Host’s opening address.

Sponsored by

Headlines claim AI is transforming software engineering overnight. Across more than 400 engineering organizations, we see patterns that challenge the hype and reveal what’s really working, and what isn’t, when AI enters the software development lifecycle.

In this talk, Justin Reock, Deputy CTO at DX, will share a data-driven “state of the union” on AI in engineering, grounded in both quantitative data from thousands of developers and on-the-ground observations.

You’ll learn:

• The current impact of AI, from benchmarks on the percentage of code authored, team PR throughput, and time savings
• Where AI adoption is creating real gains in throughput, and whether it introduces tradeoffs for quality and maintainability
• Insights and trends, including whether junior or senior developers are seeing bigger gains, the impact of structured rollouts, which tools are having the most impact, and the evolving definition of “developer”

The session will conclude with a practical framework for measuring AI’s impact, helping leaders cut through hype and understand the impact AI is having in their own organizations.

Key takeaways

Five key questions are used to highlight areas for you to focus your learning:

• Do we have the right team?
• Do we have the right technology?
• Do we have the right product vision?
• Do we have the right balance between innovation and liability?
• Do we have the right product <> market fit?

Wise onboards 50 grads annually. AI helps them code fast, but batch size is causing real senior review fatigue. I’ll share how we use automated guardrails to maintain safety without burning out the team.

I’ve spent over a decade leading engineering teams and lately the industry has shifted. Companies are asking why they should hire juniors when AI can just do the work.

At Wise we take the opposite view. We continue to hire 50 graduates every year because we believe AI is a massive enabler for unlocking early careers talent.

However, this opportunity brings a new challenge. AI allows our juniors to move incredibly fast. Our power users are producing 37% more Pull Requests than their peers. This surge in velocity proves their potential, but it brings concerns about work slop and growing PR batch sizes. We simply cannot rely on human reviewers to catch every quality issue in this new volume of code.

In this talk, I will share what guardrails we have put in place to let them move at the speed of a start up. We have introduced systems such as automated canary analysis and engineering maturity checks. This isn’t just about preventing bugs. It is about giving juniors the psychological safety to ship to production and learn faster.

I worry that if we reject early careers now, we lose our next seniors. I’ll share real stories from our academy, like interns fixing production issues on day one. You will leave with a plan to turn the AI uncertainty into your biggest advantage, using guardrails to make high velocity hiring safe and scalable.

Key takeaways

• Strategies to handle the 37% PR volume spike and increased batch sizes caused by AI-assisted juniors.
• How to mitigate work slop using your own AI engineering hub.
• Moving from “Review Gates” to “Production Guardrails” using automated canary analysis.
• A practical guide for scaling a graduate pipeline by treating AI as a productivity asset, not a replacement.

Burnout hits the engaged leaders who keep giving, not the ones who don’t care. Using the four-dimensional burnout model, this talk gives you (and your team) practical tools to recognize, prevent, and recover from burnout.

Here’s what surprised me most about leadership burnout: you have to burn to burn out. It’s not the checked-out managers who struggle – it’s the caring, committed ones.

For the past few years, leaders’ mental well-being has been my main focus. Through coaching engineering managers and navigating high-pressure leadership roles myself, I’ve built a practical framework based on burnout research – including, unexpectedly, parenting psychology.

Typical “just take a vacation” advice ignores why leaders burn out in the first place. Here’s what actually works.

The four dimensions of burnout – exhaustion, emotional distance, loss of meaning, and the gap between who you think you should be and who you actually are. Learn to spot each dimension in yourself and your team before it’s too late.

Resources vs. demands framework – a systematic way to map your emotional, physical, and mental loads against what you actually have available. Find the dangerous imbalances before they become crises.

Early warning signs – specific signals in yourself and your people that you can recognize before full burnout hits.
Action plans for each stage – what to do to prevent it, what to do with first symptoms, what to do if you’re already burned out. Different stages need different strategies.

Supporting others without burning yourself out – the do’s and don’t’s for helping your team and peers through their struggles.

Taking care of yourself is hard, especially for engineering brains that want clear systems. That’s why I built this anti-burnout framework – to turn well-being into something understandable and actually doable. Because the leaders we need most are the ones who care deeply – and we need you to stay healthy.

Key takeaways

• Four-dimensional burnout assessment – recognize exhaustion, emotional distance, loss of efficacy, and identity conflict in yourself and your team before crisis hits
• Resources vs. demands audit – map your emotional, physical, and mental loads to spot dangerous imbalances early
• Stage-specific action plans – different strategies for prevention, early symptoms, and full burnout with concrete next steps for each
• Early warning signals checklist – specific, recognizable signs you can spot in yourself and your people
• Support playbook – do’s and don’ts for helping burned-out colleagues while protecting your own well-being

Practical, repeatable inclusive-leadership patterns for global engineering teams—aligning assumptions on hierarchy, disagreement, and feedback using working agreements, inclusive meeting mechanics, decision recaps, and repair scripts to build trust and move faster.

Global teams don’t fail because people lack good intentions—they fail because they carry different assumptions about hierarchy, feedback, speed, and what “good work” looks like. Inclusive leadership across cultures is less about slogans and more about building shared operating agreements that make collaboration safe and predictable.

Drawing on my experience as the only African Joint Institute Student Ambassador in a UK–China AI institute partnership, and my leadership roles coordinating large technical cohorts in Nigerian higher education (including supervising/appraising 50+ technologists and leading a lab through external accreditation), I’ll share a practical toolkit for leading across cultural divides.

