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@soulrambler_ Wait until Modaji & his Cartel say "We see 🇺🇸&🇷🇺 equally important partners" Despite getting b!t€h slapped with sanctions & expensive junk weapons. Pu10 will likely help 🇵🇰 in the coming days. Vassal-isation of Viswaguru is in full swing 5 Footiya farzi James Bond Doctrine saar

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我问 Grok：据说特朗普第二任期至今，MAGA 阵营里除了 Alex Jones、MTG、Tucker Carlson、Joe Rogan 以外，普通人反水的情况也非常多。 给我找一些典型的普通人幡然醒悟、从坚决的 MAGA 份子变成反对川普主义的案例。说明他们主要反对的到底是什么，一次识别普通人反水 MAGA 的基本模式和动机。 Grok 回答：是的，这个说法有事实基础。 2025-2026年特朗普第二任期（尤其是2026年2月伊朗战争爆发后），确实有不少普通MAGA支持者（非Alex Jones、MTG、Tucker Carlson、Joe Rogan等公众人物）公开表达“幡然醒悟”或“后悔投票”。 这些案例多来自BuzzFeed社区投稿、NYT焦点小组、CNN街头采访、Reddit/X个人帖，以及地方新闻，尽管还不是系统性“崩盘”，但数量相当可观（民调显示约14-15%特朗普2024选民有明确遗憾，更多“失望”）。 我从可靠来源（非阴谋论或单一党派媒体）提取了几个典型普通人案例（蓝领、退休者、小企业主、固定收入者），聚焦他们从“坚决MAGA”到“反对特朗普主义”的转变，以及主要反对点。 这些不是名人，而是真实选民的自述。 典型案例 西弗吉尼亚前“MAGA junkie”联邦雇员（CNN采访，2025年3月） 一位长期特朗普支持者（自称“MAGA junkie”），2024年投票特朗普，期待“排水”和精简政府。结果DOGE（政府效率部）大幅裁员，他直接丢掉联邦工作。 主要反对：政府“大砍刀”政策伤害了普通美国工人，而不是针对“深层政府”。他后悔：“我以为会改善生活，结果自己成了受害者。” 转折：个人经济打击打破了“特朗普为小人物”的叙事。 伊利诺伊州42岁小企业主Chris（NYT焦点小组+BuzzFeed类似投稿，2026年4月） 工厂/小生意老板，2024年投票特朗普，期待移民控制和低物价。他的多数移民员工（合法+部分非法）因ICE大规模行动害怕而辞职，他损失10万美元收入。同时伊朗战争推高油价和供应链成本。 主要反对：移民政策“过头”伤到企业+伊朗战争（“没人要的战争，我们的人死了”）。他说：“特朗普只顾富人，我们更穷了。” + 转折：现实经济痛感（员工流失+物价上涨）让他觉得被背叛。 佛罗里达/北卡罗来纳固定收入退休者/阿根廷裔夫妻（BuzzFeed+地方报道，2025-2026） 一名76岁女性（匿名）：2024年投特朗普，相信“让美国更好”。伊朗战争后油价/食品暴涨，她减少外出、教堂活动，生活质量下降。 