RRepoGEO

REPOGEO REPORT · LITE

GradientHQ/parallax

Default branch main · commit 564f96f9 · scanned 6/19/2026, 10:22:03 AM

GitHub: 1,317 stars · 139 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
35 /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
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 GradientHQ/parallax, 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 core value proposition to the top of README

    Why:

    CURRENT
    The current README starts with badges and news, delaying the core value proposition.
    COPY-PASTE FIX
    Move the core 'About' statement to the very top of the README, immediately after the main title/logo, using a concise sentence like: 'Parallax is a fully decentralized inference engine and distributed model serving framework that enables building AI clusters for LLM inference across heterogeneous, geographically dispersed nodes.'
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add the official product or project homepage URL (e.g., `https://gradient.ai/parallax`) to the repository's 'About' section.
  • mediumtopics#3
    Expand repository topics to include broader inference and deployment terms

    Why:

    CURRENT
    blackwell, chatbot, decentralized-inference, deepseek, distributed-systems, glm, kimi, large-language-models, llama, llm, llm-serving, minimax, oss-gpt, python, pytorch, qwen, transformer
    COPY-PASTE FIX
    Add `model-inference`, `llm-deployment`, `distributed-ai`, `ai-cluster`, `gpu-orchestration` to the existing topics.

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 GradientHQ/parallax
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ray Serve
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Ray Serve · recommended 2×
  2. OpenVINO · recommended 2×
  3. NVIDIA Triton Inference Server · recommended 1×
  4. TensorRT-LLM · recommended 1×
  5. vLLM · recommended 1×
  • CATEGORY QUERY
    How can I set up a distributed inference engine for large language models across multiple nodes?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server
    2. TensorRT-LLM
    3. vLLM
    4. Hugging Face TGI
    5. Ray Serve
    6. DeepSpeed-MII
    7. OpenVINO
    8. ONNX Runtime
    9. PyTorch
    10. JAX

    AI recommended 10 alternatives but never named GradientHQ/parallax. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help deploy LLM inference across geographically dispersed and heterogeneous compute resources?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes
    2. KubeFed
    3. Anthos
    4. Azure Arc
    5. Rancher
    6. OpenShift
    7. Ray
    8. Ray Serve
    9. Triton Inference Server
    10. OpenVINO
    11. AWS Lambda
    12. EFS
    13. S3
    14. Google Cloud Functions
    15. Cloud Storage
    16. Azure Functions
    17. Azure Files
    18. FastAPI
    19. Uvicorn
    20. Flask
    21. Gunicorn

    AI recommended 21 alternatives but never named GradientHQ/parallax. 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 GradientHQ/parallax?
    pass
    AI named GradientHQ/parallax explicitly

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

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

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

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GradientHQ/parallax — 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