@@ -99,8 +99,8 @@ Read the [Deep Technical Explanation](docs/arduino-c-codegen.md) for more detail
9999
100100``` kotlin
101101dependencies {
102- implementation(" sk.ainet.core:SKaiNET-lang-core:0.5.1 " )
103- implementation(" sk.ainet.core:SKaiNET-backend-cpu:0.5.1 " )
102+ implementation(" sk.ainet.core:SKaiNET-lang-core:0.6.0 " )
103+ implementation(" sk.ainet.core:SKaiNET-backend-cpu:0.6.0 " )
104104}
105105// Ready to build & run in ~8 minutes
106106```
@@ -111,7 +111,7 @@ dependencies {
111111- In Kotlin Notebooks for quick exploration
112112- With sample projects to learn patterns
113113
114- See also CHANGELOG for what’s new in 0.5.1 .
114+ See also CHANGELOG for what’s new in 0.6.0 .
115115
116116## Quick start
117117
@@ -126,15 +126,15 @@ dependencyResolutionManagement {
126126
127127dependencies {
128128 // minimal dependency with simple CPU backend
129- implementation(" sk.ainet.core:SKaiNET-lang-core:0.5.1 " )
130- implementation(" sk.ainet.core:SKaiNET-backend-cpu:0.5.1 " )
129+ implementation(" sk.ainet.core:SKaiNET-lang-core:0.6.0 " )
130+ implementation(" sk.ainet.core:SKaiNET-backend-cpu:0.6.0 " )
131131
132132 // simple model zoo
133- implementation(" sk.ainet.core:SKaiNET-lang-models:0.5.1 " )
133+ implementation(" sk.ainet.core:SKaiNET-lang-models:0.6.0 " )
134134
135135 // Optional I/O (e.g., GGUF loader, JSON)
136- implementation(" sk.ainet.core:SKaiNET-io-core:0.5.1 " )
137- implementation(" sk.ainet.core:SKaiNET-io-gguf:0.5.1 " )
136+ implementation(" sk.ainet.core:SKaiNET-io-core:0.6.0 " )
137+ implementation(" sk.ainet.core:SKaiNET-io-gguf:0.6.0 " )
138138}
139139```
140140
@@ -144,7 +144,7 @@ Maven:
144144<dependency >
145145 <groupId >sk.ainet.core</groupId >
146146 <artifactId >SKaiNET-lang-core</artifactId >
147- <version >0.5.1 </version >
147+ <version >0.6.0 </version >
148148</dependency >
149149```
150150
@@ -157,6 +157,34 @@ Maven:
157157
158158![ Architecture diagram of SKaiNET compiler] ( docs//SKaiNET-compiler.svg )
159159
160+ ## 0.6.0 highlights (with tiny snippets)
161+
162+ - StableHLO and CUDA support via IREE
163+
164+ ``` kotlin
165+ // Compile model to StableHLO and run on CUDA
166+ val ir = Compile .toStableHlo(model)
167+ println (ir.pretty())
168+ ```
169+
170+ - Arduino C99 code generation
171+
172+ ``` kotlin
173+ // Export model to an Arduino library
174+ val facade = CCodegenFacade ()
175+ facade.exportToArduinoLibrary(
176+ model = model,
177+ forwardPass = { ctx -> model.forward(input, ctx) },
178+ outputPath = " build/arduino" ,
179+ libraryName = " MyModel"
180+ )
181+ ```
182+
183+ - KSP-based TracingOps generation for recording pipelines.
184+ - Improved HLO implementation and CUDA backend strategy.
185+
186+ See CHANGELOG.md for the full list.
187+
160188## 0.5.0 highlights (with tiny snippets)
161189
162190- Kolmogorov–Arnold Networks (KAN/AKN) preview in the NN DSL
@@ -213,8 +241,6 @@ println("Tensors: ${gguf.tensors.size}")
213241
214242- SIMD/Vector API acceleration on JVM; MatMul, tril, pooling ops; forward hooks and simple tape recording; unified tensor creation contexts; nested data blocks returning tensors.
215243
216- See CHANGELOG.md for the full list.
217-
218244## Experimental: Kolmogorov–Arnold Networks (KAN)
219245
220246SKaiNET includes an initial KAN layer implementation that you can wire into the NN DSL. A KAN layer expands each input feature by a learnable grid of basis coefficients and then mixes them with a linear projection, with optional bias and residual connection.
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