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Your dedicated on-chain scanner is live! I spent the whole night reverse-engineering the entire launchpad contract with AI, and finally managed to decode all the data. Tool overview: 0️⃣ The dashboard is split into two sections: top and bottom. 1️⃣ Top section: Left: real-time trade feed Top right: internal market cap leaderboard Bottom right: external market cap leaderboard 2️⃣ Bottom section: Internal and external trades are grouped by token. How to use: 1️⃣ The original frontend is basically unusable right now because the RPC is overloaded. 2️⃣ My dashboard also can’t afford some insanely powerful RPC yet. 3️⃣ So you’ll need to get your own RPC from somewhere like Alchemy and plug it in. Format example: Happy trading! @altdotfun @HyperliquidX @tradexyz

WATCH: Elon Musk was seen with his son, X Æ A-Xii, at the Great Hall of the People in Beijing. Originally named X Æ A-12, the name was modified to comply with California birth certificate laws, which prohibit digits, changing "12" to the Roman numerals "Xii"

Update: An emergency application has been filed with the court. Several witnesses and I have submitted materials to support the review. The case is not over. Full statement in English below In response to inquiries from multiple German media outlets, I am issuing this statement regarding my interviewee Xu Xiaoren (known online as Macaron), who currently faces imminent deportation from Germany. In March 2025, Xu was the only Chinese citizen I encountered on the Russian frontlines willing to appear on camera, showing his face, to condemn the war: "Every inch of land is paid for in blood. It is inhuman and immoral." For the year that followed, he endured relentless persecution and lived in flight because of this. Guided by professional ethics, I documented his journey through video, audio, and over 100,000 words of investigative notes, cross-verified by multiple witnesses, to record the risks the interviewee faced and to offer him a measure of protection. In thirty years of investigative journalism, this is the first time I have had to do this. It shows how grave I believed the situation to be. In February 2026, Xu escaped Russia. By April, I was informed that he had reached Germany, facing immediate deportation. He asked me to submit these original records to @BAMF_Dialog. I never got the chance. No evidence was reviewed. No contact was made — with me or any other witness. Instead, BAMF ruled his application "manifestly unfounded," dismissing my statement as "mere rumors and unsubstantiated assertions." An emergency application has been filed with the court. Several witnesses and I have submitted materials to support the review. I am grateful for the attention of the press. A man who admitted he entered the war by mistake, who then defected from within it, and what he became over that year, may tell us something about how wars could truly end: the reflection, the suffering, the compassion, the change. It is a battle of the human soul. I ask the German people to hear his story, and to judge for themselves. Full statement in English

There will be no AI jobpocalypse. The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment is irresponsible and damaging. Let’s put a stop to it. I’ve expressed skepticism about the jobpocalypse in previous posts. I’m glad to see that the popular press is now pushing back on this narrative. The image below features some recent headlines. Software engineering is the sector most affected by AI tools, as coding agents race ahead. Yet hiring of software engineers remains strong! So while there are examples of AI taking away jobs, the trends strongly suggest the net job creation is vastly greater than the job destruction — just like earlier waves of technology. Further, despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%. Why is the AI jobpocalypse narrative so popular? For one thing, frontier AI labs have a strong incentive to tell stories that make AI technology sound more powerful. At their most extreme, they promote science-fiction scenarios of AI “taking over” and causing human extinction. If a technology can replace many employees, surely that technology must be very valuable! Also, a lot of SaaS software companies charge around $100-$1000 per user/year. But if an AI company can replace an employee who makes $100,000 — or make them 50% more productive — then charging even $10,000 starts to look reasonable. By anchoring not to typical SaaS prices but to salaries of employees, AI companies can charge a lot more. Additionally, businesses have a strong incentive to talk about layoffs as if they were caused by AI. After all, talking about how they’re using AI to be far more productive with fewer staff makes them look smart. This is a better message than admitting they overhired during the pandemic when capital was abundant due to low interest rates and a massive government financial stimulus. To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful. (And to some, it can be fun.) I empathize with everyone affected. At the same time, this is very different from predicting a collapse of the job market. Societies are capable of telling themselves stories for years that have little basis in reality and lead to poor society-wide decision making. For example, fears over nuclear plant safety led to under-investment in nuclear power. Fears of the “population bomb” in the 1960s led countries to implement harsh policies to reduce their populations. And worries about dietary fat led governments to promote unhealthy high-sugar diets for decades. Now that mainstream media is openly skeptical about the jobpocalypse, I hope these stories will start to lose their teeth (much like fears of AI-driven human extinction have). Contrary to the predictions of an AI jobpocalypse, I predict the opposite: There will be an AI jobapalooza! AI will lead to a lot more good AI engineering jobs, and I’m also optimistic about the future of the overall job market. What AI engineers do will be different from traditional software engineering, and many of these jobs will be in businesses other than traditional large employers of developers. In non-AI roles, too, the skills needed will change because of AI. That makes this a good time to encourage more people to become proficient in AI, and make sure they’re ready for the different but plentiful jobs of the future! [Original text in The Batch newsletter.]

