AI Performance Engineering Meetup (San Francisco, Global)

Registration required on AICamp: https://www.aicamp.ai/event/eventdetails/W2026111017?utm_source=chrisfreglysv Register through this link to attend; a Meetup RSVP alone does not complete AICamp registration.
[SV] AWS Partner Showcase (11th Edition) with Anthropic, Caylent, Temporal, LaunchPad

The AWS Agentic AI Partner Showcase Extended (11th Edition) features experts from AWS, Anthropic, Caylent, Temporal, LaunchPad, who will demonstrate how to build production-ready agentic AI systems from code to deployment. This focused session presents a complete development stack through live demonstrations, technical deep dives, and interactive discussions.

In addition to main stage tech talks and demos, partner booths will be available onsite throughout the event for attendees to visit, engage in Q&A, and deep dive into each partner's solution. The speakers will share practical insights into building, monitoring, and validating agentic AI applications that enhance your business processes and drive innovation.

What You Will Learn: ✔ Production-Ready Agentic AI – See live demonstrations of the complete development stack for building agentic AI systems, from coding environments through deployment and monitoring. ✔ Technical Implementation Strategies – Gain insights from AWS and partner experts on deploying agentic AI solutions with real-world use cases and AWS integration patterns. ✔ Expert Q&A & Networking – Engage directly with technical leaders from AWS and partner companies at dedicated booth stations, plus connect with fellow developers and practitioners building agentic AI applications.

Who Should Attend:

• Developers, engineers and builders building agentic ai workflow in production.
• Engineering leaders and Decision makers seeking solutions
• Working professionals who are interested in learning agentic AI technologies

Agenda (PST)

• 5:00PM - 6:00PM: Check-In, Food and Booth Visiting
• 6:00PM - 7:45PM: Kick-off, Tech talks, Live Demo
• 7:45PM - 8:30PM: Networking, Q&A over Booths

Speakers

• Umang Shukla — Anthropic
• Tyler Burch — AWS
• Andrew Baker — Temporal
• Karson Lindstrom — Caylent
• Todd Eisemann — LaunchPad

Past sessions of the series

Venue: Plug and Play, 440 N Wolfe, Sunnyvale

Local and Global AI Community on Discord Join us on discord for local and global AI tech community: - Events chat: chat and connect with speakers and global and local attendees; - Learning AI: events, learning materials, study groups; - Startups: innovation, projects collaborations, founders/co-founders; - Jobs and Careers: job openings, post resumes, hiring managers

Registration required on AICamp: https://www.aicamp.ai/event/eventdetails/W2026111216?utm_source=chrisfreglysf Register through this link to attend; a Meetup RSVP alone does not complete AICamp registration.
[SF] AWS Partner Showcase (12th Edition) with Anthropic, Grafana, Supabase, Miggo

The AWS Agentic AI Partner Showcase Extended (12th Edition) features experts from AWS, Anthropic, Grafana, Supabase, Miggo, who will demonstrate how to build production-ready agentic AI systems from code to deployment. This focused session presents a complete development stack through live demonstrations, technical deep dives, and interactive discussions.

In addition to main stage tech talks and demos, partner booths will be available onsite throughout the event for attendees to visit, engage in Q&A, and deep dive into each partner's solution. The speakers will share practical insights into building, monitoring, and validating agentic AI applications that enhance your business processes and drive innovation.

What You Will Learn: ✔ Production-Ready Agentic AI – See live demonstrations of the complete development stack for building agentic AI systems, from coding environments through deployment and monitoring. ✔ Technical Implementation Strategies – Gain insights from AWS and partner experts on deploying agentic AI solutions with real-world use cases and AWS integration patterns. ✔ Expert Q&A & Networking – Engage directly with technical leaders from AWS and partner companies at dedicated booth stations, plus connect with fellow developers and practitioners building agentic AI applications.

Who Should Attend:

• Developers, engineers and builders building agentic ai workflow in production.
• Engineering leaders and Decision makers seeking solutions
• Working professionals who are interested in learning agentic AI technologies

Agenda (PST)

• 5:00PM - 6:00PM: Check-In, Food and Booth Visiting
• 6:00PM - 7:45PM: Kick-off, Tech talks, Live Demo
• 7:45PM - 8:30PM: Networking, Q&A over Booths

Speakers

• Tyler Burch — AWS
• Todd Dayton — Grafana Labs
• Jamal Arif — Anthropic

Past sessions of the series

Venue: AWS Loft SF, 525 Market Street, 2nd Floor, San Francisco, CA 94105

• By registering for this event, you acknowledge that you are 18 years or older and agree to follow the AWS Code of Conduct and the AWS Event Terms and Conditions
• It is mandatory to bring valid physical government ID to enter the AWS Builder loft (No Digital IDs will be accepted).
• There is no scooter/bike parking available in the building. If you plan to bring one, you will need to find parking options nearby.
• All attendees must be 18 or older to attend this event at the space

Check the Slack/Discord for more driving directions, parking, car pool, etc..

