Anthropic's original take home assignment open sourced
Recorded: Jan. 21, 2026, 11:03 a.m.
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anthropics/original_performance_takehome
mainBranchesTagsGo to fileCodeOpen more actions menuFolders and filesNameNameLast commit messageLast commit dateLatest commit History1 Committeststests .gitignore.gitignore Readme.mdReadme.md perf_takehome.pyperf_takehome.py problem.pyproblem.py watch_trace.htmlwatch_trace.html watch_trace.pywatch_trace.py View all filesRepository files navigationREADMEAnthropic's Original Performance Take-Home 2164 cycles: Claude Opus 4 after many hours in the test-time compute harness If you optimize below 1487 cycles, beating Claude Opus 4.5's best performance at launch, email us at performance-recruiting@anthropic.com with your code (and ideally a resume) so we can be appropriately impressed and perhaps discuss interviewing. About Anthropic's original performance take-home, now open for you to try! Readme Uh oh! There was an error while loading. Please reload this page. Activity Custom properties 605 1 92 Report repository Releases Packages No packages published Uh oh! There was an error while loading. Please reload this page. Languages Python HTML Footer © 2026 GitHub, Inc. Footer navigation Terms Privacy Security Status Community Docs Contact Manage cookies Do not share my personal information You can’t perform that action at this time. |
This GitHub repository, “original_performance_takehome” from Anthropic, presents a challenge centered around optimizing a Python program to achieve the lowest possible clock cycle count, ultimately surpassing the performance of Claude Opus 4.5 during its initial launch. The project’s genesis lies in a performance evaluation conducted before the emergence of Claude Opus 4.5’s superior capabilities within a significantly reduced timeframe. The core objective is to develop an algorithm or set of instructions that minimizes the number of clock cycles required by a simulated machine to solve a yet-unspecified problem – presumably a computationally intensive task. The repository’s documentation, specifically the `README.md` file, explicitly details the performance benchmarks established for comparison. These benchmarks serve as criteria for assessing the success of submitted solutions. The documented cycle counts represent the performance of several Claude models under varying computational conditions. Initially, Claude Opus 4 achieved 2164 cycles after extended use within Anthropic's “test-time compute harness.” Subsequently, Claude Opus 4.5 reached 1790 cycles during a typical, casual coding session, mirroring the best human performance achieved within the two-hour timeframe. Further refinements yielded cycle counts of 1579 for Claude Opus 4.5 after two hours in the enhanced harness, 1548 for Claude Sonnet 4.5 over extended periods, 1487 for Claude Opus 4.5 within a 11.5-hour test, and 1363 for Claude Opus 4.5 utilizing an improved test-time compute harness. The repository’s structure indicates a simple Python-based implementation, evidenced by the presence of `perf_takehome.py`, `problem.py`, `watch_trace.html`, and `watch_trace.py`. The `submission_tests.py` file is used to verify whether submitted code achieves the required performance thresholds. The inclusion of the HTML file, `watch_trace.html`, suggests a mechanism for monitoring the program’s execution and identifying potential bottlenecks which could then be addressed by optimizing the code. The `problem.py` file likely contains the core algorithmic challenge, while `watch_trace.py` provides tools for observation and debugging. The stated goal – reducing the clock cycle count to below 1487 – represents a significant hurdle, acknowledging the advanced capabilities of the Claude models. Anthropic’s recruitment team has established a direct pathway for engagement: successful optimization—achieving a cycle count below the specified threshold—requires the submission of the code and a resume to the email address performance-recruiting@anthropic.com. This incentivizes developers to dedicate themselves to the challenge. The project’s design is a carefully orchestrated performance evaluation, a competitive test case rather than a typical development exercise. It’s clear that Anthropic is seeking deeply insightful solutions, ones that demonstrate a sophisticated understanding of computational efficiency and algorithm design. The structured approach, including defined benchmarks and a clear route for evaluation, underscores the intent to identify exceptional talent within the AI development community. |