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About this Podcast
Intended for architects and senior developers.
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Building a More Appealing CLI for Agentic LLMs Based on Learnings from the Textual Framework
Will McGugan, the maker of Textual and Rich frameworks, speaks about the reasoning of developing the two two libraries and the lesson learned. Also, he shares light on Toad, his current project, which he envisions being a more visually appealing way of interacting with agentic LLMs through command line.
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Platform Engineering for AI: Scaling Agents and MCP at LinkedIn
QCon AI New York Chair Wes Reisz talks with LinkedIn’s Karthik Ramgopal and Prince Valluri about enabling AI agents at enterprise scale. They discuss how platform teams orchestrate secure, multi-agentic systems, the role of MCP, the use of foreground and background agents, improving developer experience, and reducing toil.
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GenAI Security: Defending Against Deepfakes and Automated Social Engineering
In this episode, QCon AI New York 2025 Chair Wes Reisz speaks with Reken CEO and Google Trust & Safety founder Shuman Ghosemajumder about the erosion of digital trust. They explore how deepfakes and automated social engineering are scaling cybercrime and argues defenders must move beyond default trust, utilizing behavioral telemetry and game theory to counter attacks that simulate human behavior.
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Cloud Security Challenges in the AI Era - How Running Containers and Inference Weaken Your System
Marina Moore, a security researcher and the co-chair of the security and compliance TAG of CNCF, shares her concerns about the security vulnerabilities of containers. She explains where the issues originate, providing solutions and discussing alternative routes to using micro-VMs rather than containers. Additionally, she highlights the risks associated with AI inference.
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If You Can’t Test It, Don’t Deploy It: The New Rule of AI Development?
Magdalena Picariello reframes how we think about AI, moving the conversation from algorithms and metrics to business impact and outcomes. She champions evaluation systems that don't just measure accuracy but also demonstrate real-world business value, and advocates for iterative development with continuous feedback to build optimal applications.
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About this Podcast
Discussions on intentional corporate culture and software management approaches.
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Transforming Life Sciences: AI, Vibe Coding, and Drug Development Acceleration
In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Satish Kothapalli about the transformative impact of AI and vibe coding in life sciences software development, the acceleration of drug development timelines, and the evolving roles of developers in an AI-augmented environment.
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AI Amplifies Team Strengths and Weaknesses in Software Development
In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Jon Kern and Anita Zbieg about how AI amplifies both delivery efficiency and weaknesses in development teams, the importance of fundamental collaboration practices, and maintaining holistic system thinking.
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Building a Product-First Engineering Culture in the Age of AI
In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Zach Lloyd about building a product-first engineering culture, and the critical importance of developers learning to effectively use AI tools while maintaining responsibility for code quality and understanding fundamental programming principles.
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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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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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