What the Dev?

SD Times
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What the Dev? is a podcast by the SD Times editorial team. We cover the biggest and newest topics in software development.

  1. 6 дн. тому

    365: The Rise of the Personal AI Assistant (With Gavriel Cohen of NanoCo)

    Dave Rubinstein interviews Gavriel Cohen, creator of NanoClaw, about the state of the project, an open-source multi-agent orchestration tool released in February. NanoClaw quickly gained popularity, with over 30,000 stars and 13,000 forks on GitHub, and a community of 5,000 in Discord. Cohen emphasizes the project's focus on agent isolation, credential management, and policy enforcement to prevent agents from going rogue. He discusses the importance of continuous testing and end-to-end frameworks to adapt to rapidly changing AI models. Cohen also highlights the need for robust security measures, including dedicated AI security agents, to protect against sophisticated attacks and zero-day vulnerabilities.

    365: The Rise of the Personal AI Assistant (With Gavriel Cohen of NanoCo)

  2. 25 серп.

    364: AI is Changing Who Builds Software

    Dave Rubinstein discusses with Tim Qi, lead data analyst at Linear, how AI is changing who builds software. Linear's study found that AI is accelerating team velocity and changing behavior on platforms. Companies are adopting AI in two main ways: top-down, tightly controlled rollouts, and bottom-up, allowing experimentation. Training varies, often evolving with use cases. AI adoption is similar across company sizes. The study also revealed that AI tools are making previously cumbersome tasks more manageable, leading to higher quality outputs. The industry is still figuring out how to manage the shadow AI issue.

    364: AI is Changing Who Builds Software

  3. 18 серп.

    363: The Role of AI in Mainframe Modernization

    Dave Rubinstein and Matt Whitbourne discuss the evolution and potential of mainframe modernization, particularly with the integration of AI. They highlight the long-standing challenge of maintaining legacy systems, such as COBOL, and the role of AI in enhancing innovation and efficiency. Whitbourne emphasizes the importance of understanding business logic before modernizing and the potential for AI to assist in this process. He also notes the benefits of AI in improving code serviceability, risk management, and compliance. The conversation underscores the need for a strategic approach to mainframe modernization, focusing on business goals and the unique characteristics of mainframe workloads.

    363: The Role of AI in Mainframe Modernization

  4. 11 серп.

    362: The Disconnect Between AI-generated Code and Security

    Dave Rubinstein interviews Ilya Kabanov, a former Google AI security manager, about the disconnect between AI's rapid code generation and security measures. Kabanoff, who leads the Weather Report and is a research affiliate at MIT, discusses the significant progress AI has made in writing functional code, citing a 4x increase in security over a year. However, security checks cost five times more than code writing, posing economic challenges. Kabanov emphasizes the need for education, understanding AI's limitations, and prioritizing fundamental security practices to ensure secure AI-generated code.

    362: The Disconnect Between AI-generated Code and Security

  5. 21 лип.

    361: The AI Adoption Maturity Model (With Ipek Ozkaya of CMU SEI)

    Dave and Ipek Ozkaya discuss the AI adoption maturity model created by Carnegie Mellon's Software Engineering Institute and Accenture. The model addresses issues like mismatched expectations, untested implementations, and misaligned applications that hinder AI investment returns. It includes eight core dimensions: organizational change, workforce and culture, workflow reengineering, risk and governance, data engineering, operations, technology ecosystem, and AI lifecycle engineering. The model progresses from exploratory AI to scaled AI, emphasizing the importance of strategic alignment, workflow reengineering, and ecosystem partnerships.

    361: The AI Adoption Maturity Model (With Ipek Ozkaya of CMU SEI)

  6. 30 черв.

    360: How do you nurture junior developers in an AI world? (With Barun Singh of Andela)

    SD Times editor-in-chief Dave Rubinstein and Barun Singh of tech talent company Andela discuss the impact of AI on nurturing future developers. Singh emphasizes that while AI automates coding basics, core knowledge and critical thinking are still essential. He highlights the importance of code review, planning, and security, which require senior experience. Singh believes colleges should focus on foundational knowledge, and boot camps remain relevant due to their immersive learning approach. He notes a shift towards retraining existing talent rather than just recruiting new workers.

    360: How do you nurture junior developers in an AI world? (With Barun Singh of Andela)

  7. 23 черв.

    359: The Enduring Popularity of Postgres, and what lies ahead (With Snowflake's Craig Kerstiens)

    In this episode, David Rubinstein talks with Craig Kerstiens, Snowflake's Director of Software Engineering on Postgres, about the enduring popularity of Postgres, a 30-year-old database technology, due to its reliability and recent innovations like JSON support and vector store databases. He highlighted the challenge of integrating operational (OLTP) and analytical (OLAP) data for AI applications, noting the importance of reducing latency and operational burden. Kerstiens introduced PG Lake, an open-source tool that embeds Iceberg in Postgres, simplifying data movement and querying. He also mentioned Snowflake's mirroring feature and the future of Postgres, emphasizing its continuous improvement and extension ecosystem.

    359: The Enduring Popularity of Postgres, and what lies ahead (With Snowflake's Craig Kerstiens)

  8. 16 черв.

    358: The limitations of AI models in understanding context (With Jonathan Macoskey)

    Dave Rubinstein and Jonathan Macoskey discuss the limitations of current AI models in understanding context, particularly in enterprise settings. Macoskey explains that while language models have improved with internet connectivity, they still struggle with outdated information and proprietary data. He highlights Lovelace's context engine, which integrates data from various sources into a knowledge graph, addressing issues like entity resolution and recall. This system ensures AI agents can provide accurate, cited information, enhancing trust and decision-making. The conversation also touches on the challenges of scaling large language models and the importance of grounding AI responses in reliable data.

    358: The limitations of AI models in understanding context (With Jonathan Macoskey)
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Опис

What the Dev? is a podcast by the SD Times editorial team. We cover the biggest and newest topics in software development.

Інформація

  • Автор
    SD Times
  • Роки діяльності
    2019 - 2026
  • Епізоди
    365
  • Категорія
    Не відверті
  • Авторське право
    © 2026 What the Dev?
  • Вебсайт шоу
    What the Dev?

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