Mobile App Development Frameworks

Two Years Ago, 450,000 People Tried Their App Builder. Most said the same thing: “It’s cool, but I can’t ship anything real. Like most app builders, when it breaks, it’s a nightmare to fix, and you never know what hidden bug will explode weeks later.” Dhruv Amin and Marcus Lowe heard that feedback. Stanford. MIT. Ex-Google. They could have kept iterating on what worked. Instead, they burned it down and started over. Their question wasn't "how do we make this faster?" It was "what actually stops people from shipping?" The answer: bugs they couldn't explain. Auth breaking on refresh. Checkout failing silently. Database states going haywire. The kind of problems that need a real engineer to sit with the app, click through it, watch what happens. So they built an AI that does exactly that. They call it Max. Max sees what breaks. And fixes it. That's the breakthrough: ✅ Traditional AI tools generate code and hope it works.  ✅Max uses the app like a human. Logs in. Open browsers. Clicks buttons. Fills out forms. ✅ It watches what breaks. Fixes the code. Tries again.  ✅ It runs autonomously for 30+ minutes, no babysitting prompt. Sometimes 100+ steps. ✅ In beta, it solved 97% of the hardest bugs on its own. So why does this matter beyond faster debugging? → Non-technical founders can finally ship production apps without hitting walls.  → Solo builders get a dev team's output for $200/month—not $40k agency fees.  → Spin up multiple Max agents in parallel: one fixes auth, one ships features, one runs QA.  → Apps that stalled for weeks now ship in days. To me, that's both exciting and a little surreal. Because this isn't just about building faster. It's about who gets to build at all. One founder had a "teams and invites" feature stuck for weeks. Max built the UI, routes, invite tokens, emails, and end-to-end tests—while the founder worked on launch marketing. That's not a coding assistant. That's a software engineer. Today, Max has 700k+ users. $2M ARR. $100M valuation. And the thing that got them there? Listening to what people actually needed. Not prettier code. Not faster generation. Just one thing: help me ship. It's no longer "can I afford a developer?" It's "what do I want to create?" If anyone with an idea can now build anything... What gets built next? #Anything #AnythingMax #FounderStory #StartupJourney #AI #BuildInPublic #TechStartups #NoCode

AI-assisted coding isn’t just about autocomplete anymore. It’s becoming a full lifecycle - from planning to building to reviewing. Developers are no longer just writing code, they’re orchestrating systems of agents that generate, test, and refine it. The shift is from “write code faster” to “build and ship systems end-to-end.” Here’s how the generative programmer stack is evolving 👇 𝗕𝗨𝗜𝗟𝗗 - 𝗖𝗼𝗱𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 Full-Stack App Builders: Turn ideas into working applications quickly by generating frontend, backend, and integrations in one flow. CLI-Native Agents: Work directly from the terminal to generate, edit, and execute code with tight control and speed. IDE-Native Agents: Integrate inside development environments to assist with coding, debugging, and real-time suggestions. Async Cloud Coding Agents: Run tasks in the background - writing, testing, and iterating on code without blocking your workflow. 𝗣𝗟𝗔𝗡 - 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 Spec-first Tools: Start with structured specifications that define what to build before writing any code. Ask / Plan Modes: Break down problems, explore approaches, and validate logic before jumping into implementation. Design-to-Code Inputs: Convert designs or structured inputs into working code, reducing manual translation effort. 𝗥𝗘𝗩𝗜𝗘𝗪 - 𝗥𝗲𝘃𝗶𝗲𝘄, 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Code Review Agents: Automatically analyze code for issues, improvements, and best practices before deployment. Testing & Verification: Generate and run tests to ensure reliability, correctness, and stability across different scenarios. Benchmarks: Measure performance and quality using standardized evaluation frameworks. What this means: Coding is shifting from manual effort to guided execution. The developer’s role is moving toward direction, validation, and system design. The edge is no longer just writing better code. It’s knowing how to use these tools together to ship faster and more reliably. Which part of this workflow are you using AI for the most today?

