The Oink blog
Job-search & scraping guides
Choose practical guidance for finding and evaluating roles, or implementation notes for collecting public job listings. Start with how Oink works or inspect its job-source coverage and limits.
For job seekers
Matching, freshness, location eligibility, and applying with more intent.
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I applied to 200 jobs and heard nothing — the math of spray-and-pray, and what to do instead
Applied to hundreds of jobs with no reply? Learn why spray-and-pray fails and how resume-based job scoring can produce a stronger shortlist.
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Ghost jobs: how to check a listing before you spend an hour applying
Learn the signs of a stale or ghost job listing and how to verify a role is still open before spending time on a tailored application.
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How to describe the job you want so an AI can actually find it
Turn a vague job brief into a useful AI search by defining role, stack, seniority, location eligibility, salary floor, and dealbreakers.
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“Remote (anywhere)” — except you: how to filter remote jobs by real location eligibility
Remote does not always mean anywhere. Learn how country, timezone, and payroll restrictions affect eligibility before you apply.
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Hacker News “Who is hiring?” — how to actually search a 600-comment thread
Learn practical ways to search the monthly Hacker News “Who Is Hiring?” thread for relevant roles without reading hundreds of comments.
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The “100+ applicants” wall: a fresh-job morning routine for LinkedIn
Use a daily posting-age search to find newer LinkedIn roles, while keeping the evidence and limits of application timing in view.
For developers and automation teams
Public job-board collection, normalized datasets, scheduling, and source limitations.
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How to scrape LinkedIn jobs without login or cookies
Learn how to collect public LinkedIn job postings without login or cookies, handle the result cap, and turn listings into structured data.
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Search ten job boards with one API call: job scraper bundles, explained
See how job scraper bundles combine LinkedIn, Hacker News, RemoteOK, EURAXESS, and more into one deduplicated jobs dataset.