Ray Swank @RaySwank

On CursorBench, Grok 4.5 High ranks #3 at 66.7%, right next to Fable 5 Max & Extra High.....but the insane part is the cost It's literally $1.51 per task while Fable 5 Max costs $17.32 per task Even Grok 4.5 Low ranks above Opus 4.8 Max & GPT - 5.5 Extra High Anyone can chase intelligence by burning more tokens, more steps, more compute. Grok pushes the opposite direction: high performance with less waste. That’s where Grok 4.5 is getting scary good

Announcing Grok 4.5, our first model trained specifically for coding and agents. It was trained with Cursor and offers frontier intelligence at leading speeds and cost efficiency. x.ai/news/grok-4-5

Based on strong positive feedback from customers in our beta test program, @SpaceXAI will make Grok 4.5 available to the public tomorrow. It is an Opus-class model, but faster, more token-efficient and lower cost.

In addition to their excellent and unique training data, the Cursor team is also making major engineering contributions to v9 SFT & RL. It’s an honor and a pleasure to work with them. For this 1.5T run, Cursor data was added in supplemental training, which is not quite as good as having it in initial training. The 2T run that started a few weeks ago has greatly improved data in scope & scale in almost every arena, and many upgrades/fixes to the training recipe. That will finish in late July for August release.

Grok Imagine Video 1.5 is now on Higgsfield. The best model to push creative vision further. Best-in-class realism, stronger physics, faster generations. Top-tier on @arena at 1467 Elo. Proud to officially partner with the @xai team.

📹️ Announcing the fal x @sequoia 72-Hour Video Hackathon. A 3-day global sprint for AI-native filmmakers, creative technologists, developers, designers, and storytellers building the future of video. Supported by leading AI video labs @GoogleDeepMind, @xAI, and @Kling_ai. Participants will get: - Access to frontier video models - Workshops + talks from industry leaders - Mentorship from top creators + engineers - $150k credit prize pool This is a chance to build at the cutting edge and create what wasn’t possible before. Judges, speakers and event details coming soon. Participation is by application only. Apply here: 72hourhackathon.com

