Staring at unfamiliar files? Desktop AI explains code inline

Developers—especially new hires, contractors, cross-team reviewers and security auditors—routinely spend frustrating amounts of time staring at unfamiliar files because existing tools either return high-level summaries or force context-switching between editors, search, and chat. This problem spans the 25 million-developer market and materially slows onboarding, code review and incident response in teams of all sizes. You could build an IDE/desktop-integrated assistant that explains code inline at the file and function level using code-tuned models and per-file embeddings: semantically precise summaries, variable and type explanations, call-graph snippets and "why this exists" context surfaced directly in the editor. Offer a hybrid deployment model with a cloud option and local/offline runtimes for enterprises, plus a single-window UX that eliminates tab-switching and keeps explanations anchored to the current file and revision. Market timing and economics are favorable: the $12.5B developer tooling + AI assistant market (25M devs × $500 ARR) is large, and recent advances in code-specialized models and embeddings make file-scoped explanations practical and significantly cheaper than naive LLM prompting. At the same time, enterprise demand for IP-protecting local deployments and the developer preference for in-IDE, context-driven UX both reduce adoption friction. This idea can stand out by combining file-level embeddings to cut inference costs, code-tuned models for higher semantic fidelity, and desktop/local deployment to meet enterprise security requirements; the market score of 92/100 and revenue potential 90/100 reflect that. Real challenges remain—supporting many languages and editors, proving explanation correctness, managing the cost and UX of local models, and navigating enterprise sales—but these are technical and go-to-market problems that can be addressed with focused engineering and early enterprise pilots.