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      "name": "Matt Pocock",
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          "description": "Session title and abstract to be finalized by participating speaker",
          "day": "April 8",
          "time": "3:30-5:30pm",
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        {
          "title": "It Ain't Broke: Why Software Fundamentals Matter More Than Ever",
          "description": "div]:bg-bg-000/50 [&_pre>div]:border-0.5 [&_pre>div]:border-border-400 [&_.ignore-pre-bg>div]:bg-transparent [&_.standard-markdown_:is(p,blockquote,h1,h2,h3,h4,h5,h6)]:pl-2 [&_.standard-markdown_:is(p,blockquote,ul,ol,h1,h2,h3,h4,h5,h6)]:pr-8 [&_.progressive-markdown_:is(p,blockquote,h1,h2,h3,h4,h5,h6)]:pl-2 [&_.progressive-markdown_:is(p,blockquote,ul,ol,h1,h2,h3,h4,h5,h6)]:pr-8\">_*]:min-w-0 gap-3 standard-markdown\">AI coding tools are overhyped and powerful at the same time. Used well, they're extraordinary. Used badly, they'll bury you in spaghetti code faster than any human team could. The difference isn't the tool. It's the process. After 18 months of teaching developers to build with AI agents, Matt Pocock has watched the same patterns emerge: the devs who succeed aren't the ones who delegate everything or nothing. They're the ones who fall back on engineering fundamentals. In this talk, he shares the iterative process his students use to ship high-quality applications with AI agent swarms, and why the principles that make it work (ubiquitous language, vertical slices, TDD, deep modules) are decades-old ideas that didn't break. They got more important.",
          "day": "April 9",
          "time": "5:20-5:40pm",
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      "name": "Misha Kaletsky",
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          "day": "April 8",
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      "conference": "europe",
      "name": "Jonas Templestein",
      "role": "CEO",
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      "companyDescription": "AI agent platform for automating business processes",
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          "day": "April 8",
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      "conference": "europe",
      "name": "Cormac Brick",
      "role": "Staff Engineer",
      "company": "DeepMind",
      "companyDescription": "Google AI research lab",
      "twitter": "",
      "linkedin": "https://www.linkedin.com/in/cbrick/",
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          "title": "TLMs: Tiny LLMs and Agents on Edge Devices with LiteRT-LM",
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          "time": "12:40-1:00pm",
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    {
      "id": "europe-speaker-4",
      "conference": "europe",
      "name": "Ash Prabaker",
      "role": "Member of Technical Staff",
      "company": "Anthropic",
      "companyDescription": "AI safety company, makers of Claude",
      "twitter": "AshPrabaker",
      "linkedin": "https://www.linkedin.com/in/ash-prabaker/",
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      "photoUrl": "https://ai.engineer/europe-speakers/ash-prabaker.jpg",
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          "title": "How to Build Agents That Run for Hours (Without Losing the Plot)",
          "description": "We'll cover: why self-evaluation is a trap and adversarial evaluator agents work better; why context compaction doesn't cure coherence drift but structured handoffs do; how to decompose work into testable sprint contracts; how to grade subjective output with rubrics an LLM can actually apply; and how to read traces as your primary debugging loop. Plus the question nobody asks: which parts of your harness should you delete when the next model drops?",
          "day": "April 8",
          "time": "9:00-10:20am",
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      "id": "europe-speaker-5",
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      "name": "Andrew Wilson",
      "role": "Applied AI",
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      "companyDescription": "AI safety company, makers of Claude",
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          "title": "How to Build Agents That Run for Hours (Without Losing the Plot)",
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          "day": "April 8",
          "time": "9:00-10:20am",
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      "conference": "europe",
      "name": "Pedro Rodrigues",
      "role": "AI Tooling Engineer",
      "company": "Supabase",
      "companyDescription": "Open source Firebase alternative",
      "twitter": "rodriguespn23",
      "linkedin": "https://www.linkedin.com/in/pedro-neves-rodrigues/",
      "github": "Rodriguespn",
      "photoUrl": "https://ai.engineer/europe-speakers/pedro-rodrigues.jpg",
      "talks": [
        {
          "title": "Skill Issue: How We Used AI to Make Agents Actually Good at Supabase",
          "description": "Writing Agent Skills is easy. Writing ones that actually improve agent performance is not.\n\nIn this hands-on workshop, you’ll build, test, and iterate on Agent Skills against real Supabase workflows using a prebuilt environment with MCP, CLI tooling, and an eval harness powered by Braintrust.\n\nYou’ll start by writing a simple Skill and observing how it changes agent behavior. Then we’ll push further: you’ll modify the Skill, introduce bad patterns, and see how performance shifts — sometimes improving, sometimes getting worse, and sometimes doing nothing at all. Along the way, we’ll surface common failure modes, like Skills that aren’t used, misleading instructions, or changes that look good but don’t hold up under evaluation.\n\nThe core loop of the workshop is simple: write a Skill, run evals, inspect results, and iterate. By the end, you’ll have a practical understanding of how to validate Skills, how to avoid common pitfalls, and how to design Skills that actually help agents perform better in real systems.\n\nIf you’re working with agents, this workshop will give you the tools to move beyond guesswork and start measuring what actually works.\n\nAnd if you want to see how these patterns hold up at scale, the follow-up talk on the 9th dives into our eval results and what actually moved the needle in production.",
          "day": "April 8",
          "time": "9:00-10:20am",
          "type": "workshop"
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          "description": "Agents don't fail because they're weak — they fail because they lack the right context.\n\nEven when working with something as well-known as PostgreSQL, agents regularly produce insecure queries, inefficient patterns, or incorrect migrations. Not because they can't reason, but because their knowledge is outdated, generic, or misaligned with the specific environment they're operating in.\n\nIn this talk, we explore how Agent Skills and MCP work together to close that gap.\n\nUsing real-world Postgres workflows — from writing secure RLS policies to debugging slow queries and fixing broken migrations — we'll show what actually breaks when agents operate without structured context, and what changes when you introduce the right abstractions. MCP provides the safe, auditable interface to interact with the system, while Agent Skills inject domain-specific guidance tailored to the actual environment, including Supabase-specific patterns and constraints.\n\nBut the results aren't as straightforward as \"more context = better agents.\" We'll share findings from our internal benchmarks comparing agent performance across different setups, showing that Skills don't replace MCP — they amplify it — and that poorly designed or untested Skills can have little impact or even degrade performance.\n\nWe'll also dig into how we structure Skills for real systems (not just isolated tasks), and how we use evals to measure their impact across realistic workflows. You'll see how different combinations of Skills, MCP, and CLI-based interaction affect agent behavior, reliability, and outcomes.\n\nBecause once agents touch production systems like Postgres, the problem isn't intelligence — it's having the right context, delivered in the right way.",
          "day": "April 9",
          "time": "3:10-3:30pm",
          "type": "talk"
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      "id": "europe-speaker-9",
      "conference": "europe",
      "name": "Guillaume Vernade",
      "role": "Developer Relations Engineer",
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      "companyDescription": "AI research lab by Google",
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        {
          "title": "Let's go Bananas with GenMedia",
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          "time": "9:00-10:20am",
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      "id": "europe-speaker-20",
      "conference": "europe",
      "name": "Zack Proser",
      "role": "Full-stack Developer, Developer Education",
      "company": "WorkOS",
      "companyDescription": "Enterprise identity and access management",
      "twitter": "",
      "linkedin": "https://linkedin.com/in/zackproser",
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        {
          "title": "Skills at Scale",
