Despite having worked on AI since I was a teenager, I’m now more excited than ever about what we can do with it, especially in building AI applications. Sparks are flying in our field, and 2025 will be a great year for building! One aspect of AI that I’m particularly excited about is how easy it is to build software prototypes. AI is lowering the cost of software development and expanding the set of possible applications. While it can help extend or maintain large software systems, it shines particularly in building prototypes and other simple applications quickly. If you want to build an app to print out flash cards for your kids (I just did this in a couple of hours with o1’s help), or write an application that monitors foreign exchange rates to manage international bank accounts (a real example from DeepLearning.AI’s finance team), or analyzes user reviews automatically to quickly flag problems with your products (DeepLearning.AI's content team does this), it is now possible to build these applications quickly through AI-assisted coding. I find AI-assisted coding especially effective for prototyping because (i) stand-alone prototypes require relatively little context and software integration and (ii) prototypes in alpha testing usually don’t have to be reliable. While generative AI also helps with engineering large, mission-critical software systems, the improvements in productivity there aren't as dramatic, because it’s challenging to give the AI system all the context it needs to navigate a large codebase and also to make sure the generated code is reliable (for example, covering all important corner cases). Until now, a huge friction point for getting a prototype into users’ hands has been deployment. Platforms like Bolt, Replit Agent, Vercel V0 use generative AI with agentic workflows to improve code quality, but more importantly, they also help deploy generated applications directly. (While I find these systems useful, my own workflow typically uses an LLM to design the system architecture and then generate code, one module at a time if there are multiple large modules. Then I test each module, edit the code further if needed — sometimes using an AI-enabled IDE like Cursor — and finally assemble the modules.) Building prototypes quickly is an efficient way to test ideas and get tasks done. It’s also a great way to learn. Perhaps most importantly, it’s really fun! (At least I think it is. 😄) How can you take advantage of these opportunities in the coming year? As you form new year resolutions, I hope you will: - Make a learning plan! To be effective builders, we all need to keep up with the exciting changes that continue to unfold. How many short courses a month do you want to take in 2025? If you discuss your learning plan with friends, you can help each other along. For instance, we launched a learning summary page that shows what short courses people have taken. A few DeepLearning.AI team members have agreed to a friendly competition to see who can take more courses in 2025! - Go build! If you already know how to code, I encourage you to build prototypes whenever inspiration strikes and you have a spare moment. And if you don’t yet code, it will be well worth your while to learn. Even small wins — like the flash cards I printed out, which inspired my daughter to spend an extra 20 minutes practicing her multiplication table — make life better. Perhaps you’ll invent something that really takes off. And even if you don’t, you’ll have fun and learn a lot along the way. [Original text: deeplearning.ai/the-batch/is… ]