Superflex: AI Frontend Assistant, Figma to React/Vue/NextJS/Angular (Powered by GPT & Claude)

Converts Figma design files into production-ready component code by uploading designs directly into the chat interface, then routing the design through Claude or GPT with framework-specific code generation templates. The system preserves design tokens, layout hierarchy, and responsive breakpoints from Figma's design system, outputting clean, reusable components in React, Vue, Next.js, or Angular with proper TypeScript typing and CSS/Tailwind styling.

Converts UI screenshots, mockups, or hand-drawn sketches into functional component code by uploading images to the chat interface and processing them through vision-capable AI models (Claude or GPT). The system analyzes visual layout, typography, spacing, and color information from the image, then generates corresponding HTML/CSS/JavaScript code with responsive design considerations.

Generates components with built-in responsive design and mobile-first CSS, automatically including media queries, breakpoints, and mobile-optimized layouts. The system generates code that works across device sizes without requiring manual responsive design implementation. Supports Tailwind responsive utilities and CSS media queries depending on the project's styling approach.

Generates components with semantic HTML elements (e.g., `<button>` instead of `<div>` with click handlers) and includes accessibility attributes (ARIA labels, roles, keyboard navigation) by default. The system follows WCAG guidelines and best practices for accessible component design, though accessibility compliance is not guaranteed and may require manual refinement.

Provides a chat interface within VSCode that understands the current project's code structure, allowing developers to ask questions about their codebase and receive context-aware answers. Developers can select code snippets via keyboard shortcut (⌘M) to add them to chat messages, search for specific files using a file picker, and reference project context without manually copying code. The chat maintains conversation history and can answer questions about component usage, architecture patterns, and code relationships.

Analyzes existing React, Vue, Next.js, or Angular components and refactors them while maintaining the original coding style, design patterns, and visual appearance. The system detects the component's current style (e.g., CSS-in-JS, Tailwind, CSS Modules), naming conventions, and architectural patterns, then applies refactoring suggestions (e.g., extracting sub-components, improving type safety, optimizing performance) while preserving these conventions. Changes are generated as code suggestions that can be reviewed and applied incrementally.

Generates complete, production-ready components in React, Vue, Next.js, or Angular from natural language descriptions. Developers describe the component's functionality, layout, and behavior in the chat, and the system generates fully typed, styled code with proper error handling and accessibility considerations. The generator supports both simple UI components and complex stateful components with hooks, lifecycle methods, or reactive properties depending on the framework.

Generates code with real-time streaming output, displaying generated code character-by-character as it's produced by the AI model. Developers can cancel generation mid-stream if the output is not meeting expectations, and the partially generated code is retained for editing. This streaming approach reduces perceived latency and allows developers to start reviewing code before generation completes, with support for message editing and regeneration of specific responses.

Automatically saves chat conversations within the VSCode extension, allowing developers to resume previous design-to-code or refactoring sessions without losing context. The system maintains conversation state across VSCode sessions, preserving file references, code snippets, and previous generation results. Developers can reference earlier messages in the conversation, and the AI maintains context across multiple turns without requiring manual context re-injection.

Analyzes existing project code to infer design system conventions, coding style, naming patterns, and architectural preferences, then applies these inferred patterns to all generated code. The system detects CSS-in-JS libraries, Tailwind configuration, component naming conventions, TypeScript strictness levels, and code formatting preferences from the project, ensuring generated code matches the project's existing standards without explicit configuration.

Provides keyboard shortcuts (⌘M on macOS, equivalent on Windows) to quickly select code snippets from the editor and inject them into chat messages without manual copy-paste. The selected code is automatically formatted and added to the chat context, allowing developers to reference specific code sections in their questions or generation requests. This integrates VSCode's native selection mechanism with the chat interface for seamless context injection.

Provides a file picker interface within the chat to search for and select specific project files as context for code generation or refactoring. Developers can search by filename, path, or content, then add selected files to the chat context. This allows referencing multiple files in a single generation request without manually copying code, enabling context-aware generation that understands file relationships and dependencies.