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Clem Delangue joins MTS to discuss the global open-source AI landscape, the current large language model bubble, and the future of consumer robotics.
Originally aired on MTS, Theo Jaffee and Sofia Puccini speak with Clément Delangue, CEO at Hugging Face, about the global open-source AI race, why he believes the real bubble is in API-based large language models, and how robotics could become the next major interface for AI. They also discuss AI safety, U.S.-China competition, open-weight models, and why Hugging Face became the infrastructure layer for open AI development.
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Although Cameron McCord wasn’t himself present at Edwards Air Force Base, what was about to transpire that May 2025 day in the Mojave Desert would still make it one he would never forget. AJ Piplica, CEO of Hermeus, was huddled with his team on the tarmac. No matter the outcome of the day’s flight test, he too was going to have a company-defining day. After years of labor, Piplica and his team would be testing their new hypersonic airplane engine, first taxiing down the runway, and assuming that went well, actual liftoff.
Even more nerve-wracking than the prospect of fundraising if the test failed was that of the dual taxi and liftoff test in a single day, a feat that would have been unthinkable even a few years ago. “Our data from the taxi needed to give us confidence that we’re not taking a dumb risk by attempting takeoff and landing. And that’s a huge amount of data to review, that historically took weeks, if not months, to parse between high-speed taxi and first flight,” Piplica says. The time they’d been allotted on the runway for both tests: two hours.
But his team had a new secret weapon. After years of employing a messy patchwork of data review tools that were not designed for hardware testing, Piplica had recently signed a contract with McCord’s Nominal, a company whose sole focus was testing hardware for real-world deployment. Hermeus had seen success with the platform for preliminary Hardware-In-the-Loop (HITL) tests in their Atlanta warehouse, but never in the field with the Air Force breathing down their necks, and certainly never with such high stakes and so little time.
As Hermeus’s plane started taxiing smoothly down the runway just under liftoff speed, data began to stream in through Nominal’s platform: the health of the brakes, actuators, avionics, electronics, control surfaces and more—terabytes of data giving a real-time window into the system’s health. As the plane slowed its taxi on the end of the tarmac, Piplica checked in with his engineers, hoping for good news. “Data review was done by the time we’d towed the airplane back to the other end of the runway,” he remembers, “everybody was thumbs up, so we concluded that, and we were at a safe level of risk to attempt a flight.” Minutes later, their Quarterhorse Mark One was in the air for the first time. When it landed, Piplica snapped a picture and texted it to McCord, Nominal’s CEO. Under the photo it read simply, “Hey, we got it back.”
McCord’s Mojave flight test assist was far from his first brush with the Armed Forces. He was raised on stories about his grandfather, a greatest generation archetype, who had been rushed through the Naval Academy in three years to join the war in the Pacific, witnessed the Japanese foreign minister sign the instruments of surrender, had seen nuclear weapons detonations, and flown the longest flight in history over the South Pole. McCord’s father was a federal attorney in the Department of Justice, and his mother was a special needs teacher. In their home, being of service was a mantra, and an action; most days, McCord and his siblings were explicitly urged by their parents to question how they were being a force for good in the world. For McCord, inspired by his grandfather (and uncle and cousins in the Navy), “Good,” meant joining the Armed Forces.
He paid his way through MIT by doing ROTC, which is where McCord “got bitten by the entrepreneurial bug,” he remembers, but more via osmosis than in practice. In addition to ROTC, he played varsity soccer, double majored in Nuclear Engineering and Physics and minored in Political Science, for practical reasons (“All of this engineering, how do I transition that into something that impacts the world?”). Between training and practice and problem sets and lectures and more lectures, McCord observed his classmates tinkering and creating. In particular, he remembers not infrequently passing by his fraternity brother, Jason Hoch at 4 a.m.. Hoch would still be awake, finishing a CS problem set or hacking away at a new idea as McCord, just up and in uniform on his way to ROTC training, urged Hoch to get some sleep. He admired his classmates’ will to build and create, knowing it was an exercise he couldn’t yet fully embrace, but one that he filed away to explore in some eventual, less time-constrained future.
Within days of graduation, McCord was onboard the USS Helena, SSN-725, the newest officer on the submarine. Any notion that respect would be granted by virtue of his positional authority or pedigree was quickly disabused. “You just start from scratch with first principles—how do you build trust, rapport, and respect with these people where everything you were is stripped away to zero?” says McCord. He did manage to win over his crew after they observed that what he lacked in revolutions around the sun, he made up for in thoughtful leadership, attention to detail, and commitment to understanding the nuances of the underwater behemoth they called their home. “It was my duty to understand that complexity,” says McCord. “Especially for those around me that were relying on me.” Before his service was finished, he’d have a chance to prove just how well he could navigate that complexity.
“We think it’s his appendix,” one of McCord’s crewmates informed him, as a fellow sailor nearby doubled over in pain. “It seems bad.” A few years had passed since McCord was the ‘new guy,’ during which he had experienced a midnight fire, a change in presidential administrations, and hundreds of nights underwater—but this was new. “Appendicitis on a deployed submarine on a mission is not a good thing. So the time was ticking,” says McCord. The ship’s only medically trained officer was not equipped to perform emergency surgery, and since the sub was mid-clandestine mission in North Atlantic waters, reaching a hospital before rupture meant navigating the vessel through Nordic fjords. McCord was tapped to ‘drive.’
Assuming the Conn (naval parlance for, “control of the ship”), he checked above water conditions: temperatures hovered around zero, and a blizzard made visibility nonexistent and conditions turbulent. But despite inclement conditions, this time, McCord wasn’t at a loss. Anxious, certainly, but his years of training, and his dedication to ensuring that he could be of service to his crew, paid off. “Because of my familiarity by then with all of the complexities of the submarine, I was able to gut into how to do this,” says McCord.
By then, McCord was accustomed to managing machinery designed during the Cold War, but that didn’t mean he relished the challenge. “We had to do a lot of very unnatural things with the submarine to get through this fjord and open that rear hatch.” When they neared the shore, a particularly burly soldier “essentially threw the sailor over to the Norwegian coast guard, who picked him up and rushed him to a hospital,” says McCord. Twelve hours later, a WhatsApp message from their new Nordic friend gave them the all clear health-wise.
For two more years, McCord lived many of his days underwater. In his free time, eager to stay apace with the outside world, McCord Coursera’d. “I would watch pre-downloaded Andrew Ng Stanford AI classes. This was in 2015, 2016, and I would be teaching myself and actually building out early models.” His autodidactic afternoons keeping up only reinforced how far his surroundings had fallen behind. “Learning AI, and then going into the control room where you have 1970s software, old hardware—it was a crazy cognitive dissonance.”
As he neared his five-year mark on the sub, it began dawning on McCord that the model of service and impact that had worked so well for his grandfather and uncle, both with decades-long careers in the military, might not be the right fit for him. “The military allowed them to make a huge impact on the world over 30 or so years,” says McCord. “But in today’s world, technology seemed like the way to make that impact. I wanted to use service-aligned technology, but I was not cool with the idea of having to wait 30 years.”
McCord on USS HELENA the day of the rescue missionMcCord had put in his time, “but then this incredible opportunity came by,” he says. He was selected to be one of the Navy’s liaisons to the House of Representatives. Between 2017 and 2019, McCord got a front-row seat to another deeply complex system. “You get to understand how a bill becomes a law,” says McCord. “But you also get to understand the personalities, the relationships, the executive branch, how budgets get passed, the back doors on The Hill—how it actually happens. I lived that viscerally for two years.”While his job description was to “sort of be a good steward of the Navy, tell war stories, build support,” he also made time for an extracurricular: “I was someone that members of Congress and staffers could go to to understand cutting-edge technology,” says McCord. “I think it’s hard for them to find, frankly, a low-threat way to do this. I developed this reputation of, ‘Hey, you can actually just go down to the Navy liaison shop and there’s this MIT guy Cameron who can just explain LLMs or cybersecurity or why technical stuff in this bill is relevant.” In 2020, capping off his career in public service, McCord’s technical acumen earned him an invite to help develop the House Armed Services Committee’s report on how technology was going to change the nature of warfare (and world). “It’s a little bit crazy to say now, but it was really some of the first governmental writing about AI,” says McCord, whose name is listed in the footnotes among four-star admirals and heads of agencies.
From his years on the sub, McCord was no stranger to waking up at the crack of dawn, but now he found himself doing so in a decidedly more terrestrial context. It was 2020, and he and his first private sector team, Anduril’s drone defense system engineers, were cruising through California’s Central Valley towards the desert to test their system. When they found a suitably remote, dusty patch, they unpacked their hardware—a tower covered with cameras and infrared sensors and miniature drones—and set up their Wi-Fi ‘pucks’ to run flight tests. These sessions would often last days, with team members camping in their trucks to avoid the sunrise commute. Each morning, they would begin tests anew, generating telemetry and sensor data and logs and video and images. “And it was so difficult with the tools we had to capture that data, intuitively organize it, and just answer the question: Did the test work?” As the Product Manager of the system, he led his team in long whiteboard computational sessions, hours in MATLAB, or dated academic graphing software. “None of this was production scale. And none of this was modern in any sense,” says McCord.
The process itself he enjoyed—the rolling up of sleeves, the camaraderie—all of that was pleasantly, arduously familiar. But day after dusty day, McCord remembers feeling renewed shock at the state of hardware testing. Here he was, working in a startup ecosystem where software innovation thrived on cycles of rapid iteration, but hardware testing was stalled in a different century. And it wasn’t lost on him that if Anduril, a company with “all of the venture dollars in the world and incredibly smart people had challenges getting this right, what is like at old organization X or massive company Y? In the back of my head this was just so clearly an area where better software could improve quality of life, and improve outcomes,” says McCord. “To be clear, I didn’t have the answers, but I just was obsessed with the problem.”
McCord first attempted to solve that problem with emerging data technologies: tools built for business intelligence, data marketing, SQL-based tools. In short, nothing “built for the types of telemetry and high-frequency sensor data that robotic hardware systems generate,” says McCord. “So they don’t work.” After fifteen months at Anduril, McCord couldn’t ignore his obsession any longer. Eager to get his bearings in a new complex system—the world of entrepreneurship and VC funding—McCord pitched his rough idea, improving hardware testing, and himself as a part-time entrepreneur-in-residence, to Josh Wolf at the VC firm, Lux Capital. In return for an opportunity to speak with dozens of hardware CTOs to pressure test his thesis, McCord offered to help Lux develop a dual-use, government-private sector business strategy.
For a year, co-enrolled at Harvard for his MBA, McCord effectively moonlit for Lux. “This was only possible because COVID was happening,” says McCord. “I would do Zoom classes and I would shut my Harvard Business School laptop and open my Lux laptop and basically alternate between the two.” HBS is where McCord first encountered Bryce Strauss, a kindred spirit with an aerospace background. McCord asked him to coffee, expecting a 30-minute conversation, and the pair ended up venting for nearly three hours. “It was this winding back-and-forth where we both obsessed about post-test analysis and data review, and how it’s essential to quickly get insight into performance data —whether you’re building an airplane, a car, a nuclear reactor, a drone, or a robot. Every person that builds hardware does this thing,” says McCord. “And we’re like, if we could just make that process 10 times better, we think we can build something valuable here.”
Strauss concluded they needed to turn this idea into a company together, and he wouldn’t take no for an answer. “I’m always doing a lot of things, and Bryce was this incredible unifying north star that was like, ‘Cameron, when we graduate, we’re doing this,’” says McCord. The duo decided to enlist a third, software co-founder. For McCord, there was really only one choice: “I was like, ‘Hey, I know this guy, Jason Hoch from MIT. He’s the smartest software engineer I’ve ever met. I think he’s the person to be the third leg of the stool.’” Strauss, the aerospace expert, came up with a name, a play on “All systems nominal,” common parlance for “all good” during rocket launches. 30 days before graduation, Lux Capital wrote Nominal their first check.
Nominal Cofounders Jason Hoch, Bryce Strauss, Cameron McCord“There’s a bug,” Hoch said from the backseat of their rental car. Six months into their tenure as co-founders, the trio had flown to Los Angeles to demo an early prototype of Nominal’s hardware-in-the-loop (HITL) testing system for a major satellite company, who they hoped would be their first paying customer. “Something’s off with time synchronization of different data sources,” added Hoch. With 20 minutes to go before their pitch, he’d have to do some en route hacking.
When they arrived, the trio walked into a boardroom to find roughly a dozen company leaders waiting. Strauss demoed their product, demonstrating the speed with which it would allow engineers to manage and interpret satellite test data. The last minute hack held, and the time sync across different data sources—telemetry, engine health, computation speeds and more—worked. The trio was ecstatic, even when the satellite company only signed on for an unpaid pilot of their software. “They took a bet on us and they let us learn with them, frankly, which is more valuable than anything,” says McCord.
With some assurance their product worked and had utility for customers, McCord, Hoch, and Strauss decided to hire four more full-time engineers. Still the latter days of COVID in fall 2021, everyone worked remotely half of the time, and would converge in Austin every other week, rent an Airbnb, and build from wakeup to well into Wendy’s-fueled late nights. As the company grew, their Austin reunions became every third week, then once a month. “Finally, we reached a point when we had around 40 people where we declared, we’re not going to have regularly scheduled Austin weeks anymore.”
When he wasn’t sleep deprived in Austin, McCord lived in Washington, D.C., a home that allowed him to leverage his public sector connections while growing his private sector customer base. That’s where he first met AJ Piplica for coffee at Commonwealth Joe’s. Before witnessing the pain of hardware testing as CEO of Hermeus, Piplica had experienced it in the public sector working for NASA. “Within the Department of Defense, which is a very vast test infrastructure—all the data is siloed,” he says. “You were literally moving CSVs around by hand and opening things in Excel.” After witnessing Nominal’s utility during his first Mojave flight test, Piplica recognized the step change it could represent for all hardware development. “Any organization that’s taking data out of the real world, which is, like, every major company in the world could benefit from this,” says Piplica. “Yeah, robotics and AI are cool, but what’s actually cool is when you put them together. That nexus between the digital and the physical world is what really unlocks a huge amount of growth for humanity.”
Ten days before his wedding in January 2025, McCord got a call from Alfred Lin, partner at Sequoia. Nominal was eyeing another round of fundraising to support the expansion of their team and their product. Lin, who had met McCord during Nominal’s Series A process but ultimately deferred investing (“We wanted more evidence in support of his hypothesis before investing”), understood the tailwinds accelerating Nominal’s growth, and wasn’t about to let another round pass him by. “We are living through a hardware renaissance, and we were looking for a new platform that supports modern hardware engineers on this journey”, says Sequoia partner, Anas Biad.
