Software Testing Basics

Fundamental concepts every Frontend Engineer should know: (They don't really change, even with AI) 1️⃣ Beginner → HTML: semantic HTML and the DOM → CSS fundamentals: cascade, specificity, layout (box model, flex, grid) → JavaScript: common data types, timeouts, async/await, promises → Forms: how to build forms w and w/o JavaScript → Responsive design, CSS media queries → Networking: HTTP, caching, CDNs, HTTP/2, latency mitigation, and cache invalidation strategies 2️⃣ Intermediate → Accessibility: keyboard first, ARIA, screen readers, contrast, focus management → State management: App patterns and predictable component architecture → Component design: composition, props/inputs, pure components, separation of concerns → Media optimization: formats, responsive images, decoding, lazy loading → Browser basics: rendering engines, repaint vs reflow, layout thrashing → Rendering strategies: CSR, SSR, SSG, etc → Fonts: loading strategies, fallback stacks, and layout shift considerations → Testing strategies: unit, integration, visual, and end-to-end tests, plus testability practices → Deployment basics: static hosting, edge vs origin, cache lifetimes, invalidation recipes 3️⃣ Advanced → Build systems and module formats: bundlers, tree-shaking, code-splitting → Security basics: XSS, CSRF, CSP, secure headers and safe handling of user input → Privacy and permissions: cookies, storage, same-site rules, and browser permission prompts → Offline first-patterns: service workers, PWAs, and indexedDB basics → Internationalization: i18n / l10n approaches, pluralization, date/number formats → Maintainable CSS: utility vs component styles, BEM, CSS-in-JS tradeoffs, atomic CSS → Performance: Critical rendering path and performance optimization, including paint, layout, and compositing → Design systems: Building and maintaining design systems and UI components for the web 4️⃣ Expert → Local-first systems: Offline-parity, CRDTs, and optimistic replication → Front end tooling: Monorepo orchestration, AST transformations, and custom build pipelines → Server-driven UI: JSON-defined layouts, dynamic orchestration, and cross-platform consistency → Microfrontends: Decoupled deployments, module federation, and runtime integration → WebGL / WebGPU: Shaders, hardware acceleration, and high-performance rasterization → WASM: Polyglot execution, near-native performance, and memory management → Browser internals: V8 optimization, event loop deep-dives, and rendering engine mechanics. What else would you add? ——— ♻ Repost to help others discover 📕 Save the post so you don't miss it 💡 Follow me Yangshun Tay and my company GreatFrontEnd for more

If I Had to Start Bug Bounty Again from Zero, I’d Do THIS… I wasted months doing random things when I started Bug Bounty. No plan. No structure. Just shooting arrows in the dark. If I could start again from zero, this is exactly how I’d do it: ———— 1️⃣ Learn How Websites Work (Don’t Skip This) → If you do not know how requests work, how parameters pass data, how login forms function — you will never really understand bugs. Start with: 📌 HTTP Basics 📌 GET / POST / PUT / DELETE → what do they really do? 📌 Cookies → Sessions → Authentication → Authorization (Trust me, learning this properly saves months of confusion later.) ———— 2️⃣ Pick One Vulnerability at a Time Most people start chasing everything at once. ❌ SQLi ❌ XSS ❌ CSRF ❌ IDOR No. Start with one. Learn it fully. Hunt for it in public programs. See real examples on platforms like Hacktivity. Start with IDOR → It’s everywhere. → Easy to understand. → Found on real programs. ———— 3️⃣ Don’t Just Run Tools. Learn How to Use Them. Anyone can run Subfinder or Nuclei. But do you know what they’re really doing? → If not, learn that first. 📌 Why am I doing subdomain enumeration? 📌 What is content discovery really for? 📌 Why should I fuzz this endpoint? Tools are helpers. You’re the main player. ———— 4️⃣ Follow the Right People. Avoid Noise. The internet is filled with random advice. Follow hackers who actually hunt. Learn from disclosed reports. What I would do: → Read HackerOne / Bugcrowd disclosed reports every day. → Follow 5-10 bug bounty hunters who share real tips. Skip the clickbait, learn from the work. ———— 5️⃣ Focus More on Methodology, Not Just Tools Here’s what I mean: Bad approach → “I’ll run 10 tools, I’ll surely find bugs.” Good approach → “I’ll understand how this app works → what’s the attack surface → what’s weak here → and then use tools to speed up.” Methodology beats automation every single time. ———— 6️⃣ Participate in CTFs & Labs (Side Learning) CTFs helped me build skills in a fun way. TryHackMe → Web challenges HackTheBox → Easy boxes to start PortSwigger Labs → For web bugs (must-do) Even if you don’t win, you learn. And that matters more. ———— 7️⃣ Finally → Share What You Learn Post your progress. Share your failures. You’ll build a network. You’ll get better. People will help you. It’s the reason I’m here today → because I didn’t learn alone. ———— This is exactly how I’d start if I was at zero again. No shortcuts. No magic. Just real learning. If you’re feeling lost in bug bounty → save this. And remember → consistency beats talent. Let’s grow together. ⚡ ———— Follow me for more: → Bhavesh Pardhi Join our active community of hackers and connect with like-minded individuals passionate about cybersecurity, hacking, and learning together! https://lnkd.in/dv3DmX8d https://lnkd.in/dv3DmX8d #BugBounty #CTF

