RRepoGEO

REPOGEO REPORT · LITE

callous-youth/BOAT

Default branch main · commit b950cbce · scanned 5/12/2026, 3:38:36 PM

GitHub: 1,061 stars · 148 forks

AI VISIBILITY SCORE
35 /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
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 callous-youth/BOAT, 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
    machine-learning, deep-learning, optimization, bi-level-optimization, pytorch, gradient-based, compositional-framework, solver-variants, research
  • highreadme#2
    Populate the README H1 with the project's full name and purpose

    Why:

    CURRENT
    <h1 align="center">
  
</h1>
    COPY-PASTE FIX
    <h1 align="center">BOAT: A Compositional Operation Toolbox for Gradient-based Bi-Level Optimization</h1>
  • mediumreadme#3
    Add a dedicated 'Why BOAT?' section to highlight its unique value proposition

    Why:

    COPY-PASTE FIX
    ## Why BOAT?

Unlike existing bi-level optimization libraries that typically encapsulate fixed solver routines, BOAT offers a fundamentally different approach. It factorizes the BLO workflow into **atomic, reusable primitives**, empowering researchers to **automatically compose** over **85+ solver variants** from a compact set of just **17 gradient operations**. This unique compositional framework provides unparalleled flexibility, accelerates research, and bridges the gap between theoretical modeling and practical implementation in gradient-based Bi-Level Optimization.

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 callous-youth/BOAT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Higher
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Higher · recommended 2×
  2. TensorFlow · recommended 2×
  3. JAX · recommended 2×
  4. PyTorch · recommended 1×
  5. Keras · recommended 1×
  • CATEGORY QUERY
    How to implement bi-level optimization with reusable gradient-based operations?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Higher
    3. TensorFlow
    4. Keras
    5. TensorFlow-Probability
    6. JAX
    7. Optax
    8. SciPy.optimize
    9. SymPy
    10. CasADi

    AI recommended 10 alternatives but never named callous-youth/BOAT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a flexible framework for compositional bi-level optimization beyond fixed solver routines.
    you: not recommended
    AI recommended (in order):
    1. PyTorch-Lightning
    2. JAX
    3. Higher
    4. MetaOpt
    5. TensorFlow

    AI recommended 5 alternatives but never named callous-youth/BOAT. 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 callous-youth/BOAT?
    pass
    AI named callous-youth/BOAT explicitly

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

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

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

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callous-youth/BOAT — 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