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The Place That Taught Machines to Dream

Before Hollywood learned to animate pixels, Silicon Valley learned to animate light. The first dreamers weren’t directors — they were designers and engineers who turned math into motion, built the machines behind Jurassic Park and Toy Story, and taught computers to imagine. Now, those same roots are fueling a new frontier — AI video and generative storytelling.

By Skeeter Wesinger

October 8, 2025

Silicon Valley is best known for chips, code, and capital. Yet long before the first social network or smartphone, it was quietly building a very different kind of future: one made not of transistors and spreadsheets, but of light, motion, and dreams. Out of a few square miles of industrial parks and lab benches came the hardware and software that would transform Hollywood and the entire art of animation. What began as an engineering problem—how to make a computer draw—became one of the most profound creative revolutions of the modern age.

In the 1970s, the Valley was an ecosystem of chipmakers and electrical engineers. Intel and AMD were designing ever smaller, faster processors, competing to make silicon think. Fairchild, National Semiconductor, and Motorola advanced fabrication and logic design, while Stanford’s computer science labs experimented with computer graphics, attempting to render three-dimensional images on oscilloscopes and CRTs. There was no talk yet of Pixar or visual effects. The language was physics, not film. But the engineers were laying the groundwork for a world in which pictures could be computed rather than photographed.

The company that fused those worlds was Silicon Graphics Inc., founded in 1982 by Jim Clark in Mountain View. SGI built high-performance workstations optimized for three-dimensional graphics, using its own MIPS processors and hardware pipelines that could move millions of polygons per second—unheard of at the time. Its engineers created OpenGL, the standard that still underlies most 3D visualization and gaming. In a sense, SGI gave the world its first visual supercomputers. And almost overnight, filmmakers discovered that these machines could conjure scenes that could never be shot with a camera.

Industrial Light & Magic, George Lucas’s special-effects division, was among the first. Using SGI systems, ILM rendered the shimmering pseudopod of The Abyss in 1989, the liquid-metal T-1000 in Terminator 2 two years later, and the dinosaurs of Jurassic Park in 1993. Each of those breakthroughs marked a moment when audiences realized that digital images could be not just convincing but alive. Down the road in Emeryville, the small research group that would become Pixar was using SGI machines to render Luxo Jr. and eventually Toy Story, the first fully computer-animated feature film. In Redwood City, Pacific Data Images created the iconic HBO “space logo,” a gleaming emblem that introduced millions of viewers to the look of digital cinema. All of it—the logos, the morphing faces, the prehistoric beasts—was running on SGI’s hardware.

The partnership between Silicon Valley and Hollywood wasn’t simply commercial; it was cultural. SGI engineers treated graphics as a scientific frontier, not a special effect. Artists, in turn, learned to think like programmers. Out of that hybrid came a new creative species: the technical director, equal parts physicist and painter, writing code to simulate smoke or hair or sunlight. The language of animation became mathematical, and mathematics became expressive. The Valley had turned rendering into an art form.

When SGI faltered in the late 1990s, its people carried that vision outward. Jensen Huang, Curtis Priem, and Chris Malachowsky—former SGI engineers—founded Nvidia in 1993 to shrink the power of those million-dollar workstations onto a single affordable board. Their invention of the graphics processing unit, or GPU, democratized what SGI had pioneered. Gary Tarolli left to co-found 3dfx, whose Voodoo chips brought 3D rendering to the mass market. Jim Clark, SGI’s founder, went on to co-create Netscape, igniting the web era. Others formed Keyhole, whose Earth-rendering engine became Google Earth. Alias | Wavefront, once owned by SGI, evolved into Autodesk Maya, still the industry standard for 3D animation. What began as a handful of graphics labs had by the millennium become a global ecosystem spanning entertainment, design, and data visualization.

Meanwhile, Nvidia’s GPUs kept growing more powerful, and something extraordinary happened: the math that drew polygons turned out to be the same math that drives artificial intelligence. The parallel architecture built for rendering light and shadow was ideally suited to training neural networks. What once simulated dinosaurs now trains large language models. The evolution from SGI’s Reality Engine to Nvidia’s Tensor Core is part of the same lineage—only the subject has shifted from geometry to cognition.

Adobe and Autodesk played parallel roles, transforming these once-elite tools into instruments for everyday creators. Photoshop and After Effects made compositing and motion graphics accessible to independent artists. Maya brought professional 3D modeling to personal computers. The revolution that began in a few Valley clean rooms became a global vocabulary. The look of modern media—from film and television to advertising and gaming—emerged from that convergence of software and silicon.

Today, the next revolution is already underway, and again it’s powered by Silicon Valley hardware. Platforms like Runway, Pika Labs, Luma AI, and Kaiber are building text-to-video systems that generate entire animated sequences from written prompts. Their models run on Nvidia GPUs, descendants of SGI’s original vision of parallel graphics computing. Diffusion networks and generative adversarial systems use statistical inference instead of keyframes, but conceptually they’re doing the same thing: constructing light and form from numbers. The pipeline that once connected a storyboard to a render farm now loops through a neural net.

