AI testing tools can generate test cases, maintain automated scripts, explore interfaces, locate failure causes, and assist in quality analysis. Pages compare web and mobile coverage, assertive reliability, visual regression, CI integration, test data, security isolation, and self-healing capability after product changes.
TestSprite is an AI tool for users who need a clearer way to handle focused digital work. It can support creation, automation, analysis, learning, media production, development, research, customer operations, or document workflows depending on the product scope. Start with a small low-risk task, compare the result with your own standards, and keep human review for facts, permissions, privacy, brand voice, safety, and final delivery.
testRigor is an AI tool for users who need a clearer way to handle focused digital work. It can support creation, automation, analysis, learning, media production, development, research, customer operations, or document workflows depending on the product scope. Start with a small low-risk task, compare the result with your own standards, and keep human review for facts, permissions, privacy, brand voice, safety, and final delivery.
Testbook.ai is an AI workflow tool for creating, organizing, converting, or reviewing task-specific material before final use. It should be used with clear source material, a defined output goal, and human review for accuracy, rights, privacy, and publishing quality.
SuperAnnotate helps users turn clear source material into editable results for content, media, data, learning, or operational workflows. It is best used when the goal, input, output format, and review standard are clear. Users should test it with a low-risk task first and keep human review for customer data, student work, financial information, portraits, production code, or public content.
Qase is an AI workflow tool for teams that need to create, organize, convert, or review task-specific material before final use. It should be used with clear source material, a defined output goal, and human review for accuracy, rights, privacy, and publishing quality.
QA.tech is an AI workflow tool for teams that need to create, organize, convert, or review task-specific material before final use. It should be used with clear source material, a defined output goal, and human review for accuracy, rights, privacy, and publishing quality.
QA Sphere is an AI workflow tool for teams that need to create, organize, convert, or review task-specific material before final use. It should be used with clear source material, a defined output goal, and human review for accuracy, rights, privacy, and publishing quality.
OwlityAI is an AI software quality testing platform that is mainly used to understand application interfaces through computer vision, automatically design tests, build automated processes, and discover defects. It is suitable for software teams, QA leaders, product teams and companies that need to reduce manual testing costs. Common uses include performing regression testing before going online, reducing duplication of manual QA work, and supplementing automated testing coverage for rapidly iterating products. When using it, note that autonomous testing cannot cover all business rules and boundary conditions. Complex authority, payment, compliance and core transaction processes still require manual QA to develop acceptance criteria. The page provides free trial and demonstration entrances, which is suitable for first verification with non-core applications. It is recommended to use one or two low-risk tasks to test input materials, output quality, manual modification amount and final adoption ratio before deciding whether to put them into a fixed process.
Openlayer is an observable platform for AI governance and LLM applications. It is mainly used to provide evaluation, CI/CD verification, production monitoring, safety barriers and compliance testing for AI systems, helping teams discover problems such as hallucinations, PII leaks and prompt injection. It is suitable for AI product teams, platform engineering teams, model governance leaders and enterprise security compliance teams. Common uses include performing regression testing before LLM applications go online, monitoring output quality and delay in the production environment, and establishing frameworks such as EU AI Act and NIST. Governance processes. Be careful when using it. It can help identify risks, but it cannot replace internal security, legal and data governance systems. When the test set design is insufficient, there will also be blind spots in the monitoring results. The page provides request demonstrations and pricing entrances, and is usually quoted based on team size, call volume, and governance needs. It is recommended to use one or two low-risk tasks to test input materials, output quality, manual modification amount and final adoption ratio before deciding whether to put them into a fixed process.
Nimbalyst is a visual workspace for coding agents, which is mainly used to manage agent sessions, tasks, files and documents such as Codex and Claude Code. It is suitable for developers, AI programming teams, open source maintainers, and people who need to manage agents in parallel. It can manage multiple coding agent sessions and tasks, edit Markdown, charts, mockups, and code materials, and use a visual workspace to organize files and development context. Be aware when using it, it is suitable for managing the agent collaboration process, and code merging, testing and online still need to be manually confirmed according to project specifications. It is recommended to use one or two low-risk tasks to test input materials, output quality, manual modification amount and final adoption ratio before deciding whether to put them into a fixed process.