McCord wanted to work with Sequoia, but he wanted to get married first even more. “I told Alfred, look, I’m getting married. But can we schedule time for me to come to SF right when I get back? I will walk you through everything in the business.” Lin agreed, and as promised, McCord flew straight from honeymooning in New Zealand to meet with Lin and Biad. The partners were impressed by McCord, but told him they needed to do their due diligence on the product before any decisions were made. “For days, Anas basically didn’t sleep. He called every single one of their customers,” says Lin. In the end, McCord was reassured by the seriousness with which Sequoia took the whole process. He found it grueling, but ultimately affirming. “There was something pretty powerful in having Sequoia come back and be like, we spoke to 20 customers. People really did love the product.” Ten days after their SF meeting, McCord had a term sheet from Sequoia in hand.
At the time of the final interview for this piece in late March 2026, the war with Iran had started just days earlier. News had just come out about the first casualties on both sides of the conflict, among them, three US soldiers. That reality was weighing on McCord for many reasons, but resonated particularly in the context of his chosen means of service and field of impact. “I’m obviously reading about those casualties and I’m thinking, could it have been prevented? Could Nominal’s technology in some way, shape, or form, have improved the hardware they were using and helped prevent their deaths? I have no idea,” says McCord.
He’s acutely aware that the state of the world has changed the way people think about hardware manufacturing, and he’s ambivalent about what it took for that shift to occur. “I don’t like that there’s a global land conflict in Ukraine and a war in Iran, but the reality is that it’s happening,” says McCord. “And I think it is pushing everyone to rethink and say, ‘Hey, building physical things is critical.”
McCord sees a silver lining to this macro shift in attention to hardware development. He’s hopeful it will enable innovations outside the realm of defense and war, and is actively expanding Nominal’s capabilities for teams building rockets and medical devices, tools for water desalination and electric vehicles. The shift is also apparent in Nominal’s rapid growth: as of May 2026, Nominal has achieved unicorn status, with 75 global customers across aerospace, defense, energy, and transportation, a rapidly growing team, and a constantly expanding product surface area with an eye towards enabling anyone to build hardware efficiently and intelligently. His team is integrating AI to further speed up data collection and analysis, and enable edge computing for systems operating beyond connectivity range—particularly essential in aeronautics and astronautics. McCord understands that service is an ongoing act, and the urgency his parents instilled in him to do something has only grown more acute with time. With each innovation, McCord returns to a mantra, one influenced by his upbringing surrounded by people inspiring him to be of service. “I call it the grandpa test. I basically ask myself all the time, ‘how will I feel when I’m old and sitting in my chair and the grandkids are around?’” says McCord. “I think I will look back very fondly if Nominal played a part in moving the physical ambitions of humanity forward. We talk about flying cars, but yes, there’s also defense, and advanced energy, and compute to power this next generation of AI. There’s advanced transportation, mobility, and water purification. There’s so much we want to do.”
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Rahul Vorra is the founder and CEO of Superhuman, the premium email client for power users. He previously built the Gmail plug-in Reportive and sold it to LinkedIn. He began somewhere unexpected though, as a game designer on RuneScape. In this conversation, Rahul breaks down why most founders misunderstand product market fit, why premium can actually hurt your business, and how deliberate constraint can become your biggest advantage.
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Artificial Intelligence (AI) has permeated many aspects of our daily lives, from personal support to technical assistance, learning, and even decision-making. One of the new frontiers is personal finance management.
OpenAI recently announced an expansion of its ChatGPT platform, allowing users to connect their financial accounts directly to the chatbot to receive personalized financial advice. The new feature has raised concerns among privacy and cybersecurity experts.
Powered by the latest GPT-5.5 model, the new ChatGPT financial planning platform integrates data from over 12,000 financial institutions, including major banks like Bank of America, investment firms like Charles Schwab, and brokerage platforms like Robinhood.
The rollout is facilitated by Plaid, a fintech company that connects bank accounts to third-party applications. OpenAI plans to further bolster this platform in the near future by incorporating tools from Intuit, known for its personal finance and tax software.
The goal, according to OpenAI, is to provide users with an “up-to-date view” of their portfolio performance, spending habits, subscriptions, and upcoming payments, all while leveraging the AI’s ability to analyse complex patterns and assist in financial decision-making.
Deeply Personal DataWhile OpenAI emphasizes user privacy, advocates argue these safeguards may be insufficient.
Ridhi Shetty, senior policy counsel at the Centre for Democracy and Technology’s Privacy & Data Project, warns that even without the ability to make financial transactions, the data collected paints an intimate picture of a user’s life. “The financial information it does collect can reveal deeply personal details about a person’s life, habits, vulnerabilities, and relationships,” Shetty noted.
Furthermore, there is lingering uncertainty regarding the potential commercial use of this data. Shetty pointed out that OpenAI’s announcement conspicuously lacks clarity on whether this information could be utilized for advertising or commercial targeting. There are also concerns regarding the lack of professional accountability.
Unlike human financial advisors, an AI tool does not operate under the same strict regulatory obligations to protect client privacy or act in the user’s best interests.
Recommended Security Best PracticesAs AI platforms continue to integrate with our most sensitive accounts, experts urge users to maintain a vigilant security posture. To mitigate risks when using these types of AI-integrated tools, regularly log out of unused sessions to minimize windows of opportunity for attackers. It is also important to routinely review the data the AI is storing about you and clear it when no longer needed.
PIVX. Your Rights. Your Privacy. Your Choice.
To stay on top of PIVX news please visit PIVX.org and Discord.PIVX.org.
The Rising Risks of AI-Integrated Personal Finance was originally published in PIVX on Medium, where people are continuing the conversation by highlighting and responding to this story.
Marc Andreessen joins Joe Rogan for a conversation on AI, politics, technology, and the future of American society. They discuss how artificial intelligence is rapidly moving from novelty to infrastructure, and why Andreessen believes its long-term impact will be overwhelmingly positive despite growing public fear around automation and surveillance.
The conversation covers the explosion of AI coding tools, the emergence of “AI agents,” and how these systems are already reshaping software development, medicine, and education. Andreessen argues that AI should be understood less as replacement technology and more as a universal layer of cognitive augmentation, giving individuals access to capabilities that previously required teams of experts.
They also discuss the political and cultural dynamics surrounding AI, from fears about mass unemployment and surveillance to concerns about censorship, centralized power, and China’s accelerating AI ecosystem. Along the way, the discussion expands into California politics, wealth taxes, urban decline, crime, housing, nuclear energy, and whether America can still build ambitious things at scale.
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The Zcash Foundation is committed to transparency and openness with the Zcash community and our other stakeholders. Today, we are releasing our Q1 2026 report, which provides an overview of the work undertaken by our engineering team, as well as an overview of other activities during this period.
As with our previous quarterly reports, this report describes our financial inflow and outflows, with a detailed breakdown of our expenses, and we have included a snapshot of the Foundation’s financial position, in terms of liquid assets and liabilities that must be met using those assets.
Download the Q1 2026 report here.
Our previous quarterly reports can be found here.
The post Zcash Foundation Q1 2026 Report appeared first on Zcash Foundation.
Erin Price-Wright speaks with Michael Duffey and Dino Mavrookas about what it will take to rebuild the American defense industrial base for a new era of competition. As production capacity becomes a central constraint, they outline how the system must shift toward speed, scale, and modern manufacturing.
The conversation covers the role of autonomy in both defense systems and industrial processes, and how new approaches to design, labor, and production can dramatically reduce cost and complexity. Mavrookas explains how building for software and autonomy enables entirely new classes of platforms, while Duffey emphasizes the need for structural changes in how the Department of Defense works with industry.
They also discuss the importance of commercial markets in supporting defense capabilities, the fragility of existing supply chains, and why aligning private capital with national priorities is essential to long-term resilience.
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You’ve probably seen a random post from an X account sharing a screenshot of a cool new NFT they just snagged or a brag-post about a major DeFi win. While most of these posts are farming for likes, I have stumbled on a few genuine ones. This looks innocent enough, but to someone looking to exploit data, this is a digital thread that can unravel an entire life.
Within minutes, someone can plug that address into a block explorer, trace the funding back to a KYC-compliant exchange account, match the timestamp with old tweets, discover the user’s real name, locate their home address, and estimate their exact net worth. This is known as doxxing, and in Web3, it happens every single day.
Staying safe doesn’t require you to abandon the internet and live off the grid. Here are four simple habits to help you stay safe online.
1. Practice Wallet CompartmentalizationNever use a single wallet for everything. Instead, partition your crypto activities into dedicated silos. As a rule of thumb, you should have a cold storage wallet that holds your long-term assets. This wallet does not interact with smart contracts, dApps, or Web3 websites. It only receives funds from your other secure accounts.
Next is a transactional wallet for daily activities like trading, peer-to-peer transfers, buying merchandise, or funding smaller accounts. And finally, use a dApp wallet for new Web3 websites, to mint NFTs, and to interact with experimental smart contracts. Assume this wallet’s history is noisy and potentially compromised.
By breaking the chain between these accounts, an attacker who compromises or doxxes your public dApp wallet will only see a fraction of your digital footprint, leaving your primary holdings completely invisible.
2. Hide Your Digital IP TracksEvery time you connect your wallet to a dApp or use a public node to broadcast a transaction, you leak data. Specifically, you leak your IP address, which can be tied directly to your physical location and internet service provider.
Before opening your wallets, checking block explorers, or interacting with Web3 protocols, ensure your internet traffic is routed through a trusted VPN or the Tor network to mask your true location. Treat browsing Web3 platforms with the same strict privacy hygiene you would use when accessing sensitive real-world financial accounts.
3. Sanitize Your Social FootprintThe easiest way to get doxxed is by volunteering the information yourself. If your social media handle is your real name, do not register a matching .eth or .sol domain and use it to purchase assets. Anyone can look up what that wallet holds.
If you use a specific digital collectible as your profile picture on a Web3 native site, avoid using that exact same image on your professional portfolios or LinkedIn. Cross-referencing images via reverse image search is an incredibly simple, automated OSINT technique.
4. Break the On-Chain Trail with Protocol-Level PrivacyEven with perfect operational security, standard transparent blockchains make true separation incredibly difficult. If you send funds from your personal transactional wallet to your playground wallet, the public ledger links them together forever.
To completely sever the link between your identity and your wealth, you need to obscure the on-chain trail itself. This is where moving assets through a privacy-preserving infrastructure becomes essential.
Using advanced zero-knowledge cryptography through protocols like PIVX, you can transition your funds from a completely transparent state into a shielded pool. When you transact within a shielded ecosystem like PIVX’s SHIELD protocol, the sender, receiver, and transaction amounts are entirely hidden from the public eye.
The beauty of modern privacy tech is that it doesn’t force you into a corner. PIVX allows you to use viewing keys, meaning you keep your financial data locked away from online stalkers and malicious actors on a daily basis, while retaining the absolute right to selectively share your transaction history with trusted third parties, accountants, or tax authorities whenever you choose.
PIVX. Your Rights. Your Privacy. Your Choice.
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Simple Habits to Keep Your Online Identity Separate from Your Wallet was originally published in PIVX on Medium, where people are continuing the conversation by highlighting and responding to this story.
David Ulevitch speaks with Col. Jeffrey Glover and Rahul Sidhu about how AI, drones, and sensor networks are reshaping public safety and what it takes to bring new technology into law enforcement at scale. As departments face staffing shortages, burnout, and rising complexity, they examine how the right tools can make officers more effective, safer, and better supported.
The conversation covers how drone-as-first-responder programs are changing the speed and safety of emergency response, from high-risk warrant service to Amber Alert pursuits. Glover describes how Arizona DPS is building a full technology ecosystem around its officers, including body-worn camera analytics for burnout detection, brain scan wellness checks, and international intelligence-sharing partnerships ahead of FIFA and the Olympics. Sidhu explains how Flock Safety's layered sensor network — license plate readers, gunshot detection, and drone dispatch — is turning reactive policing into proactive, data-driven response.
They also discuss what founders get wrong when building for law enforcement, why spending time on the beat matters more than any product spec, and how the next decade will fundamentally change the skills required to be a police officer in America.
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Privacy has become one of the most contested ideas of the digital age invoked very often, understood rarely, and defended inconsistently. It is treated as a preference when convenient, a right when threatened, and a liability when it complicates oversight. This inconsistency has produced a shallow public conversation, where slogans replace substance and fear often substitutes for evidence. If privacy is to be meaningfully defended, it must be argued not as an abstract ideal, but as a practical necessity. And if it is to be governed responsibly, it must be discussed in structured, serious forums. This is where Privacy Roundtable begin to matter.
To argue for privacy is not to argue against security, law enforcement, or accountability. It is to insist that individuals retain a degree of control over their personal and economic lives in a world increasingly designed to observe, record, and analyze them. Privacy is the condition that allows people to think, associate, transact, and dissent without constant scrutiny. Without it, behavior changes not always because of law, but because of the awareness of being watched. This subtle shift is difficult to measure, but its consequences are profound. Societies that normalize surveillance tend to produce conformity, caution, and, eventually, silence.
The common rebuttal that privacy enables wrongdoing rests on a selective understanding of both history and technology. Every widely used system, from cash to the internet itself, has been used for illicit purposes. Yet societies do not dismantle foundational systems simply because they can be misused. Instead, they build frameworks to manage risk while preserving utility. Privacy should be treated no differently. The presence of risk does not negate the presence of value; it demands more careful thinking about how that value is preserved.
Stake in Digital Economy:
In the digital economy, the stakes are higher because data has become both an asset and a mechanism of control. Financial transactions, communication patterns, and online behavior form detailed profiles that can be used to predict, influence, or restrict individuals. The expansion of surveillance whether driven by state policy, corporate incentives, or technological capability has outpaced the frameworks meant to govern it. As a result, decisions about privacy are often made reactively, under pressure, and with limited understanding of the systems involved.
This gap between complexity and comprehension is precisely why Privacy Roundtables are important. They create a space where different stakeholders such as developers, regulators, researchers, and users can engage with the subject beyond headlines and assumptions. Unlike public debates, which tend to reward simplification, roundtables allow for depth. They make it possible to examine how privacy technologies actually work, what risks they introduce, and what problems they are designed to solve.
More importantly, they allow for disagreement without distortion. Privacy is not a binary issue; it exists on a spectrum shaped by context, use case, and societal values. A well-structured roundtable does not aim to eliminate disagreement but to refine it and to replace vague fears with specific concerns, and broad claims with verifiable facts. This process is essential for policy. Regulation built on misunderstanding is rarely effective; it either overreaches or fails to address the real issue.
There is also a question of legitimacy. Technologies that prioritize privacy particularly in finance are often viewed with suspicion, not solely because of their function, but because of how little they are understood. When engagement is absent, narratives fill the gap. These narratives tend to be simplistic: privacy equals secrecy, secrecy equals risk, and risk justifies restriction. Roundtables disrupt this chain by introducing nuance. They allow those building the technology to explain it, and those regulating it to interrogate it directly.
