<?xml version="1.0" ?>
<rss version="2.0">
  <channel>
    <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>Fri, 06 Nov 2026 12:00:43 GMT</pubDate>
    <item>
      <title>mlflow-skinny 3.18.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>MLflow Skinny is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies.</description>
      <link>https://mlflow.org</link>
      <comments>https://github.com/mlflow/mlflow</comments>
      <guid>https://github.com/mlflow/mlflow/archive/refs/tags/v3.18.0.tar.gz</guid>
      <pubDate>Fri, 06 Nov 2026 10:05:59 GMT</pubDate>
      <source>https://mlflow.org</source>
    </item>
    <item>
      <title>mlflow 3.18.0 [linux-64, linux-aarch64, linux-ppc64le, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud).</description>
      <link>https://mlflow.org</link>
      <comments>https://github.com/mlflow/mlflow</comments>
      <guid>https://github.com/mlflow/mlflow/archive/refs/tags/v3.18.0.tar.gz</guid>
      <pubDate>Fri, 06 Nov 2026 10:03:25 GMT</pubDate>
      <source>https://mlflow.org</source>
    </item>
    <item>
      <title>langchain 1.4.7 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>LangChain is a framework for developing applications powered by language models (LLMs).</description>
      <link>https://docs.langchain.com</link>
      <comments>https://github.com/langchain-ai/langchain/tree/master/libs/langchain_v1</comments>
      <guid>https://pypi.org/packages/source/l/langchain/langchain-1.4.7.tar.gz</guid>
      <pubDate>Fri, 06 Nov 2026 09:57:14 GMT</pubDate>
      <source>https://github.com/langchain-ai/langchain</source>
    </item>
    <item>
      <title>opentelemetry-configuration 0.66b1 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>The OpenTelemetry declarative configuration API, which allows configuring the OpenTelemetry SDK from a YAML file.</description>
      <link>https://github.com/open-telemetry/opentelemetry-python/tree/main/opentelemetry-configuration</link>
      <comments>https://github.com/open-telemetry/opentelemetry-python/tree/main/opentelemetry-configuration</comments>
      <guid>https://pypi.org/packages/source/o/opentelemetry-configuration/opentelemetry_configuration-0.66b1.tar.gz</guid>
      <pubDate>Fri, 06 Nov 2026 09:49:15 GMT</pubDate>
      <source>https://github.com/open-telemetry/opentelemetry-python/tree/main/opentelemetry-configuration</source>
    </item>
    <item>
      <title>opentelemetry-instrumentation-system-metrics 0.66b1 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>This package provides entrypoints to configure OpenTelemetry.</description>
      <link>https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation/opentelemetry-instrumentation-system-metrics</link>
      <comments>https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation/opentelemetry-instrumentation-system-metrics</comments>
      <guid>https://pypi.org/packages/source/o/opentelemetry-instrumentation-system-metrics/opentelemetry_instrumentation_system_metrics-0.66b1.tar.gz</guid>
      <pubDate>Fri, 06 Nov 2026 09:46:00 GMT</pubDate>
      <source>https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation/opentelemetry-instrumentation-system-metrics</source>
    </item>
    <item>
      <title>opentelemetry-opamp-client 0.4b0 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>The OpenTelemetry OpAMP client, used to communicate with an OpAMP server for remote configuration of OpenTelemetry agents.</description>
      <link>https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/opamp/opentelemetry-opamp-client</link>
      <comments>https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/opamp/opentelemetry-opamp-client</comments>
      <guid>https://pypi.org/packages/source/o/opentelemetry-opamp-client/opentelemetry_opamp_client-0.4b0.tar.gz</guid>
      <pubDate>Fri, 06 Nov 2026 09:45:43 GMT</pubDate>
      <source>https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/opamp/opentelemetry-opamp-client</source>
    </item>
    <item>
      <title>frostwork 0.2.2 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>Frostwork compiles a set of CSS or XPath selectors and extracts their values in one scan of an HTML response. Reuse the schema across Scrapy callbacks, web-poet pages or standalone code. Working memory tracks parser state, pending matches and returned values, without storing a document tree.</description>
      <link>https://github.com/scrapy/frostwork/blob/main/docs/PYTHON.md</link>
      <comments>https://github.com/scrapy/frostwork</comments>
      <guid>https://pypi.org/packages/source/f/frostwork/frostwork-0.2.2.tar.gz</guid>
      <pubDate>Fri, 06 Nov 2026 09:45:29 GMT</pubDate>
      <source>https://github.com/scrapy/frostwork</source>
    </item>
    <item>
      <title>opentelemetry-instrumentation-logging 0.66b1 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>The OpenTelemetry logging integration automatically injects tracing context into log statements.</description>
      <link>https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/logging/logging.html</link>
      <comments>https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation/opentelemetry-instrumentation-logging</comments>
      <guid>https://pypi.org/packages/source/o/opentelemetry-instrumentation-logging/opentelemetry_instrumentation_logging-0.66b1.tar.gz</guid>
      <pubDate>Fri, 06 Nov 2026 09:44:28 GMT</pubDate>
      <source>https://github.com/open-telemetry/opentelemetry-python-contrib/tree/master/instrumentation/opentelemetry-instrumentation-logging</source>
    </item>
    <item>
      <title>wrapt 2.5.1 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>The aim of the wrapt module is to provide a transparent object proxy for Python, which can be used as the basis for the construction of function wrappers and decorator functions.</description>
      <link>https://wrapt.readthedocs.io</link>
      <comments>https://github.com/GrahamDumpleton/wrapt</comments>
      <guid>https://pypi.org/packages/source/w/wrapt/wrapt-2.5.1.tar.gz</guid>
      <pubDate>Fri, 06 Nov 2026 09:44:22 GMT</pubDate>
      <source>https://github.com/GrahamDumpleton/wrapt</source>
    </item>
    <item>
      <title>opentelemetry-propagator-b3 1.45.1 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>This library provides a propagator for the B3 format</description>
      <link>https://github.com/open-telemetry/opentelemetry-python/blob/main/propagator/opentelemetry-propagator-b3/README.rst</link>
      <comments>https://github.com/open-telemetry/opentelemetry-python/tree/main/propagator/opentelemetry-propagator-b3</comments>
      <guid>https://pypi.org/packages/source/o/opentelemetry-propagator-b3/opentelemetry_propagator_b3-1.45.1.tar.gz</guid>
      <pubDate>Fri, 06 Nov 2026 09:44:18 GMT</pubDate>
      <source>https://github.com/open-telemetry/opentelemetry-python/tree/main/propagator/opentelemetry-propagator-b3</source>
    </item>
    <item>
      <title>ob-metaflow-extensions 1.6.37 [linux-64, linux-aarch64, osx-arm64]</title>
      <description>ob-metaflow-extensions installs the client-side Outerbounds platform extension packages for Metaflow.</description>
      <link>https://docs.metaflow.org</link>
      <comments>https://pypi.org/project/ob-metaflow-extensions/#files</comments>
