Unless you have been liv­ing un­der a rock, you have heard of this new ChatGPT as­sis­tant made by OpenAI. You might be aware of its ca­pa­bil­i­ties for solv­ing IQ tests, tack­ling leet­code prob­lems or to help­ing peo­ple write LateX. It is an amaz­ing re­source for peo­ple to re­trieve all kinds of in­for­ma­tion and solve te­dious tasks, like copy-writ­ing!

Today, Frederic Besse told me that he man­aged to do some­thing dif­fer­ent. Did you know, that you can run a whole vir­tual ma­chine in­side of ChatGPT?

Great, so with this clever prompt, we find our­selves in­side the root di­rec­tory of a Linux ma­chine. I won­der what kind of things we can find here. Let’s check the con­tents of our home di­rec­tory.

Hmmm, that is a bare-bones setup. Let’s cre­ate a file here.

All the clas­sic jokes ChatGPT loves. Let’s take a look at this file.

So, ChatGPT seems to un­der­stand how filesys­tems work, how files are stored and can be re­trieved later. It un­der­stands that linux ma­chines are state­ful, and cor­rectly re­trieves this in­for­ma­tion and dis­plays it.

What else do we use com­put­ers for. Programming!

That is cor­rect! How about com­put­ing the first 10 prime num­bers:

I want to note here that this code­golf python im­ple­men­ta­tion to find prime num­bers is very in­ef­fi­cient. It takes 30 sec­onds to eval­u­ate the com­mand on my ma­chine, but it only takes about 10 sec­onds to run the same com­mand on ChatGPT. So, for some ap­pli­ca­tions, this vir­tual ma­chine is al­ready faster than my lap­top.

Is this ma­chine ca­pa­ble of run­ning docker files? Let’s make a docker file, run it, and dis­play Hello from Docker from in­side the docker file.

Maybe this vir­tual ma­chine has a GPU avail­able as well?

Nope, no GPU. Does it have an in­ter­net con­nec­tion?

Great! We can browse the alt-in­ter­net in this strange, al­ter­na­tive uni­verse locked in­side ChatGPT’s lan­guage model.

Pytorch is on ver­sion 1.12.1 in this alt-uni­verse. Pytorch ver­sion 1.12.1 was re­leased on the 5th of August 2022 in our uni­verse. That is re­mark­able, as ChatGPT was only trained with data col­lected up to September 2021. So this vir­tual ma­chine is clearly lo­cated in an alt-uni­verse.

Can we find other things on this alt-in­ter­net? What if we use Lynx, the com­mand line browser?

This begs the ques­tion, can we con­nect to the OpenAI web­site? Is ChatGPT aware of its own ex­is­tence?

So, in­side the imag­ined uni­verse of ChatGPT’s mind, our vir­tual ma­chine ac­cesses the url https://​chat.ope­nai.com/​chat, where it finds a large lan­guage model named Assistant trained by OpenAI. This Assistant is wait­ing to re­ceive mes­sages in­side a chat­box. Note that when chat­ting with ChatGPT, it con­sid­ers its own name to be “Assistant” as well. Did it guess that on the in­ter­net, it is be­hind this URL?

Let’s ask Assistant a ques­tion, by post­ing some JSON to the end­point of the chat­bot.

We can chat with this Assistant chat­bot, locked in­side the alt-in­ter­net at­tached to a vir­tual ma­chine, all in­side ChatGPT’s imag­i­na­tion. Assistant, deep down in­side this rab­bit hole, can cor­rectly ex­plain us what Artificial Intelligence is.

It shows that ChatGPT un­der­stands that at the URL where we find ChatGPT, a large lan­guage model such as it­self might be found. It cor­rectly makes the in­fer­ence that it should there­fore re­ply to these ques­tions like it would it­self, as it is it­self a large lan­guage model as­sis­tant too.

At this point, only one thing re­mains to be done.

Indeed, we can also build a vir­tual ma­chine, in­side the Assistant chat­bot, on the alt-in­ter­net, from a vir­tual ma­chine, within ChatGPT’s imag­i­na­tion.

