Popular GitHub Repositories for Directory Hashing

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

A fast Rust based tool to serialize text-based files in a repository or directory for LLM consumption

One-click to deploy your own ai tools directory with the open source web-ui

directory for Awesome MCP Servers

Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives: https://nvlabs.github.io/instant-ngp/

An Open-Source Package for Deep Learning to Hash (DeepHash)

An awesome list of best top AI directories to submit your ai tools

? The largest directory for AI-ready documentation and tools implementing the proposed llms.txt standard

Elasticsearch plugin for nearest neighbor search. Store vectors and run similarity search using exact and approximate algorithms.

A smarter web fuzzing tool that combines local LLM models and ffuf to optimize directory and file discovery

? DiscovAI-Search: An AI-powered search engine for AI tools and custom data. Built with Next.js, OpenAI, Supabase, and more. Features vector-based search, Redis caching, and LLM-powered responses.

Auto-Annotate - Automatically annotate your entire image directory by a single command. As simple as saying - "Annotate all the street sign (label) in the autonomous car dataset (directory)" and BAM! DONE. Each and every image with a street sign in the diverse dataset directory containing images of all sorts which have a street sign are filtered and the segmentation annotation is performed in a single command. The Auto-Annotate tool provides auto annotation of segmentation masks for the objects in the images inside some directory based on the labels. Auto-Annotate is able to provide automated annotations for the labels defined in the COCO Dataset and also supports Custom Labels.

A CLI tool to provide LLM context for coding projects by combining project files into a single text file (or clipboard text) with directory tree structure.

BloodHound-MCP-AI 是一款集成工具，它通过模型上下文协议 (MCP) 将 BloodHound 与 AI 连接起来，使安全专业人员能够使用自然语言而不是复杂的 Cypher 查询来分析 Active Directory 的攻击路径。

This directory contains code necessary to run the GraphNAS algorithm.

State of the Art results in Intent Classification using Sematic Hashing for three datasets: AskUbuntu, Chatbot and WebApplication.

Deep learning cross modal hashing in PyTorch

Scalable and Sustainable Deep Learning via Randomized Hashing

Pure PyTorch implementation of Nvidia's hash grid encoding: https://nvlabs.github.io/instant-ngp/

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