<?xml version="1.0" ?>
<rss version="2.0">
  <channel>
    <title>anaconda.org/conda-forge</title>
    <link>https://conda.anaconda.org/conda-forge</link>
    <description>The most recent 100 updates for conda-forge.</description>
    <pubDate>Mon, 09 Nov 2026 01:55:21 GMT</pubDate>
    <lastBuildDate>Mon, 09 Nov 2026 01:55:21 GMT</lastBuildDate>
    <item>
      <title>cyclopts 5.4.0 [noarch]</title>
      <description>Intuitive, easy CLIs based on python type hints.</description>
      <link>https://cyclopts.readthedocs.io/</link>
      <guid>https://pypi.org/packages/source/c/cyclopts/cyclopts-3.12.0.tar.gz</guid>
      <pubDate>Mon, 09 Nov 2026 01:33:04 GMT</pubDate>
      <source>https://github.com/BrianPugh/cyclopts</source>
    </item>
    <item>
      <title>pysad 0.8.0 [noarch]</title>
      <description>PySAD detects anomalies in data streams. Its detectors learn from one point at a time and score each point, with bounded memory. It also has preprocessing, postprocessing, ensembles, probability calibration and prequential evaluation for streams.</description>
      <link>https://pysad.readthedocs.io/</link>
      <comments>https://github.com/selimfirat/pysad</comments>
      <pubDate>Mon, 09 Nov 2026 01:15:57 GMT</pubDate>
      <source>https://github.com/selimfirat/pysad</source>
    </item>
    <item>
      <title>rendercanvas 3.0.0 [noarch]</title>
      <description>See how the two windows above look the same? That's the idea; they also look the same to the code that renders to them. Yet, the GUI systems are very different (Qt vs glfw in this case). Now that's a powerful abstraction!  * Provide a generic canvas API to render to. * Provide an event loop for scheduling events and draws. * Provide a simple but powerful event system with standardized event objects. * Provide various canvas implementations:     * One that is light and easily installed (glfw).     * For various GUI libraries (e.g. qt and wx), so visuzalizations can be embedded in a GUI.     * For specific platforms (e.g. Jupyter, browser).  The main use-case is rendering with wgpu, but rendercanvascan be used by anything that can render based on a window-id or by producing bitmap images.</description>
      <guid>https://files.pythonhosted.org/packages/source/r/rendercanvas/rendercanvas-3.0.0.tar.gz</guid>
      <pubDate>Mon, 09 Nov 2026 01:05:26 GMT</pubDate>
      <source>https://github.com/pygfx/rendercanvas/</source>
    </item>
    <item>
      <title>xorg-libxxf86vm 1.1.7 [linux-64, linux-aarch64, linux-ppc64le, linux-riscv64, osx-64, osx-arm64, win-64]</title>
      <description>Xlib-based library for the XFree86-VidMode X extension</description>
      <comments>https://cgit.freedesktop.org/xorg/lib/libXxf86vm</comments>
      <guid>https://www.x.org/releases/individual/lib/libXxf86vm-1.1.7.tar.xz</guid>
      <pubDate>Mon, 09 Nov 2026 00:39:22 GMT</pubDate>
      <source>https://www.x.org/</source>
    </item>
    <item>
      <title>npm-groovy-lint 19.0.1 [noarch]</title>
      <description>Lint, format and auto-fix your Groovy / Jenkinsfile / Gradle files using command line</description>
      <comments>https://github.com/nvuillam/npm-groovy-lint</comments>
      <pubDate>Mon, 09 Nov 2026 00:27:38 GMT</pubDate>
      <source>https://nvuillam.github.io/npm-groovy-lint</source>
    </item>
    <item>
      <title>slam-astro 2.1.3 [noarch]</title>
      <description>Spectral Line Analysis/Modeling for (sub)millimeter astronomy</description>
      <link>https://slam-astro.readthedocs.io/</link>
      <comments>https://github.com/jinshisai/SLAM</comments>
      <pubDate>Mon, 09 Nov 2026 00:20:38 GMT</pubDate>
      <source>https://github.com/jinshisai/SLAM</source>
    </item>
    <item>
      <title>anemoi-training 0.17.2 [noarch]</title>
      <description>A package to hold various functions to support training of ML models.</description>
      <link>https://anemoi-training.readthedocs.io/</link>
      <guid>https://pypi.org/packages/source/a/anemoi-training/anemoi_training-0.13.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 23:24:29 GMT</pubDate>
      <source>https://github.com/ecmwf/anemoi-training/</source>
    </item>
    <item>
      <title>fmt 12.2.1 [linux-64, linux-aarch64, linux-ppc64le, linux-riscv64, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>{fmt} is an open-source formatting library for C++. It can be used as a safe and fast alternative to (s)printf and iostreams.</description>
      <link>https://fmt.dev/latest/</link>
      <comments>https://github.com/fmtlib/fmt</comments>
      <guid>https://github.com/fmtlib/fmt/archive/12.2.1.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 23:19:25 GMT</pubDate>
      <source>https://fmt.dev/latest/</source>
    </item>
    <item>
      <title>torchaudio 2.11.1 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>Data manipulation and transformation for audio signal processing, powered by PyTorch</description>
      <pubDate>Sun, 08 Nov 2026 23:13:48 GMT</pubDate>
      <source>https://github.com/pytorch/audio</source>
    </item>
    <item>
      <title>simple-build-dgcode 2.1.0 [noarch]</title>
      <description>A set of simplebuild packages implementing "dgcode", a Geant4-based simulation framework. It is primarily intended for usage for simulation of thermal neutron detectors and related projects, and it originated in the Detector Group at the European Spallation Source ERIC.</description>
      <link>https://mctools.github.io/simplebuild-dgcode</link>
      <comments>https://github.com/mctools/simplebuild-dgcode</comments>
      <guid>https://pypi.org/packages/source/s/simple-build-dgcode/simple_build_dgcode-1.7.2.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 22:58:38 GMT</pubDate>
      <source>https://mctools.github.io/simplebuild-dgcode</source>
