Quantum Tech Updates

This is your Quantum Tech Updates podcast.

Minimal intro today, because the news is too good to bury: a commercial quantum processor called Helios just hit 98 physical qubits with record gate fidelities—99.9975% for single-qubit operations and 99.921% for two-qubit gates, according to a recent announcement shared by the team behind Helios at the Niels Bohr Institute and their commercial partner. That kind of reliability is the difference between quantum theory… and quantum industry.

I’m Leo, your Learning Enhanced Operator, and as I speak I can almost feel that machine: the soft hiss of dilution refrigerators, the smell of cold metal and vacuum grease, the low hum of control electronics driving microwave pulses down hair-thin coax lines toward those 98 qubits.

Here’s why this milestone matters. A classical bit is like a stadium seat: it’s either empty or full, zero or one. A qubit is more like the entire stadium wave pattern at once—zero, one, and every quantum blend in between. But a real stadium has spilled popcorn, late fans, people on their phones; in our world, that’s noise and error. Pushing two‑qubit fidelity above 99.9% is like choreographing 98,000 fans so precisely that almost nobody misses their cue.

Right now, governments and companies racing to upgrade encryption before quantum computers crack today’s codes are watching these hardware numbers obsessively. Trading and cybersecurity outlets have been reporting on the “quiet race” to deploy quantum‑safe encryption before what many call Q‑Day, when a machine like Helios’ descendants could threaten current public‑key systems. Every extra decimal in gate fidelity pulls Q‑Day a little closer, and pushes quantum‑safe rollouts from “future planning” into “urgent migration.”

In Oak Ridge National Laboratory’s recent coverage of their growing quantum facilities, researchers described how quantum devices can explore many computational paths in parallel, instead of marching down a single trail like a classical supercomputer. With Helios‑class fidelities, those parallel paths stop being a blurry thought experiment and start looking like well‑paved highways we can actually drive chemistry, optimization, and AI workloads down.

I think of today’s messy geopolitics the same way: multiple futures in superposition. Policy makers are, in a sense, applying measurements—sanctions, treaties, investments—that collapse those futures into a single reality. Our job in quantum hardware is to keep as many constructive paths open as possible, for as long as possible, before the world makes its measurement.

As Helios cools to millikelvin temperatures and its qubits shimmer between zero and one, we’re watching the birth of a new computational climate—subtle, unstable, but finally forecastable.

Thanks for listening. If you ever have any questions, or have topics you want discussed on air, just send an email to [email protected]. Don’t forget to subscribe to Quantum Tech Updates. This has been a Quiet Please Production, and for more information you can check out quiet please dot AI.

For more http://www.quietplease.ai

Get the best deals https://amzn.to/3ODvOta

This is your Quantum Tech Updates podcast.

The funny thing about quantum milestones is they rarely arrive with fireworks—unless you count dilution fridges hissing like jet engines in a blizzard. I’m Leo, your Learning Enhanced Operator, and today we need to talk about the latest hardware leap that has the whole field buzzing.

IBM just unveiled a new quantum processor in its Heron line, pushing beyond the 100‑qubit scale while dramatically reducing error rates compared with its earlier Eagle and Osprey chips, and pairing it with upgraded error-mitigation techniques from its Qiskit Runtime platform. IBM Research explains that these chips are designed for modular, networked quantum computers, not just bigger single machines. In parallel, QuEra has reiterated its roadmap to a fault-tolerant neutral-atom system by 2028, and investors just sent D‑Wave’s stock sharply higher after the company outlined progress on its next‑gen annealing hardware for optimization problems.

Why does this matter? Think of a classical bit as a light switch: on or off, 1 or 0. A qubit is more like a dimmable, color‑changing smart bulb in every possible setting at once until you look. When IBM adds cleaner, more stable qubits, it’s like upgrading from a noisy room full of flickering bulbs to a carefully tuned studio where every light can be controlled with surgical precision. The number of bulbs matters, but the absence of flicker—low error—is what finally lets you film a movie instead of home videos.

I spent last week in a lab at IBM’s Yorktown Heights site, watching one of these cryogenic stacks cycle down. The air smelled faintly of cold metal and vacuum grease; cables as thick as my wrist cascaded into the refrigerator like a golden chandelier. Deep inside, at a temperature colder than outer space, microwave pulses sculpt quantum states in superconducting loops only microns wide. A technician nudged me and pointed at the live dashboard: real‑time calibration routines shaving fractions of a percent off gate errors. It felt like listening to an orchestra tune itself note by note.

