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En 1958, cuando fue derrocado Pérez Jiménez y Betancourt regresó al país, la Guerra Fría entre USA y la URSS estaba en su clímax, y era el factor determinante para cualquier problema político. Cuba, alineada con la URSS, aterrorizaba al mundo democrático. Betancourt comenzó por colocar a Venezuela del lado occidental y AD demostró que una izquierda democrática y creativa era posible. Esta fórmula betancouriana resultó la única eficaz para derrotar al comunismo que hasta entonces parecía capaz de acabar con el principio occidental de libertades. Estados Unidos, entonces gobernado por estadistas de calidad, vio la utilidad de la fórmula representada en partidos cuyo arquetipo ha sido Acción Democrática. La alianza entre Betancourt y Kennedy derrotó la ofensiva del imperialismo ruso y éste inició su decadencia. Eso dio a la Venezuela betancouriana autoridad para crear la OPEP, que dio caja al milagro económico venezolano frustrado en la generación siguiente. Fue el momento cumbre de Venezuela en toda su Historia.

🚨 | LO TORTURARON CON BOLSAS EN LA CABEZA Y GASES TÓXICOS. HOY A SUS 71 AÑOS SALIÓ EN SILLA DE RUEDAS | El caso del general Lozada libre tras 13 años de persecución Se llama Ramón Antonio Lozada Saavedra. General retirado de la Guardia Nacional. Padre de cinco hijos. Esta noche salió del Palacio de Justicia en silla de ruedas, con un estado de salud que Alfredo Romero calificó como "gravísimo". Llevaba más de dos años postrado en una cama del Hospital Militar de Caracas. Diabetes, hipertensión, anemia, infecciones, pérdida de visión. Un cuerpo destruido por el régimen. Su historia es la de un hombre al que el chavismo intentó matar sin matarlo. En 2013 fue secuestrado y torturado durante días por funcionarios de inteligencia. Lo abandonaron en una vía de Aragua, destruido físicamente. Todo para presionar a su amigo, el general Raúl Baduel, quien después moriría bajo custodia del Estado. En 2017 lo detuvieron otra vez. Asfixia mecánica, bolsas en la cabeza, gases tóxicos, sin comida ni agua. Quedó en 40 kilos. En 2019 fueron por él una tercera vez. Estaba enfermo, en reposo en su casa en Barinas. Le cubrieron la cabeza, lo llevaron a un bosque, lo desnudaron, lo ataron e intentaron ahogarlo en un río. Él repetía: "Esta vez sí voy a morir." En 2022 su cuerpo colapsó. Fue operado del cerebro por un hematoma causado por los golpes. Esta noche el Tribunal 23 de Juicio del Área Metropolitana de Caracas lo condenó a 8 años por instigación a la rebelión. Llevaba 9 años y medio preso. Sin pruebas. Sin testigos. Sin evidencia en el expediente. Lo condenaron por debajo del tiempo que ya le habían robado. Esa fue la fórmula del régimen para sacarlo sin admitir lo que le hicieron.

🚨 Claude Code made me 6 trading bots in 15 mins In the US alone, emotional retail traders lost more than $1.8 billion on liquidations While billions of amateur traders were staring at charts, overtrading, and getting wrecked on fees, a quiet group of algorithmic traders treated prediction markets like a hyper-liquid data engine They didn't guess outcomes -> they knew the structural price gaps in advance Here is how they did it, and why manual trading is completely dead: It's all about removing emotion and deploying cross-market statistical arbitrage Linear Spread Cointegration Formula: S_t = P_P,t - β * P_K,t - μ Ornstein-Uhlenbeck Continuous Dynamics Formula: dS_t = θ(μ - S_t)dt + σ dW_t Euler-Maruyama Discretization (MLE Calibration) Formula: S_t_i = S_t_i-1 * e^(-θΔt) + μ(1 - e^(-θΔt)) + ε_t Level 1 Order Book Imbalance (OBI) Formula: I_t = (V_b(t) - V_a(t)) / (V_b(t) + V_a(t)) Volume-Weighted Micro-Price Prediction Formula: P_micro(t) = P_mid(t) + I_t * (Δspread / 2) Cross-Venue Predictive Signal Optimization Formula: ΔP_Kalshi(t + δ) = f(I_Polymarket(t), P_micro,Polymarket(t) - P_micro,Kalshi(t)) In the era of advanced AI, the winner is not the one who guesses the score, but the one who lets automated systems execute with absolute patience and discipline. AI does the hard parts now -> you don't even need a CS degree to build this The full behind-the-scenes live system build is now available to the public 📝

