What is the Scale Game principle?

The Scale Game involves testing logic at 1000x extremes—either much larger or much smaller—to reveal fundamental architectural truths that are often hidden at normal scales.

Can it help with memory leaks?

Yes. By projecting system behavior over extreme durations (e.g., simulating years of runtime), it helps identify unbounded memory growth and state persistence issues.

Is this skill only for large-scale enterprise systems?

No. Testing at both minimum and maximum extremes is useful for any code to ensure the underlying logic is fundamentally sound, regardless of the current user count.

When should I use this skill?

Use it whenever you are uncertain about system scalability, when edge cases are unclear, or when you need to validate an architecture for production volumes.

Does this skill work for API design?

Absolutely. It helps reveal why synchronous APIs might fail at global scales due to network latency and helps you transition to more resilient async patterns.

Scale Game Problem Solver

Name: Scale Game Problem Solver
Author: kienhaminh

bykienhaminh

보안 및 테스팅

Identifies architectural bottlenecks and hidden edge cases by stress-testing software logic at extreme scales.

소개

Scale Game is a diagnostic framework designed to validate software architecture by projecting performance at extreme scales. By simulating scenarios such as 1,000x user growth, instant vs. year-long durations, or near-total failure rates, it helps developers expose algorithmic complexity limits, concurrency issues, and memory leaks that remain hidden under normal operating conditions. This proactive approach allows teams to identify what breaks and what survives, ensuring systems are fundamentally sound and production-ready before a single line of scaling infrastructure is deployed.

주요 기능

Validation of error-handling adequacy under extreme failure conditions
0 GitHub stars
Long-term stability projections to detect memory leaks and state growth
Concurrency and resource limit detection for high-user scenarios
Multi-dimensional scale analysis including Volume, Speed, and Duration
Identification of algorithmic complexity limits through extreme volume testing

사용 사례

Analyzing API latency impacts on global users before production deployment
Stress-testing error-handling logic by simulating continuous system failure
Validating database schema performance for sudden million-row scaling

소개

주요 기능

Validation of error-handling adequacy under extreme failure conditions
0 GitHub stars
Long-term stability projections to detect memory leaks and state growth
Concurrency and resource limit detection for high-user scenarios
Multi-dimensional scale analysis including Volume, Speed, and Duration
Identification of algorithmic complexity limits through extreme volume testing

사용 사례

Analyzing API latency impacts on global users before production deployment
Stress-testing error-handling logic by simulating continuous system failure
Validating database schema performance for sudden million-row scaling