se fue alejando de sí mismo aunque se siguió tuteando y en ese trato distante aunque cordial fue comprendiendo que tarde o temprano acabaría reventando o aceptándose

Me and T just played a ton of arena and for the first time I kinda stopped trying so hard to win and was doing stupid shit like paladin ashe, or attack speed zilean. We actually ended up doing pretty damn good, and I ended with an ADAPt + master of duality katarina game, and I just checked and saw that I hit masters on arenasweats! It’s silly but I’m happy haha.

Si les environnements Windows ont longtemps été la cible privilégiée des cybercriminels, une récente découverte vient rappeler que les systèmes Linux, piliers de l'infrastructure cloud mondiale, sont désormais dans le collimateur d'acteurs malveillants hautement sophistiqués. Des chercheurs de Check Point ont mis au jour un nouveau framework baptisé VoidLink, un outil dont la complexité et la modularité marquent une rupture avec les malwares Linux traditionnels.

Parlons de sa structure. Il ne s'agit pas d'un simple script malveillant, mais d'un véritable écosystème offensif. Le code source révèle l'existence de plus de trente modules distincts, transformant ce logiciel en un véritable couteau suisse numérique. Cette modularité permet aux attaquants de personnaliser l'infection pour chaque machine compromise, en ajoutant ou en retirant des fonctionnalités selon l'évolution de leurs objectifs.

Le fonctionnement repose sur un chargeur en deux étapes, suivi d'un implant final qui intègre les modules de base. Fait notable, VoidLink dispose de sa propre interface de programmation (API) pour le développement de plugins, permettant au malware d'évoluer d'un simple implant vers un cadre de post-exploitation complet. Cette flexibilité offre aux opérateurs la possibilité de télécharger et d'installer de nouvelles fonctionnalités en temps réel, sans avoir à réinfecter la machine cible.

VoidLink a conscience de l'environnement dans lequel il opère. Il est spécifiquement conçu pour détecter s'il s'exécute au sein d'une infrastructure cloud publique. En interrogeant les métadonnées via les API des fournisseurs, il peut identifier s'il se trouve sur Amazon Web Services, Google Cloud Platform, Microsoft Azure, Alibaba ou Tencent. Les analystes ont même trouvé des indications suggérant que les développeurs prévoient d'étendre ces capacités de détection à d'autres fournisseurs comme Huawei, DigitalOcean et Vultr.

Cette spécialisation vers le cloud démontre une compréhension aiguë des infrastructures modernes. VoidLink ne se contente pas de savoir s'il est dans le cloud, il cherche aussi à comprendre la topologie locale. Il collecte des informations détaillées sur l'hyperviseur et détermine s'il fonctionne à l'intérieur d'un conteneur Docker ou d'un pod Kubernetes. Cette intelligence situationnelle est déterminante pour permettre des mouvements latéraux discrets et efficaces au sein des réseaux d'entreprise.

Ses fonctionnalités dépassent largement ce que l'on observe habituellement chez les attaquants opportunistes. Le framework intègre des fonctions de rootkit avancées lui permettant de se fondre dans l'activité normale du système, rendant sa détection particulièrement ardue. Il utilise des techniques d'anti-analyse et d'anti-débogage pour repérer les outils de sécurité et les mesures de durcissement installés sur la machine, adaptant son comportement pour éviter d'être repéré.

Au-delà de la dissimulation, l'objectif est bien évidemment le vol d'informations et la persistance. Le malware est capable de cartographier les processus, les services, le système de fichiers et les interfaces réseau. Il excelle particulièrement dans la récolte d'identifiants. Clés SSH, mots de passe, cookies de navigateur, identifiants Git, jetons d'authentification et clés API stockés dans le trousseau du système sont systématiquement exfiltrés. Les communications avec le serveur de commande et de contrôle sont camouflées pour ressembler à un trafic réseau légitime, compliquant encore la tâche des défenseurs.

Malgré ces capacités effrayantes, il existe une lueur d'espoir. L'analyse de l'interface de VoidLink, localisée pour des opérateurs affiliés à la Chine, ainsi que les commentaires dans le code source, indiquent que le projet est encore en phase de développement. Check Point a découvert ce malware dans des clusters de fichiers sur VirusTotal, mais n'a trouvé aucune preuve qu'il ait infecté des machines dans la nature pour le moment.

Cette absence d'attaques actives ne doit cependant pas inciter à la complaisance. Son existence même témoigne d'un investissement et d'une planification typiques des acteurs de la menace professionnelle. Cela signale que les attaquants consacrent désormais des ressources considérables pour cibler les environnements Linux et Cloud, conscients que c'est là que résident les données les plus critiques des entreprises modernes. Si aucune action immédiate n'est requise pour l'instant, la vigilance reste de mise. Les défenseurs doivent se préparer à voir émerger des menaces de ce calibre dans un avenir proche.

In today’s competitive business environment, companies that rely on vehicles need complete visibility, control, and efficiency to stay ahead. This is where gps tracking software plays a critical role. By providing live location data, actionable insights, and operational transparency, GPS-based solutions help businesses manage vehicles smarter, reduce costs, and improve overall productivity.

Smarter Control with a Real Time Tracking System

A powerful real time tracking system allows businesses to monitor vehicle movements instantly. Fleet managers can see where vehicles are, how fast they are moving, and whether they are following planned routes. This real-time visibility helps reduce unnecessary delays, control fuel consumption, and respond quickly to unexpected situations such as traffic congestion or emergencies.

Unlike traditional tracking methods, modern GPS tracking software provides live updates rather than delayed reports. This enables faster decision-making, improved customer communication, and better service reliability.

Enhanced Security with GPS Tracker for Car

Using a gps tracker for car is no longer limited to large fleets. Businesses with a few vehicles, service teams, or even company-owned cars can benefit greatly. Car trackers improve vehicle security, help prevent unauthorized use, and support theft recovery by providing accurate location data at all times.

For drivers, GPS tracking promotes responsible driving behavior. When vehicles are monitored, harsh braking, overspeeding, and excessive idling can be reduced — leading to lower accident risks and reduced vehicle wear and tear.

Optimizing Operations Through Fleet Management System

A complete fleet management system goes beyond location tracking. It centralizes multiple fleet activities into one dashboard, including vehicle monitoring, route planning, fuel analysis, and driver behavior reporting. By integrating these features, businesses gain a clear overview of fleet performance and can identify inefficiencies quickly.

Fleet management systems help organizations cut operational costs, improve delivery timelines, and maintain compliance with internal policies. This level of control is essential for logistics companies, delivery services, transportation providers, and field service operations.