Local and Global AI Community on Discord Join us on discord for local and global AI tech community: - Events chat: chat and connect with speakers and global and local attendees; - Learning AI: events, learning materials, study groups; - Startups: innovation, projects collaborations, founders/co-founders; - Jobs and Careers: job openings, post resumes, hiring managers

Monday, November 16, 2026 · 9:00–10:00 AM PST · Online
Zoom link: https://us02web.zoom.us/j/82308186562
Join us for two deep dives into hidden AI performance costs: surprise PyTorch recompilations during inference and compute, memory, and communication bottlenecks in Jev training.
Talk #0: Introductions and Meetup Updates
by Chris Fregly and Antje Barth
Talk #1: The Recompile Ambush: Taming PyTorch Inference Spikes
by Chaim Rand
`torch.compile` can accelerate steady-state inference, yet a guard failure on a new input shape or layout can trigger an expensive recompilation in the request path. Drawing on his latest post, Chaim will compare six approaches using a dynamic graph-neural-network example: cache preloading, bounded recompilation with eager fallback, Dynamo and input-layout tuning, warmup seeding, background compilation, and ahead-of-time compilation with `torch.export` and AOTInductor. He’ll explain their tradeoffs and how the right choice depends on the model, input distribution, runtime environment, and latency requirements.
Bio: Chaim Rand is a machine-learning algorithm developer who writes about PyTorch and practical AI/ML performance optimization.
Based on: Overcoming the PyTorch Recompilation Dilemma in Real-Time Inference Workloads
Talk #2: Follow the Profile: Hunting Jev’s GPU Bottlenecks
by Israel Ogbole, Co-founder and CEO of Zymtrace
Israel will explore the latest Zymtrace/TypeSafe case study on training and serving Jev. He’ll discuss how continuous CPU/GPU profiling and MCP-accessible profile data supported an iterative optimization loop across compute, memory, communication, and inference synchronization. The published case study reports approximately 46% higher training throughput on the same hardware after three successive changes, plus approximately 10% better inference performance after removing a hidden device-to-host synchronization. The talk will focus on how the team found each bottleneck, measured each change, and identified the next constraint.
Bio: Israel Ogbole is co-founder and CEO of Zymtrace, which develops continuous CPU/GPU profiling tools for performance optimization.
Based on: How TypeSafe achieved 46% higher GPU throughput training Jev — with the help of Zymtrace
Zoom link: https://us02web.zoom.us/j/82308186562
Related Links
Github Repo: http://github.com/cfregly/ai-performance-engineering/
O’Reilly Book: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/
YouTube: https://www.youtube.com/@AIPerformanceEngineering
Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm

Monday, November 30, 2026 · 9:00–10:00 AM PST · Online
Zoom link: https://us02web.zoom.us/j/82308186562
A powerful GPU can spend plenty of time waiting. What is it waiting for—and how do you find out?
Talk #0: Welcome and Meetup Updates
by Chris Fregly and Antje Barth
Talk #1: From “Why Is This Slow?” to a Faster Kernel
by Dr. Rajat “Raj” Dandekar, Co-founder of Vizuara
Join Raj for a focused tour of the engineering behind fast AI kernels, drawing on Vizuara’s Kernel Engineering course on Maven. We’ll connect the roofline model to CUDA execution and the GPU memory hierarchy, then look at how profiling guides optimizations such as coalesced memory access, tiling, and tensor-core use. Matrix multiplication and attention provide context for seeing how those ideas fit together in modern AI workloads, including FlashAttention.
The goal is a practical way to decide what to measure, which bottleneck matters, and whether an optimization helped. The session is dedicated to Raj’s talk, with time for audience questions.
Who it’s for: Engineers comfortable with Python or basic PyTorch. Prior CUDA or C++ experience is not required.
Bio: Dr. Rajat Dandekar is a co-founder of Vizuara and a Kernel Engineering course instructor. He holds a PhD in Mechanical Engineering from Purdue University and BTech and MTech degrees from IIT Madras.
Based on: Kernel Engineering: From CUDA Foundations to Frontier AI Kernels
Scheduling note: November 30 is the proposed new date; speaker confirmation is pending.
Zoom link: https://us02web.zoom.us/j/82308186562
Related Links
Github Repo: http://github.com/cfregly/ai-performance-engineering/
O’Reilly Book: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/
YouTube: https://www.youtube.com/@AIPerformanceEngineering
Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm