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「SUGA | Agust D TOUR 'D-DAY' The Original」「SUGA | Agust D TOUR 'D-DAY' The Original POSTER SET」発売決定！ 5月21日(火)11:00より予約販売開始！ #SUGA# | Agust D TOUR 'D-DAY' The Original 🎤 POSTER SET 🎤 #AgustD# #D_DAY_THE_ORIGINAL#

not all crypto rewards are created equal. when a platform advertises yield, i think the first thing to check is what holders actually receive and where that value comes from. @RebateLaunch has an interesting approach to this. instead of paying holder rewards in newly issued $rebate, eligible holders receive payouts in the pair asset. on robinhood chain, that's usdg. the platform reports: → $28.5k distributed to holders. → 120,403 individual payouts. → rewards sent several times throughout the day. → no staking or manual claiming required. but the reward distribution is only one part of the model. rebate pad also uses a declining launch tax to address early sniping. → 99% during the initial stage. → 66% in the next stage. → 33% in the following stage. these penalties go into a locked treasury that supports the buyer bonus mechanism. separately, the 0.7% platform fee is used to buy $rebate from the market and burn it. the reported cumulative burn has reached 35.3m tokens, representing 3.53% of the original 1 billion supply. what makes this model worth examining is how several mechanisms connect: ✦ early launch activity contributes to the treasury. ✦ the treasury supports buyer bonuses. ✦ trading fees fund holder payouts and the $rebate buyback-and-burn mechanism. my perspective is that paying rewards in the pair asset is an interesting distinction because holders aren't simply receiving more of the same token. however, attractive payout numbers alone don't prove sustainable yield. trading volume, fee generation, treasury health and long-term user activity still matter. i'd rather evaluate the source of rewards than focus only on the advertised percentage. also, round 3 of the creator campaign closes november 17 at 22:00 utc. the real question is whether rebate pad can maintain this economic cycle as adoption grows. $rebate

Happy 2026! Will this be the year we finally achieve AGI? I’d like to propose a new version of the Turing Test, which I’ll call the Turing-AGI Test, to see if we’ve achieved this. I’ll explain in a moment why having a new test is important. The public thinks achieving AGI means computers will be as intelligent as people and be able to do most or all knowledge work. I’d like to propose a new test. The test subject — either a computer or a skilled professional human — is given access to a computer that has internet access and software such as a web browser and Zoom. The judge will design a multi-day experience for the test subject, mediated through the computer, to carry out work tasks. For example, an experience might consist of a period of training (say, as a call center operator), followed by being asked to carry out the task (taking calls), with ongoing feedback. This mirrors what a remote worker with a fully working computer (but no webcam) might be expected to do. A computer passes the Turing-AGI Test if it can carry out the work task as well as a skilled human. Most members of the public likely believe a real AGI system will pass this test. Surely, if computers are as intelligent as humans, they should be able to perform work tasks as well as a human one might hire. Thus, the Turing-AGI Test aligns with the popular notion of what AGI means. Here’s why we need a new test: “AGI” has turned into a term of hype rather than a term with a precise meaning. A reasonable definition of AGI is AI that can do any intellectual task that a human can. When businesses hype up that they might achieve AGI within a few quarters, they usually try to justify these statements by setting a much lower bar. This mismatch in definitions is harmful because it makes people think AI is becoming more powerful than it actually is. I’m seeing this mislead everyone from high-school students (who avoid certain fields of study because they think it’s pointless with AGI’s imminent arrival) to CEOs (who are deciding what projects to invest in, sometimes assuming AI will be more capable in 1-2 years than any likely reality). The original Turing Test, which required a computer to fool a human judge, via text chat, into being unable to distinguish it from a human, has been insufficient to indicate human-level intelligence. The Loebner Prize competition actually ran the Turing Test and found that being able to simulate human typing errors — perhaps even more than actually demonstrating intelligence — was needed to fool judges. A main goal of AI development today is to build systems that can do economically useful work, not fool judges. Thus a modified test that measures ability to do work would be more useful than a test that measures the ability to fool humans. For almost all AI benchmarks today (such as GPQA, AIME, SWE-bench, etc.), a test set is determined in advance. This means AI teams end up at least indirectly tuning their models to the published test sets. Further, any fixed test set measures only one narrow sliver of intelligence. In contrast, in the Turing Test, judges are free to ask any question to probe the model as they please. This lets a judge test how “general” the knowledge of the computer or human really is. Similarly, in the Turing-AGI Test, the judge can design any experience — which is not revealed in advance to the AI (or human subject) being tested. This is a better way to measure generality of AI than a predetermined test set. AI is on an amazing trajectory of progress. In previous