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 (SF) and virtually.
AI book club is 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
7
Open-Source Video Generation: Diffusion & Transformer Models - 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 is about generating video with AI, picking up where the image generation event left off. As with images, there's no single model we're committed to — we'll explore several and see how far the open-source options have come. Open source is the focus, but commercial models (Sora, Runway, and others) are fair game if you want to compare.
Most current video models build on the same diffusion foundations as image generation, extended across time and increasingly using transformer/DiT backbones. We'll try a few open-source text-to-video and image-to-video models and talk through the practical tradeoffs: quality, clip length, speed, and hardware requirements.
Some things to look up to get started:
Open-source models:- Wan 2.2 (Alibaba Tongyi): versatile MoE model: text-to-video, image-to-video, and editing in one
- HunyuanVideo (Tencent): strong cinematic / photorealistic quality
- LTX-Video / LTX-2 (Lightricks): the fast one; ~5s clip in under a minute on a single GPU
- CogVideoX (Zhipu / THUDM): best at following detailed, multi-part prompts
- Mochi 1 (Genmo): flow-matching model known for fluid, coherent motion
- Stable Video Diffusion (Stability AI): earlier image-to-video, still widely used
Tooling:
- Hugging Face Diffusers: video pipelines: https://github.com/huggingface/diffusers
- ComfyUI: node-based workflows (popular for video too): https://github.com/comfyanonymous/ComfyUI
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).
27 attendees
AI Book Club: RAG with Python Cookbook
·OnlineOnlineJuly's book is "RAG with Python Cookbook"!
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/
-------------------------------------------------
About the book:
Title: RAG with Python Cookbook
Authors: Dominik Polzer
Published: May 2026O'rielly platform: https://learning.oreilly.com/library/view/rag-with-python/9798341600553/
Chapters:
- 1. Getting Started with RAG
- 2. Foundation Models
- 3. Loading Data
- 4. Data Preparation
- 5. Embeddings
- 6. Vector Databases and Similarity Searches
- 7. Retrieval
- 8. Agentic RAG
- 9. Graph RAG
- 10. Evaluating RAG Systems
- 11. RAG Web Apps
####
Book Description
As businesses race to unlock the full potential of large language models (LLMs), a critical challenge has emerged: How do you connect these tools to real-time, external data to solve real-world problems? Retrieval-augmented generation (RAG) is the answer. By combining LLMs with information retrieval, RAG empowers you to build everything from intelligent chatbots to autonomous, task-solving agents.
Packed with over 70 practical recipes, this go-to guide tackles a wide range of GenAI applications through structured hands-on learning. Author Dominik Polzer provides the tools you need to design, implement, and optimize RAG systems for your unique use cases. Whether you're working with simple data retrieval or designing cutting-edge autonomous agents, this cookbook will help you stay ahead of the curve.- Learn core RAG components including embedding, retrieval, and generation techniques
- Understand advanced workflows like semantic-aware chunking and multi-query prompting
- Build custom solutions such as chatbots and autonomous agents for specific data challenges
- Continuously evaluate and optimize systems for accuracy, relevance, and performance
44 attendees
Open-Source Music Generation: Text-to-Music & Lyrics-to-Song - 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 is about generating music and audio with AI. As with the image and video events, there's no single model we're locked into — the point is to explore what's out there and actually try a few. We'll focus on open-source models, but you're welcome to bring commercial ones (Suno, Udio, and friends) if you want to compare — worth noting there isn't a fully open-source Suno equivalent yet, though the gap is closing.
We'll look at the two main flavors: text-to-music (instrumental / sound design from a prompt) and lyrics-to-song (full tracks with vocals and accompaniment). Under the hood these lean on the same diffusion and transformer/language-model approaches as image and video, applied to audio. I'll research and try some of the best open-source options ahead of the stream, and we'll talk through the practical tradeoffs: quality, track length, controllability, speed, and licensing.
Some things to look up to get started:
Open-source models:- YuE (YuE AI): lyrics-to-song — full tracks up to ~5 min with synchronized vocals and accompaniment
- ACE-Step: fast and controllable — a ~4-min song in seconds; diffusion + linear-transformer design
- MusicGen (Meta / AudioCraft): versatile text-to-music with melody conditioning (note: CC BY-NC — non-commercial output license)
- Stable Audio Open (Stability AI): great for ambient/textural audio, SFX, and samples (short clips, not full songs)
Tooling:
- AudioCraft (Meta) — MusicGen / AudioGen: https://github.com/facebookresearch/audiocraft
- Hugging Face — audio models and pipelines: https://huggingface.co/models?pipeline_tag=text-to-audio
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).
11 attendees
Past events
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