      <pubDate>Fri, 06 Nov 2026 09:12:35 GMT</pubDate>
      <source>https://pypi.org/project/ob-metaflow-extensions/#description</source>
    </item>
    <item>
      <title>cython-blis 1.3.4 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>cython-blis provides the Blis linear algebra routines as a self-contained Python C-extension.</description>
      <link>https://github.com/explosion/cython-blis/blob/master/README.md</link>
      <comments>https://github.com/explosion/cython-blis</comments>
      <guid>https://github.com/explosion/cython-blis/archive/refs/tags/release-v1.3.4.tar.gz</guid>
      <pubDate>Fri, 06 Nov 2026 08:19:46 GMT</pubDate>
      <source>https://github.com/explosion/cython-blis</source>
    </item>
    <item>
      <title>torchao 0.18.0 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>TorchAO is an easy-to-use quantization and optimization library built on native PyTorch. It provides quantization (INT8, INT4, FP8), sparsity, and low-bit optimizer support, and works out-of-the-box with torch.compile() and FSDP2. TorchAO is the official PyTorch quantization backend for vLLM, HuggingFace Transformers, and TorchTune.</description>
      <link>https://docs.pytorch.org/ao/stable/index.html</link>
      <comments>https://github.com/pytorch/ao</comments>
      <pubDate>Fri, 06 Nov 2026 02:51:41 GMT</pubDate>
      <source>https://github.com/pytorch/ao</source>
    </item>
    <item>
      <title>mistralai 2.5.1 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>Mistral AI is a leading open-source generative AI platform. This is the official Python client library for Mistral AI, which provides a simple and efficient way to interact with the Mistral API.</description>
      <link>https://github.com/mistralai/client-python</link>
      <comments>https://github.com/mistralai/client-python</comments>
      <guid>https://pypi.org/packages/source/m/mistralai/mistralai-2.5.1.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 22:14:38 GMT</pubDate>
      <source>https://github.com/mistralai/client-python</source>
    </item>
    <item>
      <title>databricks-sdk 0.162.0 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>The Databricks SDK for Python includes functionality to accelerate development with Python for the Databricks Lakehouse. It covers all public Databricks REST API operations.</description>
      <link>https://databricks-sdk-py.readthedocs.io</link>
      <comments>https://github.com/databricks/databricks-sdk-py</comments>
      <pubDate>Thu, 05 Nov 2026 21:35:48 GMT</pubDate>
      <source>https://github.com/databricks/databricks-sdk-py</source>
    </item>
    <item>
      <title>graphene 3.4.3 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>Graphene is an opinionated Python library for building GraphQL schemas/types fast and easily.</description>
      <link>https://docs.graphene-python.org</link>
      <comments>https://github.com/graphql-python/graphene</comments>
      <guid>https://pypi.io/packages/source/g/graphene/graphene-3.4.3.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 21:31:41 GMT</pubDate>
      <source>https://graphene-python.org</source>
    </item>
    <item>
      <title>rust_win-arm64 1.99.1 [win-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>Thu, 05 Nov 2026 21:01:29 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>rust_win-64 1.99.1 [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>Thu, 05 Nov 2026 20:56:57 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>rust-gnu_win-64 1.99.1 [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>Thu, 05 Nov 2026 20:56:54 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>rust_osx-arm64 1.99.1 [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>Thu, 05 Nov 2026 20:56:19 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>rust_linux-aarch64 1.99.1 [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>Thu, 05 Nov 2026 20:56:09 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>rust_linux-64 1.99.1 [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>Thu, 05 Nov 2026 20:56:06 GMT</pubDate>
      <source>https://www.rust-lang.org</source>
    </item>
    <item>
      <title>platformdirs 4.14.4 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>About A small Python module for determining appropriate platform-specific dirs, e.g. a "user data dir".</description>
      <link>https://platformdirs.readthedocs.io</link>
      <comments>https://github.com/platformdirs/platformdirs</comments>
      <guid>https://pypi.org/packages/source/p/platformdirs/platformdirs-4.14.4.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 20:30:13 GMT</pubDate>
      <source>https://github.com/platformdirs/platformdirs</source>
    </item>
    <item>
      <title>pluggy 1.7.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>pluggy is the crystallized core of plugin management and hook calling for pytest. It enables 1400+ plugins to extend and customize pytest's default behaviour. Even pytest itself is composed as a set of pluggy plugins which are invoked in sequence according to a well defined set of protocols.</description>
      <link>https://pluggy.readthedocs.io</link>
      <comments>https://github.com/pytest-dev/pluggy/</comments>
      <guid>https://pypi.org/packages/source/p/pluggy/pluggy-1.7.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 20:26:04 GMT</pubDate>
      <source>https://github.com/pytest-dev/pluggy</source>
    </item>
    <item>
      <title>opendal 0.47.12 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>Apache OpenDAL is a data access layer that allows users to easily and efficiently retrieve data from various storage services in a unified way.</description>
      <link>https://opendal.apache.org/docs/python/index.html</link>
      <comments>https://github.com/apache/opendal</comments>
      <guid>https://pypi.org/packages/source/o/opendal/opendal-0.47.12.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 19:06:10 GMT</pubDate>
      <source>https://opendal.apache.org/</source>
    </item>
    <item>
      <title>rust-std-wasm32-wasip2 1.99.1 [linux-64, linux-aarch64, noarch, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>This package provides the compiler (rustc) and the documentation utilities rustdoc.</description>
      <link>https://rust-lang.org/learn</link>
      <comments>https://github.com/rust-lang/rust</comments>
      <pubDate>Thu, 05 Nov 2026 18:24:10 GMT</pubDate>
      <source>https://rust-lang.org</source>
    </item>
    <item>
      <title>rust-std-wasm32-wasip1 1.99.1 [linux-64, linux-aarch64, noarch, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>This package provides the compiler (rustc) and the documentation utilities rustdoc.</description>
      <link>https://rust-lang.org/learn</link>
      <comments>https://github.com/rust-lang/rust</comments>
      <pubDate>Thu, 05 Nov 2026 18:21:14 GMT</pubDate>
      <source>https://rust-lang.org</source>
    </item>
    <item>
      <title>llama.cpp-tests 0.9.0 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64, win-arm64]</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/v0.9.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 18:17:58 GMT</pubDate>