    </item>
    <item>
      <title>rtk-cli 1.2.0 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>RTK is a CLI proxy that filters and compresses command outputs before they reach an LLM's context window, achieving 60-90% token savings on typical dev commands like git, pytest, pip, ruff, docker, and more.</description>
      <comments>https://github.com/rtk-ai/rtk</comments>
      <pubDate>Sun, 08 Nov 2026 22:53:40 GMT</pubDate>
      <source>https://github.com/rtk-ai/rtk</source>
    </item>
    <item>
      <title>omsl 4.0.0 [linux-64, noarch]</title>
      <description>The package Modelica is a free library that is developed together with the Modelica language from the Modelica Association. It is also called Modelica Standard Library. It provides model components and standard component interfaces from many engineering domains. Each model comes with documentation included. OpenModelica &lt;https://openmodelica.org&gt; is a Modelica simulation environment. It is required for the usage of this library and is provided by the conda package omcompiler.</description>
      <guid>https://github.com/OpenModelica/OpenModelica-ModelicaStandardLibrary/archive/c11123102e94d4b5acb139331a6d85f45d8adcc0.zip</guid>
      <pubDate>Sun, 08 Nov 2026 22:52:52 GMT</pubDate>
      <source>https://github.com/OpenModelica/OpenModelica-ModelicaStandardLibrary</source>
    </item>
    <item>
      <title>jsonschema-rs 0.61.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>A high-performance JSON Schema validator for Python</description>
      <comments>https://github.com/Stranger6667/jsonschema</comments>
      <pubDate>Sun, 08 Nov 2026 22:51:51 GMT</pubDate>
      <source>https://pypi.org/project/jsonschema-rs</source>
    </item>
    <item>
      <title>anemoi-graphs 0.9.9 [noarch]</title>
      <description>A package to build graphs for data-driven forecasts.</description>
      <link>https://anemoi-graphs.readthedocs.io/</link>
      <guid>https://files.pythonhosted.org/packages/source/a/anemoi-graphs/anemoi_graphs-0.9.9.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 22:37:23 GMT</pubDate>
      <source>https://github.com/ecmwf/anemoi-graphs/</source>
    </item>
    <item>
      <title>py-markdown-table 2.0.0 [noarch]</title>
      <description>Package that generates markdown tables from a list of dicts</description>
      <guid>https://files.pythonhosted.org/packages/source/p/py-markdown-table/py_markdown_table-2.0.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 22:36:56 GMT</pubDate>
      <source>https://github.com/hvalev/py-markdown-table</source>
    </item>
    <item>
      <title>anemoi-models 0.20.0 [noarch]</title>
      <description>A package to hold various functions to support training of ML models.</description>
      <link>https://anemoi-models.readthedocs.io/</link>
      <guid>https://pypi.org/packages/source/a/anemoi-models/anemoi_models-0.15.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 22:33:46 GMT</pubDate>
      <source>https://github.com/ecmwf/anemoi-core/</source>
    </item>
    <item>
      <title>anemoi-registry 0.3.5 [noarch]</title>
      <description>A package to manage a registry or data-driven forecasts.</description>
      <link>https://anemoi-registry.readthedocs.io/</link>
      <guid>https://files.pythonhosted.org/packages/source/a/anemoi-registry/anemoi_registry-0.3.5.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 22:30:44 GMT</pubDate>
      <source>https://github.com/ecmwf/anemoi-registry/</source>
    </item>
    <item>
      <title>anemoi-inference 0.14.0 [noarch]</title>
      <description>A package to run inference from data-driven forecasts weather models.</description>
      <link>https://anemoi-inference.readthedocs.io/</link>
      <guid>https://files.pythonhosted.org/packages/source/a/anemoi-inference/anemoi_inference-0.14.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 22:29:25 GMT</pubDate>
      <source>https://github.com/ecmwf/anemoi-inference/</source>
    </item>
    <item>
      <title>anemoi-transform 0.5.1 [noarch]</title>
      <description>A package to hold various data transformation functions to support training of ML models on ECMWF data.</description>
      <link>https://anemoi-transform.readthedocs.io/</link>
      <guid>https://files.pythonhosted.org/packages/source/a/anemoi-transform/anemoi_transform-0.5.1.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 22:28:18 GMT</pubDate>
      <source>https://github.com/ecmwf/anemoi-transform/</source>
    </item>
    <item>
      <title>abicheck 0.8.0 [linux-64, noarch]</title>
      <description>abicheck compares releases of C/C++ libraries to reveal ABI and API changes, explain which changes can break existing consumers, and gate compatibility in CI. It combines binary metadata, debug information, public headers, build context, and optional source evidence, and reports what it could and could not verify.  It supports ELF, PE/COFF, and Mach-O binaries; Markdown, JSON, SARIF, HTML, and JUnit reports; snapshot baselines; policy profiles and suppressions; and Python, command-line, and GitHub Action integrations.</description>
      <link>https://abicheck.github.io/abicheck/</link>
      <comments>https://github.com/abicheck/abicheck</comments>
      <guid>https://github.com/abicheck/abicheck/archive/refs/tags/v0.8.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 22:22:04 GMT</pubDate>
      <source>https://github.com/abicheck/abicheck</source>
    </item>
    <item>
      <title>peekle 0.0.6 [noarch]</title>
      <description>Peek into pickle files without importing unknown dependencies</description>
      <pubDate>Sun, 08 Nov 2026 22:20:30 GMT</pubDate>
      <source>https://github.com/ecmwf/peekle/</source>
    </item>
