Best AI IDEs for Zed

Google Antigravity is Google’s next-generation AI IDE, purpose-built to transform software creation through agentic automation and contextual intelligence. It integrates intelligent agents directly into your coding environment, allowing them to plan, execute, and verify tasks across the editor, terminal, and browser in real time. Developers can interact with agents using natural language, refining outputs and validating code through a feedback-driven interface that fosters trust and transparency. Antigravity’s synchronized agentic control ensures that multiple agents work together efficiently, maintaining shared context across workspaces and surfaces. The IDE introduces task-oriented workflows, enabling developers to focus on higher-level architecture and design while agents handle implementation and iteration. With tab autocompletion, smart command execution, and artifact-based verification, Antigravity streamlines the development lifecycle from ideation to deployment. Available at no charge for individuals, it empowers both professionals and hobbyists to achieve new levels of productivity. By combining intelligent autonomy with human oversight, Google Antigravity bridges the gap between traditional development and the agent-first future.

Project Jupyter is dedicated to the creation of open-source tools, standards, and services that facilitate interactive computing in numerous programming languages. At the heart of this initiative is JupyterLab, a web-based interactive development environment designed for Jupyter notebooks, coding, and data manipulation. JupyterLab offers remarkable flexibility, allowing users to customize and organize the interface to cater to various workflows in fields such as data science, scientific research, and machine learning. Its extensibility and modular nature enable developers to create plugins that introduce new features and seamlessly integrate with existing components. The Jupyter Notebook serves as an open-source web application enabling users to produce and share documents that incorporate live code, mathematical equations, visualizations, and descriptive text. Common applications of Jupyter include data cleaning and transformation, numerical simulations, statistical analysis, data visualization, and machine learning, among others. Supporting over 40 programming languages—including popular ones like Python, R, Julia, and Scala—Jupyter continues to be a valuable resource for researchers and developers alike, fostering collaborative and innovative approaches to computing challenges.