The absence of such dialogue carries its own risks. Policies formed without technical insight can stifle innovation or push it into less transparent environments. At the same time, technologies developed without regulatory awareness may fail to gain acceptance, regardless of their merit. Privacy Roundtables serve as a bridge between these domains. They do not guarantee consensus, but they increase the likelihood of informed outcomes.
Ultimately, the argument for privacy is an argument about balance. It is about ensuring that the systems designed to enhance efficiency, security, and connectivity do not erode autonomy in the process. It is about recognizing that visibility, while useful, is not inherently virtuous, and that some degree of opacity is necessary for freedom to exist in practice, not just in principle.
ConclusionPrivacy is not a problem to be solved, it is a condition to be preserved. The real challenge is not choosing between privacy and security, but designing systems where both can coexist without one quietly eliminating the other. That balance cannot be achieved through assumptions, headlines, or one-sided policymaking. It requires deliberate engagement.
Privacy Roundtables matter because they introduce discipline into a conversation that is often reactive and polarized. They force clarity where there is confusion, and accountability where there are unchecked claims. In doing so, they help shift privacy from the margins of discussion to the center of decision-making.
If the digital future is being built now as it clearly is then the frameworks guiding it must be equally intentional. Ignoring structured dialogue does not preserve neutrality; it allows default systems of surveillance and control to harden without scrutiny. Engaging in these conversations, however imperfect, is how societies retain agency over the technologies they create.
In the end, the argument for privacy is not about resisting progress. It is about shaping it so that efficiency does not come at the cost of freedom, and innovation does not outpace the principles that make it worth pursuing in the first place.
PIVX. Your Rights. Your Privacy. Your Choice.
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An Argument for Privacy: Why Privacy Roundtable Counts was originally published in PIVX on Medium, where people are continuing the conversation by highlighting and responding to this story.
Sophia Dew and Binji Pande speak with Vitalik Buterin about technology, human agency, and how the internet is changing the way people think, build, and relate to the world around them. Drawing from his writings and personal reflections, Buterin discusses how his worldview has evolved over the last decade, from creating Ethereum as a teenager to thinking more deeply about the social and philosophical implications of technology today.
The conversation explores the idea of “sanctuary technology,” systems that provide safety and coordination without removing individual freedom or agency. They also discuss the changing relationship between humans and AI, the risks of over-relying on automated systems, and why actively learning and thinking for yourself may become even more important as AI capabilities improve.
Along the way, Buterin reflects on creativity, community, identity, and the challenge of staying intentional in a world that increasingly pushes people toward autopilot.
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Ben Horowitz shares lessons from building and scaling companies, drawing on his experience as a founder and CEO. He explains why a founder’s primary responsibility comes down to one thing: delivering the right product at the right time.
The conversation covers how strategy actually develops in practice, why a company’s story is inseparable from its strategy, and how founders should think about hiring, fundraising, and decision-making in fast-changing environments. Horowitz also discusses how AI is reshaping teams, the increasing importance of creativity and relationships, and why roles may evolve toward more generalist “builders.”
He also reflects on navigating uncertainty, the reality of pivots, and why defensibility still comes down to solving hard problems and building meaningful relationships with customers.
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The State of Texas has filed a lawsuit alleging that the Netflix has built a massive surveillance machinery by milking user data without genuine consent.
“We Don’t Collect Anything”For years, Netflix positioned itself as the ethical alternative to the ad-supported, data-hungry models of Big Tech. During a 2020 earnings call, then-CEO Reed Hastings famously assured investors, “We don’t collect anything… We’re not tied up with all that controversy around advertising.”
However, according to the lawsuit filed by Texas Attorney General Ken Paxton, this public-facing image was a carefully constructed facade. While executives touted privacy, the company’s internal engineering told a different story. As early as 2016, a Netflix engineer reportedly admitted at a conference that Netflix is essentially a “logging company that occasionally streams movies.”
The Mechanics of ExploitationThe lawsuit alleges that Netflix uses intentional engineering to track every facet of a user’s digital life. From granular viewing habits to precise location data pulled from IP addresses, household network details, and sensitive behavioural patterns, this data is allegedly funnelled to ad networks.
By sharing this information with data brokers like Experian and Acxiom, and ad tech platforms like Google Display & Video 360, Netflix allows its users’ private habits to be integrated into a global web of surveillance. The state claims the company earns billions every year from secretly selling consumer data to deliver hyper-targeted advertising.
Targeting the Most VulnerablePerhaps the most chilling aspect of the complaint is the alleged exploitation of children’s data. Netflix markets kids’ profiles as a safe area for those 12 and under. Yet, while the company avoids showing targeted ads directly to children, the lawsuit claims it “aggressively” collects behavioural data from these accounts.
It is obvious that the streaming giant has failed to disclose the true scope of its data practices. Netflix reportedly collects a staggering 5 petabytes of user behaviour logs every day. This mountain of data is used to “engineer highly granular audience segments,” effectively stripping users of their anonymity and turning their private relaxation time into a product for the highest bidder.
PIVX. Your Rights. Your Privacy. Your Choice.
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Netflix: The Streaming Giant Turned Surveillance Machine was originally published in PIVX on Medium, where people are continuing the conversation by highlighting and responding to this story.
Erin Price-Wright speaks with Turner Caldwell and Drew Baglino about what it will take to close America's critical minerals gap and modernize the power infrastructure that underpins the AI economy. With the US more than 50 years behind China in critical mineral supply and grid infrastructure built on systems designed a century ago, they examine where the real bottlenecks are and how to move faster.
The conversation covers how automation, reinforcement learning, and vertically integrated operations can compress the timelines for mining and refining, and why co-locating supply chains matters more than labor costs in the race to reshore manufacturing. Baglino explains how solid state transformers can replace aging mechanical grid equipment with silicon and software, while Caldwell outlines how Mariana Minerals is applying autonomous systems to remove the know-how bottleneck from critical mineral processing.
They also discuss the lessons both founders carried from Tesla — techno-optimism, appetite for risk, and mission-driven talent — and what durable industrial policy, smarter permitting, and a federal grid investment framework would unlock for American competitiveness.
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Privacy infrastructures need an economy that encourages users to use the system in ways that improve everyone else's privacy. This article explains why privacy infrastructures need their own economy, how Panther’s approach differs from earlier privacy-incentive models, and what role $ZKP plays in Panther’s economic design.
Panther has built an incentive mechanism that links protocol activity, reward issuance, and $ZKP redemption inside the same architecture. Panther uses Panther Reward Points, or PRPs, to account for activity that contributes to the health of the privacy set. Users can redeem PRPs for $ZKP through Panther’s Automated Market Maker (AMM).
In short, Panther’s flywheel works like this: users perform actions that make the anonymity set more useful, such as depositing, transacting, and interacting with applications from inside the shielded environment. Those actions can earn PRPs. PRPs can be redeemed for $ZKP through Panther’s AMM. At the same time, protocol activity generates fees, including those paid in supported assets, and the protocol automatically converts these fees back to $ZKP on DEXs to replenish the rewards pool. More useful activity can, in turn, support more rewards, and those rewards can encourage more useful activity.
The bootstrapping problem in privacy protocolsPrivacy on a public blockchain is a network effect. It is a constant battle against onchain surveillance tools and analysts. If a shielded pool only has a few users, it offers less privacy. A larger pool, with more users, more assets, and more frequent activity, gives each transaction a larger set to hide in.
This is, in a nutshell, the bootstrapping problem for shielded pools. Cryptography alone is not enough; realized privacy also depends on how many people use a shielded pool, the amount of assets entering and moving through it, and whether activity is frequent enough to make timing analysis less reliable.
Bootstrapping a large enough anonymity set is not only a marketing or UX problem but also an economic-design problem. Users often need privacy intermittently. They may want to make a sensitive transfer, protect a trading strategy, receive salary privately, or move assets without exposing all their data. At other times, they may leave funds on transparent rails because that is where most applications and liquidity are.
If a privacy protocol does not create a reason for users to keep assets within the shielded pool, make transactions, and interact with applications, the anonymity set could remain smaller and less active than the protocol’s cryptography would ideally support or than what the ecosystem would aim for. Panther’s design addresses this challenge by rewarding actions that contribute to the anonymity set.
What a shielded pool needs to be usefulSeveral factors influence the size of a shielded pool’s anonymity set.
Pool depth matters because a larger set of shielded assets increases the number of possible sources for a transaction. Activity matters because a static pool can leak information through timing. A pool that sees regular deposits, transfers, swaps, and withdrawals gives outside observers fewer simple correlations to rely on. Fresh inflows matter because a pool that stops receiving deposits could potentially become easier to analyze over time. As old positions are spent or withdrawn, the effective anonymity set could become smaller even if the historical pool size seems large. Asset composition also matters. A multi-asset shielded pool can support more flexible private activity than a single-asset shielded pool, especially where asset types and interactions are abstracted in a way that makes simple asset-in/asset-out matching less reliable. That said, asset diversity does not automatically mean every asset contributes equally to every other asset’s anonymity. Popular and frequently used assets tend to contribute more to practical privacy than assets with little activity.In Panther’s case, the effective anonymity set is also conditioned by Zones, supported assets, transaction limits, and compliance rules. Panther’s economic design can encourage activity within the protocol, but the practical privacy set still depends on the actual configuration and usage of each environment.
The economic question is therefore straightforward: how should a protocol reward actions that increase the privacy set's size, fresh inflows, and activity?
Three approaches to privacy-protocol incentivesDifferent privacy protocols have approached protocol incentives in different ways. The point of comparison is not that one model is universally right and another is wrong. Each design optimizes for a different objective.
A useful way to compare them is to ask: what activity is being rewarded, who receives the reward, and how is that reward funded or priced?
Tornado Cash: fixed-duration anonymity miningTornado Cash introduced one of the earliest serious attempts to reward users for contributing to an anonymity set. Its Anonymity Mining program allowed users to earn Anonymity Points, or AP, based on how long eligible notes remained in the Tornado Cash pools. AP was accrued privately and could later be converted into $TORN through a custom AMM. This was an important design because it recognized that privacy-set contribution should be rewarded without forcing users to reveal the details of their deposits.
The model was simple and innovative for its time. A fixed allocation of $TORN was distributed to the anonymity-mining program over a defined period. Users with AP effectively competed for the $TORN available through the AMM at the time of redemption.
The limitation here was that the incentive program was finite and externally scheduled. The reward supply did not automatically expand or contract based on ongoing protocol usage. Once the program ended, the anonymity-mining incentive did as well.
Tornado Cash, therefore, provided an important precedent: privacy-set contribution can be rewarded privately, and a custom AMM can be used to convert internal reward accounting into a public token. Panther builds on that insight, but extends it into a broader activity-incentive model.
Railgun: governance and security rewardsRailgun takes a different approach. It does not primarily reward users for depositing into or transacting within the privacy set. Instead, Railgun charges shield and unshield fees, which are collected by the decentralized governance treasury and distributed over time to eligible $RAIL stakers through Active Governor Rewards.
This is a coherent governance-security design. It rewards $RAIL stakers for remaining engaged with protocol governance and code-change processes. That has value for a privacy system, because governance participation can help protect the integrity of smart contracts and treasury-controlled components.
The trade-off is that the reward path is not primarily directed toward end-user actions that grow the anonymity set. A user who shields assets, keeps them private, and transacts inside the privacy system contributes to usage. Still, the protocol’s recurring economic reward mechanism is oriented toward eligible governance stakers rather than directly toward privacy-set contributors.
That distinction is important. Railgun’s model is not “wrong”; it is designed around a different incentive target. Panther’s model is more directly focused on rewarding user activity that improves the privacy environment itself.
Panther: activity incentives with AMM-based redemptionPanther’s design starts from a different premise: if certain actions make the privacy set more useful, the protocol should be able to reward those actions directly. Users can earn PRPs for activities that contribute to the protocol’s privacy set. These can include onboarding, deposits, internal sends, staking-related activity, and use of DeFi adaptors, depending on the protocol version and governance parameters.
PRPs are not ordinary tradable tokens. They are internal reward points used to account for contributions to Panther’s privacy environment. Users can redeem PRPs for $ZKP through Panther’s AMM. This separation matters. PRPs let Panther measure and reward protocol activity without immediately assigning a fixed $ZKP payout to every action. The AMM then converts accumulated PRPs into $ZKP based on the state of its reserves.
In simple terms: if more PRPs are redeemed against the same $ZKP reserve, the redemption rate becomes less generous. If the AMM is recharged with more $ZKP, the redemption rate improves. The rate is calculated by smart contracts rather than manually reset by the development team. This gives Panther a flexible pricing layer. Governance can set the parameters for which activities earn PRPs and how much they earn, while the AMM handles the PRP-to-$ZKP redemption rate based on reserve conditions and redemption demand.
Why the AMM mattersWithout an AMM, Panther would need to set a fixed conversion rate between PRPs and $ZKP. That would create a difficult measuring problem.
If the reward rate is too generous, the protocol could overpay for low-value activity. And, if the reward rate is too strict, users might not have enough reason to participate. If usage patterns changed, governance would need to keep adjusting the rate manually.
The AMM reduces this problem. It does not remove the need for governance, because reward parameters still matter. But it separates two questions:
Which activities should the protocol reward? What is the current redemption rate between PRP and $ZKP?Governance focuses on the first question. The AMM handles the second through a transparent, reserve-based mechanism.
This is the main reason Panther’s incentive model is different from a simple fixed-rate rewards program. It is designed to respond to actual redemption pressure and AMM reserve levels rather than relying entirely on a static schedule.
Protocol fees connected with the reward loopThe fee side of Panther’s design is important because it connects protocol usage to the resources that support PRP redemption.
When users interact with the protocol, certain actions generate fees. Some fees compensate ecosystem operators, such as relayers, zMiners, compliance providers, or other service providers involved in the transaction lifecycle. All of these fees are denominated in $ZKP, while withdrawal fees are applied to the transacted token. Withdrawal fees are especially relevant because withdrawals reduce the shielded pool, thereby reducing the anonymity set.
In Panther’s design, the withdrawal fee is applied to the transacted token, with part of the fee going to the Zone Manager and the remainder flowing back to the Protocol Rewards AMM. More generally, where fees are collected in the asset being withdrawn, the rewards AMM operates solely in $ZKP. Those fee assets need to be converted into $ZKP before they can be used to support PRP redemption. Panther’s fee model also includes operational fee recycling through user withdrawals, intended to support the rewards AMM.
This conversion is routed through open-market DEX liquidity, such as Uniswap, and creates a direct link between protocol usage and market demand for $ZKP. More activity can mean more fee generation, more fee conversion into $ZKP, and more capacity to support the PRP redemption mechanism, subject to DAO parameters and the actual configuration of each deployment.