Metabob is an AI code analysis and debugging auxiliary tool. It is mainly used to assist parallel generative programming tools for defect analysis and refactoring. It is suitable for software engineers, code reviewers, AI programming users and R & D teams. It can provide real-time intelligent code analysis, discover potential defects and security implementation issues, and assist in debugging and refactoring legacy code. Pay attention when using it that static analysis and AI recommendations require developer verification and cannot replace testing, code review and security evaluation. Before individual developer plans and team payment plans are officially adopted, it is recommended to test with low-risk samples first and record the input materials., output results, manual modifications and final adoption ratio, and then decide whether to put them into a fixed process.
Maxim is a generative AI evaluation and observability platform mainly used to simulate, evaluate and monitor the quality of AI Agents and generative applications. It is suitable for AI product teams, engineering teams, model application developers and quality leaders. It can support experiments, Agent simulation and evaluation processes, provide observability for generative AI applications, and connect development, testing and online links with a unified library. When using it, note that the evaluation platform requires the team to first define indicators, test sets, and failure criteria; if there is no stable data and online process, the value of the tool will be weakened. It is intended for use by teams and enterprises and is usually evaluated by plan or usage. Before formal adoption, it is recommended to test once with low-risk materials or small samples, record the input quality, output results, manual modifications and final adoption ratio, and then decide whether to put them into the long-term workflow.
KushoAI is an AI-native infrastructure for software maintenance, providing autonomous agents running in CI/CD for continuously handling testing, fixing, monitoring, and updating test suites as the codebase changes. It's suitable for engineering teams, QA teams, platform teams, and product development organizations that need to reduce regression risk. Confirm repository permissions, test coverage, autofix policies, CI costs, and code review responsibilities before use. AI-generated tests and fixes must be reviewed by engineers before entering the main branch. Before use, it is recommended to conduct a small-scale test with real materials, focusing on observing the output quality, review cost, payment boundaries, data permissions, and whether the team can establish a stable manual review process. Before handling formal business, it should also be judged by team processes, material authorization, and manual review criteria to avoid using automated results directly for external release or key decisions.
Katalon is an AI testing platform for software quality teams that supports low-code, full-code, and AI-driven test creation, execution, and analysis across web, mobile, API, and desktop applications. It is suitable for QA teams, test engineers, development teams, and enterprise quality leaders to unify test automation processes. The platform offers free entry and trial forever. Evaluate test scope, script maintenance, CI/CD integration, permissions, and team skill structure before landing. It's more suitable for users with clear goals, input materials, and boundaries, and small-scale testing can help you determine whether the results are worth going into the formal process faster. Before use, you should also use your own data sources, team processes, and review criteria to avoid direct automatic results into official release, submission, or business decisions.
JsRates is a custom shipping tool for Shopify merchants that allows users to write complex shipping rules in JavaScript and connect to third-party APIs. It's suitable for e-commerce teams that need to dynamically calculate shipping rates by region, weight, product mix, customer type, or external data. The platform offers free requests and AI character credits, as well as monthly subscription plans. When using it, you should first verify the code logic, boundary conditions, API errors, and checkout page performance in the test environment to avoid incorrect shipping costs affecting the order. It's more suitable for users with clear goals, input materials, and boundaries, and small-scale testing can help you determine whether the results are worth going into the formal process faster. Before use, you should also use your own data sources, team processes, and review criteria to avoid direct automatic results into official release, submission, or business decisions.
Getgud.io is an AI game behavior analysis and anti-cheat platform whose core purpose is to replay player sessions, analyze player behavior, and detect cheating and harmful behavior. It primarily revolves around game session replay, player behavior analysis, anti-cheat detection, toxic behavior detection, QA debugging, and retention analysis, making it suitable for game development and operations teams that need to understand player behavior and maintain game fairness. Before use, confirm whether the account permissions, material or data source, export format, privacy boundary, billing method, and manual review requirements match the actual process. When it comes to public publishing, sales outreach, education and learning, health, game security, code, audio and video, portraits or commercial materials, also check for authorization, compliance and the risk of misjudgment of results, and retain manual review. Before formal adoption, it is recommended to test the output quality, cost, and review process with a small sample.
Fume is an AI end-to-end test generation tool. The core positioning of the official website verifiable is to generate and maintain Playwright browser tests based on product screen recordings or Loom videos, mainly focusing on test case extraction, Playwright test generation, end-to-end testing, test running, notifications, and automatic maintenance, suitable for product engineering teams that want to quickly cover key user processes. Before use, confirm whether the account permissions, material or data source, export format, privacy boundary, billing method, and manual review requirements match the actual process. When it comes to public releases, customer communications, health, education, recruitment, audio, video, portraits, or commercial materials, also check for authorization, compliance, and the risk of misjudgment of results, and retain manual review.
