Can Cabin be vibe coded?
Privacy-friendly web analytics with simple dashboards and no cookies
The visible privacy web analytics loop is buildable, but a credible replacement needs more than the first screen. Cabin earns its keep through ingestion, storage, reliability, so expect a substantial build and a narrower personal scope.
Jump to the build brief ↓Checked Jul 2026
medium editorial confidence
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
- Build a narrow privacy web analytics collector for one site or app, ingest first-party events, and show a small set of decision-ready reports.
- 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
- bot and identity resolution
- session replay privacy tooling
- enterprise governance and integrations
- high-volume ingestion and retention
- Reliability at the vendor's scale is an operations problem, not a prompt.
- The last 20 percent is sync, migration fidelity, speed, and edge cases.
Why people still pay
Cabin: Customers pay for trusted numbers, retention, privacy controls, and a pipeline that remains accurate while traffic and schemas change.
Reliability at the vendor's scale is an operations problem, not a prompt.
The last 20 percent is sync, migration fidelity, speed, and edge cases.
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 Cabin
Context
**Cabin** — Privacy-friendly web analytics with simple dashboards and no cookies. It currently costs Variable pricing.
The visible privacy web analytics loop is buildable, but a credible replacement needs more than the first screen. Cabin earns its keep through ingestion, storage, reliability, so expect a substantial build and a narrower personal scope.
This brief describes a focused, single-operator replacement for the part of Cabin 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
Build a narrow privacy web analytics collector for one site or app, ingest first-party events, and show a small set of decision-ready reports.
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
Deploy target.
ClickHouse or SQLite for low volume.
First-party tracking script.
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 deliberately narrow personal substitute for Cabin, not a full clone.
Use exactly this stack: Next.js 15 + TypeScript + ClickHouse + PostgreSQL.
Primary job: Build a narrow privacy web analytics collector for one site or app, ingest first-party events, and show a small set of decision-ready reports.
Start from an empty folder and create the complete working project.
Make the default mode single-user and private.
Store user data locally unless the core job requires the declared self-hosted database.
Do not add analytics, telemetry, ads, or third-party accounts.
Put every secret and external credential in .env and provide .env.example.
Use realistic sample data that is clearly labelled and easy to delete.
Implement the smallest polished interface that completes the core loop end to end.
Include clear empty, loading, validation, success, and failure states.
Add import and export so the user is not trapped in the app.
Use accessible keyboard navigation, labels, focus states, and sensible contrast.
Validate untrusted input and never log secrets or private file contents.
Deliberately exclude these paid-product advantages: bot and identity resolution; session replay privacy tooling; enterprise governance and integrations.
Do not fake integrations, network effects, proprietary data, model quality, compliance, or security claims.
Where an external API is optional, keep the app useful without it and explain the degraded mode.
Write focused unit tests for the data model and the most important workflow.
Add one end-to-end smoke test that proves the core loop works.
Create a README with setup, permissions, architecture, data location, backup, and limitations.
Add scripts for install, development, test, build, and a production-style local run.
Run the tests and build before finishing, then fix errors rather than merely describing them.
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:
Bot and identity resolution.
Session replay privacy tooling.
Enterprise governance and integrations.
High-volume ingestion and retention.
Reliability at the vendor's scale is an operations problem, not a prompt.
The last 20 percent is sync, migration fidelity, speed, and edge cases.
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.
Risk
**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.
Editorial confidence in this assessment: medium. No independent one-shot implementation is linked yet.
Prior art
Working open-source software you can read, fork, or borrow from before starting:
[Plausible Analytics](https://github.com/plausible/analytics) — Open-source privacy-focused web analytics
[Umami](https://github.com/umami-software/umami) — Lightweight self-hosted web analytics
Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/cabin
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.
Open-source prior art
Before you start
Can Cabin be vibe coded?
Partly, if you narrow it. The visible privacy web analytics loop is buildable, but a credible replacement needs more than the first screen. Cabin earns its keep through ingestion, storage, reliability, so expect a substantial build and a narrower personal scope.
What can an AI coding agent reproduce from Cabin?
Build a narrow privacy web analytics collector for one site or app, ingest first-party events, and show a small set of decision-ready reports. 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 Cabin replacement still be missing?
bot and identity resolution; session replay privacy tooling; enterprise governance and integrations; high-volume ingestion and retention; Reliability at the vendor's scale is an operations problem, not a prompt.; The last 20 percent is sync, migration fidelity, speed, and edge cases.
What do I still own after building a Cabin 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.