The productive role of $ZKPIn this design, $ZKP is not only a governance token but an essential part of the Panther ecosystem. Users perform activities that contribute to the anonymity set. The protocol accounts for that contribution in PRPs. PRPs can be redeemed through the AMM for $ZKP. Protocol activity generates fees, and where those fees are collected in non-ZKP assets but intended to support the rewards AMM, they are converted into $ZKP. Depending on the protocol environment and parameters, $ZKP may also be used for protocol-related payments such as gas abstraction, compliance-related fees, staking, or other ecosystem functions.
This gives $ZKP a more direct role within the protocol’s incentive architecture, as it is used in the accounting, redemption, and reserve mechanics that connect user activity to economic rewards.
Panther's economic flyweel for privacyThe key point is not that the usage of $ZKP must be permanent or identical across all Panther deployments but that the incentive system is built around a specific loop: privacy-set activity earns PRP -> PRPs redeem into $ZKP -> protocol fees help replenish the $ZKP-denominated reward infrastructure, and $ZKP functions as the economic asset around which the reward system is organized.
What Panther’s design aims to accomplishPanther’s economic design aims to align incentives with the practical needs of the anonymity set. It rewards deposits because deposits increase pool depth. It rewards internal activity because activity makes the pool harder to analyze through simple timing correlations. It can reward the use of adaptors because private interaction with DeFi applications can make the shielded environment more useful than a passive holding pool.
It can also reward maintenance actions, such as AMM recharge functions, because the reward system itself needs to remain operational.
This is not a claim that economics alone creates privacy. The cryptography, implementation quality, supported assets, zone rules, transaction patterns, liquidity, and user behavior all matter. But economics can influence whether the system receives the kind of activity its privacy model depends on.
That is where Panther’s design is meaningfully differentiated. Tornado Cash showed that privacy-set contribution could be privately accounted for and redeemed through an AMM. Railgun shows how protocol fees can support governance/security incentives. Panther combines private activity rewards, PRP accounting, AMM-based redemption, and $ZKP reserves into a model specifically designed to encourage useful activity within the privacy environment.
A balanced approach to incentivizing the anonymity setThe strongest way to describe Panther’s advantage is not to say that other privacy protocols ignored incentives. They did not. Tornado Cash and Railgun both made important design choices around incentives, governance, and protocol economics. The better point is that Panther places the reward mechanism closer to the behavior that improves the shielded pool's anonymity set.
Instead of only rewarding governance participation, Panther can reward users for actions such as depositing, transacting, holding assets privately, or using private DeFi adaptors.
Panther separates reward accounting from redemption pricing. PRPs are earned according to DAO-set reward parameters and available reward budgets, while the amount of $ZKP received for redeemed PRPs is determined by the AMM’s reserve state rather than by a permanently fixed manual exchange rate.
Instead of treating the token as separate from the privacy infrastructure, Panther gives $ZKP a role inside the reward loop itself.
That is the productive role of $ZKP: it is the economic asset through which Panther turns anonymity-set contribution into redeemable value.
ConclusionPrivacy protocols face a practical problem: strong cryptography is not enough if the privacy set is small, inactive, or rarely used. A useful privacy system needs depth, activity, fresh inflows, and application-level reasons for users to remain inside the private environment.
Panther’s answer is to make those actions economically legible. Users earn PRPs for activity that supports the anonymity set. PRPs redeem into $ZKP through a reserve-based AMM. The redemption rate responds to reserve conditions and redemption demand rather than being fixed forever by governance. In addition, protocol fee flows can be recycled into $ZKP, helping connect real usage of the protocol to the reward infrastructure that supports PRP redemption.
This does not eliminate all challenges. Panther’s model still depends on real user adoption, careful parameter setting, sustainable reserve management, supported assets, and the design of each Zone. But it is a clear attempt to treat privacy as a network effect that requires its own incentive layer. $ZKP drives Panther’s privacy economy.
That is what makes Panther’s approach notable: it does not rely solely on users choosing privacy on principle. It tries to reward the actions that make privacy more useful in practice.
Try out Panther Protocol here: https://pantherdao.app/
To learn more about Panther Protocol, visit www.pantherprotocol.io
Billions of AI agents will soon operate independently, negotiating prices, purchasing compute, settling invoices, and trading data without a human in the loop.
These agents need money that moves as fast as they do. But traditional payment rails; banks, card networks, KYC gates, create friction that autonomous agents simply can’t navigate.
#PIVX is the currency built for this moment. #Shielded by default, settled in sixty seconds, and accessible to any agent on any machine, no permission required. Learn more: visit http://PIVX.ai
Built by PIVX_Labs, project of PIVX.org. #AI
The economy is going autonomous. was originally published in PIVX on Medium, where people are continuing the conversation by highlighting and responding to this story.
David Haber speaks with Lloyd Blankfein, former CEO of Goldman Sachs, about leadership, risk, and navigating moments of extreme uncertainty. Drawing on his experience leading Goldman through the financial crisis, Blankfein shares how organizations can build resilience, make decisions under pressure, and maintain culture while scaling.
They discuss the importance of risk management as both a discipline and a mindset, the difference between being wrong and being reckless, and how great organizations balance taking risk with protecting against it. Blankfein also reflects on Goldman’s partnership culture, how it shaped decision-making and accountability, and what it takes to build enduring institutions over time.
The conversation also touches on technology, from the role it played in transforming financial markets to the implications of AI today, including its potential, risks, and the challenges of operating in systems that are increasingly complex and harder to fully understand.
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You may not realize it, but there is a fundamental difference between “having money” and “having access” to money. For years, centralized fintech applications have bridged the gap between traditional banking and the digital age, offering sleek interfaces and one-tap convenience. However, when it comes to executing “big moves” that involve significant capital allocations, property acquisitions, or strategic business transfers, these platforms are increasingly revealing themselves as high-risk bottlenecks.
My decision to migrate large-scale transactions away from centralized apps (CeFi) isn’t about being “anti-bank”; it is about recognizing the inherent fragility of permission-based systems.
The Myth of Asset OwnershipThe primary illusion of centralized apps is the balance displayed on the screen. I have reached the valid conclusion that in a centralized environment, I do not own my assets; I own a legal claim against the company.
Every transaction is a request sent to a central authority. If that authority’s internal risk engine flags my transaction, perhaps simply because it is larger than my average spending, the send button becomes useless. And this is not me ranting, but I recently had to conduct a high-value purchase, and guess what?
My bank had (without notifying me) lowered my transaction limit and rolled out new KYC requirements. What would have ordinarily taken 60 seconds to complete took nearly two hours.
For high-value moves, the risk of an automated “Account Restricted” flag is a catastrophic variable. In decentralization, the code is the law. If my wallet has the funds and the private key signs the transaction, the network executes it without a human or algorithm second-guessing your intent.
Operational FragilityCentralized apps rely on a precarious stack of third-party servers, banking partners, and regional regulators. When a centralized app goes down for scheduled maintenance, my ability to close a time-sensitive deal vanishes. We all know that market timing can be worth thousands, if not millions.
Centralized entities are the first to be squeezed by policy shifts. Overnight, withdrawal limits can be slashed, as in my case, or specific corridors blocked to comply with new, often local, mandates. Decentralized protocols are global and indifferent to local policy shifts, ensuring that a “big move” isn’t held hostage by a bureaucrat’s pen.
The Security of the Invisible LedgerWhile centralized apps boast about security, they are often building data honey pots. To move large sums, you must provide extensive Know Your Customer data. This information is stored on central servers that are constant targets for sophisticated hacks. A breach doesn’t just lose your money; it loses your identity.
Shifting to decentralized finance (DeFi) replaces trust in a corporation with trust in mathematics. Using protocols with transparent, open-source code like PIVX ensures that the rules of the game cannot be changed mid-transaction. Your privacy remains intact because the ledger tracks the movement of value, not the personal identity of the mover.
In my opinion, centralized apps are for spending, while decentralized protocols are for building.
PIVX. Your Rights. Your Privacy. Your Choice.
To stay on top of PIVX news please visit PIVX.org and Discord.PIVX.org.
Why I Stopped Using Centralized Apps for Big Moves was originally published in PIVX on Medium, where people are continuing the conversation by highlighting and responding to this story.
Erik Torenberg speaks with Marc Andreessen about the state of AI, media, and the broader cultural and economic shifts shaping the internet. They discuss how narratives around AI, from fear to hype, are influencing public perception, and why real-world usage tells a very different story.
The conversation covers AI’s impact on jobs and productivity, the rise of “AI-native” builders, and why increased capability tends to expand work rather than eliminate it. Andreessen also examines how companies are adapting, from restructuring teams to rethinking roles around more generalist “builders.”
They also explore the changing media landscape, from the dynamics of influence and information to the breakdown of traditional authority, and what it means for trust, culture, and generational attitudes. Along the way, they touch on topics ranging from institutional power to emerging internet subcultures, offering a wide-ranging look at how technology is reshaping both systems and society.
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AI Ascent IV was our biggest and best year yet.
By Team Sequoia Published May 8, 2026On April 20, we hosted our fourth annual AI Ascent in San Francisco, bringing together more than 150 leading founders and researchers in AI, including Demis Hassabis, Andrej Karpathy, Greg Brockman, Boris Cherny of Anthropic, Dmitri Dolgov of Waymo, Jim Fan of Nvidia, and many more.
Sequoia partner Pat Grady opened the day with a frame for the moment: AI is a revolution in computation. Not faster horses, but cars. And the cars have arrived. His advice for founders building on top of the labs: get MAD. Build moats from the customer back, design for affordance, and exploit the diffusion gap between the model capabilities and what the Fortune 500 has deployed. Sonya Huang declared 2026 the year of agents, and walked through the three ingredients (models, tools, and harnesses) that have finally come together. Konstantine Buhler argued that the cognitive revolution will follow the same arc as the Industrial Revolution—just bigger and faster—and that AI is about to do to cognitive work what the Industrial Revolution did to manual labor.
The talks ranged from the long-horizon agent revolution and the endgame for robotics, to data centers in space, the frontier of data efficiency, and the emerging science behind neural networks.
Below is a selection of videos from the event. For the full lineup, visit our YouTube playlist.
Previous Video Next Video Share Share this on Facebook Share this on Twitter Share this on LinkedIn Share this via emailThe post AI Ascent 2026 appeared first on Sequoia Capital.
David Ulevitch speaks with Ben Horowitz about what it means to lead the technology industry at scale, and the responsibilities that come with it. Following the firm’s largest-ever fundraise, they discuss how venture capital, technology, and national strategy are increasingly intertwined.
The conversation covers America’s role in the next technological revolution, from AI to advanced manufacturing, and why maintaining technological leadership is critical not just for economic growth, but for global influence. Horowitz also shares his perspective on working with government, supporting national security innovation, and building systems that give more people the opportunity to contribute.
They also discuss how venture capital is evolving, the shift toward larger firms and specialized strategies, and why optimism about technology, and its potential to improve lives, remains essential even amid growing skepticism.
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As Zcash development has converged around Zebra and the broader infrastructure Zcash Foundation (ZF) maintains, we have officially assumed stewardship of three core community-facing Zcash assets:
The Zcash GitHub organization, canonical home of key repositories including librustzcash, zips, lightwalletd, and the zcashd codebase The z.cash website and domain The @Zcash handle on X
Since ZF already maintains Zebra and core protocol infrastructure, bringing these community-facing assets under the same roof simplifies coordination and ensures clear, long-term accountability. ZODL stewarded these resources well and this transition builds on that work.
ZF now manages access permissions and repository governance for the Zcash GitHub organization. This aligns with ZF’s existing role as stewards of the Zcash protocol, providing leadership and supporting its continued maintenance and improvement.
The framework remains the same:
Open Access. Existing GitHub contributors retain their access. The repositories remain open source, and pull requests, issues, and discussions remain open to everyone. Licensing. All repositories maintain their existing open-source licenses. Collaborative Development. ZODL, Shielded Labs, ZF, other organizations and independent contributors continue their work. Community Stewardship In Community HandsFor administration of z.cash and @Zcash, ZF is partnering with ZecHub under a multi-year grant. ZecHub has been one of the most consistent community contributors in the ecosystem — producing educational content, documentation, and onboarding resources that serve users across every corner of the Zcash world. Entrusting ZecHub with day-to-day management of these properties means Zcash’s most visible assets are maintained by people who are deeply embedded in the community.
The now-deprecated z.cash site will forward to zechub.wiki – consolidating key community resources into a single well-maintained reference. Zechub is now actively folding in the most relevant z.cash content to its site. Additionally, ZecHub has moved quickly to take on the daily work of managing @Zcash, reflecting their depth of experience and capability.
Both ZF and Zechub are committed to revisiting this approach regularly and refining it over time to ensure Zcash’s engagement and representation continues to strengthen and improve over time.
This structure also reflects a broader principle: ZF holds administrative accountability, but stewardship of Zcash’s public voice belongs to the community.
Looking ForwardZcash belongs to its users and ZF is committed to keeping these assets open, maintained, and accessible for the long term. We value feedback from the community, and expect this input to shape how these assets are stewarded over time. Please reach out to share your ideas and feedback with us via communications@zfnd.org.
The post Zcash Foundation Assumes Stewardship of Core Zcash Community Assets appeared first on Zcash Foundation.
After years in the making, Panther Protocol is live on Polygon — governed by Panther DAO and built by the community.
This milestone introduces a new primitive for DeFi: programmable privacy — infrastructure for confidential on-chain interactions with zero-knowledge credential verification.
The Panther interface is available at: https://pantherdao.app/
A New Phase for Privacy in DeFiPanther combines zero-knowledge cryptography, non-custodial architecture, and DAO governance to prove that privacy and accountability aren't mutually exclusive.
Users interact directly with smart contracts and retain full control of their assets. Cryptographic proofs are generated locally — in your own browser or device, never anywhere else.
Zero-Knowledge Credential VerificationThe initial deployment includes credential-based access controls, powered by independent providers like AMLBot via PureFi tooling.
Participants prove eligibility on-chain using zero-knowledge attestations — without sharing personal data or identity information with Panther DAO or the protocol. The protocol verifies only what's required. Nothing more.
Connected to Real DeFiPanther plugs into existing DeFi liquidity — it doesn't replace it. Users interact confidentially without stepping outside the broader ecosystem.
The zSwap functionality supports Quickswap, Uniswap, and Curve Finance directly through the interface.
Panther Reward Points (PRPs)PRPs recognize and reward protocol participation.
Users earn them by interacting with privacy-enabled zones and other qualifying actions, governed by Panther DAO rules. As the protocol expands across chains and integrations, PRPs are designed to keep long-term participants aligned with the ecosystem.
Panther's architecture includes Forensic Data Escrow — a mechanism for governed, conditional disclosure of encrypted metadata under defined circumstances.
The roadmap ahead includes multi-chain expansion, new integrations and adapters, and new zones and participation models.
Some protocol contracts include limited governance-controlled upgrade and emergency mechanisms — solely to protect users in the event of critical vulnerabilities, with no access to user assets.