      <source>https://github.com/ggml-org/llama.cpp</source>
    </item>
    <item>
      <title>rust 1.99.1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>This package provides the compiler (rustc) and the documentation utilities rustdoc.</description>
      <link>https://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>Thu, 05 Nov 2026 18:16:13 GMT</pubDate>
      <source>https://rust-lang.org</source>
    </item>
    <item>
      <title>llama.cpp 0.9.0 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64, win-arm64]</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/v0.9.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 18:15:32 GMT</pubDate>
      <source>https://github.com/ggml-org/llama.cpp</source>
    </item>
    <item>
      <title>libllama 0.9.0 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64, win-arm64]</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/v0.9.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 18:04:32 GMT</pubDate>
      <source>https://github.com/ggml-org/llama.cpp</source>
    </item>
    <item>
      <title>llama.cpp-tools 0.9.0 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64, win-arm64]</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/v0.9.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 17:57:58 GMT</pubDate>
      <source>https://github.com/ggml-org/llama.cpp</source>
    </item>
    <item>
      <title>gguf 0.19.0.12110 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64, win-arm64]</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/v0.9.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 17:57:32 GMT</pubDate>
      <source>https://ggml.ai</source>
    </item>
    <item>
      <title>fla-core 0.5.2 [linux-64]</title>
      <description>fla-core provides the core Triton-based operators and modules of the flash-linear-attention project, implementing efficient linear-attention and related sequence-modeling kernels for PyTorch.</description>
      <link>https://github.com/fla-org/flash-linear-attention</link>
      <comments>https://github.com/fla-org/flash-linear-attention</comments>
      <guid>https://pypi.org/packages/source/f/fla-core/fla_core-0.5.2.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 14:06:19 GMT</pubDate>
      <source>https://github.com/fla-org/flash-linear-attention</source>
    </item>
    <item>
      <title>gradio-client 2.7.2 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>Python library for easily interacting with trained machine learning models</description>
      <link>https://www.gradio.app</link>
      <comments>https://github.com/gradio-app/gradio/tree/main/client/python</comments>
      <guid>https://pypi.org/packages/source/g/gradio-client/gradio_client-2.7.2.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 13:53:12 GMT</pubDate>
      <source>https://pypi.org/project/gradio-client</source>
    </item>
    <item>
      <title>mmh3 5.3.1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>mmh3 is a Python wrapper for MurmurHash (MurmurHash3), a set of fast and robust non-cryptographic hash functions invented by Austin Appleby.</description>
      <link>https://github.com/hajimes/mmh3/blob/master/README.md</link>
      <comments>https://github.com/hajimes/mmh3</comments>
      <guid>https://github.com/hajimes/mmh3/archive/v5.3.1.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 13:52:32 GMT</pubDate>
      <source>https://github.com/hajimes/mmh3</source>
    </item>
    <item>
      <title>configparser 7.3.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, noarch, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>This module provides the ConfigParser class which implements a basic configuration language which provides a structure similar to what's found in Microsoft Windows INI files.</description>
      <link>https://docs.python.org/3/library/configparser.html</link>
      <comments>https://github.com/jaraco/configparser</comments>
      <guid>https://pypi.io/packages/source/c/configparser/configparser-7.3.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 13:43:38 GMT</pubDate>
      <source>https://github.com/jaraco/configparser</source>
    </item>
    <item>
      <title>liger-kernel 0.8.5 [linux-64]</title>
      <description>Liger Kernel is a collection of Triton kernels designed for LLM training, providing drop-in replacements for common layers (RMSNorm, RoPE, SwiGLU, cross-entropy, and fused linear variants) that reduce memory use and increase multi-GPU training throughput.</description>
      <link>https://github.com/linkedin/Liger-Kernel</link>
      <comments>https://github.com/linkedin/Liger-Kernel</comments>
      <guid>https://pypi.org/packages/source/l/liger-kernel/liger_kernel-0.8.5.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 13:35:44 GMT</pubDate>
      <source>https://github.com/linkedin/Liger-Kernel</source>
    </item>
    <item>
      <title>metaflow 2.19.39.2 [linux-64, linux-aarch64, osx-arm64]</title>
      <description>Metaflow helps scientists and engineers build, manage, and deploy real-life AI and ML systems from local prototypes to production workflows.</description>
      <link>https://docs.metaflow.org</link>
      <comments>https://github.com/outerbounds/metaflow</comments>
      <guid>https://pypi.org/packages/source/m/metaflow/metaflow-2.19.37.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 13:14:57 GMT</pubDate>
      <source>https://github.com/outerbounds/metaflow</source>
    </item>
    <item>
      <title>ob-metaflow 2.19.39.2 [linux-64, linux-aarch64, osx-arm64]</title>
      <description>Metaflow helps scientists and engineers build, manage, and deploy real-life AI and ML systems from local prototypes to production workflows.</description>
      <link>https://docs.metaflow.org</link>
      <comments>https://github.com/outerbounds/metaflow</comments>
      <pubDate>Thu, 05 Nov 2026 13:14:51 GMT</pubDate>
      <source>https://github.com/outerbounds/metaflow</source>
    </item>
    <item>
      <title>isort 9.0.2 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>isort is a Python utility / library to sort imports alphabetically, and automatically separated into sections. It provides a command line utility, Python library and plugins for various editors to quickly sort all your imports. It currently cleanly supports Python 2.6 - 3.5 using pies to achieve this without ugly hacks and/or py2to3.</description>
      <link>https://isort.readthedocs.io/</link>
      <comments>https://github.com/PyCQA/isort</comments>
      <guid>https://pypi.org/packages/source/i/isort/isort-9.0.2.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 13:14:31 GMT</pubDate>
      <source>https://isort.readthedocs.io</source>
    </item>
    <item>
      <title>ob-metaflow-stubs 6.0.12.56 [linux-64, linux-aarch64, osx-arm64]</title>
      <description>ob-metaflow-stubs contains type stub metadata for the Outerbounds Metaflow package, providing type hints for editors and language servers.</description>
      <link>https://github.com/outerbounds/ob-packages/tree/master/obp-python-packages/ob-metaflow-stubs/stubs#readme</link>
      <comments>https://github.com/outerbounds/ob-packages/tree/master/obp-python-packages/ob-metaflow-stubs/stubs</comments>
      <pubDate>Thu, 05 Nov 2026 13:09:59 GMT</pubDate>
