RRepoGEO

REPOGEO REPORT · LITE

ml-tooling/ml-workspace

Default branch main · commit 024c4053 · scanned 5/27/2026, 10:01:36 PM

GitHub: 3,540 stars · 458 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 ml-tooling/ml-workspace, 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 README opening to highlight unique value proposition

    Why:

    CURRENT
    The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines.
    COPY-PASTE FIX
    The ML workspace is a comprehensive, pre-configured, and self-hostable web-based IDE specialized for machine learning and data science. It integrates popular tools like Jupyter, VS Code, and TensorBoard into a single, ready-to-use container, enabling rapid deployment and productive ML solution building on your own infrastructure.
  • mediumcomparison#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section (e.g., 'Comparison' or 'Why ML Workspace?') that briefly outlines how `ml-workspace` differs from popular alternatives like JupyterLab, VS Code, Google Colaboratory, Deepnote, and Databricks Workspace, emphasizing its self-hostable, all-in-one, and pre-configured nature.
  • lowreadme#3
    Explicitly highlight 'Reproducible Environments' and 'GPU Support' in README

    Why:

    COPY-PASTE FIX
    Ensure the 'Features' section or a dedicated 'Key Capabilities' section explicitly highlights 'Reproducible Development Environments' and 'Seamless GPU Integration' with brief explanations of how `ml-workspace` provides these.

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 ml-tooling/ml-workspace
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
JupyterLab
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. JupyterLab · recommended 1×
  2. VS Code · recommended 1×
  3. Google Colaboratory · recommended 1×
  4. Deepnote · recommended 1×
  5. Databricks Workspace · recommended 1×
  • CATEGORY QUERY
    Seeking an integrated web development environment for machine learning and data science tasks.
    you: not recommended
    AI recommended (in order):
    1. JupyterLab
    2. VS Code
    3. Google Colaboratory
    4. Deepnote
    5. Databricks Workspace
    6. Gradient

    AI recommended 6 alternatives but never named ml-tooling/ml-workspace. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to set up a reproducible machine learning environment with GPU support?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NGC Containers
    2. Docker
    3. Podman
    4. NVIDIA Container Toolkit
    5. Conda
    6. Anaconda
    7. Miniconda
    8. conda-lock
    9. Poetry
    10. pip
    11. venv
    12. Virtualenv
    13. NixOS
    14. Nix Package Manager

    AI recommended 14 alternatives but never named ml-tooling/ml-workspace. 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 ml-tooling/ml-workspace?
    pass
    AI did not name ml-tooling/ml-workspace — likely talking about a different project

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

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

Subscribe to Pro for deep diagnoses

ml-tooling/ml-workspace — 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