RRepoGEO

REPOGEO REPORT · LITE

DaveBben/esp32-llm

Default branch main · commit 934dab4d · scanned 6/8/2026, 8:13:19 AM

GitHub: 539 stars · 57 forks

AI VISIBILITY SCORE
22 /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
1 / 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 DaveBben/esp32-llm, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0) to clarify usage rights.
  • mediumreadme#2
    Clarify the unique "LLM on ESP32" value proposition in the README summary

    Why:

    CURRENT
    # Running a LLM on the ESP32

## Summary
I wanted to see if it was possible to run a Large Language Model (LLM) on the ESP32. Surprisingly it is possible, though probably not very useful.
    COPY-PASTE FIX
    # Running a LLM on the ESP32

## Summary
This project demonstrates the surprising feasibility of running a Large Language Model (LLM) directly on a resource-constrained ESP32 microcontroller. While perhaps not yet practical for production, it pushes the boundaries of edge AI by optimizing a tinyllamas checkpoint for the ESP32-S3, achieving impressive token generation speeds.

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 DaveBben/esp32-llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Edge Impulse
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Edge Impulse · recommended 2×
  2. tensorflow/tensorflow · recommended 1×
  3. espressif/esp-dl · recommended 1×
  4. apache/tvm · recommended 1×
  5. micropython/micropython · recommended 1×
  • CATEGORY QUERY
    How to run a small language model on an embedded microcontroller like ESP32?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite for Microcontrollers (tensorflow/tensorflow)
    2. esp-dl (espressif/esp-dl)
    3. MicroTVM (apache/tvm)
    4. Edge Impulse
    5. MicroPython (micropython/micropython)
    6. ulab (vbitz/ulab)

    AI recommended 6 alternatives but never named DaveBben/esp32-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking solutions for deploying edge AI models on resource-constrained ESP32 devices.
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite for Microcontrollers
    2. MicroPython
    3. uTensor
    4. Edge Impulse
    5. ESP-DL
    6. Pytorch Mobile
    7. ONNX Runtime for Microcontrollers
    8. CMSIS-NN

    AI recommended 8 alternatives but never named DaveBben/esp32-llm. 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 DaveBben/esp32-llm?
    pass
    AI did not name DaveBben/esp32-llm — 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 DaveBben/esp32-llm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DaveBben/esp32-llm 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 DaveBben/esp32-llm solve, and who is the primary audience?
    pass
    AI did not name DaveBben/esp32-llm — 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?

Embed your GEO score

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DaveBben/esp32-llm — 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