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    <title>Anaconda repository updates</title>
    <link>http://repo.continuum.io/pkgs/</link>
    <description>Recent updates to the conda default repository</description>
    <language>en</language>
    <copyright>Copyright 2026, Anaconda, Inc.</copyright>
    <pubDate>Thu, 04 Jun 2026 16:00:52 GMT</pubDate>
    <item>
      <title>arviz-plots 1.1.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>ArviZ-plots provides plotting utilities for the ArviZ ecosystem, building on arviz-base and arviz-stats.</description>
      <link>https://python.arviz.org/projects/plots</link>
      <comments>https://github.com/arviz-devs/arviz-plots</comments>
      <guid>https://pypi.org/packages/source/a/arviz-plots/arviz_plots-1.1.0.tar.gz</guid>
      <pubDate>Thu, 04 Jun 2026 12:10:09 GMT</pubDate>
      <source>https://github.com/arviz-devs/arviz-plots</source>
    </item>
    <item>
      <title>cryptography-vectors 48.0.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-32, win-64]</title>
      <description>Cryptography is a package which provides cryptographic recipes and primitives to Python developers. Our goal is for it to be your &quot;cryptographic standard library&quot;. It supports 3.8+ / PyPy 7.3.11+. cryptography includes both high level recipes and low level interfaces to common cryptographic algorithms such as symmetric ciphers, message digests, and key derivation functions.</description>
      <link>https://cryptography.io</link>
      <comments>https://github.com/pyca/cryptography</comments>
      <guid>https://github.com/pyca/cryptography/archive/refs/tags/48.0.0.tar.gz</guid>
      <pubDate>Thu, 04 Jun 2026 10:26:16 GMT</pubDate>
      <source>https://github.com/pyca/cryptography</source>
    </item>
    <item>
      <title>cryptography 48.0.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>Cryptography is a package which provides cryptographic recipes and primitives to Python developers. Our goal is for it to be your &quot;cryptographic standard library&quot;. It supports 3.8+ / PyPy 7.3.11+. cryptography includes both high level recipes and low level interfaces to common cryptographic algorithms such as symmetric ciphers, message digests, and key derivation functions.</description>
      <link>https://cryptography.io</link>
      <comments>https://github.com/pyca/cryptography</comments>
      <guid>https://github.com/pyca/cryptography/archive/refs/tags/48.0.0.tar.gz</guid>
      <pubDate>Thu, 04 Jun 2026 10:25:38 GMT</pubDate>
      <source>https://github.com/pyca/cryptography</source>
    </item>
    <item>
      <title>go-nocgo 1.26.4 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.</description>
      <link>https://go.dev/doc</link>
      <comments>https://github.com/golang</comments>
      <pubDate>Thu, 04 Jun 2026 09:42:35 GMT</pubDate>
      <source>https://go.dev</source>
    </item>
    <item>
      <title>go 1.26.4 [linux-32, linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-32, win-64]</title>
      <description>Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.</description>
      <link>https://golang.org/doc</link>
      <comments>https://go.googlesource.com/go</comments>
      <pubDate>Thu, 04 Jun 2026 09:40:47 GMT</pubDate>
      <source>https://go.dev</source>
    </item>
    <item>
      <title>go-cgo 1.26.4 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.</description>
      <link>https://go.dev/doc</link>
      <comments>https://github.com/golang</comments>
      <pubDate>Thu, 04 Jun 2026 09:40:13 GMT</pubDate>
      <source>https://go.dev</source>
    </item>
    <item>
      <title>marimo 0.23.8 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>A reactive Python notebook that's reproducible, git-friendly, and deployable as scripts or apps</description>
      <link>https://docs.marimo.io</link>
      <comments>https://github.com/marimo-team/marimo</comments>
      <guid>https://pypi.org/packages/source/m/marimo/marimo-0.23.8.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 22:50:08 GMT</pubDate>
      <source>https://marimo.io</source>
    </item>
    <item>
      <title>trl 1.4.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Train transformer language models with reinforcement learning.</description>
      <link>https://huggingface.co/docs/trl</link>
      <comments>https://github.com/huggingface/trl</comments>
      <guid>https://github.com/huggingface/trl/archive/v1.4.0.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 20:25:32 GMT</pubDate>
      <source>https://github.com/huggingface/trl</source>
    </item>
    <item>
      <title>bitsandbytes 0.49.2 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>bitsandbytes is a library for efficient deep learning that enables 8-bit and 4-bit quantization for reduced memory usage and faster inference. It provides 8-bit optimizers for memory-efficient training, LLM.int8() for 8-bit matrix multiplication, and QLoRA for 4-bit quantization. The library is designed to work seamlessly with PyTorch and integrates with the Hugging Face ecosystem for large language model fine-tuning.</description>
      <link>https://huggingface.co/docs/bitsandbytes</link>
      <comments>https://github.com/bitsandbytes-foundation/bitsandbytes</comments>
      <guid>https://github.com/bitsandbytes-foundation/bitsandbytes/archive/refs/tags/0.49.2.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 19:45:49 GMT</pubDate>
      <source>https://github.com/bitsandbytes-foundation/bitsandbytes</source>
    </item>
    <item>
      <title>tyro 1.0.13 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>tyro.cli() generates command-line interfaces and config parsers from type-annotated Python functions and objects (dataclasses, pydantic, etc.).</description>
      <link>https://brentyi.github.io/tyro/</link>
      <comments>https://github.com/brentyi/tyro</comments>
      <guid>https://pypi.org/packages/source/t/tyro/tyro-1.0.13.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 19:30:30 GMT</pubDate>
      <source>https://github.com/brentyi/tyro</source>
    </item>
    <item>
      <title>shtab 1.8.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>- What: Automatically generate shell tab completion scripts for Python CLI apps - Why: Speed &amp; correctness. Alternatives like `argcomplete` &amp; `pyzshcomplete` are slow and have side-effects - How: `shtab` processes an `argparse.ArgumentParser` object to generate a tab completion script for your shell</description>
      <link>https://docs.iterative.ai/shtab</link>
      <comments>https://github.com/iterative/shtab</comments>
      <guid>https://pypi.org/packages/source/s/shtab/shtab-1.8.0.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 17:21:03 GMT</pubDate>
      <source>https://github.com/iterative/shtab</source>
    </item>
    <item>
      <title>streamlit-tests 1.58.0 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>Streamlit lets you turn data scripts into sharable web apps in minutes, not weeks. It's all Python, open-source, and free!</description>
      <link>https://docs.streamlit.io</link>
      <comments>https://github.com/streamlit/streamlit</comments>
      <pubDate>Wed, 03 Jun 2026 15:11:42 GMT</pubDate>
      <source>https://streamlit.io</source>
    </item>
    <item>
      <title>streamlit 1.58.0 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>Streamlit lets you turn data scripts into sharable web apps in minutes, not weeks. It's all Python, open-source, and free!</description>
      <link>https://docs.streamlit.io</link>
      <comments>https://github.com/streamlit/streamlit</comments>
      <guid>https://pypi.io/packages/source/s/streamlit/streamlit-1.41.1.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 15:11:25 GMT</pubDate>
