<metapackage xmlns:os="http://opensuse.org/Standards/One_Click_Install" xmlns="http://opensuse.org/Standards/One_Click_Install">
  <group distversion="openSUSE Tumbleweed">
    <repositories>
      <repository recommended="true">
        <name>home:Ledest:erlang:27</name>
        <summary>Erlang 27</summary>
        <description></description>
        <url>https://download.opensuse.org/repositories/home:/Ledest:/erlang:/27/openSUSE_Tumbleweed/</url>
      </repository>
      <repository recommended="true">
        <name>openSUSE:Factory</name>
        <summary>The next openSUSE distribution</summary>
        <description>openSUSE Tumbleweed: The Bleeding Edge, Perfected.
Tumbleweed is the ultimate rolling release distribution, providing the latest software as it’s released, built upon a foundation of world-class stability and testing.

* Always Current: Get the newest kernel, IDEs, desktops, and applications automatically.

* Powerfully Stable: Experience the velocity of a rolling release without sacrificing the reliability you depend on.

* Engineered for Professionals: The top choice for Developers, Power Users, and openSUSE Contributors who need the best tools for the job.

If you demand the latest stable software, your choice is Tumbleweed.

Staging dashboard is located at: https://build.opensuse.org/staging_workflows/openSUSE:Factory 

List of known devel projects: https://build.opensuse.org/package/view_file/openSUSE:Factory:Staging/dashboard/devel_projects

Have a look at http://en.opensuse.org/Portal:Factory for more details.</description>
        <url>https://download.opensuse.org/tumbleweed/repo/oss/</url>
      </repository>
      <repository recommended="true">
        <name>openSUSE:Tumbleweed</name>
        <summary>Tumbleweed</summary>
        <description>Tumbleweed is the openSUSE Rolling Release

This OBS Project represents the content of the currently published
snapshot. The newer repository for next publish can be found in openSUSE:Factory standard repository.
</description>
        <url>https://download.opensuse.org/repositories/openSUSE:/Tumbleweed/standard/</url>
      </repository>
      <repository recommended="true">
        <name>openSUSE:Tumbleweed</name>
        <summary>Tumbleweed</summary>
        <description>Tumbleweed is the openSUSE Rolling Release

This OBS Project represents the content of the currently published
snapshot. The newer repository for next publish can be found in openSUSE:Factory standard repository.
</description>
        <url>https://download.opensuse.org/tumbleweed/repo/oss/</url>
      </repository>
      <repository recommended="false">
        <name>openSUSE:Factory</name>
        <summary>The next openSUSE distribution</summary>
        <description>openSUSE Tumbleweed: The Bleeding Edge, Perfected.
Tumbleweed is the ultimate rolling release distribution, providing the latest software as it’s released, built upon a foundation of world-class stability and testing.

* Always Current: Get the newest kernel, IDEs, desktops, and applications automatically.

* Powerfully Stable: Experience the velocity of a rolling release without sacrificing the reliability you depend on.

* Engineered for Professionals: The top choice for Developers, Power Users, and openSUSE Contributors who need the best tools for the job.

If you demand the latest stable software, your choice is Tumbleweed.

Staging dashboard is located at: https://build.opensuse.org/staging_workflows/openSUSE:Factory 

List of known devel projects: https://build.opensuse.org/package/view_file/openSUSE:Factory:Staging/dashboard/devel_projects

Have a look at http://en.opensuse.org/Portal:Factory for more details.</description>
        <url>https://download.opensuse.org/repositories/openSUSE:/Factory/ports/</url>
      </repository>
    </repositories>
    <software>
      <item>
        <name>hyper</name>
        <summary>HyperLogLog for Erlang</summary>
        <description>This is an implementation of the HyperLogLog algorithm in Erlang.
Using HyperLogLog you can estimate the cardinality of very large data sets using
constant memory. The relative error is 1.04 * sqrt(2^P).
When creating a new HyperLogLog filter, you provide the precision P, allowing you
to trade memory for accuracy. The union of two filters is lossless.</description>
      </item>
    </software>
  </group>
</metapackage>
