Envisioning Stories with Generative AI
2025-09-18 12:00 - 13:00 UTC | https://openteams.com/event/envisioning-stories-with-generative-ai/
What if your e-reader had an “illustrate” option to quickly depict any passage in a book as a visual scene? Generative AI can now readily transform content from one modality to another. This enables a story written in text form to be instantly transferred to visual form, potentially enhancing readers’ engagement with the story. In this talk, Dr. Roemmele discusses the research endeavor of automatically generating scene illustrations for text-based stories, outlining current AI techniques for this task and the challenges that persist. She will also demonstrate an example implementation of this proposed e-reader “illustrate” feature using accessible tools and model APIs.

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About the Speaker
Melissa Roemmele is a research scientist exploring the use of AI and NLP technologies to augment human creativity. She is currently part of the Midjourney Storytelling Lab, a team of researchers developing tools for AI-supported storytelling. Her work focuses on how to evaluate and optimize language-based interactions with these tools. She holds a PhD in Computer Science from the University of Southern California, where her 2018 dissertation explored emerging applications of language modeling to creative writing assistance.
About the Open Source Architect Community
The Open Source Architect (OSA) Community is an invitation-only group for seasoned software architects who are passionate about open source technology. Request to join the OSA Community: https://forms.gle/7efbynVzYhhH2LCQ7
We review each application carefully. If it’s a fit, you’ll get an invite to join the space where it all happens. For the latest updates on all things open source, follow our public feed on LinkedIn.

Bodo DataFrames: HPC-based Drop-in Pandas Replacement
2025-11-20 12:00 - 13:00 | https://openteams.com/event/bodo-dataframes-hpc-based-drop-in-pandas-replacement/
Pandas is a popular library for data scientists but it struggles with large datasets; programs either become too slow or run out of memory. In this talk, Ehsan Totoni (Bodo.ai) will introduce Bodo DataFrames as a drop-in replacement for the pandas library that uses high performance computing (HPC) based techniques such as Message Passing Interface (MPI) and JIT compilation for acceleration and scaling. Ehsan will give an overview of its architecture and explain how it avoids the problems of pandas (while keeping user code the same), go over concrete examples, and finally discuss current limitations. This talk is for pandas users who would like to run their code on larger data while avoiding frustrating code rewrites to other APIs.

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About the Speaker
Ehsan Totoni is an entrepreneur, computer science researcher, and software engineer working on democratization of High Performance Computing (HPC) for data engineering, data science, and AI/ML. He received his PhD in Computer Science from the University of Illinois at Urbana-Champaign, working on various aspects of HPC and Parallel Computing. Ehsan then worked as a research scientist at Intel Labs and Carnegie Mellon University, focusing on programming systems to address the gap between programmer productivity and computing performance. He co-founded Bodo.ai in 2019 and is advancing Bodo’s mission of bringing HPC to all data applications.
About the Open Source Architect Community
The Open Source Architect (OSA) Community is an invitation-only group for seasoned software architects who are passionate about open source technology. Request to join the OSA Community: https://forms.gle/7efbynVzYhhH2LCQ7
We review each application carefully. If it’s a fit, you’ll get an invite to join the space where it all happens. For the latest updates on all things open source, follow our public feed on LinkedIn.

Plug-and-Play Zero-Shot NLP with Sieves
2025-12-12 11:00 - 12:00
Generative models are dominating the spotlight lately – and rightly so. Their flexibility and zero-shot capabilities make it incredibly fast to prototype NLP applications. However, one-shotting complex NLP problems often isn’t the best long-term strategy.
Introducing Sieves: a lightweight, model-agnostic toolkit for building plug-and-play NLP document processing pipelines. It brings together structured generation, modular components, and practical utilities to help you prototype quickly and scale confidently, without large datasets or fine-tuning.
Whether you’re building information extraction workflows, classification pipelines, or multi-step document processing systems, this tutorial will show you how to do it faster, cleaner, and more reliably.
About the Speaker
I’m a machine learning engineer, these days mostly working in natural language processing. I have soft spots for data visualization, interpretable ML, and the decentralization of AI.
I build NLP systems for a living. Occasionally they work. I don’t believe that attention is all we need. What I do believe in: pragmatism, taking a deep breath before falling for the next hype, and touching grass.
About the Open Source Architect Community
The Open Source Architect (OSA) Community is an invitation-only group for seasoned software architects who are passionate about open source technology. Request to join the OSA Community: https://forms.gle/7efbynVzYhhH2LCQ7
We review each application carefully. If it’s a fit, you’ll get an invite to join the space where it all happens. For the latest updates on all things open source, follow our public feed on LinkedIn.

