Knock vs Kubernetes
Side-by-side trajectory, velocity, and editorial themes.
Knock is rewiring notifications infrastructure to be configured by agents, not just developers.
Knock is methodically rebuilding its primitives — audiences, layouts, reusable steps, in-app guides — so they're versioned, environment-promotable, and addressable from an agent in addition to the dashboard and CLI. The recent run shows a clear pattern: each new feature ships with at least one agent-accessible path. Underneath, the engineering surface is also tightening, with reusable request input schemas making composability less guesswork.
Knock is positioning its platform as agent-buildable messaging infrastructure rather than just a developer SDK. Skills, dynamic audiences, and schema'd reusable steps are the building blocks of a future where a product team agent (or Knock's own) can spin up an entire notification flow without a developer touching code. The Layouts 2.0 refresh and Guides toolbar work in parallel to harden the human surfaces that remain.
Expect Knock to publish a more opinionated agent surface — likely an MCP-style server or an in-product agent that orchestrates skills against dynamic audiences. The reusable-input-schemas release is the kind of plumbing that precedes a 'build a workflow from a prompt' demo, so a higher-level natural-language workflow composer is the most probable next move.
Kubernetes 1.36 leans into AI/ML scheduling and control-plane scaling.
The 1.36 cycle is graduation-heavy, with PSI metrics, declarative validation, and volume group snapshots all promoted to GA. Alongside that, the project is making architectural moves around workload scheduling (a new PodGroup API), API-server safety (Mixed Version Proxy on by default), and very-large-cluster scaling (server-side sharded list and watch in alpha). Etcd 3.7 has hit beta in parallel.
Kubernetes is repositioning the control plane for two pressures at once: AI/ML batch workloads, where gang scheduling and DRA are becoming first-class concerns, and very-large clusters, where the control plane itself needs to shard. The pattern across this cycle is consolidation — old experimental scaffolding is reaching GA or being removed (ExternalIPs), while new APIs land with explicit separation of static template from runtime state. Less feature sprawl, more API hygiene.
Expect 1.37 to push server-side sharded watch toward beta and to keep extending DRA's reach into native resources like memory and networking. Workload-aware scheduling will likely accumulate scheduler-plugin-level coordination patterns next, with downstream batch frameworks starting to converge on the PodGroup shape.
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