Marco Mascorro

🚨 New: We built @a16z's personal GPU AI Workstation Founders Edition - 4x NVIDIA RTX 6000 PRO Blackwell Max-Q (384GB total VRAM) - 8TB of NVMe PCIe 5.0 storage - AMD Threadripper PRO 7975WX (32 cores, 64 threads) - 256GB ECC DDR5 RAM - 1650Watts at peak (runs on a standard 15Amp/120V circuit). For training, AI research, and deploying models locally. A datacenter-class AI rig you can keep under your desk. We are planning to make a limited number of these a16z AI Workstations. Build guide + how you can make your own 👇

we got @karpathy to ride in our self-driving golf cart!

JUST IN: SoftBank is creating a brand new company — Roze AI, that uses autonomous robots to BUILD data centers. And Masayoshi Son is already eyeing a $100 BILLION IPO by the second half of 2026. The logic is simple. The AI boom requires data centers. Data centers require construction. Construction is slow, expensive and labour-intensive. So why not automate the thing that builds the thing that runs the AI? This is infrastructure automation at a scale nobody has attempted before. Robots building the server farms that power artificial intelligence. The whole stack, automated from the ground up. The timing is aggressive, even some inside SoftBank are sceptical about the $ 100bn valuation and the IPO timeline. But Son has never been known for thinking small. For context, Jeff Bezos is reportedly running a similar playbook with Project Prometheus, a startup buying up industrial companies and modernising them with AI. The world's biggest investors are all arriving at the same conclusion: the next trillion-dollar opportunity is not in software. It's in automating the physical world. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

Did a very different format with @reinerpope – a blackboard lecture where he walks through how frontier LLMs are trained and served. It's shocking how much you can deduce about what the labs are doing from a handful of equations, public API prices, and some chalk. It’s a bit technical, but I encourage you to hang in there - it’s really worth it. There are less than a handful of people who understand the full stack of AI, from chip design to model architecture, as well as Reiner. It was a real delight to learn from him. Recommend watching this one on YouTube so you can see the chalkboard. 0:00:00 – How batch size affects token cost and speed 0:31:59 – How MoE models are laid out across GPU racks 0:47:02 – How pipeline parallelism spreads model layers across racks 1:03:27 – Why Ilya said, “As we now know, pipelining is not wise.” 1:18:49 – Because of RL, models may be 100x over-trained beyond Chinchilla-optimal 1:32:52 – Deducing long context memory costs from API pricing 2:03:52 – Convergent evolution between neural nets and cryptography

🚀 DeepSeek-V4 Preview is officially live & open-sourced! Welcome to the era of cost-effective 1M context length. 🔹 DeepSeek-V4-Pro: 1.6T total / 49B active params. Performance rivaling the world's top closed-source models. 🔹 DeepSeek-V4-Flash: 284B total / 13B active params. Your fast, efficient, and economical choice. Try it now at chat.deepseek.com via Expert Mode / Instant Mode. API is updated & available today! 📄 Tech Report: huggingface.co/deepseek-ai/De… 🤗 Open Weights: huggingface.co/collections/de… 1/n