Learning and careers decision

InsideJobs.tech

A competent developer can reproduce the job-board mechanics (scraping, DB, search, emails) using open-source prior art, but they cannot easily recreate the service's proprietary aggregated dataset and recruiter relationships that deliver the product's main value.

Visit website
You pay

$29/mo

$348/yr

Read off the official pricing page.

You’d pay instead

$100one-off80 h to build

$90/mo6 h/mo upkeep

On cash alone, building overtakes the subscription at 4 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

  • Periodically crawl recruiter and agency sites, normalize and index job posts, provide searchable filters and per-user daily digests, gate full job links behind a subscription checkout.

What it still won’t have

  • The site's existing aggregated dataset of 12k+ hidden roles (proprietary crawl/collection)
  • Search relevance and curated filtering tuned to this dataset
  • Trust and continuity of daily email alerts built from years of crawling
  • Any recruiter relationships or direct links the product has already collected

What remains hard

  • Proprietary data12,400+ tech jobs that never hit LinkedIn.
  • Proprietary data✓ Full access to every live role
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper 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 self-hosted job-board in Node.js (Fastify) + Postgres, deployable to DigitalOcean/Render. Core features: scheduled scrapers (simple HTTP fetch + CSS/XPath parsing) configurable per-domain, dedupe & normalize job records in Postgres, REST API for listing and faceted search, a minimal React front-end to browse/filter and view role summaries, Stripe subscription gating for 'unlocked' job links, daily cron worker that matches user filters and sends digests via SendGrid, admin UI to add/remove source domains. Out of scope: training ML ranking models and building large-scale distributed crawlers. Include error handling, retries for scrapers, tests for API endpoints, and Docker compose + deployment docs.
How we checked2 sources · 3/3 runs agreed · evidence score 57

How the score was reached

  • Partly verdict base52
  • 2 cited sources+1
  • Price verified on pricing page+3
  • 3/3 assessment runs agreed+4
  • Hard moats found in the evidence-3
  • Evidence score57

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.

✓ Price read off the page✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 2 moats quoted from the page