998: In Case You Missed It in May 2026
In this month’s episode of ICYMI, Jon Krohn explores how AI agents are simultaneously creating new risks and unlocking powerful new ways of working with data. Hear from Anneka Gupta, Cal Al-Dhubaib, Trevor Manz, Jazmia Henry, Jeremy Mumford, and Jacob Miller, discussing why the old cybersecurity playbook breaks down in the age of Claude Mythos, how the notebook became an AI agent’s working memory, what it really takes to build a foundation model from scratch, and why failing slowly is the most expensive mistake an AI team can make.
Additional materials: www.superdatascience.com/998
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
In this episode you will learn:
(00:40) Why Claude Mythos Changes Everything About Cybersecurity
(08:11) Why Your Notebook Should Be Your Agent’s Working Memory
(13:19) What It Actually Takes to Build a Foundation Model From Scratch
(20:46) Failing Slowly Is the Most Expensive AI Mistake
991: Pair Programming with AI in Your Python Notebook, with Dr. Trevor Manz
Dr. Trevor Manz of Marimo talks to Jon Krohn about Marimo Pair, an open-source agent skill that teaches coding agents like Claude Code how to drive a reactive Python notebook, reading cell state, running Python in the kernel, taking screenshots of cells, and iterating on data tasks the way agents iterate on traditional software. Trevor also unpacks recursive language models, his AnyWidget project that bridges Python and the web, and his journey from a Wisconsin small town and Harvard bioinformatics research to founding-engineer life at Marimo. Listen to the episode to hear why no matter where AI takes us, curiosity and going deep on a topic will always be valuable.
Additional materials: www.superdatascience.com/991
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
In this episode you will learn:
(07:04) What Marimo Pair is and how it teaches agents to use notebooks as a tool
(13:03) How agent skills work as folders of markdown files
(24:15) Trevor's day-to-day workflow combining Claude Code and Marimo Pair
(31:51) Recursive language models and why they could be the future of agentic reasoning
(57:33) Career advice on curiosity, going deep, and becoming a domain expert
Agentic Data Science Pair Programming With marimo pair
<p>How do you add agent skills to your data science workflow? How can a coding agent assist with data wrangling and research? This week on the show, Trevor Manz from marimo joins us to discuss marimo pair.</p>
<p>Trevor is a founding engineer at marimo, where he’s been working on integrating LLM tools with marimo. We discuss the balancing act of building a skill and determining how to give an agent access to all the variables in a notebook. He shares how they built a specialized reactive REPL that eliminates hidden state and allows the agent to continue constructing a reproducible Python program.</p>
<p>We dig into installing and getting started with marimo pair. Trevor also covers several of the tasks an agent can tackle in a data science workflow.</p>
<div class="alert alert-primary" role="alert">
<p><strong>Video Course Spotlight:</strong> <a href="https://realpython.com/courses/getting-started-with-marimo-notebooks/">Getting Started With marimo Notebooks</a></p>
<p>Discover how marimo notebook simplifies coding with reactive updates, UI elements, and sandboxing for safe, sharable notebooks.</p>
</div>
<p>Topics:</p>
<ul>
<li>00:00:00 – Introduction</li>
<li>00:02:26 – Trevor’s role at marimo</li>
<li>00:03:08 – Current AI tools in marimo</li>
<li>00:06:26 – Describing marimo notebooks</li>
<li>00:10:11 – What is marimo pair?</li>
<li>00:18:49 – Building an agent skill</li>
<li>00:27:34 – Setup & installation</li>
<li>00:31:16 – Video Course Spotlight</li>
<li>00:32:42 – Examples of EDA and data wrangling</li>
<li>00:45:46 – Experimenting inside of a notebook</li>
<li>00:50:40 – Managing context</li>
<li>00:53:25 – Accessing additional libraries</li>
<li>00:57:16 – Recent tools and updates from the marimo community</li>
<li>00:59:31 – What are you excited about in the world of Python?</li>
<li>01:01:10 – What do you want to learn next?</li>
<li>01:02:26 – How can people follow your work online?</li>
