establishing-benchmark-baseline

To establish a performance baseline for a language implementation, you can automate the discovery of benchmark data, fetch AreWeFastYet data, and generate a comprehensive BENCHMARK_BASELINE.md report to guide optimization.

The best way to compare your language's performance is to analyze language characteristics like type system and execution model, identify 1-2 comparable languages, and translate benchmarks locally for meaningful performance comparisons.

Yes, you can use AreWeFastYet data to guide micro-benchmark selection by discovering and fetching relevant performance data, which is then implemented and translated locally with verification for your baseline report.

Benchmark translation and verification works by fetching AreWeFastYet data, translating the benchmarks to your local language implementation, verifying their execution, and appending the reproducible results into BENCHMARK_BASELINE.md.

No, you do not need to manually orchestrate the workflow phases because the process automates the full workflow from phase-driven analysis and benchmark translation to final baseline generation in BENCHMARK_BASELINE.md.

The language characteristics analyzed to determine comparables for benchmarking include the type system, execution model, platform, and complexity of the language implementation.