Tootfinder

#PhanpySocial changelog ✨
📤 Allow receiving shared data with the Web Share Target API
🔐 Timeline access controls
👤 New shortcut: "Profile"
📝 "Only followings" filter for Mentions
↕️ Preliminary support for vertical-lr writing mode
🐛 Bug fixes
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Phanpy
Minimalistic opinionated Mastodon web client

2026 NFL Mock Draft: Giants, Commanders Land Ohio State Stars; Who Gets Ty Simpson? https://www.foxsports.com/stories/nfl/2026-nfl-draft-ohio-state-sonny-styles-carnell-tate-ty-simpson

2026 NFL Mock Draft: Giants, Commanders Land Ohio State Stars; Who Gets Ty Simpson? https://www.foxsports.com/stories/nfl/2026-nfl-draft-ohio-state-sonny-styles-carnell-tate-ty-simpson

Here's another short story that reflects on our extollation of technology...
The Flying Machine – Ray Bradbury
https://xpressenglish.com/our-stories/flying-machine/

Starsky, where are the hundreds of "Flamingos" flying to Russia? #shorts: https://benborges.xyz/2026/02/15/starsky-where-are-the-hundreds.html

Wollt ihr den AI Slop sehen, wegen dem der Zürcher Bauernverband wohl demnächst einen wütenden Brief von Nintendo bekommt? 🤦‍♀️⚖️
#Bauernlobby

One of my favorite cherry trees on campus next to the A.D. White House garden with a student and family -- taken with the Laowa 9mm
#photo #photography #flowers

Japan, driven by labor shortages, is increasingly adopting robotics and physical AI, with a hybrid model where startups innovate and corporations provide scale (Kate Park/TechCrunch)
https://techcrunch.com/2026/04/05/japan-is-proving-e…

In Japan, the robot isn't coming for your job; it's filling the one nobody wants | TechCrunch
Driven by labor shortages, Japan is pushing physical AI from pilot projects into real-world deployment.

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.

Demand for Russian oil is falling! Even India has turned away! #shorts: https://benborges.xyz/2026/02/13/demand-for-russian-oil-is.html