<li>01:03:13 – Thanks and goodbye</li>
</ul>
<p>Show Links:</p>
<ul>
<li><a href="https://marimo.io/blog/marimo-pair">Introducing marimo pair - marimo</a></li>
<li><a href="https://github.com/marimo-team/marimo-pair">marimo-pair: Drop agents inside running marimo notebook sessions</a></li>
<li><a href="https://news.ycombinator.com/item?id=47678844">Marimo pair – Reactive Python notebooks as environments for agents - Hacker News</a></li>
<li><a href="https://realpython.com/podcasts/rpp/230/">Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python</a></li>
<li><a href="https://www.youtube.com/watch?v=6uaqtchDnoc">marimo Pair - YouTube</a></li>
<li><a href="https://www.youtube.com/watch?v=VKvjPJeNRPk">We gave Claude Access to All Python Variables - YouTube</a></li>
<li><a href="https://docs.marimo.io/guides/editor_features/ai_completion/">Using the marimo editor’s AI features - marimo</a></li>
<li><a href="https://github.com/astral-sh/ty">ty: An extremely fast Python type checker and language server, written in Rust.</a></li>
<li><a href="https://molab.marimo.io/notebooks">molab - marimo</a></li>
<li><a href="https://realpython.com/marimo-notebook/">marimo: A Reactive, Reproducible Notebook – Real Python</a></li>
<li><a href="https://realpython.com/courses/investigating-quasar-data-polars-marimo-notebooks/">Investigating Quasar Data With Polars and Interactive marimo Notebooks – Real Python</a></li>
<li><a href="https://marimo.io/blog">Blog - marimo</a></li>
<li><a href="https://www.linkedin.com/in/trevor-manz/">Trevor Manz - LinkedIn</a></li>
<li><a href="https://bsky.app/profile/manzt.sh">trevor manz (@manzt.sh) — Bluesky</a></li>
</ul>
<p>Level up your Python skills with our expert-led courses:</p>
<ul>
<li><a href="https://realpython.com/courses/investigating-quasar-data-polars-marimo-notebooks/">Investigating Quasar Data With Polars and Interactive marimo Notebooks</a></li>
<li><a href="https://realpython.com/courses/getting-started-claude-code/">Getting Started With Claude Code</a></li>
<li><a href="https://realpython.com/courses/getting-started-with-marimo-notebooks/">Getting Started With marimo Notebooks</a></li>
</ul> <p><a rel="payment" href="https://realpython.com/join">Support the podcast & join our community of Pythonistas</a></p>
Reinventing the Python Notebook with Akshay Agrawal
Interactive notebooks were popularized by the Jupyter project and have since become a core tool for data science, research, and data exploration. However, traditional, imperative notebooks often break down as projects grow more complex. Hidden state, non-reproducible execution, poor version control ergonomics, and difficulty reusing notebook code in real software systems make it hard to move from exploration to production. At the same time, sharing results often requires collaborators to recreate entire environments, limiting interactivity and slowing feedback.
Marimo is an open-source, next-generation Python notebook designed to address these problems directly. Akshay Agrawal is the creator of Marimo and he previously worked at Google Brain. He joins the show with Kevin Ball to discuss the limitations of traditional notebooks, the design of reactive notebooks in Python, how marimo bridges research and production, and where notebooks fit in an increasingly agentic, AI-assisted development world.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post Reinventing the Python Notebook with Akshay Agrawal appeared first on Software Engineering Daily.
#530: anywidget: Jupyter Widgets made easy
For years, building interactive widgets in Python notebooks meant wrestling with toolchains, platform quirks, and a mountain of JavaScript machinery. Most developers took one look and backed away slowly. Trevor Manz decided that barrier did not need to exist. His idea was simple: give Python users just enough JavaScript to unlock the web’s interactivity, without dragging along the rest of the web ecosystem. That idea became anywidget, and it is quickly becoming the quiet connective tissue of modern interactive computing. Today we dig into how it works, why it has taken off, and how it might change the way we explore data.
