ThinkWithPrompt by SynotticThinkWithPrompt

Diagnose slow launches and API cold starts in my mobile app

A codebase-specific diagnosis of startup time, serverless cold starts, and network waste, with fixes ranked by impact on perceived speed.

Software DevelopmentFew-ShotStandardCodingDesignAnalyzeCode

The prompt

Analyze why my mobile app feels slow and rank what to fix. This is a diagnosis, not a refactor - do not rewrite code unless I ask. App stack: [e.g. "Expo / React Native", "Flutter", "native iOS"] Backend: [e.g. "Supabase Edge Functions", "AWS Lambda", "Firebase Cloud Functions"] Symptoms: [e.g. "first screen takes 4s", "first API call after idle is slow", "lists stutter"] Measurements I have: [timings, traces, logs - or "none yet"] Code: [PASTE app startup code, data-fetching layer, key screens, and the serverless functions they call] Check specifically: 1. App startup - work done before first render, bundle size, eager imports, font/asset loading, splash screen timing. 2. Cold starts - function bundle size and dependencies, initialization at module scope, database client creation per request, region distance from users, whether functions are called in sequence on launch. 3. Network - request waterfalls, chatty endpoints that could be batched, over-fetching, missing caching, no retry/timeout handling on poor mobile connections. 4. Perceived performance - missing skeletons or optimistic UI, blocking spinners, data that could be prefetched or cached locally. 5. Rendering - unnecessary re-renders, heavy lists without virtualization, large images. Base every finding on the code I shared; where you would need a measurement to confirm, say so and tell me how to measure it. Return a table: Issue | Evidence in the code | Likely impact (high/med/low) | Fix | Effort. Then a top-3 action list and a short plan to measure before and after.

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What to fill in

  • e.g. "Expo / React Native", "Flutter", "native iOS"
  • timings, traces, logs - or "none yet"

How this prompt is built

This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.

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