RRepoGEO

REPOGEO REPORT · LITE

openai/glow

Default branch master · commit 91b2c577 · scanned 6/22/2026, 5:50:23 PM

GitHub: 3,185 stars · 525 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
68 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 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 openai/glow, 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 comprehensive topics for better categorization

    Why:

    CURRENT
    paper
    COPY-PASTE FIX
    generative-models, deep-learning, image-generation, normalizing-flows, tensorflow, machine-learning, research-code
  • mediumreadme#2
    Reposition README opening to emphasize model implementation for training

    Why:

    CURRENT
    Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"
    COPY-PASTE FIX
    This repository provides the official TensorFlow implementation for training and reproducing results from "Glow: Generative Flow with Invertible 1x1 Convolutions", a powerful generative flow model for high-quality image synthesis.
  • lowabout#3
    Update the repository's 'About' description for clarity

    Why:

    CURRENT
    Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"
    COPY-PASTE FIX
    Official TensorFlow implementation of Glow, a generative flow model with invertible 1x1 convolutions for high-quality image generation and exact likelihood computation.

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
1 / 2
50% of queries surface openai/glow
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
NICE
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NICE · recommended 1×
  2. Real NVP · recommended 1×
  3. FFJORD · recommended 1×
  4. i-ResNet · recommended 1×
  5. i-UNet · recommended 1×
  • CATEGORY QUERY
    How to build generative models using invertible neural network architectures for image data?
    you: #3
    AI recommended (in order):
    1. NICE
    2. Real NVP
    3. Glow ← you
    4. FFJORD
    5. i-ResNet
    6. i-UNet
    7. Invertible Transformers
    Show full AI answer
  • CATEGORY QUERY
    What deep learning frameworks are suitable for training large-scale generative models on images?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. Keras
    4. JAX
    5. Flax
    6. Haiku
    7. MXNet
    8. PaddlePaddle

    AI recommended 8 alternatives but never named openai/glow. 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 openai/glow?
    pass
    AI named openai/glow explicitly

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

  • If a team adopts openai/glow in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/glow 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 openai/glow solve, and who is the primary audience?
    pass
    AI named openai/glow 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 openai/glow. 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/openai/glow.svg)](https://repogeo.com/en/r/openai/glow)
HTML
<a href="https://repogeo.com/en/r/openai/glow"><img src="https://repogeo.com/badge/openai/glow.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

openai/glow — 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