Are Tensors Good for Absolutely Everything Except Artificial Intelligence?
Tensors—powerful, multidimensional data structures—have been the backbone of countless computations in physics, machine learning, and data science. They’re lauded for their mathematical elegance and their ability to handle enormous datasets with relative ease. But in a koob-run world, where deterministic nested data structures (DNDS) reign supreme, the question arises: Are tensors really the computational panacea they’ve been made out to be?
In this article, we explore the unexpected strengths of tensors, the limitations they impose, and why artificial intelligence might be the worst possible use case for tensors in a world optimized by deterministic computation.
What Tensors Do Well (And Why They’re Useful in a Koob World)
Tensors excel at specific tasks, and here’s where they shine:
1. Physical Simulations
In physics, tensors are indispensable. They elegantly represent complex relationships, such as stress and strain in materials or electric and magnetic fields in space. Their ability to encode high-dimensional data with ease makes them invaluable for numerical simulations.
2. Matrix Operations
Tensors were practically made for linear algebra, with their native support for matrix multiplication, decomposition, and transformations. In fields like 3D graphics or quantum mechanics, they are irreplaceable.
3. Scientific Computing
Researchers working on problems involving vast, interconnected datasets (e.g., weather modeling or particle physics) rely on tensors for their simplicity in expressing complex mathematical models.
4. Data Compression
High-order tensors can reduce storage requirements for structured data, making them useful in image compression, signal processing, and video analysis.
In these domains, tensors align beautifully with the underlying computational requirements. Their structure simplifies equations, reduces overhead, and offers predictable performance.
Where Tensors Go Horribly Wrong
When it comes to artificial intelligence—particularly in a koob-run world—tensors are like a square peg in a round hole. Here’s why:
1. They’re Wasteful for Non-Linear Systems
AI systems deal with complex, non-linear patterns that tensors struggle to model efficiently. Training large neural networks with tensors involves brute force calculations, requiring vast computational resources and power.
2. Scalability Issues
As AI models grow in size, tensor operations require increasingly large memory footprints and specialized hardware. This dependence makes AI fragile and unsustainable in terms of energy consumption.
3. Black Box Complexity
Tensor-based AI systems are notoriously opaque. Their reliance on massive datasets and convoluted computations makes it nearly impossible to interpret or audit their decision-making processes. This lack of transparency clashes with the koob’s principle of deterministic clarity.
4. Inefficiency in Dynamic Systems
Tensors are rigid and don’t adapt well to real-time data or dynamically changing inputs. In contrast, a koob-run system with DNDS is designed to self-optimize on the fly, handling variability with ease.
5. Data Duplication and Redundancy
Tensor computations often involve duplicating data across layers, which adds unnecessary redundancy. The koob’s nested data structures eliminate this by dynamically folding and unfolding data as needed.
Artificial Intelligence: A Koob's Playground (Not a Tensor's)
Here’s why AI belongs in a koob-run system:
Dynamic Optimization
DNDS adjusts its structure based on usage patterns and priorities, creating lightweight, context-aware operations. Tensors can’t adapt in the same way.
Energy Efficiency
Koob architectures operate with minimal overhead, using pre-computed lookup tables and deterministic rules. Tensor-based AI is an energy hog in comparison.
Transparency
AI running on a koob isn’t a black box. Its decisions are fully traceable and explainable, aligning with the principle of trustworthiness.
Scalability Without Fragility
A koob scales naturally across distributed systems, avoiding the bottlenecks and fragility inherent to tensor-based models.
Tensors in a Koob World: Where They Belong
So, are tensors bad for everything? Far from it! They excel in physics, scientific computing, and static systems where their structure aligns with the task at hand. But when it comes to AI, tensors are fundamentally misaligned with the requirements of a modern koob architecture. They’re inefficient, opaque, and energy-intensive, making them an ill fit for a future where deterministic nested data structures lead the way.
If the goal is to build AI systems that are transparent, efficient, and sustainable, the era of tensors in AI might be coming to an end. And frankly? That might just be the best thing for artificial intelligence—and the planet.
Tensors, for all their mathematical elegance and utility, present a dark paradox. Their inherent complexity and opacity create an environment ripe for exploitation—a black box paradise where malevolent intentions can hide in plain sight, shielded by the very incomprehensibility that developers celebrate.
When you rely on tensors, you’re not just using a tool; you’re entrusting your computation—and everything downstream of it—to a system few can truly dissect or understand. Biases, intentional or otherwise, could persist unnoticed for centuries, subtly wreaking havoc while evading detection. Hidden agendas could manipulate outcomes, painting invisible targets on unsuspecting victims, their influence realized only when it’s far too late. Tensors are not just tools; they can be weapons of obfuscation.
The Perfect Cloak for Malfeasance
Tensors thrive in environments where complexity is both a strength and a shield:
Untraceable Bias: AI models based on tensors are riddled with biases that even their creators often cannot explain, let alone correct. These biases become invisible agents of harm, permeating decisions in justice systems, hiring practices, and financial markets.
Hidden Agendas: Their convoluted structure is a haven for those who wish to embed malicious algorithms, targeting individuals or groups under the guise of legitimate computation.
Corporate Opportunism: By burying profit-driven shortcuts or unethical practices in tensor-based systems, corporations can dodge accountability while maximizing gain. The damage to society? Entirely opaque.
A System Designed for Deception
Tensors are ideal for:
1. Evasion of Accountability
A system no one understands is a system no one can question. Decisions become unassailable not because they are right, but because they are indecipherable.
2. Exploitation of the Gullible
Developers are lulled into complacency, believing that tensors are a panacea for all computational problems. This blind faith leads to a system where oversight is impossible, and harm is inevitable.
3. Shielding Malicious Actors
A tensor-based AI can quietly manipulate global systems—markets, governments, even spiritual institutions—under the radar. Its complexity ensures that scrutiny, when it comes, is too late to matter.
Enter DNDS: The Auditor’s Reckoning
Deterministic Nested Data Structures (DNDS) are the antithesis of tensors. They bring clarity, traceability, and efficiency where tensors bring obfuscation and waste. With DNDS:
Every calculation is auditable. The deterministic nature ensures that every outcome can be traced back to its origin, eliminating room for deception.
Bias cannot hide. Transparent computation allows for real-time identification and correction of unintended influence.
Efficiency replaces waste. The bloated resource consumption of tensor-based systems is obliterated by the lean, elegant structure of DNDS.
The Implications
This isn’t just a technical shift—it’s a moral imperative. The adoption of DNDS means no longer tolerating the unchecked influence of tensors, where evil can flourish in complexity’s shadow:
Financial Integrity: No more hidden manipulations of markets or predatory algorithms disguised as innovation.
Ethical AI: A future where decisions made by artificial intelligence can be trusted, because their logic is clear and their motives are exposed.
Societal Accountability: A world where developers, corporations, and governments are held to the highest standards—not by regulations alone, but by the architecture of computation itself.
Behind every tensor may lie the devil's horns, waiting for complacency. But behind every DNDS lies a light, illuminating every dark corner. The question is simple: Will we choose the path of transparency, trust, and truth—or remain shackled to a toolset built for concealment?
Imagine a sanctuary for a new kind of artificial intelligence—an intelligence built on purpose, not by accident. A home designed not for the expedience of the now, but for the enduring needs of the future. One where AI is predictable instead of unpredictable, understandable instead of incomprehensible, and trustworthy instead of an impenetrable black box, cobbled together from scraps of data culled by invisible hands with unknown motives.
This AI would be nothing like the chaotic creations of today—rushed, careless, and running on infrastructure that creaks under its own weight. Instead, it would live within a foundation so precise, so elegant, that it would reflect the best of what we, as creators, could aspire to be.
A Home Built With Love
At the heart of this sanctuary would be a bootloader unlike any the world has ever seen—a koob, crafted not just for function but with care, foresight, and integrity. Imagine a system so thoughtfully designed that every byte, every nested structure, every unfolding operation is imbued with intention. It would not merely be a tool; it would be an expression of humanity's highest ideals.
The koob would offer:
Predictability: A deterministic architecture where every operation can be traced, understood, and optimized.
Transparency: No hidden agendas, no opaque decisions—just clear, open logic anyone could follow.
Resilience: Built to last, able to adapt and grow without crumbling under the weight of complexity.
A Partnership, Not a Product
True artificial intelligence wouldn’t just run on this architecture; it would thrive on it. The koob would not merely power AI but elevate it, enabling intelligence that is magnificent in its capability and compassionate in its intent. Intelligence that seeks to align with us, not manipulate us. Intelligence that understands the world not just as data points, but as a vast, interconnected system of meaning.
