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<title>demo.git/workspaces/main/weights, branch main</title>
<subtitle>Vide-coded 64k demo system</subtitle>
<id>https://git.taar-o.com/demo.git/atom?h=main</id>
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<updated>2026-03-29T14:51:22Z</updated>
<entry>
<title>update weights</title>
<updated>2026-03-29T14:51:22Z</updated>
<author>
<name>skal</name>
<email>pascal.massimino@gmail.com</email>
</author>
<published>2026-03-29T14:50:56Z</published>
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<id>urn:sha1:4bcbe13dab5ffb64d93cc61956f07ee5168a84c9</id>
<content type='text'>
</content>
</entry>
<entry>
<title>update assets and weights</title>
<updated>2026-03-29T14:39:53Z</updated>
<author>
<name>skal</name>
<email>pascal.massimino@gmail.com</email>
</author>
<published>2026-03-29T14:38:10Z</published>
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<id>urn:sha1:45f8436a5c2410e2f15a0006f6a4540de0d913a5</id>
<content type='text'>
</content>
</entry>
<entry>
<title>fix(cnn_v3): remove dec0 ReLU, load FiLM MLP at runtime</title>
<updated>2026-03-27T06:59:00Z</updated>
<author>
<name>skal</name>
<email>pascal.massimino@gmail.com</email>
</author>
<published>2026-03-27T06:59:00Z</published>
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<id>urn:sha1:fb13e67acbc7d7dd2974a456fcb134966c47cee0</id>
<content type='text'>
Two bugs blocking training convergence:

1. dec0 ReLU before sigmoid constrained output to [0.5,1.0] — network
   could never produce dark pixels. Removed F.relu in train_cnn_v3.py
   and max(0,…) in cnn_v3_dec0.wgsl. Test vectors regenerated.

2. set_film_params() used hardcoded heuristics instead of the trained MLP.
   Added CNNv3FilmMlp struct + load_film_mlp() to cnn_v3_effect.h/.cc.
   MLP auto-loaded from ASSET_WEIGHTS_CNN_V3_FILM_MLP at construction;
   Linear(5→16)→ReLU→Linear(16→72) runs CPU-side each frame.

36/36 tests pass. Parity max_err=4.88e-4 unchanged.

handoff(Gemini): retrain from scratch — needs ≥50 samples (currently 11).
See cnn_v3/docs/HOWTO.md §2-3.
</content>
</entry>
<entry>
<title>update the weights</title>
<updated>2026-03-26T07:13:43Z</updated>
<author>
<name>skal</name>
<email>pascal.massimino@gmail.com</email>
</author>
<published>2026-03-26T07:13:43Z</published>
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<id>urn:sha1:26627e8b9fee3fb3b2ec6314fc5cf45620769fcb</id>
<content type='text'>
</content>
</entry>
<entry>
<title>feat(cnn_v3): upgrade architecture to enc_channels=[8,16]</title>
<updated>2026-03-26T06:03:01Z</updated>
<author>
<name>skal</name>
<email>pascal.massimino@gmail.com</email>
</author>
<published>2026-03-26T06:03:01Z</published>
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<id>urn:sha1:8f14bdd66cb002b2f89265b2a578ad93249089c9</id>
<content type='text'>
Double encoder capacity: enc0 4→8ch, enc1 8→16ch, bottleneck 16→16ch,
dec1 32→8ch, dec0 16→4ch. Total weights 2476→7828 f16 (~15.3 KB).
FiLM MLP output 40→72 params (L1: 16×40→16×72).

16-ch textures split into _lo/_hi rgba32uint pairs (enc1, bottleneck).
enc0 and dec1 textures changed from rgba16float to rgba32uint (8ch).
GBUF_RGBA32UINT node gains CopySrc for parity test readback.

- WGSL shaders: all 5 passes rewritten for new channel counts
- C++ CNNv3Effect: new weight offsets/sizes, 8ch uniform structs
- Web tool (shaders.js + tester.js): matching texture formats and bindings
- Parity test: readback_rgba32uint_8ch helper, updated vector counts
- Training scripts: default enc_channels=[8,16], updated docstrings
- Docs + architecture PNG regenerated

handoff(Gemini): CNN v3 [8,16] upgrade complete. All code, tests, web
tool, training scripts, and docs updated. Next: run training pass.
</content>
</entry>
<entry>
<title>update weights</title>
<updated>2026-03-25T07:54:30Z</updated>
<author>
<name>skal</name>
<email>pascal.massimino@gmail.com</email>
</author>
<published>2026-03-25T07:54:30Z</published>
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<id>urn:sha1:8b4d7a49f038d7e849e6764dcc3abd1e1be01061</id>
<content type='text'>
</content>
</entry>
<entry>
<title>update weights</title>
<updated>2026-03-25T05:06:00Z</updated>
<author>
<name>skal</name>
<email>pascal.massimino@gmail.com</email>
</author>
<published>2026-03-25T05:06:00Z</published>
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<id>urn:sha1:fc74d8e36449e0040564debd381e666df40d671b</id>
<content type='text'>
</content>
</entry>
<entry>
<title>feat(cnn_v3): wire trained weights into CNNv3Effect + add timeline test sequence</title>
<updated>2026-03-22T11:53:13Z</updated>
<author>
<name>skal</name>
<email>pascal.massimino@gmail.com</email>
</author>
<published>2026-03-22T11:53:13Z</published>
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<id>urn:sha1:581c67b75aa3c089c86f764b67e6de7476a13993</id>
<content type='text'>
- CNNv3Effect constructor loads ASSET_WEIGHTS_CNN_V3 via GetAsset on startup
- seq_compiler.py: CLASS_TO_HEADER supports full #include paths for cnn_v3/ classes
- timeline.seq: add cnn_v3_test sequence at 48s (GBufferEffect → CNNv3Effect)
- test_cnn_v3_parity: zero_weights test now explicitly uploads zeros to override asset

handoff(Gemini): CNNv3Effect ready; export weights to workspaces/main/weights/ and seek to 48s to test
</content>
</entry>
<entry>
<title>Fix --mix option: blend prev layer with static p4-p7, not p0-p3</title>
<updated>2026-02-14T00:04:07Z</updated>
<author>
<name>skal</name>
<email>pascal.massimino@gmail.com</email>
</author>
<published>2026-02-14T00:01:52Z</published>
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<id>urn:sha1:1b760f3b413d28652965a51f629d3c2b8d33ce22</id>
<content type='text'>
Updated gen_identity_weights.py --mix mode to use static features
p4-p7 (uv_x, uv_y, sin20_y, bias) at channels 8-11 instead of
p0-p3 (RGB+D) at channels 4-7.

Before: 0.5*prev[i] + 0.5*static_p{i} (channels 4-7)
After:  0.5*prev[i] + 0.5*static_p{4+i} (channels 8-11)

Co-Authored-By: Claude Sonnet 4.5 &lt;noreply@anthropic.com&gt;
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