      <source>https://streamlit.io</source>
    </item>
    <item>
      <title>fribidi 1.0.16 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>One of the missing links stopping the penetration of free software in Middle East is the lack of support for the Arabic and Hebrew alphabets. In order to have proper Arabic and Hebrew support, the bidi algorithm needs to be implemented. It is our hope that this library will stimulate more free software in the Middle Eastern countries</description>
      <link>https://github.com/fribidi/fribidi</link>
      <comments>https://github.com/fribidi/fribidi</comments>
      <guid>https://github.com/fribidi/fribidi/releases/download/v1.0.16/fribidi-1.0.16.tar.xz</guid>
      <pubDate>Wed, 03 Jun 2026 13:12:19 GMT</pubDate>
      <source>https://github.com/fribidi/fribidi</source>
    </item>
    <item>
      <title>astropy-iers-data 0.2026.6.1.17.39.59 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>The iers package provides access to the tables provided by the International Earth Rotation and Reference Systems (IERS) service, in particular the Earth Orientation data allowing interpolation of published UT1-UTC and polar motion values for given times.</description>
      <link>https://docs.astropy.org/en/latest/utils/iers.html</link>
      <comments>https://github.com/astropy/astropy-iers-data</comments>
      <guid>https://pypi.org/packages/source/a/astropy-iers-data/astropy_iers_data-0.2026.6.1.17.39.59.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 12:40:13 GMT</pubDate>
      <source>https://github.com/astropy/astropy-iers-data</source>
    </item>
    <item>
      <title>s3fs 2026.4.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-32, win-64]</title>
      <description>S3FS builds on aiobotocore to provide a convenient Python filesystem interface for S3.</description>
      <link>https://s3fs.readthedocs.io</link>
      <comments>https://github.com/fsspec/s3fs</comments>
      <guid>https://pypi.org/packages/source/s/s3fs/s3fs-2026.4.0.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 12:40:07 GMT</pubDate>
      <source>https://s3fs.readthedocs.io</source>
    </item>
    <item>
      <title>triad 1.0.2 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>A collection of python utility functions for Fugue projects</description>
      <link>https://triad.readthedocs.io</link>
      <comments>https://github.com/fugue-project/triad</comments>
      <guid>https://github.com/fugue-project/triad/archive/refs/tags/v1.0.2.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 12:40:04 GMT</pubDate>
      <source>https://github.com/fugue-project/triad</source>
    </item>
    <item>
      <title>getopt-win32 1.0 [win-64]</title>
      <description>This repository contains a port of getopt which can be used with Visual C++ and clang on Windows. It is intended to be used with vcpkg and Conan.  The Visual C++ port was originally done by Ludvik Jerabek, and described in the Full getopt Port for Unicode and Multibyte Microsoft Visual C, C++, or MFC Projects article. This repository contains a copy of that code and the article.</description>
      <link>https://github.com/libimobiledevice-win32/getopt/blob/master/article.md</link>
      <comments>https://github.com/libimobiledevice-win32/getopt</comments>
      <guid>https://github.com/libimobiledevice-win32/getopt/archive/refs/tags/v1.0.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 12:36:18 GMT</pubDate>
      <source>https://vcpkg.info/port/getopt-win32</source>
    </item>
    <item>
      <title>geotiff 1.7.4 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>GeoTIFF represents an effort by over 160 different remote sensing, GIS, cartographic, and surveying related companies and organizations to establish a TIFF based interchange format for georeferenced raster imagery.</description>
      <link>https://www.ogc.org/standards/geotiff</link>
      <comments>https://github.com/OSGeo/libgeotiff</comments>
      <guid>https://download.osgeo.org/geotiff/libgeotiff/libgeotiff-1.7.4.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 12:34:07 GMT</pubDate>
      <source>https://trac.osgeo.org/geotiff</source>
    </item>
    <item>
      <title>qdrant 1.18.1 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Qdrant (read: quadrant) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.</description>
      <link>https://qdrant.tech/documentation</link>
      <comments>https://github.com/qdrant/qdrant</comments>
      <guid>https://github.com/qdrant/qdrant/archive/refs/tags/v1.18.1.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 12:31:54 GMT</pubDate>
      <source>https://qdrant.tech</source>
    </item>
    <item>
      <title>fastmcp 3.1.1 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>FastMCP is the standard framework for building MCP applications. The Model Context Protocol (MCP) connects LLMs to tools and data. FastMCP gives you everything you need to go from prototype to production</description>
      <link>https://gofastmcp.com</link>
      <comments>https://github.com/PrefectHQ/fastmcp</comments>
      <guid>https://pypi.org/packages/source/f/fastmcp/fastmcp-3.1.1.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 12:23:28 GMT</pubDate>
      <source>https://gofastmcp.com</source>
    </item>
    <item>
      <title>ds_store 1.3.2 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>ds_store lets you examine and modify .DS_Store files from Python code; since it is written in pure Python, it is portable and will run on any platform, not just Mac OS X.</description>
      <link>https://ds-store.readthedocs.io/en/latest/</link>
      <comments>https://github.com/dmgbuild/ds_store</comments>
      <guid>https://pypi.org/packages/source/d/ds_store/ds_store-1.3.2.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 12:17:09 GMT</pubDate>
      <source>https://github.com/dmgbuild/ds_store</source>
    </item>
    <item>
      <title>libtiff 4.7.1 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>This software provides support for the Tag Image File Format (TIFF), a widely used format for storing image data.</description>
      <link>https://libtiff.gitlab.io/libtiff</link>
      <comments>https://gitlab.com/libtiff/libtiff</comments>
      <guid>https://download.osgeo.org/libtiff/tiff-4.2.0.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 11:32:07 GMT</pubDate>
      <source>https://libtiff.gitlab.io/libtiff</source>
    </item>
    <item>
      <title>mac_alias 2.2.3 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>mac_alias lets you generate or read binary Alias and Bookmark records from Python code.</description>
      <link>https://mac-alias.readthedocs.io/en/latest/</link>
      <comments>https://github.com/dmgbuild/mac_alias</comments>
      <guid>https://pypi.org/packages/source/m/mac_alias/mac_alias-2.2.3.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 10:15:52 GMT</pubDate>
      <source>http://alastairs-place.net/projects/mac_alias</source>
    </item>
    <item>
      <title>exceptiongroup 1.3.1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>&quot;This is a backport of the BaseExceptionGroup and ExceptionGroup classes from Python 3.11. It contains the following:   * The exceptiongroup.BaseExceptionGroup and exceptiongroup.ExceptionGroup classes   * A utility function (exceptiongroup.catch()) for catching exceptions possibly nested in an exception group   * Patches to the TracebackException class that properly formats exception groups (installed on import)   * An exception hook that handles formatting of exception groups through TracebackException (installed on import)   * Special versions of some of the functions from the traceback module&quot;</description>