Been experimenting with AI tools in testing for a while now. Here's what I'm seeing in the real world. Where AI is genuinely helping: -Locator generation - Tools analyzing your app and suggesting stable locators. Saves hours compared to manual inspection. Example: Instead of spending 20 mins finding the perfect CSS/XPath, AI suggests 5 options in seconds with stability scores. -Test code generation - Writing boilerplate test cases from user stories or requirements. Not perfect, but gets you 70% there. You still need to review and fix, but it's faster than starting from scratch. -Analyzing test failures - AI reading stack traces and logs to pinpoint why tests failed. Instead of digging through 500 lines of logs, it tells you "API timeout on line 47" in 10 seconds. -Visual testing at scale - Catching UI changes across browsers/devices that humans might miss. -Test data generation - Creating realistic test data for different scenarios. Need 100 test users with valid emails, phone numbers, addresses? Done in seconds. Where AI is overpromised and underdelivering: "AI will write all your tests" - Nope. It writes basic happy path tests. Edge cases? Complex business logic? Still needs human brains. "No-code test automation" - Sounds great until the AI-generated test breaks and you can't debug it because you don't understand the code it wrote. Self-healing tests - Yes, it can update some selectors automatically. But it also "fixes" tests that should actually fail, hiding real bugs. 100% accurate defect prediction - AI says "this area is risky" based on code changes. Sometimes right, often wrong. Don't skip testing based on AI predictions alone. Replacing manual exploratory testing - AI follows patterns. Humans find weird unexpected bugs. Real examples from my experience: Win: Used AI to convert 50 manual test cases into automation scripts. Took 3 hours instead of 3 days. Still spent 4 hours reviewing and fixing. Fail: Tried "AI-powered" test maintenance tool. It auto-updated 30 tests after a UI change. 22 were correct. 8 were broken and I didn't notice for 2 days. Lost time debugging those false positives. Win: AI analyzing our failed test suite every morning. Started getting Slack messages like "12 tests failed due to database connection timeout, not code issues." Fail: Spent $$/month on an AI tool that "predicts which tests to run." Ran the wrong tests, missed critical bugs. My honest take: AI is a tool, not magic. Use it for: -Repetitive boring tasks (updating selectors, generating data) -First draft of test scripts (but YOU review) -Analyzing large amounts of data (logs, failures, patterns) Don't use it for: -Final decision making on test coverage -Replacing your understanding of the application -Skipping code reviews of AI-generated tests -Blindly trusting "self-healing" without verification Bottom line: AI saves me about 20-30% time on specific tasks. You still need to know testing, understand your app, and think critically. #AIInTesting

No Manual QA? Here’s What Happened to a $33B Tech Company. Not too long ago, Revolut, the fintech giant, faced widespread user backlash when a major app update broke critical functionality — including login and card management. Thousands of users were locked out, and social media exploded with complaints. What went wrong? According to internal sources and user reports, the company had increasingly prioritized automated pipelines and fast releases… while cutting back on manual exploratory testing. 🚫 No manual testers. 🚫 No human eyes reviewing complex user flows. 🚫 No one thinking outside the test scripts. The result? A polished release from a CI/CD perspective - but a broken product in users’ hands. Automation is powerful, but it doesn’t replace human intuition. Manual testers catch what automation often misses: ✅ UX issues ✅ Visual glitches ✅ Real-world usage patterns ✅ Unscripted scenarios As a QA expert, I’ve seen firsthand how the right balance of automation and manual testing protects both your brand and your users. So, the next time someone says, "We don't need manual testers", remember: It’s not about speed - it’s about quality in the hands of real people. #QA #SoftwareTesting #ManualTesting #Automation #QualityAssurance #Revolut #Bug #CI_CD #UserExperience #TechLeadership

𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: 𝗔 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝟭. 𝗠𝗮𝗻𝘂𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Manual testing involves human effort to identify bugs and ensure the software meets requirements. It includes: 𝐖𝐡𝐢𝐭𝐞 𝐁𝐨𝐱 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Focuses on the internal structure and logic of the code. 𝐁𝐥𝐚𝐜𝐤 𝐁𝐨𝐱 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Concentrates on the functionality without knowledge of the internal code. 𝐆𝐫𝐞𝐲 𝐁𝐨𝐱 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Combines both White Box and Black Box techniques, giving partial insight into the code. 𝟮. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Automation testing uses scripts and tools to execute tests efficiently, ensuring faster results for repetitive tasks. This approach complements manual testing by reducing time and effort. 𝟯. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Functional testing verifies that the application behaves as expected and satisfies functional requirements. Subtypes include: 𝐔𝐧𝐢𝐭 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Validates individual components or units of the application. 𝐔𝐬𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Ensures the application is user-friendly and intuitive. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗳𝘂𝗿𝘁𝗵𝗲𝗿 𝗲𝘅𝘁𝗲𝗻𝗱𝘀 𝘁𝗼 :- 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Tests the interaction between integrated modules. It has two methods: 𝗜𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 :- 𝐁𝐨𝐭𝐭𝐨𝐦-𝐔𝐩 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡: Starts testing with lower-level modules. 𝐓𝐨𝐩-𝐃𝐨𝐰𝐧 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡: Begins testing with higher-level modules. 𝐍𝐨𝐧-𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Tests all modules as a single unit. 𝐒𝐲𝐬𝐭𝐞𝐦 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Tests the entire system as a whole to ensure it meets specified requirements. 𝟰. 𝗡𝗼𝗻-𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 Non-functional testing evaluates the performance, reliability, scalability, and other non-functional aspects of the application. Key subtypes include: 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 :- 𝐋𝐨𝐚𝐝 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Checks the application's behavior under expected load. 𝐒𝐭𝐫𝐞𝐬𝐬 𝐓𝐞𝐬𝐭𝐢𝐧𝐠:Tests the application's stability under extreme conditions. 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Assesses the application's ability to scale up. 𝐒𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐓𝐞𝐬𝐭𝐢𝐧𝐠:Ensures consistent performance over time. 𝐂𝐨𝐦𝐩𝐚𝐭𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Verifies that the application works across various devices, platforms, or operating systems. 𝗪𝗵𝘆 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Testing ensures a bug-free, reliable, and high-performing application. By combining manual and automated approaches with functional and non-functional testing techniques, developers can deliver a robust product that meets both user expectations and business requirements. Understanding these testing types helps teams choose the right strategy to achieve software excellence!

Frontend engineering != HTML, CSS, and Javascript. That’s just for beginners. I’ve spent 10+ years as a front-end engineer (from junior to senior and then team lead engineer) The real expertise comes when you increase depth into topics, this is where the frontend gets real:  1. Playing with Binary Data - ArrayBuffer   - TypedArray (Uint8Array, Int16Array, Float32Array)   - DataView   - Blobs   - Base64 Encoding/Decoding   - Binary Parsing   2. Network - Web Sockets   - Streams (ReadableStream, WritableStream)   - WebRTC   - HTTP/2   - QUIC   - Fetch API   - Axios   - CORS   - Server-Sent Events (SSE)   - Long Polling   3. Media - Media Stream API   - Media Recorder API   - Screen Capture API   - AudioContext/Web Audio API   - Picture-in-Picture   - Subtitles and Tracks   - HTMLMediaElement   - Video and Audio Enhancements   4. DOM - DOM API   - Shadow DOM   - Intersection Observer   - Mutation Observer   - Resize Observer   - Custom Elements   - Event Delegation   - Form Validation   - Web Components   5. Data Storage - Cookies   - Session Storage   - Local Storage   - IndexedDB   - Cache API   - Service Workers   - File Reader API   - Clipboard API   6. Performance - Web Workers   - Service Workers   - Web Vitals   - Lazy Loading   - RequestAnimationFrame   - Memory Management   - Async and Defer   - Bundle Analysis   7. Graphics - Canvas API   - WebGL   - SVG Manipulation   - CSS Animations   - Web Animations API   - Image Sprites   - Dynamic Image Rendering   8. Security - Content Security Policy (CSP)   - CORS   - XSS Prevention   - Trusted Types   - Mime Sniffing   - Input Sanitization   - Permissions API   9. Build - Treeshaking   - Code Splitting   - Hot Module Replacement   - Transpilers (Babel)   - Polyfills   - Webpack Configuration   - Vite/Rollup   10. Asset - Prefetch, Preload, Preconnect   - Gzip Compression   - Brotli Compression   - Dynamic Image Loading   - Lossy and Lossless Compression   - Pixel Density Optimization   - Responsive Images

