RRepoGEO

REPOGEO REPORT · LITE

anthropics/original_performance_takehome

Default branch main · commit 5452f74b · scanned 5/26/2026, 4:03:16 PM

GitHub: 3,866 stars · 882 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface anthropics/original_performance_takehome, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ["performance-optimization", "coding-challenge", "take-home-assignment", "low-level-programming", "assembly", "benchmarking", "anthropic"]
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root, specifying the intended open-source license (e.g., MIT, Apache-2.0, or a custom license if applicable).
  • highreadme#3
    Reposition the README's opening paragraph to clarify its purpose as a challenge

    Why:

    CURRENT
    This repo contains a version of Anthropic's original performance take-home, before Claude Opus 4.5 started doing better than humans given only 2 hours.
    COPY-PASTE FIX
    This repository presents Anthropic's original performance take-home, now released as a challenging coding problem for anyone to test and improve their low-level performance optimization skills.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface anthropics/original_performance_takehome
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Intel VTune Profiler
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Intel VTune Profiler · recommended 1×
  2. AMD uProf · recommended 1×
  3. C++ · recommended 1×
  4. GCC · recommended 1×
  5. Clang · recommended 1×
  • CATEGORY QUERY
    How can I optimize my code for extreme performance, minimizing clock cycles?
    you: not recommended
    AI recommended (in order):
    1. Intel VTune Profiler
    2. AMD uProf
    3. C++
    4. GCC
    5. Clang
    6. Assembly Language
    7. NASM
    8. YASM
    9. GNU Assembler (GAS)
    10. Intel Intrinsics
    11. ARM Neon Intrinsics
    12. CUDA
    13. OpenCL
    14. OpenMP
    15. Intel TBB
    16. Rust

    AI recommended 16 alternatives but never named anthropics/original_performance_takehome. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find challenging coding problems focused on low-level performance optimization?
    you: not recommended
    AI recommended (in order):
    1. TopCoder SRM
    2. Codeforces
    3. Google Code Jam
    4. Facebook Hacker Cup
    5. LeetCode
    6. Project Euler
    7. HackerRank

    AI recommended 7 alternatives but never named anthropics/original_performance_takehome. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of anthropics/original_performance_takehome?
    pass
    AI did not name anthropics/original_performance_takehome — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts anthropics/original_performance_takehome in production, what risks or prerequisites should they evaluate first?
    pass
    AI named anthropics/original_performance_takehome explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo anthropics/original_performance_takehome solve, and who is the primary audience?
    pass
    AI did not name anthropics/original_performance_takehome — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of anthropics/original_performance_takehome. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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anthropics/original_performance_takehome — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite