Search Results for "artificial intelligence pentesting" - Page 44
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Showing 3035 open source projects for "artificial intelligence pentesting"
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Pydantic Logfire
Python observability platform for tracing apps, metrics, and logs
Pydantic Logfire is an observability platform designed to help developers monitor, analyze, and understand the behavior of their applications in real time. It is built by the team behind Pydantic and follows a philosophy of combining powerful capabilities with ease of use, making it accessible to entire engineering teams. Pydantic Logfire provides deep visibility into application performance by capturing traces, metrics, and logs through an OpenTelemetry-based architecture. It is... -
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Qodo Cover
AI tool that generates tests to improve code coverage quickly
Qodo Cover is an open source developer tool designed to automate the creation of unit tests using generative AI, helping teams improve code coverage with minimal manual effort. It operates as a command-line interface and can also be integrated into continuous integration workflows, making it adaptable to different development environments. It analyzes an existing codebase, identifies gaps in test coverage, and generates new tests that target uncovered or weakly tested areas. It follows an... -
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Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that... -
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AI-DLC
AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI
AI-DLC is an open-source workflow framework from AWS Labs designed to structure software development around AI-assisted engineering processes. The project promotes an “AI-Driven Life Cycle” methodology where coding assistants, IDE agents, and automation systems participate directly in planning, implementation, testing, and operational workflows. Rather than focusing on a single model or IDE, the framework provides reusable rules, templates, and orchestration patterns compatible with tools... -
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AG-UI
The Agent-User Interaction Protocol
AG-UI is an open, lightweight protocol for connecting AI agents to user-facing applications through a standardized event-based interface. It is designed to make agent behavior visible, interactive, and controllable inside real-time front-end experiences. Instead of treating an AI agent as a black-box chat endpoint, AG-UI defines structured events for messages, tool calls, state changes, lifecycle updates, and user interactions. This makes it easier for developers to build agent-powered apps... -
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Fli
Google Flights MCP and Python Library
Fli is a powerful Python library and command-line tool that provides direct programmatic access to Google Flights data through reverse-engineered API interactions rather than traditional web scraping. This approach enables faster, more reliable, and more stable access to flight information, avoiding the fragility associated with HTML parsing and UI changes. The library supports a wide range of flight search capabilities, including filtering by airline, departure time, number of stops, cabin... -
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Jaaz
Open source multimodal creative AI assistant with infinite canvas tool
Jaaz is an open source multimodal creative assistant designed to help users generate and organize visual media using artificial intelligence. It functions as a creative workspace where images, videos, and visual storyboards can be produced and arranged on an infinite canvas environment. It combines AI agents with visual editing tools, allowing users to generate media through prompts, sketches, or simple instructions. Jaaz supports multiple AI models and can integrate both local and cloud-based inference systems, enabling flexible creative workflows. ... -
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TensorFlow Quantum
Open-source Python framework for hybrid quantum-classical ml learning
TensorFlow Quantum is an open-source software framework designed for building and training hybrid quantum-classical machine learning models within the TensorFlow ecosystem. The framework enables researchers and developers to represent quantum circuits as data and integrate them directly into machine learning workflows. By combining classical deep learning techniques with quantum algorithms, the platform allows experimentation with quantum machine learning methods that may offer advantages... -
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Amazing-Python-Scripts
Curated collection of Amazing Python scripts
Amazing-Python-Scripts is a collaborative repository that collects a wide variety of Python scripts designed to demonstrate practical programming techniques and automation tasks. The project includes scripts ranging from beginner-level utilities to more advanced applications involving machine learning, data processing, and system automation. Its goal is to provide developers with useful coding examples that can solve everyday problems, automate repetitive tasks, or serve as learning... -
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LISA
LISA: Reasoning Segmentation via Large Language Model
LISA is an open-source multimodal AI system designed to enable language models to perform pixel-level reasoning and segmentation tasks on images. The project introduces a framework where a large language model can interpret natural language instructions and produce segmentation masks that highlight relevant regions in an image. Instead of relying solely on predefined object categories, the model is capable of reasoning about complex textual queries and translating them into visual... -
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Sparrow
Structured data extraction and instruction calling with ML, LLM
Sparrow is an open-source platform designed to extract structured information from documents, images, and other unstructured data sources using machine learning and large language models. The system focuses on transforming complex documents such as invoices, receipts, forms, and scanned pages into structured formats like JSON that can be processed by downstream applications. It combines several components, including OCR pipelines, vision-language models, and LLM-based reasoning modules to... -
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Serena
Agent toolkit providing semantic retrieval and editing capabilities
Serena is a coding-focused agent toolkit that turns an LLM into a practical software-engineering agent with semantic retrieval and editing over real repositories. It operates as an MCP server (and other integrations), exposing IDE-like tools so agents can locate symbols, reason about code structure, make targeted edits, and validate changes. The toolkit is LLM-agnostic and framework-agnostic, positioning itself as a drop-in capability for different chat UIs, orchestrators, or custom agent... -
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Fast3R
