Intuitive Coding Strategies for Developers

After spending 1000+ hours coding with AI in Cursor, here's what I learned: 1️⃣ Treat AI like your forgetful genius friend, brilliant but always needing reminders of your goals. 2️⃣ Context rules everything. Regularly reset, condense, and document your sessions. Your efficiency skyrockets when context is clear. 3️⃣ Start by sharing your vision. AI can read code but not minds; clarity upfront saves countless revisions. 4️⃣ Premium models pay off. Gemini 2.5 Pro (1M tokens) or Claude 4 Sonnet are worth every penny when tackling tough problems. 5️⃣ Brief AI as you would onboard a junior dev, clearly explain architecture, constraints, and goals upfront. 6️⃣ Leverage rules files as your hidden superpower. Preset your coding patterns and workflows to start smart every time. 7️⃣ Collaborate with AI first. Discuss and validate ideas before writing any code; it dramatically reduces wasted effort. 8️⃣ Keep everything documented. Markdown-based project logs make complex tasks manageable and ensure seamless handovers. 9️⃣ Watch your context window closely. After halfway, productivity dips, stay sharp with quick resets and concise summaries. 🔟 Version-control your rules. Team-wide knowledge-sharing ensures consistent quality and rapid onboarding. If these insights help you level up, ♻️ reshare to boost someone else's AI coding skills today!

𝗜’𝘃𝗲 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝟱𝟬+ 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 — 𝗮𝗻𝗱 𝘁𝗵𝗲𝘆 𝗮𝗹𝗹 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗺𝗶𝘀𝘁𝗮𝗸𝗲. They reread notes. Rewatch tutorials. But rarely 𝘁𝗲𝘀𝘁 what they actually understand. That’s the biggest trap in learning — confusing 𝗳𝗮𝗺𝗶𝗹𝗶𝗮𝗿𝗶𝘁𝘆 with 𝗺𝗮𝘀𝘁𝗲𝗿𝘆. When I was learning my first programming language, I did the same thing — endless repetition, zero retention. Until I discovered Richard Feynman’s principle: “If you can’t explain it simply, you don’t understand it well enough.” That line changed how I learn — and how I teach. Now I use five proven methods that turn learning into a system: 𝗧𝗵𝗲 𝗙𝗲𝘆𝗻𝗺𝗮𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲 — simplify until it’s crystal clear. 𝗔𝗰𝘁𝗶𝘃𝗲 𝗥𝗲𝗰𝗮𝗹𝗹 — test yourself, don’t just reread. 𝗧𝗵𝗲 𝗟𝗲𝗶𝘁𝗻𝗲𝗿 𝗦𝘆𝘀𝘁𝗲𝗺 — repeat less, remember more. 𝗔𝗜 𝗣𝗿𝗼𝗺𝗽𝘁𝘀 — use AI to explain, quiz, and summarize. 𝗧𝗵𝗲 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 — spaced repetition, self-testing, and feedback loops. And most importantly — 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗶𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲𝗹𝘆. Real understanding doesn’t happen in your head. It happens in action. Since then, I’ve learned faster — and helped others do the same. Because smart learning isn’t about IQ. It’s about 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲. 💬 What’s one learning habit you’d change if you could start over? — Natan Mohart

