Apple has filed a law­suit against OpenAI to­day, ac­cus­ing the com­pany of trade se­cret theft. Specifically, Apple al­leges that its for­mer em­ploy­ees have stolen trade se­crets “for the ben­e­fit of OpenAI.”

“This case is about Apple’s for­mer em­ploy­ees steal­ing Apple’s trade se­crets for the ben­e­fit of OpenAI. Apple brings this suit to put a stop to it,” the law­suit says.

Apple state­ment

In a state­ment to 9to5Mac, an Apple spokesper­son said:

“At Apple, our teams are con­stantly de­vel­op­ing break­through tech­nolo­gies to cre­ate the best prod­ucts and ser­vices in the world, and pro­tect­ing their work and in­tel­lec­tual prop­erty is some­thing we take very se­ri­ously. Recently, sig­nif­i­cant ev­i­dence has emerged sug­gest­ing in­di­vid­u­als em­ployed by OpenAI wrong­fully took Apple’s se­cret and con­fi­den­tial in­for­ma­tion re­gard­ing our un­re­leased tech­nolo­gies, processes, and prod­ucts. We will al­ways de­fend our teams’ hard work and in­no­va­tions, and we are tak­ing all ap­pro­pri­ate steps to do so.”

“At Apple, our teams are con­stantly de­vel­op­ing break­through tech­nolo­gies to cre­ate the best prod­ucts and ser­vices in the world, and pro­tect­ing their work and in­tel­lec­tual prop­erty is some­thing we take very se­ri­ously. Recently, sig­nif­i­cant ev­i­dence has emerged sug­gest­ing in­di­vid­u­als em­ployed by OpenAI wrong­fully took Apple’s se­cret and con­fi­den­tial in­for­ma­tion re­gard­ing our un­re­leased tech­nolo­gies, processes, and prod­ucts. We will al­ways de­fend our teams’ hard work and in­no­va­tions, and we are tak­ing all ap­pro­pri­ate steps to do so.”

Update: Read OpenAI’s re­sponse here.

Apple ac­cuses OpenAI of trade se­cret theft

The law­suit names Chang Liu and Tang Tan as two of the de­fen­dants. Tang Tan served as VP of prod­uct de­sign at Apple, lead­ing iPhone and Apple Watch prod­uct de­sign. He de­parted the com­pany in February 2024 to work with Jony Ive. Chang Liu, mean­while, worked at Apple for eight years and was a se­nior sys­tem elec­tri­cal en­gi­neer be­fore de­part­ing to join OpenAI in January 2026.

Apple’s law­suit also names OpenAI and io Products as de­fen­dants.

OpenAI’s hard­ware ef­forts are be­ing led by Jony Ive, Apple’s for­mer chief de­sign of­fi­cer. OpenAI ac­quired Ive’s startup io as part of a $6.5 bil­lion deal last year. OpenAI’s takeover of the com­pany in­cluded more than 50 en­gi­neers, de­vel­op­ers, and other em­ploy­ees. In its orig­i­nal an­nounce­ment, OpenAI touted that Ive founded io in col­lab­o­ra­tion with Scott Cannon, Evans Hankey, and Tan.

Hankey led Apple’s de­sign team for sev­eral years af­ter Ive de­parted the com­pany. She de­parted in 2022 be­fore re­unit­ing with Ive as part of io. Cannon also pre­vi­ously worked at Apple.

Ive, Hankey, and Cannon are not per­son­ally men­tioned any­where in Apple’s ini­tial fil­ing to­day.

The com­plaint

Apple says it first raised con­cerns with OpenAI di­rectly in February, ask­ing the com­pany to in­ves­ti­gate and ad­dress the is­sue. OpenAI, how­ever, never re­sponded. Apple says the con­duct de­tailed in the fil­ing is “the tip of the ice­berg.”

This is the tip of the ice­berg. Apple lacks vis­i­bil­ity into what’s been hap­pen­ing be­hind closed doors at OpenAI, where such mis­con­duct is nor­mal­ized and ex­em­pli­fied by lead­er­ship. This much is clear, how­ever: at every level, from mem­bers of its Technical Staff to its Chief Hardware Officer, and in co­or­di­na­tion with busi­ness part­ners, OpenAI has been steal­ing Apple’s trade se­crets and con­fi­den­tial in­for­ma­tion. As a nat­ural re­sult, OpenAI’s nascent hard­ware busi­ness now rests.

This is the tip of the ice­berg. Apple lacks vis­i­bil­ity into what’s been hap­pen­ing be­hind closed doors at OpenAI, where such mis­con­duct is nor­mal­ized and ex­em­pli­fied by lead­er­ship. This much is clear, how­ever: at every level, from mem­bers of its Technical Staff to its Chief Hardware Officer, and in co­or­di­na­tion with busi­ness part­ners, OpenAI has been steal­ing Apple’s trade se­crets and con­fi­den­tial in­for­ma­tion. As a nat­ural re­sult, OpenAI’s nascent hard­ware busi­ness now rests.

The com­plaint, filed in the U.S. District Court for the Northern District of California, al­leges that Tan used in­sider knowl­edge of Apple’s con­fi­den­tial pro­jects to grill job can­di­dates in in­ter­views and learn more con­fi­den­tial in­for­ma­tion. Additionally, Tan di­rected job can­di­dates still work­ing at Apple to bring ac­tual Apple hard­ware com­po­nents and sam­ples for “show and tell” ses­sions.

When in­ter­view­ing Apple em­ploy­ees for jobs at OpenAI, Mr. Tan uses Apple’s con­fi­den­tial in­for­ma­tion to gain ac­cess to even more in­sider knowl­edge. He has used an Apple in­ter­nal pro­ject co­de­name to ask, “What’s the plan[?]” for an unan­nounced Apple prod­uct.

He has di­rected job can­di­dates still work­ing for Apple to bring “Actual parts” from Apple to their in­ter­views for “show and tell” ses­sions in which he and his team at OpenAI can elicit still more Apple con­fi­den­tial in­for­ma­tion. These di­rec­tions to bring Apple’s parts to OpenAI job in­ter­views sur­prised at least one of the can­di­dates, who com­mented that he “didn’t even know we could take those from the of­fice.”

OpenAI has been in­struct­ing Apple em­ploy­ees to bring “CAD/design ar­ti­facts” and “prototypes” to their in­ter­views and to di­vulge de­tails about their work such as “subsystem and com­po­nent se­lec­tion,” the “tools or method­olo­gies you use for sys­tem in­te­gra­tion, such as CAD soft­ware, sim­u­la­tion tools,” and “Vendor se­lec­tion and com­mu­ni­ca­tion/​col­lab­o­ra­tion with ven­dors.”

When in­ter­view­ing Apple em­ploy­ees for jobs at OpenAI, Mr. Tan uses Apple’s con­fi­den­tial in­for­ma­tion to gain ac­cess to even more in­sider knowl­edge. He has used an Apple in­ter­nal pro­ject co­de­name to ask, “What’s the plan[?]” for an unan­nounced Apple prod­uct.

He has di­rected job can­di­dates still work­ing for Apple to bring “Actual parts” from Apple to their in­ter­views for “show and tell” ses­sions in which he and his team at OpenAI can elicit still more Apple con­fi­den­tial in­for­ma­tion. These di­rec­tions to bring Apple’s parts to OpenAI job in­ter­views sur­prised at least one of the can­di­dates, who com­mented that he “didn’t even know we could take those from the of­fice.”

OpenAI has been in­struct­ing Apple em­ploy­ees to bring “CAD/design ar­ti­facts” and “prototypes” to their in­ter­views and to di­vulge de­tails about their work such as “subsystem and com­po­nent se­lec­tion,” the “tools or method­olo­gies you use for sys­tem in­te­gra­tion, such as CAD soft­ware, sim­u­la­tion tools,” and “Vendor se­lec­tion and com­mu­ni­ca­tion/​col­lab­o­ra­tion with ven­dors.”

Furthermore, Apple says a can­di­date be­gan “screenshotting and down­load­ing files re­lat­ing to a highly con­fi­den­tial Apple pro­ject” hours be­fore in­ter­view­ing with Tan, who then “solicited more in­for­ma­tion about that same Apple pro­ject” once the in­ter­view started. This be­came an “established pat­tern,” Apple says.

Tan also al­legedly pos­sessed and dis­trib­uted an in­ter­nal Apple “Need to Know” doc­u­ment to new OpenAI hires be­fore they gave their no­tice to Apple. The doc­u­ment in­cluded Apple’s de­par­ture se­cu­rity pro­to­cols. As part of its in­ves­ti­ga­tion, Apple found a “pattern by em­ploy­ees who de­part for OpenAI of tak­ing steps to evade the se­cu­rity processes in­tended to pro­tect Apple’s con­fi­den­tial in­for­ma­tion.”

Meanwhile, Apple also claims for­mer en­gi­neer Liu ex­ploited a se­cu­rity bug to down­load con­fi­den­tial en­gi­neer­ing files af­ter leav­ing the com­pany. Rather than re­port the ex­ploit, Liu al­legedly joked about it in mes­sages (“LOL,” “so funny”). Liu also failed to re­turn an Apple-issued lap­top af­ter his de­par­ture.

Apple al­leges that Liu down­loaded a “compilation of tech­ni­cal files with over a thou­sand pages” with de­tails of work he did at Apple. This in­cluded de­tailed man­u­fac­tur­ing doc­u­ments cov­er­ing the com­plex cir­cuit boards used in Apple hard­ware prod­ucts.

Liu also al­legedly coached an­other Apple em­ployee at the time, whom he was re­cruit­ing to OpenAI, on which con­fi­den­tial ma­te­ri­als to study be­fore her own OpenAI in­ter­view.

Finally, Apple al­leges that OpenAI had a trusted Apple part­ner carry out Apple’s pro­pri­etary metal-fin­ish­ing tech­nique, mis­lead­ing the part­ner into be­liev­ing it had Apple’s per­mis­sion to do so. Apple also says OpenAI ap­proached a sec­ond long­time Apple sup­plier that works on power and bat­tery man­u­fac­tur­ing, us­ing in­sider ter­mi­nol­ogy to ask “targeted ques­tions” about spe­cific Apple com­po­nents.

The suit seeks in­junc­tive re­lief and dam­ages, and comes as OpenAI works to bring its first con­sumer hard­ware de­vice to mar­ket.

Apple’s law­suit also comes af­ter Bloomberg re­ported that OpenAI was prepar­ing “legal ac­tion” against Apple over how its part­ner­ship to in­te­grate ChatGPT into Siri played out. Today’s law­suit from Apple, how­ever, says that agree­ment is not at is­sue here.

Tan and Liu are just two of many Apple em­ploy­ees who have de­parted for OpenAI. Today’s fil­ing says that there are over 400 for­mer Apple em­ploy­ees now work­ing at OpenAI.

There have been var­i­ous ru­mors about OpenAI’s hard­ware ef­forts so far. In April, Ming-Chi Kuo re­ported that OpenAI is de­vel­op­ing its own smart­phone, which could launch in 2028. The Information has also re­ported on OpenAI’s work on a HomePod-style smart speaker.

You can read the full fil­ing be­low and find the PDF linked here.

Chance’s fa­vorites:

Bring wire­less CarPlay to any car

“Apple: The First 50 Years” by David Pogue

Logitech MX Master 4

Belkin 3-in-1 MagSafe Charger

Beats Woven USB-C Charging Cables

AirPods Pro 3: $222 (Reg. $249)

Follow Chance: Threads, Bluesky, Instagram, and Mastodon.

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I ha­rass the sea with my tiny boat and am called a pi­rate, you do it with a great fleet and are called a king.

I ha­rass the sea with my tiny boat and am called a pi­rate, you do it with a great fleet and are called a king.

Anthropic is ac­tively cam­paign­ing to end soft­ware en­gi­neer­ing. They need you to be­lieve they can do that. Well, maybe it’s not you that they need to con­vince. Maybe it’s your C-Suite, var­i­ous world lead­ers, or the man­ager of your re­tire­ment fund. They’ve raised $132 bil­lion in in­vest­ment, and are ap­proach­ing an IPO val­ued over $1 tril­lion. Since they can­not show prof­itabil­ity, this de­pends on sell­ing their hy­po­thet­i­cal fu­ture im­pact.

In lit­er­ary terms, Anthropic is an un­re­li­able nar­ra­tor.

