RRepoGEO

REPOGEO REPORT · LITE

google/visualblocks

Default branch main · commit 80749a2c · scanned 5/21/2026, 5:28:24 PM

GitHub: 1,352 stars · 179 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 google/visualblocks, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    machine-learning, ml, visual-programming, no-code, low-code, graph-editor, pipeline, dataflow, javascript, google
  • highreadme#2
    Strengthen the README's opening to emphasize ML-specific use cases

    Why:

    CURRENT
    Visual Blocks is a framework that allows any platform or application to easily integrate a visual and user-friendly interface for ML creation.
    COPY-PASTE FIX
    Visual Blocks is a framework for building visual, no-code ML pipeline editors. It allows any platform or application to easily integrate a user-friendly interface for creating and experimenting with machine learning workflows.
  • mediumreadme#3
    Add a section explaining Visual Blocks' core differentiator

    Why:

    COPY-PASTE FIX
    ## Core Differentiator: Dataflow-Oriented ML Pipelines

Unlike many block-based programming frameworks that are statement-oriented, Visual Blocks employs a dataflow-oriented programming model. This design is specifically optimized for machine learning pipelines, allowing users to intuitively connect data transformations, models, and visualizations in a clear, executable flow.

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 google/visualblocks
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DataRobot
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DataRobot · recommended 1×
  2. Google Cloud Vertex AI Workbench · recommended 1×
  3. Azure Machine Learning Studio · recommended 1×
  4. KNIME Analytics Platform · recommended 1×
  5. RapidMiner Studio · recommended 1×
  • CATEGORY QUERY
    How to build machine learning workflows visually without writing much code?
    you: not recommended
    AI recommended (in order):
    1. DataRobot
    2. Google Cloud Vertex AI Workbench
    3. Azure Machine Learning Studio
    4. KNIME Analytics Platform
    5. RapidMiner Studio
    6. H2O.ai Driverless AI

    AI recommended 6 alternatives but never named google/visualblocks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a JavaScript library to embed a drag-and-drop ML pipeline editor for users.
    you: not recommended
    AI recommended (in order):
    1. React Flow
    2. GoJS
    3. JointJS
    4. mxGraph
    5. D3.js

    AI recommended 5 alternatives but never named google/visualblocks. 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 google/visualblocks?
    pass
    AI named google/visualblocks explicitly

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

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

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

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google/visualblocks — 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