SpaceX a clôturé son premier jour de cotation à 2 100 milliards de dollars, +19%. Tout le monde regarde le chiffre. Personne ne regarde ce qu'il price réellement. Laissez-moi vous dire ce que le marché vient d'acheter, et pourquoi je pense que cette boîte vaudra 30 à 50 trillions d'ici 5 ans. D'abord, le symbole. Cette IPO est un référendum. D'un côté, 20 ans de discours sur la décroissance, la sobriété, la redistribution, la fin de l'histoire gérée par des comités. De l'autre, un homme qui a dit "je vais rendre l'humanité multiplanétaire", que tout le monde a traité de clown, et qui vient de créer la plus grosse entreprise cotée de l'histoire en partant d'un entrepôt à El Segundo. Le marché a voté. Le wokisme avait des départements RH, SpaceX avait des fusées. Les fusées ont gagné. Ensuite, la mécanique économique, parce que c'est là que tout le monde se trompe. Les analystes valorisent SpaceX comme une entreprise de lancement plus Starlink. C'est comme valoriser Internet en 1995 sur le marché du fax. Starship ne réduit pas le coût du kilo en orbite de 20%, il le divise par 100. Et chaque fois dans l'histoire qu'un coût d'infrastructure est divisé par 100, ce n'est pas le marché existant qui grossit, ce sont des industries entières qui naissent. Le coût du calcul divisé par 100 a donné Internet, le smartphone, l'IA. Le coût de l'orbite divisé par 100 va donner une économie spatiale complète. Faisons la liste de ce qui devient rentable quand le kilo en orbite coûte le prix d'un billet d'avion. Les data centers orbitaux, avec énergie solaire continue et refroidissement gratuit, au moment exact où l'IA fait exploser la demande énergétique terrestre. La fabrication en microgravité de semi-conducteurs, de fibres optiques, d'organes imprimés impossibles à produire sous gravité. Le tourisme orbital de masse, puis les hôtels lunaires, qui passeront du fantasme au business plan exactement comme la croisière de luxe au 20ème siècle. Le transport point à point terrestre, Paris-Tokyo en 40 minutes. L'industrie minière des astéroïdes, dont un seul corps de classe M contient plus de métaux que tout ce que l'humanité a extrait depuis le néolithique. Et Mars en ligne de mire, pas comme destination touristique, mais comme le plus grand projet d'infrastructure jamais entrepris, avec tout ce que ça implique de demande en énergie, matériaux, robotique, IA. SpaceX ne participera pas à ces marchés. SpaceX possède le péage d'entrée de tous ces marchés. C'est AWS, mais pour la civilisation. Apple vaut 3 500 milliards en vendant des rectangles de verre sur une seule planète. Le premier monopole d'accès à une frontière infinie à 30 ou 50 trillions dans 5 ans, ce n'est pas de l'exubérance, c'est une simple règle de trois sur l'expansion du marché adressable. Et maintenant, la partie que je préfère. Ce futur n'a pas besoin de bureaucrates. Il n'y a pas de comité consultatif en orbite. Pas de commission Théodule sur Mars. Chaque dollar de cette nouvelle économie sera créé par des ingénieurs, des techniciens, des soudeurs, des pilotes, des entrepreneurs. Les diplômés en gestion de la norme vont devoir apprendre un métier utile, et franchement, c'est une excellente nouvelle pour eux aussi : construire est infiniment plus fun que contrôler. Parce que c'est ça, le vrai signal d'aujourd'hui. Pendant 50 ans on nous a vendu un futur rétréci : moins d'énergie, moins d'enfants, moins d'ambition, gérer le déclin proprement. Et là, d'un coup, le plus gros actif financier du monde est un pari sur l'abondance, l'expansion et l'aventure. Le pessimisme vient de passer en position vendeuse sur lui-même. Le futur sera méga fun. Il y aura des hôtels avec vue sur la Terre, des honeymoons en orbite, des gamins qui diront "papa, c'était comment avant les fusées réutilisables" comme on dit "c'était comment avant Internet". Et quelque part dans les années 2030, un humain marchera sur Mars en livestream devant 5 milliards de personnes, et ce jour-là plus personne ne se souviendra du nom d'un seul de ses détracteurs. Achetez de l'optimisme. C'est encore sous-valorisé.

Built with Grok

Gopuff @gopuff

a month ago

Meet Go. Gopuff's AI shopping genius, co-developed with SpaceXAI. Just say what you need. It's already on its way.