SQUARE ENIX DEBIÓ CREAR GENSHIN IMPACT Un exdirector de Square Enix acaba de declarar que el mayor error histórico de la corporación fue permitir que otro estudio inventara la fórmula de Genshin Impact antes que ellos. Tras analizar la reciente caída financiera de 2026, Jacob Navok culpó a la burocracia por arruinar el futuro de la marca. "La empresa está tan acostumbrada a ser lenta que no puede imaginar cómo sería actuar con rapidez", sentenció, señalando que tenían toda la experiencia técnica para dominar el mercado pero prefirieron estancarse produciendo remakes. Mientras los gigantes japoneses siguen ahogados en papeleo, la competencia extranjera ya se quedó con el monopolio y el dinero de las nuevas generaciones.

Mole 1.5.0 took 6 days, 171 commits, 215 files touched, and 57k+ lines changed. Detailed changelog: 1. Menu bar: added live CPU, memory, and network stats, menu-bar-only mode, right-click quick actions, hotkey support, ghost-state protection, and the new runner animation system. 2. Status: added fan controls for supported Macs, including Auto / Cool / Quiet modes, live RPM, stricter hardware probing, safer restore behavior, and upgrade recovery for old fan presets. 3. Software updates: added update checks and install flows for Homebrew Cask, Homebrew Formula, Mac App Store, Sparkle, and Electron-style appcasts, with clearer progress and safer cancellation. 4. Startup manager: added Login Items, Launch Agents, Launch Daemons, and background item review in one place, with safer authorization behavior so viewing startup items does not ask for admin access. 5. Uninstall: improved alias search, bundle ID search, app metadata matching, Homebrew cask detection, input method discovery, WeChat Input Method support, Doubao Input Method support, and safer root-owned app removal. 6. Clean: tightened log cleanup, protected VPN and proxy app state, guarded Application Support cache cleanup, improved browser and Electron cache detection, and added stronger Trash validation. 7. Analyze: improved disk labels, breadcrumbs, drill-down behavior, folder prefetching, large-directory readability, and trash safety checks. 8. License: improved device management, activation reuse, device reclaim flows, and clearer handling when a license is already used on two Macs. 9. Reliability: fixed Homebrew child-process cancellation, sudo helper reuse, fan preset recovery, MAS inventory edge cases, menu bar ghost states, Startup permission prompts, and release-signing/appcast edge cases. 10. Website and docs: updated the 1.5.0 homepage, release notes, docs, help pages, llms.txt, appcast, and downloadable DMG.

Yesterday at #GoogleIO#, we introduced the biggest upgrade to our iconic Search box in over 25 years. Here’s what to know about the new, intelligent Search box: 🔎 More intuitive than ever, the new Search box will dynamically expand to give you space to ask whatever’s on your mind and be able to anticipate your intent, helping you formulate your questions with AI-powered suggestions that go beyond autocomplete. 📸 You’ll be able to search across modalities including text, images, files, videos or @googlechrome tabs to ask exactly what’s on your mind. 🔵 The new AI Search box is starting to roll out now, in all countries and languages where AI Mode is available.

The State of Alaska should be sued in federal court for what it's done to its people. "Permanent Fund Dividend recipients are set to receive $1,000 and $200 for energy relief, but it’s had a rocky road to reach that number. "While the statutory formula has been historically used to calculate dividends, an Alaska Supreme Court ruling in 2017 changed how the PFD is appropriated. Lawmakers have determined each year’s amount through the appropriation process since then." PFD remains the same leaving final committee, energy relief rebate increases to $200

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.