阿根廷裔夫妻：儿子持合法绿卡，却因ICE行动被短暂拘留/威胁驱逐（尽管全家投特朗普支持“合法移民优先”）。 主要反对：战争导致的通胀+移民政策“一刀切”伤到合法家庭/普通人。女性直言：“他只想打仗，我们更穷了。”夫妻感到“被骗”。 转折：战争+物价直接打击日常生活。 明尼苏达/得州普通选民+军事家庭母亲（X个人帖+Reddit，2026年） 61岁“boomer”女性：投特朗普6次、拥有多顶MAGA帽，2025年前100%忠诚。2026年后称“MAGA变成MIGA（Make Israel Great Again）”，指责特朗普向捐助者低头、政策不再“美国优先”，最后稻草是某些公开言论（包括宗教相关）。说她现在“政治无家可归”。 军事母亲：伊朗战争是最终稻草，担心儿子/退伍军人受影响 + 经济代价。 主要反对：外交政策背离“结束战争”承诺 + 优先外国/精英利益而非美国工人。 NYT 2026年4月焦点小组（12名2024特朗普选民） 12人里面有 9 人公开表示后悔投票给特朗普，称其第二任是“恐怖电影”。低学历、低收入的白人选民尤其明显。 主要反对：伊朗战争（“背叛America First”）、物价暴涨、Jeffrey Epstein相关丑闻持续发酵。 普通人反水MAGA的基本模式和动机 模式（高度一致的“觉醒路径”）： 阶段1（忠诚期）：2016-2024年因“反建制、经济民粹、无新战争”被吸引，视特朗普为“唯一能对抗精英”的人。 阶段2（裂痕出现）：2025年初DOGE裁员、移民执行初期有零星不满，但仍忍耐。 阶段3（转折点，2026年2月后）：伊朗战争+霍尔木兹海峡/油价冲击 → 物价上涨 + 个人/家庭直接损失（工作、生意、出行成本）→ 信息泡泡破裂。 阶段4（公开反水）：在Reddit、BuzzFeed、X、地方媒体自述“后悔”，常以“我不是左派，但我醒了”开头，强调“不是我变了，是特朗普变了”。 核心动机（成本-收益+行为经济学视角）： 首要：钱包受损 → 战争导致汽油/食品/生活成本上升，DOGE/移民政策反噬小业主。 理性自利计算：抽象的“美国优先”输给了现实“我的账单涨了”。 其次：承诺背叛（betrayal of “America First”） → 最大痛点是“不再结束战争，反而卷入新战争”。 很多人觉得“特朗普成了以色列复国主义者/Make Israel Great Again”，不再代表普通美国人。 第三：个人/家庭直接受害 → 合法移民家人被波及、企业员工流失、联邦工作丢掉 → 认知失调无法维持。 共同心理：不是突然“左转”或接受“觉醒主义”，而是政治上无家可归。多数人依然是保守派，反对“大政府”或“民主党”，只是对“特朗普主义”的具体政策（战争+经济失灵+精英偏向）极度失望。 损失厌恶（loss aversion）起关键作用：原以为到手的“更好生活”幻觉破灭，生活的具体打击比抽象的政治忠诚更有效。 规模：核心 MAGA 拥趸的黏性仍维持在高位（支持率80%+），但边缘/年轻/摇摆阵营转为 MAGA 阵营的者明显失速，经济上受到负面影响的蓝领从 MAGA 阵营流出已经成为显性现象。 总结：这些普通人反水不是“媒体洗脑”或单一事件，而是现实反馈回路（战争+通胀+政策副作用）打破了信息不对称。 动机高度务实：不是意识形态大逆转，而是“特朗普没兑现对我生活的承诺，反而让我更糟”。 2026中期选举前，这种“后悔票”是否扩大，将是关键观察点。目前看是局部裂痕，但对特朗普家族“捞够就走”的策略确实构成了相当压力。 综合来看，这帮人的行为调整更像是因为个人利益直接受损后接受现实，理性回归。

