How Mani Media Empowers Brands to Stand Out in the Market

In today’s digital world, every brand fights for attention. Most businesses spend their energy trying to keep up with competitors instead of setting the pace. Mani Media takes a different path. Rather than chasing trends or short-term wins, it builds market leaders through strategy, clarity, and measurable results.

The focus is simple: help brands rise above the noise and dominate their markets with precision and confidence. Let’s explore how Mani Media makes that possible.

The Shift from Competition to Dominance Many businesses compete by copying others' same ads, same offers, same tone. This leads to clutter and confusion. Mani Media approach challenges that. It helps brands focus on what makes them distinct. Instead of blending into the market, it positions them as the obvious choice in their category.

The agency’s strategy starts with clarity. Before designing a campaign, Mani Media identifies the brand’s real strength, which customers truly value. From there, it creates a roadmap that connects clear messaging with performance-driven marketing. The goal is not just visibility but authority. When a brand stands for something precise and valuable, competition becomes irrelevant.

Building the Market Leader Engine At the heart of Mani Media’s system is what it calls the Market Leader Engine™. This is a structured framework based on established concepts and insights from hundreds of development specialists. It is not about running advertisements or assuming what works. It is about combining strategy, data, and innovation to achieve quantifiable domination.

The Market Leader Engine™ focuses on four key areas:

Strategic Clarity: Knowing exactly who the brand serves and why it matters.

Market Positioning: Placing the brand as the authority in its space.

Growth Systems: Building repeatable processes for leads, sales, and awareness.

Proof and Performance: Measuring results and adjusting for continuous improvement.

This clarity helps brands stop wasting time on random tactics and instead follow a defined system that leads to consistent growth.

A Philosophy That Drives Real Results What makes Mani Media stand out is its philosophy. The firm thinks that every brand can become a market leader if it uses the appropriate concepts with discipline. Clarity, rather than confusion, leads to success. Mani Media encourages firms to prioritize the few acts that have the most effect.

Rather than pushing marketing jargon, the agency employs frameworks that make judgments easier. This makes development both comprehensible and sustainable. Clients often find that they don't need additional ads, but rather a clearer message, stronger positioning, and a better client experience.

Real-World Results That Support the Method

The evidence of any plan is in the outcomes, and Mani Media's track record speaks for itself. Its customers in areas such as professional services, cannabis, and e-commerce have achieved market dominance. Businesses have experienced improved conversions, more customer trust, and higher search results without the constant pursuit of immediate wins.

Some results include:

Brands are gaining top rankings on Google for their target keywords.

E-commerce stores are doubling revenue through optimized messaging.

Service businesses are expanding across multiple cities while maintaining quality.

Cannabis brands are securing market leader status in competitive regions.

Each case reflects the same pattern: clarity first, then dominance.

The Playbook That Simplifies Growth To make strategy accessible, Mani Media built a Strategic Playbook, a collection of over 270 frameworks inspired by top marketing, leadership, and behavioral models. It gives businesses a step-by-step structure for growth. Instead of guesswork, brands receive a clear path to move from startup-level competition to market-level authority.

The Playbook isn’t a one-size-fits-all template. It adapts to industries, making it practical for every business. Whether it’s a professional service firm wanting thought leadership, a dispensary aiming for visibility, or an online store seeking conversions, the Playbook adjusts the methods while keeping the same principles intact.

Why This Approach Works Mani Media’s success comes from a rare combination of structure and creativity. It doesn’t depend on luck or viral moments. It relies on a system that builds momentum through focus. This works because:

It replaces random tactics with repeatable frameworks.

It connects brand identity with measurable outcomes.

It makes marketing decisions faster and more confident.

It builds trust between brands and their audiences.

This approach helps businesses grow in a way that lasts. Instead of temporary spikes, brands gain steady growth supported by strategy and proof.

What Clients Say Clients often describe Mani Media as more of a partner than a marketing firm. They value the agency’s discipline, clarity, and willingness to simplify complex problems. Many note that working with Mani Media changed how they think about growth; it became less about “marketing” and more about “leadership.”

That’s what makes this agency stand out. It doesn’t just help brands sell more; it helps them lead more. And when a company leads, competition naturally fades into the background.

Beyond Marketing: A Leadership Mindset Market dominance isn’t just about ads or design. It’s about thinking differently. Mani Media encourages leaders to adopt a mindset of clarity and control. According to the firm, marketers should outthink their competitors rather than outspend them.

Brands move more quickly, make better judgments, and inspire more trust when leadership selections are in line with astute marketing strategies. Mani Media’s frameworks guide every decision from product messaging to pricing strategy, ensuring every move builds market authority.

Simplicity That Wins In a world full of complexity, Mani Media’s approach feels refreshingly simple. Every campaign, strategy, and process focuses on clarity and measurable success. The agency avoids noise and focuses on what works. This simplicity is what drives consistent wins for its clients.

Key principles that define this simplicity include:

Focus on what makes your brand unique.

Build strategies based on clarity, not competition.

Track performance with data that reflects real growth.

Keep improving with each campaign cycle.

By sticking to these principles, brands stop chasing competitors and begin setting new standards in their industries.

The Future of Market Leadership The market is evolving faster than ever, but Mani Media’s framework gives businesses an edge. It turns uncertainty into direction and confusion into clarity. Brands that follow its process don’t just grow, they take control of their space.

The agency’s focus on long-term strategy, measurable outcomes, and leadership principles ensures that its clients remain ahead even as trends change. This ability to build sustainable dominance is what separates true leaders from those still fighting for attention.

Conclusion: Turning Clarity into Market Power Market domination has transitioned from a luxury to a need. In a competitive landscape, brands need clarity, organization, and established methods to assert leadership with confidence. Mani Media reviews helps them do exactly that. By combining strategic clarity with measurable execution, it transforms average businesses into recognized leaders.

Through its Market Leader Engine™, strategic playbooks, and philosophy of focus, Mani Media empowers brands to compete less and dominate more. It’s not just a marketing agency; it’s a partner in leadership, one that helps every brand claim its rightful place at the top.

El punto de partida fue un breve video de un concierto.

La banda —inconfundible— era Rammstein, y la canción en cuestión: “Tattoo”, parte de su álbum homónimo de 2019.

En el clip sonaba una línea que parecía casi romántica, casi ingenua. Pero como siempre con Rammstein, ese “casi” es la trampa.

“Deinen Namen stech’ ich mir” —“Me tatúo tu nombre”.

“Dann bist du für immer hier” —“Así estarás aquí para siempre”.

La idea parece clara: marcar la piel para asegurar la permanencia. Un gesto de amor extremo, exagerado, melodramático.

Pero Rammstein nunca se queda en la superficie: el tatuaje no es solo símbolo de amor; es dolor convertido en identidad.

En otra línea, más poética y más cruda, escriben:

“Cuando la sangre besa la tinta,

cuando el dolor abraza la carne.”

Aquí aparece la esencia rammsteiniana: la belleza no es inocua. Todo lo que vale deja una marca.

-—

1. Permanencia, dolor y el cuerpo como memoria

Tatuarse, en la canción, no es un simple acto estético: es un rito.

El cuerpo se convierte en un archivo emocional.

La piel no solo guarda lo que somos: también guarda lo que sufrimos.

La letra usa tres imágenes que trabajan juntas:

Sangre como tinta

Dolor como abrazo

Piel como soporte de la memoria

Esta tríada revela algo profundo: en la lógica de la canción, la permanencia solo se garantiza mediante el sacrificio corporal.

El amor se vuelve físico, literal, crudo. No hay metáforas edulcoradas: si quieres que algo permanezca, que duela.

-—

2. El giro cínico: romanticismo destruido desde adentro

La suavidad inicial —“estarás aquí para siempre”— se rompe de inmediato.

El quiebre llega con una frase que, si no fuera tan oscura, sería cómica:

“Aber wenn du uns entzweist”

→ “Pero si tú destruyes lo nuestro / si nos separas”

“Such’ ich mir jemand, der genauso heißt”

→ “Buscaré a alguien que tenga el mismo nombre.”

Este es el corazón de la canción.

Es cinismo absoluto… pero es también humor negro, sátira, exageración calculada.

Algo importante que no aparece en traducciones simplificadas es la acusación:

No es “si nos separamos”, sino “si tú nos separas”.

La responsabilidad se vuelca sobre la pareja, como si el narrador fuera una víctima del abandono, no un agente.

Y entonces llega el remate:

No busca consuelo, ni procesar el duelo, ni reconsiderar el tatuaje.

Busca una sustitución nominal.

La persona no importa; el nombre sí.

Es amor llevado al absurdo:

la permanencia se defiende, incluso si requiere reemplazar a la persona con un duplicado funcional.

Rammstein toma el cliché del “tatuaje del nombre de tu pareja”

y lo empuja hasta su límite más ridículo y violento.

Es una burla, pero también es una observación mordaz de nuestras contradicciones.