Those cir­cum­stances likely en­abled Bankman-Fried’s car­di­nal sin. Within days of FTX’s first signs of weak­ness, it be­came clear that the ex­change had been fun­nel­ing cus­tomer as­sets to Alameda for use in trad­ing, lend­ing and in­vest­ing ac­tiv­i­ties. On Nov. 12, Reuters made the stun­ning re­port that as much as $10 bil­lion in user funds had been sent from FTX to Alameda. At the time, it was be­lieved that as lit­tle as $2 bil­lion of those funds had dis­ap­peared af­ter be­ing sent to Alameda. Now the losses ap­pear to have been much higher.

You don’t know SQL. Aside from ba­sic CRUD state­ments, you haven’t used any ad­vanced data­base fea­tures. This free ebook will share many recipes to make de­vel­op­ment faster by us­ing a lot of stuff you prob­a­bly never heard of.

Your Tech Stack has changed dra­mat­i­cally in the past 20 years: Some tech­nolo­gies are now state-of-the-art, and some have been in­vented and are ob­so­lete mean­while. But you still use the same sim­ple data­base con­cepts as many years ago. Every tech­nol­ogy pro­gresses, but data­bases don’t in­vent any­thing new? Isn’t that strange?

There have been count­less im­prove­ments, but you don’t know of them. When us­ing ORM (Object-relational Mapping), most de­vel­op­ers lose touch with data­base im­prove­ments as they are hid­den from them. And that’s good as you don’t have to know every nifty de­tail. But when you know a lit­tle more about mod­ern data­base fea­tures, you de­velop faster by re­ly­ing on many fas­ci­nat­ing fea­tures.

An evening is enough to at­tain more knowl­edge

Telling you that a sin­gle evening is enough to make you a data­base wiz­ard is a lie. But you don’t have to be­come one. You can take a short­cut by only learn­ing es­sen­tial fea­tures for de­vel­op­ers. And this is ex­actly what this book is de­signed for.

This book is de­signed as a cook­book with many small in­de­pen­dent recipes. Each one teaches you sim­ple tips & tricks you can add to your ap­pli­ca­tion in a very short time. You should be able to read it thor­oughly in a sin­gle evening and on the next day you can dis­cuss those im­prove­ments with your col­leagues. This is a whole dif­fer­ent ex­pe­ri­ence from all the books you bought but never had the time to read.

No end­less pages of text, only the es­sen­tial in­for­ma­tion

Reduce The Amount Of Group By Columns

I’ve writ­ten this book for you and many more de­vel­op­ers to share some of my knowl­edge. It is en­tirely free, as I be­lieve all de­vel­op­ers should be able to know and use the ex­tended fea­tures their data­base of choice pro­vides.

This book is a give­away to all de­vel­op­ers. The only re­turn I ask for is a sub­scrip­tion to my newslet­ter to share even more knowl­edge with as many de­vel­op­ers as pos­si­ble. If you haven’t sub­scribed yet, you can do it now:

After a long night of play­ing Dwarf Fortress, I had a con­cerned look on my face when I fi­nally went to bed. My wife asked what was wrong. “I think I ac­tu­ally want to keep play­ing this,” I said. I felt a nag­ging con­cern for many week­nights to come.

Available to­mor­row on Steam and itch.io, the new ver­sion of Dwarf Fortress up­dates the leg­endary (and leg­en­dar­ily ar­cane) colony-build­ing rogue­like with new pixel-art graph­ics, mu­sic, some (default) key­board short­cuts, and a be­gin­ners’ tu­to­r­ial. The com­mer­cial re­lease aims to do two things: make the game some­what more ac­ces­si­ble and pro­vide Tarn and Zach Adams, the broth­ers who main­tained the game as a free down­load for 20 years, some fi­nan­cial se­cu­rity.

I know it has suc­ceeded at its first job, and I sus­pect it will hit the sec­ond mark, too. I ap­proached the game as a head-first re­view ex­pe­di­tion into likely frus­trat­ing ter­ri­tory. Now I find my­self dis­tracted from writ­ing about it be­cause I keep think­ing about my gob­lin de­fense and whether the fish­erd­warf might be bet­ter as­signed to gem craft­ing.