This new era blurs the line between animator and algorithm. A single creator can describe a scene and watch it materialize in seconds. The tools that once required teams of engineers are being distilled into conversational interfaces. Just as the SGI workstation liberated filmmakers from physical sets, AI generation is liberating them from even the constraints of modeling and rigging. The medium of animation—once defined by patience and precision—is becoming instantaneous, fluid, and infinitely adaptive.

Silicon Valley didn’t just make Hollywood more efficient; it rewrote its language. It taught cinema to think computationally, to treat imagery as data. From the first frame buffers to today’s diffusion models, the through-line is clear: each leap in hardware has unlocked a new kind of artistic expression. The transistor enabled the pixel. The pixel enabled the frame. The GPU enabled intelligence. And now intelligence itself is becoming the new camera.

What began as a handful of chip engineers trying to visualize equations ended up transforming the world’s most powerful storytelling medium. The Valley’s real export wasn’t microchips or startups—it was imagination, made executable. The glow of every rendered frame, from Toy Story to the latest AI-generated short film, is a reflection of that heritage. In the end, Silicon Valley didn’t just build the machines of computation. It taught them how to dream big!

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The Power Law of Mediocrity

The Power Law of Mediocrity: Confessions from the Belly of the VC Beast

By Skeeter Wesinger

October 6, 2025

We all read the headlines. They hit our inboxes every week: some fresh-faced kid drops out of Stanford, starts a company in his apartment, lands millions from a “top-tier” VC, and—poof—it’s a billion-dollar exit three years later. We’re force-fed the kombucha, SXSW platitudes, and “Disruptor of the Year” awards.

The public narrative of venture capital is that of a heroic journey: visionary geniuses striking gold, a thrilling testament to the idea that with enough grit, hustle, and a conveniently privileged network, anyone can build a unicorn. It’s the Disney version of capitalism—“anyone can be a chef,” as in Ratatouille—except this kitchen serves valuations, not ratatouille.

And it’s all a delightful, meticulously crafted fabrication by PR mavens, institutional LPs, and valuation alchemists who discovered long ago that perception is liquidity.

The truth is far less cinematic. Venture capital isn’t a visionary’s playground—it’s a casino, and the house always wins. Lawyers, bankers, and VCs take their rake whether the founders strike it rich or flame out in a spectacular implosion. The real magic isn’t in finding winners; it’s in convincing everyone, especially limited partners and the next crop of naive founders, that every single bet is a winner in the making. And in the current AI gold rush, this narrative isn’t just intoxicating—it’s practically a MDMA-induced hallucination set to a soundtrack of buzzwords and TED-ready hyperbole.

Full disclosure: I’ve been on both sides of that table—VC and angel investor, and founder. So consider this less a critique and more a confession, or perhaps karmic cleansing, from someone who has seen the sausage made and lived to regret the recipe.

The Power Law of Mediocrity

The first and most inconvenient truth? Venture capital isn’t about hitting singles and doubles—it’s about swinging for the fences while knowing, with absolute certainty, that you’ll strike out 90 percent of the time.

Academic data puts it plainly: roughly 75 percent of venture-backed startups never return significant cash to their investors. A typical fund might back ten companies—four will fail outright, four will limp to mediocrity, and one or two might generate a real return. Of those, maybe one breaks double-digit multiples.

And yet, the myth persists. Why? Because returns follow a power law, not a bell curve. A single breakout win papers over nine corpses. The median VC fund barely outperforms the S&P 500, but the top decile—those with one or two unicorns—create the illusion of genius. In truth, it’s statistical noise dressed up as foresight.

The Devil in the Cap Table

Not all angels have halos. Some of them carry pitchforks.

I call them “Devil Investors.” They arrive smiling, armed with mentorship talk and a check just large enough to seem life-changing. Then, once the ink dries, they sit you down and explain “how the real world works.” That’s when the charm evaporates. Clauses appear like tripwires—liquidation preferences, ratchets, veto rights. What looked like partnership becomes ownership.

These are the quiet tragedies of the startup world: founders who lose not only their companies but their sense of agency, their belief that vision could trump capital. Venture capital thrives on asymmetry—of information, of power, of options.

So no, I don’t feel bad when VCs get hoodwinked. They’ve built an empire on the backs of the optimistic, the overworked, and the under-represented. When a fund loses money because it failed to do due diligence, that’s not misfortune—that’s karma.

For every VC who shrugs off a loss as “portfolio churn,” there’s a founder who’s lost years, health, and ownership of the very thing they built. The VC walks away with a management fee and another fund to raise. The founder walks away with debt and burnout.

The Great AI Hallucination

If the 2010s were about social apps and scooters, the 2020s are about AI euphoria. Every week, another “AI-powered” startup raises $50 million for a product that doesn’t exist, can’t scale, and often relies entirely on someone else’s model.

It’s déjà vu for anyone who remembers the dot-com bubble—companies worth billions on paper, zero on the balance sheet. But in this era, the illusion has new fuel: the hype multiplier of media and the self-referential feedback loops of venture circles. Valuation becomes validation. Paper gains become gospel.

In private, partners admit the math doesn’t add up. In public, they double down on buzzwords: foundational models, RAG pipelines, synthetic data moats. They don’t have to be right—they just have to be first, loud, and liquid enough to raise Fund IV before Fund III collapses.

