re:cinq's Napkins

When your Internal Developer Platform becomes a spaghetti mix of ArgoCD, GitLab, Rundeck, and Terraform—spread across both legacy and cloud-native apps—it's time to pause. The path forward? Build a separate, clean CN setup. Grow it slowly, while keeping clear APIs to link back to the legacy world. This lets you modernize without rewriting everything. Tooling should reflect the separation: one stack for what’s old, another for what’s next—and a migration plan in between.

9-week release cycles, handoffs between Dev, Test, and Ops, and blocked dependencies aren’t a release problem—they’re a symptom of a waterfall system in disguise. The fix isn’t just faster CI/CD—it’s merging roles, aligning incentives, and rethinking your org structure. Use Team Topologies and Platform Engineering to create autonomous teams that own delivery end to end. Automate tests, eliminate redundant approvals, and treat the “dependency problem” as a call to break down silos for good.

You’ve got a backlog of issues from app teams—where do you even start? Instead of defaulting to severity, switch to a human-impact lens. Use the four Ts: Time, Toil, Tears, and Thrashing to assess what’s worth solving first. This approach turns support into strategy, helping you prioritize what actually blocks progress. It gives engineers a shared language for urgency, aligns platform and devs on priorities, and makes ops feel less like firefighting and more like force multiplication.

As ML teams join traditional dev and platform orgs, the alignment challenge multiplies. Different assumptions, tooling, and timelines create friction. “AI Native Transformation” is a way to solve this—not by throwing more tools at the problem, but by designing for collaboration. Define personas and workflows clearly. Set shared goals and embed collaboration through task forces or sprint pods. It’s a cultural shift—one where shared ownership, mutual education, and empathy come before tooling.

Empowering dev teams to own both deployment and operations sounds great—until reality hits. Many developers lack the ops experience or time to manage the full lifecycle. To make “You build it, you run it” truly work, you need to streamline the experience. That means offering ready-made deployment templates, GitHub Actions automation, and a self-service catalog that feels like a product. Reduce friction, offload complexity, and secure exec buy-in to keep platform investment aligned with delivery.

Each LLM often requires different infrastructure, runtimes, and APIs—making reuse impossible. Instead, define a deployment contract per model and expose it via a service catalog like Backstage. This turns chaos into consistency and lets you scale AI workloads like real platform products.

If five people and two weeks are required for every deployment, the issue isn’t tooling—it’s trust and culture. Introduce risk-tiered releases, automate checks, and empower teams with GitOps and rollback support. Start with one “lighthouse” team to prove that speed and safety can go hand in hand.

Kubernetes upgrades don’t need to be scary—but they do need structure. Map deprecations, scan app compatibility, and prepare upgrade runbooks specific to your environment. Combine GitOps, observability, and tested rollback plans to upgrade with confidence—not hope.