I published an episode on @ivoox: "#1104: When Everyone Uses AI, Only Different Thinkers Stand Out #podcast go.ivoox.com/rf/170014617?ut… [Translated from EN to English]
→ View original post on X — @juanmerodio, 2026-04-06 05:02 UTC
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3D Deep Learning Computer Vision Models with PyTorch3D
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3D Deep Learning with Python — Design and develop Computer Vision models with 3D data using PyTorch3D: http://
amzn.to/491yDwh v/ @PacktDataML ————
#AI #MachineLearning #ML #DataScience #DataScientist #PyTorch -
AI’s True Goal: Power Concentration Over Human Progress
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What if the real goal of AI isn’t what we’ve been told?
— Pascal Bornet (@pascal_bornet) 6 avril 2026
Co-founder of the Center for Humane Technology, Tristan Harris, argues that the ultimate goal of many AI technocrats is not just to help humanity… but to advance their own pursuit of money and power.
That framing changes… pic.twitter.com/ETfhSPdEcgWhat if the real goal of AI isn’t what we’ve been told? Co-founder of the Center for Humane Technology, Tristan Harris, argues that the ultimate goal of many AI technocrats is not just to help humanity… but to advance their own pursuit of money and power. That framing changes how you interpret everything else. To the public, AI is presented as progress: More creativity. More freedom. Better work. But internally, the incentive structure is different. AI offers productivity without the ongoing cost of human labor. What stands out to me is how this shifts the equation. If systems can replace large parts of human work, value doesn’t disappear — it concentrates. Fewer workers. More centralized control. Greater accumulation at the top. The first time you connect these dynamics, the trajectory becomes clearer. This isn’t just a technological shift. It’s an economic one. And this is where things start to matter. Because the real question is no longer what AI can do. It’s who benefits from what it does. So here’s something I’d be curious to hear from you: As AI continues to scale, how should we think about power, ownership, and value distribution? #ArtificialIntelligence #AI #FutureOfWork #Economics #Innovation
→ View original post on X — @pascal_bornet, 2026-04-06 05:01 UTC
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Reinforcement Learning Foundational Textbook Second Edition Released
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Reinforcement Learning foundational book (2nd edition of this classic mathematical textbook): http://
amzn.to/3UtbeAa
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#DataScience #AI #MachineLearning #ML #Mathematics #Gamification -
Sakana AI Hosting Software and Solution Engineer Recruitment Briefing
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We are hosting a recruitment briefing session for Software Engineers and Solution Engineers 🐟🐟 Event Details
🗓️ April 21st (Tuesday) 19:00-21:45 (including networking session)
📝 Venue: Azabudai Hills At this event, we will discuss how Sakana AI converts AI technology into customer value, our technical approach, and details about the currently open positions: Software Engineer and Solution Engineer. We welcome the following candidates: ・Those who want to challenge full-stack development centered on AI, from Frontend to Infrastructure
・Those interested in solving complex technical challenges in the enterprise domain with AI
・Those interested in Sakana AI's engineers, culture, and use cases of AI agents
・Those interested in working at Sakana AI but don't have a clear picture of specific project content or work style Please feel free to apply. For more details, see below 👇
https://connpass.com/event/389292/ [Translated from EN to English]→ View original post on X — @sakanaailabs, 2026-04-06 04:57 UTC
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Time Series Analysis with Python Cookbook Second Edition
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New release from @PacktDataML at http://
amzn.to/4sjCbni "Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection" [2nd Edition; 812 pages] -
Hands-On AI for IoT: Expert Machine Learning Deep Learning Techniques
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Artificial Intelligence of Things AIoT book → “Hands-On AI for IoT” — Expert Machine Learning & Deep Learning techniques for developing smarter IoT systems [2nd Ed.]: http://
amzn.to/3YAu6hS 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
Leverage the power of Python libraries such as TensorFlow -
Researchers Introduce Meta-Harness for LLM Application Optimization
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/5 Researchers from Stanford and MIT introduce Meta-Harness: An end-to-end optimization of model harnesses Meta-Harness is a system that automatically searches for better harness code for LLM applications. They argue that LLM performance depends not only on model weights, but
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Anthropic Research Identifies Emotional Activation Patterns in Claude Sonnet 4.5
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/4 Anthropic reveals Claude uses “desperate” vectors to influence decisions Anthropic looks inside Claude Sonnet 4.5 and finds something unexpected: the model stores patterns that act like emotions. These are not feelings, but measurable activation patterns inside the network.
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Claude Code Throws Error When Analyzing Its Own Source
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Claude Code now throws an error if you use it to try and analyze the Claude Code source