The New Stack Podcast

The New Stack Podcast

By The New StackNewsTechnologyTech News
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The New Stack Podcast episodes

  • Hey Programming Language Developer -- Get Over Yourself

    Jean Yang, founder of API observability company Akita Software, emphasizes that programming languages should be shaped by software development needs and data, rather than philosophical ideals. Yang, a former assistant professor at Carnegie Mellon University, believes that programming tools and processes should be influenced by actual use and data, prioritizing the developer experience over the language creator's beliefs. With a background in programming languages, Yang advocates for a shift away from the outdated notion that language developers are building solely for themselves.

    In this discussion on The New Stack Makers, Yang underscores the importance of understanding the reality of developers' needs, especially as developer tools have evolved into a full-time industry. She argues for a focus on UX design and product fundamentals in developing tools, moving beyond the traditional mindset where developer tools were considered side projects.

    Yang founded Akita to address the challenges of building reliable software systems in a world dominated by APIs and microservices. The company transitioned to API observability, recognizing the crucial role APIs play in enhancing the understandability of complex systems. Yang's commitment to improving software correctness and the belief in APIs as key to abstraction and ease of monitoring align with Postman's direction after acquiring Akita. Postman aims to serve developers worldwide, emphasizing the significance of APIs in complex systems.

    Check out more episodes from The Tech Founder Odyssey series:

    How Byteboard’s CEO Decided to Fix the Broken Tech Interview

    A Lifelong ‘Maker’ Tackles a Developer Onboarding Problem

    How Teleport’s Leader Transitioned from Engineer to CEO

    27 min
  • Docker CTO Explains How Docker Can Support AI Efforts

    Docker CTO Justin Cormack reveals that Docker has been a go-to tool for data scientists in AI and machine learning for years, primarily in specialized areas like image processing and prediction models. However, the release of OpenAI's ChatGPT last year sparked a significant surge in Docker's popularity within the AI community.

    The focus shifted to large language models (LLMs), with a growing interest in the retrieval-augmented generation (RAG) stack. Docker's collaboration with Ollama enables developers to run Llama 2 and Code Llama locally, simplifying the process of starting and experimenting with AI applications. Additionally, partnerships with Neo4j and LangChain allow for enhanced support in storing and retrieving data for LLMs.

    Cormack emphasizes the simplicity of getting started locally, addressing challenges related to GPU shortages in the cloud. Docker's efforts also include building an AI solution using its data, aiming to assist users in Dockerizing applications through an interactive notebook in Visual Studio Code. This tool leverages LLMs to analyze applications, suggest improvements, and generate Docker files tailored to specific languages and applications.

    Docker's integration with AI technologies demonstrates a commitment to making AI and Docker more accessible and user-friendly.

    Learn more from The New Stack about AI and Docker:

    Artificial Intelligence News, Analysis, and Resources

    Will GenAI Take Jobs? No, Says Docker CEO

    Debugging Containers in Kubernetes — It’s Complicated

    13 min
  • What Does Open Mean in AI?

    In this episode, Stefano Maffulli, Executive Director of the Open Source Initiative, discusses the need for a new definition as AI differs significantly from open source software. The complexity arises from the unique nature of AI, particularly large language models and transformers, which challenge traditional copyright frameworks. Maffulli emphasizes the urgency of establishing a definition for open source AI and discusses an ongoing effort to release a set of principles by the year's end.

    The concept of "open" in the context of AI is undergoing a significant transformation, reminiscent of the early days of open source. The recent upheaval at OpenAI, resulting in the removal of CEO Sam Altman, reflects a profound shift in the technology community, prompting a reconsideration of the definition of "open" in the realm of AI.

    The conversation highlights the parallels between the current AI debate and the early days of software development, emphasizing the necessity for a cohesive approach to navigate the evolving landscape. Altman's ousting underscores a clash of belief systems within OpenAI, with a "safetyist" community advocating caution and transparency, while Altman leans towards experimentation. The historical significance of open source, with a focus on trust preservation over technical superiority, serves as a guide for defining "open" and "AI" in a rapidly changing environment.

    Learn more from The New Stack about AI and Open Source:

    Artificial Intelligence News, Analysis, and Resources

    Open Source Development Threatened in Europe

    The AI Engineer Foundation: Open Source for the Future of AI

    23 min
  • Debugging Containers in Kubernetes

    DockerCon showcased a commitment to enhancing the developer experience, with a particular focus on addressing the challenge of debugging containers in Kubernetes. The newly launched Docker Debug offers a language-independent toolbox for debugging both local and remote containerized applications.

    By abstracting Kubernetes concepts like pods and namespaces, Docker aims to simplify debugging processes and shift the focus from container layers to the application itself. Our guest, Docker Principal Engineer Ivan Pedrazas, emphasized the need to eliminate unnecessary complexities in debugging, especially in the context of Kubernetes, where developers grapple with unfamiliar concerns exposed by the API.

