anthropics/original_performance_takehome resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

4122 stars · 943 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Anthropic's open-sourced performance engineering take-home challenge, where you optimize a simulated machine's code from a slow baseline (232,000 cycles) toward record cycle counts. It doubles as a benchmark comparing human and LLM optimization ability and as a recruiting channel for Anthropic.

Use cases

• practice low-level performance optimization on a real challenge
• compare my optimization skills against Claude Opus 4.5's benchmark results
• try Anthropic's hiring take-home with unlimited time
• benchmark how well an AI agent performs at performance engineering
• learn cycle-level optimization and debugging with provided tools
• submit a sub-1487-cycle solution to impress Anthropic recruiters

When to choose

• you want a self-contained, well-instrumented performance optimization challenge
• you're interested in testing LLM coding agents on hard optimization tasks
• you want a shot at getting noticed by Anthropic's recruiting team

When to avoid

• you need production software or a reusable library
• you want a maintained tool with a license or ongoing support
• you're looking for a general-purpose benchmarking framework

Facets

learning-resource · maturity stable

benchmarking developer-tools simulation performance developer-tools tutorials python cli cross-platform coding-challenge performance-optimization take-home-exercise recruiting llm-benchmark low-level-optimization

Member repositories

For agents

markdown · JSON · MCP: product_card(name="anthropics/original_performance_takehome")