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Fast3R is Meta AI’s official CVPR 2025 release for “Towards 3D Reconstruction of 1000+ Images in One Forward Pass.” It represents a next-generation feedforward 3D reconstruction model capable of producing dense point clouds and camera poses for hundreds to thousands of images or video frames in a single inference pass—eliminating the need for slow, iterative structure-from-motion pipelines. Built on PyTorch Lightning and extending concepts from DUSt3R and Spann3r, Fast3R unifies multi-view... -
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Pearl
A Production-ready Reinforcement Learning AI Agent Library
Pearl is a production-ready reinforcement learning and contextual bandit agent library built for real-world sequential decision making. It is organized around modular components—policy learners, replay buffers, exploration strategies, safety modules, and history summarizers—that snap together to form reliable agents with clear boundaries and strong defaults. The library implements classic and modern algorithms across two regimes: contextual bandits (e.g., LinUCB, LinTS, SquareCB, neural... -
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VGGT
[CVPR 2025 Best Paper Award] VGGT
VGGT is a transformer-based framework aimed at unifying classic visual geometry tasks—such as depth estimation, camera pose recovery, point tracking, and correspondence—under a single model. Rather than training separate networks per task, it shares an encoder and leverages geometric heads/decoders to infer structure and motion from images or short clips. The design emphasizes consistent geometric reasoning: outputs from one head (e.g., correspondences or tracks) reinforce others (e.g., pose... -
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Tiktoken
tiktoken is a fast BPE tokeniser for use with OpenAI's models
tiktoken is a high-performance, tokenizer library (based on byte-pair encoding, BPE) designed for use with OpenAI’s models. It handles encoding and decoding text to token IDs efficiently, with minimal overhead. Because tokenization is a fundamental step in preparing text for models, tiktoken is optimized for speed, memory, and correctness in model contexts (e.g. matching OpenAI’s internal tokenization). The repo supports multiple encodings (e.g. “cl100k_base”) and lets users switch encoding... -
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OpenFold
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction
OpenFold carefully reproduces (almost) all of the features of the original open source inference code (v2.0.1). The sole exception is model ensembling, which fared poorly in DeepMind's own ablation testing and is being phased out in future DeepMind experiments. It is omitted here for the sake of reducing clutter. In cases where the Nature paper differs from the source, we always defer to the latter. OpenFold is trainable in full precision, half precision, or bfloat16 with or without... -
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DeepLabCut
Implementation of DeepLabCut
DeepLabCut™ is an efficient method for 2D and 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames). We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. The package is open source, fast, robust, and can be used to compute 3D pose estimates or for... -
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LMCache
Supercharge Your LLM with the Fastest KV Cache Layer
LMCache is an extension layer for LLM serving engines that accelerates inference, especially with long contexts, by storing and reusing key-value (KV) attention caches across requests. Instead of rebuilding KV states for repeated or shared text segments, LMCache persists and retrieves them from multiple tiers—GPU memory, CPU DRAM, and local disk—then injects them into subsequent requests to reduce TTFT and increase throughput. Its design supports reuse beyond strict prefix matching and... -
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Paper2GUI
Convert AI papers to GUI
Convert AI papers to GUI，Make it easy and convenient for everyone to use artificial intelligence technology。让每个人都简单方便的使用前沿人工智能技术 Paper2GUI: An AI desktop APP toolbox for ordinary people. It can be used immediately without installation. It already supports 40+ AI models, covering AI painting, speech synthesis, video frame complementing, video super-resolution, object detection, and image stylization. , OCR recognition and other fields. -
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Xianyu Intelligent Monitor Bot
AI tool for real-time monitoring and analysis of Goofish listings
ai-goofish-monitor is an open source automation tool designed to monitor listings on the Goofish second-hand marketplace and analyze them using artificial intelligence. It combines browser automation with AI-based analysis to automatically search, collect, and evaluate newly posted items that match a user’s purchase criteria. It uses Playwright to simulate real user interactions with the marketplace, allowing the system to retrieve product data and track updates in near real time. ai-goofish-monitor can run multiple monitoring tasks simultaneously, each configured with specific keywords, price ranges, and filtering conditions. ... -
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Qwen-Image-Layered
Qwen-Image-Layered: Layered Decomposition for Inherent Editablity
Qwen-Image-Layered is an extension of the Qwen series of multimodal models that introduces layered image understanding, enabling the model to reason about hierarchical visual structures — such as separating foreground, background, objects, and contextual layers within an image. This architecture allows richer semantic interpretation, enabling use cases such as scene decomposition, object-level editing, layered captioning, and more fine-grained multimodal reasoning than with flat image... -
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Gemma
Gemma open-weight LLM library, from Google DeepMind
Gemma, developed by Google DeepMind, is a family of open-weights large language models (LLMs) built upon the research and technology behind Gemini. This repository provides the official implementation of the Gemma PyPI package, a JAX-based library that enables users to load, interact with, and fine-tune Gemma models. The framework supports both text and multi-modal input, allowing natural language conversations that incorporate visual content such as images. It includes APIs for... -
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Perception Models
State-of-the-art Image & Video CLIP, Multimodal Large Language Models
Perception Models is a state-of-the-art framework developed by Facebook Research for advanced image and video perception tasks. It introduces two primary components: the Perception Encoder (PE) for visual feature extraction and the Perception Language Model (PLM) for multimodal decoding and reasoning. The PE module is a family of vision encoders designed to excel in image and video understanding, surpassing models like SigLIP2, InternVideo2, and DINOv2 across multiple benchmarks. Meanwhile,... -
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DeepSeek Coder
DeepSeek Coder: Let the Code Write Itself
DeepSeek-Coder is a series of code-specialized language models designed to generate, complete, and infill code (and mixed code + natural language) with high fluency in both English and Chinese. The models are trained from scratch on a massive corpus (~2 trillion tokens), of which about 87% is code and 13% is natural language. This dataset covers project-level code structure (not just line-by-line snippets), using a large context window (e.g. 16K) and a secondary fill-in-the-blank objective...