decades, overhyped expectations led to AI winters, when disappointment about AI capabilities caused reductions in interest and funding, which picked up again when the field made more progress. One of the few things that could get in the way of AI’s tremendous momentum is unrealistic hype that creates an investment bubble, risking disappointment and a collapse of interest. To avoid this, we need to recalibrate society’s expectations on AI. A test will help. If we run a Turing-AGI Test competition and every AI system falls short, that will be a good thing! By defusing hype around AGI and reducing the chance of a bubble, we will create a more reliable path to continued investment in AI. This will let us keep on driving forward real technological progress and building valuable applications — even ones that fall well short of AGI. And if this test sets a clear target that teams can aim toward to claim the mantle of achieving AGI, that would be wonderful, too. And we can be confident that if a company passes this test, they will have created more than just a marketing release — it will be something incredibly valuable. [Original text: ]

Grok Bot summary: IAC 2026 presentation by SpaceXAI President Michael Nicolls. 🎯 The main message - SpaceX was founded in 2002 to make life multi-planetary and "extend the light of consciousness to the stars." - He said the biggest risk as constellations grow is not debris. It's satellite operators not sharing data with each other. 🌍 How Starlink grew - Starlink began in 2019 with 60 V0.9 satellites. User service started in late 2020 as the "Better Than Nothing Beta." - V1.5 added space lasers, which let the satellites link into a mesh. That's when Starlink really took off, reaching oceans, Antarctica and airplanes. - V2 (2023) has 4x the capacity of V1. Falcon launches V2 satellites 2–3 times a week, and each launch adds about 3 terabits of capacity. - V3 launched on Starship about a week before the talk. Each V3 satellite has another 10x capacity, and each Starship launch carries 20–30x what a Falcon launch does. - Starlink now has over 11,000 satellites, about 60% of all active satellites in low Earth orbit. - It serves more than 13M customers in about 170 markets, plus ships and planes. - The laser mesh means Starlink doesn't depend on local ground infrastructure. During last year's fires and power outages in Spain, it kept tens of thousands of customers and emergency responders online. 🚀 The life of a Starlink satellite - **Before launch:** SpaceX checks that deploying the satellites won't bring them close to anything else, and shares where they'll be. - **Deploy:** satellites are released below 300 km, where almost no other operators fly. - **Orbit raising:** each climb path is checked against other satellites and crewed space stations, and SpaceX publishes the satellites' positions every hour on the way up. - **Lower orbits for safety:** the original plan was about 1,000 km. Now every satellite flies below 500 km, and V3 will fly at about 350–360 km. At these heights a dead satellite falls back to Earth within months instead of staying up for centuries. 🛡️ How they avoid collisions - Each satellite makes small station-keeping moves about once a day and a collision-avoidance move about every 10 days. - Across the fleet, that's about 1,000 avoidance moves a day. - Starlink's threshold for moving is nearly 100x more cautious than the industry standard, so the whole fleet's collision risk is about the same as a much smaller constellation's. - "Ducking" moves turn the satellite so it presents a smaller target during a close pass, cutting the collision risk about 10x more. - Automated software makes these decisions, which is why Starlink tries to stay in contact with every satellite at all times. 📡 Always connected - Starship's live launch video was streamed over Starlink. - The new V3 satellites connected to the network within 2 minutes of release, because Starlink terminals are built into the satellites. Their lasers link into the mesh within tens of minutes. - The goal is to make contact right away and never lose it. 🌬️ Space weather - During a 2024 solar storm, air density in orbit jumped 5x in a few hours, which throws off predictions of where satellites will be. - Starlink is "probably one of the best sensors" for this. It measures density in real time and automatically corrects its position forecasts. 🔭 Working with astronomers - **Optical:** SpaceX tried Vantablack paint and sun visors. What finally worked was a mirror-like film that reflects sunlight away from the ground, combined with careful steering. A V2 satellite now looks about as bright as a sheet of paper. This was done with groups like the Vera Rubin Observatory. - **Radio:** working with observatories like the US National Radio Astronomy Observatory (NRAO), Starlink steers its beams and frequencies around where the telescopes are pointing. As a result, the telescopes can now observe more often. ♻️ End of life - SpaceX uses statistics to predict when parts will fail and deorbits satellites early. Many of those satellites would have kept working. - Retired satellites are steered down within weeks, not left to drift for the 5 or 25 years regulations allow. - They're built to burn up completely, and reentry is aimed over the ocean. - Of about 10,000 satellites launched by early 2026, only a few dead ones remain in orbit. The goal is zero. - "Reliability and rate go together." ⚠️ The biggest danger: other operators - Tracked debris is like obstacles on a road: if you can measure it, you can avoid it. - What you can't avoid is a satellite that maneuvers without