REPOGEO REPORT · LITE

tugstugi/dl-colab-notebooks

Default branch master · commit 3622c82d · scanned 6/20/2026, 9:17:29 PM

GitHub: 1,778 stars · 460 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

22 /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

1 / 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 tugstugi/dl-colab-notebooks, 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 clarify repo's nature and focus

Why:

CURRENT

Try out deep learning models online on Colab with a single click.

COPY-PASTE FIX

A curated collection of deep learning notebooks, primarily focused on Text-to-Speech (TTS) models, runnable online on Google Colab with a single click.

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a `LICENSE` file in the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0) and adding its text.

mediumtopics#3

Add specific topics for Text-to-Speech

Why:

CURRENT

cnn, colab-notebook, deep-learning, deep-neural-networks, google-colab, google-colaboratory, jupyter-notebook, jupyter-notebooks, pytorch, tensorflow

COPY-PASTE FIX

cnn, colab-notebook, deep-learning, deep-neural-networks, google-colab, google-colaboratory, jupyter-notebook, jupyter-notebooks, pytorch, tensorflow, text-to-speech, tts, speech-synthesis

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 tugstugi/dl-colab-notebooks

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Spaces

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Spaces · recommended 2×
Gradio · recommended 2×
Streamlit · recommended 2×
Google Cloud Text-to-Speech · recommended 1×
Amazon Polly · recommended 1×

CATEGORY QUERY
How can I easily try out various deep learning text-to-speech models in a cloud environment?
you: not recommended
AI recommended (in order):
1. Google Cloud Text-to-Speech
2. Amazon Polly
3. Microsoft Azure AI Speech
4. ElevenLabs
5. Hugging Face Spaces
6. Gradio
7. Streamlit
8. Bark
9. VITS
10. Coqui TTS
11. RunPod
12. Replicate
13. Banana
14. Tacotron 2
15. FastSpeech 2
AI recommended 15 alternatives but never named tugstugi/dl-colab-notebooks. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find pre-configured deep learning model demos runnable directly in online notebooks?
you: not recommended
AI recommended (in order):
1. Hugging Face Spaces
2. Gradio
3. Streamlit
4. Hugging Face Hub
5. Google Colaboratory (Colab)
6. GitHub
7. Kaggle Notebooks
8. TensorFlow Playground
9. TensorFlow.js
10. PyTorch Examples/Tutorials
11. Jupyter notebooks
12. Papers With Code
AI recommended 12 alternatives but never named tugstugi/dl-colab-notebooks. 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 tugstugi/dl-colab-notebooks?
pass
AI did not name tugstugi/dl-colab-notebooks — 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 tugstugi/dl-colab-notebooks in production, what risks or prerequisites should they evaluate first?
pass
AI named tugstugi/dl-colab-notebooks 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 tugstugi/dl-colab-notebooks solve, and who is the primary audience?
pass
AI did not name tugstugi/dl-colab-notebooks — 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?

Embed your GEO score

Drop this badge into the README of tugstugi/dl-colab-notebooks. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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tugstugi/dl-colab-notebooks — 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