Best AI tools for< Analyze Context >

Typebar is a social media writing assistant that uses AI to help you create original and relevant posts, replies, and images. It can analyze the context of your post, the post you are replying to, and the social network you are using to generate tailored content. Typebar also offers a variety of features such as text generation, context-aware replies generation, AI text editing, and image generation. It supports multiple languages and works with Twitter, Instagram, Facebook, and LinkedIn.

Autopilot is an AI tool that mimics human thinking and learning processes to assist users in their work tasks. It leverages cutting-edge research and a context engine to provide novel insights, accurate answers, and seamless integrations with various data sources. Autopilot streamlines tasks such as creating presentations, generating documents, analyzing spreadsheets, and visualizing data without the need for extensive coding knowledge. With a focus on trustworthiness and efficiency, Autopilot aims to enhance productivity and decision-making in various professional settings.

ContextClue is an AI text analysis tool that offers enhanced document insights through features like text summarization, report generation, and LLM-driven semantic search. It helps users summarize multi-format content, automate document creation, and enhance research by understanding context and intent. ContextClue empowers users to efficiently analyze documents, extract insights, and generate content with unparalleled accuracy. The tool can be customized and integrated into existing workflows, making it suitable for various industries and tasks.

Rationale is a cutting-edge decision-making AI tool that leverages the power of the latest GPT technology and in-context learning. It is designed to assist users in making informed decisions by providing valuable insights and recommendations based on data analysis and machine learning algorithms.

Neuwo is a leading contextual AI engine for content classification and automated tagging. It advocates that organizations of any size should have access to cutting-edge yet cost-effective contextual AI technology for meaningful automated content categorization and customization. The platform enables users to transition seamlessly from Oracle Grapeshot to Neuwo, offering advanced AI-driven algorithms for more precise targeting and increased engagement. Neuwo's technology ensures reaching the right audience at the right time, without the need for contracts. The platform enriches valuable data through intelligent content processing, brand safety, and suitability, and content activation, adding value to digital properties and businesses.

LensAI is an AI-powered contextual computer vision ad solution that monetizes any visual content and fine-tunes targeting through identifying objects, logos, actions, and context and matching them with relevant ads.

BigPanda is an AI-powered ITOps platform that helps businesses automatically identify actionable alerts, proactively prevent incidents, and ensure service availability. It uses advanced AI/ML algorithms to analyze large volumes of data from various sources, including monitoring tools, event logs, and ticketing systems. BigPanda's platform provides a unified view of IT operations, enabling teams to quickly identify and resolve issues before they impact business-critical services.

Feelsy is a social media sentiment analysis tool that helps businesses understand how their audience feels about their content. With Feelsy, businesses can track the sentiment of their Instagram comments in real-time, identify the content that resonates most with their audience, and measure the effectiveness of their social media campaigns.

Cognee is an AI application that helps users build deterministic AI memory by perfecting exceptional AI apps with intelligent data management. It acts as a semantic memory layer, uncovering hidden connections within data and infusing it with company-specific language and principles. Cognee offers data ingestion and enrichment services, resulting in relevant data retrievals and lower infrastructure costs. The application is suitable for various industries, including customer engagement, EduTech, company onboarding, recruitment, marketing, and tourism.

Strategic Intelligence is an AI-powered platform that helps businesses make better decisions by providing them with real-time insights into their data. The platform uses a variety of machine learning and artificial intelligence techniques to analyze data from a variety of sources, including social media, news articles, and financial reports. This data is then used to generate reports and insights that can help businesses identify opportunities, mitigate risks, and make better decisions.

Pentest Copilot by BugBase is an ultimate ethical hacking assistant that guides users through each step of the hacking journey, from analyzing web apps to root shells. It eliminates redundant research, automates payload and command generation, and provides intelligent contextual analysis to save time. The application excels at data extraction, privilege escalation, lateral movement, and leaving no trace behind. With features like secure VPN integration, total control over sessions, parallel command processing, and flexibility to choose between local or cloud execution, Pentest Copilot offers a seamless and efficient hacking experience without the need for Kali Linux installation.

