• A new denoising diffusion probabilistic model for protein structure generation was shared (https://nanand2.github.io/proteins/), accompanied by David Baker Lab’s pre-print “Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models” (https://www.bakerlab.org/wp-content/uploads/2022/11/Diffusion_preprint_12012022.pdf) and validation tweets from @andrewwhite01 and @AndrewLBeam.

• Discussion questioned the experimental rigor behind claims that “hundreds of AI-generated proteins” were successfully tested, asking whether crystal structures match the model outputs and whether designed proteins actually perform their intended functions (e.g., binding ham).

• A pointer was provided to an ongoing open-hardware MRI project in Taiwan by @yashiro_ld, who is documenting the scratch-build of a low-cost magnetic resonance imaging system on Twitter.

• Lotka’s law (a 1/x² distribution describing author productivity in scientific fields) was introduced as a lens for understanding publication patterns in open-source biology and transhumanist research.

• A critical FreeBSD ping vulnerability (CVE-2022-23093) that enables remote code execution was flagged, raising concerns about its potential impact on embedded and lab-instrument systems running FreeBSD.