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Charlie Stross: The World is Complicated. Elegant Narratives Explaining Everything Are Wrong!

Fifteen years ago, I interviewed Charlie Stross about a short story called “Lobsters.”

This spring, a thousand people queued outside Tencent’s Shenzhen headquarters to raise one.

June 2011, Singularity 1 on 1. Back then, “singularity” was a word most people filed under astrophysics, not #AI. Charlie’s 2001 story “Lobsters,” which grew into Accelerando, was one of the sharpest early maps of what happens when intelligence stops being exclusively biological. Uploaded minds. Post-scarcity economics. Legal personhood for software. An economy run by optimization processes no human fully follows.

He wrote it six years before the iPhone.

Now look at 2026. OpenClaw, the open source agent built by Austrian developer Peter Steinberger, now at OpenAI, became the fastest-growing project in GitHub history. In China, installing it is called 养龙虾, “raising lobsters,” after the red logo. Shenzhen, Wuxi and Changshu rushed out subsidy packages. Retirees, schoolkids and office workers lined up for help. A grey market of house-call technicians appeared within days.

Any connection to Charlie’s story? None. The logo is a claw pun on Claude.

Neural networks unlock larger quantum simulations with lower computational costs

In recent years, research using artificial intelligence to predict material properties has advanced rapidly. Neural network quantum Monte Carlo methods have attracted attention as highly accurate simulation techniques. However, their extremely high computational cost has limited their application to small molecular systems. This study introduces a new computational method that overcomes this limitation.

Methods that simulate electron-level mechanisms on supercomputers are widely used to explore novel materials and understand biological phenomena. There is strong demand for new approaches that can deliver faster predictions while maintaining high accuracy.

Detecting the body’s magnetic fields with a low-power Ramsey-based magnetometer

Our bodies generate extremely weak magnetic fields as electric currents flow through the heart, brain and other tissues. These signals are used in magnetocardiography and magnetoencephalography to assess heart function and brain activity, respectively. These fields can be detected at room temperature using diamond sensors containing nitrogen-vacancy (NV) centers, in which a carbon atom is replaced by a nitrogen atom adjacent to an empty lattice site.

However, conventional NV-center sensors typically require watt-level lasers to detect the extremely weak biomagnetic fields, which are usually below the picotesla level. These high-power lasers generate significant heat, limiting how close the sensor can be placed to biological tissue. Since biomagnetic fields rapidly weaken with distance, overcoming thermal and close-proximity challenges is essential for practical biomagnetic sensing.

A research team led by Professor Takayuki Iwasaki from the Department of Electrical and Electronic Engineering, School of Engineering, Institute of Science Tokyo, Japan, has developed a diamond quantum magnetometer using a low-power laser of just 210 mW, a light-trapping diamond waveguide and a compact microwave antenna. The new sensor limits its temperature rise to only 13 K while allowing it to be placed just 2 mm (0.08 inches) from the sample, enabling close-proximity biomagnetic measurements without compromising thermal safety.

AI agent helps prepare synchrotron X-ray experimental measurements, paving the way for autonomous operation

Artificial intelligence (AI) models are now used daily by many people worldwide, both for professional and personal purposes. Over the past decades, scientists specialized in various disciplines have also started using these models to conduct research or simplify their experimental practices.

Researchers at Stanford University and SLAC National Accelerator Laboratory recently explored the possibility of using an AI-powered agent to prepare a synchrotron-based X-ray experiment. Synchrotrons are large research facilities at which electrons are accelerated to produce very bright X-rays, which can then be used to study the atomic structure of materials, molecules and biological samples.

In a paper published in Nature Machine Intelligence, the team at Stanford and SLAC proposed using an AI-based agent to prepare a real synchrotron X-ray experiment. They showed that this agent could autonomously plan actions, interpret observations and generate instrument-control commands to complete sample alignment.

New study defines conditions for successful long-term biodiversity net gain

A new study identifying the ecological conditions needed for biodiversity offsetting to achieve conservation goals could provide important guidance for governments and industries as they expand biodiversity net gain (BNG) and nature restoration policies. The research is published in the journal Conservation Biology.

Biodiversity offsetting is increasingly used to compensate for environmental damage caused by development. It sees habitat loss in one location compensated through habitat restoration or protection elsewhere, with the aim of achieving no net loss of biodiversity.

The latest research, led by Swansea University in collaboration with the UK Centre for Ecology & Hydrology (UKCEH) and Forest Research, shows that biodiversity offsets are far more likely to succeed when restoration areas are larger than impacted habitats, are protected over long timescales and are designed around how ecosystems recover over time.

Decoding of amidated aromatic Cterminus and sulfation by cholecystokinin receptors reveals conserved and divergent evolutionary mechanisms

Understanding how evolutionarily related receptors preserve recognition principles for conserved peptide post-translational modifications (PTMs) while acquiring new selectivity remains central to neuropeptide-G protein-coupled receptor (GPCR) biology. The Aplysia cholecystokinin (CCK) system provides an informative model, as its peptides combine two representative PTM-related features: an amidated aromatic C-terminus (RFamide/DFamide), and a dual-tyrosine sulfation pattern exceeding the single-site architecture typical of vertebrates.

Brain-inspired nanopore device uses current-induced heating for memory operations

Some researchers are leaning into biology for inspiration in computing. In particular, neuromorphic computing offers a brain-inspired approach to hardware that replaces traditional binary processing with systems that function more like neurons and synapses. Now, a new study, published in Nature Communications, describes an innovative design for a fluidic memristor that uses its own self-heating mechanism to induce a history-dependent memory effect.

So far, most memristor (memory resistor) devices have used solid materials with electrons or holes functioning as charge carriers. But fluidic memristors instead take advantage of the movement of ions in liquids, which more closely mimics biological signaling, like that which occurs in the brain. However, existing fluidic memristors can be difficult to fabricate and offer a limited range of memory behaviors. The authors of the new study came up with a way to overcome some of these limitations by using temperature fluctuations while also making the device more “brain-like.”

They write, The exploration of additional memristive mechanisms may be beneficial. In conventional integrated circuits, localized heating is generally regarded as an unnecessary and even harmful side effect. However, in biological neural systems, thermal signals are closely linked to essential life processes. They significantly affect neuronal functions, including ion channel activation, action potential conduction speed, and firing patterns.

The invisible wearable: New skin sensors advance health monitoring

While wearable health sensors are becoming increasingly common, current iterations are awkward to wear. For example, devices attached to the face can draw unwanted attention, increase self-consciousness and influence the signals users are trying to measure. However, recent research may have found a solution by introducing ultrathin sensors that cannot be seen by observers or felt by the wearer.

In an article published in Science Advances, researchers from the Institute of Industrial Science, The University of Tokyo, and collaborating institutions reported developing thin, stretchable on-skin electrodes that are effectively invisible when worn on the face. The new technology can measure biological signals while remaining undetectable by eye and touch, allowing monitoring to take place under more natural conditions.

Biosignals such as eye movements, facial muscle activity and brain activity provide valuable information for health care monitoring and human-machine interaction. However, conventional facial electrodes can alter a person’s appearance and affect social interactions, creating what are called appearance artifacts—changes in behavior or psychological state caused simply by wearing a device that the individual and others can see.

Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance

Joint longitudinal modelling of brain atrophy, white matter damage, and cognition in 543 older adults yielded a brain maintenance index. Poorer mental health, lower openness, and faster biological ageing predicted reduced maintenance.

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