"AI is going to replace a lot of jobs."
Ok, what do you think is gonna happen when you stop paying the "don't riot" bill? How long can you go without paying that bill before an angry mob is gonna come to collect? You can tell from their vacant eyes that they never considered that.
They're gonna go in their bunkers... And then what? They all care about getting into the bunkers and never consider that the doors can be welded shut and the vents filled with concrete.
Asahi is not only a linux distribution
#asahilinux #beer
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.
MS Now overhauls its programming schedule ahead of the 2026 US midterms, with the new weekend programming debuting June 13 and the weekday lineup on June 15 (Etan Vlessing/The Hollywood Reporter)
https://www.hollywoodreporter.com/business/busin…
Weekend Reads
* Post-quantum RPKI framework
https://arxiv.org/abs/2603.06968
* DNSSEC negative trust anchors
https://
San Jose State University
↔️
universe attains set joys
#KarmaManager #anagram #SJSU
Fascist Paramilitary Invaders Violate Use of Force Law (South Burlington, VT - 03/11/26)
If this angers you, organize to fight fascism.
Source:
#ice
Tubi becomes the first streamer to launch a native app within ChatGPT, allowing viewers to find movies or shows to watch by using conversational phrases (Lauren Forristal/TechCrunch)
https://techcrunch.com/2026/04/08/tubi-is-the-first-strea…
from my link log —
U 237C ⍼ RIGHT ANGLE WITH DOWNWARDS ZIGZAG ARROW is a symbol for azimuth.
https://ionathan.ch/2022/04/09/angzarr.html
saved 2026-03-11 …