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Software Engineering Daily

Software Engineering Daily

Technical interviews about software topics.

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  • 22 episodes
  • a few times a week
  • Avg 58 min
  • English

Episodes 22

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  • August 20 · 1 hr 12 min

    AI and the New Global Security Landscape

    The conversation about AI often focuses on software, automation, and the race between attackers and defenders in code. However, some of the most consequential risks lie further afield, in domains where a mistake is measured in human lives. Advanced models can now offer step-by-step guidance toward chemical and biological weapons, and militaries are already folding AI into targeting and battlefield assessments. These are no longer speculative fears confined to the AI doomer crowd. They are documented in red team disclosures, government legislation, and events unfolding on real battlefields. Gordon M. Goldstein is an adjunct senior fellow at the Council on Foreign Relations, where he focuses on the convergence of technology and US foreign policy. He previously spent nearly a decade as a managing director at Silver Lake, and he is the author of Lessons In Disaster: McGeorge Bundy and the Path to War in Vietnam, which is a study of national security strategy and White House decision-making. In this episode, Gordon joins Kevin Ball to discuss the credibility of AI-enabled chemical and biological weapon threats, the ad hoc safeguards meant to contain them, and the rise of autonomous warfare and its implications for human control and nuclear deterrence. Gordon speaks on his own behalf, not on behalf of the Council on Foreign Relations. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post AI and the New Global Security Landscape appeared first on Software Engineering Daily.

  • August 18 · 47 min

    How LLMs Are Reshaping Recommendation Systems

    News feeds and recommendation systems have long relied on deep learning architectures that score each candidate item independently. As LLMs have matured, they have opened up a fundamentally different approach, where a system can reason about content the way it reasons about language. However, that power comes with a fresh set of engineering challenges around cost, scale, and evaluation. LinkedIn recently rebuilt its news feed to treat content recommendation as a sequence modeling problem. The general approach is to predict what a user will want next, much like an LLM predicts the next token in a sentence. Tim Jurka has worked at LinkedIn for 13 years and is currently a VP of Engineering. In this episode, Tim joins Matt Merrill to discuss how LinkedIn re-engineered its feed, how the team combines LLMs with traditional signals, managing inference costs at massive scale, steering content quality using natural language policies, and more. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post How LLMs Are Reshaping Recommendation Systems appeared first on Software Engineering Daily.

Showing 21–22 of 22 episodes