      <link>https://docs.python.org/3/library/exceptions.html</link>
      <comments>https://github.com/agronholm/exceptiongroup</comments>
      <guid>https://pypi.org/packages/source/e/exceptiongroup/exceptiongroup-1.3.1.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 09:22:17 GMT</pubDate>
      <source>https://pypi.org/project/exceptiongroup</source>
    </item>
    <item>
      <title>jsonref 1.1.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>jsonref is a library for automatic dereferencing of JSON Reference objects for Python.</description>
      <link>https://github.com/AnacondaRecipes/jsonref-feedstock/blob/main/README.md</link>
      <comments>https://github.com/gazpachoking/jsonref</comments>
      <guid>https://github.com/gazpachoking/jsonref/archive/refs/tags/v1.1.0.tar.gz</guid>
      <pubDate>Wed, 03 Jun 2026 08:55:35 GMT</pubDate>
      <source>https://github.com/gazpachoking/jsonref</source>
    </item>
    <item>
      <title>xarray 2026.4.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-32, win-64]</title>
      <description>xarray (formerly xray) is an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!  xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy_-like arrays, which allows for a more intuitive, more concise, and less error-prone developer experience. The package includes a large and growing library of domain-agnostic functions for advanced analytics and visualization with these data structures.  xarray was inspired by and borrows heavily from pandas_, the popular data analysis package focused on labelled tabular data. It is particularly tailored to working with netCDF_ files, which were the source of xarray's data model, and integrates tightly with dask_ for parallel computing.</description>
      <link>https://docs.xarray.dev</link>
      <comments>https://github.com/pydata/xarray</comments>
      <guid>https://pypi.org/packages/source/x/xarray/xarray-2026.4.0.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 18:24:42 GMT</pubDate>
      <source>https://xarray.dev</source>
    </item>
    <item>
      <title>anaconda_powershell_prompt 1.1.1 [win-64]</title>
      <description>Package to create shortcuts for PowerShell with an activated conda environment.</description>
      <link>https://github.com/AnacondaRecipes/anaconda_powershell_prompt-feedstock/blob/main/README.md</link>
      <comments>https://github.com/AnacondaRecipes/anaconda_powershell_prompt-feedstock</comments>
      <pubDate>Tue, 02 Jun 2026 16:12:17 GMT</pubDate>
      <source>https://github.com/AnacondaRecipes/anaconda_powershell_prompt-feedstock</source>
    </item>
    <item>
      <title>anaconda_prompt 1.1.1 [win-64]</title>
      <description>Package to create shortcuts for terminals with an activated conda environment.</description>
      <link>https://github.com/AnacondaRecipes/anaconda_prompt-feedstock/blob/main/README.md</link>
      <comments>https://github.com/AnacondaRecipes/anaconda_prompt-feedstock</comments>
      <pubDate>Tue, 02 Jun 2026 16:10:56 GMT</pubDate>
      <source>https://github.com/AnacondaRecipes/anaconda_prompt-feedstock</source>
    </item>
    <item>
      <title>rust_win-64 1.96.0 [win-64]</title>
      <description>Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.</description>
      <link>https://www.rust-lang.org/learn</link>
      <comments>https://github.com/rust-lang</comments>
      <pubDate>Tue, 02 Jun 2026 15:10:18 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>rust-gnu_win-64 1.96.0 [win-64]</title>
      <description>Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.</description>
      <link>https://www.rust-lang.org/learn</link>
      <comments>https://github.com/rust-lang</comments>
      <pubDate>Tue, 02 Jun 2026 15:10:15 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>rust_osx-arm64 1.96.0 [osx-arm64]</title>
      <description>Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.</description>
      <link>https://www.rust-lang.org/learn</link>
      <comments>https://github.com/rust-lang</comments>
      <pubDate>Tue, 02 Jun 2026 15:09:54 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>rust_linux-aarch64 1.96.0 [linux-aarch64]</title>
      <description>Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.</description>
      <link>https://www.rust-lang.org/learn</link>
      <comments>https://github.com/rust-lang</comments>
      <pubDate>Tue, 02 Jun 2026 15:09:41 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>rust_linux-64 1.96.0 [linux-64]</title>
      <description>Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.</description>
      <link>https://www.rust-lang.org/learn</link>
      <comments>https://github.com/rust-lang</comments>
      <pubDate>Tue, 02 Jun 2026 15:09:39 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>winloop 0.6.3 [win-64]</title>
      <description>Windows version of uvloop</description>
      <link>https://github.com/Vizonex/Winloop/blob/main/README.md</link>
      <comments>https://github.com/Vizonex/Winloop</comments>
      <guid>https://pypi.org/packages/source/w/winloop/winloop-0.6.3.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 15:07:23 GMT</pubDate>
      <source>https://github.com/Vizonex/Winloop</source>
    </item>
    <item>
      <title>rust-std-wasm32-wasip1 1.96.0 [linux-64, linux-aarch64, noarch, osx-64, osx-arm64, win-64]</title>
      <description>This package provides the compiler (rustc) and the documentation utilities rustdoc.</description>
      <link>https://www.rust-lang.org/learn</link>
      <comments>https://github.com/rust-lang/rust</comments>
      <pubDate>Tue, 02 Jun 2026 13:46:05 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>rust-std-wasm32-wasip2 1.96.0 [linux-64, linux-aarch64, noarch, osx-64, osx-arm64, win-64]</title>
      <description>This package provides the compiler (rustc) and the documentation utilities rustdoc.</description>
      <link>https://www.rust-lang.org/learn</link>
      <comments>https://github.com/rust-lang/rust</comments>
      <pubDate>Tue, 02 Jun 2026 13:45:46 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>python-xxhash 3.7.0 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>Python binding for xxHash which is an extremely fast hash algorithm, processing at RAM speed limits. Code is highly portable, and produces hashes identical across all platforms (little / big endian).</description>
      <link>https://github.com/ifduyue/python-xxhash</link>
      <comments>https://github.com/ifduyue/python-xxhash</comments>
      <guid>https://pypi.org/packages/source/x/xxhash/xxhash-3.7.0.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 13:27:39 GMT</pubDate>
      <source>https://github.com/ifduyue/python-xxhash</source>
    </item>
    <item>
      <title>rust-gnu 1.96.0 [win-32, win-64]</title>
      <description>This package provides the compiler (rustc) and the documentation utilities rustdoc.</description>
      <link>https://www.rust-lang.org/learn</link>
      <comments>https://github.com/rust-lang/rust</comments>
      <pubDate>Tue, 02 Jun 2026 13:27:22 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>mypy_extensions 1.1.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>The 'mypy_extensions' module defines experimental extensions to the standard 'typing' module that are supported by the mypy typechecker.</description>
      <link>https://mypy.readthedocs.io</link>
      <comments>https://github.com/python/mypy_extensions</comments>
      <guid>https://pypi.org/packages/source/m/mypy_extensions/mypy_extensions-1.1.0.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 13:26:58 GMT</pubDate>
      <source>https://www.mypy-lang.org/</source>
    </item>
    <item>
