RRepoGEO

REPOGEO REPORT · LITE

mikeroyal/Neuromorphic-Computing-Guide

Default branch main · commit 7afca2ff · scanned 6/6/2026, 4:43:00 PM

GitHub: 575 stars · 80 forks

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 mikeroyal/Neuromorphic-Computing-Guide, 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
  • highreadme#1
    Clarify README opening to emphasize its role as a curated learning guide

    Why:

    CURRENT
    A guide covering Neuromorphic Computing including the applications, libraries and tools that will make you better and more efficient with Neuromorphic Computing development.
    COPY-PASTE FIX
    This is a comprehensive, curated guide and resource collection for Neuromorphic Computing. It covers essential applications, key libraries, and development tools, designed to help you learn and become more efficient in neuromorphic engineering. This repository serves as a central learning hub, distinct from specific software libraries or hardware frameworks.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root. For example, if the content is intended to be freely shared and modified, consider a permissive license like MIT or Apache-2.0, and add the appropriate text.
  • mediumtopics#3
    Refine repository topics for greater precision and focus

    Why:

    CURRENT
    artificial-neural-networks, brain-imaging, neocortex, neural-engine, neural-engineering, neural-machine-translation, neural-network, neural-networks, neural-processes, neural-radiance-fields, neural-rendering, neural-simulators, neuroimaging, neuromorphic, neuromorphic-computing, neuromorphic-engineering, neuromorphic-hardware, neuron-models, neuronal-network, neuroscience
    COPY-PASTE FIX
    brain-imaging, neocortex, neural-engine, neural-engineering, neural-processes, neural-simulators, neuroimaging, neuromorphic, neuromorphic-computing, neuromorphic-engineering, neuromorphic-hardware, neuron-models, neuronal-network, neuroscience

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 mikeroyal/Neuromorphic-Computing-Guide
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SpiNNaker
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. SpiNNaker · recommended 2×
  2. IBM TrueNorth · recommended 2×
  3. NeuralEnsemble/PyNN · recommended 1×
  4. Intel Loihi · recommended 1×
  5. lava-nc/lava · recommended 1×
  • CATEGORY QUERY
    What are the best tools and libraries for neuromorphic computing system design?
    you: not recommended
    AI recommended (in order):
    1. SpiNNaker
    2. PyNN (NeuralEnsemble/PyNN)
    3. Intel Loihi
    4. Lava (lava-nc/lava)
    5. IBM TrueNorth
    6. Corelet Programming Language (CPL)
    7. Nengo (nengo/nengo)
    8. NengoLoihi (nengo/nengo-loihi)
    9. Brian2 (brian-team/brian2)
    10. NEST (nest/nest-simulator)

    AI recommended 10 alternatives but never named mikeroyal/Neuromorphic-Computing-Guide. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find resources to learn neuromorphic engineering and build bio-inspired AI hardware?
    you: not recommended
    AI recommended (in order):
    1. Intel Loihi/Loihi 2
    2. Intel Neuromorphic Research Community (INRC)
    3. SpiNNaker
    4. Human Brain Project (HBP)
    5. IBM TrueNorth
    6. IBM Neuromorphic Computing Toolkit
    7. Prophesee Metavision Sensors
    8. SynSense DYNAP-SE/DYNAP-AI
    9. BrainChip Akida
    10. NEST
    11. Brian2
    12. Nengo

    AI recommended 12 alternatives but never named mikeroyal/Neuromorphic-Computing-Guide. 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 mikeroyal/Neuromorphic-Computing-Guide?
    pass
    AI named mikeroyal/Neuromorphic-Computing-Guide explicitly

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

  • If a team adopts mikeroyal/Neuromorphic-Computing-Guide in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mikeroyal/Neuromorphic-Computing-Guide 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 mikeroyal/Neuromorphic-Computing-Guide solve, and who is the primary audience?
    pass
    AI did not name mikeroyal/Neuromorphic-Computing-Guide — 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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  • Brand-free category queries5 vs 2 in Lite
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