One of the biggest mistakes developers are making in the 2026 job market is confusing a finished project with proof of engineering ability. Y'all need to understand something: Nobody is hiring you because your portfolio has a cool application. They ARE hiring you because of the engineering conversations that application allows you to have. Think about two candidates walking into an interview with the exact same AI-powered project. - Candidate #1 explains what the application does, which AI tools they used, and shows off the features. - Candidate #2 explains WHY they designed it the way they did. Where they used deterministic logic instead of AI. How they handle unreliable model outputs. What happens when the system fails. What tradeoffs they made and WHY. Same exact project. Two completely different interviews. One demonstrated that they could produce software. The other demonstrated that they understood how to engineer it. And this is why I keep pushing developers to understand the decisions they're generating with AI. I don't really care if AI wrote 100% of your code. But if you can't explain the architectural decisions, defend your tradeoffs, or identify where things could go wrong, you've missed one of the biggest opportunities that project was supposed to give you. AI lowered the barrier to building software. It did NOT lower the bar for understanding it and making the right decisions. We got into this on the latest episode of The Programming Podcast because I think developers need to fundamentally rethink what they're trying to accomplish with portfolio projects. #code #coding #programming #softwareengineering

Learn how to become a better software engineer from an Apple veteran with over 12 years of experience building products. This The Programming Podcast episode is with @RayFernando1337 and @leonnoel! 00:00 Meet Ray Fernando: 12 Years at Apple 01:51 How Ray Uses AI Agents to Build Software 05:24 The Real Cost of AI Agent Workflows 07:25 Automating Email with AI 10:52 A Word from Our Sponsors 13:13 Building Reliable AI Agents and Verification Systems 18:31 How to Keep Up with AI as a Software Engineer 21:01 Skill Chaining: Building Better AI Workflows 24:05 CSV vs. JSON and Why AI Constraints Matter 26:32 Claude vs. Codex vs. Gemini: Choosing the Right Model 31:17 Leon's AI Workflow for Shipping Code Faster 35:41 Behind the Scenes of Undersold AI 39:01 When Is an AI Product Ready to Launch? 43:04 Why Danny Walked Away from Two Software Projects 48:40 Fail, Pivot, Grow: Turning Failure into Progress 49:13 From Wedding DJ to Apple Engineer 53:09 How to Build Relationships and Network in Tech 58:19 What It Takes to Succeed as an Engineer at Apple 1:02:56 What Separates Great Engineers from Average Engineers? 1:06:50 Listener Q&A: What AI Portfolio Project Should You Build? 1:10:52 Why Most Developers Build the Wrong Portfolio Projects 1:14:41 Final Thoughts and Outro