Code Fundi

Provides an interactive chat panel integrated into VS Code's sidebar that accepts natural language queries about code, debugging, explanations, and generation tasks. The chat interface maintains conversation context within a session and routes user messages to a cloud-based LLM backend (codefundi.app) for processing, returning responses rendered directly in the sidebar panel without requiring context switching to external tools.

Analyzes code in the current editor file to identify bugs, errors, and logical issues, then generates explanations and suggested fixes. The capability operates by sending the active file content to the cloud backend, which applies LLM-based static analysis to detect common error patterns, runtime issues, and code quality problems, returning annotated suggestions without requiring manual test execution or stack traces.

Generates human-readable explanations of code functionality, purpose, and behavior by sending the current file or selected code to the LLM backend. The capability analyzes code structure, syntax, and logic to produce natural language descriptions suitable for documentation, code reviews, or knowledge transfer, without requiring manual annotation or external documentation tools.

Converts natural language descriptions or requirements into working code by accepting user prompts in the chat interface and generating code snippets via the LLM backend. The capability infers programming language from the current editor context and produces syntactically valid code that can be directly inserted into the file, supporting rapid prototyping and reducing boilerplate writing.

Analyzes code in the current editor file and automatically generates unit tests or test cases by sending the code to the LLM backend. The capability infers test framework and language from the editor context, producing test code that covers common code paths and edge cases, reducing manual test writing effort and improving code coverage.

Manages communication between the VS Code extension and a cloud-based LLM service (codefundi.app) using account-based authentication and session tokens. The integration handles credential storage in VS Code's secure extension storage, request routing, response parsing, and error handling, abstracting the complexity of API communication from the user while maintaining security boundaries.

Provides a free tier with unspecified usage limits and paid tiers for higher usage, managed through account-based subscription tracking on the codefundi.app backend. The extension enforces quota limits by checking account status before processing requests, returning quota-exceeded errors when limits are reached, and prompting users to upgrade for continued access.