RRepoGEO

REPOGEO REPORT · LITE

BBuf/tvm_mlir_learn

Default branch main · commit c13ec449 · scanned 6/22/2026, 3:33:14 PM

GitHub: 2,749 stars · 370 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
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 BBuf/tvm_mlir_learn, 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 improve categorization

    Why:

    COPY-PASTE FIX
    deep-learning-compiler, tvm, mlir, llvm, torchscript, relay, code-generation, scheduling, compiler-optimization, learning-resources, examples
  • highlicense#2
    Add a LICENSE file to clarify usage terms

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root to explicitly state the terms of use.
  • mediumabout#3
    Update the repository description for clarity and keywords

    Why:

    CURRENT
    compiler learning resources collect.
    COPY-PASTE FIX
    Learning notes, experiments, and examples for deep learning compilers, focusing on TVM, MLIR, LLVM, and related code generation and optimization techniques.

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 BBuf/tvm_mlir_learn
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Apache TVM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Apache TVM · recommended 1×
  2. apache/tvm · recommended 1×
  3. MLIR · recommended 1×
  4. halide/Halide · recommended 1×
  5. TensorFlow XLA · recommended 1×
  • CATEGORY QUERY
    How can I learn deep learning compiler concepts and optimize AI models?
    you: not recommended
    AI recommended (in order):
    1. Apache TVM

    AI recommended 1 alternative but never named BBuf/tvm_mlir_learn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources exist for understanding code generation and scheduling in AI compilers?
    you: not recommended
    AI recommended (in order):
    1. Apache TVM (apache/tvm)
    2. MLIR
    3. Halide (halide/Halide)
    4. TensorFlow XLA
    5. IREE (openxla/iree)
    6. LLVM (llvm/llvm-project)

    AI recommended 6 alternatives but never named BBuf/tvm_mlir_learn. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 BBuf/tvm_mlir_learn?
    pass
    AI named BBuf/tvm_mlir_learn explicitly

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

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