𝟏𝟐 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐏𝐫𝐨𝐦𝐩𝐭𝐬 𝐭𝐨 𝐃𝐞𝐛𝐮𝐠 𝐂𝐨𝐝𝐞 𝐅𝐚𝐬𝐭𝐞𝐫 Most developers debug by trial and error. These 12 prompts turn AI into your debugging partner from fixing bugs to generating test cases. 𝟏. 𝐅𝐢𝐱 𝐭𝐡𝐞 𝐁𝐮𝐠 When: Your code is not working as expected Prompt: "Help me understand why this code is failing and explain the fix in very simple terms: [your code snippet]." 𝟐. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐄𝐫𝐫𝐨𝐫 When: You encounter an error message Prompt: "I am getting this error: [error message]. What does it mean, and how can I fix it?" 𝟑. 𝐂𝐡𝐞𝐜𝐤 𝐄𝐝𝐠𝐞 𝐂𝐚𝐬𝐞𝐬 When: You want to ensure your logic is complete Prompt: "Here is what my function should do: [description]. Can you identify edge cases or scenarios I might have missed?" 𝟒. 𝐑𝐞𝐯𝐢𝐞𝐰 𝐭𝐡𝐞 𝐂𝐨𝐝𝐞 When: You want a quality check Prompt: "Review this code for bugs, security issues, and bad practices: [your code]." 𝟓. 𝐆𝐞𝐭 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐒𝐭𝐞𝐩𝐬 When: You are stuck on a tricky issue Prompt: "I am facing this issue: [describe problem]. What step-by-step approach should I take to debug it?" 𝟔. 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐞 𝐀𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧𝐬 When: You suspect incorrect logic Prompt: "I think the issue is in [part of code] because I assumed [X]. What assumptions might be wrong?" 𝟕. 𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐂𝐨𝐝𝐞 When: You do not fully understand the code Prompt: "Explain what this code does step by step in simple terms: [paste code]." 𝟖. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐓𝐞𝐬𝐭 𝐂𝐚𝐬𝐞𝐬 When: You want to test thoroughly Prompt: "Create test cases, including edge cases, for this code or feature: [description or code]." 𝟗. 𝐈𝐬𝐨𝐥𝐚𝐭𝐞 𝐭𝐡𝐞 𝐈𝐬𝐬𝐮𝐞 When: You do not know where the bug is Prompt: "Help me isolate the exact part of the code causing this issue and suggest how to verify it." 𝟏𝟎. 𝐂𝐨𝐦𝐩𝐚𝐫𝐞 𝐄𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐯𝐬 𝐀𝐜𝐭𝐮𝐚𝐥 When: Output does not match expectations Prompt: "Here is what I expected: [expected]. Here is what I got: [actual]. Where could things be going wrong?" 𝟏𝟏. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐭𝐡𝐞 𝐅𝐢𝐱 When: You have a working solution but want improvements Prompt: "This solution works, but can you suggest a cleaner, more efficient, or more scalable version?" 𝟏𝟐. 𝐀𝐝𝐝 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 When: You need better visibility into execution Prompt: "Where should I add logs or breakpoints in this code to better understand what's happening?" Debugging is not about fixing bugs faster. It is about understanding the problem, validating assumptions, testing thoroughly, and optimizing the solution. 𝐖𝐡𝐢𝐜𝐡 𝐩𝐫𝐨𝐦𝐩𝐭 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐮𝐬𝐢𝐧𝐠 𝐭𝐨𝐝𝐚𝐲? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #GenAI #AgenticAI #AIAgents

