[NOTICE] #SEJEONG#'s tvN Drama '#Record_of_Youth#' OST Part.9 'That's How I Feel' has released in all music streaming sites🙌 SEJEONG's sweet and soft voice, which perfactly matches with autumn breeze🎶 Let's listen together, Dear Friends❤ #gugudan# #Thats_How_I_Feel#

One day, we ourselves will become a probe—like those we once chased in our youth. We won’t carry racks of specialized instruments; instead, we’ll record our journey with the iPhone in our hands.

Q3 2024 Shareholder Update → — Highlights - Produced our 7 millionth vehicle at Fremont factory! - Preparations for new vehicles remain underway (including more affordable models), which we'll begin launching in the first half of 2025 - Record gross margin for @teslaenergy - COGS (cost of goods sold) per vehicle came down to lowest level ever – key to enabling affordability Vehicle US @tesla_na - Refreshed Model 3 ramp continued successfully - @cybertruck production increased sequentially & achieved a positive gross margin for the first time (congrats @cybertruck 🫡) - Preparation of the Semi factory continues and remains on track with builds scheduled to start by the end of 2025 China @Tesla_Asia - Giga Shanghai produced its 3 millionth car & exported its 1 millionth car Europe @teslaeurope - As of Q3, Model Y is the bestselling vehicle of any type in 2024 in Sweden 🇸🇪, Netherlands 🇳🇱, Denmark 🇩🇰 & Switzerland 🇨🇭 Thank you to our owners! AI/Hardware @Tesla_AI - In Q3, we released FSD Supervised 12.5, bringing improved safety & comfort thanks to a 5x increase in the number of parameters, data & training compute (will continue to scale in Q4) - We also released Actually Smart Summon & deployed FSD Supervised to @Cybertruck, including end-to-end driving on highways for the first time - Compute rules everything around us: We're already training on a 29k H100 cluster at Giga Texas – expecting to have 50k H100 capacity by end of October Vehicle & other software - Summer Release shipped with features like Parental Controls, YouTube & Amazon Music as native apps, Hands-Free Frunk, added weather forecast & other improvements Powertrain, Battery & Manufacturing - Unveiled Cybercab & Robovan at our We, Robot event in October – both purpose-built for autonomy from the ground up - Cybercab will be built on our next-gen platform, which includes a new powertrain with an estimated 5.5 mi/kWh – our most efficient one yet - Produced our 100 millionth 4680 cell & continue to progress our dry cathode manufacturing lines Energy @teslaenergy - Megafactory in Lathrop demonstrated 200 Megapacks/week (40 Gwh run rate) @Tesla_Megapack - Powerwall deployments reached a record for the 2nd quarter in a row - As of Q3, over 100k Powerwalls were enrolled in VPP programs!

A Texas federal judge ordered Rhode Island Hospital to submit voluminous transgender youth medical records for the court to hold pending the outcome of multi-district appeals, even as another district court judge rejected the same DOJ request.

The 2026 #BBWomenInMusic# Women of the Year are... EJAE, AUDREY NUNA and REI AMI, the singing voices of HUNTR/X 🎉 Answers: • "Golden" became the first-ever hit by a female K-Pop act to reach No. 1 on the Hot 100 📈 • The group became the first K-pop act to win a Grammy when "Golden" won Best Song Written for Visual Media 🏆 • The song held the No. 1 spot on the Billboard Global Excl. U.S. chart for a record 20 weeks 🌎 Watch #BBWomenInMusic# at or Billboard's YouTube Channel live on April 29

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.

