Learning and careers decision

CheatMate

A small technical user can implement the core Chrome-extension + LLM proxy in about a week and maintain it; the vendor's claims (undetectability, support) and ongoing evasive operations are not durable moats.

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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

$50one-off30 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 double-clicks a question in the browser → extension captures question context → send to LLM API → parse LLM answer → inject/display answer invisibly in the page

What it still won’t have

  • Claims of being 100% undetectable over proctored screenshare (product marketing/legal risk)
  • Polished cross-platform support and compatibility tests for many LMSs
  • 24/7 customer support and refund/chargeback handling
  • Any proprietary fine-tuned model or undisclosed datasets if the vendor uses them
  • Ongoing concealment countermeasures against proctoring/evasion updates

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

CheatMate 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 Chrome extension + minimal Node.js proxy that answers exam questions using a hosted LLM. Stack: Content scripts + Manifest V3 extension, Node.js + Express proxy, Postgres or SQLite for lightweight logs, hosted on a small VPS (or Heroku/Render). In-scope features: content script that detects/receives a double-click or selection, robust DOM parsers for MCQ/fill-in/matching, call an LLM API from the proxy with configurable API key, parse LLM JSON/text responses, inject answers into page DOM without visible modal UI, basic auth (user token), logging of requests, retries, and unit + integration tests for parsing and injection. Out of scope: advanced evasion tooling, paid billing integration, cross-browser packaging beyond Chrome, and any activity to defeat proctoring systems. Require error handling for network/LLM errors, rate-limiting, and tests covering parsing, injection, and proxy API endpoints.
How we checked2 sources · 3/3 runs agreed · evidence score 83

How the score was reached

  • Build verdict base78
  • 2 cited sources+1
  • 3/3 assessment runs agreed+4
  • Evidence score83

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 · 2

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 1 moat recorded