@hackSultan AI doesn't have a mind of it's own, it follows a designed pattern for exploit, this pattern are then mixed with other future predictive patterns with different combinations making exploits seamless and well calculated.
GOOGLE BUILT A SECRET WEAPON FOR FILE DETECTION
they ran it internally for years, gmail, drive, safe browsing, hundreds of billions of files every week
then they open sourced it
it's called magika and it exposes what files really are, not what they pretend to be
rename malware to "resume.pdf"? magika sees through it
disguise a script as an image? magika sees through it
any trick attackers use with file extensions? magika sees through all of it
ai trained on 100 million files. 200+ content types. 99% accuracy. 5ms per file
one command
`pip install magika`
the same tool protecting google's billion users is now protecting yours
https://t.co/Jr3LjmQobq
the original TurboQuant paper tested on A100 with models up to 8B.
6 days later, a bunch of strangers on the internet had it built and running on:
- Apple Silicon M1 through M5
- NVIDIA 3080 Ti through DGX Spark Blackwell
- AMD RX 6800 XT and 9070
- a 10-year-old Tesla P40
- an 8GB MacBook Air
- models from 3.8B to 70B across 6 architecture families
- 30+ independent testers
along the way we found new optimizations the paper didn't cover and failure modes it didn't test.
the fact that a loose group of people across the world can read a paper, build implementations from scratch, stress-test across hardware none of us could individually afford, and push the research further in under a week is genuinely one of the best things about this era. the tools and the community make it possible.
open source is something else.
I just implemented Google’s TurboQuant for vLLM.
My USB-charger-sized HP ZGX now fits 4,083,072 KV-cache tokens on GB10.
This may be the biggest open inference breakthrough of 2026 so far.
Training is the flex. Inference is the forever bill.
Meet a powerful new reasoning model that understands both text AND images. This 9B parameter model was distilled from top-tier reasoning architectures, bringing advanced chain-of-thought capabilities to a compact, efficient package. Perfect for developers who need multimodal smarts without massive compute.
Qwen 3.5 27b never degrades, never stops running, never has token limits, never refuses, never logs my prompts, never trains on my data, never sells my data, never runs up my credit card
17,000 requests per second on Laravel Cloud.
@dgarbs51_ breaks down exactly how he got there with k6 load testing - and what the results revealed.
https://t.co/80McjFYnz8