Open a file from the ex­plorer to be­gin”The Kernel in The Mind”Based on “The Kernel in The Mind” by Moon Hee Lee 🧠The ker­nel is­n’t a process—it’s the sys­tem. It serves user processes, re­acts to con­text, and en­forces sep­a­ra­tion and con­trol. The Kernel Is Not a Process: It’s the al­ways-pre­sent au­thor­ity bridg­ing hard­ware and soft­ware.Serv­ing the Process: Orchestrates syscalls, in­ter­rupts, and sched­ul­ing to keep user tasks run­ning.Sys­tem of Layers: Virtual, mapped, iso­lated, and con­trolled—struc­ture at run­time.1. What is the fun­da­men­tal dif­fer­ence be­tween the ker­nel and a process?The ker­nel is not a process—it’s the sys­tem it­self that serves process­es­The ker­nel is just a li­brary that processes link again­st­There is no dif­fer­ence; they are the same thing2. How does the ker­nel pri­mar­ily serve user processes?3. What char­ac­ter­izes the ker­nel’s sys­tem of lay­ers?Sim­ple and flat with no hi­er­ar­chy

Social me­dia has be­come a re­minder of some­thing pre­cious we are los­ing in the age of LLMs: unique voices.

Over time, it has be­come ob­vi­ous just how many posts are be­ing gen­er­ated by an LLM. The tell is the voice. Every post sounds like it was posted by the same so­cial me­dia man­ager.

If you rely on an LLM to write all your posts, you are mak­ing a mis­take.

Your voice is an as­set. Not just what you want to say, but how you say it.

Your voice is unique. It is formed from your life­time of lived ex­pe­ri­ences. No one’s voice will be ex­actly like yours.

Your voice be­comes rec­og­niz­able. Over many posts it be­comes some­thing peo­ple sub­con­sciously con­nect with, rec­og­nize, trust, and look for­ward to.

Your voice pro­vides the frame­work for the im­pres­sion you leave in a job in­ter­view, while net­work­ing at a meet-up, or with a co-worker.

Years ago I got a job thanks to my blog posts. A man­ager wanted my voice in­flu­enc­ing their or­ga­ni­za­tion. Your voice is an as­set.

Your voice ma­tures and be­comes even more unique with time and prac­tice.

LLMs can rob you of that voice, and the rest of us lose some­thing pre­cious in the process.

Having an LLM write “in your voice” is not the same. Your voice is not sta­tic. It changes with the tides of your life and state of mind. Your most im­pact­ful mes­sage may come be­cause it was the right mo­ment and you were in the right frame of mind.

Let your voice grow with use. Let it be unique.

Do not let one of your great­est as­sets fade into at­ro­phy, wilted by cog­ni­tive lazi­ness.

I do not care what the lin­guis­tic remix ma­chine jug­gles into be­ing.

I care what you have to say.

