# Convergence Rationale (C0)

> For the complete documentation index, see [llms.txt](https://traderx.finos.org/llms.txt).

[Learning Guide — Containerized Compose Runtime (NGINX Ingress)](https://traderx.finos.org/docs/learning/state-004-containerized-compose-runtime.md): purpose, walkthrough, and related learning states.

# Convergence Rationale (C0)

State `004-containerized-compose-runtime` is the C0 convergence baseline.

Rationale:

*   It is the first containerized, reproducible runtime that all later tracks can inherit from.
    
*   It preserves the baseline functional behavior while introducing a stable deployment substrate.
    
*   It provides the cleanest handoff point for architecture, functional, and platform deltas.
    
*   As the convergence baseline, it establishes the `C0+` CI policy: convergence states publish container images via build/publish workflow plus GHCR run-bundle artifacts.
    
*   It keeps generated patchsets aligned with current dependency security targets so descendant convergence states inherit a clean runtime baseline.
    
*   Issue #465 upgrades the inherited Java baseline to public Spring Boot 4.0.8, Spring Framework 7.0.9, Data JPA 4.0.7, and Tomcat 11.0.26. Jackson 3 and Springdoc 3 compatibility changes preserve the existing HTTP and messaging contracts without adding security suppressions.
    
*   Issue #466 makes the C0 deployment boundary require exact-snapshot build/test/security evidence and scanned image digests. Descendant demo states inherit these checks through canonical generators while retaining one snapshot commit per generated branch.
    
*   Generated CI workflows come from the canonical CI asset installer after state overlays; historical workflow patches in states 004–007 are omitted so dependency remediation and deployment hardening compose without restoring stale checks.
    

## Learning evidence at this checkpoint

At C0, the learning contract combines the inherited edge and agent-harness knowledge with application containerization and the Compose runtime. The learner traces backend and frontend Dockerfiles into images and running containers before examining how Compose connects them. A learning partner asks the learner to distinguish container startup, service readiness and the retained trade result, using separate observations for each boundary. The canonical `learning-contract.json` supplies objective mappings, diagnostic questions, graduated hints and assessment evidence for both human and agent-assisted study.