RRepoGEO

REPOGEO REPORT · LITE

knights-analytics/hugot

Default branch main · commit 789ba2f0 · scanned 6/4/2026, 11:01:59 PM

GitHub: 608 stars · 41 forks

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 knights-analytics/hugot, 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
  • hightopics#1
    Add specific topics for AI, Go, and ONNX

    Why:

    COPY-PASTE FIX
    go, golang, onnx, transformers, machine-learning, ai, nlp, embeddings, text-generation, deep-learning
  • highreadme#2
    Clarify project identity in the README's opening

    Why:

    COPY-PASTE FIX
    Insert this sentence immediately after the H1 and badges: "Hugot is a Go library for running ONNX-exported transformer models, enabling AI inference pipelines directly in your Go applications. It is *not* a static site generator or a Hugo theme."
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://pkg.go.dev/github.com/knights-analytics/hugot

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 knights-analytics/hugot
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow Serving
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Serving · recommended 1×
  2. TensorFlow Lite · recommended 1×
  3. go-tensorflow · recommended 1×
  4. ONNX · recommended 1×
  5. ONNX Runtime · recommended 1×
  • CATEGORY QUERY
    What are options for integrating AI inference pipelines into my Go backend?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Serving
    2. TensorFlow Lite
    3. go-tensorflow
    4. ONNX
    5. ONNX Runtime
    6. github.com/microsoft/onnxruntime-go (microsoft/onnxruntime-go)
    7. TorchServe
    8. Flask
    9. FastAPI
    10. PyTorch
    11. scikit-learn
    12. Hugging Face Transformers
    13. GoML
    14. goml/gobrain
    15. go-ml/ml
    16. cgo
    17. NVIDIA's TensorRT

    AI recommended 17 alternatives but never named knights-analytics/hugot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to run transformer models for text generation and embeddings natively in Go?
    you: not recommended
    AI recommended (in order):
    1. Go-Torch (github.com/gorgonia/go-torch)
    2. ONNX Runtime (github.com/microsoft/onnxruntime)
    3. llama.cpp (github.com/ggerganov/llama.cpp)
    4. TensorFlow Lite (github.com/tensorflow/tensorflow/tree/master/tensorflow/lite)
    5. Gorgonia (github.com/gorgonia/gorgonia)
    6. OpenVINO (github.com/openvinotoolkit/openvino)

    AI recommended 6 alternatives but never named knights-analytics/hugot. 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 knights-analytics/hugot?
    pass
    AI named knights-analytics/hugot explicitly

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

  • If a team adopts knights-analytics/hugot in production, what risks or prerequisites should they evaluate first?
    pass
    AI named knights-analytics/hugot 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 knights-analytics/hugot solve, and who is the primary audience?
    pass
    AI named knights-analytics/hugot 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 knights-analytics/hugot. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/knights-analytics/hugot.svg)](https://repogeo.com/en/r/knights-analytics/hugot)
HTML
<a href="https://repogeo.com/en/r/knights-analytics/hugot"><img src="https://repogeo.com/badge/knights-analytics/hugot.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

knights-analytics/hugot — 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