Zain Adil

Execution is the new capital...and these 10 founder lessons from Waterloo Tech Week might prove it. From hiring fast (and firing faster) to guarding your calendar like equity, some of Waterloo Regions’s top tech leaders dropped the kind of startup truth bombs you can't Google. Thanks to MissionSync for sponsoring and Mike Kirkupfor hosting! And a big thanks to Mila Banerjee, Sheldon McCormick, Steve Currie and Martha van Berkel for sharing their knowledge and insights with the group. Check them out 👉 https://lnkd.in/gwSVZh97

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Love this framing from Shopify. - stop debating AI adoption, start measuring outcomes. I’ve seen too many teams focus on getting people to use a tool instead of what it’s meant to solve. At Pebl, we use AI to make work easier — so our teams can focus on solving harder, more meaningful problems.

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Engineering managers: budget constraints, economic and political uncertainty, and the persistent burden of tech debt are all impacting how you lead your teams - not to mention the perception and usage of AI coding tools. The 2025 State of Engineering Management report uncovers research into all these areas. Tricia Gregg summarizes 3 takeaways from the SEMR: 🧭 Navigating Engineering Well-being and Productivity 🌊 Adapting to Evolving Workforce Dynamics 🤖 Embracing the Transformative Power of AI And she has also included considerations for each to tackle each head on here: https://lnkd.in/gS-QMYRF

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Software engineering isn’t just about shipping code—it’s about shaping teams that scale, systems that adapt, and cultures that endure. One of the biggest lessons I’ve learned in engineering leadership is that technical debt is easy to see, but organizational debt is silent — and far more expensive. We spend hours debating frameworks, performance tradeoffs, and system design. But how often do we apply that same rigor to the design of our team processes, feedback loops, or decision-making velocity? Here’s what I’ve seen work: • High-context environments beat high-control ones. Engineers thrive when they understand why, not just what. • Velocity emerges from clarity. The best teams don’t move fast because they cut corners—they move fast because they don’t waste cycles on ambiguity. • Great engineering managers are multipliers. Not the best engineer in the room, but the person who ensures every engineer is at their best. If you’re scaling a team or facing complexity creep, ask yourself: Are we debugging our systems as actively as we debug our culture? #SoftwareEngineering #Leadership #EngineeringManagement #TechCulture #TeamScaling #OrganizationalDesign ⸻ We are proud of the culture and organization we’ve built in Amazon Vancouver. Come meet us! Want to discuss about these in person? See you next Thursday, May 15th at the Amazon Payment Products event!

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In modern software delivery, waiting for a test environment is no longer an option. The pace of today’s releases means QA and DevOps teams need production-like environments ready on demand - consistent, automated, and integrated right into the CI/CD pipeline. At The Test Tribe’s 5th Vancouver Meetup, we’ll explore how Infrastructure as Code (IaC) can completely transform the way teams set up and scale test environments. With the right IaC approach, you can: - Create reliable test environments in minutes - Prevent costly delays caused by environment bottlenecks - Reduce configuration drift and eliminate manual setup errors - Scale infrastructure quickly without overspending Led by Adetokunbo Temitayo Ige - Sr. Platform Engineer, DevOps Engineer, and IaC expert - this hands-on session will give you practical strategies you can implement right away to align your testing infrastructure with the speed of your delivery pipeline. 📅 Wednesday, Sept 3, 2025 | 5:30 PM – 8:00 PM PDT 📍 Thinkific – 369 Terminal Ave #400, Vancouver, BC 🔗 Reserve your spot here: https://lu.ma/7v71mxir Thanks to our tribe champion - Vlada Zakharova for all her efforts in making this event live! #TheTestTribe #VancouverMeetup #InfrastructureAsCode #DevOps #QACommunity Ashutosh Garg Siddharth Dua

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Parallel’s best-in-class Search & Extract tools are now available in the Vercel AI SDK. Use them in your AI projects to retrieve high-quality context from across the open web, or on specific pages you define. Find everything you need to get started in the comments below.

