The Ideal Stack for AI-Assisted Development

The Ideal Stack for AI-Assisted Development

1. Why AI-Assisted Teams Need a Purpose-Built Stack

Generative tools speed up code, tests, and infra scripts—but they also multiply deployments. Your stack must let small, AI-generated changes ship safely and quickly.

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2. FaaS as the Change-Isolation Core

Goal	How FaaS Helps	Ad-Bidder Example
Ship tiny diffs	Each logical unit lives in its own function	place_bid() runs alone; no monolith rebuild
Cheap rollbacks	Version tags per function	Revert to place_bid@v42 in seconds
Cost mirrors usage	Pay only when bidding service fires	Low to zero idle spend overnight

Tech picks: AWS Lambda / Google Cloud Functions / OpenFaaS on K8s.

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3. Logic Composition: Events > REST

1. Event bus (SNS/SQS, Pub/Sub, Kafka)
2. Thin FaaS endpoints for "Bid Requested", "Bid Won"
3. Shared domain library (@ads-core) pulled in via CI

The event spine keeps each AI-generated tweak—e.g. a new scoring heuristic—contained in one function file.

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4. Blue-Green & Canary Deployments (Because ML Can Drift)

1. Blue = current, Green = candidate
2. Route 5 % traffic to Green with Linkerd/Envoy or your cloud's weighted LB
3. Compare metrics (CTR lift, latency, bid win rate)
4. Promote or auto-rollback

# Example with AWS Lambda + ALB
alb shift-weight --blue 95 --green 5

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5. Local Environment & Service Emulation

Need	Tooling	Notes
Cloud services offline	LocalStack / FakeGCP / Moto	Emulate S3, Pub/Sub, etc.
Live-reload FaaS	Tilt / Dagger / sam local	Type, save, test in <2 s
Production parity models	ONNX + Triton server	Swap in the same model docker as prod

Every dev can spin up make dev and watch ad bids flow end-to-end without Wi-Fi.

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6. CI/CD Pipeline Snapshot

git push
 └─ GitHub Actions
     ├─ Lint, unit tests
     ├─ AI-generated test delta (e.g., Diffblue)
     ├─ Build FaaS container
     ├─ terraform plan + apply
     └─ shift green traffic 5 %

Tip: keep your IaC in the same repo as FaaS code so AI tools can co-reason about schema, secrets, and code.

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7. Observability & Guardrails

• Tracing: AWS X-Ray / OpenTelemetry to see per-bid latency
• Metrics: Prometheus + Grafana dashboards ("bid fail rate < 0.1 %")
• Feature flags: LaunchDarkly or Flagr to toggle the AI scoring path vs. a rule-based fallback

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8. Pulling It Together (Ad-Bidder Walkthrough)

1. Dev writes prompt: "Improve bid score with time-decay on CTR."
2. Copilot generates a new decay_ctr() helper in @ads-core.
3. Local Tilt hot-reloads; tests run on LocalStack SQS.
4. CI builds place_bid:v43-green and shifts 5 % traffic.
5. Linkerd shows +3 % eCPM lift → auto-promote.
6. Old version remains available for instant rollback.

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9. Key Takeaways

• FaaS + events = atomic, AI-sized diffs
• Blue-green keeps experimental models safe
• Local emulation gives every dev a prod-like lab
• Feature flags & observability close the loop between code, model, and business KPI

Ad feature shipped. Zero downtime. AI doing the busy work while your team stays in control.