fraudctl-specific patterns including KNN vector search optimization, 14D vectorization, zero-allocation patterns, and performance best practices for high-throughput fraud detection APIs.
Go performance profiling patterns using pprof, benchmarks, and optimization strategies for identifying and fixing performance bottlenecks.
Configure automated git hooks using lefthook with Go project best practices including linting, testing, and coverage requirements.
Prevents destructive operations by requiring confirmation and verification before executing risky commands.
Idiomatic Go patterns, best practices, and conventions for building robust, efficient, and maintainable Go applications.
Go testing patterns including table-driven tests, subtests, benchmarks, fuzzing, and test coverage. Follows TDD methodology with idiomatic Go practices.