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Incredibly proud of this team and the work highlighted here! Quantum Metric has been an absolute game-changer for our Digital teams, helping us deeply understand customer behavior, identify friction points, and build a better mobile app experience. But what I’m most excited about is how we've pushed the boundaries of this platform beyond traditional digital experience use cases, thanks to an incredible partnership with them and Google Cloud. Because Aritzia’s data platform and QM both live natively on BigQuery, we were able to seamlessly merge that data with Google Analytics 360 and the rest of our platform data. From there, we incorporated it into our existing server-side integrations via Hightouch to power channels like paid media. To top it off, for select downstream systems that require absolute speed—like personalization—we are using BigQuery Continuous Queries. This allows us to send critical data in near real-time, with only a few seconds from the time of the event until it lands in those platforms. Thank you to Sean De La Torre and the team at Quantum Metric for being such a forward-thinking partner, and to Nick Orlove and the BigQuery team at Google Cloud for the infrastructure to make it happen. Congrats to everyone involved in raising the bar! 🎉 #QuantumMetric #GoogleCloud #BigQuery #DigitalExperience #MarTech #RetailTech

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Toronto SaaS firms are effectively killing the local hiring model by turning to global talent to slash recruitment costs by 60% and bypass a "bottleneck" that is squeezing smaller teams out of existence..

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Track nothing and you fly blind. Track everything and you risk nudging teams to optimize vanity numbers instead of real customer value. So our Engineering Management crew here at Vendasta headed to the whiteboard and asked: Which numbers would actually help us ship better software, right now? We set two ground rules: * Start small, refine fast. Perfect‑on‑paper metrics that never get implemented are useless. * Coach with data, never grade with it. Dev‑level stats exist to spark healthy habits, not to rank people. Here are just a few of the starter metrics we’re exploring: * Ramp‑up velocity ⏩ - How quickly do new engineers reach full productivity? * Roadmap vs. reactive work 🎯 - Are we spending capacity on strategic goals or fighting fires? * AI adoption & impact 🤖 - Who’s using the AI tools our org is standardizing on, and is it actually moving the productivity needle? * Hiring‑pipeline health 🏃‍♂️ - Time‑to‑fill, drop‑off rates, and how that aligns with the roadmap. This is just a taste. There are plenty more on the board, and we’ll trim or add as reality teaches us. To turn ideas into something useful Megan Cheesbrough (Engineering Manager), Lyle McRae (our new Senior Data Engineer), and I have started jamming on a proposal: a BigQuery + Dataform backbone with clear data standards so we can plug sources in once and let AI workflows keep everything fresh. Nothing’s built yet, but Lyle’s bringing proven patterns from past wins to shape a framework that fits our needs. Big unlock if we get it right. Our Principles: * Value first, vanity never. * Automate the grunt work; manage the AI. * Tell a story the whole org cares about. We’ve kicked off an MVP and will iterate. Your turn: What’s one metric that genuinely changed how your engineering org works? Drop a thought below. I’m keen to compare notes as we turn this whiteboard into reality.

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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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🧠 Tech leaders: Let’s be honest. AI is already reshaping how we build, lead, and work - and that changes the job market in Tech as well. Shopify's CEO Tobi Lütke told Shopify's leads what now VCs are telling startups: “No new hires unless AI can’t do the job.”  The effects of that are significant:  🔹 Entry-level roles? Vanishing as senior staff can now cover them with AI assistance. 🔹 Career paths? Collapsing as a consequence. 🔹 Companies? Scaling with smaller teams. The job market seems to be shrinking in Tech. ❓️ So what’s the best places to be at in terms of job perspective?  ‼️ It’s for sure the ones where you learn and build up your AI knowledge! There are a few options: 1️⃣ Obviously the big AI companies like OpenAI, Mistral or others. But there are only so many of them...  2️⃣ Companies leveraging AI - for their products or their processes. Not the big techs, but nearly all other industries! If you can help them leveling up their AI game, you have good job perspectives. 3️⃣ Yourself! Thanks to AI, starting your own business has become easier than ever. So go invent some new products or disrupt existing businesses. 👉 This is your edge. 📰 If you want to go deeper on this topic, the latest issue of my newsletter "The Tech Impact Uplift" is for you. You can access the newsletter from my profile or directly go to the issue from the comments.

