BREAKING : Anthropic released Claude Opus 4.5 and it scores 80.9% on SWE bench verified! Where is the wall?
MACHINE LEARNING
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AI Agents: Goal-Driven Systems Beyond Chatbots
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AI Agents aren’t just chatbots. They’re goal-driven systems that sense → reason → plan → act → learn. Key building blocks:
• Goals + tasks
• System prompt + oversight
• Memory (short + long term)
• Tools (web, code, apps, data, functions) The real shift: AI moving from -
Nested Learning: The Illusion of Deep Learning Architectures
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Nested Learning: The Illusion of Deep Learning Architectures Behrouz et al.: https://
openreview.net/forum?id=nbMeR
vNb7A
… #ArtificialIntelligence #DeepLearning #MachineLearning -
The AI boom explained through 16 comprehensive charts
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The AI boom, as explained in 16 charts: https://
bit.ly/4oNR8fE -
Data Avalanche: Combining Model-Free and Model-Based Methods for Robot Tasks
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Data Flywheel -> Data Avalanche: Thx to Leslie Kaebling and @Pulkitology Agrawal for suggesting "avalanche" as a better metaphor than "flywheel" for combining model-free + model-based methods to bootstrap a specific robot task to bootstrap & amplify on-policy data collection.
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24 Simultaneous YOLO Streams on Single Edge AI Card
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24 simultaneous #YOLO streams on a single #edgeAI card. That's possible, today. Independent testing shows 875 FPS on YOLOv8 whilst using less than 1 Joule per frame. Proper edge AI performance for multi-camera retail and security systems. Details: https://
eu1.hubs.ly/H0pXdz00 -
Softmax in Base 2: Fixed Point Hardware Optimization
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I wonder if doing softmax in base 2 instead of base e could be a useful optimization on some fixed point hardware. Prepend a 1 to the fractional part and shift left by the integral part as a close-ish approximation of 2^fixed_point.
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Explaining the Internal Reasoning Mechanism of AI Models
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here’s what actually happens inside:
the model maintains three things: • x = the question
• y = current answer
• z = reasoning state (like internal scratch work) each cycle: update z based on x, y, and previous z. then update y based on the new z. think of solving a Sudoku. -
Small 7M Parameter Model Outperforms Large DeepSeek R1 on Reasoning
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a 7M parameter model just beat DeepSeek R1’s 671 billion parameters on hard reasoning tasks. 45% vs 15.8%. trained in hours. fits in 28MB. runs on a single GPU. the trick is that it thinks twice. LLMs generate answers in one pass. make a mistake early, the whole thing