📣 Excited to share that I’ve joined Anyscale as a Staff Developer Advocate! This is the brilliant team behind ✨Ray✨ ray - the open-source compute engine powering AI workloads at OpenAI, Spotify, Netflix, Uber, Amazon, Pinterest, and many more! Can’t

Distributed computing is easy to start. Scaling it in production? Not so much. ray is the go-to for AI scaling — but running it at scale can get tricky. Anyscale makes Ray production-ready from the start. No hacks, no duct tape. 🗓️ Join us July 30 to learn: -Common

How Klaviyo uses Ray for data processing, training, hyperparameter tuning, and model serving! klaviyo.tech/ray-data-train…

I always love when companies use Ray for a large variety of different workloads.

DistFlow, from researchers at Shanghai Innovation Institute, looks like a cool new RL framework that builds on Ray, PyTorch, vLLM, and SGLang. arxiv.org/pdf/2507.13833 ray, PyTorch, vLLM, LMSYS Org

Interesting open source project aiming to simulate 10s of thousands of agents to model societal interactions (using ray). "Experiments demonstrate that the framework can support simulations of 30,000 agents that are faster than the wall-clock time."

🚨 Last call to register🚨 Ray gives you power - Anyscale gives you speed. Join us tomorrow at 10 AM PT to see how Anyscale simplifies Ray deployments so you can scale AI faster, without the infra hassle. Live demo + scaling tips. If you can't make tomorrow, join our global

We are seeing an emerging OSS stack for AI compute: 🔧PyTorch + 🧠vLLM + ⚡Ray + 📦Kubernetes 📽️ Robert Nishihara breaks down how these layers work together to scale LLMs + GenAI workloads 📰Full blog with examples from Pinterest, Uber, & Roblox: anyscale.com/blog/ai-comput…

If you are in NY, this awesome MCP meetup was just published! 🎉 🗓️ Happening this Friday, August 8th Featuring talks from ☁️ Amazon Web Services , 💬 Anthropic , 🐶 Datadog, Inc. ⚡️ Anyscale (I’ll be speaking as well) Join us by registering here 👉 lu.ma/lo4b24ju

“Our researchers can just write code without worrying about infrastructure... which means we can iterate faster on our models.” – Adrian Li-Bell, Member of Technical Staff Physical Intelligence See how the Anyscale platform eliminated bottlenecks and boosted research velocity in this

Scaling AI workloads? Join us at Grab ’s Singapore HQ for a Ray meetup to see how teams are using Ray and Anyscale in production – from data to training to inference. Agenda: 6pm: Networking 6:30pm: Talks from Ray experts and ML leads at Grab RSVP:

I don't think it's very widely known how big of a role Ion Stoica and the research community at UC Berkeley have played in shaping technology through research and open source. For just a few recent examples of foundational open source technologies, Ion and his students created

Excited to announce Ray on Anyscale is now available on CoreWeave to further deliver on our multi-cloud support. This gives users access to CoreWeave's purpose-built AI infrastructure, optimized for distributed AI at scale with low latency, fast startup times, and high

TBs of daily multimodal data. 16+ engineers working in parallel. Multi-cloud processing requirements. Anyscale helped Physical Intelligence process it all – at scale. Full case study 👉tinyurl.com/mubujp2z

🚨Webinar Alert!🚨 Join us Sept 4 webinar: Modernize your Spark-based AI & data platform with Ray + Anyscale. We’ll cover: • Structured + unstructured data processing with Ray Data • Actor-based scaling • Unity Catalog integration • Live embedding pipeline demo 📅 10 AM

Most of a model’s knowledge today is baked into weights through pre-training, fine-tuning, and RL. But one dimension is still relatively underexplored: feeding knowledge into context. 📚 🎥 Robert Nishihara explains why context engineering will play a big role in AI and why