Uncategorised decision
Checkwoods
A single developer can reproduce the core upload, AI-ID, geotag, map, and gamification features in ~32 hours, but they cannot reproduce the product's crowdsourced volunteer network or any live dataset the service already has.
Visit website↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off32 h to build
$50/mo3 h/mo upkeep
No published price to break even against.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Users upload photos of plants; an image-recognition API returns species predictions; the app captures GPS and timestamp; the server stores sightings in a database and renders them on a map; users earn points for validated submissions.
What it still won’t have
- the volunteer network and crowdsourced contributions
- the existing live database of sightings
- any brand, waitlist, or established community around the app
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Checkwoods does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
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What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal invasive-species sightings service using Next.js for the frontend, NextAuth for auth (email sign-in), a Postgres database (hosted on Supabase or Render), and a small Node/Express or Next API for endpoints. Core features in scope: accept photo uploads from mobile/web, capture client GPS and timestamp, send images to a third-party image-recognition API and store returned species predictions, save sightings to Postgres, show an interactive map (Leaflet or Mapbox) with filters by species and date, simple user accounts and points for submissions, and a moderator view to validate/flag sightings. Out of scope: building a proprietary ML model, advanced moderation workflows, and large-scale analytics. Include error handling for failed uploads and API errors, automated tests for API endpoints, and deployment scripts (Dockerfile + simple CI).
How we checked
How the score was reached
- Partly verdict base52
- Evidence score52
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Integrity checks
What held up, and what did not.
