There often are hotels, but it gets booked fast when weather causes delays. When I got stranded in Dallas in 2019, the Ramada made it excessively clear they were booked. But there's also tons of hotels around airports, you just have to get through security, and they don't do hourly billing like you might want if you weren't sure your replacement flight is also delayed.
> But there's also tons of hotels around airports, you just have to get through security
That works in USA where every international arrival has to be able to, and does, go landside.
In the more advanced world, you may only have authorization to stay in the terminal. Dunno what they do when shtf and people will be stuck for a few days.
The US system doesn’t seem less "advanced" to me. It cuts down on the number of people that connect through the US on non-US itineraries, but I don’t know that there are many US hubs agitating to become city-sized duty free malls like Dubai, Frankfurt, etc. And it makes our airport layouts much less complex.
There are almost no routes that would want to use the US as a midpoint due to geography. It's pretty much only routes to Central America that make any sense, and there's just not a lot of them.
So the US never felt the need to build airports with dedicated international zones.
I've almost always seen proper hotels right outside whichever airport I'm at.
But I've never seen a capsule hotel business within the airport bounds itself despite there being many stories of people wanting to sleep at the airport.
It's funny, I've been watching all the nvidia GTC keynotes from 2012-now to better understand the ecosystem and Jensen pretty clearly states a few times "its a miracle it works at all". Clearly he's intending to brag about defect rate on a 50 billion transistor chip but maybe he's more right than he realizes.
>Turns out you can compile tens of thousands of patterns and still match at line rate.
Well, yea, sort of the magic of the regular expression <-> NFA equality theorem. Any regex can be converted to a state machine. And since you can combine regexes (and NFAs!) procedurally, this is not a surprising result.
> I ran it against the first service: ~40% waste. Another: ~60%. Another: ~30%. On average, ~40% waste.
I'm surprised it's only 40%. Observability seems to be treated like fire suppression systems: all important in a crisis, but looks like waste during normal operations.
> The AI can't find the signal because there's too much garbage in the way.
There's surprisingly simple techniques to filter out much of the garbage: compare logs from known good to known bad, and look for the stuff thats' strongly associated with bad. The precise techniques seem bayesian in nature, as the more evidence (logs) you get the more strongly associated it will appear.
More sophisticated techniques will do dimensional analysis -- are these failed requests associated with a specific pod, availability zone, locale, software version, query string, or customer? etc. But you'd have to do so much pre-analysis, prompting and tool calls that the LLM that comprise today's AI won't provide any actual value.
Yeah, it's funny, I never went down the regex rabbit hole until this, but I was blown away by Hyperscan/Vectorscan. It truly changes the game. Traditional wisdom tells you regex is slow.
> I'm surprised it's only 40%.
Oh, it's worse. I'm being conservative in the post. That number represents "pure" waste without sampling. You can see how we classify it: https://docs.usetero.com/data-quality/logs/malformed-data. If you get comfortable with sampling the right way (entire transactions, not individual logs), that number gets a lot bigger. The beauty of categories is you can incrementally root out waste in a way you're comfortable with.
> compare logs from known good to known bad
I think you're describing anomaly detection. Diffing normal vs abnormal states to surface what's different. That's useful for incident investigation, but it's a different problem than waste identification. Waste isn't about good vs bad, it's about value: does this data help anyone debug anything, ever? A health check log isn't anomalous, it's just not worth keeping.
You're right that the dimensional analysis and pre-processing is where the real work is. That's exactly what Tero does. It compresses logs into semantic events, understands patterns, and maps meaning before any evaluation happens.
Well it's in the same neighborhood. Anomaly detection tends to favor finding unique things that only happened once. I'm interested in the highest volume stuff that only happens on the abnormal state side. But I'm not sure this has a good name.
> Waste isn't about good vs bad, it's about value: does this data help anyone debug anything, ever?
I get your point but: if sorting by the most strongly associated yields root causes (or at least, maximally interesting logs), then sorting in the opposite direction should yield the toxic waste we want to eliminate?
Vectorscan is impressive. It makes a huge difference if you're looping through an eval of dozens (or more) regexps. I have a pending PR to fix it so it'll run as a wasm engine -- this is a good reminder to take that to completion.
> so that customer support collapses the same day every year.
Every _month_. And it's not just the customer service desk that's a problem. With even distribution of billing and a large customer base, outflows match inflows and you don't have to do much to manage it. With all money coming in on one day you have a huge outflow of money and then it all rushes back in.
Much easier to borrow 1 dollar for a year than 30 dollars for a month.
It really depends on the definition of catch. Citi Double Cash, Fidelity, Wells Fargo and US Bank all do 2%.
Personally, I use a 2.625% cash back card with the "catch" being that I have to have enough stock in their subsidiary brokerage to qualify for the top rewards tier. Since I just buy and hold SP500 ETFs, this is an easy requirement.
Bank of America Unlimited Cash Rewards for the win :) My only regret is not realizing sooner that it existed since I used the Citi Double Cash card for so long.
Mostly flat from 2010-2021, with a recent uptick to 131 million. The discrepancy is likely due to the boomers aging out of the category, and a smaller generation coming in.
Let me put it another way: the [20, 25) and [25, 30) age cohorts are larger than any cohort aged 50+ that might have recently aged out. So that "prime age" workforce is still growing.
This could be true, but it isn't obviously true (to me). (I dispute a little bit the idea that there are many new workers in the [25, 30) demo.) There are 37M workers 55+, but only 20M in the 16-24 range: https://www.bls.gov/cps/cpsaat18b.htm (2024 numbers)
Nobody in either of those cohorts is in the BLS "prime age" group which is [25, 55). The incoming cohorts that are now 15 to 25 are larger than the outgoing cohorts that are 45 to 55.
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