Auditorium Miltiadis Evert

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.

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?

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.

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.

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.

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.

To close AWS Community Day Athens 2026, we will bring the day’s key themes together in a fireside chat with Andreas Chatzakis.

After a full day of sessions on agentic AI, security, developer tools, data platforms, and the evolving role of builders, this conversation will reflect on what these shifts mean for the AWS community in Greece, Southeast Europe, and the broader EMEA region.

After the fireside chat, we will wrap up AWS Community Day Athens with raffle giveaway announcements and the after-party.