      <title>rust 1.96.0 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>This package provides the compiler (rustc) and the documentation utilities rustdoc.</description>
      <link>https://www.rust-lang.org/learn</link>
      <comments>https://github.com/rust-lang/rust</comments>
      <guid>https://static.rust-lang.org/dist/rust-1.29.0-x86_64-unknown-linux-gnu.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 13:11:56 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>flask-jwt-extended 4.7.4 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-64]</title>
      <description>Flask-JWT-Extended not only adds support for using JSON Web Tokens (JWT) to Flask for protecting routes, but also many helpful (and optional) features built in to make working with JSON Web Tokens easier.</description>
      <link>https://flask-jwt-extended.readthedocs.io</link>
      <comments>https://github.com/vimalloc/flask-jwt-extended</comments>
      <guid>https://pypi.org/packages/source/F/Flask-JWT-Extended/flask_jwt_extended-4.7.4.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 12:58:48 GMT</pubDate>
      <source>https://github.com/vimalloc/flask-jwt-extended</source>
    </item>
    <item>
      <title>fsspec 2026.4.0 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-32, win-64]</title>
      <description>To produce a template or specification for a file-system interface, that specific implementations should follow, so that applications making use of them can rely on a common behaviour and not have to worry about the specific internal implementation decisions with any given backend.</description>
      <link>https://filesystem-spec.readthedocs.io</link>
      <comments>https://github.com/fsspec/filesystem_spec</comments>
      <guid>https://github.com/fsspec/filesystem_spec/archive/refs/tags/2026.4.0.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 12:23:18 GMT</pubDate>
      <source>https://github.com/fsspec/filesystem_spec</source>
    </item>
    <item>
      <title>pdm-pep517 1.1.4 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>This is the backend for PDM projects, but you can also use it alone. It reads the metadata of PEP 621 format and converts it to Core metadata.</description>
      <link>https://github.com/pdm-project/pdm-pep517/blob/master/README.md</link>
      <comments>https://github.com/pdm-project/pdm-pep517</comments>
      <guid>https://pypi.org/packages/source/p/pdm-pep517/pdm-pep517-1.1.4.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 09:24:01 GMT</pubDate>
      <source>https://pypi.org/project/pdm-pep517</source>
    </item>
    <item>
      <title>tbb4py 2023.0.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>oneTBB is a flexible C++ library that simplifies the work of adding parallelism to complex applications, even if you are not a threading expert.</description>
      <link>https://software.intel.com/en-us/oneapi-tbb-documentation</link>
      <comments>https://github.com/oneapi-src/oneTBB</comments>
      <guid>https://github.com/oneapi-src/oneTBB/archive/refs/tags/v2023.0.0.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 08:04:57 GMT</pubDate>
      <source>https://github.com/oneapi-src/oneTBB</source>
    </item>
    <item>
      <title>tbb-devel 2023.0.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>oneTBB is a flexible C++ library that simplifies the work of adding parallelism to complex applications, even if you are not a threading expert.</description>
      <link>https://software.intel.com/en-us/oneapi-tbb-documentation</link>
      <comments>https://github.com/oneapi-src/oneTBB</comments>
      <guid>https://github.com/oneapi-src/oneTBB/archive/refs/tags/v2023.0.0.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 08:03:16 GMT</pubDate>
      <source>https://github.com/oneapi-src/oneTBB</source>
    </item>
    <item>
      <title>tbb 2023.0.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>oneTBB is a flexible C++ library that simplifies the work of adding parallelism to complex applications, even if you are not a threading expert.</description>
      <link>https://software.intel.com/en-us/oneapi-tbb-documentation</link>
      <comments>https://github.com/oneapi-src/oneTBB</comments>
      <guid>https://github.com/oneapi-src/oneTBB/archive/refs/tags/v2023.0.0.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 08:03:03 GMT</pubDate>
      <source>https://github.com/oneapi-src/oneTBB</source>
    </item>
    <item>
      <title>anaconda-ai 0.6.0 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>Download, launch, and integrate AI models curated by Anaconda</description>
      <link>https://anaconda.cloud/docs/cli/commands/anaconda-ai.html</link>
      <comments>https://github.com/anaconda/anaconda-ai</comments>
      <guid>https://pypi.org/packages/source/a/anaconda-ai/anaconda_ai-0.6.0.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 07:09:22 GMT</pubDate>
      <source>https://anaconda.cloud</source>
    </item>
    <item>
      <title>llama.cpp-tests 0.0.9453 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>Test executables for llama.cpp</description>
      <link>https://github.com/ggml-org/llama.cpp</link>
      <comments>https://github.com/ggml-org/llama.cpp</comments>
      <guid>https://github.com/ggml-org/llama.cpp/archive/b9453.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 04:41:18 GMT</pubDate>
      <source>https://github.com/ggml-org/llama.cpp</source>
    </item>
    <item>
      <title>llama.cpp 0.0.9453 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>Server example and tools for llama.cpp, using libllama</description>
      <link>https://github.com/ggml-org/llama.cpp</link>
      <comments>https://github.com/ggml-org/llama.cpp</comments>
      <guid>https://github.com/ggml-org/llama.cpp/archive/b9453.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 04:35:04 GMT</pubDate>
      <source>https://github.com/ggml-org/llama.cpp</source>
    </item>
    <item>
      <title>libllama 0.0.9453 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>Inference of Meta's LLaMA model (and others) in pure C/C++</description>
      <link>https://github.com/ggml-org/llama.cpp</link>
      <comments>https://github.com/ggml-org/llama.cpp</comments>
      <guid>https://github.com/ggml-org/llama.cpp/archive/b9453.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 04:32:07 GMT</pubDate>
      <source>https://github.com/ggml-org/llama.cpp</source>
    </item>
    <item>
      <title>llama.cpp-tools 0.0.9453 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>Scripts and conversion tools that ship with llama.cpp, including model vocabulary files and templates</description>
      <link>https://github.com/ggml-org/llama.cpp</link>
      <comments>https://github.com/ggml-org/llama.cpp</comments>
      <guid>https://github.com/ggml-org/llama.cpp/archive/b9453.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 00:56:18 GMT</pubDate>
      <source>https://github.com/ggml-org/llama.cpp</source>
    </item>
    <item>
      <title>gguf 0.18.0.9453 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>Read and write ML models in GGUF for GGML</description>
      <link>https://github.com/ggml-org/llama.cpp/tree/master/gguf-py</link>
      <comments>https://github.com/ggml-org/llama.cpp/tree/master/gguf-py</comments>
      <guid>https://github.com/ggml-org/llama.cpp/archive/b9453.tar.gz</guid>
      <pubDate>Tue, 02 Jun 2026 00:55:51 GMT</pubDate>
      <source>https://ggml.ai</source>
    </item>
    <item>
      <title>anaconda-connector-conda 0.1.17 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Plugin that exposes conda capabilities through a custom API. Part of the Anaconda Connector ecosystem.</description>
      <link>https://github.com/anaconda/anaconda-connector</link>
