How to Use AI Instead of Traditional Coding Skills

Most developers use AI to write code faster. The best ones use it to stop writing code entirely. Today, I spend 80% of my time describing what I want, reviewing what agents build, and deciding when to step in. The other 20% is architecture and security calls that agents can't make yet. This isn't lazy. It's the new job. Anthropic's 2026 Agentic Coding Trends Report confirmed what I've been feeling: developers now integrate AI into 60% of their work while maintaining active oversight on 80-100% of delegated tasks. The role shifted from "person who writes code" to "person who directs and reviews code." Here are 5 skills I had to learn the hard way: 𝟭. 𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗦𝗽𝗲𝗰𝘀, 𝗡𝗼𝘁 𝗖𝗼𝗱𝗲 The quality of what an agent builds is directly proportional to how well you describe what you want. Vague prompt = vague code. I now spend more time writing specs than I ever spent writing implementations. 𝟮. 𝗧𝗮𝘀𝗸 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 Agents lose context on large tasks and waste time on tiny ones. The skill is finding the sweet spot: chunks big enough to be meaningful, small enough to stay accurate. 𝟯. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Agents forget everything between sessions. Your project rules, memory files, and AGENTS .md are what give them continuity. This is the most underrated skill on the list. 𝟰. 𝗥𝗲𝘃𝗶𝗲𝘄𝗶𝗻𝗴 𝗔𝗜 𝗢𝘂𝘁𝗽𝘂𝘁 Agents generate code fast. They also generate security holes, edge case gaps, and subtle architectural drift fast. Your job is catching what they miss. This is harder than writing the code yourself. 𝟱. 𝗞𝗻𝗼𝘄𝗶𝗻𝗴 𝗪𝗵𝗲𝗻 𝘁𝗼 𝗦𝘁𝗲𝗽 𝗜𝗻 Architecture decisions and security calls are still yours. Everything else? Let the agent iterate. The hardest part isn't learning to delegate. It's learning to stop grabbing the keyboard back. The developers who thrive in 2026 won't be the fastest coders. They'll be the best agent operators. Which of these 5 are you already doing?

If you've tried using #AI to code, this curve probably looks familiar… Here’s my journey through the hype cycle of coding #OperationsResearch projects with AI 👇 🤨 First, I was skeptical. I tried it, it failed miserably. I thought AI would never be useful for coding. This was just raw chatting with ChatGPT and/or Claude. It lacked enough context, so it couldn't do its job well enough. 👍 Second, I started to see its value. It was like “hey, it’s helpful; maybe not the best companion, but it gives you 80% of the code”. Not bad. I gave better context to the chat, uploaded code, and some documents if I thought they were useful. The issue here is that it creates friction: you need to constantly update your code and documents to the chat project. But it can give you good results. 💃 Third, I was so excited that I almost never read the code generated by AI. Just vibe coding. Tab, tab, tab. Apply, apply, apply. Run. Then I moved to agents like Github Copilot coupled with GPT, Claude, or Gemini. It's great as it has the context of the whole project (or at least a good summary of it), and lives inside VS Code, so the friction got removed. As a consequence, I applied more easily the changes done by the AI. 🐛 Fourth, I was disappointed. Debugging became #1 task. Also my #1 enemy. So much time and energy put here that I did a step back. As a consequence of that 'speed' of coding, it created a lot of buggy code. The real problem is that as I didn't audit the code that much, I spent more time debugging. And debugging code you didn't write is a pain. 🔁 Fifth, looking at it with new eyes. If everyone was talking about it, “how come I couldn’t get barely any value from it?”. So I tried a different approach. After realizing it, I thought there might be a different way of interacting with AI for coding. Instead of just apply the changes almost without looking at them, I started to: i) first understand what the agent/LLM understands about the task by letting it explain it in plain words, ii) let it make a plan that I can edit if I think is not complete enough, iii) start working on code that I audit so every time it suggests a new change, I understand why. 📈 Sixth, now getting value. And, at the same time, learning along the way. But I scrutinize every single line of code given by it. Now I apply that using Claude Code. I really think it's a step forward if you use it correctly. Not only that, as it explains you the plan, the code, and why it's working or not, and compile + run everything until it works, you can learn useful tips along the way. I also challenge it from time to time and put some vague words on tasks that I know exactly how to do to see if it leads me towards my solution, gives another perspective, or totally loses the point of the task. I think it’s just a matter of time that almost everyone embraces AI for coding. But I know it takes time, and you need to traverse desert. What about your experience?

