Sebastian Nilsson
Renaissance engineer - Developing great ideas into impactful solutions
Stockholm, Sweden
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Meet Sebastian, an experienced problem-solver who's professionally been blending technology with creativity since 2003. He’s helped teams bring ideas to life and build cultures that truly put people first. Whether freelancing or leading global teams, Sebastian finds clever solutions that make a real impact.
A regular speaker at tech events—and once the organizer behind Sweden’s biggest .NET meetup at the .NET Core 1.0 launch—he's always exploring fresh ways to mix tech and teamwork, proving that smart solutions can be both effective and engaging.
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Next.js: Build a State-of-the-Art E-commerce in Fullstack React
In today’s technical landscape, building a modern website means tackling diverse challenges—such as responsive performance, SEO, authentication, localized CMS content, and secure data fetching—all while delivering an interactive, state-of-the-art user experience.
Historically, combining all these features in a maintainable and extensible solution has not been easy. That’s where Next.js comes in as a true gamechanger. It offers a turnkey solution for setting up a fullstack React application, complete with out-of-the-box support for TypeScript, the full React component ecosystem, and modern web performance optimizations.
In this session, we’ll explore how Next.js makes it effortless to get started and keep high productivity as the project grows. We’ll dive into real-world scenarios from a state-of-the-art e-commerce platform—drawing on the speaker’s experience launching a leading European e-commerce site—to reveal how Next.js seamlessly addresses the complex technical demands of modern web development.
The History of C# - From v1 to v14 and Beyond
If you're a .NET developer, you most probably love C#. This language hasn't just made you extremely productive by accident. Few languages have evolved as rapidly or as thoughtfully as C#, continually adapting to solve the challenges faced by its developers.
Over the years, C# has transformed dramatically. Today, with version 14, we take for granted features like generics, LINQ, async/await, string interpolation, pattern matching, records, init-only properties, and much more. Yet, these innovations were not always part of the language—and many still remain missing in other popular languages.
Join in on a trip down memory lane as we explore the evolution of C#. Discover how the language has adapted through the years and how it continues to help us write terse, and yet highly readable, C# code.
Whether you're a seasoned veteran or new to the .NET world, this session promises a blend of nostalgia, insight, and inspiration. You will hopefully appreciate C# in its current shape and you will definitely feel assured that C# is a safe bet for the future.
AI Tooling For the Full Development Lifecycle
Software development is changing rapidly, with AI becoming a co-pilot, or even a full agent, for all the different stages. From planning and coding to testing, deployment, and monitoring. Many tools today are targeting the coding part, but there are possibilities in the other stages too.
We'll highlight the categories of tools that exist, where they fit in the lifecycle, and the tangible benefits they bring in speed, quality, and user experience. Attendees will walk away with a clearer map of the "AI tool landscape", an understanding of how different categories compare in terms of implementation effort, cost, and potential risks.
Leaders will be able to identify where their teams sit today in their AI-adoption. Technologists will get a concrete guide to tools that can make day-to-day work more productive and allow more focus on solving real problems.
From C# to Rust
Rust has been the most loved programming language for the last 8 years. For a C#-developer, it could be a powerful addition to the tool-belt.
It's a modern language, with a familiar C-style syntax, which runs with performance on the level of C and C++, but with memory safety built into the language.
The adoption of Rust does seem to be ramping up, with big players interested, like Microsoft, Facebook, Google, Apple, Cloudflare, and more.
Keep Architecture Simple, Seriously: You Don’t Need Microservices Yet
In a world where everyone talks about Microservices, containers and Kubernetes, it can be very inviting to jump on this bandwagon, no matter your actual need for it.
In this talk, we’ll look at a real-life story of scaling back from an over exaggerated Microservices architecture, in an e-com solution for an European leader within their industry. For them, Microservices was way overkill for the needs of the company and the technical organization. We’ll look at a reverse approach, where you start as simple as possible and start making things more complicated only when needed.
There are some good mental guidelines that you can think about, step by step, as a checklist for when it’s time to move forward. This might eventually end up in a Microservice architecture, but that’s very rarely the most balanced starting point.
