Foojay Podcast #62: Better Coding with AI: Friend or Enemy?
AI tools promise faster code and smarter suggestions, but how far can we trust them inside a real Java project? In this Foojay Podcast #62, we sat down with Grace Jansen, Sean Li, John Sterken, David Vlijmincx, Urs Peter, and Joost Kaan to compare notes on what works, what breaks, and where developers still need to stay in the driver’s seat.
What we talked about
- LangChain4J and bringing LLMs into Java applications
- Jakarta EE and MicroProfile in AI-powered workloads
- How Large Language Models actually help during coding
- IBM Watson Code Assistant inside VS Code
- Java at Microsoft and its AI-focused products
- Project Panama and its role for AI workloads
- Generative AI and the limits of LLM output
- Organizing an AI-focused conference for Java developers
What stood out
The panel agrees that AI helps us work faster and smarter. The same panel warns that placing too much trust in these tools causes real problems. The right balance lives somewhere between using AI for the boring parts and keeping a sharp eye on what it produces.
See the Foojay Podcast #62 for all info, shownotes, links, etc.