AI won't replace engineers.  But engineers who ship 5x faster & safer will replace those who don't. I've been shipping code with AI assistance at AWS since 2024. But it took me a few weeks to figure out how to actually use AI tools without fighting them. Most of what made the difference isn't in any tutorial.  It's the judgment you build by doing. Here's what worked for me: 1. Take the lead. •) AI doesn't know your codebase, your team's conventions, or why that weird helper function exists. You do. Act like the tech lead in the conversation. •) Scope your asks tightly. "Write a function that takes a list of user IDs and returns a map of user ID to last login timestamp" works. "Help me build the auth flow" gets you garbage. •) When it gives you code, ask it to explain the tradeoffs. 2. Use it for the boring & redundant things first •) Unit tests are the easiest win. Give it your function, tell it the edge cases you care about, let it generate the test scaffolding. •) Boilerplate like mappers, config files, CI scripts. Things that take 30 minutes but need zero creativity. •) Regex is where AI shines. Describe what you want to match and it hands you a working pattern in seconds. •) Documentation too. Feed it your code, ask for inline comments or a README draft. You'll still edit it, but the first draft is free. 3. Know when to stop prompting and start coding •) AI hallucinates confidently. It will tell you a method exists when it doesn't. It will invent API parameters. Trust but verify. •) Some problems are genuinely hard. Race conditions, complex state management, weird legacy interactions. AI can't reason about your system the way you can. •) use AI to get 60-70% there fast, then take over. The last 30% is where your judgment matters. 4. Build your own prompt library •) Always include language, framework, and constraints. "Write this in Python <desired-version>, no external dependencies, needs to run in Lambda" gets you usable code. "Write this in Python" gets you a mess. •) Context is everything. Paste the relevant types, the function signature, the error message. The more AI knows, the less you fix. •) Over time, you'll develop intuition for what AI is good at and what it's bad at. That intuition is the core skill. AI tools are multipliers. If your fundamentals are weak, they multiply confusion.  If your fundamentals are strong, they multiply speed & output. Learn to work with them, it will give you a ton of ROI.

Dear software engineers, you’ll definitely thank yourself later if you spend time learning these 7 critical AI skills starting today: 1. Prompt Engineering ➤ The better you are at writing prompts, the more useful and tailored LLM outputs you’ll get for any coding, debugging, or research task. ➤ This is the foundation for using every modern AI tool efficiently. 2. AI-Assisted Software Development ➤ Pairing your workflow with Copilot, Cursor, or ChatGPT lets you write, review, and debug code at 2–5x your old speed. ➤ The next wave of productivity comes from engineers who know how to get the most out of these assistants. 3. AI Data Analysis ➤ Upload any spreadsheet or dataset and extract insights, clean data, or visualize trends—no advanced SQL needed. ➤ Mastering this makes you valuable on any team, since every product and feature generates data. 4. No-Code AI Automation ➤ Automate your repetitive tasks, build scripts that send alerts, connect APIs, or generate reports with tools like Zapier or Make. ➤ Knowing how to orchestrate tasks and glue tools together frees you to solve higher-value engineering problems. 5. AI Agent Development ➤ AI agents (like AutoGPT, CrewAI) can chain tasks, run research, or automate workflows for you. ➤ Learning to build and manage them is the next level, engineers who master this are shaping tomorrow’s software. 6. AI Art & UI Prototyping ➤ Instantly generate mockups, diagrams, or UI concepts with tools like Midjourney or DALL-E. ➤ Even if you aren’t a designer, this will help you communicate product ideas, test user flows, or demo quickly. 7. AI Video Editing (Bonus) ➤ Use RunwayML or Descript to record, edit, or subtitle demos and technical walkthroughs in minutes. ➤ This isn’t just for content creators, engineers who document well get noticed and promoted. You don’t have to master all 7 today. Pick one, get your hands dirty, and start using AI in your daily workflow. The engineers who learn these skills now will lead the teams and set the standards for everyone else in coming years.

Agent-assisted coding transformed my workflow. Most folks aren’t getting the full value from coding agents—mainly because there’s not much knowledge sharing yet. Curious how to unlock more productivity with AI agents? Here’s what’s worked for me. After months of experimenting with coding agents, I’ve noticed that while many people use them, there’s little shared guidance on how to get the most out of them. I’ve picked up a few patterns that consistently boost my productivity and code quality. Iterating 2-3 times on a detailed plan with my AI assistant before writing any code has saved me countless hours of rework. Start with a detailed plan—work with your AI to outline implementation, testing, and documentation before coding. Iterate on this plan until it’s crystal clear. Ask your agent to write docs and tests first. This sets clear requirements and leads to better code. Create an "AGENTS.md" file in your repo. It’s the AI’s university—store all project-specific instructions there for consistent results. Control the agent’s pace. Ask it to walk you through changes step by step, so you’re never overwhelmed by a massive diff. Let agents use CLI tools directly, and encourage them to write temporary scripts to validate their own code. This saves time and reduces context switching. Build your own productivity tools—custom scripts, aliases, and hooks compound efficiency over time. If you’re exploring agent-assisted programming, I’d love to hear your experiences! Check out my full write-up for more actionable tips: https://lnkd.in/eSZStXUe What’s one pattern or tool that’s made your AI-assisted coding more productive? #ai #programming #productivity #softwaredevelopment #automation