EverSQL is an AI tool for database performance optimization. The homepage of the official website clearly states PostgreSQL, MySQL, SQL query optimization, and AI-powered DBA, with the core being to automatically optimize SQL queries and provide performance insights. Judging from the information currently verified on the official website, the core capabilities, applicable scenarios, and target users of these products are clearly written, not just a one-layer conceptual package. Whether it is truly worth using for a long time depends on whether it can stably complete a specific task after being put into your real process, rather than just appearing strong in the homepage presentation. A more practical way to judge is to directly take real materials and try them to see how they perform in terms of result quality, modification cost, and final deliverability.
Early is an AI regression protection platform for engineering teams. The homepage of the official website clearly states Regression Guard, emphasizing that it is not just looking at PR, but combining the complete code base, connected system and key business processes to identify regression risks. Judging from the information currently verifiable on the official website, the core capabilities, application scenarios and target users of these products are clearly written, and there is not just a layer of conceptual packaging. Whether the real value is worth long-term use depends on whether it can stably complete a specific thing after being put into your real process, rather than just appearing strong in the presentation on the front page. A more practical way to judge is to directly take real materials and test them and see how they perform in terms of result quality, modification cost and final deliverable.
DebuggAI is an AI programming tool designed around GitHub PR automated browser testing. The homepage of the official website states the core of the product very clearly: a browser test is automatically triggered every time a PR is submitted, and the results are returned directly to GitHub as comments. It is not a simple screenshot service or a traditional test framework tutorial station. It integrates warehouse cloning, construction, remote access and browser testing into a managed process, making it more suitable for development teams who want to reduce the friction of front-end regression testing. Judging from the information currently verifiable on the official website, their mission boundaries, application objects and main usage methods are relatively clear, and they are more suitable for starting directly with specific questions, rather than being regarded as general conceptual AI products.
ContextQA is a test automation platform for enterprise applications and AI Agent scenarios. The homepage of the official website clearly lists Enterprise App Testing and AI Agent Testing, and emphasizes automatic generation of tests, self-healing selectors, root cause analysis, and MCP integration with Cursor and Claude Code, indicating that it is not just a traditional UI automation tool, but Redefine the testing process along the AI development workflow. For those who have already started building AI agents or complex business systems, its positioning is clear: to pull testing back from script maintenance to a more automated and closer to current development methods. Judging from the information that can be confirmed on the official website, its product boundaries and target tasks are relatively clear, making it more suitable for people who already have corresponding workflows or usage scenarios to start directly, rather than treating it as a universal tool that can do everything.
Confidence AI is a quality platform for LLM engineering teams. The homepage of the official website positions it as an AI quality platform, with core coverage of evaluation, observability, red team testing and result improvement. The focus is not to help you generate content, but to help the team know whether the model system is stable or not, where problems will go wrong, and how to continue monitoring after it is launched. For people who are already doing AI applications, the value of such platforms is very practical, because from prototypes to production environments, quality and risk control are often more important than a single demonstration. Judging from the information that can be confirmed on the official website, its product boundaries and target tasks are relatively clear, making it more suitable for people who already have corresponding workflows or usage scenarios to start directly, rather than treating it as a universal tool that can do everything.
CodeSignal is an AI platform that brings skills assessment, interviews and skills development together. The homepage of the official website writes positioning as a skills verification and development platform with AI as the core, and places AI interviewers, skills assessment, real-time technical interviews, skills development and skills intelligence on the same product line. It not only serves the talent screening of enterprises, but also serves the skills improvement of learners. Therefore, it is more like a recruitment and development platform around "skills proof" rather than a single website for brushing questions. Such a platform would be very attractive for organizations that need to evaluate candidates more systematically and develop team capabilities, especially for teams that want to link recruitment evaluation with subsequent skills development.
Canditech is a skill assessment platform for HR and hiring managers. The official website states that online job simulations can be used to conduct pre-employment assessments, covering technical and soft skills. The page also displays AI Solutions, Cheating Prevention, One-Way Video Interviews, Recruitment Chatbot, 40+ ATS integrations, as well as engineering, data, marketing, sales, call center, finance, healthcare and other job scenarios, which are suitable for pre-recruitment screening and objective evaluation.
