Caley Brock

Canada has something Silicon Valley doesn't. A pan-Canadian community of 3,200+ senior tech leaders. Volunteer-led. Unsponsored. No agenda but helping each other execute faster. We don't optimize for eyeballs. We optimize for trust. The result? Real-time signal on what actually works. Peer-tested frameworks. The kind of collective intelligence that only emerges when status signaling is off the table. Communities don't scale by adding more people. They scale by deepening trust per node. When senior operators can ask the hardest questions without pretense, the entire ecosystem moves faster. That's the Canadian advantage. Not louder. Deeper.

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I spent part of my winter break building Trillium - a side project that sends a weekly summary of bills moving through Ontario's legislature, because following provincial politics via news articles was getting exhausting. But the more interesting outcome was what I learned about AI coding assistants along the way. I built two versions of the same project: one from a completely blank canvas, and one where I'd laid down some initial data models, services, and project structure before pulling in the AI. The difference was significant. The blank canvas version had dead code, logic errors, and baked-in assumptions that would've been a pain to untangle. The structured version had the AI picking up my patterns and running with them. The takeaway: a solid foundation doesn't just help you - it helps your AI assistant too. I wrote up the full case study here, including a few other things I learned about finding the right balance between leaning on AI and actually understanding your own codebase: https://lnkd.in/ge--QSWV

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🌲 Living in Vancouver means you can’t help but love the outdoors: ⛰️ mountains, lakes, 🚵 trails, all of it. So stories like this one from Xanterra Travel Collection hit close to home. They set out to upgrade their contact center, but not just for efficiency. The goal was to make guest interactions smoother and more personal, while actually driving business results. With Cresta AI Agent and real-time insights, they saw: 💡 $3.3M in new revenue 💡 74% containment rate 💡 Hundreds of thousands in cost savings That’s why AI Agents matter. Not just for trimming costs, but for creating value💰. When done right, automation can help teams connect faster, serve better, and grow smarter. Learn how: https://lnkd.in/gYF4V_JX

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It’s frustrating to see that the productivity gap between Canada and other advanced economies has widened in recent years. Meanwhile it’s inspirational seeing how advanced technology is being applied in other countries. While most Canadians are busy debating affordability, lowering taxes or increasing minimum wages, more attention can be paid on using technology to truly improve standard of living by increasing productivity. I don’t think there’s a shortage of talents or inventions coming from Canadians, but actual productization and commercialization of technology does not seem to occur frequently enough in Canada. What can individuals like myself do? Aside from working on products to help businesses identify and close productivity gaps, one thing I can think of is to bring back and share experience I have in the industry back to the university. Hopefully this could align students’ expectations and inspire better collaboration between academics and commercial application of technology. Do you agree? What would you suggest?

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I always get amped up after working with fellow Canadian founders! This week I co-hosted a powerful lunch & learn on transitioning startups from product centric to growth centric operations. Why does this shift matter, why is it rarely discussed, and why does it kill companies? It's a surprising challenging culture shift that founders rarely discuss. In the early days of building a technology business, product R&D rules and GTM strategies are put on the roadmap horizon. Then comes the crucial moment: how do you build a sales engine when the large majority of your budget operates R&D? How do your engineers react when they are required to build features based on client feedback vs pure vision? How do you enable your team to thrive through this focus shift vs create friction? The key is to engage with your tech team early and educate the team on this shift before it comes into the company walls. Be transparent that R&D is an investment and to be successful your company has to transact on that investment and earn multiples of every R&D dollar spent. Thanks MaRS Discovery District Tony Cascio, ICP-ACC and June Wang for organizing such a vital discussion. Michelle Oke, it was a pleasure hosting this event with you! AVO - Autonomous Vehicle Organization #GTM #RandD #startup #culture

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🚀 Scaling to 20 Billion QPS: How Shopify Reinvents the Global Storefront When you’re the backbone of global commerce, "downtime" isn't just a technical metric, it’s lost revenue for millions of entrepreneurs. Shopify’s footprint is staggering: - Billions of users - Millions of merchants - 175 countries - 6 continents of operation - 24/7/365 availability requirements To handle this demand, Shopify is moving toward a future powered by a distributed, globally sharded database architecture with Yugabyte. The Goal: 20 Billion QPS 📈 Yes, you read that right. Shopify is planning for a massive scale of 20 billion Queries Per Second (QPS). Moving to a cloud-native, distributed SQL database allows Shopify to: 1. Operational simplicity: Having one logical database makes it much easier to manage and do schema changes on. 2. Scale Linearly: Adding capacity becomes a matter of adding nodes, not performing risky migrations. 3. Optimize Latency: By sharding data closer to the user, they ensure a "local" feel for a global audience. So excited to be along for the ride with such a determined team! #SystemDesign #CloudNative #Scalability #Shopify #YugabyteDB #DistributedSQL #TechInnovation #SoftwareEngineering Alan Caldera Brad Dietrich Rohan J. Thomas https://lnkd.in/gspFdZKV

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Over the past few weeks, I’ve been running a series of AI in QA workshops, and it’s been one of the most energizing parts of my work lately. I’ve had the chance to lead sessions like: - Beginner to Cursor/Playwright – helping folks get hands-on with the basics. - Making Your POM with Cursor & Playwright – diving deeper into building scalable testing structures. But the one I want to highlight here is the Director-only 1:1 workshop: How Cursor Can Impact Your QA Team. I’ve run 11 of these director sessions, and they’ve all been incredibly insightful. What makes them unique is how tailored and collaborative they are. I usually start by asking directors to walk me through their QA org structure, where they’re strong, where they’re struggling, and what’s top of mind for them. From there, we build the session around their needs, whether that’s improving speed, scaling coverage, or empowering their team with AI-powered tools like Cursor. These conversations are never one-way presentations. They’re real back-and-forth exchanges. Directors share the challenges their teams face in day-to-day QA, and I get to show practical ways AI can address those challenges. Honestly, I’ve learned just as much from these sessions as the directors have, whether it’s new perspectives on QA org design, creative implementation strategies, or leadership priorities around testing at scale. What is awesome is that the majority of these 1:1s have naturally led to setting up a free private workshop for the entire QA team. Directors bring the concepts back to their teams, and we run a hands-on session where everyone gets to see what’s possible. These director sessions have shown me just how hungry QA leaders are for innovation in their space. They’re looking not just for tools, but for ways to empower their teams, reduce bottlenecks, and elevate the role of QA in their organizations. If you’re a QA leader, Engineering Manager, CTO, etc and you’re curious about how AI (and tools like Cursor) can impact your workflows and your team’s effectiveness, I’d love to connect and set up one of these conversations.

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At a Canadian startup I worked for (RIP), we chased big growth with fancy algorithms. But 80% of our purchasing challenges were solved by one thing , simple seasonal trends from past purchases. Often, the simplest data holds the real answers.

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