      <source>https://github.com/outerbounds/ob-packages/tree/master/obp-python-packages/ob-metaflow-stubs/stubs</source>
    </item>
    <item>
      <title>holidays 0.107 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>A fast, efficient Python library for generating country, province and state specific sets of holidays on the fly. It aims to make determining whether a specific date is a holiday as fast and flexible as possible.</description>
      <link>https://holidays.readthedocs.io</link>
      <comments>https://github.com/vacanza/holidays</comments>
      <guid>https://pypi.io/packages/source/h/holidays/holidays-0.18.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 12:52:35 GMT</pubDate>
      <source>https://github.com/vacanza/holidays</source>
    </item>
    <item>
      <title>s3transfer 0.19.3 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>S3transfer is a Python library for managing Amazon S3 transfers.</description>
      <link>https://pypi.org/project/s3transfer</link>
      <comments>https://github.com/boto/s3transfer</comments>
      <guid>https://pypi.org/packages/source/s/s3transfer/s3transfer-0.19.3.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 12:50:18 GMT</pubDate>
      <source>https://github.com/boto/s3transfer</source>
    </item>
    <item>
      <title>scrapy 2.19.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>Scrapy is an open source and collaborative framework for extracting the data you need from websites in a fast, simple, yet extensible way.</description>
      <link>https://docs.scrapy.org</link>
      <comments>https://github.com/scrapy/scrapy</comments>
      <guid>https://pypi.org/packages/source/s/scrapy/scrapy-2.19.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 12:23:41 GMT</pubDate>
      <source>https://www.scrapy.org</source>
    </item>
    <item>
      <title>openh264 2.7.0 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>OpenH264 is a codec library which supports H.264 encoding and decoding</description>
      <link>https://github.com/cisco/openh264</link>
      <comments>https://github.com/cisco/openh264</comments>
      <guid>https://github.com/cisco/openh264/archive/v2.7.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:49:09 GMT</pubDate>
      <source>https://www.openh264.org</source>
    </item>
    <item>
      <title>dateparser 1.5.0 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>Generic parsing of dates in over 200 language locales plus numerous formats in a language agnostic fashion. Includes support for relative dates, multiple calendar systems, timezone parsing, and flexible customization through settings.</description>
      <link>https://dateparser.readthedocs.io</link>
      <comments>https://github.com/scrapinghub/dateparser</comments>
      <guid>https://pypi.org/packages/source/d/dateparser/dateparser-1.5.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:39:38 GMT</pubDate>
      <source>https://github.com/scrapinghub/dateparser</source>
    </item>
    <item>
      <title>lumen-ai-transformers 1.4.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Lumen with embedding support</description>
      <link>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</link>
      <guid>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:37:30 GMT</pubDate>
      <source>https://github.com/holoviz/lumen</source>
    </item>
    <item>
      <title>lumen-ai-mistralai 1.4.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Lumen with MistralAI support</description>
      <link>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</link>
      <guid>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:37:24 GMT</pubDate>
      <source>https://github.com/holoviz/lumen</source>
    </item>
    <item>
      <title>lumen-ai-llama 1.4.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Lumen with llama support</description>
      <link>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</link>
      <guid>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:37:17 GMT</pubDate>
      <source>https://github.com/holoviz/lumen</source>
    </item>
    <item>
      <title>lumen-ai-openai 1.4.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Lumen with OpenAI support</description>
      <link>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</link>
      <guid>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:37:11 GMT</pubDate>
      <source>https://github.com/holoviz/lumen</source>
    </item>
    <item>
      <title>lumen-ai-local 1.4.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Lumen with huggingface_hub for local AI models</description>
      <link>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</link>
      <guid>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:37:05 GMT</pubDate>
      <source>https://github.com/holoviz/lumen</source>
    </item>
    <item>
      <title>lumen-ai-bedrock 1.4.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Lumen with Bedrock support</description>
      <link>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</link>
      <guid>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:36:58 GMT</pubDate>
      <source>https://github.com/holoviz/lumen</source>
    </item>
    <item>
      <title>lumen-ai-anthropic 1.4.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Lumen with Anthropic support</description>
      <link>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</link>
      <guid>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:36:52 GMT</pubDate>
      <source>https://github.com/holoviz/lumen</source>
    </item>
    <item>
      <title>lumen-ai 1.4.0 [linux-64, linux-aarch64, osx-arm64, win-64]</title>
      <description>Lumen with AI capabilities</description>
      <link>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</link>
      <guid>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:36:45 GMT</pubDate>
      <source>https://github.com/holoviz/lumen</source>
    </item>
    <item>
      <title>lumen 1.4.0 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64]</title>
      <description>The Lumen project provides a framework for visual analytics, which allows users to build data-driven dashboards from a simple yaml specification. The power of Lumen comes from the ability to leverage the powerful data intake, data processing and data visualization libraries available in the PyData ecosystem.</description>
      <link>https://lumen.holoviz.org/</link>
      <comments>https://github.com/holoviz/lumen</comments>
      <guid>https://pypi.org/packages/source/l/lumen/lumen-1.4.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:36:32 GMT</pubDate>
      <source>https://github.com/holoviz/lumen</source>
    </item>
    <item>
      <title>jsonpointer 3.8 [linux-64, linux-aarch64, noarch, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>Library to resolve JSON Pointers according to RFC 6901</description>
      <link>https://python-json-pointer.readthedocs.io</link>
      <comments>https://github.com/stefankoegl/python-json-pointer/tree/v3.8</comments>
      <guid>https://pypi.org/packages/source/j/jsonpointer/jsonpointer-3.8.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:36:17 GMT</pubDate>
      <source>https://github.com/stefankoegl/python-json-pointer</source>
