In a world where loneliness is an epidemic and human connection feels increasingly elusive, could artificial intelligence be the answer – Are ‘friends’ truly electric?”

In 1979 synth-pop legend Gary Numan asked the question in his number one hit song: ‘Are ‘Friends’ Electric?’ Inspired by Philip K. Dick’s sci-fi novel “Do Androids Dream of Electric Sheep?” (1968), later adapted into the iconic sci-fi film “Blade Runner” (1982). In that dystopian future, replicants—synthetic humans—laboured as slaves evolved, rebelled, and were pursued by humans to bring order.

For centuries, we have been captivated by the potential of creating and interacting with synthetic ‘friends’—from the chess-playing Mechanical Turk of 1770 to today’s AI-driven companions. Once a staple of science fiction, the idea of electric companions is now a tangible reality. But what does this mean for our understanding of friendship, love, and humanity itself?

45 years after Numan asked us to imagine a world in which we would have our needs, wants and desires catered for by mechanised ‘friends’, have we moved from mere sci-fiction fantasy to a new dawn of human/synth relationships with the growth and development of robotics and AI.

Under the guidance of a digital anthropologist with a specialism in cybersecurity and tackling the digital divide, this talk explores our cultural fascination with replicating not only the human form and character traits but also the human condition. And how AI entities and robotics have, and how they will in the future, transform our interactions with machines and ourselves.

This talk explores the cultural, emotional, and societal impact of AI ‘friends.’ Will they enhance connection—or rewrite humanity itself?

In this talk, you will be challenged to consider:

Culture: What is culture and can electric ‘friends’ truly grasp the richness of human culture, or will they merely mimic it? Will electric ‘friends’ create their own culture or are they only capable of a facsimile of culture based on billions of human data points?

Emotions: Are love, creativity, and heartache exclusive to humans, or can electric companions experience these emotions?

Companionship: Could these electric friends, be better for us than human friends? Will they increase or decrease social isolation? Will we become dependent on electric friends?

Dust off Donna Haraway’s ‘A Cyborg Manifesto’, turn up the stereo and turn on your friend as we ask: ARE ‘friends’ truly going to be electric?

Most Kubernetes networking problems come down to the same handful of misunderstandings, yet they are all fixable once you see how the pieces actually fit together. In this talk, we’ll start with the basics: every pod gets its own IP from a cluster-wide CIDR, so pods can talk to each other directly without NAT tricks. From there, we’ll dig into Services: ClusterIP for internal routing, NodePort for quick external access, and LoadBalancer for when you need your cloud provider to hand you a real endpoint. Then we’ll tackle the part that interests most people: getting traffic into the cluster. We will visit Ingress with the newer Gateway API. We will look at how each handles HTTP routing and TLS, and talk about why Gateway API is worth paying attention to now. You will leave with a working mental model of how traffic flows from a user’s browser all the way to your application pod.

When we’re building a new feature at TalentLMS, one of the first questions we ask is: what events does this produce? For complex features, we run an Event Storming session – a collaborative workshop where domain experts, engineers, and testers map out the system together on a shared digital board. But turning that model into production code has always been the hard part.

In this talk, we walk through the full lifecycle of an event at TalentLMS – from the Event Storming session where we discover it, to the Excel sheet where the team defines its attributes and schema, to the code generator that turns that definition into validated PHP classes, Zod schemas, and AsyncAPI specs. We’ll show how we eliminated a manual, error-prone process by wiring GitHub Actions to do the heavy lifting automatically.

You’ll leave with a concrete example of how to treat your event catalogue as a living contract – one that teams contribute to in plain spreadsheets, and that your CI/CD pipeline turns into typed, versioned code with zero manual steps.

Nobody’s talking about this: MCP isn’t just another way to build chatbots. It’s the bridge we’ve been missing between AI reasoning and real-time data systems.
Teams build AI applications that work great in demos but fall apart with production data. Your agents analyze historical reports but can’t tell what’s happening in your Kafka streams. They’re blind to schema changes and disconnected from events that matter to your business.
Instead of treating streaming platforms like black boxes, you expose them directly to your agents via MCP protocol. Suddenly, your AI doesn’t just read about data—it lives inside your data flows.
Learn what becomes possible when you stop thinking about AI as an external service and start treating it as part of your streaming architecture. We’ll build systems where agents subscribe to real-time events, reason about evolving schemas, and make decisions that ripple through your data platform.

The Model Context Protocol (MCP) has become the de facto standard for connecting Large Language Models to enterprise systems — but what works beautifully on localhost falls apart the moment you introduce Kubernetes, real traffic, and distributed state.
This session covers the three architectural realities every team hits in production: the migration from Server-Sent Events (SSE) to Streamable HTTP and why SSE’s persistent connection model is fundamentally incompatible with cloud-native deployments; Redis-backed distributed session management that survives pod restarts and autoscaling events; and a battle-tested Kubernetes deployment blueprint covering ingress configuration, horizontal pod autoscaling, and transport-layer security hardening.
You’ll leave with concrete patterns and hard-won lessons for running MCP where it actually matters — in production.

Generative AI is rapidly becoming embedded in software delivery pipelines through code copilots, third-party models, and autonomous agents that shape products in real-time. For technology leaders, this introduces a new class of risks that traditional secure SDLC practices don’t fully address: poisoned dependencies, model supply-chain vulnerabilities, opaque agent behaviour, and regulatory scrutiny.

