How Workflow Composer turns diagrams into code in seconds

I have created something unique for Mastering Backend users. Let me explain. - Now you can build real-world backend project and run it inside MB. - You can interact with our terminal, install things, and run commands. - You can write and execute your codes while taking a course Next Steps: - We are adding a dedicated Frontend that will integrate with the API you're developing. - Adding Kap AI to help you with development - Working on the UI to make it smooth to code with. Ask me how I built this, and I will explain.

I haven’t posted in a bit because I’ve been refactoring my personal project and learned a lot. The big takeaway: plan and document before letting Claude Code or any CLI agent touch things. Vibe coding an MVP was worth it for the discovery, and now I’m excited to share where it landed. 🤌 I renamed the app (credit to my wife) to Amicai. ☁️ Amicai now runs fully on AWS Lambda with 10 microservices covering contacts, goals, and relationship insights. Full breakdown on my blog (link in comments). 🗓️The new architecture bakes context into every service so Claude stops guessing. If there’s an auth issue, it checks AUTHENTICATION(dot)md. A root CLAUDE(dot)md routes to each service’s docs. There’s an /llm-guides folder for architecture, frontend, and UX guidance. One lesson: Claude loves spinning up its own summaries, which go stale fast. Keep a single source of truth and point it back there. 📄Pro tip: do heavy upfront planning. Create FEATURE_DEVELOPMENT_ROADMAP(dot)md and have Claude split it into sprints. Put dev rules at the top. Use unchecked boxes [ ] for tasks and start sessions with “read the guide, find the next [ ] and continue; after completion, mark [x].” 🚀 What’s next: an iOS app that feels more like a chat app, with goal setting, journaling, and an Amicai agent orchestrating tools to update goals, surface insights, and log entries. This will make the task of manual data entry about relationships less burdensome and help the user to contribute knowledge more organically while the AI calls tools to write and read from tables. 😱 Last thought: many friends say this sounds “creepy” and don’t want to share personal data with big providers. Curious if this use case feels compelling to anyone here or not. If you want deeper updates please feel free to check out the blog I’m starting. Link in comments! 

Tech With Tim: Why 1M People Tried This AI Coding Tool (Full Vibe Coding Tutorial) Dive into Emergent’s vibe coding magic and learn how to spin up production-ready apps with just a prompt—no dev experience needed (though pros will dig the deeper tips). You’ll see how to craft your initial prompt, break down web app architecture, preview live updates, save GitHub checkpoints, and roll through build-and-debug phases with AI-powered tools. By the end, you’ll be comfortable using Emergent’s advanced features—fork mode, AI integrations, credit management, and deployment workflows—to actually ship real-world projects by simply talking to your code. Watch on YouTube https://lnkd.in/gqABccHG

Thinking of transitioning your app from FlutterFlow or another no-code platform into a fully AI-assisted codebase? Here's what you need to consider before making the leap.

The Importance of Streams in Node.js: Composability, Spatial Efficiency, and Gzipping. Unlock the power of Node.js streams to enhance your app's performance and efficiency. Discover practical insights that every tech leader should know. Read more at https://lnkd.in/eucgJ5mM

Just hosted my new portfolio with an integrated AI chatbot 🤖 — a modular and high-performance system built for real-time interaction and clean architecture. Features • 🧠 RAG-powered chatbot that answers questions about me and my projects using LangChain, ChatGroq (GPT OSS 120B), and FAISS • ⚡ Real-time token streaming • 🧩 Modular architecture with clear separation of API, services, and models • 🌍 Multi-language support (English, German, French) • 🔍 Token logging for backend and frontend debugging • 💻 Modern frontend built with Vue 3, Vite, Canvas animations, and CSS3 transitions 🔗 Live at https://lnkd.in/emqfhvAi

I just published a new article, 'Exploring Modifier.Node for creating custom Modifiers in Jetpack Compose'. In this article, you will learn how to create custom modifiers using the three primary APIs, Modifier.then(), Modifier.composed(), and Modifier.Node. https://lnkd.in/gZQ-ba2t