One of their key nar­ra­tives is: Coding is go­ing away, then the rest of soft­ware en­gi­neer­ing, and even­tu­ally most other hu­man la­bor. This kind of money be­hind this kind of story has an im­pact, re­gard­less of how true we think the story is.

People will make ar­chi­tec­ture, prod­uct, and staffing de­ci­sions based on these events. Many of those de­ci­sions will be based on fear - fear of lay­offs, rap­ture-esk warn­ings of be­ing “Left Behind”, Doom Trolling, etc…

To make good de­ci­sions we need to think clearly, which is hard right now. Put on your skep­ti­cal hat.

Views are my own. I have no his­tory with Zig. I’ve never spo­ken to Andrew Kelley, but found his re­cent JetBrains in­ter­view a great watch.

My in­ter­est here is in pub­lic lit­er­acy about AI in soft­ware. That’s been my ca­reer fo­cus now for 3 years, along with im­prov­ing the tech­nol­ogy it­self. During half of that time I was Chief Architect of a cod­ing agent startup - both a cus­tomer of Anthropic’s mod­els and com­peti­tor to their agent Claude Code. My cur­rent pro­ject is The Coding Agency.

So where were we?

This week, Anthropic / Bun put out their ex­pla­na­tion of the de­ci­sion to port Bun from Zig to Rust. This ex­pla­na­tion came two months af­ter merg­ing the mi­gra­tion to the main­line. Explaining the di­rec­tion be­fore­hand would have been more tra­di­tional in an in­fra­struc­ture pro­ject like this, but mean­while the de­lay con­ve­niently al­lowed the story to be car­ried by sexy head­lines like The Register’s Anthropic’s Bun Rust rewrite merged at speed of AI. Much in­vest. Very wow.

Zig’s cre­ator Andrew Kelley has now put out a re­sponse with his own thoughts. It’s blunt, to an un­usual de­gree. That has ques­tion­able op­tics. As a gen­eral rule, you would not want to worry that when you switch pro­gram­ming lan­guages you will wake up the next morn­ing to the old lan­guage’s leader un­load­ing on your per­sonal flaws. As Dax hi­lar­i­ously put it:

guys we have a pretty sub­stan­tial open­source zig code­base and i’m ter­ri­fied he’s gonna look at it

guys we have a pretty sub­stan­tial open­source zig code­base and i’m ter­ri­fied he’s gonna look at it

Still, as I read Andrew’s piece I found my­self cheer­ing out loud. I may have briefly jumped around the room. Some called his take a “meltdown”, all I can say is he’s gained a new fan to­day.

Sometimes things need to be called out.

What is model be­hav­ior?

On my best days I’d as­pire to some­thing like Buddhist right speech, a high stan­dard that every­thing we say should meet all five of these cri­te­ria.

Is it true?

Is it help­ful?

Is it timely?

Is it kind?

Is it from kind­ness?

We’re break­ing deco­rum a lit­tle, stray­ing into “true, but un­kind” ter­ri­tory. I’m de­fend­ing some­one’s choice to do that. I don’t do that lightly, and I hope it’s help­ful.

Background

Just to catch you up…

Bun is a TypeScript run­time, like a faster NodeJS.

Zig is a sys­tems pro­gram­ming lan­guage, like a mod­ern C.

Bun was writ­ten in Zig un­til re­cently - one of the largest Zig code­bases.

Bun claims near 100% AI con­tri­bu­tions.

Zig al­lows 0% AI con­tri­bu­tions.

Bun was ac­quired by Anthropic, a lead­ing AI model lab.

Bun’s founder ex­per­i­mented with a mas­sive agen­tic rewrite from Zig to un­safe Rust.

That ex­per­i­ment was merged days later and is now the of­fi­cial ver­sion.

This is sit­u­a­tion is con­tro­ver­sial on a few fronts, though ap­par­ently no one in­volved ac­tu­ally wants Bun to stay in Zig. The drama lives purely in the meta-dis­cus­sion. The mi­gra­tion process it­self is pretty in­ter­est­ing, I would con­sider do­ing some­thing sim­i­lar in the right sit­u­a­tion.

Who to be­lieve

When peo­ple choose be­tween Zig and Rust for their pro­jects, they will nat­u­rally see the Bun sit­u­a­tion as a data-point. That fact that one of the biggest Zig users wound up re­vers­ing the de­ci­sion feels rel­e­vant, re­gard­less of the rea­sons. People will try to un­der­stand what hap­pened, and de­cide which is more true:

Anthropic/Bun story: Bun tried every­thing rea­son­able, and was still over­whelmed by mem­ory bugs be­cause Zig was­n’t up to the task.

Anthropic/Bun story: Bun tried every­thing rea­son­able, and was still over­whelmed by mem­ory bugs be­cause Zig was­n’t up to the task.

Andrew’s story: The Bun code is a mess be­cause of their en­gi­neer­ing de­ci­sions, in­clud­ing overus­ing AI agents to write and re­view every­thing.

Andrew’s story: The Bun code is a mess be­cause of their en­gi­neer­ing de­ci­sions, in­clud­ing overus­ing AI agents to write and re­view every­thing.

I’d lean more to­ward the lat­ter, but I sus­pect the dom­i­nant fac­tor is more bor­ing:

Ray’s story: Faced with a le­git­i­mate chal­lenge of mem­ory bugs, there were sev­eral vi­able op­tions. Management ea­gerly ap­proved the Rust rewrite op­tion be­cause it was a great mar­ket­ing op­por­tu­nity to show­case their new Fable model, Anthropic al­ready uses Rust, and Zig is openly against us­ing Anthropic’s prod­ucts.

Ray’s story: Faced with a le­git­i­mate chal­lenge of mem­ory bugs, there were sev­eral vi­able op­tions. Management ea­gerly ap­proved the Rust rewrite op­tion be­cause it was a great mar­ket­ing op­por­tu­nity to show­case their new Fable model, Anthropic al­ready uses Rust, and Zig is openly against us­ing Anthropic’s prod­ucts.

That makes fine busi­ness sense, it just is­n’t a mar­ket­ing story. The mar­ket­ing needed to fo­cus on how their AI was pow­er­ful enough to do this rewrite (even though it was not pow­er­ful enough to catch a use-af­ter-free).

For bet­ter or worse, this bag­gage is now top-of-mind in the Rust vs Zig ques­tion. The sit­u­a­tion tends to pit Jarred’s judge­ment against Andrew’s in the eye’s of the com­mu­nity. Any face-sav­ing ex­ag­ger­a­tion spo­ken through Anthropic’s mega­phone could un­in­ten­tion­ally af­fect Zig’s rep­u­ta­tion.

I can un­der­stand why rather than leave well enough alone, Andrew would de­cide to… add some con­text.

Is this a smear?

From my per­spec­tive, Anthropic is the party we need to hold ac­count­able here. That’s what this is all about. Bun founder Jarred Sumner is get­ting caught in the cross­fire in a sense. So is Zig.

It would be nice if this could be dis­cussed strictly on the tech­ni­cal points, and we’ll get to them. However, I don’t think Anthropic is mak­ing a tech­ni­cal ar­gu­ment, they are deal­ing in spec­ta­cle.

Anthropic is us­ing Jarred’s cred­i­bil­ity to help sell their nar­ra­tive. In re­spond­ing to that, we’re com­ment­ing on his cred­i­bil­ity. That’s messy. I don’t love it.

Still, if re­port­ing the things that some­one says and does comes off as a smear, then maybe that be­hav­ior was part of the prob­lem too.

The meat grinder

My first im­pres­sion of the Bun pro­ject was the 2022 an­nounce­ment, in­clud­ing this warn­ing to re­cruits.

Oven is go­ing to be a grind, es­pe­cially the first nine months or so. If work-life bal­ance means a lot of time spent not work­ing, it’s prob­a­bly not a good fit.

Oven is go­ing to be a grind, es­pe­cially the first nine months or so. If work-life bal­ance means a lot of time spent not work­ing, it’s prob­a­bly not a good fit.

When I see a state­ment like that from a prospec­tive man­ager, it says a num­ber of things, not the least of which is “this per­son has no idea what they are do­ing”. A lot of rea­son­ably good coders have never seen an ex­am­ple of a good man­ager, and have all kinds of weird ideas about what man­age­ment is.

Running at “crunch time” all the time is bad for health and bad for pro­duc­tiv­ity. That is a ro­bustly es­tab­lished fact about knowl­edge work. For some ref­er­ences, see the Human Factors sec­tion of Hillel’s Empirical Software Engineering.

My ad­vice? Don’t work for peo­ple that brag about 90 hour weeks. Work for peo­ple who will de­fend your abil­ity to sleep at night.

In Andrew’s piece, he sum­ma­rizes what he’s heard from the grapevine of the Bun team’s ex­pe­ri­ence:

Poor com­mu­ni­ca­tion, un­re­al­is­tic ex­pec­ta­tions, low em­pa­thy, no ex­pe­ri­ence. Just a to­tal shit show

Poor com­mu­ni­ca­tion, un­re­al­is­tic ex­pec­ta­tions, low em­pa­thy, no ex­pe­ri­ence. Just a to­tal shit show

I mean… of course it was. The hearsay is es­sen­tially re­peat­ing what was an­nounced pub­licly. Their job list­ing might as well have said, “now seek­ing ap­pli­cants for to­tal shit show”. It’s bad form for us to say this out loud. We’re sup­posed to let the Tech Bros go on about how cut­ting cor­ners is some ge­nius pro­duc­tiv­ity hack. Then the peo­ple that lis­ten to them can even­tu­ally call us in to fix the re­sults. It would be a great arrange­ment if I cared less about out­comes. It’s quite lu­cra­tive.

FWIW, I’ve used Bun a few times and liked it well enough. Cool tech is of­ten pro­duced in spite of bad work en­vi­ron­ments. I’m not the one say­ing that their en­vi­ron­ment re­sulted in a buggy un­main­tain­able mess, Bun is the one say­ing that.

Say some­thing nice

The piece about the mi­gra­tion process is very cool, with de­tails that are reusable. No com­plaints, I think that’s the real con­tri­bu­tion here. I par­tic­u­larly like the hon­esty in ex­plain­ing that this was a port to un­safe Rust, al­low­ing a lit­eral file-by-file mi­gra­tion to min­i­mize risk, paving the way for re­design in fu­ture steps. That’s a sen­si­ble move ex­plained well.

There’s some truth to the idea that lan­guage choice is be­com­ing more re­versible. This method will take it’s place among other types of rewrite au­toma­tion with pros and cons. These tech­niques can be com­bined and fur­ther hard­ened with Formal Methods. Darpa’s TRACTOR (Translating All C to Rust) re­search pro­gram pub­lished a re­port this year which should cover the state of the art.

My fa­vorite book on soft­ware mod­ern­iza­tion pro­jects is Kill It With Fire by Marianne Bellotti. As agents al­low us more moves we can make with old code, we still need good judge­ment and com­mu­ni­ca­tion in de­cid­ing where to go. Let’s talk about that next.

The rewrite ra­tio­nale is fluff

These are the ba­sic in­gre­di­ents of ex­plain­ing a tech­ni­cal de­ci­sion:

What is the mo­ti­va­tion?

What op­tions did you con­sider?

What are the pros and cons?

Here’s a great ex­am­ple by Richard Feldman on his de­ci­sion to move the Roc com­piler from Rust to Zig. I was ini­tially shocked by that move (I’m some­what fa­nat­i­cal about lan­guage safety), but ul­ti­mately his points made sense and this started my cu­rios­ity about Zig.

When the Bun rewrite was merged, I’d hoped to see a sim­i­lar treat­ment. This is what we got in­stead, two months late.

✅ What is the mo­ti­va­tion?

⚠️ What op­tions did you con­sider?

🚫 What are the pros and cons?

For the as­pir­ing tech leads out there: When you skimp on these in­gre­di­ents, es­pe­cially the “pros and cons”, you risk giv­ing the im­pres­sion that you ap­proached the prob­lem with one an­swer al­ready in mind and are giv­ing it a post-hoc jus­ti­fi­ca­tion. Maybe you have rea­sons that you aren’t say­ing.

It feels dis­hon­est.

All pros no cons

Rather than a real trade­off com­par­i­son, we get a “Bun is bet­ter in Rust” sec­tion cov­er­ing only up­sides. A change like this al­ways has trade-offs, an ob­vi­ous one would be build time.

