RRepoGEO

REPOGEO REPORT · LITE

sipeed/TinyMaix

Default branch main · commit 0532eceb · scanned 6/27/2026, 12:37:29 PM

GitHub: 1,060 stars · 169 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)

3 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 sipeed/TinyMaix, 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
    ["tinyml", "microcontrollers", "embedded-ml", "neural-network-inference", "deep-learning", "edge-ai", "machine-learning-library", "c-library", "arm-mvei", "riscv-vector"]
  • highreadme#2
    Strengthen the README's opening statement to highlight core differentiators

    Why:

    CURRENT
    # TinyMaix

[中文](README_ZH.md) | English

TinyMaix is a tiny inference Neural Network library specifically for microcontrollers (TinyML).
    COPY-PASTE FIX
    # TinyMaix: The Ultra-Lightweight Neural Network Inference Library for Microcontrollers

[中文](README_ZH.md) | English

TinyMaix is an extremely minimalist and zero-dependency neural network inference library, specifically designed for resource-constrained microcontrollers (TinyML). It enables efficient on-device AI with a core code footprint under 400 lines, making it exceptionally lightweight and easy to integrate into bare-metal embedded systems.
  • mediumhomepage#3
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    (Provide the official project homepage URL, e.g., a dedicated project page or the organization's main site if TinyMaix has a prominent section there.)

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 sipeed/TinyMaix
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MicroTVM
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MicroTVM · recommended 2×
  2. NVIDIA TensorRT · recommended 2×
  3. TensorFlow Lite Micro · recommended 1×
  4. Edge Impulse · recommended 1×
  5. CMSIS-NN · recommended 1×
  • CATEGORY QUERY
    How to run neural network inference on extremely small microcontrollers with limited memory?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite Micro
    2. Edge Impulse
    3. MicroTVM
    4. CMSIS-NN
    5. NVIDIA TensorRT
    6. Pytorch Mobile

    AI recommended 6 alternatives but never named sipeed/TinyMaix. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the most memory-efficient deep learning inference libraries for embedded systems?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. ONNX Runtime
    3. Arm NN
    4. NVIDIA TensorRT
    5. Edge Impulse EON Compiler
    6. Apache TVM
    7. MicroTVM

    AI recommended 7 alternatives but never named sipeed/TinyMaix. 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 sipeed/TinyMaix?
    pass
    AI did not name sipeed/TinyMaix — 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 sipeed/TinyMaix in production, what risks or prerequisites should they evaluate first?
    pass
    AI named sipeed/TinyMaix 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 sipeed/TinyMaix solve, and who is the primary audience?
    pass
    AI named sipeed/TinyMaix explicitly

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

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sipeed/TinyMaix — 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