Reducing Downtime with Fleet Maintenance Software

Vehicle breakdowns and unexpected repairs can significantly disrupt business operations. Fleet maintenance software helps prevent these issues by automating service schedules and tracking vehicle health. Businesses can receive timely alerts for oil changes, inspections, and part replacements, ensuring vehicles remain in optimal condition.

By maintaining vehicles proactively, companies reduce downtime, extend vehicle lifespan, and avoid expensive emergency repairs. This also contributes to better safety and consistent operational performance across the fleet.

Why GPS Tracking Software Is Essential for Growing Businesses

As businesses expand, managing vehicles manually becomes inefficient and error-prone. GPS tracking software scales easily with growing fleets, providing accurate data, detailed reports, and performance insights. This allows businesses to plan smarter routes, reduce fuel waste, and allocate resources more effectively.

Another major advantage is data-driven decision-making. With historical reports and analytics, managers can evaluate trends, optimize routes, improve driver performance, and set measurable efficiency goals.

Conclusion

Adopting gps tracking system is no longer optional for businesses that rely on vehicles. By combining a reliable gps tracker for car, an advanced real time tracking system, intelligent fleet maintenance software, and a centralized fleet management system, organizations gain full control over their fleet operations. These solutions help reduce costs, improve safety, minimize downtime, and enhance productivity — laying a strong foundation for sustainable growth and long-term success.

Read more: How to Choose the Best GPS Tracking Software for Your Business Fleet in the UAE

Language is a power that defies decription.

She wrote it simply:

“Missing you.”

And his heart shattered—as it did every time.

Then it would spend the long hours between the moment and the next explosive exchange stitching itself back together again.

And he loved it.

Every miss you, every love you, every way of saying you’re in my thoughts was a benison to his soul.

It was the power of those three letters that took an acoustic moment and made it symphonic.

ING

Not “miss” you—*missing*.
Not “love” you—*loving*.

The addition, in English, of those three silly letters! Oh, the power of the moment. As he wrote this, he felt warmth in him, and found he displaced his other thoughts, for her—so strong was the power, he felt compelled to tell her now, to pull her through the hole in the garden wall and show her his earthly delights.

Not someday. Now.
That is the power of ing.

Tonight, he does not write of her—he is writing of her, thinking of her, desiring her.

Ach—in the moment.

So perhaps, dear reader, while we slumber and refresh, our lovers will be out in the night, keeping one another—warming and sustaining each other against a cruel and indifferent world.

— #poetry #wyst #madrid

The human brain runs on roughly 20 watts. That is less power than the light bulb illuminating your desk, yet it orchestrates consciousness, creativity, memory, and the ability to read these very words. Within that modest thermal envelope, approximately 100 billion neurons fire in orchestrated cascades, connected by an estimated 100 trillion synapses, each consuming roughly 10 femtojoules per synaptic event. To put that in perspective: the energy powering a single thought could not warm a thimble of water by a measurable fraction of a degree.

Meanwhile, the graphics processing units training today's large language models consume megawatts and require industrial cooling systems. Training a single frontier AI model can cost millions in electricity alone. The disparity is so stark, so seemingly absurd, that it has launched an entire field of engineering dedicated to a single question: can we build computers that think like brains?

The answer, it turns out, is far more complicated than the question implies.

The Efficiency Enigma

The numbers sound almost fictional. According to research published in the Proceedings of the National Academy of Sciences, communication in the human cortex consumes approximately 35 times more energy than computation itself, yet the total computational budget amounts to merely 0.2 watts of ATP. The remaining energy expenditure of the brain, around 3.5 watts, goes toward long-distance neural communication. This audit reveals something profound: biological computation is not merely efficient; it is efficient in ways that conventional computing architectures cannot easily replicate.

Dig deeper into the cellular machinery, and the efficiency story becomes even more remarkable. Research published in the Journal of Cerebral Blood Flow and Metabolism has mapped the energy budget of neural computation with extraordinary precision. In the cerebral cortex, resting potentials account for approximately 20% of total energy use, action potentials consume 21%, and synaptic processes dominate at 59%. The brain has evolved an intricate accounting system for every molecule of ATP.

The reason for this efficiency lies in the fundamental architecture of biological neural networks. Unlike the von Neumann machines that power our laptops and data centres, where processors and memory exist as separate entities connected by data buses, biological neurons are both processor and memory simultaneously. Each synapse stores information in its connection strength while also performing the computation that determines whether to pass a signal forward. There is no memory bottleneck because there is no separate memory.

This architectural insight drove Carver Mead, the Caltech professor who coined the term “neuromorphic” in the mid-1980s, to propose a radical alternative to conventional computing. Observing that charges moving through MOS transistors operated in weak inversion bear striking parallels to charges flowing across neuronal membranes, Mead envisioned silicon systems that would exploit the physics of transistors rather than fighting against it. His 1989 book, Analog VLSI and Neural Systems, became the foundational text for an entire field. Working with Nobel laureates John Hopfield and Richard Feynman, Mead helped create three new fields: neural networks, neuromorphic engineering, and the physics of computation.

The practical fruits of Mead's vision arrived early. In 1986, he co-founded Synaptics with Federico Faggin to develop analog circuits based on neural networking theories. The company's first commercial product, a pressure-sensitive computer touchpad, eventually captured 70% of the touchpad market, a curious reminder that brain-inspired computing first succeeded not through cognition but through touch.

Three and a half decades later, that field has produced remarkable achievements. Intel's Loihi 2 chip, fabricated on a 14-nanometre process, integrates 128 neuromorphic cores capable of simulating up to 130,000 synthetic neurons and 130 million synapses. A unique feature of Loihi's architecture is its integrated learning engine, enabling full on-chip learning via programmable microcode learning rules. IBM's TrueNorth, unveiled in 2014, packs one million neurons and 256 million synapses onto a chip consuming just 70 milliwatts, with a power density one ten-thousandth that of conventional microprocessors. The SpiNNaker system at the University of Manchester, conceived by Steve Furber (one of the original designers of the ARM microprocessor), contains over one million ARM processors capable of simulating a billion neurons in biological real-time.

These are genuine engineering marvels. But are they faithful translations of biological principles, or are they something else entirely?

The Translation Problem

The challenge of neuromorphic computing is fundamentally one of translation. Biological neurons operate through a bewildering array of mechanisms: ion channels opening and closing across cell membranes, neurotransmitters diffusing across synaptic clefts, calcium cascades triggering long-term changes in synaptic strength, dendritic trees performing complex nonlinear computations, glial cells modulating neural activity in ways we are only beginning to understand. The system is massively parallel, deeply interconnected, operating across multiple timescales from milliseconds to years, and shot through with stochasticity at every level.

