Name: Jupyter Day
Start: 2026-10-19T16:00:00+00:00
End: 2026-10-20T00:00:00+00:00
Location: San Jose, United States

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Jupyter Day - October 2026 | Kure‑Cal

Schedule

37 events • 2 concurrent events

Workshop

12:00 am to 12:00 am

Jupyter Day

Learn more and help shape the future of AI in Jupyter through hands-on collaboration, demos, roadmap planning, and community sessions.

Registration

8:00 am to 5:00 pm

Registration + Badge Pickup

Other

8:00 am to 5:00 pm

Registration + Badge Pickup

210C Foyer

Keynote

9:00 am to 9:10 am

Welcome + Opening Remarks

Keynote

9:00 am to 9:10 am

Welcome + Opening Remarks

210C

Keynote

9:10 am to 9:30 am

Keynote: Jupyter at Twenty-Five: Curiosity, Community, and the Work Ahead

In December 2001, a graduate student shared a few hundred lines of Python designed to make interactive computing a little less tedious. There was no grand plan for the future of interactive computing. It was simply someone building a better place for people to do their work.That prompt grew into a rich shell, then an interactive notebook, then a global ecosystem. Twenty-five years later, Jupyter is how millions of people explore and share ideas, in classrooms, research labs, and quiet

Keynote

9:10 am to 9:30 am

Keynote: Jupyter at Twenty-Five: Curiosity, Community, and the Work Ahead

210C

Workshop

9:30 am to 9:45 am

Children of Jupyter: What Core Should Steal Back

Jupyter's 2011 kernel design has aged well: Colab, VS Code, Deepnote, and even ChatGPT's Python tool all run Jupyter kernels today. The notebook itself, though, still runs on the same linear REPL as in 2011, and hidden state remains its most famous complaint. A generation of descendants (Observable, Pluto, marimo, Hex, Livebook, Deepnote) has spent a decade shipping fixes for these problems. It is past time for Jupyter core to steal from its children, but first it needs a survey of

Other

9:30 am to 9:45 am

Children of Jupyter: What Core Should Steal Back

Max Klein talks about what the core of Jupyter should steal back from its children.

210C

Talk

9:50 am to 10:05 am

Notebooks Behind the Firewall: AI Agents and Real Patient Data in Jupyter

Biomedical researchers have the same AI agents as everyone else, but can't point them at their actual data. Patient records can't leave the medical center, so the agent never sees them. Institutions already host compliant model inference. The gap sits at the agent layer.This talk covers how we use Biorouter to close that gap at UCSF and connect it to Jupyter. Biorouter is an open-source research agent that routes work across institutionally hosted, local, and commercial model

Other

9:50 am to 10:05 am

Notebooks Behind the Firewall: AI Agents and Real Patient Data in Jupyter

Wanjun Gu discusses AI agents and real patient data in Jupyter notebooks behind firewalls.

210C

Exhibition

10:10 am to 10:25 am

Your AI-Generated GPU Kernel Passed the Notebook. It Is Still Wrong.

Attendees will leave able to turn a persuasive notebook demo into a reproducible correctness gate for AI-generated CUDA and Triton kernels.An AI-generated kernel can compile, run quickly, and match a reference on a few friendly tensors while still failing on boundary shapes, strides, aliasing, dtype extremes, special values, or reduction order. This talk builds the verification workflow as a Jupyter notebook: an independent CPU oracle, operator-aware input generation, explicit toleranc

Other

10:10 am to 10:25 am

Your AI-Generated GPU Kernel Passed the Notebook. It Is Still Wrong.

Dipankar Sarkar explains why AI-generated GPU kernels can be wrong despite passing notebook tests.

210C

Workshop

10:30 am to 11:00 am

Break

Assorted Bagels + Cream Cheese - Gluten-free bagel available - Vegan options availableAssorted Low Fat Yogurt (V/GF/NF) - Vegan option available

Other

10:30 am to 11:00 am

Break

210C

Talk

11:00 am to 11:15 am

Upstream Is Everyone’s Business: How Can Industry Contribute to Jupyter?

This session is intended to be a facilitated discussion about the opportunities and obstacles people encounter when trying to contribute to the Jupyter ecosystem from within a company.SESSION FORMAT We will begin with a brief framing of the question and a few common models for organizational participation in open source.Most of the session will then be dedicated to experience-sharing among participants.The final portion of the session will synthesize the discussions in

Other

11:00 am to 11:15 am

Upstream Is Everyone’s Business: How Can Industry Contribute to Jupyter?

Inessa Pawson explores how industry can contribute to Jupyter.