The House Always Wins

The cruel beauty of venture capital is that even when the bets go bad, the system pays its insiders. Management fees—usually 2 percent of committed capital—keep the lights on. Carried interest, when a unicorn hits, covers a decade of misses. It’s a model designed to appear risky while transferring the risk onto everyone else.

Founders risk their sanity, employees their weekends, and LPs their patience. The VC? He risks his reputation—which, in this industry, can always be rebranded.

A Confession, Not a Complaint

I say all this not as an outsider looking in but as someone who once believed the myth—that innovation needed gatekeepers, that disruption was noble, that capital was somehow creative. I’ve seen brilliant ideas die not for lack of ingenuity but for lack of political capital in a partner meeting.

Venture capital has produced miracles—no question. But for every transformative success, there are hundreds of broken dreams swept quietly into the footnotes of fund reports.

Pulling Back the Curtain

The next time you read about a wunderkind founder and their dazzling valuation, remember: you’re seeing the show, not the spreadsheet. Behind the curtain lies an industry that’s part casino, part cult, and wholly addicted to the illusion of inevitability.

Because in venture capital, the product isn’t innovation.
It’s a belief—and belief, conveniently, can be marked up every quarter.

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Titaniums Secret War

Titanium’s Secret War: Could Vale Be Eyeing Labrador’s Radar Project?
Story By Skeeter Wesinger
September 16, 2025

In the far reaches of Labrador, where winter stretches nine months and the land is as harsh as it is resource-rich, a junior exploration company says it may have stumbled onto one of North America’s most significant new sources of titanium. Saga Metals’ Radar Project has been promoted as road-accessible, near a port, an airstrip, and hydro power. But critics argue that in reality, it’s hell and gone from anywhere.
And yet, despite the challenges, whispers are circulating: could mining giant Vale already be circling?
Titanium is no longer just for aerospace engineers and medical implants. It’s the quiet backbone of 21st-century warfare: drones, hypersonic missiles, stealth fighters. The U.S. imports over 90% of its titanium feedstock, largely from Russia, China, and Kazakhstan. That dependency has become a glaring weakness at a time when defense spending is surging past $1 trillion. For Washington policymakers, securing a domestic or friendly-jurisdiction supply of titanium isn’t just an economic issue. It’s a national security imperative.

From communications satellites to aircraft carriers, titanium’s unmatched strength, lightness, and heat resistance make it indispensable — even the F-35 relies on it to secure America’s military advantage.

The F-35 is America’s military advantage.

Vale already has a commanding presence in Newfoundland and Labrador through its Voisey’s Bay nickel-copper-cobalt mine and Long Harbour hydromet plant. Those assets anchor Vale to the province, with billions already invested and deep relationships built with government and Indigenous stakeholders. So if Labrador is being positioned as a titanium-vanadium corridor — with Saga’s Radar Project next to Rio Tinto’s long-running Lac Tio mine — wouldn’t Vale at least be curious?
Officially, Vale has said nothing. But that silence may say less about disinterest and more about timing. Mining majors rarely move at the exploration stage. They let juniors burn cash and prove up a resource. Only once grades, tonnage, and metallurgy are de-risked do they swoop in with capital and scale. The Radar site is remote, snowbound most of the year, and would require major road, port, and power upgrades to reach production. Vale is focused on nickel and copper, metals tied to electrification and EVs, but vanadium — with its growing role in grid-scale batteries — could give them a reason to pay attention.
What if the U.S. or Canada starts subsidizing titanium development the way they did rare earths or semiconductors? That would change the math overnight. Vale, with its capital, processing expertise, and political weight, could then step in as a consolidator. It wouldn’t be the first time a major stayed quiet until the subsidies hit.
Saga’s drill results have been splashy — magnetometer readings that “maxed out the machine,” multi-metal mineralization, comparisons to China’s massive Panzhihua deposit. For now, it’s still a speculative story. But the gravity of titanium demand is real. And if Labrador is destined to become a titanium hub, Vale is already in the neighborhood.
It’s easy to dismiss Saga’s Radar Project as another hyped junior play, complete with glossy investor decks and paid promotions. But it’s also easy to forget that the world’s mining giants often wait in the wings, letting the market underestimate projects until the timing is right. In a world where titanium has become the metal behind drones, jets, and modern defense, ignoring Labrador’s potential may not be an option forever.

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Inside the ShinyHunters Breach

Inside the ShinyHunters Breach: How a Cybercrime Collective Outsmarted Google

By Skeeter Wesinger

August 26, 2025

In June 2025, a phone call was all it took to crack open one of the world’s most secure companies. Google, the billion-dollar titan that built Chrome, Gmail, and Android, didn’t fall to an exotic zero-day exploit or state-sponsored cyberweapon. Instead, it stumbled over a voice on the line.

The culprits were ShinyHunters, a name that has haunted cybersecurity teams for nearly half a decade. Their infiltration of Google’s Salesforce system—achieved by tricking an employee into installing a poisoned version of a trusted utility—didn’t yield passwords or credit card numbers. But what it did uncover, millions of names, emails, and phone numbers, was enough to unleash a global phishing storm and prove once again that the human element remains the weakest link in digital defense.

