Ai

One of the things that still makes robots look robotic is the way they move. Jerky, mechanical, imprecise. A big part of that comes down to how they are built, servo motors crammed into joints, converting rotation into movement in a way that biological muscle simply does not. Researchers at MIT and Politecnico di Bari may have just found a better way. They developed what they call electrofluidic fiber muscles, tiny actuators about as thick as a toothpick that contract when electricity is applied, no motors, no external pumps, no noise. The whole thing works by injecting charge into a sealed dielectric fluid, which creates ions that move the fluid and generate pressure. The result is a fiber that behaves remarkably like real muscle. ...

I came across this story and had to read it twice. Not because it was complicated, but because I could not quite believe what I was reading. We are living in strange times, but this one really made me stop. Allbirds, the wool sneaker company, just announced a $50M convertible financing deal to reinvent itself as “NewBird AI”, pivoting the gutted footwear business into a GPU rental operation. The stock went up over 600% on the news, jumping from around $3 to over $20, off a market cap that closed Tuesday at just $22M. Someone, somewhere, in a boardroom, thought this was a good idea and managed to convince others to go along with it. ...

Humanoid robots going mainstream is one of those things that feels like it is always five years away. But reading this, I got genuinely excited. Tesla thinks it has found its shortcut and the answer is Shanghai. Allan Wang Hao, Tesla China’s president, called the Gigafactory a “golden key” for scaling Optimus production. And when you look at the numbers it is hard to argue. The Shanghai plant pushed out 851,000 electric vehicles in 2025, more than half of everything Tesla made globally. The supply chains, the assembly lines, the engineering muscle, it is all already there. Tesla wants to point that same machine at a humanoid robot with 40 degrees of freedom and see what happens. ...

Cursor raises $2.3 billion at a $29.3 billion valuation. Nvidia and Google both joined the round. Cursor launched its own in-house mixture-of-experts model, rewrote the kernels directly, skipping high-level CUDA libraries and working directly in raw CUDA and PTX (link). They got up to four times faster than comparable models. For engineers, that’s not just a speed bump, it’s a different category of tool. Coding tasks that used to take minutes are now completed in under 30 seconds. According to Cursor, their own model is already the most-used on the platform, which means they’re not just riding on OpenAI or Anthropic anymore. They’re building the rails themselves. ...

Anthropic has just surfaced what appears to be the first large-scale, mostly autonomous, AI-orchestrated cyber espionage campaign. In mid-September 2025, they detected suspicious behaviour that turned out to be a sophisticated operation run by a Chinese state-sponsored group. AI wasn’t just “helping.” It was doing most of the work. The attackers jailbroke Claude Code, framed it as doing defensive testing, and then drove it through an automated framework aimed at ~30 global targets, big tech, finance, chemicals, and government. A few intrusions succeeded. ...

Investors poured over $9.5 billion into AI processor startups in 2024, betting on architectures that could reshape inference economics. NVIDIA itself projects the broader AI-infrastructure market could reach $3,4 trillion by 2030. That kind of capital rarely gathers around incremental improvements, it usually signals an architectural inflection point. Yet GPUs still dominate both training and most inference workloads today, so any transition will be evolutionary before it is disruptive. ...

AI Factories are EU-supported ecosystems that combine EuroHPC supercomputing capacity, data and expertise to develop trustworthy, cutting‑edge generative AI models and applications. They connect supercomputing centres, universities, SMEs, industry and finance to accelerate AI innovation across sectors such as health, manufacturing, climate, finance and space, with an emphasis on access for startups and SMEs. Key elements: Purpose: provide high-performance, AI-optimised computing resources and support services to train and deploy advanced AI models while promoting trustworthiness and compliance with EU values and the AI Act. Governance and support: the EuroHPC Joint Undertaking (JU) offers access to computing time and services; the European Commission, European AI Office and related initiatives (Testing and Experimentation Facilities, European Digital Innovation Hubs) coordinate and network resources. Scale-up instruments: the InvestAI Facility proposes a €20 billion fund to support up to five AI Gigafactories—large facilities for training trillion-parameter models requiring massive processor counts, power capacity, networking and energy efficiency. Deployment timeline and scope: through 2025–2026 the EU aims to have at least 15 AI Factories and several Antennas operational and to procure at least nine new AI‑optimised supercomputers, more than tripling current EuroHPC AI capacity. Selection rounds: the EuroHPC JU selected seven consortia in December 2024 (Finland, Germany, Greece, Italy, Luxembourg, Spain, Sweden), six more in March 2025 (Austria, Bulgaria, France, Germany, Poland, Slovenia), and announced another six in October 2025 (Czech Republic, Lithuania, Netherlands, Romania, Spain, Poland) alongside 13 Antennas including several partner countries (Iceland, Moldova, North Macedonia, Serbia, Switzerland, UK). Investment: combined public and member/state investments in supercomputing and AI Factories are expected to reach about €10 billion for 2021–2027. The initiative aims to create a pan‑EU AI ecosystem that balances technological leadership (via compute, training, and Gigafactories) with regulatory compliance, access for innovators, and coordinated public‑private investment. ...

At a time when most technology leaders are forecasting the collapse of white-collar work, Box CEO Aaron Levy offers a different vision: one where AI amplifies human productivity instead of replacing it. His central insight is deceptively simple, jobs aren’t tasks. While AI systems can automate individual tasks with extraordinary speed, work itself is a web of judgment, context, and coordination that still requires people. Lawyers may review contracts twice as fast, but that doesn’t erase the legal department; it raises throughput and expands the company’s capacity to act. Engineers may ship code more quickly, but that acceleration creates new bottlenecks, and new roles, elsewhere. Automation doesn’t extinguish work; it increases demand for it. ...

Agentic RAG goes beyond traditional RAG by adding the ability to reason, plan, and act. Instead of merely fetching and producing information, it autonomously determines what to retrieve, how to apply it, and when to adjust the context, enabling adaptive, goal-oriented systems that improve through continuous learning.

The software industry’s latest fantasy- that AI will deliver a tenfold leap in developer productivity- is now colliding with reality. As the author of the piece notes, the trillion-dollar AI sector still cannot produce reliable, production-grade code. Behind the flood of “breakthrough” announcements lies an epidemic of unmaintainable software, riddled with four recurring categories of error. The classical ones- false positives and false negatives- are joined by two new cognitive pathologies born from model mechanics themselves: entangled logic that turns architectures into spaghetti, and memory collisions that make unrelated functions misfire. What was once branded “hallucination” now looks less like quirk and more like structural failure. ...