Mark Hrynczak

I often ask leaders: “What engineering excellence practice are you passionate about but have failed to embed into culture?” The answers vary—writing UTs, maintaining solid design documentation, following agile rigour—but the underlying root causes are almost always the same. 1. Under GTM pressure, managers and leaders compromise (I am not an exception on this) on these practices. This sends a clear signal to teams: “These aren’t important enough.” The rhythm breaks, and culture erodes.  2. Stakeholders too rarely see the value—good practices are invisible until failures surface. Ironically, firefighting gets celebrated more than preventing the fire. 3. Another problem: too few believers. When leaders don’t invest in creating more champions of excellence, practices hinge on individuals. When those individuals leave, everything collapses. 4. Add to this the underinvestment in tooling—linting, automation, developer productivity platforms—and we’re left relying on people for what tools could enforce effortlessly. So,  How do we make engineering excellence practices more sustainable? 1. Choose a few critical practices—and hold to them without exception at ALL times. 2. Use incidents and customer issues to rally product and business stakeholders around the practices that would have prevented them. Incident management should be a collective responsibility (not just an engineering responsibility) 3. Focus on “cloning” believers by helping teams experience the real benefits. 4. Keep investing in developer productivity tooling—quality gates, automation, end-to-end SDLC support. Leaders must shift the culture from “just get it done” to “do it right.” That requires walking the talk, consistently. Only then can we build an engineering culture that lasts. If you are an Engineering or Senior Engineering Manager who resonates with these thoughts and would like to work with leaders like Rahul Krishnan P Rahul Aneja Prachi Sharma Shivendra Singh Gautham Ballal Ankeet Maini , Please share your resume to [email protected]. #jiohotstar #workculture  #leadership #hiring #engineeringmanager  #em 

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The other day, I was consulting with a Director of Engineering and he shared with me an overview of what his teams would be building over the next 12 months. Given my prior knowledge of the company, a few items seemed off, so I asked about the reasoning behind some of the new platform capabilities they were building. Did teams need it? Was that a business or regulatory requirement? I was simply trying to understand the "Why". He confided that several of them didn't really have a business need or requirement. As a group, his division had a vision for what a modern platform should be, and they were going to build all the necessary capabilities to achieve that vision. Also, they believe that once most of those were built, engineers would see the value and would start using them. This is a classic scenario of a platform division operating in a vacuum, leading to what is often called "Ivory Tower Platform Engineering" or the "Field of Dreams Fallacy" ("If you build it, they will come"). This approach is fundamentally flawed because it inverts the proper order of operations: - It ignores the customer: The product engineers, SREs, and product managers are the platform team's customers. Building features without understanding their real-world pain points, workflows, and constraints is like a B2C company designing a product without ever talking to a single consumer. The result is almost always a product that solves a problem no one has. - It creates waste and opportunity cost: Every engineer-hour spent building an unused "modern" capability is a direct waste of resources. More importantly, it represents a massive opportunity cost. That same engineering time could have been spent on solving, for example, a validated, high-friction problem that is currently slowing down all development teams, improving platform reliability, or reducing the toil of an existing, critical system. - It assumes rational, immediate adoption: The belief that "engineers would see the value and would start using them" is dangerously naive. Engineers are busy. They have their own deadlines and priorities. To get an engineer to adopt a new tool or platform component, the value proposition must be overwhelming. The new way must be demonstrably and significantly better than the old way, not just incrementally or theoretically better. Without solving a burning problem, adoption will be near zero. - It erodes trust: When the platform team consistently delivers features that aren't needed, they lose credibility. Development teams start to see them as a source of "fancy toys" rather than a true partner in delivering business value. This makes it harder for the platform team to get buy-in and collaboration in the future, even for genuinely good ideas. Having a clear and understood "Why?" is one of the most powerful tools in a leader's arsenal. You're not building the platform you want; you are building the platform your organization needs to win.

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Founder asked: "Can you fix our technical debt?" You have revenue? No? Then you don't have technical debt. You have a product nobody wants. Technical debt is a LUXURY problem. It means: - You have users - You have revenue   - You need to scale If you have 0 users, your problem isn't tech debt. Your problem is: - Wrong market - Wrong problem - Wrong solution - Or all three Stop hiding behind "we need to refactor." Go talk to 10 potential customers instead. Tag a founder who's been "fixing tech debt" for 6 months.

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AI is everywhere. But it's not the first wave we've seen. The Evolution We've Witnessed: 1. We started with manual testing, that’s how most of us learned the craft 2. Then automation arrived: Selenium, Cypress, Playwright, CI/CD 3. Now AI dominates every discussion, every team, every meeting Here's what's different this time: Unlike automation, which took years to settle into the industry, AI is already transforming how we work today. But here's the constant truth: Those with strong fundamentals (problem-solving, critical thinking, domain knowledge) always adapt faster.So what do we do? 📌 We keep learning. 📌 We stay curious. 📌 We explore the new. But we also hold on to what really matters. Because the people who grow the fastest and last the longest, are the ones with strong fundamentals. Ready to start your AI journey? If you’re interested in AI but aren’t sure where to begin, I’ve put together a beginner-friendly playlist on YouTube that can help you get started: https://lnkd.in/gtTBT9rr #AI #Testing #Automation #CareerGrowth #QualityEngineering #Learning #ABAutomationHub

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AI won't make your team faster on its own. Better developer experience will. I sat down with Laura Tacho, CTO at DX, to talk about what actually improves engineering team performance and what just looks like it does. We covered: 1. Why most metrics fail (and how to use them properly). 2. What developer surveys can tell you that GitHub stats never will. 3. How to track DevEx without a fancy tool, even at a 10-person startup. 4. The real impact of AI on engineering workflows (spoiler: it’s not replacing your team). The big takeaway? Developer performance isn’t about shipping faster. It’s about removing friction. Whether it’s bad tooling, unclear goals, or noisy metrics, the teams that win are the ones that make it easier to do great work.