Mastering Real-World App Performance: Our Strategy at Space-O Technologies In the dynamic world of mobile app development, testing and monitoring app performance under real-world conditions is crucial. At Space-O Technologies, we’ve developed a robust approach that ensures our apps not only meet but exceed performance expectations. Here’s how we do it, backed by real data and results. 📊📱 1. Real-User Monitoring (RUM): Our Tactic: We use RUM to gather insights on how our apps perform in real user environments. This has led to a 30% improvement in identifying and resolving user-specific issues. Benefit: By understanding actual user interactions, we've increased user satisfaction rates by 20%. 2. Load Testing in Realistic Conditions: Strategy: We simulate various user conditions, from low network connectivity to high traffic, to ensure our apps can handle real-world stresses. This approach has reduced app downtime by 40%. Outcome: As a result, we've seen a 25% increase in user retention due to improved app reliability. 3. Beta Testing with a Diverse User Base: Method: Our beta testing involves users from various demographics and tech-savviness. This diverse feedback led to a 35% increase in the app’s usability across different user groups. Impact: Enhanced user experience has led to a 15% increase in positive app reviews and ratings. 4. Performance Analytics Tools: Application: We employ advanced analytics tools to continuously monitor app performance metrics. This has helped us in optimizing app features, resulting in a 20% increase in app speed and responsiveness. Advantage: Improved performance metrics have directly contributed to a 30% growth in active daily users. 5. AI-Powered Incident Detection: Innovation: Using AI for incident detection and prediction has been a game-changer, reducing our issue resolution time by 50%. Result: Faster issue resolution has led to a 60% reduction in user complaints related to performance. 6. Regular Updates Based on Performance Data: Practice: We roll out updates based on concrete performance data, which has led to a 40% improvement in feature adoption and efficiency. Return on Investment: This strategic update process has enhanced overall app engagement by 25%. 🔍 Ensuring Peak Performance in the Real World At Space-O Technologies, we’re committed to delivering apps that perform flawlessly in the real world. Our methods are tried and tested, ensuring that our clients’ apps thrive under any condition. If you’re striving for excellence in app performance, let’s connect and share insights! https://lnkd.in/df_Pj6Ps Jasmine Patel , Bhaval Patel, Ankit Shah , Vijayant Das, Priyanka Wadhwani , Amit Patoliya , Yuvrajsinh Vaghela , Asha Kumar - SAFe Agilist #AppPerformance #RealWorldTesting #MobileAppDevelopment #TechInnovation #mobileappdevelopment #mobileapp #mobileappdesign

Day 6: 🔧 Top 10 Testing Tools Every QA Engineer Should Know in 2025 QA is no longer just about catching bugs — it's a critical enabler of fast, secure, and scalable software delivery. As projects grow more complex and timelines shrink, the right tools can make or break your testing strategy. 🛠️ Top 10 Testing Tools Shaping QA in 2025 Selenium The industry standard for web automation testing. Selenium supports multiple languages and browsers, making it a top choice for regression and functional testing. Postman A powerful platform for API development and testing, offering automation via scripts, mock servers, and robust integrations. A go-to for backend and service validation. Apache JMeter A performance testing tool is widely used to simulate heavy loads on servers and measure application behavior under stress. Essential for web and API performance testing. TestNG A versatile testing framework inspired by JUnit but more powerful—great for complex testing suites, parallel test execution, and data-driven testing, especially with Selenium. Appium The leading open-source tool for mobile automation testing. Supports native, hybrid, and mobile web apps on both Android and iOS using a single codebase. Cypress A modern end-to-end testing framework for JavaScript applications. Loved for its speed, debuggability, and real-time reloading—ideal for frontend UI tests. Playwright Developed by Microsoft, this fast-rising tool enables reliable cross-browser testing across Chromium, Firefox, and WebKit with headless support and full automation features. Burp Suite The gold standard for web security testing. Ideal for penetration testing, detecting vulnerabilities like XSS, CSRF, and more, vital in DevSecOps pipelines. TestRail A comprehensive test case management platform that helps teams organize, track, and manage testing efforts with integrations to tools like Jira and CI/CD pipelines. QTest by Tricentis Enterprise-grade test management with support for Agile workflows, real-time reporting, and seamless integration with automation frameworks and DevOps tools. These ten tools are among the most widely used, but countless others are chosen by teams worldwide based on their unique project needs, tech stacks, and testing goals. ✅ Real-World Scenario: How These Tools Work Together Imagine testing a banking app: Selenium for UI automation Postman to validate transaction APIs JMeter to simulate 1,000+ users Burp Suite to ensure data security By combining tools across layers, you ensure full test coverage, from functionality to performance and security. 💡Final Thought Tool selection isn’t about popularity, it’s about what fits your architecture, timelines, and team skills. Mastering these tools empowers you to deliver faster, test smarter, and build trust. Let tools amplify your impact, not define your limits. #SoftwareTesting #QATools #AutomationTesting #DevOps #Selenium #Postman #Testing2025 #DevSecOps #TestManagement #QAEngineering #Day6