We’ll cover three common cross-cultural failure modes and how to fix them:

1. Silence vs disagreement — how to surface dissent without forcing confrontation.
2. Hierarchy vs challenge — how to invite “safe pushback” and avoid decision-by-deference.
3. Direct vs indirect feedback — how to give clarity without disrespect, and how to receive feedback without defensiveness.

You’ll learn lightweight mechanisms you can adopt immediately: a “Working Agreements” template, inclusive meeting patterns (pre-reads, async input, rotating airtime), a decision recap format, and scripts for repairing misunderstandings when identity and power dynamics are in play.

You’ll leave with repeatable patterns to help distributed teams move faster—with less friction, more trust, and more meaningful participation.

Key takeaways

• A Working Agreements template for cross-cultural teams (communication, decision-making, feedback norms)
• Inclusive meeting patterns that reduce time-zone and hierarchy bias
• Scripts for disagreement, feedback, and repair after a cultural misread
• A decision recap format that increases clarity and reduces “re-litigating” decisions
• Practical ways to turn representation into belonging (not just diversity metrics)

When Meta’s senior engineers couldn’t explain ‘vibe-coding,’ we knew something broke. Traditional leadership tools failed, and we had to evolve. Learn how, at Meta, we changed how we hire, evaluate, and lead.

A while back, during one of Meta’s new hire orientations, a senior manager froze when new hires asked him about “vibe-coding.” He nodded politely but clearly had no idea what they meant. That moment exposed more than a generational gap, it was a warning; The gap between how fast technology evolves and how fast leaders adapt is widening, and we must do something about it. ASAP.

Junior engineers now arrive with AI superpowers. The competitive advantage shifted from raw technical skill to adaptation speed, from code quality to communication, and from knowing frameworks to catching AI hallucinations. This forced Meta to rethink how we hire, evaluate, and lead.

We built AI-native coding interviews that measure adaptation, not memorization. We learned to evaluate talent without the traditional technical signals, and shifted the skills we look for when hiring, promoting, and assessing performance.

We also changed how we lead: leaders have to use AI themselves before teaching their teams. They learn to adapt , rather than using specific tools, so they can evaluate whatever shows up next. They needed to learn how to leverage AI without the price of dependency. We realized AI accelerates junior engineers faster than any technology we’ve seen, but despite the panic about job losses, AI can help ambitious engineers break in and move up, but only if their leaders understand how to guide them.

In this talk, I’ll share what we learned, and the frameworks we developed. I’ll share the mistakes we made, and what actually works when there’s no playbook. You’ll leave knowing what skills matter now, how to evaluate talent differently, and how to lead when yesterday’s answers don’t apply anymore.

Key takeaways

• AI-native coding interviews: How we rebuilt coding interviews to evaluate adaptation and not memorization.
• New evaluation criteria: What skills actually predict engineering success when AI changes the competitive advantage
• Leadership frameworks for AI adoption: How to train leaders to use AI themselves and guide teams without creating dependency
• Data-backed insights on junior engineers: Why AI accelerates junior engineers faster than any previous technology and how to leverage that effectively despite job loss fears

In this talk, Michael Lopp will tell you a story about stories. From almost 30 years of leadership, Lopp will not only tell you stories from Slack, Pinterest, Netscape, Borland, and that start-up you’ve never heard of, but also why the art of storytelling is integral to your leadership toolkit.

Key takeaways

• The reasons humans like to hear and read stories.
• Why stories work for leaders.
• The core parts of a good story.

Your future leaders are already on your teams. They span every level from junior engineers to staff engineers, and they’re watching closely. They’re learning not just from what you say, but from how you lead, how you make decisions, and how you create opportunities for others.

So, how do we intentionally prepare individual contributors for leadership roles? How do we equip them with the skills, context, and confidence to step into leadership roles and succeed as top-tier managers within our organizations?

Because scaling teams doesn’t start with headcount or org charts. It starts with scaling yourself as a leader.

A practical playbook for empowering individuals and delivering complex technical products across distributed teams without sacrificing inclusion or autonomy.

In a world where teams are more distributed than ever, managers and senior ICs need to evolve their collaboration styles and step into the future of work. Gone are the days of huddling 10 people in a room with a whiteboard, dry erase markers, sticky notes and way too much coffee in the hope that somehow intellectual osmosis happens and we’re miraculously all aligned. That approach does kinda get the job done, let’s be honest here, but while we’re at the honesty bit, let’s also concede that eating a hamburger with a spoon also gets the job done with the same degree of efficiency.

Old-school collab can also lend itself heavily to the “get in that room and don’t come out til you’re done” vibe that not only strips autonomy and trust away from our most highly respected engineers but also creates ridiculously expensive meetings (hello, 6 staff engineers and 2 principals in one room). Don’t get me started on the impact this has on inclusion (and yes, I still care deeply about this, and you should too).

In my experience, some of the best technical decisions have been a byproduct of allowing folk to simmer over what was being discussed — to think asynchronously, sketch ideas in their own time, pressure-test assumptions, and come back sharper. The quieter, more pensive folk who aren’t the loudest in meetings tend to stare deeply into the Miro/Notion abyss… sometimes with a little help from modern tools that help them organise thoughts or explore alternatives — and then → lightbulb!

Inclusion isn’t giving someone your paint and letting them paint their own canvas. It’s allowing people to add colour to your monochromatic genius and, fam, this method we’ve been using to fuel some of our biggest projects in Pleo has resulted in some pretty spectacular technicolor masterpieces!

If you’ll have me, I have some A-M-A-Z-I-N-G tips and tricks to share about how to successfully build and launch technically complex features requiring cross-domain/cross-team collaboration with engineers, PMs and domain leads distributed all over this beautiful blue speck of ours (aka the Earth), while empowering our teams to be the best versions of themselves yet.

