Troy Gould

Seeing quite a few discussions lately about Kafka/Iceberg integrations being "zero-copy" or not. I think this is largely missing the point. First, where I agree is that this integration should be "zero-effort" for users. Materializing a Kafka topic into an Iceberg table shouldn't require more than a click of a button. Queries should provide a uniform way for accessing the data in both a topic and the corresponding table. This is the stream/table duality, and it should Just Work™. Now, whether this requires to store the bytes of data once in a Kafka topic, and a second time elsewhere for table access, shouldn't really matter from a user perspective. I'd argue storing the data twice is actually a benefit, and in fact it's a pattern well established: it resembles the design of WAL and table files known from databases for decades. I don't think anyone ever complained about this structure in their RDBMS? Which makes sense, it's an implementation detail, opaque to users. But as it turns out, having log and table data separately is even more advantageous for the deconstructed database that is Kafka and Iceberg: you can have multiple readers of the same log (Kafka topic), materializing views in multiple destinations and systems optimized for specific use cases. Maybe multiple Iceberg tables with different projections (think PII), maybe an Iceberg table and a full-text index in Elasticsearch, maybe an... you catch my drift. Furthermore, the log is replayable, so you can recreate views if needed, or you can implement new use cases you didn't originally have in mind. All in all, I think "zero copy" is mostly a red herring. Sure, it can be an optimization for certain scenarios, but mostly it's a distraction from the immense value you get from combining Kafka and Iceberg when done the right (seamless) way.

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2025 hot take: I'm not sold on Test-Driven Development (TDD). After years in the industry, I’ve seen few teams fully commit to TDD and even fewer stick with it long-term. I’ve been on teams that have completed projects with TDD and ones that started with TDD but dropped it when it became more of a blocker than a boost. The biggest untold truth? TDD success stories often come down to survivorship bias. The teams that succeed with TDD would likely succeed without it, too. Why? Because the teams that have the most success with TDD are usually made of senior engineers who’ve built similar systems countless times before. Here’s how I see it: If you’ve studied for a test so well that you could write both the questions and the answers before you take it, you’re going to ace that test no matter what. In essence, TDD works best when you already know the answers. But really then TDD isn't the main or even a contributing reason for a project's success. What do you think? Is TDD overrated or am I missing something?

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Screw vibecoding. You know what LLM coding agents are *really* useful for? Reverse-engineering legacy code! "Hey can you research and explain how the flux capacitor settings are loaded, specifically where are the margadon-level overrides applied exactly?" It's a lifesaver.

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After years of building engineering teams, I've learned that the best hires often come from conversations about real projects, not theoretical problems. I love asking: 'What's a complex technical decision you made, and would you make it differently today?' This simple question reveals so much: How they think about trade-offs, how they learn from experience, and how they communicate technical concepts. You can teach someone a new framework or language, but curiosity and critical thinking are foundational. What questions do you find reveal the most about how engineering candidates operate?

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The best engineering teams don’t just write code… They create momentum that turns complexity into growth. A project that looked overwhelming at the start suddenly begins to move with clarity and speed once the right team is in place. It’s not magic. It’s alignment. Engineers who understand the business context, who take ownership, and who know how to turn challenges into solutions. That’s the kind of momentum we build at Coderio | Software Company. Teams that don’t just deliver features, but accelerate entire companies. Because in the end, great engineering isn’t about lines of code. It’s about building the velocity that turns vision into reality.

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A surprising number of managers still judge engineers by how much code they write. Let’s set the record straight to help my fellow devs out there: > Not all problems require more code. > Not all engineers solve problems by writing more code. > Code volume ignores the invisible work of building software. > In the age of AI, anyone can generate lots of useless code. Code quantity was never a good metric. Today, it’s a terrible one. Better engineering management starts with better metrics.

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Teaching being my main gig for the last 16 years, I've seen so many times how developers don't do a thing (e.g., trunk-based development) not because it's too hard, or they think it's wrong for them or their situation, but literally because it had just never occurred to them. But this shouldn't be a surprise. If I think back to the early days of my career, there was a time when I'd never heard of Version Control Systems, had never thought to write code to perform my tests for me, hadn't given a second thought to the distribution of responsibilities in my design, or to coupling and cohesion etc etc. The game-changer in all these cases was just being made aware that they were things. "You can do that?" It's something I hear often from people who've taken one of my courses. Even very experienced developers have lightbulb moments on them.