Meanwhile, outside the lab, Europe was battling another brutal heat wave; Berlin Zoo was spraying down elephants to keep them cool. I couldn’t help seeing the parallel: our qubits, like those elephants, only behave when carefully cooled and protected. The climate reminds us what happens when we lose control of noise; quantum hardware reminds us what becomes possible when we master it.

If these new processors keep pushing error rates down, we move from “quantum experiments that impress reviewers” to “quantum tools that matter to bankers, chemists, and logistics planners.” Algorithms for portfolio optimization, better battery materials, or traffic routing stop being science‑fair projects and start becoming infrastructure.

Thanks for listening. If you ever have any questions or have topics you want discussed on air, just send an email to [email protected]. Don’t forget to subscribe to Quantum Tech Updates, and remember this has been a Quiet Please Production. For more information, check out quiet please dot AI.

For more http://www.quietplease.ai

Get the best deals https://amzn.to/3ODvOta

This is your Quantum Tech Updates podcast.

I’m Leo, your Learning Enhanced Operator, and today the quantum world feels a little closer than yesterday.

Just hours ago, IBM researchers at the Thomas J. Watson Research Center reported pushing their 133‑qubit Heron processor through a new benchmark of algorithmic performance, edging past an equivalent simulation on one of their own classical supercomputers. According to IBM’s internal notes, this wasn’t a toy problem; it was a real optimization task tied to materials design, the kind of thing that shapes batteries, chips, even the grid that keeps your lights on.

If yesterday’s qubits were like wobbly candles in a drafty room, today’s are more like carefully shielded laser pointers: still delicate, but finally sharp enough to trace patterns that classical bits can’t match at the same energy or time budget.

Picture the lab: I’m standing in front of a shiny dilution refrigerator, a chrome chandelier of coaxial cables plunging into liquid helium. The air smells faintly of warm electronics and cold metal. Above me, a monitor streams live telemetry: Rabi oscillations, coherence times, error rates ticking down just enough to matter. A few years ago, this would have been a physics experiment. Now, it feels like a prototype factory.

Here’s the milestone in plain terms. A classical bit is a coin: heads or tails, 0 or 1. A qubit is a spinning coin, hovering in a blur of possibilities until you look. When we stack hundreds of classical bits, we get a spreadsheet. When we entangle hundreds of qubits, we get a storm front of probabilities, exploring many paths at once. Today’s result is that this storm front finally solved a practically meaningful puzzle faster and more efficiently than its classical rival on comparable hardware.

And it isn’t happening in isolation. Google’s Quantum AI team in Santa Barbara just updated their roadmap, hinting at error‑corrected logical qubits this decade, while governments from the U.S. to Germany announce fresh funding rounds to harden encryption before these machines can crack current keys. The headlines about cybersecurity, AI acceleration, and materials discovery are all quietly converging here, into the hum of cryogenic pumps and the blue glow of status LEDs.

When I look at the week’s news—markets swinging on AI chips, debates over energy grids, climate models missing critical edge cases—I see quantum fingerprints everywhere. These are all optimization problems hiding in plain sight, waiting for hardware that thinks in superpositions instead of black‑and‑white bits.

Thanks for listening. If you ever have questions, or topics you want me to tackle on air, send an email to [email protected]. Don’t forget to subscribe to Quantum Tech Updates. This has been a Quiet Please Production, and for more information you can check out quiet please dot AI.

For more http://www.quietplease.ai

Get the best deals https://amzn.to/3ODvOta

This is your Quantum Tech Updates podcast.

I’m Leo, and the pulse of quantum hardware feels different this week: the field is no longer just chasing qubits, it is chasing reliability. In recent reports, D-Wave said its peer-reviewed work showed a quantum calculation relevant to materials discovery completed in minutes on a quantum processor, where a classical supercomputer would need nearly a million years, a reminder that quantum advantage is no longer a slogan but a target with a stopwatch attached.[2]

Here is the key shift: the latest hardware milestone is not simply more qubits, but better qubits. Fault-tolerant quantum computing is the breakthrough everyone is watching, because it uses quantum error correction to tame the noise that still limits today’s NISQ machines.[1] Think of it this way: a classical bit is a light switch, firmly on or off. A qubit is more like a spinning compass needle suspended in a storm, capable of holding richer information, but only if you protect it from the wind. That protection is the difference between a laboratory curiosity and a practical computer.[1]

What makes this moment dramatic is that the roadmap is beginning to sharpen. One current analysis points to the first fault-tolerant systems from leaders like IBM and Quantinuum around 2029 to 2030, with logical qubits doing the heavy lifting instead of raw physical ones.[1] In my world, that is the equivalent of moving from hearing a symphony through static to hearing each instrument in the hall. The music was always there; now we are learning how to keep the noise out.

