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About this Podcast
Intended for architects and senior developers.
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Effective Error Handling: a Uniform Strategy for Heterogeneous Distributed Systems
Jenish Shah, a back-end engineer focused on distributed systems at Netflix, provides more insights into how to handle failures in a distributed systems setup. He shares details on how he built a library that handles exceptions uniformly, regardless of the underlying communication protocol.
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Cloud and DevOps InfoQ Trends Report 2025
In this episode of the podcast, members of the InfoQ editorial staff and friends of InfoQ will discuss current trends in the cloud and DevOps domains as part of our annual trends report creation process. These reports provide InfoQ readers with a high-level overview of key topics to watch and also help the editorial team focus on innovative technologies.
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Mental Models in Architecture and Societal Views of Technology: a Conversation with Nimisha Asthagiri
In this podcast, Michael Stiefel spoke with Nimisha Asthagiri about the importance of system thinking, multi-agent systems, the consequences of society applying a technology into an area for which it was not designed, and whether we can ever have a healthy relationship with artificial intelligence.
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Elena Samuylova on Large Language Model (LLM)-Based Application Evaluation and LLM as a Judge
In this podcast, InfoQ spoke with Elena Samuylova from Evidently AI, on best practices in evaluating Large Language Model (LLM)-based applications. She also discussed the tools for evaluating, testing and monitoring applications powered by AI technologies.
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The Hidden Vulnerability of the Open Source Software Supply Chain: the Underlying Infrastructure
Software supply chain veteran Brian Fox unpacks the security implications of the new EU Cyber Resilience Act and its profound impact on open-source projects. He reveals the hidden infrastructure risks threatening open-source projects and shares insights for senior software leaders navigating this regulatory landscape.
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About this Podcast
Discussions on intentional corporate culture and software management approaches.
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GitHub Next: how their research and prototyping team operates
In this podcast, Shane Hastie, Lead Editor for Culture & Methods spoke to Idan Gazit and Eddie Aftandilian from GitHub Next how their research and prototyping team operates as a "department of fool around and find out", exploring AI-powered developer tools through rapid experimentation and user feedback.
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Trust-first Leadership and Building Great Teams
In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Natan Žabkar Nordberg about how effective leadership requires treating people as whole humans, giving trust first, implementing guided autonomy with clear boundaries, and building diverse teams through shared experiences.
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From Code to Strategy: Drive Organizational Impact Through Strategic Conversations and User Focus
In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Mark Allen about how engineers can expand their influence through strategic conversations, user-focused development practices, and excellence in incident management. Mark emphasizes the importance of building cross-organizational relationships and working on meaningful problems with positive impact.
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Productivity Through Play: Why Messing Around Makes Better Software Engineers
In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Holly Cummins about productivity in creative knowledge work like software engineering. She talks about how "messing around and having fun" actually enhances problem-solving, while exploring the shift from coding to code management with AI tools and the importance of managing cognitive load in modern development practices.
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Technology Radar and the Reality of AI in Software Development
Shane Hastie, Lead Editor for Culture & Methods spoke to Rachel Laycock, Global CTO of Thoughtworks, about how the company's Technology Radar process captures technology trends around the globe. She is sceptical of the current AI efficiency hype, emphasizing that real value of generative AI tools lies in solving complex problems like legacy code comprehension rather than just writing code faster.
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