docs(readme): show the run, not just the claim

Visitors arrive from the Show HN headline and the first screen is all prose.
Adds a 14.5s GIF directly under the 8B-on-4GB claim, above "Why Soup?".

Segment 44.0-60.0s of the demo, chosen off the .vtt cue list and confirmed
frame by frame rather than from the brief's estimate: the scene cut sits
between 47 and 48s, so the clip opens on 3.5s of the static "Layer streaming
BETA" pre-flight panel (3.60 GB store across 32 layers, 2 x 113 MB VRAM
buffers, Training started!) and then runs the measurement card up to its
settled 3.32 GB / 119.6 tok/s. Both halves read without sound or context.

Encoding: two-pass palettegen/paletteuse so terminal colours survive, 960px
wide, 10 fps, dither=none, diff_mode=rectangle -> 4.63 MB. Readability was the
binding constraint, so the budget was met by cutting duration (16s -> 14.5s)
and fps (12 -> 10) rather than width; a bayer-dithered cut of the same clip
came to 5.08 MB and was dropped. Panel lines verified legible by opening the
generated GIF, not assumed from the source resolution.

Caption numbers are from benchmarks/gate-v0.72.2-nf4.md line 314 (the
re-measurement through shipped code), matching the claim line above it.
"What's New" untouched. README 440 -> 445 lines.

README.md

@@ -47,6 +47,11 @@ resident run. Opt-in (`stream_layers: true`) and still BETA —
 [how it works](docs/performance-and-quantization.md#layer-streaming-beta-v0720-nf4-v0722-disk--wider-archs-v0723-preference-losses-v0724) ·
 [all measurements](benchmarks/) · [paper](https://doi.org/10.5281/zenodo.21771064)
 
+<p align="center">
+  <a href="https://youtu.be/T1LCErE943E"><img src="docs/assets/layer-streaming.gif" alt="soup train pre-flight for Llama-3.1-8B on a 4 GB card: a 3.60 GB base store pinned in RAM across 32 layers and two 113 MB VRAM buffers, then a measured peak of 3.32 GB at 119.6 tok/s, stopping short of the 4 GB line"></a><br>
+  <sub>Llama-3.1-8B-Instruct + NF4, LoRA, batch 1, seq 512 on an RTX 3050 Laptop 4 GB — <b>3.32 GB peak, 119.6 tok/s</b>. <a href="https://youtu.be/T1LCErE943E">Full video (90s)</a></sub>
+</p>
+
 ## Why Soup?
 
 Training LLMs is still painful. Even experienced teams spend 30-50% of their time fighting