      <comments>https://github.com/anaconda/anaconda-connector</comments>
      <pubDate>Mon, 01 Jun 2026 22:22:17 GMT</pubDate>
      <source>https://github.com/anaconda/anaconda-connector</source>
    </item>
    <item>
      <title>notebook 7.5.6 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>The Jupyter notebook is a web-based notebook environment for interactive computing.</description>
      <link>https://jupyter-notebook.readthedocs.io</link>
      <comments>https://github.com/jupyter/notebook</comments>
      <guid>https://pypi.org/packages/source/n/notebook/notebook-7.5.6.tar.gz</guid>
      <pubDate>Mon, 01 Jun 2026 21:21:42 GMT</pubDate>
      <source>https://github.com/jupyter/notebook</source>
    </item>
    <item>
      <title>requests 2.34.2 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-32, win-64]</title>
      <description>Requests is the only Non-GMO HTTP library for Python, safe for human consumption.</description>
      <link>https://requests.readthedocs.io</link>
      <comments>https://github.com/psf/requests</comments>
      <guid>https://pypi.org/packages/source/r/requests/requests-2.34.2.tar.gz</guid>
      <pubDate>Mon, 01 Jun 2026 21:20:35 GMT</pubDate>
      <source>https://requests.readthedocs.io</source>
    </item>
    <item>
      <title>black 26.5.1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-64]</title>
      <description>Black is the uncompromising Python code formatter. By using it, you agree to cease control over minutiae of hand-formatting. In return, Black gives you speed, determinism, and freedom from pycodestyle nagging about formatting. You will save time and mental energy for more important matters.</description>
      <link>https://black.readthedocs.io</link>
      <comments>https://github.com/psf/black</comments>
      <guid>https://pypi.org/packages/source/b/black/black-26.5.1.tar.gz</guid>
      <pubDate>Mon, 01 Jun 2026 21:19:51 GMT</pubDate>
      <source>https://github.com/psf/black</source>
    </item>
    <item>
      <title>anaconda-connector-core 0.1.17 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Core framework of the anaconda connector with essential runtime components. Provides the plugin system, base components, and CLI entry point for the Anaconda Connector ecosystem.</description>
      <link>https://github.com/anaconda/anaconda-connector</link>
      <comments>https://github.com/anaconda/anaconda-connector</comments>
      <pubDate>Mon, 01 Jun 2026 21:16:46 GMT</pubDate>
      <source>https://github.com/anaconda/anaconda-connector</source>
    </item>
    <item>
      <title>xlwings 0.36.0 [osx-64, osx-arm64, win-32, win-64]</title>
      <description>xlwings is a BSD-licensed Python library that makes it easy to call Python from Excel and vice versa. Note: To use xlwings PRO functionality, you need to use a license key. See https://docs.xlwings.org/en/latest/pro/license_key.html</description>
      <link>https://docs.xlwings.org</link>
      <comments>https://github.com/xlwings/xlwings</comments>
      <guid>https://pypi.org/packages/source/x/xlwings/xlwings-0.36.0.tar.gz</guid>
      <pubDate>Mon, 01 Jun 2026 21:16:43 GMT</pubDate>
      <source>https://www.xlwings.org</source>
    </item>
    <item>
      <title>zarr 3.2.1 [linux-64, linux-aarch64, linux-ppc64le, noarch, osx-64, osx-arm64, win-64]</title>
      <description>Zarr is a format for the storage of chunked, compressed, N-dimensional arrays. These documents describe the Zarr format and its Python implementation.</description>
      <link>https://zarr.readthedocs.io</link>
      <comments>https://github.com/zarr-developers/zarr-python</comments>
      <guid>https://pypi.org/packages/source/z/zarr/zarr-3.2.1.tar.gz</guid>
      <pubDate>Mon, 01 Jun 2026 21:16:02 GMT</pubDate>
      <source>https://github.com/zarr-developers/zarr-python</source>
    </item>
    <item>
      <title>anaconda-connector-utilities 0.1.17 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>General-purpose utilities for common tasks, part of the Anaconda Connector project.</description>
      <link>https://github.com/anaconda/anaconda-connector</link>
      <comments>https://github.com/anaconda/anaconda-connector</comments>
      <pubDate>Mon, 01 Jun 2026 20:41:16 GMT</pubDate>
      <source>https://github.com/anaconda/anaconda-connector</source>
    </item>
    <item>
      <title>numpy 2.4.6 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>NumPy is the fundamental package needed for scientific computing with Python.</description>
      <link>https://numpy.org/doc/stable/reference</link>
      <comments>https://github.com/numpy/numpy</comments>
      <guid>https://github.com/numpy/numpy/releases/download/v1.26.4/numpy-1.26.4.tar.gz</guid>
      <pubDate>Mon, 01 Jun 2026 20:39:47 GMT</pubDate>
      <source>https://numpy.org</source>
    </item>
    <item>
      <title>numpy-base 2.4.6 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>NumPy is the fundamental package needed for scientific computing with Python.</description>
      <link>https://numpy.org/doc/stable/reference</link>
      <comments>https://github.com/numpy/numpy</comments>
      <guid>https://github.com/numpy/numpy/releases/download/v1.26.4/numpy-1.26.4.tar.gz</guid>
      <pubDate>Mon, 01 Jun 2026 20:39:16 GMT</pubDate>
      <source>https://numpy.org</source>
    </item>
    <item>
      <title>python-kubernetes 36.0.1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-64]</title>
      <description>The official Kubernetes python client. This is a client library for Kubernetes, an open-source container-orchestration system for automating computer application deployment, scaling, and management.</description>
      <link>https://github.com/kubernetes-client/python/blob/master/README.md</link>
      <comments>https://github.com/kubernetes-client/python</comments>
      <guid>https://pypi.org/packages/source/k/kubernetes/kubernetes-36.0.1.tar.gz</guid>
      <pubDate>Sat, 30 May 2026 08:36:25 GMT</pubDate>
      <source>https://github.com/kubernetes-client/python</source>
    </item>
    <item>
      <title>pandas 3.0.3 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with &quot;relational&quot; or &quot;labeled&quot; data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal.</description>
      <link>https://pandas.pydata.org/pandas-docs/version/3.0.3/index.html</link>
      <comments>https://github.com/pandas-dev/pandas</comments>
      <guid>https://pypi.org/packages/source/p/pandas/pandas-3.0.3.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 16:30:03 GMT</pubDate>
      <source>https://pandas.pydata.org</source>
    </item>
    <item>
      <title>c-blosc2 3.1.2 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>Next generation of C-Blosc library bringing in a new container (schunk) that can optionally be persisted (frame). Also, more filters, codecs and other bells and whistles like pre-filter support have been added. The API tries to be as compatible as possible with the original C-Blosc.</description>
      <link>https://www.blosc.org/c-blosc2/c-blosc2.html</link>
      <comments>https://github.com/Blosc/c-blosc2</comments>
      <guid>https://github.com/Blosc/c-blosc2/archive/v3.1.2.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 14:35:49 GMT</pubDate>
      <source>https://github.com/Blosc/c-blosc2</source>
    </item>
    <item>
      <title>repoze.lru 0.8 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>The Repoze project is a collection of technologies which bridges the WSGI and Zope worlds.</description>
      <link>https://repoze.readthedocs.io</link>
      <comments>https://github.com/repoze</comments>
      <guid>https://pypi.org/packages/source/r/repoze.lru/repoze_lru-0.8.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 14:32:06 GMT</pubDate>