Episode sponsors
Seer: AI Debugging, Code talkpython26
PyCharm, code STRONGER PYTHON
Talk Python Courses
Links from the show
Trevor on GitHub: github.com
anywidget GitHub: github.com
Trevor's SciPy 2024 Talk: www.youtube.com
Marimo GitHub: github.com
Myst (Markdown docs): mystmd.org
Altair: altair-viz.github.io
DuckDB: duckdb.org
Mosaic: uwdata.github.io
ipywidgets: ipywidgets.readthedocs.io
Tension between Web and Data Sci Graphic: blobs.talkpython.fm
Quak: github.com
Walk through building a widget: anywidget.dev
Widget Gallery: anywidget.dev
Video: How do I anywidget?: www.youtube.com
PyCharm + PSF Fundraiser: pycharm-psf-2025 code STRONGER PYTHON
Watch this episode on YouTube: youtube.com
Episode #530 deep-dive: talkpython.fm/530
Episode transcripts: talkpython.fm
Theme Song: Developer Rap
🥁 Served in a Flask 🎸: talkpython.fm/flasksong
---== Don't be a stranger ==---
YouTube: youtube.com/@talkpython
Bluesky: @talkpython.fm
Mastodon: @talkpython@fosstodon.org
X.com: @talkpython
Michael on Bluesky: @mkennedy.codes
Michael on Mastodon: @mkennedy@fosstodon.org
Michael on X.com: @mkennedy
911: The Future of Python Notebooks is Here, with Marimo’s Dr. Akshay Agrawal
Reproducibility, Python notebooks, and data science communities: Software developer Akshay Agrawal speaks to Jon Krohn about Marimo, the next-generation computational notebook for Python, how he built and fostered a thriving community around the product, and what makes this notebook so versatile and accessible for users.
Additional materials: www.superdatascience.com/911
This episode is brought to you by Trainium2, the latest AI chip from AWS and by the Dell AI Factory with NVIDIA.
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
Revolutionizing Python Notebooks with Marimo
Summary
In this episode of the Data Engineering Podcast Akshay Agrawal from Marimo discusses the innovative new Python notebook environment, which offers a reactive execution model, full Python integration, and built-in UI elements to enhance the interactive computing experience. He discusses the challenges of traditional Jupyter notebooks, such as hidden states and lack of interactivity, and how Marimo addresses these issues with features like reactive execution and Python-native file formats. Akshay also explores the broader landscape of programmatic notebooks, comparing Marimo to other tools like Jupyter, Streamlit, and Hex, highlighting its unique approach to creating data apps directly from notebooks and eliminating the need for separate app development. The conversation delves into the technical architecture of Marimo, its community-driven development, and future plans, including a commercial offering and enhanced AI integration, emphasizing Marimo's role in bridging the gap between data exploration and production-ready applications.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing Akshay Agrawal about Marimo, a reusable and reproducible Python notebook environment
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what Marimo is and the story behind it?
What are the core problems and use cases that you are focused on addressing with Marimo?What are you explicitly not trying to solve for with Marimo?
Programmatic notebooks have been around for decades now. Jupyter was largely responsible for making them popular outside of academia. How have the applications of notebooks changed in recent years?What are the limitations that have been most challenging to address in production contexts?
Jupyter has long had support for multi-language notebooks/notebook kernels. What is your opinion on the utility of that feature as a core concern of the notebook system?
Beyond notebooks, Streamlit and Hex have become quite popular for publishing the results of notebook-style analysis. How would you characterize the feature set of Marimo for those use cases?
For a typical data team that is working across data pipelines, business analytics, ML/AI engineering, etc. How do you see Marimo applied within and across those contexts?
One of the common difficulties with notebooks is that they are largely a single-player experience. They may connect into a shared compute cluster for scaling up execution (e.g. Ray, Dask, etc.). How does Marimo address the situation where a data platform team wants to offer notebooks as a service to reduce the friction to getting started with analyzing data in a warehouse/lakehouse context?
How are you seeing teams integrate Marimo with orchestrators (e.g. Dagster, Airflow, Prefect)?
What are some of the most interesting or complex engineering challenges that you have had to address while building and evolving Marimo?\
What are the most interesting, innovative, or unexpected ways that you have seen Marimo used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Marimo?
When is Marimo the wrong choice?
