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Source channel @githubtrending · Post #14926 · Jul 8

#jupyter_notebook#artificial_intelligence#book#large_language_models#llm#llms#oreilly#oreilly_books You can learn how to use Large Language Models (LLMs) effectively through the book *Hands-On Large Language Models* by Jay Alammar and Maarten Grootendorst. This book uses nearly 300 custom illustrations to explain key concepts and practical tools for working with LLMs, including tokenization, transformers, prompt engineering, fine-tuning, and advanced text generation. It also provides runnable code examples in Google Colab, making it easy to practice and apply what you learn. This resource helps you understand and build your own LLM applications confidently, saving you time and effort in mastering complex AI technology. It’s highly recommended for anyone wanting hands-on experience with LLMs. https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

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@ai_and_law · Post #746 · 01/20/2026, 08:04 AM

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🇨🇦AI Defamation Risk: Canadian Artist Prepares Lawsuit After Google Error Canadian musician Ashley MacIsaac says a Google AI-generated summary falsely labeled him a convicted sex offender, leading a concert venue to cancel his show. MacIsaac told the Canadian Press he believes the system confused him with another individual in Canada who has similar charges, but the error directly cost him income and harmed his reputation. MacIsaac is now preparing to sue Google, arguing that the misinformation amounts to defamation and created real-world risks, including potential issues at border controls. He stated that AI companies must be held accountable for what their systems publish and what harms they can reasonably prevent, noting that he is unlikely to be the last person affected by such errors. The incident underscores how AI-generated summaries can produce high-impact false statements about individuals, with immediate legal, economic, and personal consequences, even when no human editorial judgment is involved. #AI#AIDefamation#Liability#GenerativeAI#ReputationRisk

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