Andy Wood

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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Pull Requests shouldn’t feel like this. We’ve all opened a large PR review and thought: “Well... this is going to hurt.” Sure, small PRs are great, but sometimes you need the full picture. That’s where stacked PRs help (been using Graphite for that, a tool for managing PRs and code reviews). Stacked PRs treat your feature development as a chain of commits / PRs layered on top of each other. Think of it like a Jenga tower: each block (PR) builds on top of the previous one. pr1 → main pr2 → pr1 pr3 → pr2 Now it’s getting even better. I just got early access to Graphite Chat 👇 - I can literally ask it “what’s going on in this PR?” and it gives me the full story, codebase history, past PRs, even outside context. - I can just highlight it and ask “explain this”, instant breakdown. - Flags risks before reviewers even show up. - When someone leaves feedback, it helps me make sense of it and suggests fixes. - And the best part? When CI blows up, I can just ask “why did this break?” and actually get an answer. I’ll even be able to pop open the Graphite Editor right inside Chat to view and edit the codebase. Reviews feel lighter, more collaborative. Exactly how they should.

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Read code before you write code. Lately I’ve been reading a lot more code — and it’s been just as important as writing it. Why? Because reading trains the muscle you actually need on real teams: navigating unfamiliar codebases, understanding what the code is doing, and following flow fast. How I do it (simple): - Jump on GitHub. - Find repos using my stack (Python / FastAPI / Postgres). - Follow one request end-to-end: router → deps → crud/service → models → DB. It’s helped me refactor better, name things clearer, and spot structure I’d miss in tutorials. Reading code is practice. Do it daily. #JuniorDeveloper #LearnInPublic #BuildInPublic #Python #FastAPI #DevCommunity

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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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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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Let's talk about something that's becoming way too common and completely unacceptable. Why are companies asking QA engineers to test their real products during interviews? Not a dummy app. Not a sandbox environment. Their actual live products. Let's call it what it is free labor disguised as an interview. As a QA professional, your skill set is valuable. You've spent years learning how to detect critical issues, improve user experience, and protect product integrity. That knowledge shouldn't be exploited under the pretext of "assessment." If a company truly wants to assess your ability, they should provide a test environment, dummy app, or simulation something designed for that purpose. Not their production site. Not their paying users. And if they insist otherwise? That's your cue to walk away. Your time, your mind, and your experience have value. Don't give them away freely. It's time QA professionals start setting boundaries. The moment we begin to treat our expertise as valuable, the industry will too. To every QA engineer reading this: V You're not overreacting. You're not being "difficult." You're protecting your craft. Because professionalism is not just about what you can do it's about what you refuse to do for free. #SoftwareTesting #QualityAssurance #QACommunity #Women|nTech

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Yes, the market is tough for QAs. But our Engenious University students are still getting hired. Last month alone, 5 of our AI & LLM Testing students landed new roles. 👉 One was laid off from a manual QA role 9 months ago 👉 One switched careers from a hostile work environment 👉 One had outdated manual QA experience. So what changed? ✅ They upskilled in testing GenAI apps with tools like Promptfoo ✅ Got mentorship on job search & interview strategy ✅ Worked with a real US-based AI startup through our internship training It’s 2025. Most people are still applying outdated manual skills and strategies from 2017. That doesn’t work anymore. That’s exactly why we built the AI Career Accelerator: to help testers master the #1 in-demand skill for 2025: testing AI Apps and LLMs. 🚀 This is the last cohort of 2025, and we have just 10 seats left. Check out Oct AI Testing Cohort: https://lnkd.in/e5nRzJ5k We kick off next week. Excited to help the next wave of QA professionals become AI QA leaders. #AITesting #Promptfoo #QAjobs #TechCareers #AIQA

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