Olivia Buzek
Senior Staff Developer Advocate for AI
Boulder, Colorado, United States
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Olivia has worked at the intersection of computers, language and intelligence for over 15 years. During her time working in machine learning and AI, she's worked as an MLOps Engineer, engineering manager, trustworthy AI architect, and developer advocate. Early on, she worked to scale NLP training processes to serve hundreds of classical ML models. She later led a team to share those models across the company via inner source, then took many of those concepts to work in the trustworthy and responsible AI space. Since generative AI has taken off, she's turned her focus towards innovating new patterns of AI and human interaction.
Her current focus is on building well-designed user experiences both for developers and consumers of AI applications. Olivia subscribes to neither AI hype nor AI doomerism: instead, she wants to build a world where human creativity and AI can coexist, and where builders of AI applications have a responsibility to their users to ensure that humans thrive when using AI tools. She is currently investigating how to build generative AI applications that use LLMs efficiently, effectively, and in ways that support the people whose creativity helped make AI happen.
Area of Expertise
Topics
Zen and the Art of Context Maintenance
Since the introduction of MCP, builders of AI applications have a new problem: how do you best make use of MCP to optimize limited context windows when designing AI agents? In this session, we’ll help you find harmony within the complexity of design patterns for managing agent context. Attendees will come away with a better understanding of techniques like context compression, context folding, and when to use various flavors of RAG or MCP. You’ll walk away understanding what goes into a context window, and how to keep it organized and useful.
Building Your (Local) LLM Second Brain
LLMs are hotter than ever, but most LLM-based solutions available to us require you to use models trained on data with unknown provenance, send your most important data off to corporate-controlled servers, and use prodigious amounts of energy every time you write an email.
What if you could design a “second brain” assistant with OSS technologies, that lives on your laptop?
We’ll walk through the OSS landscape, discussing the nuts and bolts of combining Ollama, LangChain, OpenWebUI, Autogen and Granite models to build a fully local LLM assistant. We’ll also discuss some of the particular complexities involved when your solution involves a local quantized model vs one that’s cloud-hosted.
In this talk, we'll build on the lightning talk to include complexities like:
* how much latency are you dealing with when you're running on a laptop?
* does degradation from working with a 7-8b model reduce effectiveness?
* how do reasoning + multimodal abilities help the assistant task?
InnerSource Summit
IBM has been in AI for decades, but our early AI technology was built in silos, making it hard to gain market relevance. When generative AI exploded, the company faced a choice: learn to collaborate or become irrelevant. We'll talk about how we rebuilt our collaboration infrastructure using InnerSource as a bridge to open source, making our best strategic technology available to all our developers. And in the end, we'll answer the question: will human collaboration survive the age of AI?
Women in Data Science - San Jose
Co-led workshops on the Granite family of models and the Docling open source project
Open Source Summit North America 2025 Sessionize Event
DevOpsDays Austin
(Lightning talk edition)
LLMs are hotter than ever, but most LLM-based solutions available to us require you to use models trained on data with unknown provenance, send your most important data off to corporate-controlled servers, and use prodigious amounts of energy every time you write an email.
What if you could design a “second brain” assistant with OSS technologies, that lives on your own laptop?
We’ll take a whirlwind tour through our second brain implementation, combining Ollama, LangChain, OpenWebUI, Autogen and Granite models to build a fully local LLM assistant.
FOSDEM
(Lightning talk edition)
LLMs are hotter than ever, but most LLM-based solutions available to us require you to use models trained on data with unknown provenance, send your most important data off to corporate-controlled servers, and use prodigious amounts of energy every time you write an email.
What if you could design a “second brain” assistant with OSS technologies, that lives on your own laptop?
We’ll walk through the OSS landscape, discussing the nuts and bolts of combining Ollama, LlamaIndex, OpenWebUI, Autogen and Granite models to build a fully local LLM assistant. We’ll also discuss some of the particular complexities involved when your solution involves a local quantized model versus one that’s cloud-hosted.
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