Problem solved

Tekk.coach addresses the inefficiency of manually identifying and scoping software improvements. It automates the discovery of codebase issues and opportunities by deploying AI agents that research and draft detailed specifications, turning vague ideas or undetected problems into actionable development plans. This reduces the time engineers spend on issue triage and solution design, while ensuring proposals are grounded in code and current best practices.

Use cases

• Automatically generating security improvement specs (e.g., secret detection, dependency updates)
• Proposing performance optimizations like query tuning or latency reduction
• Suggesting architectural refactors to reduce coupling or drift
• Enhancing AI/ML agent implementations (retrieval, evals, RAG quality)
• Coordinating team goals by spawning goal-oriented agent loops
• Integrating with existing coding agents to hand off specs for implementation
• Tracking spec-driven development from proposal to completion on a shared board

Audience

• Developers
• ML Engineers
• Product Managers
• Startups

Pros

• Automates discovery of codebase improvements, saving engineering time
• Generates detailed, code-grounded specs ready for implementation
• 24/7 continuous analysis ensures no missed opportunities
• Seamless integration with popular coding agents (Cursor, Copilot, etc.)
• Free to use, lowering barrier for teams

Cons

• Only proposes changes; implementation still requires human effort
• May generate an overwhelming number of specs if not carefully managed
• Relies on AI research accuracy, which may occasionally propose suboptimal solutions
• Limited to codebase and web research; cannot access private institutional knowledge
• Lacks direct integration with version control for automated execution