      <source>http://www.repoze.org</source>
    </item>
    <item>
      <title>jupytext 1.19.3 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>Represent Jupyter notebooks as Markdown documents or Julia, Python or R scripts. Convert any script or Markdown document to a Jupyter notebook. Round trip conversion is robust and well tested.  Use these simpler representations to do version control and collaborate on Jupyter notebooks. And refactor your notebooks encoded as scripts in your favorite IDE.  Jupytext is available directly in Jupyter Notebook and JupyterLab, and also on the command line.</description>
      <link>https://jupytext.readthedocs.io</link>
      <comments>https://github.com/mwouts/jupytext</comments>
      <guid>https://pypi.org/packages/source/j/jupytext/jupytext-1.19.3.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 14:28:55 GMT</pubDate>
      <source>https://github.com/mwouts/jupytext</source>
    </item>
    <item>
      <title>cutlass 4.5.1 [linux-64, linux-aarch64, win-64]</title>
      <description>CUTLASS is a collection of abstractions for implementing high-performance matrix-matrix multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement. CUTLASS decomposes these &quot;moving parts&quot; into reusable, modular software components and abstractions.</description>
      <link>https://docs.nvidia.com/cutlass</link>
      <comments>https://github.com/NVIDIA/cutlass</comments>
      <guid>https://github.com/NVIDIA/cutlass/archive/refs/tags/v4.5.1.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 14:28:54 GMT</pubDate>
      <source>https://github.com/NVIDIA/cutlass</source>
    </item>
    <item>
      <title>aiofile 3.9.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Real asynchronous file operations with asyncio support.</description>
      <link>https://github.com/mosquito/aiofile</link>
      <comments>https://github.com/mosquito/aiofile</comments>
      <guid>https://pypi.io/packages/source/a/aiofile/aiofile-3.9.0.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 14:15:36 GMT</pubDate>
      <source>https://github.com/mosquito/aiofile</source>
    </item>
    <item>
      <title>smartypants 2.0.2 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>Python with the SmartyPants</description>
      <link>https://smartypants.readthedocs.io</link>
      <comments>https://github.com/justinmayer/smartypants.py</comments>
      <guid>https://github.com/leohemsted/smartypants.py/archive/refs/tags/v2.0.2.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 13:59:25 GMT</pubDate>
      <source>https://github.com/justinmayer/smartypants.py</source>
    </item>
    <item>
      <title>anaconda-mcp 1.1.1 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>The Anaconda MCP Server connects your conda environments to MCP-compatible AI assistants, enabling them to create, modify, and delete environments and packages on your machine. Install only if you trust the AI assistant you intend to connect and understand it can take real actions on your machine. By installing you acknowledge: * The AI assistant you connect to this MCP server is an independent third-party model, not a product or service of Anaconda. * Anaconda is NOT responsible for the actions the AI assistant directs within your environment, including unintended changes or deletions.</description>
      <link>https://github.com/anaconda/anaconda-mcp</link>
      <comments>https://github.com/anaconda/anaconda-mcp</comments>
      <pubDate>Fri, 29 May 2026 13:48:16 GMT</pubDate>
      <source>https://github.com/anaconda/anaconda-mcp</source>
    </item>
    <item>
      <title>snowflake-connector-python 4.6.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64]</title>
      <description>The Snowflake Connector for Python provides an interface for developing Python applications that can connect to Snowflake and perform all standard operations. It provides a programming alternative to developing applications in Java or C/C++ using the Snowflake JDBC or ODBC drivers.</description>
      <link>https://docs.snowflake.net/manuals/user-guide/python-connector.html</link>
      <comments>https://github.com/snowflakedb/snowflake-connector-python</comments>
      <guid>https://github.com/snowflakedb/snowflake-connector-python/archive/refs/tags/v4.6.0.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 13:05:59 GMT</pubDate>
      <source>https://github.com/snowflakedb/snowflake-connector-python</source>
    </item>
    <item>
      <title>twisted 26.4.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>Twisted is an event-based framework for internet applications, written in Python.</description>
      <link>https://docs.twisted.org</link>
      <comments>https://github.com/twisted/twisted</comments>
      <guid>https://pypi.org/packages/source/t/twisted/twisted-26.4.0.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 12:50:20 GMT</pubDate>
      <source>https://twisted.org</source>
    </item>
    <item>
      <title>qdldl-python 0.1.9.post1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>Python interface to the [QDLDL](https://github.com/osqp/qdldl) free LDL factorization routine for quasi-definite linear systems: `Ax = b`.</description>
      <link>https://github.com/osqp/qdldl-python/blob/master/README.md</link>
      <comments>https://github.com/osqp/qdldl-python</comments>
      <guid>https://github.com/osqp/qdldl-python/archive/refs/tags/v0.1.9.post1.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 07:52:54 GMT</pubDate>
      <source>https://github.com/osqp/qdldl-python</source>
    </item>
    <item>
      <title>cfn-lint 1.51.2 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>Validate CloudFormation yaml/json templates against the CloudFormation spec and additional checks. Includes checking valid values for resource properties and best practices.</description>
      <link>https://github.com/aws-cloudformation/cfn-lint/tree/main/docs/getting_started</link>
      <comments>https://github.com/aws-cloudformation/cfn-lint</comments>
      <guid>https://github.com/aws-cloudformation/cfn-lint/archive/refs/tags/v1.51.2.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 07:49:17 GMT</pubDate>
      <source>https://github.com/aws-cloudformation/cfn-lint</source>
    </item>
    <item>
      <title>watchfiles 1.2.0 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>Watchfiles is a simple, modern and high performance file watching and code reload in python. Underlying file system notifications are handled by the Notify rust library.</description>
      <link>https://watchfiles.helpmanual.io</link>
      <comments>https://github.com/samuelcolvin/watchfiles</comments>
      <guid>https://pypi.org/packages/source/w/watchfiles/watchfiles-1.2.0.tar.gz</guid>
      <pubDate>Fri, 29 May 2026 07:49:13 GMT</pubDate>
      <source>https://watchfiles.helpmanual.io</source>
    </item>
    <item>
      <title>preliz 0.25.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Preliz provides tools for exploring and eliciting probability distributions, with integrations for the ArviZ and PyMC ecosystems.</description>
      <link>https://preliz.readthedocs.io/</link>
      <comments>https://github.com/arviz-devs/preliz</comments>
      <guid>https://pypi.org/packages/source/p/preliz/preliz-0.25.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 21:20:26 GMT</pubDate>
      <source>https://github.com/arviz-devs/preliz</source>
    </item>
    <item>
      <title>triton 3.7.0 [linux-64]</title>
      <description>This is the development repository of Triton, a language and compiler for writing highly efficient custom Deep-Learning primitives. The aim of Triton is to provide an open-source environment to write fast code at higher productivity than CUDA, but also with higher flexibility than other existing DSLs.</description>
