The New Stack Podcast

The New Stack
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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. For more content from The New Stack, subscribe on YouTube at: https://www.youtube.com/c/TheNewStack

  1. 15 hr ago

    Kubernetes won the orchestration wars. Its teams are still fighting.

    Kubernetes has become the dominant container orchestration platform, used by more than 80% of container users, but widespread adoption hasn’t eliminated tensions between developers and operations teams. Developers value Kubernetes for its self-service capabilities and ability to move quickly, while operations teams prioritize stability, governance and controlled change. In this episode, HPE Fellow David Estes explains how those competing priorities are becoming more challenging as applications spread across hybrid and edge environments and AI inference workloads increase demand for GPU scheduling and rapid capacity scaling. He also warns that excessive reliance on microservices can create unnecessary “architecture bloat.” AI, however, could help solve some of the complexity it is creating. Estes sees opportunities to use AI automation to monitor Kubernetes clusters, analyze logs, automate infrastructure tasks and potentially predict outages. With tools such as HPE OpsRamp supporting multicloud and on-premises environments, AI-driven operations could ultimately help developers and operations teams manage increasingly complex Kubernetes infrastructure more effectively. Learn more from The New Stack around the latest developments in HP Enterprise: HPE's AI agents cut root cause analysis time in half AI Is Quickly Making IT Teams and Developers From Invisible to Indispensable Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

  2. 1 day ago

    The CNCF is graduating projects faster than ever. AI agents are helping with the due diligence.

    Open-source technology has played a foundational role in the AI boom, giving companies the flexibility to scale unprecedented computing workloads across providers and hardware. OpenAI, for example, used Kubernetes as early as 2018 to balance compute across its own data centers, AWS and Azure — years before ChatGPT brought generative AI into the mainstream. CNCF Executive Director Jonathan Bryce sees a powerful feedback loop emerging between AI and open source. Early AI companies relied on open-source tools because commercial products couldn’t support the scale and complexity of their work. As those companies contribute back, enterprises across industries gain access to increasingly sophisticated AI infrastructure. AI is now accelerating open-source development itself. CNCF has used agents to assist with project due diligence, helping move projects through its graduation process faster. As AI agents increase development speed and infrastructure demands, Bryce argues that open-source communities will be critical for addressing challenges including multi-cloud computing, hardware diversity, guardrails and software capable of operating at agent speed. Learn more from The New Stack around the latest open source and AI developments: Open-source AI is just "4 months behind" closed frontier models - and 10x cheaper  Is AI killing open-source software?  Open source maintainers are drowning in AI-generated pull requests. Enterprise teams are next.  Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

  3. 2 days ago

    Dynatrace wants its AI agents to fix problems instead of adding alerts

    Dynatrace's view is that AI apps should be monitored like any other app. They still run alongside older systems, including mainframes, and when something breaks, teams need to know what it cost and why it happened. Their Bluebox agent, introduced in July, compares a team’s code with production data and proposes a fix as a pull request. Engineers can review it or let it run without them. The goal is fewer alerts, delivered in the tools developers already use, such as Claude and Codex, rather than in a separate dashboard.Learn more from The New Stack around the latest around Dynatrace's latest development: Dynatrace’s new agents can reveal the single hardest part of AI operations Beyond automation: Dynatrace unveils agentic AI that fixes problems on its own Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

  4. 6 days ago

    Bit Cloud’s next chapter starts after the AI builds your app

    For many developers, turning an AI-generated prototype into maintainable software requires more than generating code—it requires infrastructure, collaboration, testing and review. In this episode of The New Stack podcast, Bit Cloud founder and CEO Ran Mizrahi discusses Bit Cloud 2.0 and Hope, the company’s AI builder, and how they connect application creation with the work that follows. Mizrahi explains how reusable components, authentication and integrations can help teams avoid duplicating work, reduce token costs and simplify code review. The conversation also explores collaboration across developers, designers, product managers and business teams, all working from shared building blocks. Mizrahi demonstrates how development workflows can extend to mobile devices, including staging previews, build checks and code review. The discussion covers working with existing codebases, tools such as Claude Code and Cursor, and moving standard application code beyond Bit Cloud. Demonstrations show an Instagram-style prototype evolving into an application architecture and how testing can catch problems before production. Throughout, Mizrahi emphasizes building on existing work so each application can become a foundation for the next. Learn more from The New Stack around the latest in integrating AI into Development workflows Your AI Workflow Is Missing a Composable Architecture Lessons from 2 Years of Integrating AI into Development Workflows  Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