Creators and project teams today actually own a lot of assets. But most of these assets are only usage rights granted by platforms. Xiaohongshu, Douyin, TikTok, X, YouTube, Claude, OpenAI, Notion, Google Drive, GitHub, Discord. Accounts, followers, content libraries, historical interactions, recommendation weight, API access, payment channels. All of them live under someone else’s rules. TikTok’s official account safety page clearly includes processes for content removal, account bans, appeals, and data downloads. Anthropic’s transparency page also states that policy violations may lead to warnings, suspensions, or termination of access, and disclosed 1.45 million banned accounts in the second half of 2025. This is not a conspiracy theory. This is simply how platform governance works. People felt this less strongly in the past. That made sense. Back then, many people treated internet assets mainly as traffic tools. Losing an account hurt, but it did not always feel like a systemic loss of assets. That has changed. Content, customers, private communities, automated workflows, AI prompts, historical data, agent memory, community relationships, and brand credibility are all now stored online. The more assets accumulate online, the more damaging platform restrictions become. But this problem cannot be solved by a few on-chain platforms alone. Existing assets are already in a vulnerable state. People’s content, followers, transaction records, project reputation, and account weight have long been accumulated inside centralized platforms. Building a new decentralized platform usually does not solve the short-term problem, because users will not automatically migrate, and traffic will not automatically migrate either. A more realistic way to look at this is to break it into four layers. First, the content itself can be made safer. Articles, source video files, images, creative assets, prompts, model outputs, workflows, user research, and community records can all be stored in self-controlled storage, backups, knowledge bases, Git, object storage, or decentralized storage. The goal is simple: if a platform deletes your post or bans your account, you still keep the original assets. Second, identity can be made safer. Account names, domains, wallet addresses, DIDs, email lists, websites, RSS, and newsletters can form an identity layer outside any single platform. Bluesky’s AT Protocol treats account portability as a core design goal, so users can migrate their account if a Personal Data Server fails or stops operating. Nostr also separates identity from any single server through public keys and relays. Third, the social graph can be made partially safer. Follow relationships, subscriptions, address books, community members, and customer lists can be backed up and synced across platforms. But this is much harder, because social relationships have strong network effects. People interact where their habits already are. Exporting the data does not mean the interaction can be exported with it. Fourth, distribution power is extremely hard to decentralize. TikTok’s For You feed, Xiaohongshu’s recommendation system, X timeline, YouTube recommendations, the App Store, and Google Search are all traffic allocation systems. They decide who gets seen. Web3 can preserve your content and identity, but it is very hard to replace the attention-distribution power of centralized recommendation systems. Many Web3 founders die from one illusion: believing that once data is on-chain, users will naturally show up. Reality is heavier than that. Founders have to accept the algorithmic power of TikTok, Xiaohongshu, YouTube, and other major platforms, and accept that social graphs are very hard to make effective across platforms. So the more realistic direction is not to replace every platform. It is to add an escape layer. Centralized platforms can remain the traffic entrance. Your own website, domain, newsletter, private community, content library, wallet identity, and on-chain records become the asset base. Platforms are used for acquisition. The base is used for accumulation. That way, even if one platform goes wrong, your core assets can still be migrated, reused, and redistributed. AI degradation follows a similar logic. Teams should not tie their core production system entirely to one model. A more resilient approach is to keep prompts, workflows, knowledge bases, code, agent configurations, evaluation standards, and historical outputs in places they control. Claude, ChatGPT, Gemini, open-source models, and local models are all just execution layers. Models can change. Core assets and workflows should remain. So the practical strategy is not to fantasize about leaving centralized platforms. Wherever the traffic is, you keep using those platforms. But all core assets should gradually move away from dependency on any single platform. Content needs backups. Identity needs a primary entrance. Users need to be reachable again. Workflows need to be portable. AI production assets need to stay in your own hands. On-chain records should only be used for the most critical states that truly require verification. This is the realistic meaning of Agent Sovereignty. The narrative that AI has a soul, or that AI should own a wallet and make money by itself, is too far away and too likely to attract regulatory pressure. But if Agent Sovereignty means the portability and tamper-resistance of core states, such as memory, permissions, workflows, identity, reputation, and historical behavior records, then it becomes a real need. If a developer spends six months tuning a high-value agent, they absolutely cannot tolerate losing every prompt, output history, and memory because OpenAI or Claude triggers one risk-control action. At the execution level, there are still several traps to watch. First, frictionless experience is the default human preference. Adding an escape layer inevitably adds extra steps. In real life, most people strongly prefer frictionless experiences. If they can take business class on a high-speed train, they do not want to squeeze onto a bus. If they can log in with one click, they do not want to remember a seed phrase. Backups, cross-platform syncing, multisig, and maintaining an on-chain identity are naturally against user behavior. An escape layer only works if the infrastructure becomes extremely smooth. If asset continuity requires creators or developers to spend one extra hour every day maintaining the base layer, the whole solution will collapse. Second, asset portability does not equal asset reusability. A Claude-optimized prompt may produce terrible results when moved to an open-source model. Agent memory accumulated on one platform, such as a JSON file, may not be directly readable by another platform at all. So storage and backup alone are not enough. Real infrastructure also needs to solve standards and formats. Otherwise, what gets exported is unreadable dead data, not live assets that can immediately return to production. Third, only people who have felt the pain are willing to pay. This logic is defensive by nature. Before a systemic crisis happens, ordinary creators and junior developers are unlikely to pay time or money for a probabilistic risk.

Chamath: AI advantage may come less from models than from private inputs. "When labs can build similar models, the real win comes from one unique ingredient in order to monetize it well. Here is a basic thing about machine learning that is worth knowing: if you take 1,000 of the same inputs and give them to Facebook, Microsoft, Google, and Amazon, they will all come up with the same machine learning model. But if you have one extra thing, one little ingredient that all of those other companies do not have, your output can be markedly different. It is like giving two great chefs three ingredients, but giving the third chef one extra ingredient. That person has the ability to do something very special. Right now, we are in a world where everybody is crawling the open web. We are going to move to a world where, as everybody gets sophisticated enough and information is widely available, somebody is going to say, “You know what? This site, I am not going to allow anybody else to access. It is only for me, only for my models.” Those models will become better. So we have to let that play out a little bit. It is going to be a really interesting arms race. The next wave of M&A, for example, could be companies like Google, Microsoft, and Facebook looking at these companies and saying, “Can they be viable inputs to my large language models or to my other machine learning and AI models?” --- A company with unique workflows, transactions, medical records, industrial logs, legal archives, design files, or user behavior can turn boring private data into a compounding advantage. Some startups may never become great public companies on their own, yet still become valuable because they own a data stream that makes a larger AI system sharper, more differentiated, or harder to copy. That turns acquisition strategy upside down: the buyer may not be purchasing revenue, brand, or even software, but a private ingredient for intelligence. ---- From "iConnections" YouTube channel, (link in comment)