BS detector for job ads, expanded beyond Australia

What to build

A browser extension for job seekers in Australia that overlays a 'BS score' on job listings on Seek, Indeed, and LinkedIn, flagging vague salary ranges, buzzword padding, unrealistic requirement stacking, and known red-flag phrases directly on the page as they browse.

A solo-buildable red-flag detector for job ads taps into job seekers' shared frustration with low-quality listings, turning a simple heuristic scoring engine into a freemium browser extension with room to expand into bulk scanning and other English-speaking markets.

Demand

Australian job seekers browsing Seek, Indeed, and LinkedIn who are fatigued by vague, buzzword-heavy listings are the core audience; the Show HN launch suggests early curiosity from a tech-savvy crowd, though real demand from non-technical job seekers is still unproven.

• Hacker News (Show HN)Show hn post

  Show HN: Yeah Nah - a BS score for Australian job ads - 10 points, 2 comments on HN.

• Signal metadataSignal score

  Signal scored 0.42 with no historical match found, indicating this is a novel, unvalidated opportunity rather than a pattern with prior precedent in the dataset.

Stack

• Chrome/Firefox Extension Manifest V3
• JavaScript/TypeScript content scripts
• OpenAI API (for buzzword/phrase classification beyond simple regex)
• Supabase (user accounts, leaderboard data, scan history)
• Stripe (freemium subscription billing)
• Vercel (landing page + API backend)

Solo + AI difficulty

The MVP (regex/keyword-based scoring overlay on one site like Seek) is easy and buildable in a few days with AI coding help; the hard parts are keeping scraping selectors in sync as job sites change their DOM, tuning the heuristic to avoid false positives across industries and seniority levels, and navigating any ToS/defamation risk if a public 'worst offender' leaderboard names real employers. Realistic time-to-MVP is 1-2 weeks for a single-site extension with basic scoring.

Entry threshold

Buildable solo in days to a couple weeks using an LLM for text scoring plus a simple browser extension wrapper; no licences needed, main cost is scraping access and iterating on the scoring heuristic with real ad examples.

Window

3-6 months

Where to find first users

• Show HN follow-up post once paid tier ships
• r/AusFinance and r/AustralianJobs
• LinkedIn posts targeting AU job seekers
• Product Hunt launch

Counter-signals & risks

• Seek, Indeed, and LinkedIn may change page structures or add anti-scraping/anti-extension measures that break a browser-extension-based scoring tool, and could pursue takedown if the leaderboard names employers as 'worst offenders' (defamation/ToS risk).

• The 'BS score' heuristics (buzzword counts, vague salary detection) are easy to build a demo for but hard to make reliably accurate across industries, seniority levels, and ad-writing styles, risking false positives that undermine user trust and willingness to pay.

• A single Show HN post with no revenue, user, or retention data and no historical comparable signal provides weak evidence of monetizable demand beyond novelty interest from a tech audience.