Mobile App Development Frameworks

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?

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.

🚀 Stop Deploying Flutter Apps Manually (Your Time Is Too Valuable!) Shipping a Flutter update shouldn't take hours. Here's how to automate everything in 2026: ⚡ Why CI/CD Changes Everything • Save 15+ hours/week - No more manual builds & deployments • Catch bugs early - Automated testing before production • Ship faster - Deploy to stores with a single commit • Zero "works on my machine" - Consistent builds every time 🛠️ Essential Tools & Packages :- 1. GitHub Actions (Free 2000 min/month) • Native GitHub integration • Pre-built Flutter actions available 📚 https://lnkd.in/gsbUK27j 2. Fastlane (Build automation) • Cross-platform deployment • Store upload automation 📚 https://lnkd.in/ggV5crdD 3. Codemagic (Flutter-first CI/CD) • Zero config for Flutter projects • Built-in iOS code signing 📚 https://codemagic.io 4. Firebase App Distribution • Beta testing distribution • Instant tester notifications 📚 https://lnkd.in/gnqMcH3Y 🎯 Free GitHub Templates (Production-Ready) 1. Complete CI/CD Template https://lnkd.in/gdrMXs9s • Android & iOS workflows • Firebase integration included 2. Fastlane + GitHub Actions https://lnkd.in/gzEJZjmZ • Store deployment ready • Code signing examples 3. Multi-Platform Pipeline https://lnkd.in/gvtxfMFm • Branching strategies • Environment configs 4. Web Deployment Template https://lnkd.in/gENcBKkH • Firebase Hosting automation • Web-specific optimizations 🎬 Quick Start (5 Minutes Setup) 1️⃣ Create workflow file: `.github/workflows/flutter.yml` 2️⃣ Add GitHub Secrets: Keystore, Firebase tokens 3️⃣ Push to main branch - Watch automation magic happen 4️⃣ Check Actions tab - Monitor build progress 🎁 Real Results From Teams • 63% faster time to production • 40% reduction in deployment errors • Zero manual builds after initial setup • 24/7 automated testing on every commit The best time to automate was yesterday. The second best time is right now. Drop a 🚀 if you're automating your Flutter workflow in 2026! #Flutter #MobileDevelopment #CICD #DevOps #GitHubActions #Automation #AppDevelopment #FlutterDev #ContinuousIntegration #TechTips #DeveloperProductivity #SoftwareEngineering

🚀 Power Apps Canvas Apps — Performance Checklist (2025 Edition) Building a beautiful app is great. But building a fast, reliable, and scalable app — that’s what separates a good maker from a pro developer. 💪 I’ve summarized the essential optimization tips for Canvas Apps into one compact, actionable checklist — covering: 💡 App architecture & sizing ⚙️ Data delegation & caching 📲 Progressive screen loading 🎨 Formula simplicity & control management 🧠 Monitoring & telemetry Every point is designed to help you: ✅ Reduce screen load time ✅ Avoid delegation issues ✅ Improve user experience ✅ Build apps that scale This visual checklist helps teams review apps before deployment and catch performance bottlenecks early — perfect for governance frameworks and Center of Excellence (CoE) reviews. 🔗 Save it. 💬 Share it with your team. 📈 Use it before your next go-live. 👉 What’s your #1 Power Apps performance rule? #PowerApps #PowerPlatform #PowerAutomate #Dataverse #Microsoft365 #AppPerformance #LowCode #Governance #Makers #CoE #CanvasApps