What do you have planned for the future of Marimo?
Contact Info
LinkedIn
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.
Links
Marimo
Jupyter
IPython
StreamlitPodcast.__init__ Episode
Vector Embeddings
Dimensionality Reduction
Kaggle
Pytest
PEP 723 script dependency metadata
MatLab
Visicalc
Mathematica
RMarkdown
RShiny
Elixir Livebook
Databricks Notebooks
Papermill
Pluto - Julia Notebook
Hex
Directed Acyclic Graph (DAG)
Sumble Kaggle founder Anthony Goldblum's startup
Ray
Dask
Jupytext
nbdev
DuckDBPodcast Episode
Iceberg
Superset
jupyter-marimo-proxy
JupyterHub
Binder
Nix
AnyWidget
Jupyter Widgets
Matplotlib
Altair
Plotly
DataFusion
Polars
MotherDuck
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Building the future of collaborative AI development with Akshay Agrawal
In this episode of Gradient Dissent, Akshay Agrawal, Co-Founder of Marimo, joins host Lukas Biewald to discuss the future of collaborative AI development.
They dive into how Marimo is enabling developers and researchers to collaborate seamlessly on AI projects, the challenges of scaling AI tools, and the importance of fostering open ecosystems for innovation. Akshay shares insights into building a platform that empowers teams to iterate faster and solve complex AI challenges together.
Follow Weights & Biases:
https://twitter.com/weights_biases
https://www.linkedin.com/company/wandb
Join the Weights & Biases Discord Server:
https://discord.gg/CkZKRNnaf3
marimo: Reactive Notebooks and Deployable Web Apps in Python
<p>What are common issues with using notebooks for Python development? How do you know the current state, share reproducible results, or create interactive applications? This week on the show, we speak with Akshay Agrawal about the open-source reactive marimo notebook for Python.</p>
<p>Before writing any code, Akshay wrote a 2,500-word design document. He wanted to create a maintainable and reproducible tool that avoided the hidden state of traditional notebooks. We discuss solving the hidden state problem by building the notebook as a directed acyclic graph (DAG).</p>
<p>Akshay shares how marimo notebooks are stored as pure Python files, which makes them easy to read, importable, and git-friendly. We discuss serializing package requirements using PEP 723 inline metadata to create standalone reproducible notebooks. We also cover how marimo notebooks can be deployed as a web app or dashboard using Pyodide.</p>
<div class="alert alert-primary" role="alert">
<p><strong>Course Spotlight:</strong> <a href="https://realpython.com/courses/navigate-namespaces-scope/">Navigating Namespaces and Scope in Python</a></p>
<p>In this course, you’ll learn about Python namespaces, the structures used to store and organize the symbolic names created during execution of a Python program. You’ll learn when namespaces are created, how they are implemented, and how they define variable scope.</p>
</div>
<p>Topics:</p>
<ul>
<li>00:00:00 – Introduction </li>
<li>00:02:06 – Akshay’s background and studies </li>
<li>00:04:14 – Work at Google and PhD program </li>
<li>00:06:29 – Sharing notebooks </li>
<li>00:08:18 – Starting work on marimo 2 years ago </li>
<li>00:12:48 – Avoiding notebook issues and building a DAG </li>
<li>00:18:39 – The difference of reactivity </li>
<li>00:20:39 – What is a marimo notebook? </li>
<li>00:23:39 – Video Course Spotlight </li>
<li>00:24:50 – Reproducibility and managing package requirements</li>
<li>00:27:49 – Using decorators for cells </li>
<li>00:30:23 – Writing a design document before any coding </li>
<li>00:34:08 – Interactivity and UI widgets </li>
<li>00:38:20 – Design decisions and built-in widgets </li>
<li>00:42:05 – Creating a deployable web application </li>
<li>00:44:34 – Exploring examples and tutorials </li>
<li>00:46:13 – Supporting DataFrame libraries with narwhals </li>
