RRepoGEO

REPOGEO REPORT · LITE

google/tunix

Default branch main · commit 43f9eaad · scanned 5/8/2026, 4:28:01 PM

GitHub: 2,259 stars · 284 forks

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 google/tunix, 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

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

OVERALL DIRECTION
  • highreadme#1
    Explicitly clarify project identity in the README's opening

    Why:

    CURRENT
    **Tunix (Tune-in-JAX)** is a JAX based library designed to streamline the post-training of Large Language Models. It provides efficient and scalable support for:
    COPY-PASTE FIX
    **Tunix (Tune-in-JAX)** is a JAX-based library for Large Language Model (LLM) post-training. **It is not an operating system or a Unix-like environment.** It provides efficient and scalable support for:
  • mediumhomepage#2
    Add the project homepage URL

    Why:

    COPY-PASTE FIX
    https://tunix.readthedocs.io/en/latest/index.html

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 google/tunix
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. Flax · recommended 1×
  3. JAX · recommended 1×
  4. Optax · recommended 1×
  5. google/brax · recommended 1×
  • CATEGORY QUERY
    What are the best JAX-based libraries for efficient post-training and fine-tuning of large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Flax
    3. JAX
    4. Optax

    AI recommended 4 alternatives but never named google/tunix. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I perform scalable reinforcement learning for LLMs on TPUs using a JAX framework?
    you: not recommended
    AI recommended (in order):
    1. Brax (google/brax)
    2. JAX (google/jax)
    3. Flax (google/flax)
    4. Hugging Face Transformers (huggingface/transformers)
    5. RLax (deepmind/rlax)
    6. Acme (deepmind/acme)
    7. Orbax (google/orbax)
    8. TensorFlow Agents (TF-Agents) (tensorflow/agents)

    AI recommended 8 alternatives but never named google/tunix. 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 google/tunix?
    pass
    AI named google/tunix explicitly

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

  • If a team adopts google/tunix in production, what risks or prerequisites should they evaluate first?
    pass
    AI named google/tunix 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 google/tunix solve, and who is the primary audience?
    pass
    AI did not name google/tunix — 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?

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google/tunix — 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