Tootfinder

Raiders HC Klint Kubiak Makes Feelings Clear on Tom Brady https://heavy.com/sports/nfl/las-vegas-raiders/klint-kubiak-feelings-clear-tom-brady/

Sadly couldn't join myself (wife wasn't feeling good and wanted me to stay home and help parent while she napped) but my SAR team participated in a great inter-agency training this past weekend.
It's always nice to practice alongside volunteers from other teams that we work with on real incidents and get to know each other without the pressure of a real emergency.
One note: I half feel like there should be a CW on this post for mentioning law enforcement in a positiv…

One of the NFL's first female officials sues league over treatment, firing https://www.nytimes.com/athletic/7162700/2026/03/31/nfl-female-official-lawsuit/

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.

A good training day for a new team member.
Emergency release of four programs. Simple to do and a very detailed SOP was prepared. He is in the driver seat with us providing input on the details.
So far, so good. He is also anticipating what comes next which is a good sign as that means he is also following the SOP!

Internal chats: in March, xAI offered employees $420 in exchange for completed tax filings as training data for Grok, but the bonuses haven't been paid out (Carmen Arroyo/Bloomberg)

Wildfires used to 'go to sleep' at night. Climate change is turning them into prime burning hours https://phys.org/news/2026-04-wildfires-night-climate-prime-hours.html

Wildfires used to 'go to sleep' at night. Climate change is turning them into prime burning hours
Burning time for North American wildfires is going into overtime. Flames are lasting later into the night and starting earlier in the morning because human-caused climate change is extending the hotter and drier conditions that feed fires, a new study found.