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docs: Update demo number references after renumbering
Update all documentation to reflect new demo numbering:
- Chatbot Evolution is now Demo 02 (was 15)
- All other demos incremented by 1 (02→03, 03→04, etc.)
Files updated:
- README.md: Featured demo links
- slides/README.md: Demo list and inline references
- slides/week1/lecture4.md/.html: Chatbot evolution link
- TEST_COVERAGE.md: Demo test status numbers
- .gitignore: Demo directory paths
- .github/workflows/deploy-demos.yml: Deployment URLs
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <[email protected]>
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@@ -15,10 +15,10 @@ Learn by doing with our collection of **15 interactive web demonstrations** that
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### Featured Demos
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-**[ELIZA Chatbot](https://contextlab.github.io/llm-course/demos/01-eliza/)** - Chat with the groundbreaking 1966 pattern-matching therapist and experience the ELIZA effect firsthand
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-**[Embeddings Visualization](https://contextlab.github.io/llm-course/demos/03-embeddings/)** - Explore word embeddings in interactive 3D space, rotate and zoom to discover semantic relationships
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-**[Transformer Explorer](https://contextlab.github.io/llm-course/demos/05-transformer/)** - Step through the transformer architecture layer by layer with real-time visualizations of attention, feedforward networks, and residual connections
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-**[Tokenization Explorer](https://contextlab.github.io/llm-course/demos/02-tokenization/)** - Compare BPE, WordPiece, and SentencePiece algorithms side-by-side
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-**[Attention Mechanism](https://contextlab.github.io/llm-course/demos/04-attention/)** - Watch attention weights update in real-time as you modify input sequences
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-**[Embeddings Visualization](https://contextlab.github.io/llm-course/demos/04-embeddings/)** - Explore word embeddings in interactive 3D space, rotate and zoom to discover semantic relationships
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-**[Transformer Explorer](https://contextlab.github.io/llm-course/demos/06-transformer/)** - Step through the transformer architecture layer by layer with real-time visualizations of attention, feedforward networks, and residual connections
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-**[Tokenization Explorer](https://contextlab.github.io/llm-course/demos/03-tokenization/)** - Compare BPE, WordPiece, and SentencePiece algorithms side-by-side
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-**[Attention Mechanism](https://contextlab.github.io/llm-course/demos/05-attention/)** - Watch attention weights update in real-time as you modify input sequences
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