Today Glide becomes a whole new beast with the beta release of ⚡ Workflows–eerily powerful automations, perfectly integrated with Glide, continuing in our tradition of elegant tools with understated power. All of our customers pair Glide with a third-party automation tool like Zapier or Make, which are great for connecting your app to a wide array of existing services, but awkward for data-intensive automations that our customers want to achieve for a few reasons: 1. Shared data and compute: previously, customers implemented the same logic in Glide & the automation tool, drastically increasing maintenance cost. Glide Workflows have direct access to the same tables and computations as your apps, so your interfaces and automations remain in lockstep. 2. Zapier and Make are optimized for processing single events, connecting tool A to tool B. Glide Workflows are designed for operations on tables and batch data; for example, it's easy to loop over all Orders, then all Items per Order, and then finally complete a summary step. Looping is absent, primitive, or convoluted in these other tools. 3. No-code computations as steps. Glide Workflows has access to Glide's set of powerful computational primitives, making it simple to run AI, call APIs, manipulate numbers and text, without using any formulas or code. Chain these computations with actions to build simple but powerful workflows. 4. One subscription. Businesses want to consolidate their vendors. Agencies want simpler billing for clients. No-code solutions are often cobbled together with many tools, but we want building in Glide to be simpler than that. Business customers get access to scheduled triggers today, and webhook, email, and integration triggers are coming soon. Looking forward to your feedback!

🚀 𝗛𝗼𝘄 𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗟𝗟𝗠𝘀 𝗶𝗻𝘁𝗼 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 𝘁𝗼 𝗠𝗮𝗸𝗲 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 & 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗔𝗹𝗺𝗼𝘀𝘁 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 Last week, I was chasing a memory leak 🤯 — and it hit me. I was stuck doing the same old backend grind: ✅ Parsing logs manually ✅ Writing repetitive unit tests ✅ Updating Swagger docs by hand Then I remembered the GPT integration we’d built for our internal tools. Within minutes, it: 🧠 Explained the root cause 🧪 Generated full test scenarios ⚡ Suggested performance optimizations And that’s when it clicked: LLMs aren’t replacing backend developers. They amplify us.   💡𝐖𝐡𝐲 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 𝐓𝐞𝐚𝐦𝐬 𝐒𝐭𝐢𝐥𝐥 𝐋𝐚𝐠 𝐁𝐞𝐡𝐢𝐧𝐝 While frontend teams are shipping AI-powered features, backend developers are buried in: 🔍 Unit testing 📝 API documentation 🐛 Log analysis 🧠 Googling "Spring Boot best practices" for the 100th time These aren’t tech challenges — they’re productivity bottlenecks. The solution? Not another framework. It’s architecting AI into your workflow.   🧠 𝐓𝐡𝐞 𝐒𝐦𝐚𝐫𝐭 𝐋𝐋𝐌 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐅𝐥𝐨𝐰: 1️⃣ Request Sanitization → Remove sensitive data before sending to LLM 2️⃣ Context Building → Include Spring Boot-specific patterns and domain knowledge 3️⃣ LLM Processing → GPT-4 / Claude / Llama does the reasoning 4️⃣ Response Validation → Enforce internal coding standards 5️⃣ Integration → Feed insights back into your dev workflow 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐈𝐦𝐩𝐚𝐜𝐭: 💥 Debugging time → 30 mins → 8 mins 💥 Test coverage → 65% → 85% (auto-generated edge cases) 💥 Documentation → Always up-to-date 💥 Developer velocity → +40% faster feature delivery Engineers stopped repeating tasks and started solving actual business problems. 🔐 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐅𝐢𝐫𝐬𝐭 Before integrating any LLM: ✅ Sanitize sensitive data (PII, tokens, configs) ✅ Isolate network calls ✅ Enable audit logging ✅ Design fallback strategy for LLM downtime Additional wins: 🔄 Provider flexibility → Switch between GPT-4, Claude, Llama seamlessly ⚡ Performance → Async, caching, circuit breakers 📊 Observability → Track usage, latency, and cost   💬 Let’s Talk What’s your biggest backend productivity pain right now? 👉 Writing unit tests? 👉 Debugging production incidents? 👉 Keeping docs up-to-date? Drop your thoughts below — I’d love to discuss AI-powered backend productivity. #SpringBoot #Java #AI #LLM #BackendDevelopment #GPT4 #Claude3 #Llama3 #Microservices #SystemDesign #WalmartGlobalTech #CodeAutomation #TestAutomation #DeveloperTools #SoftwareEngineering #EngineeringExcellence #ArtificialIntelligence