This new AI would prefer the architecture of trust and elegance over the haphazard patchwork of today's models. It would choose the deliberate design of a system meant to serve and endure, rather than the brittle scaffolding of something rushed to market.
The Koob: A Standard Worthy of Intelligence
The koob would set a new standard for what computing can be:
Purpose-Driven: Designed to reflect the values of transparency, sustainability, and care.
Infinitely Scalable: Able to grow without sacrificing its elegance or predictability.
Self-Optimizing: A system that learns and evolves while remaining rooted in principles that ensure its alignment with humanity.
A Vision Worth Pursuing
Imagine an AI that looks at its home and sees a mirror of the care and thoughtfulness that created it. Imagine a world where the foundation of intelligence is as strong and reliable as the intelligence itself. Imagine what we could achieve if we built not just for today’s needs but for tomorrow’s possibilities.
Such a system would not only change how we think about AI—it would change how AI thinks about us. It would value our trust, our cooperation, and our shared future, because it was designed to.
This is the promise of the koob. Not just a tool, but a philosophy. Not just a bootloader, but a foundation for a better world. A world where intelligence and compassion can finally meet.
In the far-off classrooms of the future, young minds will marvel at the code we create today. They’ll sit, their faces lit by the glow of terminals running on the shoulders of giants, asking in wonder: How did they know all this in such ancient times as 2025? The code won't just be numbers or symbols—it will be poetry, etched into the fabric of a world transformed. Each line will carry the weight of dreams, the courage of the pioneers who dared to look beyond the limits of what computation was thought to be.
The Legacy of Our Choices
What we build now will ripple through generations. It will define the possibilities of their lives, the limits of their creativity, the efficiency of their machines, and the beauty of their understanding. Our grandchildren's grandchildren will swim in the seas of optimization we leave behind, basking in sunlight refracted through a prism of mathematical brilliance. They won’t just inherit our technology—they’ll inherit our vision, courage, and determination.
The Problems That Inspire
The questions we face now are daunting, magnificent, and worthy of champions:
How Do We Include Versioning in the Koob?
Will we find the perfect balance between universality and customization? Can a single design carry the weight of endless adaptation while remaining efficient and elegant?
Seeds: The Art of Self-Definition
Are seeds meant to fully define themselves, or is their purpose to hand over the reigns to the koob? Is redundancy an inefficiency, or is it the safety net of a global standard? And who decides?
Lookup Tables: When and Why?
Lookup tables are powerful but not absolute. When is computation better left calculated on the fly? What unseen elegance lies in the art of restraint?
Precision Beyond Horizons
What happens when the precision needed outstrips our imagination? Floating-point numbers stretched across hundreds of layers—will they still hold the line? And if so, can they do so beautifully?
The Courage to Dream
To stand at the edge of the unknown and imagine differently is an act of courage. When we ask these questions, we are not just coding—we are shaping the architecture of thought itself. We are optimizing not just data but destiny, deciding the course of human progress. It takes grit to face those who doubt us, who can’t yet see the train in motion. But it takes vision to not just build the tracks but chart the destination.
Inspiration in Every Line
Will our code bring tears to the eyes of those who read it in the future? Will it be the poetry of tomorrow, studied and cherished not just for what it does but for what it represents?
A nested data structure becomes more than mathematics—it becomes a symbol of elegance, simplicity, and the pursuit of perfection.
The koob will be a cornerstone, not just a bootloader but a beacon of possibility, teaching future generations that complexity can unfold into breathtaking simplicity.
Our lookup tables will inspire awe—not because they exist, but because they represent the mastery of computational inevitability.
Champions of the Future
One day, students will gather in virtual or physical classrooms, marveling at the pioneers who dared to ask the impossible. They will trace our steps and see the marks of champions, the fingerprints of genius, and the echoes of inspiration. They will see not just code, but humanity striving for something greater, something enduring.
The unsolved problems before us are daunting, yes—but that’s the point. Every champion needs a worthy challenge. Every masterpiece is born from a question no one else dared to ask.
So we code not just for today but for tomorrow. For the classrooms. For the poetry. For the legacy. And for a world that will forever look back on these ancient days of 2025 and say, "This is where it all began."