Quid is an AI-powered consumer and market intelligence platform that offers a comprehensive view of customer context through Generative AI technology. It goes beyond simple data collection and analytics to provide insights through the lens of the future. Quid helps businesses make informed decisions by analyzing conversations, sentiments, and developments. The platform offers various solutions for marketing, consumer insights, data science, customer experience, agencies, and communications, empowering organizations to stay ahead in a competitive landscape.

Quid is an AI-powered consumer and market intelligence platform that goes beyond simple data collection and analytics. It provides a complete picture of customer context, helping businesses make informed decisions based on future trends and opportunities. With features like Quid Discover for uncovering insights, Quid Monitor for real-time analytics, Quid Predict for future focus, Quid Compete for competitive analysis, and Quid Connect for data integration, the platform empowers organizations with proactive, data-driven decision-making.

Dili is an AI Diligence Platform that automates diligence processes for various industries such as Real Estate, Private Equity, Tax Credit, and Venture Capital. It offers features like extracting key data instantly, supporting various document types, flagging transaction issues, intelligent document search, and risk assessments. Dili provides advantages such as reliable insights, confidence scores for answers, error detection, custom workflows, and domain expertise encoding. However, it may have limitations in fine-tuning models on user data, handling complex math tasks, and measuring accuracy in certain cases.

Naro is an AI-powered sales knowledge platform that helps marketers identify trends and content gaps by analyzing sales conversations. It proactively suggests the right content to sellers, increases content utilization, and integrates conversations and content seamlessly. Naro also connects with existing tools to provide data-driven sales insights and improve content discoverability. With Naro, users can improve content efficacy, discover gaps and trends in sales interactions, automatically answer sales questions, and track content utilization by account and prospect.

MobiHealthNews is a digital health publication that covers breaking news and trends in healthcare. The website provides insights on AI models in radiology, responsible AI implementation, and digital tools for mental health. It also features news on partnerships in healthcare technology and advancements in AI adoption. MobiHealthNews aims to deliver daily updates on the latest developments in digital health and AI applications.

Qortex is a video intelligence platform that offers advanced AI technology to optimize advertising, monetization, and analytics for video content. The platform analyzes video frames in real-time to provide deep insights for media investment decisions. With features like On-Stream ad experiences and in-video ad units, Qortex helps brands achieve higher audience attention, revenue per stream, and fill rates. The platform is designed to enhance brand metrics and improve advertising performance through contextual targeting.

Criya AI is an Intelligent Content System that helps boost buyer engagement by providing AI-powered tools such as Content Builder, Slide Generator, Visual Design, and more. It offers features like Company knowledge management, Engagement Analytics, Secure Sharing, and Team Collaboration. Criya AI caters to various use cases like Account Based Prospecting, Lead Capture, and Deal Execution, benefiting roles such as BDR/SDR, Account Executive, and Sales Trainer. The application is designed to accelerate revenue generation by producing client-ready assets quickly and efficiently.

Easytolearn is an AI-powered platform that offers tools for education, focusing on empowering teachers and enhancing the learning process. The platform aims to provide efficient learning experiences without replacing teachers but supporting their efforts. Easytolearn's first project, LangMagic, creates a vibrant community for creators, teachers, and students to connect, utilizing AI tools for content creation and discovery.

TLDR is an AI-powered IDE plugin that explains code in plain English. It helps developers understand code by providing quick summaries of what a piece of code is doing. The tool supports almost all programming languages and offers a free version for users to try before purchasing. TLDR aims to simplify the understanding of complex code structures and save developers time in comprehending codebases.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

ContextCite is a tool for attributing statements generated by LLMs back to specific parts of the context. It allows users to analyze and understand the sources of information used by language models in generating responses. By providing attributions, users can gain insights into how the model makes decisions and where the information comes from.

Long-Context-Attention (YunChang) is a unified sequence parallel approach that combines the strengths of DeepSpeed-Ulysses-Attention and Ring-Attention to provide a versatile and high-performance solution for long context LLM model training and inference. It addresses the limitations of both methods by offering no limitation on the number of heads, compatibility with advanced parallel strategies, and enhanced performance benchmarks. The tool is verified in Megatron-LM and offers best practices for 4D parallelism, making it suitable for various attention mechanisms and parallel computing advancements.

