This dataset looks interesting but the site doesn’t instill a lot of confidence in data integrity.

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.

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pdh 2 hours ago | parent | next [-]

Agreed, this doesn't pass my smell test. November 2026 reports layoffs in NJ (with past tense) https://warnfirehose.com/blog/2026/11/week-1

The layoffs in the report are not listed in NJ's own warn notice https://www.nj.gov/labor/assets/PDFs/WARN/2026_WARN_Notice_A...

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csomar 2 hours ago | parent | prev [-]

You can think about LLM-generated UIs/apps the same way you think about LLM-generated responses. It's a bunch of garbage, but if you know what you're looking for, you might find something useful.

This doesn't seem to work at all for stats-related apps/sites though, since you can't judge the accuracy of what's being presented. If the site claims it'll "take you to space," you don't take that literally, you just treat it as another AI artifact. But with numbers, you have no way to tell what's accurate and what's just made up.

	▲	mmooss 2 hours ago \| parent [-]
		> It's a bunch of garbage, but if you know what you're looking for, you might find something useful. If you mean an LLM can be a brainstorming and hypothesis machine, and you have prior expertise to evaluate the proposals, then I can see that value. (Maybe that's what you meant, of course.) But prior expertise is absolutely necessary. Otherwise we make ourselves victims of mis/disnformation. People say the Internet is a cesspool of mis/disinfo, yet nobody thinks it could affect them - we're all too smart, of course (no really, I'm the exception!). [0] > This doesn't seem to work at all for stats-related apps/sites though, since you can't judge the accuracy of what's being presented. I don't see the difference. If it's obvious nonsense, in numbers or in text, it's detectable. Everything else, see above. [0] Research shows that thinking is a big reason people get fooled, and better educated people are easier to fool.