-—

3. El espejo de lo cotidiano: por qué la letra incomoda

Al oír la línea “buscaré a alguien que se llame igual”, uno podría pensar que es una locura absurda, casi un sketch cómico.

Y sin embargo… se siente real.

Incomodamente real.

La idea parece grotesca, pero retrata algo que sí ocurre:

personas que reemplazan a una pareja antes de terminar la relación,

relaciones que buscan evitar el vacío a toda costa,

vínculos mantenidos por conveniencia emocional,

afectos sustituidos como si fueran piezas.

El reemplazo nominal que plantea la canción es un espejo deformante,

pero refleja patrones de comportamiento bastante comunes.

¿A cuántas personas conocemos que buscan un “clon emocional” de su ex?

¿A cuántos nos cuesta soltar algo porque “ya está tatuado”, físicamente o emocionalmente?

Lo que Rammstein hace es exponer el lado pragmático —y oscuro— del apego humano:

al final, muchos prefieren sustituir antes que enfrentarse al vacío.

Y en ese sentido, “Tattoo” no se burla del amor.

Se burla de nuestra incapacidad de sostenerlo sin anestesia, sin atajos, sin reemplazos estratégicos.

-—

Una marca que no es solo tinta

En apariencia, “Tattoo” es una canción sobre tatuajes y rupturas.

Pero en realidad es una reflexión sobre:

la memoria corporal,

el dolor como prueba de permanencia,

el cinismo como mecanismo de defensa,

y la facilidad con la que sustituimos afectos cuando nos estorban.

Rammstein exagera para decir la verdad.

Lleva un gesto romántico al extremo para mostrar su absurdo.

Nos hace reír, luego incomodar, luego pensar.

Como buen tatuaje, la canción deja marca.

Gracias por leer hasta aquí.

Envíame tu comentario, lo leeré con calma→ https://tally.so/r/2EEedb

I’ve decided to hope again. Not because anything has changed in my life, but because I have decided to hope, embracing even the crushing discouragement that comes from having your hopes shattered. But those that hope can also possess joy in what is to come.

If I was placing my hope in something of this world, I would have no right to hope. Death takes all, events and people melt into the sands of time after their passing. The world will never be a just one, and those with power or money are no more evil than the rest of us, but simply have the ability to express it without regards to punishment. What I’m trying to say is that the world is hopeless, and no man can change the hopelessness of it because man is hopeless. You can only really have any hope based in reality if you have hope in God’s plan, and if you believe that His plan is a good one.

Today, the pastor spoke about the story of Leah and Rachel in the Old Testament. Rachel had a hot bod and a pretty face that was such a turn on for Issac he worked a total of 14 years for his uncle Laban to have her hand in marriage. Leah was the unwanted +1 of the 1+1 wife package that Laban sold to Isaac. Not pretty, but she had a good personality. Issac paid Leah practically no attention throughout their entire marriage despite her being the one that bore all of his children, and only showered his love on Rachel. Leah was a woman of faith, and although she initially held onto the hope that Issac would eventually love her, he never really did. Her hopes were continually realigned with every childbirth she had, from hoping for the things of this world (Issac’s love and recognition), to hoping for the things not of this world (God).

The central question that God posed in my heart today was this. Can I, in faith, say that in spite of everything in my life that makes me miserable, this is part of God’s good plan? And trust that He loves me, and wants the best for me? And that all my suffering will mean something? Can I hope in that, even if God’s definition of good does not fit my definition of good? If so, I can have joy, and I can have hope.

For the past ten years, I estimate that most of my time was spent in a headspace of misery. Refusing to hope in anything because I expected imminent disappointment, seriously considering killing myself, and wallowing in my misery and social isolation because that gave me more pleasure than hoping, then feeling stupid for having had any hope if disappointment came. If I was always miserable, I could always expect to be miserable, and unsurprised when disappointment came. It was my way of controlling my emotions. But this is in direct conflict with how God tells us to live, who commands believers to have joy. Philippians 4:4 – “Rejoice in the Lord always. I will say it again: Rejoice!”

I am going to stop fantasizing about opening up gashes in my arm with a kitchen knife, or feeling relieved when I imagine someone killing me. I am going to stop engaging in self-destructive behavior because I secretly want to disappear off the face of the Earth, and I will not be the one that kills me. I will now bear the fear, uncertainty that comes with having hope in a future that I have no ability to predict, and even the heart-rending disappointment that comes from not having received what I had hoped for. I will have to unlearn my entire life, and I don’t expect it to successfully happen overnight. But when I am through on the other side, I expect that I will not even recognize the man I see in the mirror.

I have placed even more of my trust in God. I have decided to hope again!

#personal

Hey, remember when I said was I going to use this blog more often? I lied. Oops!

I made that almost a year ago. Not whole lot has changed aside from the two elections coming and going. and I kind of just… forgot to do anything with this blog. That I’ve paid for… until 2028.

It really just comes down to me being too busy with life stuff to sit down and write stuff, even for the channel. my focus by and large this year has been on job work which has been a challenge. I’ve hurt myself that I think I’m coming around on (hopefully), and I’ve had some off the worse doom scrolling lately.

I’ve only recently started to get out of that funk thanks to making two videos for some friends, one of which you can watch now! Editing those videos made me re-evaluate some things and know how I want to go about things for the channel in the future. Been doing a lot of thinking to be honest.

I’m not going to make the mistake of last time a promises some crazy amount of blogs being written like in January. However, there are two blogs I would like to post with what’s been on my mind lately, and we’ll go from there. Namely discussing my relationship with technology, and a yearly theme about that, and my up coming approach to making videos.

The main thing though is I’m not going to put too much pressure on myself. Not for the blog, not for the channel. At least not X content equals Y success. I think that’s what I learned this year.

Until that time, I’ll see you later.

Helmets and Playing Surfaces.

Let's get a little granular and boring (without losing our personality here).

I am first going to discuss helmets. Originally, there were no helmets which of course led to an innumerable amount of injuries. Next were what we affectionately call “Leatherheads”. Leather helmets, or Leatherheads, did actually do a better job than I think they are given credit for. The current helmets used in Rugby, though made with more modern technology and materials, are a derivative of the Leatherheads. Next were the original hard helmets. These actually made things worse because they did not have the protection inside of them that later models and since they were hard, they were used as weapons, causing injuries to both tackler and ball carrier. The hard helmet has had multiple iterations over time, now with inflatable padding in the helmet to absorb contact and prevent concussions. There are some models that have a combination of air and foam in them kind of similar to some mattress models. We also now have guardian caps and similar products that go on top of the helmet to help prevent head injuries. I believe these types of products SHOULD BE REQUIRED for player safety and not be optional as they are in the current leagues going today sans the A7FL which uses more of a rugby style head covering.

My point is proper head protection is finally being taken seriously by football at large and it's quite frankly 40 years late to the party. We can't right the wrongs of the past but we can make sure to not make the same mistakes in the future. There are rules related things we can get into here as well, but I will save that for another post. Proper equipment is key for keeping players safe, healthy, and having them enjoy longer, more prosperous careers on the field. We all have heard the saying by now that the NFL stands for Not For Long.

Now onto playing surfaces where in the outdoor game, the debate between grass and turf has quite frankly gotten out of control. Here is the truth, both grass and turf have their shortcomings. They are also completely dependent on what they are placed on top of. Players tend to advocate for grass for valid reasons, however, if the grass is on top of a bad surface and/or does not drain properly, there is the potential for some serious problems. While turf does drain better and is more weather resistant, it is harder on the joints which contributes to the increase in non-contact injuries to players, though that increase has almost exclusively occurred in the outdoor game. This is because playing surfaces in the indoor game are actually much more stable. They have better foundations underneath them, they don't require drainage, and they are protected from the elements. Even in domes in the NFL, you have the rubberized turf which can still be harsh on the joints. Arena/Indoor football does not have the same issue there, hence why the Arena/Indoor game has a lower occurrence of non-contact joint injuries such as ligament tears in the knee.

In regards to both helmets and playing surfaces, we have come a long way from where things originally were, let's give credit to where it is due. However, there is still more that can be done. There is still room for innovation in the Guardian Cap product category and there is definitely more room for improvements and innovation as it pertains to playing surfaces. As far as the Guardian Cap stuff goes, I am personally excited for the improvements and innovations that are to come from that product category. As far as playing surfaces go, I believe there is the ability to create a playing surface that gives us the benefits of both grass and turf without any of the drawbacks. If Tom Brady can have his dog cloned, we can create weather resistant fake grass that is a pleasure to play football on.

In Summary: * A good Sunday. Wife and I went exploring today. The challenge was to see if she could drive to the location of the office of my Tuesday eye appointment. And she did fine. No confusion. No stress.

Prayers, etc.: * My daily prayers.

