Anson Li

iOS dev lead at Teladoc. Love making apps. I love what i do.

[MacRumors.com @MacRumors]

Everything New in iOS 27 Beta 3 https://t.co/qLlXpvaHKE

[Kapitan Jack Sparrow @jack__sparrow5]

Mnie śmieszy 😂

[Cipher | AI & Web3 @cipherr_exe]

WHAT IF FIFA WAS A BOYBAND PART 2 😭😭😭

Is this a boyband
or a girlband
LMFAOOOOOOOOOO

Someone just made this, and it's so funny 😭😭

[Elon Musk @elonmusk]

Walking the Optimus production line in Fremont

[Unique Wallpaper @just_unique0]

Here is their difference

[Tesla Semi @tesla_semi]

Incentive programs for Tesla Semi in California 

– Clean Truck & Bus Voucher Incentive Program (HVIP) gives you $120,000 off each Semi for up to 20 trucks (no scrapping required, applications re-open upon delivery)

– California Clean Fuel Rewards (CCFR) gives you an additional $120,000 off each Semi, no max truck purchase limit & no scrapping required. Stacking allowed with HVIP for small fleets only. Available starting tomorrow.

Submit your info & we'll get in touch
https://t.co/I3xX039UYk

[Blaida @kedytcom]

OpenAI just killed the worst part of building iOS apps with AI.

The new Codex "Build iOS Apps" plugin can now:

→ run your app in an in-app browser
→ open SwiftUI previews
→ hot reload edits

all without leaving Codex.

No more copy-paste-build-screenshot loop. The agent sees what it ships.

This is the loop indie devs have been begging for.

[Claude @claudeai]

New in Claude Design: it stays on brand with your design system across projects, lets you edit directly on the canvas, syncs with Claude Code, and connects to more of the tools you already use.

[Elon Musk @elonmusk]

Upcoming releases of FSD will remember your parking preferences, so that the car goes to the right location at your home, office, school drop off, etc.

Destination parking is by far the biggest reason people now intervene with FSD. Critical safety interventions are extremely rare.

[Tesla Europe, Middle East & Africa @teslaeurope]

FSD Supervised now approved in Denmark 🇩🇰

Rollout will begin soon

[Sawyer Merritt @SawyerMerritt]

SpaceX has just officially unveiled its AI1 satellite, the first generation of its AI satellite.

Overall Specs:
• 150 kW peak compute payload
• 120 kW average compute payload
• 70 kW per ton
• Compute provider interchangeable

Dimensions:
• Wingspan: 70 meters
• Deployed height: 20 meters

Thermal System:
• 110 m² deployable liquid radiator
• Redundant pumping loops
• Integrated micrometeoroid shielding
• Deployable liquid radiators

Solar Power System:
• 150 kW solar array
• 250 W/m²
• SpaceX-manufactured solar technology from Bastrop, Texas

Architecture:
• Centralized compute module
• Large deployable solar arrays
• Deployable liquid-radiator thermal management system
• AI-focused compute satellite design ("AI1 satellite")

Elon: "The AI satellite is much simpler than a Starlink satellite. The AI satellite is essentially a lot of solar cells, you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. The easier one to design for is the AI satellite. It's bigger. A lot of this is technology we've already made with the Starlink V3 satellites."

[Insider Wire @InsiderWire]

Authorities seize over 100,000 fake degree certificates in Indian H-1B fraud bust.
https://t.co/fDOILFNmp6

[Sawyer Merritt @SawyerMerritt]

Tesla has officially applied for an Autonomous Vehicle Network Company permit in Nevada.

Tesla is seeking approval for up to 5,000 robotaxis during the first 12 months after the permit is granted.

The filing covers Clark County (Las Vegas), including Harry Reid International Airport and Henderson Executive Airport, according to a new public notice from the Nevada Transport Authority.

[Mac O'Sarcástico @MacSarcastico]

Tá bom, quem fez??? hauhauhua

[NSF - NASASpaceflight.com @NASASpaceflight]

Blue Origin's New Glenn just blew up at LC-36 while attempting to Static Fire ahead of NG-4.

https://t.co/tANS0dWyIH

[Lex Fridman @lexfridman]

I've been on a wild travel journey in China for several weeks, with only a backpack, making new friends and meeting & getting to know people from all walks of life. I've been truly humbled and inspired by everyone's kindness.

Next, I'm hopping over to Taiwan (first time for me) to hang out with Jensen, attend Computex, eat a bunch of street food, and just have fun talking to all kinds of folks around the city & beyond. After that, no plans, anything goes.

As always, please give travel suggestions or fill out coffee form if you want to hang out in Taiwain or anywhere else in the world. Love you all! ❤️

[能量魔法师 @KaspaASI]

活到100岁的秘诀？
高胆固醇

一项大规模的瑞典研究追踪了超过80万人长达35年，刚刚揭示：每一个百岁老人都有高的总胆固醇。

你的LDL越高，你活得越长。🌿 🌿 🌿

[Jukan @jukan05]

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)

[Tesla Optimus @Tesla_Optimus]

Autobots, assemble!

[Tesla Robotaxi @robotaxi]

In formation