After a stroke silenced our mom's guitar and original educational songs (she was a Speech-Language Pathologist for 20 years) we stepped in and turned her songs into board books for young children, beautifully illustrated by her daughter, Meagan, and published by her son, Y. J.

Here's the #1# thing most people don't know about Warren Buffett: There is nothing special about Buffett’s stock picking. That doesn’t mean that Buffett wasn’t a great investor. He was! Buffett was, by far, the greatest investor in history, by a huge margin. Over 486 months between October 1976 and March 2017 –— 41 years –— Berkshire Hathaway’s Class A stock earned an average excess return of 18.6% per year above U.S. Tbills. Annualized volatility was 23.5%. Sharpe ratio: 0.79. Berkshire’s Sharpe ratio of (0.79) is roughly 1.6x times the broad U.S. stock market’s Sharpe ratio of 0.49 over the same period. Among all large-cap U.S. stocks and mutual funds with 30-plus-year continuous track records, those are unmatched numbers. A dollar invested in Berkshire on October 31, 1976, was worth more than $3,685 by March 31, 2017. A dollar invested in the S&P 500 with dividends reinvested over the same period was worth approximately $76. Buffett beat a passive index by a multiple of 48. But he didn’t do it with stock picking! Three researchers at AQR Capital Management –— Andrea Frazzini, David Kabiller, and Lasse Heje Pedersen –— dissected Berkshire’s 50 years of investments through 2013. They expanded and republished their findings in 2018 in the Financial Analysts Journal, which is the most highly respected industry financial journal. Their work won the Graham and Dodd Award for the best published paper of the year. The paper is called Buffett’s Alpha. They found, after accounting for cheap leverage (from the insurance float) and exposure to a handful of publicly documented factor premiums, Buffett’s investment skill –— the portion of his returns that cannot be explained by any mechanical strategy –— is 0.3% per year. That's statistically indistinguishable from zero. In other words, the alpha that Berkshire enjoyed for 50 years (as it compounded capital at 24% a year!) wasn’t due to Buffett’s stock picking. So, how did he do it? He did it by gaining access to a huge amount of investment capital that he did not own, for free. Buffett’s track record was built on leverage. That’s a dirty word for most investors, but it's the secret behind Berkshire. The AQR researchers had access to something most Buffett commentators do not: 40 years of Berkshire’s audited financial statements and the full quarterly history of the public 13F stock portfolio. The researchers asked a specific question: If I take Berkshire’s monthly stock returns from October 1976 through March 2017, and I run a linear regression against a set of well-documented risk factors –— market beta, size, value, momentum, and two newer factors called Betting-Against-Beta and Quality-Minus-Junk (detailed below) –— how much of Buffett’s performance can the factors explain? And after the factors have been stripped out, how much excess return remains? The data show clearly there are a few qualities that drove Berkshire’s results. First, Buffett has always preferred large-cap stocks, contrary to the popular image of him as a small-cap value investor. He buys elephants. Second, no surprise, Buffett buys cheap. Berkshire is almost six standard deviations away from neutral on the value axis. So far the picture is ordinary. Every large- cap value manager in America loads positively on size and on value. Buffett’s genius lies in the last two factors. These last two factors are a little complicated, but please stick with me. There’s a new factor, that, like value and size, characterizes Buffett’s strategy. It’s called Betting-Against-Beta (“BAB”). What it means is intentionally investing in stocks with very low volatility. The BAB factor captures the excess return that accrues to investors who own low-beta stocks. Low-beta stocks have historically earned higher risk-adjusted returns than high-beta stocks. Financial theory teaches that higher beta (higher risk) should mean higher return. But it doesn’t. The opposite occurs, in fact. And Buffett was one of the very first people to figure this out. Why does this factor persist? In an efficient market, once that factor is known to investors, then they should bid the price up on low- beta stocks until it no longer provides an edge. The explanation, per the theory of AQR’s Frazzini and Pedersen’s theory, is that because ordinary investors do not use leverage and seek high returns, they create persistent excess demand for more volatile stocks. (Having worked with retail investors for 30 years, I can assure you that is true.) But, an investor with access to cheap leverage –— Warren Buffett, for instance –— can exploit the mispricing by owning the low-beta names and levering them up to produce market-beating returns. And the last factor that matters to Buffett is quality. Buffett buys companies with high returns on invested capital. Quality-Minus-Junk (“QMJ”) is a factor described by Cliff Asness, also at AQR with Frazzini, and Pedersen, in a 2019 paper in Review of Accounting Studies. The QMJ factor captures the return to owning stocks of high-quality companies –— profitable, growing, safe, with high payout ratios –— against stocks lacking those characteristics. QMJ has been positive and statistically significant in every major developed equity market for which it has been measured. Berkshire’s loading is 0.37, with a t-statistic of 4.6. –– meaning it is highly significant to Berkshire’s results. In plain English: Buffett only buys large, high- quality, low-volatility stocks of the highest quality. But, Berkshire’s results were not, in any way, unusual. Any investor buying these same kinds of stocks would have earned those same returns –– about 16% a year over time. So how did Berkshire compound at 23% a year? To figure that out, AQR’s researchers built a Berkshire replica. They constructed a simple, rules-based, publicly investable portfolio that mechanically tilts toward large-cap, cheap, low-beta, high-quality stocks, and levers it 1.6- to- 1 to match Berkshire’s insurance float leverage. The correlation between their replica’s returns and Berkshire’s were virtually identical. The authors’ conclusion is unambiguous. “In summary, we find that Buffett has developed a unique access to leverage that he has invested in safe, high-quality, cheap stocks and that these key characteristics can largely explain his impressive performance.” Berkshire’s cost of insurance float has averaged almost three percentage points below the Treasury bill rate across 50fifty years of data. In roughly two-thirds of all years, Berkshire has been paid to hold other people’s money. That is not an investment strategy. That is a financing miracle. It is also the living, breathing heart of Berkshire Hathaway. It’s what Buffett built, starting in 1967 when he paid $8.6 million for National Indemnity’s $19.4 million of float. And it is the factor every retail investor admiring Berkshire’s returns has never paid any attention to. The 1.6-to-1 leverage that AQR measured over the full period, financed at this negative cost, explains the dollar magnitude of Berkshire’s returns. How do we know? An unleveraged version of the same stock portfolio –— which you can approximate by looking at the 13F holdings alone –— has earned an average excess return of 12% percent per year. It’s Berkshire’s leverage that magnifies this excess return to 18.6 %percent. How does this square with Berkshire’s reported gains? Berkshire’s 18.6% excess return, plus the T-bill rate that averaged roughly 4.7% over 1976–2017, gives you a total nominal return of roughly 23% per year, which is the figure you usually see quoted for Berkshire’s historical performance. The 23% tells you what Berkshire returned. The 18.6% tells you how much of that return was compensation for taking investment risk, as opposed to the baseline yield every lender to the U.S. government was earning anyway. With both of Berkshire’s “edges” –— systematic factor exposures to cheap, high-quality, low-volatility stocks and roughly 1.6-to-1 leverage delivered with insurance float –— you get Berkshire Hathaway’s 23% annual gains over 60 years. It’s the structure that’s genius, not the stock picking. And that's very important because it means the original Berkshire formula can work for any investor. I show you exactly how, in my new book.

