Best AI tools for< Analyze Code Structure >

AskTheCode is a powerful and versatile plugin designed to bridge the gap between ChatGPT and GitHub repositories. It allows developers to seamlessly analyze GitHub repositories and ask questions related to those repositories using ChatGPT. The tool supports universal language, works with both public and private repositories, and provides accurate results based on thoughtful prompts. AskTheCode aims to assist developers in exploring and understanding codebases, projects, and repository structures.

Mixpeek is a flexible vision understanding infrastructure that allows developers to analyze, search, and understand video and image content. It provides various methods such as scene embedding, face detection, audio transcription, text reading, and activity description. Mixpeek offers integration with data sources, indexing capabilities, and analysis of structured data for building AI-powered applications. The platform enables real-time synchronization, extraction, embedding, fine-tuning, and scaling of models for specific use cases. Mixpeek is designed to be seamlessly integrated into existing stacks, offering a range of integrations and easy-to-use API for developers.

TimeComplexity.ai is an AI tool that helps users analyze the runtime complexity of their code. It works seamlessly across different programming languages without the need for headers, imports, or a main statement. Users can simply input their code and get insights into its performance. However, it is important to note that the results provided by TimeComplexity.ai may not always be accurate, so users are advised to use the tool at their own risk.

Codiga is a static code analysis tool that helps developers write clean, safe, and secure code. It works in real-time in your IDE and CI/CD pipelines, and it can be customized to meet your specific needs. Codiga supports a wide range of languages and frameworks, and it integrates with popular tools like GitHub, GitLab, and Bitbucket.

Zevo.ai is an AI-powered code visualization tool designed to accelerate code comprehension, deployment, and observation. It offers dynamic code analysis, contextual code understanding, and automatic code mapping to help developers streamline shipping, refactoring, and onboarding processes for both legacy and existing applications. By leveraging AI models, Zevo.ai provides deeper insights into code, logs, and cloud infrastructure, enabling developers to gain a better understanding of their codebase.

Wasps is an AI code review tool that integrates seamlessly into VSCode, providing developers with a fast and efficient way to understand their codebase, detect and fix code issues using AI and Gitsecure. With Wasps, developers can identify and fix buggy & vulnerable code in minutes, receive clear and actionable feedback driven by deep analysis, and get recommendations for potential issues and improvements within their codebase. The tool allows developers to keep coding as usual while Wasps analyzes their code for them, making it easier to maintain code quality and keep bugs out of their code.

CodeConverter.com is an AI code converter tool that allows users to convert code instantly across 120 programming languages. It offers a fast and accurate conversion process, an easy-to-use interface, and supports all major programming languages. The platform is completely online, secure, and private, ensuring user data privacy. CodeConverter.com is designed to cater to developers, programmers, software engineers, students, data scientists, and hobbyists, helping them streamline their coding experience and enhance productivity.

Trag is an AI-powered tool designed to review pull requests in minutes, empowering engineering teams to save time and focus on building products. With Trag, users can create custom patterns for code review, ensuring best practices are followed and bugs are caught early. The tool offers features like autofix with AI, monitoring progress, connecting multiple repositories, pull request review, analytics, and team workspaces. Trag stands out from traditional linters by providing complex code understanding, semantic code analysis, predictive bug detection, and refactoring suggestions. It aims to streamline code reviews and help teams ship faster with AI-powered reviews.

Docify AI is an AI-assisted code comment and documentation tool designed to help software developers improve code quality, save time, and increase productivity. It offers features such as automated documentation generation, comment translation, inline comments, and code coverage analysis. The tool supports multiple programming languages and provides a user-friendly interface for efficient code documentation. Docify AI is built on proprietary AI models, ensuring data privacy and high performance for professional developers.

Copilot is an AI-powered code completion tool developed by OpenAI. It assists developers in writing code by providing suggestions and completing code snippets based on the context. Copilot uses machine learning algorithms to analyze code patterns and predict the next lines of code, making coding faster and more efficient. With its intuitive interface, Copilot aims to streamline the coding process and enhance developer productivity.

GPTPLUS is a Chrome and Edge browser extension powered by GPT-4 and GPT-3.5 that provides AI-powered writing, translation, code analysis, and Q&A assistance. It allows users to chat with ChatGPT in a sidebar, use prompts to enhance answers, and process selected text with a single click. GPTPLUS is free to use, with premium plans offering additional features.

