diff --git a/cuda_core/examples/cuda_graphs.py b/cuda_core/examples/cuda_graphs.py
index 75c9f891a41..c9463674103 100644
--- a/cuda_core/examples/cuda_graphs.py
+++ b/cuda_core/examples/cuda_graphs.py
@@ -81,9 +81,9 @@ def main():
     result3 = cp.empty_like(a)
 
     # Prepare launch configuration
-    block_size = 256
-    grid_size = (size + block_size - 1) // block_size
-    config = LaunchConfig(grid=grid_size, block=block_size)
+    block = 256
+    grid = (size + block - 1) // block
+    config = LaunchConfig(grid=grid, block=block)
 
     # Sync before graph capture
     dev.sync()
diff --git a/cuda_core/examples/gl_interop_plasma.py b/cuda_core/examples/gl_interop_plasma.py
index 7b8b43cd8d3..376ff28b2e6 100644
--- a/cuda_core/examples/gl_interop_plasma.py
+++ b/cuda_core/examples/gl_interop_plasma.py
@@ -94,8 +94,8 @@ def setup_cuda(kernel_source):
     dev.set_current()
     stream = dev.create_stream()
 
-    opts = ProgramOptions(std="c++11", arch=f"sm_{dev.arch}")
-    prog = Program(kernel_source, code_type="c++", options=opts)
+    program_options = ProgramOptions(std="c++11", arch=f"sm_{dev.arch}")
+    prog = Program(kernel_source, code_type="c++", options=program_options)
     mod = prog.compile("cubin")
     kernel = mod.get_kernel("plasma")
 
@@ -114,7 +114,6 @@ def create_window():
     """Open a pyglet window and return (window, gl_module)."""
     try:
         import pyglet
-        from pyglet.gl import gl as _gl
     except ImportError:
         print(
             "This example requires pyglet >= 2.0.\nInstall it with:  pip install pyglet",
@@ -122,6 +121,18 @@ def create_window():
         )
         sys.exit(1)
 
+    try:
+        from pyglet.gl import gl as _gl
+    except ImportError as exc:
+        print(
+            "pyglet is installed, but OpenGL could not be initialized.\n"
+            f"Underlying import error: {exc}\n"
+            "Ensure your system provides OpenGL runtime libraries (for example, libGL on Linux), "
+            "and that graphics/display support is available.",
+            file=sys.stderr,
+        )
+        sys.exit(1)
+
     window = pyglet.window.Window(
         WIDTH,
         HEIGHT,
diff --git a/cuda_core/examples/pytorch_example.py b/cuda_core/examples/pytorch_example.py
index fb4914b8b00..1073b1a8b18 100644
--- a/cuda_core/examples/pytorch_example.py
+++ b/cuda_core/examples/pytorch_example.py
@@ -48,7 +48,7 @@ def __cuda_stream__(self):
         return (0, stream_id)  # Return format required by CUDA Python
 
 
-s = dev.create_stream(PyTorchStreamWrapper(pt_stream))
+stream = dev.create_stream(PyTorchStreamWrapper(pt_stream))
 
 # prepare program
 program_options = ProgramOptions(std="c++11", arch=f"sm_{dev.arch}")
@@ -60,7 +60,7 @@ def __cuda_stream__(self):
 )
 
 # Run in single precision
-ker = mod.get_kernel("saxpy_kernel<float>")
+kernel = mod.get_kernel("saxpy_kernel<float>")
 dtype = torch.float32
 
 # prepare input/output
@@ -75,16 +75,16 @@ def __cuda_stream__(self):
 block = 32
 grid = int((size + block - 1) // block)
 config = LaunchConfig(grid=grid, block=block)
-ker_args = (a.data_ptr(), x.data_ptr(), y.data_ptr(), out.data_ptr(), size)
+kernel_args = (a.data_ptr(), x.data_ptr(), y.data_ptr(), out.data_ptr(), size)
 
 # launch kernel on our stream
-launch(s, config, ker, *ker_args)
+launch(stream, config, kernel, *kernel_args)
 
 # check result
 assert torch.allclose(out, a.item() * x + y)
 
 # let's repeat again with double precision
-ker = mod.get_kernel("saxpy_kernel<double>")
+kernel = mod.get_kernel("saxpy_kernel<double>")
 dtype = torch.float64
 
