Isaiah Williams

Yann LeCun just raised $1.03B. Not for LLMs. For what comes after them. My first thought wasn't "wow." It was: how many founders are about to miss this? Most of us are sprinting to build on top of GPT or Claude architecture. LeCun just bet a billion dollars that architecture is wrong. World models don't predict text. They understand causality. Physics. How things actually work. Not just the next token. It has happened to me over and over again. Every time I got comfortable with the current stack, the stack moved. This is the stack moving again. The founders who win the next decade are already asking different questions. Not "how do I build faster on this?" "What comes after this?" $1.03B says the AI era isn't settled. It's barely started.

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IBM just published their roadmap for bringing first-class PyTorch support to the Spyre Accelerator, fully ecosystem-first. Instead of building a custom fork, they’re extending torch.inductor with tile-based layouts, multi-core work division, and scratchpad optimizations so Spyre’s dataflow architecture plays nice with PyTorch’s compiler. Key pieces: • SuperDSC → KernelTile IR (KTIR) as a community-aligned spec for other dataflow accelerators • Full out-of-tree device registration with <5% overhead • vLLM integration with a new Spyre attention backend (no sequence-length constraints) • Layered testing + contributions back (OpenReg primitives, KTIR spec, CI patterns) This is the kind of thoughtful work that makes new hardware actually usable for real PyTorch teams, especially in healthcare AI where you need reliable, portable inference on diverse accelerators. Full technical note: https://lnkd.in/gnvg-r7F #PyTorch #AIInfrastructure #Hardware #MachineLearning

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🤖RLAD Training LLMs to Discover Abstractions for Solving Reasoning Problems. 🔍 Motivation: How do we move large language models (LLMs) beyond pattern matching—toward algorithmic, abstract reasoning on hard problems? Most current RL post-training approaches create longer chains of thought, but they often drift into verbose or degenerate exploration, missing true reasoning depth. 📄 Solution: RLAD introduces 'reasoning abstractions'—concise, natural language descriptions of procedural and factual knowledge that guide models in learning successful reasoning strategies. The approach trains two LLMs: one to generate abstractions and another to solve problems using those abstractions, via a novel RL setup. Crucially, abstractions act as high-level subgoals or skills, helping models explore diverse solution paths and generalize to harder tasks. 🔑 Results: RLAD achieves up to 44% improvement on math reasoning benchmarks (AIME 2025, DeepScaleR Hard, AMC 2023) over state-of-the-art long chain-of-thought RL methods. Moreover, the benefits scale with more compute allocated to abstraction generation versus solution sampling—a key insight for practical deployment. 💡 Implications: Abstraction-driven training broadens the reasoning abilities of LLMs, unlocking new horizons for model interpretability, strategy diversity, and generalization across complex domains. This direction is especially relevant as scaling chain-of-thought length yields diminishing returns. (Attached: Original research PDF)

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$1B+ bet on the next frontier of AI: Physical & Spatial Intelligence Today, Yann LeCun’s new startup, AMI - Advanced Machine Intelligence, announced a massive $1.03 billion funding round to challenge the dominance of Large Language Models (LLMs). Current AI is "trapped" in a digital box of text. To reach human-level autonomy, AI needs to understand the physical world. It needs to know that if you push an object, it falls; it needs to navigate a room without a map; it needs to plan complex tasks in real-time. From Digital to Physical: AI is moving into robotics, smart glasses (like Ray-Ban Meta), and industrial automation. From Prediction to Planning: Moving away from probabilistic guessing toward goal-oriented reasoning. The "Ami" (Friend) Approach: Building AI that is controllable, safe, and grounded in reality. The next frontier isn't just "generative"—it’s spatial and physical. We are moving from AI that talks to us, to AI that works alongside us in the physical world. Congrats to the AMI team on this milestone. The era of "World Models" is here. https://lnkd.in/gE99YfGJ #AI #Robotics #MachineLearning #YannLeCun #AMI #Innovation #SpatialIntelligence #TechNews

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Most LLMs generate code. Claude helps you understand it. As one engineer put it: “We don’t use Claude to generate code. We use Claude to think.” Anthropic’s latest report shows how Claude is transforming coding workflows — not just automating tasks, but helping teams reason through complexity, explore better designs, and understand legacy systems. Claude acts like a thoughtful pair programmer: • It asks clarifying questions before jumping to conclusions • It keeps context across long sessions and massive files • It writes clean, structured output with helpful comments and tests Key insights from Anthropic: • 50+ use cases • 40+ team-level productivity impacts • 30+ practical prompts and techniques Claude is already being used across: 1. Data infrastructure 2. Product development 3. Security engineering 4. Inference 5. Data science and visualization 6. API engineering 7. Growth marketing 8. Product design 9. Reinforcement learning 10. Legal and compliance We cut through the noise each in the 5 in 25 bi-monthly newsletter with Nicolas. Link to subscribe in the comment section.

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A developer in San Francisco selects MiniMax as her inference backend. The request crosses the Pacific, gets processed in a Chinese data center, returns in a second. She pays $0.30 per million tokens. Claude Opus would cost $5.00 for the same volume. No customs declaration. No tariffs. No entry in any trade database. Chinese tech media calls this Token 出海: Token Export. This month’s OpenRouter data showed it crossing a threshold: Chinese models surpassed American ones in total token consumption for the first time. This did not happen overnight. I spent February tracing the structural forces behind it: efficiency architectures, energy cost gaps, and a competitive intensity that pushes pricing below rational margins. Nvidia’s record quarter and falling stock price are part of the same story. China’s Compute Bet explains the system. https://lnkd.in/ghEBwk-s The Export That Tariffs Can’t Touch explains what happens when that system meets the global market. https://lnkd.in/gh_ajmmZ

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