I just finished reading Minding the Weather: How Expert Forecasters Think.

TL;DR it's really a book on expertise—you won't learn how to forecast the weather by reading it.

My interest in weather comes via my company's work on maritime voyage and route optimization. I finished the book with a much deeper understanding of what weather forecasters do and what it means for them to forecast the weather, generally—which was the goal (the objective being to understand what it would look like to stand up our own weather team).

Surprisingly (or maybe not surprisingly), the idea that someone could actually forecast the weather was once thought of alongside pseudoscience like astrology.

As expected, the book includes a few chapters on using machines to forecast the weather. Unfortunately, if this is what you're looking for, the coverage is way out of date—and I don't mean in the 2017 to today sense. I mean in the late 1980's to today sense—the authors talk about using expert systems to forecast the weather. What?!? I don't expect coverage of GraphCast but I do expect something other than expert systems! (Maybe no one has used a NNs on weather data in the last ten years—I'd be surprised but I guess I'll check it out next.)

That aside, it was well worth my time to read. It definitely corrected many naive assumptions, and that's what I was looking for!

#bookreview

@rahul you are one of the people running ktistec v2.0.0-9. the "dist" branch had a bug that affects the metrics—all data points are added up each run, instead of only the new data points. you may not care, in which case everything should be fine. but it you do care, i recommend building and deploying the latest "dist" (or maybe let me run it for a day or two more) and then if you want the historical metrics to be correct deleting some data from the database, which i can help you with. let me know! (if you are running off "main" then you are probably not affected.)

@jayvii you are one of the people running ktistec v2.0.0-9. the "dist" branch had a bug that affects the metrics—all data points are added up each run, instead of only the new data points. you may not care, in which case everything should be fine. but it you do, i recommend building and deploying the latest "dist" (or maybe let me run it for a day or two more) and then if you want the historical metrics to be correct deleting some data from the database, which i can help you with. let me know!

i added code to log slow queries in ktistec and it's already paying dividends. most are obviously missing indexes and it's great to fix them, but the latest example—which is missing an index—is querying a table that only has one row (in my single user instance). should that table need an index on that column? i mean, just return that row...

fwiw, a slow query is currently anything that takes longer than 50msec. i wonder if that is tight enough...?

#ktistec

i depend on visual patterns and cues, and i need so so so many colors!

after all these years it still amazes me how often i fat-finger ^X^C and accidentally exit emacs...

#emacs

getting consistent query performance, for all but the simplest queries, across different versions of sqlite feels like playing wack-a-mole. the best practice seems to be distributing a version of sqlite that's tested and works vs. depending on the system installed version?

one thing ktistec related that i haven't had the time for is working on build and deployment tools. there are a bunch of outstanding requests—and a few PRs—for docker builds, packaged deployments for various hosting environments, etc.

if you're interested in contributing, let me know. you only have to agree to maintain them—i won't be able to.

#ktistec

i went to a show and bought a cd and then had to figure out how to rip it, because that's not a workflow i've used in a while...

my vintage (c2010) macbook pro—one of the last with a cdrom drive—came to the rescue. i haven't touched it in years but thankfully it booted without problem!

buying concert tickets online is just one dark pattern after another...