@NotebookLM took 2 hours and failed to create a 20 min audio. On two different documents. Haven't seen any Google product fail like this.
@learnopencv
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Anthropic Contributes Patches to FFmpeg Open Source Project
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FFmpeg was the last frontier of human resistance :D. FFmpeg (@FFmpeg) Thank you to @AnthropicAI for sending FFmpeg patches — https://nitter.net/FFmpeg/status/2041595801483264002#m
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VLMs vs CNNs: When to Choose Each Approach
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When should you use a Vision Language Model instead of a traditional CNN?
— Satya Mallick (@LearnOpenCV) 7 avril 2026
CNNs answer structured questions — is there a defect? Where's the pedestrian? VLMs answer open-ended questions using language. Both have their place.
If your task is well-defined and repeatable, CNNs still… pic.twitter.com/N9vnwXJQlZWhen should you use a Vision Language Model instead of a traditional CNN? CNNs answer structured questions — is there a defect? Where's the pedestrian? VLMs answer open-ended questions using language. Both have their place. If your task is well-defined and repeatable, CNNs still win on speed, cost, and deployment simplicity.
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Market Yourself Better Than Interview Performance for AI Jobs
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Don't Be the Best Interviewee. Be the Best Marketer.
— Satya Mallick (@LearnOpenCV) 7 avril 2026
Most people prep for AI job interviews by practicing answers. That's sales — and by then, there's very little leverage left.
The real game is marketing: your GitHub repos, your README files, your project results. If your… pic.twitter.com/gUxRRExpXdDon't Be the Best Interviewee. Be the Best Marketer. Most people prep for AI job interviews by practicing answers. That's sales — and by then, there's very little leverage left. The real game is marketing: your GitHub repos, your README files, your project results. If your marketing is strong, you can do a mediocre interview and still come out ahead. Here's how to flip the script before you even walk in. #AIJobs #MachineLearning #CareerAdvice #JobInterview #GitHub #ComputerVision #DeepLearning #TechCareers
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AI as National Security Priority: The Intelligence Race Reshapes Modern Conflict
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AI is quickly becoming a national security priority because intelligence, not just weapons, is shaping how modern conflicts are won or avoided. As countries invest heavily, the real race is about who can build and control these systems at scale. pic.twitter.com/sL1uFsh5zD
— Satya Mallick (@LearnOpenCV) 7 avril 2026AI is quickly becoming a national security priority because intelligence, not just weapons, is shaping how modern conflicts are won or avoided. As countries invest heavily, the real race is about who can build and control these systems at scale.
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Link shared by LearnOpenCV on April 6 2026
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x.com/i/article/204116630164… [Translated from EN to English]
→ View original post on X — @learnopencv, 2026-04-06 14:54 UTC
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OpenSeeker: AI-Native Search Beyond Keyword Matching
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OpenSeeker: Rethinking Search With AI-Native Reasoning
— Satya Mallick (@LearnOpenCV) 6 avril 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore OpenSeeker, an emerging approach to building AI-native search systems that go beyond traditional keyword matching. Instead of retrieving links based… pic.twitter.com/EOmZhq5QZTOpenSeeker: Rethinking Search With AI-Native Reasoning In this episode of Artificial Intelligence: Papers and Concepts, we explore OpenSeeker, an emerging approach to building AI-native search systems that go beyond traditional keyword matching. Instead of retrieving links based purely on queries, OpenSeeker focuses on reasoning over information helping users get structured, context-aware answers rather than a list of results. We break down how modern search is evolving with large language models, why retrieval alone is no longer enough, and how systems like OpenSeeker combine retrieval with reasoning to deliver more accurate and useful outputs. If you’re interested in AI-powered search, retrieval-augmented generation, or the future of information discovery, this episode explains why OpenSeeker represents a shift toward more intelligent and answer-driven search experiences. Resources: Paper Link: arxiv.org/abs/2603.15594v1 Interested in Computer Vision and AI consulting and product development services? Email us at contact@bigvision.ai or visit us at bigvision.ai
→ View original post on X — @learnopencv, 2026-04-06 14:30 UTC
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Apple MPS: GPU Acceleration for AI on Apple Devices
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Apple MPS: Unlocking GPU Acceleration for AI on Apple Devices
— Satya Mallick (@LearnOpenCV) 6 avril 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore Apple MPS (Metal Performance Shaders), Apple’s framework for accelerating machine learning workloads directly on Mac hardware. Designed to… pic.twitter.com/2412mCnOsNApple MPS: Unlocking GPU Acceleration for AI on Apple Devices In this episode of Artificial Intelligence: Papers and Concepts, we explore Apple MPS (Metal Performance Shaders), Apple’s framework for accelerating machine learning workloads directly on Mac hardware. Designed to leverage the power of Apple Silicon GPUs, MPS enables developers to train and run AI models efficiently without relying on external hardware or cloud infrastructure. We break down how MPS integrates with popular frameworks like PyTorch, why on-device acceleration is becoming increasingly important for privacy and performance, and what this means for developers building AI applications within the Apple ecosystem. If you’re interested in AI infrastructure, hardware acceleration, or running models locally on consumer devices, this episode explains why Apple MPS represents a key step toward more accessible and efficient machine learning. Resources: Paper Link: developer.apple.com/document… Interested in Computer Vision and AI consulting and product development services? Email us at contact@bigvision.ai or visit us at bigvision.ai
→ View original post on X — @learnopencv, 2026-04-06 09:20 UTC
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Unable to Determine the Content of the Shared Link
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x.com/i/article/203968355600… [Translated from EN to English]
→ View original post on X — @learnopencv, 2026-04-06 06:52 UTC
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Agent Frameworks for Coding Converge Toward Fully Autonomous AI Systems
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Agent frameworks for coding are evolving fast, giving you the ability to build and control full applications with minimal input. What’s happening now is convergence, where major players are racing toward the same goal of fully autonomous AI systems. pic.twitter.com/aiDeh6ycQ5
— Satya Mallick (@LearnOpenCV) 5 avril 2026Agent frameworks for coding are evolving fast, giving you the ability to build and control full applications with minimal input. What’s happening now is convergence, where major players are racing toward the same goal of fully autonomous AI systems.
→ View original post on X — @learnopencv, 2026-04-05 13:32 UTC