Best AI tools for< Defend Beliefs >

Darktrace is a cybersecurity platform that leverages AI technology to provide proactive protection against cyber threats. It offers cloud-native AI security solutions for networks, emails, cloud environments, identity protection, and endpoint security. Darktrace's AI Analyst investigates alerts at the speed and scale of AI, mimicking human analyst behavior. The platform also includes services such as 24/7 expert support and incident management. Darktrace's AI is built on a unique approach where it learns from the organization's data to detect and respond to threats effectively. The platform caters to organizations of all sizes and industries, offering real-time detection and autonomous response to known and novel threats.

Fletch is the world's first cyber threat AI application that helps users stay ahead of cyber threats by automating busywork with AI agents. It continuously trends the threat landscape, forecasts impact, prioritizes alerts, generates tailored advice, and provides daily proactive insights to guide users in defending against threats. Fletch filters and prioritizes alerts, uncovers weaknesses in SaaS supply chains, and offers timely tactical advice to act fast in critical moments. The application also assists in articulating threat messages and provides instant answers through AskFletch chat. Fletch integrates with existing tools, simplifying users' lives and offering hands-on guidance for businesses of all sizes.

TAID is a cutting-edge AI tool that specializes in analyzing text to determine whether it was created by a human or generated by artificial intelligence models like ChatGPT. It helps users combat misinformation, ensure transparency, and maintain trust in online communication by verifying the authenticity of the text they encounter. TAID utilizes advanced machine learning algorithms to achieve impressive accuracy in detecting AI-generated content, offering a free detection service with unlimited usage and no hidden fees or subscriptions.

Operant is a cloud-native runtime protection platform that offers instant visibility and control from infrastructure to APIs. It provides AI security shield for applications, API threat protection, Kubernetes security, automatic microsegmentation, and DevSecOps solutions. Operant helps defend APIs, protect Kubernetes, and shield AI applications by detecting and blocking various attacks in real-time. It simplifies security for cloud-native environments with zero instrumentation, application code changes, or integrations.

DDoS-Guard is a web security service that protects websites from distributed denial-of-service (DDoS) attacks. It checks the user's browser before granting access to the website, ensuring a secure browsing experience. The service provides automatic protection against DDoS attacks and ensures the smooth functioning of websites. DDoS-Guard is trusted by many websites to safeguard their online presence and maintain uninterrupted service for their users.

Reclaim.ai is an AI-powered scheduling application designed to optimize users' schedules for better productivity, collaboration, and work-life balance. The app offers features such as Smart Meetings, Scheduling Links, Calendar Sync, Buffer Time, and Time Tracking. It helps users analyze their time across meetings, tasks, and work-life balance metrics. Reclaim.ai is trusted by over 300,000 people across 40,000 companies, with a 4.8/5 rating on G2. The application is known for its ability to defend focus time, automate daily plans, and manage smart events efficiently.

Mimecast is an AI-powered email and collaboration security application that offers advanced threat protection, cloud archiving, security awareness training, and more. With a focus on protecting communications, data, and people, Mimecast leverages AI technology to provide industry-leading security solutions to organizations globally. The application is designed to defend against sophisticated email attacks, enhance human risk management, and streamline compliance processes.

MixMode is the world's most advanced AI for threat detection, offering a dynamic threat detection platform that utilizes patented Third Wave AI technology. It provides real-time detection of known and novel attacks with high precision, self-supervised learning capabilities, and context-awareness to defend against modern threats. MixMode empowers modern enterprises with unprecedented speed and scale in threat detection, delivering unrivaled capabilities without the need for predefined rules or human input. The platform is trusted by top security teams and offers rapid deployment, customization to individual network dynamics, and state-of-the-art AI-driven threat detection.

Tracecat is an open-source security automation platform that helps you automate security alerts, build AI-assisted workflows, orchestrate alerts, and close cases fast. It is a Tines / Splunk SOAR alternative that is built for builders and allows you to experiment for free. You can deploy Tracecat on your own infrastructure or use Tracecat Cloud with no maintenance overhead. Tracecat is Apache-2.0 licensed, which means it is open vision, open community, and open development. You can have your say in the future of security automation. Tracecat is no-code first, but you can also code as well. You can build automations fast with no-code and customize without vendor lock-in using Python. Tracecat has a click-and-drag workflow builder that allows you to automate SecOps using pre-built actions (API calls, webhooks, data transforms, AI tasks, and more) combined into workflows. No code is required. Tracecat also has a built-in case management system that allows you to open cases directly from workflows and track and manage security incidents all in one platform.

Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, speech recognition, generation, certification, etc.).

hackingBuddyGPT is a framework for testing LLM-based agents for security testing. It aims to create common ground truth by creating common security testbeds and benchmarks, evaluating multiple LLMs and techniques against those, and publishing prototypes and findings as open-source/open-access reports. The initial focus is on evaluating the efficiency of LLMs for Linux privilege escalation attacks, but the framework is being expanded to evaluate the use of LLMs for web penetration-testing and web API testing. hackingBuddyGPT is released as open-source to level the playing field for blue teams against APTs that have access to more sophisticated resources.

EasyEdit is a Python package for edit Large Language Models (LLM) like `GPT-J`, `Llama`, `GPT-NEO`, `GPT2`, `T5`(support models from **1B** to **65B**), the objective of which is to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. It is designed to be easy to use and easy to extend.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

DevOpsGPT is an AI-driven software development automation solution that combines Large Language Models (LLM) with DevOps tools to convert natural language requirements into working software. It improves development efficiency by eliminating the need for tedious requirement documentation, shortens development cycles, reduces communication costs, and ensures high-quality deliverables. The Enterprise Edition offers features like existing project analysis, professional model selection, and support for more DevOps platforms. The tool automates requirement development, generates interface documentation, provides pseudocode based on existing projects, facilitates code refinement, enables continuous integration, and supports software version release. Users can run DevOpsGPT with source code or Docker, and the tool comes with limitations in precise documentation generation and understanding existing project code. The product roadmap includes accurate requirement decomposition, rapid import of development requirements, and integration of more software engineering and professional tools for efficient software development tasks under AI planning and execution.

PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.

TaskWeaver is a code-first agent framework designed for planning and executing data analytics tasks. It interprets user requests through code snippets, coordinates various plugins to execute tasks in a stateful manner, and preserves both chat history and code execution history. It supports rich data structures, customized algorithms, domain-specific knowledge incorporation, stateful execution, code verification, easy debugging, security considerations, and easy extension. TaskWeaver is easy to use with CLI and WebUI support, and it can be integrated as a library. It offers detailed documentation, demo examples, and citation guidelines.

This repository tracks the latest research on machine unlearning in large language models (LLMs). It offers a comprehensive list of papers, datasets, and resources relevant to the topic.

Awesome-Jailbreak-on-LLMs is a collection of state-of-the-art, novel, and exciting jailbreak methods on Large Language Models (LLMs). The repository contains papers, codes, datasets, evaluations, and analyses related to jailbreak attacks on LLMs. It serves as a comprehensive resource for researchers and practitioners interested in exploring various jailbreak techniques and defenses in the context of LLMs. Contributions such as additional jailbreak-related content, pull requests, and issue reports are welcome, and contributors are acknowledged. For any inquiries or issues, contact [email protected]. If you find this repository useful for your research or work, consider starring it to show appreciation.

This repository is a collection of papers on Generative AI Machine Unlearning, categorized based on modality and applications. It includes datasets, benchmarks, and surveys related to unlearning scenarios in generative AI. The repository aims to provide a comprehensive overview of research in the field of machine unlearning for generative models.

AutoAudit is an open-source large language model specifically designed for the field of network security. It aims to provide powerful natural language processing capabilities for security auditing and network defense, including analyzing malicious code, detecting network attacks, and predicting security vulnerabilities. By coupling AutoAudit with ClamAV, a security scanning platform has been created for practical security audit applications. The tool is intended to assist security professionals with accurate and fast analysis and predictions to combat evolving network threats.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

LangGraph Studio is a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications. It offers visual graphs and state editing to better understand agent workflows and iterate faster. Users can collaborate with teammates using LangSmith to debug failure modes. The tool integrates with LangSmith and requires Docker installed. Users can create and edit threads, configure graph runs, add interrupts, and support human-in-the-loop workflows. LangGraph Studio allows interactive modification of project config and graph code, with live sync to the interactive graph for easier iteration on long-running agents.

Aioaws is an asyncio SDK for some AWS services, providing clean, secure, and easily debuggable access to services like S3, SES, and SNS. It is written from scratch without dependencies on boto or boto3, formatted with black, and includes complete type hints. The library supports various functionalities such as listing, deleting, and generating signed URLs for S3 files, sending emails with attachments and multipart content via SES, and receiving notifications about mail delivery from SES. It also offers AWS Signature Version 4 authentication and has minimal dependencies like aiofiles, cryptography, httpx, and pydantic.

KubeAI is a highly scalable AI platform that runs on Kubernetes, serving as a drop-in replacement for OpenAI with API compatibility. It can operate OSS model servers like vLLM and Ollama, with zero dependencies and additional OSS addons included. Users can configure models via Kubernetes Custom Resources and interact with models through a chat UI. KubeAI supports serving various models like Llama v3.1, Gemma2, and Qwen2, and has plans for model caching, LoRA finetuning, and image generation.

Numpy.NET is the most complete .NET binding for NumPy, empowering .NET developers with extensive functionality for scientific computing, machine learning, and AI. It provides multi-dimensional arrays, matrices, linear algebra, FFT, and more via a strong typed API. Numpy.NET does not require a local Python installation, as it uses Python.Included to package embedded Python 3.7. Multi-threading must be handled carefully to avoid deadlocks or access violation exceptions. Performance considerations include overhead when calling NumPy from C# and the efficiency of data transfer between C# and Python. Numpy.NET aims to match the completeness of the original NumPy library and is generated using CodeMinion by parsing the NumPy documentation. The project is MIT licensed and supported by JetBrains.

Explains and defends the Catholic view of the Trinity.

Ask me anything about the ATT&CK

Post-apocalyptic game guide with real-world survival tips.

Expert on the Book of Mormon, providing insights and explanations.

Witty and sharp-tongued comeback generator

Strategic leader in zombie crisis scenarios.

Defends HTMX with wit

A 3D Printed Construction wiz trained on expert interviews. Use creatively, don't depend on 3DCP Guru GPT for factually accurate info (although it's pretty darn good)