PAIN TRIGGERS
Job scraping is blocked by platform anti-scraping, preventing reliable filtered results.
Scrapers and prompts break when platforms update their protections.
SaaS· job seekersPain 7.00/10WTP 6.0/10Market 7.0/10Validation 8.0Confidence 78%May 3, 2026
HarvestJobs: Maintained Multi-Site Job Scraper with Smart Filters
Anti-scraping protections on major job boards block reliable extraction of filtered listings by salary, posting date, and description details, with scrapers breaking on every platform update.
automationdata-managementdevtoolsjob-searchjob-seekersproductivitysaasscraping
1
STAGE 01 · PROBLEM
Is the problem real?
CANONICAL PROBLEM
Scraping job listings from LinkedIn, Indeed, Glassdoor, and Dice is blocked by anti-scraping measures, making it hard to filter by date, salary, job description, and posting time quickly.
FREQUENCY
Multiple repeated complaints in the post and comments.
INTENSITY
Users explicitly describe existing tools as bloated/overkill and mention workaround behavior.
EVIDENCE
Built a prompt for a month. Not sure to sell it or not.
SideProject25
job scraping that respects filters and salary is a real pain point
commentif it actually works consistently that's genuinely useful, job scraping that respects filters nd salary is a real pain point. test it with a small paid group first before going wide, scraping prompts break when platforms update nd u want to know how fragile it is before u have paying customers
how long until a new update stops it would be a concern
commentYes if it works would be a great resource but how long until a new update stops it would be a concern. But with the job situation right now, to the right person it would be very useful.
2
STAGE 02 · CUSTOMER
Who feels this pain?
TARGET USERS
job seekersTech Job Seekers Building Tools
Developers and engineers in tough job markets who spend hours daily across LinkedIn, Indeed, Glassdoor, and Dice to find roles matching salary, recency, and keyword filters.
Context
Efficiently scrape and filter job postings from major platforms while respecting search criteria like salary and recency.
Manually searching job sites without efficient filtering or automation.
Building custom prompts or scrapers that require ongoing maintenance.
Current Workarounds
Manual searching and tab-switching across sites
Building and constantly fixing custom prompts or scrapers
Using incomplete free aggregators that lose filters
3
STAGE 03 · MARKET
Where's the gap?
EXISTING SOLUTION GAPS
Standard scraping tools are blocked by anti-scraping on LinkedIn, Indeed, etc.
Existing methods fail to maintain consistent filters (salary, date, posting time) and break on updates.
OPPORTUNITY & VALUE
Why Now
Multiple mentions of anti-scraping blocks, fragility after updates, and time invested in workarounds across job seeker communities.
Value Proposition
Focus on job-specific filters and automatic update resilience instead of generic web scraping platforms.
Product Direction
Cloud SaaS that runs and maintains headless browsers/proxies to deliver clean, filtered job feeds via dashboard, API, and alerts from LinkedIn, Indeed, Glassdoor, and Dice.
4
STAGE 04 · BUSINESS
How does it make money?
MONETIZATION
$29/moUp to 3 concurrent scrapes · 10k results/mo
Model
SaaS subscription
WILLINGNESS TO PAY
Users already invest a month building custom prompts and repeatedly complain about breakage; $29 is trivial compared to hours saved weekly in painful job searches.
5
STAGE 05 · EXECUTION
How do you ship it?
MVP PLAN
“Pull filtered jobs from four major sites daily without maintenance headaches.”
Cloud SaaS that runs and maintains headless browsers/proxies to deliver clean, filtered job feeds via dashboard, API, and alerts from LinkedIn, Indeed, Glassdoor, and Dice.
Core Features
Pre-built scrapers for LinkedIn/Indeed/Glassdoor/Dice with salary and date filters
Daily refreshed dashboard and CSV export
Email/Slack alerts for new matching roles
Weekly Roadmap
1
W1-W2
Core scraping engine runs reliably for one platform.
- •Set up headless browser infrastructure with proxies
- •Build basic scraper for LinkedIn job listings
- •Implement salary and date filters
2
W3-W4
Multi-platform support with unified output.
- •Add Indeed, Glassdoor, and Dice scrapers
- •Normalize data into common schema
- •Create dashboard for filtered viewing and export
3
W5
Alert system and internal dogfooding complete.
- •Build daily refresh scheduler and email alerts
- •Test with 5 beta tech job seekers
- •Add basic error monitoring and retry logic
4
W6
Public beta launch with first subscribers.
- •Implement Stripe billing for paid tier
- •Deploy to Product Hunt and Reddit
- •Collect feedback and first month retention metrics
Launch Strategy
Launch on r/cscareerquestions, r/jobs, r/SideProject, and Hacker News with free tier for initial data pulls
RISKS & ASSUMPTIONS
Top Risks
Legal and ToS risks from scraping
Major platforms prohibit scraping; service could face blocks, lawsuits, or shutdown pressure.
Maintenance burden on scraper reliability
Frequent UI changes require constant updates, potentially exceeding 6-week MVP assumptions.
Low conversion from free manual users
Job seekers may tolerate manual pain rather than pay for a tool that still carries risk.
Data quality inconsistency
Anti-scraping evasion may miss listings or return incomplete salary/posting info.
6
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 "automation", "data-management", "devtools", 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 "HarvestJobs: Maintained Multi-Site Job Scraper with Smart Filters" 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 automation?
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.