Toastbroti

@Spamdrain165105 @reddit_lies so europeans should get AC because america has weather so hot the european mind cannot comprehend?

@diesesToastbrot @reddit_lies Lmao Phoenix, AZ has on average 100 days of the year over 38 degrees the European mind cannot fathom this

The absolute poorest Americans in the US, the ones you look down on and berate, can afford AC and you cannot.

@The_OrangeBlack @reddit_lies which is why its retarded to compare europeans not having AC to americans not having it. we don't ever get close to 40 degrees

@diesesToastbrot @reddit_lies 30 degrees °C isn’t even that hot 😂 Many states go through 12 weeks of 40 degrees °C every year

@justalexoki name a more iconic couple

math and amphetamines, name a more iconic duo

@anon_and_off @reddit_lies this is apples to oranges because the EU heat death numbers in this graph measure excess death rate, not actual cause of death.

@diesesToastbrot @reddit_lies

@justalexoki why does this erdos guy have so many problems

1) what (the fuck)

@HAv2RtrnVdeoTps TIL the US only has a single hospital

“In hospital”

@seconds_0 If polls matter you're not thinking anti-voter ID enough

One of the worst possible positions for democrats to take is being anti-voter ID. It polls so well its an insane own goal

@Yjvitor Chinese and Japanese are hard because they don't use any spaces at all

The fact that people actually believe german is hard because "no space" is ridiculous There are thousands of other reasons why German is difficult

@justalexoki The EU heat deaths in this chart are measured akin to counting everyone who died while carrying a gun as a gun death

lol ok europoors permanently lost the right to complain about gun violence because genuinely how hard is it to just install AC

@gfodor How much are you spending to unsloppify and how much is it actually going to save you in token cost going forward? What is the sloptimum for how soon and how often to unsloppify again?

This is a pure refactoring pass to de-slopify 25k lines of code across 180 checkpoints with regression testing via a custom harness. When someone says "AI produces slop" they are just admitting they have have chosen to tweet instead of working the problem.

@KeyTryer @Dlassche1 which no one could have predicted by drawing a straight line over and over again

@Dlassche1 Yes, absolutely. Technological advancements and cost reductions in renewables have been much faster than forecasts predicted. carbonbrief.org/analysis-how-h… ourworldindata.org/cheap-renewabl…

Yes, THANKS TO THE ADOPTION OF RENEWABLES

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

@BecomingCritter >Nikita calls out annoying retard I think I've never heard of before >Look inside >I have already muted the retard in question

Nikita says this guy does undisclosed promotions Guy asks nikita to compile a list since he’s got nothing to hide Nikita compiles a list

@KeyTryer Yeah it seems obvious for training data and generalized mass production for now

Maybe cope, but I think it's self-evident to me that humanoids will dominate most industries more than any other kind of robot simply because they can be made to do this and like a thousand other jobs without having to make new designs for every task.

@justalexoki I'm vegan except for all the days that end in -day

im vegan but i eat chicken and tuna sometimes :) and a lot of dairy like greek yoghurt. occasional ham and beef. and eggs for my protein pancakes

@theo Why is X paying the third world to post ai written slop that misrepresents the research

Always read the system prompt before coming to conclusions

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)

Same here. By way of background for those who care, I spent a lot of time last week with senior members of the Anthropic team to understand what they do to ensure Claude is good for humanity and was impressed. Everyone I met was highly competent and cared a great deal about doing the right thing. No one set off my evil detector. So long as they engage in critical self-examination, Claude will probably be good. After that, I was ok leasing Colossus 1 to Anthropic, as SpaceXAI had already moved training to Colossus 2.

We’ve agreed to a partnership with @SpaceX that will substantially increase our compute capacity. This, along with our other recent compute deals, means that we’ve been able to increase our usage limits for Claude Code and the Claude API.