Storm Stoking + Leapfrogging

Tower Beams initiating primary cell - Tower to the south turns off - Notice how the cell changes once the tower turns off - Gets attracted to next tower up - Leap frog it to the next tower - Notice the storm cell has to abruptly shift direction to get to the last tower - once it does they kick up the vortex and break it apart

6-29-26

https://t.me/doomsdayspray

Special thanks to @sigoak for all his hard work in @DoomsdaySpray Channel

Introducing Our Latest Channel
To The Doomsday Empire / Azazel News Empire

Doomsday Spray

https://t.me/doomsdayspray

Welcome to Doomsday Spray, Telegrams largest archive of Chemtrail Operations (otherwise know as Spray Ops)

Here, we do End Of Day reports showing the magnitude of Chemtrails over the Western Hemisphere

Created by @john
It’s his baby/kingdom

https://t.me/doomsdayspray

Special thanks to @sigoak for all his hard work in @DoomsdaySpray Channel

GM

GM

Hydrate Nigga

You fat fuck
Go to the Gym!!!!

Yummers automation

Haters will say it’s AI

Advanced methods & tactics that explore elite levels of human performance
https://t.me/metahumandoomsday

It’s 2026 and you don’t have a Cyberdeck Protected by a Faraday Cage/Bag

Warlord my Ass!!!

https://t.me/DoomsdayTradecraft

Meanwhile in Tasmania

Free gm for all!

Berlin police turn water cannons into cooling mist as heatwave hits 39.9°C

🄳🄾🄾🄼🄿🤖🅂🅃🄸🄽🄶

This is a magnetoencephalography (MEG) machine

Meta trained brain2qwerty v2 by having 9 volunteers wear one while typing for 10 hours each

Meta: We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2.

Building on v1, which was published today in Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication.

We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating.

We trained Brain2Qwerty v2 on ~22,000 sentences from 9 volunteers, each recorded for 10 hours wearing an MEG device while typing.

By using end-to-end deep learning on raw brain signals from MEG devices and fine-tuning LLMs, the system effectively bridges the gap between noisy neural data and coherent language.

The results are promising:
- Avg word accuracy of 61% across participants
- 78% word accuracy and 50%+ of sentences decoded with ≤ 1 word error for the top-performing participant
- Performance scales log-linearly with data volume

To help accelerate neuroscience breakthroughs, we're releasing the full training code for Brain2Qwerty v1 and v2, and our partner, bcbl_, is releasing the v1 dataset.