The chip made for the AI in­fer­ence era — the Google TPUAs I find the topic of Google TPUs ex­tremely im­por­tant, I am pub­lish­ing a com­pre­hen­sive deep dive, not just a tech­ni­cal overview, but also strate­gic and fi­nan­cial cov­er­age of the Google TPU. The his­tory of the TPU and why it all even started?The dif­fer­ence be­tween a TPU and a GPU?Where are the prob­lems for the wider adop­tion of TPUsGoogle’s TPU is the biggest com­pet­i­tive ad­van­tage of its cloud busi­ness for the next 10 yearsHow many TPUs does Google pro­duce to­day, and how big can that get?Gem­ini 3 and the af­ter­math of Gemini 3 on the whole chip in­dus­try­The his­tory of the TPU and why it all even started?The story of the Google Tensor Processing Unit (TPU) be­gins not with a break­through in chip man­u­fac­tur­ing, but with a re­al­iza­tion about math and lo­gis­tics. Around 2013, Google’s lead­er­ship—specif­i­cally Jeff Dean, Jonathan Ross (the CEO of Groq), and the Google Brain team—ran a pro­jec­tion that alarmed them. They cal­cu­lated that if every Android user uti­lized Google’s new voice search fea­ture for just three min­utes a day, the com­pany would need to dou­ble its global data cen­ter ca­pac­ity just to han­dle the com­pute load.At the time, Google was re­ly­ing on stan­dard CPUs and GPUs for these tasks. While pow­er­ful, these gen­eral-pur­pose chips were in­ef­fi­cient for the spe­cific heavy lift­ing re­quired by Deep Learning: mas­sive ma­trix mul­ti­pli­ca­tions. Scaling up with ex­ist­ing hard­ware would have been a fi­nan­cial and lo­gis­ti­cal night­mare.This sparked a new pro­ject. Google de­cided to do some­thing rare for a soft­ware com­pany: build its own cus­tom sil­i­con. The goal was to cre­ate an ASIC (Application-Specific Integrated Circuit) de­signed for one job only: run­ning TensorFlow neural net­works.2013-2014: The pro­ject moved re­ally fast as Google both hired a very ca­pa­ble team and, to be hon­est, had some luck in their first steps. The team went from de­sign con­cept to de­ploy­ing sil­i­con in data cen­ters in just 15 months—a very short cy­cle for hard­ware en­gi­neer­ing.2015: Before the world knew they ex­isted, TPUs were al­ready pow­er­ing Google’s most pop­u­lar prod­ucts. They were silently ac­cel­er­at­ing Google Maps nav­i­ga­tion, Google Photos, and Google Translate.This ur­gency to solve the “data cen­ter dou­bling” prob­lem is why the TPU ex­ists. It was­n’t built to sell to gamers or ren­der video; it was built to save Google from its own AI suc­cess. With that in mind, Google has been think­ing about the »costly« AI in­fer­ence prob­lems for over a decade now. This is also one of the main rea­sons why the TPU is so good to­day com­pared to other ASIC pro­jects.The dif­fer­ence be­tween a TPU and a GPU?To un­der­stand the dif­fer­ence, it helps to look at what each chip was orig­i­nally built to do. A GPU is a “general-purpose” par­al­lel proces­sor, while a TPU is a “domain-specific” ar­chi­tec­ture.The GPUs were de­signed for graph­ics. They ex­cel at par­al­lel pro­cess­ing (doing many things at once), which is great for AI. However, be­cause they are de­signed to han­dle every­thing from video game tex­tures to sci­en­tific sim­u­la­tions, they carry “architectural bag­gage.” They spend sig­nif­i­cant en­ergy and chip area on com­plex tasks like caching, branch pre­dic­tion, and man­ag­ing in­de­pen­dent threads.A TPU, on the other hand, strips away all that bag­gage. It has no hard­ware for ras­ter­i­za­tion or tex­ture map­ping. Instead, it uses a unique ar­chi­tec­ture called a Systolic Array.The “Systolic Array” is the key dif­fer­en­tia­tor. In a stan­dard CPU or GPU, the chip moves data back and forth be­tween the mem­ory and the com­put­ing units for every cal­cu­la­tion. This con­stant shuf­fling cre­ates a bot­tle­neck (the Von Neumann bot­tle­neck).In a TPU’s sys­tolic ar­ray, data flows through the chip like blood through a heart (hence “systolic”).It passes in­puts through a mas­sive grid of mul­ti­pli­ers.The data is passed di­rectly to the next unit in the ar­ray with­out writ­ing back to mem­ory.What this means, in essence, is that a TPU, be­cause of its sys­tolic ar­ray, dras­ti­cally re­duces the num­ber of mem­ory reads and writes re­quired from HBM. As a re­sult, the TPU can spend its cy­cles com­put­ing rather than wait­ing for data.Google’s new TPU de­sign, also called Ironwood also ad­dressed some of the key ar­eas where a TPU was lack­ing:They en­hanced the SparseCore for ef­fi­ciently han­dling large em­bed­dings (good for rec­om­men­da­tion sys­tems and LLMs)It in­creased HBM ca­pac­ity and band­width (up to 192 GB per chip). For a bet­ter un­der­stand­ing, Nvidia’s Blackwell B200 has 192GB per chip, while Blackwell Ultra, also known as the B300, has 288 GB per chip.Im­proved the Inter-Chip Interconnect (ICI) for link­ing thou­sands of chips into mas­sive clus­ters, also called TPU Pods (needed for AI train­ing as well as some time test com­pute in­fer­ence work­loads). When it comes to ICI, it is im­por­tant to note that it is very per­for­mant with a Peak Bandwidth of 1.2 TB/s vs Blackwell NVLink 5 at 1.8 TB/s. But Google’s ICI, to­gether with its spe­cial­ized com­piler and soft­ware stack, still de­liv­ers su­pe­rior per­for­mance on some spe­cific AI tasks.The key thing to un­der­stand is that be­cause the TPU does­n’t need to de­code com­plex in­struc­tions or con­stantly ac­cess mem­ory, it can de­liver sig­nif­i­cantly higher Operations Per Joule.For scale-out, Google uses Optical Circuit Switch (OCS) and its 3D torus net­work, which com­pete with Nvidia’s InfiniBand and Spectrum-X Ethernet. The main dif­fer­ence is that OCS is ex­tremely cost-ef­fec­tive and power-ef­fi­cient as it elim­i­nates elec­tri­cal switches and O-E-O con­ver­sions, but be­cause of this, it is not as flex­i­ble as the other two. So again, the Google stack is ex­tremely spe­cial­ized for the task at hand and does­n’t of­fer the flex­i­bil­ity that GPUs do.As we de­fined the dif­fer­ences, let’s look at real num­bers show­ing how the TPU per­forms com­pared to the GPU. Since Google is­n’t re­veal­ing these num­bers, it is re­ally hard to get de­tails on per­for­mance. I stud­ied many ar­ti­cles and al­ter­na­tive data sources, in­clud­ing in­ter­views with in­dus­try in­sid­ers, and here are some of the key take­aways.The first im­por­tant thing is that there is very lim­ited in­for­ma­tion on Google’s newest TPUv7 (Ironwood), as Google in­tro­duced it in April 2025 and is just now start­ing to be­come avail­able to ex­ter­nal clients (internally, it is said that Google has al­ready been us­ing Ironwood since April, pos­si­bly even for Gemini 3.0.). And why is this im­por­tant if we, for ex­am­ple, com­pare TPUv7 with an older but still widely used ver­sion of TPUv5p based on Semianalysis data:TPUv7 has 192GB of mem­ory ca­pac­ity vs TPUv5p 96GBTPUv7 mem­ory Bandwidth is 7,370 GB/s vs 2,765 for v5pWe can see that the per­for­mance leaps be­tween v5 and v7 are very sig­nif­i­cant. To put that in con­text, most of the com­ments that we will look at are more fo­cused on TPUv6 or TPUv5 than v7.Based on an­a­lyz­ing a ton of in­ter­views with Former Google em­ploy­ees, cus­tomers, and com­peti­tors (people from AMD, NVDA & oth­ers), the sum­mary of the re­sults is as fol­lows.Most agree that TPUs are more cost-ef­fec­tive com­pared to Nvidia GPUs, and most agree that the per­for­mance per watt for TPUs is bet­ter. This view is not ap­plic­a­ble across all use cases tho.»If it is the right ap­pli­ca­tion, then they can de­liver much bet­ter per­for­mance per dol­lar com­pared to GPUs. They also re­quire much lesser en­ergy and pro­duces less heat com­pared to GPUs. They’re also more en­ergy ef­fi­cient and have a smaller en­vi­ron­men­tal foot­print, which is what makes them a de­sired out­come.The use cases are slightly lim­ited to a GPU, they’re not as generic, but for a spe­cific ap­pli­ca­tion, they can of­fer as much as 1.4X bet­ter per­for­mance per dol­lar, which is pretty sig­nif­i­cant sav­ing for a cus­tomer that might be try­ing to use GPU ver­sus TPUs.«Similarly, a very in­sight­ful com­ment from a Former Unit Head at Google around TPUs ma­te­ri­ally low­er­ing AI-search cost per query vs GPUs:»TPU v6 is 60-65% more ef­fi­cient than GPUs, prior gen­er­a­tions 40-45%«This in­ter­view was in November 2024, so the ex­pert is prob­a­bly com­par­ing the v6 TPU with the Nvidia Hopper. Today, we al­ready have Blackwell vs V7.Many ex­perts also men­tion the speed ben­e­fit that TPUs of­fer, with a Former Google Head say­ing that TPUs are 5x faster than GPUs for train­ing dy­namic mod­els (like search-like work­loads).There was also a very eye-open­ing in­ter­view with a client who used both Nvidia GPUs and Google TPUs as he de­scribes the eco­nom­ics in great de­tail:»If I were to use eight H100s ver­sus us­ing one v5e pod, I would spend a lot less money on one v5e pod. In terms of price point money, per­for­mance per dol­lar, you will get more bang for TPU. If I al­ready have a code, be­cause of Google’s help or be­cause of our own work, if I know it al­ready is go­ing to work on a TPU, then at that point it is ben­e­fi­cial for me to just stick with the TPU us­age.In the long run, if I am think­ing I need to write a new code base, I need to do a lot more work, then it de­pends on how long I’m go­ing to train. I would say there is still some, for ex­am­ple, of the work­load we have al­ready done on TPUs that in the fu­ture be­cause as Google will add newer gen­er­a­tion of TPU, they make older ones much cheaper.