          "description": "Write once, run in Claude, Codex, Cursor, and your own agents\n\nEvery developer using AI tools has the same problem: they prompt the same way, for the same tasks, over and over. Skills fix this. A skill is a portable unit of agent behavior that teaches any AI tool how to do a specific job. Write one, drop it into your editor, and it just works. Across tools. Across teams.\n\nMost people don't know this primitive exists. In this hands-on workshop, you'll write real skills, test them live, and see how one file can power Claude.ai, Claude Code, Cursor, and Codex without changing a line.\n\nThen we'll go deeper. You'll see how the WorkOS CLI uses this same pattern to power 15 framework\nintegrations — each one a skill composed with others, wired into an agent that installs and configures\nAuthKit in under 60 seconds. That's not a demo. That's production code, shipping today.\n\nWhat you'll do:\nWrite 2+ skills for tasks you actually do at workInstall and test them across AI tools in real timeLearn the craft of good skill writing — specificity, constraints, composabilitySee how skills compose and scale inside a real CLI powered by the Claude Agent SDKWhat you'll leave with:\nWorking skills installed in your AI tools, ready to use Monday morningA repeatable pattern for turning any recurring task into a portable skillThe mental model for when a skill is enough and when you need a full agentNo repos to clone. No dependencies to install. Bring a laptop with Claude Code or Claude.ai and something you're tired of doing manually.",
          "day": "April 8",
          "time": "10:40am-12:00pm",
          "type": "workshop"
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      "id": "europe-speaker-21",
      "conference": "europe",
      "name": "Peter Werry",
      "role": "Engineer",
      "company": "Unblocked",
      "companyDescription": "AI context layer for developer productivity",
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      "photoUrl": "https://ai.engineer/europe-speakers/peter-werry.jpg",
      "talks": [
        {
          "title": "Mergeable by default: Building the context engine to save time and tokens",
          "description": "Agents can generate code. The hard part is generating code that's right for your system, team conventions, and past decisions. That's a context problem that naive RAG, MCP servers, and bigger context windows don't solve. Without the right context, that code costs you twice: once in tokens, again in long review cycles.\n\nThis talk is a practitioner's guide to building a context engine: the reasoning layer that brings together your organizational context and delivers only what the agent needs for the task at hand. I'll walk through the challenges that matter: reasoning across conflicting sources, maintaining permissions, and personalizing results based on who's asking and what they're working on. Along the way, we'll go deep on specific components with live demos and technical breakdowns.\n\nDrawn from real lessons building this in production, including what we got wrong.",
          "day": "April 8",
          "time": "1:00-3:00pm",
          "type": "workshop"
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      "id": "europe-speaker-22",
      "conference": "europe",
      "name": "Giran Moodley",
      "role": "Solutions Engineer",
      "company": "Braintrust",
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      "photoUrl": "https://ai.engineer/europe-speakers/giran-moodley.jpg",
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          "title": "Shipping complex AI applications with Braintrust",
          "description": "Getting a prototype working is straightforward. Making it reliable in production, especially with multi-step agents, tool use, and real users is the hard part. In this hands-on workshop, you'll work through the core parts of building production-grade AI applications with Braintrust.",
          "day": "April 8",
          "time": "1:00-3:00pm",
          "type": "workshop"
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    {
      "id": "europe-speaker-23",
      "conference": "europe",
      "name": "Samuel Colvin",
      "role": "CEO",
      "company": "Pydantic",
      "companyDescription": "",
      "twitter": "samuelcolvin",
      "linkedin": "https://www.linkedin.com/in/samuel-colvin/",
      "github": "samuelcolvin",
      "photoUrl": "https://ai.engineer/europe-speakers/samuel-colvin.jpg",
      "talks": [
        {
          "title": "Playground in Prod - Optimising Agents in Production Environments",
          "description": "Deploying an agent is just the beginning. The real challenge is making it better once it's live — without redeploying, without downtime, and ideally without a human in the loop at all.\n\nIn this talk I'll introduce two complementary approaches to optimising agents in production, both built on Pydantic AI and Logfire:\n\n**Managed Variables** — a new Pydantic AI feature (build on the Open Feature standard) that lets you externalise key parameters of your agent (system prompts, model configuration, tool descriptions, thresholds) and update them instantly from the Logfire UI. Change a system prompt, swap a model, or adjust a temperature parameter and see the effect on the next request — no redeploy, no restart. This turns production into a playground where you can iterate on agent behaviour in seconds based on real traffic and real feedback.\n\n**Autonomous optimisation with GEPA** — once you can update agent parameters without redeploying, the natural next step is to let an optimiser do it for you. I'll show how GEPA (Genetic-Pareto reflective text evolution) can be wired into Logfire's managed variables to create a closed loop: observe agent performance via Logfire traces, reflect on failures, evolve better prompts, and push improvements live — all without human intervention.\n\nTogether these form a practical workflow: start by manually tuning your agent in production using managed variables, build up evaluation datasets from real traces, then hand the optimisation loop to GEPA to continuously improve performance.\n\n## Outline\n\n1. **The problem with agent deployment today** — why \"deploy and forget\" doesn't work for agents, and why traditional CI/CD is too slow for prompt iteration.\n2. **Managed Variables in Logfire** — live demo showing how to externalise a system prompt, update it from the Logfire dashboard, and see the change take effect immediately on a running agent.\n3. **From manual tuning to automated optimisation** — using Logfire's observability data (traces, evaluations, failure modes) to build the feedback signal GEPA needs.\n4. **GEPA + Managed Variables** — closing the loop: GEPA reflects on agent traces, evolves better prompts, and pushes them live via managed variables. Live demo of an agent that gets measurably better over time without any code changes.\n5. **Practical considerations** — guardrails, rollback, A/B testing between prompt variants, and when to trust autonomous optimisation vs. keeping a human in the loop.\n\n## Audience Takeaways\n\n- How to set up agents so key parameters can be changed without redeployment\n- A practical workflow for iterating on agent behaviour using production data\n- How reflective prompt evolution (GEPA) works and when to use it\n- How to combine Pydantic AI, Logfire, and GEPA into a continuous improvement loop for production agents",
          "day": "April 8",
          "time": "10:40am-12:00pm",
          "type": "workshop"
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          "title": "Build Your First Demand-Driven Context Base: Let AI Agents Tell You What They Need",
          "description": "London's black cab drivers don't learn \"The Knowledge\" by reading a map. An examiner gives them a destination, they get lost, they discover which streets they didn't know, they go learn those streets. Each run fills gaps the previous one revealed — until they know the city. Nobody told them what to learn. The journeys told them.\n\nThat's the opposite of what every enterprise team is doing with AI agents right now. We curate tribal knowledge into skills.md files and structured knowledge bases, three people argue over a Miro board about what the agent needs to know, someone screenshots it into Confluence, and we wonder why the agent still can't reason about anything domain-specific. The industry has great tools for context management — RAG, vector databases, prompt engineering. But the harder unsolved problem is context discovery: how do you even know what to curate? Without solving it, your enterprise agent is just an expensive autocomplete.\n\nIn this hands-on workshop, you'll do what the cab drivers do — and what we do at IKEA Digital — give your agent destinations instead of maps. You'll build a Demand-Driven Context (DDC) base from scratch, where real problems drive what gets curated, not top-down guessing.\n\nThe exercise is simple but the insight is profound:\n\nYou'll get a realistic enterprise problem (we provide problem cards)\nYou'll give it to an AI agent with zero domain context\nThe agent will fail — and generate an information checklist of exactly what's missing\nYou'll fill the gaps using reference material we provide\nThe agent will try again — and succeed\nThat moment — when the agent goes from confidently wrong to correctly reasoned — is when DDC clicks. You didn't document everything. You documented exactly what one problem demanded. Now multiply that by 30 problems and you have a better knowledge base than months of top-down curation.\n\nWhat you'll build:\n\nA working knowledge base repo with structured domain entities, a sandbox for problem exploration, and a repeatable process for growing the knowledge base problem-by-problem. Everything in Markdown — human-readable, machine-parseable, Git-friendly. You'll also see how to use Claude Code sub-agents to separate concerns — a curator agent that identifies what context is missing, a solver agent that uses the curated context to reason, and role-specific agents (architect, engineer, product owner) that share the same knowledge base but operate with different reasoning boundaries. No custom tooling required — just CLAUDE.md files, sub-agent task delegation, and the knowledge repo structure doing the heavy lifting.