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.

Hello world 🌍 I’m a product engineer based in Hangzhou, building simple tools that make everyday work and life a little easier. Meet the Tw93 Open Source Family. Kaku is the dad. He helps you code in the terminal and build faster in the AI era. Pake is the mom. She turns web apps into desktop apps and helps good products ship. MiaoYan is the older brother. A native macOS Markdown editor for writing, thinking, and notes. Waza is the older sister. A skill system that turns AI workflows into daily habits. Kami is the younger sister. A design system that makes AI-written documents clear and beautiful. Mole is the family pet. A Mac cleaning tool that digs out junk and keeps things tidy. Which member did you meet first? Meet the family:

Mole 1.35 is here, codename Swift 🦅 The Mac cleaning tool that can free up tens of GBs in one go. 48K stars. The codename is also a small hint for what may come next. Here’s what matters: · mo clean now supports cleanup for AI coding agents, including Claude Code, Cursor, Windsurf, Cline, and Aider. It also respects your whitelist more consistently and adds cleanup for unavailable Xcode simulators. · Scan speed is faster for common 50-500 item cleanup batches, thanks to a better path deduplication pipeline. · mo optimize now starts almost instantly, with the old 6-second startup delay removed. Disk verify is also disabled by default to avoid unnecessary sudo prompts and slow checks. · mo uninstall is safer by default. App removals now go through Trash first, with --permanent available when you really want direct deletion. A new --list flag also makes installed app inventory easier. · mo analyze and mo purge are more stable on huge directory trees, with fixes for worker pool exhaustion, better throttling, and artifact age shown during selection. There are also smaller fixes for stale Homebrew taps, Touch ID detection, launch daemon matching, deletion logging, CJK alignment, and a new mo analyse alias. Swift is not a flashy release. It is mostly about faster scans, safer cleanup, fewer hangs, and better coverage for the tools developers actually use every day. If Mole helps, I’d love your ideas on where to dig deeper for safe cleanup and more hidden junk.

Mole 1.34 is live. The Mac cleaning tool that can free up tens of GBs in one go. 36K stars. Here’s what matters from the last two releases: · mo optimize: now runs optimization tasks automatically with no confirmation prompts, and adds regular maintenance, quarantine cleanup, broken LaunchAgent repair, .DS_Store protection, and disk SMART checks. · mo analyze: gives a much clearer view of reclaimable space, including iOS backups, old Downloads, and real cache usage from Xcode, Gradle, JetBrains, Docker, pip, and more. It also shows cleanable items like Trash, system caches, and Xcode artifacts before you run cleanup. · mo clean: expands cleanup coverage for Zed, Warp, Ghostty, Cursor, Stremio, Brave Service Worker caches, expired iOS/iPadOS firmware, Chrome graphics caches, Stocks app cache, Office container logs, wallpaper thumbnails, and more. Whitelist rules are handled more consistently. · mo uninstall: now supports mo uninstall directly, with better leftover detection, orphan file cleanup, and improved handling of app-related residue. · mo check and mo status: better visibility into system health, including battery health and uptime scoring, broken LaunchAgents, missing developer tools, and common version conflicts in local dev environments. These two releases make Mole more useful in day-to-day cleanup, especially for developers and long-used Macs. If Mole helps, I’d love your ideas on where to dig deeper for safe cleanup and more hidden junk.

Mole 1.32 is live. The Mac cleaning tool that can free up tens of GBs in one go. 44.3K stars. Here’s what’s new: · Performance: significantly faster scanning across mo clean, mo purge, and mo optimize, with parallel cache discovery, less subprocess overhead, and smoother large project handling. · mo clean: fixes missed cleanup for macOS wallpaper agent and Messages preview caches, improves dry-run accuracy, preserves important runtime assets by default, and adds cleanup support for PCSX2 and RPCS3 caches and logs without touching save data or configs. · mo status: improves disk capacity reporting accuracy, adds persistent high-CPU process alerts, and fixes rendering issues during terminal resize. · mo purge: shows full paths more clearly, compresses long paths more intelligently, and speeds up size calculation to avoid menu hangs on large projects. · Install, uninstall, and system behavior: smoother install flow with fewer repeated sudo prompts, better Touch ID detection on newer macOS versions, cleaner uninstall targets, and more consistent protection for system files, symlinks, and active data. This release makes Mole faster, safer, and more accurate across everyday cleanup workflows. If Mole helps, I’d love your ideas on where to dig deeper for safe cleanup and more hidden junk.

🐹 Just shipped Mole V1.30.0. If you have cleaned tens of GB of junk from your Mac in one go, you know the feeling. Since October, Mole has gone from v1 to 30. This release improves reliability with safer orphan cleanup and better edge case handling.