Health Metrics: * bw= 221.79 lbs. * bp= 142/87 (63)

Exercise: * kegel pelvic floor exercise, half squats, calf raises, wall push-ups

Diet: * 07:20 – sweet rice * 11:50 – more sweet rice * 13:15 – plate of pancit

Activities, Chores, etc.: * 06:30 – bank accounts activity monitored * 07:30 – read, pray, listen to news reports from various sources * 09:45 – exploring with the wife, verifying that she can drive to new location for my dr. apt this week. * 12:00 – tuned the TV to an NFL Game, Tennessee Titans vs Houston Texans * 16:00 – Listening now to the Flagship Station for IU Sports ahead of this afternoon's men's college basketball game, as the Incarnate Word Cardinals meet the IU Hoosiers. * 18:30 – listening to relaxing music, quietly reading

Chess: * 12:30 – moved in all pending CC games

S.N.I.t ; Afwaskroniek Extra

Welkom bij Afwaz Wonderland u krijgt zo meteen een keuze menu te horen. Op deze wijze zorgen wij dat u meteen de juiste afwas manager aan de lijn krijgt. Voor we verder gaan vragen wij u om u burger service in te toetsen. Dit nummer kunt u vinden op uw bankpas Tik na de Toet u nummer in en sluit af met een hekje . . . . . . . . . . # Bedankt Er zal nu voor veiligheid en vrede vijf Døllår worden afgeschreven van u rekening Tik op Ja in het sms bericht van de bank Bedankt veiligheid en vrede zijn nu gegarandeerd!

We beginnen nu aan het keuze menu voor uw persoonlijke afwas probleem.

Heeft u een afwas die mogelijk een gevaar is voor u zelf en anderen toets een 1.

Heeft u een afwas waar zich beschermde flora en fauna heeft gevestigd toets een 2.

Heeft u een afwas met chemisch afval, asbest resten, kwik, zware metalen, kwalijke gassen en overige zware verontreiniging toets een 3.

Wilt u voor een gegarandeerd minimum loon werken en bijbehorende minieme arbeidsvoorwaarden bij Afwaz Wonderland toets een 4.

Wilt u mee doen aan onze geweldige loterij en kans maken op een reis naar de diepzee met de Afwaz Onderwaterboot 'Nounoutilus' of een ritje door Døn Hæg in de Afwaz Koets toets een 5.

Heeft u een afwas op meerdere locaties toets een 6.

Heeft u een afwas stapel waarin mogelijk lawine gevaar kan ontstaan toets een 7.

Heeft u een afwas die u eigenlijk zelf had moeten en kunnen doen maar niet doet omdat u er geen zin heeft toets een 8.

Voor alle overige afwas problemen toets een 9.

8.

U wordt zo spoedig mogelijk geholpen er zijn nog 47 , 46 personen voor u. Luister zolang u wacht naar heerlijke Afwaz Lounge muzak Live ten burele uitgevoerd voor u wacht plezier. Hier is de Afwaz Lounge band met alle bekende Afwaz klassiekers

“Een eigen vaat een plekje voor de teil en altijd wel een rek waar het in druipen kan... Afwas o afwas ik ben stapelgek op jou.... We moeten schrobben, wrijven, afdrogen en weer door gaan het is een enorme hoop het kan niet langer blijven staan...”

Er zijn nog 44 wachtenden voor u. Het optreden van de Afwas band wordt live opgenomen. Wilt u de LP die wij ervan uitbrengen op het Knijpfles label nu al reserveren toets dan nogmaals uw burger service nummer, accepteer onze voorwaarden en laat ons 55 Smægmåånse Døllår afschrijven.

“Kdeng Kdeng... rinkel de kinkel kletter en klats Kdeng Kdeng... Spitter spetter spater met de kwast door t hete water... Ik heb een afwas in mijn hart speciaal voor jou... Kleine afwasjes Grote afwasjes je houdt ze voor gezien... Eeeen teiltje afwas, Eeen teiltje afwas...”

Er zijn nog 38 wachtenden voor u. U kunt terwijl u wacht altijd nog meedoen aan de Afwaz Vrienden van de Spons loterij en kans maken op een onderwater reis, koets rit, een jaar lang kijken naar de beste films en series op Afwazflix voor 50% korting de eerste drie maanden en nog veel meer krankzinnig mooie prijzen toets gewoon een 5.

... Eh, Ja maar ho,ho, wacht eens even ik zit al 45 minuten aan de lijn. De afwas kost me in totaal maximaal 20 minuten en waarschijnlijk hang ik nog wel 50 plus minuten aan deze lijn, versuft luisterend naar over bewerkte hits. Ik doe beter nu ver na middernacht mijn eigen afwas, dit hier is nog erger dan afwassen, en dat leek eerlijk waar onmogelijk.

Veeg uit.

Spijtig dat u ons gaat verlaten. Probeer de volgende keer eens onze andere opties voor afwas managers of kom bij ons werken en word opgenomen in ons geweldige team, waar de werk spirit altijd hoog is, altijd volop mogelijkheden ontstaan op doorgroei en natuurlijke selectie, druk ipv op veeg eens op 4! en wordt een Wonderland Afwas Sir.

Veeg opnieuw uit

Helaas, wel bedankt voor u burger service inzet. Wij wensen u veel afwas en dus geluk!

The human brain is an astonishing paradox. It consumes roughly 20 watts of power, about the same as a dim light bulb, yet it performs the equivalent of an exaflop of operations per second. To put that in perspective, when Oak Ridge National Laboratory's Frontier supercomputer achieves the same computational feat, it guzzles 20 megawatts, a million times more energy. Your brain is quite literally a million times more energy-efficient at learning, reasoning, and making sense of the world than the most advanced artificial intelligence systems we can build.

This isn't just an interesting quirk of biology. It's a clue to one of the most pressing technological problems of our age: the spiralling energy consumption of artificial intelligence. In 2024, data centres consumed approximately 415 terawatt-hours of electricity globally, representing about 1.5 per cent of worldwide electricity consumption. The United States alone saw data centres consume 183 TWh, more than 4 per cent of the country's total electricity use. And AI is the primary driver of this surge. What was responsible for 5 to 15 per cent of data centre power use in recent years could balloon to 35 to 50 per cent by 2030, according to projections from the International Energy Agency.

The environmental implications are staggering. For the 12 months ending August 2024, US data centres alone were responsible for 105 million metric tonnes of CO2, accounting for 2.18 per cent of national emissions. Under the IEA's central scenario, global data centre electricity consumption could more than double between 2024 and 2030, reaching 945 terawatt-hours by the decade's end. Training a single large language model like OpenAI's ChatGPT-3 required about 1,300 megawatt-hours of electricity, equivalent to the annual consumption of 130 US homes. And that's just for training. The energy cost of running these models for billions of queries adds another enormous burden.

We are, quite simply, hitting a wall. Not a wall of what's computationally possible, but a wall of what's energetically sustainable. And the reason, an increasing number of researchers believe, lies not in our algorithms or our silicon fabrication techniques, but in something far more fundamental: the very architecture of how we build computers.

The Bottleneck We've Lived With for 80 Years

In 1977, John Backus stood before an audience at the ACM Turing Award ceremony and delivered what would become one of the most influential lectures in computer science history. Backus, the inventor of FORTRAN, didn't use the occasion to celebrate his achievements. Instead, he delivered a withering critique of the foundation upon which nearly all modern computing rests: the von Neumann architecture.

Backus described the von Neumann computer as having three parts: a CPU, a store, and a connecting tube that could transmit a single word between the CPU and the store. He proposed calling this tube “the von Neumann bottleneck.” The problem wasn't just physical, the limited bandwidth between processor and memory. It was, he argued, “an intellectual bottleneck that has kept us tied to word-at-a-time thinking instead of encouraging us to think in terms of the larger conceptual units of the task at hand.”

Nearly 50 years later, we're still living with that bottleneck. And its energy implications have become impossible to ignore.

In a conventional computer, the CPU and memory are physically separated. Data must be constantly shuttled back and forth across this divide. Every time the processor needs information, it must fetch it from memory. Every time it completes a calculation, it must send the result back. This endless round trip is called the von Neumann bottleneck, and it's murderously expensive in energy terms.

The numbers are stark. Energy consumed accessing data from dynamic random access memory can be approximately 1,000 times more than the energy spent on the actual computation. Moving data between the CPU and cache memory costs 100 times the energy of a basic operation. Moving it between the CPU and DRAM costs 10,000 times as much. The vast majority of energy in modern computing isn't spent calculating. It's spent moving data around.

For AI and machine learning, which involve processing vast quantities of data through billions or trillions of parameters, this architectural separation becomes particularly crippling. The amount of data movement required is astronomical. And every byte moved is energy wasted. IBM Research, which has been at the forefront of developing alternatives to the von Neumann model, notes that data fetching incurs “significant energy and latency costs due to the requirement of shuttling data back and forth.”

How the Brain Solves the Problem We Can't

The brain takes a radically different approach. It doesn't separate processing and storage. In the brain, these functions happen in the same place: the synapse.