For ex­am­ple, when they came out with v4, I re­mem­ber the price of v2 came down so low that it was prac­ti­cally free to use com­pared to any NVIDIA GPUs.Google has got a good promise so they keep sup­port­ing older TPUs and they’re mak­ing it a lot cheaper. If you don’t re­ally need your model trained right away, if you’re will­ing to say, “I can wait one week,” even though the train­ing is only three days, then you can re­duce your cost 1/5.«Another valu­able in­ter­view was with a cur­rent AMD em­ployee, ac­knowl­edg­ing the ben­e­fits of ASICs:»I would ex­pect that an AI ac­cel­er­a­tor could do about prob­a­bly typ­i­cally what we see in the in­dus­try. I’m us­ing my ex­pe­ri­ence at FPGAs. I could see a 30% re­duc­tion in size and maybe a 50% re­duc­tion in power vs a GPU.«We also got some num­bers from a Former Google em­ployee who worked in the chip seg­ment:»When I look at the pub­lished num­bers, they (TPUs) are any­where from 25%-30% bet­ter to close to 2x bet­ter, de­pend­ing on the use cases com­pared to Nvidia. Essentially, there’s a dif­fer­ence be­tween a very cus­tom de­sign built to do one task per­fectly ver­sus a more gen­eral pur­pose de­sign.«What is also known is that the real edge of TPUs lies not in the hard­ware but in the soft­ware and in the way Google has op­ti­mized its ecosys­tem for the TPU.A lot of peo­ple men­tion the prob­lem that every Nvidia »competitor« like the TPU faces, which is the fast de­vel­op­ment of Nvidia and the con­stant »catching up« to Nvidia prob­lem. This month a for­mer Google Cloud em­ployee ad­dressed that con­cern head-on as he be­lieves the rate at which TPUs are im­prov­ing is faster than the rate at Nvidia:»The amount of per­for­mance per dol­lar that a TPU can gen­er­ate from a new gen­er­a­tion ver­sus the old gen­er­a­tion is a much sig­nif­i­cant jump than Nvidia«In ad­di­tion, the re­cent data from Google’s pre­sen­ta­tion at the Hot Chips 2025 event backs that up, as Google stated that the TPUv7 is 100% bet­ter in per­for­mance per watt than their TPUv6e (Trillium).Even for hard Nvidia ad­vo­cates, TPUs are not to be shrugged off eas­ily, as even Jensen thinks very highly of Google’s TPUs. In a pod­cast with Brad Gerstner, he men­tioned that when it comes to ASICs, Google with TPUs is a »special case«. A few months ago, we also got an ar­ti­cle from the WSJ say­ing that af­ter the news pub­li­ca­tion The Information pub­lished a re­port that stated that OpenAI had be­gun rent­ing Google TPUs for ChatGPT, Jensen called Altman, ask­ing him if it was true, and sig­naled that he was open to get­ting the talks back on track (investment talks). Also worth not­ing was that Nvidia’s of­fi­cial X ac­count posted a screen­shot of an ar­ti­cle in which OpenAI de­nied plans to use Google’s in-house chips. To say the least, Nvidia is watch­ing TPUs very closely.Ok, but af­ter look­ing at some of these num­bers, one might think, why aren’t more clients us­ing TPUs?Where are the prob­lems for the wider adop­tion of TPUsThe main prob­lem for TPUs adop­tion is the ecosys­tem. Nvidia’s CUDA is en­graved in the minds of most AI en­gi­neers, as they have been learn­ing CUDA in uni­ver­si­ties. Google has de­vel­oped its ecosys­tem in­ter­nally but not ex­ter­nally, as it has used TPUs only for its in­ter­nal work­loads un­til now. TPUs use a com­bi­na­tion of JAX and TensorFlow, while the in­dus­try skews to CUDA and PyTorch (although TPUs also sup­port PyTorch now). While Google is work­ing hard to make its ecosys­tem more sup­port­ive and con­vert­ible with other stacks, it is also a mat­ter of li­braries and ecosys­tem for­ma­tion that takes years to de­velop.It is also im­por­tant to note that, un­til re­cently, the GenAI in­dus­try’s fo­cus has largely been on train­ing work­loads. In train­ing work­loads, CUDA is very im­por­tant, but when it comes to in­fer­ence, even rea­son­ing in­fer­ence, CUDA is not that im­por­tant, so the chances of ex­pand­ing the TPU foot­print in in­fer­ence are much higher than those in train­ing (although TPUs do re­ally well in train­ing as well — Gemini 3 the prime ex­am­ple).The fact that most clients are multi-cloud also poses a chal­lenge for TPU adop­tion, as AI work­loads are closely tied to data and its lo­ca­tion (cloud data trans­fer is costly). Nvidia is ac­ces­si­ble via all three hy­per­scalers, while TPUs are avail­able only at GCP so far. A client who uses TPUs and Nvidia GPUs ex­plains it well:»Right now, the one biggest ad­van­tage of NVIDIA, and this has been true for past three com­pa­nies I worked on is be­cause AWS, Google Cloud and Microsoft Azure, these are the three ma­jor cloud com­pa­nies.Every com­pany, every cor­po­rate, every cus­tomer we have will have data in one of these three. All these three clouds have NVIDIA GPUs. Sometimes the data is so big and in a dif­fer­ent cloud that it is a lot cheaper to run our work­load in what­ever cloud the cus­tomer has data in.I don’t know if you know about the egress cost that is mov­ing data out of one cloud is one of the big­ger cost. In that case, if you have NVIDIA work­load, if you have a CUDA work­load, we can just go to Microsoft Azure, get a VM that has NVIDIA GPU, same GPU in fact, no code change is re­quired and just run it there.With TPUs, once you are all re­lied on TPU and Google says, “You know what? Now you have to pay 10X more,” then we would be screwed, be­cause then we’ll have to go back and rewrite every­thing. That’s why. That’s the only rea­son peo­ple are afraid of com­mit­ting too much on TPUs. The same rea­son is for Amazon’s Trainium and Inferentia.