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A few months ago I was considering buying RTX Pro 6000s and thinking that $7,000 was too much. 🤡

Apple M5 Ultra 512GB is live on Apple store! 27K EURO!!! with 4TB SSD in Italy 👀 Availability 23rd November!

arstechnica.com/security/202… The private keys for these certs are literally the numbers 4, 56, 66, 115, 155, 156, 230, and 237 (life imitates XKCD, again).

We keep improving performance across Claude, and you may have noticed it starts responding sooner! Claude Code now gzips the conversation it sends on every turn, so it reaches the model faster. Cloud sessions also start up as soon as you start typing.

GPU prices have been climbing for a year, and I wanted to know whether this is a genuine shortage or a Labubu moment. So I spent sometime mapping what this hardware actually sells for. It is both, and the split shows up the moment you separate what sellers ask from what buyers pay. Asking prices run 40 to 60% above what transactions actually close at. The transactions themselves are real, and they only go one way. HOW I CHECKED 489 dated observations across 14 sources and 11 machines. Four price tiers, never mixed. First party list price, retail street price for new stock, second hand asking price, second hand sold price. Sold beats asking, asking beats list, and when a card has both I print both. Sold prices come from a weekly sold listing series, 101 observations from January 17 to October 6. Asking prices come from daily asking medians, 367 observations from July 28 to October 11. List prices come from the vendors themselves. Street prices come from retail trackers and store pages. Windows differ per card because the archives differ, so I label which window each number sits in instead of averaging them together. USED PRICES, OCTOBER 2026 (sold listings, what buyers paid) RTX 3090, 2020, 24GB: $1,206, up 56% since January RTX 4090, 2022, 24GB: $2,214, down 6% this year, still 38% above its launch price RTX 5090, 32GB: $4,843, up 70% since January, no down month RTX A6000, 48GB: $5,108, up 28% since July RTX 6000 Ada, 48GB: $7,590, up 28% since July RTX PRO 6000, 96GB: $14,400 new, against roughly $8,565 in launch listings DGX Spark, 128GB: $6,950 list, launched at $3,999 The premium over launch price tracks memory tier, not compute. The 24GB cards are the only ones trading near their launch prices. 32GB is 2.4x. 96GB is up 1.7x at the cheapest listing I could verify, closer to 2x on the street. Apple is the cleanest proof, since they raised the M3 Ultra by $1,300 in June and deleted the 512GB option entirely, blaming memory supply. NVIDIA did it to the Spark twice, $3,999 to $4,699 to $6,950. Different silicon, different vendors, same line on the chart. WHERE THE BARGAINS ARE Treating each machine as a memory carrier, here is what a gigabyte of fast memory costs and how fast it is. Mac Studio M5 Ultra, 256GB: $37/GB, 1,200 GB/s MacBook Pro M5 Max, 128GB: $40/GB, 614 GB/s used RTX 3090, 24GB: $50/GB, 936 GB/s DGX Spark, 128GB: $54/GB, 273 GB/s used RTX 4090, 24GB: $92/GB, 1,008 GB/s used RTX A6000, 48GB: $106/GB, 768 GB/s RTX 5090, 32GB: $151/GB, 1,792 GB/s Two of these stand out and for opposite reasons. The 3090 is the only used GPU worth buying for capacity. It is the last consumer card NVIDIA shipped with NVLink, 112.5 GB/s between a pair against 31 GB/s per direction over PCIe 4.0. The 4090 and 5090 dropped the connector entirely. Two 3090s and a bridge give you a 48GB pool for about $2,400. One 48GB pro card costs $5,108. The catch is software. NVLink only pools under a tensor-parallel runtime, so vLLM or a tensor-parallel llama.cpp build. Plain Ollama sees two separate 24GB cards. The 5090 is the most expensive memory in the table that most people are actually shopping for, at $151/GB. It is the fastest thing here and it still costs four times what an M5 Ultra costs per gigabyte. MAC IS THE BEST VALUE IF YOU IGNORE CUDA The cheapest fast memory I have found is not a GPU at all. At $37/GB with 1,200 GB/s, a 256GB Mac Studio undercuts every GPU in this table on both axes, and it does that at retail list price, not on the used market. You ALSO get the whole computer with it, so it's even a better deal than it looks. DGX SPARK IS A WEIRD OUTLIER And the Spark, which I want to be fair to, because it is a genuinely good machine with a real problem at the till. $6,950 for 128GB at 273 GB/s. Its actual sweet spot is two of them, connected over ConnectX for 405B models, which is $13,900 before you have bought anything else. That is a lot to ask for a box thats memory bus will be a achilles heel forever. Yet the DGX Spark runs on NVIDIA software, it pools 128gb of vram together, with connect7 ports that allows clustering. In current benchmarks, it still stands mostly equal to the M5 Class of Macs, despite the fact that its memory bandwidth, other things about the DGX Spark makes up for the low memory bandwidth, such as it's NVLink C2C (5x the speed of PCI-E Bus). FINAL SUMMARY - MEMORY SOLD IS NOT ALL EQUAL Two boxes in this table are both Blackwell, and they have nothing in common. The 5090 and the RTX PRO 6000 run GDDR7 at 1,792 GB/s. The Spark runs LPDDR5X at 273 GB/s, and it asks $54/GB for that against $50/GB for a six year old 3090 that is three times faster per gigabyte. Cards here punch above their price against newer silicon. Bandwidth is harder to work around. The key here is that cards can go into future-rigs, boxes cannot. On break-even maths anything thats runs on RTX cards completely obliterates the Macs and Sparks on speed. Money buys capacity. Bandwidth is forever. The market is asking $6,786 for a 5090 while buyers pay $4,843. That gap is $1,943 and it widened all year. That gap is also the honest answer to my Labubu question. Memory is the product, but where the memory sits is really important to consider. Sources in reply 👇

