World Models with V-JEPA 2: prediction in representation space

About this event

Welcome 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 (https://github.com/facebookresearch/vjepa2?utm_source=luma) • ​V-JEPA 2 in Transformers: https://huggingface.co/docs/transformers/model_doc/vjepa2 (https://huggingface.co/docs/transformers/model_doc/vjepa2?utm_source=luma) • ​V-JEPA 2 paper: https://huggingface.co/papers/2506.09985 (https://huggingface.co/papers/2506.09985?utm_source=luma) • ​Meta AI research overview: https://ai.meta.com/research/vjepa/ (https://ai.meta.com/research/vjepa/?utm_source=luma) ​​​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).