The next evolution of collaboration is human-led, AI-assisted, and designed to amplify thinking and level the playing field — so no one feels like they don’t belong.

Key takeaways

• A clear picture of what effective collaboration looks like in 2026 and beyond – moving past synchronous, room-centric rituals toward more inclusive, distributed ways of working.
• Why empowering engineers and cross-functional partners isn’t a “nice to have,” but a practical lever for higher engagement, better technical decisions, and faster, more sustainable delivery.
• Concrete patterns for enabling asynchronous thinking that give quieter voices space to contribute and improve the quality of outcomes.
• Real-world examples from complex, cross-domain initiatives showing what worked, what didn’t, and what we’d do differently next time.
• Actionable techniques attendees can apply immediately in their next project – from shaping collaboration norms to structuring work so teams can do their best thinking.

Engineers have Cursor; leaders play “Calendar Tetris.” This case study shows how to build a personal AI assistant using commercial tools. Learn to automate toil, manage high-volume context, and reclaim time for strategic impact.

The AI market is currently obsessed with the Individual Contributor. Tools like Cursor and Cloud Code have revolutionized the IDE, allowing engineers to ship features and crush complexity at record speeds. But as Engineering Leaders, we have been largely left out of this revolution. Our “IDE” isn’t a code editor; it is a fragmented ecosystem of calendars, documents, and messaging apps.

While our teams move faster, we find ourselves bogged down, playing “Calendar Tetris” and drowning in context switching. Since the release of ChatGPT 2.5, I have been on a mission to fix this imbalance. I wanted to answer a simple question: Can I build a personal AI assistant that actually understands the context of engineering leadership?

In this session, I will walk you through the architecture of my personal AI system built using existing commercial tools. We will move beyond basic chat prompts and look at how to automate the “heavy lifting” of management. We will cover how to use AI to:

• Streamline meeting preparation and context gathering.
• Stay on top of a high volume of conversations across multiple teams and projects.
• Reduce the manual toil required for operational data analysis.
• Act as a “strategic coach” to help you prioritize between short-term fires and long-term goals.

You will leave with a blueprint for building your own assistant—one that handles the noise so you can focus on the signal, the strategy, and your people.

Key takeaways

• The “”Leader’s Stack””: A walkthrough of the specific commercial tools and integrations used to build a low-maintenance, high-impact personal assistant.
• Managing Scale: How to use AI to summarize and track context across dozens of concurrent streams without losing the details.
• The AI Coach: Techniques for using LLMs to analyze your schedule and tasks, helping you enforce prioritization between the urgent and the important.
• From Toy to Tool: How to move past the novelty of LLMs and make it into a centralised IDE that help you become a more impactful leader?

Host’s closing remarks.

Host’s opening address.

At Cursor, we’re on the edge of the edge: we’re both builders and heavy users of AI-assisted development tools, which means we feel the gap between “it seems to work” and “we trust it enough to ship” more acutely than most. We’ve spent a lot of time trying to close that gap — building evals, interpreting usage data, and navigating the surprisingly tricky question of how to train and deploy models when your feedback signal is noisy and your users have very high expectations.

This talk gives an honest look at where we are: what our validation process looks like today, what the data from millions of engineering sessions has taught us, where it’s left us with more questions than answers, and what we wish we’d known earlier about the operational realities of running our own models.

If you’re building in this space and finding that the standard playbook doesn’t quite fit, this one’s for you.

We dive into the technical details of three areas in the context of shipping AI agents: security, reliability and performance.

This is a talk written by engineers, for engineers. At Gradient Labs we build AI agents for financial services companies. In this talk, we share techniques and best practices that we’ve developed in shipping AI agents in high-stakes environments. Specifically, we cover three areas, outlined below.

Security

We talk about how we apply the principle of least privilege to AI agents, as well as some specific practical examples where we’ve had to think through security (e.g. identity verification with voice agents).

Reliability

We talk about how we handle failures, rate limits, latency spikes, durable execution and retries, as well as how we protect our agent from external factors like spikes in load.

Performance

We talk about testing non-deterministic systems using production conversations as test fixtures, LLM-as-judge evaluation, and the metrics that actually predict customer satisfaction.

Key takeaways

• How to manage the risk of AI agents reading sensitive data and performing high-stakes actions
• How to ensure reliable execution in the presence of failures when working with AI agents
• How to test AI agents when traditional unit tests don’t work; synthetic evaluation pipelines, LLM-as-judge, and metrics that catch regressions before customers do

Building reliable systems is a core tenet of good engineering. Yet, when reliable components are glued together, deviant behaviour often emerges. These can manifest as tricky bugs that take significant time and effort to identify, or broader incidents that hamper availability. Reliability needs to be considered across the sum of all parts rather than in just individual components.

The Monzo engineering teams have built the UKs largest digital bank from scratch over a period of many years. In this talk, Matej discusses lessons learned and the properties engineering organisations need to have to build and maintain reliability at the core.

Small things going wrong can quickly snowball. The cascading failure is often a nightmare scenario for any system. An initial problem, which in isolation seems like such a minor problem, can kick off a chain reaction of ever-increasing failures, potentially leading to catastrophic results.

When a failure of a single component results in the failure of other connected elements, this is known as a progressive collapse. In this talk, Sam Newman looks at this phenomenon in more detail, and he’ll examine how it has manifested in major disasters. Based on lessons learned from other industries, Sam will share three key techniques that can be used to mitigate against the progressive collapse occurring in your own system.

This talk will help you understand how to architect your systems in such a way that small failures stay small.

