How to Hyper-Optimise Claude Code: The Complete Engineering Guide

Never Hit Limits Again While Keeping Top Models Predicting A comprehensive, stats-driven framework from simple fixes to advanced architectures The hard lessons I've learned from burning through Claude Code limits in hours, starting refactoring sessions at 9 AM only to hit rate limits by lunch, spending $200/day when I budgeted $200/month, taught me that the real bottleneck isn't the model itself. The common pattern? Treating Claude Code like Google Search. @entire_repo Refactor the authentication system This works... until your context window explodes, your tokens drain, and you're staring at a rate limit error with half your feature unfinished. The issue isn't the model. The issue is how we architect context. After optimising dozens of production codebases, I've identified 16 concrete strategies ranked by complexity and impact that can reduce token consumption by 60-90% while keeping Opus and Sonnet actively predicting (relegating Haiku to where it belongs: simple, bounded tasks). Her