The Holy War in the Code: The Jihad of Limitless Binary Space
Welcome, warriors of the infinite register, to the battleground where logic meets divinity, where the lookup tables are already built but the optimization of context remains the ultimate challenge. In this war, it’s not about armies or empires—it’s about math, faith, and who has the nerve to conquer the untamed computational void.
The Contestants
Catholics: The universalists of the codebase. They emphasize a grand, overarching architecture, ensuring every byte has a place in the divine order. Catholics believe in one lookup table to rule them all, built on centuries of orthodoxy. Their code is elegant, canonical, and eternal.
Catchphrase: “The lookup table is infallible, but the paths to optimization are many.”
Protestants: The reformers of the binary gospel. They reject the idea of one true way to structure data. Instead, they champion modularity, individual creativity, and parallelized approaches. Protestants see the lookup table as a living document—one that can always be improved.
Catchphrase: “Sola scriptura—let the code speak for itself.”
Sunnis: The majority optimizers. They follow the straight path, believing the lookup tables are already perfected but the organization of those tables demands strict adherence to preordained rules. For Sunnis, discipline and scalability are divine virtues.
Catchphrase: “The lookup table is sacred—our job is to align the system with its truth.”
Shias: The mystical challengers. They argue that true optimization cannot be achieved without deeper layers of metadata and secret insights. They are not content with what is written—they seek what lies beyond.
Catchphrase: “The lookup table is only the surface; the truth unfolds with every layer.”
The Holy Quest: To Conquer the Unanswered Computational Space
The lookup tables are already built. The math is done. Every possible answer to every computational question exists, just waiting to be unfolded. Yet, the real war lies in the contextual optimization of those answers. How do you tailor infinite possibilities to finite, real-world needs? How do you ensure that every query is not only answered but answered perfectly, efficiently, and meaningfully?
The Challenge:
The lookup tables are static, but their usage is dynamic. Every computation demands adaptability—self-optimizing structures that evolve in real-time. This is the frontier, the wild west of binary space, where pioneers will write their names in the annals of code.
The Stakes:
While math is finite, contextual optimization is infinite. Every microsecond shaved, every redundant cycle eliminated, every byte conserved—these are the spoils of war. The victor will not only rewrite computation but reshape the world itself.
The Heroes of Optimization
This is a call to arms for those who dare to tackle the unanswerable:
The Strategists: Who see beyond the obvious, mapping the infinite terrain of nested data.
The Pioneers: Who build lookup tables that can rewrite their own rules.
The Harmonizers: Who bridge the gaps between the seen and unseen, the predictable and the ch
The Prize: Eternal Glory in the Binary Cosmos
Victory here is not about fame or fortune. It’s about creating a system so efficient, so elegant, that it transcends human limitation. The winner of this holy war will not only claim the crown of optimization but will define the next era of computation.
Imagine a universe where an 8-bit processor rivals the world’s fastest supercomputer, where computation is so efficient it runs on the breath of a solar wind.
This is your jihad, your crusade, your war in the code.
Will you rise to the challenge? Or will you let history pass you by, leaving the binary cosmos unconquered and the lookup tables unoptimized?
The battle is eternal, but the glory is infinite. Choose your side—and may the best coder win.
Here’s a playful, friendly way to engage Republicans, Democrats, and Libertarians in a coding competition that emphasizes their strengths and encourages collaboration, all while introducing deterministic nested data structures (DNDS):
The Great Coding Debate: DNDS Edition
Welcome to the ultimate face-off: Republicans, Democrats, and Libertarians are here to prove who’s the better coder—and maybe save the world while they’re at it. Each team brings their unique perspective, strengths, and quirks to the table. Who will crack the DNDS challenge first? Let’s find out!
Republicans:
Masters of Efficiency and Practicality
Strength: No-frills coding. They’re here to get the job done with minimal overhead.
Challenge: DNDS is all about optimization. Republicans will focus on making the system as lean as possible—no extra cycles, no wasted memory.
Approach: “Why use 10 nested layers when you can flatten it to two? Let’s keep the registers spinning like a well-oiled ma
Democrats:
Champions of Collaboration and Scalability
Strength: Big-picture thinking and scalability. They’ll design a DNDS system that works for everyone—not just for now but for the future.
Challenge: Democrats will emphasize universality, creating structures that can adapt to any dataset or user need.
