Platform Engineering Podcast

Did you know that software engineers often "learn things the hard way" because they lack a standardized system to share knowledge about reliability issues? While security professionals have CVEs to catalog vulnerabilities, reliability engineers have been left to reinvent the wheel with each new bug or outage.

Tony Meehan, co-founder and CTO of Prequel, introduces us to Common Reliability Enumerations (CREs) - an open-source approach that's doing for reliability what CVEs did for security. After spending a decade at the NSA hunting vulnerabilities, Tony recognized that the same community-driven approach could revolutionize how we handle reliability issues.

This conversation covers:

• How CREs help developers detect and mitigate reliability issues before they cause outages
• The open-source tools Preq and CRE that allow teams to leverage community knowledge
• Practical ways to implement these tools in your development workflow (locally, in CI/CD, and production)
• How this approach can reduce cloud costs by identifying issues rather than over-provisioning
• Tips for debugging mysterious production issues when no CRE exists yet

What if you could turn a five-year software project into a one-month endeavor? Gene Kim, co-founder of IT Revolution and author of The Phoenix Project, reveals how AI-powered Vibe Coding is transforming the way developers work.

Kim shares insights from his upcoming book about how developers are achieving unprecedented productivity, including how his co-author produces 12,000 lines of production-ready code daily using AI assistance. But it's not just about speed - learn how this approach enables developers to tackle previously impossible projects and explore larger design spaces.

From DevOps evolution to practical AI implementation, Kim discusses:

• What Vibe Coding really means and how it differs from traditional development
• Real examples of AI accelerating development without sacrificing quality
• Common pitfalls to avoid when implementing AI in your development workflow
• How AI is making developers more ambitious rather than replacing them
• The critical role of testing and feedback loops in successful AI implementation

Whether you're a seasoned developer or a tech leader wondering about AI's place in your development workflow, this conversation provides practical insights into the future of software development.

Security often feels like a roadblock to developers, but what if it could be seamlessly integrated into the development process? As software delivery becomes increasingly automated and self-service, the traditional approach to security needs a major overhaul.

Danny Allan, CTO at Snyk, shares practical insights on transforming security from a bottleneck into an enabler of developer productivity. Drawing from his extensive experience at IBM, VMware, and Veeam, Allan discusses how security teams can shift left effectively without creating friction.

Key topics covered:

• Building successful security champions programs that cultivate curiosity rather than relying solely on senior developers
• Practical approaches to embedding security controls into development pipelines, from IDE integration to PR checks
• Strategies for measuring security team success beyond just vulnerability counts
• The role of pre-hardened containers and infrastructure-as-code scanning in platform security
• How AI is transforming both code generation and security tooling, including Snyk's approach to vulnerability detection

Are your platform teams constantly saying "no" to requests for new Kubernetes clusters? The traditional approach to Kubernetes multi-tenancy forces organizations to choose between cluster sprawl or restrictive namespaces - neither of which fully meets the needs of modern development teams.

Lukas Gentele, CEO and co-founder of Loft Labs, shares how vCluster is transforming the way organizations handle multi-tenancy in Kubernetes. By running virtual Kubernetes control planes inside namespaces, vCluster enables teams to experiment with different versions, operators, and configurations while maintaining efficient resource usage.

Key topics covered:

• How vCluster solves the limitations of namespace-based multi-tenancy
• Running multiple Kubernetes versions in the same cluster for testing and gradual upgrades
• Managing bare metal GPU resources efficiently for AI/ML workloads
• Balancing standardization with developer autonomy in platform engineering
• Using virtual clusters for cost-effective testing across multiple Kubernetes versions

Whether you're a platform engineer looking to say "yes" more often or a development team seeking greater autonomy within Kubernetes, this discussion offers practical insights into modern multi-tenancy approaches.