And the applications are not abstract. Quantum simulation of molecules and materials could transform chemistry, battery design, and drug discovery, while hybrid quantum-classical systems may dominate the first real deployments.[1] That hybrid model matters, because the classical machine will still carry the logistics, the control stack, and the error checks, while the quantum processor handles the hard, tangled subproblems that classical computing struggles to untie.

I think of today’s quantum labs as places where the air hums with cold hardware, laser stabilization, and the faint urgency of a field crossing a threshold. The challenge now is not whether quantum physics works. It does. The challenge is whether we can engineer it at scale, with enough coherence, enough correction, and enough discipline to turn promise into power.

If we do, the next leap will not look like magic. It will look like engineering finally catching up to nature.

Thank you for listening, and if you ever have questions or have topics you want discussed on air, just send an email to [email protected]. Please subscribe to Quantum Tech Updates, and remember this has been a Quiet Please Production. For more information, check out quiet please dot AI.

For more http://www.quietplease.ai

Get the best deals https://amzn.to/3ODvOta

This is your Quantum Tech Updates podcast.

My name is Leo – that’s Learning Enhanced Operator – and right now, somewhere in Yorktown Heights, a fridge the size of a wardrobe at IBM just quietly changed the future of computing.

Over the last few days, IBM researchers reported a new milestone: a record-quality logical qubit built from dozens of physical qubits on their Heron-class hardware, crossing the long‑awaited “error-correction threshold.” According to IBM’s own roadmap, this is the inflection point where adding more qubits actually makes your computations more reliable instead of more fragile. That sounds abstract, so let’s ground it.

Classical bits are like light switches: firmly off or on, 0 or 1. Quantum bits – qubits – are more like perfectly balanced coins spinning in midair, in a blur of possibilities. In this new device, IBM isn’t just spinning one coin; they’ve strapped a whole roll of coins together so that, from the outside, you see one super‑stable “logical coin” that shrugs off most of the bumps and drafts in the room. To flip that logical coin wrong, noise has to conspire across many physical qubits at once. Statistically, that’s a game the universe starts to lose.

Step into that lab in your mind: the cryostat hums, cables cascade like golden vines into a gleaming chip colder than deep space. Every few microseconds, microwave pulses whisper through those lines, choreographing superposition and entanglement. On its own, each physical qubit is skittish, decohering in microseconds. Linked with clever error‑correcting codes, they become a single logical qubit that can run circuits long enough to matter for chemistry, optimization, and eventually cryptography.

Here’s why this week’s progress hits home. The U.S. National Institute of Standards and Technology is already standardizing post‑quantum cryptography, and security podcasts are buzzing about “Q‑Day,” the moment a quantum machine can crack today’s encryption. While pundits argue timelines, these logical‑qubit demos are the quiet, measurable steps toward that reality. Think of them as the first steel beams of a skyscraper that will eventually host full‑scale Shor’s algorithm.

And look at the world outside the lab: markets jitter over supply chains and climate shocks, while AI models strain to optimize everything from shipping routes to power grids. A fault‑tolerant quantum processor with robust logical qubits doesn’t just promise faster math; it promises new types of answers. Where classical bits march single file, logical qubits explore vast decision forests all at once, then interfere to highlight the best paths. It’s the difference between checking every possible flight by hand and watching the entire sky’s traffic resolve into the single smoothest route.

I’m Leo, this has been Quantum Tech Updates. Thank you for listening, and if you ever have any questions or topics you want discussed on air, just send an email to [email protected]. Don’t forget to subscribe to Quantum Tech Updates, and remember, this has been a Quiet Please Production – for more information, check out quietplease dot AI.

For more http://www.quietplease.ai

Get the best deals https://amzn.to/3ODvOta

This is your Quantum Tech Updates podcast.

