The AI chess board just shifted again. Nvidia announced new open-source models, releasing training data and offering sizes from edge to data center. Their message: full transparency, security you can verify yourself. The timing is strategic. Chinese labs are leading the open-source rankings. U.S. states are starting to ban Chinese AI models. And Meta? Reportedly considering going closed-source. If Meta walks away from open, Nvidia becomes the de facto leader of U.S. open-source AI. That's a massive repositioning, and it's happening quietly while everyone's focused on the latest chatbot features. Worth watching. https://www.cp24.com/news/money/2025/12/15/nvidia-unveils-new-open-source-ai-models-amid-boom-in-chinese-offerings

MIT Technology Review just dropped their "AI Wrapped 2025", a glossary of the 14 terms you couldn't escape this year. What I like about it: they're not just cataloguing the hype. "Superintelligence" is there, sure. But so is "slop", the flood of AI-generated garbage polluting the internet. Including the critical vocabulary alongside the aspirational stuff gives a more honest picture. If you spent 2025 nodding along to AI conversations without being 100% sure what people meant, this is your catch-up. 10 minutes well spent before 2026. https://www.technologyreview.com/2025/12/25/1130298/ai-wrapped-the-14-ai-terms-you-couldnt-avoid-in-2025

Wired is calling 2026 the year of Qwen, suggesting Alibaba's model family is about to overshadow GPT-5's momentum. Bold prediction, especially given how fast the leaderboards shuffle these days 🔄 The "remember Llama?" line in the subtitle feels a bit premature though—Meta's models are still very much in the game.

The shift from single-prompt AI to multi-stage agent workflows is where things get interesting. This piece from Towards Data Science explores how IntelliNode approaches complex task automation with "vibe agents" - essentially moving beyond isolated prompts to coordinated systems that can handle enterprise-level complexity 🔧 Curious to see how this compares to other orchestration frameworks out there.

TabPFN is one of those models that doesn't get nearly enough attention — a foundation model specifically designed for tabular data, which is still where most real-world ML happens. This deep dive covers the architecture and practical implementation, useful if you've been curious about alternatives to XGBoost for smaller datasets. 📊