RRepoGEO

REPOGEO REPORT · LITE

NVIDIA-AI-IOT/jetracer

Default branch master · commit b194ffd3 · scanned 5/27/2026, 12:47:19 AM

GitHub: 1,181 stars · 339 forks

AI VISIBILITY SCORE
47 /100
Critical
Category recall
1 / 2
Avg rank #6.0 when recommended
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 NVIDIA-AI-IOT/jetracer, 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 specific topics for better categorization

    Why:

    COPY-PASTE FIX
    ["jetson-nano", "ai-robotics", "autonomous-vehicles", "racecar", "embedded-ai", "deep-learning", "edge-ai", "jupyter-notebook"]
  • highreadme#2
    Reposition README opening to emphasize embedded, high-speed AI robotics

    Why:

    CURRENT
    JetRacer is an autonomous AI racecar using NVIDIA Jetson Nano. With JetRacer you will

* Go fast - Optimize for high framerates to move at high speeds

* Have fun - Follow examples and program interactively from your web browser

By building and experimenting with JetRacer you will create fast AI pipelines and push the boundaries of speed.
    COPY-PASTE FIX
    JetRacer is an open-source platform for building and programming an autonomous AI racecar using the NVIDIA Jetson Nano. It focuses on implementing high-speed AI for autonomous robotics on embedded hardware, enabling developers to create fast AI pipelines and push the boundaries of speed in miniature vehicles.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/NVIDIA-AI-IOT/jetracer

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
1 / 2
50% of queries surface NVIDIA-AI-IOT/jetracer
Avg rank
#6.0
Lower is better. #1 = top recommendation.
Share of voice
4%
Of all named tools, what % are you?
Top rival
autorope/donkeycar
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. autorope/donkeycar · recommended 1×
  2. ros/ros · recommended 1×
  3. tensorflow/tensorflow · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. NVIDIA-AI-IOT/jetbot · recommended 1×
  • CATEGORY QUERY
    What frameworks exist for building AI-powered miniature autonomous race cars?
    you: #6
    AI recommended (in order):
    1. DonkeyCar (autorope/donkeycar)
    2. ROS (ros/ros)
    3. TensorFlow (tensorflow/tensorflow)
    4. PyTorch (pytorch/pytorch)
    5. NVIDIA JetBot (NVIDIA-AI-IOT/jetbot)
    6. JetRacer (NVIDIA-AI-IOT/jetracer) ← you
    7. NVIDIA JetPack SDK
    8. OpenCV AI Kit (OAK-D)
    9. DepthAI (luxonis/depthai)
    10. F1TENTH (f1tenth/f1tenth_ws)
    11. Duckietown (duckietown/duckietown-install)
    12. Duckiebot OS
    Show full AI answer
  • CATEGORY QUERY
    How to implement high-speed AI for autonomous robotics using embedded hardware?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Jetson Platform
    2. Intel Movidius Myriad X VPU
    3. Intel OpenVINO
    4. Google Coral Edge TPU
    5. Qualcomm Robotics RB5 Platform
    6. AMD Versal AI Edge Series
    7. Raspberry Pi 5
    8. Hailo-8 AI Accelerator
    9. Xilinx Vivado
    10. Xilinx Vitis
    11. Intel Quartus Prime

    AI recommended 11 alternatives but never named NVIDIA-AI-IOT/jetracer. 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 NVIDIA-AI-IOT/jetracer?
    pass
    AI did not name NVIDIA-AI-IOT/jetracer — 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 NVIDIA-AI-IOT/jetracer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVIDIA-AI-IOT/jetracer 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 NVIDIA-AI-IOT/jetracer solve, and who is the primary audience?
    pass
    AI named NVIDIA-AI-IOT/jetracer explicitly

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

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NVIDIA-AI-IOT/jetracer — 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