Slides and recording will be available here: github.com/terrytangyua...
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Yuan Tang
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Senior Principal Software Engineer at Red Hat AI | Open Source Leader at KServe, Argo, Kubeflow, Kubernetes, CNCF | Maintainer of XGBoost, TensorFlow | Keynote Speaker | Author | Technical Advisor
More info: http://terrytangyuan.xyz
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What they're sharing
GitHub - terrytangyuan/public-talks: Slides, videos, and supporting files for my public talks github.com
GitHub - omnigent-ai/omnigent: Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device. github.com
Deploying distributed AI inference: Blueprints & troubleshooting | Red Hat Developer developers.redhat.com
Optimizing distributed AI inference: Advanced deployment patterns | Red Hat Developer developers.redhat.com
Designing distributed AI inference: Core concepts and scaling dimensions | Red Hat Developer developers.redhat.com
Cloud Native AI + Inference Day | LF Events events.linuxfoundation.org
Articles & links
Check it out here: github.com/omnigent-ai/omnigent
📖 Part 3 - Blueprints & Troubleshooting: https://developers.redhat.com/articles/2026/06/26/deploying-distributed-ai-inference-blueprints-troubleshooting Which of these blueprints is closest to your current setup? What would you change?
📖 Part 2 - Advanced Deployment Patterns: https://developers.redhat.com/articles/2026/06/24/optimizing-distributed-ai-inference-advanced-deployment-patterns
📖 Part 1 - Core Concepts & Scaling Dimensions: https://developers.redhat.com/articles/2026/06/22/designing-distributed-ai-inference-core-concepts-and-scaling-dimensions
We will be hosting Cloud Native AI + Inference Day again as a co-located event with KubeCon North America! @cncf.io Looking forward to your proposals (by June 21) and sponsorship (by September 21)! events.linuxfoundation.org/kubecon-clou...
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Recent commentary
Excited to share Part 1 of our blog series on Red Hat Developer: 𝘋𝘦𝘴𝘪𝘨𝘯𝘪𝘯𝘨 𝘋𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘦𝘥 𝘈𝘐 𝘐𝘯𝘧𝘦𝘳𝘦𝘯𝘤𝘦: 𝘊𝘰𝘳𝘦 𝘊𝘰𝘯𝘤𝘦𝘱𝘵𝘴 𝘢𝘯𝘥 𝘚𝘤𝘢𝘭𝘪𝘯𝘨 𝘋𝘪𝘮𝘦𝘯𝘴𝘪𝘰𝘯𝘴. LLM inference is two workloads pretending to be one. The prefill phase is compute-bound, processing entire prompts in parallel to populate the KV cache.
📢 𝗧𝗵𝗲 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗠𝗼𝗱𝗲𝗹 𝗦𝗲𝗿𝘃𝗶𝗻𝗴 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝗶𝗲𝘀: 𝗝𝘂𝗻𝗲 𝗘𝗱𝗶𝘁𝗶𝗼𝗻 𝗶𝘀 𝗼𝘂𝘁! We recently launched our newsletter publicly after sharing it internally at Red Hat AI for over a year.
I’ve had a great experience using Omnigent and have been collaborating with the Databricks team to land a few features and fixes, including: - Kubernetes & OpenShift deployment - NVIDIA OpenShell sandbox integration - Claude on Vertex AI auto-detection - Podman support
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