RRepoGEO

REPOGEO REPORT · LITE

higgsfield-ai/higgsfield

Default branch main · commit d12a36e6 · scanned 5/17/2026, 10:51:56 PM

GitHub: 3,709 stars · 621 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 higgsfield-ai/higgsfield, 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 PyTorch foundation in README's opening sentence

    Why:

    CURRENT
    Higgsfield is an open-source, fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters, such as Large Language Models (LLMs).
    COPY-PASTE FIX
    Higgsfield is an open-source, fault-tolerant, highly scalable GPU orchestration and a machine learning framework built specifically for PyTorch, designed for training models with billions to trillions of parameters, such as Large Language Models (LLMs).
  • highhomepage#2
    Add the official project homepage

    Why:

    COPY-PASTE FIX
    Add the official project homepage URL to the repository's 'About' section.
  • mediumtopics#3
    Expand topics to include more specific distributed training terms

    Why:

    CURRENT
    cluster-management, deep-learning, distributed, llama, llama2, llm, machine-learning, mlops, pytorch
    COPY-PASTE FIX
    cluster-management, deep-learning, distributed, distributed-training, fault-tolerance, gpu-orchestration, large-language-models, llama, llama2, llm, machine-learning, mlops, pytorch, pytorch-distributed

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 higgsfield-ai/higgsfield
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 2×
  2. Megatron-LM · recommended 1×
  3. FairScale · recommended 1×
  4. Colossal-AI · recommended 1×
  5. PyTorch FSDP · recommended 1×
  • CATEGORY QUERY
    How can I efficiently train trillion-parameter language models across a distributed GPU cluster?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. Megatron-LM
    3. FairScale
    4. Colossal-AI
    5. PyTorch FSDP
    6. TensorFlow Mesh
    7. JAX

    AI recommended 7 alternatives but never named higgsfield-ai/higgsfield. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source solutions manage GPU resources and provide fault-tolerant distributed deep learning?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow
    2. Ray
    3. PyTorch Lightning
    4. Horovod
    5. DeepSpeed
    6. Slurm Workload Manager
    7. Open MPI

    AI recommended 7 alternatives but never named higgsfield-ai/higgsfield. 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 higgsfield-ai/higgsfield?
    pass
    AI named higgsfield-ai/higgsfield explicitly

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

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

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

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higgsfield-ai/higgsfield — 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
higgsfield-ai/higgsfield — RepoGEO report