Regardless of if someone is going through hypergrowth, just onboarding their first customers, or has already scaled ten times over, this talk will cover how to not let perfect get in the way of good when on a tight timeline, and how “more time” needs to be paired with removal of bias towards existing patterns, continuous cross-functional advocacy, and strong metrics and milestones to drive success.

“If you build it, they will come” works great for sports fiction, but the reality is we don’t start out building for scale – we build to see if it works first. For some, the scale never comes. For one telehealth company, the scale came pretty much overnight – and we fumbled it pretty hard.

In this talk, we’ll follow the story of how to prepare for high scale (and the gotchas that got us) with a scrappier approach in a seven-day timeframe, and how having more time alone didn’t guarantee us the win next time around. We’ll dive into the playbook for scaling the architecture while operating it in parallel – not only the services and tools, but also the larger technical strategy and cultural shifts that were required to break us out of our structural slump and help us score a major scaling goal.

When approaching scaling challenges (or any large system-wide architectural challenge):

• Start somewhere – don’t let perfect be the enemy of good
• More time does not mean better outcomes on its own
• Understand your critical path and follow it from start to finish
• Find your guiding metric/north star and measure against it regularly and consistently

We will discuss load testing tooling, automation approaches, system benchmarking, as well as organizational/technical strategy elements.

Facing fragmented microservices, Duolingo launched a Golden Path to standardize development. By packaging best practices into templates with superior tooling and automated security, adoption followed organically, boosting velocity without mandates while empowering autonomous engineering teams.

After more than a decade of rapid growth, Duolingo’s microservice ecosystem was highly functional, but also highly fragmented. This technical diversity created significant friction, slowing down developer velocity and making consistent security and maintenance a formidable challenge. To address this, we initiated a “Golden Path” project to standardize how services are built and operated, but we immediately confronted a classic dilemma: how to drive adoption across autonomous engineering teams without affecting productivity. The traditional “stick” approach of enforcing mandates from the top-down felt counter to our culture, while a “carrot” approach required genuine value to development teams in order to gain long-term traction. For example, security, while its importance was universally acknowledged in principle, in practice it was often deprioritized by developers focused on shipping features.

Our breakthrough came from realizing that we could use our existing microservice templates as the central place to improve and commit standards, essentially treating repositories as updatable packages. Instead of prescribing standards from the top down, we systematically identified, refined, and codified the best practices and tools that had emerged organically within our engineering culture over the past ten years. We focused on making the Golden Path the path of least resistance by providing superior tooling, faster build and deployment times, and automated security that was seamlessly integrated and provided immediate benefits to existing microservices. This transformed security and cost controls from compliance burdens into free features. By standardizing our most successful homegrown solutions, we achieved widespread adoption not through heavy enforcement, but by offering a tangible competitive advantage that empowers our developers to build faster and more securely.

Key takeaways

• Top-down mandates can conflict with culture, focus on “carrots” not “sticks”
• Existing service templates can be treated as updatable packages to embed and evolve standards
• AI code migration tools work better for what is unique about a service, static tools work better for what is common among services
• Identify and codify homegrown best practices rather than a brand new set of standards if possible
• Make the Golden Path the easiest option to drive voluntary, rather than forced adoption

Replacing a 20-year-old system while remaining on-air is the ultimate engineering stress test. Talk shares ITV’s framework for visualising “invisible” technical debt to justify the modernisation of critical infrastructure, moving from gut-feel to data-driven decommissioning.

Every engineering leader fears the “untouchable” legacy system, the 20-year-old monolith that runs the core business but blocks innovation.
At ITV, this was our linear scheduling infrastructure, a critical system underpinning our broadcast channels, yet it was effectively difficult to maintain and manage.

We couldn’t afford a high-risk “big bang” rewrite, nor could we let the debt continue to accumulate. In this session, I will share the “Technical Debt Framework” that we used to derisk this modernisation.

We moved beyond the abstract by categorising debt (Visible vs. Invisible, Positive vs. Negative Value) to secure stakeholder buy-in on a modernisation strategy.

I will cover:

• The “Visible vs. Invisible” Matrix: How to map architectural decay against business risk to show non-technical leadership why a “working” system is actually closer to failure.
• A Case Study: The specific engineering trade-offs of modernising an old estate, including the decision to build tactical in-house microservices versus adopting third-party tooling.
• The “25% Rule”: The road we took to identify the operational reality of negotiating a fixed capacity for debt repayment to prevent the “spiral of reduced morale and productivity.

Key takeaways

• A Classification Framework: A practical model for categorising debt (Architecture vs. Code vs. Infrastructure) to make “invisible” drag visible to the C-suite.
• Modernisation Strategy: How to choose between a tactical in-house rebuild and third-party vendor adoption when replacing core legacy systems.
• Observability for Operationalising Repayment: How to implement a standard approach that allocates up to 25% of sprint capacity to debt servicing without derailing product delivery.

A reliable system isn’t just about your infrastructure. Learn to effectively leverage game days to train engineers and build resilience before real incidents force you to.

No one likes responding to incidents – but they’re also where you learn the most. Every outage reveals weaknesses across your systems and processes that you might not have found any other way.

The problem is, waiting for your biggest incidents isn’t a realistic strategy for building a more reliable system. You can introduce stress tests or extrapolate worst-case behaviours, but there’s an unpredictability to how systems and people behave in combination under real pressure that is hard to fully account for.

Game days are your opportunity to test that combination without frantic firefighting. If you’re not running realistic tests, your safeguards are theoretical: you don’t want to learn whether they work in a disaster scenario.

At incident.io, we use game days to train engineers, validate fixes from past outages, and build genuine confidence in our systems and response processes. Most recently, we ran game days to successfully validate that we would be resilient in the face of an AWS outage. Every time we run a game day, engineers are asking when the next one will be.

