Can AppsFlyer be vibe coded?
Mobile measurement platform that attributes app installs and in-app events to the ad campaigns that caused them.
You can absolutely build a click tracker: log clicks, generate deep links, receive install pings from your own SDK, join them in SQLite. What you cannot build is the reason AppsFlyer exists, which is that Meta, Google, TikTok and Apple do not hand raw attribution data to random self-hosted endpoints. Those self-attributing networks report only to certified measurement partners, and SKAdNetwork/AdAttributionKit postbacks are aggregated and privacy-thresholded by design. Add install fraud detection, which is a data problem across billions of devices rather than a code problem, and the gap stops being about engineering effort. The honest consolation build is a first-party attribution tracker for channels you control: your own links, emails, influencer codes, organic web to app.
Jump to the build brief ↓Legacy-calibrated assessment
Checked Aug 2026
medium editorial confidence
Tracked separately from the pricing check
Buildability by layer
Screens, forms, and focused interactions
The repeatable job the product performs
Availability and legality of required data
Uptime, queues, support, and maintenance
Security, compliance, and user confidence
The achievable core
- A self-hosted link tracker that issues tagged deep links, logs clicks, and matches them to install and event pings from your own app SDK to give you first-party attribution for channels you control.
- Ingest a known data source, calculate a focused metric set, and render a useful dashboard.
- A responsive interface with real empty, loading, success, and error states.
The parts a prompt cannot buy
- Self-attributing network data from Meta, Google, TikTok and Apple Search Ads, which is restricted to certified measurement partners
- SKAdNetwork and AdAttributionKit postback handling, decoding and conversion value modeling
- Install and click fraud detection, which depends on cross-advertiser device data you will never have
- Postbacks and audience syncs to thousands of ad partners and MMP-only integrations
- Connectors, OAuth flows, and vendor API changes require constant upkeep.
- The useful dataset is owned, accumulated, or expensive to reproduce.
Build, switch, or keep paying
Narrower, with trade-offs
A self-hosted link tracker that issues tagged deep links, logs clicks, and matches them to install and event pings from your own app SDK to give you first-party attribution for channels you control.
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Compare the prior art below or build only the workflow you need.
Recommended
Because attribution is not a computation, it is an entitlement. The moment a meaningful slice of your spend goes to Meta or Google or TikTok, your homegrown tracker sees a blob of untagged traffic and shrugs, while the ad networks themselves only talk to partners they have certified. On top of that, media buyers want a neutral scorekeeper both sides accept, finance wants one number, and nobody wants to argue with a partner about whose SQL is right. The price is the referee, not the dashboard.
Visit AppsFlyer ↗Why people still pay
Because attribution is not a computation, it is an entitlement. The moment a meaningful slice of your spend goes to Meta or Google or TikTok, your homegrown tracker sees a blob of untagged traffic and shrugs, while the ad networks themselves only talk to partners they have certified. On top of that, media buyers want a neutral scorekeeper both sides accept, finance wants one number, and nobody wants to argue with a partner about whose SQL is right. The price is the referee, not the dashboard.
Connectors, OAuth flows, and vendor API changes require constant upkeep.
The useful dataset is owned, accumulated, or expensive to reproduce.
Compliance, licensing, and legal accountability are core features.
The brief
Context, requirements, acceptance criteria, non-goals, and the full production standard — as Markdown, ready for any coding agent.
Build brief — a focused alternative to AppsFlyer
Context
AppsFlyer — Mobile measurement platform that attributes app installs and in-app events to the ad campaigns that caused them.
You can absolutely build a click tracker: log clicks, generate deep links, receive install pings from your own SDK, join them in SQLite. What you cannot build is the reason AppsFlyer exists, which is that Meta, Google, TikTok and Apple do not hand raw attribution data to random self-hosted endpoints. Those self-attributing networks report only to certified measurement partners, and SKAdNetwork/AdAttributionKit postbacks are aggregated and privacy-thresholded by design. Add install fraud detection, which is a data problem across billions of devices rather than a code problem, and the gap stops being about engineering effort. The honest consolation build is a first-party attribution tracker for channels you control: your own links, emails, influencer codes, organic web to app.
This brief describes a focused, single-operator replacement for the part of AppsFlyer that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.
What you are building
A self-hosted link tracker that issues tagged deep links, logs clicks, and matches them to install and event pings from your own app SDK to give you first-party attribution for channels you control.
Ingest a known data source, calculate a focused metric set, and render a useful dashboard.
A responsive interface with real empty, loading, success, and error states.
Requirements
Functional
A server with a public HTTPS domain for click and postback endpoints.
Ability to add a small SDK or HTTP call to your own iOS/Android app.
Apple App Site Association and Android assetlinks.json files served for deep linking.
Your own ad account exports if you want spend numbers next to installs.
Non-functional
Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
Security: server-side secrets, validated input, and no credentials in the client bundle.
Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
Portability: the operator can export their data and leave without losing it.
Implementation brief
Build a self-hosted first-party mobile attribution tracker. TypeScript, Fastify, SQLite via better-sqlite3, Vite plus React for the dashboard, Docker Compose for deployment. No accounts, no cloud services, no telemetry.
What it does:
1. Link service: POST /api/links creates a tracked link with campaign, source, medium, and an optional destination path in my app. Each link gets a short id served at GET /l/:id.