A Panther DAO-approved grant will fund open-source development toward a potential future deployment on Base.
About Panther Protocol Foundation
Panther Protocol Foundation is a non-profit supporting the ecosystem through research funding, open-source development grants, and ecosystem initiatives. It does not operate the protocol, host interfaces, custody assets, execute or intermediate transactions, or provide financial services.
The Panther dApp is a non-custodial interface — users interact directly with smart contracts from their own wallets, signing all transactions themselves. Compliance credentials are issued and managed by independent third-party providers.
Please review the applicable notices, disclosures, and jurisdictional restrictions available through the Panther interface before interacting with the protocol.
For more information, visit www.panther.org
To learn more about Panther Protocol, visit www.pantherprotocol.io
Robert Hackett speaks with the general partners at a16z crypto about the launch of their fifth crypto fund and the current state of the industry. They reflect on how crypto has evolved from an ideological movement into a more pragmatic, product-focused ecosystem, shaped by real-world use cases and increasing regulatory clarity.
The conversation covers the rise of stablecoins, onchain finance, and new market infrastructure, as well as the growing overlap between crypto and AI. The group also discusses how founders are shifting toward building products that work within existing systems, rather than attempting to replace them, and why this moment may represent a new phase of mainstream adoption.
They also look ahead to what success looks like for the next generation of crypto companies, from onboarding billions of users to enabling AI agents as economic actors, and the role crypto could play in shaping more open, decentralized systems in an increasingly consolidated technology landscape.
Resources:
Chris Dixon on X: https://twitter.com/cdixon
Ali Yahya on X: https://twitter.com/alive_eth
Eddy Lazzarin on X: https://twitter.com/eddylazzarin
Guy Wuollet on X: https://twitter.com/guywuolletjr
Robert Hackett on X: https://twitter.com/rhackett
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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We are pleased to welcome Valerie Leisten, who joins ZF as our Global Events and Operations Manager.
Valerie has more than eight years of experience spanning operations, project management, and stakeholder coordination within international, multi-stakeholder environments. She has led event and program delivery, supported senior leadership, and acted as a reliable first point of contact for diverse stakeholders and community members. Valerie’s work is driven by a passion for social and climate justice, informed by experience across the UK and East Africa, and supported by a multilingual, culturally-attuned approach.
In her new role, Valerie will serve as the primary logistical coordinator for ZF events, including Zcon, Zcash Dev Summits, and regional meetups, while also providing operational support to the leadership team. She will additionally contribute to community and ecosystem engagement and support the ongoing development of the Shielded Aid Initiative.
Welcome, Valerie!
The post Zcash Foundation Welcomes a New Global Events and Operations Manager appeared first on Zcash Foundation.
The German federal cabinet recently pushed a legislative package that arguably shifts the country’s approach to digital policing. The proposed bill grants law enforcement the authority to use automated biometric image matching against publicly available data on the internet.
Currently, German officers must perform manual searches of social networks and other websites to locate photos of suspects. The new bills would modernize this process, allowing police to use AI-driven tools to upload a photo and automatically scour the web for matching images.
While the government defends the move, stating it will not create a permanent state-controlled database or include real-time surveillance from public cameras, the proposal has met fierce resistance. A coalition of over a dozen civil society organizations has condemned the package, arguing it fuels digital dragnets and contradicts the constitutional responsibility to protect citizens from automated mass surveillance. What could possibly go wrong, you ask? Well, here’s what I think.
1. The “Mission Creep” EffectThe government claims no permanent database will be created. However, history shows that once the infrastructure for automated searching is built, the requirements for its use often expand. What begins as a tool for serious crime can easily scale into a routine check for minor administrative offenses or political monitoring, effectively creating a de facto database through repeated, systematic queries.
2. Validating Data ScrapingBy legalizing police use of tools that scrape the public web, the state is essentially validating the business model of controversial third-party facial recognition engines. If the government relies on data harvested without consent from social media and blogs, it undermines its own standing to regulate or ban private companies that do the same, leading to a wild west of biometric exploitation.
3. The Chill of the “Digital Dragnet”When citizens know that any photo posted online, whether by them, a friend, or a stranger, can be instantly biometrically linked to their identity by the state, behaviour changes. This chilling effect discourages free expression, attendance at protests, or even simple social participation. The result is a society that self-censors to avoid being picked up” by an algorithm.
4. False Positives and Algorithmic BiasAI image matching is not infallible. Automated systems are known to produce false positives, particularly for marginalized groups. In an automated system, a match could lead to dawn raids or detentions before a human officer ever verifies the context, placing the burden of proof on the innocent citizen to prove the algorithm was wrong.
5. Vulnerability to Data PoisoningIf law enforcement becomes dependent on internet-scraped data for investigations, bad actors can exploit this by poisoning the well. By flooding the web with AI-generated or manipulated images designed to trigger or bypass biometric filters, criminals could lead investigators down false paths or frame innocent individuals with digital evidence that the automated tools aren’t yet sophisticated enough to debunk.
PIVX. Your Rights. Your Privacy. Your Choice.
To stay on top of PIVX news please visit PIVX.org and Discord.PIVX.org.
What Could Possibly Go Wrong with Germany’s Pivot Toward Automated Surveillance was originally published in PIVX on Medium, where people are continuing the conversation by highlighting and responding to this story.
Morgan Brennan speaks with NASA Administrator Jared Isaacman about the next phase of American space exploration and the urgency behind returning to the moon. They discuss the Artemis program, the challenges of cost, speed, and execution, and how a new competitive landscape is reshaping NASA’s priorities.
The conversation covers the role of public-private partnerships, the rise of commercial space companies, and the need to rebuild core capabilities within NASA. Isaacman also outlines how the agency is shifting toward faster iteration, clearer demand signals for industry, and a more focused strategy to compete in what he describes as a new space race.
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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David Haber speaks with Tony James about building enduring firms across multiple eras of finance. From joining DLJ when it was a subscale firm to helping grow Blackstone into one of the largest asset managers in the world, James reflects on the decisions, structures, and cultural principles that enabled long-term success.
They discuss the origins of leveraged buyouts, the evolution of private markets, and how identifying structural opportunities early can create lasting competitive advantage. James also shares lessons from backing companies like Costco, where culture, customer focus, and long-term thinking drove exceptional outcomes.
The conversation covers leadership, talent development, and the challenges of scaling organizations while maintaining performance. James also reflects on succession, firm-building, and why culture, incentives, and alignment ultimately determine whether an organization compounds or stagnates.
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We are releasing Zebra 4.4.1 today. This release contains a fix for a consensus-critical security vulnerability, and we strongly encourage all node operators to upgrade immediately. You can update directly to it if you have not updated for the last couple of releases.
Note that the 4.4.0 release was just three days ago. If you have already upgraded, unfortunately you will need to upgrade again.
Security Advisories GHSA-pvmv-cwg8-v6c8: Zebra still accepts V5 SIGHASH_SINGLE without a corresponding outputZebra failed to enforce a ZIP-244 consensus rule for V5 transparent transactions: when an input is signed with SIGHASH_SINGLE and there is no transparent output at the same index as that input, validation must fail. Zebra instead asked the underlying sighash library to compute a digest, and that library produced a digest over an empty output set rather than failing. An attacker could craft a V5 transaction with more transparent inputs than outputs that Zebra accepts but zcashd rejects, creating a consensus split between Zebra and zcashd nodes.
A previous fix (GHSA-cwfq-rfcr-8hmp) addressed a closely related case in the same area of the code, but did not cover this specific one.
Thanks to @sangsoo-osec, @zmanian, and @fivelittleducks for reporting the issue.
UpgradingWe strongly recommend all Zebra node operators upgrade to 4.4.1 as soon as possible, particularly due to the consensus vulnerabilities described above. There are no known workarounds — upgrading is the only way to ensure your node remains on the correct chain and is protected against the issues listed in this release. You can find the release on GitHub.
Thank You to Our ContributorsThis release was made possible by the work of @alchemydc, @arya2, @conradoplg, @daira, @gustavovalverde, @mpguerra, @oxarbitrage, @schell, and @upbqdn. Thank you for your continued contributions to Zebra.
Zebra is the Zcash Foundation’s independent, Rust-based implementation of the Zcash protocol. Learn more at github.com/ZcashFoundation/zebra.
The post Zebra 4.4.1: Critical Security Fix appeared first on Zcash Foundation.
Are you trusting your government to protect your digital rights and privacy? Well, think again because the global digital rights community was dealt a heavy blow this week after the Zambian government abruptly cancelled RightsCon, one of the world’s most influential conferences dedicated to human rights in the digital age.
The event, which was set to begin on May 5 in Lusaka, was shuttered just days before the opening ceremony, leaving thousands of delegates in a state of uncertainty.
A Last-Minute ShutdownAccess Now, the advocacy organization behind the summit, expressed heavy hearts in their announcement, confirming that the event would not proceed either in person or online. The cancellation followed a series of vague warnings from Zambian officials. Minister of Technology and Science Felix Mutati initially cited incomplete security clearances and a need for the dialogue to align with national procedures and diplomatic protocols.
However, as more details emerged, the narrative shifted from administrative delays to deeper geopolitical and thematic concerns. Thabo Kawana, the Secretary for Information and Media, later suggested that the cancellation was necessitated by a lack of comprehensive disclosure regarding the specific topics proposed for discussion.
Geopolitical Pressure and Local SurveillanceThe sudden move has raised eyebrows among international observers. One freedom of expression advocacy group claimed that pressure from foreign governments likely influenced the decision.
Local reports point toward a specific point of contention: the inclusion of Taiwanese delegates. The conference was scheduled to take place at the Mulungushi International Conference Center, a venue constructed via a $30 million grant from the Chinese government.
With RightsCon having been hosted in Taiwan in 2025, analysts suggest that the prospect of delegates speaking against Chinese policies in a Chinese-funded venue may have triggered the diplomatic shutdown.
The incident highlights a troubling trend within Zambia itself. In recent years, the country has seen a tightening of digital expression. Legislative updates have drawn criticism for enabling government surveillance and limiting free speech.
For a conference dedicated to the protection of digital freedoms, its forced cancellation serves as a stark reminder of the very challenges: censorship, surveillance, and geopolitical gatekeeping.
PIVX Voices: writings from PIVXcommunity
To stay on top of PIVX news please visit PIVX.org and Discord.PIVX.org.
The Sudden Silence in Lusaka: Digital Freedom Gets Grounded in Zambia was originally published in PIVX on Medium, where people are continuing the conversation by highlighting and responding to this story.
Katherine Boyle speaks with Sarah Rogers, Under Secretary for Public Diplomacy, about the intersection of AI, free speech, and global information systems. They discuss how major technological shifts, from the printing press to the internet to AI, have reshaped communication and power, and why this moment may be even more consequential.
Recorded at the a16z American Dynamism Summit, the conversation explores the role of public diplomacy in the digital age, the risks of censorship and overregulation, and how governments are approaching AI as both a national security priority and a platform for global influence. Rogers also highlights the importance of maintaining “AI with a Western soul,” and why preserving open systems and freedom of expression will shape the future of innovation.
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Panther Reward Points (PRPs) are a unique solution designed specifically for Panther, providing an economic flywheel to bootstrap protocol activity together with Panther’s Automated Market Maker (AMM). On this blog, we explain why PRPs are important, how they function, and what the role of PRPs are when it comes to protecting your onchain privacy.
How does Panther achieve Privacy?When users deposit their assets into Panther´s Multi-Asset Shielded Pool (MASP), they become private. For each transaction, there’s a ‘’receipt’’ in the form of an Unspent Transaction Output (UTXO). The protocol tracks the UTXO and will then use the deposited assets onchain upon user request, internally tracking who did what by spending and generating new UTXOs for the said user. Onchain analysts can see a number of transactions all being handled by the protocol addresses, but can not deduce who did what. If there are only 10 users in the protocol, or if there is minimal volume, it will be easier for onchain analysts to link certain transactions to users. So privacy first comes in the form of actions (internally represented via UTXOs), and is then practically achieved via the volume of actions relative to the total number of users. Commonly referred to as the anonymity- or privacy set, the collection of all possible users that could have performed a specific action, preventing onchain analysts or AI from linking transactions to users.
Why does Panther have Reward Points?Panther’s privacy works via the anonymity set: more users and more actions equal better privacy, or a larger anonymity set. Realistically, this is what a user would care about the most, assuming privacy is the end goal. That is why the Panther Protocol values and encourages these elements: through Panther Reward Points. PRPs are not tradable assets. It exists only inside private accounts, accessible only to its owner, increment-only by protocol rules, decrement-only by zero-knowledge proofs. Each action, from signing up to depositing assets and holding them inside the protocol, rewards users with PRPs, analogous to loyalty points. As Panther expands the protocol's DeFi adaptors, such as zSwap, users will find more ways to use their private assets and generate PRPs. The protocol expands its anonymity set, offering greater privacy with each action. Users earn PRPs for helping the protocol.
How are PRPs calculated?PRP calculations are configured and voted in through the Panther DAO. Some actions are simple and easily identifiable, such as PRP vouchers for signing up or doing DeFi swaps. But others are more complex, for example, PRPs earned on deposit and the PRPs generated over time.
PRP rewards come from three main sources:
Flat reward: 10 PRP for every MASP transaction, regardless of the transaction size. Time-based yield: Targets 10% APY on shielded assets based on how long and how much value is shielded. Deposit bonus: A reward of 0.01% of the deposited amount. Understanding UTXOs and Their LimitationsAs mentioned earlier, UTXOs act as receipts that are conceptually simple but more complex than one might assume. UTXOs are a static representation of what has happened onchain. If a user deposits 1 ETH, there would be a UTXO saying that the user has 1 ETH. And if a user deposits another 0.5 ETH, there would be a UTXO showing that the given user has 1 ETH, plus another UTXO showing that the user has 0.5 ETH. When a withdrawal is being made, the protocol spends the relevant UTXOs and then creates new ones with the remaining balance. For example, withdrawing 0.8 ETH might spend both the 1 ETH and 0.5 ETH UTXOs, leaving a new UTXO of, say, 0.7 ETH. A UTXO doesn't display the current market value of 1 ETH, nor the rest of the user's holdings, nor how long each has been held. How do we go about determining an asset's annual yield if all of the information is trapped in a static UTXO that cannot infer on real-time values? The problem here is solved with PRPs, and why $ZKP isn't directly attributed. Instead, Panther users swap PRPs for $ZKP via the one-sided automated market maker (AMM). Just ponder for a moment on how you would attach a $ZKP value to a UTXO without running into fair value or liquidity issues. Additionally, using real-time price feeds would compromise privacy as external oracle calls could leak information about user activity patterns.
