Learning and careers decision

NSW School Reports

A focused replacement that generates NESA-aligned comments and implements token purchases is realistic for a competent developer using open-source tooling and LLM APIs, but reproducing the vendor's tuned prompts, product polish, and support/brand experience would be costly.

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Built by Pieter van Wyk, who ships 10 products in this index

You pay

$10/mo

$120/yr

Read off the official pricing page.

You’d pay instead

$100one-off120 h to build

$15/mo4 h/mo upkeep

On cash alone, building overtakes the subscription at 3 seats.

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

  • Teacher selects student/subject/achievement level, enters brief notes, system generates a NESA-aligned draft comment which the teacher edits and exports; purchase one-off token packs to allow generation.

What it still won’t have

  • Vendor-tuned NESA / NSW curriculum prompt engineering and iterative quality improvements
  • Priority support and product polish (demo, UX adjustments, content library, resources)
  • Token marketplace trust and pre-purchased token economics as marketed
  • Any proprietary training data or closed prompt recipes the vendor uses

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 3 seats.

Paid seatsseats

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 NSW-aligned report comment generator using Next.js (React) frontend, Node.js/Express backend, Postgres for de-identified workspace data, Stripe for one-off token purchases, and OpenAI-compatible LLM API for generation. Core features in scope: account sign-up and login (email), dashboard showing token balance, purchase flow for token packs, form UI to select student/subject/year/achievement/tone/length and enter teacher notes, server-side prompt templating that maps selections to curriculum-aligned prompts, call to LLM API with retries and rate-limit handling, save generated drafts and allow edit/export as DOCX (use docxtemplater) and copy-to-clipboard. Out of scope: training proprietary models, long-term analytics dashboards, multi-school admin billing, integrations with SENTRAL. Require input validation, error handling, unit tests for backend routes and prompt generation, and a README with deployment steps for a single small server (Heroku/Render) and estimated monthly costs.
How we checked3 sources · 3/3 runs agreed · evidence score 62

How the score was reached

  • Partly verdict base52
  • 3 cited sources+3
  • Price verified on pricing page+3
  • 3/3 assessment runs agreed+4
  • Evidence score62

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.

Integrity checks

What held up, and what did not.

✓ Price read off the page✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 1 moat recorded