RRepoGEO

REPOGEO REPORT · LITE

brevdev/launchables

Default branch main · commit 4a0ba27b · scanned 6/22/2026, 11:52:39 PM

GitHub: 1,820 stars · 312 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 brevdev/launchables, 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 H1 and intro to clarify core purpose

    Why:

    CURRENT
    # Brev Launchables

Launchables are a feature of NVIDIA Brev that deliver preconfigured, fully optimized compute and software environments. Fast and easy to deploy, launchables allow you to start projects without extensive setup or configuration.

This repo contains helpful AI/ML notebooks for LLMs, multi-modal models, image segmentation, and more. Each notebook has been coupled with the minimum GPU specs required to use them along with a 1-click deploy badge that starts each notebook on a GPU.
    COPY-PASTE FIX
    # Brev Launchables: Curated AI/ML Notebooks for GPU Environments

This repository provides Brev Launchables, a collection of helpful AI/ML notebooks for LLMs, multi-modal models, image segmentation, and more. Launchables are a feature of NVIDIA Brev that deliver preconfigured, fully optimized compute and software environments, allowing you to start projects without extensive setup or configuration. Each notebook includes minimum GPU specs and a 1-click deploy badge to launch it on a GPU.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ai, ml, deep-learning, llm, multi-modal, image-segmentation, notebooks, gpu, nvidia, brev, machine-learning-notebooks, data-science
  • mediumlicense#3
    Clarify the existing license(s) in the README

    Why:

    COPY-PASTE FIX
    ## License
This repository contains a custom license. Please refer to the [LICENSE](LICENSE) file for full details on the terms and conditions.

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 brevdev/launchables
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Colaboratory (Colab)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Colaboratory (Colab) · recommended 1×
  2. Kaggle Kernels (Notebooks) · recommended 1×
  3. Paperspace Gradient Community Notebooks · recommended 1×
  4. Google Cloud AI Platform Notebooks (Vertex AI Workbench) · recommended 1×
  5. Amazon SageMaker Studio Lab · recommended 1×
  • CATEGORY QUERY
    How to quickly start deep learning projects without extensive GPU environment setup?
    you: not recommended
    AI recommended (in order):
    1. Google Colaboratory (Colab)
    2. Kaggle Kernels (Notebooks)
    3. Paperspace Gradient Community Notebooks
    4. Google Cloud AI Platform Notebooks (Vertex AI Workbench)
    5. Amazon SageMaker Studio Lab
    6. Deepnote
    7. RunPod.io (Serverless GPU)

    AI recommended 7 alternatives but never named brevdev/launchables. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find curated AI/ML notebooks for LLMs, multi-modal, and image segmentation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Hub
    2. Gradio
    3. Streamlit
    4. Hugging Face Datasets
    5. Kaggle Notebooks
    6. PyTorch
    7. PyTorch Hub
    8. TensorFlow
    9. Keras
    10. GitHub
    11. Papers With Code
    12. Google Colaboratory

    AI recommended 12 alternatives but never named brevdev/launchables. 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 brevdev/launchables?
    pass
    AI named brevdev/launchables explicitly

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

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

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

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brevdev/launchables — 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