RRepoGEO

REPOGEO REPORT · LITE

NervanaSystems/neon

Default branch master · commit 8c3fb8a9 · scanned 5/19/2026, 10:31:59 AM

GitHub: 3,866 stars · 807 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 NervanaSystems/neon, 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
  • highabout#1
    Update the repository description to reflect its archived status

    Why:

    CURRENT
    Intel® Nervana™ reference deep learning framework committed to best performance on all hardware
    COPY-PASTE FIX
    ARCHIVED: Intel® Nervana™ reference deep learning framework, historically known for its high performance on various hardware. This project is no longer maintained.
  • mediumreadme#2
    Add a 'Historical Significance and Legacy' section to the README

    Why:

    COPY-PASTE FIX
    ## Historical Significance and Legacy
Neon was a pioneering deep learning framework from Intel Nervana, recognized for its exceptional performance and efficiency during its active development period. It played a significant role in advancing deep learning research and hardware integration, particularly with Nervana's specialized AI hardware. While no longer maintained, this repository serves as a valuable historical reference for the evolution of deep learning frameworks.
  • lowreadme#3
    Add a note about the historical context of performance benchmarks in the README

    Why:

    CURRENT
    For fast iteration and model exploration, neon has the fastest performance among deep learning libraries (2x speed of cuDNNv4, see benchmarks).
    COPY-PASTE FIX
    During its active development, neon was recognized for its exceptional performance among deep learning libraries (e.g., 2x speed of cuDNNv4, see historical benchmarks). These benchmarks reflect its capabilities at the time of its development.

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 NervanaSystems/neon
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. TensorFlow · recommended 2×
  3. JAX · recommended 2×
  4. MXNet · recommended 1×
  5. ONNX Runtime · recommended 1×
  • CATEGORY QUERY
    What deep learning framework offers top performance across various CPU and GPU hardware?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. JAX
    4. MXNet
    5. ONNX Runtime

    AI recommended 5 alternatives but never named NervanaSystems/neon. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python deep learning library for fast iteration and easy model exploration.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Keras
    3. TensorFlow
    4. Fastai
    5. JAX

    AI recommended 5 alternatives but never named NervanaSystems/neon. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 NervanaSystems/neon?
    pass
    AI named NervanaSystems/neon explicitly

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

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

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

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MARKDOWN (README)
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NervanaSystems/neon — 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