Silicon, by contrast, prefers clean digital logic. Transistors want to be either fully on or fully off. The billions of switching events in a modern processor are choreographed with picosecond precision. Randomness is the enemy, meticulously engineered out through redundancy and error correction. The very physics that makes digital computing reliable makes biological fidelity difficult.

Consider spike-timing-dependent plasticity, or STDP, one of the fundamental learning mechanisms in biological neural networks. The principle is elegant: if a presynaptic neuron fires just before a postsynaptic neuron, the connection between them strengthens. If the timing is reversed, the connection weakens. This temporal precision, operating on timescales of milliseconds, allows networks to learn temporal patterns and causality.

Implementing STDP in silicon requires trade-offs. Digital implementations on platforms like SpiNNaker must maintain precise timing records for potentially millions of synapses, consuming memory and computational resources. Analog implementations face challenges with device variability and noise. Memristor-based approaches, which exploit the physics of resistive switching to store synaptic weights, offer elegant solutions for weight storage but struggle with the temporal dynamics. Each implementation captures some aspects of biological STDP while necessarily abandoning others.

The BrainScaleS system at Heidelberg University takes perhaps the most radical approach to biological fidelity. Unlike digital neuromorphic systems that simulate neural dynamics, BrainScaleS uses analog circuits to physically emulate them. The silicon neurons and synapses implement the underlying differential equations through the physics of the circuits themselves. No equation gets explicitly solved; instead, the solution emerges from the natural evolution of voltages and currents. The system runs up to ten thousand times faster than biological real-time, offering both a research tool and a demonstration that analog approaches can work.

Yet even BrainScaleS makes profound simplifications. Its 512 neuron circuits and 131,000 synapses per chip are a far cry from the billions of neurons in a human cortex. The neuron model it implements, while sophisticated, omits countless biological details. The dendrites are simplified. The glial cells are absent. The stochasticity is controlled rather than embraced.

The Stochasticity Question

Here is where neuromorphic computing confronts one of its deepest challenges. Biological neural networks are noisy. Synaptic vesicle release is probabilistic, with transmission rates measured in vivo ranging from as low as 10% to as high as 50% at different synapses. Ion channel opening is stochastic. Spontaneous firing occurs. The system is bathed in noise at every level. It is one of nature's great mysteries how such a noisy computing system can perform computation reliably.

For decades, this noise was viewed as a bug, a constraint that biological systems had to work around. But emerging research suggests it may be a feature. According to work published in Nature Communications, synaptic noise has the distinguishing characteristic of being multiplicative, and this multiplicative noise plays a key role in learning and probabilistic inference. The brain may be implementing a form of Bayesian computation, sampling from probability distributions to represent uncertainty and make decisions under incomplete information.

The highly irregular spiking activity of cortical neurons and behavioural variability suggest that the brain could operate in a fundamentally probabilistic way. One prominent idea in neuroscience is that neural computing is inherently stochastic and that noise is an integral part of the computational process rather than an undesirable side effect. Mimicking how the brain implements and learns probabilistic computation could be key to developing machine intelligence that can think more like humans.

This insight has spawned a new field: probabilistic or stochastic computing. Artificial neuron devices based on memristors and ferroelectric field-effect transistors can produce uncertain, nonlinear output spikes that may be key to bringing machine learning closer to human cognition.

But here lies a paradox. Traditional silicon fabrication spends enormous effort eliminating variability and noise. Device-to-device variation is a manufacturing defect to be minimised. Thermal noise is interference to be filtered. The entire thrust of semiconductor engineering for seventy years has been toward determinism and precision. Now neuromorphic engineers are asking: what if we need to engineer the noise back in?

Some researchers are taking this challenge head-on. Work on exploiting noise as a resource for computation demonstrates that the inherent noise and variation in memristor nanodevices can be harnessed as features for energy-efficient on-chip learning rather than fought as bugs. The stochastic behaviour that conventional computing spends energy suppressing becomes, in this framework, a computational asset.

The Memristor Revolution

The memristor, theorised by Leon Chua in 1971 and first physically realised by HP Labs in 2008, has become central to the neuromorphic vision. Unlike conventional transistors that forget their state when power is removed, memristors remember. Their resistance depends on the history of current that has flowed through them, a property that maps naturally onto synaptic weight storage.

Moreover, memristors can be programmed with multiple resistance levels, enhancing information density within a single cell. This technology truly shines when memristors are organised into crossbar arrays, performing analog computing that leverages physical laws to accelerate matrix operations. The physics of Ohm's law and Kirchhoff's current law perform the multiplication and addition operations that form the backbone of neural network computation.

Recent progress has been substantial. In February 2024, researchers demonstrated a circuit architecture that enables low-precision analog devices to perform high-precision computing tasks. The secret lies in using a weighted sum of multiple devices to represent one number, with subsequently programmed devices compensating for preceding programming errors. This breakthrough was achieved not just in academic settings but in cutting-edge System-on-Chip designs, with memristor-based neural processing units fabricated in standard commercial foundries.

In 2025, researchers presented a memristor-based analog-to-digital converter featuring adaptive quantisation for diverse output distributions. Compared to state-of-the-art designs, this converter achieved a 15-fold improvement in energy efficiency and nearly 13-fold reduction in area. The trajectory is clear: memristor technology is maturing from laboratory curiosity to commercial viability.

Yet challenges remain. Current research highlights key issues including device variation, the need for efficient peripheral circuitry, and systematic co-design and optimisation. By integrating advances in flexible electronics, AI hardware, and three-dimensional packaging, memristor logic gates are expected to support scalable, reconfigurable computing in edge intelligence and in-memory processing systems.

The Economics of Imitation

Even if neuromorphic systems could perfectly replicate biological neural function, the economics of silicon manufacturing impose their own constraints. The global neuromorphic computing market was valued at approximately 28.5 million US dollars in 2024, projected to grow to over 1.3 billion by 2030. These numbers, while impressive in growth rate, remain tiny compared to the hundreds of billions spent annually on conventional semiconductor manufacturing.

Scale matters in chip production. The fabs that produce cutting-edge processors cost tens of billions of dollars to build and require continuous high-volume production to amortise those costs. Neuromorphic chips, with their specialised architectures and limited production volumes, cannot access the same economies of scale. The manufacturing processes are not yet optimised for large-scale production, resulting in high costs per chip.

This creates a chicken-and-egg problem. Without high-volume applications, neuromorphic chips remain expensive. Without affordable chips, applications remain limited. The industry is searching for what some call a “killer app,” the breakthrough use case that would justify the investment needed to scale production.

