I worked at a 4 1/2 nines (99.997) place and it was pretty standard to speak of half-nines at least (although on reflection, perhaps not everyone understood that 0.7 was 1/2 of 0.9 and thought 99.995 was "4 and a half nines") - we went from 3 nines in 2015 to 4 1/2 in 2025 (maybe slide back a little in 2026) - fun task but takes sustained high-level interest in reliability for a long time.
I don't think that people often understand that if reliability doesn't increase before an adoption cycle, the lack of it will limit the success of the adoption cycle.
If I'm tasked with getting a tool to be used at least 2x as much, my first task is to reduce the failures per 1000 runs by 4x. In that way, if adoption increases by 2.5, 3x instead of the the minimum we are looking for, then the number of errors reported per week still goes down instead of up.
We like to think of things as percentages but the moment they start increasing in the time domain everyone gets mad, because they asked for and received the wrong thing.
And that's on top of the fact that when people are 'forced' to use a tool, any errors they experience will be lumped onto the Learned Helplessness theater they've been engaging in to avoid being made to use a tool that is going to make all of our lives easier.
"Failed customer interactions" - if you have a way to actually see requests before they hit your datacenter, e.g. some async third party client libraries.
On this topic, as a service operator, it's really nice when you also own your SDKs, and have client-side telemetry about failed requests. Gives you a much clearer picture of end-to-end reliability (at least for the subset of customers who opt-in to telemetry)
To the extent the LLM copied the plagiarized work, no. Like a number of the Erdos problem solutions turned out to be based on forgotten literature. So the drama matters even for the assessment of the mega agent work.
Because the mathematicians consulted 25 or so years ago believed their solutions would lead to the greatest amount of interesting new maths to explore, and because they had been validated as being hard by being attempted and not solved for a long time.
I like jquery. I only do websites for data viz things so I don't follow all the frameworks and so on, I just want some nice D3 graphs and tabular reports and to have it be clear enough JavaScript that I can look at the math and maybe edit it. JQuery serves very well for that and for keeping me out of browser compatibility issues, although that seems to be less of an issue than in 2008 when I started with it.
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