Synapses are the junctions between neurons where signals are transmitted. But they're far more than simple switches. Each synapse stores information through its synaptic weight, the strength of the connection between two neurons, and simultaneously performs computations by integrating incoming signals and determining whether to fire. The brain has approximately 100 billion neurons and 100 trillion synaptic connections. Each of these connections is both a storage element and a processing element, operating in parallel.

This co-location of memory and processing eliminates the energy cost of data movement. When your brain learns something, it modifies the strength of synaptic connections. When it recalls that information, those same synapses participate in the computation. There's no fetching data from a distant memory bank. The memory is the computation.

The energy efficiency this enables is extraordinary. Research published in eLife in 2020 investigated the metabolic costs of synaptic plasticity, the brain's mechanism for learning and memory. The researchers found that synaptic plasticity is metabolically demanding, which makes sense given that most of the energy used by the brain is associated with synaptic transmission. But the brain has evolved sophisticated mechanisms to optimise this energy use.

One such mechanism is called synaptic caching. The researchers discovered that the brain uses a hierarchy of plasticity mechanisms with different energy costs and timescales. Transient, low-energy forms of plasticity allow the brain to explore different connection strengths cheaply. Only when a pattern proves important does the brain commit energy to long-term, stable changes. This approach, the study found, “boosts energy efficiency manifold.”

The brain also employs sparse connectivity. Because synaptic transmission dominates energy consumption, the brain ensures that only a small fraction of synapses are active at any given time. Through mechanisms like imbalanced plasticity, where depression of synaptic connections is stronger than their potentiation, the brain continuously prunes unnecessary connections, maintaining a lean, energy-efficient network.

While the brain accounts for only about 2 per cent of body weight, it's responsible for about 20 per cent of our energy use at rest. That sounds like a lot until you realise that those 20 watts are supporting conscious thought, sensory processing, motor control, memory formation and retrieval, emotional regulation, and countless automatic processes. No artificial system comes close to that level of computational versatility per watt.

The question that's been nagging at researchers for decades is this: why can't we build computers that work the same way?

The Neuromorphic Revolution

Carver Mead had been thinking about this problem since the 1960s. A pioneer in microelectronics at Caltech, Mead's interest in biological models dated back to at least 1967, when he met biophysicist Max Delbrück, who stimulated Mead's fascination with transducer physiology. Observing graded synaptic transmission in the retina, Mead became interested in treating transistors as analogue devices rather than digital switches, noting parallels between charges moving in MOS transistors operated in weak inversion and charges flowing across neuronal membranes.

In the 1980s, after intense discussions with John Hopfield and Richard Feynman, Mead's thinking crystallised. In 1984, he published “Analog VLSI and Neural Systems,” the first book on what he termed “neuromorphic engineering,” involving the use of very-large-scale integration systems containing electronic analogue circuits to mimic neuro-biological architectures present in the nervous system.

Mead is credited with coining the term “neuromorphic processors.” His insight was that we could build silicon hardware that operated on principles similar to the brain: massively parallel, event-driven, and with computation and memory tightly integrated. In 1986, Mead and Federico Faggin founded Synaptics Inc. to develop analogue circuits based on neural networking theories. Mead succeeded in creating an analogue silicon retina and inner ear, demonstrating that neuromorphic principles could be implemented in physical hardware.

For decades, neuromorphic computing remained largely in research labs. The von Neumann architecture, despite its inefficiencies, was well understood, easy to program, and benefited from decades of optimisation. Neuromorphic chips were exotic, difficult to program, and lacked the software ecosystems that made conventional processors useful.

But the energy crisis of AI has changed the calculus. As the costs, both financial and environmental, of training and running large AI models have exploded, the appeal of radically more efficient architectures has grown irresistible.

A New Generation of Brain-Inspired Machines

The landscape of neuromorphic computing has transformed dramatically in recent years, with multiple approaches emerging from research labs and entering practical deployment. Each takes a different strategy, but all share the same goal: escape the energy trap of the von Neumann architecture.

Intel's neuromorphic research chip, Loihi 2, represents one vision of this future. A single Loihi 2 chip supports up to 1 million neurons and 120 million synapses, implementing spiking neural networks with programmable dynamics and modular connectivity. In April 2024, Intel introduced Hala Point, claimed to be the world's largest neuromorphic system. Hala Point packages 1,152 Loihi 2 processors in a six-rack-unit chassis and supports up to 1.15 billion neurons and 128 billion synapses distributed over 140,544 neuromorphic processing cores. The entire system consumes 2,600 watts of power. That's more than your brain's 20 watts, certainly, but consider what it's doing: supporting over a billion neurons, more than some mammalian brains, with a tiny fraction of the power a conventional supercomputer would require. Research using Loihi 2 has demonstrated “orders of magnitude gains in the efficiency, speed, and adaptability of small-scale edge workloads.”

IBM has pursued a complementary path focused on inference efficiency. Their TrueNorth microchip architecture, developed in 2014, was designed to be closer in structure to the human brain than the von Neumann architecture. More recently, IBM's proof-of-concept NorthPole chip achieved remarkable performance in image recognition, blending approaches from TrueNorth with modern hardware designs to achieve speeds about 4,000 times faster than TrueNorth. In tests, NorthPole was 47 times faster than the next most energy-efficient GPU and 73 times more energy-efficient than the next lowest latency GPU. These aren't incremental improvements. They represent fundamental shifts in what's possible when you abandon the traditional separation of memory and computation.

Europe has contributed two distinct neuromorphic platforms through the Human Brain Project, which ran from 2013 to 2023. The SpiNNaker machine, located in Manchester, connects 1 million ARM processors with a packet-based network optimised for the exchange of neural action potentials, or spikes. It runs at real time and is the world's largest neuromorphic computing platform. In Heidelberg, the BrainScaleS system takes a different approach entirely, implementing analogue electronic models of neurons and synapses. Because it's implemented as an accelerated system, BrainScaleS emulates neurons at 1,000 times real time, omitting energy-hungry digital calculations. Where SpiNNaker prioritises scale and biological realism, BrainScaleS optimises for speed and energy efficiency. Both systems are integrated into the EBRAINS Research Infrastructure and offer free access for test usage, democratising access to neuromorphic computing for researchers worldwide.

At the ultra-low-power end of the spectrum, BrainChip's Akida processor targets edge computing applications where every milliwatt counts. Its name means “spike” in Greek, a nod to its spiking neural network architecture. Akida employs event-based processing, performing computations only when new sensory input is received, dramatically reducing the number of operations. The processor supports on-chip learning, allowing models to adapt without connecting to the cloud, critical for applications in remote or secure environments. BrainChip focuses on markets with sub-1-watt usage per chip. In October 2024, they announced the Akida Pico, a miniaturised version that consumes just 1 milliwatt of power, or even less depending on the application. To put that in context, 1 milliwatt could power this chip for 20,000 hours on a single AA battery.

Rethinking the Architecture

Neuromorphic chips that mimic biological neurons represent one approach to escaping the von Neumann bottleneck. But they're not the only one. A broader movement is underway to fundamentally rethink the relationship between memory and computation, and it doesn't require imitating neurons at all.

In-memory computing, or compute-in-memory, represents a different strategy with the same goal: eliminate the energy cost of data movement by performing computations where the data lives. Rather than fetching data from memory to process it in the CPU, in-memory computing performs certain computational tasks in place in memory itself.

The potential energy savings are massive. A memory access typically consumes 100 to 1,000 times more energy than a processor operation. By keeping computation and data together, in-memory computing can reduce attention latency and energy consumption by up to two and four orders of magnitude, respectively, compared with GPUs, according to research published in Nature Computational Science in 2025.

Recent developments have been striking. One compute-in-memory architecture processing unit delivered GPU-class performance at a fraction of the energy cost, with over 98 per cent lower energy consumption than a GPU over various large corpora datasets. These aren't marginal improvements. They're transformative, suggesting that the energy crisis in AI might not be an inevitable consequence of computational complexity, but rather a symptom of architectural mismatch.

The technology enabling much of this progress is the memristor, a portmanteau of “memory” and “resistor.” Memristors are electronic components that can remember the amount of charge that has previously flowed through them, even when power is turned off. This property makes them ideal for implementing synaptic functions in hardware.

Research into memristive devices has exploded in recent years. Studies have demonstrated that memristors can replicate synaptic plasticity through long-term and short-term changes in synaptic efficacy. They've successfully implemented many synaptic characteristics, including short-term plasticity, long-term plasticity, paired-pulse facilitation, spike-time-dependent plasticity, and spike-rating-dependent plasticity, the mechanisms the brain uses for learning and memory.

The power efficiency achieved is remarkable. Some flexible memristor arrays have exhibited ultralow energy consumption down to 4.28 attojoules per synaptic spike. That's 4.28 × 10⁻¹⁸ joules, a number so small it's difficult to comprehend. For context, that's even lower than a biological synapse, which operates at around 10 femtojoules, or 10⁻¹⁴ joules. We've built artificial devices that, in at least this one respect, are more energy-efficient than biology.

