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  • Next-Gen Encryption for Post-Quantum Security | CLEAR by Quantum Knight Icon
    Next-Gen Encryption for Post-Quantum Security | CLEAR by Quantum Knight

    Lock Down Any Resource, Anywhere, Anytime

    CLEAR by Quantum Knight is a FIPS-140-3 validated encryption SDK engineered for enterprises requiring top-tier security. Offering robust post-quantum cryptography, CLEAR secures files, streaming media, databases, and networks with ease across over 30 modern platforms. Its compact design, smaller than a single smartphone image, ensures maximum efficiency and low energy consumption.
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  • Skillfully - The future of skills based hiring Icon
    Skillfully - The future of skills based hiring

    Realistic Workplace Simulations that Show Applicant Skills in Action

    Skillfully transforms hiring through AI-powered skill simulations that show you how candidates actually perform before you hire them. Our platform helps companies cut through AI-generated resumes and rehearsed interviews by validating real capabilities in action. Through dynamic job specific simulations and skill-based assessments, companies like Bloomberg and McKinsey have cut screening time by 50% while dramatically improving hire quality.
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  • 1
    bufferline.nvim

    bufferline.nvim

    A snazzy bufferline for Neovim

    A snazzy buffer line (with tab page integration) for Neovim built using Lua. This plugin shamelessly attempts to emulate the aesthetics of GUI text editors/Doom Emacs. It is advised that you specify either the latest tag or a specific tag and bump them manually if you'd prefer to inspect changes before updating.
    Downloads: 10 This Week
    Last Update:
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  • 2
    char-rnn

    char-rnn

    Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN)

    char-rnn is a classic codebase for training multi-layer recurrent neural networks on raw text to build character-level language models that learn to predict the next character in a sequence. It supports common recurrent architectures including vanilla RNNs as well as LSTM and GRU variants, letting users compare behavior and output quality across model types. It is straightforward: you provide a single text file, train the model to minimize next-character prediction loss, then sample from the trained network to generate new text one character at a time in the style of the dataset. The project is designed for experimentation, offering tunable settings for depth, hidden size, dropout, sequence length, and sampling temperature to control creativity and coherence. ...
    Downloads: 0 This Week
    Last Update:
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