ShinyHunters first burst onto the scene in 2020, when massive troves of stolen data began appearing on underground forums. Early hits included databases from Tokopedia, Wattpad, and Microsoft’s private GitHub repositories. Over time, the group built a reputation as one of the most prolific sellers of stolen data, often releasing sample leaks for free to advertise their “work” before auctioning the rest to the highest bidder. Unlike some cybercrime groups that focus on a single specialty—ransomware, banking trojans, or nation-state espionage—ShinyHunters thrive on versatility. They have carried out brute-force intrusions, exploited cloud misconfigurations, and, as Google’s case shows, mastered social engineering. What ties their operations together is a single goal: monetization through chaos. Their name itself comes from the Pokémon community, where “shiny hunters” are players obsessively searching for rare, alternate-colored Pokémon. It’s a fitting metaphor—ShinyHunters sift through digital landscapes looking for rare weaknesses, exploiting them, and then flaunting their finds in dark corners of the internet.

The attack on Google was as elegant as it was devastating. ShinyHunters launched what cybersecurity experts call a vishing campaign—voice phishing. An employee received a convincing phone call from someone posing as IT support. The hacker guided the target into downloading what appeared to be Salesforce’s Data Loader, a legitimate tool used by administrators. Unbeknownst to the victim, the tool had been tampered with. Once installed, it silently granted ShinyHunters remote access to Google’s Salesforce instance. Within hours, they had siphoned off contact data for countless small and medium-sized business clients. The breach didn’t expose Gmail passwords or financial records, but in today’s digital ecosystem, raw contact data can be just as dangerous. The stolen information became ammunition for phishing campaigns that soon followed—calls, texts, and emails impersonating Google staff, many of them spoofed to look as though they came from Silicon Valley’s “650” area code.

This wasn’t ShinyHunters’ first high-profile strike. They’ve stolen databases from major corporations including AT&T, Mashable, and Bonobos. They’ve been linked to leaks affecting over 70 companies worldwide, racking up billions of compromised records. What sets them apart is not sheer volume but adaptability. In the early days, ShinyHunters focused on exploiting unsecured servers and developer platforms. As defenses improved, they pivoted to supply-chain vulnerabilities and cloud applications. Now, they’ve sharpened their social engineering skills to the point where a single phone call can topple a security program worth millions. Cybersecurity researchers note that ShinyHunters thrive in the gray zone between nuisance and catastrophe. They rarely pursue the destructive paths of ransomware groups, preferring instead to quietly drain data and monetize it on dark web markets. But their growing sophistication makes them a constant wildcard in the cybercrime underworld.

Google wasn’t the only target. The same campaign has been tied to breaches at other major corporations, including luxury brands, airlines, and financial institutions. The common thread is Salesforce, the ubiquitous customer relationship management platform that underpins business operations worldwide. By compromising a Salesforce instance, attackers gain not only a list of customers but also context—relationships, communication histories, even sales leads. That’s gold for scammers who thrive on credibility. A phishing email that mentions a real company, a real client, or a recent deal is far harder to dismiss as spam. Google’s prominence simply made it the most visible victim. If a company with Google’s security apparatus can be tricked, what chance does a regional retailer or midsize manufacturer have?

At its core, the ShinyHunters breach of Google demonstrates a troubling shift in cybercrime. For years, the focus was on software vulnerabilities—buffer overflows, unpatched servers, zero-days. Today, the battlefield is human psychology. ShinyHunters didn’t exploit an obscure flaw in Salesforce. They exploited belief. An employee believed the voice on the phone was legitimate. They believed the download link was safe. They believed the Data Loader tool was what it claimed to be. And belief, it turns out, is harder to patch than software.

Google has confirmed that the incident did not expose Gmail passwords, and it has urged users to adopt stronger protections such as two-factor authentication and passkeys. But the broader lesson goes beyond patches or new login methods. ShinyHunters’ success highlights the fragility of digital trust in an era when AI can generate flawless fake voices, craft convincing emails, and automate scams at scale. Tomorrow’s vishing call may sound exactly like your boss, your colleague, or your bank representative. The line between legitimate communication and malicious deception is blurring fast. For ShinyHunters, that blurring is the business model. And for the rest of us, it’s a reminder that the next major breach may not come from a flaw in the code, but from a flaw in ourselves. And these ShinyHunters use fake Gmail accounts, which will get them caught.

