Talks

As an online grocery store, we often wonder what happens in the exact moments people make a decision. What makes you spontaneously add something to the shopping cart? Why do you decide to buy ingredients for three dinners instead of four? To get closer to what happens in these moments, we combined diary studies with interviews. Participants recorded short video diaries after each shopping trip. Later, during the interviews, we could revisit specific moments and trips they had shared with us, using them to better understand not just what happened, but why.

This presentation is about why we needed this specific method, what made the diary study actually work (as a team, we all had a few unsuccessful diary attempts under our belts), and the things we definitely hope to do better next time.

AI has fundamentally changed how engineers work, but most hiring processes haven’t changed at all.

Coding assistants can generate solutions in seconds. System design answers can be refined with prompts. Candidates walk into interviews with powerful AI tools at their side. Yet many companies are still evaluating engineers as if AI didn’t exist.

So how do you actually assess engineering ability in this new reality?

After interviewing hundreds of engineers at Elementor, I redesigned my hiring process for the AI era. Instead of trying to prevent candidates from using AI, I changed what we evaluate. Today we test how engineers think, how they collaborate with AI, and how they apply judgment when AI-generated solutions are incomplete, incorrect, or misleading.

In this talk, I’ll share what changed in our hiring process and what actually works in practice:
how we evaluate coding ability when AI is involved, which system design questions reveal AI orchestration skills, and what behavioral signals distinguish high-impact engineers from candidates who rely on AI to mask gaps.

Whether you’re hiring engineers, preparing for interviews, or leading an engineering organization, you’ll leave with a practical framework for evaluating engineering talent in the AI era.

We’re in the middle of a shift: companies are buying AI agents, vendors are selling them, but the rules are still unclear. Pricing models range from tokens to subscriptions to outcomes—each with tradeoffs—and questions around security, onboarding, and real value remain unresolved. In this talk, Eduards brings structure to the chaos by comparing AI agents to software subscriptions and human employees. Each follows a different model, with distinct pricing, security, and user experience assumptions. You’ll gain a clearer framework for deciding when and how to deploy AI agents effectively in your organization.

The hiring market has flipped. After years of frenzied growth, tech is now defined by mass layoffs, cautious headcount, hiring freezes, and hundreds of applicants for every role. Hiring cycles have grown longer: more interviews, more barriers, lower confidence.

The problem isn’t the market, it’s that most hiring managers treat recruiting as marketing. Transparent recruiting fixes both sides of the equation by giving candidates the clarity and equity they need to perform their best, while giving hiring managers a faster, more productive hiring process.

With global examples from companies big and small, you’ll see how HubSpot hired 16 highly qualified content designers in just 5 months by focusing on a simple idea: people perform their best when they know what’s expected of them.

Audience takeaways:
– A simple strategy and set of tactics you that transforms your recruiting practices, especially in a slow market where every hire counts
– Compelling ways to set expectations for candidates before they even talk with you, reducing noise in your pipeline and building a strong employer brand
– How to rewrite your job posts to speak directly to your target audience — their goals, motivations, and frustrations — so your roles stand out in a sea of sameness
– How to help your best candidates succeed faster with enablement materials and radically transparent recruiting tools

We put students in front of a webcam to protect the integrity of remote standardized tests. Then our production pipeline started flagging kids for phone use while they drank from a water bottle. On the hard cases, two times out of three.

This talk is about how to build near-real-time detection on vision LLMs. I’ll walk the pipeline end to end: segmenting live video, running VLM passes over short windows, and turning raw model output into signals you can trust.

Every lesson here was measured against a human-annotated data set. The video format you send moves accuracy more than the model you pick: one input change cut false positives by 27 points. Telling the model to “be careful” buys almost nothing, because vision-model language priors override the pixels, a failure two 2026 papers quantify. Two frontier models were unusable for us.