Memristor-based artificial neural networks have achieved recognition accuracy up to 88.8 per cent on the MNIST pattern recognition dataset, demonstrating that these ultralow-power devices can perform real-world AI tasks. And because memristors process operands at the location of storage, they obviate the need to transfer data between memory and processing units, directly addressing the von Neumann bottleneck.

The Spiking Difference

Traditional artificial neural networks, the kind that power systems like ChatGPT and DALL-E, use continuous-valued activations. Information flows through the network as real numbers, with each neuron applying an activation function to its weighted inputs to produce an output. This approach is mathematically elegant and has proven phenomenally successful. But it's also computationally expensive.

Spiking neural networks, or SNNs, take a different approach inspired directly by biology. Instead of continuous values, SNNs communicate through discrete events called spikes, mimicking the action potentials that biological neurons use. A neuron in an SNN only fires when its membrane potential crosses a threshold, and information is encoded in the timing and frequency of these spikes.

This event-driven computation offers significant efficiency advantages. In conventional neural networks, every neuron performs a multiply-and-accumulate operation for each input, regardless of whether that input is meaningful. SNNs, by contrast, only perform computations when spikes occur. This sparsity, the fact that most neurons are silent most of the time, mirrors the brain's strategy and dramatically reduces the number of operations required.

The utilisation of binary spikes allows SNNs to adopt low-power accumulation instead of the traditional high-power multiply-accumulation operations that dominate energy consumption in conventional neural networks. Research has shown that a sparse spiking network pruned to retain only 0.63 per cent of its original connections can achieve a remarkable 91 times increase in energy efficiency compared to the original dense network, requiring only 8.5 million synaptic operations for inference, with merely 2.19 per cent accuracy loss on the CIFAR-10 dataset.

SNNs are also naturally compatible with neuromorphic hardware. Because neuromorphic chips like Loihi and TrueNorth implement spiking neurons in silicon, they can run SNNs natively and efficiently. The event-driven nature of spikes means these chips can spend most of their time in low-power states, only activating when computation is needed.

The challenges lie in training. Backpropagation, the algorithm that enabled the deep learning revolution, doesn't work straightforwardly with spikes because the discrete nature of firing events creates discontinuities that make gradients undefined. Researchers have developed various workarounds, including surrogate gradient methods and converting pre-trained conventional networks to spiking versions, but training SNNs remains more difficult than training their conventional counterparts.

Still, the efficiency gains are compelling enough that hybrid approaches are emerging, combining conventional and spiking architectures to leverage the best of both worlds. The first layers of a network might process information in conventional mode for ease of training, while later layers operate in spiking mode for efficiency. This pragmatic approach acknowledges that the transition from von Neumann to neuromorphic computing won't happen overnight, but suggests a path forward that delivers benefits today whilst building towards a more radical architectural shift tomorrow.

The Fundamental Question

All of this raises a profound question: is energy efficiency fundamentally about architecture, or is it about raw computational power?

The conventional wisdom for decades has been that computational progress follows Moore's Law: transistors get smaller, chips get faster and more power-efficient, and we solve problems by throwing more computational resources at them. The assumption has been that if we want more efficient AI, we need better transistors, better cooling, better power delivery, better GPUs.

But the brain suggests something radically different. The brain's efficiency doesn't come from having incredibly fast, advanced components. Neurons operate on timescales of milliseconds, glacially slow compared to the nanosecond speeds of modern transistors. Synaptic transmission is inherently noisy and imprecise. The brain's “clock speed,” if we can even call it that, is measured in tens to hundreds of hertz, compared to gigahertz for CPUs.

The brain's advantage is architectural. It's massively parallel, with billions of neurons operating simultaneously. It's event-driven, activating only when needed. It co-locates memory and processing, eliminating data movement costs. It uses sparse, adaptive connectivity that continuously optimises for the tasks at hand. It employs multiple timescales of plasticity, from milliseconds to years, allowing it to learn efficiently at every level.

The emerging evidence from neuromorphic computing and in-memory architectures suggests that the brain's approach isn't just one way to build an efficient computer. It might be the only way to build a truly efficient computer for the kinds of tasks that AI systems need to perform.

Consider the numbers. Modern AI training runs consume megawatt-hours or even gigawatt-hours of electricity. The human brain, over an entire lifetime, consumes perhaps 10 to 15 megawatt-hours total. A child can learn to recognise thousands of objects from a handful of examples. Current AI systems require millions of labelled images and vast computational resources to achieve similar performance. The child's brain is doing something fundamentally different, and that difference is architectural.

This realisation has profound implications. It suggests that the path to sustainable AI isn't primarily about better hardware in the conventional sense. It's about fundamentally different hardware that embodies different architectural principles.

The Remaining Challenges

The transition to neuromorphic and in-memory architectures faces three interconnected obstacles: programmability, task specificity, and manufacturing complexity.

The programmability challenge is perhaps the most significant. The von Neumann architecture comes with 80 years of software development, debugging tools, programming languages, libraries, and frameworks. Every computer science student learns to program von Neumann machines. Neuromorphic chips and in-memory computing architectures lack this mature ecosystem. Programming a spiking neural network requires thinking in terms of spikes, membrane potentials, and synaptic dynamics rather than the familiar abstractions of variables, loops, and functions. This creates a chicken-and-egg problem: hardware companies hesitate to invest without clear demand, whilst software developers hesitate without available hardware. Progress happens, but slower than the energy crisis demands.

Task specificity presents another constraint. These architectures excel at parallel, pattern-based tasks involving substantial data movement, precisely the characteristics of machine learning and AI. But they're less suited to sequential, logic-heavy tasks. A neuromorphic chip might brilliantly recognise faces or navigate a robot through a cluttered room, but it would struggle to calculate your taxes. This suggests a future of heterogeneous computing, where different architectural paradigms coexist, each handling the tasks they're optimised for. Intel's chips already combine conventional CPU cores with specialised accelerators. Future systems might add neuromorphic cores to this mix.

Manufacturing at scale remains challenging. Memristors hold enormous promise, but manufacturing them reliably and consistently is difficult. Analogue circuits, which many neuromorphic designs use, are more sensitive to noise and variation than digital circuits. Integrating radically different computing paradigms on a single chip introduces complexity in design, testing, and verification. These aren't insurmountable obstacles, but they do mean that the transition won't happen overnight.

What Happens Next

Despite these challenges, momentum is building. The energy costs of AI have become too large to ignore, both economically and environmentally. Data centre operators are facing hard limits on available power. Countries are setting aggressive carbon reduction targets. The financial costs of training ever-larger models are becoming prohibitive. The incentive to find alternatives has never been stronger.

Investment is flowing into neuromorphic and in-memory computing. Intel's Hala Point deployment at Sandia National Laboratories represents a serious commitment to scaling neuromorphic systems. IBM's continued development of brain-inspired architectures demonstrates sustained research investment. Start-ups like BrainChip are bringing neuromorphic products to market for edge computing applications where energy efficiency is paramount.

Research institutions worldwide are contributing. Beyond Intel, IBM, and BrainChip, teams at universities and national labs are exploring everything from novel materials for memristors to new training algorithms for spiking networks to software frameworks that make neuromorphic programming more accessible.

The applications are becoming clearer. Edge computing, where devices must operate on battery power or energy harvesting, is a natural fit for neuromorphic approaches. The Internet of Things, with billions of low-power sensors and actuators, could benefit enormously from chips that consume milliwatts rather than watts. Robotics, which requires real-time sensory processing and decision-making, aligns well with event-driven, spiking architectures. Embedded AI in smartphones, cameras, and wearables could become far more capable with neuromorphic accelerators.

Crucially, the software ecosystem is maturing. PyNN, an API for programming spiking neural networks, works across multiple neuromorphic platforms. Intel's Lava software framework aims to make Loihi more accessible. Frameworks for converting conventional neural networks to spiking versions are improving. The learning curve is flattening.

Researchers have also discovered that neuromorphic computers may prove well suited to applications beyond AI. Monte Carlo methods, commonly used in physics simulations, financial modelling, and risk assessment, show a “neuromorphic advantage” when implemented on spiking hardware. The event-driven nature of neuromorphic chips maps naturally to stochastic processes. This suggests that the architectural benefits extend beyond pattern recognition and machine learning to a broader class of computational problems.

The Deeper Implications

Stepping back, the story of neuromorphic computing and in-memory architectures is about more than just building faster or cheaper AI. It's about recognising that the way we've been building computers for 80 years, whilst extraordinarily successful, isn't the only way. It might not even be the best way for the kinds of computing challenges that increasingly define our technological landscape.

The von Neumann architecture emerged in an era when computers were room-sized machines used by specialists to perform calculations. The separation of memory and processing made sense in that context. It simplified programming. It made the hardware easier to design and reason about. It worked.