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A few weeks ago I wrote about how I use Claude Code for candidate sourcing. Then I listened to the ''How Webflow's CPO built an AI chief of staff to manage her calendar and drive internal AI adoption'' episode of How I AI with Claire Vo and Rachel Wolan. I HIGHLY recommend this episode. I listened twice, took the advice, and started looking around for other possible workflows and optimized all my mailboxes. I called Viviane Sauvé De Guise and Danielle Coburn (thank you both for bearing with my hyperfocused state), sleep deprived, and completely in awe. My newest post is about this: How I Built an AI Chief of Staff with Claude Code. It goes through the setup and what it does: meeting prep, calendar triage, email triage, LinkedIn reconciliation, and all the jazz. If you're in Ops/HR/People/fractional and you've been building with agents -  what are you making? Tag anyone who's in this space. I want to see everything. Link: https://lnkd.in/eRZSXasx

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With tight iOS privacy rules, almost 40–60% of installs go unattributed (or at least mis-attributed) on many tools. That means you can be spending on ads, while half your data is invisible. If only there was something to help you reclaim ~95% of that, and use creative-level performance to guide how to optimize for retention, LTV, and player engagement... Oh nevermind, there is Audiencelab by Geeklab. https://audiencelab.ai/

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When Tobi Lütke of Shopify announced their AI-first policy last week, it got a lot of people talking. At LTV, we've also been moving towards a similar AI-driven vision — and one big question kept coming up: "How do you make AI feel fun and empowering, instead of like it’s replacing parts of people's jobs?" Recently, I tried an experiment with 1 of my teams, inspired by Mark Rober’s "Super Mario Effect" TED Talk — the idea that people succeed more when challenges are gamified. 🎮 The Idea: Gamify AI learning through custom GPT-powered games during our remote team happy hour! Here’s what we built: - pAIctionary: Draw doodles, upload them, and have a custom GPT guess them — points for every correct guess! - codenAImes: A word-association game where teams craft clues to make GPT guess the right words without accidentally hitting the wrong one. - pAIsswords: Like "Password" but with AI — players get the AI to guess a secret word without using restricted hints. - AIseeAIcompare: Players prompt GPT to recreate given images, and a custom tool scores how closely they match. The Results? - Each of our 6 vertical teams competed in fast-paced 10-minute games. - Each team was required to have 2 champions participating actively in each game. The champions strategized, but everyone contributed — cheering, suggesting prompts, and collaborating. - The competition brought energy and laughter, but more importantly, it built confidence and creativity around using AI tools. 👉 Key takeaway: When you gamify AI adoption, you shift the mindset from fear to fun; from threat to tool. 💬 How are you encouraging AI adoption on your teams? I would love to hear any creative ideas or strategies you’ve seen work! 🚀 #AI #Leadership #CultureBuilding #Teamwork #Innovation #FutureOfWork #LearningThroughPlay

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I’ve been hearing several people in senior technical leadership roles (Head of Engineering at Shopify, CEO at Sourcegraph) talking about how they’re happy to have every engineer in their team spend $1000/month or more on tokens using coding agents if it produces results. What this means at first is that budget that is allocated for product development and used for hiring new engineers is now being used by engineers made of silicon. So there are fewer hires. But at some point you will speed up your delivery and can serve more customers, which means that you can spend more, you’ll hire more developers again to babysit the silicon engineers. The landscape is shifting fast. How are you adapting? #engineering #ai #llm #codingagent

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This is a big one for us! 🙌 We’ve signed one of our first larger North American customers, and our 2nd PE customer. We will start by providing data engineering solutions to one of TorQuest Partners’s portfolio companies within the logistics sector, and look to expand to other use cases including Agentic AI adoption further down the line. A special thank you to Mike for helping and trusting us, Adam for the initial intro, my team for working hard to land this, and our legendary EWOR coach, Paul for his BD wisdom which has been super useful in these conversations over the past few weeks! Let’s go!

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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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