Learning and careers decision

Math AI - Solve math by screenshot

A single competent developer can build a useful screenshot-to-steps math extension in about one week using existing OCR, SymPy, and LLM APIs; no durable moats are evident on the product page.

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You pay

Not priced

No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.

You’d pay instead

$100one-off40 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

  • User takes screenshot → OCR/math parsing → symbolic/LLM solver computes steps → render step-by-step solution in the extension UI.

What it still won’t have

  • Polished UX and multi-language localization provided by the published extension
  • Chrome Web Store listing, ratings, and existing user base
  • Any proprietary back-end optimizations or in-app purchase infrastructure the publisher may provide

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

Math AI - Solve math by screenshot 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

Keep paying
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Subscription price × seats × 12

Build it
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AI build —APIs + hosting —

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

Not run yet
Build a minimal Chrome extension + backend that replicates screenshot-based math solving: stack: Chrome extension (JS/TS) for capture and UI; Node.js + Express backend; Python microservice (Flask) for OCR and symbolic math using Tesseract (or Mathpix API) and SymPy; OpenAI (or equivalent) for natural-language step explanations. Core features in scope: screenshot capture from page, upload to backend, OCR -> LaTeX extraction, SymPy-based solution attempt, LLM prompt to generate step-by-step explanation when needed, JSON API between services, in-extension renderer showing steps and ability to copy/export. Out of scope: payments/in-app purchases, analytics dashboards, multi-user accounts. Require: error handling for failed OCR/parse/solver, retries and timeouts for external APIs, unit and integration tests for OCR->parse->solve flow, and CI that builds the extension manifest and runs tests.
How we checked1 sources · 2/3 runs agreed · evidence score 78

How the score was reached

  • Build verdict base78
  • Evidence score78

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.

Integrity checks

What held up, and what did not.

! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat recorded