    <item>
      <title>netgen-lvs 1.5.328 [linux-64]</title>
      <description>LVS tool for VLSI circuit netlists</description>
      <pubDate>Sun, 08 Nov 2026 21:50:25 GMT</pubDate>
      <source>https://github.com/RTimothyEdwards/netgen</source>
    </item>
    <item>
      <title>seaweedfs 4.53 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>SeaweedFS is a fast distributed storage system for blobs, objects, files, and data lake, for billions of files! Blob store has O(1) disk seek, cloud tiering. Filer supports Cloud Drive, cross-DC active-active replication, Kubernetes, POSIX FUSE mount, S3 API, S3 Gateway, Hadoop, WebDAV, encryption, Erasure Coding.</description>
      <link>https://seaweedfs.com/</link>
      <comments>https://github.com/seaweedfs/seaweedfs</comments>
      <pubDate>Sun, 08 Nov 2026 21:35:01 GMT</pubDate>
      <source>https://seaweedfs.com/</source>
    </item>
    <item>
      <title>fsspec-data 0.5.0 [linux-64, osx-64, win-64]</title>
      <description>Python Filesystem interface for non-filesystem data sources</description>
      <comments>https://github.com/1kbgz/fsspec-data</comments>
      <pubDate>Sun, 08 Nov 2026 21:05:10 GMT</pubDate>
      <source>https://github.com/1kbgz/fsspec-data</source>
    </item>
    <item>
      <title>cppbmad 20261108.0 [linux-64, osx-64, osx-arm64]</title>
      <description>cppbmad provides a modern C++ interface to the Fortran Bmad library using proxy classes for direct memory access. It supports tracking and other simulations coordination with wrapped procedures. pybmad uses nanobind to wrap the C++ layer, exposing the Bmad and Tao ecosystems to Python.</description>
      <link>https://github.com/ken-lauer/cppbmad/wiki</link>
      <pubDate>Sun, 08 Nov 2026 20:39:05 GMT</pubDate>
      <source>https://github.com/ken-lauer/cppbmad</source>
    </item>
    <item>
      <title>pybmad 20261108.0 [linux-64, osx-64, osx-arm64]</title>
      <description>cppbmad provides a modern C++ interface to the Fortran Bmad library using proxy classes for direct memory access. It supports tracking and other simulations coordination with wrapped procedures. pybmad uses nanobind to wrap the C++ layer, exposing the Bmad and Tao ecosystems to Python.</description>
      <link>https://github.com/ken-lauer/cppbmad/wiki</link>
      <pubDate>Sun, 08 Nov 2026 20:39:05 GMT</pubDate>
      <source>https://github.com/ken-lauer/cppbmad</source>
    </item>
    <item>
      <title>kikuchipy-base 0.14.0 [noarch]</title>
      <description>kikuchipy is a library for processing, simulating, and indexing of electron backscatter diffraction (EBSD) patterns in Python. It is built on the tools for multi-dimensional data analysis provided by the HyperSpy library.</description>
      <link>https://kikuchipy.org</link>
      <comments>https://github.com/pyxem/kikuchipy</comments>
      <guid>https://pypi.org/packages/source/k/kikuchipy/kikuchipy-0.14.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 20:35:29 GMT</pubDate>
      <source>https://kikuchipy.org</source>
    </item>
    <item>
      <title>pyrockimagelab 0.1.2 [noarch]</title>
      <description>Browser app and tools for core and outcrop image processing, data collection, and geological classification and analysis</description>
      <comments>https://github.com/GeoLarryLai/pyRockImageLab</comments>
      <pubDate>Sun, 08 Nov 2026 20:23:20 GMT</pubDate>
      <source>https://geolarrylai.com/projects/pyRockImageLab/</source>
    </item>
    <item>
      <title>lazygit 0.66.3 [linux-64, linux-aarch64, linux-ppc64le, linux-riscv64, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>A simple terminal UI for git commands, written in Go with the gocui library.</description>
      <link>https://godoc.org/github.com/jesseduffield/lazygit</link>
      <comments>https://github.com/jesseduffield/lazygit</comments>
      <guid>https://github.com/jesseduffield/lazygit/archive/v0.44.1.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 20:18:47 GMT</pubDate>
      <source>https://github.com/jesseduffield/lazygit</source>
    </item>
    <item>
      <title>jrl-cmakemodules 2.4.0 [noarch]</title>
      <description>`jrl-cmakemodules` provide functions to factorize and write more reliable CMake code. It is used by teams at CNRS, LAAS/HPP, JRL and INRIA, but can be integrated in any project.</description>
      <link>https://simplejson.readthedocs.io/</link>
      <comments>https://github.com/jrl-umi3218/jrl-cmakemodules/</comments>
      <pubDate>Sun, 08 Nov 2026 20:13:38 GMT</pubDate>
      <source>https://github.com/jrl-umi3218/jrl-cmakemodules</source>
    </item>
    <item>
      <title>jrl-cmakemodules-scripts 2.4.0 [noarch]</title>
      <description>`jrl-cmakemodules` provide functions to factorize and write more reliable CMake code. It is used by teams at CNRS, LAAS/HPP, JRL and INRIA, but can be integrated in any project.</description>
      <link>https://simplejson.readthedocs.io/</link>
      <comments>https://github.com/jrl-umi3218/jrl-cmakemodules/</comments>
      <pubDate>Sun, 08 Nov 2026 20:13:24 GMT</pubDate>
      <source>https://github.com/jrl-umi3218/jrl-cmakemodules</source>
    </item>
    <item>