And evaluation, not generation, is the hard part. When two annotators watched the same video and disagreed on what counted as cheating, we handed the same footage to ten people, just to force the argument out into the open. That pushed us to write an objective grading rubric for our annotation team, then to rebuild the scoring logic around difficulty and ambiguity instead of a flat pass or fail.

You leave with a repeatable way to build and evaluate vision detection pipelines, and a sharper distrust of your own metrics.

As AI coding tools evolve from session-based assistants into always-on agents, the role of the engineer is changing. In this talk, Emilie Schario explores what it means to build software with agents that can act, follow up, and carry work forward without waiting to be prompted at every step.

She’ll cover what OpenClaw got right, where it falls short, and how Kilo is closing the gap with KiloClaw — along with what this shift means for the future of how software gets built.

What happens when a successful monolith needs to evolve into a platform capable of serving users across continents?

This talk follows the transformation from a region-bound, synchronously connected architecture toward an event-driven platform built around domain boundaries, business events, sagas, and globally distributed read models.

We will explore an architecture in which writes remain centralized while reads are served closer to users through regional projections. Along the way, we will examine the practical consequences of this model: eventual consistency, delayed and out-of-order events, retries, idempotency, data freshness, partial failures, and recovery.

The technical architecture is only part of the challenge. Moving beyond the monolith also requires dozens of teams to change how they design services, publish events, model data, and reason about failure.

Rather than presenting a perfect final architecture, this is a practical account of the decisions, trade-offs, and lessons involved in building an event-driven platform across continents.

At GitGuardian, 80 to 90% of our code is now written by AI. We’re building towards what we call a “dark factory”: systems that take a well-written spec and ship a working feature with very little human typing.

GitGuardian is a 7-year-old, post-Series C cybersecurity startup with a 70-person engineering org that’s scaling fast, and we’re SOC 2 Type II certified. Customers trust us to protect their secrets, so there’s very little room for mistakes. We have to reach our goal of getting to a software dark factory while holding the same quality bar as before, not quietly lowering it to make the numbers look good. Our journey has been like changing the engine of a car while it’s still driving!

This is the practical version of that story. Getting here took far more than handing everyone Cursor and Claude Code. I’ll walk through the building blocks we’ve had to put in place to make agentic engineering safe at our scale, from the infrastructure to run fleets of background agents to keeping a lid on spiralling token costs, and I’ll be honest about what worked, what broke, and what it cost us.

I’ll also talk about the human side, because that’s been the hardest part. What it does to how people learn, the stress it creates, the questions about what engineering careers look like from here, and the real work of driving adoption and meeting resistance head-on.

If your team is going down this path, you’ll leave with the how of what we did and not just the why — the technical scaffolding, the changes to how we work, and the lessons we’re still learning about the people side.

One happy surprise worth a mention: going faster hasn’t meant fewer engineers. We’re hiring heavily.

This talk explores the often-neglected superpower of product design: vision. Not just strategy decks or north star flows, but emotionally charged, speculative, team-energising visionary ideas and thinking, the kind that lifts teams out of the backlog and helps companies imagine what’s next.

Through a cinematic, storytelling-driven format, I’ll use familiar movie moments to illustrate why visionary design matters, how to start practicing it, and how to push through resistance when no one’s asking for it. This talk will be honest, fun, and deeply grounded in personal and team experiences.

As AI rapidly reshapes the way we build products, lead teams, and make decisions, many organisations are becoming faster, but not necessarily wiser. In the rush toward optimisation, automation, and scale, something more fundamental is quietly being tested: our ability to remain human inside increasingly machine-shaped systems.

This talk introduces the Luminary Framework, a leadership and cultural model developed through real-world experience leading high-performing engineering organisations through complexity, growth, and transformation. Rather than treating AI as purely a technical challenge, the framework explores the human capabilities that become more valuable as intelligent systems become more powerful.