It’s easy to run a simple drill, but half the value of a game day comes from thorough preparation. Figuring out what’s actually feasible to break gives you confidence in the parts of your system that are solid. Finding the holes you *can* poke through gives you avenues to follow up, even before you’ve run anything. And preparing for the different response paths your engineers might take lets you create realistic pressure, making it feel like a real incident rather than a repetitive drill.

In this this talk, I’ll cover what we’ve learned running game days, and how you can get the most value out of them. Running a successful game day can make your systems more resilient, but you’ll also be building a culture of reliability in your organisation.

Key takeaways

• Why game days are one of the most effective tools for reliability and getting ahead of the next potential outage
• Learn to run efficient and worthy game days that can give you both confidence in your systems and a clear list of areas to improve, as well as a chance for your engineers to experience realistic pressure without risking production
• How to build a culture of reliability by making game days and incidents something engineers find valuable and want to repeat

Host’s closing remarks.

Host’s opening address.

Engineering friction compounds across your organization, costing you velocity, quality, and talent. This talk provides a data-driven framework for measuring, prioritizing, and scaling DevEx improvements that deliver measurable business impact.

Your engineers spend hours waiting on builds, navigating undocumented systems, and context-switching between 12 different tools. That’s not just frustrating—it’s expensive. Every friction point compounds across your organization, costing you velocity, quality, and your ability to retain top talent.

As AI transforms software development, this problem is about to get worse. AI-generated code increases deployment frequency and system complexity. If your baseline friction is already high, AI won’t speed you up—it will amplify every bottleneck and create new categories of problems you haven’t seen yet.

This talk presents a systematic framework for identifying and eliminating engineering friction at scale. You’ll learn how to measure what’s actually slowing your teams down, build data-driven business cases for DevEx investment, and prioritize changes that deliver measurable ROI. We’ll cover how to scale improvements from a single team to your entire engineering organization, and why the teams that fix their DevEx foundation now will outpace competitors in the AI era.

You’ll walk away with practical frameworks, measurement approaches, and stakeholder templates you can use immediately—whether you’re making the case for investment, leading an improvement initiative, or scaling best practices across teams.

Key takeaways

• A measurement framework for quantifying engineering friction and connecting it to business outcomes (velocity, quality, retention)
• Proven stakeholder templates for building data-driven business cases that secure DevEx investment
• A scalable playbook for rolling out DevEx improvements from single teams to entire engineering organizations

What tools and techniques do top-performing teams incorporate into their software delivery practices in 2026? In this talk, you’ll learn about ways to improve your DORA metrics to become an elite-performing team that can effectively leverage AI-assisted development, and how benefits compound exponentially from adopting the interlocking modern developer productivity and platform engineering stack.

You’ll see concrete, vendor-agnostic examples of teams gaining confidence in their delivery process through hermetic build artifacts, continuous delivery pipelines, feature flagging, observability, code ownership, blameless incident analysis, shared platforms/infrastructure, Service Level Objectives (SLOs), and chaos engineering.

Key takeaways

• How to build platform and structure platform teams
• Which platform practices to embrace in which order
• How AI changes (or doesn’t change) your dev workflows

Accessibility is often seen as a velocity killer. I’ll share how Giffgaff transformed our legacy monolith. We proved inclusive code costs zero extra time and unlocks the massive economic potential of the ‘Purple Pound’.

Accessibility is often treated as a compliance checkbox or a final cleanup phase. It becomes a form of exclusionary tech debt that teams fear. With the ‘Purple Pound’ (the spending power of disabled households) worth an estimated £274 billion in the UK alone, the business case is clear. Yet at Giffgaff, we faced a hard reality with our legacy monolith. We had a daunting backlog, and it was feared that fixing it would destroy productivity and derail our roadmap.

In this talk, I’ll share how we turned this challenge into an opportunity to build a high-performing, inclusive engineering culture. We moved beyond reactive bug fixes to prove a controversial truth. Writing accessible code doesn’t have to take extra time.

I will demonstrate how we built an accessibility-first mindset by:

• Modernising the monolith and securing leadership buy-in by coupling accessibility standards with our technical migration to a modern tech stack.
• Scaling knowledge through documentation and training to empower developers, rather than relying on experts.
• Building culture by establishing an Employee Resource Group and hosting an ‘Accessibility Culture Day’ to shift our perspective from obligation to opportunity.
• Moving from a fix-it-later approach to an accessibility-first mindset, proven by a drop from 43 accessibility issues to zero in our new phones journey, without delaying release.

This talk is for leaders looking to move accessibility from the backlog to the definition of done, proving that quality, inclusion, and speed are not trade-offs.

Key takeaways

• Learn how to pay down accessibility debt by aligning it with technical modernisation.
• Discover how to quantify the impact of inclusion to secure leadership buy-in.
• Understand how to scale accessibility knowledge via documentation and tooling.
• Gain strategies to build a culture of empathy that shifts the mindset from compliance to opportunity.

Shipping your first LLM feature requires more than code. This talk explains how we navigated new strategic demands, from opt-in stances to evaluation workflows, to find a pragmatic path to production.

Even for mature engineering organisations, the first LLM-powered feature introduces a paradigm shift. The actual coding often feels like the easy part. The real challenge lies in a daunting list of new “supporting” activities. From defining evaluation frameworks and prompt versioning to deciding on customer “opt-out” stances and communication strategies, how do you decide where to focus your limited energy?

In this session, I will share the “battle scars” from our journey to production. I’ll break down our approach to the 80/20 rule: identifying the activities that required deep investment, those we found a “light” way to handle, and those we intentionally parked for later.

