About us
Welcome to our AI Meetup! We are a passionate community dedicated to building and learning about artificial intelligence. Whether you're an expert or just starting out, join us to share knowledge, collaborate on projects, and explore the fascinating world of AI together.
We'll be getting different events off the ground, both locally (Seattle) and virtually.
I'd like to have an AI book club going again in 2024, so if you have recommendations for us to read, let us know!
We'll AI cover topics such as Machine Learning (ML), Large Language Models (LLMs), Deep Learning, Data engineering, MLOps, Python, Computer Vision, Natural Language Processing (NLP), the Latest AI developments, and more!
Questions? Reach out to Sage Elliott on LinkedIn: https://www.linkedin.com/in/sageelliott/
Upcoming events
3
World Models with NVIDIA Cosmos: Physical AI - AI Build & Learn
·OnlineOnlineWelcome to AI Build & Learn, a weekly AI engineering stream where we pick a new topic and learn by building together.
This event goes bigger on world models with NVIDIA Cosmos, an open family of world foundation models built for physical AI. Where DreamerV3 learns a small world model for a single agent, Cosmos is a large generative model that simulates the physical world itself: predicting future video, running action-conditioned rollouts, and generating synthetic data to train robots and autonomous machines. It ties directly back to the Isaac Sim event.
Cosmos 3 (released 2026) exposes two surfaces: a Reasoner for understanding and planning, and a Generator for world simulation and future prediction. The weights are open (OpenMDW license) and available on Hugging Face.
Some things to look up to get started:
Model:
- NVIDIA Cosmos (open platform of world models): https://github.com/nvidia/cosmos
- Cosmos overview: https://www.nvidia.com/en-us/ai/cosmos/
- Cosmos Cookbook (runnable recipes): https://nvidia-cosmos.github.io/cosmos-cookbook/
Resources
- GitHub: https://github.com/sagecodes/ai-build-and-learn
- Events Calendar: https://luma.com/ai-builders-and-learners
- Slack (Discuss during the week): https://slack.flyte.org/
- Hosted by Sage Elliott: https://www.linkedin.com/in/sageelliott/
In this stream
- Intro to topic
- Community Discussion
- Practical examples
Community challenge (optional)
Try spending 30–90 minutes during the week learning or building something related to the topic, then share what you’re working on in Slack.Note on Flyte / Union
You may see Flyte used in some demos. Flyte is an open-source AI orchestration platform maintained by Union (where I work) for building scalable, durable, and observable AI workflows. You do not need to use Flyte to participate.- Union: https://www.union.ai/
- Flyte: https://flyte.org/
Drop a comment with ideas for future topics (agents, RAG, MLOps, robotics, frameworks, and more).
7 attendees
World Models with V-JEPA 2: prediction in representation space
·OnlineOnlineWelcome to AI Build & Learn, a weekly AI engineering stream where we pick a new topic and learn by building together.
This event is about world models with V-JEPA 2, Meta's self-supervised video model that learns by predicting in representation space instead of generating pixels. We'll load the model, hide parts of a video clip, watch it predict the missing pieces as embeddings rather than images, and measure how good those predictions actually are.
V-JEPA 2 is trained on internet-scale video with no labels, and it has no decoder at all: the predictor emits vectors, not frames. That constraint makes it a great topic to build around, because "show me what it predicted" stops being a screenshot and starts being a measurement. We'll explore how the model represents video, how to probe those representations with a single frozen linear layer, why cosine similarity can quietly mislead you, and where the pretrained predictor stops behaving like a world model.
Depending on where people want to go, we can also compare it against pixel-space world models like NVIDIA Cosmos and latent world models like DreamerV3, and look at how JEPA-style representations get used for robotics, planning, and perception.
Some things to look up to get started:
- V-JEPA 2 (Meta): https://github.com/facebookresearch/vjepa2
- V-JEPA 2 in Transformers: https://huggingface.co/docs/transformers/model_doc/vjepa2
- V-JEPA 2 paper: https://huggingface.co/papers/2506.09985
- Meta AI research overview: https://ai.meta.com/research/vjepa/
Resources
- GitHub: https://github.com/sagecodes/ai-build-and-learn
- Events Calendar: https://luma.com/ai-builders-and-learners
- Slack (Discuss during the week): https://slack.flyte.org/
- Hosted by Sage Elliott: https://www.linkedin.com/in/sageelliott/
In this stream
- Intro to topic
- Community Discussion
- Practical examples
Community challenge (optional)
Try spending 30–90 minutes during the week learning or building something related to the topic, then share what you’re working on in Slack.Note on Flyte / Union
You may see Flyte used in some demos. Flyte is an open-source AI orchestration platform maintained by Union (where I work) for building scalable, durable, and observable AI workflows. You do not need to use Flyte to participate.- Union: https://www.union.ai/
- Flyte: https://flyte.org/
Drop a comment with ideas for future topics (agents, RAG, MLOps, robotics, frameworks, and more).
3 attendees
AI Book Club: Vision Language Models (VLMs)
·OnlineOnlineSeptember's book is "Vision Language Models!" (due to travel its pushed out 1 week from normal date)
This is a casual-style event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back.
Feel free to join the discussion even if you have not read the book chapters! :)
Want to discuss the contents during the reading week? Join the Flyte MLOps Slack group https://slack.flyte.org/
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About the book:
Title: Vision Language Models
Authors: Merve Noyan, Andrés Marafioti, Miquel Farré, Orr Zohar
Published: June 2026O'rielly: https://learning.oreilly.com/library/view/vision-language-models/9798341624030/
Chapters:
- 1. Introduction to Vision and Language
- 2. Vision Language Model Applications
- 3. Vision Language Model Training
- 4. Training Data and Preprocessing for VLMs
- 5. Post-Training Vision Language Models
- 6. Core Architectures of Vision Language Models
- 7. Deploying Models for Inference at Scale
- 8. Document AI
- 9. Video-Language Models
- 10. Any-to-Any Models
- 11. Advanced Topics and Cutting-Edge Research
Book Description
Vision language models (VLMs) combine computer vision and natural language processing to create powerful systems that can interpret, generate, and respond in multimodal contexts. Vision Language Models is a hands-on guide to building real-world VLMs using the most up-to-date stack of machine learning tools from Hugging Face, Meta (PyTorch), NVIDIA (Cuda), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farré, Andrés Marafioti, and Orr Zohar. From image captioning and document understanding to advanced zero-shot inference and retrieval-augmented generation (RAG), this book covers the full VLM application and development lifecycle.
Designed for ML engineers, data scientists, and developers, this guide distills cutting-edge VLM research into practical techniques. Readers will learn how to prepare datasets, select the right architectures, fine-tune and deploy models, and apply them to real-world tasks across a range of industries.- Explore core model architectures and alignment techniques
- Train and fine-tune VLMs with Hugging Face, PyTorch, and others
- Deploy models for applications like image search and captioning
- Implement advanced inference strategies, from zero-shot to agentic systems
- Build scalable VLM systems ready for production use
7 attendees
Past events
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