Energy costs may provide that driver. Training a single large language model can consume electricity worth millions of dollars. Data centres worldwide consume over one percent of global electricity, and that fraction is rising. If neuromorphic systems can deliver on their promise of dramatically reduced power consumption, the economic equation shifts.

In April 2025, during the annual International Conference on Learning Representations, researchers demonstrated the first large language model adapted to run on Intel's Loihi 2 chip. It achieved accuracy comparable to GPU-based models while using half the energy. This milestone represents meaningful progress, but “half the energy” is still a long way from the femtojoule-per-operation regime of biological synapses. The gap between silicon neuromorphic systems and biological brains remains measured in orders of magnitude.

Beyond the Brain Metaphor

And this raises a disquieting question: what if the biological metaphor is itself a constraint?

The brain evolved under pressures that have nothing to do with the tasks we ask of artificial intelligence. It had to fit inside a skull. It had to run on the chemical energy of glucose. It had to develop through embryogenesis and remain plastic throughout a lifetime. It had to support consciousness, emotion, social cognition, and motor control simultaneously. These constraints shaped its architecture in ways that may be irrelevant or even counterproductive for artificial systems.

Consider memory. Biological memory is reconstructive rather than reproductive. We do not store experiences like files on a hard drive; we reassemble them from distributed traces each time we remember, which is why memories are fallible and malleable. This is fine for biological organisms, where perfect recall is less important than pattern recognition and generalisation. But for many computing tasks, we want precise storage and retrieval. The biological approach is a constraint imposed by wet chemistry, not an optimal solution we should necessarily imitate.

Or consider the brain's operating frequency. Neurons fire at roughly 10 hertz, while transistors switch at gigahertz, a factor of one hundred million faster. IBM researchers realised that event-driven spikes use silicon-based transistors inefficiently. If synapses in the human brain operated at the same rate as a laptop, as one researcher noted, “our brain would explode.” The slow speed of biological neurons is an artefact of electrochemical signalling, not a design choice. Forcing silicon to mimic this slowness wastes most of its speed advantage.

These observations suggest that the most energy-efficient computing paradigm for silicon may have no biological analogue at all.

Alternative Paradigms Without Biological Parents

Thermodynamic computing represents perhaps the most radical departure from both conventional and neuromorphic approaches. Instead of fighting thermal noise, it harnesses it. The approach exploits the natural stochastic behaviour of physical systems, treating heat and electrical noise not as interference but as computational resources.

The startup Extropic has developed what they call a thermodynamic sampling unit, or TSU. Unlike CPUs and GPUs that perform deterministic computations, TSUs produce samples from programmable probability distributions. The fundamental insight is that the random behaviour of “leaky” transistors, the very randomness that conventional computing engineering tries to eliminate, is itself a powerful computational resource. Simulations suggest that running denoising thermodynamic models on TSUs could be 10,000 times more energy-efficient than equivalent algorithms on GPUs.

Crucially, thermodynamic computing sidesteps the scaling challenges that plague quantum computing. While quantum computers require cryogenic temperatures, isolation from environmental noise, and exotic fabrication processes, thermodynamic computers can potentially be built using standard CMOS manufacturing. They embrace the thermal environment that quantum computers must escape.

Optical computing offers another path forward. Researchers at MIT demonstrated in December 2024 a fully integrated photonic processor that performs all key computations of a deep neural network optically on-chip. The device completed machine-learning classification tasks in less than half a nanosecond while achieving over 92% accuracy. Crucially, the chip was fabricated using commercial foundry processes, suggesting a path to scalable production.

The advantages of photonics are fundamental. Light travels at the speed of light. Photons do not interact with each other, enabling massive parallelism without interference. Heat dissipation is minimal. Bandwidth is essentially unlimited. Work at the quantum limit has demonstrated optical neural networks operating at just 0.038 photons per multiply-accumulate operation, approaching fundamental physical limits of energy efficiency.

Yet photonic computing faces its own challenges. Implementing nonlinear functions, essential for neural network computation, is difficult in optics precisely because photons do not interact easily. The MIT team's solution was to create nonlinear optical function units that combine electronics and optics, a hybrid approach that sacrifices some of the purity of all-optical computing for practical functionality.

Hyperdimensional computing takes inspiration from the brain but in a radically simplified form. Instead of modelling individual neurons and synapses, it represents concepts as very high-dimensional vectors, typically with thousands of dimensions. These vectors can be combined using simple operations like addition and multiplication, with the peculiar properties of high-dimensional spaces ensuring that similar concepts remain similar and different concepts remain distinguishable.

The approach is inherently robust to noise and errors, properties that emerge from the mathematics of high-dimensional spaces rather than from any biological mechanism. Because the operations are simple, implementations can be extremely efficient, and the paradigm maps well onto both conventional digital hardware and novel analog substrates.

Reservoir computing exploits the dynamics of fixed nonlinear systems to perform computation. The “reservoir” can be almost anything: a recurrent neural network, a bucket of water, a beam of light, or even a cellular automaton. Input signals perturb the reservoir, and a simple readout mechanism learns to extract useful information from the reservoir's state. Training occurs only at the readout stage; the reservoir itself remains fixed.

This approach has several advantages. By treating the reservoir as a “black box,” it can exploit naturally available physical systems for computation, reducing the engineering burden. Classical and quantum mechanical systems alike can serve as reservoirs. The computational power of the physical world is pressed into service directly, rather than laboriously simulated in silicon.

The Fidelity Paradox

So we return to the question posed at the outset: to what extent do current neuromorphic and in-memory computing approaches represent faithful translations of biological principles versus engineering approximations constrained by silicon physics and manufacturing economics?

The honest answer is: mostly the latter. Current neuromorphic systems capture certain aspects of biological neural computation, principally the co-location of memory and processing, the use of spikes as information carriers, and some forms of synaptic plasticity, while necessarily abandoning others. The stochasticity, the temporal dynamics, the dendritic computation, the neuromodulation, the glial involvement, and countless other biological mechanisms are simplified, approximated, or omitted entirely.

This is not necessarily a criticism. Engineering always involves abstraction and simplification. The question is whether the aspects retained are the ones that matter for efficiency, and whether the aspects abandoned would matter if they could be practically implemented.

Here the evidence is mixed. Neuromorphic systems do demonstrate meaningful energy efficiency gains for certain tasks. Intel's Loihi achieves performance improvements of 100 to 10,000 times in energy efficiency for specific workloads compared to conventional approaches. IBM's TrueNorth can perform 46 billion synaptic operations per second per watt. These are substantial achievements.