  5. 30 Sept

    CloudBees just committed to an AI-first pivot. Here's why it matters for enterprise DevOps teams

    CloudBees CEO Mo Plassnig is leading the CI/CD company through a major transformation as generative AI reshapes software development. Returning to CloudBees eight years after joining through its acquisition of CodeShip, which he co-founded, Plassnig says the emergence of generative AI renewed his interest in DevOps and the opportunities ahead. His central concern is the dramatic increase in code generated by AI. Rather than focusing on predictions that autonomous agents will replace developers, Plassnig argues that enterprises face a more immediate challenge: safely managing, governing, and deploying an unprecedented volume of machine-generated software. After meeting with Fortune 500 companies, public organizations, and global enterprises, Plassnig found a significant gap between AI hype and real-world adoption. Enterprises recognize the potential of agentic coding but must contend with complex process changes, governance requirements, and security concerns. His strategy is to reposition CloudBees as an AI-first company while rethinking how its Jenkins automation platform can support this new era of software development. Learn more from The New Stack around the latest update with CloudBees and CI/CD: CloudBees CEO: Why Migration Is a Mirage Costing You Millions Why coding agents will break your CI/CD pipeline (and how to fix it) Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

  6. 23 Sept

    A third option is emerging in the fight over AI and your data

    The AI industry has faced a growing enterprise dilemma: companies want access to powerful proprietary AI models without risking sensitive data or intellectual property, while AI labs want to protect their model weights from being exposed to customers. Traditionally, businesses had to choose between proprietary models with potential data-leakage concerns or open-weight models that lagged behind the frontier. Vast Data co-founder Jeff Denworth argues that a new approach can address both sides of the trust problem. Vast Data’s DataEnclave uses Nvidia’s Confidential Computing technology to let enterprises run proprietary AI models securely on their own infrastructure, while preventing either the company’s data or the AI lab’s model weights from being exposed. Denworth says the timing reflects rapidly increasing enterprise AI adoption, particularly after agentic coding tools drove demand and usage. As AI agents create new requirements at the data layer, the podcast explores how enterprises are approaching AI, the security challenges involved, and the untapped potential of enterprise data. Learn more from The New Stack around the latest in AI trust: VAST Data tackles the enterprise AI trust gap Google, Microsoft, and OpenAI join forces to help create AI’s missing trust layer Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

  7. 7 Sept

    Drowning in AI pull requests: Harness's field CTO on code review and a Git repo built for agents

    Harness Field CTO Martin Reynolds joins The New Stack to talk about what happens after coding agents start opening pull requests faster than anyone can review them. He explains how he first saw the bottleneck during early GitHub Copilot trials, the three ways enterprises are coping with the volume now, and why Harness rebuilt its Code Repository and launched AI Code Review for agent traffic. The conversation also covers GitHub's recent outages, the software delivery knowledge graph behind Harness's reviewer, and how much of the delivery pipeline should stay deterministic.   Learn more from The New Stack around the latest in coding agents: AI coding agents can write code, Crafting wants to help them ship it Git real: AI agents aren't just for solo developers anymore Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

  8. 3 Sept

    How to find failures without drowning in tracing data

    Traces provide a detailed view of a request’s journey through data, microservices and applications, helping SREs pinpoint where failures occur and resolve issues faster. But while tracing can reduce downtime and developer burnout, collecting every trace creates its own problems. Storing massive volumes of data is expensive, can burden the systems being monitored and makes it harder to find the information that actually matters. The solution isn’t abandoning tracing, but being smarter about what gets retained. Head sampling captures only a portion of traces upfront, while tail sampling evaluates completed traces and keeps those most valuable for troubleshooting. Dynamic sampling goes further by filtering repetitive or nearly identical traces before they overwhelm storage. On The New Stack podcast, Sarah Hudspeth of Chronosphere, a Palo Alto Networks company, explains how teams can build a more effective tracing strategy. She breaks down how thoughtful sampling and observability design can turn tracing from a data-hoarding problem into a practical tool for production troubleshooting.  Learn more from The New Stack around the latest in tracing: Sampling: the philosopher’s stone of distributed tracing How OpenTelemetry Works: Tracing, Metrics and Logs on Kubernetes Why Synthetic Tracing Delivers Better Data, Not Just More Data Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

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About

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. For more content from The New Stack, subscribe on YouTube at: https://www.youtube.com/c/TheNewStack

Information

  • Creator
    The New Stack
  • Years Active
    2014 - 2026
  • Episodes
    300
  • Rating
    Clean
  • Copyright
    © All rights reserved
  • Show Website
    The New Stack Podcast

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