If you're not building AI literacy now, you're already behind. Not because AI is replacing engineers. But because the engineers who understand how to use it are moving faster than those who don't. Here are the AI must-have skills you need to focus on in 2026: 1. Prompt engineering for technical work Learn how to use AI to speed up code reviews, debug faster, and generate boilerplate. The skill isn't using ChatGPT. It's knowing how to ask the right questions to get reliable outputs. 2. Understanding AI limitations in your domain Know when AI helps and when it halts progress. Can it generate your unit tests? Yes. Can it architect your entire system? No. Learn the difference so you don't waste time or credibility. 3. AI-assisted documentation and communication Use AI to turn technical complexity into clear explanations. Draft design docs faster. Translate jargon for non-technical stakeholders. This saves hours and makes you more valuable to leadership. 4. Evaluating AI-generated code Don't just copy and paste. Learn to audit AI outputs for security risks, edge cases, and maintainability. The engineers who blindly trust AI create technical debt. The ones who verify it build better systems. 5. Using AI for learning and upskilling Use AI as a personal tutor to learn new frameworks, languages, or concepts faster. Ask it to explain complex topics, generate practice problems, or review your learning path. It's like having a senior engineer on call 24/7. 6. Staying current without getting distracted Pick one AI tool relevant to your work and master it. Don't chase every new model. Don't get lost in hype. Depth beats breadth. The engineers getting promoted aren't the ones using every AI tool. They're the ones using AI to deliver faster, think clearer, and solve harder problems. If you are a high-level engineer that wants to start building AI muscle, comment 'AI,’ and I'll send you a cheat sheet of tools worth learning.

2025 saw a massive shift in how we perceive coding. It's 2026 now, and companies are still lagging behind. I used to think you needed developers to build products. Then I launched Searchable... And validated the entire idea with AI in 48 hours. At that level, I didn't need to know a single line of code. But if you're planning to replace real engineering work,  You'll need to create a proper plan of action. AI coding makes it easier than ever to build. But you still need to input clear ideas and know how it works. There are three levels of AI coding founders should understand: (See the visual for more details 👇) 1. Vibe Coding Level: Non-technical founders What it is: Turning rough ideas into working prototypes by describing what you want in plain English and letting AI handle the code. Business use case: → Validating startup ideas fast → Building landing pages, MVPs, internal tools → Testing demand before hiring engineers Tools to use: → Lovable - Product prototypes and signup flows → Bolt - Fast web app generation → Replit - Build and deploy without setup → Make - Connect tools and workflows 2. AI-Assisted Coding Level: Technical or semi-technical teams What it is: AI working alongside a human developer to speed up writing, debugging, and refactoring code. Business use case: → Building production-ready software faster → Improving developer output without growing headcount → Reducing bugs and repetitive work Tools to use: → Cursor - AI-first code editor → GitHub Copilot - Inline code assistance → Continue - Open-source AI coding assistant → Google Antigravity - Context aware completions 3. Agentic Coding Level: Advanced team and operators What it is: AI agents that can plan, write, test, and refine entire chunks of software from a single objective. Business use case: → Large feature builds → Legacy code refactors → Automating repetitive engineering tasks → Spinning up internal systems fast Tools to use: → Claude Code - Agent-driven deployment → OpenAI Codex - Autonomous coding tasks → Devin - Full software agent → Gemini CLI - Command-line agent workflows These tools let you validate first and hire second… Yet another way AI allows founders to move faster than ever before. If you’re building right now, this is leverage you can’t ignore. Are you familiar with AI coding? How are you using it?  Drop a comment below with your process.  At Searchable, we're using AI to build an autonomous SEO and AEO growth engine. It analyses, fixes, and scales websites to drive customers automatically. If you're a founder who wants to stay visible when people search with ChatGPT, Perplexity, or Google AI... This is built for you. Learn more and get started with a 14-day free trial here:  https://lnkd.in/epgXyFmi ♻️ Repost to share this breakdown with founders in your network.  And follow Chris Donnelly for more on building smarter. 