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Scattered Spider

Scattered Spider: Impersonation, and Cybersecurity in the Age of Cloud Computing

By Skeeter Wesinger
June 29, 2025

In an era where companies have moved their infrastructure to the cloud and outsourced much of their IT, one old-fashioned tactic still defeats the most modern defenses: impersonation.
At the center of this threat is Scattered Spider, a cybercriminal collective that doesn’t exploit code—they exploit people. Their operations are quiet, persuasive, and dangerously effective. Instead of smashing through firewalls, they impersonate trusted employees—often convincingly enough to fool help desks, bypass multi-factor authentication, and gain access to critical systems without ever tripping an alarm.
This is the cybersecurity challenge of our time. Not ransomware. Not zero-days. But trust itself.
Who Is Scattered Spider?
Known to threat intelligence teams as UNC3944, Muddled Libra, or 0ktapus, Scattered Spider is an English-speaking group that has compromised some of the most security-aware companies in North America. Their breaches at MGM Resorts and Caesars Entertainment made headlines—not because they used sophisticated malware, but because they didn’t have to.
Their weapon of choice is the phone call. A help desk technician receives a request from someone claiming to be a senior executive who lost their device. The impersonator is articulate, knowledgeable, and urgent. They know internal jargon. They cite real names. Sometimes, they even use AI-generated voices.
And too often, it works. The attacker gets a password reset, reroutes MFA codes, and slips in undetected.
The Illusion of Familiarity
What makes these attackers so dangerous is their ability to sound familiar. They don’t just say the right things—they say them the right way. They mirror internal language. They speak with confidence. They understand hierarchy. They’re skilled impersonators, and they prey on a simple reflex: the desire to help.
In the past, we might have trusted our ears. “It sounded like them,” someone might say.
But in the age of AI, “sounding like them” is no longer proof of identity. It’s a liability.
When Cloud Isn’t the Cure
Many organizations have moved to cloud-based environments under the assumption that centralization and managed services will reduce their exposure. In some ways, they’re right: the cloud simplifies infrastructure and offloads security operations. But here’s the truth: you can’t outsource responsibility. The human layer remains—and that’s precisely where Scattered Spider operates.
They don’t need to breach Azure or AWS. They just need to impersonate someone with access to it.
It’s time we stop treating “trust but verify” as a cliché and start treating it as operational policy. Better yet: trust—but always verify. Every request. Every reset. Every exception.
Verification today means more than checking a box. It requires multi-channel authentication. It means never resetting MFA or passwords based solely on a phone call, no matter how credible the caller seems. It means locking down help desk protocols so impersonation doesn’t slip through the cracks.
Security teams must also monitor legitimate tools—like AnyDesk, TeamViewer, and ScreenConnect—that attackers often use once inside. These aren’t inherently malicious, but in the wrong hands, they’re devastating.
And above all, organizations must train their frontline personnel—especially support staff—to treat every identity request with healthy skepticism. If your instinct says something feels off, pause and verify through secure channels. Escalate. Slow down. Ask the questions attackers hope you won’t.
Scattered Spider doesn’t hack your servers. They hack your systems of trust. They bypass encryption by impersonating authority. And they exploit the one vulnerability no software can patch: assumption.
As we continue shifting toward remote work, outsourced IT, and cloud-based everything, the real threat isn’t technical—it’s personal. It’s the voice on the line. The urgent request. The person who “sounds right.”
In this world, cybersecurity isn’t just about what you build. It’s about what you believe—and what you’re willing to question.
Therefore, you have to train your teams. Harden your protocols. And remember in the age of the cloud, the most important firewall is still human.
Trust—but always verify!

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When the Dead Speak

When the Dead Speak: AI, Ethics, and the Voice of a Murder Victim
By Skeeter Wesinger
May 7, 2025

In a Phoenix courtroom not long ago, something happened that stopped time.

A voice echoed through the chamber—steady, direct, unmistakably human.

“To Gabriel Horcasitas, the man who shot me: it is a shame we encountered each other that day in those circumstances.”

It was the voice of Chris Pelkey, who had been dead for more than three years—killed in a road rage incident. What the judge, the defendant, and the grieving family were hearing was not a recording. It was a digital recreation of Chris, constructed using artificial intelligence from photos, voice samples, and memory fragments.

For the first time, a murder victim addressed their killer in court using AI.

Chris’s sister, Stacey Wales, had been collecting victim impact statements. Forty-nine in total. But one voice—the most important—was missing. So she turned to her husband Tim and a friend, Scott Yentzer, both experienced in emerging tech. Together, they undertook a painful and complicated process of stitching together an AI-generated likeness of Chris, complete with voice, expression, and tone.

There was no app. No packaged software. Just trial, error, and relentless care.

Stacey made a deliberate choice not to project her own grief into Chris’s words. “He said things that would never come out of my mouth,” she explained. “But I know would come out his.”

What came through wasn’t vengeance. It was grace.

“In another life, we probably could’ve been friends. I believe in forgiveness and in God who forgives. I always have and I still do.”

It left the courtroom stunned. Judge Todd Lang called it “genuine.” Chris’s brother John described it as waves of healing. “That was the man I knew,” he said.

I’ve written before about this phenomenon. In January, I covered the digital resurrection of John McAfee as a Web3 AI agent—an animated persona driven by blockchain and artificial intelligence. That project blurred the line between tribute and branding, sparking ethical questions about legacy, consent, and who has the right to speak for the dead.

But this—what happened in Phoenix—was different. No coin. No viral play. Just a family trying to give one man—a brother, a son, a victim—a voice in the only place it still mattered.

And that’s the line we need to watch.

AI is going to continue pushing into the past. We’ll see more digital likenesses, more synthesized voices, more synthetic presence. Some will be exploitative. Some will be powerful. But we owe it to the living—and the dead—to recognize the difference.

Sometimes, the most revolutionary thing AI can do isn’t about what’s next.

It’s about letting someone finally say goodbye.

Let’s talk:
➡ Should AI have a role in courtrooms?
➡ Who owns the voice of the deceased?
➡ Where should we draw the ethical boundary between tribute and manipulation?