Professional Standout: Be Invaluable to Your Current & Future Employer
Do you want to make sure you stand out in your workplace and within your field? There are some clear strategies to do so. These points are important to keep in mind no matter if you want to excel in your current workplace, apply for a highly sought after position, minimize chances of being made redundant or to get back in the game after a company downsize.
We will look at how you can lead yourself to your full potential. Also, we'll look at how you can lead others by setting a great example and maybe even consider leading others by taking on an official leadership role. Leveraging all this, you can even start looking at how to lead the industry, either based on the domain you work in or the specific technical areas you can innovate within.
Fullstack AI: The Building Blocks for Production Ready AI-Powered Software
Generative AI has evolved from basic chatbots into agentic systems capable of much more than just conversation. This talk explores what it takes to build a production ready AI agent, which doesn't just answer questions, but understands context, uses tools, and takes actions to solve real problems. Even the smartest LLM is limited without access to up-to-date knowledge or the ability to interact with the world. We'll look at how augmenting a model with memory, external data, and action capabilities turns a passive chatbot into a proactive, helpful assistant.
We'll dive into what the modern building blocks are for building AI-powered software. Techniques like Retrieval-Augmented Generation (RAG), embeddings, fine-tuning, and targeted tool use help ground your AI in real-world context, improving both precision and reliability. You’ll learn about open standards like the Model Context Protocol (MCP), a standardized interface for external tools and data. Along the way, we’ll consider how to balance speed, accuracy, and cost when designing systems for real-world performance.
We’ll touch on other important concepts to be aware of to enable you to build the AI solutions you need. Prompt and context engineering are core techniques for correctly guiding model behavior and reducing hallucinations by providing just the right information at the right time. You'll leave with tools and inspiration for building AI-powered software which are more than a wrapper on top of a mainstream LLM.
Don't Just "Trust Me, Bro" with Your AI
Remember back when we had to convince developers that unit tests, observability and automated testing were important? We are now in the same situation with AI solutions being built.
In today's world of fast moving LLMs and generative AI, "trust me" isn't a strategy, it's a liability. AI outputs are inherently non‑deterministic, especially given the factors of different models, temperature settings, provider switches and more. So, “it worked on my AI” is not a sustainable approach. This is why you need AI-aware observability, not just classic logging.
What really defines a working AI solution? At least, it has to be measurable, monitorable, and reliable. We need robust evaluation frameworks. We'll need things like automated evals, human-in-the-loop assessment, user-feedback, or even LLM-driven judging, to confirm expected behavior and detect regressions.
You wouldn't ship a classic app without telemetry, so why treat AI differently? This talk equips developers with the mindset and tools to build AI systems that are intelligent and production-ready: traceable, testable, monitored, and trustworthy.
From Traditional to Agentic AI Dev Team - Secrets From the Trenches
AI-powered development tools are evolving at an extraordinary pace. Currently, many frontrunners are guarded about their detailed experiences, withholding valuable knowledge to the community and slowing down progression within the area. This session breaks that pattern, drawing from hands-on experience inside a large enterprise. It will share the experiences of moving a traditional development model toward an agentic AI-augmented team, including how to handle enterprise limitations, align and upskill developers, designing AI-centric workflows, and confronting lessons learned from implementation reality, from the trenches.
Modern AI coding tools are no longer limited to autocomplete. They now can operate across the entire software delivery lifecycle, assisting with specification clarification, architectural scaffolding, structured code generation, automated test design, defect reproduction, pull request pre-screening, documentation synthesis, systematic refactoring, and much more. Some are evolving toward agentic patterns, capable of planning and executing multi-step tasks under human direction. When deliberately integrated, they reshape how individuals work, how teams collaborate, and how organizations think about throughput and engineering leverage.
Becoming an agentic AI development team is not about installing a tool. It's a mental shift and a journey of learning a new way of working. It requires rethinking how value is created in software teams. It requires structured experimentation with emerging capabilities, staying current without chasing every trend, defining shared vocabularies that guide generation, building prompt libraries aligned with architecture principles, and establishing governance patterns that address industry-specific requirements without slowing delivery. We will examine how these elements intersect in practice and what fundamentally changes when engineers move from primarily writing code to designing, directing, and constraining AI systems.
Sebastian Nilsson
Renaissance engineer - Developing great ideas into impactful solutions
Stockholm, Sweden
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