Reacting to AI (reducing shadow AI practices)
Platform Engineering teams have inherited distributed multi-agent systems and they are struggling to support teams. Option paralysis is not something that these teams can afford now. Taming Shadow AI is the challenge of 2026.
🇪🇸Spring I/O 2026
Spring I/O 2026 in Barcelona brought AI, agentic patterns, and Spring 7.x/Boot 4.x to the forefront. Spring AI 2.0's native MCP support, maturing observability standards, and protocol convergence are reshaping how Java teams build intelligent systems.
Manage and distribute skills with `skills-oci`
Manage and distribute agent skills with OCI-compliant images, enabling versioning, distribution, reuse of existing infrastructure, and leveraging the OCI ecosystem for signing, verifying provenance, and security scanning.
New Beginnings, same principles, still pushing for Open Source and Open Standards
I am hyped to be joining Dash0 as an Ecosystem Engineer because OpenTelemetry is the foundation for understanding agentic systems.
DevEx in the age of AI
TLTR: coding agents are replacing the tasks developers perform in their inner loops. What tools and practices can help you tame these agents to be more efficient and get the most out of these tools? What would it take to adopt these coding agents at scale in a secure and reliable way?