<li>00:48:00 – Migrating from a Jupyter notebook </li>
<li>00:52:02 – Working with cells and not running code </li>
<li>00:54:30 – A couple favorite tutorials </li>
<li>00:56:17 – What are you excited about in the world of Python? </li>
<li>00:57:39 – What do you want to learn next? </li>
<li>00:59:34 – How can people follow the project and yourself? </li>
<li>01:00:12 – Thanks and goodbye</li>
</ul>
<p>Show Links:</p>
<ul>
<li><a href="https://marimo.io/">marimo - a next-generation Python notebook</a></li>
<li><a href="https://www.youtube.com/watch?v=9R2cQygaoxQ">marimo: an open-source reactive notebook for Python - Akshay Agrawal (Nbpy2024) - YouTube</a></li>
<li><a href="https://www.tensorflow.org/">TensorFlow</a></li>
<li><a href="https://marimo.io/gallery">Made with marimo - marimo</a></li>
<li><a href="https://docs.marimo.io/faq.html#faq-problems">FAQ - marimo</a></li>
<li><a href="https://plutojl.org/">Pluto.jl — interactive Julia programming environment</a></li>
<li><a href="https://observablehq.com/">Observable: Build expressive charts and dashboards with code</a></li>
<li><a href="https://blog.jetbrains.com/datalore/2020/12/17/we-downloaded-10-000-000-jupyter-notebooks-from-github-this-is-what-we-learned/">We Downloaded 10,000,000 Jupyter Notebooks From Github – This Is What We Learned - The Datalore Blog</a></li>
<li><a href="https://leomurta.github.io/papers/pimentel2019a.pdf">A Large-scale Study about Quality and Reproducibility of Jupyter Notebooks</a></li>
<li><a href="https://marimo.io/blog/lessons-learned">Lessons learned reinventing the Python notebook - marimo</a></li>
<li><a href="https://realpython.com/podcasts/rpp/226/">Episode #226: PySheets: Spreadsheets in the Browser Using PyScript</a></li>
<li><a href="https://peps.python.org/pep-0723/">PEP 723 – Inline script metadata</a></li>
<li><a href="https://packaging.python.org/en/latest/specifications/inline-script-metadata/#inline-script-metadata">Inline script metadata - Python Packaging User Guide</a></li>
<li><a href="https://marimo.io/blog/sandboxed-notebooks">Serializing package requirements in marimo notebooks - marimo</a></li>
<li><a href="https://astral.sh/blog/uv-unified-python-packaging">uv: Unified Python packaging</a></li>
<li><a href="https://marimo.io/blog/newsletter-7">marimo Newsletter 7 - Jupyter to marimo</a></li>
<li><a href="https://docs.marimo.io/guides/integrating_with_marimo/custom_ui_plugins.html">Custom UI elements - marimo</a></li>
<li><a href="https://anywidget.dev/">anywidget - anywidget</a></li>
<li><a href="https://docs.marimo.io/guides/interactivity.html">Interactive elements - marimo</a></li>
<li><a href="https://realpython.com/podcasts/rpp/224/">Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries</a></li>
<li><a href="https://calmcode.io/course/marimo/introduction">Calmcode - marimo: Introduction</a></li>
<li><a href="https://marimo.io/discord">Join the marimo Discord</a> </li>
<li><a href="https://marimo.io/newsletter">marimo newsletter</a> </li>
<li><a href="https://twitter.com/marimo_io">marimo on Twitter</a> </li>
<li><a href="https://www.linkedin.com/company/marimo-io/">marimo on LinkedIn</a> </li>
<li><a href="https://akshayagrawal.com/">Akshay Agrawal’s website</a> </li>
<li><a href="https://x.com/akshaykagrawal">Aksahy on Twitter</a></li>
</ul>
<p>Level up your Python skills with our expert-led courses:</p>
<ul>
<li><a href="https://realpython.com/courses/navigate-namespaces-scope/">Navigating Namespaces and Scope in Python</a></li>
<li><a href="https://realpython.com/courses/python-decorators-101/">Python Decorators 101</a></li>
<li><a href="https://realpython.com/courses/using-jupyter-notebooks/">Using Jupyter Notebooks</a></li>
</ul> <p><a rel="payment" href="https://realpython.com/join">Support the podcast & join our community of Pythonistas</a></p>
