Never miss TODOs in code — AI highlights, classifies & prioritizes

Across an estimated 25 million professional developers, TODOs and inline markers in code commonly become unstandardized, untriaged and forgotten, creating hidden tech debt and missed deliverables. This problem affects individual contributors who lose context, engineering managers who cannot reliably prioritize work, and distributed teams where informal markers become de facto coordination points. You could build an AI-powered developer tool composed of an IDE extension (e.g., VS Code, JetBrains), CI/repo integrations, and a lightweight dashboard that surfaces, semantically classifies, and ranks TODOs in near real-time. Using code-aware LLMs plus repo metadata it would label TODOs (bug, improvement, test, security, temporary), estimate risk and impact, suggest next steps or PR templates, and sync decisions with issue trackers to close the loop. The timing is favorable: modern LLMs increasingly understand code semantics, IDE extension ecosystems have lowered distribution friction, and remote engineering practices raise demand for centralized marker tracking. The market is sizable — roughly $7.5B in annual tooling spend (25M developers × $300/year) — so modest share capture could sustain a focused product. To stand out you must emphasize precision, explainability, and enterprise-friendly privacy modes: fine-tune models on large, multi-language corpora, offer private-inference or on-prem options, and integrate tightly with CI and issue-tracking workflows to make suggestions verifiable. The main challenges are earning developer trust by minimizing false positives, competing in a medium-competition space, and aligning pricing with measurable productivity gains, but a clear focus on ROI and low-friction adoption can create defensible value.