210C

Talk

11:15 am to 11:30 am

When LLMs Get Infrastructure Wrong: Building Reliable AI Workflows in Jupyter

LLMs can summarize logs, explain failures, analyze configurations, and suggest fixes in seconds. But infrastructure engineering has an uncomfortable requirement: an answer that merely sounds correct isn't good enough.In this session, I’ll use Jupyter to build and evaluate an AI-assisted infrastructure analysis workflow where LLM-generated conclusions are treated as hypotheses rather than facts. We’ll examine common failure modes including hallucinated root causes, missing context,

Other

11:15 am to 11:30 am

When LLMs Get Infrastructure Wrong: Building Reliable AI Workflows in Jupyter

Sowmya Varakala discusses building reliable AI workflows in Jupyter when LLMs get infrastructure wrong.

210C

Workshop

11:35 am to 11:50 am

How Does Jupyter Display a Plot? Making Passagemath Plots Work in Any Notebook

When plotting in passagemath, I noticed that plots sometimes displayed text instead of an image. passagemath is a modular version of the math software SageMath. SageMath has its own display system, but the system is not in many notebook environments. I learned that IPython's DisplayFormatter collects the available representations and the notebook frontend chooses which one to display. In contrast, SageMath asks the frontend what it supports and renders just one thing. By adding reprs

Other

11:35 am to 11:50 am

How Does Jupyter Display a Plot? Making Passagemath Plots Work in Any Notebook

Samuel Chen and Matthias Köppe explain how Passagemath plots work in any notebook.

210C

Networking

11:55 am to 12:15 pm

Lightning Intros

Ever wished you could meet everyone at a conference in one go? This is your chance!During Lightning Intros, each participant gets 30 seconds to introduce themselves to the full group and share what topics they’re most excited to talk about. Then, during the reception that follows, you can seek each other out and dive into those conversations.No slides, no prep—just you and a microphone.

Lightning intros

11:55 am to 12:15 pm

Lightning Intros

210C

Meal

12:15 pm to 1:29 pm

Lunch

The Lunch BoxSandwich: - Chicken Caesar Roll Up | Grilled Chicken Breast, Romaine Lettuce, Gouda Cheese, Sun Dried Tomatoes, Caesar Dressing, Spinach Tortilla (NF) - So California Sandwich | Smoked Turkey, Maple Bacon, Garlic Aioli, Arugula, Whole Wheat Multigrain Hero (DF/NF) - Romaine Salad | Turnips, Lentils, Cranberries, Apple Cider Vinaigrette (Vegan/GF/NF) - Chipotle Steak (GF/DF/NF) - Soy Ginger Tofu (Vegan/GF)Sides: - Kettle Chips (V/NF/GF) - Honey C

Meal

12:15 pm to 1:29 pm

Lunch

210C

Workshop

1:35 pm to 2:15 pm

The Future of Jupyter: Building the Next Core, Together

Project Jupyter's future is being built in the open, but much of it lives outside the default install. This panel brings maintainers from multiple organizations to align on what's next.We'll debate the foundational capabilities that would benefit everyone: disconnection-resilient execution (a reload never loses a running cell), robust real-time collaboration, native diffing, performance for large notebooks, and simpler, better-documented server APIs developers can build

Other

1:35 pm to 2:15 pm

The Future of Jupyter: Building the Next Core, Together

David Qiu, Kirstie Whitaker, Zach Sailer, Min RK, Chris Holdgraf, and Jason Grout discuss building the next core of Jupyter together.

210C

Workshop

2:15 pm to 2:30 pm

Invisible Infrastructure at Scale: Operating 5,000 Daily Notebook Servers Across Multiple Kubernetes

Starting a Jupyter notebook appears to be one simple action. At approximately 5,000 real notebook-server sessions on a typical day, that action becomes considerably more complex - especially because the workloads are not identical. Each notebook may require a different combination of CPU, memory, GPU capacity, container image, storage, and runtime configuration, with some resources available only in particular clusters.This talk presents how we extended KubeSpawner to separate the Jupy

Other

2:15 pm to 2:30 pm

Invisible Infrastructure at Scale: Operating 5,000 Daily Notebook Servers Across Multiple Kubernetes

Karthik Ravi and Karthik Banala share insights on operating 5,000 daily notebook servers across multiple Kubernetes clusters.

210C

Workshop

2:35 pm to 2:50 pm

Running GPU Jupyter Workspaces on Kubernetes: Scheduling, Autoscaling, and Policy Lessons

Kubernetes autoscaling is built for workloads that can be killed and rescheduled at any moment. A notebook is the opposite: its state lives in a running kernel, an eviction interrupts a person mid-work, and its GPU cannot be shared. We added GPU workspaces to jupyter-k8s, an open-source Kubernetes operator for Jupyter, and most of the hard questions turned out to be one question: which layer should own this knob, cloud instances, autoscaler, operator, infrastructure-as-code, or UI.The

Other

2:35 pm to 2:50 pm

Running GPU Jupyter Workspaces on Kubernetes: Scheduling, Autoscaling, and Policy Lessons

Andrii Ieroshenko and Gaurav Bhanderi discuss scheduling, autoscaling, and policy lessons for running GPU Jupyter workspaces on Kubernetes.

