ing with joins/​sorts/​sub­queries etc. In to­tal Postgres has over 40 dif­fer­ent types of plan nodes.

After gen­er­at­ing the query plan, Postgres passes it to the query en­gine. The Postgres query en­gine is the part of Postgres that takes the query plan and ac­tu­ally re­trieves the rows and per­forms the ag­gre­ga­tion. Postgres uses a style of ex­ecu­tor known as the “Volcano model.” To get a sense of how it works, here’s a minia­ture im­ple­men­ta­tion of the Postgres query en­gine:

use std::hint::black­_box;

trait Node { fn next(&mut self) -> Option<f64>; }

struct SeqScan<’a> { table: &’a [f64], pos: usize, }

impl Node for SeqScan<’_> { fn next(&mut self) -> Option<f64> { if self.pos >= self.table.len() { re­turn None; // end of table } let value = self.table[self.pos]; self.pos += 1; Some(value) } }

struct SumAggregate<’a> { child: Box<dyn Node + ’a>, to­tal: f64, done: bool, }

impl Node for SumAggregate<’_> { fn next(&mut self) -> Option<f64> { if self.done { re­turn None; } while let Some(value) = self.child.next() { self.to­tal += value; } self.done = true; Some(self.total) } }

let table: Vec<f64> = (1..=500_000_000usize).map(|i| i as f64).col­lect();

let mut plan = SumAggregate { child: black­_box(Box::new(Se­qS­can { table: &table, pos: 0 })), to­tal: 0.0, done: false, };

let sum = plan.next().un­wrap();

(The black­_box is needed to pre­vent com­piler op­ti­miza­tions from thwart­ing our bench­mark)

The key fea­ture of the Volcano model is the `next()` method, which is sup­ported by all nodes in the query plan. The job of `next()` is to re­turn a sin­gle row. `next()` in a se­quen­tial scan re­turns the next row in the se­quen­tial scan. `next()` on an ag­gre­ga­tion will com­pute the en­tire ag­gre­ga­tion and then re­turn the sin­gle row re­sult. Executing a query plan is just a mat­ter of call­ing `next()` on the root plan node un­til it no longer re­turns any rows. The ad­van­tage of the Volcano model is that it’s very sim­ple. You im­ple­ment a sin­gle method for each of your plan nodes, and that’s it. While sim­pli­fied, the above code is very close to what Postgres does in­ter­nally.

While the Volcano model makes things sim­ple, it also adds a lot of over­head. When I run this ex­am­ple, it takes 1.3s. That’s much faster than the Postgres ver­sion be­cause we’re re­mov­ing a lot of the non-query en­gine pieces, but it’s still slower than the raw for loop be­cause of the Volcano mod­el’s over­head.

The biggest per­for­mance hit in the code above is that `next()` processes only one row at a time. There’s no batch­ing. The `SeqScan.next()` func­tion is called once per row. That adds sig­nif­i­cant over­head, es­pe­cially be­cause many CPU op­ti­miza­tions, such as pipelin­ing, don’t work well when you are call­ing a func­tion that is­n’t known un­til run­time. The first op­ti­miza­tion we can im­ple­ment is batch­ing:

const BATCH: usize = 1024;

trait BatchNode { fn nex­t_­batch(&mut self, out: &mut [f64; BATCH]) -> usize; }

struct BatchSeqScan<’a> { table: &’a [f64], pos: usize, }

impl BatchNode for BatchSeqScan<’_> { fn nex­t_­batch(&mut self, out: &mut [f64; BATCH]) -> usize { let n = (self.table.len() - self.pos).min(BATCH); out[..n].copy­_from_s­lice(&self.table[self.pos..self.pos + n]); self.pos += n; n } }

struct BatchSumAggregate<’a> { child: Box<dyn BatchNode + ’a>, to­tal: f64, }

impl BatchSumAggregate<’_> { fn run(&mut self) -> f64 { let mut buf = [0.0f64; BATCH]; loop { let n = self.child.nex­t_­batch(&mut buf); if n == 0 { break; } for &value in &buf[..n] { self.to­tal += value; } } self.to­tal } }

let mut plan = BatchSumAggregate { child: black­_box(Box::new(Batch­Se­qS­can { table: &table, pos: 0 })), to­tal: 0.0, }; let sum = plan.run();

Batching on its own elim­i­nates most of the over­head. It brings the time to run the query from 1.3 sec­onds down to around 480ms. Still slower than the for loop, but much closer. One very im­por­tant de­tail is that the batch buffer is al­lo­cated on the stack. This means the ag­gre­ga­tion node does­n’t need to al­lo­cate any mem­ory while it’s run­ning. Allocating mem­ory tends to be one of the slower op­er­a­tions. Therefore, when writ­ing ul­tra-fast code, you’ll want to min­i­mize the num­ber of mem­ory al­lo­ca­tions you make.

