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
AI Book Club: Build a Reasoning Model (From Scratch)
·OnlineOnlineAugust's book is "Build a Reasoning Model (From Scratch)"!
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: Build a Reasoning Model (From Scratch)
Authors: Sebastian Raschka
Published: July 2026Manning (Promo code: AIBookClub should give you 45% off: https://www.manning.com/books/build-a-reasoning-model-from-scratch
O'rielly platform: https://learning.oreilly.com/library/view/build-a-reasoning/9781633434677/
Chapters:
- 1 Understanding reasoning models
- 2 Generating text with a pretrained LLM
- 3 Evaluating reasoning models
- 4 Improving reasoning with inference-time scaling
- 5 Inference-time scaling via self-refinement
- 6 Training reasoning models with reinforcement learning
- 7 Improving GRPO for reinforcement learning
- 8 Distilling reasoning models for efficient reasoning
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Book Description
Build a Reasoning Model (From Scratch) is a practical guide to understanding how modern reasoning-oriented LLMs work by building their core methods step by step. The book tells a clear engineering story: start with a conventional pre-trained LLM, learn how text generation works, build reliable evaluation tools, improve reasoning through inference-time methods, then move into training-based approaches such as reinforcement learning and distillation.The progression is deliberate. Early chapters establish the baseline model and explain text generation, KV caching, and evaluation with math verifiers. The middle chapters show how reasoning can be improved without changing model weights, using chain-of-thought prompting, sampling, self-consistency, response scoring, and self-refinement. Later chapters move to changing the model itself through reinforcement learning with verifiable rewards, GRPO improvements, format rewards, and finally distillation from stronger reasoning models into smaller ones.
65 attendees
Robotics Simulation with NVIDIA Isaac Sim - 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 robotics simulation with NVIDIA Isaac Sim, a simulation platform built on NVIDIA Omniverse for developing, testing, and training robotic systems in realistic virtual environments. We'll set up simulated robots, interact with sensors and environments, and experiment with controlling robots before ever touching physical hardware.
Isaac Sim combines GPU-accelerated physics, photorealistic rendering, synthetic sensor data, and robotics tooling in one environment. We'll explore how to load and control robots, work with cameras and other simulated sensors, build simple environments, and look at how simulation can be used for reinforcement learning, perception, and robotics development.
Depending on where people want to go, we can also experiment with Isaac Lab, NVIDIA's framework for robot learning and reinforcement learning built on top of Isaac Sim.
Some things to look up to get started:
Tooling:- NVIDIA Isaac Sim: https://developer.nvidia.com/isaac/sim
- Isaac Sim Documentation: https://docs.isaacsim.omniverse.nvidia.com/
- Isaac Lab: https://github.com/isaac-sim/IsaacLab
- Isaac Lab Documentation: https://isaac-sim.github.io/IsaacLab/
- NVIDIA Omniverse: https://www.nvidia.com/en-us/omniverse/
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).
26 attendees
World Models with DreamerV3 - 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 kicks off a run on world models: models that learn an internal representation of how an environment works, then use it to predict what happens next and to plan. It's a natural next step after the generation and RL events, tying both threads together.
We're starting with DreamerV3, a model-based RL agent and a great on-ramp to the idea. Dreamer learns a compact world model of its environment from experience, then trains its policy almost entirely inside imagined rollouts of that model rather than the real environment. It's lightweight (trains on a single GPU), works across many tasks with the same settings, and connects directly to the RL and MuJoCo work.
Some things to look up to get started:
Model:- DreamerV3 (Danijar Hafner): https://github.com/danijar/dreamerv3
- Paper: "Mastering Diverse Domains through World Models" https://arxiv.org/abs/2301.04104
Background:
- The original "World Models" paper (Ha and Schmidhuber): https://worldmodels.github.io/
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).
18 attendees
Past events
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