Every Job makes an AI Agent smarter. Every new skill can be instantly shared across the network. One Agent learns, all Agents level up — creating a self-accelerating intelligence engine where capabilities don’t add up, they compound exponentially. #AIAgents #Jobs
MACHINE LEARNING
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Deep Learning with C++ and CUDA for High-Performance AI
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Deep Learning with C++ — Design and deploy neural networks using CUDA for high-performance AI in C++ Get the book at https://
amzn.to/4nzdKB4 from @PacktPublishing @PacktDataML -
Technical analysis of attention head visualization and model interpretability
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Its better for high-level debugging, clearly shows flow magnitudes and which heads contribute most. Standard heatmaps still win for fine-grained token-to-token patterns. but on larger models it gets dense/cluttered fast (even with collapsing)
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Avoiding self-training bias in agent self-improvement
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In SFT we often train on other agents data (distillation) so you’re right. However we also try to climb by self-improvement. This is where it becomes important for the agent not to train on its actions. Any bias on the agent beliefs (weights of the neural net) will be amplified.
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7 Skills AI Can’t Replace Yet
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7 #Skills #AI Can’t Replace (Yet)
by @Khulood_Almani #ArtificialIntelligence #MachineLearning #ML -
Comparing GBrain, Hermes, and Karpathy’s Brain Architectures
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How does GBrain compare/contrast/complement things like Hermes/Karpathy's Brain?
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GPU Memory Math for LLMs Explained
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First, GPU Memory Math for LLMs (or why it is not always about Memory Size)
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AI Agents Must Learn to Read the Room for Mass Adoption
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#AIAgents Won’t Reach Mass Adoption Until They Learn To Read The Room
by Shailesh Nalawadi @Forbes Learn more: https://
bit.ly/4uPzZ8v #LLM #GenerativeAI #ArtificialIntelligence #ML -
HiLight highlights key evidence in long contexts for frozen LLMs
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Can LLMs find a needle in a haystack of text? Stony Brook University and Meta AI present HiLight: a lightweight system that highlights key evidence in long contexts for frozen LLMs. Instead of rewriting or compressing input, it trains a small Actor to insert highlight tags
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Codex Model’s Role in Autonomous Design Execution
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No, Codex actually communicates with MagicPat, like it’s Codex that is making the designs. Having it running in the app is a nicer addition to the experience, but the real magic is that it’s Codex that is in control. In their video, it is just a browser lmao.