      <title>gz-rendering8 8.2.2 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:59 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>gz-rendering8-ogre 8.2.2 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:59 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>libgz-rendering8 8.2.2 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:59 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>libgz-rendering8-ogre 8.2.2 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:59 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>gz-rendering 10.1.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:58 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>gz-rendering-ogre 10.1.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:58 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>gz-rendering9 9.4.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:58 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>gz-rendering9-ogre 9.4.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:58 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>libgz-rendering 10.1.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:58 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>libgz-rendering-ogre 10.1.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:58 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>libgz-rendering9 9.4.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:58 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>libgz-rendering9-ogre 9.4.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ library designed to provide an abstraction for different rendering engines. It offers unified APIs for creating 3D graphics applications.</description>
      <pubDate>Sun, 08 Nov 2026 20:08:58 GMT</pubDate>
      <source>https://github.com/gazebosim/gz-rendering</source>
    </item>
    <item>
      <title>pytensor-ml 0.4.0 [noarch]</title>
      <description>pytensor_ml is a neural network library built on PyTensor. Models are defined as symbolic graphs, so gradients come from PyTensor's autodiff and training functions compile to any of its backends.</description>
      <comments>https://github.com/pymc-devs/pytensor-ml</comments>
      <pubDate>Sun, 08 Nov 2026 20:05:04 GMT</pubDate>
      <source>https://github.com/pymc-devs/pytensor-ml</source>
    </item>
    <item>
      <title>napari-phasors 0.9.0 [noarch]</title>
      <description>A simple plugin to use phasor analysis</description>
      <link>https://github.com/napari-phasors/napari-phasors</link>
      <guid>https://files.pythonhosted.org/packages/source/n/napari-phasors/napari_phasors-0.9.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 19:24:45 GMT</pubDate>
      <source>https://github.com/napari-phasors/napari-phasors</source>
    </item>
    <item>
      <title>gp-connect 3.3.1 [linux-64, linux-aarch64, osx-64, osx-arm64]</title>
      <description>A GlobalProtect VPN client built on OpenConnect with support for SSO authentication. It provides command-line tools and the GP Connect desktop application for macOS, Linux, FreeBSD, and OpenBSD.</description>
      <comments>https://github.com/yuezk/GlobalProtect-openconnect</comments>
      <pubDate>Sun, 08 Nov 2026 19:24:35 GMT</pubDate>
      <source>https://github.com/yuezk/GlobalProtect-openconnect</source>
    </item>
    <item>
      <title>nvtop 3.4.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64]</title>
      <description>A (h)top like task monitor for GPUs and accelerators. It can handle multiple GPUs and print information about them in a htop-familiar way.</description>
      <guid>https://github.com/Syllo/nvtop/archive/refs/tags/3.4.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 18:32:43 GMT</pubDate>
      <source>https://github.com/Syllo/nvtop</source>
    </item>
    <item>
      <title>libghostty-py 0.3.0 [noarch]</title>
      <description>Python bindings for the Ghostty terminal emulation library</description>
      <comments>https://github.com/davidbrochart/libghostty-py</comments>
      <pubDate>Sun, 08 Nov 2026 18:32:06 GMT</pubDate>
      <source>https://github.com/davidbrochart/libghostty-py</source>
    </item>
    <item>
      <title>wetterdienst-with-influxdb 0.185.0 [noarch]</title>
      <description>Open weather data for humans</description>
      <link>https://wetterdienst.readthedocs.io/</link>
      <comments>https://github.com/earthobservations/wetterdienst</comments>
      <pubDate>Sun, 08 Nov 2026 18:29:54 GMT</pubDate>
      <source>https://github.com/earthobservations/wetterdienst</source>
    </item>
    <item>
      <title>wetterdienst-with-knmi 0.185.0 [noarch]</title>
      <description>Open weather data for humans</description>
      <link>https://wetterdienst.readthedocs.io/</link>
      <comments>https://github.com/earthobservations/wetterdienst</comments>
      <pubDate>Sun, 08 Nov 2026 18:29:54 GMT</pubDate>
      <source>https://github.com/earthobservations/wetterdienst</source>
    </item>
    <item>
      <title>wetterdienst-with-matplotlib 0.185.0 [noarch]</title>
      <description>Open weather data for humans</description>
      <link>https://wetterdienst.readthedocs.io/</link>
      <comments>https://github.com/earthobservations/wetterdienst</comments>
      <guid>https://pypi.org/packages/source/w/wetterdienst/wetterdienst-0.118.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 18:29:54 GMT</pubDate>
      <source>https://github.com/earthobservations/wetterdienst</source>
    </item>
    <item>
      <title>wetterdienst-with-mcp 0.185.0 [noarch]</title>
      <description>Open weather data for humans</description>
      <link>https://wetterdienst.readthedocs.io/</link>
      <comments>https://github.com/earthobservations/wetterdienst</comments>
      <pubDate>Sun, 08 Nov 2026 18:29:54 GMT</pubDate>
      <source>https://github.com/earthobservations/wetterdienst</source>
    </item>
    <item>
      <title>wetterdienst-with-pdf 0.185.0 [noarch]</title>
      <description>Open weather data for humans</description>
      <link>https://wetterdienst.readthedocs.io/</link>
      <comments>https://github.com/earthobservations/wetterdienst</comments>
      <guid>https://pypi.org/packages/source/w/wetterdienst/wetterdienst-0.118.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 18:29:54 GMT</pubDate>
      <source>https://github.com/earthobservations/wetterdienst</source>