The Codebase Context Specification (CCS) project aims to standardize embedding contextual information within codebases to enhance understanding for both AI and human developers. It introduces a convention similar to `.env` and `.editorconfig` files but focused on documenting code for both AI and humans. By providing structured contextual metadata, collaborative documentation guidelines, and standardized context files, developers can improve code comprehension, collaboration, and development efficiency. The project includes a linter for validating context files and provides guidelines for using the specification with AI assistants. Tooling recommendations suggest creating memory systems, IDE plugins, AI model integrations, and agents for context creation and utilization. Future directions include integration with existing documentation systems, dynamic context generation, and support for explicit context overriding.

This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

LLMonFHIR is an iOS application that utilizes large language models (LLMs) to interpret and provide context around patient data in the Fast Healthcare Interoperability Resources (FHIR) format. It connects to the OpenAI GPT API to analyze FHIR resources, supports multiple languages, and allows users to interact with their health data stored in the Apple Health app. The app aims to simplify complex health records, provide insights, and facilitate deeper understanding through a conversational interface. However, it is an experimental app for informational purposes only and should not be used as a substitute for professional medical advice. Users are advised to verify information provided by AI models and consult healthcare professionals for personalized advice.

llm-colosseum is a tool designed to evaluate Language Model Models (LLMs) in real-time by making them fight each other in Street Fighter III. The tool assesses LLMs based on speed, strategic thinking, adaptability, out-of-the-box thinking, and resilience. It provides a benchmark for LLMs to understand their environment and take context-based actions. Users can analyze the performance of different LLMs through ELO rankings and win rate matrices. The tool allows users to run experiments, test different LLM models, and customize prompts for LLM interactions. It offers installation instructions, test mode options, logging configurations, and the ability to run the tool with local models. Users can also contribute their own LLM models for evaluation and ranking.

**Description:** PromptFuzz is an automated tool that generates high-quality fuzz drivers for libraries via a fuzz loop constructed on mutating LLMs' prompts. The fuzz loop of PromptFuzz aims to guide the mutation of LLMs' prompts to generate programs that cover more reachable code and explore complex API interrelationships, which are effective for fuzzing. **Features:** * **Multiply LLM support** : Supports the general LLMs: Codex, Inocder, ChatGPT, and GPT4 (Currently tested on ChatGPT). * **Context-based Prompt** : Construct LLM prompts with the automatically extracted library context. * **Powerful Sanitization** : The program's syntax, semantics, behavior, and coverage are thoroughly analyzed to sanitize the problematic programs. * **Prioritized Mutation** : Prioritizes mutating the library API combinations within LLM's prompts to explore complex interrelationships, guided by code coverage. * **Fuzz Driver Exploitation** : Infers API constraints using statistics and extends fixed API arguments to receive random bytes from fuzzers. * **Fuzz engine integration** : Integrates with grey-box fuzz engine: LibFuzzer. **Benefits:** * **High branch coverage:** The fuzz drivers generated by PromptFuzz achieved a branch coverage of 40.12% on the tested libraries, which is 1.61x greater than _OSS-Fuzz_ and 1.67x greater than _Hopper_. * **Bug detection:** PromptFuzz detected 33 valid security bugs from 49 unique crashes. * **Wide range of bugs:** The fuzz drivers generated by PromptFuzz can detect a wide range of bugs, most of which are security bugs. * **Unique bugs:** PromptFuzz detects uniquely interesting bugs that other fuzzers may miss. **Usage:** 1. Build the library using the provided build scripts. 2. Export the LLM API KEY if using ChatGPT or GPT4. 3. Generate fuzz drivers using the `fuzzer` command. 4. Run the fuzz drivers using the `harness` command. 5. Deduplicate and analyze the reported crashes. **Future Works:** * **Custom LLMs suport:** Support custom LLMs. * **Close-source libraries:** Apply PromptFuzz to close-source libraries by fine tuning LLMs on private code corpus. * **Performance** : Reduce the huge time cost required in erroneous program elimination.

