What changed
FACT: A cheap, fast Gemini tier (Gemini 3.7 Flash) is now publicly available via API, making per-listing text classification economically viable at free-tier scale (cited Google blog). FACT: Job seekers are publicly documenting LinkedIn ghost jobs with hard specifics — a listing reposted 7 hours prior that stopped accepting applications after only 36 applicants (cited HN thread).
Why now
Ghost-job frustration is peaking publicly during a seeker-heavy 2026 job market (second HN thread notes more seekers than hirers and jobs being 'rotated/reposted'), while the model-cost floor just dropped enough that a broke solo dev can classify every listing a user views for near-zero marginal cost, client-side, with no infra.
Converging signals
(1) PAIN: HN complaint thread with concrete ghost-job documentation (id 6006); (2) PAIN: HN 2026 job-market thread reporting repost/spam patterns; (3) CAPABILITY: Gemini 3.7 Flash cheap tier (id 7957). Complaint x cheap-capability — the canonical quick-win shape, not a mandate shape.
Customer pain
Each tailored application to a ghost listing costs an evening (resume tailoring, cover letter, application forms) and delivers zero chance of hire. Seekers cannot distinguish live listings from evergreen pipeline-builders, compliance postings, or stale reposts. The pain is acute, recurring daily during a search, and emotionally charged — exactly the profile that converts complaint volume into card payments.
Who pays
Active job seekers mid-search (white-collar/tech skew). HYPOTHESIS: this segment already pays for Teal/Simplify/LinkedIn Premium-class tools, so a $6/mo painkiller is within demonstrated spending behavior — but no direct spend evidence for ghost-detection specifically is in the input, so existing_spend is scored moderately, not high.
Solved today
Manual heuristics (checking repost dates, applicant counts, Glassdoor sleuthing), Reddit/HN crowd-sourced company blacklists, and a few standalone sites (ghostjobs.io reports, hiring.cafe filters). LinkedIn's own 'actively reviewing applicants' signals are weak and gameable.
Why current solutions are bad
Manual sleuthing takes minutes per listing — the exact time the tool should save. Standalone report sites require leaving the job board and only cover reported companies. Nothing scores the listing in-context, at the moment of decision, using repost frequency + applicant velocity + language classification together.
Proposed product
Chrome/Edge extension that renders a 0-100 ghost-score badge directly on LinkedIn and Indeed listing pages. Signals: repost frequency for the same req (fingerprinted title+company+description hash), listing age vs. applicant velocity, company-level posting patterns (crowd-aggregated across users, anonymized), and Gemini Flash classification of boilerplate/evergreen-req language. Free tier: 10 scans/day. Paid: unlimited + company ghost-history + weekly 'don't bother' digest.
MVP version
Extension that works on the listing page the user is already viewing: parse visible fields client-side, call Gemini Flash with the user's activity batched, score against simple repost/age/velocity heuristics, render badge. No backend beyond a thin scoring API and a shared repost-fingerprint store. Ship in ~2 weeks.
30-day build
Week 1-2 build MVP for LinkedIn only. Week 3 launch free tier into the exact complaint threads (HN, r/jobs, r/recruitinghell) and Chrome Web Store. Week 4 add Indeed support. Target 500 installs per the kill test.
60-day build
Turn on $6/mo / $29/yr paywall at scan cap. Add company ghost-history pages (SEO surface: 'is [company] posting ghost jobs'). Instrument conversion. Kill per the pre-registered test if paid conversion <2% after 3 weeks at 500+ installs.
90-day revenue plan
HYPOTHESIS: 3,000 installs via complaint-thread virality + Chrome store search, 3% paid conversion = ~90 subs = ~$540 MRR, growing with the SEO company-pages flywheel. Secondary revenue probe: aggregate ghost-rate data as a report/API for job boards and researchers (the recruiter-side is the bigger long-term buyer).
Distribution path
Post directly into the complaint ecosystem that generated the signal (HN, r/jobs, r/recruitinghell, TikTok/LinkedIn creators who rant about ghost jobs — this topic is reliably viral), Chrome Web Store search ('ghost job'), and programmatic SEO on company ghost-history pages. No paid acquisition needed to start — fits the broke-solo-dev bar.
Pricing hypothesis
$6/mo or $29/yr, free tier capped at 10 scans/day. Card payment, no procurement. Price anchored under Teal ($9/wk) and LinkedIn Premium ($40/mo).
Technical difficulty
Low-moderate. Client-side DOM parsing is fragile (LinkedIn changes markup and obfuscates classes) and the cross-user repost/velocity database has a cold-start problem — heuristics + LLM classification must carry the score until aggregation density builds. Ongoing maintenance burden as boards change markup.
Legal / regulatory risk
Moderate and platform-shaped, not regulatory: LinkedIn's ToS prohibit automated collection and LinkedIn has a documented history of C&Ds against extensions/scrapers. The extension reads pages the user is already viewing (user-directed, client-side), which is the most defensible posture post-hiQ, but LinkedIn can still ban user accounts or send a C&D. This is the single biggest structural risk — flagged platform_policy_risk.
Platform dependency
High: LinkedIn/Indeed DOM stability, LinkedIn's tolerance of overlay extensions, Chrome Web Store approval, and Gemini API pricing. LinkedIn could also neutralize the product by shipping credible native verification (it has been moving this direction with verification badges).
Founder fit
Mixed. Browser extensions and complaint-mining are explicitly in the founder's preferred product list, the build is squarely within fast AI-assisted prototyping strength, and it sells through demonstrated value. But it is a consumer-adjacent, churn-heavy market (customers leave when hired), distribution depends on virality rather than a forced deadline, and it has none of the government-portal/forced-filer leverage where his proven edge lies. A lesson (conf 0.65) says mandate-shaped opportunities fit him best — this is not one, and per the quick-win rubric that does not disqualify it, but fit is mid, not maximal.
Breakout potential
Moderate: the consumer extension is the wedge, but the aggregated ghost-rate dataset (company-level posting integrity) is a sellable data product to job boards, researchers, and press — a defensible asset no weekend clone starts with. 'Ghost job index' press coverage is a plausible breakout vector.
Final recommendation
BUILD as a fast, cheap kill-test — this is a legitimate quick-win: acute documented pain, days-to-weeks build on near-zero infra, card-paying buyer, and a pre-registered kill criterion. Cap total investment at the ~30-day test; the pre-registered 2%-conversion kill test is the decision gate. Do not scale spend before it passes, and treat the aggregated dataset (not the subscription) as the real long-term asset. It is NOT a primary-thesis play and should not displace mandate-shaped work.
Next action
Ship the LinkedIn-only MVP in 2 weeks (DOM parse + heuristics + Gemini Flash classify + badge), then post the free tier into the cited HN thread and r/recruitinghell the same day and start the 500-install / 2%-conversion clock.