We will explore:

• The Effort Flip: How we restructured our workflow to move from “getting it to work” to “ensuring it’s good enough,” reallocating engineering time from writing code to context engineering and evaluation.
• Strategic Stances: How we tackled non-code needs like opt-in/opt-out policies, customer FAQs, and internal upskilling.
• Triage in Action: What we over-invested in, what I wish we’d done sooner (like early internal documentation on feature capabilities) and what we successfully deferred (like automation of evals).

This isn’t a prescriptive guide, but a look behind the curtain at how we defined our own “production-ready” standard. You will leave with a framework for auditing your own AI roadmap to ensure you’re focused on the activities that truly drive reliability and trust.

Key takeaways

• Evaluation First: Robust evaluation matters more for reliable AI systems than perfecting the initial code.
• Prioritising based on a North Star: We treated security and trust as non-negotiables, informing what to prioritise, including OWASP Top 10 reviews and data privacy decisions.
• Design the First Feature to Scale: The initial rollout can establish reusable patterns that accelerate every AI feature that follows.
• Manual Before Automated: Starting with human-in-the-loop evaluation saved time and helped us understand failure modes before investing in automated testing.

Replace fragile legacy scripts with cloud-native stability by mastering documentation, phased delivery, and knowledge transfer to eliminate single points of failure in your architecture.

Moving legacy systems to the cloud is often sold as a solution, but a “lift-and-shift” without a longer-term plan only relocates the problem. When mission-critical architecture relies on decades of legacy Bash and PL/SQL scripts maintained by a single person, that individual becomes a single point of failure. Their departure doesn’t just leave a vacancy; it leaves a “black box” monster that no one understands and everyone is afraid to touch.

This platform-agnostic session provides a survival guide for inheriting these systems. We will explore why the foundation of modernization isn’t code, but documentation and knowledge gathering. We’ll discuss how to audit undocumented scripts to build a source of truth before a single line of code is rewritten.

A key focus will be practical application of the strangler fig pattern. Instead of a risky “big bang” rewrite, we examine how to balance ongoing maintenance of the old system with the development of the new. You will learn how to establish realistic dates and deliverables that prioritize stability—such as replacing outer Bash wrappers first while letting complex but stable PL/SQL logic run until a later phase. This talk provides a blueprint for turning a fragile, person-dependent script library into a resilient, documented, and modernisable cloud asset.

Key takeaways

• Identify and mitigate “”Single Points of Failure”” by converting tribal knowledge into technical documentation
• Develop a phased “”piecemeal”” migration roadmap that prioritizes wrapping scripts over deep logic
• Balance the “”business as usual”” maintenance of legacy code with the development of new architecture
• Establish a framework for setting realistic deliverables and milestones during a high-risk cloud transition

How modernising our quality practices created a cycle of continuous improvement, transforming tools, processes, and culture, and ultimately delivering organisational change that achieved what had previously been considered impossible.

This talk shares how we introduced modern quality engineering into a legacy environment and in doing so reduced our lead time to deployment by 480 hours while also dramatically improving the developer experience. We’ll break down the technical, cultural, and procedural shifts that enabled faster feedback loops, more reliable deployments, and higher team confidence. From early test-automation efforts and the introduction of ephemeral environments, to standardising deployments and gradually replacing brittle manual steps, this is a practical, honest account of how meaningful change actually happens. Attendees will leave with a clear understanding of what quality engineering really looks like in practice, how to introduce it sustainably, and what timescales to expect when pursuing long-term ROI and organisational transformation.

Here’s what we’ll cover:

Background

• State of things when we started
• Issues this caused

First steps

• Introducing test automation to create faster feedback loops
• How we prioritised out test creation and determined what was the most valuable.

Unlocking deployments

• Introducing tooling and processes that standardised and automated deployments.
• Enabled more people to be involved, understand, and contribute to improving our processes.

Shifting left

• Improving development ways of working that enabled more testing, more often, and earlier in the process.
• Introducing ephemeral test environments
• What they are, how they work, and how they improved testing

Trust takes time

• A pause in discussing the improvements to focus on why its importnant to iterate slowly over time and build confidence gradually when making significant changes to processes and ways of working

Replacing manual with automated

• How automating those final manual steps allowed all the gradual improvements to come together

Summary

• Where did we end up
• What did we learn?
• How long did it take?

Key takeaways

• Explain what modern quality engineering looks like in practice, beyond traditional QA, and identify where to start in their own teams.
• Prioritise the right early investments (automation, feedback loops, workflow changes) to generate momentum and visible value.
• Design sustainable improvements using iterative change, gradual trust-building, and appropriate timescales for organisational ROI.
• Apply practical techniques for introducing test automation, standardising deployments, and using ephemeral environments to accelerate delivery.
• Recognise the cultural, technical, and process shifts needed to reduce lead time and improve developer experience across an organisation.

In established ecosystems, “Chaos Engineering” is a simulation. In Nigeria, it is a daily reality. Learn the “Zero-Trust” patterns and leadership strategies we use to build bulletproof systems when the infrastructure is constantly failing us.

In established ecosystems, engineering leaders obsess over “Chaos Monkey”, software designed to randomly crash servers to test resilience. In Abuja Nigeria, we don’t need Chaos Monkey. We operate on a national power grid that collapsed over 20 times in the last two years, amidst hundreds of fiber cuts and network disruptions.

For my team, failure isn’t a theoretical risk to be simulated; it is the default state of our existence.

This session explores the unique engineering discipline required to build world-class products in a “low-trust” infrastructure environment. I will share the war story of a Black Friday launch for our high-volume fintech platform where we faced the “Perfect Storm”: a major fiber cut took our office offline, our primary payment partner suffered a latency spike, and the power grid collapsed, all within an hour.