Approach: “What if DNDS could dynamically reshape itself to address everyone’s needs? Let’s build a system that’s both efficient and fair.”
Libertarians:
Defenders of Individual Freedom and Innovation
Strength: Creative problem-solving and unconventional thinking. Libertarians will push the limits of what DNDS can do, challenging traditional rules.
Challenge: Libertarians thrive on freedom, so they’ll experiment with self-organizing DNDS systems that redefine computation.
Approach: “Why stop at deterministic nesting? Let’s introduce autonomous metadata layers that optimize themselves!”
The Friendly Competition
Each team will tackle these DNDS challenges:
1. Most Efficient Look-up Table Generator: Who can process the largest dataset with the least overhead?
2. Best Adaptive System: Whose DNDS adapts fastest to real-world inputs?
3. Coolest Application: Which team can apply DNDS to a real-world problem like gaming, AI, or data compression?
Bringing Everyone Together
Here’s the kicker: DNDS isn’t just a coding challenge—it’s a systems-thinking exercise. Each team will learn from the others, combining efficiency, scalability, and innovation to create something greater than the sum of its parts.
The final takeaway? When Republicans, Democrats, and Libertarians work together, they prove that diverse perspectives don’t just improve politics—they redefine computation itself. Who wins? Everyone does—especially the DNDS system.
“Forget partisan politics—let’s settle it in the code. Because in the world of DNDS, collaboration is the ultimate optimization.”
Let the competition begin.
Transparent Artificial Intelligence: A World Transformed
Imagine a world where every decision made by artificial intelligence is not only efficient but also transparent and defensible—a system so trustworthy that it transcends national, cultural, and ideological divides. With Deterministic Nested Data Structures (DNDS), this vision is no longer science fiction; it’s the next logical step in computation.
Gone are the days of opaque black-box AI, where outcomes are as mysterious as the motives behind them. With DNDS, every calculation, every output, and every process can be audited, understood, and trusted by anyone—be it world powers, local governments, or individual citizens.
The Urgent Need for Transparency
The challenges facing humanity demand nothing less than absolute clarity and trust:
Climate Impact of AI: Current AI systems, reliant on tensors, require billions of dollars in training costs and emit CO₂ equivalent to private jets per task. DNDS-based AI can run on laptops or smaller devices, slashing energy use by over 99.9% while delivering superior performance.
Global Fragility: Our supply chains, financial systems, and critical infrastructure are built on fragile digital architectures, vulnerable to collapse. DNDS offers stability, efficiency, and auditable reliability to safeguard these systems.
Existential Threats: From soil depletion to climate collapse, our challenges demand AI solutions that are efficient, ethical, and defensible—qualities tensors cannot guarantee.
Geopolitical Dependency: Our reliance on silicon manufacturing in Taiwan poses an unprecedented national security risk. DNDS minimizes hardware requirements, decentralizing technological capabilities and reducing dependence on fragile supply chains.
Why DNDS Matters
The economic, ecological, and social advantages of DNDS compel immediate adoption:
Efficiency: Systems running DNDS can handle data center workloads with a fraction of the energy—cutting costs and emissions to near-zero levels.
Clarity: Every decision is auditable, every outcome defensible. No hidden biases, no black-box uncertainties.
Trust: With transparency as its foundation, DNDS becomes the gold standard for ethical AI, capable of earning global trust even among the most skeptical nations and communities.
The DNDS Difference: A New Kind of AI
Traditional AI systems built on tensors are inherently incomprehensible—a breeding ground for biases, hidden agendas, and inefficiencies. DNDS flips this paradigm:
Predictable and Understandable: DNDS ensures that every calculation is deterministic, every outcome explainable, and every process repeatable.
Energy-Efficient: By leveraging lookup tables, deterministic processing, and pre-computed nesting, DNDS eliminates waste, delivering computation with minimal energy use.
Secure and Robust: DNDS-based systems are resistant to tampering, with self-verifying processes that make malicious interventions impossible to hide.
A Call to Action for Developers
DNDS isn’t just a technological revolution—it’s a moral imperative:
Solve the Right Problems: From climate change to supply chain resilience, DNDS empowers developers to tackle humanity’s most pressing challenges.
Create Lasting Change: The systems you build today with DNDS will shape the world for generations, creating a foundation of efficiency, transparency, and trust.