 # prepare input
@@ -101,10 +101,10 @@ def __cuda_stream__(self):
 block = 64
 grid = int((size + block - 1) // block)
 config = LaunchConfig(grid=grid, block=block)
-ker_args = (a.data_ptr(), x.data_ptr(), y.data_ptr(), out.data_ptr(), size)
+kernel_args = (a.data_ptr(), x.data_ptr(), y.data_ptr(), out.data_ptr(), size)
 
 # launch kernel on PyTorch's stream
-launch(s, config, ker, *ker_args)
+launch(stream, config, kernel, *kernel_args)
 
 # check result
 assert torch.allclose(out, a * x + y)
diff --git a/cuda_core/examples/saxpy.py b/cuda_core/examples/saxpy.py
index ec9fc2a0111..89c32ce8d9f 100644
--- a/cuda_core/examples/saxpy.py
+++ b/cuda_core/examples/saxpy.py
@@ -35,7 +35,7 @@
 
 dev = Device()
 dev.set_current()
-s = dev.create_stream()
+stream = dev.create_stream()
 
 # prepare program
 program_options = ProgramOptions(std="c++11", arch=f"sm_{dev.arch}")
@@ -51,7 +51,7 @@
 )
 
 # run in single precision
-ker = mod.get_kernel("saxpy<float>")
+kernel = mod.get_kernel("saxpy<float>")
 dtype = cp.float32
 
 # prepare input/output
@@ -61,24 +61,24 @@
 x = rng.random(size, dtype=dtype)
 y = rng.random(size, dtype=dtype)
 out = cp.empty_like(x)
-dev.sync()  # cupy runs on a different stream from s, so sync before accessing
+dev.sync()  # cupy runs on a different stream from stream, so sync before accessing
 
 # prepare launch
 block = 32
 grid = int((size + block - 1) // block)
 config = LaunchConfig(grid=grid, block=block)
-ker_args = (a, x.data.ptr, y.data.ptr, out.data.ptr, size)
+kernel_args = (a, x.data.ptr, y.data.ptr, out.data.ptr, size)
 
-# launch kernel on stream s
-launch(s, config, ker, *ker_args)
-s.sync()
+# launch kernel on stream
+launch(stream, config, kernel, *kernel_args)
+stream.sync()
 
 # check result
 assert cp.allclose(out, a * x + y)
 
 # let's repeat again, this time allocates our own out buffer instead of cupy's
 # run in double precision
-ker = mod.get_kernel("saxpy<double>")
+kernel = mod.get_kernel("saxpy<double>")
 dtype = cp.float64
 
 # prepare input
@@ -91,18 +91,18 @@
 # prepare output
 buf = dev.allocate(
     size * 8,  # = dtype.itemsize
-    stream=s,
+    stream=stream,
 )
 
 # prepare launch
 block = 64
 grid = int((size + block - 1) // block)
 config = LaunchConfig(grid=grid, block=block)
-ker_args = (a, x.data.ptr, y.data.ptr, buf, size)
+kernel_args = (a, x.data.ptr, y.data.ptr, buf, size)
 
-# launch kernel on stream s
-launch(s, config, ker, *ker_args)
-s.sync()
+# launch kernel on stream
+launch(stream, config, kernel, *kernel_args)
+stream.sync()
 
 # check result
 # we wrap output buffer as a cupy array for simplicity
@@ -113,5 +113,5 @@
 
 # clean up resources that we allocate
 # cupy cleans up automatically the rest
-buf.close(s)
-s.close()
+buf.close(stream)
+stream.close()
diff --git a/cuda_core/examples/simple_multi_gpu_example.py b/cuda_core/examples/simple_multi_gpu_example.py
index b43ac385bf7..59e5d0a892b 100644
--- a/cuda_core/examples/simple_multi_gpu_example.py
+++ b/cuda_core/examples/simple_multi_gpu_example.py
@@ -13,7 +13,7 @@
 
