Tough game but France got it done. Between Argentina and this game today, refereeing is becoming a major issue in this tournament. 3 yellows for France and zero for Paraguay who played like thugs? Also, France, stop passing the ball to Rabiot for long-range shots lol

At Miami airport, seeing Cabo Verde fans who look like they cried all night. The reality of the World Cup is that 47 teams will go home heartbroken but you can tell that coming so agonizingly close, against the odds, crowd and referee hit hard. All a deeply human adventure.

Nobody can define what a sandbox is because the goalpost keeps moving. The evolution of what agents need from a sandbox: Stage 1 (code execution) Your agent needs to run Python, analyze a CSV, and solve a math equation that ChatGPT can't do natively. So you spin up a tiny isolate Stage 2 (coding agents) Now the agent needs to clone a GitHub repo, edit code, install packages, run it, and preview the output. You need a full Linux machine Stage 3 (agent lives inside the sandbox) Now security matters. Can the agent see your tokens and credentials? What can it access on the internet? Do you need a firewall? Stage 4 (RL workloads) Now you want speed, throughput, concurrency, and spin up anywhere between 50,000 and 500,000 sandboxes simultaneously in seconds Stage 5 (general-purpose knowledge work) Legacy apps, internal tooling, workflows - everything lives in Windows. Linux sandboxes won't cut it here The tools that serve Stage 1 don't fit into Stage 3. The ones that fit into Stage 3 don't work well in Stage 4. And so on. What your agent needed a sandbox for 18 months ago is completely different from what it needs today. And what it'll need in 6 months doesn't exist yet.

Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (@ctnzr). @nvidia is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed

Fascinated by Lime going public - in an age where AI gets all the attention, how does a scooter company with $1B in debt pull off a successful IPO literally after expressing "substantial doubt" that they might not even survive the year? * Impressive financial engineering - the IPO paid off the toxic loans and converted the rest to equity, so the slate is clean * Uber owns 22% of Lime and refers riders directly to Lime, so obviously a great backstop and partner * They've actually been FCF positive for 3 consecutive years with revenue growing nearly 30% YoY * Something about being the last man standing and surviging the micromobility bloodbath (RIP Bird). Never give up!

Smart glasses and goggles, a history: Silicon Valley, 2013 (Google): “you really want this” Everyone: “no we don’t” Silicon Valley, 2016 (Microsoft): “ok but what if it’s for the enterprise” Enterprise: “maybe, but also, no” Silicon Valley, 2023 (Meta): “ok but what if they look normal and have AI” Everyone: “wait… maybe? … Actually, no” Silicon Valley, 2024 (Apple): “ok but what if it’s $3,499 and covers your whole face” Everyone: “absolutely not” Silicon Valley, 2026 (Snap): “ok but this time for real” Everyone: “we admire the persistence but still no”

State of AI compute 2026: my conversation with @stephenbalaban of @LambdaAPI on the neocloud boom, data centers, GPUs and what's ahead 00:00 — Cold open 01:21 — Why GPU compute was never a commodity 02:45 — The H100 price index and what it gets wrong 04:02 — The real moat: technology or financing? 05:57 — Winner-take-all, or room for many neoclouds 06:48 — Are we overbuilding or underbuilding AI compute? 09:26 — What if AI gets 10x more compute-efficient? 10:44 — The real bottleneck: land, power, and shell 11:38 — The backlash against data centers — and the misinformation 15:00 — Opening the hood: from photons to tokens 17:11 — Extracting more value from the same chip 19:26 — Frontier inference and distributed training, explained 23:26 — What actually drives compute cost 25:21 — Lambda's chip stack and the NVIDIA relationship 26:17 — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat 28:59 — Networking, storage, and the one-click cluster 34:46 — Renting vs. owning, and full vertical integration 36:24 — How global is Lambda? Does location still matter? 38:44 — The financing stack: off-take agreements, SPVs, and credit 41:16 — Why a 2023 GPU leases for more today 42:36 — A futures market for compute? 43:54 — Origin story: facial recognition, Perceptio, and Apple 47:03 — The Lambda hat and Dream Scope 48:59 — The $60K bet that became a cloud business 52:00 — Holding the team together through the hard times 54:30 — Bringing on a new CEO; Stephen as CTO 57:33 — Matching xAI on high-velocity deployment 59:29 — "AI won't write software — it will become the software" 01:01:30 — Neural software vs. vibe coding 01:04:25 — Do agents change the compute layer 01:06:14 — Self-assembling software inside Lambda 01:08:18 — Gigawatt-scale AI factories 01:08:57 — One person, one GPU 01:12:04 — Hot takes: overrated and underrated in AI