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🚨⚡️ Xi was showing off 300-year-old ancient trees in the secret Zhongnanhai gardens. Trump ignores the trees and asks: "Do you bring other Presidents here?" Xi shakes his head: "NO." -: Trump just wanted to make sure he’s the favorite. Peak Main Character Energy! 🤣😂

Deep in NW China’s Gansu Province, a high-speed railway winds its way through rolling loess hills. Boring tunnels through mountains has never been an easy feat—yet China makes it happen, all for the people waiting beyond the peaks.

📊 April 2026 #USDD# Transparency Report Key highlights: 🔹 April peak supply reached $1.56B, up 10.87% MoM 🔹 Month-end collateral value reached $2.16B, maintaining a 146% collateral ratio 🔹 Smart Allocator earnings surpassed $16.24M, with $2.46M added in April 🔹 New WBTC Vaults introduced to diversify collateral structure 🔹 AI ecosystem integration to support the growth of AI Agents USDD reserves remain 100% publicly verifiable across TRON, Ethereum, and BNB Chain. Looking ahead, USDD will continue strengthening stability, optimizing yield infrastructure, enhancing transparency, and expanding ecosystem adoption — with the goal of becoming trusted stablecoin infrastructure for DeFi and the AI Agent economy. More details ⬇️

Effective today, we are: 1) Doubling Claude Code’s 5-hour rate limits for Pro, Max, and Team plans; 2) Removing the peak hours limit reduction on Claude Code for Pro and Max plans; and 3) Substantially raising our API rate limits for Opus models.

Microsoft's MIT licensed VibeVoice speech-to-text model (think Whisper with speaker diarization) is really good - my notes on running the 5.71GB 4bit MLX conversion on an M5 MacBook, using about 60GB of RAM at peak and transcribing 1hr of audio in ~9 mins

Stablecoin market cap at a new ATH: ~$320B. <$5B in 2020 → $185B peak in 2022 → a steady multi-year climb to today's high. The supply side has never been deeper, and the slope hasn't broken since early 2024.

Without getting all the way down to performance counters, GPU power from nvidia-smi is a better indicator of true utilization than job scheduling or “gpu busy”. I would love to see animated “heat maps” of the big data centers, with each pixel being an individual GPU’s power draw. I am confident that inference and frontier training at the big labs is highly efficient, but I wonder how many GPUs would be dark due to scheduling and inefficient research code. With a little calibration for base load and peak, just the power bill for the datacenter would be a pretty good first order indicator of utilization.