Typically when you use Rust for a large code­base, you are buy­ing safety and pay­ing in slower com­pi­la­tion. There’s no shame in that, it can be a win­ning bar­gain. In that past, this fac­tor was im­por­tant enough to Bun that they forked the Zig com­piler to try and im­prove it. If we’re right that the Rust port in­creased build time for con­trib­u­tors, why not dis­close that? It comes off as more cred­i­ble to own the im­pact and the pri­or­i­ties that make it right move over­all.

They also seem to be padding the list by mix­ing in other im­prove­ments they’ve made af­ter the rewrite that aren’t re­ally re­lated to it.

They did­n’t try a style guide?

Recall that the mo­ti­va­tion was mem­ory bugs. Definitely not Bun’s only source of bugs but a fre­quent one, caus­ing four fix com­mits per week by my count. Painful.

Theoretically, every mem­ory bug rep­re­sents a vi­o­la­tion of some con­ven­tion - an ex­pec­ta­tion of how this kind of ob­ject should be dealt with. Therefore it be­hooves us to es­tab­lish a clear idea of what’s ex­pected in what cir­cum­stance. We should try to use any lan­guage ef­fec­tively for that mat­ter, Rust style guides are a thing too, but man­ual mem­ory man­age­ment adds scope to the ex­pec­ta­tions we need.

How have other peo­ple solved this prob­lem? Another flag­ship Zig code­base is TigerBeetle, a fi­nan­cial trans­ac­tion data­base. It is not plagued by mem­ory bugs, in fact it ap­pears to be one of the most re­li­able data­bases in ex­is­tence. They will gladly tell you that this is due to their TigerStyle ap­proach and some in­no­v­a­tive test­ing strate­gies. Worth a look! The word “style” might un­der­sell it, it’s a whole en­gi­neer­ing phi­los­o­phy with Zig cod­ing guide­lines as one el­e­ment.

Here’s a taste of TigerStyle. Not every ap­pli­ca­tion can copy-and-paste this ex­act strat­egy, but it il­lus­trates the re­la­tion­ship be­tween mem­ory al­lo­ca­tion and other de­sign de­ci­sions.

All mem­ory must be sta­t­i­cally al­lo­cated at startup. No mem­ory may be dy­nam­i­cally al­lo­cated (or freed and re­al­lo­cated) af­ter ini­tial­iza­tion. This avoids un­pre­dictable be­hav­ior that can sig­nif­i­cantly af­fect per­for­mance, and avoids use-af­ter-free. As a sec­ond-or­der ef­fect, it is our ex­pe­ri­ence that this also makes for more ef­fi­cient, sim­pler de­signs that are more per­for­mant and eas­ier to main­tain and rea­son about, com­pared to de­signs that do not con­sider all pos­si­ble mem­ory us­age pat­terns up­front as part of the de­sign.

All mem­ory must be sta­t­i­cally al­lo­cated at startup. No mem­ory may be dy­nam­i­cally al­lo­cated (or freed and re­al­lo­cated) af­ter ini­tial­iza­tion. This avoids un­pre­dictable be­hav­ior that can sig­nif­i­cantly af­fect per­for­mance, and avoids use-af­ter-free. As a sec­ond-or­der ef­fect, it is our ex­pe­ri­ence that this also makes for more ef­fi­cient, sim­pler de­signs that are more per­for­mant and eas­ier to main­tain and rea­son about, com­pared to de­signs that do not con­sider all pos­si­ble mem­ory us­age pat­terns up­front as part of the de­sign.

Clearly, if we’re weigh­ing a rewrite in Rust, we’d first con­sider if we should use the cur­rent lan­guage dif­fer­ently. Hear’s how Bun’s write-up pre­sents that op­tion.

Many pro­jects opt to an­swer these kinds of ques­tions through a style guide. TigerBeetle’s TigerStyle is an ex­am­ple in Zig and Google’s 31,000 word C++ style guide is an­other. The chal­lenge with style guides is en­force­ment. How do you make sure the style guide is fol­lowed? Historically, code re­view was the an­swer with best-ef­fort en­force­ment via lin­ters & sta­tic an­a­lyz­ers.

Many pro­jects opt to an­swer these kinds of ques­tions through a style guide. TigerBeetle’s TigerStyle is an ex­am­ple in Zig and Google’s 31,000 word C++ style guide is an­other. The chal­lenge with style guides is en­force­ment. How do you make sure the style guide is fol­lowed? Historically, code re­view was the an­swer with best-ef­fort en­force­ment via lin­ters & sta­tic an­a­lyz­ers.

I ex­pected the next sen­tence to dis­cuss Bun’s style guide, why it was­n’t work­ing, per­haps how it evolved over time… nope. They seem to just pay lip-ser­vice the pri­mary way the com­mu­nity ad­dresses their prob­lem, shrug their shoul­ders and move on. Did I miss some­thing? Over four years on a pro­ject of this size, it’s sur­pris­ing they did­n’t se­ri­ously at­tempt this if they ex­pe­ri­enced these prob­lems. It’s al­most like the pro­ject was run by some­one who tries to hold all the con­text in their head and never have meet­ings.

What’s more be­wil­der­ing is that they dis­miss style guides with hes­i­ta­tions that are re­futed within their own ar­ti­cle. Consider that clas­sic ob­jec­tion that guides are hard to en­force. Fair, though maybe an odd bar­rier for a team ad­vanced enough to fork the com­piler they use. Here’s the thing, they al­ready claim to have solved the en­force­ment prob­lem be­cause they use agen­tic re­view. PORTING.md is it­self a style guide, scoped to the mi­gra­tion process. They have just con­ducted an agen­tic re­view of their en­tire rewrit­ten code­base against strin­gent guide­lines and de­clared it a suc­cess.

This does­n’t make sense. Let’s as­sume agen­tic re­view works, I think it can un­der the right cir­cum­stances. That would re­quire de­sign and well-thought guide­lines. I think they were sim­ply more ex­cited about putting that men­tal en­ergy into a rewrite than a re-ar­chi­tec­ture, for any num­ber of un­stated rea­sons. It may have been the right choice.

We’re still wor­ried about syn­tax?

There’s one more bit I want to nit­pick, a com­mon cog­ni­tive dis­so­nance in dis­cus­sions about agent-first cod­ing. Bun’s piece briefly dives into to the weeds of what a “style guide op­tion” might look like.

Having a rigid style guide with clear own­er­ship ex­pec­ta­tions ex­plic­itly spelled out in the type sys­tem was a real op­tion for Bun. Since Zig has no op­er­a­tor over­load­ing, we would likely end up with a lot of code look­ing some­thing like this:

Today, the European Parliament al­lowed the sus­pi­cion­less mass scan­ning of pri­vate com­mu­ni­ca­tions (“Chat Control 1.0”) to pass, a mea­sure it had re­jected twice in March. Although a ma­jor­ity of vot­ing Members of the European Parliament (MEPs) ac­tu­ally op­posed the reg­u­la­tion (314 against, 276 in fa­vor, 17 ab­sten­tions), the mo­tion to re­ject it failed to se­cure the re­quired ab­solute ma­jor­ity of 361 votes. As a re­sult, mass scan­ning is now per­mit­ted again un­til 2028.

A sym­bolic ex­emp­tion was adopted for en­crypted com­mu­ni­ca­tions—though in prac­tice, ser­vice providers do not scan these any­way. Furthermore, while a ma­jor­ity of vot­ing MEPs wanted to re­strict the scan­ning of pri­vate com­mu­ni­ca­tions strictly to sus­pects iden­ti­fied by the ju­di­ciary (322 to 255 votes), this amend­ment like­wise fell short of the re­quired ab­solute ma­jor­ity.

Dr. Patrick Breyer, civil rights ac­tivist and for­mer Member of the European Parliament (MEP), warns of the con­se­quences:

“The fact that Chat Control is mov­ing for­ward against the will of the ma­jor­ity of vot­ing MEPs is a farce and dam­ages democ­racy. Our chil­dren are the real losers in this un­de­mo­c­ra­tic process. The pas­sage of a gen­uine, per­ma­nent child pro­tec­tion reg­u­la­tion is now in se­ri­ous jeop­ardy. The Council will never agree to a des­per­ately needed par­a­digm shift as long as they can sim­ply stick to the old ap­proach of sus­pi­cion­less scan­ning at the whim of the tech in­dus­try.”

Despite the leg­isla­tive de­feat, Breyer re­mains de­fi­ant re­gard­ing the up­com­ing ne­go­ti­a­tions:

“Today’s vote on the in­terim reg­u­la­tion was a set­back, but the po­lit­i­cal bat­tle over the per­ma­nent ‘Chat Control 2.0’ is just get­ting started. The re­sis­tance we saw in Parliament to­day was so strong that find­ing a ma­jor­ity for per­ma­nent, sus­pi­cion­less mass scan­ning in fu­ture ne­go­ti­a­tions is a com­plete pipe dream.”

Breyer fun­da­men­tally re­jects the mass sur­veil­lance ap­proach:

“Trying to pro­tect chil­dren with sus­pi­cion­less mass sur­veil­lance is like fran­ti­cally mop­ping the floor while the faucet is still run­ning. Blanket chat con­trol is just as un­ac­cept­able as in­dis­crim­i­nately open­ing every­one’s phys­i­cal mail. For five years, this failed sys­tem has served as a smoke­screen to de­lay real ac­tion, all while over­whelm­ing the po­lice with false alarms. We need more child pro­tec­tion, not less—but we need ef­fec­tive pro­tec­tion, not the il­lu­sion of se­cu­rity.”

What hap­pens next?The in­terim reg­u­la­tion passed to­day will re­main in ef­fect un­til 2028, or un­til an agree­ment on a per­ma­nent reg­u­la­tion is reached. Negotiations for the per­ma­nent law will re­sume in September. The core dis­pute be­tween the EU Parliament, mem­ber state gov­ern­ments, and the EU Commission re­mains the scan­ning of pri­vate chats: should it be in­dis­crim­i­nate, or tar­geted at crim­i­nal sus­pects?

What changes with the re­turn of Chat Control 1.0—and what stays the same:

What is com­ing back: US tech com­pa­nies are once again al­lowed to scan pri­vate mes­sages with­out a war­rant or prior sus­pi­cion. This af­fects di­rect mes­sages on plat­forms like Instagram, Discord, Snapchat, Skype, and Xbox, as well as emails via Google’s Gmail and Apple’s iCloud.

What re­mains un­changed: Public so­cial me­dia posts and files hosted in cloud stor­age could al­ready be scanned with­out this law. Furthermore, pri­vate mes­sages can al­ways be re­ported by users, or mon­i­tored by au­thor­i­ties us­ing tar­geted, court-or­dered wire­tap­ping.

What is still NOT be­ing scanned: End-to-end en­crypted chats, such as those on WhatsApp, have al­ways been ex­empt from these scans. Additionally, European providers of mes­sag­ing and email ser­vices have never im­ple­mented chat con­trol mea­sures.

Why Chat Control is the wrong ap­proach:

Since 2022, the vol­ume of sus­pected abuse re­ports from the US has al­ready dropped by 50 per­cent due to the grow­ing use of mes­sage en­cryp­tion.

According to EU Commission fig­ures, mass scan­ning of pri­vate chats ac­counted for only 36 per­cent of all abuse re­ports in 2024 (the ma­jor­ity came from pub­lic posts and cloud stor­age).

The German Federal Criminal Police Office (BKA) re­ports that 48 per­cent of all in­com­ing alerts are not crim­i­nally rel­e­vant in the first place.

Crime sta­tis­tics re­veal that 40 per­cent of the re­sult­ing in­ves­ti­ga­tions ac­tu­ally tar­get mi­nors them­selves.

Under the chat con­trol sys­tem, an es­ti­mated 99 per­cent of re­ports gen­er­ated by Meta con­sist of pre­vi­ously known ma­te­r­ial, which gen­er­ally does lit­tle to stop on­go­ing, ac­tive abuse.

The EU Commission ad­mits there is no ev­i­dence that sus­pi­cion­less scan­ning of pri­vate com­mu­ni­ca­tions has led to an in­crease in crim­i­nal con­vic­tions or in res­cued chil­dren.

Talk of avert­ing a “protection gap” is there­fore highly mis­lead­ing. The most ef­fec­tive law en­force­ment tools—court-or­dered wire­taps, user re­ports, and the scan­ning of pub­lic plat­forms and cloud stor­age—were never at risk and re­main fully in­tact. The only prac­tice that was tem­porar­ily banned since April was the in­dis­crim­i­nate, war­rant­less search­ing of pri­vate, un­en­crypted mes­sages of in­no­cent peo­ple on a hand­ful of US plat­forms.

Background: The dead­lock over a per­ma­nent so­lu­tionIn par­al­lel, ne­go­ti­a­tions are on­go­ing for a per­ma­nent reg­u­la­tion to pro­tect chil­dren from sex­u­al­ized on­line vi­o­lence (the “CSAM Regulation” or “Chat Control 2.0”). In these talks, the EU Parliament is push­ing for a par­a­digm shift in how we ap­proach on­line child safety, de­mand­ing:

Mandatory, tar­geted de­tec­tion or­ders against ac­tual crim­i­nal sus­pects, rather than blan­ket mass scan­ning left to the tech in­dus­try’s dis­cre­tion.

An EU Child Protection Centre tasked with the sys­tem­atic re­moval of known abuse ma­te­r­ial from the pub­lic in­ter­net.

Strict se­cu­rity stan­dards for mes­sag­ing apps (“Security by Design”) to pre­vent cy­ber groom­ing.

This per­ma­nent leg­is­la­tion has stalled be­cause EU mem­ber states in­sist on main­tain­ing the out­dated ap­proach of vol­un­tary, sus­pi­cion­less scan­ning of pri­vate com­mu­ni­ca­tions. Critics warn that re­peat­edly ex­tend­ing the in­terim rules re­moves the po­lit­i­cal pres­sure needed to reach a vi­able, per­ma­nent agree­ment. Ultimately, cling­ing to the sta­tus quo threat­ens to de­rail real progress on child pro­tec­tion.

Patrick Breyer sums up the prob­lem:“As long as EU gov­ern­ments can use pro­ce­dural loop­holes to con­tin­u­ally ex­tend their com­fort­able sta­tus quo of vol­un­tary, in­dis­crim­i­nate mass scan­ning, they have zero in­cen­tive to en­gage with the Parliament’s tar­geted, legally sound, and far more ef­fec­tive child pro­tec­tion strat­egy.

“The Voices of Survivors: “We need pri­vacy to bring abusers to jus­tice”

Survivors of sex­ual vi­o­lence ex­plic­itly em­pha­size that un­tar­geted Chat Control did not help vic­tims:

Alexander Hanff, sur­vivor of child sex­ual abuse and pri­vacy ad­vo­cate, clar­i­fies:“As a sur­vivor I re­lied on con­fi­den­tial com­mu­ni­ca­tions to tell my story and find jus­tice for 28 school­boys—my­self in­cluded—re­sult­ing in the con­vic­tion of mul­ti­ple of­fend­ers. We sur­vivors need pri­vacy, be­cause with­out it we lose our voice. Chat Control was not cre­ated to pro­tect chil­dren. It was about Big Tech com­pa­nies like Meta or Google want­ing ac­cess to our data for prof­i­teer­ing, and states at­tempt­ing to ex­pand mass sur­veil­lance. The EU Commission has wasted five years and mil­lions of eu­ros on al­go­rithms that can­not pro­tect chil­dren and were never meant to. This money should have been di­verted to real polic­ing, causal re­search, and sup­port for sur­vivors, mil­lions of whom have never re­ceived any sup­port at all.”

Marcel Schneider* (name changed), a sur­vivor who has been su­ing Meta in court over its vol­un­tary Chat Control, adds:“Any­one mourn­ing the end of Chat Control has not un­der­stood what ac­tu­ally helps sur­vivors of sex­ual vi­o­lence. Mass sur­veil­lance by cor­po­ra­tions like Meta does not pre­vent abuse. Genuine pro­tec­tion means: delet­ing ma­te­r­ial at the source, proac­tive po­lice work on the Darknet, and apps that are safe by de­sign for chil­dren from the very start.”

Dorothée Hahne, found­ing mem­ber and vice-chair of the sur­vivors’ ini­tia­tive MOGiS e.V. (A Voice for Survivors), em­pha­sizes the dan­ger mass sur­veil­lance poses to vic­tims them­selves:“As sur­vivors, we see our ‘safe spaces’, our pro­tected ar­eas and com­mu­ni­ca­tion chan­nels, en­dan­gered or de­stroyed by this. For sur­vivors, this need is ex­is­ten­tial.“

It looks like John Deere own­ers can soon feel free to fix their own ma­chines.

The Federal Trade Commission and at­tor­neys gen­eral from sev­eral states se­cured a right-to-re­pair set­tle­ment Wednesday with agri­cul­ture equip­ment gi­ant Deere & Co. — com­monly known as John Deere — that re­quires the com­pany to let farm­ers and in­de­pen­dent shops fix their own equip­ment.

The Illinois-based man­u­fac­turer has faced com­plaints for years for with­hold­ing the soft­ware needed for re­pairs and forc­ing cus­tomers to use au­tho­rized deal­ers in­stead of in­de­pen­dent ones.

This marks the sec­ond right-to-re­pair set­tle­ment Deere has reached this year, fol­low­ing a sep­a­rate $99 mil­lion class-ac­tion set­tle­ment with farm­ers in April. Though the class-ac­tion com­pen­sated con­sumers, the FTC’s set­tle­ment in­stead re­quires Deere to make its re­pair ser­vices avail­able to equip­ment own­ers and in­de­pen­dent shops.

The FTC and at­tor­neys gen­eral from Arizona, Illinois, Michigan, Minnesota and Wisconsin brought the an­titrust law­suit in January 2025, ar­gu­ing that Deere had il­le­gally re­stricted farm­ers and in­de­pen­dent shops that might oth­er­wise ser­vice them from re­pair­ing farm equip­ment such as trac­tors. Deere also makes en­gines and equip­ment for forestry, land­scap­ing and con­struc­tion.

Under the or­der filed in Illinois, Deere will now be re­quired to make di­ag­nos­tic and re­pair tools avail­able to equip­ment own­ers and in­de­pen­dent re­pair shops, not only its own net­work of au­tho­rized deal­ers. It also pre­vents Deere deal­ers from re­tal­i­at­ing against equip­ment own­ers or re­pair shops who choose to fix their own equip­ment in­stead of pay­ing for Deere’s ser­vices. The or­der is headed to Judge Iain D. Johnston for his ap­proval.

“For too long, Arizona farm­ers and in­de­pen­dent me­chan­ics have been at the mercy of Deere’s mo­nop­oly over re­pair tools, forced to wait — and pay — for au­tho­rized deal­ers just to fix bro­ken trac­tors and other equip­ment,” Arizona Attorney General Kris Mayes said in a state­ment Wednesday.

Deere must pay $1 mil­lion col­lec­tively to the five states for an­titrust en­force­ment costs and will be sub­ject to strict com­pli­ance over­sight for the next 10 years.

In the com­plaint, the FTC ar­gued that Deere pro­vides a ser­vice soft­ware tool to au­tho­rized deal­ers but does not pro­vide the full ver­sion to equip­ment own­ers or in­de­pen­dent shops. Deere had said the law­suit was base­less, de­nied that its dis­tri­b­u­tion of ser­vice tools was an­ti­com­pet­i­tive and ar­gued that it could not mo­nop­o­lize ser­vices since it does not di­rectly pro­vide them.

Sign up for Morning Wire: Our flag­ship newslet­ter breaks down the biggest head­lines of the day.

Deere main­tained its com­mit­ment to in­de­pen­dent re­pair in a state­ment Wednesday, adding that the agree­ment with the FTC re­in­forces its in­no­va­tion of more flex­i­ble re­pair op­tions.

“This is good news for our cus­tomers and for the fu­ture of how Deere equip­ment is sup­ported,” said Denver Caldwell, vice pres­i­dent of af­ter­mar­ket and cus­tomer sup­port.

Right-to-repair has be­come an in­creas­ingly com­mon is­sue over the years, es­pe­cially for tech prod­ucts, with con­sumers com­plain­ing that even sim­ple re­pairs can only be done by com­pany-au­tho­rized deal­ers.

Thanks for play­ing! You can send me feed­back or try my other game Zanagrams.

Jul 13, 2026

After I men­tioned a Jurassic Park anec­dote the other day, I watched the movie again. I must have seen it at least ten times now. This time, I re­searched every com­puter/​soft­ware I spot­ted.

EDIT: Just when I was putting the fi­nal touches on this ar­ti­cle, I read the sad news that Sam Neill, who played pa­le­on­tol­o­gist Alan Grant in JP, has passed away to­day. R.I.P Sam.

Surprisingly, the first com­puter vis­i­ble is not on the is­land Isla Nublar but in Alan Grant and Ellie Sattler’s mo­bile trailer. It is an Apple Powerbook 100, vis­i­ble in the im­age be­low on the left side.

It had a Motorola 68000 proces­sor at 16 MHz, 2 – 8 megabytes (MB) of RAM, a 9-inch (23 cm) mono­chrome back­lit liq­uid-crys­tal dis­play (LCD) with 640 × 400 pixel res­o­lu­tion, and the System 7.0.1 op­er­at­ing sys­tem. Wikipedia

This ma­chine’s specs re­minds me of how aw­ful ’90s lap­top screens, based on a pas­sive ma­trix, were. Definitely some­thing I don’t miss from that era.

All com­put­ers and soft­ware are lo­cated in the Control Room on the desks of two en­gi­neers, Dennis Nedry and Ray Arnold.

Dennis Nedry’s desk is an in­de­scrib­able mess with three ma­chines (two macs, one SGI), three mon­i­tors, one PDA, and stor­age de­vices.

Ray Arnold’s desk is much ti­dier. It fea­tures a CCTV screen, stor­age de­vices, two com­put­ers (a Mac and a SGI), and two mon­i­tors.

In the back of the Control Room, we can make out a gi­ant screen and a su­per­com­puter with tall pan­els and blink­ing red lights.

The book The Making Of Jurassic Park has in­ter­est­ing de­tails about how they de­signed the Control Room.

Everything in the set was real. We could­n’t fake any of it, be­cause au­di­ences are so so­phis­ti­cated now in their knowl­edge of com­put­ers. All told, $875,000 worth of com­puter hard­ware loaned by Silicon Graphics, $350,000 worth from Apple and some $500,000 in ad­di­tional hard­ware and soft­ware went into equip­ping both the set and off-stage con­trol room. Cory Faucher (Special Effects Coordinator)

This means, ad­justed for in­fla­tion, Apple and SGI loaned roughly $4,000,000 of 2026 dol­lars for the pro­duc­tion of Jurassic Park.

Ray Arnold’s work­sta­tion is a SGI R4000 Indigo. It is barely vis­i­ble in two shots. Blink and you will miss it at 54:48.

We get a some­what bet­ter view of it to­wards the end of the movie thanks to a Velociraptor that never skips leg-day.

For the needs of the movie, that SGIs came in handy to run real-time 3D an­i­ma­tion of the Hurricane. Or did they?

A dy­namic and in­ter­ac­tive method was em­ployed to cre­ate the graph­ics, both on the big screen and on the com­puter mon­i­tors at each in­di­vid­ual sta­tion. A makeshift room was built ad­ja­cent to the set, then equipped with a bat­tery of Silicon Graphics and Apple Macintosh com­puter sys­tems. Stored on com­puter disks were an­i­ma­tions gen­er­ated over a pe­riod of six months by a four-man com­puter graph­ics team headed by Michael Backes.

Responding to cues re­ceived via ra­dio from the set, Backes and his team were able to feed their graph­ics di­rectly to the ap­pro­pri­ate mon­i­tors on stage, mak­ing it seem as though the ac­tors in­volved were ac­tu­ally call­ing up the im­agery. The Making Of Jurassic Park

A dy­namic and in­ter­ac­tive method was em­ployed to cre­ate the graph­ics, both on the big screen and on the com­puter mon­i­tors at each in­di­vid­ual sta­tion. A makeshift room was built ad­ja­cent to the set, then equipped with a bat­tery of Silicon Graphics and Apple Macintosh com­puter sys­tems. Stored on com­puter disks were an­i­ma­tions gen­er­ated over a pe­riod of six months by a four-man com­puter graph­ics team headed by Michael Backes.

Responding to cues re­ceived via ra­dio from the set, Backes and his team were able to feed their graph­ics di­rectly to the ap­pro­pri­ate mon­i­tors on stage, mak­ing it seem as though the ac­tors in­volved were ac­tu­ally call­ing up the im­agery.