      <link>https://triton-lang.org/</link>
      <comments>https://github.com/triton-lang/triton</comments>
      <guid>https://github.com/triton-lang/triton/archive/refs/tags/v3.7.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 14:59:10 GMT</pubDate>
      <source>https://github.com/triton-lang/triton</source>
    </item>
    <item>
      <title>py-key-value-aio 0.4.4 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>This library provides a pluggable interface for key-value stores with support for multiple backends, TTL handling, type safety, and extensible wrappers.</description>
      <link>https://strawgate.com/py-key-value/</link>
      <comments>https://github.com/strawgate/py-key-value</comments>
      <pubDate>Thu, 28 May 2026 14:22:17 GMT</pubDate>
      <source>https://github.com/strawgate/py-key-value</source>
    </item>
    <item>
      <title>traceloop-sdk 0.57.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Traceloops Python SDK allows you to easily start monitoring and debugging your LLM execution. Tracing is done in a non-intrusive way, built on top of OpenTelemetry. You can choose to export the traces to Traceloop, or to your existing observability stack.</description>
      <link>https://www.traceloop.com/docs/openllmetry/introduction</link>
      <comments>https://github.com/traceloop/openllmetry</comments>
      <guid>https://pypi.org/packages/source/t/traceloop-sdk/traceloop_sdk-0.57.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 12:02:34 GMT</pubDate>
      <source>https://www.traceloop.com/openllmetry</source>
    </item>
    <item>
      <title>pytensor-distributions 0.1.3 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Probability distributions implemented on top of PyTensor for use in PyMC and related libraries.</description>
      <link>https://pytensor-distributions.readthedocs.io/</link>
      <comments>https://github.com/pymc-devs/pytensor-distributions</comments>
      <guid>https://pypi.org/packages/source/p/pytensor-distributions/pytensor_distributions-0.1.3.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 10:51:58 GMT</pubDate>
      <source>https://github.com/pymc-devs/pytensor-distributions</source>
    </item>
    <item>
      <title>libqdldl 0.1.9 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>A free LDL factorisation routine for quasi-definite linear systems.</description>
      <link>https://github.com/osqp/qdldl/blob/master/README.md</link>
      <comments>https://github.com/osqp/qdldl</comments>
      <guid>https://github.com/osqp/qdldl/archive/v0.1.9.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 10:03:09 GMT</pubDate>
      <source>https://github.com/osqp/qdldl</source>
    </item>
    <item>
      <title>allure-robotframework 2.16.0 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>Allure Framework is a flexible lightweight multi-language test report tool that not only shows a very concise representation of what have been tested in a neat web report form, but allows everyone participating in the development process to extract maximum of useful information from everyday execution of tests.</description>
      <link>https://allurereport.org/docs/</link>
      <comments>https://github.com/allure-framework/allure-python</comments>
      <guid>https://github.com/allure-framework/allure-python/archive/2.16.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 09:17:59 GMT</pubDate>
      <source>https://github.com/allure-framework/allure-python</source>
    </item>
    <item>
      <title>allure-pytest-bdd 2.16.0 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>Allure Framework is a flexible lightweight multi-language test report tool that not only shows a very concise representation of what have been tested in a neat web report form, but allows everyone participating in the development process to extract maximum of useful information from everyday execution of tests.</description>
      <link>https://allurereport.org/docs/</link>
      <comments>https://github.com/allure-framework/allure-python</comments>
      <guid>https://github.com/allure-framework/allure-python/archive/2.16.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 09:17:44 GMT</pubDate>
      <source>https://github.com/allure-framework/allure-python</source>
    </item>
    <item>
      <title>allure-pytest 2.16.0 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>Allure Framework is a flexible lightweight multi-language test report tool that not only shows a very concise representation of what have been tested in a neat web report form, but allows everyone participating in the development process to extract maximum of useful information from everyday execution of tests.</description>
      <link>https://allurereport.org/docs/</link>
      <comments>https://github.com/allure-framework/allure-python</comments>
      <guid>https://github.com/allure-framework/allure-python/archive/2.16.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 09:17:30 GMT</pubDate>
      <source>https://github.com/allure-framework/allure-python</source>
    </item>
    <item>
      <title>allure-nose2 2.16.0 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>Allure Framework is a flexible lightweight multi-language test report tool that not only shows a very concise representation of what have been tested in a neat web report form, but allows everyone participating in the development process to extract maximum of useful information from everyday execution of tests.</description>
      <link>https://allurereport.org/docs/</link>
      <comments>https://github.com/allure-framework/allure-python</comments>
      <guid>https://github.com/allure-framework/allure-python/archive/2.16.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 09:17:17 GMT</pubDate>
      <source>https://github.com/allure-framework/allure-python</source>
    </item>
    <item>
      <title>allure-behave 2.16.0 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>Allure Framework is a flexible lightweight multi-language test report tool that not only shows a very concise representation of what have been tested in a neat web report form, but allows everyone participating in the development process to extract maximum of useful information from everyday execution of tests.</description>
      <link>https://allurereport.org/docs/</link>
      <comments>https://github.com/allure-framework/allure-python</comments>
      <guid>https://github.com/allure-framework/allure-python/archive/2.16.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 09:17:03 GMT</pubDate>
      <source>https://github.com/allure-framework/allure-python</source>
    </item>
    <item>
      <title>allure-python-commons-test 2.16.0 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>Allure Framework is a flexible lightweight multi-language test report tool that not only shows a very concise representation of what have been tested in a neat web report form, but allows everyone participating in the development process to extract maximum of useful information from everyday execution of tests.</description>
      <link>https://allurereport.org/docs/</link>
      <comments>https://github.com/allure-framework/allure-python</comments>
      <guid>https://github.com/allure-framework/allure-python/archive/2.16.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 09:16:49 GMT</pubDate>
      <source>https://github.com/allure-framework/allure-python</source>
    </item>
    <item>