telling anyone. Starlink has had passes as close as tens of meters this way. - Starlink has maneuvered around satellites from about 650 operators in recent months, but shares position data with only about half of them. "That number is too low." - SpaceX shares data with China's space station and some Chinese operators. Many operators don't share at all, including some in the US, Europe and Japan. ⭐ Stargaze: Starlink's own space tracking - Over 30,000 star trackers on Starlink satellites spot passing objects as streaks against the stars. - Stargaze now tracks over 17,000 objects, including about 90% of the maneuvering satellites that cross Starlink's orbits. - In one example, an untracked satellite's predicted 9 km miss shrank to about 60 m after it maneuvered toward Starlink. Stargaze caught the move and the Starlink satellite dodged in time. - He said it's still no substitute for operators sharing their plans in advance. 🤝 The ask: share your data - SpaceX runs its own space safety data platform, and over 100 operators have signed up. - Operators can upload their satellite positions and check them against Starlink and against Stargaze tracking. - His message to every operator was to join the platform and share their positions. 🧠 StarMind: AI compute in space - StarMind is SpaceX's planned AI satellite constellation, starting launches and scaling next year. - The goal is to move AI compute off Earth. He expects StarMind to drive demand for Starship launches the way Starlink did for Falcon. - On the ground, SpaceX is building over 2 GW of compute in Memphis. He said AI demand will be nearly limitless, and Earth's resources make that hard to keep scaling there. - In space, the satellites run on solar power and shed heat through large radiators. - Each satellite is simpler than a Starlink: basically a computer with big solar panels and radiators. - Each one has about 250 kW, roughly one NVL72 rack of compute. - They'd fly in tight clusters of about 10 satellites linked by about 10 terabits of bandwidth, then connect to Starlink. - SpaceX has filed for up to **1 million satellites**, in low-inclination and sun-synchronous orbits. Building them could mean thousands of satellites a week. - New challenges include much higher reliability and redundancy, flying in close formation, and micrometeoroids. Dead satellites might be sent away from Earth instead of burning up in the atmosphere. 🌙 Beyond Earth - Starlink is developing very high-bandwidth lasers to connect the Moon, then Mars, building an "interplanetary mesh." - All of that depends on getting safety right in low Earth orbit first. 💡 Key takeaways - Starlink is the world's biggest constellation, and it's moving lower to be safer. - It makes about 1,000 collision-avoidance moves a day, with nearly 100x more caution than the industry standard. - The biggest risk is operators who don't share where their satellites are going. - StarMind could scale to 1 million AI satellites, with launches starting next year. - "Coordination is a key element for realizing all of our ambitions."

Today a crazy quantum story just got wilder. On March 31, the Google Quantum AI team published a landmark result on Shor's algorithm for elliptic curve cryptography. Technically, the paper was a bombshell: a dramatic 10x improvement over the state-of-the-art. As a stunt and wakeup call to the blockchain space, those optimisations were illustrated on secp256k1, the elliptic curve underlying Bitcoin and Ethereum signatures. But perhaps the most striking part of the paper was sociological, not technical. Instead of following standard academic process, the optimisations were kept secret, hidden behind a zero-knowledge (ZK) proof. Google's accompanying blog post mentions they "engaged with the U.S. government". The ZK proof demonstrates the existence of algorithmic improvements without leaking details. Academic censorship with ZK, a historic first! As a co-author of the Google paper I witnessed some of the context surrounding this censorship. To be honest, multiple aspects of that context don't sit well with me. As much as I believe the general public ought to know more, I am limited in my ability to whistleblow. Though let me be clear about one thing: the Google team's professionalism has been absolutely exemplary, and they deserve nothing but praise. Censorship has a way of backfiring. The Streisand effect, where an attempt to bury something only draws more attention to it, is exactly what's unfolding today. First, Google's key optimisation has been rediscovered by the French. And in a thrilling turn of events, a collaborative Shor-at-home challenge just launched. The initiative, available at ecdsa[.]fail, breached a new Shor world record in a matter of hours. Let's start with the rediscovery. Just two months after Google's paper, French quantum expert André Schrottenloher cracks the main secret optimisation. His paper, titled "Optimized Point Addition Circuits for Elliptic Curve Discrete Logarithms", landed on the arXiv today. Big congrats to André, who beat several other nerdsnipped experts to it. In a blog post also published today, Craig Gidney, the world expert on Shor optimisations, revealed that he'd been sitting on this very optimisation for a whole year under censorship pressure. Interestingly, André missed a handful of minor optimisations, both from Google's original publication and from improvements found since. It's plausible there's still plenty of juice left to squeeze out of Shor, and this is exactly what the ecdsa[.]fail challenge is about. The verifier program developed for the ZK proof does double duty, automatically