Kadoa
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Pricing model
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Kadoa is an AI-driven tool that enables users to swiftly extract data from websites, PDFs, and databases within seconds. It removes the necessity for coding custom scrapers and provides unimpeded access to data. Additionally, it offers a robust API and integrations for straightforward access and utilization of the extracted data. Kadoa is applicable for tasks such as price monitoring, lead generation, finance and investment, business intelligence, and market research.
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Riku is a platform enabling users to develop AI models without coding. It offers various features, integrations, and resources that empower users to create content, refine models, design prompts, and more.
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VibeScan is an AI-based platform designed for scanning and correcting code, specifically tailored to analyze AI-generated code for vulnerabilities, performance issues, and quality concerns. The tool seamlessly integrates into modern development workflows through UI, CLI, and API interfaces, offering developers and engineering teams automated security checks and one-click fixes, eliminating common errors in AI-generated outputs. By utilizing AI to detect and resolve potential issues before deployment, VibeScan provides a crucial layer of security for organizations increasingly relying on AI coding assistants, enabling teams to release AI-generated code with confidence, reducing security risks, preventing performance bottlenecks, and maintaining high code quality standards without slowing down development cycles.
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RunCell is an AI-driven assistant seamlessly incorporated into Jupyter notebooks, allowing users to create and run code using natural language commands instead of manual coding. This tool revolutionizes the data analysis process by allowing data scientists, researchers, and developers to simply state their objectives, and then RunCell generates and executes the necessary code automatically. By introducing conversational AI features to the notebook setting, RunCell greatly cuts down coding time, reduces the technical entry barrier for newcomers, and aids seasoned programmers in quickly prototyping ideas. It enables users to concentrate on their analytical objectives rather than syntax intricacies, thus making complex data tasks more approachable and simplifying the experimental workflow for anyone using data in Jupyter contexts.