import marimo

__generated_with = "0.1.0"
app = marimo.App()

@app.cell
def __():
    # Define a variable - changes here propagate automatically
    data_source = "production_database"  # Try changing this to "test_database"
    return data_source,

@app.cell
def __(data_source):
    # This cell REACTS to changes in data_source
    if data_source == "production_database":
        query = "SELECT * FROM users WHERE active = TRUE"
    else:
        query = "SELECT * FROM test_users LIMIT 100"
    
    print(f"Running query on {data_source}:\n{query}")
    return query,

@app.cell
def __(data_source, query):
    # Another reactive cell - depends on BOTH previous cells
    import pandas as pd
    
    # Simulated data fetch based on the reactive variables
    if "production" in data_source:
        df = pd.DataFrame({"user_id": [1, 2, 3], "status": ["active", "active", "inactive"]})
    else:
        df = pd.DataFrame({"user_id": [101, 102], "status": ["test", "test"]})
    
    print(f"DataFrame shape: {df.shape}")
    print(df.head())
    return df,

if __name__ == "__main__":
    app.run()

The Notebook Problem: We've Been Doing It Wrong This Whole Time

Let's be honest: Jupyter notebooks are the digital equivalent of that drawer in your kitchen where you throw random cables, takeout menus, and batteries of questionable charge. They start organized, with beautiful Markdown cells explaining your brilliant hypothesis, then devolve into a labyrinth of experimental code, half-baked visualizations, and cells that only work if you execute them in a very specific order that you forgot three weeks ago.

marimo looks at this mess and says, "What if cells just... knew about each other?" Revolutionary concept, I know. It's like suggesting that maybe the different parts of your car should communicate rather than operating as independent fiefdoms. The reactive part means when you change a variable in one cell, every other cell that depends on it automatically updates. No more manually running cells in the "right" order. No more "I swear this worked yesterday."

The "AI-Native" Buzzword Bingo Square

Of course, it wouldn't be a 2025 tech product without throwing "AI-native" into the description. What does that even mean? Does the editor finish your sentences with "as an AI language model"? Does it suggest variable names like "hyperparameter_tuning_matrix_final_v2"? Or does it just mean they used ChatGPT to write the documentation? (Spoiler: They probably did.)

The real innovation here isn't the AI part—it's the fact that marimo notebooks are stored as pure Python files. This is so obvious it's painful. For years, we've been dealing with .ipynb files that are basically JSON nightmares, impossible to diff properly in git, prone to merge conflicts that look like digital hieroglyphics, and containing enough metadata to reconstruct your entire thought process (including the 3 AM "what if I just try this" moments).

What Actually Matters: The Git-Friendly Part

Let's talk about the real hero here: version control that doesn't make you want to cry. Traditional Jupyter notebooks in git are like trying to version control a PDF by tracking every individual pixel change. marimo's pure Python approach means you get actual, meaningful diffs. You can see what changed in the logic, not just that cell 47 now has a different UUID and the output cache updated.

This alone could save data science teams approximately 47% of their meeting time currently spent on phrases like "Wait, which version are you running?" and "I think you need to clear the kernel and run it from the top."

The Deployment Fantasy vs. Reality

The promise of "deploy as an app" is particularly amusing. Because what every data scientist wants after spending weeks on an analysis is to become a frontend developer overnight! But seriously, the ability to turn a notebook into a shareable web app without rewriting everything in Flask or Streamlit is genuinely useful. It's like discovering your sketchbook drawings can suddenly become an art gallery exhibition without having to learn how to frame pictures.

The SQL integration is nice too—because nothing says "modern data workflow" like still using technology from the 1970s. But hey, if it means one less context switch between your notebook and your database client, we'll take it.

The Real Test: Will Anyone Actually Use This?

Here's the thing about new tools in the data science ecosystem: They need to be at least 10x better than the existing solution to overcome inertia. Jupyter might be a hot mess, but it's our hot mess. We know its quirks. We've developed elaborate rituals to work around its limitations. We have extensions upon extensions that add just enough functionality to keep us from rioting.

marimo has 18,031 GitHub stars as of this writing, which in startup math translates to "everyone is talking about it but nobody is actually using it in production." The real test will be whether teams can convince their most stubborn senior data scientist to switch from the notebook environment they've been using since their PhD.

The "execute as a script" feature might be the Trojan horse here. Being able to run your notebook from the command line like a normal Python script? That's not just convenient—that's professional. It means you can actually put this stuff in pipelines. It means you can stop pretending that clicking "Run All Cells" is a production deployment strategy.

The Dark Secret of Reproducibility

Let's address the elephant in the room: No tool can fix the real reproducibility problem, which is that half of data science involves downloading datasets from URLs that will 404 in six months, using undocumented APIs that change without warning, and relying on proprietary black-box models from companies that might pivot to making AI-generated cat videos next quarter.

marimo can make your code reproducible, but it can't make your data sources reliable. It can version your analysis, but it can't version the constantly shifting landscape of external dependencies. Still, it's better than what we have now, which is basically digital wishful thinking.