Solving the Valuation Problem: Scaling and WeightingPanther Protocol solves this limitation by first predetermining the asset's value for the UTXO to recognize. This is done via scaling and weighting factors. Each token may use a different decimal value, so a scaling factor is applied to normalize the values among the rest. This scaling factor is set once when a token becomes a zAsset and can never be changed. Then a weighting factor is applied against this value to approximate that token's US dollar value. The weighting factor is periodically updated via DAO votes to follow token price changes, and could even be adjusted to make one asset more desirable than another. This method allows tracking the approximate value of UTXOs, but the limitation is that it is not a real-time value; it is a preconfigured one that becomes outdated between updates.
The PRP Value AssumptionGiven that PRPs are only exchangeable for $ZKP and the access point is a one-sided AMM, another assumption is required: a target ratio. The Panther DAO’s target launch AMM ratio is "10 PRP: 1 $ZKP.’’ This real-time ratio will fluctuate over time, depending on the number of users claiming rewards or waiting for the AMM to recharge (recharges occur when $ZKP is added to the AMM), as explained in this article. Due to the limitations of UTXOs, all yields are calculated statically. The worth of $ZKP can be changed at a later date via weighting factor updates.
PRPs to Bootstrap Protocol ActivityThe purpose of PRPs are to encourage protocol usage and should not be classed as a primary yield-bearing mechanism with a fixed return. There are multiple dynamically changing values being used that no other privacy protocol has attempted. PRPs are a key feature of Panther protocol, and help solve a bootstrap problem that other privacy protocols have - together with Panther’s AMM! PRPs help protect the users' on-chain privacy, and are an important part of Panther’s solution to stimulate protocol activity within its core architecture.
About Panther Protocol Foundation
Panther Protocol Foundation is a non-profit organization that supports the ecosystem through research funding, open-source development grants, and ecosystem initiatives.
The Foundation does not operate the protocol, deploy smart contracts, host interfaces, custody assets, or provide financial or digital asset services.
For more information, visit www.panther.org
To learn more about Panther Protocol, visit www.pantherprotocol.io
We are releasing Zebra 4.4.0 today. This release contains fixes for multiple security vulnerabilities, including several consensus-critical issues, and we strongly encourage all node operators to upgrade immediately.
Security Advisories GHSA-28xj-328h-72vm: Permanent Block Discovery Halt via Gossip Queue Saturation + Syncer PoisoningZebra’s block discovery pipeline contained a composite denial-of-service vulnerability that allowed a remote attacker to permanently halt all new block discovery on a targeted node. The attack exploited three independent weaknesses in the gossip, syncer, and download subsystems — all exercisable from a single TCP connection — to create a monotonically growing block deficit that never self-heals.
The gossip path was vulnerable because there was no per-connection rate limit on inv messages. A single connection could send enough sequential inv messages with fake block hashes to fill the entire gossip download queue in under a millisecond, and the FullQueue return value was silently ignored. The syncer backup path could be degraded by responding with empty inv to FindBlocks requests (valid protocol, zero misbehavior penalty) and with NotFound to block download requests. The attack produced zero misbehavior score, zero bans, and zero disconnections.
The fix drops connections that send empty responses to FindBlocks and FindHeaders messages, preventing attackers from degrading the syncer path without consequence.
Zebra’s block validator undercounted transparent signature operations against the 20,000-sigop block limit, allowing it to accept blocks that zcashd rejects. Two distinct undercounts were identified:
zcashd includes the coinbase input’s scriptSig in its sigop count. Zebra skipped the coinbase input entirely, and up to ~98 sigops could be hidden inside the coinbase scriptSig without being charged against the block limit.
Aggregate P2SH sigops. zcashd parses each P2SH input’s redeem script and sums those sigops into the block-wide total. Zebra computed P2SH sigops only in mempool transactions and never accumulated them during block validation. A block whose aggregate redeem-script sigops exceed 20,000 would be accepted by Zebra and rejected by zcashd.
The fix adds coinbase scriptSig to the legacy sigop iterator, introduces a shared p2sh_sigop_count function that mirrors zcashd‘s GetP2SHSigOpCount, and accumulates both legacy and P2SH sigops in the block-validation path.
Thanks to sangsoo-osec for finding and reporting this issue.
GHSA-gq4h-3grw-2rhv: Consensus Divergence in Transparent Sighash Hash-Type Handling due to Stale BufferThe fix for a previous sighash advisory (GHSA-8m29-fpq5-89jj) introduced a separate issue due to insufficient error handling when the sighash hash type is invalid. Zebra’s transparent script verification calls Bitcoin Script verification code in C++ through a foreign function interface (FFI), with a Rust callback that computes the sighash. The previous fix correctly returned None for undefined hash types, but the FFI bridge only writes to the C++ sighash buffer when the callback returns Some, and the C++ checker reads that buffer unconditionally — so the failure signal was lost.
An attacker could exploit this by constructing a transparent output spent by a script that runs a valid OP_CHECKSIGVERIFY immediately before an OP_CHECKSIG with an undefined hash type. The first opcode primes the C++ sighash buffer with a valid digest; the second causes Zebra’s callback to return None while the C++ checker verifies the invalid signature against the stale digest. Zebra would accept the spend while zcashd would reject it, creating a consensus split.
The fix fills the sighash output buffer with random bytes on validation failure, which makes signature verification fail as expected with overwhelming probability. This is a workaround that avoids a breaking release of the zcash_script crate; a future release will propagate the error correctly for a direct fix.
Thanks to sangsoo-osec for finding and reporting this issue.
GHSA-cwfq-rfcr-8hmp: TransparentSIGHASH_SINGLE Corresponding-Output Handling
A divergence between Zebra and zcashd was reported in how SIGHASH_SINGLE is handled for V5 transparent transactions when an input index has no corresponding output. In zcashd, this case triggers a script failure under ZIP-244; in Zebra, the sighash engine computed a digest for the missing-output case rather than failing.
We do not consider this a practically exploitable security issue: the scenario requires an attacker to both submit a malformed transaction through Zebra’s mempool and have an external miner include it in a block — a chain of events with no economic incentive that has never occurred on mainnet. Nonetheless, we have added an explicit pre-check that rejects these transactions, matching zcashd‘s behavior and closing the divergence. (#10510)
Thanks to sangsoo-osec and defuse for finding and reporting this issue.
GHSA-438q-jx8f-cccv: Allocation Amplification in Inbound Network DeserializersSeveral inbound deserialization paths in Zebra allocated buffers sized against generic transport or block-size ceilings before the tighter protocol or consensus limits were enforced. An unauthenticated or post-handshake peer could force the node to preallocate and parse orders of magnitude more data than the protocol intended, amplifying per-message memory and parse cost. Four vectors were identified:
headers message receive cap. The CountedHeader vector was deserialized via the generic TrustedPreallocate path, allowing up to ~1,409 entries per message. The protocol ceiling of 160 was only enforced on the send side, creating an ~8.8× preallocation gap on receive. This was reachable before the version handshake completed.
Equihash solution length. The equihash solution was decoded as a generic Vec<u8> and only checked against the consensus size (1,344 bytes on mainnet) afterwards. A single fixed-size header field could be inflated to nearly the full block-size ceiling before rejection.
Sapling spend vectors in coinbase transactions. V5 spend_prefixes and V4 shielded_spends were allocated with block-size-derived ceilings (~5,681 / ~5,208 entries) before the consensus rule that coinbase transactions have zero Sapling spends was enforced in the verifier. The fix reads the Sapling spend count and rejects coinbase transactions with any spends before allocating the spend vector.
Coinbase script bytes. The coinbase script was read as a generic Vec<u8> up to the message-size cap before enforcing the consensus rule that coinbase scripts are between 2 and 100 bytes.
Each individual case is bounded by the 2 MiB transport ceiling or the block-size cap, so no single message causes unbounded allocation, but the cumulative gap between intended and actual limits is significant and stackable across concurrent connections. All four vectors have been fixed by capping allocations to the protocol-specified limits before deserialization begins.
Thanks to Zk-nd3r for finding and reporting these issues.
Security Improvements Indexer gRPC Server Resource LimitsThe indexer gRPC server had no connection or subscription cap, and used a 4,000-message per-stream queue with full block payloads. The fix switches indexer streams to use try_send, which drops slow consumers instead of backpressuring the server, and reduces the per-stream buffer from 4,000 to 64 messages. Note: gRPC subscribers that fall behind are now dropped instead of backpressuring the server. Well-behaved clients are unaffected.
The RPC compatibility middleware read, parsed, and re-serialized the full HTTP request body before applying any local size cap. The fix bounds the HTTP request body via http_body_util::Limited before allocation, with the limit derived from MAX_BLOCK_BYTES to accommodate submitblock.
getrawtransaction Block Hash Validation Race
A TOCTOU race condition existed in getrawtransaction between block hash validation and txid lookup during chain forks, which could return incorrect results. The fix reuses the caller-provided block hash and its best-chain flag from the initial query, avoiding a third state lookup that could race with a reorg.
The RPC authentication cookie file was written without explicit 0600 permissions, potentially allowing other local users to read it on multi-user systems. The fix sets restrictive file permissions on the cookie file at creation time on Unix, and also rejects symlinks at the cookie path.
Thanks to Zk-nd3r for finding and reporting the four issues above.
New FeaturesnTx Field in getblock Response
The getblock RPC response now includes the nTx field, reporting the number of transactions in a block. This improves compatibility with downstream tooling that expects this field. (#10498)
A Criterion-based benchmark suite and CI workflow have been added, giving the team a systematic way to track and catch performance regressions across releases. (#10444)
Sentry Environment AlignmentSentry CI metadata and environment tagging have been aligned, improving how crash and error reports are categorized across development, staging, and production deployments. (#10490)
Bug Fixesgetrawtransaction Confirmations
A bug in the getrawtransaction RPC that returned incorrect confirmation counts has been fixed. (#10507)
librustzcash
The entire librustzcash dependency stack has been migrated to the latest version. This replaces the yanked core2 crate with corez 0.1.1, clearing the RUSTSEC-2026-0105 advisory. (#10522)
zebra-consensus, completing the cleanup after the Sapling verifier migration. (#10436)
cargo-vet configuration has been fixed to run correctly after releases. (#10504)
Upgrading
We strongly recommend all Zebra node operators upgrade to 4.4.0 as soon as possible, particularly due to the consensus vulnerabilities described above. There are no known workarounds — upgrading is the only way to ensure your node remains on the correct chain and is protected against the issues listed in this release. You can find the release on GitHub.
Thank You to Our ContributorsThis release was made possible by the work of @alchemydc, @arya2, @conradoplg, @daira, @gustavovalverde, @mpguerra, @oxarbitrage, @schell, and @upbqdn. Thank you for your continued contributions to Zebra.
Zebra is the Zcash Foundation’s independent, Rust-based implementation of the Zcash protocol. Learn more at github.com/ZcashFoundation/zebra.
The post Zebra 4.4.0: Critical Security Fixes appeared first on Zcash Foundation.
In this episode, host Friederike Ernst is joined by Kubi Mensah, CEO and co-founder of Gattaca, the company behind Titan Builder. Kubi sheds light on the highly competitive and often opaque world of Ethereum block building, explaining how Gattaca evolved from a centralized exchange proprietary trading firm to one of the three dominant builders responsible for constructing the vast majority of Ethereum blocks today .
They dissect the true "journey of a transaction," revealing why over half of all Ethereum transaction value bypasses the public mempool in favor of private order flow auctions and MEV-protection services . Kubi explains the intricate mechanics of top-of-block bidding by high-frequency DeFi arbitrageurs, the necessity of extreme latency optimization, and the "flywheel effect" that makes block building a natural oligopoly . Finally, the discussion turns to the future of the Ethereum roadmap, unpacking how upcoming upgrades like ePBS (enshrined Proposer-Builder Separation) and FOCIL (Fork-Choice Enforced Inclusion Lists) aim to permanently alter the power dynamics between block builders, validators, and originators .
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Theo Jaffee speaks with Balaji Srinivasan and Taylor Lorenz about how AI is reshaping media, trust, and online communication. Building on prior public disagreements between the two, the conversation revisits core tensions around media, technology, and power in a rapidly changing information environment.
They discuss the breakdown of traditional information systems, the rise of AI-generated content, and why new models for verifying identity and truth may be necessary. The conversation lays out competing visions for the future of media, from decentralized “webs of trust” and cryptographic verification to the role of journalism, privacy, and public accountability.
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Could pixels hold the keys to training useful agents?
The race to scale language models — and the agent ecosystem around them — is white-hot. Coding agents, which reason through problems and write code to solve them, have already taken us very far.
But one ambitious young team is making a different bet: that the most promising path to general computer agents may not run through language, screenshots, and tool calls, but through scaling raw video.
Standard Intelligence’s thesis is that the best way to build a general agent is through full video pre-training on computer use, because it is the only approach that can truly scale action data. Instead of predicting text tokens, the model learns to use a computer from raw screen data, predicting the next mouse movement, click, and keystroke from the pixels in front of it.
It is the Tesla FSD approach applied to knowledge work on computer screens.
That makes the bet both deeply contrarian and deeply “bitter lesson”-pilled. Rather than hand-engineering workflows or wrapping language models in increasingly elaborate harnesses, Standard Intelligence is betting on a new pre-training paradigm: feed the model the raw stream of computer use, scale it aggressively, and let the generality emerge from the data.
“We’re not video people”
Video is unwieldy. It is computationally expensive, economically expensive, and technically unforgiving. Prior attempts to scale video toward AGI have often died on the vine.
The Standard Intelligence team is emphatically “not video people.” They did not arrive with a decade of inherited assumptions about how to work with video as a medium. Instead, they have had to reason through each challenge from first principles, and have met those challenges with unusual optimism, creativity, and scrappiness.
The results are striking. An 11-million-hour computer action dataset — the largest in the industry. A video encoder that is roughly 50× more token-efficient than competing approaches, enabling nearly two hours of 30 FPS video to fit inside a 1-million-token context window. A 30-petabyte storage cluster racked in San Francisco for under $500K, roughly 20× cheaper than hyperscaler alternatives.
FDM-1, their first foundation model trained directly on computer-use video at scale, offers an early glimpse of what this paradigm could become. It is a general model that can extrude a CAD gear in Blender, drive a car around a San Francisco block after an hour of fine-tuning, and find bugs in software by exploring its state space the way a curious human might.
Conscientious young founders
Founders Galen Mead and Devansh Pandey met as teenagers during the Atlas Fellowship in 2022, a selective fellowship for high-school students interested in AI alignment and AGI.
Galen and Devansh are unusually serious about reaching AGI, and unusually conscientious about doing so safely. Both founders are wise beyond their years (21 and 20 respectively), and both left their undergraduate programs out of a sense of urgency to work on this problem.
Galen and Devansh stand out for their combination of taste, scrappiness, technical courage, and ambition. It shows up in the product thinking, in the research direction, and in the FDM-1 report itself.
The full team of six is small but mighty. Neel, Yudhister, Ulisse, and Ryan are each quirky and exceptional. They have chosen to turn down the conventional path (fancy degrees and offers from big token) and pursue this courageous mission together.
