SKaiNET-developers · michalharakal · Aug 11, 2026 · Aug 10, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -2,6 +2,16 @@
 
 ## [Unreleased]
 
+## [0.39.0] - 2026-08-10
+
+Headline: **on-device AI on Android becomes real.** A JNI NEON kernel backend
+(`skainet-backend-jni-cpu`) brings hand-tuned ARM matmul to Android — where the
+FFM provider can never run — measured at ~24 tok/s SmolLM2-135M Q8_0 decode on a
+Pixel 8a versus ~3.8 scalar (6.4x), clearing the on-device usability bar. The
+release also hardens the GGUF load path on Android (streaming instead of
+full-file heap loads), makes published Kotlin/Native kernel klibs linkable, and
+lands a batch of tensor-storage correctness fixes.
+
 ### CI
 
 - **Release workflow publishes the `skainet-backend-jni-cpu` AAR.** `./gradlew publish` now

diff --git a/README.md b/README.md
@@ -44,7 +44,7 @@ Add the core dependencies (Gradle Kotlin DSL):
 ```kotlin
 dependencies {
     // Recommended: import the umbrella BOM and drop versions on the engine modules.
-    implementation(platform("sk.ainet:skainet-bom:0.38.0"))
+    implementation(platform("sk.ainet:skainet-bom:0.39.0"))
 
     implementation("sk.ainet.core:skainet-lang-core")
     implementation("sk.ainet.core:skainet-backend-cpu")
@@ -288,7 +288,14 @@ val withoutLabel = dataPipeline<RawDataset>()
 
 ---
 
-## What's New in 0.38.0
+## What's New in 0.39.0
+
+- **On-device AI on Android — a NEON kernel backend.** New `skainet-backend-jni-cpu` module: the hand-tuned ARM matmul kernels reach Android through a JNI bridge (ART has no `java.lang.foreign`, so the FFM provider can never run there). Two `.so` tiers are built from the same sources and selected at load time from `/proc/cpuinfo` — a baseline `armv8-a` build that runs on every 64-bit core, and an `armv8.2-a+dotprod` build for the `vdotq_s32` Q4_K/Q6_K paths — so a single artifact is safe from Cortex-A53 up. Measured on a Pixel 8a: **~24 tok/s** SmolLM2-135M Q8_0 decode versus ~3.8 scalar (6.4x), clearing the on-device usability bar. The provider auto-registers via `ServiceLoader`; an app just adds the AAR.
+- **Android GGUF loading no longer OOMs.** `createRandomAccessSource` returned `null` on Android, forcing every model load through a full-file heap read that exhausted the ART heap on real devices. It now streams via positional `FileChannel` reads across `skainet-io-gguf` / `-safetensors` / `-onnx`.
+- **Published Kotlin/Native kernel klibs are linkable.** The static kernel archive is now embedded into the cinterop klib, so downstream K/N consumers of `skainet-backend-native-cpu` (`-linuxx64` / `-linuxarm64`, and the path future Apple targets will use) link with no manual setup. A NEON body was also added for the Q4_0 matmul kernel.
+- **Tensor-storage correctness pass.** Fail-fast on unsupported GGUF quant types instead of silently dropping weights; truthful ownership labels and real byte counts in the storage layer; a materializable `FileBacked`/`Aliased` transfer path; and a rank-safe default `copyToFloatArray`.
+
+### Previously, in 0.38.0
 
 - **Streaming KV-cache decode (dynamic dimensions)** — a first-class `Dim` vocabulary makes "dynamic extent" explicit instead of an overloaded `-1`, and the StableHLO emitter renders it as an MLIR `?`. One compiled vmfb now serves every autoregressive decode step with a growing cache, instead of one fixed cache length. Verified end-to-end: the full FunctionGemma `with_past` decode graph and the Moonshine v2 decoder (dynamic self *and* cross caches) self-compile from the DSL to a CPU vmfb — graphs that could not be compiled before. Static graphs are emitted byte-for-byte unchanged.
 - **Narrow-float (BF16 + FP16) weights kept packed** — SafeTensors F16 and GGUF F16/BF16 weights load `KEEP_NATIVE`, two bytes per element at rest instead of widening to FP32, and reach format-specific matmul kernels still packed. Narrow floats are a storage width only: kernels widen to f32 lanes and accumulate in f32.