But they remain far from biological efficiency. The brain achieves femtojoule-per-operation efficiency; current neuromorphic hardware typically operates in the picojoule range or above, a gap of three to six orders of magnitude. Researchers have achieved artificial synapses operating at approximately 1.23 femtojoules per synaptic event, rivalling biological efficiency, but scaling these laboratory demonstrations to practical systems remains a formidable challenge.

The SpiNNaker 2 system under construction at TU Dresden, projected to incorporate 5.2 million ARM cores distributed across 70,000 chips in 10 server racks, represents the largest neuromorphic system yet attempted. One SpiNNaker2 chip contains 152,000 neurons and 152 million synapses across its 152 cores. It targets applications in neuroscience simulation and event-based AI, but widespread commercial deployment remains on the horizon rather than in the present.

Manufacturing Meets Biology

The constraints of silicon manufacturing interact with biological metaphors in complex ways. Neuromorphic chips require novel architectures that depart from the highly optimised logic and memory designs that dominate conventional fabrication. This means they cannot fully leverage the massive investments that have driven conventional chip performance forward for decades.

The BrainScaleS-2 system uses a mixed-signal design that combines analog neural circuits with digital control logic. This approach captures more biological fidelity than purely digital implementations but requires specialised fabrication and struggles with device-to-device variation. Memristor-based approaches offer elegant physics but face reliability and manufacturing challenges that CMOS transistors solved decades ago.

Some researchers are looking to materials beyond silicon entirely. Two-dimensional materials like graphene and transition metal dichalcogenides offer unique electronic properties that could enable new computational paradigms. By virtue of their atomic thickness, 2D materials represent the ultimate limit for downscaling. Spintronics exploits electron spin rather than charge for computation, with device architectures achieving approximately 0.14 femtojoules per operation. Organic electronics promise flexible, biocompatible substrates. Each of these approaches trades the mature manufacturing ecosystem of silicon for potentially transformative new capabilities.

The Deeper Question

Perhaps the deepest question is whether we should expect biological and silicon-based computing to converge at all. The brain and the processor evolved under completely different constraints. The brain is an electrochemical system that developed over billions of years of evolution, optimised for survival in unpredictable environments with limited and unreliable energy supplies. The processor is an electronic system engineered over decades, optimised for precise, repeatable operations in controlled environments with reliable power.

The brain's efficiency arises from its physics: the slow propagation of electrochemical signals, the massive parallelism of synaptic computation, the integration of memory and processing at the level of individual connections, the exploitation of stochasticity for probabilistic inference. These characteristics are not arbitrary design choices but emergent properties of wet, carbon-based, ion-channel-mediated computation. The brain's cognitive power emerges from a collective form of computation extending over very large ensembles of sluggish, imprecise, and unreliable components.

Silicon's strengths are different: speed, precision, reliability, manufacturability, and the ability to perform billions of identical operations per second with deterministic outcomes. These characteristics emerge from the physics of electron transport in crystalline semiconductors and the engineering sophistication of nanoscale fabrication.

Forcing biological metaphors onto silicon may obscure computational paradigms that exploit silicon's native strengths rather than fighting against them. Thermodynamic computing, which embraces thermal noise as a resource, may be one such paradigm. Photonic computing, which exploits the speed and parallelism of light, may be another. Hyperdimensional computing, which relies on mathematical rather than biological principles, may be a third.

None of these paradigms is necessarily “better” than neuromorphic computing. Each offers different trade-offs, different strengths, different suitabilities for different applications. The landscape of post-von Neumann computing is not a single path but a branching tree of possibilities, some inspired by biology and others inspired by physics, mathematics, or pure engineering intuition.

Where We Are, and Where We Might Go

The current state of neuromorphic computing is one of tremendous promise constrained by practical limitations. The theoretical advantages are clear: co-located memory and processing, event-driven operation, native support for temporal dynamics, and potential for dramatic energy efficiency improvements. The practical achievements are real but modest: chips that demonstrate order-of-magnitude improvements for specific workloads but remain far from the efficiency of biological systems and face significant scaling challenges.

The field is at an inflection point. The projected 45-fold growth in the neuromorphic computing market by 2030 reflects genuine excitement about the potential of these technologies. The demonstration of large language models on neuromorphic hardware in 2025 suggests that even general-purpose AI applications may become accessible. The continued investment by major companies like Intel, IBM, Sony, and Samsung, alongside innovative startups, ensures that development will continue.

But the honest assessment is that we do not yet know whether neuromorphic computing will deliver on its most ambitious promises. The biological brain remains, for now, in a category of its own when it comes to energy-efficient general intelligence. Whether silicon can ever reach biological efficiency, and whether it should try to or instead pursue alternative paradigms that play to its own strengths, remain open questions.

What is becoming clear is that the future of computing will not look like the past. The von Neumann architecture that has dominated for seventy years is encountering fundamental limits. The separation of memory and processing, which made early computers tractable, has become a bottleneck that consumes energy and limits performance. In-memory computing is an emerging non-von Neumann computational paradigm that keeps alive the promise of achieving energy efficiencies on the order of one femtojoule per operation. Something different is needed.

That something may be neuromorphic computing. Or thermodynamic computing. Or photonic computing. Or hyperdimensional computing. Or reservoir computing. Or some hybrid not yet imagined. More likely, it will be all of these and more, a diverse ecosystem of computational paradigms each suited to different applications, coexisting rather than competing.

The brain, after all, is just one solution to the problem of efficient computation, shaped by the particular constraints of carbon-based life on a pale blue dot orbiting an unremarkable star. Silicon, and the minds that shape it, may yet find others.

