PAIN TRIGGERS
Job listings stay live for extended periods with no feedback or closure after applications.
SaaS· software engineers job huntingPain 7.00/10WTP 6.0/10Market 9.0/10Validation 8.0Confidence 72%May 17, 2026
GhostFilter: Real-Time Ghost Job Detector for Tech Roles
Job listings remain open for months or years with no responses or closures, wasting massive application effort on ghost jobs that aren't truly active.
analyticsautomationbrowser-extensioncareer-toolsdevelopersfreelancersjob-searchproductivitysaastech-professionals
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STAGE 01 · PROBLEM
Is the problem real?
CANONICAL PROBLEM
Tech job seekers experience ghosting where applications receive no response and job listings remain open for months or years without apparent hiring activity.
FREQUENCY
Multiple repeated complaints in the post and comments.
INTENSITY
Users explicitly describe existing tools as bloated/overkill and mention workaround behavior.
EVIDENCE
Show HN: Got ghosted by tech companies so I built a tool to track ghost jobs
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Show HN: Got ghosted by tech companies so I built a tool to track ghost jobs
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Are you sure these are talent shortages and not H1B postings?
commentAre you sure these are talent shortages and not H1B postings? Are you filtering for changing JDs or taking something like the URL being up as canonical?
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STAGE 02 · CUSTOMER
Who feels this pain?
TARGET USERS
software engineers job huntingSoftware Engineers Job Hunting
Mid-to-senior software engineers applying to 30-100+ roles monthly across LinkedIn, Indeed, and company sites while dealing with prolonged silence and stale listings.
Context
Identify and prioritize real, active job opportunities while avoiding wasted effort on ghost jobs.
Building a custom tracking system to monitor posting dates and longevity across thousands of listings.
Current Workarounds
Manually tracking posting dates in spreadsheets
Building custom scripts to monitor 35k+ listings for longevity
Applying broadly then ghosting companies back after weeks of silence
Relying on personal networks to validate active hiring
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STAGE 03 · MARKET
Where's the gap?
EXISTING SOLUTION GAPS
Standard job boards and company career pages do not indicate posting age, activity, or whether roles are ghosted.
No built-in signals for hiring momentum or pipeline health visible to applicants.
OPPORTUNITY & VALUE
Why Now
Strong repetition around long-open listings (700+ days), zero feedback after applications, and skepticism of real hiring activity.
Value Proposition
Focuses exclusively on ghost detection and freshness signals rather than matching or resume tools; leverages large-scale posting longevity data.
Product Direction
Browser extension and web app that scores any job posting for ghost probability using age, company patterns, and activity signals, prioritizing real opportunities.
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STAGE 04 · BUSINESS
How does it make money?
MONETIZATION
$15/moIndividual job seeker plan
Model
SaaS subscription
WILLINGNESS TO PAY
Engineers already invest dozens of hours building custom trackers and applying to dead listings; signals show frustration with wasted effort and users actively seek better filtering to save time, making low monthly fee a clear ROI.
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STAGE 05 · EXECUTION
How do you ship it?
MVP PLAN
“Spot real hiring signals and skip ghost jobs before you apply.”
Browser extension and web app that scores any job posting for ghost probability using age, company patterns, and activity signals, prioritizing real opportunities.
Core Features
Job posting age and ghost score overlay on LinkedIn/Indeed
Personal dashboard tracking applied roles with status decay alerts
Company-level hiring momentum heatmap from public listings
Exportable active-only job feed
Weekly Roadmap
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W1-W2
Core ghost scoring engine and basic browser extension built.
- •Build posting age parser and simple decay model
- •Create Chrome extension skeleton with overlay UI
- •Store user-tracked listings in backend
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W3-W4
LinkedIn and Indeed overlays functional with dashboard.
- •Implement LinkedIn DOM injection for scores
- •Add manual URL import and company heatmap
- •User authentication and basic tracking
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W5
Polish, internal testing, and 20 beta users onboarded.
- •UI refinements and error handling
- •Recruit beta testers from r/cscareerquestions
- •Validate scores against known long-open listings
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W6
Public launch with first paying subscribers.
- •Stripe integration for subscriptions
- •Launch on Product Hunt and relevant subreddits
- •Track conversion from free scans to paid
Launch Strategy
Launch as Chrome extension on Product Hunt and Reddit (r/cscareerquestions, r/jobs, r/ExperiencedDevs); target tech Twitter/X job hunting threads.
RISKS & ASSUMPTIONS
Top Risks
Data scraping and platform restrictions
LinkedIn and Indeed may block or limit automated analysis needed for real-time scoring.
Inaccurate ghost signals
Some long-open roles may still be valid; false positives could erode user trust.
Low willingness to pay
Job seekers in transition may prefer free tools even if frustrated.
Seasonal job market dependency
Demand may drop significantly outside peak hiring periods.
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STAGE 06 · DECISION
Should you build it?
NEED A CLEARER CALL?
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoWhat this score means
This idea scores in the upper-middle range of opportunities surfaced by MonetScope, with a validation sub-score of 8/10 against 3 independently sourced evidence signals. A "promising" rating usually indicates a real pain has been detected and discussed in the open, but the pipeline did not find enough signal to flag it as urgent or high-frequency. These opportunities can still produce excellent businesses — they often correspond to "boring" problems that established players have ignored — but the founder should expect a longer customer-development cycle to confirm willingness to pay.
Why this matters for SaaS founders
It sits at the intersection of "analytics", "automation", "browser-extension", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
Frequently asked questions
Is "GhostFilter: Real-Time Ghost Job Detector for Tech Roles" a real validated startup idea or just an AI-generated suggestion?
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
How recent is the underlying data for analytics?
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
What's the difference between "overall score" and "validation score"?
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.