RRepoGEO

REPOGEO REPORT · LITE

kingoflolz/mesh-transformer-jax

Default branch master · commit f8315e30 · scanned 5/22/2026, 7:48:47 PM

GitHub: 6,370 stars · 883 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
28 /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
2 / 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 kingoflolz/mesh-transformer-jax, 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 specific topics to the repository

    Why:

    COPY-PASTE FIX
    jax, haiku, transformer, model-parallelism, tpu, deep-learning, large-language-models, llm, distributed-training
  • highreadme#2
    Reposition the README's first sentence to highlight core differentiator

    Why:

    CURRENT
    A haiku library using the `xmap`/`pjit` operators in JAX for model parallelism of transformers.
    COPY-PASTE FIX
    Mesh Transformer JAX is a Haiku library providing a JAX-native implementation for highly scalable model parallelism of large transformer networks, specifically optimized for efficient distributed training on Google TPUs using `xmap`/`pjit` operators.
  • mediumhomepage#3
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    Add a link to a project homepage, documentation, or a relevant blog post that provides more context or a live demo.

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 kingoflolz/mesh-transformer-jax
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
JAX's pjit
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. JAX's pjit · recommended 1×
  2. Flax · recommended 1×
  3. Hugging Face Accelerate · recommended 1×
  4. Google's Praxis · recommended 1×
  5. Manually implementing JAX communication primitives · recommended 1×
  • CATEGORY QUERY
    How can I implement model parallelism for large transformer networks using JAX?
    you: not recommended
    AI recommended (in order):
    1. JAX's pjit
    2. Flax
    3. Hugging Face Accelerate
    4. Google's Praxis
    5. Manually implementing JAX communication primitives

    AI recommended 5 alternatives but never named kingoflolz/mesh-transformer-jax. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What libraries help scale transformer models efficiently on TPUs with model sharding?
    you: not recommended
    AI recommended (in order):
    1. JAX (google/jax)
    2. Flax (google/flax)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. PyTorch/XLA (pytorch/xla)
    6. torch.distributed.tensor.parallel
    7. TensorFlow (tensorflow/tensorflow)
    8. tf.experimental.dtensor
    9. Mesh TensorFlow (tensorflow/mesh)

    AI recommended 9 alternatives but never named kingoflolz/mesh-transformer-jax. 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 kingoflolz/mesh-transformer-jax?
    pass
    AI did not name kingoflolz/mesh-transformer-jax — 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 kingoflolz/mesh-transformer-jax in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kingoflolz/mesh-transformer-jax 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 kingoflolz/mesh-transformer-jax solve, and who is the primary audience?
    pass
    AI named kingoflolz/mesh-transformer-jax explicitly

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

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kingoflolz/mesh-transformer-jax — 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