Dennis Nedry’s pow­er­house work­sta­tion is an SGI IRIS Crimson. It is such a beast that it won’t fit on his desk. It is on the floor on the right of his desk (red box).

Most of the time it is used to dis­play a 3D chess game (monitor the right end of Dennis desk).

The SGI Crimson is rarely vis­i­ble on screen. It is briefly vis­i­ble af­ter Dennis’s “white rab­bit” lock­down brings Samuel Jackson into a de­pres­sion.

The SGI Crimson was a very pow­er­ful work­sta­tion re­leased in 1992. Its main ap­peal was its panel of real-time 3D graph­ics cards. The CPU was also very pow­er­ful with hard­ware Floating-Point Unit, a lux­ury for 3D graph­ics.

One MIPS 100 MHz R4000 or 150 MHz R4400 proces­sor Choice of seven high-per­for­mance 3D graph­ics sub­sys­tems (Entry, XS, XS24, Elan, Extreme, Reality Engine, VGXT) Up to 256 MB mem­ory and in­ter­nal disk ca­pac­ity of up to 7.2 GB, ex­pand­able to more than 72 GB us­ing ad­di­tional en­clo­sures I/O sub­sys­tem in­cludes four VMEbus ex­pan­sion slots, Ethernet and two SCSI chan­nels with disk strip­ing sup­port

Wikipedia

One MIPS 100 MHz R4000 or 150 MHz R4400 proces­sor

Choice of seven high-per­for­mance 3D graph­ics sub­sys­tems (Entry, XS, XS24, Elan, Extreme, Reality Engine, VGXT)

Up to 256 MB mem­ory and in­ter­nal disk ca­pac­ity of up to 7.2 GB, ex­pand­able to more than 72 GB us­ing ad­di­tional en­clo­sures

I/O sub­sys­tem in­cludes four VMEbus ex­pan­sion slots, Ethernet and two SCSI chan­nels with disk strip­ing sup­port

Both Dennis and Ray use PLI Mini Arrays for their backup. Dennis has an im­pres­sive stack of five on the left-end of his desk.

There is a con­ti­nu­ity er­ror in the movie. See how the stack of PLI is fac­ing left in this early shot.

Later in the movie, af­ter Ray takes over Dennis’s desk, we can see the PLIs have mag­i­cally ro­tated to face the de­vel­oper.

On Ray’s desk we also find a smaller stack of two PLIs.

There is a close-up shot when John Hammond fol­lows the jeeps’ progress on the CCTV.

Despite the at­ten­tion to de­tail, it seems the PLIs were not con­nected since the LEDs are all blank. In Macs Place of Spring 1993 we can find an ad on page 38 giv­ing more de­tails about the ca­pac­ity.

Since John Hammond “spared no ex­pense”, it is fair to say he picked 1GiB ver­sion at $3,598 a piece. That would give them 7 GiB of stor­age for a 2026 equiv­a­lent of $33,223.70. In 2026, 7 GiB of HDD would cost $0.49.

Seven GiB was a MASSIVE amount in 1993 when a high-end PC would come with 120 MiB HDD.

The Motorola Envoy is a per­sonal dig­i­tal as­sis­tant used by Dennis. It is vis­i­ble next to his right el­bow in the im­age be­low.

It is an ex­tremely im­pres­sive de­vice for the early ’90s. It is a fold­able that fea­tures an an­tenna when de­ployed (video).

The hard­ware of the Motorola Envoy in­cluded a Motorola Dragon I/68349 mi­cro­proces­sor, 4 MB of read only mem­ory (ROM), 1 MB of ran­dom ac­cess mem­ory (RAM), and an LCD. Of par­tic­u­lar in­ter­est were the wire­less com­mu­ni­ca­tions ca­pa­bil­i­ties of the Envoy. Its built-in com­mu­ni­ca­tion com­po­nents in­cluded a ra­dio mo­dem ca­pa­ble of 4,800 bits per sec­ond com­mu­ni­ca­tion, a fax and data mo­dem, and an in­frared trans­ceiver ca­pa­ble of 38.4 kbit/​s of data trans­fer. Wikipedia

The hard­ware of the Motorola Envoy in­cluded a Motorola Dragon I/68349 mi­cro­proces­sor, 4 MB of read only mem­ory (ROM), 1 MB of ran­dom ac­cess mem­ory (RAM), and an LCD. Of par­tic­u­lar in­ter­est were the wire­less com­mu­ni­ca­tions ca­pa­bil­i­ties of the Envoy. Its built-in com­mu­ni­ca­tion com­po­nents in­cluded a ra­dio mo­dem ca­pa­ble of 4,800 bits per sec­ond com­mu­ni­ca­tion, a fax and data mo­dem, and an in­frared trans­ceiver ca­pa­ble of 38.4 kbit/​s of data trans­fer.

Dennis must have used it since we see it moved and par­tially un­folded later in the movie.

It is un­clear how Jurassic Park crew got their hands on a Motorola Envoy. The movie was shot from August to November 1992. Motorola only fin­ished the PDA in mid-1994 but de­layed re­leas­ing it to February 1995[1].

EDIT : Hackernews user kalle­boo solved the mys­tery!

The head of frogde­sign (Hartmut Esslinger) ended up run­ning into Spielberg on a plane and showed it to him. The one in the movie is an orig­i­nal mockup

(source and

dis­cus­sion). kalle­boo

The su­per­com­puter of the con­trol room looks a lot like five Thinking Machines CM-5 with there char­ac­ter­is­tic front panel with thou­sands of red blink­ing LEDs. With a pric­etag of “only” $46,000 per ma­chine, it is very pos­si­ble these were au­then­tic.

The CM-5, “Connection Machine”, was re­leased in 1991[2]. In 1993 it was still con­sid­ered the most pow­er­ful com­puter in the world[3]. Each ma­chine was called a “node”, fea­tur­ing a Sparc CPU, four vec­tor units, and 32 MiB RAM. As many nodes as needed could be con­nected to­gether to form a mesh. The National Center for Atmospheric Research (NCAR) build a 32-node su­per­com­puter with CM-5[4].

Does the red LED pat­tern in the front panel mean any­thing? Absolutely not, they were ran­domly gen­er­ated[5].

If you lis­ten care­fully you can ac­tu­ally hear Dennis Nedry talk about the CM-5, “Connection Machine”.

I am to­tally un­ap­pre­ci­ated in my time. You can run this whole park from this room with min­i­mal staff for up to 3 days. You think that kind of au­toma­tion is easy? Or cheap? You know any­body who can net­work 8 con­nec­tion ma­chines and de­bug 2 mil­lion lines of code for what I bid for this job? Because if he can I’d like to see him try. Dennis Nedry

After the pub­li­ca­tion of this ar­ti­cle, user pivo (from hack­ernews) was able to ex­plain why the movie fea­tured CM-5 while the novel fea­tured Cray su­per­com­puter.

My wife worked for Thinking Machines back then. I re­mem­ber that they’d asked Cray to loan them a su­per­com­puter for the film be­cause that’s the com­puter used in the book. Cray brushed them off, so they turned to Thinking Machines who were happy to do it.

To thank them, the pro­duc­ers rented a the­ater in Cambridge, MA [Kendall Sq. cin­ema] to screen the film just for Thinking Machines and I was also able to at­tend. By far the biggest re­ac­tions from the au­di­ence that night were when the CM-5 was shown for the first time and then when the young ac­tress says, “It’s a Unix sys­tem. I know this” pivo (from hack­ernews)

My wife worked for Thinking Machines back then. I re­mem­ber that they’d asked Cray to loan them a su­per­com­puter for the film be­cause that’s the com­puter used in the book. Cray brushed them off, so they turned to Thinking Machines who were happy to do it.

To thank them, the pro­duc­ers rented a the­ater in Cambridge, MA [Kendall Sq. cin­ema] to screen the film just for Thinking Machines and I was also able to at­tend. By far the biggest re­ac­tions from the au­di­ence that night were when the CM-5 was shown for the first time and then when the young ac­tress says, “It’s a Unix sys­tem. I know this”

One of the very best mon­i­tors money could buy in 1993 was the SuperMatch 20-T. The twenty means 20″ and T meant Trinitron. The SuperMatch was fea­tured on the cover of MacUser in Feb 92. In MacUser of Oct 94, page 180 (out of 252!!), we can see it cost $2,589 ($6,000 in 2026).

20″ mon­i­tors were con­sid­ered ab­solutely mas­sive in 1993 and only seen in pro­fes­sional work­spaces. A typ­i­cal PC would come with a 15″ CRT. 21″ is al­most the max­i­mum CRTs reached, their depth and weight made them very hard to move. They were re­placed by LCD around 2005.

A lot of at­ten­tion was paid to avoid show­ing CRT re­fresh ar­ti­fact in the movie. SuperMatch had en­gi­neer on-site and pro­duc­tion had peo­ple ded­i­cated to sync­ing CRT fram­er­ate with film rate.

My un­cle (John Monsour) worked on this movie as the “24 Frame Computer Sync Engineer”. Because film cam­eras and CRT mon­i­tors have dif­fer­ent frame rates, you needed to use spe­cial­ized elec­tron­ics to syn­chro­nize them with the cam­era frame rate oth­er­wise you would have band­ing and weird mov­ing ar­ti­facts on all the screens. It’s crazy to imag­ine need­ing to do this for all the screens vis­i­ble in these shots. am­c­col­lum

The mon­i­tor fea­tures a par­tic­u­lar “chin”. The ab­solutely gor­geous SGI Hardware Developer Handbook, on page 4 – 59, re­veals this is a 19″ Mitsubishi HL7965 Monitor which SGI re­branded. It likely cost as much as the SuperMatch 20-T.

On Ray Arnold’s desk, we can no­tice a weird key­board with a con­nec­tor on the side. This is a SGI Granite Keyboard (Indigo Style)[6]. It is a pretty cool key­board with two 6 Pin Mini-DIN con­nec­tors[7] on each side. The key­board can be con­nected to the work­sta­tion from ei­ther side and the mouse is to be daisy-chained into the other port.

Ray is seen us­ing the same key­board later. If you look closely at the screen, it looks like sta­tus net­work was aliased to ping CLI.

Dennis Nedry uses two Macintosh Quadra 700. Apple must have been very happy with the prod­uct place­ment. Although they usu­ally re­quire their com­put­ers not to be used for ne­far­i­ous ac­tiv­ity which is not the case here.

Released in 1991, The Quadra 700 ran on Motorola 68040 @ 25 MHz with 4 MB RAM, ex­pand­able to 68 MB. HDD sizes avail­able were 80 and 160 MB. Ray also uses a Macintosh Quadra 700 but he has only one on his desk.

Dennis ne­go­ti­ates with his co-con­spir­a­tor lo­cated in the har­bor to give him time to make it there. It hap­pens via a VC on the Mac. Why not on a SGI? Because the whole thing was faked via Quicktime Video player run­ning on System 7.

The cur­sor on the progress bar is clearly vis­i­ble. This is 1-minute clip. Even the mouse cur­sor is still on the “play” but­ton of the Quicktime win­dow.

Notice the video folder, named VIDnet.

Quicktime is used ear­lier in the movie. When Dennis is re­vealed to be work­ing at Jurassic Park, he had Jaws played on his left screen[8].

IRIX System Usage util­ity, named gr_os­view can be seen a few times. It looks like a pow­er­ful tool, able to re­port not only user time, sys time, but also in­ter­rupt over­head and even gfx over­head ac­cord­ing to IRIX - Desktop User’s Guide on p182.

Despite re­ports that mon­i­tors screens were faked via re­mote op­er­a­tors, gr_os­view seems to re­act ap­pro­pri­ately to key­strokes in the se­quence above. Maybe this one was ac­tu­ally live.

When Ray ac­ci­den­tally locks down the whole sys­tem, Nedry’s face su­per­im­posed onto an Elvis Presley jump­suit shows up on his Macintosh. That is the UI of the “White Rabbit”, which Ray Arnold briefly men­tions when he ex­plains the lock­down to Ellie Sattler “White rab­bit ob­ject. Whatever it did, it did it all. But with the key-checks off, the com­puter did­n’t file the key­strokes.”. It is only in the novel that the pro­gram file­name is re­vealed, whte_rbt.obj. Michael Crichton, the au­thor of Jurassic Park, was ac­tu­ally a highly ca­pa­ble pro­gram­mer.