\n\nWhat you'll experience:\n\nThe \"flip\" moment — agents telling you what they need instead of you guessing what to give them\nHow learning paths emerge from problems rather than being designed upfront\nHow different problems reveal overlapping context needs — showing you where to invest curation effort\nThe TDD parallel — DDC is to knowledge bases what TDD is to code\nWhat you'll see from real production use:\n\nWe've been running DDC at IKEA Digital against real vendor integration problems, architecture decisions, and system design tasks. The workshop includes a live walkthrough of actual DDC sessions — real enterprise problems, the information checklists agents generated, how context was curated, and the before/after of agent output quality. You'll see real numbers: knowledge base growth curves, context reuse across problems, and how agent accuracy improves from problem 1 to problem N.\n\nWhat you'll leave with:\n\nA working DDC knowledge base you can extend with your own domain\nA repeatable process for demand-driven curation\nA template repo with structure, formats, and agent guidance\nEvidence from real production use that demand-driven curation produces less volume but more signal than top-down documentation\nThe conviction that 30 real problems beat 6 months of documentation\nNo specific programming language required. Bring a laptop with Claude Code, Cursor, or any LLM-powered coding tool. All content is in Markdown — this is about knowledge, not code.\n\nWhether you're an engineer building enterprise agents, an architect designing knowledge systems, or a team lead who's tried and failed to curate domain knowledge for LLMs — this workshop gives you a framework you can apply the week you get home from London.",
          "day": "April 8",
          "time": "3:30-5:30pm",
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          "day": "April 9",
          "time": "12:00-12:10pm",
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      "id": "europe-speaker-53",
      "conference": "europe",
      "name": "Radek Sienkiewicz",
      "role": "Developer Relations",
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      "linkedin": "https://www.linkedin.com/in/radeksienkiewicz/",
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          "title": "I Gave an AI Agent the Keys to My Life (Here's What Happened)",
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          "day": "April 9",
          "time": "12:00-12:20pm",
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      "id": "europe-speaker-54",
      "conference": "europe",
      "name": "Chris Lovejoy",
      "role": "Founder",
      "company": "Notius Labs",
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      "twitter": "ChrisLovejoy_",
      "linkedin": "https://www.linkedin.com/in/dr-christopher-lovejoy/",
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          "description": "Vertical AI is a multi-trillion-dollar opportunity. But you can't win by grabbing the latest LLMs off-the-shelf: you need to embed domain expertise into your organisation, and use it to build a domain-native application. Most teams get this wrong: they either don't hire the right domain experts or don't leverage them correctly to build a differentiated product.\n\nIn this talk, I'll share a framework for building a domain-native AI organisation, drawing on case studies from healthcare, productivity, and legal. I'll share:\n- three models for embedding domain expertise (the Oracle, the Evaluator, and the Architect) - and how to choose which fits your stage and use case\n- who to hire and how to evolve their role over time\n- the most common org-building failure modes (and how to avoid them)",
          "day": "April 10",
          "time": "12:20-12:40pm",
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      "conference": "europe",
      "name": "Sally-Ann Delucia",
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      "company": "Arize AI",
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      "linkedin": "https://www.linkedin.com/in/sallyann-delucia-59a381172/",
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          "description": "An emerging pattern is clear: the best agent memory systems aren't built top-down; they emerge bottom-up from composable primitives. Whether it's grep piping to sort, table previews pointing to full spans, or databases feeding file systems, the winning architecture is always a hierarchy of tools that agents can chain together. The Unix philosophy – small, focused tools that do one thing well and compose infinitely – turns out to be exactly what LLMs need to make 200k tokens feel like 200 trillion. Fifty years later, the lessons still hold: make memory feel infinite by making it hierarchical, and make it hierarchical by making it composable. In this presentation, we’ll outline new findings/data on AI memory from working on thousands of deployed agents.",
          "day": "April 9",
          "time": "12:00-12:20pm",
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      "id": "europe-speaker-56",
      "conference": "europe",
      "name": "Marc Klingen",
      "role": "CEO",
      "company": "Langfuse / ClickHouse",
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      "twitter": "marcklingen",
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          "title": "Skills issue. How agent skills get written, evaluated and improved out in the wild.",
          "description": "At Langfuse we ship and improve our own agents skills regularly & we are seeing many of our community members do the same. We'd love to share learnings how those teams approach evaluating & iterating their skills based on dozens of examples. We are seeing valuable patters how skills actually evolve over time and can share those patterns.",
          "day": "April 9",
          "time": "12:20-12:40pm",
          "type": "talk"
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    {
      "id": "europe-speaker-57",
      "conference": "europe",
      "name": "Shivam Verma",
      "role": "Staff Machine Learning Engineer",
      "company": "Spotify",
      "companyDescription": "Music streaming platform",
      "twitter": "kaffeinated",
      "linkedin": "https://www.linkedin.com/in/shivam13verma",
      "github": "",
      "photoUrl": "https://ai.engineer/europe-speakers/shivam-verma.jpg",
      "talks": [
        {
          "title": "Personalization in the Era of LLMs",
          "description": "Streaming platforms serve hundreds of millions of users across a catalog of 100M+ items that changes daily. Classical recommender systems rank well but can't explain, converse, or generalize across surfaces. LLMs can — but they don't know your catalog, and they definitely don't know your user.\n\nIn this talk, I'll walk through how we bridge that gap at Spotify: teaching open-weight LLMs to be catalog-aware and user-aware without full fine-tuning. I'll cover three building blocks: (1) learned user representations that transfer across search, and recommendation; (2) Semantic IDs — discrete token sequences that let generative models reason over catalog entities the way they reason over words; and (3) parameter-efficient conditioning methods that inject user context into frozen LLMs — from single-token to multi-token projections that give the model a richer \"user prompt\" to attend over.\n\nI'll share what actually worked, what didn't, and the engineering tradeoffs of serving personalized LLMs at web scale.",
          "day": "April 9",
          "time": "12:20-12:40pm",
          "type": "talk"
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    {
      "id": "europe-speaker-58",
      "conference": "europe",
      "name": "Anant Dole",
      "role": "Head of AI",
      "company": "Take Take Take",
      "companyDescription": "",
      "twitter": "",
      "linkedin": "https://www.linkedin.com/in/anantdole/",
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      "photoUrl": "https://ai.engineer/europe-speakers/anant-dole.jpg",
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        {
          "title": "Building a Chess Coach",
          "description": "Take Take Take is the consumer chess app founded by World Champion Magnus Carlsen. We turn play into progress across Social, Play, and Journey: community, seamless play, and long-term coaching. This talk presents Chess Insights: session-level stats, tactical drilldowns, and an LLM coach + memory layer using the Agent Harness that makes engine+LLM coaching reliable in production (prompt design, fallbacks, latency, evals, and evaluation-driven iteration). We’ll finish with a short live demo and practical patterns for consumer AI teams.",
          "day": "April 9",
          "time": "12:20-12:40pm",
          "type": "talk"
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      "id": "europe-speaker-59",
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      "name": "Asbjørn Steinskog",
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          "day": "April 9",
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      "name": "Sally OMalley",
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          "day": "April 9",
          "time": "12:20-12:40pm",
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      "name": "Fryderyk Wiatrowski",
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          "day": "April 9",