I started coding again when the first ChatGPT launched in November 2022—curiosity turned into obsession. Since then, I’ve tried nearly every AI coding tool out there. Recently, I’ve become hooked on Cursor. It’s common to see two extremes: • New/junior devs often overestimate what AI can do. • Senior engineers usually distrust it entirely. Both are wrong! The sweet spot is using AI as an empowering partner, not a full dev replacement. You’re still in control—AI can help you go faster and think deeper, but only if you stay in the loop. After months of heavy use, here are some practical tips and a prompt sequence I rely on for deep code reviews and debugging in Cursor 👇 ⸻ 🔁 1. LLMs have no memory. Every chat is stateless. If you close the tab or start a new thread, you must reintroduce the code context—especially for complex systems. 📌 2. Think in steps, not monolith prompts. Work in multi-step prompts within the same chat session. Review each output before proceeding. ⚠️ 3. LLMs tend to do more than asked. Start by asking: “What are you going to do?” Then approve and ask: “Now do only that.” 💾 4. Commit before you go. Save your last working state. AI edits can be powerful—and sometimes destructive. 🧠 5. Use the right model for the job. • Lightweight stuff → Sonnet 4 • Deep analysis or complex refactoring → Opus 4 or O3 (these cost more, but they’re worth it) ⸻ 👨💻 Prompt Workflow Example: Reviewing a Complex App with Legacy Code Here’s a sequence I use inside a single Cursor chat session: ⸻ 🧩 Prompt 1 “As a senior software architect, review this app. Focus on [e.g. performance, architecture, state management, UI]. Provide an .md doc with findings, code diagrams, and flow logic.” ✅ Carefully review what’s generated. Correct or expand anything that feels off. Save it for reuse. ⸻ 🔍 Prompt 2 “Based on this understanding, identify the top 5 most critical issues in the app—explain their impact and urgency.” Ask for clarification or expansion if needed. ⸻ 💡 Prompt 3 “For issue #3, suggest 2–3 possible solutions (no code yet). For each, list pros/cons and outline what needs to change.” Choose the most viable solution. ⸻ 🛠️ Prompt 4 “Now implement the selected solution step by step. After each step, run ESLint (and if available, unit tests).” ⸻ 🔬 Pro tip: Ask Cursor to generate a full unit test suite before editing. Then validate every change via tests + linting. ⸻ This is how I use AI coding tools today: as a thought partner and execution aid, not a replacement. Would love to hear your workflows too. #CursorIDE #PromptEngineering l #DeveloperTips #CodingWithAI

Sharing Vibe Coding Manifesto which i learned, it mirrors how I actually think and build when working with tools like Cursor. It’s not about throwing code at a wall and waiting for tests to fail. It’s about co-creating with an intelligent system that respects your context, your constraints, and even your intuition. When you code in this mode what I’d call agent-augmented flow you start noticing something powerful: you’re no longer managing syntax. You’re managing intent, abstraction, and feedback. The biggest unlock? Realizing that prompts aren’t just throwaway instructions. They’re structured thought patterns. Once you start versioning them like functions, threading them by feature scope, and tracking which ones lead to successful outputs, you’ve essentially built a prompt-native architecture. In fact, I’ve started treating Composer threads like living documentation: design journals that can explain not just what we built, but why it evolved that way. Every time I get a working output or a good insight, I save the prompt chain that got me there. I replay, refine, and reuse it like a library of internal APIs. These “prompt recipes” often outperform code snippets—because they solve intent problems, not syntax ones. vibe coding still feels like coding with a gifted partner who forgets what they said two minutes ago. We need true agent collaboration—where a test agent, an optimizer, and a refactor agent can pass memory and coordinate. And honestly, where’s my AI observability dashboard? I want to see why my agent made a call, what model paths it took, and where it hesitated. I don’t think vibe coding is about replacing developers. I think it’s about expanding the way we think. When I’m deep in a Composer thread, asking “what’s the tradeoff between composability and latency here?”, I’m not just coding I’m conversing with the architecture itself. That’s a fundamentally different experience from clicking through boilerplate or memorizing API quirks. Value as a developer isn’t how fast you type it’s how clearly you think and how well you guide your AI collaborators. And to me, that’s a future worth building toward. https://lnkd.in/ex9tVJha