50 Real-World Android Debugging ⚠️ ANRs (App Not Responding) 1. ANR = Main thread blocked for 5+ sec 2. Common cause = disk/network/db on main thread 3. Use StrictMode to detect main thread abuse 5. onClick → delay = bad UX, risk of ANR ⚠️ Crashes 6. Always read the last 5 lines of the crash log 7. NullPointerException = you trusted something that didn’t exist 9. Fatal Exception = unhandled or unrecoverable 10. Use try-catch only around risky code, not everything ⚠️ Lifecycle Bugs 11. Crash during rotation? You're leaking context or holding old views 13. Accessing Views after onDestroyView() = crash 14. Use viewLifecycleOwner in Fragment observers 15. Observe LiveData in onViewCreated(), not onCreate() ⚠️ Memory Leaks 16. Leak = long-lived reference to dead object (e.g., Activity) 17. Holding reference to Context = silent leak 18. Listeners not unregistered = strong ref leak 19. Anonymous inner classes hold outer class = leak 20. Use LeakCanary regularly, not just once ⚠️ Performance 21. App slow? First step = check Logcat + Systrace 22. Avoid deep view hierarchies — layout inflation is expensive 23. Watch for overdraw using Dev Options 24. Large bitmaps in ImageView = OOM crash 25. Use RecyclerView.setHasFixedSize(true) if dimensions won’t change ⚠️ Tools 26. Logcat = your best friend in war 27. Use adb shell dumpsys meminfo to check memory use 28. Profile with Android Studio Profiler before blaming Compose 29. Network slow? Inspect with Charles / OkHttp logging 30. Use adb shell am stack to debug backstack weirdness ⚠️ Coroutine / Flow Bugs 31. Not cancelling coroutines = memory leak 32. Crash in Flow? You probably missed catch 33. Wrong dispatcher = UI freeze or slow response 34. Missing debounce = too many requests fired 35. collectLatest cancels previous collector — use wisely ⚠️ UI Debugging 36. Invisible View? Check visibility, layout bounds, and constraints 37. ConstraintLayout bug? Turn on layout bounds 39. Text not showing? Font size overflow or invisible text color 40. Scroll issue? Wrap in NestedScrollView or debug scroll flags ⚠️ Testing & Release 41. Crash only in release? Probably Proguard/R8 42. Enable minifyEnabled true only after setting Proguard rules 43. Always test on at least 2 real devices 44. Debuggable builds hide some crashes 45. ANRs may not appear unless Play Console shows it ⚠️ Production Bugs 46. User says crash but no logs? Integrate Crashlytics 47. App crash at startup? Could be Application class issue 49. App not installing? Corrupted APK or conflicting signature 50. Permissions crash? Happens post-Android 6.0 — always check before use Bonus Flashcard #51 “Most bugs aren’t from bad code. They’re from bad assumptions.” Final Interview Hook “Say this: ‘Before touching the code, I always debug the flow and read the logs backward.’ That one sentence makes you sound senior." Let's Connect Imthiyas Alam

This how a Lead Test Automation Engineer Designs the Test Framework. No matter you use Selenium WebDriver or Playwright, This strategy can be used with it! Most automation frameworks I've seen (including my earlier ones) make the same mistakes: - Auth tokens regenerated before every API test - WebDriver instances causing conflicts in parallel runs - Failed tests with zero context for debugging - Test data hardcoded everywhere Here's the setup that solved all of this: - @BeforeSuite — Generate auth token once, save to file Why hit the login API 100 times when you can do it once? One token, shared across all API tests. - @BeforeClass — Initialize WebDriver using ThreadLocal Singleton Each parallel thread gets its own isolated browser. No race conditions. No tests stepping on each other. - @AfterMethod — Capture failure evidence automatically If a test fails: screenshot + page source + test name → straight into the report. Debugging at 3 AM becomes 10x easier. - @AfterClass — Clean driver shutdown Quit the driver, but check for null first. Prevents unnecessary exceptions cluttering your logs. API Tests — Read token from file No redundant authentication. Fast execution. Clean separation. UI Tests — Read config file for credentials & URLs One config file controls all environments. Change from QA to Staging? Update one property, not 50 test files. The result? → API and UI tests in the same suite → Parallel execution without conflicts → Rich failure reports with full context → Environment changes in seconds This isn't cutting-edge tech. It's just proper test engineering. But it took me years of painful debugging sessions to get here. What's the one framework decision you wish you'd made earlier? -x-x- To learn and implement similar strategies and concept, Explore my Full Stack QA Automation Engineer Course for 2026: https://lnkd.in/gcFkyxaK #japneetsachdeva

