Join us in Iasi in 2025!

When people describe technical debt they are often describing understandability issues – it takes too long to understand the code and its context well enough to plan or make changes correctly. In this keynote Michael Feathers will describe ways of developing understanding and certainty in large existing code bases using a variety of tools and practices.

There are no best design practices in Software architecture–everything is a trade-off. But how do you figure out what those are? The answer to every question in software architecture is “It depends.” This keynote starts to answer the follow up question: “Depends on what?!?” It proves a variety of techniques and tools to help architects and other teams members understand and evaluate trade-offs, including how to perform iterative design and how to avoid common trade-off traps.

What if your greatest tool as a developer wasn’t just your code but your mindset? In this talk, we’ll explore how your reasoning patterns can revolutionize your approach to software design and decision-making. By shifting your perspective, you’ll learn to make more impactful choices, grow your skills, and contribute meaningfully to your projects.

We’ll explore strategies for making more intelligent decisions, designing more resilient systems, and driving personal and professional growth. It’s time to think beyond code and start building with purpose.

OpenAI’s Assistants API lets you build rich, interactive virtual assistants without incurring the time and expense of fine-tuning an LLM. Imagine an AI component that can book appointments on a calendar, query a database, produce charts and graphics, prioritize support tickets, retrieve information from your company’s internal documents, generate spreadsheets, and even run code. The Assistants API makes all this and more possible, and it represents Phase 2 of the LLM revolution in which LLMs are supplemented by “tools” that perform tasks LLMs can’t. Learn how to put the Assistants API to work in your business and see some jaw-dropping examples of the tasks it can perform, complete with sample code.

It’s been over 70 years since Alan Turing defined what many still consider to be the ultimate test for a computer system — Can a machine exhibit intelligent behavior that is indistinguishable from that of a human? Originally coined the imitation game, the Turing test involves having someone evaluate text conversations between a human and a machine designed to respond like a human. The machine passes the test if the evaluator cannot reliably tell the difference between the human versus machine-generated text. Although the Turing test generally serves as a starting point for discussing AI advances, some question its validity as a test of intelligence.  After all, the results do not require the machine to be correct, only for its answers to resemble those of a human.

Whether it’s due to artificial “intelligence” or imitation, we live in an age where machines are capable of generating convincingly realistic content. Generative AI does more than answer questions, it writes articles and poetry, synthesizes human faces and voices, creates music and artwork, and even develops and tests software. But what are the implications of these machine-based imitation games? Are they a glimpse into a future where AI reaches general or super intelligence? Or is it simply a matter of revisiting or redefining the Turing test?  Join Tariq King as he leverages a live audience of software testing professionals to probe everything from generative adversarial networks (GANs) to generative pre-trained transformers (GPT). Let’s critically examine the Turing test and more because it’s judgment day — and this time, we are the judges!

Since .NET Core saw the light of day, it has become more opinionated and flexible—especially when it comes to design patterns. Many patterns that developers once dabbled in have now become front and center in ASP.NET Core, and MAUI and .NET overall. In this session, I’ll cover some of the main design patterns at the forefront, a few lesser-known ones, and some we probably could do without—like the “Best Intentions Pattern” and “Over-Engineering Pattern.”

Join me for a code heavy and practical, yet slightly tongue-in-cheek, look at how these patterns show up in the frameworks we use every day, and how they shape (and sometimes complicate) the way we code.

The difference between an effective and an ineffective team is staggering. You can’t “fix” bad teams by throwing more money or better technology at the problem. After recruiting, leading, coaching, consulting and training hundreds of teams, and always carefully obsessing about what separates the greats from the good and the bad, I’m ready to present my own model for Team Effectiveness, the 3S Model: Structure, Stewardship, Spirit.