RRepoGEO

REPOGEO REPORT · LITE

alpa-projects/alpa

Default branch main · commit b8078a9f · scanned 5/22/2026, 12:06:55 AM

GitHub: 3,187 stars · 361 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 alpa-projects/alpa, 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
  • highabout#1
    Update the repository description to reflect its status as a research artifact

    Why:

    CURRENT
    Training and serving large-scale neural networks with auto parallelization.
    COPY-PASTE FIX
    A foundational research artifact for auto-parallelization in large-scale neural networks, with core algorithms now integrated into XLA.
  • mediumtopics#2
    Add more specific topics to highlight its research and XLA integration

    Why:

    CURRENT
    alpa, auto-parallelization, compiler, deep-learning, distributed-computing, distributed-training, high-performance-computing, jax, llm, machine-learning
    COPY-PASTE FIX
    alpa, auto-parallelization, compiler, deep-learning, distributed-computing, distributed-training, high-performance-computing, jax, llm, machine-learning, research-artifact, xla-integration, compiler-optimization, distributed-ml
  • lowreadme#3
    Refine the README's opening sentence to reinforce its historical context

    Why:

    CURRENT
    Alpa is a system for training and serving large-scale neural networks.
    COPY-PASTE FIX
    Alpa was a pioneering system for training and serving large-scale neural networks, demonstrating automatic parallelization techniques now integrated into XLA.

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 alpa-projects/alpa
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 2×
  2. Hugging Face Accelerate · recommended 2×
  3. PyTorch Distributed (torch.distributed) · recommended 1×
  4. TensorFlow Distributed Strategy API · recommended 1×
  5. Horovod · recommended 1×
  • CATEGORY QUERY
    How to automatically parallelize deep learning models across a distributed cluster?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Distributed (torch.distributed)
    2. TensorFlow Distributed Strategy API
    3. Horovod
    4. DeepSpeed
    5. Ray Train
    6. Hugging Face Accelerate
    7. PaddlePaddle Fleet API

    AI recommended 7 alternatives but never named alpa-projects/alpa. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help train very large language models efficiently with automatic scaling?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud TPUs
    2. AWS SageMaker Distributed Training
    3. Microsoft Azure Machine Learning
    4. Hugging Face Accelerate
    5. PyTorch FSDP
    6. DeepSpeed

    AI recommended 6 alternatives but never named alpa-projects/alpa. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 alpa-projects/alpa?
    pass
    AI named alpa-projects/alpa explicitly

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

  • If a team adopts alpa-projects/alpa in production, what risks or prerequisites should they evaluate first?
    pass
    AI named alpa-projects/alpa 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 alpa-projects/alpa solve, and who is the primary audience?
    pass
    AI named alpa-projects/alpa explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite