How to Use AI Instead of Traditional Coding Skills

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!

Everyone thinks AI coding agents can replace engineers. This is wrong. Here’s how to actually make them useful: Agents generate code that almost works. Engineers close the loop: prompt → review → edit → test → repeat. The teams that win are the ones who make that loop fast and tight. Most teams are still stuck in “ask once, hope it compiles” habits. But here’s what actually works now: 1. Give agents real context → Keep a project guide (agents[.]md, rules[.]md, etc.) → Define goals, edge cases, and examples → Make it clear what “done” looks like before asking for code 2. Treat agents like junior devs → First: ask them to explain changes → Turn that into a clear checklist → Decide how much autonomy they get each run 3. Stay inside a diff-first loop → Always review diffs before merging → Separate shell commands from prompts → Reference files directly to tighten context 4. Edit small things yourself → Quick copy or logic fixes are faster by hand → Recompile and test right after each accepted diff → Let the agent continue in parallel on the next task 5. Capture what you learn → Document tricky fixes for reuse → Save commands, patterns, and notes → Every iteration gets faster with history 6. Ship with guardrails → Commit and PR from the terminal → Keep human review in the loop → Ship confidently, not blindly Agents don’t replace engineers. They amplify engineers who know how to run a tight feedback loop. Here's the link if you want to start building: https://go.warp.dev/aadit

90% of engineers using AI coding tools are doing it wrong. They're treating AI like a code monkey. Fire prompt → Get code → Accept all changes → Ship. That's why we see 128k-line AI pull requests that became memes (look this up, it's a fun read). After spending quite a bit of time using AI dev tools, I discovered the real game isn't about generating more code faster. It's about rapid engineering while managing cognitive load. My workflow now: 1. Start with AI-generated system diagrams 2. Ask questions until I understand the architecture 3. Create detailed change plans 4. Break down into AI-manageable chunks 5. Maintain context throughout This isn't coding. It's orchestration. The best engineers aren't typing anymore. They're conducting symphonies of AI agents, each handling specific complexity while the human maintains the vision. Think about it → We're moving from IDEs to "Cognitive Load Managers." Tools that auto-generate documentation, visualize dependencies in real-time, and explain impact before you commit. The future isn't AI writing code. It's AI helping you understand what code to write. The billion-dollar opportunity? Build the tool that turns every engineer into a systems architect who happens to code. We're not being replaced. We're being promoted. Who else sees this shift? #AI #SoftwareEngineering #DevTools #FutureOfCoding #TechLeadership

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?