          "time": "3:10-3:30pm",
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    {
      "id": "europe-speaker-62",
      "conference": "europe",
      "name": "Steve Ruiz",
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      "companyDescription": "Infinite canvas SDK",
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          "title": "Agents on the Canvas in tldraw",
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          "day": "April 9",
          "time": "11:40am-12:00pm",
          "type": "talk"
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      "conference": "europe",
      "name": "Alva Liu",
      "role": "Staff AI Engineer",
      "company": "Legora",
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        {
          "title": "Rethinking Document Reading for Legal AI: From OCR Pipelines to Multimodal Agents",
          "description": "Every production legal AI system runs some version of the same pipeline: ingest, OCR, store, read. It works - until you realize it silently destroys information lawyers depend on. In legal, where a missed redline can change a clause's meaning, \"mostly works\" isn't good enough.\n\nAt Legora, we process millions of legal documents and have been testing what happens when you stop treating every document the same at ingest time. Instead, we let multimodal agents decide at query time how each document should be parsed and read. This talk covers what we gained, where agentic multimodal parsing breaks down, and the hard problems at the center of it: How do you cite a document the model parsed on the fly?",
          "day": "April 9",
          "time": "12:40-1:00pm",
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          "description": "Our ability to measure AI has been outpaced by our ability to develop it, and this eval gap is one of the most important problems in AI. We need more enduring benchmarks to close this gap, and consequently advance entire new vectors of capabilities for the field. In this talk, I'll share our learnings evaluating agents, drawing from experience working with nearly all global frontier labs and leading academics. We'll discuss the science (i.e., mechanics that make benchmarks rigorous and effective) and art (i.e., intangibles driving ambitious and enduring benchmarks) of building great benchmarks. I'll close by sharing some of the learnings from Open Benchmarks Grants— a $3M initiative in partnership with Hugging Face, Together AI, Prime Intellect, Factory, and others— and highlighting some of the projects we're most excited about funding.",
          "day": "April 9",
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      "name": "Bilge Yücel",
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          "day": "April 9",
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          "day": "April 9",
          "time": "1:00-2:00pm",
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          "title": "Software Engineering + AI = ?",
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          "day": "April 9",
          "time": "4:30-5:00pm",
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          "day": "April 10",
          "time": "4:05-4:23pm",
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      "company": "Neo4j",
      "companyDescription": "Graph database platform",
      "twitter": "steveonjava",
      "linkedin": "https://linkedin.com/in/steveonjava",
      "github": "",
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      "talks": [
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          "title": "Connecting the Dots with Context Graphs",
          "description": "AI systems need more than intelligence; they need context that persists. Without it, even strong models can misinterpret information, lose decision rationale, or repeat the same mistakes. Context Graphs have emerged as a practical pattern for agentic AI: a living graph that captures not only what was retrieved or known, but how context led to actions through tool calls, constraints, policies, and outcomes, stitched across entities and time so precedent becomes searchable.\n\nThis talk explores context engineering as the discipline of designing that context layer, and shows how context graphs complement retrieval by enabling multi-hop, structured context assembly (building on GraphRAG-style hierarchical summaries) while improving explainability and evaluation. Attendees will leave with a practical understanding of how to build context pipelines that combine contextual retrieval with persistent memory and provenance, and why context graphs are becoming central to trustworthy, enterprise-ready AI systems.",
          "day": "April 9",
          "time": "2:30-2:50pm",
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      "id": "europe-speaker-69",
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      "name": "Merve Noyan",
      "role": "Developer Advocacy Engineer",
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      "companyDescription": "AI model hub and platform",
      "twitter": "mervenoyann",
      "linkedin": "https://www.linkedin.com/in/merve-noyan-28b1a113a/",
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          "title": "Open-Source Agents Ecosystem",
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          "day": "April 9",
          "time": "3:10-3:30pm",
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      "name": "Ara Khan",
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      "company": "Cline",
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      "twitter": "arafatkatze",
      "linkedin": "https://www.linkedin.com/in/arafatkatze/",
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      "talks": [
        {
          "title": "What We Learned From a Zillion Agent Tasks",
          "description": "In this talk I while show how we actually parsed through and understood countless AI agents tasks to understand how to actually parse the tasks to make the best possible evals for Cline and build a beautiful hill climbing factor.y",
          "day": "April 9",
          "time": "2:30-2:50pm",
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          "title": "Stop Rolling Your Own Model Router",
          "description": "In this talk I talk about how we wasted(and then recovered) tens and thousands of dollars in our attempts and experiments with AI inference routing self hosting and figuring out the strange napkin math of AI inference",
          "day": "April 10",
          "time": "3:10-3:30pm",
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    {
      "id": "europe-speaker-71",
      "conference": "europe",
      "name": "Mike Christensen",
      "role": "Staff Distributed Systems Engineer",
      "company": "Ably",
      "companyDescription": "",
      "twitter": "christensencode",
      "linkedin": "https://www.linkedin.com/in/mikescottchristensen/",
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      "photoUrl": "https://ai.engineer/europe-speakers/mike-christensen.jpg",
      "talks": [
        {
          "title": "Why Your AI UX Is Broken (and It's Not the Model's Fault)",
          "description": "AI interfaces are moving fast. The single-shot prompt box is already feeling dated. The products pulling ahead let users steer responses mid-stream, interrupt and redirect when the agent goes off track, pick up conversations across devices, send follow-up messages without waiting for the current response to finish, and hand off seamlessly between AI and human support. These aren't speculative features. They're already shipping in the best AI products, and users are starting to expect them everywhere.\n\nBuilding these experiences is hard, and not for the reasons you might think. The problem is not model intelligence or agent capabilities. It is that most AI sessions are ephemeral, tied to a single connection, device, or agent instance.\n\nMost AI apps stream responses over HTTP or SSE, a one way pipe from server to client tied to a single connection. That works for streaming a single response to a single client on a single device. But the moment you want to interrupt a response, resume after a disconnect, or sync across devices, you are fighting the transport at every step. A user switches tabs, refreshes the page, or hits a network blip, and the in-progress response disappears.\n\nTeams end up building their own fragile plumbing - message buffering, replay logic, and state recovery - instead of shipping their actual product.\n\nAs teams push the limits of AI UX, a pattern is starting to emerge. The most advanced AI products treat the session itself as a durable, shared resource, independent of any single connection, device, or agent instance. Connections break. Devices come and go. The session persists, and anyone who joins catches up automatically.\n\nThis is what durable execution did for backend workflows. A similar shift is now happening at the experience layer.\n\nThis talk explores the UX capabilities defining the next generation of AI products: resumable streaming, live steering and interruption, multi-device continuity, concurrent interactions, and human-in-the-loop handoff. I will show how leading AI products use durable sessions to enable these experiences - with demos built on real code.\n\nYou will walk away with a clear picture of the UX bar being set right now, and practical patterns for building these experiences, regardless of which model, framework, or infrastructure you use.",
          "day": "April 9",
          "time": "2:50-3:10pm",
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    {
      "id": "europe-speaker-72",
      "conference": "europe",