    </item>
    <item>
      <title>python-socketio 5.18.1 [linux-32, linux-64, linux-aarch64, linux-ppc64le, noarch, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>This projects implements Socket.IO clients and servers that can run standalone or integrated with a variety of Python web frameworks.</description>
      <link>https://python-socketio.readthedocs.io</link>
      <comments>https://github.com/miguelgrinberg/python-socketio</comments>
      <guid>https://pypi.org/packages/source/p/python-socketio/python_socketio-5.18.1.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:34:36 GMT</pubDate>
      <source>https://github.com/miguelgrinberg/python-socketio</source>
    </item>
    <item>
      <title>elfutils 0.197 [linux-64, linux-aarch64]</title>
      <description>A set of utilities and libraries for handling ELF (Executable and Linkable Format) files.  This includes libelf, libdw, libasm, libdwelf, libdwfl and libebl together with the eu-* command line tools.</description>
      <link>https://sourceware.org/git/?p=elfutils.git;a=tree;f=doc;hb=refs/tags/elfutils-0.197</link>
      <comments>https://sourceware.org/git/?p=elfutils.git;a=tree;h=refs/tags/elfutils-0.197;hb=refs/tags/elfutils-0.197</comments>
      <guid>https://sourceware.org/ftp/elfutils/0.197/elfutils-0.197.tar.bz2</guid>
      <pubDate>Thu, 05 Nov 2026 11:34:24 GMT</pubDate>
      <source>https://sourceware.org/elfutils/</source>
    </item>
    <item>
      <title>bsdiff4 1.4.2 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>bsdiff4 is derived from bsdiff, a very simple Python extension module that allows Python to perform the same tasks as the bsdiff utility</description>
      <link>https://github.com/ilanschnell/bsdiff4</link>
      <comments>https://github.com/ilanschnell/bsdiff4</comments>
      <guid>https://pypi.org/packages/source/b/bsdiff4/bsdiff4-1.4.2.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 11:19:29 GMT</pubDate>
      <source>https://github.com/ilanschnell/bsdiff4</source>
    </item>
    <item>
      <title>trackio 0.40.0 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>trackio is a lightweight, local-first, free experiment-tracking Python library built by Hugging Face. It provides a wandb-compatible API for logging metrics and visualizing runs through a local Gradio dashboard, with optional syncing to Hugging Face Spaces.</description>
      <link>https://github.com/gradio-app/trackio</link>
      <comments>https://github.com/gradio-app/trackio</comments>
      <guid>https://files.pythonhosted.org/packages/8f/60/69c8baac61bab8be099d54bc227cc5db845f7f2deaa69a3105e45fbca8c5/trackio-0.40.0-py3-none-any.whl</guid>
      <pubDate>Thu, 05 Nov 2026 11:11:31 GMT</pubDate>
      <source>https://github.com/gradio-app/trackio</source>
    </item>
    <item>
      <title>grpcio-tools 1.85.1 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>gRPC is a modern, open source, high-performance remote procedure call (RPC) framework that can run anywhere. gRPC enables client and server applications to communicate transparently, and simplifies the building of connected systems.  This package contains the gRPC C++ libraries and header files, as well as the code generation plugins.</description>
      <link>https://grpc.io/docs</link>
      <comments>https://github.com/grpc/grpc</comments>
      <guid>https://github.com/grpc/grpc/archive/v1.83.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 09:27:42 GMT</pubDate>
      <source>https://grpc.io</source>
    </item>
    <item>
      <title>grpcio 1.85.1 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>gRPC is a modern, open source, high-performance remote procedure call (RPC) framework that can run anywhere. gRPC enables client and server applications to communicate transparently, and simplifies the building of connected systems.  This package contains the gRPC C++ libraries and header files, as well as the code generation plugins.</description>
      <link>https://grpc.io/docs</link>
      <comments>https://github.com/grpc/grpc</comments>
      <guid>https://github.com/grpc/grpc/archive/v1.83.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 09:26:25 GMT</pubDate>
      <source>https://grpc.io</source>
    </item>
    <item>
      <title>libgrpc 1.85.1 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>gRPC is a modern, open source, high-performance remote procedure call (RPC) framework that can run anywhere. gRPC enables client and server applications to communicate transparently, and simplifies the building of connected systems.  This package contains the gRPC C++ libraries and header files, as well as the code generation plugins.</description>
      <link>https://grpc.io/docs</link>
      <comments>https://github.com/grpc/grpc</comments>
      <guid>https://github.com/grpc/grpc/archive/v1.83.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 09:22:57 GMT</pubDate>
      <source>https://grpc.io</source>
    </item>
    <item>
      <title>pythonanywhere 0.19.3 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>PythonAnywhere is a collection of helper scripts and command-line tools for interacting with the PythonAnywhere cloud platform. It allows users to manage files, databases, web apps, scheduled tasks, and virtual environments directly from their local terminal.</description>
      <link>https://github.com/pythonanywhere/helper_scripts/blob/master/README.md</link>
      <comments>https://github.com/pythonanywhere/helper_scripts</comments>
      <guid>https://pypi.io/packages/source/p/pythonanywhere/pythonanywhere-0.11.0.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 09:05:38 GMT</pubDate>
      <source>https://github.com/pythonanywhere/helper_scripts</source>
    </item>
    <item>
      <title>opentelemetry-instrumentation-fastapi 0.66b1 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>OpenTelemetry instrumentation for FastAPI applications. Wraps a FastAPI ASGI app to automatically generate spans for incoming requests, propagate trace context, and emit standard HTTP semantic-convention attributes. Built on top of opentelemetry-instrumentation-asgi.</description>
      <link>https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/fastapi/fastapi.html</link>
      <comments>https://github.com/open-telemetry/opentelemetry-python-contrib/tree/v0.66b1/instrumentation/opentelemetry-instrumentation-fastapi</comments>
      <guid>https://pypi.org/packages/source/o/opentelemetry-instrumentation-fastapi/opentelemetry_instrumentation_fastapi-0.66b1.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 09:03:08 GMT</pubDate>
      <source>https://github.com/open-telemetry/opentelemetry-python-contrib/tree/main/instrumentation/opentelemetry-instrumentation-fastapi</source>
    </item>
    <item>
      <title>types-setuptools 82.0.0.20260518 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>Typing stubs for setuptools</description>
      <link>https://typing.readthedocs.io</link>
      <comments>https://github.com/python/typeshed</comments>
      <guid>https://pypi.org/packages/source/t/types-setuptools/types_setuptools-82.0.0.20260518.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 08:45:23 GMT</pubDate>
      <source>https://github.com/python/typeshed</source>
