RRepoGEO

REPOGEO REPORT · LITE

pyro-ppl/numpyro

Default branch master · commit 662efd57 · scanned 6/25/2026, 9:27:11 AM

GitHub: 2,708 stars · 288 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
93 /100
Healthy
Category recall
2 / 2
Avg rank #1.0 when recommended
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 pyro-ppl/numpyro, 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
    Emphasize JAX as the core differentiator in the README's opening

    Why:

    CURRENT
    # NumPyro

Probabilistic programming powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
    COPY-PASTE FIX
    # NumPyro

NumPyro: a lightweight probabilistic programming library built exclusively on JAX for high-performance autograd and JIT compilation to GPU/TPU/CPU.
  • mediumtopics#2
    Add 'machine-learning' topic

    Why:

    CURRENT
    bayesian-inference, hmc, inference-algorithms, jax, mcmc, numpy, probabilistic-programming, pyro
    COPY-PASTE FIX
    bayesian-inference, hmc, inference-algorithms, jax, machine-learning, mcmc, numpy, probabilistic-programming, pyro
  • lowabout#3
    Refine the repository description

    Why:

    CURRENT
    Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
    COPY-PASTE FIX
    Lightweight probabilistic programming library for Bayesian inference, leveraging JAX for high-performance autograd and JIT compilation to GPU/TPU/CPU.

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
2 / 2
100% of queries surface pyro-ppl/numpyro
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
17%
Of all named tools, what % are you?
Top rival
BlackJAX
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. BlackJAX · recommended 2×
  2. PyMC · recommended 2×
  3. TensorFlow Probability (TFP) · recommended 1×
  4. Stan · recommended 1×
  5. CmdStanPy · recommended 1×
  • CATEGORY QUERY
    Looking for a probabilistic programming library that integrates well with JAX and NumPy.
    you: #1
    AI recommended (in order):
    1. NumPyro ← you
    2. BlackJAX
    3. PyMC
    4. TensorFlow Probability (TFP)
    5. Stan
    6. CmdStanPy
    7. PyStan
    Show full AI answer
  • CATEGORY QUERY
    Need a tool for efficient MCMC and Bayesian inference leveraging JAX for acceleration.
    you: #1
    AI recommended (in order):
    1. NumPyro ← you
    2. BlackJAX
    3. PyMC
    4. TensorFlow Probability
    5. JAX
    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 pyro-ppl/numpyro?
    pass
    AI named pyro-ppl/numpyro explicitly

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

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

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

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pyro-ppl/numpyro — 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