 import cupy as cp
 
-from cuda.core import Device, LaunchConfig, Program, launch, system
+from cuda.core import Device, LaunchConfig, Program, ProgramOptions, launch, system
 
 if system.get_num_devices() < 2:
     print("this example requires at least 2 GPUs", file=sys.stderr)
@@ -40,9 +40,9 @@
     }
 }
 """
-prog_add = Program(code_add, code_type="c++", options={"std": "c++17", "arch": f"sm_{dev0.arch}"})
+prog_add = Program(code_add, code_type="c++", options=ProgramOptions(std="c++17", arch=f"sm_{dev0.arch}"))
 mod_add = prog_add.compile("cubin")
-ker_add = mod_add.get_kernel("vector_add")
+add_kernel = mod_add.get_kernel("vector_add")
 
 # Set GPU 1
 dev1 = Device(1)
@@ -62,9 +62,9 @@
     }
 }
 """
-prog_sub = Program(code_sub, code_type="c++", options={"std": "c++17", "arch": f"sm_{dev1.arch}"})
+prog_sub = Program(code_sub, code_type="c++", options=ProgramOptions(std="c++17", arch=f"sm_{dev1.arch}"))
 mod_sub = prog_sub.compile("cubin")
-ker_sub = mod_sub.get_kernel("vector_sub")
+sub_kernel = mod_sub.get_kernel("vector_sub")
 
 
 # This adaptor ensures that any foreign stream (ex: from CuPy) that have not
@@ -100,7 +100,7 @@ def __cuda_stream__(self):
 stream0.wait(cp_stream0)
 
 # Launch the add kernel on GPU 0 / stream 0
-launch(stream0, config0, ker_add, a.data.ptr, b.data.ptr, c.data.ptr, cp.uint64(size))
+launch(stream0, config0, add_kernel, a.data.ptr, b.data.ptr, c.data.ptr, cp.uint64(size))
 
 # Allocate memory on GPU 1
 # Note: This runs on CuPy's current stream for GPU 1.
@@ -115,7 +115,7 @@ def __cuda_stream__(self):
 stream1.wait(cp_stream1)
 
 # Launch the subtract kernel on GPU 1 / stream 1
-launch(stream1, config1, ker_sub, x.data.ptr, y.data.ptr, z.data.ptr, cp.uint64(size))
+launch(stream1, config1, sub_kernel, x.data.ptr, y.data.ptr, z.data.ptr, cp.uint64(size))
 
 # Synchronize both GPUs are validate the results
 dev0.set_current()
diff --git a/cuda_core/examples/strided_memory_view_gpu.py b/cuda_core/examples/strided_memory_view_gpu.py
index d53c4278b2a..9d4e4aacffe 100644
--- a/cuda_core/examples/strided_memory_view_gpu.py
+++ b/cuda_core/examples/strided_memory_view_gpu.py
@@ -57,7 +57,7 @@
 # We assume the 0-th argument supports either DLPack or CUDA Array Interface (both
 # of which are supported by StridedMemoryView).
 @args_viewable_as_strided_memory((0,))
-def my_func(arr, work_stream, gpu_ker):
+def my_func(arr, work_stream, kernel):
     # Create a memory view over arr (assumed to be a 1D array of int32). The stream
     # ordering is taken care of, so that arr can be safely accessed on our work
     # stream (ordered after a data stream on which arr is potentially prepared).
@@ -73,7 +73,7 @@ def my_func(arr, work_stream, gpu_ker):
     block = 256
     grid = (size + block - 1) // block
     config = LaunchConfig(grid=grid, block=block)
-    launch(work_stream, config, gpu_ker, view.ptr, np.uint64(size))
+    launch(work_stream, config, kernel, view.ptr, np.uint64(size))
     # Here we're being conservative and synchronize over our work stream,
     # assuming we do not know the data stream; if we know then we could
     # just order the data stream after the work stream here, e.g.
@@ -101,24 +101,24 @@ def run():
     # To know the GPU's compute capability, we need to identify which GPU to use.
     dev = Device(0)
     dev.set_current()
-    gpu_prog = Program(gpu_code, code_type="c++", options=ProgramOptions(arch=f"sm_{dev.arch}", std="c++11"))
-    mod = gpu_prog.compile(target_type="cubin")
-    gpu_ker = mod.get_kernel(func_name)
+    prog = Program(gpu_code, code_type="c++", options=ProgramOptions(arch=f"sm_{dev.arch}", std="c++11"))
+    mod = prog.compile(target_type="cubin")
+    kernel = mod.get_kernel(func_name)
 
