By this weekend, xAI will have three Grok Build models in training simultaneously
MACHINE LEARNING
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GLM-5-Turbo performance and inference speed discussion
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Inference speed is the new battlefield, and GLM-5-Turbo is heavily armed. Blown away by the initial specs here. Can't wait to test the API.
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RULE Framework Tackles Noisy Multimodal Entity Alignment
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Is inconsistent data making multi-modal entity alignment a nightmare? Researchers at Sichuan University, China, have a breakthrough! They introduce RULE, a framework designed to conquer "Dual-level Noisy Correspondence" (DNC). RULE intelligently assesses the reliability of
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Comprehensive Roster of Current and Upcoming Large Language Models
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Here is the full roster! – Llama 3 8B
– OLMo 2 7B
– DeepSeek V3
– DeepSeek R1
– Gemma 3 27B
– Mistral Small 3.1 24B
– Llama 4 Maverick
– Qwen3 235B-A22B
– Qwen3 32B
– Qwen3 8B
– Qwen3 4B
– SmolLM3 3B
– Kimi K2
– GLM-4.5 355B
– GPT-OSS 20B
– GPT-OSS 120B
– Grok 2.5 270B
– Qwen3 -
Comprehensive Visual LLM Architecture Gallery Released
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Someone built the ultimate visual LLM Architecture Gallery, packing 38 models from 2024-2026 into a single hub It completely breaks down the complexity for you. Inside:
→ Annotated diagrams
→ Key design choices
→ Actual code implementations link to the gallery in ↓ -
Attention Residuals: Selective Computation in Deeper Transformers
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“Attention Residuals” is now available on AlphaXiv! In standard transformer, every layer just inherits an equal sum of all earlier layers, so as models get deeper, useful computations get diluted instead of being selectively reused. The research team at @Kimi_Moonshot proposes
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AI Deep Learning Framework Advances Sustainable Bio-Oil Prediction
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Thrilled to share that I’m a co-author of a newly published paper in Scientific Reports — a Nature Portfolio, Q1-ranked journal — highlighting the power of AI-driven innovation in sustainable energy systems. Our paper, “Deep learning enhanced prediction framework for bio oil
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OpenAI Introduces Subagents in Codex for Parallel Codebase Management
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🚨 @OpenAI just launched Subagents in Codex, and it changes how we handle massive codebases.
— Charly Wargnier (@DataChaz) 17 mars 2026
You no longer have to rely on a single agent.
You can now split complex features across multiple specialized workers running in parallel 🔥
By spinning up Codex’s new custom subagents,… pic.twitter.com/4JXFeavgXA@OpenAI just launched Subagents in Codex, and it changes how we handle massive codebases. You no longer have to rely on a single agent. You can now split complex features across multiple specialized workers running in parallel By spinning up Codex’s new custom subagents,
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MaxRL: Rethinking Reinforcement Learning Optimization
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RL Isn’t Actually Optimizing What We Think, And That’s a Problem Come join us for this AI4Science talk: Maximum Likelihood Reinforcement Learning (MaxRL). In this session, the author of MaxRL @FahimTajwar10 will cover their paper that takes a step back and asks a fundamental
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AI-Driven Scientific Discovery Using Bayesian Surprise Methods
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Curious about how AI is starting to do science on its own? Come join us for this AI4Science talk: Data-driven Discovery at Ai2, Open-ended Scientific Discovery via Bayesian Surprise. At Ai2, this line of work has been building toward the idea of using AI to extract genuinely