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References and Sources

1. “Communication consumes 35 times more energy than computation in the human cortex, but both costs are needed to predict synapse number.” Proceedings of the National Academy of Sciences (PNAS). https://www.pnas.org/doi/10.1073/pnas.2008173118

2. “Can neuromorphic computing help reduce AI's high energy cost?” PNAS, 2025. https://www.pnas.org/doi/10.1073/pnas.2528654122

3. “Organic core-sheath nanowire artificial synapses with femtojoule energy consumption.” Science Advances. https://www.science.org/doi/10.1126/sciadv.1501326

4. Intel Loihi Architecture and Specifications. Open Neuromorphic. https://open-neuromorphic.org/neuromorphic-computing/hardware/loihi-intel/

5. Intel Loihi 2 Specifications. Open Neuromorphic. https://open-neuromorphic.org/neuromorphic-computing/hardware/loihi-2-intel/

6. SpiNNaker Project, University of Manchester. https://apt.cs.manchester.ac.uk/projects/SpiNNaker/

7. SpiNNaker 2 Specifications. Open Neuromorphic. https://open-neuromorphic.org/neuromorphic-computing/hardware/spinnaker-2-university-of-dresden/

8. BrainScaleS-2 System Documentation. Heidelberg University. https://electronicvisions.github.io/documentation-brainscales2/latest/brainscales2-demos/fp_brainscales.html

9. “Emerging Artificial Neuron Devices for Probabilistic Computing.” Frontiers in Neuroscience, 2021. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.717947/full

10. “Exploiting noise as a resource for computation and learning in spiking neural networks.” Cell Patterns, 2023. https://www.sciencedirect.com/science/article/pii/S2666389923002003

11. “Thermodynamic Computing: From Zero to One.” Extropic. https://extropic.ai/writing/thermodynamic-computing-from-zero-to-one

12. “Thermodynamic computing system for AI applications.” Nature Communications, 2025. https://www.nature.com/articles/s41467-025-59011-x

13. “Photonic processor could enable ultrafast AI computations with extreme energy efficiency.” MIT News, December 2024. https://news.mit.edu/2024/photonic-processor-could-enable-ultrafast-ai-computations-1202

14. “Quantum-limited stochastic optical neural networks operating at a few quanta per activation.” PMC, 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC11698857/

15. “2025 IEEE Study Leverages Silicon Photonics for Scalable and Sustainable AI Hardware.” IEEE Photonics Society. https://ieeephotonics.org/announcements/2025ieee-study-leverages-silicon-photonics-for-scalable-and-sustainable-ai-hardwareapril-3-2025/

16. “Recent advances in physical reservoir computing: A review.” Neural Networks, 2019. https://www.sciencedirect.com/science/article/pii/S0893608019300784

17. “Brain-inspired computing systems: a systematic literature review.” The European Physical Journal B, 2024. https://link.springer.com/article/10.1140/epjb/s10051-024-00703-6

18. “Current opinions on memristor-accelerated machine learning hardware.” Solid-State Electronics, 2025. https://www.sciencedirect.com/science/article/pii/S1359028625000130

19. “A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks.” PMC, 2015. https://pmc.ncbi.nlm.nih.gov/articles/PMC4438254/

20. “Updated energy budgets for neural computation in the neocortex and cerebellum.” Journal of Cerebral Blood Flow & Metabolism, 2012. https://pmc.ncbi.nlm.nih.gov/articles/PMC3390818/

21. “Stochasticity from function – Why the Bayesian brain may need no noise.” Neural Networks, 2019. https://www.sciencedirect.com/science/article/pii/S0893608019302199

22. “Deterministic networks for probabilistic computing.” PMC, 2019. https://ncbi.nlm.nih.gov/pmc/articles/PMC6893033

23. “Programming memristor arrays with arbitrarily high precision for analog computing.” USC Viterbi, 2024. https://viterbischool.usc.edu/news/2024/02/new-chip-design-to-enable-arbitrarily-high-precision-with-analog-memories/

24. “Advances of Emerging Memristors for In-Memory Computing Applications.” PMC, 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC12508526/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

Revelation 11 is one of the most misunderstood, misused, and misrepresented chapters in all of Scripture, not because it is too strange, but because it is too honest. It exposes something many people do not want to face: the war between truth and power. It is a chapter about witnesses who cannot be bought, silenced, or erased, and a world that panics when it realizes that truth still speaks even when buried. When John wrote this vision on the island of Patmos, he was not trying to give Christians a puzzle to solve. He was giving them a mirror. Revelation 11 is not about distant future speculation as much as it is about what always happens when God’s voice collides with human systems that want control instead of repentance.

The chapter opens not with thunder or beasts, but with a measuring rod. John is told to measure the temple of God, the altar, and those who worship there. That instruction alone carries enormous meaning. In Scripture, measuring is never about curiosity. Measuring is about ownership, protection, and distinction. When God measures something, He is claiming it. He is saying, “This belongs to Me.” In a world where everything feels unstable, Revelation 11 begins by saying that God knows exactly who belongs to Him and exactly where they stand. Even when the outer court is given over to trampling, even when the city is overrun, even when chaos rules the streets, God still knows where His people are.

That detail matters, because Revelation 11 is not a chapter about escape. It is a chapter about endurance. The holy city is trampled for forty-two months. God’s witnesses prophesy for 1,260 days. These are not random numbers. They represent a season of pressure that is limited, not endless. Evil never gets eternity. It only gets a window. God allows opposition for a time, but He never surrenders sovereignty.

Then come the two witnesses, the figures that have fueled centuries of speculation. Some have tried to identify them as Moses and Elijah. Others as Enoch and Elijah. Others as two literal prophets yet to appear. But Scripture does something far more powerful here. It uses symbolism that points beyond two individuals into a larger spiritual reality. These two witnesses are called “the two olive trees and the two lampstands that stand before the Lord of the earth.” That language reaches back into Zechariah, where olive trees supply oil to a lampstand so that light never goes out. Oil represents the Spirit. Light represents testimony. These witnesses are not just two men. They are the Spirit-filled, truth-bearing people of God who refuse to stop speaking even when the world tells them to.

They wear sackcloth, which means they are not performing. They are mourning. They are calling people to repentance, not applause. Their message is not popular. It is not trendy. It is not safe. It is confrontational because truth always confronts what is false.

And then comes one of the most shocking details in the chapter: fire comes out of their mouths and devours their enemies. This is not literal flamethrower imagery. It is the fire of God’s Word. Jeremiah was told that God’s Word is fire. Hebrews says the Word is living and active, sharper than any sword. When truth is spoken in a world built on lies, it burns. It exposes. It judges. It destabilizes false narratives. That is what these witnesses do. They do not kill with weapons. They destroy with truth.

They have power to shut the sky so it does not rain. They have power to turn waters to blood. They have power to strike the earth with plagues. These are images drawn from Elijah and Moses, not because the witnesses are those men reincarnated, but because they carry the same prophetic authority. They confront systems that refuse to repent. They speak to kings and crowds alike. They do not bow.

But then something terrifying happens. The beast that comes up from the abyss makes war on them, overcomes them, and kills them. That is not how we expect God’s story to go. We expect victory, not defeat. We expect rescue, not bodies in the street. But Revelation 11 is brutally honest: sometimes God’s witnesses are silenced. Sometimes the truth is murdered. Sometimes the faithful lose in the short term.

