We heard you. And we agree.
In light of recent developments in physical media, GitHub is proud to announce that you can now obtain your public repo on CD-ROM.
Keep it. Lend it to friends. Pass it on to your children.
Your code is physically yours, forever. Until you lose it, let's be real.
Order yours today.
https://t.co/z041pdMH7h
Meta researchers used LLMs to predict the text a person was typing just from non-invasive brain recording!
Literal mind reading!
"For our best participant, the model accurately decodes half of the sentences with one word error or less." โ INSANE
Let's learn about how it works!
Weโre 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.
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We believe in broad access and plan to make GPT-5.6 Sol, Terra, and Luna generally available in the coming weeks.
For now, at the request of the U.S. government, weโre starting with a limited preview among a small group of trusted partners in Codex and the API.
Zyphra is sharing our first work in continual learning where we study: Can LLMs learn forever from new data?
Many see continual learning as a path to AGI through recursive self-improvement (RSI).
The first obstacle is plasticity loss. We derive a scaling law for its onset ๐งต