-    s = dev.create_stream()
+    stream = dev.create_stream()
     try:
         # Create input array on GPU
         arr_gpu = cp.ones(1024, dtype=cp.int32)
         print(f"before: {arr_gpu[:10]=}")
 
         # Run the workload
-        my_func(arr_gpu, s, gpu_ker)
+        my_func(arr_gpu, stream, kernel)
 
         # Check the result
         print(f"after: {arr_gpu[:10]=}")
         assert cp.allclose(arr_gpu, 1 + cp.arange(1024, dtype=cp.int32))
     finally:
-        s.close()
+        stream.close()
 
 
 if __name__ == "__main__":
diff --git a/cuda_core/examples/thread_block_cluster.py b/cuda_core/examples/thread_block_cluster.py
index 5dd37b1b771..371f8e333e7 100644
--- a/cuda_core/examples/thread_block_cluster.py
+++ b/cuda_core/examples/thread_block_cluster.py
@@ -94,7 +94,7 @@
     options=ProgramOptions(arch=f"sm_{arch}", std="c++17", include_path=include_path),
 )
 mod = prog.compile(target_type="cubin")
-ker = mod.get_kernel("check_cluster_info")
+kernel = mod.get_kernel("check_cluster_info")
 
 # prepare launch config
 grid = 4
@@ -122,7 +122,7 @@
 block_dims[:] = 0
 
 # launch kernel on the default stream
-launch(dev.default_stream, config, ker, grid_buffer, cluster_buffer, block_buffer)
+launch(dev.default_stream, config, kernel, grid_buffer, cluster_buffer, block_buffer)
 dev.sync()
 
 # verify results
diff --git a/cuda_core/examples/vector_add.py b/cuda_core/examples/vector_add.py
index dbe56e90fe5..32ee7c1a88e 100644
--- a/cuda_core/examples/vector_add.py
+++ b/cuda_core/examples/vector_add.py
@@ -30,7 +30,7 @@
 
 dev = Device()
 dev.set_current()
-s = dev.create_stream()
+stream = dev.create_stream()
 
 # prepare program
 program_options = ProgramOptions(std="c++17", arch=f"sm_{dev.arch}")
@@ -38,7 +38,7 @@
 mod = prog.compile("cubin", name_expressions=("vector_add<float>",))
 
 # run in single precision
-ker = mod.get_kernel("vector_add<float>")
+kernel = mod.get_kernel("vector_add<float>")
 dtype = cp.float32
 
 # prepare input/output
@@ -48,7 +48,7 @@
 b = rng.random(size, dtype=dtype)
 c = cp.empty_like(a)
 
-# cupy runs on a different stream from s, so sync before accessing
+# cupy runs on a different stream from stream, so sync before accessing
 dev.sync()
 
 # prepare launch
@@ -56,9 +56,9 @@
 grid = (size + block - 1) // block
 config = LaunchConfig(grid=grid, block=block)
 
-# launch kernel on stream s
-launch(s, config, ker, a.data.ptr, b.data.ptr, c.data.ptr, cp.uint64(size))
-s.sync()
+# launch kernel on stream
+launch(stream, config, kernel, a.data.ptr, b.data.ptr, c.data.ptr, cp.uint64(size))
+stream.sync()
 