«These prob­lems are well known at Google, so it is no sur­prise that in­ter­nally, the de­bate over keep­ing TPUs in­side Google or start­ing to sell them ex­ter­nally is a con­stant topic. When keep­ing them in­ter­nally, it en­hances the GCP moat, but at the same time, many for­mer Google em­ploy­ees be­lieve that at some point, Google will start of­fer­ing TPUs ex­ter­nally as well, maybe through some neo­clouds, not nec­es­sar­ily with the biggest two com­peti­tors, Microsoft and Amazon. Opening up the ecosys­tem, pro­vid­ing sup­port, etc., and mak­ing it more widely us­able are the first steps to­ward mak­ing that pos­si­ble.A for­mer Google em­ployee also men­tioned that Google last year formed a more sales-ori­ented team to push and sell TPUs, so it’s not like they have been push­ing hard to sell TPUs for years; it is a fairly new dy­namic in the or­ga­ni­za­tion.Google’s TPU is the biggest com­pet­i­tive ad­van­tage of its cloud busi­ness for the next 10 years­The most valu­able thing for me about TPUs is their im­pact on GCP. As we wit­ness the trans­for­ma­tion of cloud busi­nesses from the pre-AI era to the AI era, the biggest take­away is that the in­dus­try has gone from an oli­gop­oly of AWS, Azure, and GCP to a more com­modi­tized land­scape, with Oracle, Coreweave, and many other neo­clouds com­pet­ing for AI work­loads. The prob­lem with AI work­loads is the com­pe­ti­tion and Nvidia’s 75% gross mar­gin, which also re­sults in low mar­gins for AI work­loads. The cloud in­dus­try is mov­ing from a 50-70% gross mar­gin in­dus­try to a 20-35% gross mar­gin in­dus­try. For cloud in­vestors, this should be con­cern­ing, as the fu­ture pro­file of some of these com­pa­nies is more like that of a util­ity than an at­trac­tive, high-mar­gin busi­ness. But there is a so­lu­tion to avoid­ing that fu­ture and re­turn­ing to a nor­mal mar­gin: the ASIC.The cloud providers who can con­trol the hard­ware and are not be­holden to Nvidia and its 75% gross mar­gin will be able to re­turn to the world of 50% gross mar­gins. And there is no sur­prise that all three AWS, Azure, and GCP are de­vel­op­ing their own ASICs. The most ma­ture by far is Google’s TPU, fol­lowed by Amazon’s Trainum, and lastly Microsoft’s MAIA (although Microsoft owns the full IP of OpenAI’s cus­tom ASICs, which could help them in the fu­ture).While even with ASICs you are not 100% in­de­pen­dent, as you still have to work with some­one like Broadcom or Marvell, whose mar­gins are lower than Nvidia’s but still not neg­li­gi­ble, Google is again in a very good po­si­tion. Over the years of de­vel­op­ing TPUs, Google has man­aged to con­trol much of the chip de­sign process in-house. According to a cur­rent AMD em­ployee, Broadcom no longer knows every­thing about the chip. At this point, Google is the front-end de­signer (the ac­tual RTL of the de­sign) while Broadcom is only the back­end phys­i­cal de­sign part­ner. Google, on top of that, also, of course, owns the en­tire soft­ware op­ti­miza­tion stack for the chip, which makes it as per­for­mant as it is. According to the AMD em­ployee, based on this work split, he thinks Broadcom is lucky if it gets a 50-point gross mar­gin on its part.With­out hav­ing to pay Nvidia for the ac­cel­er­a­tor, a cloud provider can ei­ther price its com­pute sim­i­larly to oth­ers and main­tain a bet­ter mar­gin pro­file or lower costs and gain mar­ket share. Of course, all of this de­pends on hav­ing a very ca­pa­ble ASIC that can com­pete with Nvidia. Unfortunately, it looks like Google is the only one that has achieved that, as the num­ber one-per­form­ing model is Gemini 3 trained on TPUs. According to some for­mer Google em­ploy­ees, in­ter­nally, Google is also us­ing TPUs for in­fer­ence across its en­tire AI stack, in­clud­ing Gemini and mod­els like Veo. Google buys Nvidia GPUs for GCP, as clients want them be­cause they are fa­mil­iar with them and the ecosys­tem, but in­ter­nally, Google is full-on with TPUs.As the com­plex­ity of each gen­er­a­tion of ASICs in­creases, sim­i­lar to the com­plex­ity and pace of Nvidia, I pre­dict that not all ASIC pro­grams will make it. I be­lieve out­side of TPUs, the only real hy­per­scaler shot right now is AWS Trainium, but even that faces much big­ger un­cer­tain­ties than the TPU. With that in mind, Google and its cloud busi­ness can come out of this AI era as a ma­jor ben­e­fi­ciary and mar­ket-share gainer.Re­cently, we even got com­ments from the SemiAnalysis team prais­ing the TPU:»Google’s sil­i­con su­premacy among hy­per­scalers is un­matched, with their TPU 7th Gen ar­guably on par with Nvidia Blackwell. TPU pow­ers the Gemini fam­ily of mod­els which are im­prov­ing in ca­pa­bil­ity and sit close to the pareto fron­tier of $ per in­tel­li­gence in some tasks«How many TPUs does Google pro­duce to­day, and how big can that get?Here are the num­bers that I re­searched:This post is for paid sub­scribers

