Filip Procházka

JetBrains just announced they're moving to a single, unified IntelliJ distribution starting with version 2025.3. No more choosing between Community and Ultimate - it's one IDE with features unlocked by subscription. Why does this matter? • Some previously paid features (Spring support, database tools) are becoming FREE • The open-source commitment remains, but with a twist • This signals bigger changes in how dev tools compete in the AI era I spent time analyzing the announcement and its implications. This isn't just about IntelliJ - it's about the future of IDEs in a world where AI assistants are changing how we write code. Full analysis here → https://lnkd.in/gBpSNWUu Curious: How many of you are using IntelliJ Community Edition today? What's your take on this change?

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You know that one senior dev on your team; let's call her Sarah. She makes everything look effortless: - Debugs issues in minutes while you're still stuck after hours - Writes clean code that just works - Explains complex concepts so clearly even non-tech people get it - Stays calm when production breaks You watch Sarah and think:  “I want to code like her.” So you try copying what you see; her debugging approach, coding style, PR structure. But it doesn't work the same for you. Here's why:  You're copying her methods, not her system. What you see: - Sarah writes clean code on first try - Quick, methodical debugging  - Clear technical communication What you DON'T see: - 2 years building her debugging framework - Her collection of refined code snippets - Daily 15-min learning ritual - Mistake log she updates after every bug - Weekly reflection sessions analyzing what worked/didn't Sarah's invisible debugging system: 1. Always reproduce the bug first 2. Check the same 5 places every time 3. Set 20-min timer, if stuck, ask for help 4. Update her "common issues" doc after each fix Her calm confidence?  It comes from having systems she trusts. When production breaks, she's not stressed, she has a proven process. You get stressed because you're improvising every time. Start building YOUR system: Week 1: Start a debug log Week 2: Create a 15-min daily learning ritual  Week 3: Build your first checklist/template Don't copy Sarah's methods. Build the system that will create YOUR methods. The senior dev others want to emulate?  That's who you become when you stop copying outputs and start building systems. What system are you building this week? 👇

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𝘞𝘩𝘺 𝘠𝘖𝘜𝘙 Automation 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬 𝘸𝘪𝘭𝘭 𝘍𝘈𝘐𝘓 𝘪𝘯 6 𝘮𝘰𝘯𝘵𝘩𝘴 (𝘢𝘯𝘥 𝘩𝘰𝘸 𝘵𝘰 𝘧𝘪𝘹 𝘪𝘵) I just audited a $50M company's automation framework. Result? 73% of their tests were failing randomly. Their CTO asked me one question: "How did we go from 10 passing tests to complete chaos?" The Brutal Truth: 85% of Selenium Projects Die the Same Death Month 1: "Our automation is amazing!" Month 6: "Why does everything break when we deploy?" Here's what kills every framework (and the fix): 1️⃣ The "Everything in One Folder" Disaster ❌ Death pattern: UI, API, utils all mixed together ✅ Fix: Dedicated packages → UI, API, POJO, services separated Reality check: Good teams onboard new devs in 2 hours, not 2 weeks. 2️⃣ The "Hardcoded Hell" Problem ❌ Death pattern: URLs, data, timeouts scattered everywhere ✅ Fix: Environment property files + externalized test data Game changer: Switch DEV→QA→PROD with one command. 3️⃣ The "No POJO = No Scale" Trap ❌ Death pattern: Raw JSON strings, manual API payloads ✅ Fix: Request/Response POJOs + schema validation Impact: API tests become 10x more maintainable. 4️⃣ The "Debug Nightmare" Issue ❌ Death pattern: "Test failed" with zero context ✅ Fix: Extent Reports + screenshots + API logs Truth: Debug time drops from 2 hours → 5 minutes The Framework That Actually Scales I've built a production-ready structure that includes: 🏗️ Proper separation of UI/API/POJO layers 🔧 External configurations for all environments 📊 Rich reporting with screenshots & metrics 🚀 CI/CD ready with Docker & Jenkins support 🎯 BDD structure that business teams understand The Bottom Line: Stop building "quick automation scripts." Start building software systems that scale. Your framework should work at test #10 AND test #1000. Want the complete folder structure? 👇 Comment "𝑭𝑹𝑨𝑴𝑬𝑾𝑶𝑹𝑲" and I'll send it to your inbox! Found this helpful? Share with someone struggling with flaky tests! 🚀 -x-x- Full Stack QA & Automation Framework Course with Clearing SDET Coding Rounds: https://lnkd.in/g7tn6Uif #japneetsachdeva

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Every Developer at a Growing E-Commerce Has Faced This Question. How do we scale order creation during a flash sale? You’ve got 4 options. Only one of them actually works. Let’s break them down. Option A: Synchronous order creation Blocks the request. DB write in the hot path. Crashes under load. Not built for scale. Option B: Asynchronous with message queue ✅ Accept the order. Drop it in a queue. Return fast. Process later. This decouples the API from the workload. It scales. It survives spikes. It gives you control. It wins because queues absorb pressure. Option C: Centralized order service One big box. One big bottleneck. Simpler code, same problems. Breaks at scale. Option D: Microservice everything Looks modern. Adds latency, coordination, and failure points. Still doesn’t buffer the spike. Wrong tool for this job. Trade-offs? (always) 1- Eventual consistency 2- You need idempotency (it is critical anyway) 3- You must monitor queue health 4- You need compensating transactions 5- Just remember, Queues don’t solve everything by themselves But those are engineering problems. A sync API crashing under load is a business problem. If you want to practice real system design problems like this every day, with streaks, leaderboards, and feedback, check out joinenginuity.com Because the best time to learn system design isn’t during a flash sale. It’s before one.

