If I were starting a new software project today, I wouldn't try to force every task into one AI tool. I'd choose a small toolkit that covers the journey from idea to working product. First, I'd explore Greta.sh for quickly turning a product concept into a website or app prototype. Being able to see an idea as an interface makes it easier to discuss and improve. Next, I'd consider Cursor for projects that need direct code editing and more detailed control. Claude would be useful for thinking through requirements, understanding unfamiliar code, and working through technical questions. Finally, I'd use GitHub to track code changes and maintain a history of the project. Each tool has a different role, which is precisely why combining them can make sense. AI app development is becoming more accessible, but that doesn't mean the planning, testing, and decision-making disappear. A prototype still needs to be checked, and a real product needs appropriate security, data handling, and deployment practices. If you're planning to build a SaaS product, start by identifying your biggest obstacle. Is it getting the first version built, understanding the code, or managing changes? Choose the tool that addresses that problem first. A small, well-chosen AI development stack can be far more practical than subscribing to every tool you see recommended online.
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