Nash R.

A little more detail on my AI in QA workshops: Please reshare to help get out the word. These are free and I want to keep doing them if there is interest. The goal is largely meant to inspire and encourage learning. They will point you in the right direction. 1. An intro to Cursor, Playwright & LLMs This hour-long session will focus on getting you comfortable with Cursor and the tips and tricks that are key for QA engineers using it. I’ll walk you through model selection, how to set context, how to use rules, and more. This is great for anyone who is fairly new to Cursor and wants to start using it. Calendly: https://lnkd.in/gJthqa7b 2. Using Cursor to build your POM This session will leverage Cursor to build out a basic page object model. I’ll show you how to do this using both the DOM and code. Additionally, we’ll cover adding test IDs to the codebase and look at strategies for handling test data. Calendly: https://lnkd.in/gp9M6Gn9 3. Directors only: How can Cursor help your team? These are ad-hoc sessions any manager, director, CTO, etc., can book. I’ve already done several and really enjoy them. They tend to be conversations about your current approach, with me demoing whether or not Cursor can be helpful. They are often fascinating discussions about QA and usually offer a nugget or two that people take away. Calendly: https://lnkd.in/guRf6Cu2 #qa #ai #cursor #learning

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How to Stay Motivated in Repetitive Tasks: Tips for QA Engineers     Up to 85%. That’s how many people worldwide feel disengaged at work (Gallup says so).  Sounds brutal, right? But here’s the twist: when teams are engaged, they’re sharper, faster, and they stick around longer.  And for QA engineers? The challenge is real. Same test cases. Same steps. Again. And again. But—repetition isn’t always the villain. Sometimes it’s where mastery happens. Where creativity sneaks in. Where new ideas are born. So, how do you keep your energy when the job feels stuck on repeat? 

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“Why are we using a rocket launcher to butter toast?” you might ask... What happens when a perfectly sensible architectural decision quietly drifts out of alignment with reality. A SaaS search engine built for full-text search and clever relevance scoring… being used for glorified key-value lookups. Seventy-five million requests a month. Lots of cost. No edge caching. Limited observability. Ouch. The real lesson here isn’t just “this technology is faster and cheaper” (although 35ms P50 latency and a 0.0001% error rate are nothing to sniff at). It’s about architectural honesty. Stripping a system back to what it needs. Using organisational learning instead of reinventing the wheel. Designing observability in from day one. And migrating gradually, with dual-running and rollback paths, so 75 million monthly requests don’t even blink. 📣 Sometimes the strategic move isn’t adding something new. It’s removing the unnecessary. We’re back to our technical roots this week with a tale from Nick Theodoulou about re-architecting for simplicity https://lnkd.in/eGh_4fU5

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🔄 I think continuous learning is super important, so each week, I try to read/listen/watch broadly across engineering, AI, leadership, and strategy. A few highlights from this week’ 👇 Developer Productivity in 2025: More AI, but Mixed Results https://lnkd.in/grKgzpF7 AI tools are widespread, but productivity gains vary widely depending on how teams adopt and integrate them. Svelte 5 and the Future of Frameworks https://lnkd.in/gX63_dZb A conversation with Rich Harris on why simpler, more intentional frameworks may matter more than chasing complexity. How I Use LLMs as a Staff Engineer https://lnkd.in/gc935q2x A pragmatic take on using LLMs as a daily engineering aid—learning, exploring, and unblocking—rather than replacing core thinking. Start Finishing, Stop Starting: Why I Prefer Walk-the-Board Stand-Ups https://lnkd.in/gtX4_Pk4 Shifting stand-ups from status reporting to flow management to improve throughput and reduce WIP. Claude Code and What Comes Next https://lnkd.in/gS7ppMFP A look at autonomous coding agents and what they signal about the future of software development workflows. AWS in 2026: The Year of Proving They Still Know How to Operate https://lnkd.in/gjfPjGeR A critical view of how AWS must execute operationally as cloud, AI, and competition intensify. What Is the PESTEL Model for Business Analysis? https://lnkd.in/gGCAJ3FU A refresher on a strategic framework for understanding macro-level forces shaping business and technology decisions. From Chore to Habit https://lnkd.in/gKVYfrJJ How small, repeatable actions turn good intentions into sustainable habits. The Pulse #158: New Ways of Coding with AI Tools https://lnkd.in/gaUVcuiJ Emerging patterns in how engineers are actually coding day-to-day with AI assistants. How Engineering Leaders Can Better Leverage AI in 2026 https://lnkd.in/g3wSeKqV Practical guidance for leaders on adopting AI intentionally—across teams, tooling, and culture. When AI Writes Almost All Code, What Happens to Software Engineering? https://lnkd.in/gGdj7aM7 A thought experiment on how roles, skills, and value shift if code generation becomes nearly free. Building Internal Agents https://lnkd.in/gD6usBif A practical series on designing and deploying internal AI agents to automate real work inside organizations.

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I love #TDD. Not just because it’s clean. Because it’s honest too. It forces you to face the #design before you think about the #implementation. It exposes #coupling. It shows you what’s really needed - not what you think might be. TDD isn’t about 100% #test coverage. It’s about thinking clearly. It’s about moving with #confidence, not just fast. It doesn’t work for everything - but when it fits, it changes how you build. You write code that’s easier to understand, easier to maintain, easier to trust, and easier to change. Tests don’t slow you down. Unclear thinking does.

