Job seekers waste months applying to and interviewing for positions that don't actually exist — posted only for HR optics, investor reports, or internal compliance
Aggregate signals (posting age, repost frequency, company hiring velocity, Glassdoor/LinkedIn data, user reports) to assign a 'realness score' to each job listing; flag likely ghost jobs before candidates invest time
Freemium — free browser extension with basic scores, $9.99/mo premium for detailed analytics, company-level hiring pattern reports, and alert filters
Job seekers routinely report spending 3-6 months applying to positions that never existed. The emotional toll — wasted interview prep, ghosted after rounds, false hope — is severe. The HN thread sentiment ('most of them have dummy openings') reflects widespread frustration. This is a top-3 pain point for anyone actively job searching.
US alone has ~6M active job seekers at any time, EU adds another ~10M+. Mid-to-senior tech professionals are a subset (~2-3M addressable). At $9.99/mo with even 1% penetration of addressable market, that is ~$2.5-3M ARR. TAM is meaningful but niche — this is not a billion-dollar market unless it expands beyond tech or adds B2B employer-facing products.
Job seekers are historically reluctant to pay for job search tools — LinkedIn Premium being the notable exception and even that sees high churn. The $9.99/mo price point is reasonable but competes with free alternatives (Reddit communities, manual checking). Best signal: LinkedIn Premium proves some WTP exists. Conversion will likely be low (2-5%) unless the accuracy is demonstrably high. European markets may be slightly more WTP-friendly.
A solo dev can build a basic browser extension MVP in 4-8 weeks, BUT accuracy is the hard part. Aggregating signals from LinkedIn, Glassdoor, and company career pages requires scraping that violates most ToS and is subject to breakage. Building a reliable multi-signal scoring model needs training data that does not cleanly exist — there is no labeled dataset of confirmed ghost vs real jobs. The extension shell is easy; the intelligence layer is genuinely hard to get right.
No dominant player exists. The space is fragmented with tiny indie tools. LinkedIn/Indeed are structurally misaligned (ghost jobs boost their metrics). Glassdoor has the data but not the product. This is a clear whitespace where the first well-executed product wins. The browser extension overlay approach — scoring jobs on any platform — is particularly unaddressed.
Job searching is inherently episodic, not continuous. A user might subscribe for 3-6 months during active search, then churn. This creates a high-churn subscription model unless you add always-on value (market monitoring, passive opportunity alerts, salary benchmarking). Compare to LinkedIn Premium which has the same problem — most users subscribe during job search then cancel. LTV will be capped by search duration.
- +Clear whitespace — no dominant competitor and structural misalignment prevents incumbents from building this
- +Strong emotional resonance — ghost jobs are a visceral frustration with growing media/regulatory attention
- +Browser extension distribution model is proven (Honey, Grammarly) and has low CAC via Chrome Web Store
- +European market focus is smart — less competitive than US, strong privacy/worker-protection regulatory tailwinds
- +Community/crowdsource layer creates a data moat that improves with scale
- !Accuracy is existential — false positives (flagging real jobs) will destroy trust faster than false negatives. Without a labeled training dataset, building reliable scoring is the core technical risk
- !Scraping dependency — LinkedIn, Glassdoor, and Indeed actively fight scrapers. A ToS violation or API lockout could break the product overnight
- !Episodic usage = high churn — job seekers subscribe during search then cancel, capping LTV at ~$30-60 per user
- !Legal risk — companies whose listings get flagged as ghost jobs may push back legally, especially in EU jurisdictions
- !Feature, not product — any major job platform could add a 'listing freshness/realness' score as a feature, commoditizing the standalone tool
Community-driven platform where job seekers crowdsource reports of suspected ghost job listings. Users flag listings they believe are fake, building a shared database of questionable postings.
Shows 'actively hiring' badges, application counts, posting age, and recruiter responsiveness indicators. Premium tier adds insights on who viewed your profile and how you compare to other applicants.
Provides company reviews, interview experience reports, salary data, and hiring process transparency. Job seekers can indirectly assess legitimacy by reading interview reviews and company ratings.
Curated job platform that vets companies before listing positions. Focuses on quality over quantity, primarily serving tech/startup ecosystem with hand-picked job listings.
Job search productivity suites that help track applications, optimize resumes, manage pipelines, and organize the job hunt. Some are beginning to add basic job quality signals.
Chrome/Firefox extension that overlays a simple red/yellow/green 'realness score' on LinkedIn and Indeed job listings. V1 signals: posting age, repost frequency, number of applicants vs. company size, and a community report button. Skip company-level analytics for MVP — just nail the per-listing score. Add a simple feedback loop where users report outcomes (got interview, got ghosted, position filled) to train the model over time.
Free extension with basic red/yellow/green scores (drives installs and data collection) → $9.99/mo premium for detailed breakdowns, company hiring pattern reports, and ghost job alerts → $29.99/mo pro tier with API access and bulk analysis for recruiters/career coaches → B2B pivot: sell anonymized hiring signal data to workforce analytics firms or job boards wanting to differentiate on quality
8-12 weeks to MVP launch, 3-4 months to first paying users. Realistic path: weeks 1-6 build extension + basic scoring, weeks 7-8 beta with HN/Reddit job seeker communities, weeks 9-12 iterate on accuracy based on feedback, month 4 launch premium tier. First meaningful revenue ($1K MRR) likely at month 5-6. The long pole is accuracy — launching too early with bad scores will poison the well.
- “most of them have just dummy openings for internal procedures/investor reports”
- “No companies are hiring and all jobs are fake”
- “HR wants you to think so, otherwise even they are not safe”
- “jobs and interviews for vacancies that don't exist so those still left in HR can seem busy”
- “Agencies simply collect data and will consume your time with intake interviews”