"Freedom of Money" is @cz_binance Changpeng Zhao's ( #CZ#, founder of Binance) new memoir and manifesto, released in April 2026. It's part personal story, part defense of #crypto#'s core ideals, written in bursts on a prison computer during his short federal sentence. Here are some genuinely positive things about the book and its message:Inspiring Personal JourneyCZ shares his rise from a modest childhood in rural China to building Binance, one of the most impactful companies in fintech history. It highlights resilience, high-frequency trading roots, and bold decisions amid volatility and pressure. The book feels raw and authentic — written in 15-minute daily sessions on limited prison resources — which adds a layer of humility and determination that many readers appreciate. Strong Advocacy for Financial FreedomIt’s a passionate manifesto for "freedom of money" — the idea that individuals should control their own finances without excessive gatekeepers, borders, or intermediaries. CZ ties this to broader themes of empowerment, innovation, and user protection in crypto. Many see it as a timely reminder of crypto’s original promise: decentralization, access for the unbanked, and resistance to over-regulation. Impact and ReceptionIt quickly became a bestseller (e.g., high rankings on Amazon Kindle in crypto categories) and sparked widespread discussion in the industry. Proceeds reportedly support charity, and CZ has used it as a platform for reflection, closure, and looking forward (with mentions of focus on AI + crypto post-Binance). Fans and reviewers praise its insider perspective on crypto’s explosive growth, the challenges faced, and CZ’s unapologetic belief in the technology’s long-term potential. Overall, it’s positioned as both a compelling underdog story and a defense of why financial sovereignty matters. If you’re into crypto history, founder memoirs, or the philosophy behind decentralized finance, it delivers on those fronts with CZ’s straightforward style. It’s available on Kindle and Audible, with limited physical editions.

Updates regarding the Community Round: All participants who have chosen to refund their contribution will receive a bonus of 5% in stablecoins in addition to a refund of their original capital. We defined the selection period to allow participants sufficient time to be contacted, respond, make a choice during the time period before and after our TGE. We appreciate the engaged participation of community round participants in building and scaling Billions Network. So, as a result, additional consideration will be given to those contributors in the Billions Rewards program. This is on top of the bonus detailed above for refunded users, as well as users who are opting in for bonus rewards and ongoing participation in Billions Network. The selection period will still remain open as scheduled, allowing full time for those who have not yet submitted their choice to follow the TGE and make an informed decision.

Kagurabachi anime adaptation set for April 2027! Original Work: Takeru Hokazono (Serialized in Weekly Shonen Jump) Production: Cypic Director: Tetsuya Takeuchi Character Design: Keigo Sasaki