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#PR# ZEESEA様(@CosmeticZeesea )より星空リキッドアイシャドウを頂きました✨リキッドアイシャドウ今まで苦手だったけどこれは本当に使いやすくて発色もめちゃくちゃ綺麗✨✨ ME02ウキウキがお気に入りです♡♡ #ZEESEAコスメ# #ZEESEA#

ZEESEA様からダイヤモンドシリーズ 星空リキッドアイシャドウの新色をいただきました〜！ 初めてのリキッドアイシャドウ！発色が良くて擦ってもなかなかとれないからこれからの季節沢山使えそうです🤤🤍 #ZEESEA# #ZEESEAコスメ#

ZEESEA様よりアイシャドウを頂きました♡！ とっても使いやすいブラウンです！ 普段のメイクにも使えるし、コスプレにも使えます！✌️ とっても良かったので是非みんなにも使って欲しい〜♡ #ZEESEA# #ZEESEAコスメ# @CosmeticZeesea #PR#

ZEESEA様(@CosmeticZeesea )よりブロウペンシルと夢幻燦爛動物シリーズのアイシャドウパレットを頂きました🐈 今回はピンク系の色を使って見ました💗特に真ん中右上のラメがお気に入り✨ペンシルも凄く描きやすいです✏️公式にアイシャドウの塗り方も出ているので是非チェックして見て下さい🙌#ZEESEA#

ZEESEA(@CosmeticZeesea)さんから星空リキッドシャドウいただきました💫 ラメがザクザク入ってて可愛いし、リキッドなのでメイクしやすいです…✨ #ZEESEA# #ZEESEA桜メイク# #マスクメイク#

如果你进银行抢 100 万美元，你要坐牢。 但如果 7-Eleven 抢你 100 万，这叫“商业合同到期”。 很多普通人的终极梦想，是攒一笔钱，加盟一个像 7-Eleven 这样的全球大连锁，图个旱涝保收。 Javed 就是这么想的。 他在悉尼南部花 100 万美元买下了一家 7-Eleven。 辛辛苦苦干了十年，把生意做得红红火火。 结果，十年加盟协议和租约到期了。 正常人觉得，生意这么好，大家继续续约，合作共赢对吧？ 总部可不这么想。 协议快到期时，总部逼他卖掉店铺。 等 Javed 找好了买家，总部却直接出手，连续三次否决、否决、再否决，活生生把交易拖到流产。 一直拖到合同截止日期的那一天。 早上六点整，天刚蒙蒙亮。 7-Eleven 总部直接派了一队穿着反光背心的“彪形大汉”，带着锁匠，直接开进店里。 拉起隔离带，关掉油价牌，强行清点库存。 然后换锁，把 Javed 的公司名字从窗户上抠掉，换成总部的直营实体名称。 Javed 和他的兄弟被迫站在车道上看着，当场被剥夺了进入的权利。 一分钱补偿都没有，直接扫地出门。 Javed 辛苦养大了十年的“孩子”，被亲爹合法强占了。 他说，当年签约时，总部的人跟我说了上百次，“我们是一家人，是伙伴”。 现在看，哪个家里人会干这种事？ 这还真不是个例。 另一个叫 Zeeshan 的加盟商，也是花了 60 万美元买下店铺，最后被用同样的方式剥夺一切，背了一屁股贷款被踢出门。 墨尔本一个花 70 万买店的加盟商，现在每天活在极度恐慌里，只想半价亏本把店贱卖了逃生。 但总部就是不批准新买家，摆明了是想把时间耗光，等协议一到期就无偿接管。 最操蛋的是什么？ 这一切，居然完全是合法的。 资本的合同条款早就设计好了陷阱。 协议到期，总部有权不续约。 你以为你是借着巨头的翅膀创业，其实你只是自备干粮、替地主开荒的奴隶。 等你把最难熬的阶段熬过去，把选址、客源、运营全部跑通，把这家店做成了下金蛋的鸡。 不好意思，合同到期，地主收回，直接变直营。 连之前你自己花巨资升级的设备和装修，都成了总部的免费资产。 怎么分辨你常去的 7-Eleven 是加盟商在苦苦支撑，还是已经被总部“合法抢劫”了？ 看收银台旁边的玻璃窗。 如果是加盟店，上面会写着店主自己的小公司名字（比如 Javed 的店写着 Alpha Fusion）。 如果是直营店，上面只会有冷冰冰的 “7 Eleven Stores Proprietary Limited”。 在资本的绞肉机里，从来就没有温情。 你把人家当“家庭”，人家只把你当一次性“开荒耗材”。

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)

🍀Lady Luck is on my side🍀 * A little side by side of my Domino cosplay,, I'm really proud of this costume & it was so much fun to get to run around the con in * #Deadpool2# #Deadpool# #cosplayer#