Best Practices for Writing Clean and Maintainable Code One of the worst headaches is trying to understand and work with poorly written code, especially when the logic isn’t clear. Writing clean, maintainable, and testable code—and adhering to design patterns and principles—is a must in today’s fast-paced development environment. Here are a few strategies to help you achieve this: 1. Choose Meaningful Names: Opt for descriptive names for your variables, functions, and classes to make your code more intuitive and accessible. 2. Maintain Consistent Naming Conventions: Stick to a uniform naming style (camelCase, snake_case, etc.) across your project for consistency and clarity. 3. Embrace Modularity: Break down complex tasks into smaller, reusable modules or functions. This makes both debugging and testing more manageable. 4. Comment and Document Wisely: Even if your code is clear, thoughtful comments and documentation can provide helpful context, especially for new team members. 5. Simplicity Over Complexity: Keep your code straightforward to enhance readability and reduce the likelihood of bugs. 6. Leverage Version Control: Utilize tools like Git to manage changes, collaborate seamlessly, and maintain a history of your code. 7. Refactor Regularly: Continuously review and refine your code to remove redundancies and improve structure without altering functionality. 8. Follow SOLID Principles & Design Patterns: Applying SOLID principles and well-established design patterns ensures your code is scalable, adaptable, and easy to extend over time. 9. Test Your Code: Write unit and integration tests to ensure reliability and make future maintenance easier. Incorporating these tips into your development routine will lead to code that’s easier to understand, collaborate on, and improve. #CleanCode #SoftwareEngineering #CodingBestPractices #CodeQuality #DevTips

Vibe coding is a skill. The more you understand how an AI works, the more value you can get. Here are some basic tips that might help. 1. Start Small Begin with one simple page or feature. Starting small helps you see results quickly and avoid breaking too many things at once. Example (Landing Page): “Create a simple landing page with a headline that says ‘Grow Your Email List’ and one ‘Join Now’ button.” 2. Break Big Ideas Into Steps Large ideas work best when split into smaller parts. Build one piece, make sure it works, then move on. Example (Blog): “Show a list of blog post titles with short descriptions.” “Now add a page where I can write and publish a new blog post.” 3. Build What You Can See First Focus on how things look before worrying about how data works. Seeing the page early makes it easier to improve layout and wording. Example (Website): “Design a homepage for a personal website with a hero section, an ‘About Me’ section, and a contact button.” 4. Be Very Specific Clear instructions lead to better results. Describe exactly what you want to see and how it should behave. Example (Waitlist): “Create a waitlist form with an email field and a ‘Join the Waitlist’ button. Show a success message after submission.” 5. Explain What Should Happen Tell the AI what should happen in normal and unusual situations. This avoids confusion later. Example (Survey): “When a user submits the survey, show a thank-you message. If no option is selected, show a gentle error message.” 6. Change One Thing at a Time Ask for only one change per prompt. This makes it easy to understand what each update does. Example (CRM): “Add a new field to store a customer’s company name.” (Next prompt) “Now show that company name in the customer list.” 7. Use the Preview to Fix Issues If something looks wrong or doesn’t work, describe it in your next prompt. You don’t need to start over. Example (Blog): “The blog list shows empty cards when there are no posts. Please show a message that says ‘No posts yet.’” 8. Think About the User Flow Guide how users move from one step to the next. This makes your app feel smooth and complete. Example (Waitlist): “After someone joins the waitlist, redirect them to a thank-you page with next steps.” 9. Use Real Apps as Inspiration Referencing familiar apps helps the AI understand the style you want. This often leads to cleaner and more professional designs. Example (Survey Tool): “Style the survey layout similar to Google Forms, with clean spacing and simple question cards.” 10. Reuse What Works When something turns out well, build on it. Reuse the same wording and structure in future prompts. Example (Small Business Website): “The contact form looks great. Use the same style for the newsletter signup form.”

