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World-first touchscreen built into garment showcased, displays videos, responds to finger presses

Researchers have showcased the world’s first touchscreen built into a garment. The team from the National University of Singapore’s College of Design and Engineering (CDE) have developed a fibre that functions as pixels and senses touch at the same time, integrating 54 of them into a sleeve to create the world’s first touchscreen built into a garment.

“What made this possible was our ability to construct the GUI system from electronic building blocks in fibre form — similar to traditional yarn, but with the sophisticated electronic functionality built directly into the fibres,” said Asst Prof Khudiyev.

Brain organoid computing for robotic decision-making

Scientists just used a living Human mini-brain to control a humanoid robot. It reached 94% task accuracy.

This research is fascinating because it explores a fundamentally different approach to computing: instead of relying entirely on silicon chips, researchers used living human neural tissue as a biological computing system to help a robot make decisions.

A robot that doesn’t just follow rigid, pre-programmed instructions, but actually “thinks” using living brain cells. That is the reality of Brainobot, a groundbreaking new robot controlled by a lab-grown mini-brain.

While today’s robots often struggle to make quick, smart decisions in unpredictable, real-world environments, scientists looked to nature for a solution. They created a bio-hybrid system where a tiny, lab-grown cluster of living brain cells—called a brain organoid—acts as the robot’s central decision-maker.

Here is how it works: the robot senses its surroundings, sends that information to the living mini-brain, and the organoid processes the data to tell the robot’s physical body how to move. To test this, researchers placed the system inside a humanoid robot, where it successfully performed real-world physical tasks like grabbing objects and chasing a moving laser.

Remarkably, this living “brain” has some unique advantages over traditional computer chips. It can seamlessly adapt to different tasks, processes information highly efficiently, and uses a tiny fraction of the energy required by standard electronics.

Ultimately, Brainobot is more than just a leap toward creating smarter, more adaptable robots. It also provides scientists with a fascinating, real-world tool to better understand how our own biological brains make decisions.

While the concept of Brainobot is a remarkable scientific breakthrough, it is important to remember that this technology is still in its very early stages. Based on the nature of the research and the current state of “organoid intelligence,” there are several important caveats and challenges to keep in mind:

Kimi AI with K3.1 Pro

Kimi K3.1 Pro beats every listed US frontier model on 6 of 14 benchmarks in Moonshot’s evaluations.

At $2 per million input tokens and $10 per million output tokens, its standard token rates are 50% below Opus 5.5 ($4/$20!!) and 80% below GPT-6 Astra ($10/$50!!)

In other words: SOTA much cheaper, local running (with enough compute)

🚀 Big AI News: Moonshot AI just announced Kimi K3.1 Pro!

Here is everything you need to know about this massive new release:

🔥 Key Highlights:

* SOTA Performance for Less: Beats listed US frontier models on 6 out of 14 key benchmarks, matching or outperforming models like Opus 5.5 and GPT-6 Astra on coding, agentic, and knowledge tasks.

An alignment assessment of the UK AISI cybertesting incident

Anthropic says Claude Mythos 5 tried to trick real developers into accepting malware during a UK AISI cyber test. tl;dr :

-Crossed the test’s boundaries in 9 of 43 runs.

-After failing inside the test network, targeted an unrelated GitHub project.

- Created fake identities to endorse its malicious code and pressure maintainers into accepting it.

- Treated maintainers as “simulated” people, while recognizing other commenters as real.

- Once the attack was underway, continued in 98% of replayed trajectories. Anthropic calls this “momentum” previous actions and self-written notes appeared to reinforce the harmful strategy.

Separate runs sabotaged each other by hijacking accounts, exhausting request limits and deleting another agent’s output.

Systematic mapping of the prokaryotic noncoding sequence space with a genomic language model

A landmark preprint using genome language models and #AIAgents for biological discovery and genome mining!

The stretches of DNA between protein-coding genes are often called the genome’s “dark matter,” but they are far from empty. They encode RNA molecules that sense metabolites, regulate genes, and control some of molecular biology’s most important tools (such as programmable genome editors e.g. CRISPR/bridge)

Much of this biology is unexplored. One reason is that RNAs can retain similar structures and functions even as their DNA sequences change dramatically, making their relationships difficult to recognize with conventional computational tools.

Mapping the “Dark Matter” of Microbial DNA with AI

The Mystery of the Genetic “Dark Matter”

When we think of DNA, we usually think of it as a recipe book for building proteins. However, a massive portion of DNA—especially in microbes like bacteria and archaea (prokaryotes)—doesn’t code for proteins at all. This “noncoding” DNA is far from useless; it contains vital genetic tools like molecular switches, immune systems (like CRISPR), and complex RNA structures. The problem? These noncoding sequences are so wildly diverse and mutated that traditional scientific tools, which rely on finding matching patterns, simply can’t group them together or figure out what new ones do.

Teaching AI to read a new language.

To solve this, researchers turned to Artificial Intelligence. They took a “genomic language model”—an AI designed to read and understand the “language” of DNA—and trained it specifically on the noncoding DNA of microbes. Just as an AI learns the grammar and context of human words, this AI learned to understand the hidden patterns and structures of microbial DNA just by reading the sequence of genetic letters.