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 (NY) and virtually.
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
5
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).
21 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).
6 attendees
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).
3 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).
1 attendee
Past events
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