What we did in re­sponse

I was work­ing on a bug in Chocolate Quake net­code. The is­sue was an edge case where start­ing two clients on the same ma­chine re­sulted in the sec­ond one zomb­i­fy­ing the first one. When the bug oc­curred there was no dis­con­nec­tion but the client could no longer move. Instead the screen would show an “indicator” look­ing like an un­plugged Ethernet ca­ble in the up­per left cor­ner.

As I dug into the code, I learned there were more of these. Located in­side pak0.pak and nested in gfx.wad are files TURTLE, DISC, RAM, and NET. I could not find any­thing about these “indicators” so I doc­u­mented them here.

The TURTLE in­di­ca­tor shows up on screen when the fram­er­ate goes be­low 10 fps. It is un­likely to have been in­tended for play­ers but rather for peo­ple at id Software dur­ing de­vel­op­ment. Programmers could see where the en­gine was not fast enough. More im­por­tantly map de­sign­ers could see if they had too many poly­gons in spe­cific ar­eas of their map.

The TURTLE in­di­ca­tor can be en­abled/​dis­abled with com­mand show­tur­tle 1/0. The code is all in func­tion SCR_DrawTurtle, where host_fram­e­time is the time in sec­onds it took to draw the last frame.

There is a scr_show­tur­tle in Quake 2 source code but it does not do any­thing.

The icon does­n’t ac­tu­ally de­pict a tur­tle but a tor­toise. A tur­tle swims in the wa­ter while a tor­toise walks on land.

Quake does not ren­der poly­gons us­ing di­rectly a tex­ture and a lightmap. Instead it com­bines these two into a “surface” which is then fed to the ras­ter­izer. After be­ing used sur­faces are not dis­carded but cached be­cause the next frame is likely to need the same sur­face again.

The RAM in­di­ca­tor is here to warn when the en­gine evicts from the cache sur­faces that were gen­er­ated and cached on the same frame. This means the geom­e­try of the map forces the en­gine to op­er­ate be­yond its sur­face cache ca­pac­ity. Under this con­di­tion, the ren­derer en­ters a cat­a­strophic “death spi­ral” where it evicts sur­faces that will be needed later in the frame. Needless to say the fram­er­ate suf­fers greatly.

This was likely a fea­ture in­tended for map de­sign­ers to warn them of scenes go­ing be­yond the amount of sur­face cache mem­ory Quake pro­vi­sioned. See D_SCAlloc where thrash­ing is de­tected to learn more about it.

Alike the tur­tle one, this in­di­ca­tor can also be en­abled/​dis­abled with com­mand showram 1/0.

The DISC in­di­ca­tor wraps HDD ac­cess done via Sys_FileRead. It is un­likely it was used by de­vel­op­ers to di­ag­nose any­thing since its screen lo­ca­tion over­laps with the TURTLE in­di­ca­tor. It is just here to give feed­back to play­ers that the game is load­ing.

Because the icon is hid­den when Sys_FileRead re­turns, it is nor­mal to see it flicker on the screen (and it also looks kinda cool). The code for this in­di­ca­tor is in Draw_BeginDisc.

The NET in­di­ca­tor is dis­played when a client has not re­ceived any pack­ets from the server in the last 300ms. This was likely aimed at play­ers to help them de­ter­mine how bad their con­nec­tion was (a dis­tant server would eas­ily have a 500ms ping in these dial-up over PPP mo­dem days) or if they had plainly lost con­nec­tion to the server.

The code for this in­di­ca­tor is in SCR_DrawNet.

The NET in­di­ca­tor is pre­vent and ac­tive in Quake 2. The code is still in SCR_DrawNet but the im­age is no longer in a wad. It is store in pak0.pak at pics/​net.pcx.

Below, a ter­ri­ble user ex­pe­ri­ence where the frame made the en­gine thrash its sur­face cache, the fram­er­ate dropped be­low 10fps, and the en­gine last re­ceived pack­ets from the server more than 300ms ago.

Under the harsh lights of an op­er­at­ing the­atre in the Indian cap­i­tal, Delhi, a woman lies mo­tion­less as sur­geons pre­pare to re­move her gall­blad­der. She is un­der gen­eral anaes­the­sia: un­con­scious, in­sen­sate and ren­dered com­pletely still by a blend of drugs that in­duce deep sleep, block mem­ory, blunt pain and tem­porar­ily paral­yse her mus­cles.Yet, amid the hum of mon­i­tors and the steady rhythm of the sur­gi­cal team, a gen­tle stream of flute mu­sic plays through the head­phones placed over her ears.Even as the drugs si­lence much of her brain, its au­di­tory path­way re­mains partly ac­tive. When she wakes up, she will re­gain con­scious­ness more quickly and clearly be­cause she re­quired lower doses of anaes­thetic drugs such as propo­fol and opi­oid painkillers than pa­tients who heard no mu­sic.That, at least, is what a new peer-re­viewed study from Delhi’s Maulana Azad Medical College and Lok Nayak Hospital sug­gests. The re­search, pub­lished in the jour­nal Music and Medicine, of­fers some of the strongest ev­i­dence yet that mu­sic played dur­ing gen­eral anaes­the­sia can mod­estly but mean­ing­fully re­duce drug re­quire­ments and im­prove re­cov­ery.The study fo­cuses on pa­tients un­der­go­ing la­paro­scopic chole­cys­tec­tomy, the stan­dard key­hole op­er­a­tion to re­move the gall­blad­der. The pro­ce­dure is short - usu­ally un­der an hour - and de­mands a par­tic­u­larly swift, “clear-headed” re­cov­ery.To un­der­stand why the re­searchers turned to mu­sic, it helps to de­code the mod­ern prac­tice of anaes­the­sia.“Our aim is early dis­charge af­ter surgery,” says Dr Farah Husain, se­nior spe­cial­ist in anaes­the­sia and cer­ti­fied mu­sic ther­a­pist for the study. “Patients need to wake up clear-headed, alert and ori­ented, and ide­ally pain-free. With bet­ter pain man­age­ment, the stress re­sponse is cur­tailed.” Achieving that re­quires a care­fully bal­anced mix of five or six drugs that to­gether keep the pa­tient asleep, block pain, pre­vent mem­ory of the surgery and re­lax the mus­cles.

