Timothy S.

What happens when two Cloud Native Computing Foundation (CNCF) Ambassadors talk open source and cloud-native innovation? Intuit’s Lisa-Marie Namphy and Carlos Santana from Amazon Web Services (AWS) discuss Argo, AI agents, and the future of collaboration. Watch the conversation 👇 Cloud Native Computing Foundation (CNCF) Intuit Open Source Argo Project

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“MI300X is unlocking new possibilities for AI.” Yee Jiun Song of AI at Meta shares how MI300X accelerators are driving Llama 3 & 4 inference and why their performance is now powering even more critical workloads like training and recommendations. #AdvancingAI

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In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for...

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“In a very binary sense, Metronome made self-service at Anyscale possible.” That’s Matt Weber, Software Engineer, reflecting on Anyscale's expansion into new revenue channels. As the team added PLG and cloud marketplaces (AWS, GCP, and Azure), their manual billing setup couldn’t support the volume or complexity. Reporting, reconciliation, and manual workflows were slowing them down. With Metronome as their billing foundation, Anyscale unified every revenue motion on one platform and accelerated growth across the business. They launched self-serve billing end-to-end, brought AWS, GCP, and Azure marketplaces live in under three weeks each, and freed up three engineers and a PM from dedicated billing work. 🎉🤝 Big congratulations to the Anyscale team on the expansion—this is what it looks like when innovative product and modern monetization infrastructure come together. Read the full story at the link in the comments.

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We are rolling out new capabilities across the 3 pillars of the Anyscale platform for Ray. Developer Central Unified workspace to build faster. Cloud IDE on real clusters with in context observability. New Lineage Tracking private beta built on OpenLineage, improved dashboards for data, train, tasks, and clusters, plus Custom Workspace Templates for consistent onboarding. Anyscale Runtime Performance engine for Ray. API compatible with open source Ray. No code changes. Internal tests on identical clusters show 2 to 3x throughput for image batch inference, 2x lower cost on preemptible, up to 3x lower cost on autoscaling, up to 10x gains on some data pipelines, up to 5x QPS for video with direct ingress, and up to 8x higher scale for APPO. Cluster Controller Run reliably anywhere. Deploy in AWS, GCP, and now Azure. New GRS observability shows queue health and preemptions. Multi resource cloud chooses the best place to start a single cluster at launch. Multi cloud inference runs one service across regions and providers with latency, availability, or cost based routing and automatic failover. 👉 Read the roll up blog: https://lnkd.in/gj8xrmqY

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As AI grows more specialized, infrastructure must keep up. F5 and NVIDIA are helping enterprises build scalable, secure AI factories with BIG-IP Next for Kubernetes on BlueField-3 DPUs—enabling faster inference, smarter prompt routing, and better performance for LLMs. Explore how we're supporting the shift to purpose-built AI: https://go.f5.net/q3cy5o44 #F5 #GTCParis #NVIDIA #Kubernetes #LLM #AgenticAI

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We just crossed 150 production-ready models on Runcrate. LLMs. Vision. Embeddings. Multimodal. All under one API endpoint. The old way: pick a use case, find a model, negotiate vendor access, get API keys, build integrations, and manage bills. The new way: one API key. One credit balance. Switch models in a single line of code. Here's why this matters for AI teams: → Prototype with a lightweight model, scale to a frontier model without re-architecting → Run vision and text inference from the same endpoint → Compare outputs across models without juggling multiple SDKs → One bill. One set of credentials. No vendor lock-in. The fragmentation tax on AI development is real. Teams spend 30-40% of their engineering time on infrastructure glue instead of product. We built the platform we wished existed two years ago. What models are you currently evaluating for production? Drop them below — we'll make sure they're available. Check us out → runcrate.ai

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It all starts here 🌉 ​ ​ The San Francisco Bay Area will be the first market to welcome the Uber-exclusive robotaxi, powered by Nuro and Lucid, expected to begin in late 2026.​ ​ On-road development with our Robotaxi Engineering Fleet is now underway, marking the start of an exciting new chapter in our Global Robotaxi Program. ​ ​ Over the coming months, more than 100 Nuro-driven Lucid Gravitys will join the fleet as we continue to test, validate, and advance the future of safe, sustainable rides.​ ​ The future is on its way! ⚡​ #AutonomousVehicles #Technology #Innovation #Partnership #ElectricVehicles

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We just published a walkthrough on building a RAG application using Vercel's AI SDK and integrating it with Maxim AI’s observability suite. 👉 What we covered: How to set up a simple Next.js RAG app with Vercel’s AI SDK Using Postgres + Drizzle Studio to store and visualize embeddings/resources Adding & fetching resources via tool calls Integration with Maxim’s observability to log sessions, track LLM calls, costs, tokens, and traces This is a hands-on demo showing how to go from RAG setup → observability → insights in one flow.

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“1 million rows per second.” In his opening keynote, our CEO Max Liu walked through what that really means in production: - TiDB processing 1M rows/sec - 10x higher throughput for cross-region replication - A data layer built to make multi-tenant architectures actually practical at scale If you’re thinking about SaaS, multi-region, or AI-heavy workloads, this is the kind of infrastructure ceiling you want. 🎥 Watch the full keynote from Max Liu at #TiDBSCaiLE to see what’s behind the numbers: https://ow.ly/BgKl50YetBq #TiDB #DistributedSQL #MultiTenancy #CloudNative #Scalability

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