TL;DR
By early June 2026, layoff trackers counted roughly 184,000 tech workers cut across 247 events — Oracle (30,000), Intel (15,000), and many more — with 55% of announcements citing AI. For recruiters, talent teams, and analysts, two public data sources turn that turmoil into a sourcing and market-intelligence edge: state WARN Act notices (official, public filings of mass layoffs) and layoff trackers. ScrapeMaster extracts both in one click — auto-detecting the table, handling pagination, exporting to CSV/XLSX/JSON — with your data staying in your browser. Here's the playbook.
The short answer: WARN notices are the underused goldmine
Everyone watches the headline layoff trackers. Far fewer people systematically work the WARN notices — and that's where the edge is. Under the federal WARN Act (and stricter state versions, especially California's), employers must file advance public notice of mass layoffs and plant closings with the state. These filings list the company, location, number of affected employees, and often the effective date — frequently before the layoff is widely reported. State labor departments publish them as public web pages or spreadsheets. Scrape them on a schedule and you get an early, structured, official feed of exactly which companies are about to release talent and where. Pair that with the broad trackers for context, and you have a real talent-intelligence pipeline.
Why 2026 makes this worth automating
The volume is the reason. ~184,000 tech cuts by early June, averaging over a thousand a day, across 247 distinct events. Oracle alone cut ~30,000; Intel announced 15,000 after net income fell 85% in a quarter. Manually reading trackers and state WARN portals at that pace is a part-time job. Automating the extraction turns hours of copy-paste into a weekly one-click pull. And the "55% cite AI" framing matters for how you read the data — analysts note significant "AI washing," where AI is blamed for what's really over-hiring or cost-cutting, so the companies and headcounts in WARN filings are more reliable signal than the stated reasons.
The two data sources
| Source | What it gives you | Cadence | Why it's useful |
|---|---|---|---|
| State WARN notice portals (e.g. CA EDD, NY DOL, others) | Company, location, # affected, effective date | Updated as filed | Early, official, structured — often ahead of news |
| Layoff trackers (layoffs.fyi, trueup, etc.) | Company, count, date, sometimes role/function | Continuous | Breadth and context across the whole market |
Both are public web pages. Both are tabular or list-structured — exactly what an auto-detecting scraper is good at.
How to extract them with ScrapeMaster
- Open a state WARN notices page (or a layoff tracker).
- Click the ScrapeMaster icon. The side panel opens and auto-detects the repeating rows in 2–4 seconds, naming columns (Company, Location, Employees, Date) intelligently — no CSS selectors, no code.
- Rename or drop columns to match your schema.
- Enable pagination. WARN portals and trackers often span many pages or use "load more" / infinite scroll. ScrapeMaster auto-detects next-page buttons, numbered pagination, and infinite scroll, and walks through them with live progress.
- Follow detail pages if a filing links to a detail view with extra fields (effective date ranges, reason, contact) — ScrapeMaster opens each in a background tab and merges the extra fields back into your table.
- Click Extract, then export to CSV, XLSX, JSON, or copy straight into Google Sheets or your ATS.
- Save the config. ScrapeMaster remembers your column setup and pagination rules per domain, so next week's pull on the same portal is one click.
Because JavaScript-heavy portals render in your browser, ScrapeMaster sees the page after it loads — SPAs and dynamic tables are fine. And your extracted data stays local (stored in IndexedDB, never uploaded); only the page's structure is analyzed during auto-detect.
Turning the data into intelligence
Once you have clean CSVs, the analysis writes itself:
- Early outreach lists. WARN filings name companies releasing talent before the news cycle. Build a candidate-sourcing list for those companies and roles ahead of competitors.
- Geographic mapping. WARN notices include locations — spot which metros are shedding talent in your target function.
- Market timing. Cross-reference tracker counts over time to see whether the wave is accelerating or cooling in your sector.
- Client briefings. For agency recruiters, a tidy "who's cutting, where, how many" report is a genuine value-add.
To freeze a particular WARN page or tracker view as evidence for a report — dated, exactly as it read — pair ScrapeMaster with Convert: Web to PDF: scrape the data, snapshot the source page. And if you assemble a market-intelligence packet, Convert: Anything to PDF merges your CSV exports and notes into one report PDF.
A note on doing this responsibly
WARN notices are public records and the trackers publish aggregated company data — extracting them is low-risk. But the moment you're collecting data about individuals (a layoff list naming people, say), GDPR/CCPA-style obligations can attach: purpose limitation, retention limits, and treating people decently. Reaching out to recently-laid-off people is sensitive; lead with genuine help, not a cold pitch built on someone's bad week. ScrapeMaster is a neutral tool — the responsibility for ethical use is yours. (We keep these tools free and private on principle; our manifesto is the long version.)
Frequently asked questions
What are WARN notices and are they public?
WARN Act notices are advance filings employers must submit to state labor departments before mass layoffs or plant closings, listing the company, location, number of affected employees, and often the effective date. States publish them publicly, frequently before the layoff hits the news — which is what makes them valuable.
Can ScrapeMaster handle multi-page WARN portals?
Yes. It auto-detects next-page buttons, numbered pagination, "load more" buttons, and infinite scroll, and extracts page by page with live progress. It can also follow links to detail pages and merge those fields back in.
What export formats can I use?
CSV, XLSX (Excel-compatible), JSON, or direct clipboard copy into Google Sheets, Excel, or your ATS/CRM. Exports save to your downloads folder.
Does my extracted layoff data get uploaded anywhere?
No. Records are stored locally in your browser's IndexedDB and never uploaded. Only the page's HTML structure (not its content) is sent during auto-detect to suggest columns.
Will scraping these sites get me blocked?
Public WARN portals and trackers are generally far friendlier than aggressive anti-bot sites. ScrapeMaster uses your normal browser session and you can set delays to throttle. It doesn't rotate proxies, so be reasonable with volume.
Do data-protection laws apply when the layoff list names individuals?
Yes. Collecting personal data — even from public sources — can trigger GDPR/CCPA obligations like purpose limitation and retention limits. Aggregate company data is lower-risk than named-individual data; treat the latter carefully.
Which browsers does it work on?
Chrome, Edge, Brave, Arc, and any Chromium browser. Firefox and Safari aren't supported (Side Panel API).
Bottom line
The 2026 layoff wave is a crisis for workers and a structured data opportunity for the people trying to re-employ them. Public WARN notices are an early, official, underused feed of who's cutting and where; layoff trackers add breadth. ScrapeMaster turns both into clean CSVs in one click, keeps your data local, and remembers your setup for next week. Build the pipeline once, then lead with help — not a cold pitch.