Generative AI is forcing us to rethink the very foundations of computing—what it means to program, to test, and even to “know.” This talk explores how the shift from deterministic systems to reasoning, agentic models is reshaping development practices, cost structures, and the role of the developer. From context engineering to AI economics, from “vibe coding” to specs-driven systems, we’ll trace the rapid evolution of GenAI and what it reveals about the future of software creation. Join to discover why everything you thought was stable in computing is being rewritten—faster than any technological shift before it.

AI didn’t wait for your security review. Developers are already using agentic AI, coding assistants, and MCP servers to write, review, and ship code, often without shared security standards or governance. In large orgs, this becomes sprawl: multiple IDEs, many agents, inconsistent rules, and rising risk. The instinctive reaction is to block AI tools entirely. That approach fails fast as developers work around it, security loses visibility, delivery slows, and risk grows.

AI can write your Terraform, generate your CI/CD pipelines, and spin up infrastructure in seconds. So do we still need platform teams, DevOps practices, and carefully designed cloud architectures?

Sinopsis In this talk we will walk you through our journey of experimenting with different ETL solutions for creating and updating a dataset in the new AWS service: Table Buckets. Source data resides in glue tables and RDS. We start experimentation with Athena and eventually select Glue with Apache Spark as a solution. The presentation is focused on exploring the upsides and limitations of each solution, costs, challenges we faced, solutions we considered and local setup of an apache spark job.

The era when organizations relied solely on skilled programmers to write countless lines of code is fading. Developers who once enjoyed crafting well-structured and well-documented code in their preferred programming languages are now openly turning to their Generative AI coding partners to generate code. Here, language is no longer a barrier.

It’s gradually becoming clear that when it comes to coding productivity, human developers are no match for the code-dumping co-pilots. As this trend continues and gains momentum, how do you survive as a software engineer?

Your CEO doesn’t want SQL, neither does he or she want dashboards. What if instead you could give them an application that talks to your data? Join me to learn how Cloudwalker built Echo — a conversational analytics engine built on Amazon Bedrock and Amazon Redshift for the betting industry. I’ll share what worked, what broke, and what we’d do differently — from schema-aware prompting to guardrails that keep AI honest.

We all love Lambda. However, developers often overuse it. In this session and true event-driven fashion, we will explore functionless architecture - connecting AWS services directly through native integrations rather than Lambda and SDK. Eliminating the need to bundle and deploy these functions, as well as saving on cost. During the demo, you’ll see a functionless application build with AWS CDK, its direct integration to various AWS Services such as API Gateway, EventBridge, S3, DynamoDB, Step Functions, handling business logic and data flow.

Every org enforcing Terraform standards eventually hits the same wall: policy tools can flag issues, but they cannot fix module code. The result is a graveyard of forked modules that drift from upstream and turn upgrades into a full-time job. This talk introduces policy transformation: automatically rewriting Terraform modules at download time so teams get compliant code without maintaining forks.

I will demo eight real transformation rules across four categories: lifecycle management (prevent_destroy, ignore tag drift, protect KMS keys), block removal (strip provisioners), attribute restriction (deny GPU or specialty instances), and content sanitization (safe regex cleanups). You will see the before-and-after HCL, plus the safety model that makes this production-ready: deterministic outputs, collision detection, preview diffs, and a four-level risk classification.

This talk will take you on a journey through the pursuit of performance in AWS serverless services. For those who have embarked on a similar path, you’d agree that it’s rarely a smooth ride. Complexities often arise, especially when navigating the mysteries of AWS (serverless) black boxes.

We’ll dive into XM’s backend architecture, which powers the real-time delivery of hundreds of thousands of financial market data to thousands of clients.

This session provides a practical guide to designing and deploying modern web architectures on Amazon Web Services and an open-source reference implementation to support your deployments. Attendees will learn how to transform a codebase into a fully operational, world-class application running on AWS, emphasizing best practices in scalability, security, and observability.

We’ll walk through the architectural decisions behind Presence, including how to structure microservices, leverage managed AWS services, and implement robust CI/CD pipelines.

In the AWS world, some architectures shine, others… offer lessons on what to avoid. We’ll analyze real-world examples of anti-patterns: unmanageable architectures, ineffective scaling, and questionable technology choices. We’ll see what went wrong, turning every facepalm into a lesson. With humor and technique, we’ll learn to design robust and scalable solutions. If you want to learn from (others’!) mistakes while improving your cloud architectures, this is the talk for you!

Food and drinks are two of my major hobbies, and I love mixing cocktails at home. But when I host friends, I always end up as the menu. People keep asking what I have and what I can make.

In this talk I will show how I built an AI bartender on AWS that lets guests interact with a drink menu using natural language. Instead of listing options or explaining what’s available, guests can ask for things like “something fruity” or “a gin drink”, and the AI Bartender respond with suggestions powered by Amazon Bedrock.