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Why did xAI hand over a 220,000-GPU cluster to Anthropic? The technical backdrop to xAI's decision to hand Colossus 1 over to Anthropic in its entirety is more interesting than it appears. xAI deployed more than 220,000 NVIDIA GPUs at its Colossus 1 data center in Memphis. Of these, roughly 150,000 are estimated to be H100s, 50,000 H200s, and 20,000 GB200s. In other words, three different generations of silicon are mixed together inside a single cluster — a "heterogeneous architecture." For distributed training, however, this configuration is close to a disaster, according to engineers familiar with the setup. In distributed training, 100,000 GPUs must finish a single step simultaneously before the cluster can advance to the next one. Even if the GB200s finish their computation first, the remaining 99,999 chips have to wait for the slower H100s — or for any GPU that has hit a stack-related snag — to catch up. This is known as the straggler effect. The 11% GPU utilization rate (MFU: the share of theoretical FLOPs actually realized) at xAI recently reported by The Information can be read as the numerical fallout of this problem. It stands in stark contrast to the 40%-plus MFU figures achieved by Meta and Google. The problem runs deeper still. As discussed earlier, NVIDIA's NCCL has traditionally been optimized for a ring topology. It works beautifully at the 1,000–10,000 GPU scale, but once you push into the 100,000-unit range, the latency of data traversing the ring once around becomes punishingly long. GPUs need to churn through computations rapidly to keep MFU high, but while they sit waiting endlessly for data to arrive over the network fabric, more than half of the silicon falls into idle. Google sidestepped this bottleneck with its own custom topology (Google's OCS: Apollo/Palomar), but xAI, by my read, has not yet reached that stage. Layer Blackwell's (GB200) "power smoothing" issue on top, and the picture comes into focus. According to Zeeshan Patel, formerly in charge of multimodal pre-training at xAI, Blackwell GPUs draw power so aggressively that the chip itself includes a hardware feature for smoothing power delivery. xAI's existing software stack, however, was optimized for Hopper and does not understand the characteristics of the new hardware; when it imposes irregular loads on the chip, the silicon physically destructs — literally melts. That means the modeling stack must be rewritten from scratch, which in turn means scaling is far harder than most of us imagine. Pulling all of this together points to a single conclusion. xAI judged that training frontier models on Colossus 1 simply was not efficient enough to be worthwhile. It therefore moved its own training workloads wholesale onto Colossus 2, built as a 100% Blackwell homogeneous cluster. Colossus 1, on the other hand — whose mixed architecture is far less crippling for inference, which parallelizes more forgivingly — was leased in its entirety to an Anthropic that desperately needed inference capacity. Many observers point to what looks like a contradiction: Elon Musk poured enormous capital into building Colossus, only to hand the core asset over to a direct competitor in Anthropic. Others read it as xAI capitulating because it is a "middling frontier lab." But these are surface-level reads. Look at the numbers and a different picture emerges. xAI today holds roughly 550,000+ GPUs in total (on an H100-equivalent performance basis), and Colossus 1 (220,000 units) accounts for only about 40% of the total available capacity. Colossus 2 — built entirely on Blackwell — is already operational and continuing to expand. Elon kept the all-Blackwell homogeneous cluster (Colossus 2) for himself and leased out the older, mixed-generation Colossus 1. In other words, he handed the pain of rewriting the stack — the MFU-11% debacle — to Anthropic, while keeping his own focus on training the next generation of models. The real point, then, is this. Elon's objective appears to be positioning ahead of the SpaceXAI IPO at a $1.75 trillion valuation, currently floated for as early as June. The narrative SpaceXAI now needs is that xAI — long the "sore finger" — is not merely a research lab burning cash, but a business with a "neo-cloud" model in the mold of AWS, capable of leasing surplus assets at high yields. From a cost-of-capital perspective, an "AGI cash incinerator" is far less attractive to investors than a "data-center landlord generating cash." As noted above, the most important detail of the Colossus 1 lease is that it is for inference, not training. Unlike training, inference requires far less tightly synchronized inter-GPU communication. Even when the chips are heterogeneous, the workload parcels out cleanly across them in parallel. The straggler effect — the chief weakness of a mixed cluster — is essentially neutralized for inference workloads. Furthermore, with Anthropic occupying all 220,000 GPUs as a single tenant, the network-switch jitter (unanticipated latency) that arises under multi-tenancy disappears. The two sides' technical weaknesses end up complementing each other almost exactly. One insight follows. As a training cluster mixing H100/H200/GB200, Colossus 1 was an asset that could only deliver an MFU of 11%. The moment it was handed over to a single inference customer, however, that asset transformed into a cash-flow asset rented out at roughly $2.60 per GPU-hour (a weighted average of the lease rates across GPU types). For xAI, what was a "cluster from hell" for training has become a "golden goose" minting $5–6 billion in annual revenue when redeployed for inference. Elon's genius, I would argue, lies not in the model but in this asset-rotation structure. The weight of that $6 billion becomes clearer when set against xAI's income statement. Annualizing xAI's 1Q26 net loss yields roughly $6 billion in losses per year. The $5–6 billion in annual revenue generated by leasing Colossus 1 to Anthropic, in other words, almost perfectly hedges xAI's loss figure. This single deal effectively pulls xAI to break-even. Heading into the SpaceXAI IPO, this functions as a core line of financial defense. From a cost-of-capital standpoint, if the image shifts from "research lab burning cash" to "infrastructure tollgate stably printing $6 billion a year," the entire tone of the offering can change. (May 8, 2026, Mirae Asset Securities)