Red Flags Every QA Professional Should Watch For In the dynamic world of software development, Quality Assurance (QA) isn’t just about detecting bugs—it’s about ensuring excellence in the user experience, system performance, and product stability. But what happens when things go off track? Recognizing the red flags early can save teams from major pitfalls. Here are some critical red flags every QA expert must watch out for: 1. Undefined Requirements If user stories or business requirements are ambiguous or missing, your testing foundation is weak. This leads to misaligned expectations and inconsistent test coverage. Red Flag: “We’ll finalize the requirements later.” 2. Last-Minute QA Involvement QA must be involved from the requirement gathering phase. If testing is seen only as a final step, it usually results in rushed testing and missed defects. Red Flag: “We’ll add QA just before the release.” 3. No Time for Regression Testing Skipping regression testing or performing it under tight deadlines increases the risk of breaking existing features—a major cause of production defects. Red Flag: “Let’s skip regression for now.” 4. Lack of Test Environments An unstable or shared test environment often leads to inconsistent test results, impacting productivity and delaying defect validation. Red Flag: “The environment is being used by another team.” 5. Poor Communication Between Teams If developers, product managers, and QA work in silos, it leads to misunderstandings and incomplete testing. Red Flag: “I assumed that was already tested.” 6. Minimal or No Automation In 2025, relying solely on manual testing for repetitive tasks is a red flag. Test automation is essential for faster feedback cycles and scalable testing. Red Flag: “We don’t have time to automate this.” 7. No Defect Triage Process Without regular triage meetings, defects are ignored, poorly prioritized, or closed without resolution—damaging product quality. Red Flag: “We’ll review bugs before release—hopefully.” 8. Overreliance on Happy Path Testing If the focus is only on expected scenarios, edge cases and failure conditions are neglected, leading to critical bugs post-launch. Red Flag: “We only tested the main workflow.” Final Thoughts: A great QA professional doesn’t just find bugs—they prevent them by identifying process-level gaps early. If you’re noticing these red flags, raise your voice, realign with the team, and advocate for quality-first development. Let’s champion quality—every sprint, every release. #QualityAssurance #SoftwareTesting #QA #QATips #BugHunting #TestAutomation #AgileTesting #DevOps #ManualTesting #QALife #RedFlagsInQA #TestProcess #TechLeadership #SudhanshuYadav #QualityExpert

𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗔𝘀𝘀𝘂𝗿𝗮𝗻𝗰𝗲 (𝗤𝗔) 𝘃𝘀. 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 (𝗤𝗖): While Quality Assurance (QA) and Quality Control (QC) are often used interchangeably, they serve different purposes in the pursuit of excellence. Understanding their unique roles is essential for organizations striving to deliver superior products and services while minimizing errors and inefficiencies. 🔹 Quality Control (QC) is a reactive, output-focused process designed to identify and correct defects in finished products or services. Key activities include: • Conducting inspections and tests • Detecting and resolving inconsistencies • Ensuring deliverables align with predefined standards • Utilizing tools like statistical sampling and performance metrics QC is vital for catching issues before they reach the end-user, but its scope is limited to addressing problems after they occur. 🔹 Quality Assurance (QA), in contrast, is a proactive, process-driven approach aimed at preventing defects by embedding quality into every stage of production or service delivery. It involves: • Defining and standardizing workflows • Establishing and communicating quality benchmarks • Conducting regular process evaluations and audits • Training teams to adhere to best practices • Implementing methodologies like Six Sigma and Lean for continuous improvement • Monitoring key performance indicators (KPIs) to identify areas for enhancement • Building systems that prioritize defect prevention over correction 💡 QC fixes problems, but QA prevents them! A robust QA framework ensures that quality is woven into the fabric of an organization’s operations, reducing reliance on QC for issue resolution. Together, QA and QC form a holistic quality management system that drives customer satisfaction and operational success. Organizations that excel in both QA and QC reap significant benefits, including: ✅ Consistently delivering high-quality products and services ✅ Minimizing rework, waste, and costs tied to defects ✅ Strengthening customer trust and loyalty ✅ Boosting process efficiency and productivity ✅ Cultivating a culture of ongoing improvement 🚀 Is your organization solely focused on QC, or are you harnessing the power of QA to embed quality into your processes? Let’s discuss in the comments! 𝑫𝒊𝒔𝒄𝒍𝒂𝒊𝒎𝒆𝒓: 𝘐 𝘩𝘢𝘷𝘦 𝘮𝘢𝘥𝘦 𝘦𝘷𝘦𝘳𝘺 𝘦𝘧𝘧𝘰𝘳𝘵 𝘵𝘰 𝘦𝘯𝘴𝘶𝘳𝘦 𝘢𝘤𝘤𝘶𝘳𝘢𝘤𝘺, 𝘣𝘶𝘵 𝘮𝘪𝘴𝘵𝘢𝘬𝘦𝘴 𝘤𝘢𝘯 𝘴𝘵𝘪𝘭𝘭 𝘰𝘤𝘤𝘶𝘳. 𝘐 𝘸𝘦𝘭𝘤𝘰𝘮𝘦 𝘧𝘦𝘦𝘥𝘣𝘢𝘤𝘬 𝘵𝘰 𝘤𝘰𝘳𝘳𝘦𝘤𝘵 𝘢𝘯𝘺 𝘦𝘳𝘳𝘰𝘳𝘴. #QualityAssurance #QualityControl #QA #QC #ProcessExcellence #ContinuousImprovement #OperationalEfficiency #CustomerExperience #SixSigma #LeanManagement #QualityStandards #BusinessSuccess #DefectPrevention #QualityCulture #Audit #Testing #Inspection #QualityManagement #WorkflowOptimization