The session will explore three core dimensions: Narrative Shift, Emotional Literacy, and Systemic Clarity, and why these are becoming critical leadership competencies in the AI era. It will examine how teams lose coherence when leaders optimise for output over meaning, why psychological safety becomes harder in highly accelerated environments, and how organisations risk amplifying bias, fragmentation, and disconnection when human depth is removed from decision-making.

In 2025, Fin (formerly Intercom) took on an ambitious goal: double the throughput of its engineering team, not by building fancy demos, but by using AI agents to get real features, built on a large existing SaaS codebase, into the hands of paying customers. In this talk Brian will go past the hype into the practicalities of scaling agentic engineering at the organisational level: the layers that actually matter, such as skills and codebase preparation, provenance, the SDLC process around agents, designing feedback and verification loops, and building evals that improve the system itself. He’ll share what has and hasn’t worked on the way beyond 2x. This talk is aimed at engineering leaders and staff+ engineers in established organisations trying to get real results out of working with AI agents.

Craftsmanship is the fundamentals of quality. This talk presents a practical framework—Fit, Flow, Fidelity—that helps product teams deliver digital experiences that work for real people across diverse bodies, minds, and contexts.

⁜ Fit combines Intuitiveness and Predictability to make interactions clear and consistent across modalities.
⁜ Flow combines Efficiency and Optimization to conserve time and energy by reducing friction.
⁜ Fidelity combines Polish and Reliability to signal care and ensure access endures through updates and sessions.

Through Microsoft examples, we bring each facet to life with clear, relatable scenarios—like how returning focus after closing a dialog shows predictability, or how precise caption timing reflects polish. Rather than listing checklists, the talk focuses on stories and practical illustrations that make Fit, Flow, and Fidelity easy to grasp, demonstrating how these fundamentals of craft turn ordinary interactions into experiences that feel intentional and inclusive for disabled users.

AI will not replace data engineers tomorrow. But it is already changing how we work — including, occasionally, helping us do stupid things with more confidence.

Used badly, AI will make us weaker engineers. We outsource judgment, trust the output too quickly, miss the details, and slowly lose the habit of critical thinking. Used well, it can make us sharper. AI can help with the parts our brains are weaker at: holding lots of context, exploring many options, drafting code, explaining errors, and moving faster.

This talk is inspired by a real example: Claude Code building a dbt project on DuckDB from API data. It created models, tests, docs, incremental loads, and freshness checks. Impressive. Until you realise it missed API pagination, dropped useful fields, and made questionable modeling choices. The pipeline looked like it worked, but it did not.

AI is great at speeding up the mechanical parts of data engineering: writing boilerplate, debugging dbt errors, generating SQL, suggesting tests, and exploring unfamiliar code. But it does not remove the need for judgment.

Someone still needs to ask: Is the data complete? Is the model correct? Are the assumptions safe? Would I trust this dashboard in a real business decision?

So this is not a talk about “AI instead of data engineers.”, it is about data engineers using AI well. Not to delegate thinking, but to help us magnify our skills.

A year ago, PFF had a ChatGPT subscription. That was it. Today we’ve achieved 10x developer output and built more in the last six months than we did in the previous two years with a team one-fifth its former size.

This talk is about how we got there, and everything that happened along the way; the stuff that worked, the stuff that didn’t, and the stuff nobody warned us about.
I’ll cover the obvious and the non-obvious. Why hackathons aren’t the answer. Why executive AI committees are a sign your leadership is detached from the problem, not engaged with it. Why bolting AI onto your existing processes is the most expensive way to achieve nothing. And why things started really well for us, then got hard, and what we did about it.

I’ll share the practical checklists we built through trial and error and how to actually transform your org before your competitors figure it out. Not theory. Not a vendor pitch. Just what we learned from doing it for real, getting it wrong a few times, and ending up somewhere genuinely different.

If your company’s AI strategy is still a subscription and a Slack channel, this talk is for you.