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Beyond Euclidean Memory

Beyond Euclidean Memory: Quantum Storage Architectures Using 4D Hypercubes, Wormhole-Looped States, and Braided Qubit Paths

By Skeeter Wesinger
April 16, 2025

Abstract In the evolving landscape of quantum technology, traditional memory systems rooted in Euclidean geometry are hitting their limits. This post explores three radical constructs—4D hypercubes, wormhole-looped memory states, and braided qubit paths—that are redefining how information is stored, accessed, and preserved in quantum systems. Together, these approaches promise ultradense, energy-efficient, and fault-tolerant memory networks by moving beyond conventional spatial constraints.

  1. Introduction Classical memory architecture assumes linear addressability in a 2D or 3D layout—structures that struggle to scale in the face of today’s power, thermal, and quantum coherence constraints. Quantum memory design, on the other hand, opens the door to higher-dimensional and non-local models. This article outlines a new conceptual framework for memory as a dynamic, entangled fabric of computation, rather than a passive container of bits.
  2. The 4D Hypercube in Memory Design The tesseract, or 4D hypercube, expands traditional 3D memory lattices by adding a fourth spatial axis. This architecture allows non-linear adjacencies and exponential addressability.

2.1 Spatial Folding and Compression

  • Logical neighbors can occupy non-contiguous physical space
  • Memory density increases without amplifying thermal output
  • Redundant access paths collapse, reducing latency

2.2 Picobots and MCUs

  • Picobots manage navigation through hyperedges
  • Micro-Control Units (MCUs) translate 4D coordinates into executable memory requests
  1. Wormhole-Looped Memory States Quantum entanglement allows two distant memory nodes to behave as if adjacent, thanks to persistent tunneling paths—or wormhole-like bridges.

3.1 Topological Linking

  • Entangled nodes behave as spatially adjacent
  • Data can propagate with no traversal through intermediate nodes

3.2 Redundancy and Fault Recovery

  • Instant fallback routes minimize data loss during decoherence events
  • Eliminates thermal hotspots and failure zones
  1. Braided Qubit Paths Borrowed from topological quantum computing, braided qubit paths encode information not in particle states, but in the paths particles take.

4.1 Topological Encoding

  • Logical data is stored in the braid pattern
  • Immune to transient local noise and electromagnetic fluctuations

4.2 Persistent Logic Structures

  • Braids can be reconfigured without data corruption
  • Logical gates become pathways, not gates per se
  1. Non-Local 3D Topologies: The Execution Layer Memory in these architectures is not stored in a fixed location—it lives across a distributed, entangled field.

5.1 Flattening Physical Constraints

  • Logical proximity trumps physical distance
  • Reduces energy costs associated with moving data

5.2 Topological Meshes and Networked Tensors

  • MCUs dynamically reconfigure access paths based on context
  • Enables self-healing networks and true parallel data operations
  1. Conclusion Quantum systems built around 4D hypercubes, wormhole-bridged memory states, and braided qubit paths promise not just new efficiencies, but a reimagining of what memory is. These systems are not static repositories—they are active participants in computation itself. In escaping the confines of Euclidean layout, we may unlock memory architectures capable of evolving with the data they hold.

Welcome to memory without location.

Follow Skeeter Wesinger on Substack  For more deep dives into quantum systems, speculative computing, and post-classical architecture. Questions, insights, or counter-theories? Drop a comment below or reach me at skeeter@skeeter.com.

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In a classic phishing move

In a classic phishing move: spoofing a legit security company like VadeSecure to make the email look trustworthy. Irony at its finest—phishers pretending to be the anti-phishing experts.

Here’s what’s likely going on:

  • vadesecure.com is being spoofed—the return address is faked to show their domain, but the email didn’t actually come from Vade’s servers.

  • Or the phishers are using a lookalike domain (e.g., vadesecure-support.com or vadesecure-mail.com) to trick people not paying close attention.

If you still have the email:

  • You can check the email headers to see the real “from” server (look for Return-Path and Received lines).

  • If the SPF/DKIM/DMARC checks fail in the headers, that’s confirmation it’s spoofed.

  • You can also report it to VadeSecure directly at: abuse@vadesecure.com

By Skeeter Wesinger

March 26, 2025

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Schrödinger’s Cat Explained & Quantum Computing

Schrödinger’s Cat Explained & Quantum Computing

Schrödinger’s cat is a thought experiment proposed by physicist Erwin Schrödinger in 1935 to illustrate the paradox of quantum superposition and observation in quantum mechanics.

Google’s Sycamore Processor EXPOSED What’s Next for Quantum Supremacy

The Setup:

Imagine a cat placed inside a sealed box along with:

  1. A radioactive atom that has a 50% chance of decaying within an hour.
  2. A Geiger counter that detects radiation.
  3. A relay mechanism that, if the counter detects radiation, triggers:
    • A hammer to break a vial of poison (e.g., hydrocyanic acid).
    • If the vial breaks, the cat dies; if not, the cat lives.

The Paradox:

Before opening the box, the quantum system of the atom is in a superposition—it has both decayed and not decayed. Since the cat’s fate depends on this, the cat is both alive and dead at the same time until observed. Once the box is opened, the wavefunction collapses into one state—either dead or alive.

This paradox highlights the odd implications of quantum mechanics, particularly the role of the observer in determining reality.

How Does Antimony Play into This?