The rise of AI Agents has transformed coding in just 3 years Here's the evolution most leaders are completely missing... If your team is still manually writing every line of code, you're already behind. The coding landscape has shifted from Traditional → Vibe → AI-Assisted → Agentic, and each stage requires a different mindset. 📌 Let me break down when to use each approach: 1/ Traditional Coding - Writing code manually line-by-line in a programming language.  - You build PRDs, write syntax, compile/interpret, debug errors, test for issues, then deploy. - Use: When you need full control, custom logic, or complex architecture that AI can't handle yet. Tools: VsCode, IntelliJ, Sublime Text Best for: Production systems where every line matters and security is critical. 2/ Vibe Coding - Describe what you want in plain language and let AI generate the entire app.  - Choose the right tool, write a query in natural language, let the LLM build your idea, add tools and databases, get feedback, then test and deploy. - Use: When you need quick prototypes, simple apps, or you're learning new frameworks. Tools: Bolt.new, Lovable, Replit Agent Best for: MVPs, landing pages, or internal tools where speed beats perfection. 3/ AI-Assisted Coding - You write code while AI suggests completions, like having a senior dev pair-programming with you.  - Build PRDs, developer verifies code, AI shares suggestions, you run debugging, write test cases, and maintain compliance. - Use: When you need production-grade projects requiring oversight but want 3x speed. Tools: Github Copilot, Code Whisperer Best for: Enterprise applications where human review is mandatory. 4/ Agentic Coding - AI agents autonomously code in iterative loops, building plans, writing code, fixing errors, checking test cases, and deploying with minimal human intervention. - Use: When you need complex workflows or end-to-end automation but you are willing to spend time reviewing the entire code. Tools: Claude Code, OpenAI Codex Best for: Automating repetitive tasks, batch processing, or multi-step workflows. The biggest mistake I see? Teams trying to use the same approach for everything. Traditional coding for a quick prototype? You'll waste days. Agentic coding for mission-critical banking software? A disaster. Here's the truth: The best teams in 2025 aren't the ones who code the fastest; they're the ones who know which method to use when. Master this evolution, and you'll 10x your output while others debate whether AI will replace them. 📌 If you want to understand AI agent concepts deeper, my free newsletter breaks down everything you need to know: https://lnkd.in/g5-QgaX4 Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents

You don’t need to be a developer to work with AI. And you definitely don’t need to start with math, coding, or complex models. Most non-tech professionals get stuck because they think AI entry means “learn Python first” or “become technical overnight.” It doesn’t. This guide shows how non-tech people are actually getting into AI - step by step, using practical skills that compound fast. It walks through a clear progression: • Understanding AI fundamentals without math • Learning how LLMs really work (so you don’t blindly trust outputs) • Becoming a daily AI power user for real work • Writing practical prompts for emails, reports, and analysis • Thinking in AI + business terms, not tools • Building no-code automations • Creating simple AI workflows • Using APIs without becoming a developer • Building small but real projects • Learning RAG and AI agents at a beginner level • Creating a portfolio without heavy coding • Turning skills into real career opportunities The key idea is simple: AI rewards applied thinking, not technical depth at the start. You don’t need to know how models are trained. You need to know how work flows and where AI fits. If you’re in business, operations, marketing, consulting, or product, this is a realistic path into AI without burning out or getting stuck in tutorials. Swipe through. Start small. Build proof, not certificates. Questions about O-1, EB-1A, or EB-5? Book a free consult - https://lnkd.in/gqJUQ-8X Join our Open Atlas community for visa-friendly job drops and free resume reviews - https://lnkd.in/gqVU84qW 🔔 Follow to stay updated on high-skilled immigration, jobs, and tech

Hot tip: If you're using an AI to help you code, don't ask it for code. - Start by telling it what your goals are for the project - Ask it for a project plan outlining how it would execute your project - Have it create a scope of work - Challenge what it produces around simplification, core purpose, and useability - Ask it to revise what it created - Have it simulate the flow of the project code - Review it again and challenge it again - Then, when you're satisfied that it: 1/ understands what you wanted to achieve 2/ is doing so based on your ideas 3/ is not over engineering anything 4/ has developed a stable plan THEN - Ask it do produce an artifact that contains the final plan, scope, and project layout Next, YOU start the coding. Create the layout, structure, directories, file names YOU put in the basics, and go as far as you can Then ask for help. Give the AI the project plan it created with your guidance, tell it where you're stuck, then ask it to teach you how to meet the goals, line by line but only when asked, and only for what you're asking about. That's how you learn, and that's how you get things done. There is no easy button but there can be symbiosis. #artificialintelligence #coding #LLMs #code #devops