Prequel is launching a new developer-focused service aimed at democratizing software error detection—an area typically dominated by large cloud providers. Co-founded by Lyndon Brown and Tony Meehan, both former NSA engineers, Prequel introduces a community-driven observability approach centered on Common Reliability Enumerations (CREs). CREs categorize recurring production issues, helping engineers detect, understand, and communicate problems without reinventing solutions or working in isolation. Their open-source tools, cre and prereq, allow teams to build and share detectors that catch bugs and anti-patterns in real time—without exposing sensitive data, thanks to edge processing using WebAssembly.The urgency behind Prequel’s mission stems from the rapid pace of AI-driven development, increased third-party code usage, and rising infrastructure costs. Traditional observability tools may surface symptoms, but Prequel aims to provide precise problem definitions and actionable insights. While observability giants like Datadog and Splunk dominate the market, Brown and Meehan argue that engineers still feel overwhelmed by data and underpowered in diagnostics—something they believe CREs can finally change.Learn more from The New Stack about the latest Observability insights Why Consolidating Observability Tools Is a Smart MoveBuilding an Observability Culture: Getting Everyone Onboard Join our community of newsletter subscribers to stay on top of the news and at the top of your game.
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5:13
Arm’s Open Source Leader on Meeting the AI Challenge
At Arm, open source is the default approach, with proprietary software requiring justification, says Andrew Wafaa, fellow and senior director of software communities. Speaking at KubeCon + CloudNativeCon Europe, Wafaa emphasized Arm’s decade-long commitment to open source, highlighting its investment in key projects like the Linux kernel, GCC, and LLVM. This investment is strategic, ensuring strong support for Arm’s architecture through vital tools and system software.Wafaa also challenged the hype around GPUs in AI, asserting that CPUs—especially those enhanced with Arm’s Scalable Matrix Extension (SME2) and Scalable Vector Extension (SVE2)—are often more suitable for inference workloads. CPUs offer greater flexibility, and Arm’s innovations aim to reduce dependency on expensive GPU fleets.On the AI framework front, Wafaa pointed to PyTorch as the emerging hub, likening its ecosystem-building potential to Kubernetes. As a PyTorch Foundation board member, he sees PyTorch becoming the central open source platform in AI development, with broad community and industry backing.Learn more from The New Stack about the latest insights about Arm: Edge Wars Heat Up as Arm Aims to Outflank Intel, Qualcomm Arm: See a Demo About Migrating a x86-Based App to ARM64Join our community of newsletter subscribers to stay on top of the news and at the top of your game.
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18:21
Why Kubernetes Cost Optimization Keeps Failing
In today’s uncertain economy, businesses are tightening costs, including for Kubernetes (K8s) operations, which are notoriously difficult to optimize. Yodar Shafrir, co-founder and CEO of ScaleOps, explained at KubeCon + CloudNativeCon Europe that dynamic, cloud-native applications have constantly shifting loads, making resource allocation complex. Engineers must provision enough resources to handle spikes without overspending, but in large production clusters with thousands of applications, manual optimization often fails. This leads to 70–80% resource waste and performance issues. Developers typically prioritize application performance over operational cost, and AI workloads further strain resources. Existing optimization tools offer static recommendations that quickly become outdated due to the dynamic nature of workloads, risking downtime. Shafrir emphasized that real-time, fully automated solutions like ScaleOps' platform are crucial. By dynamically adjusting container-level resources based on real-time consumption and business metrics, ScaleOps improves application reliability and eliminates waste. Their approach shifts Kubernetes management from static to dynamic resource allocation. Listen to the full episode for more insights and ScaleOps' roadmap.Learn more from The New Stack about the latest in scaling Kubernetes and managing operational costs: ScaleOps Adds Predictive Horizontal Scaling, Smart Placement ScaleOps Dynamically Right-Sizes Containers at Runtime Join our community of newsletter subscribers to stay on top of the news and at the top of your game.
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17:22
How Heroku Is ‘Re-Platforming’ Its Platform
Heroku has been undergoing a major transformation, re-platforming its entire Platform as a Service (PaaS) offering over the past year and a half. This ambitious effort, dubbed “Fir,” will soon reach general availability. According to Betty Junod, CMO and SVP at Heroku (owned by Salesforce), the overhaul includes a shift to Kubernetes and OCI standards, reinforcing Heroku’s commitment to open source. The platform now features Heroku Cloud Native Buildpacks, which let developers create container images without Dockerfiles. Originally built on Ruby on Rails and predating Docker and AWS, Heroku now supports eight programming languages. The company has also deepened its open source engagement by becoming a platinum member of the Cloud Native Computing Foundation (CNCF), contributing to projects like OpenTelemetry. Additionally, Heroku has open sourced its Twelve-Factor Apps methodology, inviting the community to help modernize it to address evolving needs such as secrets management and workload identity. This signals a broader effort to align Heroku’s future with the cloud native ecosystem. Learn more from The New Stack about Heroku's approach to Platform-as-a-Service:Return to PaaS: Building the Platform of Our DreamsHeroku Moved Twelve-Factor Apps to Open Source. What’s Next?How Heroku Is Positioned To Help Ops Engineers in the GenAI EraJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.
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18:01
Container Security and AI: A Talk with Chainguard's Founder
In this episode of The New Stack Makers, recorded at KubeCon + CloudNativeCon Europe, Alex Williams speaks with Ville Aikas, Chainguard founder and early Kubernetes contributor. They reflect on the evolution of container security, particularly how early assumptions—like trusting that users would validate container images—proved problematic. Aikas recalls the lack of secure defaults, such as allowing containers to run as root, stemming from the team’s internal Google perspective, which led to unrealistic expectations about external security practices.The Kubernetes community has since made strides with governance policies, secure defaults, and standard practices like avoiding long-lived credentials and supporting federated authentication. Aikas founded Chainguard to address the need for trusted, minimal, and verifiable container images—offering zero-CVE images, transparent toolchains, and full SBOMs. This security-first philosophy now extends to virtual machines and Java dependencies via Chainguard Libraries.The discussion also highlights the rising concerns around AI/ML security in Kubernetes, including complex model dependencies, GPU integrations, and potential attack vectors—prompting Chainguard’s move toward locked-down AI images.Learn more from The New Stack about Container Security and AIChainguard Takes Aim At Vulnerable Java LibrariesClean Container Images: A Supply Chain Security RevolutionRevolutionizing Offensive Security: A New Era With Agentic AI Join our community of newsletter subscribers to stay on top of the news and at the top of your game.
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