RRepoGEO

REPOGEO REPORT · LITE

breandan/kotlingrad

Default branch master · commit 7fff271c · scanned 6/12/2026, 7:31:58 PM

GitHub: 546 stars · 23 forks

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 breandan/kotlingrad, 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
  • highreadme#1
    Emphasize JVM and compile-time shape safety in README's opening sentence

    Why:

    CURRENT
    Kotlin∇ is a type-safe automatic differentiation framework written in Kotlin. It allows users to express differentiable programs with higher-dimensional data structures and operators. We attempt to restrict syntactically valid constructions to those which are algebraically valid and can be checked at compile-time.
    COPY-PASTE FIX
    Kotlin∇ is a type-safe automatic differentiation framework for the JVM, written in Kotlin. It uniquely enforces compile-time shape safety and algebraic validity, eliminating common runtime errors in differentiable programs.
  • mediumcomparison#2
    Add a 'Comparison with Alternatives' section to README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

Unlike general-purpose machine learning frameworks such as TensorFlow, PyTorch, or Deeplearning4j, Kotlin∇ focuses specifically on providing a type-safe, compile-time shape-checked automatic differentiation library for the JVM. While other Kotlin wrappers exist (e.g., Keras-Kotlin), Kotlin∇ offers native, deep integration of shape-safety directly into the type system, preventing a class of runtime errors common in dynamic frameworks.
  • lowtopics#3
    Add 'jvm' to repository topics

    Why:

    CURRENT
    ["algebraic-data-types", "array-programming", "automatic-differentiation", "chinese", "computer-algebra", "differentiable-programming", "gradient-descent", "kotlin", "linear-algebra", "message-passing", "multi-stage-programming", "optimization", "shape-safety", "symbolic-differentiation", "types"]
    COPY-PASTE FIX
    ["algebraic-data-types", "array-programming", "automatic-differentiation", "chinese", "computer-algebra", "differentiable-programming", "gradient-descent", "kotlin", "linear-algebra", "message-passing", "multi-stage-programming", "optimization", "shape-safety", "symbolic-differentiation", "types", "jvm"]

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 breandan/kotlingrad
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow · recommended 2×
  2. Deeplearning4j · recommended 1×
  3. nd4j · recommended 1×
  4. Keras · recommended 1×
  5. Keras-Kotlin · recommended 1×
  • CATEGORY QUERY
    What are the best automatic differentiation frameworks available for Kotlin on the JVM?
    you: not recommended
    AI recommended (in order):
    1. Deeplearning4j
    2. nd4j
    3. Keras
    4. Keras-Kotlin
    5. TensorFlow
    6. KotlinDL
    7. TensorFlow
    8. MXNet

    AI recommended 8 alternatives but never named breandan/kotlingrad. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to achieve compile-time shape safety in differentiable programs to avoid runtime errors?
    you: not recommended
    AI recommended (in order):
    1. JAX
    2. Equinox
    3. Type-Safe JAX
    4. PyTorch
    5. TorchScript
    6. MyPy
    7. TensorFlow.js
    8. TypeScript
    9. tfjs-ts-types
    10. Julia
    11. StaticArrays.jl
    12. Flux.jl
    13. TensorFlow.jl
    14. Rust
    15. ndarray
    16. tch-rs
    17. LibTorch
    18. Scala
    19. Shapeless
    20. Spire
    21. ND4S
    22. ND4J

    AI recommended 22 alternatives but never named breandan/kotlingrad. 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 breandan/kotlingrad?
    pass
    AI named breandan/kotlingrad explicitly

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

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

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

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breandan/kotlingrad — 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