Best AI tools for< Troll People Online >
3 - AI tool Sites
Trolly.ai
Trolly.ai is an AI-powered tool that helps you create and manage your online store. With Trolly.ai, you can easily create a beautiful online store, add products, and start selling online in minutes. Trolly.ai also provides you with a range of powerful features to help you grow your business, such as marketing automation, inventory management, and customer support.
Trill
Trill is an AI-powered research assistant designed to streamline the user research process. It helps users move from interviews to insights quickly by providing relevant insights and observations based on project objectives. With features like instant themes and categories, organizing findings, and a user-friendly editor, Trill aims to simplify and accelerate the research analysis process. Currently in beta, Trill offers a free trial for users to experience its capabilities and provide feedback for further improvements.
Watchdog
Watchdog is an AI-powered chat moderation tool designed to fully automate chat moderation for Telegram communities. It helps community owners tackle rulebreakers, trolls, and spambots effortlessly, ensuring consistent rule enforcement and user retention. With features like automatic monitoring, customizable rule enforcement, and quick setup, Watchdog offers significant cost savings and eliminates the need for manual moderation. The tool is developed by Ben, a solo developer, who created it to address the challenges he faced in managing his own community. Watchdog aims to save time, money, and enhance user experience by swiftly identifying and handling rule violations.
8 - Open Source AI Tools
awesome-generative-ai
A curated list of Generative AI projects, tools, artworks, and models
Top-AI-Tools
Top AI Tools is a comprehensive, community-curated directory that aims to catalog and showcase the most outstanding AI-powered products. This index is not exhaustive, but rather a compilation of our research and contributions from the community.
ReasonablePlanningAI
Reasonable Planning AI is a robust design and data-driven AI solution for game developers. It provides an AI Editor that allows creating AI without Blueprints or C++. The AI can think for itself, plan actions, adapt to the game environment, and act dynamically. It consists of Core components like RpaiGoalBase, RpaiActionBase, RpaiPlannerBase, RpaiReasonerBase, and RpaiBrainComponent, as well as Composer components for easier integration by Game Designers. The tool is extensible, cross-compatible with Behavior Trees, and offers debugging features like visual logging and heuristics testing. It follows a simple path of execution and supports versioning for stability and compatibility with Unreal Engine versions.
llmware
LLMWare is a framework for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows. This project provides a comprehensive set of tools that anyone can use - from a beginner to the most sophisticated AI developer - to rapidly build industrial-grade, knowledge-based enterprise LLM applications. Our specific focus is on making it easy to integrate open source small specialized models and connecting enterprise knowledge safely and securely.
x-crawl
x-crawl is a flexible Node.js AI-assisted crawler library that offers powerful AI assistance functions to make crawler work more efficient, intelligent, and convenient. It consists of a crawler API and various functions that can work normally even without relying on AI. The AI component is currently based on a large AI model provided by OpenAI, simplifying many tedious operations. The library supports crawling dynamic pages, static pages, interface data, and file data, with features like control page operations, device fingerprinting, asynchronous sync, interval crawling, failed retry handling, rotation proxy, priority queue, crawl information control, and TypeScript support.
Efficient_Foundation_Model_Survey
Efficient Foundation Model Survey is a comprehensive analysis of resource-efficient large language models (LLMs) and multimodal foundation models. The survey covers algorithmic and systemic innovations to support the growth of large models in a scalable and environmentally sustainable way. It explores cutting-edge model architectures, training/serving algorithms, and practical system designs. The goal is to provide insights on tackling resource challenges posed by large foundation models and inspire future breakthroughs in the field.