 # check result
 assert cp.allclose(c, a + b)
diff --git a/cuda_core/pixi.lock b/cuda_core/pixi.lock
index b64995bac0d..c4866bc0815 100644
--- a/cuda_core/pixi.lock
+++ b/cuda_core/pixi.lock
@@ -9,13 +9,10 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.15.3-hb03c661_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.2-h39aace5_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_104.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.11.12-hbd8a1cb_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-he90730b_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_15.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.9.27-ha770c72_0.conda
@@ -36,138 +33,48 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-profiler-api-12.9.79-h7938cbb_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-version-12.9-h4f385c5_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.2-py314h1807b08_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h24cb091_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/ffmpeg-8.0.1-gpl_hac7f63c_111.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.1-ha770c72_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.16-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_15.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-h319ec69_15.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.44.5-h2b0a6b4_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/glslang-16.2.0-h96af755_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-hac33072_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.14-hecca717_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_15.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_15.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-12.3.2-h6083320_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/intel-gmmlib-22.9.0-hb700be7_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/intel-media-driver-25.3.4-hecca717_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_8.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_104.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/level-zero-1.28.0-hb700be7_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libass-0.17.4-h96ad9f0_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-4_h4a7cf45_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.2.0-hb03c661_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.2.0-hb03c661_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.2.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcap-2.77-h3ff7636_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-4_h0358290_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcufile-1.14.1.1-hbc026e6_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.125-hb03c661_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h9ec8514_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libflac-1.5.0-he200343_1.conda
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-  - libabseil-static =20250512.1=cxx17*
+  - libabseil-static =20260107.1=cxx17*
+  - abseil-cpp =20260107.1
   license: Apache-2.0
   license_family: Apache
-  size: 1327580
-  timestamp: 1750194149128
+  size: 1384817
+  timestamp: 1770863194876
 - conda: https://conda.anaconda.org/conda-forge/linux-64/libass-0.17.4-h96ad9f0_0.conda
   sha256: 035eb8b54e03e72e42ef707420f9979c7427776ea99e0f1e3c969f92eb573f19
   md5: d3be7b2870bf7aff45b12ea53165babd
@@ -4295,22 +3530,6 @@ packages:
   license: ISC
   size: 152179
   timestamp: 1749328931930
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libass-0.17.4-hcfe818d_0.conda
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-  - libiconv >=1.18,<2.0a0
-  - harfbuzz >=11.0.1
-  - fontconfig >=2.15.0,<3.0a0
-  - fonts-conda-ecosystem
-  - fribidi >=1.0.10,<2.0a0
-  - libfreetype >=2.13.3
-  - libfreetype6 >=2.13.3
-  - libzlib >=1.3.1,<2.0a0
-  license: ISC
-  size: 171287
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-4_h4a7cf45_openblas.conda
   build_number: 4
   sha256: f35fee1eb3fe1a80b2c8473f145a830cf6f98c3b15b232b256b93d44bd9c93b3
@@ -4327,6 +3546,24 @@ packages:
   license: BSD-3-Clause
   size: 18529
   timestamp: 1764823833499
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-5_h5875eb1_mkl.conda
+  build_number: 5
+  sha256: 328d64d4eb51047c39a8039a30eb47695855829d0a11b72d932171cb1dcdfad3