Modern Amazon RDS and Aurora PostgreSQL deployments must balance performance, reliability, and cost, especially at scale. In this session, we’ll explore practical strategies for improving database performance while simultaneously reducing infrastructure spend. We’ll cover right-sizing, modern instance adoption (including Graviton), storage and I/O optimization, benchmarking techniques, and observability-driven tuning. Based on real production experience, these approaches often lead to measurable performance gains and 20–50% cost reductions without compromising availability.

AI agents are the next big step after chatbots — but the path from a local prototype to a scalable cloud solution is full of pitfalls. In this talk, I’ll walk through my project ai-secure.dev, where I built a fully automated security compliance agent using AWS Bedrock AgentCore that audits websites against frameworks such as ISO 27001, NIST, and SOC 2. AgentCore is essentially “Fargate for AI agents”: you provide the container, and AWS takes care of scaling, networking, and infrastructure. Particularly exciting is the AgentCore Browser — a managed Chromium environment that enables web automation without maintaining your own Playwright infrastructure.

Securing and managing network traffic efficiently is crucial for modern cloud architectures. AWS Cloud WAN, combined with Network Firewall and Route 53 DNS Firewall, provides a robust framework for protecting both egress and east-west traffic. This presentation explores how these security services enhance resilience, prevent threats, and enforce compliance across distributed environments. By integrating firewall policies and DNS security, organizations can strengthen their cloud infrastructure against cyber threats while maintaining optimal performance. Learn how to build a secure, scalable, and resilient network with AWS’s advanced security solutions.

How do you build a security platform that ingests terabytes of logs daily, runs real-time threat detection, and deploys seamlessly across SaaS, cloud-connected, and self-hosted environments—all without managing a single server?

In this session, we’ll take you inside Panther, a cloud-native SIEM built entirely on AWS serverless technologies. You’ll learn how we leverage Lambda and Fargate with dynamic compute selection, S3, DynamoDB, and SQS to build a resilient log processing pipeline and achieve true tenant isolation with dedicated AWS accounts per customer.

Amazon Aurora DSQL is a serverless distributed SQL database with virtually unlimited scale, highest availability, and zero infrastructure management. In this talk we’ll use a sample serverless application that uses Amazon API Gateway, AWS Lambda and Aurora DSQL to explore the architecture and features of this database. We’ll explore the scaling behaviour of DSQL, measure performance including cold starts, deep dive into its pricing and current limitations.

We’ll also use Strands Agent with Amazon Aurora DSQL to talk to our application using natural language. We’ll wrap up with some suggestions when currently to use DSQL comparing to DynamoDB, RDS and Aurora (Serverless) and what to pay attention to when migrating your existing application to Aurora DSQL.

At Skroutz we have lately been experimenting with building customised Agentic workflows for various security use cases.

This is when we started exploring Amazon Bedrock AgentCore and its ability to deploy server less agentic components easily as AWS-native primitives.

This session will cover our lessons learned, things that worked and things that didn’t so hopefully they can help the community experiment with Amazon’s latest agentic capabilities.

For many years developer productivity improved through better frameworks, faster runtimes, and stronger cloud platforms. Today, the biggest shift is happening elsewhere: how we work.

In this session, I’ll demonstrate how agentic AI tools and LLMs are transforming everyday development tasks: from planning the project to coding, debugging and code reviewing, documentation and data extraction.

We’ll explore how traditional techniques are being replaced or augmented by LLM-based approaches, such as using natural-language reasoning instead of complex parsing logic based on regular expressions. The session includes practical examples using tools like Kiro, Claude Code, and foundation models hosted on Amazon Bedrock.

Creating high-quality video content at scale is time-consuming, resource-intensive, and heavily reliant on manual editing workflows. For modern newsrooms and content creators, the bottleneck isn’t capturing footage - it’s everything that comes after.

In this session, I’ll walk through the architecture of a fully automated, serverless video pipeline built on AWS that transforms raw video into publish-ready content. The pipeline combines AI-powered transcription enhancement, detailed per-frame visual analysis, automated content generation, and intelligent editing decisions - all orchestrated without a single server to manage.

As organizations increasingly rely on custom foundation models (FMs) for generative AI applications, the challenge shifts from model training experimentation to delivering low-latency, high-throughput, cost-efficient inference at production scale.

In this session, learn how Omilia uses Amazon SageMaker AI to deploy and scale custom FMs for enterprise conversational AI, optimizing for reliability, performance, and cost.

Serverless computing abstracts infrastructure away, but that abstraction hides critical architectural decisions.

Let’s explore how to use Kiro Specs to make those decisions explicit, AWS MCP servers to understand what AWS services offer and what your architecture requires, and AWS CDK to generate infrastructure from specifications.

A live demo shows the complete flow: from spec to generated infrastructure.