Card: link the sample app (CoreAISearchiOS) and record the iPhone 18 Pro JIT run of 2026-10-09 (bit-identical to the 17 Pro h18p index)

README.md

@@ -26,6 +26,8 @@ This model has no row on [DeviceMark](https://devicemark.github.io/), the on-dev
 
 Zoo card, recipe and gate transcript: [coreai-model-zoo/models/granite-embedding-97m](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/granite-embedding-97m/README.md).
 
+Sample app: [CoreAISearchiOS](https://github.com/john-rocky/coreai-samples/tree/main/CoreAISearchiOS) downloads `ios/fp32-s128` from this repo and searches your own notes by meaning, 16.5 ms per query on an iPhone 18 Pro (iOS 27.2, 2026-10-09).
+
 IBM's 97M-parameter **multilingual text embedder** — a ModernBERT encoder, 384-d CLS-pooled
 unit vectors, Japanese and English among its languages — as a static `.aimodel` for macOS 27
 and iOS 27, with bundles compiled ahead of time for the iPhone 17 Pro beside it.
@@ -121,7 +123,11 @@ per graph
 
 The iPhone gate above (the iPhone 17 Pro rows) ran the `h18p` export, now in `ios-h18p/`. The JIT IR in
 `ios/` was only loaded and called once on the iPhone 18 Pro. That call returned a 384-value float32
-embedding with no non-finite values; its parity with the reference was not re-measured.
+embedding with no non-finite values. On 2026-10-09 the sample app above embedded 9,859 sentences on an
+iPhone 18 Pro (iOS 27.2, 24B5099f) with `ios/fp32-s128`, GPU preferred: the 9,859 × 384 float32 matrix has the
+same SHA-256 as the one the `h18p` bundle produced on the iPhone 17 Pro on 2026-09-20 (bit-identical), so the
+five example queries return the same top 5 with the same scores. First load 0.44 s, first call 612 ms, load
+after a relaunch 0.054 s, 4.91 ms per sentence over the 9,859, one run.
 
 The fixed grid computes every position, so pick the smallest grid that covers the text: S=128
 for queries and short notes, S=512 for passages. **fp32 is the default.** w8 is a storage