How to Adapt Coding Skills for AI

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.

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?

As AI rapidly transforms our industry, I've been thinking about which tech roles will survive – and which won't. Testing code used to require specialized skills. Today, AI can write test scripts that rival those created by mid-level engineers. Tomorrow? 𝗕𝗮𝘀𝗶𝗰 𝘁𝗲𝘀𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗮 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆 𝘀𝗸𝗶𝗹𝗹. This isn't fear-mongering. It's our new reality. The engineers who thrive won't be those who simply write test code, but those who architect entire testing environments, design integration strategies, and optimize the full delivery pipeline. I recently watched this transformation happen in real-time with Rody, a test automation specialist with 13 years of experience. He recognized the shifting landscape and made a critical decision: to rise above the commodity skills and master DevOps. His journey began with a challenge: implementing test automation for a company without a test environment. Instead of treating this as "not my job," he collaborated with a DevOps engineer to build a Kubernetes-based testing environment from scratch. This experience sparked something profound: the realization that the most valuable engineers aren't just coders – they're architects and problem solvers 💡 Over 18 months (while balancing a new baby, a move, and job changes), Rody transformed his skill set. He now creates Flask applications deployed in Kubernetes clusters, builds Terraform projects integrated with Jenkins, and automates server configuration with Ansible. The AI revolution creates two distinct career paths for engineers: 1. 𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝗰𝗼𝗺𝗽𝗲𝘁𝗲 with AI at tasks it will inevitably master 2. 𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲 AI while focusing on skills AI struggles with: system design, integration strategy, and holistic problem-solving Rody chose the second path. He's no longer at risk of becoming another replaceable test engineer in a sea of mediocrity. This pattern will repeat across our industry. The engineers who survive won't be those writing the most code – they'll be those who 🟢 understand how systems connect 🟢 can architect solutions across multiple domains 🟢 continually adapt to change Read his full story here: https://lnkd.in/dXEUBFmP 💬 What skills are you developing that AI can't easily replicate? 💬 How are you ensuring you stay on the right side of this divide?

The headline says AI’s writing 25% of Google’s code, but it skips the part about software engineers still reviewing and validating it. How much time is really being saved? That’s not mentioned either. GenAI does really simple coding well, and that’s what junior software engineers are hired to do today. Experienced engineers are used to reviewing GenAI/junior-level code. Those roles won’t change…yet. Entry-level positions will be harder to come by. What happens at Google today spreads to the rest of tech in a year and filters into traditional domains in 2 years. What can people entering the field do to adapt and thrive? 🟢 They must still learn to code, but they will learn to do it with an AI assistant to augment their work. They should have a mid-level developer’s capabilities with the AI’s support. 🟢 Prompting and generating code based on documentation must be core capabilities. The key is to be highly proficient at augmented coding methods to deliver solutions faster. 🟢 Software engineering architecture, security, optimization, documentation, patterns, and best practices become even more critical. 🟢 Code reviews, validation, and testing should be core capabilities. Software engineers won’t disappear, but their role will significantly change. Businesses will need fewer of them and expect higher productivity levels. Adaptation is the only option. #ArtificialIntelligence #Coding #GenAI

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 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 to write code. Now I write prompts. And my productivity has exploded. The most valuable skill for developers today isn't knowing every syntax detail – it's knowing how to explain what you want clearly to AI. I've been building with Claude AI lately. When I provide the right context and guidelines, it generates solutions in minutes that would have taken me days to code myself. 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐢𝐬 𝐬𝐡𝐢𝐟𝐭 𝐡𝐚𝐬 𝐭𝐚𝐮𝐠𝐡𝐭 𝐦𝐞: 🔹The developer's role is transforming Instead of typing out every line of code, I'm now an architect and director. I focus on the "what" instead of the "how." 🔹Context is the new coding The quality of my output directly correlates with how well I can articulate my requirements. Clear communication beats technical prowess. 🔹Iteration is still king AI doesn't replace the feedback loop - it accelerates it. I can test 10 approaches in the time it used to take for one. 🔹Deep knowledge still matters Understanding fundamentals helps me evaluate AI output, spot errors, and know what's possible. 🔹The productivity gap is widening Developers embracing this paradigm shift are outpacing those clinging to traditional-only methods by orders of magnitude. My workday has transformed from writing functions to writing specifications. 𝐅𝐫𝐨𝐦 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐝𝐞𝐭𝐚𝐢𝐥𝐬 𝐭𝐨 𝐝𝐞𝐬𝐜𝐫𝐢𝐛𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐧𝐭𝐢𝐨𝐧𝐬. 𝐅𝐫𝐨𝐦 𝐡𝐨𝐰 𝐭𝐨 𝐰𝐡𝐚𝐭. This doesn't make development obsolete - 𝐢𝐭 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐬𝐮𝐩𝐞𝐫𝐜𝐡𝐚𝐫𝐠𝐞𝐝. The future belongs to developers who can clearly communicate their vision and leverage #AI as a multiplier. P.S. What percentage of your coding time is now spent writing prompts instead of code? 0%? 50%? 100%?