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The shine is starting to wear off vibe coding. I know, because I’ve been there. One moment I’m in Cursor, impressed by what a large language model can produce. The next, I’m staring at the clock, realising I’ve lost hours because it couldn’t navigate the complexity of the codebase I’m working on - and I find myself back in PyCharm, questioning the value of the detour. One answer that’s emerging is "Spec-First Development". You don’t toss vague instructions at an AI and hope for magic. Instead, you craft the clearest, most rigorous specification you can - and let the agent build from that. Better still, treat specifications as code artefacts: versioned, under source control, and continually refined with compacted, curated context. For me, this isn’t a radical shift. I’ve long worked with test-driven development, where the unit test is the specification. And LLMs thrive on this. Unit tests aren’t just notes in prose; they’re executable specifications - formal, computable, and unforgiving in all the right ways. That’s the deeper lesson: when you hand a spec to an LLM, the more formalisation, the better. Natural language is fine, but structure wins. Code loves tests. Maths loves notation. Business, however, has no equivalent - no unit test for strategy documents, compliance rules, or process design. Enter ontologies and knowledge graphs. They give us a way to formalise business semantics, capturing domains in rigorous, computable detail. Paired with an LLM, they don’t just guide the generation - they also validate it. The future of agentic coding - and of LLMs in business more broadly - won’t be built on “vibes.” It will be driven by how well we can formalise intent: transforming ambiguity into something structured, testable, and executable. ⭕ Spec-kit: https://lnkd.in/dPZCgUeq ⭕ What is an Ontology: https://lnkd.in/ePS7ha8z ⭕ What is a Knowledge Graph: https://lnkd.in/e5ed_f8g

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Every bug I fixed in my early coding days wasn’t just a technical win. It was a leadership lesson in disguise. When a system crashed, the solution wasn’t panic. It was patience. Step through the process. Understand where the logic broke. Fix with intention. That’s leadership too. Teams don’t fail because they lack talent. They fail when clarity breaks, when assumptions slip through, when no one checks the “logic” of decisions. What software taught me about leadership? → Debug issues, don’t debug people. → Documentation matters. In code and in culture. → The best solutions are rarely complex. They’re clear, simple, and scalable. At iTechnoSol Inc., we approach leadership like we approach software: Clarity first. Complexity later. Because in the end, building software isn’t about machines. It’s about people and the clarity we give them to succeed. P.S.: The best leadership frameworks aren’t written in books. They’re hidden in the lessons of everyday work. #FromCodeToClarity #Leadership #TechMindset #ThoughtLeader #ChirayuDalwadi #iTechnoSol

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I once had an engineering manager say to me, “Matt, do you know what you get when you hire a new engineer?” I said, “I’m hoping we get a bunch of great new features shipped. “ “No, Matt.” He said, “You get a new engineer.” He makes a fair and accurate point that it takes time for new hires to get up to speed and contribute at the level that drives the business. The problem is that the attrition rate of new employees is high and has been getting worse every year for the past five years. That means that over 30% of the time, new employees are leaving before they truly hit their stride. And then the cycle begins again. Poor retention is a significant drag on the momentum of teams. In sales, in engineering, and everywhere else. A new report published today by Revelio Labs presents a compelling argument that new hires are leaving at a higher rate than in the past, mainly due to a lack of career opportunities. To quote the report “What aspects have reportedly worsened most for early leavers amid the decline in new employee retention? Compared to 2022, when new employee retention was last stable, career opportunity ratings have dropped the most (again controlling for business outlook ratings and seniority)” BOTTOM LINE? Companies should pay more attention to talent development, which includes providing career progression clarity and skill-based training to promote growth. CONSIDER THIS... 52%+ of employees say there is little to no transparency into their career path at their organization. (Grant Thorton, State of Work in America 2024) 54% of employees felt their employer did not provide adequate opportunities to learn skills helpful to their career. (PwC - Hopes & Fears Workforce Survey 2024) ONE MORE IMPORTANT POINT The Revelio study also showed that newer employees stay longer when their more tenured colleagues are getting promoted. This correlation suggests that a culture that emphasizes internal mobility and growth improves retention for everyone. 9 in 10 employees feel stuck, and 84% say a clear career path would boost their commitment. (Dayforce/Ceridian, Pulse of Talent)

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Second to bad management, there's one thing that kills engineering productivity and morale more than anything else. Slowness in running and deploying code. This past Friday, someone asked for a non-urgent bug fix at 4:25pm. Got our local environment up and running in seconds, instantly refreshed as I made changes, got immediate code reviews from our team channel, and deployed in minutes. This was the difference between me responding a half hour later that the fix was live in prod vs. asking them to file a JIRA ticket for my team to handle next week. Here's the limits you want to stay under: Time to launch your local env - 30 seconds Time to refresh your local env - A few seconds Time to get a trivial code review - 10 minutes Time to get a beefier code review - A few hours Time to deploy - 15 minutes (pic from the early days of my startup Scoutlit when we were just shipping)

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