mgks/ai-context-optimization
Techniques and tools for optimizing how AI coding assistants understand your codebase, with a focus on cost reduction and efficiency.
Provides practical tools including a Cursor MAX Optimizer that generates single-file codebase summaries to replace expensive multi-step tool calls, and a role-based workflow pattern that allocates expensive reasoning models to architecture while delegating implementation to cost-effective alternatives. Both approaches use file-based context passing and work across different AI editors and LLMs, with documented token savings up to 97% for supported models.
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Jul 01, 2025
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