RRepoGEO

REPOGEO REPORT · LITE

espressif/esp-dl

Default branch master · commit f027c9d8 · scanned 5/23/2026, 11:46:47 AM

GitHub: 1,023 stars · 202 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
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 espressif/esp-dl, 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
    esp32, esp-dl, deep-learning, neural-network-inference, edge-ai, iot, embedded, microcontroller, aiot, quantization
  • highreadme#2
    Strengthen README's opening sentence to emphasize embedded/IoT focus

    Why:

    CURRENT
    ESP-DL is a lightweight and efficient neural network inference framework designed specifically for ESP series chips.
    COPY-PASTE FIX
    ESP-DL is a lightweight and efficient neural network inference framework for deploying deep learning models on Espressif ESP series chips, specifically designed for embedded AIoT applications.
  • mediumhomepage#3
    Add the official documentation link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://docs.espressif.com/projects/esp-dl/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 espressif/esp-dl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow Lite
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Lite · recommended 2×
  2. PyTorch Mobile · recommended 2×
  3. ONNX Runtime · recommended 2×
  4. OpenVINO Toolkit · recommended 2×
  5. NVIDIA TensorRT · recommended 1×
  • CATEGORY QUERY
    How to deploy and run deep learning models efficiently on embedded IoT devices?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. ONNX Runtime
    4. OpenVINO Toolkit
    5. NVIDIA TensorRT
    6. Edge Impulse
    7. Arm NN

    AI recommended 7 alternatives but never named espressif/esp-dl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable neural network inference and model quantization for edge AI applications?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. LibTorch
    4. ONNX Runtime
    5. OpenVINO Toolkit
    6. Apache TVM (apache/tvm)
    7. Core ML

    AI recommended 7 alternatives but never named espressif/esp-dl. 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 espressif/esp-dl?
    pass
    AI named espressif/esp-dl explicitly

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

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

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

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espressif/esp-dl — 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