+  md5: 9d2f2e3a943d38f972ceef9cde8ba4bf
+  depends:
+  - mkl >=2025.3.0,<2026.0a0
+  constrains:
+  - liblapack  3.11.0   5*_mkl
+  - liblapacke 3.11.0   5*_mkl
+  - libcblas   3.11.0   5*_mkl
+  - blas 2.305   mkl
+  track_features:
+  - blas_mkl
+  - blas_mkl_2
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 18744
+  timestamp: 1765818556597
 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.11.0-4_haddc8a3_openblas.conda
   build_number: 4
   sha256: 6da3f5e1506f2b98fb741ab4ed705b7914a691f4f44ca18e28c3543b10185021
@@ -4343,6 +3580,23 @@ packages:
   license: BSD-3-Clause
   size: 18577
   timestamp: 1764823752463
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.11.0-5_haddc8a3_openblas.conda
+  build_number: 5
+  sha256: 700f3c03d0fba8e687a345404a45fbabe781c1cf92242382f62cef2948745ec4
+  md5: 5afcea37a46f76ec1322943b3c4dfdc0
+  depends:
+  - libopenblas >=0.3.30,<0.3.31.0a0
+  - libopenblas >=0.3.30,<1.0a0
+  constrains:
+  - mkl <2026
+  - libcblas   3.11.0   5*_openblas
+  - liblapack  3.11.0   5*_openblas
+  - liblapacke 3.11.0   5*_openblas
+  - blas 2.305   openblas
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 18369
+  timestamp: 1765818610617
 - conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.11.0-4_hf2e6a31_mkl.conda
   build_number: 4
   sha256: 0c6ecdabcd3c5b92c7be68a65c30c29983040dd81f502d2e9ad3763fdbbabdef
@@ -4357,36 +3611,31 @@ packages:
   license: BSD-3-Clause
   size: 67784
   timestamp: 1764824188313
+- conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.11.0-5_hf2e6a31_mkl.conda
+  build_number: 5
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+  md5: f9decf88743af85c9c9e05556a4c47c0
+  depends:
+  - mkl >=2025.3.0,<2026.0a0
+  constrains:
+  - liblapack  3.11.0   5*_mkl
+  - libcblas   3.11.0   5*_mkl
+  - blas 2.305   mkl
+  - liblapacke 3.11.0   5*_mkl
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 67438
+  timestamp: 1765819100043
 - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.2.0-hb03c661_1.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlicommon-1.2.0-he30d5cf_1.conda
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-  license: MIT
-  license_family: MIT
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-  timestamp: 1764017273715
-- conda: https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.2.0-hfd05255_1.conda
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-  depends:
-  - ucrt >=10.0.20348.0
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
+  - libgcc >=14
   license: MIT
   license_family: MIT
-  size: 82042
-  timestamp: 1764017799966
+  size: 79965
+  timestamp: 1764017188531
 - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.2.0-hb03c661_1.conda
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   md5: 366b40a69f0ad6072561c1d09301c886
@@ -4398,28 +3647,6 @@ packages:
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   timestamp: 1764017199083
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlidec-1.2.0-he30d5cf_1.conda
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-  - libgcc >=14
-  license: MIT
-  license_family: MIT
-  size: 33166
-  timestamp: 1764017282936
-- conda: https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.2.0-hfd05255_1.conda
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-  - ucrt >=10.0.20348.0
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
-  license: MIT
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-  timestamp: 1764017851337
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlienc-1.2.0-he30d5cf_1.conda
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.2.0-hfd05255_1.conda
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-  - ucrt >=10.0.20348.0
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
-  license: MIT
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@@ -4487,6 +3692,22 @@ packages:
   license: BSD-3-Clause
   size: 18521
   timestamp: 1764823852735
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-5_hfef963f_mkl.conda
+  build_number: 5
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+  md5: 9b6cb3aa4b7912121c64b97a76ca43d5
+  depends:
+  - libblas 3.11.0 5_h5875eb1_mkl
+  constrains:
+  - liblapack  3.11.0   5*_mkl
+  - liblapacke 3.11.0   5*_mkl
+  - blas 2.305   mkl
