25 au 27 février, 2026
Montréal, Canada
Top 20 sessions
Tests et QualitéIntelligence Artificielle
Code reviews often get stuck on nits like spacing, naming, minor style. AI can automate the noise, freeing reviewers to focus on design, maintainability, and business impact. This talk shows how to leverage AI as a review partner, improving code quality and developer collaboration without losing the human touch.
DevOpsIntelligence Artificielle
AI’s potential in software development goes far beyond code generation or IDE chatbots. This talk explores five AI capability families (Learning, Coding Assistants, Automation & Agents, Planning, and Data Exploration) that can reshape the CI/CD lifecycle. From process assessment to VM migrations, centering AI integration around developers throughout the entire lifecycle of planning, coding, deployment, and monitoring unlocks greater ROI.
Architecture & Design Patterns
Clean Architecture is not the final word in application design. This talk traces the history of software architecture patterns from the 1970s to today. We will categorize them by their core principles to understand the driving forces behind their evolution. You will leave with a clear framework to look beyond today's popular patterns, helping you make informed architectural choices that improve the testability and productivity of your team.
InfonuagiqueIntelligence Artificielle
Being a developer these days means you’re not only writing code, but also a Linux admin, a DevOps engineer, you name it! Thankfully, the Model Context Protocol (MCP) has made it significantly easier for me to ask my natural language questions, such as “What’s consuming the most memory in our dev cluster right now?”, and quickly receive a response. Hint: It was Prometheus. Learn the most popular MCP Servers, and how to chain them together too!
Intelligence Artificielle
Agents fail for two reasons: the model you're using isn’t good enough, or the context is a mess. Today, we focus on the mess. LLMs are pure functions, what goes in determines what comes out. That’s why context is everything. This session covers context engineering: how to write, select, compress, and isolate information. You’ll learn to avoid context rot, when to use memory, and how to split problems so models don’t drown in tokens.
Intelligence Humain
Stepping into leadership as a developer is tricky: you’re expected to guide peers without becoming a manager. This talk shares strategies to influence without authority, avoid micromanagement, and balance coding with people skills. We will give practical insights to build trust, give feedback, and help your team thrive.
InfonuagiqueCTO
As tools develop and teams grow, the complexity increases. With monorepos regrouping multiple shared packages with multiple technologies, how can we effectively manage this complexity and adopt proper practices to build, scale and support your business? Real life experience regarding the set-up and scale-up of a multi-monorepo (firmware, edge (arhm7), cloud (x86_64) and multi-language (C, go, python, nodejs, pnpm, bun).
Intelligence Artificielle
AI coding assistants excel at small tasks, but complex codebases require upfront thinking. This talk introduces spec-driven development as a way to scale AI for real features. You’ll see how tools like Kiro break work into planning, execution, and validation phases, explore real-world tradeoffs, and learn how to apply this approach to your own projects immediately.
Intelligence Artificielle
This presentation examines how AI automation through MCP servers revolutionizes development team management and project workflows. Attendees explore practical implementations of AI-powered ticket assessment, automated bug resolution, UI design, and Full SDLC management. Participants gain actionable strategies to implement intelligent automation that reduces overhead while improving productivity and delivery speed.
Tests et QualitéIntelligence Artificielle
LLM as a judge uses a language model to evaluate responses based on criteria you define, and prompt optimization is the process of refining the evaluation prompt to ensure the LLM judge is consistent, accurate, and fair in its scoring. Tools and techniques exist to automate this optimization, helping to capture complex evaluation criteria even with limited human supervision, improving performance, and reducing costly manual iterations.
StartupIntelligence Humain
After five years of building our team, we’ve distilled seven practical habits—no Scrum, no dogma—that fuel delivery, motivation, and continuous improvement:
1. Learn how to prioritize pragmatically,
2. Kill complexity,
3. Own your work
4. Communicate relentlessly
5. Build microteams
6. Deliver continuously
7. Have fun.
Full of real-world stories and takeaways for thriving teams. We've had no churn in 5 years
1. Learn how to prioritize pragmatically,
2. Kill complexity,
3. Own your work
4. Communicate relentlessly
5. Build microteams
6. Deliver continuously
7. Have fun.
Full of real-world stories and takeaways for thriving teams. We've had no churn in 5 years
DevOpsIntelligence Artificielle
AI isn’t coming for the future of software delivery, it’s already here, rewriting the rules in real time. Models are being trained, deployed, and scaled faster than ever, but while DevOps gave us speed and reliability for traditional applications, it wasn’t built for the messy, unpredictable world of machine learning. That’s where MLOps comes in, the next evolution of DevOps, engineered for the age of AI.
Architecture & Design PatternsIntelligence Artificielle
Coding Agents succeed when you give them structure. This session runs like a workshop: we’ll demonstrate how to build a feature with coding agents by defining a spec, managing context, and breaking the work into smaller subtasks. Along the way, you’ll see real prompts, failure cases, and handoff rhythms that keep quality high while boosting delivery speed.
Architecture & Design PatternsIntelligence Artificielle
Are you looking to better understand how to better leverage claude.ai and why it behaves this way? Dealing with claude.ai is counter-intuitive for many people. I will present why claude.ai is behaving this way and how to use the ML perspective to better constrain him to do what you really want with prompts.
Intelligence Artificielle Python
Large Language Models are everywhere, but how they actually work “under the hood” often remains a mystery. In this live-coding talk, we’ll skip the slides and build an LLM step by step, not just a toy model, but a state-of-the-art one. Along the way we’ll dive into tokenization, transformer architecture, small-scale training, and inference, showing how these components come together to power today’s most advanced models.
Bases de données
Many applications suffer from inefficient database use, causing poor performance, downtime, and security risks. This talk shares foundational best practices for MySQL, PostgreSQL, and MongoDB. We’ll cover schema design, query execution, indexing, and scalability—equipping developers with the skills to build robust, secure, and efficient applications through smarter database operations.
Architecture & Design PatternsIntelligence Humain
Thinking tools to solve thorny problems. Software engineering isn’t just about production, it’s about learning in complex, uncertain situations. This talk explores how useful (if imperfect) models and practical heuristics can help you reframe problems, cut through complexity, and make better technical decisions.
Intelligence HumainCTO
In many large organizations, leadership defines team structures without considering their impact on software. The result? Conway’s Law inevitably shapes your systems in ways you may not expect.
In this session, we'll explore how ignoring software architecture undermines development, delivery, and ambitions. Then, we will see what can be done to better align teams with the intended targets.
In this session, we'll explore how ignoring software architecture undermines development, delivery, and ambitions. Then, we will see what can be done to better align teams with the intended targets.
DevOps
Things we might take for granted today: comprehensive testing, automated deployment, local dev environments on demand. How do you take a 20 year old web application, including its runtime dependence on a live database, and retrofit it with these modern best practices? Well, testing was the (relatively) easy part, it was the deployment cycle, and spinning up local dev environments that was harder, but not impossible. Let us show you how we did it.
Intelligence Artificielle Intelligence Humain
What do they have in common? This session explores AI through the lens of neuroscience, drawing parallels between how the brain processes information and AI developments. We’ll highlight key definitions, milestones, and evaluation gaps in both fields, and consider how close we are to achieving AGI.
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