I built Aura-IDE, a native desktop LLM coding harness for AI assisted software engineering.

The idea is not just “chat with your codebase.” Aura wraps models in a structured engineering loop:

repo awareness → Planner spec → Worker execution → surgical edits → validation → recovery → final receipt

The Planner reads the project and writes a spec. The Worker executes that spec with filesystem tools, diff approval, terminal validation, and recovery behavior. The goal is to make ordinary models produce better code by giving them better process, context, and tools.

The unusual part: Aura has been heavily dogfooded on itself. Roughly 98% of the codebase was generated, edited, or refined through Aura’s own Planner/Worker workflow under human direction. During May, visible DeepSeek usage crossed 1.1B tokens and nearly 30k API requests while improving Aura’s reliability, edit mechanics, UI, updater flow, README, demo flow, and product polish.

It is built with Python and PySide6. It supports DeepSeek, OpenAI, Anthropic, Gemini, OpenRouter, and CLI backends. It can run carefully with diff review, or faster with auto-dispatch/auto-approve.

I’d love feedback from people building coding agents, IDEs, or local/dev tools.