Software Design Principles Great software isn't just about making things work, it's about creating systems that are maintainable, scalable, and resilient. These fundamental design principles guide developers toward writing better code. 1./ 𝐊𝐈𝐒𝐒 (𝐊𝐞𝐞𝐩 𝐈𝐭 𝐒𝐢𝐦𝐩𝐥𝐞, 𝐒𝐭𝐮𝐩𝐢𝐝) ➟ The most elegant solutions are often the simplest. ➟ Avoid unnecessary complexity, keep code clear and concise, and focus on essential features. Remember that code is read far more often than it's written. 2./ 𝐃𝐑𝐘 (𝐃𝐨𝐧'𝐭 𝐑𝐞𝐩𝐞𝐚𝐭 𝐘𝐨𝐮𝐫𝐬𝐞𝐥𝐟) ➟ Every piece of knowledge in a system should have a single, unambiguous representation. 3./ 𝐘𝐀𝐆𝐍𝐈 (𝐘𝐨𝐮 𝐀𝐢𝐧'𝐭 𝐆𝐨𝐧𝐧𝐚 𝐍𝐞𝐞𝐝 𝐈𝐭) ➟ Resist implementing features "just in case." ➟ Build what's needed today. 4./ 𝐒𝐎𝐋𝐈𝐃 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 the backbone of object-oriented design: ➟ Single Responsibility - Classes should do one thing well ➟ Open/Closed - Open for extension, closed for modification ➟ Liskov Substitution - Subtypes must be substitutable for their base types ➟ Interface Segregation - Many specific interfaces beat one general interface ➟ Dependency Inversion - Depend on abstractions, not concrete implementations 5./ 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 𝐨𝐟 𝐋𝐞𝐚𝐬𝐭 𝐀𝐬𝐭𝐨𝐧𝐢𝐬𝐡𝐦𝐞𝐧𝐭 ➟ Software should behave as users expect. ➟ Consistency in terminology, conventions, and error messages creates intuitive experiences. 6./ 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 𝐨𝐟 𝐌𝐨𝐝𝐮𝐥𝐚𝐫𝐢𝐭𝐲 ➟ Well-defined, independent modules make systems easier to understand, maintain, and test. 7./ 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 𝐨𝐟 𝐀𝐛𝐬𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 ➟ Hide implementation details to reduce cognitive load. ➟ Users of your code shouldn't need to know how it works internally, just how to use it. 8./ 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 𝐨𝐟 𝐄𝐧𝐜𝐚𝐩𝐬𝐮𝐥𝐚𝐭𝐢𝐨𝐧 ➟ Protect the internal state of objects from external manipulation. ➟ This creates more robust systems by preventing unexpected side effects. 9./ 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 𝐨𝐟 𝐋𝐞𝐚𝐬𝐭 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 (𝐋𝐚𝐰 𝐨𝐟 𝐃𝐞𝐦𝐞𝐭𝐞𝐫) ➟ Components should have limited knowledge of other components. ➟ This "need-to-know basis" approach creates more modular, flexible systems. 10./ 𝐋𝐨𝐰 𝐂𝐨𝐮𝐩𝐥𝐢𝐧𝐠 & 𝐇𝐢𝐠𝐡 𝐂𝐨𝐡𝐞𝐬𝐢𝐨𝐧 ➟ Minimize dependencies between modules while ensuring each module has a clear, unified purpose. ➟ This balance makes systems more maintainable and adaptable. You’d probably agree, It's easy to nod along with design principles when reading them, but much harder to catch when drifting away from them in real code. That's where tools like CodeRabbit can be valuable. During pull requests, it identifies potential issues that developers might overlook, such as unnecessary complexity or signs of tight coupling, without being intrusive or slowing down the development process. Understand, these tools don't replace human judgment but provide an additional layer of verification that can help maintain code quality over time.👊 coderabbit.ai

A single CLAUDE.md file just hit 15K+ GitHub stars. No framework. No infra. No fine-tuning. Just… better instructions. This idea is inspired by Andrej Karpathy, who pointed out something most people ignore: "LLMs don’t fail randomly. They fail predictably." - Overengineering simple tasks - Making silent assumptions - Editing things you didn't ask for - Writing 10x more code than needed If the mistakes are predictable → you can design against them. That's exactly what this CLAUDE.md does. It turns AI coding from: "generate code" into "engineer behavior" Here are the 4 core principles inside: 1️⃣ Think Before Coding → Force the model to state assumptions, surface ambiguity, and ask questions 2️⃣ Simplicity First → Minimum code. No speculative abstractions. No unnecessary flexibility 3️⃣ Surgical Changes → Only touch what’s required. No “drive-by refactoring” 4️⃣ Goal-Driven Execution → Define success criteria (tests, checks) instead of vague instructions This is the real shift happening right now: We're moving from "AI writes code" to "we design systems that make AI write good code" And the most powerful tools? Not always libraries. Sometimes… just well-crafted prompts.