Their bodies lie in the street of the great city, which is spiritually called Sodom and Egypt, where their Lord was crucified. That line alone tells us this is not just about geography. Sodom represents moral corruption. Egypt represents spiritual slavery. Jerusalem, which once held the temple, has become a place that looks more like oppression than worship. When a city rejects God, it becomes indistinguishable from every other system of rebellion.

People from every tribe, language, and nation look at the bodies and refuse to bury them. In ancient culture, refusing burial was the ultimate humiliation. It was a declaration that these witnesses were not worthy of dignity. The world celebrates their death. They exchange gifts. They throw parties. Why? Because these two prophets had tormented those who lived on the earth. Truth always torments those who love lies. Light always irritates darkness. The world does not hate Christians for being kind. It hates them for being honest.

This is where Revelation 11 becomes painfully relevant to our own time. We live in an age that celebrates the silencing of voices it finds inconvenient. We call it canceling. We call it deplatforming. We call it accountability. But often what is really happening is that uncomfortable truth is being buried in the street while the crowd cheers. Revelation 11 tells us that this is not new. It has always been this way.

But the story does not end with bodies in the street.

After three and a half days, the breath of life from God enters them, and they stand on their feet. That moment is one of the most powerful in all of Scripture. The world thought it was over. The witnesses thought it was over. The silence felt permanent. And then God breathed. Resurrection is always God’s response to premature celebrations of death. When God decides to restore, no amount of mockery can stop it.

Great fear falls on those who see them. Of course it does. Resurrection always terrifies those who built their lives on the assumption that God was finished. Then a loud voice from heaven says, “Come up here,” and they go up in a cloud while their enemies watch. That is not just ascension. That is vindication. God does not always rescue His people from suffering, but He always rescues their story.

Then comes the earthquake. A tenth of the city falls. Seven thousand people are killed. The rest are terrified and give glory to God. Notice what changes. Before, the world celebrated the witnesses’ deaths. After, the world is shaken into recognition. Sometimes the only way people wake up is when the systems they trusted collapse.

Revelation 11 then moves into the sounding of the seventh trumpet. Loud voices in heaven declare that the kingdom of the world has become the kingdom of our Lord and of His Christ, and He will reign forever and ever. This is not the start of God’s reign. It is the announcement that His reign can no longer be ignored. Heaven celebrates not because God finally took over, but because the illusion of human control has finally been exposed.

The elders fall on their faces and worship. They give thanks that God has taken His great power and begun to reign. They declare that the nations were angry, but God’s wrath has come, and the time has arrived to judge the dead, reward His servants, and destroy those who destroy the earth. That last phrase matters more than we realize. God is not against the earth. He is against those who ruin it through greed, violence, and corruption. Judgment is not about revenge. It is about restoration.

The chapter ends with the temple of God in heaven being opened, and the ark of His covenant being seen. That means access has been restored. The ark represented God’s presence. For centuries it was hidden. Now it is revealed. Lightning, thunder, an earthquake, and hail follow. When God’s presence is revealed, everything false shakes.

Revelation 11 is not about two strange prophets in the future. It is about what happens whenever faithful people speak truth in a world addicted to lies. It is about how the system always fights back. It is about how silence is never permanent. It is about how resurrection is never optional for God.

The two witnesses are every believer who has ever refused to stop speaking even when it cost them everything. They are pastors who lost their churches because they would not compromise. They are parents who taught their children truth even when the culture mocked them. They are whistleblowers, martyrs, missionaries, and ordinary people who chose obedience over comfort.

Revelation 11 tells you something crucial about your own life. If you follow Christ, you are a witness. And if you are a witness, the world will eventually push back. You may not be killed in the street, but you may be ignored, ridiculed, or dismissed. You may be labeled intolerant, outdated, or dangerous. But God is still measuring His temple. He still knows who belongs to Him. And He still breathes life into stories that look finished.

This chapter is not meant to scare you. It is meant to steel you. It is God telling His people, “I see you. I know the cost. And I am not done.”

And if God is not done, then neither are you.

The reason Revelation 11 feels so intense is because it strips away the fantasy that faith is supposed to be safe. Somewhere along the way, modern Christianity was sold a version of the gospel that promised comfort, popularity, and cultural approval. Revelation 11 destroys that illusion. It shows us what faith looks like when it actually collides with power, money, politics, ego, and fear. God does not raise witnesses to be admired. He raises them to be heard.

The two witnesses are not powerful because they are protected. They are powerful because they are faithful. They do not survive because they are untouchable. They survive because God’s purpose for them is not finished. And when their purpose is finished, even their death becomes part of their testimony. That is the part of this chapter that changes how you see suffering. The world saw their bodies in the street as proof that truth had lost. Heaven saw it as the final act before resurrection.

This is where Revelation 11 becomes deeply personal. You have probably experienced your own version of being left in the street. You spoke up and were misunderstood. You stood for something and were pushed out. You told the truth and lost relationships. You did what was right and paid for it. In those moments, it feels like God has gone silent. It feels like your voice no longer matters. Revelation 11 says otherwise. God never stops counting days. He never forgets witnesses. He never loses track of His people just because the world celebrates their downfall.

The forty-two months of trampling and the 1,260 days of prophecy tell us something essential: God sets the limits. Evil always feels permanent while it is happening. But in heaven, it is measured. Oppression always looks unstoppable from the ground. But in heaven, it has an expiration date. Your suffering is not random. Your endurance is not wasted. Your faithfulness is not invisible.

When the breath of life entered the two witnesses, fear fell on those who watched. That is always the result of resurrection. When God restores what the world buried, it shakes everything. It forces people to admit they were wrong. It exposes how shallow their celebrations were. It reveals how temporary their victories really were.

And notice what happens next. The voice from heaven calls the witnesses upward. God does not ask their enemies for permission. He does not wait for public opinion to change. He simply calls His people home. Vindication does not come from being proven right by the crowd. It comes from being called by God.

Then comes the earthquake. Revelation uses earthquakes to describe moments when false structures collapse. When God moves, systems shake. Careers fall. Reputations crumble. Institutions that looked untouchable suddenly fracture. This is not chaos for chaos’ sake. It is judgment that makes room for truth. A tenth of the city falls, not all of it. God’s judgment is precise. He does not destroy more than what needs to be exposed.

The survivors give glory to God, not because they suddenly became holy, but because fear finally pierced their denial. Sometimes people do not repent because they love God. Sometimes they repent because they realize God is real. Revelation 11 shows both are possible.

Then heaven erupts with worship. The seventh trumpet sounds, and the declaration is made: the kingdom of the world has become the kingdom of our Lord and of His Christ. That line does not mean the world suddenly changed governments. It means the lie that humans were in charge has finally collapsed. Christ does not have to overthrow what never truly belonged to us in the first place. He simply reveals what was always true.