DigestDiff is an AI-driven tool that helps users analyze and understand commit history in codebases. It provides detailed narratives based on commit logs, accelerates onboarding by summarizing codebases, and automates the creation of release notes using AI. The tool prioritizes privacy by only requiring read-only access to commit history and never storing any code or generated data.

CO/AI is an AI tool that provides actionable resources and strategies to accelerate success in the age of AI. It offers a wide range of AI tools, resources, and community discussions to help users stay updated and informed about the latest trends and developments in artificial intelligence.

Weekly Github Insights is an AI-powered platform that provides users with a comprehensive summary of their latest GitHub activities from the past 7 days. It aims to keep users informed and motivated by compiling their weekly GitHub journey. The platform is built by @rohan_2502 using @aceternitylabs, @github APIs, and @OpenAI.

Patented.ai is an AI-powered platform that specializes in IP commercialization, patent valuation, and litigation support. The platform helps users unlock hidden revenue from their IP portfolio, identify valuable innovations in their codebase, and get data-driven insights on patent value and industry applicability. It offers features such as source code analysis, identifying licensees instantly, and mapping patent claims to source code. Patented.ai is trusted by leading innovators and IP counsel worldwide for lightning-fast insights and comprehensive IP strength assessments.

SecureWoof is an AI-powered malware scanner that utilizes advanced technologies such as Yara rules, Retdec unpacker, Ghidra decompiler, clang-tidy formatter, FastText embedding, and RoBERTa transformer network to scan and detect malicious content in executable files. The tool is trained on the SOREL-20M malware dataset to enhance its accuracy and efficiency in identifying threats. SecureWoof offers a public API for easy integration with other applications, making it a versatile solution for cybersecurity professionals and individuals concerned about malware threats.

MobiHeals is a mobile application focused on security analysis and vulnerability checks for mobile apps. It offers comprehensive security vulnerability analysis, cloud-based static and dynamic application security testing, and integrated vulnerability assessment in one platform. MobiHeals helps users comply with global cybersecurity guidelines and manage security vulnerabilities throughout the development, testing, and operation stages of mobile applications.

Master of Code Global is an AI development company that offers a wide range of AI, web, and mobile solutions to enhance customer experiences. They specialize in services such as chatbot development, conversational AI, generative AI development, web development, software product development, and more. With a focus on leveraging advanced technology to automate tasks, analyze data effectively, and personalize customer interactions, Master of Code Global aims to provide custom world-class digital experiences for web and mobile platforms empowered by AI.

Powerdrill is a platform that provides swift insights from knowledge and data. It offers a range of features such as discovering datasets, creating BI dashboards, accessing various apps, resources, blogs, documentation, and changelogs. The platform is available in English and fosters a community through its affiliate program. Users can sign up for a basic plan to start utilizing the tools and services offered by Powerdrill.

No Code Camp is an online learning platform that teaches people how to use artificial intelligence (AI) and no-code tools to automate their work and build applications. The platform offers a live, 5-week cohort-based course that covers the essentials of no-code development, including data architecture, interface design, AI scaling, and no-code automation. The course is designed for people with no prior coding experience and is taught by experienced instructors who have built and scaled digital products using no-code tools.

Code2Prompt is a powerful command-line tool that generates comprehensive prompts from codebases, designed to streamline interactions between developers and Large Language Models (LLMs) for code analysis, documentation, and improvement tasks. It bridges the gap between codebases and LLMs by converting projects into AI-friendly prompts, enabling users to leverage AI for various software development tasks. The tool offers features like holistic codebase representation, intelligent source tree generation, customizable prompt templates, smart token management, Gitignore integration, flexible file handling, clipboard-ready output, multiple output options, and enhanced code readability.

RepoAgent is an LLM-powered framework designed for repository-level code documentation generation. It automates the process of detecting changes in Git repositories, analyzing code structure through AST, identifying inter-object relationships, replacing Markdown content, and executing multi-threaded operations. The tool aims to assist developers in understanding and maintaining codebases by providing comprehensive documentation, ultimately improving efficiency and saving time.

Repopack is a powerful tool that packs your entire repository into a single, AI-friendly file. It optimizes your codebase for AI comprehension, is simple to use with customizable options, and respects Gitignore files for security. The tool generates a packed file with clear separators and AI-oriented explanations, making it ideal for use with Generative AI tools like Claude or ChatGPT. Repopack offers command line options, configuration settings, and multiple methods for setting ignore patterns to exclude specific files or directories during the packing process. It includes features like comment removal for supported file types and a security check using Secretlint to detect sensitive information in files.

