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@cdarwin@c.im
2026-02-15 05:24:34

A citizen of Rome in 117 AD,
under Emperor Trajan,
would've found it difficult to imagine the empire not existing.
The roads, the aqueducts, the legal system, the trade networks stretching from Britain to Mesopotamia:
all of it seemed to be a near-fact of nature, like gravity
Edward Gibbon gave us six volumes explaining how that feeling turned out to be wrong,
and even he couldn't fully untangle all the causes.
But the overarching theme mig…

The empire always falls
A citizen of Rome in 117 AD, under Emperor Trajan, would've found it difficult to imagine the empire not existing. The roads, the aqueducts, the legal system, the trade networks stretching from Britain to Mesopotamia: all of it seemed to be a near-fact of nature, like gravity // the Mediterranean itself.

@annsev@troet.cafe
2026-03-13 13:16:43

RE: #Söder
Er hat sich seither selbst in einem ungeheueren Ausmaß korrumpiert.

@cheeaun@mastodon.social
2026-02-15 10:23:52

RE: #PhanpySocialDev

Demo of "Only followings" filter on the Mentions page.

@Mediagazer@mstdn.social
2026-04-14 10:05:37

[Thread] A dashboard shows the past 25 years of NYT coverage, across 1.5B words, 2.2M articles, and 26K reporters; Israel-Gaza dominates international coverage (Ted Alcorn/@tedalcorn)
https://x.com/tedalcorn/status/2043760109302464583

Ted Alcorn (@TedAlcorn) on X
I built a dashboard to explore the last 25+ years of @nytimes coverage. 1.5B words, 2.2M articles, 26K reporters. It's fascinating to look at the world’s preeminent news organization not as daily stories but as patterns of attention, ebbing and flowing. https://t.co/CoE22qsBtb

@aredridel@kolektiva.social
2026-04-14 14:22:42

So to follow up on this, I've caught it in action. Models, when quantized a bit, just do a bit more poorly with short contexts. Even going from f32 (as trained) to bf16 (as usually run) to q8 tends to do okay for "normal" context windows. And q4 you start feeling like "this model is a little stupid and gets stuck sometimes” (it is! It's just that it's still mostly careening about in the space of "plausible" most of the time. Not good guesswork, but still in the zone). With long contexts, the probability of parameters collapsing to zero are higher, so the more context the more likelihood you are to see brokenness.
And then at Q2 (2 bits per parameter) or Q1, the model falls apart completely. Parameters collapse to zero easily. You start seeing "all work and no play makes jack a dull boy” sorts of behavior, with intense and unscrutinized repetition, followed by a hard stop when it just stops working.
And quantization is a parameter that a model vendor can turn relatively easily. (they have to regenerate the model from the base with more quantization, but it's a data transformation on the order of running a terabyte through a straightforward and fast process, not like training).
If you have 1000 customers and enough equipment to handle the requests of 700, going from bf16 to q8 is a no-brainer. Suddenly you can handle the load and have a little spare capacity. They get worse results, probably pay the same per token (or they're on a subscription that hides the cost anyway so you are even freer to make trade-offs. There's a reason that subscription products are kinda poorly described.)
It's also possible for them to vary this across a day: use models during quieter periods? Maybe you get an instance running a bf16 quantization. If you use it during a high use period? You get a Q4 model.
Or intelligent routing is possible. No idea if anyone is doing this, but if they monitor what you send a bit, and you generally shoot for an expensive model for simple requests? They could totally substitute a highly quantized version of the model to answer the question.
There are •so many tricks• that can be pulled here. Some of them very reasonable to make, some of them treading into outright misleading or fraudulent, and it's weirdly hard to draw the line between them.