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43 articles
Learn to implement and experiment with fundamental AI concepts including neural networks, transformers, and attention mechanisms through hands-on coding exercises.
This explainer explores how advanced AI technologies enable the creation of fully AI-generated films like 'Dreams of Violets,' examining the neural architectures, data requirements, and creative implications of this breakthrough.
Learn to build a practical fact-checking tool using Python and Hugging Face Transformers that can verify claims against evidence using state-of-the-art NLP models.
Learn to build a high-precision retrieve-and-rerank pipeline using the zeroentropy/zerank-2-reranker model, combining fast retrieval with advanced reranking for improved search quality.
Learn to build an AI interpretability tool that analyzes how language models make decisions by examining attention patterns and gradients, following principles discussed by Anthropic's Chris Olah.
Learn to build an AI-powered sentiment analysis tool that can process text and determine sentiment, similar to what companies like Samsung might use to understand labor tensions during AI implementation.
Learn how to work with large language models that support long context windows, similar to Alibaba's Qwen3.7-Max. This beginner-friendly tutorial teaches you to prepare inputs, generate responses, and optimize memory usage for handling long-horizon tasks.
This explainer explores how AI video generation serves as a pathway to world models, the theoretical framework for creating general-purpose AI systems that understand and predict complex environments.
Learn how to implement basic LLM distillation techniques to train smaller, more efficient models that mimic larger pre-trained models.
This explainer examines how ChatGPT's Chinese deployment exhibits systematic linguistic tics that differ from its English version, revealing important insights about multilingual LLM behavior and training data effects.
Learn how to implement multi-token prediction for text generation using Google's Gemma 4 model, demonstrating how generating multiple tokens simultaneously can speed up text generation by up to three times.
Learn how to set up and use basic AI classification models using Python and the Hugging Face Transformers library, inspired by recent Pentagon AI partnerships.