A new pre-training regime
Video has long been a powerful training ground for AI. DQN showed that agents could learn rich behavior directly from pixels in Atari environments. Tesla scaled video models to make self-driving cars and robots navigate the physical world.
But in the race toward general knowledge agents, video-first pre-training remains an unconventional idea.
Standard Intelligence is betting that it will not stay unconventional for long.
We are thrilled to lead Standard Intelligence’s Series A alongside Miko and Yasmin from Spark Capital.
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Elena Burger speaks with Joe Schmidt, partner on the enterprise team at a16z, about the future of enterprise software in the age of AI. Using Workday as a case study, they discuss why many of today’s most important enterprise systems feel broken, how platform shifts reshape entire categories, and what an AI-native replacement might look like.
The conversation covers the limits of legacy SaaS, why “AI revenue” may be overstated, and how agents could fundamentally change how companies manage workflows, permissions, and internal systems. They also explore why even the most defensible software businesses may now be vulnerable to replatforming.
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Theo Jaffee and Gabriel Dickinson speak with Cremieux about China’s rapid rise to the top of global clinical trial output. They discuss the regulatory reforms that accelerated China’s progress, the surge in novel drug development, and what the US would need to change to stay competitive in biomedical innovation.
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This interview with Stripe cofounders John and Patrick Collison originally aired on TBPN. They discuss Stripe's 34% growth and new employee tender offer, how agent commerce and stablecoins may require high-throughput blockchains built for millions of transactions per second, and why the economics of software are shifting from mass-produced products to bespoke, on-demand systems cooked fresh at the moment of use.
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We are pleased to welcome Andrés Rodríguez, who joins ZF as a Platform Engineer (DevOps/SRE).
Andrés has more than eight years of experience spanning cloud infrastructure, ERP systems, and platform automation across both the private and public sectors. He has helped organizations modernize their systems and adopt DevOps practices, building scalable and reliable platforms for high-traffic APIs and leading large-scale digital transformation initiatives. His career has also included leading teams and collaborating with stakeholders across technical, operational, and business functions.
Andrés is driven by solving real business problems through technology, and is eager to bring that approach to ZF’s infrastructure and platform operations. He brings hands-on experience designing resilient architectures and automating complex workflows to help ZF continue to scale its engineering efforts.
Welcome, Andrés!
The post Zcash Foundation Welcomes a New Platform Engineer appeared first on Zcash Foundation.
What does it mean to build a mind that is not a copy of our own?
Today, we are excited to announce that Sequoia is partnering with David Silver and Ineffable Intelligence, a new AI research lab based in London with a singular mission: to make first contact with superintelligence.
Ineffable is building what David calls a superlearner: a system that discovers all knowledge directly through its own experience, from elementary motor skills to profound intellectual breakthroughs. No pre-training. No imitation. Just an agent learning endlessly from the consequences of its own actions in a world built to teach it.
A Reinforcement Learning-based superlearner has the potential to rediscover and then transcend the greatest inventions in human history: language, science, mathematics and technology. Imagine a machine that derives the laws of physics from first principles. That invents new branches of mathematics we never thought to ask about. That designs materials, medicines and computers we don’t yet have the vocabulary to describe. This is the prize David is reaching for.
A Different Path
The current generation of AI was built by training on the entirety of the human internet. It is an extraordinary achievement. But a system trained on human data may also have fundamental limitations.
Ineffable Intelligence is scaling reinforcement learning from a clean base: no pre-training, no human data to shortcut the system. Guided by the Era of Experience as a north star, David is proving that agents trained purely from an environment can develop non-human strategies for reasoning about problems we don’t yet know how to solve.
David led some of the defining breakthroughs in deep reinforcement learning, most famously in the devilishly difficult game of Go. Go is the ultimate machine intelligence test because it cannot be brute-forced by a computer: a combinatorially explosive O(10^170) possible legal board positions vastly exceeds the O(10^80) atoms in the observable universe. It was thought to be simply too hard for machines to solve.
At DeepMind, David drove the key breakthrough that finally solved the game of Go: self-play. Self-play drove the ~800 ELO-point leap that led to the historic AlphaGo vs. Lee Sedol showdown in March 2016. David pushed the idea further still with AlphaGo Zero: removing human pre-training entirely and learning purely through self-play increased the system’s ELO rating from ~3,700 to 5,000+. The result was a system that reached decisively superhuman performance, and with somewhat non-human mannerisms.
That’s the lineage David has spent his career building. He was the lead researcher and technical force behind the Alpha series at DeepMind, where, for a brilliant period, his approach was the dominant paradigm: AlphaGo, AlphaZero, AlphaStar, AlphaProof and more.
Even with the arrival of LLMs, David never stopped believing. He is one of the very few people on earth with the conviction, the technical depth and the team to scale reinforcement learning.
What’s Ahead
The work ahead is hard, the timeline to superintelligence is uncertain, and the bet is genuinely contrarian. That is exactly what excites us. The largest leaps in AI have always come from people willing to ignore the consensus. David has ignored more consensus, more correctly, than almost anyone in the field.
We are honored to co-lead Ineffable’s first round and to partner with David on what may be the most ambitious scientific mission of our generation.
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Anjney Midha, founder of AMP PBC, speaks with Ben Horowitz, cofounder of a16z, about how venture capital changed from a small, relationship-driven business into a scalable system for backing new technology companies. They discuss network effects, firm design, leadership, culture, and how AI is reshaping both the capital race and the kinds of companies that can be built now.
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Now that Panther’s DAO-operated Mainnet deployment is near, it is time to take a closer look at how its Automated Market Maker (AMM) works! On this blog, we explain the role of Panther’s Reward Points (PRPs) and how their conversion to $ZKP works. We explain the conversion model's calculations, the factors that influence the PRP/$ZKP ratio, and why there is an Automated Market Maker in the first place. Let’s dive in!
What are PRPs and their role?PRPs are rewards earned for holding shielded $ZKP (zZKP) in Panther’s Multi-Asset Shielded Pool (MASP) and for performing certain user actions within the protocol. zAssets like zZKP are digital assets within Panther’s MASP, in this case, $ZKP tokens within the protocol. Originally, PRPs were intended to be exchanged for zZKP determined by supply and demand once Panther’s Mainnet was operational. However, this has changed as there won’t be a transition from Panther’s MASP v0.5 (also known as Advanced Staking) to Mainnet (v1.0), nor a corresponding upgrade of v0.5 PRPs to v1.0 PRPs. Instead, there will be a floating rate starting at 10 PRPs for 1 $ZKP through the AMM, whereas 1 zZKP is equivalent to 1 ZKP – distinguishing it from the prior PRP exchange mechanism.
Automating claims and user involvement through the AMMIn the current setup, PRPs can be redeemed by exchanging them for $ZKP from the protocol's reserves. Each redemption reduces the $ZKP reserves while Panther users receive $ZKP. On the Limited Mainnet Beta version of Panther’s Mainnet, daily additions (e.g., approximately 33,300 $ZKP per day) significantly affected the PRP and $ZKP reserve balances, thereby influencing the ratio. In contrast, Panther’s Canary environment initially lacked a recharge AMM pool feature, which can skew the ratio by introducing large amounts of ZKP without a corresponding inflow of PRPs.
As there is no way to automate the claiming process, it must be performed manually by PRP holders through the AMM. This design encourages active participation and aligns with the protocol's decentralized nature.
Rewards Redemption Breakdown How the AMM conversion ratio is calculatedPanther’s rewards functionality operates on a specific logic for issuing rewards, which has implications for the PRP-to-$ZKP conversion ratio. When protocol rewards are deposited, the reward controller first sends the total accrued amount to the Automated Market Maker (AMM), which can lead to a high initial conversion ratio. To manage this, options include depositing the accrued amount and claiming it to align with current values, or redeploying the smart contract to reset the start date. The $ZKP amount a user receives when redeeming PRP is determined entirely by smart contracts, with no modifications made by the development team. The Panther dApp retrieves this amount, displays it, and calculates the displayed ratio. If a user has no PRP available to redeem, the conversion ratio shows as 0 PRP = 0 $ZKP.
Panther’s smart contracts use the following formula for the PRP to $ZKP conversion:
zkpOut = (prpIn × zkpReserve) / (prpReserve + prpIn)
Where:
prpIn: Amount of PRP being converted. zkpReserve: Total ZKP in the pool. prpReserve: Virtual PRP balance in the pool. zkpOut: ZKP tokens received.The rate is then zkpOut / prpIn.
Whereas an example calculation would be:
Pool state: 1,000,000 PRP reserve, 500,000 ZKP reserve. Converting 1,000 PRP: zkpOut = (1,000 × 500,000) / (1,000,000 + 1,000) = 500,000,000 / 1,001,000 ≈ 499.5 ZKP. Rate = 499.5 / 1,000 = 0.4995 ZKP per PRP.These calculations depend on the user's available PRP and the current zkpReserve and prpReserve balances, which update with every redemption or AMM pool recharge. This means the $ZKP amount and ratio are recalculated dynamically for all dApp users on the Redeem PRP page.
Factors Affecting the Ratio and Potential SolutionsThe PRP-to-$ZKP ratio could become unfavorable due to low user participation—specifically, few users consistently adding significant PRP to the ZKP reserves. In scenarios with only a handful of users (e.g., up to three) redeeming and recharging sequentially in production, the ratio can fluctuate dramatically, for example, 1 PRP = 30 ZKP (as seen in certain screenshots from testers). However, with dozens or hundreds of users engaging 24/7—recharging the AMM pool and redeeming PRP at rates they find favorable—the ratio should stabilize and not spike excessively. To address unfavorable ratios, solutions like a buffer mechanism could be considered if they prove effective and secure.
About Panther Protocol FoundationPanther Protocol Foundation is a non-profit organization dedicated to supporting the growth, sustainability, and responsible use of Panther Protocol. While it does not operate the protocol or facilitate digital asset services, the Foundation plays a critical role in promoting adoption, supporting open-source development, advancing research, and raising awareness around the protocol’s core privacy-preserving technologies.
By empowering users, developers, and permissioned actors within DeFi and web3, the Foundation contributes to building a more secure and confidential digital future.
For more information, visit www.panther.org
To learn more about Panther Protocol, visit www.pantherprotocol.io.
Contact
Panther Protocol Foundation
📧 Email: general@panther.org
🌐 Website: www.panther.org
The room is warm. The light is low. Someone dealt the cards a long time ago, before you arrived, and the rules were explained just clearly enough for you to feel like a participant.
You played. Most people do. It seemed the reasonable thing — the only thing, really. Everyone else was at the table. The stakes felt manageable. And the dealer had a reassuring face, a practiced manner, a way of making the whole arrangement feel not just normal but necessary. Democratic, even.
It took a long time to notice that the other players always seemed to know something you didn’t. That the house never lost. That the rules, read carefully, had been written by people who were not sitting at your end of the table.
And that the door, the whole time, had been right behind you.
* * *
Some people thought they had found a way to change the game from within.
Not so long ago, they were promised disruption. An outsider. Someone who had nothing to owe the machine, because he had never needed it — or so the story went. The swamp would be drained. The people who had been dealt the worst hands, year after year, would finally have their turn.
They were not stupid for wanting this. They were human. When the table has been rigged long enough, the promise of a new dealer feels like salvation.
But disruption, it turned out, has its own donors. Its own revolving doors. Its own preferred industries, preferred policies, preferred exemptions. The faces around the table shifted. The table did not.
This belief — that the right person, the right movement, the right wave of anger could finally overturn the house — is not stupidity. It is loyalty. And loyalty, once extended, is astonishingly difficult to withdraw — because withdrawing it means admitting something painful: that the years of compliance, the forms filled out, the taxes paid, the faith extended, the ballots cast, were not investments in a shared future. They were contributions to someone else’s private one.
Some people are only now beginning to admit this.
But they are.
* * *
Watch the dealer carefully, and the technique becomes visible.
A politician takes the stage. The lighting is warm. The words are practiced. There is talk of ordinary people, of shared sacrifice, of the hard work ahead. There is a flag somewhere, applause at the right moments. The cameras find faces in the crowd — worried faces, hopeful faces, faces that want to believe they are watching something real.
And then the cameras stop rolling.
The calls begin. Not to constituents, but to donors. The legislation takes shape in rooms that smell like money rather than democracy. The final text — thousands of pages, released hours before the vote — was not written by the people who will vote on it. It was written by the people who paid for the people who will vote on it.
This is not cynicism dressed up as insight. It is the operational reality of modern governance in most of the world’s wealthiest democracies. Lobbyists don’t influence legislation. In many cases, they author it.
But the deepest trick happens before any of this — before the cameras, before the speeches, before the vote. It happens in the hand selection. Candidates who might genuinely threaten the donor class find their funding dry up, their media coverage thin, their party support quietly withdrawn. By the time a name appears on a ballot, the serious vetting has already occurred. Not by voters. By money. The players at your end of the table get to choose between the cards the house has already approved.
Democracy, in this reading, doesn’t begin at the ballot box.
It ends there.
* * *
People know this. They have known it for a long time, in the way you know, somewhere in the back of your mind, that the odds in a casino are not in your favour — and keep playing anyway, because what else would you do?
Opinion surveys in country after country show the same result: collapsing trust in governments, in institutions, in the premise that those in power answer to those who placed them there. But the surveys measure a feeling. What they do not capture is the mechanism. It is not simply that politicians stop listening after they are elected. It is that the politicians who would have listened were never permitted to get that far. The selection happens upstream, in the funding rounds, the endorsement calls, the quiet conversations about viability that precede any public process entirely.
The filter is invisible. That is the point of it.
If the game is fixed at the point of selection, what do you do? You cannot simply vote harder — the cards available to you were chosen by someone else. You cannot protest your way to the table where decisions are actually made. You cannot opt out of the economy — you still need to eat, to pay rent, to move through a world that requires participation at every turn.
So most people do the only thing that seems available: they carry on. They absorb the dissonance. They reserve their outrage for the dinner table, the comment section, the private conversation. And they continue, dutifully, to lay down their cards.
And to fill out the forms.
* * *
Here is where the arrangement reveals its sharpest edge.
You will provide your name. Your address. Your date of birth. Your government-issued identification. Proof of your source of funds. Explanation of the purpose of your transaction. Not because you are suspected of wrongdoing, but because the system has decided that your continued participation requires justification.
Know Your Customer. The phrase has the texture of care — as though the institution asking is motivated by concern for you, for your protection, your best interests.
But consider what is not asked in return.
Does the customer know the institution? Does the customer know how the bank’s balance sheet is structured, which risks it carries, which political campaigns its executives quietly fund, which industries its investment arm finances in your name? Does the customer know which lobbyist wrote the regulation that defines what the institution may do — and what it need not disclose?
The answer, of course, is no.