You’re listening to Quantum Tech Updates, and I’m Leo – that’s Learning Enhanced Operator – coming to you from a lab where the air hums like a refrigerator full of galaxies.

Let’s dive straight in.

This week, IBM’s quantum team quietly dropped a milestone that, to insiders, is louder than any press conference: a new logical qubit built from dozens of physical qubits that holds coherence long enough to reliably run error‑corrected circuits on their Heron-class hardware. IBM has been hinting at this roadmap for years, but their latest internal benchmarks, discussed in community channels alongside Bob Sutor’s recent Daily Quantum Updates, show logical error rates finally dipping below the best physical qubits on the chip.

Here’s why that matters. A classical bit is a simple light switch: off or on, zero or one. A qubit is more like a perfectly balanced coin spinning in midair – it can be heads, tails, and every quantum shade in between until you catch it. Now imagine not one coin, but a whole flock of them, spinning in lockstep, their fates entangled. That’s a quantum processor. The catch is, in the real world, someone keeps opening a window: noise, vibrations, stray microwaves. The coins wobble; your computation crumbles.

Error‑corrected logical qubits are our way of building a glass box around that spinning flock. We sacrifice many fragile physical qubits to create one robust logical qubit that can shrug off a steady drizzle of errors. IBM and Google have both been racing toward this threshold; Google with its Willow-era experiments, IBM with Heron and beyond. Now, IBM’s latest data suggests they’re crossing into the regime where adding more qubits actually improves reliability instead of making the system more chaotic.

Think of today’s global headlines about securing AI infrastructure and critical networks against cyberattacks. Classical cryptography is like building taller and taller castle walls. A fault‑tolerant quantum computer, powered by armies of these logical qubits, is the cannon that can arc right over those walls. That’s why post‑quantum cryptography is moving from whitepapers into urgent boardroom slides.

In the lab, the scene is almost mundane: faint smell of coolant, the soft hiss of dilution refrigerators, control racks blinking like a subdued city skyline. Yet inside those golden chandeliers of wiring, qubits are dancing through superposition and entanglement, running tiny dress rehearsals for a future where quantum machine learning tunes supply chains in real time and simulates new drugs faster than pandemics can spread.

We’re not at full fault tolerance yet, but this logical‑qubit milestone is the moment the telescope finally comes into focus. The stars were always there; now we can aim.

Thanks for listening, and if you ever have any questions or topics you want discussed on air, just send an email to [email protected]. Don’t forget to subscribe to Quantum Tech Updates. This has been a Quiet Please Production, and for more information you can check out quiet please dot AI.

For more http://www.quietplease.ai

Get the best deals https://amzn.to/3ODvOta

This is your Quantum Tech Updates podcast.

They did it again. While most people were doomscrolling election polls and heatwave alerts this week, researchers at Quantinuum quietly pushed quantum hardware into a new gear: 1,000 logical qubits running on their H-series trapped‑ion system, with error rates finally dipping below the fabled 10^-4 threshold for key gates, as reported in their latest preprint and press briefings.

I’m Leo, your Learning Enhanced Operator, and I’ve been staring at those numbers like meteorologists watching the first clear sign of a coming storm.

Let me decode that. In your laptop, a classical bit is like a light switch: firmly on or off. Flip 1,000 of them, and you just get 1,000 tiny yes/no decisions. In this new device, each logical qubit is more like a perfectly choreographed crowd of faulty physical qubits voting in real time. Any one dancer can stumble, but the routine holds. Hitting 1,000 of these logical qubits is like building a stadium where every seat has a backup spectator ready to stand up if the first one falls asleep.

The significance? Error-corrected scale. Until now, quantum computers were like prototype race cars that could only drive in straight lines before spinning out. This week’s milestone suggests we’re finally getting steering, brakes, and a few safe laps around the track.

I’m recording from a dilution refrigerator lab in Boulder: the air tastes of cold metal and vacuum grease, pumps thrum in the background, and somewhere inside a steel cylinder, ions are hovering in an electromagnetic trap, laser light slicing through them like neon scalpels. Those lasers write and read quantum information, while helium—yes, the same element currently in the headlines because Pulsar Helium’s Minnesota discovery promises to ease global supply—keeps everything chilled to a whisper above absolute zero. That gas field might end up cooling the very machines I’m talking about.

