Health, home and travel decision
Taller
A technically-capable developer can build a useful height-prediction and tracking replacement (prediction + plans + AI Q&A) in a few weeks, but matching Taller's claimed accuracy, community, mobile polish, and any proprietary model/data is not realistic without the vendor's data or extensive ML work.
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-off54 h to build
$120/mo6 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
- Collect user profile and measurements, run a prediction model to estimate potential height, generate a personalized daily habit/nutrition routine, track progress and update predictions monthly, and provide an AI chat interface for Q&A.
What it still won’t have
- Proprietary training data and any vendor-tuned prediction models used by Taller
- Established community and moderated private forums
- Polish and cross-platform native mobile app UX
- Any product-sourcing or commerce/recommendation features promised as “coming soon”
- Trust/marketing value from an existing user base and accuracy claims
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Taller 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 self-hosted web PWA in Next.js + Typescript with a Postgres backend (hosted on a small cloud VPS or managed DB) and a Node/Express API. Implement: (1) user auth (email/password + session), (2) user profile and measurement model (age, sex, baseline height, habit inputs) persisted in Postgres, (3) a height-prediction microservice: start with a simple explainable regression model (scikit-learn) packaged as a REST endpoint and seed it with synthetic rules, (4) endpoints to submit measurements and a monthly job that recalculates predicted potential and writes history, (5) a templated personalized routine generator (rule-based) that outputs daily habits, (6) AI chat UI that forwards user questions plus the user's anonymized profile to a hosted LLM (OpenAI) and returns context-aware answers, (7) scheduled job and email/push reminder integration via a provider (e.g., SendGrid + FCM/web push). Out of scope: building or training a large proprietary foundation model, native iOS/Android apps, running paid community moderation. Include error handling, input validation, unit tests for API endpoints, and integration tests for the monthly job; provide Dockerfiles and a deployment script for a single VPS and a README with run/deploy steps.
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 →Cited sources · 1
Every page the run actually retrieved.
- official productTaller App - Official Height Prediction & Growth App
Integrity checks
What held up, and what did not.
