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

You don’t need to be a developer to build intelligent AI workflows anymore, but you need a thing or two about how agents work. With no-code platforms like Make, n8n, and Zapier, deploying AI agents has become faster, more visual, and scalable for business automation. Here’s a step-by-step breakdown of how to deploy AI agents without writing a single line of code 👇 1.🔹Identify the Use Case Focus on repetitive manual tasks, customer queries, or data bottlenecks. Tools: Notion AI, Airtable, ChatGPT, Make.com, Zapier 2.🔹Define Objectives & Scope Outline expected outcomes, key integrations, and KPIs for success. Tools: Miro, Whimsical, Google Sheets, ClickUp 3.🔹Select the Right No-Code Platform Evaluate features, pricing, and scalability before choosing. Recommended: Make.com, n8n, Zapier, Pabbly Connect 4.🔹Design the Workflow Blueprint Map triggers, processes, and output flow visually. Tools: Draw.io, Whimsical, Make.com visual builder 5.🔹Integrate Data & APIs Connect CRMs, email tools, or databases to your automation. Tools: Make.com API modules, n8n HTTP Node, Postman 6.🔹Add AI Components Embed GPT, Claude, or Gemini to enable contextual reasoning and automation. Tools: OpenAI API, Flowise, Langflow, MindStudio 7.🔹Test & Validate Workflows Run real-time test cases and monitor accuracy, latency, and performance. Tools: Make.com Scenario Testing, n8n Test Mode, Postman Monitors 8.🔹Train End-Users Provide clear training materials and internal demos for adoption. Tools: Loom, Notion, Slack, Microsoft Teams 9.🔹Deploy & Monitor Go live with tracking for API usage, success rates, and performance. Tools: Make,com Dashboard, n8n Logs, Datadog 10.🔹Continuous Improvement Refine workflows, add new AI models, and scale to multi-agent systems. Tools: Airtable, LangFuse, Relevance AI, Vercel Ready to deploy your own AI agent without coding? Save this post and start experimenting with tools like Make or n8n today. #AIAgent

Can AI-driven “vibe coding” get good enough to get non-coders to produce fully functional tools, perhaps even enterprise-level software?? I’ve been “vibe coding” and pleasantly surprised by the outcome(s) – this got me thinking about what level of skills a non-coder would have to acquire in order to take advantage of these capabilities. The short answer is, we need foundational tech literacy combined with some high-level framing and some AI-specific capabilities. Here’s a small list of skills I think are necessary if you are looking to get started with vibe coding (or you’re a college/grad school student looking to update yourself beyond the coursework available) • Basic digital and software concepts like files, folders, version control (e.g., GitHub), cloud storage, APIs, and how software is deployed. This foundational tech literacy ensures users can manage projects and code artifacts effectively • Problem framing and decomposition – this is where a lot of non-tech folks would have an advantage.Breaking down real-world problems into clear, structured tasks that AI-powered tools can help solve means learning to formulate actionable questions, accept iterative refinement, and specify requirements in ways software can address • Prompt engineering and AI interaction – again, non-tech folks, this would cake walk for you. Skills in effectively communicating with AI coding tools, such as crafting prompts, guiding AI suggestions, and validating AI-generated outputs to produce quality code or software components. • Familiarity with No-Code/Low-Code platforms that can provide exposure to intuitive tools that allow building applications without deep coding, including drag-and-drop AI model builders, chatbot designers, and API integration platforms. This bridges the gap between understanding AI assistance and executing hands-on building. • Mindset to include testing, debugging, interpreting error messages, and iterative improvement to ensure solutions work as intended. This complements AI’s assistance with user oversight and quality control. There are tons of tools available to help with testing and debugging as well • Basic Software Development Workflow Awareness like version control, collaboration through GitHub, documentation standards, and deployment pipelines even if not coding manually, to align with professional-grade software practices. This is just a ‘starter pack’ – essentially, a combination of foundational digital fluency, problem-solving skills, and AI interaction techniques will best prepare us to capitalize on AI-driven “vibe coding” capabilities in the near future. It’s time to give it a go! Emergent Google Google Colab Manus AI image source : MIT review

Rise of genAI tools – Democratization and Reinvention of Work   Domain experts, even non-techies are building systems and products to automate work today.   genAI has opened up access to information and skills across roles and business functions. You can build your first AI workflow in a few minutes, even if you do not have programming skills. Most executives think AI automation requires a development team and a six-figure budget. I come from an IT services background, trained in the eighties. Building systems (even using legacy AI a decade ago) was an elaborate process, requiring a lot of time and elaborate team work. This week, I attended a wonderful workshop which taught us the use of genAI tools for automating systems. Among the many systems we built hands-on, one really surprised me with the ease and speed of development and left a deep mark. It involved building a personal portfolio website. The first personal website I had developed during the lockdown had taken me a few days and nights of intense effort. In the workshop, we downloaded our LinkedIn profile. We needed a few detailed text prompts to generate the first draft of our website. The solution for solving many business problems isn't necessarily learning Python or hiring developers for automating many systems. Here's what works: No-code AI platforms like rocket and many others let you connect apps and add AI using prompts and dropdown menus. I built my first draft of the website ready in just about 15 minutes. Of course, making it secure and robust would involve additional effort. The new technology ensures you don’t become the executive who waits for your IT team, while your competitor automates the same work this week. Tomorrow, pick one repetitive task that takes you more than 2 hours weekly. Try out a few AI tools and platforms, and browse their system for something close to your task. You'll find yourself building the automation system that saves hours of manual monthly effort and proves you're adapting, and not resisting change.   The systems you build today become the skills that keep you relevant tomorrow. #GrowthSchool

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.