@@ -326,6 +333,10 @@ We love contributions! Whether it's a new operator, documentation, or a bug fix:
 
 Browse the full codebase documentation on [DeepWiki](https://deepwiki.com/SKaiNET-developers/SKaiNET).
 
+### Contributors (0.39.0)
+
+- **Michal Harakal** ([@michalharakal](https://github.com/michalharakal)) — Android JNI NEON kernel backend with runtime dotprod dispatch (#943, #945), Android `createRandomAccessSource` streaming loads (#922), cinterop klib archive embedding (#942), Q4_0 NEON kernel (#939), GGUF loader fail-fast (#919), tensor-storage correctness fixes (#927, #928, #929, #930, #931), AAR release publishing (#947)
+
 ### Contributors (0.38.0)
 
 - **Michal Harakal** ([@michalharakal](https://github.com/michalharakal)) — dynamic tensor dimensions for streaming KV-cache decode (#891), shared narrow-float BF16/FP16 layer (#886), zero-copy transpose for input-major narrow weights (#895), native FP16 matmul kernel (#896), read-once weight tiling in the native narrow kernels (#897)

diff --git a/docs/antora.yml b/docs/antora.yml
@@ -15,7 +15,7 @@ asciidoc:
     framework_name: SKaiNET
     # Current SKaiNET release — bump once per release; referenced as
     # {skainet_version} in dependency snippets (blocks need subs="attributes+").
-    skainet_version: 0.38.0
+    skainet_version: 0.39.0
     ksp_version: 2.2.21-2.0.5
     dokka_version: 2.1.0
     asciidoctorj_version: 3.0.0
diff --git a/docs/modules/ROOT/pages/reference/kernel-support-matrix.adoc b/docs/modules/ROOT/pages/reference/kernel-support-matrix.adoc
@@ -1,7 +1,7 @@
 = Kernel × platform support matrix
 :description: Which compute-kernel provider serves each weight format on each KMP target.
 
-Generated from `kernel-support.json` (version `0.31.2`) by `KernelSupportMatrixTest` — registry introspection of the registered `KernelProvider` implementations. Do not edit by hand; run `./gradlew generateKernelMatrix` to refresh.
+Generated from `kernel-support.json` (version `0.39.0`) by `KernelSupportMatrixTest` — registry introspection of the registered `KernelProvider` implementations. Do not edit by hand; run `./gradlew generateKernelMatrix` to refresh.
 
 Each cell is the best (highest-priority) provider that serves `Float32 × format` `matmul` on that platform: *native-ffm* (100) → *panama-vector* (50) → *scalar* (0). An empty cell (`—`) means no provider carries a kernel there (the format is dequant-to-FP32 only).
 
@@ -11,11 +11,11 @@ Each cell is the best (highest-priority) provider that serves `Float32 × format
 
 | `Float32` | native-ffm | panama-vector | scalar | scalar | scalar 
 | `BFloat16` | native-ffm | panama-vector | scalar | scalar | scalar 
-| `Q8_0` | native-ffm | panama-vector | scalar | scalar | scalar 
-| `Q4_0` | native-ffm | panama-vector | scalar | scalar | scalar 
-| `Q4_K` | native-ffm | panama-vector | scalar | scalar | scalar 
-| `Q6_K` | panama-vector | panama-vector | scalar | scalar | scalar 
-| `Q5_K` | native-ffm | panama-vector | scalar | scalar | scalar 
+| `Q8_0` | native-ffm | native-jni | scalar | scalar | scalar 
+| `Q4_0` | native-ffm | native-jni | scalar | scalar | scalar 
+| `Q4_K` | native-ffm | native-jni | scalar | scalar | scalar 
+| `Q6_K` | panama-vector | native-jni | scalar | scalar | scalar 
+| `Q5_K` | native-ffm | native-jni | scalar | scalar | scalar 
 | `Q5_1` | panama-vector | panama-vector | scalar | scalar | scalar 
 | `Q5_0` | panama-vector | panama-vector | scalar | scalar | scalar 
 |===

diff --git a/gradle.properties b/gradle.properties
@@ -1,5 +1,5 @@
 GROUP=sk.ainet.core
-VERSION_NAME=0.38.0
+VERSION_NAME=0.39.0
 POM_DESCRIPTION=SKaiNET
 
 POM_URL=https://github.com/SKaiNET-developers/skainet/