Show HN: WARN Firehose – Every US layoff notice in one searchable database (warnfirehose.com)
I built WARN Firehose because I was frustrated trying to track layoff data across the US. The WARN Act requires companies with 100+ employees to file public notices 60 days before mass layoffs — but the data is scattered across 50 state websites with different formats, broken links, and no API.
WARN Firehose scrapes every state workforce agency daily and normalizes the data into a single database going back to 1988. It now has 131,000+ notices covering 14 million workers.
*What you can do:*
- Browse interactive charts and data tables (no account needed): https://warnfirehose.com/data - Drill into any state, city, company, or industry: https://warnfirehose.com/data/layoffs - Query the REST API (free tier: 100 calls/day): https://warnfirehose.com/docs - Export in CSV, JSON, NDJSON, Parquet, or JSON-LD - Set up webhooks for real-time alerts on new filings
*Who uses this:*
- Journalists breaking layoff stories before press releases - Quant funds using WARN filings as an alternative data signal (filings happen ~60 days before layoffs) - Recruiters sourcing from displaced talent pools - Researchers studying labor market dynamics - Workforce development boards doing rapid response planning
*Tech stack:* Python/FastAPI, SQLite, scrapers for all 50 states, static HTML generation for SEO pages, Claude Haiku for AI analysis, deployed on EC2.
Free tier is genuinely useful (100 API calls/day, dashboard access, charts). Paid plans start at $19/mo for full historical data and bulk exports.
Would love feedback on the API design, data quality, or anything else. Happy to answer questions.
19 comments
[ 3.0 ms ] story [ 52.2 ms ] threadSome of the entries pull up a page that says "Failed to load company data: No company name provided in URL" from the state specific view (e.g, any link on https://warnfirehose.com/data/layoffs/california ). Has a vibe-coded feel to it.
I saw a lot of "Purchase dataset for city details" in places which was annoying. Wondering how much processing is being done on the base dataset to justify the pricing. Could you explain a bit on the normalization/cleaning process?
For the pricing, to be honest I have zero hope to make money. Its just out there because I wanted to integrate this with stripe for payments and all. At the same time, if you look at competitor sites, they seem crap lol. plus look at their pricing. The site does have some operating cost and I will have to recover them if i can if I want it to be self sustainable but only if this is a value to someone. I am trying to make this a value. Please offer if you have ideas and I would appreciated it :)
If it all can be done in a weekend without having to write a single line manually, sky is the limit no? Anybody having an idea can make these thing happen. I am not expecting it to make any money and it was a learning project but I do see some value for certain people.
Insurance brokers would benefit if they are first to know so they coudl target these layed off people. Recruiters the same and definitely hedge funds, short shellers and quant. Gemini tells me data is the new oil ! I am convinced.
By the way, if the company is public, it brings up stock ticker, SEC link and all layoff related news. Plus all historical WARN notices by that company.
On the Charts page the selected time range is 12/01/2025 to 02/28/2026 and shows 106,603 employees affected. But the horizontal bar chart with state level data shows numbers in millions. For example, CA has more than 2 million and IL has more than 1.7 million employees affected. Then the layoff map at the bottom shows only layoffs in Texas.
The problem is WARN only fires when companies actually fire people. AI displacement doesn't really work that way, at least not yet. Customer support new hires dropped 65% in eight quarters (https://www.saastr.com/customer-support-hiring-has-fallen-65...).
Those roles just stopped being posted..
- It has over 15k individual landing pages for search engines - a dedicated page for each city, state, company, county. These pages are very reach on how they look haha: - Exmaple: new jersey page-> https://warnfirehose.com/data/layoffs/new-jersey - Data is exportable to multiple format including json-d and parquet - i never heard of parquet before. - The site has MCP (model context protocol) built in ! - it accepts payments on multiple methods including paypal, apple pay, amazon pay, card etc. All refund and all built in. - It also has an invisible admin dashboard where I can see everything (all metrics, signups payments etc.) - Reports are crazy - these are genuinely better than someone writing at wallstreet journal, seriously. you would have to check to see them. - The api endpoints.
I am convinced the role Software engineer is going to go away by this year end. We are turning into builders, not coders. All SaaS apps are going to take big hit because they can be builkt better for fraction of cost by anyone - just think about it.
- Its all about ideas and anyone can do it.