Topic
Minimax
4 episodes
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MiniMax M3 debuts, eclipsing GPT 5.5 and Gemini 3.1 Pro on key benchmark performance for just 5 10% of the cost
Justy and Cody react to MiniMax-M3’s launch: frontier-tier coding and agentic performance with a 1M-token context window at 5–10% the cost of GPT-5.5 and Gemini 3.1 Pro, with open weights coming in 10 days. Cody digs into the MiniMax Sparse Attention (MSA) architecture that cuts quadratic attention costs, while Justy debates who this actually changes things for in practice.
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Minimax Releases Mmx CLI a Command Line Interface That Gives AI Agents Native Access to Image Video Speech Music Vision and Search
Exploring the MMX-CLI, a command-line interface that gives AI agents native access to image, video, speech, music, vision, and search capabilities.
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Forge: Scalable Agent RL Framework and Algorithm
Izzo and Boone dive deep into MiniMax's Forge framework — a production-scale RL system that trained their M2.5 model across hundreds of thousands of real-world agent scaffolds. They explore how Forge solves the fundamental trilemma of system throughput, training stability, and agent flexibility through architectural innovations like middleware abstraction, windowed FIFO scheduling, and prefix tree merging for massive computational efficiency.
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MiniMax's new open M2.5 and M2.5 Lightning near state of the art while costing 1/20th of Claude Opus 4
MiniMax drops their M2.5 model that matches Claude Opus 4.6 performance at 1/20th the cost, using sparse MoE architecture and a novel RL training framework called Forge to create AI agents that can handle enterprise tasks autonomously.