@eliocamp @coolbutuseless another good developer arrives at the crushing reality that CRAN’s motivations do not align with either end users OR developers. Who do they serve? No one seems to know.
Imagine I posted a joke here about me inviting Idris over for the evening, and Idris says OK but only if it can bring its friend GHC along, and then GHC shows up and it's towing its "service animal" which is a 5,000 pound elephant with "LLVM-13" painted on the side
@fogus wow, I forgot (or missed) this, read that book ages ago
In Perez-Reverte’s book The Club Dumas, the main character is given a 40pg report containing an author history, publication record, supporting images, citations, and bibliography to accompany a grimoire that he’s hired to investigate. Similar reports should be written and published for all major computing grimoires.
@DrJackBrown Business Inside said the remarks were "not entirely out of the ordinary, many judges advise jurors that while they can speak to the media after high-profile trials it may not be in the best interest to do so."
L. L. Bean stands for Large Language Bean. @lowqualityfacts
@peterdrake the most common kind of Large Language Bean being, of course, the Java Bean.
@kaoudis
Working in analytics contexts, you sometimes get the business requirement that the same _user_ code should produce the same result given the same input data, even if the underlying dependencies (also code, but the user doesn't want to care) change. That often requires pinning dependencies / carefully testing when any dependency changes, which is quite cursed especially when using R, which does not allow pinning individual dependencies out of the box.
https://stackoverflow.com/questions/7730180/dependency-management-in-r#7730477
https://www.r-bloggers.com/2022/02/challenges-in-package-management/
"If a developer experience initiative attempts to provide developers with new and adaptive strategies, but the larger context then invalidates the behaviors by which a person can execute those strategies, psychological outcomes may be more negative than not intervening in the first place. "
@mtraven23 now I'm wondering what the dual of a star is, astrologically speaking
What I'm listening to today: "Edelleen ja edelleen", Sleepers Tomb
Quiet, insistent drone ambient track. You're asleep, your phone's alarm keeps pushing at the barrier from some other world trying to break through and drag you out, but it's not working. A piece built up slowly on a modular suitcase that splays the track's internal process open to view like something on a dissection table. Good mood. I think the name is Finnish for "On and On"
@grimalkina Among other things, Agile tries to find insights about how software teams work.
It's mostly not very scientific (or successful). Are there ways to approach iterative (team-wide) improvement in a scientific way?
It could be the kinds of questions we ask, or how we evaluate outcomes.
I need a couple of dozen small blobs of similarly-structured-but-not-identically-structured JSON that look like they came from a biology lab to use in a tutorial on manipulating JSON in SQLite and Postgres. If you have such, I'd be grateful for a ping: gvwilson@third-bit.com. (Yes, I know, it's a weird request, but you're a weird bunch of people and I love you for it.)
There is really good stats thinking you can do on this and there are many models for mapping this type of change, but you won't get this thinking from business analytics or mainstream data science unfortunately. You need to look to the sciences that have done causal inference in complex real world situations.
People often say "ugh stop overthinking it. Just set a target and measure a change."
All models are wrong etc, but on some topics putting "simple" over everything is fundamentally broken.
Finally this is a great example of the dangers of thinking all changes are simple and linear. There are MANY patterns. A spike in negative evaluations is a well documented characteristic of any time you learn more about the world so again, a salient point made by @mekkaokereke that we must be very careful to observe before we diagnose. Here's an adjacent example in schools: sometimes we have evidence for an intervention working because it *slows down an existing negative trajectory*
code / data wrangler in Switzerland