Lazy Buckaroo: Analyze Big Data on Your Laptop
2026-01-15 12:00 - 13:00 | https://openteams.com/event/lazy-buckaroo-analyze-big-data-on-your-laptop/
Big datasets don’t wait for your RAM to catch up. When local files brush up against memory limits, most tools buckle, forcing you into blind sampling and guesswork. Buckaroo flips the script by rendering only what’s visible, leaning on Polars’ lazy engine for the heavy lifting, and surfacing insights in real time as the UI updates.
In this talk, Paddy Mullen will walk us through how Buckaroo makes large-scale, local data exploration fast, fluid, and surprisingly joyful. No clusters. No cloud. Your favorite laptop is all you need.

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About the Speaker
Paddy Mullen is a full‑stack engineer and data‑tooling builder. An early employee at Anaconda, he contributed to the Bokeh visualization library. He has built data tools and led teams at hedge funds and startups. Since 2023 he has been developing Buckaroo, an interactive dataframe viewer for notebook environments.
About the Open Source Architect Community
The Open Source Architect (OSA) Community is an invitation-only group for seasoned software architects who are passionate about open source technology. Request to join the OSA Community: https://forms.gle/7efbynVzYhhH2LCQ7
We review each application carefully. If it’s a fit, you’ll get an invite to join the space where it all happens.
For the latest updates on all things open source, follow our public feed on LinkedIn.

Reducing LLM Costs Through Programmatic Tooling
2026-02-19 12:00 - 13:00 | https://openteams.com/event/reducing-llm-costs-through-programmatic-tooling/
The Model Context Protocol (MCP) provides a standardized way for large language models (LLMs) to discover and invoke external tools through a client–server architecture. In its most common usage, MCP tools are called directly by the model during inference, one action at a time, via structured tool calls. While effective, this approach can become limiting when workflows grow more complex.
In this session, we’ll introduce MCP Code Mode, a paradigm shift in how LLMs interact with tools. Rather than emitting a series of discrete tool calls, the model generates executable code that invokes, sequences, and coordinates MCP tools directly. The result: clearer intent, tighter control flow, and more powerful orchestration.
We’ll start with a quick tour of MCP’s architecture and the standard tool-calling patterns most of us are familiar with, then dig into where those patterns begin to creak under real-world complexity. From there, we’ll explore how Code Mode unlocks a more expressive and efficient way for LLMs to reason about actions by using code as the glue.
To make it concrete, we’ll walk through live demos that compare direct tool calling with code-based orchestration, highlighting where Code Mode shines in practice and why it can be a game-changer for building robust, scalable AI systems.

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About the Speaker
Eric Charles is an active contributor and committer to several open source projects, including Jupyter and Apache. He is the founder and CEO of Datalayer (https://datalayer.ai), a platform for AI-driven data analysis. Prior to founding Datalayer, Eric collaborated with leading SaaS companies to design and implement innovative open source solutions.
About the Open Source Architect Community
The Open Source Architect (OSA) Community is an invitation-only group for seasoned software architects who are passionate about open source technology. Request to join the OSA Community: https://forms.gle/7efbynVzYhhH2LCQ7
We review each application carefully. If it’s a fit, you’ll get an invite to join the space where it all happens.
For the latest updates on all things open source, follow our public feed on LinkedIn.