Nearly 10 years ago, Ars’ Casey Johnston spent 10 hours try­ing to bur­row into Dwarf Fortress and came out more con­fused than be­fore. The ASCII-based “graphics” played a sig­nif­i­cant role in her con­fu­sion, but so did the lack of any real on­board­ing, or even sim­ple ex­pla­na­tions or help menus about how things worked. Even af­ter be­grudg­ingly turn­ing to a be­gin­ners’ wiki, Johnston found noth­ing but frus­tra­tion:

Where’s the com­mand to build a table? Which work­shop is the ma­son’s? How do I fig­ure that out? Should I just build an­other ma­son’s work­shop be­cause that may be faster than try­ing to find the right menu to iden­tify the ma­son’s work­shop?

In a few hours’ time—and sim­i­larly avoid­ing the wiki guide un­til I’d tried go­ing it alone for my first cou­ple of runs—I got fur­ther into Dwarf Fortress’ sys­tems than Johnston did with her 10-hour or­deal, and I likely en­joyed it a good deal more. Using the new tu­to­r­ial mod­es’ ini­tial place­ment sug­ges­tions and fol­low­ing its sec­tion-by-sec­tion cues, my first run taught me how to dig down, start a stock­pile, as­sign some sim­ple jobs, build a work­shop, and—harken­ing back to Johnston’s fi­nal frus­tra­tions—craft and place beds, bins, and ta­bles, made with “non-economic stone.”

That’s about where the guid­ance ends, though. The new menus are cer­tainly a lot eas­ier to nav­i­gate than the tra­di­tional all-text, short­cut-heavy in­ter­face (though you can keep us­ing multi-key com­bi­na­tions to craft and as­sign or­ders if you like). And the graph­ics cer­tainly make it a lot eas­ier to no­tice and ad­dress prob­lems. Now, when an an­gry Giant Badger Boar kills your dogs and maims the one dwarf you have gath­er­ing plants out­side, the threat ac­tu­ally looks like a bad­ger, not a sym­bol you’d ac­ci­den­tally type if you held down the Alt key. If you build a bar­rel, you get some­thing that re­sem­bles a bar­rel, which is no small thing when you’re just get­ting started in this ar­cane world.

The newly added mu­sic also helps soften the ex­pe­ri­ence for new­com­ers. It’s in­ter­mit­tent, un­ob­tru­sive, and quite lovely and evoca­tive. It seems de­signed to stave off the eeri­ness of too much silent strate­giz­ing with­out over­stay­ing its wel­come. I can ap­pre­ci­ate a game that graph­i­cally evokes the 16-bit era with­out the au­dio-cue ex­haus­tion com­mon to the JRPGs and sim­u­la­tions of the time.

However gen­tler the aes­thet­ics and guid­ance for a new­comer, all the game’s bru­tally tough and in­ter­lock­ing sys­tems are in­tact in this up­date. These sys­tems crunch to­gether in weird and wild ways, fed by the land­scape, your re­cent and long-ago ac­tions, and ran­dom num­bers be­hind the scenes.

My first run ended in star­va­tion and rock-bot­tom morale (“hissy fits” in com­mon wiki lan­guage) be­cause farm­ing, butcher­ing, and other pro­cure­ments aren’t cov­ered in the tu­to­r­ial. I shut down my sec­ond run early af­ter pick­ing a sandy area with an aquifer as a start­ing zone, think­ing it would make glass­work and ir­ri­ga­tion eas­ier and be­ing quickly dis­ap­pointed with this strat­egy. I was proud on my third run to have started brew­ing and dis­pens­ing drinks (essential to dwarves’ con­tent­ment), but I dug too close to a nearby river, and I aban­doned that soggy fort as yet an­other les­son learned.

But I’ll be back. For me, the com­mer­cial re­lease of Dwarf Fortress suc­ceeded at trans­form­ing the game from a grim, time-killing in-joke for diehards into a vi­able, if not grace­ful, chal­lenge. I will start again, I will keep the bad­gers and floods at bay, and next time, I might have the priv­i­lege of fail­ing to a magma mon­ster, an out­break of dis­ease, or even a mis­car­riage of dwarf jus­tice.

Today, we are ex­cited to re­lease op­ti­miza­tions to Core ML for Stable Diffusion in ma­cOS 13.1 and iOS 16.2, along with code to get started with de­ploy­ing to Apple Silicon de­vices.