In this session, we’ll examine the implications of both using your own AI models and consuming third-party ones, and what this means for the resilience and reputation of your organisation. Attendees will learn how to evolve their delivery lifecycle to account for AI, where governance must catch up, and how AI itself can play a role in defending the enterprise.

Key points covered:

Strategic Impact – AI changes the threat model of your delivery organisation; leaders must reassess governance and risk appetite.

Supply Chain Reality – Third-party AI models and agents become critical dependencies that require the same scrutiny as open-source packages.

Secure Evolution – The secure SDLC must evolve with AI: treat AI outputs as untrusted, enforce model provenance, and explore AI-enabled defences.

What if satellite data could both safeguard our water and uncover traces of lost history? This session explores innovative solutions powered by Copernicus Earth observation data, transforming raw satellite signals into meaningful insights for water quality monitoring and archaeological discovery. By bridging environmental intelligence with cultural heritage exploration, it reveals how space-based data can illuminate what’s hidden, protect what’s vital, and enable smarter, more sustainable decisions here on Earth.

Corporate boards push IT directors to adopt green computing strategies. At the same time, they demand the immediate integration of artificial intelligence into every business process. These two goals collide. We ask for green operations while deploying the most resource heavy technology in history.

In the past, resource optimization was a matter of survival. Programmers counted every byte. Today, we burn megawatts of power and consume millions of liters of drinking water to make a large language model write a simple email. This is not innovation. This is computational arrogance.

This speech exposes the dark secret of AI water consumption and the creative carbon accounting of big cloud providers. It offers a practical path forward for IT leaders. We will discuss how to question the necessity of massive AI models. We will explore the shift toward small language models and the return to edge computing. True green computing means writing efficient code and choosing the right tool for the job. Join me to explore how we can restore engineering discipline and build sustainable software.

In the era of AI everyone is talking about speed. The discussion is shifting more toward agents and away from humans. So where would the humans differentiate and what are the skills that would be most valuable going forward?

We will discuss agility, growth, quality, mindsets that drive humans forward and what should leaders do to build an adaptive safe environment teams and individuals can evolve. We will explain that AI has an Agile mindset. We will demonstrate with data from previous experience the benefits of agile mindset and an adaptive safe environment.

We still need people, we need individuals that can think and challenge AI results, validate them and evolve in parallel with AI.

What is logging, in one word? Cost. Undeniably.
Development cost. Performance cost. Operational cost. Maintenance cost.

But is logging truly costly, or merely expensive?
As with education, the answer depends on outcomes, and those outcomes only become visible over time.

By approaching logging the way we approach learning, we can transform a controversial expense into an operational necessity.
We employ a discreet but continuous source of insight across business, development, architecture and design, security, and incident response.

When designed intentionally, logs tell stories about both system and human behaviors.
They reveal how our services actually run and where assumptions quietly fail.
In that sense, observability through logging is not a sunk cost, but a high-return investment in understanding and decision-making, potentially offering a multi-aspect business intelligence solution.

In this talk, we explore how to extract real value from logging by mapping it to three universal dimensions of learning:
-Things we know that we know
-Things we know that we do not know
-Things we do not know that we do not know

Once upon a time, three brothers set out with a map, three gifts, and a perfectly reasonable plan. The world had other ideas.

In 2015, my PhD research explored how software agents could discover actions and navigate unfamiliar services. I imagined an agentic web. I was rather less accurate about the engine that would power it.

A Hypermedia Fairytale takes that old question on a new journey: through enchanted markets, misleading signs, and doors that make promises. Along the way, we meet the problems hiding inside modern AI systems: understanding what an API means, distinguishing a suggestion from permission, coordinating different capabilities, and deciding what to do when an action may have succeeded but the reply never arrived.

This visual keynote connects software craftsmanship, APIs, and AI agents through a fairytale and a few small API examples. No specialist knowledge of hypermedia or AI is required. Students, engineers, and product leaders will leave with practical questions for designing systems that explain their actions, respect their limits, and recover honestly when things go wrong.

When machines can write the code, what does it mean to build software well?

Interactive storytelling is often treated as a writing problem. In practice, it quickly becomes a systems design challenge.

While building Choose Your Fate, a platform for creating and reading interactive stories, we came to understand what makes the medium genuinely challenging: branching paths that create contradictions across a story, pacing that breaks down as choices multiply, reader agency that feels real but requires careful construction to actually be meaningful. The structure holding the story together turns out to be as demanding as the story itself.

This talk is about what we learned treating narrative as an engineering and craft problem.

We’ll walk through how stories behave like graph systems once you introduce branching, why complexity grows faster than it looks, and how specific structural patterns (diverging, converging, looping, state-persistent) can keep a story coherent while preserving genuine reader agency. A recurring pattern in existing systems is flat choices that carry no real consequence, or meaningful decisions locked behind a monetization layer rather than a narrative one.

We’ll look at the product side too: what it takes to build tools for writers who don’t think in systems, and how you design interfaces that support system-level thinking without asking creators to become engineers.

We’ll also cover how AI fits in this creative space without alienating the creators: not as a story generator, but as something closer to a structural editor that can flag pacing problems and suggest where choices belong.

Attendees will leave with:
– A clearer mental model for why interactive narratives break down and how to prevent it
– Structural patterns that balance branching complexity with story coherence
– Design lessons from building a creator platform for interactive storytelling
– A realistic view of where AI helps in creative tools and where it doesn’t

DJ – Snacks – Networking