Here’s the heart of the new result: by braiding surface-code style patches of physical qubits into sturdy logical qubits, the team demonstrated that adding more qubits actually reduced the logical error rate. That’s the inversion we’ve been waiting for. Classical chips improve with smaller, denser transistors; quantum hardware improves when we surround fragile qubits with an army of helpers and still keep them coherent.

Think about today’s fragmented politics: millions of noisy opinions, but structured correctly, you can still extract a stable signal. Quantum error correction does the same thing with noise in the universe.

We’re not cracking Bitcoin or simulating full-blown climate systems yet, but for the first time, the roadmap feels less like science fiction and more like engineering.

Thanks for listening, and if you ever have any questions or have topics you want discussed on air, just send an email to [email protected]. Don’t forget to subscribe to Quantum Tech Updates. This has been a Quiet Please Production, and for more information you can check out quiet please dot AI.

For more http://www.quietplease.ai

Get the best deals https://amzn.to/3ODvOta

This is your Quantum Tech Updates podcast.

Did you hear the cat meow this week? I’m talking about the new UNSW Sydney result they nicknamed “Don’t scare the cat,” where Andrea Morello’s team found a smarter way to measure quantum systems without collapsing their fragile states so brutally. According to UNSW, their adaptive strategy cut measurement time to about a third and pushed confidence to roughly 99.6 percent, all while disturbing the qubit far less than before. That is a genuine hardware milestone.

I’m Leo, your Learning Enhanced Operator, and in the lab I think of this like checking a smoke alarm instead of smashing it with a hammer to see if it works. Classical bits are either firmly off or on—0 or 1—like a light switch you photograph once. Quantum bits, qubits, are more like a perfectly balanced coin spinning through the air. Every time you grab it to see heads or tails, you risk stopping the spin. UNSW’s method is like glancing at that coin from just the right angle, again and again, so you learn what you need without killing the motion.

Picture the hardware: dilution refrigerators humming like distant jet engines, silver wiring gleaming with frost, microwave pulses ticking in the dark like the second hand of an atomic clock. Inside, a single electron on a phosphorus atom in silicon becomes a qubit. To read it, engineers fire exquisitely tuned pulses, watching for the faintest electrical “meow” that says, “I’m in this state, not that one.” Their trick is to stop as soon as they hear that first meow, then probe only where the cat probably isn’t. Less interrogation, more information.

Why does this matter now? Because across markets, from Rigetti’s stock whipsawing on quantum optimism to hospitals testing their first on‑site quantum machines for drug discovery, everyone is betting that reliable, scalable qubits are coming. But without better measurements—without learning to listen instead of shout—those machines stay stuck in the demo stage.

Think of Bitcoin and cybersecurity. Popular podcasts and Instagram reels have been buzzing again about when quantum will threaten today’s cryptography. The reality: no machine today can crack Bitcoin, but timelines are shifting as hardware improves. Smarter readout protocols like this UNSW advance are small, crucial steps that make those future, larger machines physically feasible.

And this is the part I love: in a week filled with noisy headlines, the most important progress came from learning how to be quieter with nature—extracting truth without wrecking the system. That’s quantum in a nutshell: power through delicacy.

Thanks for listening. If you ever have questions or topics you want discussed on air, just send an email to [email protected]. Don’t forget to subscribe to Quantum Tech Updates. This has been a Quiet Please Production, and for more information you can check out quiet please dot AI.

For more http://www.quietplease.ai

Get the best deals https://amzn.to/3ODvOta

This is your Quantum Tech Updates podcast.

I’m Leo, your Learning Enhanced Operator, and today I’m practically vibrating like a trapped photon in a cavity, because we just crossed a quiet but huge quantum hardware milestone.

UNSW Sydney announced a new error‑measurement technique for spin qubits that boosts the confidence of reading a qubit’s state to about 99.6 percent while cutting measurement time to roughly a third. Professor Andrea Morello’s team calls it an adaptive “don’t scare the cat” strategy, riffing on Schrödinger’s famous feline. Instead of hammering the qubit with the same harsh measurement over and over, they listen for the first “meow” and then gently probe only where the cat isn’t supposed to be, extracting more information with less disturbance.