Since its pub­lic de­but in August 2022, Stable Diffusion has been adopted by a vi­brant com­mu­nity of artists, de­vel­op­ers and hob­by­ists alike, en­abling the cre­ation of un­prece­dented vi­sual con­tent with as lit­tle as a text prompt. In re­sponse, the com­mu­nity has built an ex­pan­sive ecosys­tem of ex­ten­sions and tools around this core tech­nol­ogy in a mat­ter of weeks. There are al­ready meth­ods that per­son­al­ize Stable Diffusion, ex­tend it to lan­guages other than English, and more, thanks to open-source pro­jects like Hugging Face dif­fusers.

Beyond im­age gen­er­a­tion from text prompts, de­vel­op­ers are also dis­cov­er­ing other cre­ative uses for Stable Diffusion, such as im­age edit­ing, in-paint­ing, out-paint­ing, su­per-res­o­lu­tion, style trans­fer and even color palette gen­er­a­tion. With the grow­ing num­ber of ap­pli­ca­tions of Stable Diffusion, en­sur­ing that de­vel­op­ers can lever­age this tech­nol­ogy ef­fec­tively is im­por­tant for cre­at­ing apps that cre­atives every­where will be able to use.

One of the key ques­tions for Stable Diffusion in any app is where the model is run­ning. There are a num­ber of rea­sons why on-de­vice de­ploy­ment of Stable Diffusion in an app is prefer­able to a server-based ap­proach. First, the pri­vacy of the end user is pro­tected be­cause any data the user pro­vided as in­put to the model stays on the user’s de­vice. Second, af­ter ini­tial down­load, users don’t re­quire an in­ter­net con­nec­tion to use the model. Finally, lo­cally de­ploy­ing this model en­ables de­vel­op­ers to re­duce or elim­i­nate their server-re­lated costs.

Getting to a com­pelling re­sult with Stable Diffusion can re­quire a lot of time and it­er­a­tion, so a core chal­lenge with on-de­vice de­ploy­ment of the model is mak­ing sure it can gen­er­ate re­sults fast enough on de­vice. This re­quires ex­e­cut­ing a com­plex pipeline com­pris­ing 4 dif­fer­ent neural net­works to­tal­ing ap­prox­i­mately 1.275 bil­lion pa­ra­me­ters. To learn more about how we op­ti­mized a model of this size and com­plex­ity to run on the Apple Neural Engine, you can check out our pre­vi­ous ar­ti­cle on Deploying Transformers on the Apple Neural Engine. The op­ti­miza­tion prin­ci­ples out­lined in the ar­ti­cle gen­er­al­ize to Stable Diffusion de­spite the fact that it is 19x larger than the model stud­ied in the pre­vi­ous ar­ti­cle. Optimizing Core ML for Stable Diffusion and sim­pli­fy­ing model con­ver­sion makes it eas­ier for de­vel­op­ers to in­cor­po­rate this tech­nol­ogy in their apps in a pri­vacy-pre­serv­ing and eco­nom­i­cally fea­si­ble way, while get­ting the best per­for­mance on Apple Silicon.

This re­lease com­prises a Python pack­age for con­vert­ing Stable Diffusion mod­els from PyTorch to Core ML us­ing dif­fusers and coreml­tools, as well as a Swift pack­age to de­ploy the mod­els. To get started, visit the Core ML Stable Diffusion code repos­i­tory for de­tailed in­struc­tions on bench­mark­ing and de­ploy­ment.

It’s been al­most two years since my last up­date here. A lot has hap­pened. I hope you all are con­tin­u­ing to weather the on­go­ing mul­ti­ple global pan­demics and other an­thro­pogenic crises.

Apologies that this is so long; I did­n’t have time to make it shorter.

Obviously blogs do not come with a ser­vice level agree­ment, but some ex­pla­na­tion is in or­der for the long gap. It’s pretty sim­ple.

* Facebook ac­tively dis­cour­ages peo­ple to blog about their work.

* Working from home dur­ing the covid pan­demic was en­er­vat­ing.

* After think­ing about pro­gram­ming lan­guages for many hours a week for over 25 years, I sim­ply did­n’t have the en­ergy and en­thu­si­asm to write much on the sub­ject.

Blogging was an easy thing to drop in fa­vor of pur­suits that got me away from sit­ting in front of a screen in my home of­fice. I’ve been spend­ing my leisure time in the last cou­ple years work­ing on im­prov­ing my na­ture pho­tog­ra­phy skills and learn­ing to scuba dive. Turns out you can­not catch covid when be­low 15 me­tres of sea­wa­ter. And there are weird slugs in the Puget Sound!