filtering for valid submissions. Dozens of compounding small and micro improvements are rolling in. As of the time of writing there's an 8.4% improvement to Google's circuit, as measured by the product of logical qubit count and Toffoli gate count. Nice! The nerdsnipping ran deeper than anyone expected. Over the last few weeks it became clear it extended well beyond André and other quantum experts. Behind the scenes, a small army of amateurs quietly got to work. Inspired by Karpathy-style autoresearch, they turned AI on Shor. Ironically, the verifier program for the ZK proof makes an ideal reward function for AIs. The barrier to entry for this modern style of research is refreshingly low, with several non-experts, even a teenager, finding nice optimisations. Get in touch if you'd like to join a Telegram group with fellow autoresearchers :) Part 2: neutral atoms and qday The story doesn't end with Google. On the same day Google went public, a stealthy startup called Oratomic published its own Shor paper in a coordinated release. It made a splash, ultimately becoming the most upvoted paper on scirate[.]com, a website ranking arXiv papers. Oratomic's claim was wild. By building on Google's logical optimisations and applying custom physical optimisations for neutral atoms, they claimed just 10K physical qubits were sufficient to run Shor's algorithm on secp256k1. That number is mind-bogglingly low. Knowing essentially nothing about neutral atoms when Oratomic's paper landed, I was intrigued and decided to learn more about the tech. I fell straight down the rabbit hole and spent a couple hundred hours on the topic. I got a little obsessed and watched every YouTube video I could find and spoke to a bunch of experts. My conclusion? The tech is real, very real. Even Google recently decided to start a neutral atom lab, a notable pivot from their sole focus on superconducting qubits. If you care about qday, i.e. the day a quantum computer will break the first piece of cryptography in production, neutral atoms demand your attention. I shared some of my learnings on Shor and neutral atoms in a 30min talk at the ZKProof cryptography conference. You can find it on YouTube by searching "zkproof neutral atom". Here's an interesting observation about this duo of breakthrough papers: neither Google nor Oratomic say a word about what their results mean for qday. No timelines. Zero. Nada. That is especially baffling given that the whole point of whitehat quantum cryptanalysis is to inform qday estimations and help the general public make good decisions. So let me attempt to partially fill the silence, similarly to what Scott Aaronson did in his April 29 post. Given everything I know, including scary non-public information, I now put the odds of qday by 2032 at 50%. 10% by 2030. Anecdotally, the US government has its own date: 2035. Originating at the NSA and later adopted by NIST, it's when branches of the US government will be disallowed from using quantum-vulnerable cryptography. In plain language: with hindsight, that date is a joke and should be discounted entirely. I don't see how NIST avoids being forced to pull it forward by years. Part 3: post-quantum cryptography There are good reasons to sound the alarm today, but please do not panic. Rushing carelessly towards immature post-quantum cryptography is a recipe for disaster. IMO a good target date for migration is 2029, roughly 3.5 years out. 2029 happens to be the date selected by Google, Cloudflare, and the Ethereum Foundation. These days most of my time goes to safely migrating Ethereum towards post-quantum cryptography as part of the broader lean Ethereum effort. There's a lot to do. We need to rip out and replace BLS signatures at the consensus layer, KZG commitments at the data layer, and ECDSA signatures at the execution layer. The plan to get there is compelling, and is based on hash-based cryptography. Within the Ethereum Foundation we've developed a Swiss army knife called leanVM (github[.]com/leanEthereum/leanVM) powered by the magic of hash-based SNARKs. Thanks to truly exceptional work by Emile, Thomas, and others, its performance is derisked. Regarding security, leanVM is a jewel, a minimal zkVM crafted for end-to-end formal verification and maximum security. Want to help? There are two $1M initiatives. First, the Proximity Prize (proximityprize[.]org). Solve a long-standing mathematical conjecture in coding theory, improve hash-based SNARKs, and go home a millionaire. Second, the Poseidon Initiative (poseidon-initiative[.]info), offers $1M for breaking Poseidon, the SNARK-friendly hash function.

📢 BrownDust2 × Shinovi Master Senran Kagura NEW LINK Collaboration Rerun Update Countdown! D-Day The shinobi have officially joined the battle! To thank all of our players who’ve been waiting for the collaboration rerun update, we’ve prepared special events. Join in now! [Collaboration Rerun Update Countdown Login Event] 📅 Event Period - April 9, 12:00 AM (UTC) – April 12, 02:59 PM (UTC) 🎁 Rewards - 🎫2 Draw Tickets [Collaboration Rerun Countdown Mission Event] 📅 Event Period - From the time of posting on April 9 to April 12, 02:59 PM (UTC) 📝 How to Participate ① Follow the official BrownDust2 X (Twitter) account ② Like, comment on, or repost (RP) the countdown post (Complete at least one action to participate!) 🎁 Mission & Reward - When the total number of Likes + Comments + Reposts (RP) reaches 10,000, 🎫 2 Draw Tickets will be distributed 🎉 Results Announcement - April 13 (Mon) #BrownDust2# #BD2# #ShinobiMaster# #SenranKagura# #Collaboration# #Update# #April9#

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