Turkey reported 880 Tesla sales and 1.1% market share in April. BEV penetration is 21% and Tesla has 5.2% of this segment. 🇹🇷 • Market share is 10 basis points or 10% above the 3-month trailing average of 1.0% • 100% Model Y • +577% vs. April last year and +80% compared to January the first month of the previous quarter • Best April ever • 3rd best first month of the quarter ever and +80% vs. the previous one • Last three months -44.6% vs. November - January • Year-to-date -15% over same period last year • Year-to-date is 10% or 1.2/12 of last year's total

Dynamic Airdrop Conversion 3.0 is LIVE Conversion 3.0 is the first seasonal incentives model from River that balances contributions and sustains token distribution through recurring Seasons. Built on the concept of time-encoded tokenomics from 1.0 and conversion shaped by real participation from 2.0. Convert your River Pts into RIVER any time before the Season ends.

The Claude Agent SDK is pre-1.0 (v0.2.x) — APIs break frequently, functions get renamed, parameters change. A static skill would teach Claude outdated patterns that produce broken code. The pipeline keeps this skill accurate by tracking version bumps, researching new issues, and updating rules daily. Without it, advice written for v0.2.30 silently becomes wrong when the SDK moves to v0.3.0. So I've written this:

THE GLOBAL FINANCIAL SYSTEM JUST BROKE IN TOKYO Japan’s 30-year bond yield hit 3.41% today. That number means nothing to you. Here’s why it should terrify you. Japan owes 230% of everything it produces. It’s the most indebted nation in human history. For 35 years, they kept the lights on by borrowing at near-zero rates. That era ended this morning. Here’s What Just Happened Core inflation is running at 3.0%. Government bond yields are spiking to levels not seen since 1999. China just conducted its 25th military incursion near Japanese waters this year. Japan is now forced to spend 2% of GDP on defense … nearly 9 trillion yen annually. The Bank of Japan is trapped between two impossible choices: raise rates and trigger a debt collapse, or keep rates low and watch inflation destroy savings. They chose door number two. Why You Should Care Every major bank, hedge fund, and institution on Earth has borrowed yen at cheap rates and invested it elsewhere for 30 years. This “carry trade” could be worth anywhere from $350 billion to $4 trillion. Nobody knows the real number because it’s hidden in derivatives. When Japan’s system breaks, this money unwinds. Fast. The last time we saw a preview … July 2024 … the Nikkei dropped 12.4% in a single day. The Nasdaq fell 13%. That was a small tremor. The earthquake is coming. The Math Is Simple! Japan’s government pays interest on $9 trillion in debt. Every 0.5% increase in rates costs them $45 billion annually. At current yields, debt service will consume 10% of all tax revenue. That’s the death spiral threshold. The yen is trading at 157 to the dollar. If it strengthens to 152, the entire carry trade becomes unprofitable. Unwinding begins. Emerging market currencies could drop 10-15%. The Nasdaq could fall 12-20% as funds are forced to sell. What Happens Next December 18-19, the Bank of Japan meets. Markets are pricing 51% odds they raise rates another 0.25%. If they do, volatility explodes. If they don’t, inflation accelerates and the problem gets worse. There is no way out. Japan’s fiscal dominance is now permanent. They must keep the yen weak to service their debt. This means the free money that powered global markets since 1990 is ending. The Bottom Line Interest rates worldwide are going up 0.5-1.0% permanently. Not because of inflation. Because the world’s largest creditor nation can no longer subsidize global growth. Your mortgage, your car loan, your credit card … all repricing higher. Stock valuations built on cheap money … all compressing. The everything bubble … all deflating. This is not a recession. This is a regime change. The largest liquidity engine in financial history just seized up, and most people won’t understand what happened until their portfolios are down 30%. Tokyo broke the world today. You’ll feel it tomorrow.​​​​​​​​​​​​​​​​ Read the full data driven deep dive article -

Announcing agentic performance benchmarking for Speech to Speech models on Artificial Analysis. We use 𝜏-Voice to measure tool calling and customer interaction voice agent capabilities in realistic customer service scenarios Even the strongest Speech to Speech (S2S) models today resolve only about half of realistic customer service scenarios end-to-end - a meaningful gap relative to frontier text-based agents on the same tasks. Voice channels introduce significant complexity: challenging accents, background noise, and packet loss, all while requiring fast responses, consistency across long multi-turn conversations, and reliable tool use. Performance also varies considerably by audio condition: in clean audio some models perform notably better, but realistic conditions continue to pose a challenge. Conversation duration also varies meaningfully across models, with implications for both customer experience and operational cost. About 𝜏-Voice: Our Agentic Performance benchmark is based on 𝜏-Voice (Ray, Dhandhania, Barres & Narasimhan, 2026), which extends 𝜏²-bench into the voice modality to evaluate S2S models on realistic customer service tasks. It measures multi-turn instruction following, support of a simulated customer through a complete interaction, and tool use against simulated customer service systems. The simulated user combines an LLM-driven decision model with realistic audio synthesis: diverse accents, background noise, and packet loss modelled on real network conditions. This complements our Big Bench Audio benchmark measuring intelligence and Conversational Dynamics (Full Duplex Bench subset) benchmark measuring conversational naturalness. Scores are the average of three independent pass@1 trials. We evaluate under realistic audio conditions using the 𝜏²-bench base task split across three domains: ➤ Airline (50 scenarios): e.g., changing a flight, rebooking under policy constraints ➤ Retail (114 scenarios): e.g., disputing a charge, processing a return ➤ Telecom (114 scenarios): e.g., resolving a billing issue, troubleshooting a service problem Task success is determined by deterministic checks against expected actions and final database state, consistent with the 𝜏²-bench evaluator. Key results: xAI's Grok Voice Think Fast 1.0 is the clear leader at 52.1%, averaging 5.6 minutes per conversation, the second-longest overall. OpenAI's GPT-Realtime-2 (High) (39.8%, 3.0 min) and GPT-Realtime-1.5 (38.8%, 4.8 min) follow, with Gemini 3.1 Flash Live Preview - High close behind at 37.7% (3.8 min). Speech to Speech is a fast evolving modality and we expect movement in rankings as we continue to add new models with these capabilities, and model robustness improves. Congratulations @xAI @elonmusk! See below for further detail ⬇️

Spool 0.3.1 significantly optimizes indexing performance for thousands of claude sessions! Thanks for the feedback from @zhouliangLen that prioritizes this optimization!

Grok Voice Think Fast 1.0 ranks #1# on the Artificial Analysis τ-Voice benchmark for real-world agentic customer service resolution Absolutely outperforming GPT-Realtime-2 (High) and Gemini 3.1 Flash by a huge margin That's a massive 12%+ lead over OpenAI's best model that just released a few days ago Grok is running real-time background reasoning without the latency penalty, which is why it is already handling live Starlink phone operations autonomously at scale

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.