Can DataFast be vibe coded?
Web analytics that ties revenue back to the marketing channel that produced it
The pageview half of DataFast is the same contained build as Plausible or Umami. The revenue half is where it stops being a contained effort. Attribution is only worth anything if the anonymous visitor who found your launch post on a phone is still recognisably the customer who pays from a laptop three weeks later, and that stitching is exactly what an agent will hand you a naive version of. A localStorage id plus an email match at signup does get you a channel table that is directionally right for a single-domain solo product, which is genuinely worth having. It also quietly under-counts every cross-device path, every privacy browser that clears storage between visits, and every customer who pays with a different address than they signed up with. You can build the dashboard in a contained effort. Trusting it enough to move ad spend is the part that keeps costing you weekends.
Jump to the build brief ↓Checked Aug 2026
What you pay today, before any DIY hosting
high 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
- Record pageviews with their UTM and referrer channel, bind the anonymous visitor to a customer at signup, then take Stripe webhooks and show revenue per channel.
- 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
- identity stitching across devices, browsers and cleared storage
- bot and AI-crawler filtering that stays current without you
- one-click installs for Shopify, Webflow, WordPress and 20 other platforms
- the live visitor feed and purchase-likelihood scoring
- Connectors, OAuth flows, and vendor API changes require constant upkeep.
- Reliability at the vendor's scale is an operations problem, not a prompt.
Why people still pay
An attribution number you do not trust is worse than no number, because you spend against it. Paying keeps someone else maintaining the bot filters, the Stripe and Shopify connectors and the retention window while you sell, and at $9 a month that is cheaper than the contained each quarter you would spend keeping your own version honest.
Connectors, OAuth flows, and vendor API changes require constant upkeep.
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 DataFast
Context
**DataFast** — Web analytics that ties revenue back to the marketing channel that produced it. It currently costs $9/mo.
The pageview half of DataFast is the same contained build as Plausible or Umami. The revenue half is where it stops being a contained effort. Attribution is only worth anything if the anonymous visitor who found your launch post on a phone is still recognisably the customer who pays from a laptop three weeks later, and that stitching is exactly what an agent will hand you a naive version of. A localStorage id plus an email match at signup does get you a channel table that is directionally right for a single-domain solo product, which is genuinely worth having. It also quietly under-counts every cross-device path, every privacy browser that clears storage between visits, and every customer who pays with a different address than they signed up with. You can build the dashboard in a contained effort. Trusting it enough to move ad spend is the part that keeps costing you weekends.
This brief describes a focused, single-operator replacement for the part of DataFast 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
Record pageviews with their UTM and referrer channel, bind the anonymous visitor to a customer at signup, then take Stripe webhooks and show revenue per channel.
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
Hosted server.
Database.
Tracker script.
Domain/SSL.
Bot filtering.
Data and integrations
Stripe webhook secret.
Each of these needs a real account, credential, or quota. Set them up before writing feature code.
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 me a revenue attribution dashboard for one site, to replace DataFast. Requirements:
Node + Express + better-sqlite3, one process behind Caddy on my own VPS.
Server-rendered pages, no frontend framework, no build step.
A tracker snippet under 2 KB: navigator.sendBeacon sends path, referrer and
any utm_* params, keyed to a first-party visitor id in localStorage. No
third-party cookies.
Attribution is the whole point. Per visitor store first-touch and last-touch
channel, from utm_source/utm_medium/utm_campaign, else by parsing the
referrer host into google / x / reddit / hn / direct. Never overwrite
first-touch.
An /identify endpoint I call after signup with the user's email, which binds
the anonymous visitor id to a customer row.
A Stripe webhook for checkout.session.completed, invoice.paid and
customer.subscription.deleted: verify the signature, match on email, write
revenue against that visitor. Webhook secret and API key from .env.
Dashboard on localhost behind one bearer token from .env: a channel table
with visitors, signups, customers, MRR and revenue per visitor over 7/30/90
days. Tables and one inline SVG bar chart, nothing else.
Drop known bots against a user-agent blocklist before anything is counted. No
accounts, no telemetry, one SQLite file I can copy off the box.
Out of scope: cross-device identity stitching, multi-touch models, the live
visitor feed, purchase-likelihood scoring, team seats and an MCP server. One
domain, single-touch, single-device.
README: the script tag, the /identify call, `stripe listen` for testing
webhooks locally, and an honest paragraph on where the numbers lie · a
phone-to-laptop journey counts as two visitors, cleared localStorage counts
as a new one, and a customer who pays from a different address never matches
at all.
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:
Identity stitching across devices, browsers and cleared storage.
Bot and AI-crawler filtering that stays current without you.
One-click installs for Shopify, Webflow, WordPress and 20 other platforms.
The live visitor feed and purchase-likelihood scoring.
Connectors, OAuth flows, and vendor API changes require constant upkeep.
Reliability at the vendor's scale is an operations problem, not a prompt.
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
**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.
Editorial confidence in this assessment: high. No independent one-shot implementation is linked yet.
Prior art
Working open-source software you can read, fork, or borrow from before starting:
[PostHog](https://github.com/PostHog/posthog) — Open source and self-hostable, with revenue analytics and channel attribution already built
[Plausible](https://github.com/plausible/analytics) — Open source analytics with goals and revenue goals; the Stripe join is still yours to write
[Umami](https://github.com/umami-software/umami) — Lightweight self-hosted analytics with UTM tracking and no revenue side at all
Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/datafast
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.
Open-source prior art
Open source and self-hostable, with revenue analytics and channel attribution already built
View project ↗PlausibleOpen source analytics with goals and revenue goals; the Stripe join is still yours to write
View project ↗UmamiLightweight self-hosted analytics with UTM tracking and no revenue side at all
View project ↗Before you start
Can DataFast be vibe coded?
Partly, if you narrow it. The pageview half of DataFast is the same contained build as Plausible or Umami. The revenue half is where it stops being a contained effort. Attribution is only worth anything if the anonymous visitor who found your launch post on a phone is still recognisably the customer who pays from a laptop three weeks later, and that stitching is exactly what an agent will hand you a naive version of. A localStorage id plus an email match at signup does get you a channel table that is directionally right for a single-domain solo product, which is genuinely worth having. It also quietly under-counts every cross-device path, every privacy browser that clears storage between visits, and every customer who pays with a different address than they signed up with. You can build the dashboard in a contained effort. Trusting it enough to move ad spend is the part that keeps costing you weekends.
What can an AI coding agent reproduce from DataFast?
Record pageviews with their UTM and referrer channel, bind the anonymous visitor to a customer at signup, then take Stripe webhooks and show revenue per channel. 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 DataFast replacement still be missing?
identity stitching across devices, browsers and cleared storage; bot and AI-crawler filtering that stays current without you; one-click installs for Shopify, Webflow, WordPress and 20 other platforms; the live visitor feed and purchase-likelihood scoring; Connectors, OAuth flows, and vendor API changes require constant upkeep.; Reliability at the vendor's scale is an operations problem, not a prompt.
What do I still own after building a DataFast 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.