May 11 · 12 min read · Why LLM Inference Costs Are the Wrong Unit of Measure Every time an LLM solves a problem, it forgets how it solved it. Feed the model the same class of problem tomorrow, and it starts from scratch — burning the same tokens, taking the same computatio...
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Apr 13 · 11 min read · Artificial intelligence has an energy problem that could define its limits. Training a single frontier large language model can emit as much carbon as five average American cars produce over their entire lifetimes. Running production AI systems at sc...
Join discussionApr 4 · 4 min read · We are used to thinking of AI as a tool—a fancy autocomplete that helps us write code, generate images, or summarize emails. But what happens if you remove the human from the driver's seat entirely? W
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Mar 21 · 7 min read · The RL Renaissance: Why Three Papers in One Day Signal the Death of Imitation Learning for AI Agents Reinforcement learning is the future of agent training, and imitation learning is a dead end. Three independent papers all converge on the same thesi...
Join discussionMar 21 · 7 min read · The Future of Coding Agents Isn't Writing Code. It's Debugging It. FAIR's "Neural Debuggers" paper quietly redefines what LLM-powered development should look like. Every AI coding tool today is obsessed with generation. Type a comment, get a functio...
Join discussionMar 21 · 7 min read · Most of Your Reasoning Model's Thinking Is Actively Harmful 57% of chain-of-thought tokens make your model dumber. Here's the proof and the dead-simple fix. Here's a number that should make you uncomfortable: on MATH-500, you can delete 57-59% of a ...
Join discussionFeb 9 · 8 min read · You hear about new AI every day. Some write stories. Some make pictures. Some chat with you. But Genie 3 is different. It doesn't just create things - it creates entire worlds. Let's compare it to other AI and see why it's special. How Is Genie 3 Dif...
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