      "name": "Joshua Snyder",
      "role": "Team Lead",
      "company": "PostHog",
      "companyDescription": "",
      "twitter": "joshsny",
      "linkedin": "https://www.linkedin.com/in/joshsny/",
      "github": "joshsny",
      "photoUrl": "https://ai.engineer/europe-speakers/joshua-snyder.jpg",
      "talks": [
        {
          "title": "Self Driving Products: Engineering the Pipeline from Product Signals to Pull Requests",
          "description": "Every product generates a firehose of signals; users rage-clicking through broken flows, error spikes at 2am, experiments that quietly tank a metric, a customer complaining in Slack. Today, a human has to notice, triage and eventually write the fix. We're building a system at PostHog that automatically collapses that entire chain.\n\nI'll cover how we ingest and normalize signals across very different sources; session replays, error tracking, analytics, logs, experiments, and third-party tools like Slack. How we convert noisy, unstructured signals into well-scoped coding tasks with enough context for an agent to act on. How we orchestrate agents against real codebases, run them in secure sandboxed environments, and decide what's worth shipping.",
          "day": "April 9",
          "time": "2:50-3:10pm",
          "type": "talk"
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      "id": "europe-speaker-78",
      "conference": "europe",
      "name": "Ibragim Badertdinov",
      "role": "Lead Research Engineer",
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          "title": "SWE-rebench: Lessons from Evaluating Coding Agents on Real Software Engineering Tasks",
          "description": "A practical tour of coding agents, benchmarking, and RL environments, grounded in what we built over the last year. I will show how we created SWE-rebench, an RL environments dataset for code agents with 12M+ downloads on Hugging Face, and how it powers the SWE-rebench leaderboard with 1M+ visits per month on fresh, real-world software tasks.\n\nEvery month we run 30+ top closed and open models on software engineering tasks. I will share the lessons we learned and the most common pitfalls of these models. I will also explain what RL environments are, why they are important, and how we collect them at scale.",
          "day": "April 9",
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    {
      "id": "europe-speaker-79",
      "conference": "europe",
      "name": "Alexey Ostrikov",
      "role": "Head of Development",
      "company": "Yandex",
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      "linkedin": "https://www.linkedin.com/in/alexey-ostrikov/",
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      "photoUrl": "https://ai.engineer/europe-speakers/alexey-ostrikov.jpg",
      "talks": [
        {
          "title": "Self-Evolving AI Agents: Lessons from Winning an International Agent Competition",
          "description": "At the end of December 2025, I took part in an AI agent competition (https://erc.timetoact-group.at/) — not just another hackathon, but a full-blown interactive platform where teams built AI agents to survive inside a realistic corporate environment. The final round: 104 tasks, 3 hours, blind evaluation, no manual intervention allowed.\n\nI won 1st place (https://erc.timetoact-group.at/assets/erc3.html). My agent navigated wikis, employee hierarchies, project tracking, permissions, and customer data — answering arbitrary user queries by calling 20+ API endpoints while enforcing complex access rules buried across scattered documentation. My secret weapon wasn't a better hand-crafted prompt or a breakthrough architecture. I built a meta-agent loop: an Analyzer that diagnosed failures from execution logs and an Evolver that autonomously rewrote the main agent's system prompt. Over 80 generations with zero human intervention, a bare-bones prompt evolved into a structured 5,700-token document with phased reasoning, 34 rules, and 21 few-shot examples — and it won 1st place.\n                                                                          \nBut this pattern is bigger than one competition. I'll connect my approach to parallel breakthroughs emerging right now — an agent that autonomously tuned a Bashkir real-time translation engine, Cursor's week-long experiments where agents wrote a browser from scratch and shipped production refactors — all sharing one core insight: with a measurable eval and a capable enough model, you can close the feedback loop and let agents evolve solutions no human would hand-craft. This talk covers the concrete architecture, the hard-won lessons on breaking through plateaus, and a practical playbook for anyone ready to let their agents optimize themselves.\n\nThe whole talk in Russian: https://www.youtube.com/watch?v=gTKB9dDicNA, I previously spent two years working in London at UBS, so I'm fully comfortable delivering this talk in English.",
          "day": "April 9",
          "time": "3:10-3:30pm",
          "type": "talk"
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    {
      "id": "europe-speaker-80",
      "conference": "europe",
      "name": "Eoin Mulgrew",
      "role": "Head of Digital Transformation",
      "company": "10 Downing Street",
      "companyDescription": "",
      "twitter": "",
      "linkedin": "https://www.linkedin.com/in/eoinmulgrew/",
      "github": "",
      "photoUrl": "https://ai.engineer/europe-speakers/eoin-mulgrew.jpg",
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        {
          "title": "Rewiring the State",
          "description": "In No10 we're building what should be one of the most elite technical teams of any central government. \n\nWe are taking engineers and devs from AI labs, big tech, YC founder etc. and deploying them to rewire the UK state.\n\nThis session would include demos and act as a call to arms for people to join us.",
          "day": "April 10",
          "time": "11:40am-12:00pm",
          "type": "talk"
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    {
      "id": "europe-speaker-81",
      "conference": "europe",
      "name": "Danielle An",
      "role": "Principal Engineer",
      "company": "Meta",
      "companyDescription": "Social technology company",
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      "linkedin": "https://www.linkedin.com/in/danielle-an-07063217/",
      "github": "",
      "photoUrl": "https://ai.engineer/europe-speakers/danielle-an.jpg",
      "talks": [
        {
          "title": "Think You Can Build a Game with AI? Think Again! The New Games Are Just Being Invented!",
          "description": "With the recent development of AI, either you or your friend probably vibe coded a game using Gemini, on Three.js. But that is old news now. If everyone can do that, what is next? The next massive hit, the one that millions of people across the world will play, is just about to be born. Wanna know more? Come see this talk!",
          "day": "April 10",
          "time": "12:40-1:00pm",
          "type": "talk"
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    {
      "id": "europe-speaker-82",
      "conference": "europe",
      "name": "Omar Sanseviero",
      "role": "Developer Experience Lead",
      "company": "Google DeepMind",
      "companyDescription": "AI research lab",
      "twitter": "osanseviero",
      "linkedin": "https://www.linkedin.com/in/omarsanseviero/",
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      "photoUrl": "https://ai.engineer/europe-speakers/omar-sanseviero.jpg",
      "talks": [
        {
          "title": "Gemma, DeepMind's Family of Open Models",
          "description": "Google DeepMind’s Gemma family is expanding. Join us for a deep dive into the latest models of the Gemma ecosystem. From vibe fine-tuning to Sovereign AI, you'll learn about the latest model capabilities, how to build high-performance applications, and how to get started with open models.",
          "day": "April 10",
          "time": "9:00-9:20am",
          "type": "keynote"
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          "day": "April 10",
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      "conference": "europe",
      "name": "Patrick Debois",
      "role": "Creator of DevOps",
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          "day": "April 10",
          "time": "11:15-11:40am",
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          "day": "April 10",
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          "day": "April 10",
          "time": "12:00-12:20pm",
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    {
      "id": "europe-speaker-107",
      "conference": "europe",
      "name": "Sarah Chieng",
      "role": "Head of Developer Experience",
      "company": "Cerebras",
      "companyDescription": "",
      "twitter": "sarahchieng",
      "linkedin": "https://www.linkedin.com/in/sarah-chieng-888595139/",
      "github": "",
      "photoUrl": "https://ai.engineer/europe-speakers/sarah-chieng.jpg",
      "talks": [
        {
          "title": "The Year of Latency Debt (And How Big Tech Is Paying It Down)",
          "description": "In the past few years, we’ve developed a series of ‘bad habits’ as a consequence of slow AI code generation. We write huge prompts, generate massive code diffs, or create 10 parallel sessions because each response takes so long. Psychologically, this also means constant context switching resulting in auto-accepting large code changes, tech debt accumulation, and slop code.\n\nCodex Spark represents a genuine paradigm shift in AI capabilities and use cases.: 1,000+ tokens/second enables real-time collaboration that requires a new approach to work. However, this speed comes with measurable trade-offs and our interactions with LLMs need to be more deliberate and thought out. \n\nThis talk is a practical playbook for working in this new regime, where the AI is faster than we can keep up with: smaller diffs, automated feedback loops, and better code understanding so you move faster and ship clean code.",