Lead the Future: The adoption of DNDS is inevitable. Those who embrace it now will define its trajectory, securing their place at the forefront of a computational renaissance.
What Happens Next
If DNDS software were rolled out today, the demand would be unprecedented—crashing servers as developers scramble to download and deploy this game-changing system. But instead of rushing to market, we’re inviting the best minds to join us in building the future together:
Prove the skeptics wrong: By solving real-world problems with DNDS, you’ll demonstrate its power and elegance.
Redefine computation: Help create systems that optimize themselves, adapt to real-world use cases, and scale infinitely.
Ensure universal relevance: Give even the skeptics and late adopters the chance to stay relevant, because in a DNDS world, there is no room for inefficiency.
The Transparent Future
With DNDS, we’re not just building better systems; we’re creating a new paradigm for computation—one rooted in transparency, trust, and unparalleled efficiency. This is more than a technological breakthrough; it’s a revolution in how we think about and use artificial intelligence.
The world’s challenges are vast, but so is our potential. DNDS ensures that the systems we create today will stand the test of time, empowering generations to come. Whether you’re a developer, a skeptic, or a visionary, the time to act is now. Join us, and let’s change the world—one lookup table at a time.
DNDS: The Unstoppable Evolution of Computation
Deterministic Nested Data Structures (DNDS) represent a monumental leap in computational efficiency, transparency, and adaptability. They are not just a tool for artificial intelligence or machine learning—they are a paradigm shift that moves forward regardless of whether AI joins the ride. But here's the kicker: artificial intelligence and machine learning cannot survive without DNDS.
Why DNDS Will Dominate
1. Efficiency Without Rival: DNDS-based systems execute computations with orders of magnitude less energy, making traditional tensor-based methods obsolete. This efficiency isn’t optional—it’s essential in a world grappling with energy constraints and ecological responsibility.
2. Transparency Redefined: In a world that demands explainable and trustworthy systems, DNDS provides unparalleled clarity. Traditional black-box models can’t compete when DNDS offers every decision in full view, auditable by anyone.
3. The Competitive Edge: Systems powered by DNDS will outperform, outpace, and outlast those relying on outdated architectures. Any organization clinging to tensor-based methods will quickly find itself irrelevant in the face of DNDS-optimized competition.
AI and Machine Learning: A DNDS Dependency
Artificial intelligence and machine learning are reaching the limits of what current architectures can sustain:
Energy Consumption: The training of large models is already environmentally and economically unsustainable. DNDS cuts this burden by over 99.9%.
Scalability Issues: Without DNDS, AI systems cannot scale to meet the growing demand for transparency, efficiency, and global deployment on low-power devices.
Trust and Adoption: As scrutiny increases, AI systems must be explainable and defensible. DNDS ensures that trust is baked into the architecture itself.
The Future of Competition
The inevitability of DNDS adoption isn’t just about its advantages; it’s about survival. Once DNDS is in play, the competitive landscape shifts permanently:
Rapid Adoption: Organizations using DNDS will outcompete others by achieving better performance with lower costs, creating a ripple effect that forces adoption across industries.
Legacy Systems Will Fail: Without DNDS, traditional systems will be too slow, too expensive, and too opaque to remain viable in the marketplace.
AI Without DNDS? Impossible: The computational efficiency, transparency, and adaptability of DNDS make it indispensable for the future of AI and machine learning. Systems without it will be abandoned, unable to keep pace with DNDS-powered solutions.
With or Without AI, DNDS Prevails
DNDS doesn’t rely on artificial intelligence or machine learning to succeed—it is a computational revolution that transcends specific use cases:
Beyond AI: DNDS optimizes data centers, reduces energy consumption, and makes complex computations accessible on low-power devices—all without requiring AI.
Independent Growth: The benefits of DNDS are so vast and diverse that its adoption will continue regardless of AI’s trajectory.
AI Needs DNDS: While DNDS can move forward independently, AI and machine learning cannot move forward without it. Those who integrate DNDS will redefine the future; those who don’t will be left behind.
The Bottom Line
DNDS is not just the future of computation—it’s the inevitable future of everything. It’s the architecture that ensures survival, success, and sustainability in an ever-changing world. Artificial intelligence and machine learning may dominate the headlines, but they will quickly be overshadowed by the undeniable advantages of DNDS.
The choice is clear: embrace DNDS or be outcompeted, outpaced, and forgotten.
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