Oct 11, 2026

AI Apps in a Flash: Ship to GPUs Without Docker — Dean Quiñanola, Runpod

Dean Quiñanola, a staff engineer at Runpod, presents Runpod Flash, a way to run Python on cloud GPUs as if they were local, with no Dockerfiles, image builds, or registry pushes. The talk covers serverless and load-balanced endpoints, endpoint-to-endpoint calls, network volumes with warm caching, and live serverless iteration. A live demo moves from GPU detection to text and image generation and then deploys to production with a single command.

Sep 30, 2026

AslanX Flow in Action: Training & Deploying a CFD Surrogate Model | Deep Dive — Mason Seo

Mason Seo, CTO of Narnia Labs, demonstrates AslanX Flow, a platform enabling engineers to train, evaluate, and deploy surrogate models for 3D field prediction (e.g., CFD aerodynamics) without a dedicated machine learning engineer. The deep dive covers building a training workflow from raw engineering data, comparing model architectures like PointNet, Transolver, and Linear Neural Operator, and deploying the winning model as a no-code inference application. A key insight shared is the importance of evaluating models on specific engineering data, as public leaderboard performance doesn't always translate, alongside a preview of AI-driven workflow configuration.

Sep 28, 2026

Dedicated Inference

A walkthrough of CoreWeave's three inference paths (Serverless, Dedicated, and Inference on CKS), covering when each fits, how a multi-agent research assistant uses OpenAI-compatible endpoints across model types, and live demos of gateways, deployments, and Kubernetes-based vLLM, Dynamo, and LLM-D serving.

Aug 06, 2026

How Open Source Became AI's Backbone | Inferact with a16z

Simon Mo, CEO of Inferact and lead maintainer of vLLM, discusses how open-source AI, exemplified by vLLM, transformed into critical infrastructure. The conversation highlights the technical complexities of serving LLMs, the evolving economics and licensing of open-weight models, the need for control over guardrails, and the rapidly disappearing capability gap between open and proprietary AI.

Jul 21, 2026

2026 State of AI Engineering — Barr Yaron, Amplify Partners

Barr Yaron's 2026 AI engineering survey reveals key trends: audio and image generation are rapidly gaining traction, while cost is now a primary engineering constraint. Agents are evolving to take actions within systems, but control mechanisms remain primitive. Evaluation (eval) is still the top infrastructure challenge. AI positively impacts job satisfaction and experimentation but also raises concerns about technical skill erosion and non-developers shipping code, fundamentally changing engineering culture. Predictions include a likely AGI declaration within five years and a shift away from Transformers as state-of-the-art.

May 08, 2026

How Transformers Finally Ate Vision – Isaac Robinson, Roboflow

Isaac Robinson from Roboflow explains why Vision Transformers (ViTs), despite their initial disadvantages in computational complexity and lack of inductive bias, ultimately surpassed Convolutional Neural Networks (CNNs) for computer vision tasks. The talk covers the critical roles of massive, ViT-specific pre-training methods like MAE and DINO, the architectural evolution through models like Swin, ConvNeXt, and Hiera, and optimizations borrowed from the LLM ecosystem. It culminates in a discussion on the practical deployment challenges of large foundation models like SAM and how Neural Architecture Search can bridge the gap.