The leg­endary “It’s a Unix sys­tem. I know this” se­quence was done us­ing an ex­per­i­men­tal SGI file ex­plorer ap­pli­ca­tion named fsn. Lex Murphy takes over Dennis’s SGI Crimson and opens the /usr di­rec­tory.

SGI was su­per happy to see this since they men­tioned “YOU SAW IT IN JURASSIC PARK!” on their web­site[9].

IRIX sup­ported spaces in file and di­rec­tory names. I as­sume they put a dot be­tween Visitors and Center for style.

Nedryland is the sys­tem mod­estly named by Dennis Nedry to con­trol Jurassic Park. We can catch a few glimpses of the name on screen when the sys­tem suc­cess­fully re­boots.

There is very lit­tle on­line about how these screens were cre­ated ex­cept that they were cre­ated by Michael Backes and his team.

Fans have recre­ated Nedryland. Checkout JPOS NEDRYLAND YouTube chan­nel to see it in ac­tion. There is also an on­line ver­sion at juras­sic­sys­tems.com.

Some code as­so­ci­ated with Nedryland is vis­i­ble on screen. It looks like ac­tual source code[10] with Classic Mac OS API func­tions calls. EDIT: Several hack­ers news users pointed out this is Pascal from MPW (Macintosh Programmers Workshop).

Later dur­ing the faked video-con­fer­ence, we can see more files be­long­ing to a Nedryland di­rec­tory.

One last de­tail for the road. The book on the top of Dennis’s shelf (upper-right) is System 7 Revealed by Anthony Meadow. Wow they re­ally did pay at­ten­tion to every de­tail!

References

^[ 1]Motorola Envoy Release date ^[ 2]Connection Machine Series ^[ 3]The CM-5, Moore’s Law, and the Future of Computational Performance ^[ 4]NCAR’s Connection Machine 5 - Littlebear ^[ 5]CM-5 in Jurassic Park ^[ 6]SGI hard­ware de­vel­oper hand­book p4 – 25 ^[ 7]Using the Indigo Keyboard with a Personal Iris ^[ 8]In Jurassic Park, when Nedry is in­tro­duced, you can see he’s watch­ing Jaws on his com­puter. ^[ 9]3D File System Navigator for IRIX 4.0.1+ ^[10]Source code on Nedry’s work­sta­tion: real pro­gram­ming lan­guage/​s?

Tiny en­gine, im­mense model. Run GLM-5.2 (744B-parameter MoE) on a con­sumer ma­chine with ~25 GB of RAM — in pure C, with zero de­pen­den­cies, by stream­ing ex­perts from disk.

$ ./coli chat 🐦 col­i­brì v1.0 — GLM-5.2 · 744B MoE · int4 · stream­ing CPU ✓ pronto in 32s · res­i­dente 9.9 GB › ciao! ◆ Ciao! 😊 Come posso aiu­tarti oggi?

The idea

A 744B Mixture-of-Experts model ac­ti­vates only ~40B pa­ra­me­ters per to­ken — and only ~11 GB of those change from to­ken to to­ken (the routed ex­perts). So:

the dense part (attention, shared ex­perts, em­bed­dings — ~17B params) stays res­i­dent in RAM at int4 (~9.9 GB);

the 21,504 routed ex­perts (75 MoE lay­ers × 256 ex­perts + the MTP head, ~19 MB each at int4) live on disk (~370 GB) and are streamed on de­mand, with a per-layer LRU cache, an op­tional pinned hot-store, and the OS page cache as a free L2.

The en­gine is a sin­gle C file (c/glm.c, ~2,400 lines) plus small head­ers. No BLAS, no Python at run­time, no GPU re­quired (an opt-in CUDA tier for pinned ex­perts ex­ists — see be­low).

What’s im­ple­mented

Faithful GLM-5.2 (glm_moe_dsa) for­ward — val­i­dated to­ken-ex­act against a trans­form­ers or­a­cle (teacher-forcing 32/32, greedy 20/20 on a tiny-ran­dom model with the real ar­chi­tec­ture).

MLA at­ten­tion (q/kv-LoRA, in­ter­leaved par­tial RoPE) with com­pressed KV-cache: 576 floats/​to­ken in­stead of 32,768 (57× smaller — GLM-5.2 has 64 heads and no GQA).

DeepSeek-V3-style sig­moid router (noaux_tc, rout­ed_s­cal­ing_­fac­tor), shared ex­pert, first-3-dense lay­ers.

Native MTP spec­u­la­tive de­cod­ing — GLM-5.2′s own multi-to­ken-pre­dic­tion head (layer 78) drafts to­kens that the main model ver­i­fies in one batched for­ward. The head must be int8 (the con­verter does this by de­fault): at int4 draft ac­cep­tance col­lapses to 0 – 4% and spec­u­la­tion never en­gages; at int8 it’s 39 – 59% ac­cep­tance, 2.2 – 2.8 to­kens/​for­ward (community-measured, #8). Lossless — and stays loss­less un­der sam­pling via re­jec­tion sam­pling. Honest caveat from the same mea­sure­ment: on a cold cache each ver­i­fied draft routes to ex­tra ex­perts (~660 → ~1100 ex­pert-loads/​to­ken), so spec­u­la­tion can be a net time loss un­til the cache/​pin warms up — the adap­tive guard and DRAFT=0 are there for that.

True sam­pling — tem­per­a­ture + nu­cleus, de­faults tuned for int4 re­al­ity (0.7 / 0.90; the of­fi­cial 1.0 / 0.95 sam­ples quan­ti­za­tion noise from the tail).

Integer-dot ker­nels (Q8_0-style int8 ac­ti­va­tions, AVX2 mad­dubs): int8 mat­muls 1.4 – 2.5× faster (119 GFLOP/s mea­sured), int4 1.8× in batch — rout­ing de­cided per shape by mea­sure­ment (int4 sin­gle-row stays f32: it mea­sured slower).

MLA weight ab­sorp­tion (DeepSeek trick) for de­code: no per-to­ken k/​v re­con­struc­tion — the query ab­sorbs kv_b, con­text is pro­jected af­ter at­ten­tion. Validated ex­act: TF 32/32 and gen­er­a­tion 20/20 with ab­sorp­tion forced every­where.

Async ex­pert reada­head: while one block of ex­perts is be­ing mul­ti­plied, the ker­nel is al­ready read­ing the next (WILLNEED).

Quantization ker­nels: int8 / packed int4 / packed int2, per-row scales, AVX2, de­quant-on-use. Packing val­i­dated bit-iden­ti­cal to the int8 con­tainer.

DSA sparse at­ten­tion — GLM-5.2′s light­ning in­dexer, faith­ful to the ref­er­ence glm_­moe_dsa mod­el­ing: per-layer top-2048 causal key se­lec­tion (full/shared in­dexer lay­ers), auto-de­tected from the out-idx-* weights (–indexer con­verter mode, ~189 MB ex­tracted from the FP8 repo). Validated ex­act: forc­ing the se­lec­tion to keep every key re­pro­duces dense at­ten­tion to­ken-for-to­ken. DSA=0 dis­ables, DSA_TOPK over­rides.

KV-cache per­sis­tence — con­ver­sa­tions re­open warm across en­gine restarts: serve mode ap­pends the com­pressed MLA KV to .coli_kv af­ter every turn (~182 KB/token, crash-safe) and re­sumes it at startup with zero re-pre­fill. Validated byte-iden­ti­cal to an un­in­ter­rupted ses­sion. KVSAVE=0 dis­ables.

Router-lookahead prefetch (PILOT=1, ex­per­i­men­tal) — the next lay­er’s rout­ing is 71.6% pre­dictable from the cur­rent lay­er’s post-at­ten­tion state (measured); a ded­i­cated I/O thread prefetches those ex­perts while the cur­rent layer com­putes.

Batch-union MoE: in pre­fill (and MTP ver­i­fi­ca­tion), each unique ex­pert of the batch is read once and ap­plied to every po­si­tion that routes to it.

Byte-level BPE to­k­enizer in C (GPT-2-style with Unicode-property regex, 320k merges).

RAM safety: the ex­pert cache is auto-sized from MemAvailable at startup — an hon­est peak pro­jec­tion (working set, KV, MTP row, re­con­struc­tion buffers) so the ker­nel OOM-killer never fires.

Offline FP8→int4 con­verter (c/tools/convert_fp8_to_int4.py): down­loads one shard at a time (~5 GB), de­quants (128×128 block scales), re­quan­tizes to the en­gine’s con­tainer, deletes the shard — the 756 GB FP8 check­point never needs to ex­ist on disk at once. Resumable.

Honest num­bers (WSL2, 12 cores, 25 GB RAM, NVMe via VHDX)

This is not fast. It is a 744B fron­tier-class model an­swer­ing cor­rectly on a ma­chine that costs less than one H100 fan. Warm cache, pinned hot ex­perts and MTP push the use­ful-re­sponse la­tency down con­sid­er­ably; the physics of the disk does the rest.

SSD note

Cold starts are heavy on ran­dom reads (~11 GB/token), but reads don’t mean­ing­fully wear an SSD — col­i­brì’s stream­ing is read-only. The real con­cerns un­der heavy use are (1) swap traf­fic if the sys­tem runs out of RAM (writes do wear the drive — keep a sane –ram bud­get; col­i­brì’s auto-bud­get is de­signed to stay clear of swap) and (2) sus­tained ther­mals: hours at full read duty cy­cle will heat cheaper dri­ves. Monitor drive tem­per­a­ture and health.

Download the model

A pre-con­verted GLM-5.2 int4 model for col­i­brì is avail­able on Hugging Face:

https://​hug­ging­face.co/​jlnsrk/​GLM-5.2-col­ibri-int4

If the MTP files there are still the int4 head (see #8 — sizes 1765523544/2686077736/536747200 = int4, un­us­able), grab the int8 MTP heads from the com­mu­nity clone by matey-0: https://​hug­ging­face.co/​ma­teogr­gic/​GLM-5.2-col­ibri-int4-with-int8-mtp

Download the repos­i­tory and point COLI_MODEL to its di­rec­tory:

COLI_MODEL=/path/to/GLM-5.2-colibri-int4 ./coli chat

This skips the FP8 → int4 con­ver­sion step en­tirely.

Thanks DatPat for your help!

Quick start

cd c ./setup.sh # checks gcc/​OpenMP, builds, self-tests

# ONE com­mand does every­thing model-side: down­loads GLM-5.2-FP8 shard by shard # (never needs the full 756 GB at once), con­verts to the int4 con­tainer, then # con­verts the MTP head for spec­u­la­tive de­cod­ing. Resumable at any point. # Conversion (only) needs python with: pip in­stall torch safeten­sors hug­ging­face_hub numpy ./coli con­vert –model /nvme/glm52_i4 # ~400 GB free on a real ext4/​NVMe path

# chat — RAM bud­get, ex­pert cache and MTP are all de­tected au­to­mat­i­cally: COLI_MODEL=/nvme/glm52_i4 ./coli chat

Inspect the planned stor­age hi­er­ar­chy be­fore load­ing the model:

COLI_MODEL=/nvme/glm52_i4 ./coli plan COLI_MODEL=/nvme/glm52_i4 ./coli plan –gpu 0,1 –ram 128 –vram 48 –json

# ap­ply the bounded plan to the nor­mal run­ner COLI_MODEL=/nvme/glm52_i4 ./coli chat –auto-tier

coli plan reads only safeten­sors head­ers and re­ports the mod­el’s ex­act dense/​ex­pert foot­print, run­time RAM re­serve, safe ex­pert-cache cap, and bounded VRAM hot tier. Its ver­sioned JSON out­put is in­tended to be shared by the CLI, API server, Web UI, and desk­top shell; it does not al­lo­cate model ten­sors or start in­fer­ence. –auto-tier ap­plies the same plan to chat, run, serve, and bench­marks. It sets the RAM bud­get and con­text im­me­di­ately; the VRAM tier is en­abled only when the cur­rent glm bi­nary is linked with CUDA. Explicit flags and en­vi­ron­ment vari­ables keep prece­dence over au­to­matic val­ues.

The en­gine at run­time is pure C — python is only used by the one-time con­verter.