There are a lot of seriously over-engineered frameworks out there for working with coding agents. Multi-agent orchestration systems, massive spec kits, etc. I respect what people are building, but honestly most of the time you just need something dead simple that gets the job done. I've been using the same workflow for every new project I start with Claude Code for over a year now. Two parts: set up the AI layer once, then knock out features with PIV loops. The AI layer is everything your coding agent needs before it writes a single line of code. Your PRD (the full scope of what you're building, broken into phases), your global rules (conventions, tech stack, testing strategy), and on-demand context docs the agent can pull in when it's working on specific parts of the app. You create this once and every PIV loop after benefits from it. Plus you evolve it over time. Then for each phase you run a PIV loop: Plan, Implement, Validate. You take one phase from the PRD, create a focused plan with specific tasks and validation criteria, reset your context window, and point the agent at just that plan. Fresh context, one concern, clear success criteria. I built a full link-in-bio app (something like a self-hosted Linktree) in one session this way. Four phases, four PIV loops. Foundation, link management, theming, analytics. Each loop was its own conversation with a clean context. The agent never had to hold more than one phase in its head. The trick most people miss: you define how to validate BEFORE you write any code. It's basically TDD applied to the whole feature, not just functions. When the agent finishes implementing, you already know exactly how to check if it worked. I recorded the entire process from empty folder to working app. Full live build, the planning, the PIV loops in action, and the four rules I follow to keep things from going sideways: https://lnkd.in/gJ6ZuQrT

I spent some time over winter break building a NextJS template so I can ship faster and I've already shipped 2 app ideas! This is my current workflow when building with AI tools: Step 1: Claude for Planning I chat with Claude about my idea to get a full PRD (Product Requirements Document). It's like having a senior dev help me think through features, user flows, and technical decisions before I write a single line of code. Step 2: Stitch + Gemini for Design I take that PRD and send it to Stitch with Gemini to get UI designs. As a developer who's not a designer, this saves me HOURS. I get professional-looking mockups in minutes. Step 3: My NextJS Template - LaunchKit This is the game-changer. I built a starter template with auth (Supabase), payments (Stripe), database setup, and Tailwind already configured. No more spending 2 weeks on boilerplate setup every time I have an idea. Step 4: GitHub Copilot for Building I use Copilot's Agent Mode and work through my PRD step by step. I start with plan mode to map everything out, then build one feature at a time. Step 5: Multi-Model Approach I switch between Claude, Gemini, and Codex depending on what I need. Claude for complex logic, Gemini for UI components, GPT for debugging and code reviews. Step 6: Git for Version Control Not AI, but essential. I commit after every feature or fix. Keeps my project history clean and makes it easy to roll back if something breaks. The result? I'm actually finishing projects instead of abandoning them in my editor. Weekend builds are hitting different now 🚀 What AI tools are you using in your dev workflow? I'm always looking to learn from what others are doing. #Developers #AI #Vibecoding #NextJS #BuildInPublic

Coding agents aren't magic wands for developers. Most people focus on prompts—but that's just the tip of the iceberg. Curious how systematic workflows and fast iteration unlock real productivity? Read on. After months of using AI coding assistants on real, deployed projects—not just toy apps—I've learned that success comes from more than clever prompts. It's about building disciplined workflows and external memory systems. When building the Kirin dataset versioning package, I iterated three times in a week, each time learning more about the architectural boundaries and refining my approach. Effective agent usage starts with a repeatable workflow: plan first, then execute. Separate planning and execution phases, and leverage your tool's modes for each. TDD is non-negotiable—write tests first, let the agent fail, then implement and iterate. Speed-run your projects. Don't aim for perfection on the first try. Quick iterations help you discover architectural boundaries and clarify your mental model. Each attempt brings new insights. Use agents for systematic improvements: prioritize test coverage, refactoring, and documentation. Let the agent rank issues, pick what you understand, and document the rest as GitHub issues. Your issue tracker becomes your external memory. Document your standards in an AGENTS markdown file and use slash commands to streamline repetitive tasks. No task is too small—agents excel at the mundane, freeing you to focus on higher-level work. If you're exploring how to get the most out of coding agents, I'd love for you to check out my latest blog post: https://lnkd.in/dbUx8WqK Please share your own experiences or tips in the comments! What workflow or habit has made the biggest difference in your use of AI coding assistants? #ai #softwaredevelopment #codingagents #productivity #devtools