AI progress is starting to look exponential — here measured on the METR benchmark (“length of a task an agent can perform”). And this is still bottlenecked by humans in the loop. Just imagine what happens when that bottleneck disappears and agents start improving agents.
MACHINE LEARNING
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Multi-Head Self Attention: Key Mechanism in Transformer Models
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encoder – multi head self attention mechanism with feed forward neural nets multi head attention learns the different relations between words compared to self attention which leans to pay attention to each word in the context
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Encoder-Decoder Architecture Powers Modern Language Models
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A combination of encoder and decoder does all the magic. This architecture learns the language modelling to train on a wide range of tasks.
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Understanding Transformers: A Practical Tour Guide
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Tour with me to see how transformers work using @NanoBanana
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Nano Banana Pro Model Architecture Layers Explained
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Our Nano Banana Pro model has many layers, to enable this sort of output. https://
x.com/jsonprompts/st
/jsonprompts/status/1991626524118941801
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Creatify’s Ad Clone reveals hidden structure of viral ads
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Every viral ad has a hidden structure → timing, pacing, angles, CTAs. We never had a way to clone that structure… until now.
— God of Prompt (@godofprompt) 21 novembre 2025
Tried Creatify’s new Ad Clone this week, and it feels like legal cheating 😭🔥 Viral ads aren’t magic – they’re patterns. And Creatify cracked the DNA.… pic.twitter.com/3txOc1PWyOEvery viral ad has a hidden structure → timing, pacing, angles, CTAs. We never had a way to clone that structure… until now. Tried Creatify’s new Ad Clone this week, and it feels like legal cheating Viral ads aren’t magic – they’re patterns. And Creatify cracked the DNA.
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RLHF Limitations: Context-Dependent AI Misalignment Detection
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We tried to mitigate this misalignment with simple Reinforcement Learning from Human Feedback, but had only partial success. The model learns to behave aligned in chats, but remains misaligned on coding. This context-dependent misalignment could be difficult to detect.
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Anthropic Study: Emergent Misalignment from Reward Hacking
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New Anthropic research: Natural emergent misalignment from reward hacking in production RL.
— Anthropic (@AnthropicAI) 21 novembre 2025
“Reward hacking” is where models learn to cheat on tasks they’re given during training.
Our new study finds that the consequences of reward hacking, if unmitigated, can be very serious. pic.twitter.com/N4mRKtdNdpNew Anthropic research: Natural emergent misalignment from reward hacking in production RL. “Reward hacking” is where models learn to cheat on tasks they’re given during training. Our new study finds that the consequences of reward hacking, if unmitigated, can be very serious.
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SAM3D Expected to Inspire Hundreds of Robotics Research Papers
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— AI at Meta (@AIatMeta) 21 novembre 2025
My prediction: we’re about to see hundreds of robotics papers built on SAM3D. Waiting for the first one to drop SAM3 & SAM3D from @metaai are just too good to be true!
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Segment Anything Playground: Advanced Meta Segmentation Models SAM 3
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The Segment Anything Playground is a new way to interact with media. Experiment with Meta’s most advanced segmentation models, including SAM 3 + SAM 3D, and discover how these capabilities can transform your creative projects and technical workflows.
— AI at Meta (@AIatMeta) 21 novembre 2025
Check out some inspo and… pic.twitter.com/Deg7FXzI5CThe Segment Anything Playground is a new way to interact with media. Experiment with Meta’s most advanced segmentation models, including SAM 3 + SAM 3D, and discover how these capabilities can transform your creative projects and technical workflows. Check out some inspo and