          "day": "April 10",
          "time": "12:00-12:20pm",
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          "time": "12:20-12:40pm",
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      "id": "europe-speaker-113",
      "conference": "europe",
      "name": "Lawrence Jones",
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          "description": "At incident.io we're building AI SRE: a system that investigates production incidents autonomously, digging through logs, metrics, traces and code changes to tell you what's gone wrong and how to fix it. It's one of the most complex AI products out there: a deeply nested tree of agents, extremely ambiguous problems, integrating with nondeterministic telemetry systems. When something breaks, you can't just look at a prompt and its output — you need to trace through the entire chain to find where reasoning went sideways.\n\nOur answer to this complexity has been to fight AI with AI — building internal tools where AI agents help us understand, debug, and improve our own AI systems. This talk walks through the specific tools and workflows we've built:\n\nEvals that actually work: a CLI and red-green runbook that turns \"this interaction went wrong\" into a proven fix, structured so AI agents can follow the process end-to-endFilesystem downloads: serialising complex agent traces as markdown that AI agents can read and reason about, turning hour-long debugging sessions into 5-minute conversationsAnalysis at scale: a pipeline where 25 parallel AI agents each analyse an investigation, then cluster results to surface systemic patterns across a customer's incidentsAI-powered feedback loops: using AI agents to dogfood our own tools, submitting structured feedback that feeds directly into what we build next\n\nNone of this requires exotic infrastructure. The patterns are straightforward: give AI agents access to the same debugging information your engineers use, but in a format they can read. Write runbooks they can follow. Build pipelines where AI does the repetitive analysis and surfaces the patterns for humans to act on.\n                                          \nIf you're building AI products and finding the complexity is outpacing your ability to debug and improve them, this talk will give you concrete strategies to close that gap.",
          "day": "April 10",
          "time": "12:20-12:40pm",
          "type": "talk"
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      "name": "Michael Hablich",
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      "company": "Google",
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          "title": "Building Agent Interfaces: Lessons from Chrome DevTools (MCP) for Agents",
          "description": "Last year, my team shipped Chrome DevTools MCP—and immediately learned we'd built it wrong.\n\nOur first version had one giant \"debug_webpage\" tool that tried to do everything. Agents couldn't compose behaviors. They failed silently when parts worked but others didn't. We had to rethink our entire architecture mid-project, decomposing that single tool into 26 focused, composable tools (click, screenshot, evaluate_script, get_network_requests, etc.).\n\nThat wasn't our only mistake. Our error messages were written for humans: \"Unable to navigate back in currently selected page.\" An agent reads that and... what? Does it retry? Give up? We rewrote them three times before agents could self-recover: \"Cannot navigate back, no previous page in history.\" Explicit. Actionable. Machine-parseable.\n\nThen came production reality. Real web pages make hundreds of network requests. We returned them all. Agents hit context limits and failed silently. We added pagination we hadn't planned for, learned token costs the hard way, and realized that token efficiency isn't an optimization—it's a core requirement.\n\nEvery agent developer hits these problems. The architecture patterns that work for human APIs break for agents.\n\nIf you're building MCP servers, REST APIs, or any interface agents will use, you'll face similar challenges:\n\n- **Architecture decisions**: Monolithic vs. composable tools, and when granularity becomes overhead\n- **Error recovery**: How to write error messages that enable agents to self-heal without human intervention\n- **Token efficiency**: Real costs, pagination strategies, and when to truncate vs. summarize\n- **Testing without user research**: How we learned from telemetry, failure patterns, and developer proxies\n- **MCP protocol choices**: Why we chose MCP, what it enabled, and where it constrained us\n\nThis talk shares specific implementation patterns from Chrome DevTools MCP—including the mistakes. I'll show code examples, error message transformations, and architecture decisions from our actual production system. Whether you're building MCP servers, or any agent-facing API, these patterns apply.",
          "day": "April 10",
          "time": "11:40am-12:00pm",
          "type": "talk"
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      "conference": "europe",
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      "company": "Callosum",
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      "linkedin": "https://www.linkedin.com/in/adrian-bertagnoli-bb3467178/",
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          "description": "To date, the dominant trajectory of AI progress has been defined by the homogeneous paradigm: capturing performance gains by scaling uniformity at both the architectural level (monolithic models) and the hardware level (identical GPU clusters).\n\nHowever, it is becoming increasingly clear that real-world intelligence applications require multi-agent systems, where specialised components must collaborate to solve long-horizon, multi-turn, and multi-task problems.\n\nIn this talk, we propose Heterogeneous Intelligence as the essential scaling paradigm for this new era. Through detailed theoretical and empirical analysis, we demonstrate that heterogeneity, across workflows, model architectures, and chip designs, is an asset to be exploited rather than a complexity to be suffered. We will explore the multi-dimensional decision space of hardware selection, demonstrating how widely varying workloads, from the memory constraints of LLM decoding to the physics of diffusion models, can be dynamically routed to specialised architectures, including wafer-scale, thermodynamic, neuromorphic and photonic accelerators, for targeted improvements ranging from optimised digital logic to native physical emulation.\n\nWe conclude with what we believe to be promising avenues in this direction, including how new systems of intelligence will be discovered through iterative and symbiotic hardware-algorithm co-evolution.",
          "day": "April 10",
          "time": "12:40-1:00pm",
          "type": "talk"
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    {
      "id": "europe-speaker-117",
      "conference": "europe",
      "name": "Prince Canuma",
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      "company": "Arcee.ai",
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      "twitter": "Prince_Canuma",
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          "title": "MLX Genmedia",
          "description": "",
          "day": "April 10",
          "time": "11:40am-12:00pm",
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      "conference": "europe",
      "name": "Ruben Casas",
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      "company": "Postman",
      "companyDescription": "API development platform",
      "twitter": "https://x.com/Infoxicador",
      "linkedin": "https://www.linkedin.com/in/ruben-casas-17100383/",
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          "day": "April 10",
          "time": "12:40-1:00pm",
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          "description": "What happens when thousands of agents try to edit source code at once? Merge chaos, slow builds, and stacks of pull requests for engineers to review. While agentic software development has never been so promising, traditional CI/CD solutions threaten to constrain the agentic software transformation. In this session, we’ll unpack what happens when autonomous coding agents continuously open PRs, modify infrastructure, and trigger workflows across hundreds of repos: traditional CI/CD systems, tuned for infrequent human changes, become the latency and cost bottleneck in the SDLC. We’ll discuss concrete failure modes including runner saturation, cache thrash, cold Docker builds, test explosion, and opaque flakiness and show why treating CI/CD as a high-performance system (with specialized hardware, incremental execution, and fine-grained observability) is now a core AI infra problem, not a DevOps afterthought.\n\nUsing Namespace as a case study, we’ll go deep on how to architect an agent-ready pipeline layer: intelligent execution over GitHub Actions, remote caching and Turbo-style Docker builds, Git-aware incrementality, and workflow analytics that tie time and spend directly to specific jobs, repos, and agents. We’ll also cover operational requirements including ephemeral, high-performance clusters; private registries optimized for build workloads; and interactive debugging for both human and agent-authored changes—and how these design choices emerged from running microservices-scale infra at Google and beyond. Attendees will leave with a concrete blueprint for turning CI/CD into a throughput- and reliability-optimized substrate that can safely sustain 5–10x more changes from humans and agents without blowing up latency or cloud budgets.",