    Another Docker project, Tape, simplifies deployment by consolidating Kubernetes artifacts into a single package, streamlining the process for developers. The ultimate goal is to facilitate debugging of slim containers with minimal dependencies, optimizing security and user experience in Kubernetes development.

    While progress is being made, bridging the gap between developer practices and platform engineering expectations remains an ongoing challenge.

    Learn more from The New Stack about Kubernetes and Docker:

    Kubernetes Overview, News, and Trends

    Docker Rolls out 3 Tools to Speed and Ease Development

    Will GenAI Take Jobs? No, Says Docker CEO

    16 min
  • Integrating a Data Warehouse and a Data Lake

    TNS host Alex Williams is joined by Florian Valeye, a data engineer at Back Market, to shed light on the evolving landscape of data engineering, particularly focusing on Delta Lake and his contributions to open source communities. As a member of the Delta Lake community, Valeye discusses the intersection of data warehouses and data lakes, emphasizing the need for a unified platform that breaks down traditional barriers.

    Delta Lake, initially created by Databricks and now under the Linux Foundation, aims to enhance reliability, performance, and quality in data lakes. Valeye explains how Delta Lake addresses the challenges posed by the separation of data warehouses and data lakes, emphasizing the importance of providing asset transactions, real-time processing, and scalable metadata.

    Valeye's involvement in Delta Lake began as a response to the challenges faced at Back Market, a global marketplace for refurbished devices. The platform manages large datasets, and Delta Lake proved to be a pivotal solution in optimizing ETL processes and facilitating communication between data scientists and data engineers.

    The conversation delves into Valeye's journey with Delta Lake, his introduction to Rust programming language, and his role as a maintainer in the Rust-based library for Delta Lake. Valeye emphasizes Rust's importance in providing a high-level API with reliability and efficiency, offering a balanced approach for developers.

    Looking ahead, Valeye envisions Delta Lake evolving beyond traditional data engineering, becoming a platform that seamlessly connects data scientists and engineers. He anticipates improvements in data storage optimization and envisions Delta Lake serving as a standard format for machine learning and AI applications.

    The conversation concludes with Valeye reflecting on his future contributions, expressing a passion for Rust programming and an eagerness to explore evolving projects in the open-source community. 

    Learn more from The New Stack about Delta Lake and The Linux Foundation:

    Delta Lake: A Layer to Ensure Data Quality

    Data in 2023: Revenge of the SQL Nerds

    What Do You Know about Your Linux System?

    21 min
  • WebAssembly's Status in Computing

    Liam Crilly, Senior Director of Product Management at NGINX, discussed the potential of WebAssembly (Wasm) during this recording at the Open Source Summit in Bilbao, Spain. With over three decades of experience, Crilly highlighted WebAssembly's promise of universal portability, allowing developers to build once and run anywhere across a network of devices.

    While Wasm is more mature on the client side in browsers, its deployment on the server side is less developed, lacking sufficient runtimes and toolchains. Crilly noted that WebAssembly acts as a powerful compiler target, enabling the generation of well-optimized instruction set code. Despite the need for a virtual machine, WebAssembly's abstraction layer eliminates hardware-specific concerns, providing near-native compute performance through additional layers of optimization.

    Learn more from The New Stack about WebAssembly and NGINX:

    WebAssembly Overview, News and Trends

    Why WebAssembly Will Disrupt the Operating System

    True Portability Is the Killer Use Case for WebAssembly

    4 Factors of a WebAssembly Native World

    24 min
  • PostgreSQL Takes a New Turn

    Jonathan Katz, a principal product manager at Amazon Web Services, discusses the evolution of PostgreSQL in an episode of The New Stack Makers. He notes that PostgreSQL's uses have expanded significantly since its inception and now cover a wide range of applications and workloads. Initially considered niche, it faced competition from both open-source and commercial relational database systems. Katz's involvement in the PostgreSQL community began as an app developer, and he later contributed by organizing events.

    PostgreSQL originated from academic research at the University of California at Berkeley in the mid-1980s, becoming an open-source project in 1994. In the mid-1990s, proprietary databases like Oracle, IBM DB2, and Microsoft SQL dominated the market, while open-source alternatives like MySQL, MariaDB, and SQLite emerged.

    PostgreSQL 16 introduces logical replication from standby servers, enhancing scalability by offloading work from the primary server. The meticulous design process within the PostgreSQL community leads to stable and reliable features. Katz mentions the development of Direct I/O as a long-term feature to reduce latency and improve data writing performance, although it will take several years to implement.