In pro­ce­dures like la­paro­scopic gall­blad­der re­moval, anaes­the­si­ol­o­gists now of­ten sup­ple­ment this drug reg­i­men with re­gional “blocks” - ul­tra­sound-guided in­jec­tions that numb nerves in the ab­dom­i­nal wall. “General anaes­the­sia plus blocks is the norm,” says Dr Tanvi Goel, pri­mary in­ves­ti­ga­tor and a for­mer se­nior res­i­dent of Maulana Azad Medical College. “We’ve been do­ing this for decades.“But the body does not take to surgery eas­ily. Even un­der anaes­the­sia, it re­acts: heart rate rises, hor­mones surge, blood pres­sure spikes. Reducing and man­ag­ing this cas­cade is one of the cen­tral goals of mod­ern sur­gi­cal care. Dr Husain ex­plains that the stress re­sponse can slow re­cov­ery and worsen in­flam­ma­tion, high­light­ing why care­ful man­age­ment is so im­por­tant. The stress starts even be­fore the first cut, with in­tu­ba­tion - the in­ser­tion of a breath­ing tube into the wind­pipe.To do this, the anaes­the­si­ol­o­gist uses a laryn­go­scope to lift the tongue and soft tis­sues at the base of the throat, ob­tain a clear view of the vo­cal cords, and guide the tube into the tra­chea. It’s a rou­tine step in gen­eral anaes­the­sia that keeps the air­way open and al­lows pre­cise con­trol of the pa­tien­t’s breath­ing while they are un­con­scious.“The laryn­goscopy and in­tu­ba­tion are con­sid­ered the most stress­ful re­sponse dur­ing gen­eral anaes­the­sia,” says Dr Sonia Wadhawan, di­rec­tor-pro­fes­sor of anaes­the­sia and in­ten­sive care at Maulana Azad Medical College and su­per­vi­sor of the study.“Al­though the pa­tient is un­con­scious and will re­mem­ber noth­ing, their body still re­acts to the stress with changes in heart rate, blood pres­sure, and stress hor­mones.“To be sure, the drugs have evolved. The old ether masks have van­ished. In their place are in­tra­venous agents - most no­tably propo­fol, the hyp­notic made in­fa­mous by Michael Jackson’s death but prized in op­er­at­ing the­atres for its rapid on­set and clean re­cov­ery. “Propofol acts within about 12 sec­onds,” notes Dr Goel. “We pre­fer it for short surg­eries like la­paro­scopic chole­cys­tec­tomy be­cause it avoids the ‘hangover’ caused by in­hala­tional gases.“The team of re­searchers wanted to know whether mu­sic could re­duce how much propo­fol and fen­tanyl (an opi­oid painkiller) pa­tients re­quired. Less drugs means faster awak­en­ing, stead­ier vi­tal signs and re­duced side ef­fects.So they de­signed a study. A pi­lot in­volv­ing eight pa­tients led to a full 11-month trial of 56 adults, aged roughly 20 to 45, ran­domly as­signed to two groups. All re­ceived the same five-drug reg­i­men: a drug that pre­vents nau­sea and vom­it­ing, a seda­tive, fen­tanyl, propo­fol and a mus­cle re­lax­ant. Both groups wore noise-can­celling head­phones - but only one heard mu­sic.“We asked pa­tients to se­lect from two calm­ing in­stru­men­tal pieces - soft flute or pi­ano,” says Dr Husain. “The un­con­scious mind still has ar­eas that re­main ac­tive. Even if the mu­sic is­n’t ex­plic­itly re­called, im­plicit aware­ness can lead to ben­e­fi­cial ef­fects.”

Patients ex­posed to mu­sic re­quired lower doses of propo­fol and fen­tanyl. They ex­pe­ri­enced smoother re­cov­er­ies, lower cor­ti­sol or stress-hor­mone lev­els and a much bet­ter con­trol of blood pres­sure dur­ing the surgery. “Since the abil­ity to hear re­mains in­tact un­der anaes­the­sia,” the re­searchers write, “music can still shape the brain’s in­ter­nal state.“Clearly, mu­sic seemed to qui­eten the in­ter­nal storm. “The au­di­tory path­way re­mains ac­tive even when you’re un­con­scious,” says Dr Wadhawan. “You may not re­mem­ber the mu­sic, but the brain reg­is­ters it.“The idea that the mind be­hind the anaes­thetic veil is not en­tirely silent has long in­trigued sci­en­tists. Rare cases of “intraoperative aware­ness” show pa­tients re­call­ing frag­ments of op­er­at­ing-room con­ver­sa­tion. If the brain is ca­pa­ble of pick­ing up and re­mem­ber­ing stress­ful ex­pe­ri­ences dur­ing surgery - even when a pa­tient is un­con­scious - then it might also be able to reg­is­ter pos­i­tive or com­fort­ing ex­pe­ri­ences, like mu­sic, even with­out con­scious mem­ory.“We’re only be­gin­ning to ex­plore how the un­con­scious mind re­sponds to non-phar­ma­co­log­i­cal in­ter­ven­tions like mu­sic,” says Dr Husain. “It’s a way of hu­man­is­ing the op­er­at­ing room.“Mu­sic ther­apy is not new to med­i­cine; it has long been used in psy­chi­a­try, stroke re­ha­bil­i­ta­tion and pal­lia­tive care. But its en­try into the in­tensely tech­ni­cal, ma­chine-gov­erned world of anaes­the­sia marks a quiet shift. If such a sim­ple in­ter­ven­tion can re­duce drug use and speed re­cov­ery - even mod­estly - it could re­shape how hos­pi­tals think about sur­gi­cal well­be­ing.As the re­search team pre­pares its next study ex­plor­ing mu­sic-aided se­da­tion, build­ing on ear­lier find­ings, one truth is al­ready hum­ming through the data: even when the body is still and the mind asleep, it ap­pears a few gen­tle notes can help the heal­ing be­gin.

