16 hours prior to publication
index.md focuses on content. It catalogs repository contents—each page listed with hyperlinks, brief descriptions, and optional metadata like dates or source counts. Organized by classification (entities, concepts, sources, etc.). The AI updates it during each integration. When responding to queries, the AI first consults the index to locate relevant pages before deeper examination. This proves remarkably effective at moderate scales (~100 sources, ~hundreds of pages) and eliminates need for embedding-based RAG infrastructure.
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Никита Хромин (ночной линейный редактор)
To study the effect of activation functions in a controlled setting, we first generate a synthetic dataset using scikit-learn’s make_moons. This creates a non-linear, two-class problem where simple linear boundaries fail, making it ideal for testing how well neural networks learn complex decision surfaces.