MoonDream 3 is impressive, but the API surface is pretty messy right now. There are three ways to use MoonDream 3 right now. Option 1 (Hugging Face Transformers – model download only) You need a Hugging Face token to download the model. It is gated. With some tweaks, this
@learnopencv
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Moondream 3 MPS Compatibility Tweaks for Apple Silicon
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Moondream 3 doesn't work on Apple MPS out of the box but a a couple of tweaks can make it work.
— Satya Mallick (@LearnOpenCV) 28 mars 2026
1. use float16 on MPS
2. disable flex decoding on MPS (and CPU fallback)
You can also make it work on the CPU, but that option is really bad. It is about 20x slower than MPS. https://t.co/GbdBWMEnVXMoondream 3 doesn't work on Apple MPS out of the box but a a couple of tweaks can make it work. 1. use float16 on MPS
2. disable flex decoding on MPS (and CPU fallback) You can also make it work on the CPU, but that option is really bad. It is about 20x slower than MPS. -
AI Agents: Beyond Chatbots to Autonomous Problem-Solving Systems
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We are moving past the era of chatbots and into a world where AI agents break down problems and execute commands across APIs and databases. Software development has fundamentally changed because you are no longer just building interfaces for human users but creating systems that… pic.twitter.com/6XwM2KZqPW
— Satya Mallick (@LearnOpenCV) 27 mars 2026We are moving past the era of chatbots and into a world where AI agents break down problems and execute commands across APIs and databases. Software development has fundamentally changed because you are no longer just building interfaces for human users but creating systems that
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ReCoSplat: 3D Scene Reconstruction From Sparse Visual Data
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ReCoSplat: Reconstructing 3D Worlds From Sparse Visual Data
— Satya Mallick (@LearnOpenCV) 27 mars 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore ReCoSplat, a novel approach to 3D scene reconstruction that leverages sparse visual inputs to generate detailed spatial representations.… pic.twitter.com/aCZsG6IH4yReCoSplat: Reconstructing 3D Worlds From Sparse Visual Data In this episode of Artificial Intelligence: Papers and Concepts, we explore ReCoSplat, a novel approach to 3D scene reconstruction that leverages sparse visual inputs to generate detailed spatial representations.
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Vibe Coding: Productivity Illusion or Addictive Building Cycle
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💻 Vibe Coding: Productivity or Addiction?
— Satya Mallick (@LearnOpenCV) 26 mars 2026
Vibe coding doesn’t save time it consumes it. When building becomes effortless, ambition grows, projects multiply, and sleep disappears. As Andrej Karpathy noted, it’s less about efficiency and more about being stuck in constant build… pic.twitter.com/lvcOdwe1uOVibe Coding: Productivity or Addiction? Vibe coding doesn’t save time it consumes it. When building becomes effortless, ambition grows, projects multiply, and sleep disappears. As Andrej Karpathy noted, it’s less about efficiency and more about being stuck in constant build
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Space-Based AI Infrastructure: The Future of Supercomputing Beyond Earth
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The future of AI infrastructure may move off the planet entirely as space offers continuous solar energy and a natural vacuum for radiating massive GPU heat. If launch costs continue to fall the biggest supercomputers will no longer sit in terrestrial data centers but will orbit… pic.twitter.com/6Can9U9iJ1
— Satya Mallick (@LearnOpenCV) 26 mars 2026The future of AI infrastructure may move off the planet entirely as space offers continuous solar energy and a natural vacuum for radiating massive GPU heat. If launch costs continue to fall the biggest supercomputers will no longer sit in terrestrial data centers but will orbit
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Video Understanding: Teaching AI to Interpret Motion and Time
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Video Understanding: Teaching AI to Make Sense of Motion and Time
— Satya Mallick (@LearnOpenCV) 26 mars 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore Video Understanding, a rapidly evolving area of AI focused on helping models interpret not just images, but sequences of events over… pic.twitter.com/QIl9DGcz4vUnderstanding: Teaching AI to Make Sense of Motion and Time In this episode of Artificial Intelligence: Papers and Concepts, we explore Understanding, a rapidly evolving area of AI focused on helping models interpret not just images, but sequences of events over
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YOLOv3: Speed Meets Accuracy in Object Detection
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⚡YOLOv3: Speed Meets Accuracy in Object Detection
— Satya Mallick (@LearnOpenCV) 26 mars 2026
YOLO changed the game with fast detection but accuracy needed a boost. In 2018, YOLOv3 arrived with Darknet-53, residual connections, and multi-scale predictions.
It improved small object detection, enabled multi-label… pic.twitter.com/FNfCXKmaSPYOLOv3: Speed Meets Accuracy in Object Detection YOLO changed the game with fast detection but accuracy needed a boost. In 2018, YOLOv3 arrived with Darknet-53, residual connections, and multi-scale predictions. It improved small object detection, enabled multi-label
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Penguin-VL: Vision-Language Model With Advanced Reasoning Capabilities
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Penguin-VL: Advancing Vision–Language Models With Stronger Reasoning
— Satya Mallick (@LearnOpenCV) 25 mars 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore Penguin-VL, a new vision–language model designed to improve how AI systems understand and reason across images and text. Moving beyond… pic.twitter.com/nJXr2ILIZoPenguin-VL: Advancing Vision–Language Models With Stronger Reasoning In this episode of Artificial Intelligence: Papers and Concepts, we explore Penguin-VL, a new vision–language model designed to improve how AI systems understand and reason across images and text. Moving beyond
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RetinaNet and Focal Loss: Solving Class Imbalance in Object Detection
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🎯 RetinaNet & Focal Loss: Fixing Class Imbalance in Object Detection
— Satya Mallick (@LearnOpenCV) 25 mars 2026
Single stage detectors were fast but struggled with class imbalance. In 2017, researchers at Facebook AI introduced RetinaNet with a new loss function Focal Loss.
By down-weighting easy background examples… pic.twitter.com/1gQ9p8Ku7jRetinaNet & Focal Loss: Fixing Class Imbalance in Object Detection Single stage detectors were fast but struggled with class imbalance. In 2017, researchers at Facebook AI introduced RetinaNet with a new loss function Focal Loss. By down-weighting easy background examples