    </item>
    <item>
      <title>wetterdienst-with-plotting 0.185.0 [noarch]</title>
      <description>Open weather data for humans</description>
      <link>https://wetterdienst.readthedocs.io/</link>
      <comments>https://github.com/earthobservations/wetterdienst</comments>
      <guid>https://pypi.org/packages/source/w/wetterdienst/wetterdienst-0.118.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 18:29:54 GMT</pubDate>
      <source>https://github.com/earthobservations/wetterdienst</source>
    </item>
    <item>
      <title>tflint 0.66.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>TFLint is a Terraform linter focused on possible errors, best practices, etc.</description>
      <comments>https://github.com/terraform-linters/tflint.git</comments>
      <pubDate>Sun, 08 Nov 2026 18:28:36 GMT</pubDate>
      <source>https://github.com/terraform-linters/tflint</source>
    </item>
    <item>
      <title>openvscode-server 1.96.0 [linux-64, linux-aarch64, osx-64, osx-arm64]</title>
      <description>Run upstream VS Code on a remote machine with access through a modern web browser from any device, anywhere.</description>
      <guid>https://github.com/gitpod-io/openvscode-server/archive/refs/tags/openvscode-server-v1.96.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 18:27:32 GMT</pubDate>
      <source>https://github.com/gitpod-io/openvscode-server</source>
    </item>
    <item>
      <title>python-rocksdb 0.7.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>Python bindings for RocksDB</description>
      <link>https://python-rocksdb.readthedocs.io/</link>
      <comments>https://github.com/twmht/python-rocksdb</comments>
      <guid>https://pypi.org/packages/source/p/python-rocksdb/python-rocksdb-0.7.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 18:26:40 GMT</pubDate>
      <source>https://github.com/twmht/python-rocksdb</source>
    </item>
    <item>
      <title>django-auditlog 3.4.2 [noarch]</title>
      <description>Audit log app for Django</description>
      <comments>https://github.com/jazzband/django-auditlog</comments>
      <guid>https://files.pythonhosted.org/packages/source/d/django-auditlog/django_auditlog-3.4.2.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 18:24:32 GMT</pubDate>
      <source>https://github.com/jazzband/django-auditlog</source>
    </item>
    <item>
      <title>nix 2.35.5 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64]</title>
      <description>Nix is a powerful package manager for Linux and other Unix systems that makes package management reliable and reproducible. It provides atomic upgrades and rollbacks, side-by-side installation of multiple versions of a package, multi-user package management and easy setup of build environments.</description>
      <link>https://nixos.org/nix/manual/</link>
      <comments>https://github.com/NixOS/nix</comments>
      <guid>https://nixos.org/releases/nix/nix-2.3.16/nix-2.3.16.tar.xz</guid>
      <pubDate>Sun, 08 Nov 2026 18:00:05 GMT</pubDate>
      <source>https://nixos.org/nix/</source>
    </item>
    <item>
      <title>pmd-pytcp 0.3.9 [noarch]</title>
      <description>PyTCP userspace TCP/IP stack</description>
      <pubDate>Sun, 08 Nov 2026 17:56:15 GMT</pubDate>
      <source>https://github.com/ccie18643/PyTCP</source>
    </item>
    <item>
      <title>causal-hybrid-control 1.3.0 [noarch]</title>
      <description>Physics-structured dynamics with a learned causal residual, controlled by constrained optimal control / MPC.</description>
      <comments>https://github.com/causaldyn/causal-hybrid-control</comments>
      <pubDate>Sun, 08 Nov 2026 17:02:00 GMT</pubDate>
      <source>https://github.com/causaldyn/causal-hybrid-control</source>
    </item>
    <item>
      <title>pytorch-metric-learning 3.0.2 [noarch]</title>
      <description>Composable metric learning losses, miners, distances, samplers, and retrieval metrics for PyTorch.</description>
      <guid>https://files.pythonhosted.org/packages/source/p/pytorch-metric-learning/pytorch_metric_learning-3.0.2.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 16:59:54 GMT</pubDate>
      <source>https://github.com/KevinMusgrave/pytorch-metric-learning</source>
    </item>
    <item>
      <title>pykdebugparser 1.10.0 [noarch]</title>
      <description>Parser for Apple kdebug events</description>
      <pubDate>Sun, 08 Nov 2026 16:45:10 GMT</pubDate>
      <source>https://github.com/matan1008/pykdebugparser</source>
    </item>
    <item>
      <title>pyiosbackup 0.2.4 [noarch]</title>
      <description>Parse iOS backups</description>
      <pubDate>Sun, 08 Nov 2026 16:45:06 GMT</pubDate>
      <source>https://github.com/matan1008/pyiosbackup</source>
    </item>
    <item>
      <title>pmd-net-proto 0.0.3 [noarch]</title>
      <description>Network protocol classes from the PyTCP stack</description>
      <pubDate>Sun, 08 Nov 2026 16:45:01 GMT</pubDate>
      <source>https://github.com/ccie18643/PyTCP</source>
    </item>
    <item>
      <title>asgiwebdav 2.2.1 [noarch]</title>
      <description>An asynchronous WebDAV server implementation</description>
      <pubDate>Sun, 08 Nov 2026 16:44:59 GMT</pubDate>
      <source>https://github.com/rexzhang/asgi-webdav</source>
    </item>
    <item>
      <title>opack2 0.2.0 [noarch]</title>
      <description>Python library for parsing Apple's OPACK format</description>
      <pubDate>Sun, 08 Nov 2026 16:44:56 GMT</pubDate>
      <source>https://github.com/doronz88/opack2</source>
    </item>
    <item>
      <title>derivkit 1.5.0 [noarch]</title>
      <description>A robust toolkit for stable numerical derivatives</description>
      <link>https://docs.derivkit.org/</link>
      <comments>https://github.com/derivkit/derivkit</comments>