And yet this same system — the one that requires your complete financial transparency — has a long and documented record of looking the other way when the person in question is wealthy enough, connected enough, or useful enough to the right people. Files have surfaced over the years. Names attached to extraordinary behaviour in extraordinary places — behaviour that would have ended ordinary lives. The consequences for those concerned remained largely theoretical. But what those files also revealed, to those paying attention, is that the names within them were not the architects of the arrangement. They were guests. The people who truly control such situations are rarely found in the documents at all. They are the ones who decide what gets released, and when, and to whom.
The information flows in one direction only: toward them, about you. Analysed, cross-referenced, sold, leaked, stolen — fed into systems that will judge your future based on choices you made with no idea they were being permanently recorded.
This is what they call safety. What they call the price of participation.
They did not ask if you agreed to these terms. They simply changed them. And the cards kept coming.
* * *
Now, in the background, the house is introducing a new kind of chip.
It goes by several names. In some places, a Central Bank Digital Currency. In others it arrives more quietly, dressed as a stablecoin — neutral, efficient, modern. Progress. The natural next step in a journey toward frictionless payment that has only ever moved in one direction.
What it actually is, is the logical conclusion of everything that came before. The final card in a hand that has been building for decades.
Physical cash has always been the problem, from the house’s perspective. Cash is anonymous. It cannot be frozen by an algorithm, redirected by a policy update, or expired on a schedule designed to encourage spending at politically convenient moments. A centrally-issued digital currency solves all of these inconveniences — not from your perspective, of course, but from theirs.
Money has always been, at its core, a tool of individual agency — the ability to exchange value without permission, to save without approval, to move resources without explaining yourself to anyone. A programmable currency issued by a central authority is the opposite of that. It is money with conditions attached — updated silently, applied selectively, enforced automatically. Spend it here, not there. Before this date, not after. On these goods, not those.
The technology is neutral. The intentions behind it are not.
A leash. Made of code. Attached, gently and for your own good, to everything you own.
* * *
The natural response, still, is to look for a reformer. A new dealer. Someone honest enough to change the rules from within.
But the rules were not written by the dealer. The dealer is an employee. And the people who own the house have spent decades purchasing not just politicians but the entire machinery through which politicians might be held to account — the regulators who return to the industries they once oversaw, the legislation drafted before the public vote, the campaign structures that determine which reformers are viable before a single ballot is cast.
To believe that the next election, the next administration, the next wave of righteous anger will alter this architecture is not hope.
It is the house’s most valuable asset.
There is an old idea that has grown newly urgent. When an institution fails you, the theory goes, you have a choice between voice and exit. For generations, exit was treated as surrender — the responsible citizen raised their voice, organised, demanded to be heard.
But that framing assumed the microphone was real. That the feedback loop between citizens and those who govern them remained intact.
When that loop is severed — not just at the point of governance but earlier, at the point of selection, where money has already determined which voices will ever reach the room — exit changes its character entirely.
Pushing back your chair and walking away from a rigged table is not defeat.
It is the only move that cannot be countered.
* * *
What does walking away actually look like?
Not the fantasy of the hermit — disconnected, self-sufficient, responsible to no one. That is simply a different kind of illusion. You still live in the world. You still need to move through it.
It looks, instead, like a steady and deliberate migration of your financial life away from systems that require your total legibility as the price of participation. A quiet reduction of surface area. Not a protest anyone needs to witness. A personal decision, made in private, for reasons that are nobody else’s business.
It looks like understanding that the money in your bank account is a liability on someone else’s balance sheet — subject to their policies, their freezing mechanisms, their reporting requirements. The account number is yours. The money, increasingly, is conditional.
It looks like recognising that continuing to fund decisions you were never asked about — made in rooms you will never enter, for reasons explained to you only afterward, if at all — is not citizenship. It is subsidy.
And it looks like asking, calmly and without drama, what an alternative architecture for your financial life might actually look like. Not as a political statement. Not as a form of protest that anyone needs to notice.
As a personal decision. Private. Practical. Self-respecting.
* * *
This is where something built without venture capital timelines, without the approval of institutions, and without any particular interest in being respectable, begins to matter.
PIVX does not promise to fix the table. It offers something more modest and more useful: a way to leave it.
You can hold wealth in a form that no government can freeze, no institution can seize, no algorithm can expire. You can stake that wealth — participate in the network’s operation — and receive quiet, compounding rewards that require neither employer nor bank account nor form to access. You can operate a masternode and participate in a system that functions identically for everyone, regardless of jurisdiction, identity, or political inconvenience.
But PIVX offers something beyond the financial — something the current system has not managed in a generation: a community where voice is not for sale.
Development decisions are not handed down from a boardroom or shaped by the preferences of venture capital. They emerge from open discussion among people who understand what they are building and why — a meritocracy in the original sense, where the quality of an argument matters more than the status of the person making it. This is how PIVX has, more than once, arrived at the frontier of privacy technology before larger, better-funded projects found their way there. Not despite its structure, but because of it.
Masternode owners do not merely earn rewards. They vote. On the treasury. On proposals. On the direction the project takes next. Real votes, on real decisions, that move real resources. The outcome is not predetermined by whoever wrote the largest cheque. It is determined by the people who chose to show up.
This is what representative democracy was supposed to look like. The fact that it is functioning here — in a privacy network, governed by pseudonymous participants across dozens of countries — while failing so visibly in the institutions that claim to embody it, is not a coincidence. It is a consequence of design. Systems that cannot be bought tend to behave differently from systems that can.
And when you need to re-enter the world you still live in — because you do still live in it — the paths exist. Into Bitcoin, something close to digital gold after more than seventeen years: a store of value outside the reach of monetary policy, resistant to dilution by decree. Or directly into daily life — through gift cards, through merchants, through the growing network of places where value held outside the system can be spent inside it, on your terms, without leaving the trail that someone else will use to build their model of who you are.
This is not escape.
It is the freedom to move through the world without the house watching every hand you play.
* * *
The objection will come — it always does — that this is only for those with something to hide.
That phrase has done extraordinary work for the people it protects, and it deserves examination.
Everyone has something to hide, in the precise sense that everyone has things they consider private. Medical choices. Religious convictions. Political donations. The contents of their conscience. These are not shameful things. They are human things. The right to hold them without narrating them to an institution is not a privilege for the suspicious. It is a basic condition of dignity.
The people demanding your total financial transparency are, themselves, comprehensively opaque. The donor relationships, the backroom arrangements, the regulatory decisions made over private dinners — none of this is visible to you. It is visible to the participants, who are careful about what they commit to paper.
They have chosen their privacy carefully, and defended it at every turn.
They would simply prefer that the option not be available to you.
* * *
The room is still there. The table is still set. The dealer still has that practiced manner, that reassuring face.
But something has changed.
You can see the door now.
Not the dramatic exit of the revolutionary — coat thrown on, bridges burning behind them. The quieter exit of someone who has simply looked clearly at what is being asked: total financial legibility, permanent behavioral records, participation in a monetary system whose direction has been consistent for decades. And decided, without anger, without theater, that this particular arrangement no longer requires their cooperation.
Becoming ungovernable is not a statement. It is a posture. Maintained privately. Practiced daily. Invisible to the people it no longer serves.
It does not fix the room. Nothing so modest could claim that.
But it does something the house cannot easily counter: it reduces your dependence on it. Quietly. One decision at a time. Building, in the unhurried way of someone who has stopped rushing toward a finish line that was never theirs, a financial life that does not require total submission as the price of admission.
And in that reduction, something returns that is difficult to name but immediately recognizable once felt.
Not the performed agency of the voter who believes their ballot reaches the rooms where things are actually decided. Not the exhausted agency of the protester who shouts at a wall. The real kind. The private kind. The kind that requires no audience, no validation, no permission.
The kind that was always yours.
The door was always there.
It is remarkably easy to walk through.
PIVX Voices: writings from PIVXcommunity
To stay on top of PIVX news please visit PIVX.org and Discord.PIVX.org.
The Game They Need You To Keep Playing was originally published in PIVX on Medium, where people are continuing the conversation by highlighting and responding to this story.
Let the stories come to you. Catch up on the best of the community and market actions every week with the Pulse.
Market Pulse Masternode Count: Over the last seven days, the PIVX active masternode count adjusted from 2,119 to 2,131. While fluctuations are standard, the network ended the week with 12 more active nodes than it started with. Price Check: Trading for PIVX remained flat over the past seven days, with the Daily USD Value fluctuating within the $0.08 corridor. Despite this sideways movement in prices, the weekly average declined from $0.0862 to $0.0782, a 9.28% from last week. Trading Buzz: Reflecting the decline in daily prices, trading activity saw a reduction this week. Total weekly volume contracted from $22.2 million to $17.1 million, a 22.97% drop. The reduction in activity suggests a transition from aggressive speculative trading to a period of consolidation, as the lack of a clear price catalyst led to thinner order books.PIVX. Your Rights. Your Privacy. Your Choice.
To stay on top of PIVX news please visit PIVX.org and Discord.PIVX.org.
PIVX Weekly Pulse (Apr. 17th, 2026 — Apr. 23th, 2026) was originally published in PIVX on Medium, where people are continuing the conversation by highlighting and responding to this story.
Steven Sinofsky, board partner at a16z, Aaron Levie, CEO of Box, and Martin Casado, general partner at a16z, discuss the reality of AI inside enterprises. They cover the gap between Silicon Valley and the rest of the world, why most AI initiatives fail in large organizations, and how agents, infrastructure, and workflows are evolving beyond the hype.
Resources:
Follow Aaron Levie on X: https://twitter.com/levie
Follow Steve Sinofsky on X: https://twitter.com/stevesi
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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We have just released FROST v3.0.0. You can find frost-core and the ciphersuite crates available on crates.io. This stable release follows v3.0.0-rc.0, which introduced the bulk of the changes in this major version. If you are upgrading from v2.x, please read the RC release post for the full picture—the changes described here are what’s new since the release candidate. Full release notes are on GitHub, and the updated documentation book is at frost.zfnd.org.
The changes between the release candidate and the final release are modest but meaningful. On the security front, SigningKey is no longer Copy and now implements ZeroizeOnDrop, meaning signing keys are automatically wiped from memory when they go out of scope. dkg::round2::Package has received the same treatment. These changes reduce the window during which sensitive key material exists in memory and bring FROST in line with security best practices for cryptographic implementations.
There is also a bug fix: verify_signature_share() now correctly calls the Ciphersuite::pre_commitment_aggregate() hook, which was added in the RC to support custom pre-aggregation logic but was inadvertently omitted from the per-share verification path.
Finally, PublicKeyPackage::new() now takes min_signers as an Option. Passing None is useful when the threshold is not known at construction time—for example, when deserializing packages from older versions.
Thank you to everyone who contributed to this release: @conradoplg, @natalieesk and @BeeFlea.
The post FROST Release v3.0.0 appeared first on Zcash Foundation.
Erik Torenberg speaks with Martin Shkreli, American investor and businessman, about how he sees the AI landscape, from OpenAI to Anthropic, and what actually matters beyond the hype. They also talk through the future of computing, the limits of “vibe coding,” and why biotech and pharma remain some of the toughest industries to get right.
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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Erik Torenberg and Theo Jaffee speak with Balaji Srinivasan, angel investor, entrepreneur, and author of The Network State, about how AI is transforming media, eroding trust, and reshaping how information is created and verified. They discuss why systems like hiring, journalism, and online communication are breaking under synthetic content, and what replaces them. The conversation also examines the role of cryptography, on-chain data, and new models of proof in rebuilding trust online.
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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This is the 37th post in an ongoing series describing new privacy features in Brave. This post describes work done by Vadym Struts (Senior Software Engineer) and Agustín Ruiz (DesignOps Lead), and was written by Shivan Kaul Sahib (VP, Privacy and Security).
Following the success of the Shred button on the Brave iOS browser, Brave is bringing the Shred feature to the Brave Android app. Now available with version 1.89, the Shred button on Android allows users to quickly delete site data that could be used to re-identify users across visits.
Shred lets you instantly erase any data a site stores on your device. Shred on Android offers the same easy and site-specific data erasure as Shred on iOS. This means you can instantly wipe one website’s stored data without being forcibly logged out of all websites, eliminating the need for complex site-by-site exceptions. This sets Shred apart from similar features in other privacy-focused browsers.
You can also set up Auto Shred to automatically delete site data when you close website tabs or the browser. Auto Shred replaces the existing “forget me when I close this site” feature on Android. The Shred feature unifies the user experience across Android and iOS by providing a more consistent approach to both manual (tapping the Shred button) and automatic (using Auto Shred) data clearing. If you previously used the Android forgetful-browsing feature, your preferences will be automatically migrated to the new Auto Shred settings.
The Shred button on Android, just like on iOS, is a powerful tool against the first-party tracking that might otherwise allow sites to:
Monitor your visits and limit content access (e.g. “you have 5 articles left this month”) Invisibly share your data with partners (e.g. via server-side tracking)By allowing you to clear data stored by the site, Shred disrupts this tracking. Shred can help prevent companies from building detailed user profiles or monitoring your interactions with them over extended periods.
How to use Shred Shred data nowYou can immediately Shred a site in three different ways:
From the tab switcher From the contextual menu By selecting the new Shred option in ShieldsShred works on a per-site basis, since that is the privacy boundary for cookies and storage. When you shred a site, all tabs open to that site are closed, and locally stored data for that site is erased.
Automatically shredAuto Shred lets you automatically delete data for specific sites, so you don’t have to remember to manually clear site data. As mentioned above, Auto Shred replaces the “forget me when I close this site” feature. Similar to the “forget me when I close this site” setting, Auto Shred has a 30 second delay after tabs close before all data is cleared for the site, to give you a chance to restore tabs.
To set up Auto Shred:
Open Shields. Tap Shred site’s data. Tap Auto shred.You can configure Auto Shred to happen when all tabs for a site are closed, or on browser restart. Setting Auto Shred to Site Tabs Closed means that whenever you close the last tab for a site, Brave will automatically Shred that site’s data.
You can also set Auto Shred to happen for all sites:
Open Settings. Tap Brave Shields & privacy. Tap Auto Shred. What data does the Shred feature delete?Shredding deletes data explicitly stored by the site (e.g. cookies and local storage) as well as implicit data (e.g. network-related caches) available to the site. Android doesn’t have the same platform restrictions as iOS, so Brave has more control over the storage associated with a particular website. This means we can more effectively clean up all first-party storage visible to the site.
Note that your local browsing history is not available to websites, and is not currently cleared by Shred. Let us know if you would like the ability to shred local browsing history.
Privacy should be simple (and fun)Our iOS users love the Shred button, and we’re excited to finally bring the same experience to our Android users. As always, Brave is committed to making privacy protections simple, effective, and fun for everyone. We’re also exploring new capabilities, such as shredding tab groups. Tell us how we can make Shred work even better for you!