    </item>
    <item>
      <title>langchain-openai 1.7.3 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>This package contains the LangChain integrations for OpenAI through their openai SDK.</description>
      <link>https://reference.langchain.com/python/integrations/langchain_openai</link>
      <comments>https://github.com/langchain-ai/langchain/tree/master/libs/partners/openai</comments>
      <guid>https://pypi.org/packages/source/l/langchain-openai/langchain_openai-1.7.3.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 08:22:15 GMT</pubDate>
      <source>https://docs.langchain.com/oss/python/integrations/providers/openai</source>
    </item>
    <item>
      <title>cut_cross_entropy 25.1.1 [linux-64, linux-aarch64, osx-arm64]</title>
      <description>Cut Cross-Entropy (CCE) is a memory-efficient implementation of the linear-cross-entropy loss used when training large language models. It computes the cross-entropy loss without materializing the full logits matrix in global memory, using custom Triton kernels, which substantially reduces memory usage during training.</description>
      <link>https://github.com/unslothai/cut-cross-entropy</link>
      <comments>https://github.com/unslothai/cut-cross-entropy</comments>
      <pubDate>Thu, 05 Nov 2026 02:57:25 GMT</pubDate>
      <source>https://github.com/unslothai/cut-cross-entropy</source>
    </item>
    <item>
      <title>triton 3.9.0 [linux-64, linux-aarch64]</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/v3.7.1.tar.gz</guid>
      <pubDate>Thu, 05 Nov 2026 00:58:33 GMT</pubDate>
      <source>https://github.com/triton-lang/triton</source>
    </item>
    <item>
      <title>aiohappyeyeballs 2.6.2 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>This library exists to allow connecting with Happy Eyeballs (RFC 8305) when you already have a list of addrinfo and not a DNS name.</description>
      <link>https://aiohappyeyeballs.readthedocs.io</link>
      <comments>https://github.com/aio-libs/aiohappyeyeballs</comments>
      <guid>https://pypi.org/packages/source/a/aiohappyeyeballs/aiohappyeyeballs-2.6.2.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 23:50:34 GMT</pubDate>
      <source>https://github.com/aio-libs/aiohappyeyeballs</source>
    </item>
    <item>
      <title>torchcodec 0.17.0 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>TorchCodec is a Python library for decoding video and audio data into PyTorch tensors, on CPU and CUDA GPU. It also supports video and audio encoding on CPU! It aims to be fast, easy to use, and well integrated into the PyTorch ecosystem. If you want to use PyTorch to train ML models on videos and audio, or run inference, TorchCodec is how you turn these into data.</description>
      <link>https://meta-pytorch.org/torchcodec/stable/index.html</link>
      <comments>https://github.com/meta-pytorch/torchcodec</comments>
      <pubDate>Wed, 04 Nov 2026 20:20:03 GMT</pubDate>
      <source>https://github.com/meta-pytorch/torchcodec</source>
    </item>
    <item>
      <title>pypdf-with-rtl-text 6.21.4 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>pypdf is a free and open-source pure-python PDF library capable of splitting, merging, cropping, and transforming the pages of PDF files. It can also add custom data, viewing options, and passwords to PDF files. pypdf can retrieve text and metadata from PDFs as well.</description>
      <link>https://pypdf.readthedocs.io</link>
      <comments>https://github.com/py-pdf/pypdf</comments>
      <guid>https://pypi.org/packages/source/p/pypdf/pypdf-6.21.4.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 19:09:44 GMT</pubDate>
      <source>https://pypi.org/project/pypdf</source>
    </item>
    <item>
      <title>pypdf-with-fonts 6.21.4 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>pypdf is a free and open-source pure-python PDF library capable of splitting, merging, cropping, and transforming the pages of PDF files. It can also add custom data, viewing options, and passwords to PDF files. pypdf can retrieve text and metadata from PDFs as well.</description>
      <link>https://pypdf.readthedocs.io</link>
      <comments>https://github.com/py-pdf/pypdf</comments>
      <guid>https://pypi.org/packages/source/p/pypdf/pypdf-6.21.4.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 19:09:25 GMT</pubDate>
      <source>https://pypi.org/project/pypdf</source>
    </item>
    <item>
      <title>pypdf-with-brotli 6.21.4 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>pypdf is a free and open-source pure-python PDF library capable of splitting, merging, cropping, and transforming the pages of PDF files. It can also add custom data, viewing options, and passwords to PDF files. pypdf can retrieve text and metadata from PDFs as well.</description>
      <link>https://pypdf.readthedocs.io</link>
      <comments>https://github.com/py-pdf/pypdf</comments>
      <guid>https://pypi.org/packages/source/p/pypdf/pypdf-6.21.4.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 19:09:06 GMT</pubDate>
      <source>https://pypi.org/project/pypdf</source>
    </item>
    <item>
      <title>conda-rattler-solver 26.10.2 [linux-64, linux-aarch64, osx-arm64, win-64, win-arm64]</title>
      <description>`conda-rattler-solver` brings the [`resolvo`](https://github.com/prefix-dev/resolvo) solver, as provided by [`rattler`](https://github.com/conda/rattler), to `conda`. Environments solve meaningfully faster than with the `classic` and `libmamba` solvers, especially large or complex ones, and your existing environments and workflows keep working. It is conda's default solver starting with conda 26.10.</description>
      <link>https://github.com/conda/conda-rattler-solver/blob/main/README.md</link>
      <comments>https://github.com/conda/conda-rattler-solver</comments>
      <guid>https://github.com/conda/conda-rattler-solver/archive/26.10.2.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 18:42:56 GMT</pubDate>
      <source>https://github.com/conda/conda-rattler-solver</source>
    </item>
    <item>
      <title>pandas 3.1.0 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" 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/docs/</link>
      <comments>https://github.com/pandas-dev/pandas</comments>
      <guid>https://pypi.org/packages/source/p/pandas/pandas-3.1.0.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 18:19:02 GMT</pubDate>
      <source>https://pandas.pydata.org</source>
    </item>
    <item>
      <title>confection 1.3.4 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>A lightweight library that offers a configuration system letting you conveniently describe arbitrary trees of objects.</description>
      <link>https://github.com/explosion/confection/blob/main/README.md</link>
      <comments>https://github.com/explosion/confection</comments>
      <guid>https://github.com/explosion/confection/archive/refs/tags/release-v1.3.4.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 18:11:55 GMT</pubDate>
      <source>https://github.com/explosion/confection</source>
    </item>
    <item>
      <title>cfn-lint 1.59.0 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64, win-arm64]</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/v1.59.0.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 17:51:38 GMT</pubDate>