Bugster is a developer tool that builds the future of QA. The official website description includes e2e testing agents for devs, db branches for AI agents, session replay bug detection, and visual regression monitoring. It is suitable for engineering teams to integrate end-to-end testing, playback defect identification, visual regression, and database branch testing into the development process. However, the official website information is relatively streamlined, and specific integration and pricing need to be confirmed within the product. It provides a more complete QA entry than a single point script for teams that are repairing automated test coverage, monitoring production page changes, or wanting to spot defects from user playback.
Browserless is a browser automation platform. The official website describes that it can be used for scraping, automation, PDFs & screenshots, testing, and provides BrowserQL, Browsers as a Service, REST APIs, Session Replay, Live Debugger, Lighthouse Testing, Smart Scrape API, captcha solving, and Puppeteer/Playwright WebSocket endpoint. It is suitable for development teams to expand their browser automation infrastructure.
BotGauge is an Autonomous QA as a Solution platform. The official website states that it uses AI agents and QA experts to create, maintain and execute end-to-end tests to allow teams to deliver reliable versions faster. The page emphasizes AI-powered E2E testing, zero setup, no extra headcount, forward-deployed QA pods and 80% test coverage in 4 months, which are suitable for engineering teams to outsource and automate QA.
Beagle Security is an AI-powered AppSec platform for Web applications, APIs, GraphQL, WordPress, OWASP, DevSecOps and compliance test scenarios that performs comprehensive penetration testing and provides executable fix insights. It is suitable for developers, security engineers and enterprise teams to manage application and interface security risks. The official website title reads AI-powered AppSec platform and Web application & API penetration testing. Use cases such as API security testing, GraphQL security testing, OWASP security testing, DevSecOps, and compliance are also listed in the navigation. Automated scanning may misreport and fail to understand business logic loopholes. A formal security plan should include rights control, code review, manual testing, patch processes, and retest.
Arize is an AI and Agent Engineering Platform, the official website describes it as the Unified LLM Observability and Agent Evaluation Platform for AI Applications, covering development, observability, evaluation, Agent Tracing, Evaluators, Experiments, Prompts, Monitoring, Annotations, etc. The official website also distinguishes Arize AX, Phoenix OSS, and Alyx AI Engineering Agent, and displays the positioning of enterprise observability, production AI, open source and open standards. It is suitable for R&D and platform teams to monitor LLM/Agent application quality, but it is not a substitute for test design and manual error analysis.
Applitools is an AI-powered end-to-end testing platform with a description of Visual AI, GenAI, and no-code approaches for functional, visual, API, accessibility, cross-browser/device, component testing, PDF documents and compliance testing. The platform includes Autonomous Eyes, SDKs, Codeless Recorder, NLP Builder, Visual AI, Cross Browser Testing, Accessibility Testing, Test Orchestration, Self-Healing Tests, Root Cause Analysis, and Automated Maintenance, for QA, SDET, developers, and compliant industry test teams.
micro1 is a "human intelligence" data and talent infrastructure platform for AI laboratories and enterprises, providing human data production and AI recruitment capabilities at its core. micro1 organizes experts and annotation teams through Data Engine to transform industry experience into high-quality datasets for model training, evaluation, and reinforcement learning environment construction; At the same time, with the help of the AI recruitment assistant Zara, candidate search, screening, interviews, and qualification assessments are automatically completed, accelerating AI talent recruitment and global employment matching. Whether it's building a human feedback data process, expanding data annotation capacity, or improving AI recruitment efficiency, micro1 can help teams obtain higher-quality talent and data supply at a lower cost.
Teachable Machine is Google's web-based machine learning model builder that allows anyone to quickly train their own classification model without programming. Teachable Machine allows you to train with image, sound, and pose data, acquire, train, and test in real-time in a few steps, and export the model for use on websites, apps, or creative interactive projects. With Teachable Machine, you can build prototypes, demonstrations, or bring ideas to your product faster, while deploying and iterating on common front-end and mobile solutions.