2. On click, /l/:id records a click row: timestamp, short id, user agent, platform guess from UA, referrer, IP truncated to /24 for IPv4 and /48 for IPv6, and a random click token set as a cookie. Then 302 to a universal link if the platform is iOS or Android, otherwise to a configured web fallback.
3. Serve /.well-known/apple-app-site-association and /.well-known/assetlinks.json from files in ./public so real deep links work.
4. Ingest endpoint: POST /api/events accepts { deviceId, eventName, clickToken?, platform, appVersion, ts, value? } authenticated by a shared secret in APP_INGEST_KEY. Store raw events untouched in an events table.
5. Attribution job: for each first_open event, attribute deterministically if clickToken matches a click. Otherwise do a probabilistic match against clicks in the last 24 hours on the same truncated IP and platform, and mark it as probabilistic with a confidence field. Never overwrite a deterministic match. Store results in an attributions table so the logic can be re-run idempotently.
6. Dashboard at /: table of campaigns with clicks, installs, deterministic versus probabilistic split, and any named post-install events. Date range picker. CSV export.
7. CLI script: npm run import-spend that loads a CSV of campaign,date,spend so the dashboard can show cost per install for channels I bought myself.
Explicitly out of scope, and say so in the README: SKAdNetwork and AdAttributionKit postbacks, any integration with Meta, Google, TikTok or Apple Search Ads, fraud detection, audience syncing, and a native SDK. The client side is a documented HTTP contract only.
Secrets in .env: APP_INGEST_KEY, PUBLIC_BASE_URL, WEB_FALLBACK_URL. Include a seed script with fake clicks and installs, and vitest tests covering deterministic match, probabilistic match, and the no-double-attribution rule.
Delivery standard
Inspect the repository first, then write a short implementation plan before writing code.
Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
Add structured logs around every external call and return actionable errors without leaking sensitive details.
Write unit tests for the core logic and one automated test of the main user journey.
Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.
Acceptance criteria
A clean install starts the app using only the README and .env.example.
The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
Tests, type checking, linting, and a production build all pass with no ignored failures.
No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.
Non-goals
Do not build these, and do not claim to have replaced them:
Self-attributing network data from Meta, Google, TikTok and Apple Search Ads, which is restricted to certified measurement partners.
SKAdNetwork and AdAttributionKit postback handling, decoding and conversion value modeling.
Install and click fraud detection, which depends on cross-advertiser device data you will never have.
Postbacks and audience syncs to thousands of ad partners and MMP-only integrations.
A maintained SDK that survives every OS release, ATT prompt change and privacy policy update.
Deterministic cross-device and cross-platform identity resolution.
What you still own after launch
Run migrations, backups, restores, and dependency updates.
Test the critical journey after every model, API, or hosting change.
Monitor failures and fix the edge cases a first prompt will miss.
Maintain every third-party integration as APIs and OAuth rules change.
Risk
High consequence. Use this as a prototype or personal aid. Keep a qualified human and an established provider in the loop for consequential decisions.
Editorial confidence in this assessment: medium. No reviewed project implementation is linked yet.
Generated by Can It Be Vibe Coded? · Full report: https://www.canitbevibecoded.com/appflyer
You still own the product
- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.
- Maintain every third-party integration as APIs and OAuth rules change.
Projects built from this idea
No reviewed implementation has been linked for AppsFlyer yet. A submission is evidence for review, not automatic proof that the whole product was replaced.
Built a version of AppsFlyer?Submit the project as evidence for this report.
Before you start
Can AppsFlyer be vibe coded?
Not faithfully. You can absolutely build a click tracker: log clicks, generate deep links, receive install pings from your own SDK, join them in SQLite. What you cannot build is the reason AppsFlyer exists, which is that Meta, Google, TikTok and Apple do not hand raw attribution data to random self-hosted endpoints. Those self-attributing networks report only to certified measurement partners, and SKAdNetwork/AdAttributionKit postbacks are aggregated and privacy-thresholded by design. Add install fraud detection, which is a data problem across billions of devices rather than a code problem, and the gap stops being about engineering effort. The honest consolation build is a first-party attribution tracker for channels you control: your own links, emails, influencer codes, organic web to app.
What can an AI coding agent reproduce from AppsFlyer?
A self-hosted link tracker that issues tagged deep links, logs clicks, and matches them to install and event pings from your own app SDK to give you first-party attribution for channels you control. Ingest a known data source, calculate a focused metric set, and render a useful dashboard. A responsive interface with real empty, loading, success, and error states.
What will a DIY AppsFlyer replacement still be missing?
Self-attributing network data from Meta, Google, TikTok and Apple Search Ads, which is restricted to certified measurement partners; SKAdNetwork and AdAttributionKit postback handling, decoding and conversion value modeling; Install and click fraud detection, which depends on cross-advertiser device data you will never have; Postbacks and audience syncs to thousands of ad partners and MMP-only integrations; Connectors, OAuth flows, and vendor API changes require constant upkeep.; The useful dataset is owned, accumulated, or expensive to reproduce.
What do I still own after building a AppsFlyer alternative?
Run migrations, backups, restores, and dependency updates. Test the critical journey after every model, API, or hosting change. Monitor failures and fix the edge cases a first prompt will miss. Maintain every third-party integration as APIs and OAuth rules change.