Episode #20: Meta’s Comeback, Design Disruption, and How AI is Redefining Tech Skills
Meta again trying to make a Smartwatch Revival with Cameras
Meta is bringing back its smartwatch project after stopping it in 2022. This time, the focus is on cameras and AI, not health tracking. The new watch is designed to work with Meta’s VR headsets and smart glasses, so you can take photos, make video calls, and use smart features from your wrist. We might see an official reveal at the Meta Connect conference in September. The big question: Will people actually want a camera on their watch?
Why “Designer” May Soon Be an Outdated Role in Tech
Design isn’t just about looks anymore—it’s about understanding how AI really works. As AI tools get smarter and can do more on their own, design has shifted to more of a supporting role, while the real innovation happens in engineering and product teams. With easy-to-use tools and polished templates, good design has become the starting point, not something special. Today, the biggest impact and best opportunities go to people who know how to build and optimize AI systems—not just make things look nice. If you want a bigger role in tech and the chance to shape the future, learning technical skills and diving deep into AI matters more than ever. The future belongs to those who aren’t afraid to get technical and help build what comes next.
Focus Time Is Overrated: Why Impactful Engineers Leave the IDE
Some engineers believe they need long hours of uninterrupted focus to do their best work, but that’s not always true. While deep coding can feel great, your real job is to make an impact—like solving key problems, finding mistakes early, or helping your team move faster. Often, the most valuable moments happen outside the code editor: asking smart questions in meetings, clarifying assumptions before building, or just being available to help others. The best engineers balance focused coding with staying connected to the team. Productivity isn’t measured by hours spent coding, but by the value you deliver—did you help ship something important or prevent a costly error? Find the right rhythm to mix solo work with teamwork, communicate your schedule, and remember: solving the right problems usually matters more than just writing more code. Sometimes your biggest win comes when you step away from the keyboard.
Three Ways to Think About Software Complexity—And Why It Matters
Software complexity means how hard a program is to understand or change. Experts describe it in different ways:
- Rich Hickey says things are simple if they do just one thing and aren’t tangled up with other parts. The more your code mixes together different jobs, the more confusing it gets.
- John Ousterhout thinks complexity is anything that makes software hard to work with—like too many dependencies, or when you have to search all over to make a small change.
- Zach Tellman explains complexity as how much you’d have to explain to someone else so they can understand or fix your code. In short, complexity is anything that makes your code harder to read, change, or explain. Keeping things simple and clear makes software much easier for everyone.
Coding with AI: Why Strategy, Not Syntax, Is the New Superpower
AI coding assistants are great at turning your ideas into working code, but your biggest value is in architecting the big picture—not just writing syntax. Before you start coding, focus on designing how parts of your system interact, pick the patterns that will scale, and clarify what problems you’re actually solving. Write out clear specifications so the AI doesn’t build the wrong thing—treat specs as first-class files in your project. Let AI handle the boilerplate and routine tasks while you review its work carefully. Always double-check AI-generated code; trust your instincts if something feels off. The best results come from giving exact instructions, asking your AI to explain its thinking, and double-checking tricky problems with multiple tools. Embrace planning—map out major changes and review tasks as you go. Keep your documentation updated so everyone (including the AI) is always on the same page. Remember, as AI gets better at code, your job is to make smart decisions, ask the right questions, and steer the project. The teams that succeed use AI as a partner, not a replacement, unlocking more time for strategy and creative problem solving.