Codecamp_Brasov 2024

Agile abandoned belief in product requirements, focusing instead on a development process of iteration and feedback as its key to creating value. Faced with growing disillusion with agile and with inordinate focus on process, now “product-led organizations” shift the value focus from that process to its output: the product. But where does the value lie? In software, value lies in the end-user process: an outcome tied partly to that output. Perhaps the focus does lie in in the process — that of the client — rather than in the developer process, and the product is only a side effect of both. Looking even further leads to a more holistic view not only sound economics but of being a responsible world citizen.

Has implementing Clean Architecture become more of an ideal than a reality in your projects? Despite its clear rules and intended separations, the complexity of growing codebases and the crunch of time often render these principles invisible in practice. When projects technically fail, the culprit is frequently uncontrolled complexity – where Clean Architecture remains a concept on paper, not in code.

In this session, I will show how to get predictability by implementing Clean Architecture through structure, rather than relying on discipline and code reviews only. I’ll show how to create a structure that makes it easy to write the code that follows your architecture and at the same time it makes it difficult to write the code that doesn’t.

You will walk away with a recipe and building blocks for creating a foundation in code that sustains Clean Architecture and the level of quality in your code that can control the complexity of the project.

This structure will enforce separation of concerns and how dependencies are created. It will deliver predictability by creating a Code Desing that is maintainable, extensible and reusable.

Oracle and Tremend, a subsidiary of Publicis Sapient, have partnered to develop groundbreaking AI solutions tailored for the enterprise market. This collaboration focuses on creating a highly specialized Generative AI (GenAI) system, utilizing advanced Oracle technologies like Database 23ai, Select AI and other GenAI services on Oracle Cloud Infrastructure (OCI).

The data confidentiality and sovereignty, inherently present in the Oracle Cloud Infrastructure, are the cornerstone of the system. Its dedicated platform for data curation and validation takes into account the specifics of data sources in each business domain. Not only that but the answers it provides are grounded in the provided data sources, effectively minimizing the errors (“hallucinations”), making it ideal for various business sectors.

This journey proved that a successful GenAI implementation requires more than just advanced technology; it demands a clear strategic vision, robust data management, and a focus on impactful applications like knowledge bases to drive growth. AI success also hinges on a well-rounded plan, the right skills, and measurable outcomes. Only by aligning technology with purpose, the solutions developed are both sustainable and meaningful for clients.

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.

Have you ever noticed peculiar things while working with Java? Concepts you were taught should work differently, only to find them embedded right in the JDK? Maybe you’ve wondered: Why is SQLException a checked exception? Why aren’t local variables final by default? How is it that the Time APIs have been rewritten three times since the language’s inception? And why didn’t virtual threads exist from the start? Join me as I unravel the story behind these design choices and more. Let me tell you a story…

There’s an incredible amount of noise around artificial intelligence these days, but very little reliable signal. AI will bring doom and destruction, or a world where cheerful robots feed us peeled grapes while we lie on the couch. Some say AI is already replacing the jobs of creative people; others say creative people will improve their jobs by using AI. Meanwhile, it’s not even clear on what people even mean by AI. How do we make sense of it all?

Serious testers know how to make sense of things: we test. That’s sometimes tricky, and AI in particular brings some special problems. AI is by nature obscure and fragile. It intrudes into human social like, it comes packaged with wishful claims, and the current flood of hype makes criticism socially challenging.

The good news is that real testing can step up and work around the obstacles. The skills of analysis, investigation, and critical thinking — and an understanding of testing testing’s essential missions — apply to any socio-technical product. Now testers and developers need to apply these skills more than ever. One thing is certain: traditional, formalized, procedurally structured test cases won’t do the job. And something else is certain, too: perspectives will change between the writing of this abstract and the delivery of this talk!

Michael Bolton will describe his experiences of analyzing AI- and LLM-based products, and will offer a set of heuristics to address the challenges that AI presents in today’s ever-changing technological landscape (ahem!).