Arthur Conan Doyle delved into men’s men­tal health through his Sherlock Holmes sto­ries. (Image: Wikimedia Commons/Canva , Public do­main)

Note: This ar­ti­cle is re­pub­lished from The Conversation un­der a Creative Commons li­cense. It in­cludes links to ex­ter­nal sites that may earn a com­mis­sion for pur­chases. We did not add these links and have kept the orig­i­nal con­tent in­tact.

Arthur Conan Doyle was not just one of the world’s best crime fic­tion writ­ers. He was a pro­gres­sive word­smith who brought light to con­tro­ver­sial and taboo sub­jects. One of those taboo sub­jects was male vul­ner­a­bil­ity and men­tal health prob­lems — a topic of per­sonal sig­nif­i­cance to the au­thor.

Doyle was a vul­ner­a­ble child. His fa­ther, Charles, was an al­co­holic, which led to fi­nan­cial trou­bles in the fam­ily. Charles was ad­mit­ted to an asy­lum in 1881 and spent the next 12 years in var­i­ous men­tal care es­tab­lish­ments. So be­gan Doyle’s in­ter­est in male vul­ner­a­bil­ity and men­tal health.

The char­ac­ter of Sherlock Holmes is a true ex­pres­sion of male vul­ner­a­bil­ity that does not equate it with weak­ness. Doyle does not rep­re­sent Holmes as in­fal­li­ble, but as a man oth­ers can re­late to — he bat­tles with drug ad­dic­tion, lone­li­ness and de­pres­sion. His ge­nius thrives in part be­cause of these vul­ner­a­bil­i­ties, not de­spite them.

(function(w,q){w[q]=w[q]||[];w[q].push([“_mgc.load”])})(window,“_mgq”);

Many of Doyle’s Sherlock Holmes sto­ries ex­am­ine male char­ac­ters fac­ing emo­tional cat­a­stro­phe, be­trayal or moral dilem­mas. In works such as The Man with the Twisted Lip (1891), The Adventure of the Engineer’s Thumb (1892) and The Stockbroker’s Clerk (1894), Holmes’s male clients ap­proach him with prob­lems lay­ered with emo­tional tur­moil, fear and fail­ure.

In The Man with the Twisted Lip, for ex­am­ple, a man named Neville St Clair hides his dou­ble life. He tells his fam­ily that he is a re­spectable en­tre­pre­neur go­ing to London on busi­ness. In re­al­ity he is beg­ging on the city streets. He lives this dou­ble life due to fear and shame over the in­abil­ity to pay off his debts. “It was a long fight be­tween my pride and the money,” he ex­plains, “but the dol­lars won at last.”

Also Read: Who Looks Smarter: The Quick Thinker or the Careful Thinker?

“I would have en­dured im­pris­on­ment, ay, even ex­e­cu­tion, rather than have left my mis­er­able se­cret as a fam­ily blot to my chil­dren,” St Clair says. In hav­ing his char­ac­ter con­sider ex­e­cu­tion to pro­tect his and his fam­i­ly’s rep­u­ta­tion, Doyle ex­plored the so­ci­etal ex­pec­ta­tions of Victorian mas­culin­ity and how men strug­gled with such pres­sures.

(function(w,q){w[q]=w[q]||[];w[q].push([“_mgc.load”])})(window,“_mgq”);

The Stockbroker’s Clerk also ex­am­ines male sui­cide, as well as eco­nomic and pro­fes­sional anx­i­eties. When Holmes re­veals the crimes of Harry Pinner, the man at­tempts sui­cide rather than face prison.

In The Engineer’s Thumb, hy­draulic en­gi­neer Victor is treated phys­i­cally by Watson and men­tally by Holmes. As Doyle writes: “Round one of his hands he had a hand­ker­chief wrapped, which was mot­tled all over with blood­stains. He was young, not more than five-and-twenty, I should say, with a strong mas­cu­line face; but he was ex­ceed­ingly pale and gave me the im­pres­sion of a man who was suf­fer­ing from some strong ag­i­ta­tion, which it took all his strength of mind to con­trol.”

The phys­i­cal in­jury marks Victor as a vic­tim of phys­i­cal vi­o­lence. Watson sug­gests that Victor is us­ing all his men­tal ca­pa­bil­i­ties to keep calm about his se­vere pain. Holmes treats Victor’s mind as he lis­tens to his story: “Pray lie down there and make your­self ab­solutely at home. Tell us what you can, but stop when you are tired, and keep up your strength with a lit­tle stim­u­lant.”

Also Read: Study of 3 Million Finnish Adults Finds Non-Voters Tend to Die Earlier

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Holmes is a pro­tec­tor, a con­fi­dante and a com­forter in this scene. He pro­vides Victor with break­fast, in­duces him to lie down and of­fers him a stim­u­lant (more than likely brandy).

The ex­trem­ity of vi­o­lence that Victor has en­dured has es­ca­lated to men­tal trauma. In hav­ing Holmes treat Victor’s men­tal trauma while Watson treats his phys­i­cal pain, Doyle showed the im­por­tance psy­cho­log­i­cal sup­port for men of the age.

Holmes was a highly pop­u­lar char­ac­ter. To con­tem­po­rary read­ers, his drug use and dys­func­tional clients were seen as mark­ers of his ge­nius rather than a re­flec­tion of the sig­nif­i­cant so­cial is­sues that men faced dur­ing this pe­riod. But to­day, they of­fer a win­dow into the men­tal strug­gles of Victorian men, and a point of con­nec­tion be­tween read­ers of the past and pre­sent.