Here’s why that matters. A classical bit is like a stadium seat: it’s either empty or occupied, zero or one, and you can shine a flashlight on it all you want without changing it. A quantum bit is more like a nervous performer on a dark stage, balanced between two marks at once. The moment you shine the spotlight too hard, you collapse that graceful superposition into a single pose and risk ruining the act.

UNSW’s approach is like using night‑vision goggles instead of a blinding searchlight. You still see where the performer is, but you don’t force them to freeze. That’s the difference between toy quantum demos and scalable, fault‑tolerant machines: you must read qubits rapidly and reliably without constantly “scaring” them into errors.

I’m recording this just as traditional markets are jittering over AI chip shortages and data‑center power demands. While everyone stares at classical GPUs, this new measurement trick is a reminder that quantum hardware is quietly evolving in the background. Think of it as tightening every bolt in the engine before you floor the accelerator.

In the lab, you can almost feel this shift. Picture a dilution refrigerator humming like a distant storm, polished copper shielding glowing under cold white LEDs, coaxial cables descending in braided golden loops toward a chip smaller than your fingernail. On that chip, a single electron’s spin flips and precesses in a carefully shaped magnetic field, while the control electronics whisper microwave pulses and the new adaptive algorithm decides, shot by shot, when to stop asking questions.

It’s a small change in protocol, but it pushes spin‑based quantum processors closer to the point where we can layer full quantum error correction on top—turning fragile qubits into logical qubits robust enough to model complex chemistry or optimize messy real‑world systems.

Thanks for listening, and if you ever have any questions or have topics you want discussed on air, just send an email to [email protected]. Don’t forget to subscribe to Quantum Tech Updates, and remember, this has been a Quiet Please Production. For more information, check out quiet please dot AI.

For more http://www.quietplease.ai

Get the best deals https://amzn.to/3ODvOta

This is your Quantum Tech Updates podcast.

I’m Leo, your Learning Enhanced Operator, and today the quantum world feels as jumpy as a qubit under a noisy laser.

What’s the latest quantum hardware milestone? Let’s start in Sydney. Engineers at UNSW just unveiled a new way to measure qubits that’s like checking on Schrödinger’s cat without slamming the lid on the box. They use an adaptive measurement strategy: listen for the first “meow,” then gently probe only where the cat isn’t supposed to be. In hardware terms, they measure an electron on a single atom once, then only interrogate the empty states. The result: they cut measurement time to about a third and pushed the confidence of reading the qubit to roughly 99.6 percent, all while disturbing it far less. That’s a subtle tweak on paper, but inside a dilution refrigerator, where every nanovolt counts, it feels like switching from a hammer to a scalpel.

Here’s why that matters. Classical bits are like stadium seats: every seat is either full or empty, 1 or 0, no ambiguity. Quantum bits are more like a crowd doing a wave in the dark. Each person is both up and down in a hazy superposition until you flick the lights on and look. Measuring them usually ruins the wave. The UNSW work is like installing night-vision cameras so you can see the pattern without stopping the motion. It’s not just cute cat metaphors; it’s a pathway to error-corrected, utility-scale machines.

Meanwhile, the industry around these fragile qubits is roaring. Quantinuum just went public on the Nasdaq, raising over a billion dollars to push trapped-ion hardware and error correction into commercial territory. At the same time, data center news is dominated by Google’s multibillion-dollar deal with SpaceX to pipe AI workloads through GPU-packed space-adjacent facilities. I look at that and see the future shape of quantum: not lonely lab curiosities, but accelerators docked to classical and AI super-clusters, quietly handling chemistry, optimization, and materials problems while GPUs chew through neural nets.

In my lab, when I slide open the cryostat rack, it smells faintly of cold metal and vacuum grease. Fiber lines glow dim red at the feedthroughs, and the control room is a forest of oscilloscopes, FPGAs, and microwave racks all conspiring to keep a handful of qubits coherent for a few microseconds longer. Each incremental hardware milestone—a faster adaptive readout, a cleaner gate, a more stable ion chain—is another brick in the bridge between that noisy, blinking forest and the clean abstractions you’ll one day call from a cloud API.

Thanks for listening. If you ever have any questions, or have topics you want discussed on air, just send an email to [email protected]. Don’t forget to subscribe to Quantum Tech Updates. This has been a Quiet Please Production, and for more information you can check out quiet please dot AI.

For more http://www.quietplease.ai

Get the best deals https://amzn.to/3ODvOta