How to Boost Productivity With AI Coding Assistants

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

I've been using AI coding tools for a while now & it feels like every 3 months the paradigm shifts. Anyone remember putting "You are an elite software engineer..." at the beginning of your prompts or manually providing context? The latest paradigm is Agent Driven Development & here are some tips that have helped me get good at taming LLMs to generate high quality code. 1. Clear & focused prompting ❌ "Add some animations to make the UI super sleek" ✅ "Add smooth fade-in & fade out animations to the modal dialog using the motion library" Regardless of what you ask, the LLM will try to be helpful. The less it has to infer, the better your result will be. 2. Keep it simple stupid ❌ Add a new page to manage user settings, also replace the footer menu from the bottom of the page to the sidebar, right now endless scrolling is making it unreachable & also ensure the mobile view works, right now there is weird overlap ✅ Add a new page to manage user settings, ensure only editable settings can be changed. Trying to have the LLM do too many things at once is a recipe for bad code generation. One-shotting multiple tasks has a higher chance of introducing bad code. 3. Don't argue ❌ No, that's not what I wanted, I need it to use the std library, not this random package, this is the 4th time you've failed me! ✅ Instead of using package xyz, can you recreate the functionality using the standard library When the LLM fails to provide high quality code, the problem is most likely the prompt. If the initial prompt is not good, follow on prompts will just make a bigger mess. I will usually allow one follow up to try to get back on track & if it's still off base, I will undo all the changes & start over. It may seem counterintuitive, but it will save you a ton of time overall. 4. Embrace agentic coding AI coding assistants have a ton of access to different tools, can do a ton of reasoning on their own, & don't require nearly as much hand holding. You may feel like a babysitter instead of a programmer. Your role as a dev becomes much more fun when you can focus on the bigger picture and let the AI take the reigns writing the code. 5. Verify With this new ADD paradigm, a single prompt may result in many files being edited. Verify that the code generated is what you actually want. Many AI tools will now auto run tests to ensure that the code they generated is good. 6. Send options, thx I had a boss that would always ask for multiple options & often email saying "send options, thx". With agentic coding, it's easy to ask for multiple implementations of the same feature. Whether it's UI or data models asking for a 2nd or 10th opinion can spark new ideas on how to tackle the task at hand & a opportunity to learn. 7. Have fun I love coding, been doing it since I was 10. I've done OOP & functional programming, SQL & NoSQL, PHP, Go, Rust & I've never had more fun or been more creative than coding with AI. Coding is evolving, have fun & let's ship some crazy stuff!

I used an AI coding agent with $𝟱𝟬𝟬 in credits to build a yugioh card game engine with documentation in 1 week (side project hours). To build this I used: AI Coding Agent ➜ Claude Code CLI AI Architect ➜ Google Gemini Pro 2.5 Everything was built by prompting Claude Code through my CLI I used Google Gemini Pro 2.5 to throw the entire codebase into an LLM and discuss architectural design patterns for complex tasks. 𝗧𝗼𝗽 𝟯 𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝘁𝗼 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝘁𝗵𝗲 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝘀𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗰𝗼𝘀𝘁 𝗻𝗲𝘅𝘁 𝘁𝗶𝗺𝗲 𝗮𝗿𝗼𝘂𝗻𝗱: 1. In every new session, provide "Architectural principles to code by", which is a set of clean code practices that the LLM should follow. Restart your session frequently to re-input these principles if the context gets too long. 2. Always tell the LLM "Do not code" and have it come up with an approach then explain why it works before allowing it to code. When fixing bugs tell the LLM "Do not code, investigate and report back with rationale for whats broken and how to fix it" 3. Use another LLM (such as Gemini) to ideate on a concrete architectural design before having the coding agent tackle any refactors or implement complex features. Example prompt contexts that I used can be found in the comments

Ever wished you could keep coding with AI assistance during your commute? I ride BART every day, and those 30-40 minute underground stretches used to be dead time. Not anymore. Using Continue's agent mode with Ollama, I've turned my daily commute into focused coding sessions—completely offline. The key? Context engineering. Before losing signal, I prepare PRDs (Product Requirement Documents) that outline exactly what I want to build. I've set up rules that teach the AI our team's conventions. And I've indexed my entire codebase locally. When the train goes underground, I have everything needed for productive work. No Stack Overflow rabbit holes, no Twitter distractions—just focused implementation with AI assistance. Research shows 69% of developers lose over 8 hours per week to inefficiencies, and it takes 52 minutes of uninterrupted time to reach flow state. My BART rides give me that uninterrupted time twice daily. Over a week, those 30-minute sessions compound into real features shipped and technical debt addressed. The best part? Complete privacy. Your code never leaves your machine. No telemetry, no data retention policies. Whether you're in a secure environment, on a flight, or just trying to focus, your AI coding assistant works anywhere. Sometimes the best code really does get written underground. #DeveloperProductivity #AIAssistedCoding #LocalFirstDevelopment