Antimony (Sb) is relevant to Schrödinger’s cat in a few ways:

  1. Radioactive Isotopes of Antimony

Some isotopes of antimony, such as Antimony-124 and Antimony-125, undergo beta decay—which is similar to the radioactive decay process in Schrödinger’s experiment. This means that an antimony isotope could replace the radioactive atom in the setup, making it a more tangible example.

  1. Antimony’s Role in Detection
  • Antimony trioxide (Sb₂O₃) is used in radiation detectors.
  • In Schrödinger’s experiment, the Geiger counter detects radiation to trigger the poison release.
  • Some radiation detectors use antimony-doped materials to enhance sensitivity, making it potentially a critical component in the detection mechanism.
  1. Antimony and Quantum Mechanics Applications
  • Antimony-based semiconductors are used in quantum computing and superconducting qubits—which are crucial for studying quantum superposition, the core idea behind Schrödinger’s paradox.
  • Antimonides (like Indium Antimonide, InSb) are used in infrared detectors, which relate to advanced quantum experiments.

 

  1. Schrödinger’s Cat and Quantum Computing

The paradox of Schrödinger’s cat illustrates superposition, a key principle in quantum computing.

Superposition in Qubits

  • In classical computing, a bit is either 0 or 1.
  • In quantum computing, a qubit (quantum bit) can exist in a superposition of both 0 and 1 at the same time—just like Schrödinger’s cat is both alive and dead until observed.
  • When measured, the qubit “collapses” to either 0 or 1, similar to opening the box and determining the cat’s fate.

Entanglement and Measurement

  • In Schrödinger’s thought experiment, the cat’s fate is entangled with the state of the radioactive atom.
  • In quantum computing, entanglement links qubits so that the state of one affects another, even over long distances.
  • Measurement in both cases collapses the system, meaning observation forces the system into a definite state.
  1. How Antimony Plays into Quantum Computing

Antimony is significant in quantum computing for materials science, semiconductors, and superconductors.

  1. Antimony in Qubit Materials
  • Indium Antimonide (InSb) is a topological insulator with strong spin-orbit coupling, which is important for Majorana qubits—a type of qubit promising for error-resistant quantum computing.
  • Superconducting qubits often require materials like antimony-based semiconductors, which have been used in Josephson junctions for superconducting circuits in quantum processors.
  1. Antimony in Quantum Dots
  • Antimony-based quantum dots (tiny semiconductor particles) help create artificial atoms that can function as qubits.
  • These quantum dots can be controlled via electric and magnetic fields, helping develop solid-state qubits for scalable quantum computing.
  1. Antimony in Quantum Sensors
  • Antimony-doped detectors improve sensitivity in quantum experiments.
  • Quantum computers rely on precision measurements, and antimony-based materials contribute to high-accuracy quantum sensing.
  1. The Big Picture: Quantum Computing and Schrödinger’s Cat
  • Schrödinger’s cat = Superposition and measurement collapse.
  • Entanglement = Cat + radioactive decay connection.
  • Antimony = Key material for qubits and quantum detectors.

Schrödinger’s cat symbolizes the weirdness of quantum mechanics, while antimony-based materials provide the physical foundation to build real-world quantum computers.

 

  1. Topological Qubits: A Path to Error-Resistant Quantum Computing

Topological qubits are one of the most promising types of qubits because they are more stable and resistant to errors than traditional qubits.

  1. What is a Topological Qubit?
  • A topological qubit is a qubit where quantum information is stored in a way that is insensitive to small disturbances—this makes them highly robust.
  • The key idea is to use Majorana fermions—hypothetical quasi-particles that exist as their own antiparticles.
  • Unlike traditional qubits, where local noise can cause decoherence, topological qubits store information non-locally, making them more stable.
  1. How Antimony is Involved

Antimony-based materials, particularly Indium Antimonide (InSb) and Antimony Bismuth compounds, are crucial for creating these qubits.

  1. Indium Antimonide (InSb) in Topological Qubits
  • InSb is a topological insulator—a material that conducts electricity on its surface but acts as an insulator internally.
  • It exhibits strong spin-orbit coupling, which is necessary for the creation of Majorana fermions.
  • Researchers use InSb nanowires in superconducting circuits to create conditions for topological qubits.
  1. Antimony-Bismuth Compounds in Topological Computing
  • Bismuth-Antimony (BiSb) alloys are another class of topological insulators.
  • These materials help protect quantum states by preventing unwanted environmental interactions.
  • They are being explored for fault-tolerant quantum computing.
  1. Why Topological Qubits Matter
  • Error Correction: Traditional quantum computers need error-correction algorithms, which require many redundant qubits. Topological qubits naturally resist errors.
  • Scalability: Microsoft and other companies are investing heavily in Majorana-based quantum computing because it could scale up more efficiently than current quantum architectures.
  • Longer Coherence Time: A major problem with quantum computers is that qubits lose their quantum states quickly. Topological qubits could last thousands of times longer.
  1. Superconducting Circuits: The Heart of Modern Quantum Computers

While topological qubits are still in the research phase, superconducting circuits are the most widely used technology in quantum computers today.