Today we're standing up XBOX LABS (labs.xbox.com). Here is the note that I sent to the team: Team, The first video games emerged some 75 years ago. In the time since, there have been booms and busts, but every year there are more great games to play, more devices to play them, and best of all, more people than ever playing. That growth helped make XBOX what it is today, and we've helped shape the industry in return, from bringing hard drives to consoles and online gaming through Xbox Live to Game Pass and cloud gaming, alongside franchises that have brought millions of people together. In recent weeks, we've seen games rapidly decompiled and rebuilt to run on hardware they were never originally designed to support. Windows PC builds have been translated to run on Apple Silicon or ported to run inside a browser through WebAssembly and WebGPU, the latter now approaching 90% global browser support. Perhaps more surprisingly, we've seen many of the biggest games in the world have been remixed into the source code of another. These experiments challenge long-held assumptions about our industry: the tether between software and hardware, the boundary between creation and play, and how people discover and experience games. While it is hard to predict the future of gaming, it is easy to see the possibilities in making great games easier to access, giving old games new life and allowing more players to become creators and co-creators. These are unmistakably good outcomes for players, even when (and sometimes because) they challenge our existing business models. To explore these possibilities, I’ve asked Scott to stand-up XBOX LABS, a small group of game makers, engineers and scientists whose job is to turn emerging ideas into things our players and developers can experience. We'll build in the open, learn from what people do, and measure success by whether more people are playing and creating. I think XBOX has an important role to play here. Coding became the first major workload for agents because they can write, run, and test software. Games share many of those same properties, and people spend more than 72 billion hours a year playing XBOX games, roughly five times the estimated time spent writing code worldwide. We've spent 25 years learning how to make great games, and I want our game makers to help shape what these new technologies make possible. As we learn, we’ll share the tools we build with developers across the industry, just as we always have. Our priorities aren't changing. We remain committed to the 4Cs, to making great games, to our hardware and to being responsible stewards of IP and the communities built around it. LABS gives us a way to explore new technology that is game design led without abandoning the work already underway. Asha

Devs: "QA found another bug." "How do we fix it?" "I don't know. No one wrote it. It's all slop code." Multiply that by 20000 times. Per game.

me telling Codex about the quasi-Riemann hypothesis proof and telling it implement a new kernel to speed up matmuls in mlx