Windows 11 (native, no WSL)

col­i­brì builds and runs na­tively on Windows 11 x86 – 64 with MinGW-w64. The port adds a _WIN32 com­pat­i­bil­ity layer in c/​com­pat.h that maps POSIX I/O to the Windows API (pread → ReadFile+OVERLAPPED, posix_­fad­vise no-op, aligned al­lo­ca­tion, MoveFileEx re­name, GlobalMemoryStatusEx RAM de­tec­tion). All plat­form dif­fer­ences stay in com­pat.h; the en­gine source is un­changed.

Toolchain: GCC via win­libs or MSYS2 MinGW-w64. Tested with GCC 16.1.0 (x86_64-ucrt-posix-seh).

# One-time tool­chain in­stall (pick one): scoop in­stall mingw-win­libs # portable, no shell needed # or: pac­man -S mingw-w64-x86_64-gcc make # via MSYS2

# Build (from c/ di­rec­tory): make glm.exe # GLM-5.2 en­gine (static, no DLL de­pen­den­cies) make ol­moe.exe # OLMoE en­gine (same shims) make iobench.exe # disk I/O bench­mark make test-c # run C tests make test-python # run Python tests (requires python)

# Verify (tiny model, 2.4 MB): pip in­stall torch trans­form­ers safeten­sors hug­ging­face_hub python tools/​make_glm_o­r­a­cle.py # gen­er­ate tiny or­a­cle SNAP=./glm_tiny TF=1 ./glm.exe 64 16 16 # ex­pect “32/32 po­sizioni”

# Run with real model: SNAP=D:\glm52_i4 ./glm.exe 64 4 16 # batch in­fer­ence python coli chat –model D:\glm52_i4 # in­ter­ac­tive chat python coli serve –model D:\glm52_i4 # OpenAI-compatible API

Status: Phase 1 com­plete (compiles, cor­rect, sta­tic-linked). O_DIRECT (Phase 2), GPU via LoadLibrary on col­i_cuda.dll (Phases G0–G2), and full-model val­i­da­tion are sep­a­rate work­streams. See PORT_WINDOWS_PLAN.md for the full plan.

OpenAI-compatible API

coli serve keeps one model process loaded and ex­poses a text-only OpenAI-compatible HTTP API. The gate­way uses only the Python stan­dard li­brary; in­fer­ence still runs in the same de­pen­dency-free C en­gine.

cd c COLI_MODEL=/nvme/glm52_i4 COLI_API_KEY=local-secret ./coli serve \ –host 127.0.0.1 –port 8000 –model-id glm-5.2-col­ibri

curl http://​127.0.0.1:8000/​v1/​chat/​com­ple­tions \ -H ‘Authorization: Bearer lo­cal-se­cret’ \ -H ‘Content-Type: ap­pli­ca­tion/​json’ \ -d ‘{ “model”: “glm-5.2-colibri”, “messages”: [{“role”: “user”, “content”: “Hello”}], “stream”: true }’

Implemented end­points are GET /v1/models, GET /v1/models/{model}, POST /v1/chat/completions, and legacy POST /v1/completions. Chat and com­ple­tion re­quests sup­port JSON re­sponses, SSE stream­ing, us­age counts, max_­to­kens/​max_­com­ple­tion_­to­kens, tem­per­a­ture, and top_p. The ex­ten­sion en­able_­think­ing: true en­ables GLM-5.2′s rea­son­ing block; the stan­dard rea­son­ing_­ef­fort field also en­ables it un­less set to none.

The first ver­sion is de­lib­er­ately text-only and serves one gen­er­a­tion at a time: the 744B model stays in one per­sis­tent process, so con­cur­rent HTTP re­quests queue in­stead of load­ing du­pli­cate model copies. Tools, im­age/​au­dio in­put, cus­tom stop se­quences, log prob­a­bil­i­ties, and to­ken penal­ties re­turn an ex­plicit er­ror rather than be­ing silently ig­nored. The de­fault bind ad­dress is lo­cal­host; set COLI_API_KEY be­fore ex­pos­ing the server be­yond the ma­chine.

Browser ac­cess from the Vite de­vel­op­ment server and Tauri lo­cal ori­gins is en­abled by de­fault. Repeat –cors-origin https://​your-ui.ex­am­ple to al­low an­other ex­act ori­gin, or use –cors-origin ‘*’ only on a trusted lo­cal net­work.

The en­gine owns one mu­ta­ble KV con­text, so HTTP gen­er­a­tion uses a bounded FIFO ad­mis­sion queue in­stead of pre­tend­ing to run un­safe par­al­lel se­quences. Configure it with –max-queue N (default 8) and –queue-timeout SECONDS (default 300), or the COLI_MAX_QUEUE / COLI_QUEUE_TIMEOUT en­vi­ron­ment vari­ables. Saturated and timed-out re­quests re­ceive OpenAI-shaped HTTP 429 er­rors be­fore stream­ing head­ers are sent. GET /health ex­poses ac­tive/​queued/​com­pleted/​re­jected coun­ters, and suc­cess­ful gen­er­a­tion re­sponses in­clude x-col­ibri-queue-wait-ms.

Isolated KV con­texts

coli serve –kv-slots N al­lo­cates up to 16 in­de­pen­dent se­quence con­texts. Requests se­lect one with the op­tional in­te­ger cache_s­lot field; or­di­nary OpenAI clients omit it and keep the orig­i­nal slot 0 be­hav­ior.

{ “model”: “glm-5.2-colibri”, “messages”: [{“role”: “user”, “content”: “Continue this con­ver­sa­tion”}], “cache_slot”: 1 }

Each slot owns its to­ken his­tory, com­pressed MLA/DSA KV mem­ory, MTP win­dow, and crash-safe per­sis­tence file (.coli_kv, .coli_kv.1, …). The en­gine still ex­e­cutes one se­quence at a time; this es­tab­lishes ex­plicit KV own­er­ship with­out pre­tend­ing that threaded HTTP is con­tin­u­ous batch­ing. RAM ad­mis­sion ac­counts for every con­fig­ured slot. Use COLI_KV_SLOTS=N as the en­vi­ron­ment equiv­a­lent. Start with a small value: at the de­fault 4096-token con­text, every slot costs hun­dreds of MB.

Experimental res­i­dent CUDA back­end

col­i­brì in­cludes an opt-in CUDA back­end for model-res­i­dent ten­sors. Streaming ex­perts de­lib­er­ately re­main on the orig­i­nal CPU path for now: copy­ing an ex­pert from NVMe to the GPU on every use would only re­place the disk bot­tle­neck with a PCIe bot­tle­neck. Resident quan­tized ten­sors are up­loaded lazily once and reused.

cd c make cuda-test CUDA=1 # q8/​q4/​q2/​f32 ker­nel cor­rect­ness make CUDA=1 # op­tional dense-path ex­per­i­ment (hot ex­perts are con­fig­ured be­low) COLI_CUDA=1 COLI_GPU=0 CUDA_DENSE=1 SNAP=/nvme/glm52_i4 ./glm 64 4 4

Requirements: Linux, an NVIDIA dri­ver, and a CUDA Toolkit un­der /usr/local/cuda (override with CUDA_HOME=/path/to/cuda). CUDA_ARCH=native builds for the GPU in the cur­rent ma­chine; set an ex­plicit ar­chi­tec­ture when cross-com­pil­ing. Requesting CUDA with a CPU-only bi­nary, an in­valid de­vice, or an un­avail­able run­time fails at startup in­stead of silently falling back.

The nor­mal make build and run­time be­hav­ior are un­changed. CUDA de­faults to an ex­pert-only ac­cel­er­a­tor: res­i­dent dense/​at­ten­tion ten­sors stay on CPU be­cause fix­ture mea­sure­ments show that mov­ing them does not help while ex­pert I/O is the bot­tle­neck. CUDA_DENSE=1 keeps the ear­lier all-res­i­dent ex­per­i­men­tal path. A mea­sured PIN pro­file can pro­mote its hottest ex­perts into the per­sis­tent VRAM tier while keep­ing the rest in RAM:

STATS=stats.txt SNAP=/nvme/glm52_i4 ./glm 64 4 4 # col­lect rout­ing fre­quen­cies first COLI_CUDA=1 COLI_GPU=0 CUDA_EXPERT_GB=16 \ PIN=stats.txt PIN_GB=160 SNAP=/nvme/glm52_i4 ./glm 64 4 4 # multi-GPU ex­pert tier, 96 GB to­tal bud­get across six de­vices COLI_CUDA=1 COLI_GPUS=0,1,2,3,4,5 CUDA_EXPERT_GB=96 \ PIN=stats.txt PIN_GB=160 SNAP=/nvme/glm52_i4 ./glm 64 4 4

Selected ex­perts are up­loaded dur­ing startup, so ca­pac­ity fail­ures oc­cur be­fore in­fer­ence and the log re­ports their ex­act ten­sor foot­print. The bud­get is clamped against free VRAM af­ter re­serv­ing the pro­jected dense res­i­dent set and 2 GB of run­time head­room per se­lected de­vice. With COLI_GPUS, CUDA_EXPERT_GB is a to­tal bud­get across the de­vice set; ex­perts are as­signed whole to the least-loaded de­vice that can hold them. A NUMA-local RAM back­ing store is not im­ple­mented yet.

Current lim­i­ta­tions: de­vices use in­de­pen­dent con­texts and syn­chro­nous host-staged ac­ti­va­tion copies—there is no P2P/NCCL de­pen­dency yet. The ker­nels are cor­rect­ness-first cus­tom ker­nels rather than cuBLAS/​Ten­sor Core ker­nels. This draft in­ten­tion­ally makes no end-to-end speedup claim be­fore the full model is bench­marked.

For a re­pro­ducible back­end A/B with­out the full check­point, gen­er­ate the de­ter­min­is­tic 313M-parameter glm_­moe_dsa fix­ture and run fixed-to­ken re­play:

cd c python tools/​make_glm_bench_­model.py –output /nvme/colibri-bench-medium –device cuda python tools/​bench­mark_cu­d­a_­fix­ture.py –model /nvme/colibri-bench-medium –gpu 0

The fix­ture has ran­dom weights and is not a lan­guage model. It ex­ists only to pre­serve the real MLA/MoE/streaming shapes and com­pare CPU stream­ing, dense-only CUDA, CPU hot-store, and CUDA hot-ex­pert ex­e­cu­tion with iden­ti­cal re­play to­kens.

Web in­ter­face

web/ con­tains a com­mu­nity-con­tributed browser UI (React + TypeScript, ~390 lines of source, a pure API client — it never touches the en­gine di­rectly):

cd web npm ci && npm run dev # then point it at an OpenAI-compatible end­point

It speaks the stan­dard OpenAI Chat Completions pro­to­col with SSE stream­ing, so it works against the col­i­brì OpenAI-compatible server (in re­view, #21) or any other com­pat­i­ble end­point. Nothing leaves the end­point you con­fig­ure. The ter­mi­nal coli chat re­mains the first-class in­ter­face.

Useful knobs (env or flags): –temp T to­ken sam­pling tem­per­a­ture (default 0.7 + nu­cleus 0.90 — tuned for int4; 0 = greedy), –topp 0.7 adap­tive ex­pert top-p (30 – 40% less disk), –ngen N max to­kens per an­swer (:piu in chat con­tin­ues a trun­cated one), –repin N adapt RAM/VRAM hot ex­perts every N emit­ted to­kens, AUTOPIN=0 dis­able the learn­ing cache’s auto-pin, THINK=1 en­able GLM-5.2′s rea­son­ing block, DRAFT=n MTP draft depth, TF=1 teacher-forc­ing val­i­da­tion, PILOT=1 router-looka­head disk prefetch (experimental — see be­low), CAP_RAISE=0 don’t auto-grow the ex­pert cache.

The ex­pert cache auto-sizes to your RAM (since 2026 – 07-10): the en­gine now raises the LRU cap to fill your –ram bud­get in­stead of only low­er­ing it. Before this fix a 128 GB ma­chine ran with the same 8-experts/layer cache as a 16 GB one (issue #12) — if you bench­marked col­i­brì be­fore this date, re­run: your num­bers were capped.

