RRepoGEO

REPOGEO REPORT · LITE

zwang4/awesome-machine-learning-in-compilers

Default branch master · commit d768a971 · scanned 5/26/2026, 2:32:58 AM

GitHub: 1,675 stars · 178 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 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 zwang4/awesome-machine-learning-in-compilers, 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 `awesome-list` topic to clarify repository type

    Why:

    CURRENT
    artificial-intelligence, auto-tuning, compiler, machine-learning, multi-cores, operating-systems, optimisation, parallel-computing, parallel-programming, parallelisation, parallelism
    COPY-PASTE FIX
    artificial-intelligence, auto-tuning, compiler, machine-learning, multi-cores, operating-systems, optimisation, parallel-computing, parallel-programming, parallelisation, parallelism, awesome-list
  • mediumhomepage#2
    Add repository URL as homepage

    Why:

    COPY-PASTE FIX
    https://github.com/zwang4/awesome-machine-learning-in-compilers
  • mediumreadme#3
    Clarify README's opening to emphasize its role as a comprehensive reference

    Why:

    CURRENT
    A curated list of awesome research papers, datasets, and tools for applying machine learning techniques to compilers and program optimisation.
    COPY-PASTE FIX
    This repository is a comprehensive, curated list of research papers, datasets, and tools for applying machine learning techniques to compilers and program optimisation, serving as a primary reference for the field.

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 zwang4/awesome-machine-learning-in-compilers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LLVM-ML
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LLVM-ML · recommended 1×
  2. OpenTuner · recommended 1×
  3. Intel Advisor · recommended 1×
  4. CompilerGym · recommended 1×
  5. Polly · recommended 1×
  • CATEGORY QUERY
    How can machine learning techniques be applied to improve compiler performance and program optimization?
    you: not recommended
    AI recommended (in order):
    1. LLVM-ML
    2. OpenTuner
    3. Intel Advisor
    4. CompilerGym
    5. Polly
    6. TensorFlow XLA
    7. AlphaCode
    8. PROSE
    9. CodeQL

    AI recommended 9 alternatives but never named zwang4/awesome-machine-learning-in-compilers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for research and tools applying artificial intelligence to system performance and parallelization.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym
    2. Ray RLlib
    3. TensorFlow Lite Micro
    4. Apache TVM
    5. Intel oneAPI
    6. oneAPI DPC++
    7. oneAPI VTune Profiler
    8. NVIDIA CUDA Toolkit
    9. Nsight Systems
    10. Nsight Compute
    11. Google AutoPerf
    12. AutoScheduler
    13. MLIR
    14. OpenMP
    15. OpenACC

    AI recommended 15 alternatives but never named zwang4/awesome-machine-learning-in-compilers. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 zwang4/awesome-machine-learning-in-compilers?
    pass
    AI did not name zwang4/awesome-machine-learning-in-compilers — 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 zwang4/awesome-machine-learning-in-compilers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zwang4/awesome-machine-learning-in-compilers 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 zwang4/awesome-machine-learning-in-compilers solve, and who is the primary audience?
    pass
    AI did not name zwang4/awesome-machine-learning-in-compilers — 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 zwang4/awesome-machine-learning-in-compilers. 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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zwang4/awesome-machine-learning-in-compilers — 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