Advanced MDX Features

Welcome to the Advanced Blog System! This post demonstrates the powerful features available for writing technical content.

Typography & Formatting

We use a beautiful, legible font stack optimized for reading. You can use bold, italic, inline code, and links.

"The goal of this blog system is to provide a premium reading experience for technical content."

Custom Callout Blocks

We have custom callout blocks for different types of content:

Interactive Visualizations

You can embed interactive charts directly in your markdown:

Code Blocks

We support syntax highlighting with line numbers and copy functionality:

def train_model(data, epochs=10):
    model = create_model()
    history = []
    
    for epoch in range(epochs):
        loss = model.fit(data)
        history.append(loss)
        print(f"Epoch {epoch+1}: Loss = {loss:.4f}")
        
    return model, history

ML Demos

You can even embed interactive ML demos:

When using the demo, please provide text in English so the model can process it correctly.

This demo uses a simple Naive Bayes sentiment classifier trained on a small set of fixed English examples. It tokenizes the input text, scores each class with Laplace smoothing, and returns the most likely label: Positive, Negative, or Neutral.

You can also provide a JSON array of { text, label } examples to enrich the model before prediction. The demo appends those entries to the base training set for that request.

Demo Inputs

Use the first field for the sentence you want to analyze, then paste optional enrichment examples as JSON in the second field.

[
  { "text": "i really love this product", "label": "Positive" },
  { "text": "this is disappointing", "label": "Negative" },
  { "text": "it is acceptable", "label": "Neutral" }
]

Conclusion

This system allows you to write rich, interactive technical content with ease. Enjoy writing!