Self-improving software won't produce Skynet (contalign.jefflunt.com)

A new approach to self-improving software has emerged, addressing the challenge of "documentation debt" that often hampers both developers and AI agents. This innovative model utilizes agentic AI, which can not only comprehend existing code and documentation but also autonomously update them after implementing changes. This creates a continuous feedback loop where documentation evolves alongside the code, significantly reducing onboarding time for new agents and minimizing the risk of hallucinations from outdated information. Importantly, this self-improvement paradigm is fundamentally different from sci-fi fears of rogue AIs like Skynet. Instead, it emphasizes a pragmatic and controlled approach where AI tools remain guided by human input, automating iterative processes that developers already use. By closing the gap between code and documentation, this approach promises to enhance system resilience and maintainability, setting the stage for tackling complexities, including managing legacy codebases. This progressive shift could revolutionize software development, making it more efficient and collaborative between human and AI contributions.