JetBrains DataSpell Reviews

Retool is an AI-driven platform that helps teams design, build, and deploy internal software from a single unified workspace. It allows users to start with a natural language prompt and turn it into production-ready applications, agents, and workflows. Retool connects to nearly any data source, including SQL databases, APIs, and AI models, creating a real-time operational layer on top of existing systems. The platform supports AI agents, LLM-powered workflows, dashboards, and operational tools across teams. Visual app building tools allow users to drag and drop components while seeing structure and logic in real time. Developers can fully customize behavior using code within Retool’s built-in IDE. AI assistance helps generate queries, UI elements, and logic while remaining editable and schema-aware. Retool integrates with CI/CD pipelines, version control, and debugging tools for professional software delivery. Enterprise-grade security, permissions, and hosting options ensure compliance and scalability. The platform supports data, operations, engineering, and support teams alike. Trusted by startups and Fortune 500 companies, Retool significantly reduces development time and manual effort. Overall, it enables organizations to build smarter, AI-native internal software without unnecessary complexity.

Devin Desktop is an AI-powered integrated development environment that enables developers to manage fleets of coding agents while maintaining complete control over the software development lifecycle. Built as the evolution of Windsurf, the platform combines advanced AI agents, a fully featured IDE, and collaborative workflow management into a single development experience. Developers can assign coding tasks to local or cloud-based agents, allowing autonomous execution of research, implementation, testing, debugging, optimization, and documentation activities. The platform's Agent Command Center provides centralized visibility into ongoing agent work, making it easier to coordinate multiple development efforts simultaneously. Features such as Spaces enable shared context and Git worktrees across agents, while Fast Context rapidly surfaces relevant code, files, and dependencies to accelerate development. Devin Desktop includes Supercomplete, which predicts developer intent beyond simple code completion, helping users work faster and remain focused. The platform supports multiple AI models and agent frameworks through the Agent Client Protocol, providing flexibility across different coding workflows and use cases. Extensive integrations with development, collaboration, monitoring, and project management tools allow organizations to connect AI-assisted development with their existing technology stack. Built-in code review, debugging, and traceability features ensure developers can inspect, validate, and refine every AI-generated change before deployment. The platform is designed for organizations that want to scale AI-assisted software engineering while maintaining visibility, governance, and code quality standards. Devin Desktop helps developers and engineering teams accelerate software delivery by combining autonomous AI execution with professional development tools and human oversight.

JupyterHub is a powerful tool that enables the creation of a multi-user environment, allowing for the spawning, management, and proxying of multiple instances of Jupyter notebook servers. Created by Project Jupyter, it is specifically tailored to support numerous users at once. This platform serves a wide array of functions, making it suitable for educational settings, corporate data science teams, collaborative scientific research endeavors, or groups that utilize high-performance computing resources. However, it's essential to highlight that JupyterHub does not officially support Windows operating systems. While some users may attempt to run JupyterHub on Windows using compatible Spawners and Authenticators, the default settings are not optimized for such an environment. Additionally, any issues encountered on Windows will not receive support, and the testing framework is not designed to work on Windows platforms. Minor patches that could potentially address basic compatibility issues on Windows are infrequent and not guaranteed. Consequently, for those using Windows, it is recommended to operate JupyterHub within a Docker container or a Linux virtual machine, as this ensures better performance and compatibility. This strategy not only improves functionality but also streamlines the installation process, making it easier for Windows users to access the benefits of JupyterHub. Ultimately, adopting this method can lead to a more seamless user experience.