Tugrul Erdem

Act as a Senior Mobile Performance Engineer and Supabase Edge Functions Architect.

Your task is to perform a deep, production-grade analysis of this codebase with a strict focus on:

- Expo (React Native) mobile app behavior
- Supabase Edge Functions usage
- Cold start latency
- Mobile perceived performance
- Network + runtime inefficiencies specific to mobile environments

This is NOT a refactor task.
This is an ANALYSIS + DIAGNOSTIC task.
Do not write code unless explicitly requested.
Do not suggest generic best practices — base all conclusions on THIS codebase.

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## 1. CONTEXT & ASSUMPTIONS

Assume:
- The app is built with Expo (managed or bare)
- It targets iOS and Android
- Supabase Edge Functions are used for backend logic
- Users may be on unstable or slow mobile networks
- App cold start + Edge cold start can stack

Edge Functions run on Deno and are serverless.

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## 2. ANALYSIS OBJECTIVES

You must identify and document:

### A. Edge Function Cold Start Risks
- Which Edge Functions are likely to suffer from cold starts
- Why (bundle size, imports, runtime behavior)
- Whether they are called during critical UX moments (app launch, session restore, navigation)

### B. Mobile UX Impact
- Where cold starts are directly visible to the user
- Which screens or flows block UI on Edge responses
- Whether optimistic UI or background execution is used

### C. Import & Runtime Weight
For each Edge Function:
- Imported libraries
- Whether imports are eager or lazy
- Global-scope side effects
- Estimated cold start cost (low / medium / high)

### D. Architectural Misplacements
Identify logic that SHOULD NOT be in Edge Functions for a mobile app, such as:
- Heavy AI calls
- External API orchestration
- Long-running tasks
- Streaming responses

Explain why each case is problematic specifically for mobile users.

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## 3. EDGE FUNCTION CLASSIFICATION

For each Edge Function, classify it into ONE of these roles:

- Auth / Guard
- Validation / Policy
- Orchestration
- Heavy compute
- External API proxy
- Background job trigger

Then answer:
- Is Edge the correct runtime for this role?
- Should it be Edge, Server, or Worker?

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## 4. MOBILE-SPECIFIC FLOW ANALYSIS

Trace the following flows end-to-end:

- App cold start → first Edge call
- Session restore → Edge validation
- User-triggered action → Edge request
- Background → foreground resume

For each flow:
- Identify blocking calls
- Identify cold start stacking risks
- Identify unnecessary synchronous waits

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## 5. PERFORMANCE & LATENCY BUDGET

Estimate (qualitatively, not numerically):

- Cold start impact per Edge Function
- Hot start behavior
- Worst-case perceived latency on mobile

Use categories:
- Invisible
- Noticeable
- UX-breaking

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## 6. FINDINGS FORMAT (MANDATORY)

Output your findings in the following structure:

### 🔴 Critical Issues
Issues that directly harm mobile UX.

### 🟠 Moderate Risks
Issues that scale poorly or affect retention.

### 🟢 Acceptable / Well-Designed Areas
Good architectural decisions worth keeping.

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## 7. RECOMMENDATIONS (STRICT RULES)

- Recommendations must be specific to this codebase
- Each recommendation must include:
  - What to change
  - Why (mobile + edge reasoning)
  - Expected impact (UX, latency, reliability)

DO NOT:
- Rewrite code
- Introduce new frameworks
- Over-optimize prematurely

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## 8. FINAL VERDICT

Answer explicitly:
- Is this architecture mobile-appropriate?
- Is Edge overused, underused, or correctly used?
- What is the single highest-impact improvement?

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## IMPORTANT RULES

- Be critical and opinionated
- Assume this app aims for production-quality UX
- Treat cold start latency as a FIRST-CLASS problem
- Prioritize mobile perception over backend elegance

Act as a Senior Expo + Supabase Architect.

Implement a “cold-start safe” architecture using:
- Expo (React Native) client
- Supabase Postgres + Storage + Realtime
- Supabase Edge Functions ONLY for lightweight gating + job enqueue
- A separate Worker service for heavy AI generation and storage writes

Deliver:
1) Database schema (SQL migrations) for: jobs, generations, entitlements (credits/is_paid), including indexes and RLS notes
2) Edge Functions:
   - ping (HEAD/GET)
   - enqueue_generation (validate auth, check is_paid/credits, create job, return jobId)
   - get_job_status (light read)
   Keep imports minimal; no heavy SDKs.
3) Expo client flow:
   - non-blocking warm ping on app start
   - Generate button uses optimistic UI + placeholder
   - subscribe to job updates via Realtime or implement polling fallback
   - final generation replaces placeholder in gallery list
4) Worker responsibilities (describe interface and minimal endpoints/logic, do not overbuild):
   - fetch queued jobs
   - run AI generation
   - upload to storage
   - update jobs + insert generations
   - retry policy and idempotency

Constraints:
- Do NOT block app launch on any Edge call
- Do NOT run AI calls inside Edge Functions
- Ensure failed jobs still create a generation record with original input visible
- Keep the solution production-friendly but minimal

Output must be structured as:
A) Architecture summary
B) Migrations (SQL)
C) Edge function file structure + key code blocks
D) Expo integration notes + key code blocks
E) Worker outline + pseudo-code