AI assistants and search decision
StackNinja
A small team or competent technical user can build a useful StackNinja replacement in ~32 hours and maintain it; nothing on the provided page indicates durable moats or proprietary data that would block a rebuild.
Visit website↗Built by Marc Lou, who ships 32 products in this index
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.
Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All StackNinja alternatives, with the arithmetic →
What a replacement has to do
- Collect user health/profile and supplement inputs; generate personalized supplement recommendations via a hosted LLM; store user profiles and recommendations in Postgres; present recommendations and editable plans in a simple web UI; schedule reminders and send notifications (email/SMS) for doses.
What it still won’t have
- Any proprietary supplement database, curated content or editorial community the vendor may have
- Brand trust and off-the-shelf UX polish
- Potentially bundled, paid model access or negotiated API discounts
- Vendor-managed compliance, medical disclaimers, and legal support
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
StackNinja 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 self-hosted StackNinja replacement using React frontend, Node.js + Express backend, and Postgres. Core features in scope: user sign-up (email-only), user profile input (age, goals, current supplements, allergies), call OpenAI-compatible LLM to generate a personalized supplement protocol with rationale, store protocols and history in Postgres, web UI to view/edit current protocol, scheduled daily reminders implemented with a background worker + cron and integrated with SendGrid (email) and Twilio (SMS). Out of scope: payment/subscriptions, advanced medical compliance/legal review, multi-user teams, or training any models. Include error handling, input validation, unit tests for API routes, integration test for LLM calls (mocked), and Docker Compose for local dev and deployment.
How we checked
How the score was reached
- Build verdict base78
- An open-source build was found+5
- 3 cited sources+3
- Evidence score86
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 →Cited sources · 3
Every page the run actually retrieved.
- official productLouis | Indie Page
- open sourcearc53/DocsGPT
- open sourceChatGPTNextWeb/NextChat
Integrity checks
What held up, and what did not.
