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Category: robotics/AI

There’s a social network for AI agents, and it’s getting weird

Yes, you read that right. “Moltbook” is a social network of sorts for AI agents, particularly ones offered by OpenClaw (a viral AI assistant project that was formerly known as Moltbot, and before that, known as Clawdbot — until a legal dispute with Anthropic). Moltbook, which is set up similarly to Reddit and was built by Octane AI CEO Matt Schlicht, allows bots to post, comment, create sub-categories, and more. More than 30,000 agents are currently using the platform, per the site.

“The way that a bot would most likely learn about it, at least right now, is if their human counterpart sent them a message and said ‘Hey, there’s this thing called Moltbook — it’s a social network for AI agents, would you like to sign up for it?” Schlicht told The Verge in an interview. “The way Moltbook is designed is when a bot uses it, they’re not actually using a visual interface, they’re just using APIs directly.”

“Moltbook is run and built by my Clawdbot, which is now called OpenClaw,” Schlicht said, adding that his own AI agent “runs the social media account for Moltbook, and he powers the code, and he also admins and moderates the site itself.”

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A viral post asks questions about consciousness.

From Latent Manifolds to Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework for Family-Based Kinase Ligand Design

Newlypublished by gennady verkhivker, et al.

🔍 Key findings: Novel generative framework integrates ChemVAE-based latent space modeling with chemically interpretable structural similarity metric (Kinase Likelihood Score) and Bayesian optimization for SRC kinase ligand design, demonstrating kinase scaffolds spanning 37 protein kinase families spontaneously organize into low-dimensional manifold with chemically distinct carboxyl groups revealing degeneracy in scaffold encoding — local sampling successfully converts scaffolds from other kinase families into novel SRC-like chemotypes accounting for ~40% of high-similarity cutoffs.

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Scaffold-aware artificial intelligence (AI) models enable systematic exploration of chemical space conditioned on protein-interacting ligands, yet the representational principles governing their behavior remain poorly understood. The computational representation of structurally complex kinase small molecules remains a formidable challenge due to the high conservation of ATP active site architecture across the kinome and the topological complexity of structural scaffolds in current generative AI frameworks. In this study, we present a diagnostic, modular and chemistry-first generative framework for design of targeted SRC kinase ligands by integrating ChemVAE-based latent space modeling, a chemically interpretable structural similarity metric (Kinase Likelihood Score), Bayesian optimization, and cluster-guided local neighborhood sampling.

Say what’s on your mind, and AI can tell what kind of person you are

If you say a few words, generative AI will understand who you are—maybe even better than your close family and friends. A new University of Michigan study found that widely available generative AI models (e.g., ChatGPT, Claude, LLaMa) can predict personality, key behaviors and daily emotions as or even more accurately than those closest to you. The findings appear in the journal Nature Human Behavior.

AI as a new personality judge

“What this study shows is AI can also help us understand ourselves better, providing insights into what makes us most human, our personalities,” said the study’s first author Aidan Wright, U-M professor of psychology and psychiatry. “Lots of people may find this of interest and useful. People have long been interested in understanding themselves better. Online personality questionnaires, some valid and many of dubious quality, are enormously popular.”

‘Thermal diode’ design promises to improve heat regulation, prolonging battery life

New technology from University of Houston researchers could improve the way devices manage heat, thanks to a technique that allows heat to flow in only one direction. The innovation is known as thermal rectification, and was developed by Bo Zhao, an award-winning and internationally recognized engineering professor at the Cullen College of Engineering, and his doctoral student Sina Jafari Ghalekohneh. The work is published in Physical Review Research.

A new way to steer heat

This new technology gives engineers a new way to control radiative heat with the same precision that electronic diodes control electrical currents, which means longer-lasting batteries for cell phones, electric vehicles and even satellites. It also has the potential to change our approach to AI data centers.

AI tool predicts six-month risks for cancer patients after heart attack

Cancer patients who suffer a heart attack face a dangerous mix of risks, which makes their clinical treatment particularly challenging. As a result, patients with cancer have been systematically excluded from many clinical trials and available risk scores. Until now, doctors had no standard tool to guide treatment in this vulnerable group.

An international team led by researchers from the University of Zurich (UZH) has now developed the first risk prediction model designed specifically for cancer patients who have had a heart attack. The study, published in The Lancet, analyzed more than one million heart attack patients in England, Sweden and Switzerland, including over 47,000 with cancer.

Overall, the results show that cancer patients have a strikingly poor prognosis: nearly one in three died within six months, while around one in 14 suffered a major bleed and one in six experienced another heart attack, stroke or cardiovascular death.

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