Tootfinder

Court filing: lobbyist Jack Abramoff spent $12,800 buying press for AML Bitcoin prior to his 2020 indictment; Forbes and the Daily Caller were among recipients (Will Sommer/The Bulwark)
https://www.thebulwark.com/p/another-right-wi…

Folks, We’ve Got Yet Another Right-Wing Media Payola Scandal
This one involves Jack Abramoff’s crypto venture!

The BBC removes BAFTAs, telecast on a two-hour delay, from iPlayer after failing to cut the N-word involuntarily shouted by an audience member with Tourette's (Jake Kanter/Deadline)
https://deadline.com/2026/02/bbc-apologizes-bafta-film-award…

BBC Removes BAFTA Film Awards From iPlayer After Initially Failing To Cut N-Word From Ceremony
The BBC has apologized after its coverage of the BAFTA Film Awards failed to cut a racial slur shouted by an audience member with Tourette syndrome.

Sources: DOJ's review of Netflix's WBD takeover examines whether Netflix wields anticompetitive leverage over creators in violation of Clayton and Sherman Acts (Josh Sisco/Bloomberg)
https://www.bloomberg.com/news/articles/20

Sources: DOJ's review of Netflix's WBD takeover examines whether Netflix wields anticompetitive leverage over creators in violation of Clayton and Sherman Acts (Josh Sisco/Bloomberg)
https://www.bloomberg.com/news/articles/20

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.

The Senate confirms Army Lt. Gen. Joshua Rudd to lead the NSA and US Cyber Command, filling a vacancy created when Gen. Timothy Haugh was fired in April 2025 (Martin Matishak/The Record)
https://therecord.media/rudd-confirmed-nsa-cyber-command-chief

Rudd confirmed to head NSA, Cyber Command after near year-long vacancy
Rudd, who was confirmed 71-29 to serve as the “dual-hat” leader of the organizations, takes the reins as the U.S. faces mounting aggression in cyberspace from foreign adversaries at the same time the Trump administration has sought to shrink the size of the federal government.