​An event dedicated to exploring all things AI Engineering!

​Agenda

​​​​​🕒 5:30 pm – 5:40 pm
Welcome and Opening Remarks

​🎤 Melissa Cheng, Event and Field Marketing Lead at Sentry​​​​

​🎤 Marijan Cipcic, Principal Events Manager at Daytona

​​​​​​🕒 5:40 pm - 6:00 pm
Keynote Talk "AX is the only Experience that Matters"

​​🎤 Ivan Burazin, Co-Founder & CEO at Daytona

​​​Outline:

​If you’re building devtools for humans, you’re building for the past. Already a quarter of Y Combinator’s latest batch used AI to write 95% or more of their code. AI agents are scaling at an exponential rate and soon, they’ll outnumber human developers by orders of magnitude.The real bottleneck isn’t intelligence. It’s tooling. Terminals, local machines, and dashboards weren’t built for agents. They make do… until they can’t. In this talk, I’ll share how we killed the CLI at Daytona, rebuilt our infrastructure from first principles, and what it takes to build devtools that agents can actually use. Because in an agent-native future, if agents can’t use your tool, no one will.

​🕒 6:00 pm - 6:20 pm
Keynote Talk "Bad DX Becomes Worse AX: Building Toward Self-Optimizing Software"

​​🎤 Indragie Karunaratne, Director of Engineering at Sentry

​​​Outline:

​Self-optimizing software has been a fantasy in performance engineering for decades, but the missing piece wasn’t data: it was a user that could actually act on it. We already know how to measure what’s slow: profiles, traces, and profile-guided optimization have existed forever, wrapped in tools with notoriously bad DX. In an agentic world, that bad DX turns into even worse AX: LLMs struggle with complex tooling setup and data firehoses that eat tokens. This talk is about building simplified abstractions that present runtime context to agents as clear statistical guidance instead of raw telemetry. If we treat agents as first-class users of debugging tools, we get a realistic path to software that continuously measures, reasons, and rewrites itself.

​​🕒 6:20 pm - 6:40 pm
Keynote Talk "OpenThoughts: Data Recipes for Reasoning Models"

​​🎤 Etash Guha, PhD at Stanford University

​​​Outline:

​Reasoning models have made rapid progress on many benchmarks involving math, code, and science. Yet, there are still many open questions about the best training recipes for reasoning since state-of-the-art models often rely on proprietary datasets with little to no public information available. To address this, the goal of the OpenThoughts project is to create open-source datasets for training reasoning models. After initial explorations, our OpenThoughts2-1M dataset led to OpenThinker2-32B, the first model trained on public reasoning data to match DeepSeek-R1-Distill-32B on standard reasoning benchmarks such as AIME and LiveCodeBench. We then improve our dataset further by systematically investigating each step of our data generation pipeline with 1,000+ controlled experiments, which led to OpenThoughts3. Scaling the pipeline to 1.2M examples and using QwQ-32B as teacher yields our OpenThoughts3-7B model, which achieves state-of-the-art results: 53% on AIME 2025, 51% on LiveCodeBench 06/24-01/25, and 54% on GPQA Diamond - improvements of 15.3, 17.2, and 20.5 percentage points compared to the DeepSeek-R1-Distill-Qwen-7B. All of our datasets and models are available on this https URL.

​​🕒 6:40 pm - 7:00 pm
Keynote Talk TBA

​​🎤 Alex Shaw, Founding Member of the Technical Staff at Laude Institute

​​​Outline:

​TBA

​​​​​​​​​​🕒 7:00 pm - 8:30 pm

​​Networking

​With pizzas and beverages

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​​About event

​This is dynamic gathering for AI enthusiasts, innovators, and professionals to collaborate, share ideas, and explore the latest advancements in artificial intelligence. Whether you're building AI products, researching cutting-edge algorithms, or simply passionate about the field, join us to connect, learn, and drive the future of AI forward.