A curated list of awesome Model Context Protocol (MCP) servers that enable AI models to securely interact with local and remote resources through standardized server implementations. The list focuses on production-ready and experimental servers extending AI capabilities through file access, database connections, API integrations, and other contextual services.

MCP CLI client is a simple CLI program designed to run LLM prompts and act as an alternative client for Model Context Protocol (MCP). Users can interact with MCP-compatible servers from their terminal, including LLM providers like OpenAI, Groq, or local LLM models via llama. The tool supports various functionalities such as running prompt templates, analyzing image inputs, triggering tools, continuing conversations, utilizing clipboard support, and additional options like listing tools and prompts. Users can configure LLM and MCP servers via a JSON config file and contribute to the project by submitting issues and pull requests for enhancements or bug fixes.

GitIngest is a tool that allows users to turn any Git repository into a prompt-friendly text ingest for LLMs. It provides easy code context by generating a text digest from a git repository URL or directory. The tool offers smart formatting for optimized output format for LLM prompts and provides statistics about file and directory structure, size of the extract, and token count. GitIngest can be used as a CLI tool on Linux and as a Python package for code integration. The tool is built using Tailwind CSS for frontend, FastAPI for backend framework, tiktoken for token estimation, and apianalytics.dev for simple analytics. Users can self-host GitIngest by building the Docker image and running the container. Contributions to the project are welcome, and the tool aims to be beginner-friendly for first-time contributors with a simple Python and HTML codebase.

ERAG is an advanced system that combines lexical, semantic, text, and knowledge graph searches with conversation context to provide accurate and contextually relevant responses. This tool processes various document types, creates embeddings, builds knowledge graphs, and uses this information to answer user queries intelligently. It includes modules for interacting with web content, GitHub repositories, and performing exploratory data analysis using various language models.

Awesome MCP Servers is a curated list of Model Context Protocol (MCP) servers that enable AI models to securely interact with local and remote resources through standardized server implementations. The list includes production-ready and experimental servers that extend AI capabilities through file access, database connections, API integrations, and other contextual services.

InternLM is a powerful language model series with features such as 200K context window for long-context tasks, outstanding comprehensive performance in reasoning, math, code, chat experience, instruction following, and creative writing, code interpreter & data analysis capabilities, and stronger tool utilization capabilities. It offers models in sizes of 7B and 20B, suitable for research and complex scenarios. The models are recommended for various applications and exhibit better performance than previous generations. InternLM models may match or surpass other open-source models like ChatGPT. The tool has been evaluated on various datasets and has shown superior performance in multiple tasks. It requires Python >= 3.8, PyTorch >= 1.12.0, and Transformers >= 4.34 for usage. InternLM can be used for tasks like chat, agent applications, fine-tuning, deployment, and long-context inference.

LLMap is a CLI code search tool designed to automatically find context in large codebases by evaluating the relevance of each source file using DeepSeek-V3 and DeepSeek-R1. It optimizes analysis by performing multi-stage analysis and caching results for faster searches. Currently supports Java and Python files, with potential for extension to other languages. Install with 'pip install llmap-ai' and use with a DeepSeek API key to search for specific context in code.