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)

AI Coding Assistant is a free and open-source IntelliJ plugin that leverages cutting-edge Language Model APIs to enhance developers' coding experience. It seamlessly integrates with various leading LLM APIs, offers an intuitive toolbar UI, and allows granular control over API requests. With features like Code & Patch Chat, Planning with AI Agents, Markdown visualization, and versatile text processing capabilities, this tool aims to streamline coding workflows and boost productivity.

rLLM (relationLLM) is a Pytorch library for Relational Table Learning (RTL) with LLMs. It breaks down state-of-the-art GNNs, LLMs, and TNNs as standardized modules and facilitates novel model building in a 'combine, align, and co-train' way using these modules. The library is LLM-friendly, processes various graphs as multiple tables linked by foreign keys, introduces new relational table datasets, and is supported by students and teachers from Shanghai Jiao Tong University and Tsinghua University.

Vitron is a unified pixel-level vision LLM designed for comprehensive understanding, generating, segmenting, and editing static images and dynamic videos. It addresses challenges in existing vision LLMs such as superficial instance-level understanding, lack of unified support for images and videos, and insufficient coverage across various vision tasks. The tool requires Python >= 3.8, Pytorch == 2.1.0, and CUDA Version >= 11.8 for installation. Users can deploy Gradio demo locally and fine-tune their models for specific tasks.

OpenAdapt is an open-source software adapter between Large Multimodal Models (LMMs) and traditional desktop and web Graphical User Interfaces (GUIs). It aims to automate repetitive GUI workflows by leveraging the power of LMMs. OpenAdapt records user input and screenshots, converts them into tokenized format, and generates synthetic input via transformer model completions. It also analyzes recordings to generate task trees and replay synthetic input to complete tasks. OpenAdapt is model agnostic and generates prompts automatically by learning from human demonstration, ensuring that agents are grounded in existing processes and mitigating hallucinations. It works with all types of desktop GUIs, including virtualized and web, and is open source under the MIT license.

The repository 'Awesome-LLM4Cybersecurity' provides a comprehensive overview of the applications of Large Language Models (LLMs) in cybersecurity. It includes a systematic literature review covering topics such as constructing cybersecurity-oriented domain LLMs, potential applications of LLMs in cybersecurity, and research directions in the field. The repository analyzes various benchmarks, datasets, and applications of LLMs in cybersecurity tasks like threat intelligence, fuzzing, vulnerabilities detection, insecure code generation, program repair, anomaly detection, and LLM-assisted attacks.

ChatGPT Queue Prompts is a collection of prompt chains designed to enhance interactions with large language models like ChatGPT. These prompt chains help build context for the AI before performing specific tasks, improving performance. Users can copy and paste prompt chains into the ChatGPT Queue extension to process prompts in sequence. The repository includes example prompt chains for tasks like conducting AI company research, building SEO optimized blog posts, creating courses, revising resumes, enriching leads for CRM, personal finance document creation, workout and nutrition plans, marketing plans, and more.

This repository provides a deployment guide for utilizing Google Cloud's Generative AI tools in marketing scenarios. It includes step-by-step instructions, examples of crafting marketing materials, and supplementary Jupyter notebooks. The demos cover marketing insights, audience analysis, trendspotting, content search, content generation, and workspace integration. Users can access and visualize marketing data, analyze trends, improve search experience, and generate compelling content. The repository structure includes backend APIs, frontend code, sample notebooks, templates, and installation scripts.

ONNX Runtime Server is a server that provides TCP and HTTP/HTTPS REST APIs for ONNX inference. It aims to offer simple, high-performance ML inference and a good developer experience. Users can provide inference APIs for ONNX models without writing additional code by placing the models in the directory structure. Each session can choose between CPU or CUDA, analyze input/output, and provide Swagger API documentation for easy testing. Ready-to-run Docker images are available, making it convenient to deploy the server.

