How to Build MVPs with AI: 7 Essential Rules

🚀 How I Use AI in My Daily Work I’ve been experimenting with AI in different areas of software development and beyond. Here are a few lessons and practices I follow that make AI a powerful teammate. 1️⃣ Build step by step, Not All at Once A common mistake is asking an AI to code an entire project in one go. This often leads to complex, error-prone, and hard-to-manage code. Instead, I break down projects into small, manageable components. I start with the architectural skeleton, then build out features one by one, checking for errors for each step and update accordinly before moving to the next step. This iterative process, guided by specific prompts, ensures a robust and clean codebase. 2️⃣ Demand Constructive Answers, Not Just "Yes" reply When faced with a problem or a choice, I don't let the AI simply agree with me. I prompt for a "constructive answer" or "critical analysis" to get a more reasoned response. This helps me explore alternative solutions, consider edge cases, and think more deeply about the problem at hand. 3️⃣ Control complexity: Prompt for Exactly What You Need AI often gives lengthy, over-engineered solutions. I make sure my prompts clearly state what exactly I need to avoid unnecessary complexity. For example, instead of "write a function to get user data," I'd write, "write a function to fetch JSON data from api.example.com/users/{id} and handle potential connection errors." This specificity ensures the output is clean, minimal, and directly addresses the task, saving me time on refactoring AI Code. 4️⃣ Let AI Handle the Boring Stuff For complex logic, I prefer to stay in control and write code myself. I use AI for repetitive/boring parts (boilerplate, refactoring my existing code to improve readability and performance, tests). It frees me up to focus on creative problem-solving and high-level architecture. 5️⃣ Great AI use cases in dev work => Prototyping design => Testing => Docker & CI/CD scripts => Generation of commit message 6️⃣ AI for Documentation and Learning => Fast generating Document writing, budheting, planning and generating presentation => desinging different types of diagrams => Technology suggestions for project requirements => Logo design, image generator, image editing => Learning new technologies 7️⃣. Personal Projects and Fun Finally, I use AI on personal projects and hobby experiments—things that I've always wanted to build but never had the time. It also helps me to research for Writing social content. ✨ For me, AI is not a replacement—it’s a collaborator that speeds up repetitive tasks, enhances creativity, and helps me explore new ideas faster. How do you utilize AI in your workflow? Which AI products you Love in your work?

Stop treating AI like magic. Here's how to use it properly as a front-end developer: �� AI amplifies you, it doesn't replace you - You still own the quality and client trust. 📋 Plan first, code second - Let AI ask YOU questions to clarify requirements before coding. �� Create coding guidelines - Use files like AGENTS.md to get consistent output instead of cleaning up messy code later. 🏗️ Build file structure manually - This forces planning mode and keeps you in control. The key? You're still the developer. AI is just a powerful ally that works best with clear direction. I wrote a detailed guide on this approach with real examples. Full article: https://lnkd.in/dTFfsy96 #FrontendDevelopment #AI #WebDevelopment #Coding

🚨 THE AI TRICK THAT WILL MAKE YOU FASTER 🚨 Everyone's talking about AI replacing developers. But the real game-changer isn't replacement — it's delegation. Knowing what to hand off to AI and what to keep for yourself is the new superpower. What is effective AI delegation? 🌐 Effective AI delegation in a frontend context is the strategic process of offloading specific, repetitive, or data-intensive tasks to an AI tool while reserving high-value, creative, and critical-thinking tasks for human intelligence. It's about treating AI not as a magic black box, but as a junior dev with incredible speed and no common sense. 📌 Pro Tips You Can Use Today  ☑️ Repetition: If you're doing a task more than once, consider if you can delegate it to AI. Think boilerplate code, documentation, or static data mock-ups. ☑️ Abstraction: The more abstract and open-ended a problem is, the more it requires your unique human skills. AI is great at solving the "how," but you're the one who needs to define the "what" and the "why." ☑️ Risk: The higher the risk of a mistake, the more you need to keep a tight human loop on it. Use AI to get a first draft, but never commit the code without a manual review. ☑️ Creativity: AI can generate a thousand variations of a UI, but it can't choose the one that aligns with your brand's emotional tone or solves a subtle user experience problem. The creative leap is still yours. 🌐 Use Cases: What to Delegate & What to Own Delegate to AI 💡 ☑️ Boilerplate Code: Generating a new React component with props and state hooks, creating a form with standard inputs, or setting up a file structure. ☑️ Test Cases: Writing unit tests for a specific function or generating edge-case scenarios for a UI component. ☑️ Documentation: Writing JSDoc comments for functions or creating a README for a new library. ☑️ Refactoring: Converting a class component to a functional one, or migrating from an older CSS-in-JS library to a newer one. Keep for Yourself 💡 ☑️ Architectural Decisions: Choosing a framework, designing the state management strategy, or deciding on the overall component architecture. ☑️ User Experience Flow: Mapping out a complex user journey, identifying potential friction points, and designing a delightful interaction. ☑️ Debugging a Complex Bug: AI can help you find a line of code, but the detective work of understanding why it's failing in a specific environment requires your contextual knowledge. ☑️ Code Reviews: AI can check for syntax errors, but only a human can truly understand the intent, context, and long-term implications of a change. The best developers I know aren't scared of AI. They're using it to escape the mundane and focus on the truly impactful work. What do you think? Do we rely on AI too much for the wrong things, or not enough for the right ones? Let's discuss in a comment below👇 #FrontendDevelopment #AI #TechTrends #Productivity #DeveloperLife 