PromptLayer is a prompt and agent management platform for teams, helping you manage prompt templates, versions, and release processes in AI programming and product iteration. PromptLayer supports prompt version control and visual editing, which is convenient for non-technical members to collaborate and modify. At the same time, it provides evaluation and regression testing capabilities, and uses data to compare the effects of different prompts or models. Through the request tracking and observability panel, PromptLayer can also monitor call links, costs, latency, and errors, quickly locate issues, and continuously optimize online performance, allowing prompt management, evaluation, and monitoring to form a closed loop.
Artificial Analysis is an independent AI model evaluation and comparison platform, focusing on AI detection and benchmarking with a unified caliber, helping you quickly select models and inference services that are more suitable for your business. Artificial Analysis provides AI model rankings and comparison pages, covering key indicators such as quality performance, price and cost, output speed, latency, and context, and is organized by provider and model version, making it easy to conduct horizontal evaluation and solution selection. Whether you are working on an AI chat application, an AI programming assistant, or an enterprise-level agent, Artificial Analysis can use a clear data view and evaluation system to lower the decision-making threshold of "selecting a model, calculating costs, and comparing performance".
Jules is Google's autonomous AI programming agent that integrates directly with GitHub repositories to help developers delegate "chores" to agents. Jules handles multi-step tasks such as fixing bugs, supplementing tests, updating dependencies and version upgrades, refining documentation, implementing new features, and runs asynchronously: After you submit a requirement, Jules pulls code in an isolated cloud environment, understands the project context, generates an execution plan, and produces reviewable code changes that can be pulled back into the team process. For teams that require continuous iteration and improved delivery efficiency, Jules can significantly reduce repetitive development costs, bringing AI programming closer to everyday engineering practices.
Maestro is an all-in-one, end-to-end UI automation testing platform for mobile and web. Core capabilities include visual recording and element detection for the Maestro Studio desktop app, cross-platform coverage for iOS/Android/Web, and AI-assisted use case generation and command suggestion powered by MaestroGPT. It offers a free CLI and desktop version that can be used in less than 5 minutes, and seamlessly integrates continuous integration/continuous delivery processes for parallel execution and stable playback in the cloud. Concise definition: Maestro = end-to-end UI automation and AI programming assistance testing platform for mobile applications and web pages, suitable for enterprises and start-up teams to ensure quality at scale. Common scenarios: regression test, smoke test, visual acceptance.
CodeRabbit is an advanced AI-powered code review platform designed to enhance development efficiency and code quality. By integrating large language models like Claude, CodeRabbit enables context-aware line-by-line review of every commit and pull request, providing intelligent suggestions and one-click fixes. The platform supports GitHub, GitLab, Bitbucket, and Azure DevOps, and is compatible with mainstream IDEs such as VS Code and Cursor, allowing developers to obtain real-time review feedback locally. CodeRabbit also integrates more than 20 static analysis and security tools, combined with codegraph analysis, to provide comprehensive quality assurance. Its custom review rules and learning mechanisms enable the AI to continuously refine review strategies based on team feedback. The platform supports both SaaS and self-hosted deployments, complies with SOC 2 Type II security standards, and is suitable for a wide range of development scenarios, from startups to large organizations.
Qodo (formerly CodiumAI) is an AI programming platform focused on code quality, offering full-process support from code generation and testing to review. Its core products include Qodo Gen (IDE plugin), Qodo Merge (PR review assistant), and Qodo Cover (test coverage tool), supporting mainstream development environments such as VS Code and JetBrains. Qodo leverages Retrieval-Augmented Generation (RAG) technology to deeply understand the context of the codebase and generate code and test cases that align with project specifications. The platform supports multiple programming languages, integrates large language models such as GPT-4, Claude, and Gemini, and provides a self-developed AlphaCodium process to improve the accuracy and efficiency of code generation. Qodo also provides the open-source tool Cover-Agent to automate test generation and improve coverage. The platform is SOC 2 Type II certified for both individual developers and enterprise teams, helping to improve development efficiency and code quality.
CodeGeeX is an intelligent programming assistant based on large models launched by Zhipu AI, aiming to improve developers' coding efficiency and code quality. Its core model has 13 billion parameters and supports more than 100 programming languages, including Python, Java, C++, JavaScript, Go, etc., and is suitable for mainstream development environments such as VS Code, JetBrains IDE, Visual Studio, and Vim. CodeGeeX provides features such as code generation and completion, automatic annotations, cross-language code translation, intelligent Q&A, unit test generation, code review and fix, function refactoring, debug log generation, README document generation, and more. The platform also integrates Code Interpreter, which supports batch file processing and data visualization analysis. CodeGeeX is completely free for individual users and has been widely used in various industries such as technology, finance, and healthcare, making it a powerful tool for developers to improve productivity.
