Eighth phase of Arc 3. Integration, not new content. Multi-cloud basecamp. The repo goes public.

Phases 17-23 added a lot: kernel internals, networking, containers from scratch, K8s, distributed systems, IaC, AWS. This phase doesn’t add an eighth thing. It proves the previous seven transferred. GCP is the compare-cloud step at year scale. Multi-cloud basecamp is the integration. basecamp going public is the artifact.

This is also the moment Arc 3 stops being internal practice and starts being a portfolio. basecamp flips from private to public. Anyone landing on its GitHub repo now sees a multi-cloud GitOps platform with forge + beacon + observability + mesh. That’s the senior-engineer signal.


Prerequisites

  • All of Phases 17-23 complete
  • GCP $300 credits available
  • basecamp substrate + forge alive on homelab + AWS
  • You accept: this phase is the proof, not the lesson. Lessons happened in Phases 17-23. Here you make them visible.

Why this phase exists

A capstone integration phase serves two purposes. First, it forces previous phases’ work to actually compose: most engineering pain happens at integration boundaries. Second, it produces a visible artifact (the public basecamp repo) that proves Arc 3 happened.

The GCP compare is the year-scale instance of the COMPARE step. If GCP feels easy after Phase 23’s AWS, the pattern transferred. If GCP feels foreign, AWS taught you AWS-specific naming.


The pattern-first frame

Same eight steps.


1. PROBLEM

You have a working platform on homelab + AWS. You have IaC + GitOps + paved-road discipline. The question: did the patterns transfer to a different cloud? If yes, basecamp runs on GCP with minimal changes. If no, you’ll discover AWS-specific assumptions leaked into the platform.


2. PRINCIPLES

2.1 Multi-tenancy

A multi-tenant platform serves multiple isolated workloads on shared substrate. At Arc 3 scale, “multi-cloud” is a form of multi-tenancy.

→ Pattern: multi-tenancy: first OUTLINE

Investigate:

2.2 Cloud abstraction (the limits)

You discovered in Phase 22 that IaC doesn’t actually abstract clouds. The platform layer has more abstraction potential: the developer says “deploy this service” and the platform decides which cloud.

Investigate:

2.3 Cost across clouds

Each cloud has different pricing curves. Egress expensive on AWS, less on GCP. GPU instances easier to reserve on GCP for short bursts. Storage costs vary.

Investigate:

2.4 Capstone discipline

A capstone is subtractive. What do you cut to keep the narrative clear?

Investigate:


3. TRADE-OFFS

DecisionOptionsCost
Multi-cloud scopeAll workloads; selected critical; one workloadWider scope = exponentially more ops. One workload everywhere is enough to prove pattern.
GCP managedGKE Autopilot; GKE Standard; raw VMsAutopilot: locked-in. Standard: comparable to EKS. VMs: closest to homelab K3s.
Repo visibilityFully public; public with private submodules; stagedFully public: strongest signal, requires hygiene. Sub-modules: pragmatic. Staged: hedging.
Integration scopeAdd GCP; integrate everything; cut somethingSubtract to clarify.

4. TOOLS (as of 2026-06)

Reading


5. MASTERY: Multi-cloud basecamp

5.1 The integration

5.2 Going public

Flip basecamp’s repo from private to public. Before flipping:

5.3 Operational depth checklist

[ ] GKE small cluster provisioned via forge; destroyed cleanly
[ ] ArgoCD multi-cluster: same Application synced to both EKS and GKE
[ ] ascent --target gke working end-to-end (foreshadow Phase 26)
[ ] basecamp repo scanned for secrets; clean
[ ] basecamp public on GitHub; README explains in 5 minutes
[ ] Two services running on all 3 clouds; same Helm chart, different cluster targets
[ ] Cost dashboard showing AWS + GCP + homelab side-by-side
[ ] Demo: deploy a new service via ascent to all 3 clouds in one command

6. COMPARE: Cost analysis

Run the same workload on AWS, GCP, homelab for 1 week. Capture cost. Build a small markdown table showing total cost per service per cloud.

400-word reflection: where did surprises come from? Predictable from docs vs not? Single cloud for next employer, and which and why?


7. OPERATE


8. CONTRIBUTE


What ships from this phase


Validation criteria

[ ] Multi-cloud basecamp working (K3s + EKS + GKE)
[ ] basecamp repo public; README + LICENSE + architecture diagram
[ ] ascent --target flag working for all 3 clouds (foreshadow Phase 26)
[ ] All 8 operational depth checks
[ ] Cost comparison reflection (400 words)
[ ] 3-4 multi-cloud runbooks
[ ] Pattern entries:
    - multi-tenancy → first OUTLINE
    - all prior Arc 3 patterns reinforced toward DEEP
[ ] Exit Test passed

Exit Test

Time: 3 hours.

Part 1: Build (90 min)

Provision fresh GKE cluster via forge; connect to ArgoCD; deploy hello to it. Verify mTLS works between hello on GKE and beacon on EKS via mesh federation (or document why federation is too expensive at this scale).

Part 2: Articulate (90 min)

~1200 words: “basecamp is now multi-cloud. Defend the choice. Cover developer experience, operational cost, resilience, vendor lock-in, and what specifically you’d change to add a third cloud (Azure). Cite Arc 3 patterns.”


Anti-patterns

Anti-patternWhy
Going public with secrets in Git historyBiggest career-impacting mistake. Scan thoroughly.
Treating multi-cloud as goal, not testPoint is “the patterns transferred.” Celebrate that, not the diagram.
Adding GCP services not present in AWSCompare requires symmetry.
Sprawling READMEReviewer has 5 minutes. Lead with what basecamp is + a diagram.

Patterns touched this phase


→ Next: Phase 25: Service Mesh + Zero-Trust Networking