demo.git - Vide-coded 64k demo system

Age	Commit message (Collapse)	Author
17 hours	feat: Add multi-layer CNN support with framebuffer capture and blend control	skal
	Implements automatic layer chaining and generic framebuffer capture API for multi-layer neural network effects with proper original input preservation. Key changes: - Effect::needs_framebuffer_capture() - generic API for pre-render capture - MainSequence: auto-capture to "captured_frame" auxiliary texture - CNNEffect: multi-layer support via layer_index/total_layers params - seq_compiler: expands "layers=N" to N chained effect instances - Shader: @binding(4) original_input available to all layers - Training: generates layer switches and original input binding - Blend: mix(original, result, blend_amount) uses layer 0 input Timeline syntax: CNNEffect layers=3 blend=0.7 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
19 hours	docs: Update and streamline CNN training documentation	skal
	- Document coordinate-aware layer 0 architecture - Add checkpointing examples and options table - Consolidate training workflow with practical examples - Clarify CoordConv2d usage and size impact - Streamline training/README.md structure Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
22 hours	docs: Add CNN effect documentation and update project status	skal
	New Documentation: - `doc/CNN_EFFECT.md` (223 lines): Comprehensive implementation guide - Architecture overview (file structure, shader composition) - Usage examples (C++ API, timeline integration) - Training workflow (planned) - Implementation details (convolution signatures, weight storage) - Size budget breakdown (~5-8 KB total) - Testing and troubleshooting Updated Documentation: - `doc/CNN.md`: Added implementation status section - Completed items (✅ modular shaders, C++ class, tests) - Pending items (⏳ training script, multi-layer, quantization) - Size impact summary - `PROJECT_CONTEXT.md`: - Added "Effects: CNN post-processing foundation" to Current Status - Added `CNN_EFFECT.md` to Technical Reference list Summary: CNN effect foundation complete with modular WGSL architecture, ready for training script integration. All tests passing (36/36). ~5-8 KB footprint. handoff(Claude): Documentation complete for CNN effect implementation