210C

Workshop

2:55 pm to 3:10 pm

Cartographers on Your Hub

JupyterGIS is an in-development ecosystem project to enable collaboration and reproducibility in Geospatial Science. Cartographers, designers who specialize in representing spatial data, are not the users it was most obviously built for, but I'd argue they may be a major component of its first serious userbase.Professional cartographers do surprisingly little of their work in GIS. They use it for cleaning, filtering, and basic symbolization, then hand off to a design tool for the

Other

2:55 pm to 3:10 pm

Cartographers on Your Hub

Benjamin Szeghy talks about cartographers on your hub.

210C

Other

3:10 pm to 3:30 pm

Break

210C

Workshop

3:35 pm to 3:50 pm

Roadmapping to Improve Community Capacity, Alignment, and Team Productivity

In The Voices of JupyterHub (<a href="https://voicesofjupyterhub.orgmycology.com" target="_blank" rel="noopener noreferrer">https://voicesofjupyterhub.orgmycology.com</a>) ethnographic report, the JupyterHub leadership team identified that our community "struggles with onboarding new contributors and delegating responsibility", and that "project management, prioritization, and triage processes are a source of stress for maintainers and confusion for newcomers". Building on

Workshop

3:50 pm to 4:05 pm

Fantastic Environments and Where to Find Them

Frontier labs pay millions for a single RL environment representing one high-value task: software engineering, cybersecurity, project management. That closed industry is a big part of why frontier agents perform.This talk is a field guide to cracking it in the open. I'll cover how the environment business works, from research and reporting; the open source counterparts, Prime Intellect, TRL, and OpenEnv; and an end-to-end walkthrough of training a cybersecurity agent for free. You

Talk

4:10 pm to 4:25 pm

Beyond WER: Interactive Failure Analysis for Multilingual Audio Models in Jupyter

Word Error Rate is often treated as the final answer when evaluating speech recognition models. But a single aggregate score can hide where a model actually fails.In multilingual and conversational speech, two models with similar WER can behave very differently across dialects, code-switching, acoustic conditions, speaking styles, and data sources.In this talk, I will show how Jupyter can turn model evaluation from a static benchmark into an interactive and reproducible investig

36 concurrent sessions

Session

Jupyter Day

12:00 am to 12:00 am

workshop

Learn more and help shape the future of AI in Jupyter through hands-on collaboration, demos, roadmap planning, and community sessions.

Overview

Schedule

Jupyter Day

Registration + Badge Pickup

Registration + Badge Pickup

Welcome + Opening Remarks

Welcome + Opening Remarks

Keynote: Jupyter at Twenty-Five: Curiosity, Community, and the Work Ahead

Keynote: Jupyter at Twenty-Five: Curiosity, Community, and the Work Ahead

Children of Jupyter: What Core Should Steal Back

Children of Jupyter: What Core Should Steal Back

Notebooks Behind the Firewall: AI Agents and Real Patient Data in Jupyter

Notebooks Behind the Firewall: AI Agents and Real Patient Data in Jupyter

Your AI-Generated GPU Kernel Passed the Notebook. It Is Still Wrong.

Your AI-Generated GPU Kernel Passed the Notebook. It Is Still Wrong.

Break

Break

Upstream Is Everyone’s Business: How Can Industry Contribute to Jupyter?

Upstream Is Everyone’s Business: How Can Industry Contribute to Jupyter?

When LLMs Get Infrastructure Wrong: Building Reliable AI Workflows in Jupyter

When LLMs Get Infrastructure Wrong: Building Reliable AI Workflows in Jupyter

How Does Jupyter Display a Plot? Making Passagemath Plots Work in Any Notebook

How Does Jupyter Display a Plot? Making Passagemath Plots Work in Any Notebook

Lightning Intros

Lightning Intros

Lunch

Lunch

The Future of Jupyter: Building the Next Core, Together

The Future of Jupyter: Building the Next Core, Together

Invisible Infrastructure at Scale: Operating 5,000 Daily Notebook Servers Across Multiple Kubernetes

Invisible Infrastructure at Scale: Operating 5,000 Daily Notebook Servers Across Multiple Kubernetes

Running GPU Jupyter Workspaces on Kubernetes: Scheduling, Autoscaling, and Policy Lessons

Running GPU Jupyter Workspaces on Kubernetes: Scheduling, Autoscaling, and Policy Lessons

Cartographers on Your Hub

Cartographers on Your Hub

Break

Roadmapping to Improve Community Capacity, Alignment, and Team Productivity

Fantastic Environments and Where to Find Them

Beyond WER: Interactive Failure Analysis for Multilingual Audio Models in Jupyter

Jupyter Day

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