Week 47

The week of November 21-27, 2022 (W47) was exceptionally quiet in the AI community, with only one piece of content recorded across all tracked sources. This single video from Andrej Karpathy dominated the week's output, making it a focused period on deep learning architecture education rather than a diverse news cycle. The absence of GitHub repositories, blog posts, Bluesky discussions, and X (Twitter) posts suggests either a lull in public releases or that major developments were concentrated in private research or corporate settings during this Thanksgiving week in the United States. Andrej Karpathy's YouTube video 'Building makemore Part 5: Building a WaveNet' served as the sole educational beacon, continuing his popular series on character-level language modeling. With 268,962 views, this technical tutorial demonstrated how to evolve a simple 2-layer MLP into a deeper, tree-like convolutional neural network architecture, specifically arriving at a WaveNet structure. The video's substantial view count indicates strong ongoing interest in hands-on neural network implementation tutorials from authoritative figures in the field. Given the single-source nature of this week's content, no cross-source patterns or community discussions could be identified. The lack of GitHub activity suggests no major open-source AI projects gained significant traction during this period, while the absence of social media posts from AI leaders on Bluesky and X indicates minimal public commentary on industry developments. This week stands in contrast to more active periods where multiple sources typically reinforce emerging trends through coordinated discussion across platforms. The focused educational content from Karpathy represents a continuation of his commitment to making advanced AI concepts accessible through practical implementation. While the broader AI community appeared quiet across other channels, this deep dive into convolutional architectures and WaveNet implementation provided valuable learning material for practitioners interested in the foundational building blocks of modern neural networks, particularly in sequence modeling applications.