Build complex AI workflows in minutes, not days, with Genkit. In this guide, a Genkit Go contributor walks you through Genkit's core strength: the “Flow” system that simplifies complex, multi-step AI workflows into simple, manageable functions: http://goo.gle/46wkacq

The cost to get an initial code example has gone down drastically thanks to AI models, but it doesn't always mean that in the beginning it will make developers "faster". What it does FOR SURE is allow the developer to spend time in OTHER areas. 1️⃣ Before $150/hr senior dev spends 8 hours reading docs of an unfamiliar and complex system that they need to integrate to. Does their best to put the pieces together in their head or write down their findings. No code generated yet. Alternatively, they spend 2 hours reading the docs to get a superficial understanding of it. Then they create a small proof of concept, and incrementally increase the difficulty to include the full requirements. That turns into 3 hours per iteration with at least 2 or 3 iterations. Total Cost: $1200-1650 Analysis: In this particular scenario, the developer needs to spend a lot more upfront time to understand what they are integrating to so that they know how to integrate to it. Risk: Still not knowing enough of the system because the documentation is scattered and not tailored to the task that they need to implement Solution: Spends more time reading the docs to reveal parts of the system that could affect the features that they are working on 2️⃣ After $150/hr senior dev gives AI model docs to look at, has conversations with the docs with answers structured in a way that they understand. Gives full requirements from the beginning and receives potential code examples tailored to those exact requirements. Spends 2 hours in prompting, 1 hour reviewing, 4-8 hours of integrating the code, iterating through problems, and understanding the 3rd party system at a deeper level. Total: $1050-1650 Analysis: In this particular scenario, the developer doesn't need to spend as much time reading the documentation initially because the AI model can cut through to the parts of the documentation that they actually care about. Risk: The developer has implemented the features requested, but they didn't spend enough time to understand the 3rd party system deeply. This introduces problems down the line that they didn't account for. They also need to be familiar with how AI models respond which could cause friction for them or even total rejection of using AI. Solution: Similar as above, spends more time asking the AI model specific questions that deepens their knowledge of the 3rd party system. This reveals parts of the system that could affect the features that they are working on. -- How much value you get out from your devs using AI models to code in this particular scenario depends on how quickly you would like to see results. If you value your devs creating a solution that has a solid foundation in a tighter timeline, then allowing them to use AI models give them the opportunity to get a deeper understanding of a system in a way that they understand. In general, the time saved is usually spent is some other high-value area.

AI is making code reviews faster (and better). Here’s how: Without AI, by the time a PR is reviewed, the engineer has often moved on. Context is cold. Iteration is slow. That's why CodeRabbit provided not just code reviews in pull requests, but in the IDE. They've just taken that a step further by releasing a CLI purpose-built for code reviews in the terminal. This shifts reviews to another place closer to where code is written (and generated). Here’s what it unlocks: • Catch issues before they spread ↳ Review diffs locally and flag bugs, logic gaps, and “AI slop” before a commit or PR. • Increased Automation ↳ It’s the first CLI that can hand off review context to your AI coding agent for automated fixes, when you choose. • Works with all CLI coding agents ↳ Seamless integration with Claud Code, Cursor CLI, Gemini, Codex, etc. • Stay in flow ↳ Code, review, commit, without leaving your terminal. Remember, the more eyes reviewing, the better. The earlier those reviews occur, the better. Best part? Code reviews in the CLI are free (rate limits apply). Try the new CLI here: https://lnkd.in/gXpqQsUM Thanks to CodeRabbit for building a great tool and partnering on this post. 💬 Are you using AI in your code reviews? ↓