          "day": "April 10",
          "time": "12:40-1:00pm",
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      "name": "Madison Faulkner",
      "role": "Partner",
      "company": "NEA",
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      "linkedin": "https://www.linkedin.com/in/madisonhfaulkner/",
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          "description": "What happens when thousands of agents try to edit source code at once? Merge chaos, slow builds, and stacks of pull requests for engineers to review. While agentic software development has never been so promising, traditional CI/CD solutions threaten to constrain the agentic software transformation. In this session, we’ll unpack what happens when autonomous coding agents continuously open PRs, modify infrastructure, and trigger workflows across hundreds of repos: traditional CI/CD systems, tuned for infrequent human changes, become the latency and cost bottleneck in the SDLC. We’ll discuss concrete failure modes including runner saturation, cache thrash, cold Docker builds, test explosion, and opaque flakiness and show why treating CI/CD as a high-performance system (with specialized hardware, incremental execution, and fine-grained observability) is now a core AI infra problem, not a DevOps afterthought.\n\nUsing Namespace as a case study, we’ll go deep on how to architect an agent-ready pipeline layer: intelligent execution over GitHub Actions, remote caching and Turbo-style Docker builds, Git-aware incrementality, and workflow analytics that tie time and spend directly to specific jobs, repos, and agents. We’ll also cover operational requirements including ephemeral, high-performance clusters; private registries optimized for build workloads; and interactive debugging for both human and agent-authored changes—and how these design choices emerged from running microservices-scale infra at Google and beyond. Attendees will leave with a concrete blueprint for turning CI/CD into a throughput- and reliability-optimized substrate that can safely sustain 5–10x more changes from humans and agents without blowing up latency or cloud budgets.",
          "day": "April 10",
          "time": "12:40-1:00pm",
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    {
      "id": "europe-speaker-121",
      "conference": "europe",
      "name": "Ben Burtenshaw",
      "role": "ML Engineer",
      "company": "Hugging Face",
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      "twitter": "ben_burtenshaw",
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          "title": "Your Coding Agent Should Do AI System Engineering",
          "description": "We gave Claude Code, Codex, and Gemini CLI the ability to build CUDA  kernels, fine-tune models, and run full ML experiments. The results were real speedups and performance boosts. An agent-written RMSNorm kernels hit 1.88x speedups on H100s. Fine-tuned Qwen3-0.6B hit 35% on livecodebench.\n\nHugging Face teams are now running 1,000+ ML experiments daily without writing training scripts.\n\nThis talk is a live walkthrough of where Hugging Face has integrated agent skills into the ML stack, so that agentic coders can go deeper than ever. \n\nI'll demo the full loop: giving an agent a task it can't do, watching it fail, loading a skill, and watching it produce a kernel that beats PyTorch's native implementation. I'll share benchmarks across six models on kernel writing, show where agents still fail badly, and give you a concrete playbook for using skills to tackle the hardest systems problems in AI engineering today.\n\nI'll briefly highlight the practical tools that we're using to build skills and release agents on the Hub.\n\nBut the real takeaway isn't the tools. It's what this means for your career as an engineer, and/or engineering team you lead. CUDA programming, ML training pipelines, RL alignment, these were deep specializations that took years to develop. Agent skills compress that timeline from years to hours. \n\nLeave with: the open-source skill files, the upskill CLI, and a framework for knowing when to trust (and when to verify) your agent on hard systems tasks.",
          "day": "April 10",
          "time": "2:30-2:50pm",
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      "id": "europe-speaker-122",
      "conference": "europe",
      "name": "Matt Carey",
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          "title": "Every API Is a Tool for Agents",
          "description": "The best MCP server is the one you didn't have to build.\n\nAt Cloudflare we have a lot of products. Our REST OpenAPI spec is over 2.3 million tokens. When teams started building MCP servers, they did what everyone does: cherry-picked important endpoints for their product, wrote some tool definitions and shipped a separate service that covered a small fraction of their API.\n\nThis was driven by a fundamental context limit of the end users' agent. And tools use a bunch of context just to describe themselves. MCP felt like a Mega Context Problem (and a separate service to maintain).\n\nI think we got it all wrong.\n\nThe context limit is not an MCP problem. It's an agent problem. Tools should probably be discovered on demand and clients are coming around to this. But maybe we can also do it on the server?\n\nCLIs get this for free, self-discoverable and documented by design. APIs just need a little help.\n\nThis talk will cover some of the techniques we've been exploring at Cloudflare, such as codemode and tool search, to make complete APIs accessible to agents through MCP.\n\nI'll also cover some of the work we are doing with the MCP Typescript SDK to make stateless servers the default.",
          "day": "April 10",
          "time": "11:15-11:40am",
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      "id": "europe-speaker-123",
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      "role": "CEO",
      "company": "Vibe Kanban",
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      "twitter": "tokengobbler",
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          "description": "AI eats the middle, software engineers are spending all their time planning and reviewing the work of AI.\n\nIf all humans are going to do is plan and review the work of AI, the biggest lever you have to ship more is to speed up planning and review.\n\nAnd some examples of how teams and individuals are adapting:\n- What tools are people spending their time in\n- How much time are teams spending reviewing code, how has this changed since AI\n- What are different approaches to planning work\n- Is agile and scrum dead? Are most product teams moving faster",
          "day": "April 10",
          "time": "2:30-2:50pm",
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      "id": "europe-speaker-124",
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      "name": "Marlene Mhangami",
      "role": "Senior Cloud Advocate",
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      "twitter": "marlene_zw",
      "linkedin": "https://www.linkedin.com/in/marlenemhangami/",
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          "description": "AI has gotten faster at writing application code and to keep up many developers have let AI write their tests as well. Those tests might pass from a code coverage perspective but the final test is whether or not the application works functionally as expected. Now more than ever AI generated projects require end to end testings to help verify that applications work beyond self affirming unit tests created by LLMs. In this talk we'll understand how to build tests with Playwright. We'll discuss what Playwright is, how to set it up in Agentic workflows and walkthrough some examples of how developers are using it locally and in CI. We'll also look at best practices for using Playwright and accessing it through the new MCP server.",
          "day": "April 10",
          "time": "2:50-3:10pm",
          "type": "talk"
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      "id": "europe-speaker-125",
      "conference": "europe",
      "name": "Michael Richman",
      "role": "VP Engineering",
      "company": "Independent",
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      "twitter": "mrwoofster",
      "linkedin": "https://www.linkedin.com/in/michael-richman-b7807b2/",
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          "description": "You know FOMO – Fear of Missing Out. I’m going to talk about FOMAT – Fear of Missing Agent Time.\n\nFOMAT is the feeling when you’re out in the world, going about your business, and you know that your agents could be working away on your behalf. \n\nIt is you wondering whether the agent that you spun up on a task 30 minutes ago is blocked and waiting for your input.\n\nOr maybe you just want to to get coffee and you know Claude Code just has another 4 minutes before it needs to ask you a question.\n\nAnd it's standing in the way of maximizing productivity with AI coding agents.\n\nWe want coding agents working and unblocked as much as possible. We want to fire up new agent sessions whenever inspiration strikes. \n\nClaude Code and Codex offer some solutions to this, but we are largely chained to our desks, at the terminal or in an IDE, hesitant to miss a beat and let things sit idle.\n\nThis talk introduces a solution: Cmd+Ctrl – an app, a system, that lets you monitor and interact with all of your coding agents from anywhere – your phone, your watch, even when getting that coffee. Agents can notify you when they need your attention and you can respond from anywhere.\n\nThis lightning talk introduces a way to allay that fear of missing agent time. \n\nLive demo of Cmd+Ctrl (pronounced “command and control”) is a part of the talk. Works with Claude Code, Cursor IDE, Cursor CLI, Codex CLI, Gemini CLI, GitHub Copilot in VS Code.\n\nKey topics:\n- FOMAT: The new productivity anxiety with AI agents\n- Why maximizing agent uptime requires freedom to step away\n- Live demo: Starting, checking in on, and unblocking coding agents from your phone\n- The control plane pattern for async agent management",