  1. How Superconducting Circuits Work
  • Superconducting quantum computers rely on Josephson junctions, which are made of two superconductors separated by a thin insulating barrier.
  • These junctions allow Cooper pairs (pairs of electrons) to tunnel through, enabling quantum superposition and entanglement.
  • Quantum processors made by Google, IBM, and Rigetti use this technology.
  1. How Antimony Helps Superconducting Qubits
  • Some superconducting materials use antimony-based compounds to enhance performance.
  • Antimony-doped niobium (NbSb) and indium-antimonide (InSb) are being tested to reduce decoherence and improve qubit stability.
  • Antimony-based semiconductors are also used in the control electronics needed to manipulate qubits.
  1. Superconducting Qubit Applications
  • Google’s Sycamore Processor: In 2019, Google’s Sycamore quantum processor used superconducting qubits to perform a calculation that would take a classical supercomputer 10,000 years to complete in just 200 seconds.
  • IBM’s Eagle and Condor Processors: IBM is scaling its superconducting quantum processors, aiming for over 1,000 qubits.

By Skeeter Wesinger

February 21, 2025

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DeepSeek’s Mind-Blowing AI Solution Caught in a Cover-Up?

DeepSeek, a rising CCP AI company, was under siege. The company’s official statement, issued in careful, bureaucratic phrasing, spoke of an orchestrated “distributed denial-of-service (DDoS) attack” aimed at crippling its systems. A grave and urgent matter, to be sure. Yet, for those who had followed the firm’s meteoric rise, there was reason for skepticism

DeepSeek had, until this moment, presented itself as a leader in artificial intelligence, one of the few entities capable of standing alongside Western firms in the increasingly cutthroat race for dominance in machine learning. It was a firm backed, either openly or in whispered speculation, by the unseen hand of the Chinese state. The company’s servers, housed in mainland China, were reportedly fueled by NVIDIA H800 GPUs, their interconnections optimized through NVLink and InfiniBand. A formidable setup, at least on paper

But then came the curious measures. Whole swaths of IP addresses, particularly from the United States, were unceremoniously blocked. The platform’s registration doors were slammed shut. And in the vague, elliptical style of official Chinese pronouncements, the public was assured that these were emergency steps to preserve service stability. What the company did not say—what they could not say—was that these actions bore all the hallmarks of a hasty retreat, rather than a tactical defense

For a true DDoS attack—one launched by sophisticated adversaries—there were measures to mitigate it. Content delivery networks. Traffic filtering. Rate-limiting techniques refined over decades by those who had fought in the trenches of cybersecurity. Yet DeepSeek’s response was not one of resilience, but of restriction. They were not filtering the bad actors; they were sealing themselves off from the world

A theory began to take shape among industry watchers. If DeepSeek had overestimated its own technological prowess, if its infrastructure was ill-prepared for rapid growth, the sudden influx of new users might have looked, to their own internal systems, like an attack. And if the company was not merely a commercial enterprise but an entity with deeper ties—perhaps to sectors of the Chinese government—it would not do to admit such failings publicly. To confess that their AI could not scale, that their systems could not bear the weight of global interest, would be an unpardonable humiliation.

The consequences of such a revelation would be severe. The markets had already felt the tremors of cyberattacks; the global economy had bled $1.5 trillion due to disruptions of this nature. If DeepSeek, a firm hailed as the vanguard of China’s AI ambitions, was faltering under its own weight, the financial and political repercussions would extend far beyond the walls of its server farms. The illusion of invulnerability had to be maintained

Thus, the narrative of a “DDoS attack” was not merely convenient—it was necessary. It allowed DeepSeek to take drastic action while obscuring the truth. Blocking foreign IPs? A countermeasure against cyber threats. Suspending new users? A precaution against infiltration. A firm whose technological backbone was more fragile than its reputation suggested had suddenly found an excuse to withdraw from scrutiny under the guise of self-defense

It is in such moments that history leaves its telltale fingerprints. The annals of technological development are filled with entities that stumbled not due to sabotage, but due to their own shortcomings, concealed under layers of propaganda and misdirection. One wonders if, years from now, when the documents are unsealed and the real story emerges, historians will look back at DeepSeek’s so-called DDoS crisis not as an act of foreign aggression—but as a moment of revelation, when the cracks in the edifice became too great to hide

Also, the DeepSeek app has been removed from both Apple’s App Store and Google’s Play Store in Italy. This action occurred after Italy’s data protection authority, known as the Garante, requested information from DeepSeek regarding its handling of personal data. Users attempting to access the app in Italy received messages indicating that it was “currently not available in the country or area you are in” on Apple’s App Store and that the download “was not supported” on Google’s platform. As reported by REUTERS.CO

Regarding Ireland, the Irish Data Protection Commission has also reached out to DeepSeek, seeking details about how it processes data related to Irish users. However, as of now, there is no confirmation that the app has been removed from app stores in Ireland. As reported by THEGUARDIAN.COM

Currently there is no publicly available information indicating that DeepSeek has specifically blocked access from Apple, Google, or individual reporters’ servers. It’s possible that access issues could be related to the broader measures DeepSeek has implemented in response to recent events, but without specific details, it’s difficult to determine the exact cause.

For now, the truth remains elusive, hidden behind digital firewalls and the careful hand of censorship. But as in all such cases, history is patient. It waits for those who will dig deeper, who will look beyond the official statements and ask: Was it an attack? Or was it something else entirely?

Story By Skeeter Wesinger

January 30, 2025

 

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