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Coding with AI changes everything. It changes how we design, test, and improve our software projects. Today, I’m talking to generative AI expert James Phoenix.
He’s written the book on prompt engineering and shares his hard-earned AI insights freely on the show — including a crash course in developing effectively with the Cursor IDE.
Are developers just AI wranglers now? Technical managers? Will we ever code again?
You’ll find out today.
This episode is sponsored by Paddle.com — if you're looking for a payment platform that works for you so you can focus on what matters, check them out.
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In this section, James Phoenix discusses how the role of software developers is transforming due to AI advancements. He emphasizes the shift from traditional coding to an AI-first approach, where engineers act more like project managers, overseeing AI tools that generate high-quality code quickly. With large language models (LLMs) now able to produce effective code almost instantly, the way engineers interact with programming has evolved significantly.
James raises concerns about a potential skills gap, where developers, particularly those using AI-assistant tools, may lose the foundational knowledge needed to understand the code they produce. He advises developers to remain grounded in the lower-level primitives of coding languages and technologies to avoid becoming overly reliant on AI tools.
The discussion then shifts to Cursor IDE, with James sharing insights on how to effectively utilize this AI-powered coding environment. He explains the various modes in Cursor — inline edits, chat mode, and composer mode — and suggests best practices for each, including taking advantage of the problems tab to resolve errors efficiently.
Addressing the importance of testing, James advocates for integration testing as a more efficient method for indie developers. He outlines a balanced approach to testing that emphasizes the value of quickly implemented tests while also allowing for adaptability as projects evolve. He introduces the idea of 'defensible programming' as a strategy for catching errors in runtime rather than relying solely on pre-defined testing.
Finally, the conversation explores the future landscape of software engineering in light of AI's continued evolution. James reflects on how new frameworks might struggle to gain traction if they aren't supported by robust AI training data, and he shares optimism for future advancements in LLMs that will reduce the current limitations faced by developers.
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