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🚀 Will AI Replace Software Engineers? 🤔 You’ve seen the headlines and the hot takes—but here’s what I’ve experienced firsthand: AI isn’t here to take our jobs, it’s here to turbocharge our workflows. Over the last few months, I’ve leaned on AI as my true co-pilot, and the results are game-changing: - Brainstorming at scale - MVPs in days, not months - Faster iterations - Automated grunt work The net effect? We’re shipping features at lightning speed at EverUptime, validating concepts with customers sooner, and learning faster. ⚡️ So, will AI replace software engineers? No. But will it elevate software engineers? Absolutely. Developers, engineers, and builders—how are you using AI in your daily workflow? Which tools have become your go-to co-pilots? Share your stories and favourite prompts below! 👇 #AI #SoftwareEngineering #MVP #Productivity #DevOps #EverUptime

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Accidentally discovered a new way to work with GitHub Copilot today: write new tests based off test coverage reports. Copilot (or I) run this command to generate a report of all the uncovered lines in the current diff: pytest --cov --cov-report=xml && \ diff-cover coverage.xml --html-report coverage_report.html && \ open coverage_report.html Then Copilot reviews the report, summarizes the uncovered situations, and proposes new test cases to add, or if the code is truly unreachable, suggests deleting it. Just another reason why you should add diff-cover (or similar tool) to your projects!

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Developers: Are you your team’s silent safety net? Every dev team has that one engineer  The one who stays late fixing broken scripts. Reviews everyone’s pull requests. Debugs someone else’s code at 9PM. The unspoken catcher of all things dropped. But here’s the harsh truth: If you’re constantly doing their job… who’s doing yours? 🚨 Helping too much can hurt your growth. You’ll miss your own deliverables, delay feature launches, and even get overlooked in performance reviews all while untangling someone else’s tech debt. 🚫 You might be propping up bad practices. If others rely on you to fix their bugs, it hides deeper problems. Sometimes, they even get rewarded for work you quietly finished. 📉 You’re unknowingly weakening the team. If delivery depends on a hidden hero, you’re building a fragile culture ,not just fragile code. ✅ A better way? • Make your contributions visible. • Advocate clean processes. • Promote code ownership and accountability. And when someone falls short .let the system respond. They’ll either level up, or step aside. 💡 In tech, silent heroics don’t scale. Clarity and accountability do. Don’t just cover , contribute with visibility. #SoftwareEngineering #TeamCulture #Leadership #DevTips #CodeOwnership #EngineeringExcellence

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I am glad folks at Cypress.io read my blog post "Pick E2E Tests To Run Using AI Summaries" and now are showing test summaries on dashboard. Next thing they will probably suggest tests to run. You can easily derive these text descriptions. https://lnkd.in/gUh2Srtm 

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Engineering Management in 2025 is 𝘊𝘩𝘢𝘯𝘨𝘪𝘯𝘨. The end of ZIRP (Zero Interest Rate Policy) and the rise of generative AI have sent ripples across the industry. Leadership assumptions that were true last year may be wrong next year. You read posts on how this impacts software engineers. But what about software engineering managers? ❓ What is changing? ❓ How can you use LLMs for your job? ❓ What should you avoid using LLMs for? ❓ What will stay the same, even a decade from now? Check out: "How to Manage in the Age of AI" April 2nd, 12 PM EDT sign-up for free: https://lnkd.in/exFsAegW

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Stop stuffing your Quarto repo with figure files like an overpacked suitcase... If you're storing generated figures in your Quarto project's main repository, you're creating a mess. Slow clones, bloated repos, unhappy teammates. Git submodules = lean main repo + organized assets + happy developers See it working in Quarto projects: 📂 Main repo: https://lnkd.in/gUtitFyW 🖼️ Figures repo: https://lnkd.in/gHViUFCH Implementation guide: https://lnkd.in/gzgf3tMg #quarto #git #submodules

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