Loading…

Subscribe to the podcast

Get The Stack Overflow Podcast at your favorite listening service.

Being unambiguous in what you want: the software engineer in a vibe coding world

Quinn Slack, CEO and co-founder of Sourcegraph, joins the show to dive into the implications of AI coding tools on the software engineering lifecycle. They explore how AI tools are transforming the work of developers from syntax-focused tasks to higher-level design and management roles and how AI will integrate into enterprise environments.

Diving into the results of the 2025 Developer Survey

Ryan and Eira welcome Erin Yepis, Senior Analyst at Stack Overflow, to the show to discuss the newly released 2025 Developer Survey results. They explore the decline in trust in AI tools, shifts in popular programming technologies, and the patterns Erin saw in salary growth among developers.

How your favorite movie is changing language learning technology

Ryan sits down with CTO Aruna Srivastava and CPO Ruslan Mukhamedvaleev from Koel Labs to talk about how they’re innovating speech technology with the help of AI and classic movies. They also tell Ryan about their time in the Mozilla Builders Accelerator and their experiences as student co-founders in an ever-changing economic and technological landscape.

Attention isn’t all we need; we need ownership too

Ryan welcomes Illia Polosukhin, co-author of the original "Attention Is All You Need" Transformers paper and co-founder of NEAR, on the show to talk about the development and impact of the Transformers model, his perspective on modern AI and machine learning as an early innovator of the tech, and the importance of decentralized, user-owned AI utilizing the blockchain.

“We’re not worried about compute anymore”: The future of AI models

Ryan Donovan and Ben Popper sit down with Jamie de Guerre, SVP of Product at Together AI, to discuss the evolving landscape of AI and open-source models. They explore the significance of infrastructure in AI, the differences between open-source and closed-source models, and the ethical considerations surrounding AI technology. Jamie emphasized the importance of leveraging internal data for model training and the need for transparency in AI practices.