Min Ragan-Kelley

North Bay Python

No Project Scope Survives Contact with Users - Min Ragan-Kelley - 2026

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Practical Development Skills: Mastering web scraping, designing Python APIs for volatile data, and building civic tech on a budget.

Open Source and Community Building: Learning best practices for project scope management, contributing to large open-source projects, and technical communication.

Explore advanced topics in Python development, system reliability, ethical AI, and community-driven open source projects.

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A critical examination of the tech industry's ideological roots, connecting eugenics, data labor, and techno-optimism. Learn to build technology with social justice and empathy.

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A practical framework for applying technology to local activism and civic engagement, focusing on maximizing impact with minimal resources.

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Learn to build robust Python scrapers and APIs for volatile open web data, focusing on handling silent failures and maintaining data contracts.

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Explore essential technical communication and crisis communication skills needed to navigate rapid technological change and social uncertainty.

Crisis (Technical) Communication: Teaching Survival Skills You Didn’t Know...

A personal journey detailing the growth of an open-source project, emphasizing community building, vulnerability, and technical contribution.

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Applying feminist theory to network engineering, arguing that systemic problems (like latency) require intersectional, non-linear thinking.

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A case study on maintaining project scope in large open-source projects, crucial for sustainable software architecture.

No Project Scope Survives Contact with Users

North Bay Python 2026

Defining scope requires explicitly stating what problems the project is *not* trying to solve, which is a powerful way to manage expectations and say 'no'.

The Jupyter architecture—separating the UI (browser) from the execution (kernel) via a web server—allowed for flexible deployment, but this flexibility led to scope creep.

Early scope limitations (e.g., targeting small academic groups, Linux-only, no high availability) were necessary to keep the project manageable and focused.

As the project gained adoption (from small workshops to large institutions like Berkeley), the scope naturally expanded, leading to increased complexity (e.g., Kubernetes integration, high availability requirements).

The lesson learned is that while saying 'yes' feels good, it can incur significant long-term costs, making clear, documented scope communication essential for project health.

Learn how defining and defending a project scope is critical for open-source success, using the evolution of JupyterHub as a case study.

Min Ragan-Kelley begins by defining 'scope' not just as what a project aims to build, but what problems it explicitly chooses *not* to solve. In open-source work, having a clear scope allows developers to say 'no' effectively and efficiently, which is vital when external users propose changes or features.

The Jupyter project has grown from a simple IPython tool to a massive ecosystem managing millions of lines of code across dozens of repositories. The core architecture separates the user interface (UI) from the code execution (kernel) via a web server. This separation was initially chosen for accessibility (running on various platforms) and allowed the execution environment to be decoupled from the UI, enabling remote deployment.

When JupyterHub was first developed, the scope was intentionally limited to manage complexity. Constraints included: <ul><li>**Target Users:** Small academic groups (tens of users), not large enterprises.</li><li>**Technical Limits:** Linux-only support, requiring Python 3, and avoiding complex features like high availability (HA) or advanced security mechanisms.</li><li>**Goal:** To provide a single host with a single URL/port, simplifying IT policy and deployment for users.</li></ul>These limitations were crucial for keeping the initial development effort manageable.

The project's adoption quickly challenged its initial scope. Early adopters (like Jessica Ham) used it for small classes, but as institutions scaled up (e.g., Berkeley's Data Hub supporting thousands of students), the requirements shifted dramatically. The community began demanding features like: <ul><li>High Availability (HA) guarantees.</li><li>Support for massive scale (thousands of users).</li><li>Integration with advanced deployment tools (e.g., Kubernetes).</li></ul>This shift forced the project to expand its scope significantly, moving from a simple tool to complex infrastructure.

The talk concludes by highlighting the cost of scope creep. The initial small project became a massive undertaking, pulling the developer away from other core Jupyter work. The key takeaway is that while the desire to say 'yes' to every feature request is natural, it can impose a significant, sometimes crippling, cost on the project's focus and maintainability. Clear, documented scope communication is the most critical tool for sustainable open-source development.

No Project Scope Survives Contact with Users - 2026

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