Most teams would have folded. Mine didn’t even pause.

I will detail the “Zero-Trust” framework that allowed us to survive, and how you can apply it to your own stack:

1. The Architecture of Distrust: How we moved from synchronous coupling to an aggressive asynchronous model. We assume every external API (Stripe, Twilio, AWS) will fail 50% of the time. I will show how we used Redis-backed persistent queues and the Outbox Pattern to allow our app to “hold its breath” for hours without crashing.
2. The “Air-Gapped” Developer: How we built “Offline-First” dev environments. By dockerizing our entire stack locally (including mocked third-party services), we empowered engineers to ship code even while tethered to 3G mobile hotspots, a lesson in eliminating cloud dependencies that speeds up any team.
3. Leading Through “Outage Fatigue”: Constant firefighting burns out engineers. I will share my playbook for psychological safety, shifting the culture from “Who broke it?” to “How did the system absorb the shock?”

Attendees will leave with a new perspective: You don’t need to be in Africa to face failure. By applying these “survivalist” constraints, you can build systems that are bulletproof anywhere.

Key takeaways

1. Organic Chaos Engineering: Why “simulated” failure pales in comparison to real-world constraints, and how to audit your system for “happy path” bias.
2. Asynchronous by Default: Implementing specific patterns (Circuit Breakers, Leaky Buckets, Outbox) to decouple uptime from third-party dependencies.
3. Psychological Safety in a Firefight: A manager’s guide to preventing burnout when the environment is constantly fighting against you.

Discover how the biggest e-commerce company in Turkey, used LLMs and automation workflows to boost engineering speed by 30%–improving tests, documentation, and delivery, while keeping production stable, developers confident, and AI adoption practical, measurable.

Every engineering leader wants AI-driven productivity–but few achieve it without chaos. In this talk, I’ll share how our teams integrated LLMs and automation tools like n8n, Cursor, A2A, and MCPs into a 2,000-engineer organization to deliver a 30% improvement in delivery speed while keeping production stable.

This isn’t a story about hype or magic prompts–it’s about engineering discipline, governance, and workflow design. You’ll see how we automated testing, documentation, and code reviews; built smart pipelines; and measured impact in ways that developers trusted. I’ll also share what didn’t work–over-automation, misaligned incentives, and cultural resistance–and how we overcame them.

Attendees will leave with practical strategies to safely scale AI adoption, boost team productivity, and blend human creativity with machine efficiency–all without breaking the systems or the culture that make great engineering teams thrive.

Key takeaways

Whether you’re AI-curious or already experimenting, you’ll leave with a playbook for:

• Learn how to integrate LLMs and automation without disrupting production stability.
• Discover practical methods to boost engineering productivity by 30%.
• See real examples of AI-driven testing, documentation, and code reviews.
• Understand how to build trust and governance for safe AI adoption.
• Take home a proven framework for scaling AI across large engineering teams.

Host’s closing remarks.

You know it: Fire fighting, reacting, and chasing information aren’t how you should you spend your time. But doing so much of this is exactly what makes it even harder to get out of it.

So how can you shift gears? What systems can (should?) you put in place to get the visiblity you need to do your job well? How can you make sure you’re able to spot issues and course-correct early without micromanaging everyone and everything at all times?

From this session run by a seasoned senior leader, you’ll take away concrete ideas to start exactly where you and your org are at right now, improve your organisational observability, and get a kit to help you evolve from there.

Connect with folks over shared experiences

Everyone’s journey into leadership is tough, but folks who are underrepresented in tech often face their own unique set of challenges. And that can lead to people feeling isolated in their experiences. 

Come to the community representation groups, where you’ll be able to connect with folks who might have similar backgrounds and experiences to yours, and where you’ll be able to build a peer network for support throughout your journey.

Connect with folks over shared experiences

Everyone’s journey into leadership is tough, but folks who are underrepresented in tech often face their own unique set of challenges. And that can lead to people feeling isolated in their experiences. 

Come to the community representation groups, where you’ll be able to connect with folks who might have similar backgrounds and experiences to yours, and where you’ll be able to build a peer network for support throughout your journey.

Connect with folks over shared experiences

Everyone’s journey into leadership is tough, but folks who are underrepresented in tech often face their own unique set of challenges. And that can lead to people feeling isolated in their experiences. 

Come to the community representation groups, where you’ll be able to connect with folks who might have similar backgrounds and experiences to yours, and where you’ll be able to build a peer network for support throughout your journey.

Connect with folks over shared experiences

Everyone’s journey into leadership is tough, but folks who are underrepresented in tech often face their own unique set of challenges. And that can lead to people feeling isolated in their experiences. 

Come to the community representation groups, where you’ll be able to connect with folks who might have similar backgrounds and experiences to yours, and where you’ll be able to build a peer network for support throughout your journey.

If you’re inspired by a talk, want to learn more, or just want to chat about it with other folks, head to our Office Hours area where our speakers will be waiting for you.

Watch or participate in free 1:1 coaching with Mathias Meyer.

Learning new information is always great, but sometimes solutions can feel too one-size-fits-all for your specific circumstances. How can advice be applied to a team of 10 or a team of 10,000?

On the other hand, coaching is directly tailored to your specific challenges and the specific environment they exist in. Mathias will be hosting one-on-one coaching sessions with attendees to get into the nuts and bolts of the challenges they’re facing. Come and have a conversation, or peek into the challenges other leaders are facing.

Quiet quitting. Rage quitting. Disengagement, burnout, mental health breaks. Stalled careers, unclear expectations, slanging matches, stolen credit, and betrayal. Poisoned relationships, broken promises. Politics.