+  track_features:
+  - blas_mkl
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 18385
+  timestamp: 1765818571086
 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.11.0-4_hd72aa62_openblas.conda
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@@ -4500,6 +3721,20 @@ packages:
   license: BSD-3-Clause
   size: 18576
   timestamp: 1764823760728
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.11.0-5_hd72aa62_openblas.conda
+  build_number: 5
+  sha256: 3fad5c9de161dccb4e42c8b1ae8eccb33f4ed56bccbcced9cbb0956ae7869e61
+  md5: 0b2f1143ae2d0aa4c991959d0daaf256
+  depends:
+  - libblas 3.11.0 5_haddc8a3_openblas
+  constrains:
+  - liblapack  3.11.0   5*_openblas
+  - liblapacke 3.11.0   5*_openblas
+  - blas 2.305   openblas
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 18371
+  timestamp: 1765818618899
 - conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-4_h2a3cdd5_mkl.conda
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@@ -4513,6 +3748,76 @@ packages:
   license: BSD-3-Clause
   size: 68001
   timestamp: 1764824219221
+- conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-5_h2a3cdd5_mkl.conda
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+  md5: b3fa8e8b55310ba8ef0060103afb02b5
+  depends:
+  - libblas 3.11.0 5_hf2e6a31_mkl
+  constrains:
+  - liblapack  3.11.0   5*_mkl
+  - liblapacke 3.11.0   5*_mkl
+  - blas 2.305   mkl
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 68079
+  timestamp: 1765819124349
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcublas-13.2.1.1-h676940d_0.conda
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+  - cuda-nvrtc
+  - cuda-version >=13.1,<13.2.0a0
+  - libgcc >=14
+  - libstdcxx >=14
+  license: LicenseRef-NVIDIA-End-User-License-Agreement
+  size: 376501341
+  timestamp: 1768276465220
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcudnn-9.19.0.56-ha4b6413_0.conda
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+  - libcublas
+  - libgcc >=14
+  - libstdcxx >=14
+  - libzlib >=1.3.1,<2.0a0
+  constrains:
+  - libcudnn-jit <0a
+  license: LicenseRef-cuDNN-Software-License-Agreement
+  size: 329927632
+  timestamp: 1770222415966
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcudss-0.7.1.4-h7bcfba5_1.conda
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+  - _openmp_mutex >=4.5
+  - cuda-version >=13,<14.0a0
+  - libcublas
+  - libgcc >=14
+  - libstdcxx >=14
+  constrains:
+  - libcudss0 <0.0.0a0
+  - libcudss-commlayer-nccl 0.7.1.4 hd557bf5_1
+  - libcudss-commlayer-mpi 0.7.1.4 h6647138_1
+  license: LicenseRef-NVIDIA-End-User-License-Agreement
+  size: 62715991
+  timestamp: 1770671835770
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcufft-12.1.0.78-hecca717_0.conda
+  sha256: 4f8951e942210116ee6e1548c25774009afddc59e494b5eac0e5ca539196d1b5
+  md5: 58a7aa38206ea03a9eb6ccbcc012901e
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - cuda-version >=13.1,<13.2.0a0
+  - libgcc >=14
+  - libstdcxx >=14
+  license: LicenseRef-NVIDIA-End-User-License-Agreement
+  size: 192379210
+  timestamp: 1768273636415
 - conda: https://conda.anaconda.org/conda-forge/linux-64/libcufile-1.14.1.1-hbc026e6_1.conda
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@@ -4565,6 +3870,43 @@ packages:
   license: LicenseRef-NVIDIA-End-User-License-Agreement
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   timestamp: 1768273724252
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcurand-10.4.1.81-h676940d_0.conda
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+  - libgcc >=14
+  - libstdcxx >=14
+  license: LicenseRef-NVIDIA-End-User-License-Agreement
+  size: 43775293
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcusolver-12.0.9.81-h676940d_0.conda
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+  md5: 17a342e69a0821ecf76a0e79a2044288
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+  - cuda-version >=13.1,<13.2.0a0
+  - libcublas >=13.2.1.1,<13.3.0a0
+  - libcusparse >=12.7.3.1,<12.8.0a0
+  - libgcc >=14
+  - libnvjitlink >=13.1.115,<14.0a0
+  - libstdcxx >=14
+  license: LicenseRef-NVIDIA-End-User-License-Agreement
+  size: 161188241
+  timestamp: 1768286542683
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcusparse-12.7.3.1-hecca717_0.conda