After 5+ years of making DevOps tutorials on YouTube (yeah, we started back in Oct 2019), we're trying something completely new 👇 And honestly? I have no idea if you'll love it or hate it. But we had to try. This is Rody. 13 years in QA and test automation. Senior level. And he had a realization that changed everything. I sat down with him to talk about the future of QA, the real impact of AI on testing roles, and the specific path he took to become what he calls "future-proof." Watch it here: https://lnkd.in/gAm8GeTZ 𝗕𝘂𝘁 𝗵𝗲𝗿𝗲'𝘀 𝘄𝗵𝗲𝗿𝗲 𝘄𝗲 𝗱𝗶𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁: We didn't just dump the interview online. We broke it down. Added explanations. Built in my own insights. Made it actually engaging to watch from start to finish. (Hopefully 🙂) Think of it like a documentary-style deep dive into real career transformations. You'll learn the specific skills that matter, understand how to learn them without quitting your job, and throughout the conversation, I break down what this means for you. So whether you're in QA, thinking about DevOps, transitioning from a different background, or just trying to understand where engineering careers are heading, this interview will be interesting for you. This is the first of a new career series featuring: ↳ DevOps bootcamp graduates who came from completely different backgrounds (QA, network engineering, non-tech) and how they actually made it work ↳ Industry experts like Kelsey Hightower sharing insights you won't find anywhere else ↳ Real day-to-day work perspectives from Lead DevOps Engineers, DevSecOps specialists etc From non-tech backgrounds to DevOps lead. From manual QA to platform engineering. From network engineer to DevSecOps engineer. These aren't fairy tales. These are real people who put in the work and made it happen. 𝗙𝘂𝗹𝗹 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆: This isn't some official permanent format we're committing to forever 😄 We're experimenting. Learning the tools is one thing. But understanding real career paths? That's what this series is about. So we want to show you what paths are actually possible and give you the behind-the-scenes perspective on how people really learn this stuff. The editing took us 3 weeks longer than a normal "interview dump". But I think it's worth it. 💬 Now I need your help - what should we call this series? Drop your ideas below. And please - tell me what you think 🙏 What do you want more of? What should we skip? Because ultimately, we're doing this for you. If this format helps one person make a better career decision, it's worth it. More interviews already in the pipeline. This is just the beginning. — Thanks Rody, for being so open and sharing your journey with the community 💙

According to Copia Automation, 81% of teams spend less than 4 hours/month reviewing code but 45 hours/month debugging it. In some industries, debugging spikes to 77 hours/month. 𝐖𝐡𝐲 𝐭𝐡𝐞 𝐢𝐦𝐛𝐚𝐥𝐚𝐧𝐜𝐞? Because it’s easier to rush code through the door than to stop and check for cracks. But here’s the catch: skipping reviews doesn’t save time—it costs it. Debugging isn’t just tedious; it’s expensive, delays innovation, and risks safety in industrial environments. 𝐓𝐡𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧?  Shift left. Prioritize quality with better code reviews, automation, and a DevOps mindset. The best bug is the one that never existed. Let's spend more time innovating and less time firefighting. 𝐑𝐞𝐚𝐝 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐝𝐞𝐭𝐚𝐢𝐥𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐭𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦, 𝐭𝐡𝐞 𝐢𝐦𝐩𝐚𝐜𝐭 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧:  https://lnkd.in/e9FKHU6T ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!