⚡️ Turn Jupyter Notebooks into Blog Posts
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⚡️ Turn Jupyter Notebooks into Blog Posts

Jul 15, 2024·
Thibaut Germain
Thibaut Germain
· 3 min read
Image credit: HugoBlox
blog

As a researcher or data scientist, your work often lives in Jupyter Notebooks. But sharing those insights effectively usually means taking screenshots, messy copy-pasting, or exporting to PDF.

Hugo Blox changes that. With the {{< notebook >}} shortcode, you can render your actual .ipynb files directly as beautiful, interactive blog posts or project pages. Keep your code, outputs, and narrative in one place.

Table of Contents

Why publish notebooks?

Tip

Reproducible Research: By publishing the actual notebook, you allow others to download and run your code, verifying your results and building upon your work.

  • No more screenshots – Render crisp code and vector plots directly from your source.
  • Theme consistent – Notebooks automatically adapt to your site’s theme (including dark mode).
  • Flexible sourcing – Display notebooks from your assets/ folder, page bundles, or even directly from a remote GitHub URL.
  • Interactive – Users can copy code blocks or download the full notebook to run locally.

Example: Data Science Workflow

Below is a live example of a notebook rendered right here in this post. Notice how the markdown, code, and outputs (text, HTML, and JSON) are all preserved and styled.

Launch Readiness Analysis

Python · Kernel: Python 3 · nbformat 4.5 · 6 cells

Download notebook
Markdown

Ship Notebook Stories in Minutes

Hugo Blox Notebook renderer turns your .ipynb experiments into beautiful long-form posts. Use this sample to see how markdown, code, and outputs flow together.

Markdown
  1. Drop notebooks inside assets/notebooks/ (or import them as page resources).
  2. Reference them with {{</* notebook src="your.ipynb" */>}}.
  3. Control code, outputs, metadata badges, and download links via shortcode params.
In [1]
demo quickstart
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import math
accuracy = 0.982
print("Collecting data...")
print("Training notebook-ready block...")
print("Done!")
accuracy
Collecting data...
Training notebook-ready block...
Done!
0.982
In [2]
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from IPython.display import HTML
HTML("<div style='font-family:Inter,ui-sans-serif;'><strong>Launch Readiness:</strong> <span style='color:#22c55e;'>98.2% confidence</span><br><em>Notebook blocks are theme-aware and dark-mode friendly.</em></div>")
Launch Readiness: 98.2% confidence
Notebook blocks are theme-aware and dark-mode friendly.
In [3]
metrics
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metrics = {
    'metrics': {
        'engagement_rate': 0.73,
        'read_time_minutes': 4.6,
        'subscribers': 1280
    }
}
metrics
{
  "metrics": {
    "engagement_rate": 0.73,
    "read_time_minutes": 4.6,
    "subscribers": 1280
  }
}
Markdown

Tip: Pair this block with Call-to-Action cards or the Embed shortcode to link to GitHub repos, datasets, or ARXIV preprints.

Powered by Hugo Blox Kit - https://github.com/HugoBlox/kit

How to add a notebook

  1. Save your notebook. Place your .ipynb file in assets/notebooks/ (for global access) or inside a page bundle (like content/blog/my-post/analysis.ipynb).
  2. Add the shortcode. In any Markdown page, simply use: {{< notebook src="analysis.ipynb" >}}
  3. Customize. You can hide code cells for non-technical audiences (show_code=false) or just show the output (show_outputs=true).
Important

Hugo Blox respects your privacy. Notebook rendering happens statically at build time—no third-party services required.

Next steps

  • Try it out: Drop one of your existing notebooks into this site and see how it looks.
  • Link your papers: Use the Embed shortcode to link your notebook to your latest arXiv preprint or GitHub repository.
  • Get help: Join the community on Discord or check the documentation.

Happy researching! 🚀

Thibaut Germain
Authors
Postdoctoral Researcher

My research combines machine learning and signal processing with geometric methods to study time series and dynamical systems.

Since March 2025, I have been a postdoctoral researcher at CMAP - Ecole Polytechnique, working with Karim Lounici and Rémi Flamary. My work combines operator learning and optimal transport to compare dynamical systems, uncover shared dynamical structure, and transfer knowledge across systems.

Previously, I completed my PhD at Centre Borelli, under the supervision of Charles Truong and Laurent Oudre. I developed methods tailored for the discovery and statistical analysis of time series patterns with a particular focus on biomedical applications.