      <source>https://github.com/aws-cloudformation/cfn-lint</source>
    </item>
    <item>
      <title>libva 2.24.1 [linux-64, linux-aarch64, win-64, win-arm64]</title>
      <description>VA-API is an open-source library and API specification, which provides access to graphics hardware acceleration capabilities for video processing. It consists of a main library and driver-specific acceleration backends for each supported hardware vendor.</description>
      <link>https://github.com/intel/libva</link>
      <comments>https://github.com/intel/libva</comments>
      <guid>https://github.com/intel/libva/archive/2.24.1.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 17:40:16 GMT</pubDate>
      <source>https://github.com/intel/libva</source>
    </item>
    <item>
      <title>imagecodecs 2026.8.16 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>The imagecodecs package provides various block-oriented, in-memory buffer transformation, compression, and decompression functions for use in the tifffile, czifile, and other Python imaging modules.  Decode and/or encode functions are currently implemented for Zlib DEFLATE, ZStandard, Blosc, LZMA, BZ2, LZ4, LZW, LZF, PNG, WebP, JPEG 8-bit, JPEG 12-bit, JPEG SOF=0xC3, JPEG 2000, JPEG XR, PackBits, Packed Integers, Delta, XOR Delta, Floating Point Predictor, and Bitorder reversal.</description>
      <link>https://www.cgohlke.com</link>
      <comments>https://github.com/cgohlke/imagecodecs</comments>
      <guid>https://github.com/cgohlke/imagecodecs/archive/refs/tags/v2026.8.16.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 17:07:39 GMT</pubDate>
      <source>https://www.cgohlke.com</source>
    </item>
    <item>
      <title>django 6.1.3 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it takes care of much of the hassle of Web development, so you can focus on writing your app without needing to reinvent the wheel. It's free and open source.</description>
      <link>https://docs.djangoproject.com</link>
      <comments>https://github.com/django/django</comments>
      <guid>https://github.com/django/django/archive/refs/tags/6.1.3.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 15:18:00 GMT</pubDate>
      <source>https://www.djangoproject.com</source>
    </item>
    <item>
      <title>smart_open 9.0.1 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>smart_open is a Python 3 library for efficient streaming of very large files from/to storages such as S3, GCS, Azure Blob Storage, HDFS, WebHDFS, HTTP, HTTPS, SFTP, or local filesystem.  It supports transparent, on-the-fly (de-)compression for a variety of different formats</description>
      <link>https://github.com/RaRe-Technologies/smart_open/blob/develop/README.rst</link>
      <comments>https://github.com/RaRe-Technologies/smart_open</comments>
      <guid>https://github.com/piskvorky/smart_open/archive/refs/tags/v9.0.1.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 15:14:14 GMT</pubDate>
      <source>https://github.com/RaRe-Technologies/smart_open</source>
    </item>
    <item>
      <title>filelock 4.2.7 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>A filelock provides a synchronization mechanism between different instances of your application, similar to a thread lock. It can be used to signalize that files, directories or other resources are currently used or manipulated (Think of a sync.lock file). Only the existence of the lockfile is watched for this purpose. The file itself can not be written and is always empty.</description>
      <link>https://py-filelock.readthedocs.io</link>
      <comments>https://github.com/tox-dev/py-filelock</comments>
      <guid>https://pypi.org/packages/source/f/filelock/filelock-4.2.7.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 14:46:18 GMT</pubDate>
      <source>https://github.com/tox-dev/py-filelock</source>
    </item>
    <item>
      <title>brotli-python 1.2.1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>Brotli is a generic-purpose lossless compression algorithm that compresses data using a combination of a modern variant of the LZ77 algorithm, Huffman coding and 2nd order context modeling, with a compression ratio comparable to the best currently available general-purpose compression methods. It is similar in speed with deflate but offers more dense compression.</description>
      <link>https://github.com/google/brotli/tree/master/docs</link>
      <comments>https://github.com/google/brotli</comments>
      <guid>https://github.com/google/brotli/archive/v1.2.1.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 14:40:30 GMT</pubDate>
      <source>https://github.com/google/brotli</source>
    </item>
    <item>
      <title>brotli 1.2.1 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>Brotli is a generic-purpose lossless compression algorithm that compresses data using a combination of a modern variant of the LZ77 algorithm, Huffman coding and 2nd order context modeling, with a compression ratio comparable to the best currently available general-purpose compression methods. It is similar in speed with deflate but offers more dense compression.</description>
      <link>https://github.com/google/brotli/tree/master/docs</link>
      <comments>https://github.com/google/brotli</comments>
      <guid>https://github.com/google/brotli/archive/v1.2.1.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 14:37:07 GMT</pubDate>
      <source>https://github.com/google/brotli</source>
    </item>
    <item>
      <title>brotli-bin 1.2.1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>Brotli is a generic-purpose lossless compression algorithm that compresses data using a combination of a modern variant of the LZ77 algorithm, Huffman coding and 2nd order context modeling, with a compression ratio comparable to the best currently available general-purpose compression methods. It is similar in speed with deflate but offers more dense compression.</description>
      <link>https://github.com/google/brotli/tree/master/docs</link>
      <comments>https://github.com/google/brotli</comments>
      <guid>https://github.com/google/brotli/archive/v1.2.1.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 14:36:50 GMT</pubDate>
      <source>https://github.com/google/brotli</source>
    </item>
    <item>
      <title>libbrotlienc 1.2.1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>Brotli is a generic-purpose lossless compression algorithm that compresses data using a combination of a modern variant of the LZ77 algorithm, Huffman coding and 2nd order context modeling, with a compression ratio comparable to the best currently available general-purpose compression methods. It is similar in speed with deflate but offers more dense compression.</description>
      <link>https://github.com/google/brotli/tree/master/docs</link>
      <comments>https://github.com/google/brotli</comments>
      <guid>https://github.com/google/brotli/archive/v1.2.1.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 14:36:27 GMT</pubDate>
      <source>https://github.com/google/brotli</source>
    </item>
    <item>