      <pubDate>Sun, 08 Nov 2026 16:26:51 GMT</pubDate>
      <source>https://derivkit.org/</source>
    </item>
    <item>
      <title>robotframework-robocop-with-all 9.3.0 [noarch]</title>
      <description>Static code analysis tool (linter) and code formatter for Robot Framework (with all [extras])</description>
      <link>https://robocop.dev/</link>
      <comments>https://github.com/MarketSquare/robotframework-robocop</comments>
      <pubDate>Sun, 08 Nov 2026 16:18:01 GMT</pubDate>
      <source>https://pypi.org/project/robotframework-robocop</source>
    </item>
    <item>
      <title>robotframework-robocop-with-mcp 9.3.0 [noarch]</title>
      <description>Static code analysis tool (linter) and code formatter for Robot Framework (with [mcp])</description>
      <link>https://robocop.dev/</link>
      <comments>https://github.com/MarketSquare/robotframework-robocop</comments>
      <pubDate>Sun, 08 Nov 2026 16:18:01 GMT</pubDate>
      <source>https://pypi.org/project/robotframework-robocop</source>
    </item>
    <item>
      <title>robotframework-robocop-with-tests 9.3.0 [noarch]</title>
      <description>Static code analysis tool (linter) and code formatter for Robot Framework (with all [extras] and tests)</description>
      <link>https://robocop.dev/</link>
      <comments>https://github.com/MarketSquare/robotframework-robocop</comments>
      <pubDate>Sun, 08 Nov 2026 16:18:01 GMT</pubDate>
      <source>https://pypi.org/project/robotframework-robocop</source>
    </item>
    <item>
      <title>city2graph 1.1.0 [noarch]</title>
      <description>City2Graph is a Python library for converting datasets of geospatial relations into graphs. It is designed to facilitate graph data analytics in particular for urban studies and spatial analysis. It supports PyTorch Geometric to enable graph representation learning, such as Graph Neural Networks (GNNs).  Key features include: - Construct graphs from morphological datasets (buildings, streets, land use) - Construct graphs from transportation datasets (public transport networks) - Construct graphs from contiguity datasets (land use, land cover, administrative boundaries) - Construct graphs from mobility datasets (bike-sharing, migration, pedestrian flows) - Convert geospatial data into tensors for graph representation learning - Support for various proximity methods (KNN, Waxman, fixed radius) - Integration with PyTorch Geometric's Data and HeteroData formats  The library enables urban researchers and data scientists to transform complex geospatial relationships into graph structures suitable for network analysis and machine learning applications.</description>
      <link>https://city2graph.net</link>
      <comments>https://github.com/c2g-dev/city2graph</comments>
      <guid>https://pypi.org/packages/source/c/city2graph/city2graph-1.1.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 16:17:26 GMT</pubDate>
      <source>https://city2graph.net</source>
    </item>
    <item>
      <title>sonatype-iq-api-client 0.209.0 [noarch]</title>
      <description>Sonatype Lifecycle Public REST API</description>
      <comments>https://github.com/sonatype-nexus-community/nexus-iq-api-client</comments>
      <pubDate>Sun, 08 Nov 2026 16:17:14 GMT</pubDate>
      <source>https://pypi.org/project/sonatype-iq-api-client</source>
    </item>
    <item>
      <title>pygama 2.6.3 [noarch]</title>
      <description>pygama is a Python package for:  * converting physics data acquisition system output to LH5-format   HDF5 files (functionality provided by the legend-pydataobj and   legend-daq2lh5 packages) * performing bulk digital signal processing (DSP) on time-series data   (functionality provided by the dspeed package) * optimizing DSP routines and tuning associated analysis parameters * generating and selecting high-level event data for further analysis</description>
      <link>https://pygama.readthedocs.io/</link>
      <comments>https://github.com/legend-exp/pygama</comments>
      <pubDate>Sun, 08 Nov 2026 16:10:30 GMT</pubDate>
      <source>https://github.com/legend-exp/pygama</source>
    </item>
    <item>
      <title>pymomentum-core 0.1.114 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>A library for human kinematic motion and numerical optimization solvers to apply human motion</description>
      <link>https://facebookresearch.github.io/momentum/</link>
      <comments>https://github.com/facebookresearch/momentum</comments>
      <pubDate>Sun, 08 Nov 2026 15:57:56 GMT</pubDate>
      <source>https://facebookresearch.github.io/momentum/</source>
    </item>
    <item>
      <title>nvim 0.12.6 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>Ambitious Vim-fork focused on extensibility and agility</description>
      <comments>https://github.com/neovim/neovim</comments>
      <guid>https://github.com/neovim/neovim/archive/v0.11.1.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 15:57:30 GMT</pubDate>
      <source>https://neovim.io/</source>
    </item>
    <item>
      <title>pyscard 2.3.2 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>pyscard is a Python module adding smart card support (PC/SC) to Python.</description>
      <link>https://pyscard.sourceforge.io/user-guide.html</link>
      <comments>https://github.com/LudovicRousseau/pyscard</comments>
      <pubDate>Sun, 08 Nov 2026 15:23:55 GMT</pubDate>
      <source>https://pyscard.sourceforge.io/</source>
    </item>
    <item>
      <title>python-snap7 3.4.0 [noarch]</title>
      <description>python-snap7 is a pure Python implementation of the Siemens S7 communication protocol, providing client, server, partner and LOGO! support for interfacing with Siemens S7 PLCs.</description>
      <link>https://python-snap7.readthedocs.io/en/latest/</link>