    Amazon Web Services has built Amazon RDS on PostgreSQL to simplify application development for developers. This managed service handles operational tasks such as deployment, backups, and monitoring, allowing developers to focus on their applications. Amazon RDS supports multiple PostgreSQL releases, making it easier for businesses to manage and maintain their databases.

    Learn more from The New Stack about PostgreSQL and AWS:

    PostgreSQL 16 Expands Analytics Capabilities

    Powertools for AWS Lambda Grows with Help of Volunteers

    How Donating Open Source Code Can Advance Your Career

    22 min
  • The Limits of Shift-Left: What’s Next for Developer Security

    The practice of "shift left," which involves moving security concerns to the code level and increasing developers' responsibility for security, is facing a backlash, with both developers and security professionals expressing concerns. Peter Klimek, director of technology at Imperva, discusses the reasons behind this backlash in this episode.

    Some organizations may have exhausted the benefits of shift left, while the main challenge for many isn't finding vulnerabilities but finding time to address them. Security attacks are now targeting business logic vulnerabilities rather than dependencies, which shift left tools are better at identifying. These business logic vulnerabilities are often tied to authorization decisions, making them harder to address through code-level tools. Additionally, attacks increasingly focus on the frontend, such as API development and cart attacks.

    Klimek emphasizes the need for development and security teams to collaborate and advocates for using DORA metrics to assess the impact of security efforts on the development pipeline. Some organizations may reach a point where the tools added to the development lifecycle become counterproductive, he notes. DORA metrics can help determine when this occurs and provide valuable insights for security teams.

    Learn more from The New Stack about Developer Security and Imperva:

    Why Your APIs Aren’t Safe — and What to Do about It

    What Developers Need to Know about Business Logic Attacks

    Are Your Development Practices Introducing API Security Risks?

    23 min
  • How AI and Automation Can Improve Operational Resiliency

    Operational resiliency, as explained by Dormain Drewitz of PagerDuty, involves the ability to bounce back and recover from setbacks, not only technically but also in terms of organizational recovery. True resiliency means maintaining the willingness to take risks even after facing challenges. In a conversation with Heather Joslyn on the New Stack Makers podcast, Drewitz discussed the role of AI and automation in achieving operational resiliency, especially in a context where teams are under pressure to be more productive.

    Automation, including generative AI code completion tools, is increasingly used to boost developer productivity. However, this may lead to shifting bottlenecks from developers to operations, creating new challenges. Drewitz emphasized the importance of considering the entire value chain and identifying areas where AI and automation can assist. For instance, automating repetitive tasks in incident response, such as checking APIs, closing ports, or database checks, can significantly reduce interruptions and productivity losses.

    PagerDuty's AI-powered platform leverages generative AI to automate tasks and create runbooks for incident handling, allowing engineers to focus on resolving root causes and restoring services. This includes drafting status updates and incident postmortem reports, streamlining incident response and saving time. Having an operations platform that can generate draft reports at the push of a button simplifies the process, making it easier to review and edit without starting from scratch.

    Learn more from The New Stack about AI, Automation, Incident Response, and PagerDuty:

    Operationalizing AI: Accelerating Automation, DataOps, AIOps

    Three Ways Automation Can Improve Workplace Culture

    Incident Response: Three Ts to Rule Them All

    Four Ways to Win Executive Buy-In for Automation

    21 min
  • Will GenAI Take Developer Jobs? Docker CEO Weighs In

    In this episode, Scott Johnston, CEO of Docker, highlights the evolving role of developers, emphasizing their increasing importance in architectural decision-making and tool development for applications. This shift in prioritizing a great developer experience and rapid tool development has led to substantial spending in the industry.

    Johnston expressed confidence that integrating generative AI into the developer experience will drive business growth and expand the customer base. He downplayed concerns about AI taking jobs, explaining that it would alleviate repetitive tasks, enabling developers to focus on more complex problem-solving. Johnston likened this evolution to expanding bike lanes in a city, leading to increased bike traffic, equating it to the development of more apps due to increased speed and efficiency.

    In his talk with TNS host, Alex Williams, Johnston emphasized that each advancement in programming languages and tools has expanded the developer market and driven greater demand for applications. Notably, the demand for over 750 million apps in the next two years, as reported by IDC, demonstrates the ever-increasing appetite for creative solutions from developers.

    Overall, Johnston sees the integration of generative AI and increasing development velocity as a multifaceted expansion that benefits developers and meets growing demand for applications in the market.

    Learn more from The New Stack about Generative AI and Docker:

    Generative AI News, Analysis, and Resources

    Docker Launches GenAI Stack and AI Assistant at DockerCon

    Docker Rolls out 3 Tools to Speed and Ease Development

    22 min

About The New Stack Podcast

From the publisher's feed

The New Stack Podcast is all about the developers, software engineers and operations people who build at-scale architectures that change the way we develop and deploy software.

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