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SE Radio 724: Jure Leskovec on Relational Graph and Foundational Models

1 h 2 min · Eilen
jakson SE Radio 724: Jure Leskovec on Relational Graph and Foundational Models kansikuva

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Jure Leskovec, Professor of Computer Science at Stanford University and Chief Scientist at Kumo.ai, speaks with host Sriram Panyam about relational and graph language models and their transformative impact on enterprise decision-making and predictive modeling. Jure begins by establishing the critical importance of predictive modeling across industries - from fraud detection in financial institutions to customer churn prediction, lifetime value estimation, product recommendations, and healthcare risk assessment. He notes that while AI has made remarkable advances in natural language understanding and computer vision, predictive modeling over enterprise operational data stored in relational databases has been largely left behind, still relying on 30-year-old machine learning approaches that are expensive, time-consuming, and require manual feature engineering. His proposed solution to the fundamental problem with current approaches is relational deep learning and relational transformers. The discussion explores how this approach differs from traditional graph neural networks (GNNs), which Jure pioneered and deployed successfully at Pinterest. Jure concludes with practical guidance for software engineers and data scientists interested in exploring this technology.

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jakson SE Radio 724: Jure Leskovec on Relational Graph and Foundational Models kansikuva

SE Radio 724: Jure Leskovec on Relational Graph and Foundational Models

Jure Leskovec, Professor of Computer Science at Stanford University and Chief Scientist at Kumo.ai, speaks with host Sriram Panyam about relational and graph language models and their transformative impact on enterprise decision-making and predictive modeling. Jure begins by establishing the critical importance of predictive modeling across industries - from fraud detection in financial institutions to customer churn prediction, lifetime value estimation, product recommendations, and healthcare risk assessment. He notes that while AI has made remarkable advances in natural language understanding and computer vision, predictive modeling over enterprise operational data stored in relational databases has been largely left behind, still relying on 30-year-old machine learning approaches that are expensive, time-consuming, and require manual feature engineering. His proposed solution to the fundamental problem with current approaches is relational deep learning and relational transformers. The discussion explores how this approach differs from traditional graph neural networks (GNNs), which Jure pioneered and deployed successfully at Pinterest. Jure concludes with practical guidance for software engineers and data scientists interested in exploring this technology.

Eilen1 h 2 min
jakson SE Radio 722: Dwayne McDaniel on the Engineering Challenges of Secrets Management kansikuva

SE Radio 722: Dwayne McDaniel on the Engineering Challenges of Secrets Management

Dwayne McDaniel [https://trail.gitguardian.com/api/t/c/usr_XgQEQPgwFZ78282oE/tsk_CSQx5hsyFYHpAHx2r/enc_U2FsdGVkX19aR9lGtbabCxEhb9Yde_hsokM0Br2H8cO0MuhkXtGOlxqoSa2kzhx9AJEkM4SrYvH4PEzf842ZL9fm-omZUuEVXLdnzhA74ugphvs8lMXgwE63YENVZ9Ax], developer advocate at GitGuardian.com [http://gitguardian.com/], joins host Priyanka Raghavan to talk about the engineering challenges of secrets management. They explore what "secrets" really are in modern systems—far beyond passwords—including API keys, tokens, certificates, and machine identities, and how "secret sprawl" emerges across the SDLC. Drawing on reports from GitGuardian and Verizon, they discuss the growing scale of secret leaks and why credential abuse and phishing remain dominant attack vectors. They examine common leak points—from code repos and logs to CI/CD pipelines, containers, and SaaS integrations—and how cloud, DevOps, and AI tooling are amplifying risks. Priyanka quizzes Dwayne about recent supply chain attacks from pyPi and trivy ecosystems, highlighting recurring root causes like poor access control, long-lived credentials, and weak security hygiene. Finally, they consider detection, response, and modern solutions—short-lived credentials, secret scanning, and identity-based approaches like OWASP NHIR and SPIFFE/SPIRE—ending with practical advice for engineers to reduce blast radius and design for secure secret lifecycle management.

27. touko 202652 min
jakson SE Radio 721: Rob Moffat on Risk-First Software Development kansikuva

SE Radio 721: Rob Moffat on Risk-First Software Development

In this episode, Rob Moffat, author of Risk-First Software Development and chief technical architect at the FinTech Open Source Software Foundation (FINOS), speaks with host Brijesh Ammanath about how all of software development is actually risk management. Rob introduces the concept of 'risk-first software development,' which sits in the context of existing methodologies like scrum and kanban. Showcasing multiple real-world project patterns to illustrate how things can go wrong when risk is ignored, he makes the case for why risk should be the primary lens behind every development decision, from architecture to prioritization. Through various examples, he shows how every developer action can be viewed as a risk trade-off and why making that explicit can lead to better outcomes. The conversation takes a deep dive into the risk-first framework and how teams can apply it in their existing processes.

20. touko 202652 min