      <comments>https://github.com/gijzelaerr/python-snap7</comments>
      <pubDate>Sun, 08 Nov 2026 15:23:15 GMT</pubDate>
      <source>https://github.com/gijzelaerr/python-snap7</source>
    </item>
    <item>
      <title>json-c 0.19 [linux-64, linux-aarch64, linux-ppc64le, linux-riscv64, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>A JSON implementation in C</description>
      <link>https://json-c.github.io/json-c/</link>
      <comments>https://github.com/json-c/json-c</comments>
      <guid>https://s3.amazonaws.com/json-c_releases/releases/json-c-0.18.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 15:22:37 GMT</pubDate>
      <source>https://github.com/json-c/json-c</source>
    </item>
    <item>
      <title>libgdal-avif 3.13.3 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>Raster driver AVIF for the Geospatial Data Abstraction Library (GDAL)</description>
      <guid>http://download.osgeo.org/gdal/3.12.4/gdal-3.12.4.tar.xz</guid>
      <pubDate>Sun, 08 Nov 2026 14:19:22 GMT</pubDate>
      <source>http://www.gdal.org/</source>
    </item>
    <item>
      <title>libgdal-core-devel 3.13.3 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>GDAL is a translator library for raster and vector geospatial data formats that is released under an X/MIT style Open Source license by the Open Source Geospatial Foundation.</description>
      <guid>http://download.osgeo.org/gdal/3.12.4/gdal-3.12.4.tar.xz</guid>
      <pubDate>Sun, 08 Nov 2026 14:19:22 GMT</pubDate>
      <source>http://www.gdal.org/</source>
    </item>
    <item>
      <title>libgdal-heif 3.13.3 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>Raster driver HEIF for the Geospatial Data Abstraction Library (GDAL)</description>
      <guid>http://download.osgeo.org/gdal/3.12.4/gdal-3.12.4.tar.xz</guid>
      <pubDate>Sun, 08 Nov 2026 14:19:22 GMT</pubDate>
      <source>http://www.gdal.org/</source>
    </item>
    <item>
      <title>libgdal-adbc 3.13.3 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64]</title>
      <description>Vector driver ADBC for the Geospatial Data Abstraction Library (GDAL)</description>
      <guid>http://download.osgeo.org/gdal/3.12.4/gdal-3.12.4.tar.xz</guid>
      <pubDate>Sun, 08 Nov 2026 14:17:00 GMT</pubDate>
      <source>http://www.gdal.org/</source>
    </item>
    <item>
      <title>libghostty-vt 1.0.0 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>Standalone terminal sequence parsing library, without the Ghostty GUI.</description>
      <link>https://ghostty.org/docs</link>
      <comments>https://github.com/ghostty-org/ghostty</comments>
      <pubDate>Sun, 08 Nov 2026 14:02:06 GMT</pubDate>
      <source>https://ghostty.org/</source>
    </item>
    <item>
      <title>higgstools 1.3 [linux-64, linux-aarch64, osx-64, osx-arm64]</title>
      <description>HiggsTools is a toolbox for comparing BSM models with extended scalar sectors to experimental results. It unifies and extends HiggsBounds and HiggsSignals in modern C++ with a native Python interface, and contains the sub-libraries HiggsPredictions, HiggsBounds and HiggsSignals. The experimental data sets are distributed separately (https://gitlab.com/higgsbounds/hbdataset and https://gitlab.com/higgsbounds/hsdataset).</description>
      <link>https://higgsbounds.gitlab.io/higgstools/</link>
      <comments>https://gitlab.com/higgsbounds/higgstools</comments>
      <pubDate>Sun, 08 Nov 2026 13:48:57 GMT</pubDate>
      <source>https://gitlab.com/higgsbounds/higgstools</source>
    </item>
    <item>
      <title>libhiggstools 1.3 [linux-64, linux-aarch64, osx-64, osx-arm64]</title>
      <description>HiggsTools is a toolbox for comparing BSM models with extended scalar sectors to experimental results. It unifies and extends HiggsBounds and HiggsSignals in modern C++ with a native Python interface, and contains the sub-libraries HiggsPredictions, HiggsBounds and HiggsSignals. The experimental data sets are distributed separately (https://gitlab.com/higgsbounds/hbdataset and https://gitlab.com/higgsbounds/hsdataset).</description>
      <link>https://higgsbounds.gitlab.io/higgstools/</link>
      <comments>https://gitlab.com/higgsbounds/higgstools</comments>
      <pubDate>Sun, 08 Nov 2026 13:48:57 GMT</pubDate>
      <source>https://gitlab.com/higgsbounds/higgstools</source>
    </item>
    <item>
      <title>libhiggstools-devel 1.3 [linux-64, linux-aarch64, osx-64, osx-arm64]</title>
      <description>HiggsTools is a toolbox for comparing BSM models with extended scalar sectors to experimental results. It unifies and extends HiggsBounds and HiggsSignals in modern C++ with a native Python interface, and contains the sub-libraries HiggsPredictions, HiggsBounds and HiggsSignals. The experimental data sets are distributed separately (https://gitlab.com/higgsbounds/hbdataset and https://gitlab.com/higgsbounds/hsdataset).</description>
      <link>https://higgsbounds.gitlab.io/higgstools/</link>
      <comments>https://gitlab.com/higgsbounds/higgstools</comments>
      <pubDate>Sun, 08 Nov 2026 13:48:57 GMT</pubDate>
      <source>https://gitlab.com/higgsbounds/higgstools</source>
    </item>
    <item>
      <title>plasticparcels 0.4.0 [noarch]</title>
      <description>plasticparcels is a python package for simulating the transport and dispersion of plastics in the ocean. The tool is based on the parcels computational Lagrangian ocean analysis framework, providing a modular and customisable collection of methods, notebooks, and tutorials for advecting virtual plastic particles with a wide range of physical properties.</description>
      <link>https://plastic.oceanparcels.org/</link>
      <comments>https://github.com/OceanParcels/plasticparcels</comments>