There are endless stupid ways we can accidentally lose our best people, or turn them into our worst – this is a session to share some of your people-related mistakes, and hear about the worst mistakes of your peers.

Supported by Ceri Newton-Sargunar, a behavioural scientist and skilled senior leadership coach, you’ll also get some understanding along the way, of why things go as badly as they do – so you can take steps to make sure they, and you don’t fall into the same traps (again).

Comparing notes on shared challenges that come with your role has never been easier. Peer Hangouts are 30-minute, casual networking sessions for folks to talk through their experiences and challenges with peers who understand the context they’re working in. Drop in and join a conversation!

Watch or participate in free 1:1 coaching with Mathias Meyer.

Learning new information is always great, but sometimes solutions can feel too one-size-fits-all for your specific circumstances. How can advice be applied to a team of 10 or a team of 10,000?

On the other hand, coaching is directly tailored to your specific challenges and the specific environment they exist in. Mathias will be hosting one-on-one coaching sessions with attendees to get into the nuts and bolts of the challenges they’re facing. Come and have a conversation, or peek into the challenges other leaders are facing.

Watch or participate in free 1:1 coaching with Mathias Meyer.

Learning new information is always great, but sometimes solutions can feel too one-size-fits-all for your specific circumstances. How can advice be applied to a team of 10 or a team of 10,000?

On the other hand, coaching is directly tailored to your specific challenges and the specific environment they exist in. Mathias will be hosting one-on-one coaching sessions with attendees to get into the nuts and bolts of the challenges they’re facing. Come and have a conversation, or peek into the challenges other leaders are facing.

LGBTQIA+ and BIPOC communities, neurodivergent, disabled people, and women have long been grossly underrepresented in tech, and even more so at senior management levels, where being “the first [insert your identity here]” in your company at your level is a common experience. We want to create this space specifically for you to connect and find community. During the session, you will receive guiding questions to discuss with your peers and a space to get to know, learn from, and celebrate each other, together.

Who this is for: We welcome any senior managers (Director and above) who are members of underrepresented groups in tech. We want to create a space for the many groups with little representation at those levels, so if you belong to a group that isn’t specifically mentioned here, please join and let the hosts know. You don’t need a DirectorPlus pass to attend.

Dedicated sessions on how to take the next step in your career for tackling the role of a Tech Lead

Sponsored by

Explore solutions for your technical issues.

Make the most of your breaks with the demo stage sessions, where leading tech providers will showcase their innovative solutions in informative and concise presentations. Each carefully curated 10 minute demo will show the very best engineering solutions available.

Sponsored by

Explore solutions for your technical issues.

Make the most of your breaks with the demo stage sessions, where leading tech providers will showcase their innovative solutions in informative and concise presentations. Each carefully curated 10 minute demo will show the very best engineering solutions available.

Sponsored by

Join these 10-minute talks to explore how our partners are helping teams tackle real-world engineering challenges. Each session shares practical examples, lessons learned, and insights into the decisions behind the solutions — giving you ideas you can take back to your own teams and systems.

Sponsored by

Join these 10-minute talks to explore how our partners are helping teams tackle real-world engineering challenges. Each session shares practical examples, lessons learned, and insights into the decisions behind the solutions — giving you ideas you can take back to your own teams and systems.

Sponsored by

Explore solutions for your technical issues.

Make the most of your breaks with the demo stage sessions, where leading tech providers will showcase their innovative solutions in informative and concise presentations. Each carefully curated 10 minute demo will show the very best engineering solutions available.

Sponsored by

Explore solutions for your technical issues.

Make the most of your breaks with the demo stage sessions, where leading tech providers will showcase their innovative solutions in informative and concise presentations. Each carefully curated 10 minute demo will show the very best engineering solutions available.

Sponsored by

Join these 10-minute talks to explore how our partners are helping teams tackle real-world engineering challenges. Each session shares practical examples, lessons learned, and insights into the decisions behind the solutions — giving you ideas you can take back to your own teams and systems.

Sponsored by

Join these 10-minute talks to explore how our partners are helping teams tackle real-world engineering challenges. Each session shares practical examples, lessons learned, and insights into the decisions behind the solutions — giving you ideas you can take back to your own teams and systems.

Sponsored by

Explore solutions for your technical issues.

Make the most of your breaks with the demo stage sessions, where leading tech providers will showcase their innovative solutions in informative and concise presentations. Each carefully curated 10 minute demo will show the very best engineering solutions available.

Sponsored by

Explore solutions for your technical issues.

Make the most of your breaks with the demo stage sessions, where leading tech providers will showcase their innovative solutions in informative and concise presentations. Each carefully curated 10 minute demo will show the very best engineering solutions available.

Sponsored by

Join these 10-minute talks to explore how our partners are helping teams tackle real-world engineering challenges. Each session shares practical examples, lessons learned, and insights into the decisions behind the solutions — giving you ideas you can take back to your own teams and systems.

Sponsored by

Join these 10-minute talks to explore how our partners are helping teams tackle real-world engineering challenges. Each session shares practical examples, lessons learned, and insights into the decisions behind the solutions — giving you ideas you can take back to your own teams and systems.

Sponsored by

Explore solutions for your technical issues.

Make the most of your breaks with the demo stage sessions, where leading tech providers will showcase their innovative solutions in informative and concise presentations. Each carefully curated 10 minute demo will show the very best engineering solutions available.

Sponsored by

Explore solutions for your technical issues.

Make the most of your breaks with the demo stage sessions, where leading tech providers will showcase their innovative solutions in informative and concise presentations. Each carefully curated 10 minute demo will show the very best engineering solutions available.