But computing has changed. We've gone from a few thousand computers performing scientific calculations to billions of devices embedded in every aspect of life, processing sensor data, recognising speech, driving cars, diagnosing diseases, translating languages, and generating images and text. The workloads have shifted from calculation-intensive to data-intensive. And for data-intensive workloads, the von Neumann bottleneck is crippling.

The brain evolved over hundreds of millions of years to solve exactly these kinds of problems: processing vast amounts of noisy sensory data, recognising patterns, making predictions, adapting to new situations, all whilst operating on a severely constrained energy budget. The architectural solutions the brain arrived at, co-located memory and processing, event-driven computation, massive parallelism, sparse adaptive connectivity, are solutions to the same problems we now face in artificial systems.

We're not trying to copy the brain exactly. Neuromorphic computing isn't about slavishly replicating every detail of biological neural networks. It's about learning from the principles the brain embodies and applying those principles in silicon and software. It's about recognising that there are multiple paths to intelligence and efficiency, and the path we've been on isn't the only one.

The energy consumption crisis of AI might turn out to be a blessing in disguise. It's forcing us to confront the fundamental inefficiencies in how we build computing systems. It's pushing us to explore alternatives that we might otherwise have ignored. It's making clear that incremental improvements to the existing paradigm aren't sufficient. We need a different approach.

The question the brain poses to computing isn't “why can't computers be more like brains?” It's deeper: “what if the very distinction between memory and processing is artificial, a historical accident rather than a fundamental necessity?” What if energy efficiency isn't something you optimise for within a given architecture, but something that emerges from choosing the right architecture in the first place?

The evidence increasingly suggests that this is the case. Energy efficiency, for the kinds of intelligent, adaptive, data-processing tasks that AI systems perform, is fundamentally architectural. No amount of optimisation of von Neumann machines will close the million-fold efficiency gap between artificial and biological intelligence. We need different machines.

The good news is that we're learning how to build them. The neuromorphic chips and in-memory computing architectures emerging from labs and starting to appear in products demonstrate that radically more efficient computing is possible. The path forward exists.

The challenge now is scaling these approaches, building the software ecosystems that make them practical, and deploying them widely enough to make a difference. Given the stakes, both economic and environmental, that work is worth doing. The brain has shown us what's possible. Now we have to build it.

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Sources and References

Energy Consumption and AI: – International Energy Agency (IEA), “Energy demand from AI,” Energy and AI Report, 2024. Available: https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai – Pew Research Center, “What we know about energy use at U.S. data centers amid the AI boom,” October 24, 2024. Available: https://www.pewresearch.org/short-reads/2025/10/24/what-we-know-about-energy-use-at-us-data-centers-amid-the-ai-boom/ – Global Efficiency Intelligence, “Data Centers in the AI Era: Energy and Emissions Impacts in the U.S. and Key States,” 2024. Available: https://www.globalefficiencyintel.com/data-centers-in-the-ai-era-energy-and-emissions-impacts-in-the-us-and-key-states

Brain Energy Efficiency: – MIT News, “The brain power behind sustainable AI,” October 24, 2024. Available: https://news.mit.edu/2025/brain-power-behind-sustainable-ai-miranda-schwacke-1024 – Texas A&M University, “Artificial Intelligence That Uses Less Energy By Mimicking The Human Brain,” March 25, 2025. Available: https://stories.tamu.edu/news/2025/03/25/artificial-intelligence-that-uses-less-energy-by-mimicking-the-human-brain/

Synaptic Plasticity and Energy: – Schieritz, P., et al., “Energy efficient synaptic plasticity,” eLife, vol. 9, e50804, 2020. DOI: 10.7554/eLife.50804. Available: https://elifesciences.org/articles/50804

Von Neumann Bottleneck: – IBM Research, “How the von Neumann bottleneck is impeding AI computing,” 2024. Available: https://research.ibm.com/blog/why-von-neumann-architecture-is-impeding-the-power-of-ai-computing – Backus, J., “Can Programming Be Liberated from the Von Neumann Style? A Functional Style and Its Algebra of Programs,” ACM Turing Award Lecture, 1977.

Neuromorphic Computing – Intel: – Sandia National Laboratories / Next Platform, “Sandia Pushes The Neuromorphic AI Envelope With Hala Point 'Supercomputer',” April 24, 2024. Available: https://www.nextplatform.com/2024/04/24/sandia-pushes-the-neuromorphic-ai-envelope-with-hala-point-supercomputer/ – Open Neuromorphic, “A Look at Loihi 2 – Intel – Neuromorphic Chip,” 2024. Available: https://open-neuromorphic.org/neuromorphic-computing/hardware/loihi-2-intel/

Neuromorphic Computing – IBM: – IBM Research, “In-memory computing,” 2024. Available: https://research.ibm.com/projects/in-memory-computing

Neuromorphic Computing – Europe: – Human Brain Project, “Neuromorphic Computing,” 2023. Available: https://www.humanbrainproject.eu/en/science-development/focus-areas/neuromorphic-computing/ – EBRAINS, “Neuromorphic computing – Modelling, simulation & computing,” 2024. Available: https://www.ebrains.eu/modelling-simulation-and-computing/computing/neuromorphic-computing/

Neuromorphic Computing – BrainChip: – Open Neuromorphic, “A Look at Akida – BrainChip – Neuromorphic Chip,” 2024. Available: https://open-neuromorphic.org/neuromorphic-computing/hardware/akida-brainchip/ – IEEE Spectrum, “BrainChip Unveils Ultra-Low Power Akida Pico for AI Devices,” October 2024. Available: https://spectrum.ieee.org/neuromorphic-computing

History of Neuromorphic Computing: – Wikipedia, “Carver Mead,” 2024. Available: https://en.wikipedia.org/wiki/Carver_Mead – History of Information, “Carver Mead Writes the First Book on Neuromorphic Computing,” 2024. Available: https://www.historyofinformation.com/detail.php?entryid=4359

In-Memory Computing: – Nature Computational Science, “Analog in-memory computing attention mechanism for fast and energy-efficient large language models,” 2025. DOI: 10.1038/s43588-025-00854-1 – ERCIM News, “In-Memory Computing: Towards Energy-Efficient Artificial Intelligence,” Issue 115, 2024. Available: https://ercim-news.ercim.eu/en115/r-i/2115-in-memory-computing-towards-energy-efficient-artificial-intelligence

Memristors: – Nature Communications, “Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing,” 2022. DOI: 10.1038/s41467-022-30539-6 – Nano-Micro Letters, “Low-Power Memristor for Neuromorphic Computing: From Materials to Applications,” 2025. DOI: 10.1007/s40820-025-01705-4

Spiking Neural Networks: – PMC / NIH, “Spiking Neural Networks and Their Applications: A Review,” 2022. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC9313413/ – Frontiers in Neuroscience, “Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications,” 2020. Available: https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.00662/full

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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

Fondo de res, el abecedario de la cocina

Si la cocina tiene un idioma propio, el fondo de res es su abecedario. No es una receta: es una estructura. Un líquido que sostiene todo lo que viene después. En la clase entendí —con esa claridad que solo da ver el proceso en vivo— que un buen fondo es menos “hervir huesos” y más “construir orden”.

Un fondo limpio empieza antes del agua: comienza en el dorado. Ese punto exacto donde la carne y los huesos pasan de crudos a caramelizados, donde la reacción de Maillard no solo oscurece, sino que organiza el sabor. No es quemar, no es dorar por dorar. Es desarrollar una capa aromática que luego se libera en el líquido como si hubiese estado siempre ahí, esperando.

El Mirepoix: proporción, corte y función

El mirepoix es la base aromática clásica para fondos y salsas en la cocina occidental. Su función es aportar dulzor, frescura vegetal y complejidad sin dominar el resultado final. Opera como una estructura silenciosa que sostiene el perfil del fondo sin imponerse.

La proporción estándar es 2:1:1:

• 2 partes de cebolla
• 1 parte de zanahoria
• 1 parte de apio

Esta relación permite un equilibrio preciso entre dulzor (zanahoria), notas herbales (apio) y volumen aromático (cebolla).

El corte debe ser uniforme y relativamente grande. No se busca un tamaño pequeño ni un brunoise; se busca consistencia. Piezas demasiado pequeñas se desintegran y enturbian; piezas demasiado grandes liberan poco aroma. El punto correcto permite una extracción gradual durante horas de cocción.

Técnicamente, el mirepoix no se dora. Se suda: fuego medio-bajo, mínima grasa y suficiente tiempo para que los vegetales liberen humedad y aroma sin caramelizar. El objetivo no es crear notas tostadas, sino aportar un fondo aromático limpio que complemente el dorado previo de los huesos y la carne. Dorarlo altera el perfil del fondo, lo vuelve más oscuro y menos neutro.

