A lab gets better when the last experiment changes the next design.
Using LiteFold, we ran a peptide campaign against BHRF1. We got 47% in hit rate.
That measurement is the whole point of LiteFold: your data, your target, the next round smarter than the last.
Lots of blogs and technical reports are on the way!
I have been following!! this course!, changed my life for real while doing Deep learning
Stanford cs312, its a must for all
#Deeplearning#Stanford#learning
https://t.co/TW0L8rFV3d
@Joshua_WD exactly @Joshua_WD I believe this is the most important aspect! in understanding, it always seems like it always creates this missteps of understanding my intentions
So have been using Fable 5.2! it's making a lot of chronologically wrong decision with respect to my intent, that even models like OPUS does not do!! its like ASTRA all over again! let's see! if they patch it up
#AI#Anthropic
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: https://t.co/bOWCcFhCVq