Posts

  • Oct 2, 2025

    npm-install

    GitHub Action for install npm dependencies with caching without any configuration

    GitHub Action for install npm dependencies with caching without any configuration

    Tags: #javascript • github-actions • npm-install

  • Oct 1, 2025

    PyDebloatX

    Python GUI for uninstalling the default Windows 10 apps.

    Pre-built binaries are available from the releases page.

    PyDebloatX installer is also available from the Windows Package Manager Winget:

    winget install --id=Teraskull.PyDebloatX -e

    Tags: #python • windows-10 • python3

  • Oct 1, 2025

    rubyinstaller2

    MSYS2 based RubyInstaller for Windows

    This project provides an Installer for Ruby-2.4 and newer on Windows based on the MSYS2 toolchain. It is the successor to the MSYS1 based RubyInstaller which was used for Ruby-2.3 and older. It is licensed under the 3-clause Modified BSD License.

    In contrast to the old RubyInstaller it does not provide its own DevKit, but makes use of the rich set of MINGW libraries from the MSYS2 project. It therefore integrates well into MSYS2 after installation on the target system to provide a build-and-runtime environment for installation of gems with C-extensions. This and more changes are documented in the CHANGELOG.

    Tags: #ruby • msys2 • rubyinstaller

  • Oct 1, 2025

    analytics.usa.gov

    The US federal government’s web traffic.

    Analytics.usa.gov is a product of the Digital Analytics Program (DAP), which collects and publishes web analytics from thousands of public-facing US federal government websites per the “Delivering a Digital-First Public Experience” requirement built in support of the 21st Century Integrated Digital Experience Act (IDEA).

    The process for adding features to this project is described in Development and deployment process.

    Tags: #javascript • analytics • dap

  • Sep 30, 2025

    HTTP-Proxy-Servlet

    Smiley’s HTTP Proxy implemented as a Java servlet

    The following is a list of parameters that can be configured

    • log: A boolean parameter name to enable logging of input and target URLs to the servlet log.
    • forwardip: A boolean parameter name to enable forwarding of the client IP
    • preserveHost: A boolean parameter name to keep HOST parameter as-is
    • preserveCookies: A boolean parameter name to keep COOKIES as-is
    • preserveCookiePath: A boolean parameter name to keep cookie path unchanged in Set-Cookie server response header
    • http.protocol.handle-redirects: A boolean parameter name to have auto-handle redirects
    • http.socket.timeout: A integer parameter name to set the socket connection timeout (millis)
    • http.read.timeout: A integer parameter name to set the socket read timeout (millis)
    • http.connectionrequest.timeout: A integer parameter name to set the connection request timeout (millis)
    • http.maxConnections: A integer parameter name to set max connection number
    • useSystemProperties: A boolean parameter whether to use JVM-defined system properties to configure various networking aspects.
    • targetUri: The parameter name for the target (destination) URI to proxy to.

    Tags: #java

  • Sep 30, 2025

    copper-engine

    COPPER - a high performance Java workflow engine

    Run ./gradlew eclipse once. This will create Eclipse project files which you can import. This also creates proper code style settings. Before committing you should always reformat the code. You can configure Eclipse to do this automatically on each save.

    Every time a dependency changes in build.gradle you must run ./gradlew eclipse again. You don’t need to restart Eclipse for this, simply press F5 on the projects.

    Tags: #java

  • Sep 29, 2025

    nodevectors

    Fastest network node embeddings in the west

    pip install nodevectors

    This package depends on the CSRGraphs package, which is automatically installed along it using pip. Most development happens there, so running pip install --upgrade csrgraph once in a while can update the underlying graph library.

    Tags: #python

  • Sep 29, 2025

    xai

    XAI - An eXplainability toolbox for machine learning

    XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning.

    You can find the documentation at https://ethicalml.github.io/xai/index.html. You can also check out our talk at Tensorflow London where the idea was first conceived - the talk also contains an insight on the definitions and principles in this library.

    Tags: #python • explainability • xai

  • Sep 29, 2025

    pe_tree

    Python module for viewing Portable Executable (PE) files in a tree-view using pefile and PyQt5. Can also be used with IDA Pro and Rekall to dump in-memory PE files and reconstruct imports.

    PE Tree is a Python module for viewing Portable Executable (PE) files in a tree-view using pefile and PyQt5. It can also be used with IDA Pro, Ghidra, Volatility, Rekall and minidump to view and dump in-memory PE files, as well as perform import table reconstruction.

    Tags: #python

  • Sep 28, 2025

    training

    Reference implementations of MLPerf® training benchmarks

    This is a repository of reference implementations for the MLPerf training benchmarks. These implementations are valid as starting points for benchmark implementations but are not fully optimized and are not intended to be used for “real” performance measurements of software frameworks or hardware.

    Please see the MLPerf Training Benchmark paper for a detailed description of the motivation and guiding principles behind the benchmark suite. If you use any part of this benchmark (e.g., reference implementations, submissions, etc.) in academic work, please cite the following:

    @misc{mattson2019mlperf,
    title={MLPerf Training Benchmark},
    author={Peter Mattson and Christine Cheng and Cody Coleman and Greg Diamos and Paulius Micikevicius and David Patterson and Hanlin Tang and Gu-Yeon Wei and Peter Bailis and Victor Bittorf and David Brooks and Dehao Chen and Debojyoti Dutta and Udit Gupta and Kim Hazelwood and Andrew Hock and Xinyuan Huang and Atsushi Ike and Bill Jia and Daniel Kang and David Kanter and Naveen Kumar and Jeffery Liao and Guokai Ma and Deepak Narayanan and Tayo Oguntebi and Gennady Pekhimenko and Lillian Pentecost and Vijay Janapa Reddi and Taylor Robie and Tom St. John and Tsuguchika Tabaru and Carole-Jean Wu and Lingjie Xu and Masafumi Yamazaki and Cliff Young and Matei Zaharia},
    year={2019},
    eprint={1910.01500},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

    These reference implementations are still very much “alpha” or “beta” quality. They could be improved in many ways. Please file issues or pull requests to help us improve quality.

    Tags: #python • benchmark • machine-learning

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