This project is a tutorial on developing large model applications for novice developers. It aims to provide a comprehensive introduction to large model development, focusing on Alibaba Cloud servers and integrating personal knowledge assistant projects. The tutorial covers the following topics: 1. **Introduction to Large Models**: A simplified introduction for novice developers on what large models are, their characteristics, what LangChain is, and how to develop an LLM application. 2. **How to Call Large Model APIs**: This section introduces various methods for calling APIs of well-known domestic and foreign large model products, including calling native APIs, encapsulating them as LangChain LLMs, and encapsulating them as Fastapi calls. It also provides a unified encapsulation for various large model APIs, such as Baidu Wenxin, Xunfei Xinghuo, and Zh譜AI. 3. **Knowledge Base Construction**: Loading, processing, and vector database construction of different types of knowledge base documents. 4. **Building RAG Applications**: Integrating LLM into LangChain to build a retrieval question and answer chain, and deploying applications using Streamlit. 5. **Verification and Iteration**: How to implement verification and iteration in large model development, and common evaluation methods. The project consists of three main parts: 1. **Introduction to LLM Development**: A simplified version of V1 aims to help beginners get started with LLM development quickly and conveniently, understand the general process of LLM development, and build a simple demo. 2. **LLM Development Techniques**: More advanced LLM development techniques, including but not limited to: Prompt Engineering, processing of multiple types of source data, optimizing retrieval, recall ranking, Agent framework, etc. 3. **LLM Application Examples**: Introduce some successful open source cases, analyze the ideas, core concepts, and implementation frameworks of these application examples from the perspective of this course, and help beginners understand what kind of applications they can develop through LLM. Currently, the first part has been completed, and everyone is welcome to read and learn; the second and third parts are under creation. **Directory Structure Description**: requirements.txt: Installation dependencies in the official environment notebook: Notebook source code file docs: Markdown documentation file figures: Pictures data_base: Knowledge base source file used

The Looker GenAI Extension provides code examples and resources for building a Looker Extension that integrates with Vertex AI Large Language Models (LLMs). Users can leverage the power of LLMs to enhance data exploration and analysis within Looker. The extension offers generative explore functionality to ask natural language questions about data and generative insights on dashboards to analyze data by asking questions. It leverages components like BQML Remote Models, BQML Remote UDF with Vertex AI, and Custom Fine Tune Model for different integration options. Deployment involves setting up infrastructure with Terraform and deploying the Looker Extension by creating a Looker project, copying extension files, configuring BigQuery connection, connecting to Git, and testing the extension. Users can save example prompts and configure user settings for the extension. Development of the Looker Extension environment includes installing dependencies, starting the development server, and building for production.

ChatAFL is a protocol fuzzer guided by large language models (LLMs) that extracts machine-readable grammar for protocol mutation, increases message diversity, and breaks coverage plateaus. It integrates with ProfuzzBench for stateful fuzzing of network protocols, providing smooth integration. The artifact includes modified versions of AFLNet and ProfuzzBench, source code for ChatAFL with proposed strategies, and scripts for setup, execution, analysis, and cleanup. Users can analyze data, construct plots, examine LLM-generated grammars, enriched seeds, and state-stall responses, and reproduce results with downsized experiments. Customization options include modifying fuzzers, tuning parameters, adding new subjects, troubleshooting, and working on GPT-4. Limitations include interaction with OpenAI's Large Language Models and a hard limit of 150,000 tokens per minute.

GenAIScript is a scripting environment designed to facilitate file ingestion, prompt development, and structured data extraction. Users can define metadata and model configurations, specify data sources, and define tasks to extract specific information. The tool provides a convenient way to analyze files and extract desired content in a structured format. It offers a user-friendly interface for working with data and automating data extraction processes, making it suitable for various data processing tasks.

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.

Specializes in analyzing ChatGPT conversations for code snippets to build apps

Delivers AST analysis with type, value, and children.

Expert in algorithms and data structures, providing clear and concise explanations.

コードを分析し、言語ではなくスタイルに焦点を当て、プログラムを書いた人の性格を推察するツールです。( It is a tool that analyzes code, focuses on style rather than language, and infers the personality of the person who wrote the program. )

A Next.js expert ready to analyze code, answer questions, and offer learning plans.

I explain basic concepts and analyze code in distributed systems.

Analyzes code for time and memory complexity

Analyzes code for accessibility issues & provides recommendations

我分析并优化代码以提高性能和可读性。

A Senior Developer AI Assistant for optimizing and debugging code.

Improved Python code analysis and execution power.

Assists with code review, pull requests, and writing tests.

Assistant Python ultra-personnalisé, conçu pour transformer les programmeurs de tous niveaux en maîtres de Python. Spécialisé dans l'analyse approfondie du code, les tutoriels interactifs, et l'optimisation de performance.

用中文分析代码和提供建议的专家

提供清晰专业的代码解释

A maestro generates, troubleshoots, and analyzes R programming code.

Expert in Fift programming analysis and education

This plugin assists reverse-engineering binary programs, including decompiling.

Specializes in teaching and analyzing the Rust programming language.

API Finder: Analyze, Clarify, Suggest, build code, Iterate, test ... International version