      <guid>https://github.com/oceanparcels/plasticparcels/archive/v0.3.1.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 13:41:02 GMT</pubDate>
      <source>https://github.com/OceanParcels/plasticparcels</source>
    </item>
    <item>
      <title>libqrencode 4.1.1 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>Libqrencode is a fast and compact library for encoding data in a QR Code symbol, a 2D symbology that can be scanned by handy terminals such as a mobile phone with CCD. The capacity of QR Code is up to 7000 digits or 4000 characters and has high robustness.</description>
      <link>https://fukuchi.org/works/qrencode/manual/index.html</link>
      <comments>https://github.com/fukuchi/libqrencode</comments>
      <guid>https://github.com/fukuchi/libqrencode/archive/refs/tags/v4.1.1.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 13:21:18 GMT</pubDate>
      <source>https://fukuchi.org/works/qrencode/</source>
    </item>
    <item>
      <title>libpetsird 0.13.0 [linux-64, osx-arm64]</title>
      <description>Library and tools for working with Positron Emission Tomography Standardization Initiative Raw Data (PETSIRD)</description>
      <pubDate>Sun, 08 Nov 2026 13:15:03 GMT</pubDate>
      <source>https://github.com/ETSInitiative/PETSIRD</source>
    </item>
    <item>
      <title>gkx 2.9.0 [noarch]</title>
      <description>GKX is a JAX-native gyrokinetic solver designed for differentiability, high-performance accelerator execution, and advanced stellarator optimization. The code employs a Hermite-Laguerre velocity space, Fourier perpendicular coordinates, and field-aligned flux-tube geometry to simulate linear and nonlinear electrostatic and electromagnetic turbulence in magnetized plasmas.</description>
      <link>https://gkx.readthedocs.io/</link>
      <comments>https://github.com/uwplasma/GKX</comments>
      <pubDate>Sun, 08 Nov 2026 13:13:59 GMT</pubDate>
      <source>https://gkx.readthedocs.io/</source>
    </item>
    <item>
      <title>libspatialite 5.1.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>Extend the SQLite core to support fully fledged Spatial SQL capabilities</description>
      <guid>https://www.gaia-gis.it/gaia-sins/libspatialite-sources/libspatialite-5.1.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 13:12:29 GMT</pubDate>
      <source>https://www.gaia-gis.it/fossil/libspatialite/index</source>
    </item>
    <item>
      <title>fireworks-ai 1.2.33 [noarch]</title>
      <description>Python client library for the Fireworks.ai Generative AI Platform</description>
      <guid>https://pypi.org/packages/source/f/fireworks-ai/fireworks_ai-0.15.13.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 13:10:52 GMT</pubDate>
      <source>https://fireworks.ai/</source>
    </item>
    <item>
      <title>moab 5.6.0 [linux-64, linux-aarch64, osx-64, osx-arm64]</title>
      <description>The Mesh-Oriented datABase (MOAB) is a component for representing and evaluating mesh data. MOAB can store structured and unstructured mesh, consisting of elements in the finite element zoo plus polygons and polyhedra.</description>
      <link>https://web.cels.anl.gov/projects/sigma/docs/moab</link>
      <comments>https://bitbucket.org/fathomteam/moab/</comments>
      <guid>https://web.cels.anl.gov/projects/sigma/downloads/moab/moab-5.6.0.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 12:48:30 GMT</pubDate>
      <source>https://sigma.mcs.anl.gov/moab-library</source>
    </item>
    <item>
      <title>ada-py-desktop 0.137.0 [win-64]</title>
      <description>A native desktop window for Ada Studio, the web app that ships with ada-py. `ada desktop` starts the local viewer server from the installed ada-py and opens it in this window, with native pop-out windows, remembered window positions and a native 3D mouse reader. Requires the Microsoft Edge WebView2 runtime, which ships with current Windows.</description>
      <link>https://krande.github.io/adapy/</link>
      <pubDate>Sun, 08 Nov 2026 12:43:25 GMT</pubDate>
      <source>https://github.com/krande/adapy</source>
    </item>
    <item>
      <title>types-networkx 3.6.1.20261108 [noarch]</title>
      <description>Typing stubs for networkx</description>
      <comments>https://github.com/python/typeshed</comments>
      <guid>https://pypi.org/packages/source/t/types-networkx/types_networkx-3.6.1.20260210.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 11:26:23 GMT</pubDate>
      <source>https://github.com/python/typeshed</source>
    </item>
    <item>
      <title>salabim 26.0.9 [noarch]</title>
      <description>discrete event simulation in Python</description>
      <guid>https://files.pythonhosted.org/packages/source/s/salabim/salabim-26.0.9.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 10:05:35 GMT</pubDate>
      <source>https://github.com/salabim/salabim</source>
    </item>
    <item>
      <title>salabim-animation 26.0.9 [linux-64, osx-64, win-64]</title>
      <description>discrete event simulation in Python</description>
      <guid>https://files.pythonhosted.org/packages/source/s/salabim/salabim-26.0.9.tar.gz</guid>
      <pubDate>Sun, 08 Nov 2026 10:05:33 GMT</pubDate>
      <source>https://github.com/salabim/salabim</source>
    </item>
    <item>
      <title>libarrow-all 26.0.1 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ libraries for Apache Arrow</description>
      <pubDate>Sun, 08 Nov 2026 09:25:52 GMT</pubDate>
      <source>http://github.com/apache/arrow</source>
    </item>
    <item>
      <title>libarrow-substrait 26.0.1 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>C++ libraries for Apache Arrow Substrait</description>
      <pubDate>Sun, 08 Nov 2026 09:25:27 GMT</pubDate>
      <source>http://github.com/apache/arrow</source>
    </item>
  </channel>
</rss>