The elders worship because history has reached a turning point. God is no longer being ignored. His reign is no longer being mocked. His justice is no longer being delayed. The time has come to reward the faithful and confront those who destroyed the earth. That phrase is not just environmental. It is moral. Those who destroy relationships, families, communities, and souls are also destroyers of the earth. God’s judgment is not narrow. It is holistic. He cares about what is broken in every form.

The temple in heaven opens. The ark of the covenant is revealed. That is the ultimate image of access. The presence of God, once hidden behind curtains and walls, is now visible. The separation is gone. The distance is closed. Revelation 11 ends not with destruction, but with revelation. God is not hiding anymore.

That is why this chapter matters so much for believers today. You are not living in a time where faith is becoming irrelevant. You are living in a time where faith is becoming dangerous again. And dangerous faith is always the kind that changes the world.

The two witnesses are still speaking. They speak through pastors who preach truth even when it costs them donors. They speak through parents who raise their children in faith in a culture that mocks it. They speak through Christians who refuse to bend their conscience for applause. They speak through you when you choose integrity over comfort.

Revelation 11 does not promise that you will always be protected. It promises that you will always be known. It does not promise you will always be celebrated. It promises you will always be remembered. And when God remembers, resurrection is never far behind.

You may feel like your voice does not matter. You may feel like your faith is quiet, small, and easily ignored. But heaven keeps records. Heaven keeps count. Heaven keeps breath ready.

And one day, when the world thinks it has buried the truth for good, God will breathe again.

That is what Revelation 11 really says.

Not that the witnesses were only two.

But that they were never alone.

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Your friend, Douglas Vandergraph

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hello again it’s me!

You know me! I am growing as a person right now, of course it hurts

But right now I am growing. Around the waist, and in my mind!

Did you know during fitness class I spotted my thighs again; they are muscular!

Not like in my prime: in my prime I had to buy larger trousers just for the legs

Because they were ridiculously strong

For some reason, I took great pleasure in having muscular thighs. It’s not exactly sexy, but I didn’t wear them to please others — they were just for me.

Functional, to be sure! I could roundhouse kick with mighty force.

Tomorrow I have a street dance class, let’s go!

I love dancing, it’s one of the many manifestations of Art: Dance !! and music !!

I believe it will connect us to a greater being!

I feel that I enter this trance

Where my mind will soar like previously described

I feel I am a swan, or even something floating in space — a comet with a blazing tail?

Sometimes I catch myself in the mirrors of the gym. I see my broad smile, and my muscular thighs.

Am I good at dancing? — that’s beside the point

The point is I love dancing!!

That’s the only thing which counts when it comes to Art!!!!

In Summary: * Listening now to the pregame show ahead of tonight's basketball game, Michigan State Spartans vs Indiana University Hoosiers. Listening to the call of that game, then finishing my night prayers will occupy me as long as I plan to stay awake. Hopefully a good night's sleep will follow.

Prayers, etc.: *I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night. Details of that regimen are linked to my link tree, which is linked to my profile page here.

Health Metrics: * bw= 220.02 lbs. * bp= 141/85 (64)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups

Diet: * 07:50 – 1 cheese sandwich * 09:00 – fresh watermelon * 12:15 – 2 steak burger patties with mushroom, peppers, and onions gravy, white rice * 16:20 – fresh watermelon

Activities, Chores, etc.: * 05:00 – listen to local news talk radio * 06:10 – bank accounts activity monitored * 06:20 – read, pray, follow news reports from various sources, surf the socials, nap * 12:00 to 13:15 – watch old game shows and eat lunch at home with Sylvia * 13:45 – read, pray, follow news reports from various sources, surf the socials, nap * 16:00 – catching the last hour of the Jack Riccardi Show * 17:00 – have tuned the radio to The Flagship Station for IU Sports ahead of tonight's NCAA men's college basketball game between the Michigan Spartans and the Indiana Hoosiers.

Chess: * 15:45 – moved in all CC games

Midwinter Sun

The holidays settled just past a solstice and the world was dark in many ways

The rain was too much and people forgot their way that kindness is all that matters

A field lay north of a south line of firs that the sun didn't peak past since the fall

The sky became clear and the light came over top limbs laden with needles warming tangled tall grass

A bit of good news at the same time delivered and I remembered the thing I forgot

Darkness doesn't cast though the days shorten through summer and the winter can't last either too

Das weiß ich nicht. Dies ist das X. mal im Laufe der letzten Jahre, dass ich mir einen Blog zugelegt habe. In der Vergangenheit meist selbst-gehostete, verschiedene Lösungen. Zu einem echten Beitrag kam es aber nie – nach der Installation und Einrichtung des Servers und der (Blog)Software verlor ich schnell die Lust und widmete mich anderen Hobbies. Außerdem wusste ich nicht, zu welchem Thema ich schreiben sollte. Oder wie ich beginnen soll.

Das weiß ich auch jetzt nicht. Aber ich schreibe gern. Nur tu ich es nie.

…und das ist dir sicher bereits aufgefallen.

Vermutlich werde ich dies als eine Art öffentliches Tagebuch nutzen und über alles Mögliche schreiben, was mir gerade durch den Kopf schwirrt. Es ist z. B. gut möglich, dass ich hier von Zeit zu Zeit über meine Arbeit spreche.

Wo ich gerade dabei bin: Ich arbeite auf einem Bauernhof. Ein Biohof. Der Hof meiner Familie. Vor ein paar Jahren bin ich dort “mit eingestiegen” und kann mir jetzt nichts anderes mehr vorstellen – obwohl ich es manchmal gerne täte. Bis vor einigen Monaten lebte ich auch auf dem Hof, bin aber umgezogen in meine eigene Wohnung – 10 Minuten mit dem Rad. Etwas Abstand tut mir gut.

Soviel dazu. Manchmal schreibe ich hier bestimmt über irgendwelche Videospiele, die mich gerade beschäftigen. Oder Filme und neue Musik. Vielleicht erwähne ich sogar mal ein Buch, welches ich erworben habe. Ich sage bewusst nicht “gelesen”, da meine gekauften Bücher meist ungelesen im Regal landen. Wer weiß, vielleicht lese ich ja mal wirklich eines davon, nur um hier darüber schreiben zu können.

Ich bin ein Typ, der ständig neue Hobbies hat. Darüber werde ich vermutlich ebenfalls schreiben…oder nimm es mir zumindest vor.

Damit verabschiede ich mich jetzt erst einmal.

Tschüss