AI everywhere. Companies hustling not to stay behind. However, things are not as easy as we are usually told if we want to leverage the power of AI successfully.

In this session, we will discuss 7 pitfalls that are often lost in the omnipresent AI clamor: Dangers of anthropomorphization, leaky abstractions, review fatigue, productivity traps, AI vampires, sovereignty issues, and more. Not all are easy to fix, but we need to find answers on our journey towards AI. Let’s explore together!

Our north star: a data expert at every collaborator’s side. A PM, a CSM, a GTM analyst, a marketer — anyone — should be able to get an analyst’s answer whenever they need one.

Two modes:
Reactive — ask in natural language, get the answer a senior analyst would have given, with the same caveats and context.
Proactive — the analyst shows up before you ask: the Monday read on last week’s launch, the “so what?” behind a metric move, the draft of your weekly update. This is less about answering faster and more about helping the whole team decide faster.

The model is the easy part. The hard part is the foundation underneath it: a governed semantic layer of data products and semantic views, exposed through a persona-aware harness that only lets the agent see what the user’s role is allowed to see, with skills that make it reason — and eventually act — like an analyst.

It works: on our first pilot use case, this foundation lifted end-to-end accuracy from ~40% to >90% on the eval set.

These days, everyone is talking about Agentic AI and how to create entirely new apps with just a few prompts. We are living the AI hype. I’ve stopped counting how many apps have proudly launched AI assistants and other features nobody ever uses.
In this talk, I’ll walk you through how we at Wise scale our product with AI. I will cover our journey from discovery to experimentation and delivery, showing how we identify the biggest opportunities and manage the risks around them. You will see a number of real examples where we apply this technology to build money without borders for more than 15 million customers.

In my experience building products at companies like Google, Spotify, and Microsoft, I’ve learned one powerful truth: the most loved products aren’t just useful—they’re emotionally resonant. They spark joy, create meaningful connections, and leave lasting impressions. That’s what I call Product Delight.

In this talk, I’ll introduce the Delight Framework I’ve developed to help product teams go beyond functional features and create moments of deep delight. I’ll share how to uncover emotional motivators, design for both functional and emotional needs, and move beyond the “nice-to-have” mindset—making delight a core product strategy.

How do we future-proof not only our careers but also our products and business models in the age of AI? Mirela explores the shift from simply using LLM to speed up PM work to building AI-first products where and when it matters. And three case studies.

Everyone is talking about AI adoption. Few are being honest about what it actually looks like inside a real product team.

This talk is built on firsthand interviews with product leaders at non-big-tech companies navigating AI transformation right now. You will hear what really changes in the product-design-engineering workflow, why faster output doesn’t automatically mean better results, and what skills become non-negotiable when the machine can build. Closing with the most honest advice practitioners have for peers starting today – no hype, no vendor stories, just the messy truth.

The talk delves into how new emerging conversational CX and agentic experiences reshape human and machine interactions, and the whole digital ecosystem.
– How to accelerate the agentic CX innovation by being customer-first, not AI-first
– How to drive intent-driven personalization for conversational CX and agentic experiences
– How to rethink systemic workflows to effectively combine product design, generative AI and agentic capabilities

Parallel programming can be intimidating, but doesn’t need to be! Tile-based programming models make GPU parallelism more newcomer-friendly, highly productive, and still fast by letting you write sequential, array-centric code while the framework handles parallelization, synchronization, and data movement. In this talk, we’ll present cuTile, NVIDIA’s new tile programming stack and Tile IR, the new compiler stack that it is built with.

This session argues that simply investing in advanced technology or AI tools does not guarantee meaningful insights or success. Instead, high-performing teams use AI to enhance already strong decision-making, supported by clear goals, data literacy, and thoughtful integration into existing workflows. Success depends on defining metrics upfront and balancing automation with human judgment. Ultimately, it emphasizes that building the right culture and capabilities matters more than spending heavily on tools.