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.

A clear path into AI engineering using 10 GitHub repos Step-by-step plan you can follow and show as proof of work Foundations 1. Learn the basics of machine learning and deep learning • ML for Beginners, AI for Beginners Output: 3 small projects with short READMEs that explain the goal, data, and result. Go deeper 2) Build neural nets from scratch • Neural Networks: Zero to Hero Output: a tiny GPT trained on a toy dataset, plus notes on what you changed and why. Read papers in code 3) Study real architectures by walking through annotated implementations • DL Paper Implementations Output: pick one model and re-implement a minimal version. Write what you simplified. Ship real software 4) Move from notebooks to apps and services • Made With ML Output: refactor one project with a simple API, tests, and a one-click run script. Work with LLMs 5) Learn the core pieces end to end • Hands-on LLMs Output: a basic RAG app (retrieval augmented generation) that answers questions on a small knowledge base. Make RAG better 6) Compare advanced techniques • Advanced RAG Techniques Output: run A/B tests on 3 settings and report latency, accuracy, and cost in a table. Learn agents 7) Build simple agents that take steps toward a goal • AI Agents for Beginners Output: an agent that checks a site, writes a summary, and files a ticket. Take agents toward production 8) Add memory, orchestration, and basic security • Agents Towards Production Output: logging, retry logic, and input checks. Note what fails and how you fixed it. Round out your portfolio 9) Adapt working examples • AI Engineering Hub Output: 2 more apps that solve real tasks, each with a clear demo and setup guide. How to pace this • One repo per week is a good rhythm. • Keep a single repo called “ai-engineering-journey” with subfolders per step. • After each step, post a short write-up with a 30-second screen recording. What hiring managers look for • Working code that runs on first try. • Clear README, data source, and limits. • Small tests and a simple eval, even if manual. • Changelog that shows steady progress. Save this and start with step 1 today. Repos and links 1. ML for Beginners — https://lnkd.in/dQ6nAJRC 2. AI for Beginners — https://lnkd.in/dXwJJjMm 3. Neural Networks: Zero to Hero — https://lnkd.in/dagQ3kmA 4. DL Paper Implementations — https://lnkd.in/dyw54m73 5. Made With ML — https://lnkd.in/duHjr2CY 6. Hands-On Large Language Models — https://lnkd.in/dxEGzsgc 7. Advanced RAG Techniques — https://lnkd.in/dd2TKA5P 8. AI Agents for Beginners — https://lnkd.in/deznrHdf 9. Agents Towards Production — https://lnkd.in/dz-WgU-3 10. AI Engineering Hub — https://lnkd.in/d9cNqy7c

Is AI automating away coding jobs? New research from Anthropic analyzed 500,000 coding conversations with AI and found patterns that every developer should consider: When developers use specialized AI coding tools: - 79% of interactions involve automation rather than augmentation - UI/UX development ranks among the top use cases - Startups adopt AI coding tools at 2.5x the rate of enterprises - Web development languages dominate:          JavaScript/TypeScript: 31%          HTML/CSS: 28% What does this mean for your career? Three strategic pivots to consider: 1. Shift from writing code to "AI orchestration"     If you're spending most of your time on routine front-end tasks, now's the time to develop skills in prompt engineering, code review, and AI-assisted architecture. The developers who thrive will be those who can effectively direct AI tools to implement their vision. 2. Double down on backend complexity     The data shows less AI automation in complex backend systems. Consider specializing in areas that require deeper system knowledge like distributed systems, security, or performance optimization—domains where context and specialized knowledge still give humans the edge. 3. Position yourself at the startup-enterprise bridge     With startups adopting AI coding tools faster than enterprises, there's a growing opportunity for developers who can bring AI-accelerated development practices into traditional companies. Could you be the champion who helps your organization close this gap? How to prepare: - Learn prompt engineering for code generation - Build a personal workflow that combines your expertise with AI assistance - Start tracking which of your tasks AI handles well vs. where you still outperform it - Experiment with specialized AI coding tools now, even if your company hasn't adopted them - Focus your learning on architectural thinking rather than syntax mastery The developer role isn't disappearing—it's evolving. Those who adapt their skillset to complement AI rather than compete with it will find incredible new opportunities. Have you started integrating AI tools into your development workflow? What's working? What still requires the human touch?