Sourcery is an AI-powered code review and refactoring platform designed to improve development efficiency and code quality. It supports over 30 programming languages and integrates with GitHub, GitLab, and major IDEs like VS Code, JetBrains, etc., providing instant code feedback and optimization suggestions. Sourcery automatically identifies potential bugs and security issues, generating PR summaries, review guides, and visual charts to help teams quickly understand code changes. Its built-in Coding Assistant supports generating test cases, documents, code explanations, and real-time refactoring suggestions. The platform supports custom review rules, learns from team feedback, and continuously optimizes review strategies. Sourcery offers strict data security guarantees, supports zero-code retention, and self-hosted deployments, making it suitable for both individual developers and enterprise teams.
DeepCode AI is an AI-powered code security analysis platform launched by Snyk, focusing on static application security testing (SAST) and automated code review. The platform combines symbolic AI and generative AI, supports 19+ programming languages, including Java, Python, JavaScript, TypeScript, C/C++, and more, and can identify and automatically fix security vulnerabilities in real-time in developers' IDEs. Its core feature, DeepCode AI Fix, offers up to 80% accuracy in auto-fixes, significantly reducing mean time to fix (MTTR). DeepCode AI ensures data privacy through self-hosted deployment, making it suitable for enterprise-grade security needs. The platform has been adopted by organizations around the world to help developers secure their applications while maintaining efficient coding.
CircleCI is a leading continuous integration and continuous delivery (CI/CD) platform designed to help development teams automate the build, test, and deployment process to accelerate software delivery cycles. The platform supports a variety of programming languages and frameworks, including Go, Java, Python, Node.js, etc., and is compatible with mainstream version control systems such as GitHub, Bitbucket, and GitLab. Users can define pipelines through configuration files (config.yml) and use Orbs to achieve configuration reuse and improve development efficiency. CircleCI offers both cloud and on-premises deployment options, supporting various execution environments such as Docker, Linux, macOS, Windows, and more, catering to teams of different sizes. Its Insights Dashboard provides real-time monitoring and analytics to help teams optimize their build processes and improve code quality. As a key tool in DevOps practices, CircleCI has been adopted by many enterprises, including Meta, Adobe, Spotify, and more, to enable efficient and reliable software delivery.
Tongyi Lingma is an intelligent coding assistant launched by Alibaba Cloud, which provides efficient automatic code generation and intelligent Q&A capabilities based on the Tongyi model. The platform supports multi-language and multi-framework, with cross-file semantic understanding, auto-completion functions, batch refactoring, and test case generation. Seamlessly integrate VS Code, JetBrains series, and Web IDEs to synchronize code and comments in real time for multi-person collaboration. At the same time, it provides access to privatized knowledge bases and permission management to ensure enterprise source code security and compliance. Tongyi Lingma helps the development team significantly improve R&D efficiency, reduce repetitive labor, and achieve a full-process intelligent programming experience from demand to delivery.
aiXcoder is an intelligent software development tool launched by Sixin Technology, relying on deep learning and large-scale code training, providing functions such as natural language to method-level code generation, token-level and row-level intelligent completion, and cross-file intelligent search. It supports Java, Python, C++, JavaScript, Go and other languages, and is compatible with VS Code, IntelliJ IDEA, PyCharm, Android Studio and other mainstream IDEs, and can complete code snippet generation, unit testing, and automatic comment addition without leaving the editor. Privatization ensures enterprise code security and helps teams significantly improve development efficiency and code quality.
Raccoon family Raccoon is an AI intelligent productivity series launched by SenseTime, based on SenseTime's self-developed large language model, covering two major members: "Code Raccoon" and "Office Raccoon". Code Raccoon supports requirements analysis, architecture design, code writing and testing, and is compatible with 100+ mainstream languages such as Python, Java, C++, as well as VS Code and IDEA plug-ins, helping developers speed up by more than 50%. The office raccoon can connect multiple files and complex tables to achieve data cleaning, calculation, trend analysis and visualization, and significantly improve the efficiency of data processing and reporting. This series has served hundreds of thousands of enterprise users and is widely used in software research and development and intelligent office scenarios.