I was today years old when I discovered the Testing feature in Vscode. That’s what I’ve been missing for years! A good code is a tested code. Sometimes, an error occurs during tests, and it becomes quite hard to understand the source of the error if the exception message is not clear. That’s particularly my case when I develop API with exception handling. I found a way to use the debugger by configuring the launch.json file to handle debugging with pytest. It worked, but the process was still painful. Until I discover that VSCode already implement a testing feature amongst its basic features… 🤦 “That was what this lab bottle was for…” Nobody talks about it, yet it is extremely powerful. So let me do it for you: 🧪 Automatic discovery using Pytest or Unittest Indicate the tests/ folder and let VSCode do the rest. It gives you an overview of all single tests and run them. In one look, you know which tests are failing. 🧪 Run tests individually With my previous setup, I had to run the CLI command to run all tests, even if only one was failing. Lot of time and energy wasted. Now, one click = one test. Just efficient. 🧪 Debugger integrated with Testing Probably the best feature of all! Not only can I run all of a single test, the VSCode debugger also works straight out of the box. It means adding break points and checking the content of data objects. Incredibly useful! Never too late to discover basic features! Have you any other tips I’m missing?

Life is Short – Use Testing Tools Smartly! In QA, tools are not just add-ons — they are our backbone for bug tracking, test planning, automation, CI/CD, and reporting. Here’s a complete toolbox every QA Engineer / Tester should know 👇 🔴 Bug & Defect Tracking Jira – Usage: Log & track bugs. Example: User sends money → deducted but not credited → tester logs in Jira for fix. Bugzilla / Mantis / YouTrack / Monday / ClickUp / Asana / Redmine / Trac – Usage: Report issues to devs. Example: “App crashes on Pay Bill” recorded for dev to resolve. 🟠 Test Planning & Management TestRail / Zephyr / Xray / QMetry / TestCollab – Usage: Write, organize & run test cases. Example: Tester creates case: “Add product → Apply coupon → Checkout → Verify discount.” 🟡 Automation Testing Selenium – Usage: Automate browser actions. Example: Auto-check login & balance 100 times instead of manual. Appium – Usage: Automate mobile apps. Example: Auto-test “Send money → Get SMS confirmation.” Playwright / Cypress / Katalon – Usage: Fast end-to-end testing. Example: Test “Signup with OTP” flow works on all browsers. 🟢 Automation Frameworks TestNG / JUnit / Pytest / Mocha / Gauge – Usage: Organize automation code. Example: Run regression suite for “Loan EMI calculation.” Cucumber – Usage: Write tests in plain English (BDD). Example: “Given user logs in → When they transfer money → Then receipt is shown.” 🔵 CI/CD (Continuous Testing) Jenkins / GitLab / Bamboo / CircleCI / TeamCity / CodePipeline – Usage: Run tests automatically after updates. Example: After new release, Jenkins runs smoke tests: login, balance check, transfer. 🟣 Test Execution & Reporting BrowserStack / LambdaTest / HeadSpin – Usage: Test across devices & locations. Example: Tester checks payment page on iPhone + Samsung. Allure / Extent Reports – Usage: Generate reports for stakeholders. Example: Share dashboard showing passed/failed test cases. ✅ Key Takeaway: The right tool at the right stage saves time, effort, and cost — while ensuring quality at speed. 🔗 Which of these tools do you use daily in your QA journey? Let’s share best practices! #QA #SoftwareTesting #Automation #BugTracking #TestManagement #CI/CD #QualityAssurance

I just finished documenting two frameworks that solve the biggest pain point with agentic workflows: How to actually deploy them so other services can use them. Giving both away for free. The problem: Local workflows are great until you need to trigger them remotely, run on a schedule, or let other services call them. Most deployment guides assume you're a DevOps expert (I'm not). So I built two frameworks: 1/ Modal Cloud Execution Deploys your workflows to Modal with a single prompt. Webhooks respond in 2-3 seconds, auto-scale, and cost almost nothing (I've sent hundreds of requests for 1 cent). 2/ Local Server Execution Runs on your computer, exposes public URLs via Cloudflare. Perfect for development without cloud costs. Both include: • Complete setup documentation • Real examples (lead scraping, proposals, hiring systems) • API integration patterns for any service • Troubleshooting for actual errors I hit Modal handles cold starts in seconds instead of minutes. Local framework lets you iterate faster while staying remotely accessible. Not claiming these are perfect—there are probably edge cases I haven't hit yet. But I figured sharing them might save some people a few dozen hours of trial and error. To get both frameworks: Comment "FRAMEWORKS" below and I'll DM you the links. If you find bugs or edge cases I missed, let me know—still learning this stuff too.