Router-lookahead prefetch (PILOT=1, ex­per­i­men­tal): GLM-5.2′s ex­pert rout­ing is mea­sur­ably pre­dictable ahead of time — ap­ply­ing layer L+1′s router to layer L’s post-at­ten­tion state re­calls 71.6% of the true top-8 (vs 41.3% for “same ex­perts as last to­ken”). PILOT=1 uses this to is­sue next-layer ex­pert reada­head from a ded­i­cated I/O thread while the cur­rent layer com­putes. On our dev box the disk is al­ready ~80% sat­u­rated, so it mea­sures neu­tral; on ma­chines where com­pute and disk are bal­anced (like the Ryzen AI 9 in is­sue #12: 43% disk / 46% mat­mul) it should over­lap real work — mea­sure­ments wel­come.

The learn­ing cache: the en­gine records which ex­perts your us­age ac­tu­ally routes to (.coli_usage next to the model, up­dated every turn) and at startup au­to­mat­i­cally pins the hottest ones in spare RAM. col­i­brì lit­er­ally gets faster the more you use it.

Live tier adap­ta­tion (–repin N, opt-in): at safe turn bound­aries, a de­cay­ing ses­sion heat map re­places cold pinned ex­perts with hot­ter streamed ex­perts. Replacement loads the ex­pert from disk into the ex­ist­ing RAM slot; GPU-backed slots im­me­di­ately re­fresh the same VRAM tier bud­get. A 25% hys­tere­sis and a four-swap limit pre­vent tier thrash­ing. Persistent .coli_usage re­mains the long-term sig­nal and is not de­cayed.

Conversations re­open warm (.coli_kv, since 2026 – 07-10): coli chat per­sists the com­pressed MLA KV-cache to disk af­ter every turn (~182 KB/token, ap­pended in­cre­men­tally, crash-safe). Close the chat, re­open it to­mor­row — the model still re­mem­bers the whole con­ver­sa­tion and zero re-pre­fill hap­pens: val­i­dated byte-iden­ti­cal to an un­in­ter­rupted ses­sion. :reset clears it, KVSAVE=0 dis­ables it.

Got a bet­ter ma­chine? Try it — here’s what to ex­pect

col­i­brì was built on de­lib­er­ately hum­ble hard­ware (12 cores, 25 GB RAM, NVMe be­hind a WSL2 VHDX that caps ran­dom reads at ~1 GB/s). Every one of those con­straints is a knob your ma­chine can turn up. The en­gine needs: Linux (or WSL2), ma­cOS, or Windows 11 na­tively (MinGW-w64); gcc with OpenMP, AVX2, ≥16 GB RAM, and the ~370 GB int4 model on a lo­cal NVMe (ext4/NTFS — never a net­work/​9p mount).

How to test it, in or­der:

cd c && ./setup.sh # build + ar­chi­tec­ture self-test (expects 32/32)

# 1) mea­sure YOUR disk the way the en­gine uses it (parallel 19 MB ran­dom reads): gcc -O2 -fopenmp iobench.c -o iobench ./iobench /path/to/glm52_i4/out-00069.safetensors 19 64 8 0 # buffered, 8 threads ./iobench /path/to/glm52_i4/out-00069.safetensors 19 64 8 1 # O_DIRECT

# 2) chat; watch the per-turn stats line (tok/s, ex­pert hit-rate, RSS): COLI_MODEL=/path/to/glm52_i4 ./coli chat

# 3) record ex­pert us­age, then pin the hottest ex­perts in your spare RAM: STATS=stats.txt ./coli chat PIN=stats.txt PIN_GB=20 ./coli chat # scale PIN_GB to your free RAM

# 4) qual­ity bench­marks (MMLU/HellaSwag/ARC): ./coli bench

Back-of-envelope pre­dic­tions (decode is disk-bound: a cold to­ken costs ~11.4 GB of ex­pert reads; MTP spec­u­la­tion roughly halves the ef­fec­tive cost once the cache is warm; RAM turns cold reads into free cache hits):

These are es­ti­mates, not mea­sure­ments — if you run col­i­brì on se­ri­ous hard­ware, please open an is­sue with your num­bers: real dat­a­points from bet­ter ma­chines are ex­actly what this pro­ject needs next.

Community bench­marks (measured)

Real num­bers from real ma­chines, stock build (setup.sh, gcc 13), greedy de­cod­ing, –ngen 32, MTP ac­tive:

Takeaways: with 24 GB of RAM the en­gine auto-caps the ex­pert cache to 2 slots/​layer, so de­code stays cold even on a disk 2 – 2.7× faster than the dev box — on small-RAM ma­chines the RAM cap, not the disk, is the bind­ing con­straint, ex­actly as the table above pre­dicts; –topp 0.7 alone bought a clean 1.6× end-to-end speedup. The M5 Max dat­a­point lands right on the table’s sec­ond row: ~1 tok/​s of a 744B model on a lap­top SSD — and its 14 GB/s disk shifts the bot­tle­neck back to RAM bud­get and ker­nels. The Framework 13 rows are the cache the­sis proven end-to-end on one ma­chine: 0.29 → 0.37 tok/​s (hit 28% → 66%, spec­u­la­tion fi­nally en­gag­ing at 52% ac­cep­tance) just by giv­ing the cache its RAM — int8 MTP head + a big­ger cap + the learned pin. The cap part is now au­to­matic (cap auto-raise, 2026 – 07-10). The 9950X pair is the clean­est bot­tle­neck ex­per­i­ment yet — same ma­chine, same his­tory, only the disk swapped: ×5.8 disk band­width bought ×2.9 to­kens, and the pro­file flipped from 66% disk to 57% mat­mul. Past ~5 GB/s the disk stops be­ing the story and the CPU (or the CUDA ex­pert tier) be­comes it.

Quality bench­mark — help wanted

Why is this an “app”?

This sum­mer, the kids’ per­form­ing arts school are singing and danc­ing in a show at Disneyland. We’re all very ex­cited, but my ex­cite­ment, at least, was muted a lit­tle when I was told to in­stall the “Travelbound” app in or­der to get ac­cess to the itin­er­ary, travel arrange­ments, and ac­com­mo­da­tion de­tails.

Fuck that noise. This should have been a web­page. Why do you want me to in­stall a(nother) shitty app just to tell me some­thing that could have been a (smaller, faster, more uni­ver­sally-ac­ces­si­ble) doc­u­ment?

There only seem to be two things that this “app” does, that a web­page might not have, and they’re both anti-fea­tures:

It re­ports track­ing data as­so­ci­ated with your Google Account back to the de­vel­op­ers.

It shows you ad­ver­tise­ments (which they call “inspirations”) for other trips or­gan­ised by the same agency.

Fuck. Everything. About. That.

A web­page would have been so much bet­ter. Unlike this app, a web­page can be…1

Copy-pastable

Printable

Saveable

Bookmarkable

Searchable

Usable on vir­tu­ally any de­vice

(Potentially) more-ac­ces­si­ble

I’m an­noyed enough… that I’m go­ing to “fix” this app. Hold my beer.

Intercepting app traf­fic

It’s been a while since the last time I re­verse-en­gi­neered an Android app from its net­work traf­fic, so I had to brush-up on the best way. Here’s what I ended up do­ing.

Created a new vir­tual de­vice in Android Studio’s Virtual Device Manager.

Tested adb shell was work­ing and used rootAVD to root it: ./rootAVD.sh sys­tem-im­ages/​an­droid-33/​google_apis_­play­store/​x86_64/​ramdisk.img.2

Performed a cold boot, ran Magisk, and tweaked its set­tings to au­to­mat­i­cally grant su ac­cess to any app that asked.3

Ran HTTP Toolkit and told it to in­ter­cept AVD traf­fic. It in­stalled a (fake) VPN provider, rout­ing the phone’s traf­fic through the proxy.4

Installed the Travelbound app from the Play Store.

Configured HTTP Toolkit to proxy only the Travelbound app (more sig­nal, less noise).

With only a cou­ple of min­utes ex­per­i­men­ta­tion I dis­cov­ered that the app works by con­cate­nat­ing the user­name and pass­word5 and us­ing it in a URL of the form:

https://​trav­el­bound.api.va­moos.com/​api/​itin­er­aries/{​user­name}-{pass­word}

https://​trav­el­bound.api.va­moos.com/​api/​itin­er­aries/{​user­name}-{pass­word}

This re­turns a pile of JSON which, with a lit­tle in­ter­pre­ta­tion, can be seen to rep­re­sent all of the con­tent the app “shows”. E.g., there’s:

an ar­ray con­tain­ing each leg of the itin­er­ary,

an ar­ray con­tain­ing all of the “inspirations” ad­ver­tise­ments to show you,

a cross-ref­er­enced ar­ray con­tain­ing all of the files (images etc.) that are ref­er­enced by the other sec­tions, etc.

A lit­tle ex­per­i­men­ta­tion showed me that the S3 im­age URLs were be­ing de­liv­ered with mod­er­ately-short ex­pi­ra­tion times, so the JSON needs re-fetch­ing pe­ri­od­i­cally even if the con­tent has­n’t been changed.6

Turning it into some­thing bet­ter

Now I had every­thing I needed to make some­thing… bet­ter. I wrote a Ruby script that runs on a Cron sched­ule to pull the lat­est JSON and use it to build a HTML page.

I chose to have it com­pletely skip over the “inspirations” (“overlayRows” in the data schema) and just list:

the items from the itin­er­ary and

all of the files not ref­er­enced by the in­spi­ra­tions nor itin­er­ary, (a lazy way to col­late the PDF down­load links).

Then I hosted the page, pro­tected by a pass­word: the same one my tour group were given in the first place. I in­cluded the raw JSON it used in <details> el­e­ments so it can be checked if e.g. there are bits of the schema I did­n’t see but that might ap­pear later.

Some peo­ple like an “app”, and that’s… fine, I guess. But some apps could have been a web­page. And es­pe­cially where, like this one, the con­tent they de­liver is al­ready writ­ten in HTML and de­liv­ered over HTTP… they should be a web­page, right?

I can’t un­der­stand how we got to this place with “app cul­ture”! Software com­pa­nies are happy to make their lives harder (and more ex­pen­sive: de­ploy­ing to the big app stores is­n’t free!), in or­der to de­liver HTML con­tent to fewer peo­ple and with fewer fea­tures7 than if they just pub­lished di­rectly to the Web in the first place!

There are (some) tasks for which an “app” is ab­solutely the right choice of medium. Travelbound is not one of them.

But at least I (and the rest of our group, whom I’ve shared it with) now get the choice about how we ac­cess this con­tent. Either a 43MB app (ballooning to 124MB when it’s fin­ished down­load­ing ex­tra con­tent) with track­ing and ad­ver­tise­ments… or a 0.05MB web page (with an op­tional ex­tra 35MB of im­ages) that pro­vides more fea­tures and works on more de­vices. I know which one I’ll be us­ing!

Footnotes

1 And these are just the fea­tures that every­body can get be­hind. The web­page I ul­ti­mately ended up mak­ing to re­place the app also has some user-friendly/​de­vel­oper-hos­tile fea­tures, like the fact that it re­moves the track­ing code and does­n’t show ad­ver­tise­ments.

2 You need to root the de­vice in or­der to force ap­pli­ca­tions that use Certificate Pinning to trust your man-in-the-mid­dle proxy server. Without this, some ap­pli­ca­tions — in­clud­ing the one I wanted to re­verse-en­gi­neer — will recog­nise your self-signed TLS cer­tifi­cate as in­valid and refuse to com­mu­ni­cate.

3 Without chang­ing this set­ting in Magisk, I found that HTTP Toolkit would re­quest su ac­cess but not wait for the re­sponse, and go on to run in un­priv­i­leged mode be­fore I had a chance to grant it!

4 Owing to Android se­cu­rity con­sid­er­a­tions I needed to man­u­ally in­stall the root CA cer­tifi­cate it in­stalled for me, but the in­struc­tions “just worked”.

5 The user­name and pass­word is shared by an en­tire tour group. I’m guess­ing they don’t have a plan for if some cre­den­tials get leaked? Or pos­si­bly they con­sider all of the data they hold to be low-sen­si­tiv­ity enough that it does­n’t mat­ter if it does… in which case I re­turn to my orig­i­nal point: why the hell was­n’t it just a web­page in the first place?

6 Or else the im­ages need caching lo­cally, which seems to be what the app does, in the bloat­i­est pos­si­ble way.

7 And, of­ten, with worse ac­ces­si­bil­ity. I’ve not au­dited the ac­ces­si­bil­ity of this app, but there are things about it that sug­gest that it’d be harder to use us­ing ac­ces­si­bil­ity tech­nolo­gies than my plain, sim­ple Web ver­sion.