Software Engineering Daily

Software Engineering Daily

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Software Engineering Daily episodes

  • Cross-functional Incident Management with Ashley Sawatsky and Niall Murphy
    Incident management is the process of managing and resolving unexpected disruptions or issues in software systems, especially those that are customer-facing or critical to business operations. Implementing a robust incident management system is often a key challenge in technical environments. Rootly is a platform to handle incident management directly from Slack, and is used by
    51 min
  • AI for Software Delivery with Birgitta Böckeler
    AI-assisted software delivery refers to the utilization of artificial intelligence to assist, enhance, or automate various phases of the software development lifecycle. AI can be used in numerous aspects of software development, from requirements gathering to code generation to testing and monitoring. The overarching aim is to streamline software delivery, reduce errors and, ideally, reduce
    1 hr 2 min
  • Expanding B2B Payments with Sohil Pandya
    The Buy Now, Pay Later model, or BNPL, is traditionally a business-to-consumer model that is gaining traction in the business-to-business domain. Adoption of BNPL in business-to-business scenarios offers benefits like improved cash flow management for buyers, and increased sales for suppliers, often leading to higher order value. However, this model introduces complexities, including the need
    52 min
  • Database Caching with Ben Hagan
    Database caching is a fundamental challenge in database management and there are hundreds of techniques to satisfy different caching scenarios. PolyScale is a fully automated database cache. It offers an innovative approach to database caching, leveraging AI and automated configuration to simplify the process of determining what should and should not be cached. Ben Hagan
    36 min
  • An update about SED from Jeff’s family
    In memory of Software Engineering Daily Founder, Jeff Meyerson. 1988 – 2022 Jeff founded Software Engineering Daily in 2015 and hosted the podcast until 2022. He was willful, hard working, and a strong advocate for self-direction. To hear more about what Jeff was like, please listen to Remembering Jeff Meyerson with Erika Hokanson. If you’d
    2 min
  • Simplifying Documentation with Sébastien Lorber
    In the realm of modern software development, clear and comprehensive documentation is essential for guiding users and contributors alike. Developers often struggle to create, manage, and maintain documentation that is both user-friendly and visually appealing. In this episode, we sit down with Sebastian Lorber, the lead maintainer of Docusaurus, a widely acclaimed open-source static site
    50 min
  • SDKs for your API with Sagar Batchu
    APIs are ubiquitous and critical to building modern software, and developers must frequently develop custom APIs to streamline user access to their services. However, making an API that provides a great developer experience can be a time-consuming endeavor. As a result, API teams often leave the final mile of integration up to their users. Speakeasy
    38 min
  • Ethical GPTs with Amruta Moktali
    Generative pre-trained transformer models, or GPT models, have countless applications and are being rapidly deployed across a wide range of domains. However, using GPT models without appropriate safeguards can lead to leakage of sensitive data. This concern underscores the critical need for privacy and data protection. Skyflow LLM Privacy Vault prevents sensitive data from reaching
    52 min
  • CAP Theorem 23 Years Later with Eric Brewer
    The CAP theorem, also known as Brewer’s theorem, is a fundamental principle in distributed systems that states that it is impossible to simultaneously achieve three desirable properties in a distributed data system: Consistency, Availability, and Partition tolerance. Eric Brewer is the VP of Infrastructure & Google Fellow at Google and he joins us today. This
    47 min
  • Data-Centric AI with Alex Ratner
    Companies have high hopes for Machine learning and AI to support real-time product offerings, prevent fraud and drive innovation. But there was a catch – training models require labeled data that machines can digest. As data volumes increase, the opportunity to get great ML results rises, but so does the problem of labeling all the
    51 min

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Technical interviews about software topics.

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