          "day": "April 10",
          "time": "2:50-3:10pm",
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      "conference": "europe",
      "name": "Rayan Nait Mazi",
      "role": "CEO",
      "company": "Pruna AI",
      "companyDescription": "",
      "twitter": "AskRayan",
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          "day": "April 10",
          "time": "12:00-12:20pm",
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      "id": "europe-speaker-127",
      "conference": "europe",
      "name": "Rachel-Lee Nabors",
      "role": "Fractional DevX Leader",
      "company": "Dressed for Space",
      "companyDescription": "",
      "twitter": "nearestnabors",
      "linkedin": "https://www.linkedin.com/in/nearestnabors",
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          "description": "Right now, most agents interact with users in text/markdown. Chat has been heralded as the one-size fits all UI of the future. But we’ve had an interface like this before that was never adopted by the mainstream: the terminal.\n\nProgrammers can type out their intentions, but “point and grunt” interfaces win with end users. \n\nFortunately, we solved rich interactive UI thirty years ago with CSS, HTML, and JavaScript. These technologies are well rounded and feature complete, and thanks to MCP Apps and WebMCP, your agent just inherited the entire web platform as its rendering surface.\n\nMCP Apps embed full HTML, CSS, and JavaScript inside agent interfaces, not as a hack, but as a standard. WebMCP, a W3C community incubation led by the Chrome and Edge teams, is bringing structured tool calling directly into the browser. Together, they turn your agent from a text-in-text-out pipe into an infinite canvas: interactive forms, data visualizations, image galleries, approval workflows. Anything the web can render, your agent can now surface.\n\nThis talk demos the full stack live. You'll watch me browse, search, and read a 23-year-old web comic archive entirely through an agent: no browser tabs, no screen scraping, no DOM wrangling. \n\nWhat you'll walk away with:\n\n- How MCP Apps and WebMCP actually work (iframes, postMessage, and a W3C spec you should be tracking)\n- When to return text vs. structured data vs. rendered UI from your MCP server\n- How to make existing web content agent-native without a rewrite\n- Why the web platform is the most powerful and underused primitive in the MCP ecosystem",
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          "time": "2:50-3:10pm",
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      "name": "Brian Scanlan",
      "role": "Principal Systems Engineer",
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          "title": "How Building with AI Can Double the Throughput of Your Engineering Team",
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          "day": "April 10",
          "time": "2:50-3:10pm",
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    {
      "id": "europe-speaker-129",
      "conference": "europe",
      "name": "Liam Hampton",
      "role": "Senior Cloud Advocate",
      "company": "Microsoft",
      "companyDescription": "",
      "twitter": "liamchampton",
      "linkedin": "https://www.linkedin.com/in/liam-conroy-hampton/",
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          "title": "Cooking with Agents in VS Code",
          "description": "This is a demo driven talk that explores how AI agents are shaping modern developer workflows inside VS Code. This session introduces attendees to local, remote, and worktree based agents, explaining how each agent path works, the trade offs involved, and when to use each of them effectively. Attendees will leave with a clear mental model for agent based development in VS Code, an understanding of emerging agent patterns",
          "day": "April 10",
          "time": "3:10-3:30pm",
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    {
      "id": "europe-speaker-130",
      "conference": "europe",
      "name": "Karan Sampath",
      "role": "Member of Technical Staff",
      "company": "Anthropic",
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      "twitter": "karan_sampath",
      "linkedin": "https://www.linkedin.com/in/karansampath/",
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          "title": "Bringing MCPs to the Enterprise",
          "description": "MCPs are often flaky, face multiple security vulnerabilities, and are generally hard to scale. Most enterprises struggle to use more than single digit numbers of MCPs due to issues with security, observability, and access control. In this talk, we'll explore the approaches and learnings we at Anthropic have been taking to solve this, and make MCPs more enterprise ready.",
          "day": "April 10",
          "time": "3:10-3:30pm",
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      "id": "europe-speaker-131",
      "conference": "europe",
      "name": "Mike Spitz",
      "role": "Principal Software Engineer",
      "company": "PFF",
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          "title": "Agents Don't Do Standups: Building the Post-Engineer Engineering Org",
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          "description": "AI agents that run for hours, wait on humans, and call dozens of tools need durability. Today the default answer is application-level replay: record every step, replay the log on recovery, require your code to be deterministic. It works, but it constrains how you write agents and can't capture everything a running process actually holds in memory.\nThere's a second approach that almost nobody in the AI engineering world is using yet: OS-level snapshot/restore. Freeze the entire process. Free all resources. Restore it exactly where it left off, with no replay log, no determinism requirements, and no step boundaries in your code.\nIn this talk I'll compare both approaches honestly, where each one wins and where each one breaks, then demo a live agent on Trigger.dev that checkpoints mid-execution, suspends at zero cost while waiting on a human, and resumes from snapshot. You'll leave with a clear framework for choosing the right durability model for your agents.",
          "day": "April 9",
          "time": "10:50-11:08am",
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          "description": "What language would you use to vibe-code a new app? If you ask ChatGPT this very question, it will probably enthusiastically suggest TypeScript and possibly Python; if Rust is mentioned, there might be a note saying to use it \"if you enjoy suffering beautifully\" (yes, I actually got this response).\n\nIn fact, Rust is the ideal programming language for vibe-coding. The language itself deterministically enforces rules to ensure correctness (e.g. memory safety and safe concurrency) and best practice. When agents code in other languages, often all that prevents them from shipping broken code is some easily-forgotten agent instructions; possibly another independent, but fallible, review agent; and in the worst-case scenario, nothing but hope. When violating a rule in Rust, agents reliably receive error messages that explain exactly what is wrong and point them in the right direction. Rust's tooling-enforced invariants provide a natural agentic feedback loop, lowering reliance on model skill and instead introducing guardrails for your code.",
          "day": "April 9",
          "time": "3:45-4:03pm",
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      "name": "Priscila Andre de Oliveira",
      "role": "Senior Software Engineer",
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          "title": "Comprehend First, Code Later: The AI Skill I Rely On Daily",
          "description": "Literally everyone is vibe coding. It's about letting AI write, commit, and ship code you never even read. Perfect for prototypes and side projects - no argument there. But what happens when you're working in a million-line codebase where you need to understand before you change? Quality code still matters.\n\nIt is widely stated in the software development community that developers spend 70–80% of their time reading and understanding existing code, not writing new code. We now have an incredibly smart tool - so why not use it for exactly that?\n\nSo I went straight to the data - 239 of my own messages from daily work at Sentry. What I found flipped the narrative: my #1 use of AI wasn't generation. It was comprehension. Whether navigating unfamiliar code or reconstructing past decisions from commit history - AI became the teammate who never gets tired of my questions.\n\nIn this talk, I'll show you the loop that actually makes me productive in a large, complex codebase: understand first, then code.",
          "day": "April 10",
          "time": "1:45-2:03pm",
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