RRepoGEO

REPOGEO REPORT · LITE

NervanaSystems/neon

Default branch master · commit 8c3fb8a9 · scanned 6/30/2026, 7:17:09 PM

GitHub: 3,866 stars · 805 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
  • highreadme#1
    Reposition README's main description to reflect archived status

    Why:

    CURRENT
    neon is Intel's reference deep learning framework committed to best performance on all hardware.
    COPY-PASTE FIX
    neon *was* Intel's reference deep learning framework, known for its commitment to best performance on all hardware, and now serves as a historical reference for deep learning research.
  • mediumtopics#2
    Add topics reflecting archived and historical status

    Why:

    CURRENT
    deep-learning, fast, mkl, neon, neural-network, performance, python
    COPY-PASTE FIX
    deep-learning, fast, mkl, neon, neural-network, performance, python, archived, historical-framework, deep-learning-history
  • lowabout#3
    Update 'About' description to reflect archived status

    Why:

    CURRENT
    Intel® Nervana™ reference deep learning framework committed to best performance on all hardware
    COPY-PASTE FIX
    Intel® Nervana™ reference deep learning framework, now archived, that was committed to best performance on all hardware.

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 2×
  5. ONNX Runtime · recommended 1×
  • CATEGORY QUERY
    What deep learning framework offers top performance across different hardware platforms?
    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 neural networks with good performance and common layers.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. JAX
    4. Flax
    5. Haiku
    6. Keras
    7. MXNet

    AI recommended 7 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?

Embed your GEO score

Drop this badge into the README of NervanaSystems/neon. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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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