Untangling Side-Effects in Jetpack Compose I just came across an excellent article on ProAndroidDev about side-effects in Jetpack Compose, and it’s a must-read for anyone building Compose apps. At first glance, “side-effects” might sound like a programming buzzword — but in Compose, they’re a crucial concept when you go beyond pure UI rendering: A side-effect is something your UI triggers that reaches beyond just emitting composables (for example: navigation, showing snackbars, launching coroutines). Because Compose can recompose unexpectedly, managing side-effects in a controlled, lifecycle-aware way is essential to avoid bugs or unintended behavior. The article walks through the core Compose “effect APIs” you’ll want to know (and use correctly): - LaunchedEffect — launch a coroutine when a key changes when a coroutine first launched. - DisposableEffect — perform cleanup logic when composable leaves composition - rememberUpdatedState — keep references fresh in long-running effects - produceState, derivedStateOf — bridge non-Compose state or derived transformations - sideEffect : when publishing composition state on non-compose code with every recomposition(after every successful recomposition) - snapShot : when you want to convert the compose state into flow (better when using operators) What stood out to me: You should ideally keep composable functions side-effect free. When effects are needed, encapsulate them in the right effect scope so they run predictably. This resonates strongly with the Compose philosophy: your UI layer focuses on declaring state → UI, while side-effects live in structured scopes you control. If you’ve wrestled with unexpected behavior due to recomposition or lifecycle — this piece will help you make your Compose code more robust, readable, and bug-resistant. Read it here: 🔗 Side-effects in Jetpack Compose — ProAndroidDev https://lnkd.in/djsUNF_b #android #developer #kotlin #jetpack #compose #sideEffects #ProAndroidDev

To successfully build products and get maximum out of LLMs the iterative smallest possible code in one go is the best approach. Why? Ideas evolve in our head as we think more about them and immerse ourselves. Often the best move occurs in our head once we have made the move. In case of LLMs if we give it a macro task and expect it to do everything using best practices it will end up generating great looking 10k+ lines of code. Now this is a booby trap regardless of it works or not. If it works, you will end up building mode on top of something you don't fully comprehend or have bandwidth to do so and if it doesn't work the LLM would start biting its own tail resulting into "ohh yeah lemme fix it, it should work now" "wait yeah you are right, I overlooked, lemme try again" Instead of asking it to create Uber from scratch, and letting the LLM internally run a prompt like "what are technical tenants of typical uber kind of app looks like" and then generate database schema, ci cd pipeline, a web app and code for Android, ios and all the platforms out there, start with "help me define key features of an uber app". if it's your own novel product idea, then skip this step and make a list of mutually exclusive features/functions/background-jobs. Now revisit this list and remove whatever is not needed on day 1. Revisit it and remove whatever you won't need for next 3 months. Now define your design taste as well as code structuring/framework related taste. On the framework side, keep it as simple as possible. Something you can manually change if needed. You can independently chat with llm chat-bot to come up with this. I used "help me define the design language, vibe, philosophy behind apple cupertino design system " and put the long manual in the markdown file after removing redundant or something which I didn't like or didn't want. Now revisit our minimal product laundry list, and referencing that discussion with any LLM bot to come up with mutually exclusive units/components. For the web I prefer vanilla Web components defined using typescript. PS: given these units do need to communicate with each other eventually, define a pattern behind how they will communicate with each other. Just like design taste, keep it in a markdown file. Now generate all the units/components, one by one by attaching your design taste as well as inter component interaction pattern. If you want your debugging life easier, you can ask it to generate simple views to quickly review if the unit/component is working or not. if it's not working, you can try again in fresh context, fix the minor bugs manually. This way you can scale your LLM driven development while staying in control of code quality, product*development* scalability (ps: don't confuse with runtime performance or scalability, that would require a totally different thought process and more thinking at your end). Thanks Saif Khan for the motivation for this post. Wrote it omw to office. Das auto!!

🔥 You won’t believe this one! I just built a Scientific Calculator App using SpecKit CLI with the Spec-Driven Development approach — and yes… it was powered by Qwen AI 🤖 No tedious setup. No boilerplate. I simply described the app I wanted — and the CLI handled everything: ✨ React + TypeScript structure ✨ Vite configuration ✨ Scientific functions and logic ✨ Styled UI with Tailwind Within moments, I had a complete, working AI-generated calculator capable of handling everything from basic arithmetic to sin, cos, tan, log, and even constants like π and e. Seeing AI not just assist but actually build a project end-to-end is a game-changer. This feels like the next evolution in coding — where we design, and AI builds. ⚙️ 🔗 Check out the full project here: Github: https://github/Asiya-Akhtar/react-scientific-calculator