"This Changes Everything" AI is not just helping with code reviews, it's fundamentally changing the developer workflow. The traditional model of waiting for a PR review often means the original engineer has moved on, and context is lost. This is where CodeRabbit's new CLI is a true game-changer. By bringing code reviews directly into the terminal, they're enabling us to: 1. Catch issues early: Spot bugs and "AI slop" before a commit is even made. 2. Stay in flow: Code, review, and commit without ever leaving the terminal. 3. Automate fixes: Hand off review context to your AI coding agent for instant, automated solutions. This isn't just about speed; it's about shifting quality left → ensuring code is clean and correct from the very beginning. Kudos to Nikki Siapno for highlighting this. This is the kind of genuine, practical innovation that every engineering team needs to be looking at right now. #AI #CodeReview #DeveloperTools #SoftwareEngineering #DevOps

This is HUGE! Nikki Siapno just dropped a fantastic breakdown of how AI is revolutionizing code reviews—making them faster AND better! 🚀 The days of cold context and slow iterations are officially numbered. Tools like CodeRabbit, now with CLI integration, are empowering developers to catch issues locally, review diffs before a PR, and automate reviews with AI agents. This isn't just an improvement; it's a paradigm shift! Imagine the speed, the quality, and the sheer productivity gains! This is exactly how AI should augment our workflows. Developers, get ready for a whole new level of efficiency!

Open-source AI just became more valuable than knowing React. And 90% of engineers haven't noticed yet. xAI dropped Grok 2.5 as open source this week, I realized something game-changing. Elon Musk just made xAI's Grok 2.5 model completely open source on Hugging Face, with Grok 3 following in about 6 months. This isn't just another model release - it's a 500GB powerhouse that was xAI's flagship in 2024. Here's what most engineers are doing: -Waiting for companies to build AI features for them -Relying solely on API calls to closed models -Missing the chance to understand how frontier AI actually works Meanwhile, the ones positioning for senior roles are: -Downloading and experimenting with open-source models like Grok 2.5 -Building custom AI solutions using accessible model weights -Learning system architecture by studying production-grade AI implementations. 📌 The reality is: The license allows commercial use with guardrails but prohibits training other foundation models meaning you can build real products, but you can't create competing AI companies. Here are 3 ways senior engineers can leverage this: -Study Production Architecture: The model requires 8 GPUs with 40GB+ memory each and uses SGLang for inference perfect for learning enterprise-scale AI deployment patterns -Build Custom AI Features: Use the model weights to create specialized tools for code review, documentation generation, or technical interviews - differentiating yourself as someone who builds, not just consumes AI -Master AI Integration: Understanding how to work with 500GB model weights and multi-GPU setups positions you for the inevitable AI infrastructure roles every company will need. The shift is happening now. While others debate whether AI will replace developers, smart engineers are learning to work with these systems at the foundational level. Ready to turn your AI knowledge into interview wins? Understanding systems like Grok 2.5 is exactly what sets you apart in technical interviews get my complete guide to crushing every round: https://lnkd.in/d64MhyMr

🚀 Gradio – Build & Deploy AI Demos in Minutes 🚀 ------------------------------------------------------------------------------------- Last week, I explored Gradio for building AI/ML demos, and I was genuinely impressed. ✨ What stood out to me was how simple, intuitive, and lightweight it is — just a few lines of code and you have a working, interactive UI. Whether it’s a basic model demo or a chatbot interface for LLMs, Gradio makes it seamless to go from idea ➝ prototype ➝ shareable app. 🔹 Why I liked it: - Tiny amount of code, but big impact 🚀 - Extremely easy to set up and run - Works across text, images, audio, video, and chat - Can be deployed easily on Hugging Face Spaces or even containerized with Docker 💡 With just a few lines of Python, you can turn your model into a shareable web app: ----------- import gradio as gr def greet(name):   return f"Hello {name}, welcome to Gradio!" demo = gr.Interface(fn=greet, inputs="text", outputs="text") demo.launch() Run this script, and you instantly have a working UI in your browser. 💬 And it gets even better -- Gradio makes building Chat UIs effortless: ------------------------------------------------ import gradio as gr def chatbot(message, history): return f"You said: {message}" demo = gr.ChatInterface(fn=chatbot) demo.launch() 👉 This creates a clean, ready-to-use chatbot interface, perfect for showcasing LLMs or custom NLP models. Exploring Gradio reminded me how powerful tools can be when they prioritize developer experience. I’m excited to see how others in the community are using it! #AI #MachineLearning #Gradio #LLM #DataScience #GenerativeAI #OpenSource #HuggingFace #Docker