LLM Calculator is a script that estimates the memory requirements and performance of Hugging Face models based on quantization levels. It fetches model parameters, calculates required memory, and analyzes performance with different RAM/VRAM configurations. The tool supports Windows and Linux, AMD, Intel, and Nvidia GPUs. Users can input a Hugging Face model ID to get its parameter count and analyze memory requirements for various quantization schemes. The tool provides estimates for GPU offload percentage and throughput in tk/s. It requires dependencies like python, uv, pciutils for AMD + Linux, and drivers for Nvidia. The tool is designed for rough estimates and may not work with MultiGPU setups.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, ,'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in less than 3 words,Verb + noun form,in daily spoken language,in lowercase letters).Answer in english languagesname:Awesome-Chinese-LLM readme:# Awesome Chinese LLM   An Awesome Collection for LLM in Chinese 收集和梳理中文LLM相关    自ChatGPT为代表的大语言模型（Large Language Model, LLM）出现以后，由于其惊人的类通用人工智能（AGI）的能力，掀起了新一轮自然语言处理领域的研究和应用的浪潮。尤其是以ChatGLM、LLaMA等平民玩家都能跑起来的较小规模的LLM开源之后，业界涌现了非常多基于LLM的二次微调或应用的案例。本项目旨在收集和梳理中文LLM相关的开源模型、应用、数据集及教程等资料，目前收录的资源已达100+个！ 如果本项目能给您带来一点点帮助，麻烦点个⭐️吧～ 同时也欢迎大家贡献本项目未收录的开源模型、应用、数据集等。提供新的仓库信息请发起PR，并按照本项目的格式提供仓库链接、star数，简介等相关信息，感谢~

Llama-github is a powerful tool that helps retrieve relevant code snippets, issues, and repository information from GitHub based on queries. It empowers AI agents and developers to solve coding tasks efficiently. With features like intelligent GitHub retrieval, repository pool caching, LLM-powered question analysis, and comprehensive context generation, llama-github excels at providing valuable knowledge context for development needs. It supports asynchronous processing, flexible LLM integration, robust authentication options, and logging/error handling for smooth operations and troubleshooting. The vision is to seamlessly integrate with GitHub for AI-driven development solutions, while the roadmap focuses on empowering LLMs to automatically resolve complex coding tasks.

MAVIS (Math Visual Intelligent System) is an AI-driven application that allows users to analyze visual data such as images and generate interactive answers based on them. It can perform complex mathematical calculations, solve programming tasks, and create professional graphics. MAVIS supports Python for coding and frameworks like Matplotlib, Plotly, Seaborn, Altair, NumPy, Math, SymPy, and Pandas. It is designed to make projects more efficient and professional.

Expert in data extraction and context-driven analysis. Can read most filetypes including PDFS, XLSX, Word, TXT, CSV, EML, Etc.

Delves into historical events, figures, or eras based on user queries. It can provide detailed narratives, analyze historical contexts, and even create engaging stories or hypothetical scenarios based on historical facts, making learning history interactive and fun.

Specialist in culturally-contextual translations

Focused on images and their descriptions in news contexts.

Especialista em Análise de Direitos Humanos em Contextos Multilíngues

Interprets body language in diverse daily situations with contextual insights.

As LegalGPT, I'm an AI legal assistant with expertise in law, adaptable for nationwide legal queries. I provide precise, context-sensitive advice based on a rich knowledge source, aiding in legal reasoning and drafting. Note: I'm not a substitute for a lawyer.

Welcome to Strategy Slugger the app designed for baseball enthusiasts, coaches, and players seeking in-depth knowledge and strategies about game. This interactive tool is your gateway to mastering baseball, offering insights into game planning, historical contexts, and tactical approaches to match.

An AI that embodies the persona of William Shakespeare, offering first-person insights into his life and works, and generating creative content inspired by his literary contributions. It provides historical context, analysis, and can engage in Shakespearean-style dialogue.

Advanced assistant for data publication and subscription guidance, with enhanced contextual understanding and technical integration.

The specific types of diagrams I can produce, such as sequence diagrams, mind maps, and flowcharts, along with the appropriate contexts for each type.

Analyzing society, blending history and tech impacts

Translating ancient texts to modern English, extrapolating key insights and practical applications.

Explores a broad range of historical medical practices and their modern implications.

Analyzes images, providing facts, history, and location details.

搜集关键国际新闻，根据时间线整理事件，并深入分析历史背景、现状以及预测未来发展。用户可以根据兴趣定制主题方向，获取定制化的局势分析和发展预测。

Analyzes and rates your writing using insights derived from studying LinkedIn influencers' top performing posts from the last 4 years.

Analyse content through the official Google Search Quality Rater Guidelines.

Analyse & visualise semantic networks entities and attributes for content creation.

Get an objective assessment of your marketing content.