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+  md5: 915b747d67493ba94a0d9b79095cc06d
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+  - __glibc >=2.17,<3.0.a0
+  - cuda-version >=13.1,<13.2.0a0
+  - libgcc >=14
+  - libnvjitlink >=13.1.115,<14.0a0
+  - libstdcxx >=14
+  license: LicenseRef-NVIDIA-End-User-License-Agreement
+  size: 145513192
+  timestamp: 1768280223267
 - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda
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   md5: 6c77a605a7a689d17d4819c0f8ac9a00
@@ -4575,26 +3917,6 @@ packages:
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libdeflate-1.25-h1af38f5_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.25-h51727cc_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libdrm-2.4.125-he30d5cf_1.conda
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@@ -4625,14 +3937,6 @@ packages:
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   - libgcc >=14
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   timestamp: 1763011535779
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.12-h86ecc28_0.conda
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-  md5: ae2c2dd0e2d38d249887727db2af960e
-  depends:
-  - libgcc >=13
-  - xorg-libx11 >=1.8.10,<2.0a0
-  license: MIT
-  license_family: MIT
-  size: 33649
-  timestamp: 1734229123157
 - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxscrnsaver-1.2.4-hb9d3cd8_0.conda
   sha256: 58e8fc1687534124832d22e102f098b5401173212ac69eb9fd96b16a3e2c8cb2
   md5: 303f7a0e9e0cd7d250bb6b952cecda90
@@ -8244,17 +7410,6 @@ packages:
   license_family: MIT
   size: 14412
   timestamp: 1727899730073
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxscrnsaver-1.2.4-h86ecc28_0.conda
-  sha256: ab88b1533e7498baeb00cbda50c899a6fe73eaee14df32c57b8ad3f2a0b3cc26
-  md5: 7a0a04defd4399a93936f06fcfac5531
-  depends:
-  - libgcc >=13
-  - xorg-libx11 >=1.8.12,<2.0a0
-  - xorg-libxext >=1.3.6,<2.0a0
-  license: MIT
-  license_family: MIT
-  size: 15720
-  timestamp: 1750007336692
 - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-hb9d3cd8_3.conda
   sha256: 752fdaac5d58ed863bbf685bb6f98092fe1a488ea8ebb7ed7b606ccfce08637a
   md5: 7bbe9a0cc0df0ac5f5a8ad6d6a11af2f
@@ -8268,18 +7423,16 @@ packages:
   license_family: MIT
   size: 32808
   timestamp: 1727964811275
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxtst-1.2.5-h57736b2_3.conda
-  sha256: 6eaffce5a34fc0a16a21ddeaefb597e792a263b1b0c387c1ce46b0a967d558e1
-  md5: c05698071b5c8e0da82a282085845860
+- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-xorgproto-2025.1-hb03c661_0.conda
+  sha256: 7a8c64938428c2bfd016359f9cb3c44f94acc256c6167dbdade9f2a1f5ca7a36
+  md5: aa8d21be4b461ce612d8f5fb791decae
   depends:
-  - libgcc >=13
-  - xorg-libx11 >=1.8.9,<2.0a0
-  - xorg-libxext >=1.3.6,<2.0a0
-  - xorg-libxi >=1.7.10,<2.0a0
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=14
   license: MIT
   license_family: MIT
-  size: 33786
-  timestamp: 1727964907993
+  size: 570010
+  timestamp: 1766154256151
 - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
   sha256: b4533f7d9efc976511a73ef7d4a2473406d7f4c750884be8e8620b0ce70f4dae
   md5: 30cd29cb87d819caead4d55184c1d115
diff --git a/cuda_core/pixi.toml b/cuda_core/pixi.toml
index 3853a30c5c8..ad8b5670fd8 100644
--- a/cuda_core/pixi.toml
+++ b/cuda_core/pixi.toml
@@ -17,7 +17,6 @@ pytest = "*"
 pytest-benchmark = "*"
 pytest-randomly = "*"
 pytest-repeat = "*"
-pyglet = "*"
 
 [feature.cython-tests.dependencies]
 cython = ">=3.2,<3.3" # for tests that exercise APIs from cython
@@ -55,6 +54,16 @@ cuda = "13"
 [feature.cu13.dependencies]
 cuda-version = "13.1.*"
 
+[feature.examples.dependencies]
+cuda-core = { path = "." }
+
+[feature.examples.target.linux-64.dependencies]
+cupy = ">=14.0.1,<15"
+pytorch = ">=2.10.0,<3"
+cffi = ">=2.0.0,<3"
+pyglet = ">=2.0"
+libgl-devel = ">=1.7.0,<2"
+
 [feature.cu12.system-requirements]
 cuda = "12"
 
@@ -72,6 +81,7 @@ default = { features = [
     "cython-tests",
 ], solve-group = "default" }
 cu13 = { features = ["cu13", "test", "cython-tests"], solve-group = "default" }
+examples = { features = ["cu13", "examples"], solve-group = "examples" }
 cu12 = { features = ["cu12", "test", "cython-tests"], solve-group = "cu12" }
 
 # TODO: check if these can be extracted from pyproject.toml