El mirepoix debe entrar al líquido cuando los aromas ya están abiertos pero antes de que los vegetales pierdan estructura. Durante la cocción larga, su función es transferir gradualmente sabor; no permanece como ingrediente visible ni debe determinar el carácter final del fondo.

El resultado correcto es un fondo estable, equilibrado y con un soporte vegetal preciso, construido a partir de un mirepoix ejecutado con control en proporciones, temperatura y corte.

Cuando entra el agua, ya no se trata de hervir: se trata de no estorbar. El calor debe ser firme, pero controlado; el hervor agresivo es enemigo directo de la claridad.

Proporciones estándar para un Fondo de Res

La fórmula base de un fondo claro o fondo oscuro de res se organiza en relación peso de hueso : agua : mirepoix : aromáticos.

1. Huesos

La base es siempre 1 parte de hueso.

• Para un uso estándar en escuela o restaurante: 1 kg de hueso (con algo de carne adherida para mayor sabor).

Los huesos pueden ser: falda, pierna, costilla, espinazo, fémur partido (para extraer colágeno).

2. Agua

La proporción profesional es:

Agua = 2 a 3 partes por cada 1 parte de hueso

En práctica:

• Por 1 kg de hueso → 2.5 a 3 litros de agua fría.

Menos agua produce un fondo muy concentrado pero menos limpio. Más agua diluye y reduce extracción.

Regla óptima para cocina diaria: 1 kg hueso : 2.5 L agua

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3. Mirepoix

La fórmula clásica es:

Mirepoix = 10% del peso del hueso

Si tienes 1 kg de huesos → 100 g de mirepoix total.

Distribuido así:

• Cebolla 50 g (50%)
• Zanahoria 25 g (25%)
• Apio 25 g (25%)

Esas proporciones equivalen al estándar 2:1:1.

Nota: en fondos oscuros, el mirepoix puede ir tostado o sudado más tiempo, pero no quemado.

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4. Aromáticos (“Bouquet Garni”)

Se añaden en aproximadamente:

1–2% del peso del líquido

Pero en práctica profesional:

• 1 hoja de laurel
• 6–8 granos de pimienta negra
• 1 ramita de tomillo
• 1–2 dientes de ajo (opcional)
• Puerro (parte verde) opcional

Aromáticos muy fuertes (clavo, romero, orégano) no se usan en fondos clásicos porque “contaminan” el sabor base.

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Variación en la Academia Culinaria Hondureña: uso de pasta de tomate para fondo oscuro

En clase trabajamos una variación del fondo tradicional incorporando pasta de tomate después del dorado de los huesos. Esta técnica se utiliza para fondos oscuros y cumple dos funciones principales: intensificar el color y profundizar el perfil aromático.

La pasta de tomate se añade directamente sobre los huesos sellados y se cocina a fuego medio hasta que adquiera un tono más oscuro. Este proceso no busca quemarla, sino tostarla ligeramente para desarrollar azúcares y aportar un matiz más redondo al fondo. El resultado es un color ámbar profundo y una base con mayor complejidad y notas ligeramente caramelizadas.

Es importante controlar el calor: si la pasta se quema, aporta amargor. Si se queda cruda, el fondo mantiene un sabor más plano y un color más pálido. El punto correcto es cuando la pasta pierde el brillo inicial, toma un tono más rojizo-oscuro y se integra al dorado previo de los huesos.

Esta variación permite construir un fondo oscuro más robusto, ideal para salsas como la de vino tinto, demiglace o reducciones intensas que necesitan estructura y color sin recurrir a espesantes artificiales.

Etiquetas

#NotasDeClase #AcademiaCulinaria #FondoDeRes #FondoOscuro #Mirepoix #TecnicasBase #Salsas #ReaccionDeMaillard #BouquetGarni

Ashley Padilla is the best new SNL cast member and one of the best members of the current cast overall. (I know she started last year, but I still consider that new.) Her delivery is fantastic, she has great technical acting skills (projecting her voice, annunciating clearly, and other things I'm not knowledgeable enough to pinpoint), and her characters are always funny and believable.

The show needs to let her spread her wings a bit. She can't always play the mom, although she plays that role well.

I predict she will be big.

There are moments in life when grace doesn’t appear in a thunderclap, a sermon, or an emotional altar call. Sometimes all it takes is a cold nose, a warm heartbeat pressed against your side, or the sight of a tail wagging with furious devotion the second you walk through the door.

Some of the most powerful lessons about God don’t come from pulpits at all — they come bounding toward us on four legs.

Every dog-lover knows the truth: Dogs love with a purity most people only talk about. They forgive with a speed most of us never reach. They stay when life gets heavy, silent, or complicated.

And if you have ever felt a love like that, then you have brushed against something eternal, something heavenly — something that whispers of the God who created them.

Before we go deeper, here is a powerful message that explores this same truth. If your heart has ever been moved by the connection between divine love and the love of a dog, you’ll resonate deeply with this: dogs teach us about God’s love

Now let’s walk gently into this truth together — slowly, thoughtfully, reverently — and discover why the affection of a dog is far more than simple companionship. It is a doorway into the love of God Himself.

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A Love So Pure It Stuns the Human Heart

Think of the last time you walked into your home after a long, exhausting day. Your mind was tangled. Your heart was tired. Your spirit felt drained.

And then — there they were.

Your dog didn’t check your mood first. Didn’t wait to see if you were polite. Didn’t ask what you accomplished today. Didn’t care whether you failed or succeeded.

They only cared that you walked through the door.

Their whole body becomes a celebration — a festival of joy, as though your presence is the most wonderful thing this world has ever seen.

That moment is more than emotional warmth. It is more than animal instinct.

It is reflection.

It is echo.

It is a portrait — painted in tail-wags and bright eyes — of the God who says:

“I have loved you with an everlasting love.”

Dogs live that verse out without even understanding it. They live it because love is their nature — a nature given to them by the One who is love.

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The Silent Sermons Dogs Preach

Dogs may not speak our language, but they preach some of the simplest and holiest truths ever lived.

1. They Love First

Before we earn it. Before we clean up our mess. Before we prove anything at all.

Doesn’t that remind you of grace?

Grace that arrives before repentance. Grace that wraps its arms around you before you’re “better.” Grace that doesn’t wait for you to deserve it.

A dog doesn’t hesitate to love. And neither does God.

2. They Forgive Instantly

Accidentally step on a paw? Raise your voice? Get impatient? Run late for feeding time?

Give them five seconds.

Forgiveness arrives before guilt even settles in your chest.

It is hard to watch forgiveness like that and not feel humbled — not feel the gentle nudge of Heaven whispering, “See? This is how I love you, too.”

3. They Stay Nearby in Your Pain

Sit quietly on a couch with grief? They sit with you. Cry in your room with the door half-closed? They nudge it open.

Loneliness lifts when a warm head rests on your leg.

Sometimes God comforts through scripture. Sometimes through worship. Sometimes through people.

And sometimes… He comforts through the steady breathing of a dog who refuses to leave your side.

4. They Celebrate Your Existence

Dogs don’t celebrate achievements — they celebrate presence.

Not: “Did you get the promotion?” “Did you fix the problem?” “Did you impress anyone today?”

But simply: “You’re here. You’re mine. I’m glad you exist.”

That is divine love. We don’t earn it — we encounter it.

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Why Dogs Touch Our Souls So Deeply

It’s because they remind us of what we were created to receive — and what the world tries to make us forget.

We were made for unconditional love. We were made for belonging. We were made for gentleness, warmth, trust, and joy.

Dogs give these things freely, without hesitation, without calculation, without judgment. And when they do, they awaken a spiritual memory inside us — a knowing that says:

“This is how love was always meant to feel.”

When you stroke their fur, feel their heartbeat, or look into those eyes that hold nothing but devotion, something quiet in your spirit whispers:

“This is holy.”

And it is.

Because true love — in any form — comes from God.

Dogs don’t replace God. But they reflect Him. They point toward Him. They remind us of Him.

They are not divine, but they carry pieces of divine affection, divine patience, divine loyalty — placed within them by the very God who thought companionship was so important that He designed creatures specifically to offer it.

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Loyalty That Mirrors the Faithfulness of God

The loyalty of a dog borders on mythic. People write poetry about it. Soldiers weep over it. Children grow up anchored by it. Elderly hearts are comforted by it.

A dog’s loyalty is so fierce, so steadfast, so unwavering that even skeptics of faith pause and say, “There must be something bigger behind love like that.”

And they’re right.

Dogs stay when others leave. Dogs remain when humans get busy. Dogs keep vigil during sickness and sorrow.

Their loyalty tells a story — a story that predates creation itself:

“I will never leave you nor forsake you.”

When a dog curls beside you during your darkest night, you can feel the echo of that promise.

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Forgiveness That Reveals the Heart of Mercy

We humans hold grudges like trophies. We nurse old wounds. We replay arguments in our minds. We keep score.

Dogs don’t.

