Posts

  • Jul 21, 2022

    webvr-boilerplate

    A starting point for web-based VR experiences that work on all VR headsets.

    A THREE.js-based starting point for cross-platform web-based VR experiences.

    This project relies heavily on the WebVR Polyfill to provide VR support if the WebVR API is not implemented. It also uses the WebVR UI project to render the UI to enter VR and magic window modes. See here for a live demo.

    Tags: #javascript

  • Jul 20, 2022

    selection

    ✨ Viselect - A simple and lightweight library to add a visual way of selecting elements, just like on your Desktop. Zero dependencies, super small. Support for major frameworks.

    • 🌟 Modern bundle
    • 🔩 Ultra tiny (<4kb)
    • 👌 Simple usage
    • ⚡ Highly optimized
    • ✔ Zero dependencies
    • 📱 Mobile / touch support
    • 🖱 Vertical and horizontal scroll support
    • 💪 Hardened (over 3 years old and used in many apps)
    • 🖼 Support for major frameworks (WIP)

    Tags: #typescript • selection • selections

  • Jul 20, 2022

    typegraphql-nestjs

    TypeGraphQL integration with NestJS

    Basic integration of TypeGraphQL in NestJS.

    Allows to use TypeGraphQL features while integrating with NestJS modules system and dependency injector.

    Tags: #typescript • typegraphql • type-graphql

  • Jul 20, 2022

    enum4linux-ng

    A next generation version of enum4linux (a Windows/Samba enumeration tool) with additional features like JSON/YAML export. Aimed for security professionals and CTF players.

    • support for YAML and JSON export
    • colored console output (can be disabled via NO_COLOR)
    • ldapsearch und polenum are natively implemented
    • support for multiple authentication methods
    • support for legacy SMBv1 connections
    • auto detection of IPC signing support
    • ‘smart’ enumeration will automatically disable tests which would otherwise fail
    • timeout support
    • SMB dialect checks
    • IPv6 support (experimental)

    Tags: #python • enum4linux • security

  • Jul 19, 2022

    pyprob

    A probabilistic programming system for simulators and high-performance computing (HPC), based on PyTorch

    We support front ends in multiple languages through the PPX interface that allows execution of models and inference engines in separate programming languages, processes, and machines connected over a network.

    Tags: #python

  • Jul 19, 2022

    ntc-ansible

    Multi-vendor network modules

    You need to perform two steps to start using these modules.

    1. Ensure this repository is in your Ansible module search path
    2. Install Dependencies

    Tags: #python

  • Jul 19, 2022

    archivesspace

    The ArchivesSpace archives management tool

    ArchivesSpace is released under the Educational Community License, version 2.0. See the COPYING file for more information.

    Tags: #ruby • archivesspace • archives

  • Jul 18, 2022

    mry

    Mry Migrates .Rubocop.Yml

    Mry Migrates .Rubocop.Yml :muscle:

    RuboCop has many many breaking changes, because it is before version 1.0. So, if you update your RuboCop version, .rubocop.yml breaks in many cases… This tool supports migrating .rubocop.yml. It automatically renames the configuration in your .rubocop.yml that was renamed by the updated RuboCop version. So, with this tool, you do not have to be afraid of updating RuboCop anymore!

    Tags: #ruby

  • Jul 18, 2022

    gitbeaker

    🤖 GitLab API NodeJS library with full support of all the Gitlab API services.

    Tags: #typescript • gitlab • api

  • Jul 18, 2022

    EvalAI

    :cloud: :rocket: :bar_chart: :chart_with_upwards_trend: Evaluating state of the art in AI

    • Custom evaluation protocols and phases: We allow creation of an arbitrary number of evaluation phases and dataset splits, compatibility using any programming language, and organizing results in both public and private leaderboards.

    • Remote evaluation: Certain large-scale challenges need special compute capabilities for evaluation. If the challenge needs extra computational power, challenge organizers can easily add their own cluster of worker nodes to process participant submissions while we take care of hosting the challenge, handling user submissions, and maintaining the leaderboard.

    • Evaluation inside environments: EvalAI lets participants submit code for their agent in the form of docker images which are evaluated against test environments on the evaluation server. During evaluation, the worker fetches the image, test environment, and the model snapshot and spins up a new container to perform evaluation.

    • CLI support: evalai-cli is designed to extend the functionality of the EvalAI web application to your command line to make the platform more accessible and terminal-friendly.

    • Portability: EvalAI is designed with keeping in mind scalability and portability of such a system from the very inception of the idea. Most of the components rely heavily on open-source technologies – Docker, Django, Node.js, and PostgreSQL.

    • Faster evaluation: We warm-up the worker nodes at start-up by importing the challenge code and pre-loading the dataset in memory. We also split the dataset into small chunks that are simultaneously evaluated on multiple cores. These simple tricks result in faster evaluation and reduces the evaluation time by an order of magnitude in some cases.

    Tags: #python • ai • machine-learning

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