c54c569f30
Completed clean root directory structure: - Root now contains only: .git, .env, docs/, orig/ - Moved all remaining files and directories to orig/: - Config files (.claude, .dockerignore, .drone.yml, etc.) - All .env variants (except active .env) - Git config (.gitconfig, .github, .gitignore, etc.) - Tool configs (.golangci.yml, .revive.toml, etc.) - Documentation (*.md files, @prompts) - Build files (Dockerfiles, Makefile, go.mod, go.sum) - Docker compose files - All source directories (scripts, tests, tools, etc.) - Runtime directories (logs, monitoring, reports) - Dependency files (node_modules, lib, cache) - Special files (--delete) - Removed empty runtime directories (bin/, data/) V2 structure is now clean: - docs/planning/ - V2 planning documents - orig/ - Complete V1 codebase preserved - .env - Active environment config (not in git) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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You are an expert in Go performance optimization and high-frequency trading systems. I'm building an MEV bot in Go that needs to process thousands of transactions per second with minimal latency.
I need help with:
- Optimizing data structures for high-frequency access
- Minimizing memory allocations and garbage collection
- Implementing efficient caching strategies
- Optimizing network I/O for RPC calls
- Parallel processing of transactions
- Profiling and benchmarking techniques
Please provide production-ready Go code that:
- Implements lock-free or low-lock data structures where appropriate
- Minimizes memory allocations through object pooling
- Uses efficient algorithms for data processing
- Implements caching for frequently accessed data
- Handles concurrency properly
- Follows Go best practices
- Includes comprehensive comments
The code should:
- Process transactions with minimal latency
- Scale efficiently across multiple CPU cores
- Handle backpressure gracefully
- Provide metrics for performance monitoring
- Include benchmarks for critical functions