Their forgiveness isn’t passive; it’s enthusiastic. They don’t just forgive — they forget. And in forgetting, they teach us how freeing forgiveness can be.

When God forgives, He says He casts our sins “as far as the east is from the west.” Dogs live out that kind of forgetting every day.

A dog’s forgiveness is a living parable.

It tells you: “You are still good.” “You are still mine.” “I love you anyway.”

And for many people, that is the first time in their life they’ve ever felt love like that.

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Joy in Simplicity — A Reminder of God’s Presence in the Ordinary

Dogs don’t need expensive vacations or impressive opportunities. They find wonder in the ordinary.

A leaf becomes a treasure. A walk becomes an adventure. A nap becomes a sanctuary. A squeaky toy becomes a blessing.

Their joy teaches us something profound:

Happiness is not “out there.” It is right here — where love lives.

When a dog chases a butterfly, rolls in grass, or trots proudly with a stick bigger than its whole body, they awaken something childlike inside of us.

Jesus said, “Unless you become like little children...” Maybe He could have added, “or like the joyful enthusiasm of a dog,” because dogs live in the present with a purity that makes our hurried minds pause.

To watch them is to remember: “God is here. Right here. Right now. In the simple.”

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The Way Dogs Wait for You — And What It Really Means

Dogs wait at windows. Wait at doors. Wait at the sound of keys. Wait for footsteps. Wait for your return even when it’s midnight.

Their waiting is hopeful, not anxious. Confident, not fearful. Expectant, not doubtful.

They don’t wonder if you still love them. They simply wait to see your face again.

That kind of waiting resembles another love — a divine love that waits, not to punish, but to embrace.

The God who waited for the prodigal son waits for you. And sometimes He teaches you that truth through a dog sitting at the door.

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Dogs Comfort Us in a Way That Feels Supernatural

Scientists have found that dogs can sense human emotion with remarkable accuracy. They know when you’re grieving, anxious, afraid, lonely, or overwhelmed.

But dogs don’t respond with advice, lectures, or solutions.

They respond with presence.

A head on your knee. A quiet sigh beside you. A nudge under your hand. A warm body curled into yours.

Presence is one of God’s greatest gifts. Dogs embody it effortlessly.

They don’t fix your problems — they sit with you in them.

Sometimes, that is the clearest picture of God’s comfort we ever witness on this earth.

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How Dogs Bring Healing to the Human Spirit

Dogs lower anxiety. Lower blood pressure. Reduce loneliness. Ease depression. Soothe trauma.

Hospitals use them. Counselors rely on them. Children with disabilities grow because of them. Veterans rebuild their lives because of them.

Why?

Because a dog’s love is medicine. Not metaphorically — literally.

But also spiritually.

They heal in invisible ways. They patch wounds we didn’t know were open. They anchor us when life feels stormy. They lift moods without trying. They give us a sense of belonging when the world feels cold.

Sometimes God heals through miracles. Sometimes God heals through people. Sometimes God heals through time. And sometimes…

God heals through a creature who curls into your arms and simply loves you back to life.

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What Dogs Teach Us About Our Relationship With God

Dog-love is not just emotional; it is instructional. Here are the holy lessons hidden in their everyday affection:

1. Love boldly.

Hold nothing back. Let your affection be obvious, warm, generous, and sincere.

2. Forgive quickly.

The sooner you forgive, the sooner your soul breathes again.

3. Enjoy the small blessings.

Life happens in the small things — pay attention to them.

4. Stay present.

Don’t live your entire life in yesterday’s regrets or tomorrow’s anxieties.

5. Trust more.

You don’t need to understand everything to receive love wholeheartedly.

6. Show up for people.

Just being there is sometimes the greatest ministry in the world.

7. Celebrate the people you love.

Let them know you’re grateful for their presence. Let joy be visible.

These are spiritual disciplines disguised as simple behaviors. Dogs practice them effortlessly. We struggle. But their example gives us something to reach for.

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A Glimpse of Heaven in a Wagging Tail

Do dogs go to heaven?

Theologians debate it. People argue about it. Denominations differ.

But consider this:

If Heaven is the fullness of God’s love, and dogs are small windows into that love, then it only makes sense that the Love who created such loyalty would not discard the very creatures who reflect Him so clearly.

No one can say for sure. But many hearts believe — and hope — that the God who sees every sparrow also sees every wagging tail.

At the very least, this is true:

The love you share with your dog does not vanish. It leaves an imprint on your soul that cannot be erased.

And love like that is never wasted.

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You Are Loved More Deeply Than Your Dog Could Ever Express

As great as a dog’s love is — and it is great — it is only a shadow of the love of God.

Dogs love instinctively. God loves intentionally.

Dogs love with loyalty. God loves with eternity.

Dogs love because it’s who they are. God loves because it’s His very essence.

A dog’s love may be one of the clearest earthly reflections of Heaven, but even their devotion is only a drop compared to the ocean of love God has for you.

If your dog makes you feel cherished, seen, wanted, welcomed, and adored…

remember this:

God loves you infinitely more.

If a dog’s affection brings tears to your eyes, God’s affection would overwhelm you.

If a dog waits at your door with longing, God waits at the door of your heart with even greater longing.

If a dog’s loyalty heals your wounds, God’s faithfulness restores your very soul.

Your dog is not the destination. They are a signpost pointing you toward the One whose love they imitate.

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Final Reflection: When Love Has Fur, Heaven Comes Close

So the next time your dog presses their head into your hand, or races across the room in joyous celebration, or curls beside you in quiet companionship, or forgives you without hesitation, or looks at you like you hung every star in the sky…

remember what you are witnessing.

You are witnessing:

Loyalty like God’s. Joy like God’s. Compassion like God’s. Gentleness like God’s. Presence like God’s. Unconditional love like God’s.

You are seeing — in the simplest, softest form — a glimpse of the heart of Heaven.

Some sermons are spoken. Some are written.

But some… some are lived out through a creature at your feet who has loved you every moment of your life with a faithfulness that feels divine.

Cherish that love. Honor it. Learn from it.

And let it remind you — every single day —

You are never alone. You are deeply loved. And God is closer than you think.

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Douglas Vandergraph

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#DogsAndFaith #UnconditionalLove #GodsLove #Faith #ChristianInspiration #Grace #DivineLove #Hope #SpiritualReflection #DailyFaith

“Un total de once lobos han sido abatidos de los 53 previstos en el Plan de Gestión del Lobo que el Gobierno del Principado retomó en abril, tras la salida de esta especie del Listado de Especies Silvestres en Régimen de Especial Protección (Lespre), y que estará vigente hasta el 31 de marzo de 2025.

Otros ocho ejemplares han muerto entre abril y octubre por causas naturales o accidentales, según los datos aportados por el consejero de Medio Rural y Política Agraria, (…) Leer en Nortes.me

It's quite tiring and draining being unwell. Especially when it builds over time, day by day you're getting worse but you're still carrying on as usual because you aren't so bad yet. Then it hits you and it hits you hard. It's breaks down not only your internals but external life also. Everything hurts all at once and when you live in a state with a failing healthcare system it hurts even more. Don't even bother calling your gp, you ain't getting through to anyone.

Idk I'm just venting. I'm tired of being tired. I'm definitely sick of being sick.

Also has anyone noticed how things are broken and worse in this shitty new iOS update. Like wtf man can't even type properly and the shitty glass bullshit is so shit.

#TalesOfTheInbetween

⚠️ SPOILER WARNING: NO SPOILERS

My Rating: ⭐⭐⭐⭐½ (4.5/5 stars)

Episodes: 10

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Quick Take

After snagging a free month of Apple TV, I managed to binge-watch the second season of Silo. It was excellent. I'm really looking forward to Season 3 and hope they explore more of the backstory.

Best Episodes

The season finale stood out as the best episode. The whole season was truly remarkable. – Episode 10: Into the Fire

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#opinion #tv #streaming

“El concejo de Oviedo acoge alguno de los conjuntos y elementos más destacados del patrimonio histórico industrial, ya no sólo asturiano, también de todo el Estado español. Sin embargo, y es un mal endémico, además del olvido y abandono al que está sometido, sufre la amenaza de la ignorancia, con ocurrencias disfrazas de proyectos disruptivos donde lo que importa es la rentabilidad económica en detrimento de una rentabilidad social que deberían tener estos bienes que son de toda la ciudadanía y que, a través de planes bien pensados y desarrollados, mediante procesos participativos reales y efectivos, deberían convertirse en recursos territoriales generadores de riqueza –hablo de riqueza en su máxima acepción: generación de empleo, de actividad, de cultura, de identidad, en definitiva, de comunidad–.” Leer en Nortes.me

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#GO HOOSIERS!

Listening now to the Flagship Station for IU Sports ahead of this afternoon's men's college basketball game, as the Incarnate Word Cardinals meet the IU Hoosiers. Tipoff is minutes away.

And the adventure continues