TLDR
I built Cauldron, a recipe app that imports from anywhere, generates recipes with on-device AI, and connects you with friends, almost entirely using Claude Code. This post shares what I learned about AI-assisted development: it's not magic, and "I built an app in 5 minutes" is a myth. The real power comes from being explicit, maintaining context, and treating AI as a collaborator that amplifies your vision rather than replaces it.
After publishing my first app PlayCount last year, my dad came to me with a problem: he had no way to organize our family recipes. We'd always talked about creating a family cookbook, but I felt like there could be something better.
Looking at apps on the App Store, every option had a limit or an outdated design. I wanted a modern app that showcases your recipes, lets you categorize and share them, and generates new ones when you don't know what to make. That's how Cauldron came to be.
Cauldron lets you import recipes from anywhere (URLs, YouTube videos, Instagram posts, TikTok) with intelligent parsing that extracts ingredients and steps automatically. It has a cook mode with built-in timers and Live Activities for your lock screen, a smart grocery list that organizes items by category, and on-device AI recipe generation powered by Apple Intelligence. You can connect with friends, share recipes, and browse what others are cooking. It's everything I wished existed in one place.
I built Cauldron almost entirely with Claude Code, and it fundamentally changed how I work. Instead of implementation-first, I went product-first. Prototyping was a prompt away. I could test different UI options side by side instead of building something and hoping it sticks.
But let's be clear: anyone who says they "built an app in 5 minutes" is either lying or built a mediocre app. It takes more than five minutes just to write a decent prompt to get 10% of your vision. LLMs make assumptions and decisions without asking. If I'd just said "make a recipe app with sharing and image support," Claude would have created an app that may compile and run, but wouldn't match my vision.
Models like Claude Opus 4.5 and GPT 5.2-xhigh may already be better engineers than I am, and they're improving faster than any human ever could. But they still can't read your mind. Creating good code with AI has gone from impossible to common, but you must know how to use these tools to get the result you are imagining in your head.
Here are the top lessons I learned (some the hard way) while building Cauldron:
Be explicit. Coding agents rarely ask clarifying questions. They fill in the blanks, leading to redundant code that has to be undone once you realize the app doesn't match your vision. By testing the results and explicitly saying what you want - whether it is a specific modifier or style, it is much easier for the agent to implement if you use technical terms.
Be purposeful. Research platforms, tools, APIs, and frameworks that could improve your app. You can use the coding agent itself like a chat app to brainstorm and develop a shared vision. Always start every session with plan mode and maintain a roadmap.md file so you and the agent stay aligned.
Build big, then chip away. Test everything. Keep a running note of what's broken or not quite right, and feed it back to the agent as you encounter issues. Take breaks from implementing new features so the backlog of issues doesn't exponentially grow.
Enforce modularity. AI agents tend to write long files (1000+ lines). Periodically check file lengths and specify in your CLAUDE.md or AGENTS.md that files must remain short. Otherwise, the agent only reads the first few hundred lines, leading to bugs that slip past the compiler and linger unless thoroughly tested.
Manage context with intention. Maintain a detailed but concise instructions file that tells the agent what it's working on and where to find specific guidance: style guidelines, framework documentation for things released after its training cutoff, roadmaps. Another rising concept is continuity ledgers: telling the agent to track its changes so you can start new sessions exactly where you left off. This saves precious context and improves output quality.
Building with AI is the next frontier of software engineering, turning you into a 100x engineer, product manager, and decision maker in any codebase. As we use these new tools to accelerate our software output, apps will increase in functionality exponentially, becoming personalized for every user with features we've barely begun to explore. This will create even more demand for engineering skills. Engineers who adopt and stay ahead of these AI tools will be the leaders in the agentic
future—their traditional development skills give them a significant head start.
I'm already applying these lessons to my next app, and coming up with new ways I can incorporate AI into my product building workflow. I'm sure I'll write another post soon sharing what else I've been building... If you want to see what building this way looks like, download Cauldron on the App Store, add me by signing up with my referral code (2BEB0E) or searching @nadav, and check out my challah recipe.