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Waiting half an hour for your tests to finish? That’s not feedback - that’s punishment 🧪 You might have been there: Every new integration test adds another minute to your overall build time. Twenty minutes turn to thirty. CI times explode. Developers stop writing integration tests and become unhappy. Flaky tests appear. Bugs slip through. Releases slow down. But what if you could slash that time, without deleting a single test? With Spring Test, that's possible. It's a hidden gem called Spring TestContext Context Caching 🏆 So, how do you know how many contexts are spinning up? More importantly, how do you figure out why they aren’t being reused? That’s why I built Spring Test Profiler ⏱️ Meet Spring Test Profiler - the free, open-source x-ray for your test suite. Integration is as simple as this three-step process: 1. Add the dependency (Maven/Gradle snippets appear on screen). 2. Run your tests. 3. Open the report at: target/spring-test-profiler/report.html. Within the report, you’ll see: • How many contexts started, and why. • Which tests are sharing configs versus starting their own. • Exact diffs: profiles, properties, or annotations blocking cache reuse. The overall goal is to help identify cache misses and align your tests to reuse contexts for faster feedback 🚤 Spring Test Profiler: Profile. Optimize. Deliver. Try it yourself: https://lnkd.in/djXVJiNE Below is a demo of a section in the report to analyze and compare two contexts 🆚

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I’ve been thinking a lot about where multiplatform development is heading. With tools like Kotlin Multiplatform becoming truly practical, the expectation to share more logic across platforms is growing fast. Lately I started exploring Rust as a multiplatform option. Rust is usually aimed at C++ developers and systems programmers, but I keep coming back to this question: What if Rust were approachable for mobile developers too? One of the big reasons Rust interests me is its mix of performance and safety. You get native speed without garbage collection or ARC, strong guarantees around memory safety, and the ability to ship the same core module to iOS, Android, desktop, backend, or even WebAssembly without rewriting it per platform. That makes it another compelling option for shared business logic or core components across platforms. Some apps already use Rust as a strong core layer. I'm exploring to see how viable it is for mobile devs to try out, even if it's for a small module rather than replacing a full-blown business layer across multiple apps. I want to see how we can make it less intimidating and more accessible for everyday app developers. I’m exploring this and will share more as I go. If there is enough interest, I’ll go deeper. I'll share my findings on https://lnkd.in/emVy_hFv

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From Developer to CEO Part 03: 5 to 10 years of experience Congratulations, you’re now a senior developer. But you’re not a senior just because you’ve reached 5 years of experience. You’re a senior because you’ve seen more problems— and you know how to prevent them. At this stage, you should be able to build software from A to Z. But your main focus must now shift to business impact. Not just how to code, but how to solve real business problems. Code shouldn’t be your challenge. Code should be your tool—to deliver value. For that, start asking the right questions: • What’s the real problem the customer is trying to solve? • Why does the client need this feature? • What’s the expected benefit for their business? • Is there a better solution? You’re not just a developer anymore. You should become a technical partner to your clients, not just someone who takes orders. Your new mindset: 👉 How can I maximize my customer’s success? If you follow these tips, you’ll level up faster. ⸻ After 20+ years in web development, I’m sharing what I wish I had known when I was younger. This post is a follow-up to Julien Rajerison’s post. Tomorrow, we’ll talk about developers with 10+ years of experience. ⸻ You’ve got this. I’m Toky RABESON. I’m a seasoned web developer.

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Flaky tests feel random. They’re not. The patterns are clear: * Timing issues (async workflows, race conditions) * Shared state (bad test isolation, order dependencies) * Environment drift (things that work locally but fail in CI) By definition, though, they’re not obvious to spot. A missing await, an unseeded random number, a fragile assertion. These problems tend to show up after the developer who wrote them has moved on to the next thing, reducing the likelihood that anyone will take time to fix them. AI is going to multiply these issues. It’s *also* going to help us catch them faster. The challenge for leaders: don’t accept flaky tests as inevitable. They’re just tech debt we’ve been too slow to pay down.

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'We only hire senior developers' Congrats! You're building a time bomb. Here's why: Senior-only teams create: • Echo chambers of outdated practices • Zero knowledge transfer • Expensive solutions to simple problems • 10 architects, no builders What you're really missing: • Fresh perspectives • Hunger to learn • Questions that challenge assumptions • Future leaders The brutal truth: • Your 'senior' requirements are fear-based • Your '10 years experience' is arbitrary • Your 'no junior devs' policy is expensive technical debt Why this matters: • Innovation needs new eyes • Growth requires teaching • Sustainability demands succession • AI is leveling the experience gap anyway How to fix it: 1. Hire for potential, not just experience 2. Create mentorship structures 3. Balance your team pyramid 4. Accept that juniors today code better than seniors did 5 years ago Remember: Today's 'risky' junior is tomorrow's 'savior'. What's your take on the senior-only trend? Drop your thoughts below 👇 *** P.S. If your entire team is senior developers, who's asking the 'stupid' questions that prevent stupid mistakes?

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MediatR is powerful. But here's the truth: 👉 Using it for a simple project is like using a chainsaw to slice bread. Technically possible, but massively overkill. If your team and your app are small, chances are you don't need pipelines, handlers, and all the extra complexity. Stick to direct calls, filters, and middlewares — you'll ship faster with less cognitive load. 💡 The real flex? Choosing the simplest architecture that solves the actual problem.

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