I tried vibe-coding. It sucked. Here’s why. I skipped all structure and planning. I accepted every change Cursor suggested. It took one day to regret it: ❌ I debugged more than I built ❌ My code turned into chaos fast ❌ Every quick fix created three new problems.. I was ready to quit. Sound familiar? the lesson: good code isn't just written. It's engineered. So I learned vibe-engineering. Here is my high-level process that works: 1/ Define your real MVP Stop trying to build the whole app at once. → Identify the core feature → Ignore the rest → Prove the idea before expanding 2/ Turn it into a proper PRD Clarity saves weeks. → Ask AI to interview you → Answer everything → Let it draft your PRD Refine until nothing is vague. 3/ Break the architecture into components AI performs 10x better with boundaries. → Separate frontend + backend → Split features into modules → Keep every piece independently testable 4/ Set up your environment for success Your project needs a source of truth. Create: → claude.md - coding rules, patterns, stack → code_map.md - where everything lives → API spec - the contract your app follows This keeps AI consistent and prevents hallucinated structure. 5/ Define tests BEFORE coding Tests are the rails AI builds between. → One test suite per component → Debugging becomes trivial → Refactors don’t break everything And seriously: never let AI edit test files. 6/ Implement incrementally Tiny steps = fast progress without disasters. → One change at a time → Run tests → Update code_map.md → Commit with a clear message Repeat. Don’t rush. 7/ Connect things together. Build. Deploy. This is where everything clicks. → Connect UI with your API → Test flows end-to-end → Deploy with Vercel/Netlify/Fly.io → Validate real behavior, not assumptions 🎁 Bonus tip: Stop asking AI to “fix it.” Ask it to explain it - step by step. Follow this system and AI coding stops feeling like chaos and starts feeling like engineering. Planning doesn't slow you down. It prevents the slowdowns. Which step should I break down next? ♻️ Repost to help other builders avoid vibe-coding disasters ➕ Follow me (Basia Kubicka) for more systems that make coding feel less chaotic and more predictable 🔔 Subscribe to my newsletter for deeper breakdowns on systems that work https://air-scale.kit.com/

Most developers treat AI coding agents like magical refactoring engines, but few have a system, and that's wrong. Without structure, coding with tools like Cursor, Windsurf, and Claude Code often leads to files rearranged beyond recognition, subtle bugs, and endless debugging. In my new post, I share the frameworks and tactics I developed to move from chaotic vibe coding sessions to consistently building better, faster, and more securely with AI. Three key shifts I cover: -> Planning like a PM – starting every project with a PRD and modular project-docs folder radically improves AI output quality -> Choosing the right models – using reasoning-heavy models like Claude 3.7 Sonnet or o3 for planning, and faster models like Gemini 2.5 Pro for focused implementation -> Breaking work into atomic components – isolating tasks improves quality, speeds up debugging, and minimizes context drift Plus, I share under-the-radar tactics like: (1) Using .cursor/rules to programmatically guide your agent’s behavior (2) Quickly spinning up an MCP server for any Mintlify-powered API (3) Building a security-first mindset into your AI-assisted workflows This is the first post in my new AI Coding Series. Future posts will dive deeper into building secure apps with AI IDEs like Cursor and Windsurf, advanced rules engineering, and real-world examples from my projects. Post + NotebookLM-powered podcast https://lnkd.in/gTydCV9b