Jukan @jukan05

2 months ago

What the SpaceX–Anthropic Deal Means Two weeks ago, we published a note laying out what GPT-5.5's release implied. The conclusion was simple: whoever secures compute first, in greater volume, and with greater reliability ultimately takes the win. With OpenAI's 30GW roadmap

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SpaceXAI and @cursor_ai are now working closely together to create the world’s best coding and knowledge work AI. The combination of Cursor’s leading product and distribution to expert software engineers with SpaceX’s million H100 equivalent Colossus training supercomputer will allow us to build the world’s most useful models. Cursor has also given SpaceX the right to acquire Cursor later this year for $60 billion or pay $10 billion for our work together.

The market moves in real time... Now Tori does too! 🔥 Powered by @grok, Tori brings live sentiment from X, remembers your context, and manages Agent Portfolios through conversation. From insight to action, all in one place. Tori - Your AI Edge 👇 etoro.tw/4cguWrj

Colossus 2. 1 gigawatt. Built in 122 days. Let that sit. Seven models training at once. Not queued. Not waiting. Parallel. Up to 10 trillion parameters. Real scale, not slides. Others still stitching clusters across cloud partners. xAI just built the whole thing. @xai

Elon Musk @elonmusk

3 months ago

SpaceXAI Colossus 2 now has 7 models in training: - Imagine V2 - 2 variants of 1T - 2 variants of 1.5T - 6T - 10T Some catching up to do.

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To all Sora Customers: xAI is open for business on image and video generation. Send X Chat or post a reply with contact info and we will get in touch.

Sora @soraofficialapp

4 months ago

We’re saying goodbye to the Sora app. To everyone who created with Sora, shared it, and built community around it: thank you. What you made with Sora mattered, and we know this news is disappointing. We’ll share more soon, including timelines for the app and API and details on

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x.com/i/article/2034…

The Grok 4.2 release candidate (public beta) is now available for use. You need to select it specifically. Critical feedback is appreciated. Unlike prior versions of Grok, 4.2 is able to learn rapidly, so there will be improvements every week with release notes.

@DKSportsbook @LAClippers = the new Skid Row, #SkidBros

Awesome article by Carl Eschenbach and Kais Khimji on behalf of Team Sequoia Capital & Ben Horowitz on behalf of Andreessen Horowitz on the power of Cresta. #ExpertiseAI 🌊 "The ROI is immediate: higher conversion, better NPS,…lnkd.in/ee8D8C7 lnkd.in/eeSHgpN

Join Dynamic Signal's Chief People Officer, Regan Cain, and Chief Financial Officer, Bong Suh, on Friday, August 21 at 10 a.m. PT to hear how every executive at the leadership table has a role to play in improving employee wellness. #EmployeeExperience bit.ly/2XrwhVm

Brightview Senior Living on the #DySi platform: “It means so much when our leadership is able to speak directly to all of our frontline associates during such a challenging time." #EmployeeComms #EmployeeExperience bit.ly/2CRJgZv

Misleading headline. BR is totally twisting words to make a “story.” Listen to the entire quote from @DGreen_14 on his podcast & you’ll understand why this is inaccurate. x.com/br_nba/status/…

Bleacher Report NBA @BR_NBA

8 years ago

Danny Green unknowingly played with a torn groin last season, went undetected by Spurs medical staff bit.ly/2A6IUvT

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