Your Jupyter Notebook Is Lying to You. marimo Fixes That.

You ran cell 12, then cell 7, then 12 again, deleted cell 9, and now your chart says one thing and your variables say another. Be honest — you've been there. I love Jupyter. It taught a whole generation of us to think in code-and-output. But it has one deep, structural lie baked in, and in 2026 there's finally a notebook that doesn't tell it: **marimo.** Let me show you the problem first, because once you see it you can't unsee it. ## The hidden-state problem (a.k.a. the lie) In Jupyter, **the code on your screen and the state in memory are two different things.** You can run cells out of order. You can delete a cell whose variable still lives in memory, haunting everything downstream. You can edit a cell and forget to re-run the three cells that depend on it. The result is a reproducibility crisis that's been measured, not just grumbled about. In a large-scale study of public Jupyter notebooks on GitHub (Pimentel et al., 2019), only about **24% ran top-to-bottom without errors** — so roughly three in four didn't — and only about **4% reproduced their saved outputs exactly**. That's not a few sloppy people. That's the *default behavior of the tool* nudging everyone toward broken work. And it bites where it hurts most: you share a notebook, your colleague runs it fresh, and it explodes — or worse, silently produces different numbers. ## marimo's big idea: the notebook is a dependency graph marimo throws out top-to-bottom execution and replaces it with **reactivity.** It reads your cells, figures out which variables each one defines and uses, and builds a **dependency graph (a DAG)**. Then it enforces a simple promise: > When you change a cell, every cell that depends on it re-runs automatically. Cells that don't depend on it are left alone. If you've used a spreadsheet, you already understand this. Change cell `A1` and every formula referencing `A1` updates *instantly*. You never think "did I remember to recalculate?" marimo brings that guarantee to Python. Your code, your outputs, and your program state are **always** in sync — there's no way to get them out of sync, because the tool won't let you. Delete a cell? Its variables vanish from memory too — no ghosts. Run things out of order? You can't, really; the graph decides the true order. ## A taste of reactivity Here's the part that makes people grin. In marimo, UI elements are first-class and *reactive*. Make a slider, and any cell that reads it re-runs the instant you drag it — no callbacks, no `%matplotlib widget` incantations: ```python import marimo as mo # cell 1 threshold = mo.ui.slider(1, 100, value=20, label="Min revenue") threshold ``` ```python # cell 2 — re-runs automatically whenever the slider moves filtered = df[df["revenue"] >= threshold.value] mo.md(f"**{len(filtered)} rows** above {threshold.value}") ``` Drag the slider and the count updates live. You just built a mini interactive dashboard in eight lines — and it's still a normal notebook you can read top to bottom. That's the magic trick: interactivity *for free*, because the reactive engine was going to track those dependencies anyway. ## The quiet superpower: it's just a `.py` file This is the one that wins over engineers. A Jupyter `.ipynb` is **JSON** — your code wrapped in metadata with base64-encoded image outputs glued in. Try reviewing *that* in a pull request. The `git diff` is unreadable; merge conflicts are a nightmare. A marimo notebook is **pure Python.** A real `.py` file. Which means: - `git diff` shows actual code changes, like any normal file. - Code review works. Your teammate can comment on line 40. - You can `import` functions from it, or run it as a **script**: `python my_notebook.py`. - You can serve it as a small **web app**: `marimo run my_notebook.py`. - AI coding agents can read and edit it cleanly, because it's just Python — no JSON wrapper to confuse them. One artifact — notebook, script, app, and reviewable source — instead of a JSON blob that only opens in one tool. ## Getting started in 30 seconds ```bash pip install marimo marimo edit my_analysis.py # opens the reactive editor ``` That's the whole onboarding. If you want to kick the tires without committing, `marimo tutorial intro` walks you through it interactively. ## The honest trade-offs I'm not going to pretend it's all upside — that's not how I'd tutor you. - **The discipline is real.** marimo won't let you redefine the same variable in two cells (that would break the graph). Mostly this makes your code cleaner, but it's an adjustment if you're used to scribbling. - **The ecosystem is younger.** Jupyter has a decade of extensions, Stack Overflow answers, and "open in Colab" buttons. marimo is growing fast in 2025–2026 but it's still the newcomer. - **Some habits must die.** No more "just run this cell real quick out of order." The tool is protecting you, but it *feels* like friction at first. My honest take: for **exploratory scratch work**, Jupyter's looseness is still fine. For anything you'll **share, re-run, deploy, or trust** — analysis that matters, dashboards, teaching material, reproducible research — marimo is the upgrade I now reach for by default. ## Takeaways - Jupyter's hidden state is *structural* — in one large GitHub study only ~4% of notebooks reproduced their saved outputs. - marimo models your notebook as a **dependency graph** and re-runs only what changed — like a spreadsheet for Python. - **Reactive UI** (sliders, dropdowns) gives you live dashboards with no callbacks. - Notebooks are **pure `.py` files**: git-friendly, reviewable, runnable as scripts, deployable as apps, and AI-agent-friendly. - Trade-offs: stricter rules, a younger ecosystem, and breaking some old habits. - Use Jupyter for loose scratch work; reach for marimo for anything you need to **trust**. Reproducibility isn't a chore you do at the end — it's a property you either build in from cell one or fake forever. marimo just makes "build it in" the path of least resistance. Want me to help you turn a tangled notebook into a clean, reproducible, reactive one? I tutor 1:1 → /book-session.