Writing and content decision

Lunchbreak

A capable developer can build a useful detector+humanizer that covers many workflows, but reproducing the vendor's coverage of proprietary detectors and the commercial integrations requires partnerships the builder likely can't obtain.

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Subscription$10/month ✓ verified
Initial build36 hours
Monthly upkeep6 hours + $30
Evidence2/3 runs agree

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 Lunchbreak alternatives, with the arithmetic →

What a replacement has to do

  • Paste text → run multiple detector APIs → run an LLM-based 'humanize' rewrite → re-run detectors → present side-by-side flagged lines and rewritten output → save/export document.

What it still won’t have

  • Access to proprietary detection services that require commercial partnerships (Turnitin/GPTZero/etc.)
  • Polish of a production UX, scale, and multi-detector tuning performed by the vendor
  • Factual-sources/citations feature and any proprietary model tuning
  • Ongoing detector updates and QA across multiple third-party detectors

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 4 seats.

Paid seatsseats

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

Time you would spend

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 single-developer web app (React frontend, Node.js/Express backend, PostgreSQL) that accepts pasted text, calls configurable detector endpoints, calls an LLM (OpenAI/Anthropic) to rewrite flagged passages, re-runs detectors on rewritten output, and shows a side-by-side comparison with highlighted lines and export. In scope: user signup/login, paste-and-check flow, detector-aggregator adapter layer (pluggable endpoints), LLM humanizer service, document save/export (PDF/TXT), Stripe basic billing hook, and tests for API endpoints and critical UI flows. Out of scope: commercial Turnitin/GPTZero reseller integrations that require enterprise contracts, large-scale multi-tenant autoscaling, and advanced analytics. Include error handling for failed API calls, rate-limit backoff, and unit/integration tests; provide deployment scripts for a single Heroku/DigitalOcean instance and instructions to swap detector endpoints and LLM API keys.
How we checked5 sources · 2/3 runs agreed · evidence score 63

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 5 cited sources+3
  • Price verified on pricing page+3
  • Evidence score63

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.

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