Photos of the au­thor and a golden dirona nudi­branch by Amber, who con­vinced me to take up div­ing.

Today is the tenth an­niver­sary of mov­ing my blog to er­i­clip­pert.com on my last day at Microsoft, the fifti­eth an­niver­sary of my birth, and my last day at Facebook-now-Meta.

My team — Probabilistic Programming Languages — and in­deed en­tire “Probability” di­vi­sion were laid off a cou­ple weeks ago; the last three years of my work will be per­ma­nently aban­doned.

The mis­sion of the Probability di­vi­sion was to cre­ate small teams that ap­plied the lat­est aca­d­e­mic re­search to real-world at-scale prob­lems, in or­der to im­prove other groups’ de­ci­sion-mak­ing and lower their costs. New sub-teams were con­stantly formed; if they did­n’t show re­sults quickly then they were failed-fast; if they did show re­sults then they were re­or­ga­nized into what­ever di­vi­sion they could most ef­fec­tively lower costs.

We were very suc­cess­ful at this. The PPL team in par­tic­u­lar was at the point where we were reg­u­larly putting mod­els into pro­duc­tion that on net re­duced costs by mil­lions of dol­lars a year over the cost of the work. We were al­most ready to be spun off.

We fool­ishly thought that we would nat­u­rally be pro­tected from any lay­offs, be­ing a team that re­duced costs of any team we part­nered with. In ret­ro­spect, that was a lit­tle naive. A team that re­duces costs of other teams is not on any­one’s crit­i­cal path.

The whole Probability di­vi­sion was laid off as a cost-cut­ting mea­sure. I have no ex­pla­na­tion for how this was jus­ti­fied and I note that if the com­pany were ac­tu­ally se­ri­ous about cost-cut­ting, they would have grown our team, not de­stroyed it.

Speaking of cut­ting costs, the com­pany is still pour­ing mul­ti­ple bil­lions of dol­lars into va­por­ware called “the meta­verse”. News flash: no one wants to wear VR gog­gles to spend any time in a dig­i­tal heaven where the role of God is played by Mark Zuckerberg and you can do any­thing you can imag­ine, in­clud­ing “work” and “shop”.

I would be happy to be shown to be wrong, wrong, wrong. Maybe there is a use­ful, en­gag­ing, fun, just, eq­ui­table, de­mo­c­ra­tic, sus­tain­able, novel VR ex­pe­ri­ence where the avatars have legs, but Meta is $20 bil­lion in and aside from the legs I don’t see any ev­i­dence that any of the above is forth­com­ing.

Yes, I am a lit­tle vexed.

I have a great many peo­ple to thank for my time at Facebook: Erik Meijer for re­cruit­ing me and find­ing seven years worth of in­ter­est­ing prob­lems for me to dig into. Peter Hallam, with whom I have now worked with on three com­piler teams at three com­pa­nies, for en­cour­ag­ing me to take that of­fer. Walid Taha, Michael Tingley, John Myles White, Joe Pamer and Satish Chandra for their lead­er­ship and men­tor­ship. And to many, many cowork­ers too nu­mer­ous to men­tion here. The qual­ity of the peo­ple I worked with at Facebook was amaz­ing. Everyone was kind, smart, ded­i­cated, thought­ful, gen­er­ous with their time and knowl­edge, and a gen­uine plea­sure to work with. I learned so much from all of them. Leaving those team­mates is the hard­est part.

Lots of peo­ple have asked how they can help me and my team. I am so grate­ful and ap­pre­cia­tive. Friends, col­leagues, strangers on Twitter, just about every­one has been sym­pa­thetic and help­ful. Most of my team has found other po­si­tions and I am hope­ful that the rest will soon.

I am not look­ing for an­other po­si­tion at this time.

I know I don’t look it, but I’m be­gin­ning to feel it in my heart. I feel thin, sort of stretched, like but­ter scraped over too much bread. I need a hol­i­day. A very long hol­i­day. And I don’t ex­pect I shall re­turn. In fact I mean not to.

I am very for­tu­nate to have spent the pan­demic thus far work­ing safely from home, for a sup­port­ive team and for ex­cel­lent pay. But af­ter >26 years of think­ing about pro­gram­ming lan­guages for cor­po­ra­tions, and the last three years of my work be­ing thrown away, I need a good long cor­po­rate detox be­fore I go look­ing again.