Now, if you pro­file the batched ver­sion, the hotspot is now `copy_from_slice`. Even though we are now batch­ing, we still need to copy the items into the buffer. This over­head can be elim­i­nated with what’s known as “operator fu­sion”. If there are com­mon op­er­a­tions that we know will be per­formed to­gether, we can cre­ate a sin­gle node that re­places two nodes. In our case, we can cre­ate a sin­gle `SumAggregateSequentialScan` node that com­bines the logic of both the se­quen­tial scan and the sum:

struct SumAggregateSequentialScan<’a> { table: &’a [f64], done: bool, }

impl Node for SumAggregateSequentialScan<’_> { fn next(&mut self) -> Option<f64> { if self.done { re­turn None; } self.done = true; let mut to­tal = 0.0; for &value in self.table { to­tal += value; } Some(total) } }

This gives us the same per­for­mance as the straight for loop be­cause it is lit­er­ally the same code as the for loop. Now this may seem like cheat­ing, and it def­i­nitely is be­cause we’re hard­cod­ing in an op­ti­miza­tion for a spe­cific query we know in ad­vance. With op­er­a­tor fu­sion, it makes sense to hard­code a cou­ple of the most com­mon cases, but you’ll still pretty quickly hit cases you did­n’t pre­pare for ahead of time.

This can be solved with JIT com­pi­la­tion. With JIT com­pi­la­tion, you can gen­er­ate the ideal code you wish you had and “cheat” in every query. JIT com­pi­la­tion lets you gen­er­ate the per­fect code for your query, no mat­ter what the query is and al­ways do op­er­a­tor fu­sion. Unfortunately, this post is long enough as is, so I’ll have to talk about how pgrust lever­ages JIT com­pi­la­tion an­other time.

For one fi­nal op­ti­miza­tion, we can look to SIMD. SIMD refers to a set of CPU op­er­a­tions that can per­form one op­er­a­tion across mul­ti­ple pieces of data si­mul­ta­ne­ously. Using SIMD to per­form an op­er­a­tion against mul­ti­ple rows si­mul­ta­ne­ously is usu­ally much faster than per­form­ing the op­er­a­tion on one row at a time. If we change our code to use SIMD:

#[cfg(target_arch = “aarch64”)] struct SumAggregateSequentialScanSimd<’a> { table: &’a [f64], done: bool, }

#[cfg(target_arch = “aarch64”)] impl Node for SumAggregateSequentialScanSimd<’_> { fn next(&mut self) -> Option<f64> { if self.done { re­turn None; } self.done = true; use std::arch::aarch64::*; let mut acc = un­safe { [vdupq_n_f64(0.0); 4] }; let (chunks, rest) = self.table.as_chunks::<8>(); for chunk in chunks { for lane in 0..4 { un­safe { let v = vld1q_f64(chunk.as_ptr().add(2 * lane)); acc[lane] = vad­dq_f64(acc[lane], v); } } } let mut tail = 0.0; for &value in rest { tail += value; } Some(unsafe { let s01 = vad­dq_f64(acc[0], acc[1]); let s23 = vad­dq_f64(acc[2], acc[3]); vad­dvq_f64(vad­dq_f64(s01, s23)) + tail }) } }

Our code now takes 135ms which is now al­most 3x faster than the for loop and 10x faster than our orig­i­nal Volcano code. While it is com­mon for com­pil­ers to re­place for loops with SIMD equiv­a­lents, I chose this ex­am­ple so that would­n’t hap­pen. Compilers will usu­ally avoid in­tro­duc­ing SIMD when op­er­at­ing on floats, be­cause they will pro­duce a slightly dif­fer­ent re­sult. This is be­cause float­ing point arith­metic is not as­so­cia­tive, so chang­ing the or­der in which you do a sum can pro­duce a slightly dif­fer­ent re­sult.

—

All in all, with three sim­ple op­ti­miza­tions, we were able to make our query 10x faster. Here’s where things ended up:

These types of op­ti­miza­tions, and many more, en­able pgrust to per­form hun­dreds of times faster than Postgres for an­a­lyt­i­cal queries.

Benchmark setup: AWS c8g.4xlarge (Graviton4, 16 vCPU), PostgreSQL 18.4 with `max_parallel_workers_per_gather = 0`, data warm in shared buffers, me­dian of 5 runs. Rust built with `cargo build –release`, 4 runs per im­ple­men­ta­tion, all mea­sured in one process on one ma­chine.

Thanks for read­ing, and if you want to sup­port the pro­ject, the best way to sup­port pgrust is to give us a star on GitHub. If you want to fol­low along:

1. GitHub 2. Discord 3. Mailing list — weekly up­dates on pgrust, in­clud­ing the fol­low-up on JIT com­pi­la­tion 4. pgrust.com