Artificial intelligence has reshaped modern software development, and developers are now relying heavily on AI-assisted coding environments to improve productivity, automate repetitive tasks, and accelerate deployment cycles. From code generation to debugging and documentation, AI-powered development tools are becoming essential in everyday engineering workflows. Yet one important question remains: should developers trust expensive closed-source ecosystems,
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How Well Does Kilo Code Handle Large Projects Without Losing Context Compared to GitHub Copilot’s Workspace?
As software systems scale, maintaining architectural coherence and contextual awareness becomes a non-trivial engineering challenge. Developers working on enterprise-grade applications, often spanning thousands of files and multiple services, require tools that can intelligently interpret and persist context across sessions. This is where AI-assisted development tools come into focus. But how do newer tools compare with
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