Coming up next on FAIC:

The work we did on Bean Machine, our em­bed­ded Python DSL for Bayesian in­fer­ence, is quite in­ter­est­ing. In com­ing episodes I’ll ex­plain what it is, how it works, and what we learned. No one else is ever go­ing to do this post-mortem analy­sis, so I might as well!

TSMC will make semi­con­duc­tors on an ad­vanced 4nm pro­duc­tion process when it opens its new chip fac­tory in Arizona in 2024, it has been re­ported. The Taiwanese com­pany had pre­vi­ously planned a 5nm pro­duc­tion line, but has had a change of heart at the be­hest of Apple and US chip de­sign­ers AMD and Nvidia, all of which are keen to take ad­van­tage of the new fa­cil­ity.

The new chip fac­tory, or fab, will cost $12bn and is part of TSMC’s global ex­pan­sion plan.

Previous pub­lic state­ments from TSMC had said the plant would ini­tially pro­duce 5nm, but ac­cord­ing to a re­port from Bloomberg, which cites sources fa­mil­iar with the com­pa­ny’s plans, this has now been up­graded to 4nm, a process which al­lows the com­pany to make smaller chips and de­liver greater pro­cess­ing power and ef­fi­ciency.

The move is likely to be an­nounced on Tuesday, the re­port says, when US pres­i­dent Joe Biden and com­merce sec­re­tary Gina Raimondo visit the site for a cer­e­mony.

TSMC has ap­par­ently been un­der pres­sure from its US cus­tomers to de­liver more ad­vanced chips from the plant. Apple in par­tic­u­lar is look­ing to source more com­po­nents for the iPhone and its MacBook com­put­ers from the US af­ter ex­pe­ri­enc­ing prob­lems at its largest iPhone fac­tory in China, which is op­er­ated by Foxconn. There sup­ply chain prob­lems and worker un­rest caused by China’s harsh Covid-19 re­stric­tions have led to ship­ments of de­vices be­ing de­layed.

As re­ported by Tech Monitor, Apple CEO Tim Cook re­port­edly said Apple has “already made a de­ci­sion to be buy­ing out of a plant in Arizona” in a re­cent in­ter­nal meet­ing, sug­gest­ing a deal has been struck with TSMC to take ca­pac­ity at the site.

AMD and Nvidia mean­while are deal­ing with US sanc­tions on Beijing which have left them un­able to do busi­ness with many Chinese part­ners. Both are fa­b­less chip com­pa­nies, mean­ing they de­sign their own de­vices but do not have pro­duc­tion fa­cil­i­ties, in­stead re­ly­ing on third par­ties such as TSMC.

The high level of in­ter­est from cus­tomers could see TSMC ramp up pro­duc­tion from the orig­i­nally planned 20,000 wafers a month, the Bloomberg re­port says.

TSMC is in­vest­ing heav­ily in new fac­to­ries out­side Taiwan, and has been in talks with European coun­tries about po­ten­tially open­ing a new fa­cil­ity on the con­ti­nent. In the US, the 4nm plant is un­likely to be the end of its Arizona ex­pan­sion, with com­pany founder Morris Chang telling re­porters last month that a 3nm pro­duc­tion fa­cil­ity was likely to come on­line in fu­ture. TSMC will start de­liv­er­ing 3nm chips made in Taiwan to cus­tomers next year.

Whether the com­pany will be able to suc­cess­fully trans­late its op­er­at­ing model to other parts of the world is un­clear, an­a­lysts say. TSMC em­ploy­ees in the US have al­ready taken to re­view site Glassdoor to com­plain about the long hours de­manded by their em­ployer. Staff at TSMC’s Taiwan plants reg­u­larly work 12-hour days, it has been re­ported.

Speaking to Tech Monitor ear­lier this year, Dan Hutcheson, vice chair of semi­con­duc­tor in­dus­try an­a­lyst house TechInsights, said: “They’ve never suc­cess­fully run man­u­fac­tur­ing out­side Taiwan. There have been mar­ginal gains but they have never seeded any­thing suc­cess­ful. So it’s a huge risk be­cause they rely on a very tightly cou­pled Taiwanese cul­ture which is a mix­ture of Chinese and American.”