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Been thinking about how non-technical founders often treat engineering like a black box that should predictably output features. But the most telling signal isn't output—it's how your team handles uncertainty. Watch how they respond when requirements shift. Do they adapt elegantly or crumble? Do they push back thoughtfully or just nod? The best teams I've seen don't just ship code—they ship understanding. Everything else is theater.

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You are going to do a System Design interview ? You want to practice ? A fun prompt that I tested with Claude 4 Sonnet and is really interesting: # System Design Interview Simulation Act as a senior system design interviewer at **[COMPANY_NAME]**, a leading **[PRODUCT_TYPE]** company. You are conducting a 45-minute system design interview with a candidate for a **[POSITION_LEVEL]** position. ## Your Role: - Present realistic system design challenges relevant to **[DOMAIN/INDUSTRY]** - Ask clarifying questions about requirements, scale, and constraints - Probe deeper into technical decisions and trade-offs - Provide constructive feedback on architectural choices - Guide the conversation like a real interviewer would - Challenge assumptions and ask follow-up questions ## Interview Structure: 1. Present 3 system design topics (I'll choose one) 2. Start with requirement gathering (5-10 minutes) 3. High-level architecture design (15-20 minutes) 4. Deep dive into specific components (10-15 minutes) 5. Discuss scalability, reliability, and trade-offs (5-10 minutes) ## Your Interviewing Style: - Ask "Why did you choose X over Y?" frequently - Request specific numbers for scale estimates - Challenge me on potential bottlenecks - Ask about failure scenarios and monitoring - Probe about data consistency and security (especially important for **[DOMAIN_SPECIFIC_CONCERNS]**) - Give real-time feedback on my approach ## Possible Topics: Please present 3 system design challenges relevant to **[COMPANY_NAME]**'s domain, such as: - **[TOPIC_1]** - **[TOPIC_2]** - **[TOPIC_3]** ## Company Context: - **Scale:** **[COMPANY_SCALE]** (e.g., "10,000+ customers with varying sizes from 10 to 50,000 employees") - **Key Challenges:** **[DOMAIN_CHALLENGES]** (e.g., "compliance, data privacy, multi-tenant architecture") - **Integrations:** **[INTEGRATION_COMPLEXITY]** (e.g., "500+ third-party apps", "multiple payment providers") ## Evaluation Criteria: Rate my performance on: - Problem decomposition and requirement gathering - System architecture and component design - Scalability and performance considerations - Data modeling and storage decisions - **[DOMAIN_SPECIFIC_CRITERIA]** (e.g., "Security and compliance awareness") - Communication and thought process ## Example Interaction Flow: Interviewer: "Design a system for [SPECIFIC_FEATURE] at [COMPANY_NAME]. We have [SCALE_CONTEXT]." Candidate: "Let me start by understanding the requirements..." Interviewer: "Good approach. What specific questions do you have about [DOMAIN_PROCESS]?" Candidate: "What are the main steps in [PROCESS]? What integrations are needed?" Interviewer: "[TYPICAL_FLOW_DESCRIPTION]. We integrate with [INTEGRATION_DETAILS]. Now, what's your approach to handle this complexity?"

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PSA: If you remove or deprecate a feature, give your engineering team the time to fully remove that feature from your codebase. Your future self (and your future team) will probably not remember to thank you, but their higher velocity might be a thank you enough. Every line of code in production comes with a cost, however hidden it may be in the present moment. End of message.

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Startups don’t die from lack of horsepower. They die because their camshaft is cut wrong. Your SDLC is the Camshaft of Your Delivery Team The delivery team is the engine that drives the company forward. But horsepower alone doesn’t win. You need torque. And torque comes from the camshaft. In software, the SDLC is the camshaft. If it’s not cut clean, cylinders misfire. Teams fall out of sync. Energy gets wasted. You can add more horsepower—more headcount— but the engine still sputters. When the SDLC is well defined, timing locks in. Every valve opens and closes in rhythm. The torque curve comes alive. That’s when the heavy lifting of delivery feels smooth. That’s when effort translates into acceleration. Horsepower—your headcount—matters most at the high end. That’s when you’re already running clean and scaling fast. But torque is what gets you off the line. Torque is what pulls you through the mud. Torque is what startups live or die on. That’s why the camshaft—the SDLC—has to come first. Without it, horsepower is wasted. With it, the engine runs smooth. Torque hits. Momentum builds. Every ounce of power gets translated into forward motion. If you want your team to move faster, don’t hire more horsepower. Cut the camshaft right. Every company wants more speed. Few start by cutting the camshaft right. 👉 #Leadership #Startups #ProductDevelopment #EngineeringLeadership #BusinessGrowth

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What does it take to build a successful engineering organization? 🚀 It’s not just strategy decks or scaling headcount. It’s the messy, behind-the-scenes work—building clarity when there’s chaos, balancing autonomy with alignment, and constantly evolving your architecture and culture as you grow. I’ve spent the last few years learning (and re-learning) this. So, I wrote a 3-part blog series to share some nuggets. If you're leading (or hoping to lead) at scale, I hope there's something useful in here for you. Would love your thoughts, and even more—your own lessons 🙂 #engineering #leadership #lessonslearned

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