      <title>allure-python-commons 2.16.0 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>Allure Framework is a flexible lightweight multi-language test report tool that not only shows a very concise representation of what have been tested in a neat web report form, but allows everyone participating in the development process to extract maximum of useful information from everyday execution of tests.</description>
      <link>https://allurereport.org/docs/</link>
      <comments>https://github.com/allure-framework/allure-python</comments>
      <guid>https://github.com/allure-framework/allure-python/archive/2.16.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 09:16:34 GMT</pubDate>
      <source>https://github.com/allure-framework/allure-python</source>
    </item>
    <item>
      <title>arviz-stats 1.1.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>arviz-stats provides statistical computation and diagnostics for the ArviZ ecosystem, building on arviz-base and xarray.</description>
      <link>https://python.arviz.org/projects/stats</link>
      <comments>https://github.com/arviz-devs/arviz-stats</comments>
      <guid>https://pypi.org/packages/source/a/arviz-stats/arviz_stats-1.1.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 09:13:53 GMT</pubDate>
      <source>https://python.arviz.org</source>
    </item>
    <item>
      <title>arviz-stats-core 1.1.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>arviz-stats provides statistical computation and diagnostics for the ArviZ ecosystem, building on arviz-base and xarray.</description>
      <link>https://python.arviz.org/projects/stats</link>
      <comments>https://github.com/arviz-devs/arviz-stats</comments>
      <guid>https://pypi.org/packages/source/a/arviz-stats/arviz_stats-1.1.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 09:13:38 GMT</pubDate>
      <source>https://python.arviz.org</source>
    </item>
    <item>
      <title>langgraph-prebuilt 1.1.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Library with high-level APIs for creating and executing LangGraph agents and tools.</description>
      <link>https://github.com/langchain-ai/langgraph/blob/main/libs/prebuilt/README.md</link>
      <comments>https://github.com/langchain-ai/langgraph/tree/main/libs/prebuilt</comments>
      <guid>https://pypi.org/packages/source/l/langgraph-prebuilt/langgraph_prebuilt-1.1.0.tar.gz</guid>
      <pubDate>Thu, 28 May 2026 07:02:23 GMT</pubDate>
      <source>https://www.github.com/langchain-ai/langgraph</source>
    </item>
    <item>
      <title>mcp-compose 0.1.12 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>A powerful, production-ready framework for composing and orchestrating Model Context Protocol (MCP) servers with advanced management capabilities, REST API, and modern Web UI.</description>
      <link>https://github.com/datalayer/mcp-compose</link>
      <comments>https://github.com/datalayer/mcp-compose</comments>
      <guid>https://github.com/datalayer/mcp-compose/archive/refs/tags/v0.1.12.zip</guid>
      <pubDate>Wed, 27 May 2026 22:04:19 GMT</pubDate>
      <source>https://github.com/datalayer/mcp-compose</source>
    </item>
    <item>
      <title>environments-mcp-server 1.0.7 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>The Environments MCP Server connects your conda environments to MCP-compatible AI assistants, enabling them to create, modify, and delete environments and packages on your machine. Install only if you trust the AI assistant you intend to connect and understand it can take real actions on your machine. By installing you acknowledge:   * The AI assistant you connect to this MCP server is an independent third-party model, not a product or service of Anaconda.   * Anaconda is NOT responsible for the actions the AI assistant directs within your environment, including unintended changes or deletions.</description>
      <link>https://github.com/anaconda/environments-mcp</link>
      <comments>https://github.com/anaconda/environments-mcp</comments>
      <pubDate>Wed, 27 May 2026 21:08:39 GMT</pubDate>
      <source>https://github.com/anaconda/environments-mcp</source>
    </item>
    <item>
      <title>tzdata 2026b [noarch]</title>
      <description>The Time Zone Database (called tz, tzdb or zoneinfo) contains code and data that represent the history of local time for many representative locations around the globe.  It is updated periodically to reflect changes made by political bodies to time zone boundaries, UTC offsets, and daylight-saving rules.</description>
      <link>https://data.iana.org/time-zones/tz-link.html</link>
      <comments>https://github.com/eggert/tz</comments>
      <pubDate>Wed, 27 May 2026 16:45:50 GMT</pubDate>
      <source>https://www.iana.org/time-zones</source>
    </item>
    <item>
      <title>libwebp 1.6.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>WebP is a method of lossy and lossless compression that can be used on a large variety of photographic, translucent and graphical images found on the web. The degree of lossy compression is adjustable so a user can choose the trade-off between file size and image quality. libwebp-base provides the headers and shared libraries. For cwebp and dwep, binaries install libwebp.</description>
      <link>https://developers.google.com/speed/webp/docs/using</link>
      <comments>https://chromium.googlesource.com/webm/libwebp</comments>
      <guid>https://storage.googleapis.com/downloads.webmproject.org/releases/webp/libwebp-1.6.0.tar.gz</guid>
      <pubDate>Wed, 27 May 2026 16:42:34 GMT</pubDate>
      <source>https://developers.google.com/speed/webp</source>
    </item>
    <item>
      <title>cuda-pathfinder 1.5.5 [linux-64, linux-aarch64, win-64]</title>
      <description>Public API for loading NVIDIA Dynamic Libraries</description>
      <link>https://nvidia.github.io/cuda-python/cuda-pathfinder</link>
      <comments>https://github.com/NVIDIA/cuda-python/tree/main/cuda_pathfinder</comments>
      <guid>https://github.com/NVIDIA/cuda-python/releases/download/cuda-pathfinder-v1.5.5/cuda-python-cuda-pathfinder-v1.5.5.tar.gz</guid>
      <pubDate>Wed, 27 May 2026 14:44:53 GMT</pubDate>
      <source>https://nvidia.github.io/cuda-python/cuda-pathfinder</source>
    </item>
    <item>
      <title>pybind11 3.0.4 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64]</title>
      <description>pybind11 is a lightweight header-only library that exposes C++ types in Python and vice versa, mainly to create Python bindings of existing C++ code. Its goals and syntax are similar to the excellent Boost.Python library by David Abrahams: to minimize boilerplate code in traditional extension modules by inferring type information using compile-time introspection.</description>
      <link>https://pybind11.readthedocs.io</link>
      <comments>https://github.com/pybind/pybind11</comments>
      <guid>https://github.com/pybind/pybind11/archive/v3.0.4.tar.gz</guid>
      <pubDate>Wed, 27 May 2026 14:38:33 GMT</pubDate>
      <source>https://github.com/pybind/pybind11</source>
    </item>
    <item>
      <title>pybind11-global 3.0.4 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64]</title>
      <description>pybind11 is a lightweight header-only library that exposes C++ types in Python and vice versa, mainly to create Python bindings of existing C++ code. Its goals and syntax are similar to the excellent Boost.Python library by David Abrahams: to minimize boilerplate code in traditional extension modules by inferring type information using compile-time introspection.</description>
      <link>https://pybind11.readthedocs.io</link>
      <comments>https://github.com/pybind/pybind11</comments>
      <guid>https://github.com/pybind/pybind11/archive/v3.0.4.tar.gz</guid>
      <pubDate>Wed, 27 May 2026 14:38:02 GMT</pubDate>
      <source>https://github.com/pybind/pybind11</source>
    </item>
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