Looking for some­thing good? Cut through the noise with a care­fully cu­rated se­lec­tion of the lat­est re­leases, live events and ex­hi­bi­tions, straight to your in­box every fort­night, on Fridays. Sign up here.

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This ar­ti­cle fea­tures ref­er­ences to books that have been in­cluded for ed­i­to­r­ial rea­sons, and may con­tain links to book­shop.org. If you click on one of the links and go on to buy some­thing from book­shop.org The Conversation UK may earn a com­mis­sion.

Emma Linford, Honorary re­search as­so­ci­ate, English lit­er­a­ture, University of Hull

This ar­ti­cle is re­pub­lished from The Conversation un­der a Creative Commons li­cense. Read the orig­i­nal ar­ti­cle.

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Arthur Conan Doyle was not just one of the world’s best crime fic­tion writ­ers. He was a pro­gres­sive word­smith who brought light to con­tro­ver­sial and taboo sub­jects. One of those taboo sub­jects was male vul­ner­a­bil­ity and men­tal health prob­lems — a topic of per­sonal sig­nif­i­cance to the au­thor.

Doyle was a vul­ner­a­ble child. His fa­ther, Charles, was an al­co­holic, which led to fi­nan­cial trou­bles in the fam­ily. Charles was ad­mit­ted to an asy­lum in 1881 and spent the next 12 years in var­i­ous men­tal care es­tab­lish­ments. So be­gan Doyle’s in­ter­est in male vul­ner­a­bil­ity and men­tal health.

The char­ac­ter of Sherlock Holmes is a true ex­pres­sion of male vul­ner­a­bil­ity that does not equate it with weak­ness. Doyle does not rep­re­sent Holmes as in­fal­li­ble, but as a man oth­ers can re­late to — he bat­tles with drug ad­dic­tion, lone­li­ness and de­pres­sion. His ge­nius thrives in part be­cause of these vul­ner­a­bil­i­ties, not de­spite them.

Many of Doyle’s Sherlock Holmes sto­ries ex­am­ine male char­ac­ters fac­ing emo­tional cat­a­stro­phe, be­trayal or moral dilem­mas. In works such as The Man with the Twisted Lip (1891), The Adventure of the Engineer’s Thumb (1892) and The Stockbroker’s Clerk (1894), Holmes’s male clients ap­proach him with prob­lems lay­ered with emo­tional tur­moil, fear and fail­ure.

In The Man with the Twisted Lip, for ex­am­ple, a man named Neville St Clair hides his dou­ble life. He tells his fam­ily that he is a re­spectable en­tre­pre­neur go­ing to London on busi­ness. In re­al­ity he is beg­ging on the city streets. He lives this dou­ble life due to fear and shame over the in­abil­ity to pay off his debts. “It was a long fight be­tween my pride and the money,” he ex­plains, “but the dol­lars won at last.”

“I would have en­dured im­pris­on­ment, ay, even ex­e­cu­tion, rather than have left my mis­er­able se­cret as a fam­ily blot to my chil­dren,” St Clair says. In hav­ing his char­ac­ter con­sider ex­e­cu­tion to pro­tect his and his fam­i­ly’s rep­u­ta­tion, Doyle ex­plored the so­ci­etal ex­pec­ta­tions of Victorian mas­culin­ity and how men strug­gled with such pres­sures.

The Stockbroker’s Clerk also ex­am­ines male sui­cide, as well as eco­nomic and pro­fes­sional anx­i­eties. When Holmes re­veals the crimes of Harry Pinner, the man at­tempts sui­cide rather than face prison.

In The Engineer’s Thumb, hy­draulic en­gi­neer Victor is treated phys­i­cally by Watson and men­tally by Holmes. As Doyle writes: “Round one of his hands he had a hand­ker­chief wrapped, which was mot­tled all over with blood­stains. He was young, not more than five-and-twenty, I should say, with a strong mas­cu­line face; but he was ex­ceed­ingly pale and gave me the im­pres­sion of a man who was suf­fer­ing from some strong ag­i­ta­tion, which it took all his strength of mind to con­trol.”

The phys­i­cal in­jury marks Victor as a vic­tim of phys­i­cal vi­o­lence. Watson sug­gests that Victor is us­ing all his men­tal ca­pa­bil­i­ties to keep calm about his se­vere pain. Holmes treats Victor’s mind as he lis­tens to his story: “Pray lie down there and make your­self ab­solutely at home. Tell us what you can, but stop when you are tired, and keep up your strength with a lit­tle stim­u­lant.”

Holmes is a pro­tec­tor, a con­fi­dante and a com­forter in this scene. He pro­vides Victor with break­fast, in­duces him to lie down and of­fers him a stim­u­lant (more than likely brandy).

The ex­trem­ity of vi­o­lence that Victor has en­dured has es­ca­lated to men­tal trauma. In hav­ing Holmes treat Victor’s men­tal trauma while Watson treats his phys­i­cal pain, Doyle showed the im­por­tance psy­cho­log­i­cal sup­port for men of the age.

Holmes was a highly pop­u­lar char­ac­ter. To con­tem­po­rary read­ers, his drug use and dys­func­tional clients were seen as mark­ers of his ge­nius rather than a re­flec­tion of the sig­nif­i­cant so­cial is­sues that men faced dur­ing this pe­riod. But to­day, they of­fer a win­dow into the men­tal strug­gles of Victorian men, and a point of con­nec­tion be­tween read­ers of the past and pre­sent.

Looking for some­thing good? Cut through the noise with a care­fully cu­rated se­lec­tion of the lat­est re­leases, live events and ex­hi­bi­tions, straight to your in­box every fort­night, on Fridays. Sign up here.

This ar­ti­cle fea­tures ref­er­ences to books that have been in­cluded for ed­i­to­r­ial rea­sons, and may con­tain links to book­shop.org. If you click on one of the links and go on to buy some­thing from book­shop.org The Conversation UK may earn a com­mis­sion.