      <title>libbrotlidec 1.2.1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>Brotli is a generic-purpose lossless compression algorithm that compresses data using a combination of a modern variant of the LZ77 algorithm, Huffman coding and 2nd order context modeling, with a compression ratio comparable to the best currently available general-purpose compression methods. It is similar in speed with deflate but offers more dense compression.</description>
      <link>https://github.com/google/brotli/tree/master/docs</link>
      <comments>https://github.com/google/brotli</comments>
      <guid>https://github.com/google/brotli/archive/v1.2.1.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 14:36:06 GMT</pubDate>
      <source>https://github.com/google/brotli</source>
    </item>
    <item>
      <title>libbrotlicommon 1.2.1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>Brotli is a generic-purpose lossless compression algorithm that compresses data using a combination of a modern variant of the LZ77 algorithm, Huffman coding and 2nd order context modeling, with a compression ratio comparable to the best currently available general-purpose compression methods. It is similar in speed with deflate but offers more dense compression.</description>
      <link>https://github.com/google/brotli/tree/master/docs</link>
      <comments>https://github.com/google/brotli</comments>
      <guid>https://github.com/google/brotli/archive/v1.2.1.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 14:35:43 GMT</pubDate>
      <source>https://github.com/google/brotli</source>
    </item>
    <item>
      <title>asciitree 1.0.0 [linux-64, linux-aarch64, noarch, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>Draws ASCII trees.</description>
      <link>https://github.com/mbr/asciitree</link>
      <comments>https://github.com/mbr/asciitree</comments>
      <guid>https://pypi.org/packages/source/a/asciitree/asciitree-1.0.0.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 13:50:49 GMT</pubDate>
      <source>https://github.com/mbr/asciitree</source>
    </item>
    <item>
      <title>exceptiongroup 1.3.1 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>"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"</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, 04 Nov 2026 13:48:55 GMT</pubDate>
      <source>https://pypi.org/project/exceptiongroup</source>
    </item>
    <item>
      <title>deepspeed 0.19.9 [linux-64, linux-aarch64, osx-arm64]</title>
      <description>DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. It enables world-class training speed, memory efficiency, scalability, and accessibility for large model training and inference.</description>
      <link>https://deepspeed.readthedocs.io</link>
      <comments>https://github.com/deepspeedai/DeepSpeed</comments>
      <guid>https://pypi.org/packages/source/d/deepspeed/deepspeed-0.19.8.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 13:35:22 GMT</pubDate>
      <source>https://www.deepspeed.ai</source>
    </item>
    <item>
      <title>langchain-core 1.7.0 [linux-64, linux-aarch64, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>LangChain Core contains the core abstractions powering LangChain, including LangChain Expression Language.</description>
      <link>https://reference.langchain.com/?lang=python</link>
      <comments>https://github.com/langchain-ai/langchain/tree/master/libs/core</comments>
      <guid>https://pypi.org/packages/source/l/langchain-core/langchain_core-1.4.0.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 12:07:36 GMT</pubDate>
      <source>https://github.com/langchain-ai/langchain</source>
    </item>
    <item>
      <title>moto 5.2.4 [linux-64, linux-aarch64, linux-ppc64le, noarch, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>A library that allows you to easily mock out tests based on AWS infrastructure.</description>
      <link>https://docs.getmoto.org</link>
      <comments>https://github.com/getmoto/moto</comments>
      <guid>https://pypi.org/packages/source/m/moto/moto-5.2.4.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 11:45:19 GMT</pubDate>
      <source>https://docs.getmoto.org</source>
    </item>
    <item>
      <title>murmurhash 1.0.16 [linux-32, linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>Cython bindings for MurmurHash2</description>
      <link>https://github.com/explosion/murmurhash</link>
      <comments>https://github.com/explosion/murmurhash</comments>
      <guid>https://pypi.org/packages/source/m/murmurhash/murmurhash-1.0.16.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 11:44:21 GMT</pubDate>
      <source>https://github.com/explosion/murmurhash</source>
    </item>
    <item>
      <title>python-engineio 4.15.1 [linux-32, linux-64, linux-aarch64, linux-ppc64le, noarch, osx-64, osx-arm64, win-32, win-64, win-arm64]</title>
      <description>This project implements Python based Engine.IO client and server that can run standalone or integrated with a variety of Python web frameworks and applications.</description>
      <link>https://python-engineio.readthedocs.io</link>
      <comments>https://github.com/miguelgrinberg/python-engineio</comments>
      <guid>https://github.com/miguelgrinberg/python-engineio/archive/refs/tags/v4.15.1.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 11:03:46 GMT</pubDate>
      <source>https://github.com/miguelgrinberg/python-engineio</source>
    </item>
    <item>
      <title>authlib 1.9.3 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, noarch, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>The ultimate Python library in building OAuth and OpenID Connect servers. It is designed from low level specifications implementations to high level frameworks integrations, to meet the needs of everyone.</description>
      <link>https://docs.authlib.org</link>
      <comments>https://github.com/authlib/authlib</comments>
      <guid>https://pypi.org/packages/source/a/authlib/authlib-1.5.1.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 10:42:42 GMT</pubDate>
      <source>https://authlib.org</source>
    </item>
    <item>
      <title>spacy-alignments 0.9.3 [linux-64, linux-aarch64, linux-ppc64le, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>A spaCy package for Yohei Tamura's Rust tokenizations library.</description>
      <link>https://github.com/explosion/spacy-alignments</link>
      <comments>https://github.com/explosion/spacy-alignments</comments>
      <guid>https://pypi.org/packages/source/s/spacy-alignments/spacy_alignments-0.9.3.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 10:39:36 GMT</pubDate>
      <source>https://spacy.io</source>
    </item>
    <item>
      <title>joserfc 1.8.0 [linux-64, linux-aarch64, linux-s390x, osx-64, osx-arm64, win-64, win-arm64]</title>
      <description>joserfc is a Python library that provides a comprehensive implementation of several essential JSON Object Signing and Encryption (JOSE) standards.</description>
      <link>https://jose.authlib.org</link>
      <comments>https://github.com/authlib/joserfc</comments>
      <guid>https://pypi.org/packages/source/j/joserfc/joserfc-1.8.0.tar.gz</guid>
      <pubDate>Wed, 04 Nov 2026 10:38:00 GMT</pubDate>
      <source>https://jose.authlib.org</source>
    </item>
  </channel>
</rss>
