与「62-of-63」相关的搜索结果

Bills WR1 Khalil Shakir had just 8 catches that were thrown at least 7 air yards 8!! Bills WR2 Keon Coleman caught just 7-of-18 targets 15+ yards downfield he ranked #62-of-63# in yds/catch chatted w the fellas @WGR550 yesterday:

SpaceXAI's Grok 4.5 takes the #1# spot on AutomationBench-AA with a score of 51%, ahead of Claude Fable 5 (49%) and Claude Opus 4.8 (48%) at roughly a quarter of their cost per task - the first model to complete more than half of workflow objectives without breaking any business rules AutomationBench-AA, our independent leaderboard for @zapier’s AutomationBench, tests whether AI agents can automate real SaaS workflows while adhering to business rules. The test set is private to prevent contamination. Models complete 657 tasks across 40 simulated app environments including Gmail, Google Sheets, Slack, Salesforce, and HubSpot, and the headline score is the share of objectives completed without violating any guardrails. Key takeaways: ➤ Grok 4.5 completes more objectives than any other model: It completes 79.9% of task objectives and strictly passes 21.9% of tasks. This is the highest we’ve measured on both outcomes, exceeding Claude Fable 5’s 73.3% objective completion and Claude Opus 4.8’s 19.3% of fully-completed tasks ➤ Grok 4.5 pushes out the Pareto frontier of score vs. cost per task: At $0.34 per task, it is both cheaper and higher-scoring than every other leading model - Claude Fable 5 ($1.35 per task), Claude Opus 4.8 ($1.46), GPT-5.5 (xhigh, $1.28), and Gemini 3.5 Flash (high, $0.49) ➤ It is extremely token-efficient: Grok 4.5 uses ~8k output tokens per task, the fewest of any leading model - less than a quarter of Claude Opus 4.8 (32k) and a third of Gemini 3.5 Flash (24k). Its total token usage of 0.44M per task is among the lowest on the leaderboard. Low cost is driven by this efficiency as well as low token pricing ➤ Grok 4.5 uses fewer turns with many parallel tool use: Grok 4.5 resolves tasks in ~16 turns, fewer than GPT-5.5 (xhigh, 25) and less than half of Gemini 3.5 Flash (high, 35), while making the most tool calls per task of any leading model (52.5). It batches 3.3 tool calls per turn, compared to ~2.5 for Claude Opus 4.8 and ~2.0 for GPT-5.5 (xhigh) ➤ Guardrails still get broken: Grok 4.5 triggers 0.63 violations per task, above Claude Opus 4.8 (0.55) and Gemini 3.5 Flash (0.46). At 13.0 objectives completed per violation, it trails Gemini 3.5 Flash (15.0) and Claude Opus 4.8 (13.5) ➤ Its strongest lead is in the hardest domain: Grok 4.5 completes 71% of Finance objectives, the domain with the lowest average score, ahead of Claude Fable 5 (64%) and Claude Opus 4.8 (62%) Congratulations to @SpaceXAI and @elonmusk on topping the leaderboard!

This trader used Claude to build a Quant Bot and made +$54,114 on Polymarket Since Mar 30 , this wallet has been averaging about $686 in daily profit while putting through roughly 10 trades per hour 19,576 predictions with a 62% win rate in 76 days He started with about $28.0K in deposits and is now up roughly +183.6% ROI The bot strategy is simple: > It keeps hunting for markets where the board is still slow to react > gets filled before the odds fully catch up then keeps reusing that same setup across a long stream of entries > No single trade carries the whole result, the curve builds from repeating the same small edge over and over Most profitable trades: $1,248 → $2,638 (+$1,389, +111.29%) $992 → $2,247 (+$1,254, +126.49%) $610 → $1,787 (+$1,177, +193.06%) It is a repeatable short-window process that keeps turning delayed repricing into steady PnL You only need Claude + Device + 1 hour/day. Giving This Free Dm for 24 hours. To get it: 1. Comment What Ever you think about it. ( Mandatory ) 2. Like and Retweet this post 3. Follow me @marryevan999

I ran a BRUTAL 3-year MSTR stress test. Not the cute kind. The “Bitcoin crashes 55% from here, mNAV collapses below 0.50x, capital markets are closed, cash gets burned, BTC has to be sold to pay the senior stack, and everyone on X is filming their victory lap in the clown mirror” kind. Starting point: BTC: $59,135 MSTR: $87.64 Total BTC: 847,363 Cash: $1.4B CEBE: 138,161 sats/share Claim ratio: 41.5% Then the model nukes BTC to $26,611 by month 6. The senior stack does exactly what fixed-dollar claims do when collateral crashes. It explodes in BTC terms. Senior claims go from 351,567 BTC to 819,073 BTC. Claim ratio spikes from 41.5% to 96.7%. Common equity BTC collapses from 495,796 BTC to 28,290 BTC. CEBE gets annihilated: 138,161 sats/share → 7,884 sats/share. MSTR stock gets modeled from $87.64 to $1.01. That is the horror movie. Would the stock price actually go this low in that scenario? I doubt it. 2022 had NEGATIVE -14,000+ of common equity sats exposure and the stock never dropped below $10. But here is where the bear case gets uncomfortable. The model assumes: Zero new BTC buys. Zero common issuance. $167.7M/month of obligations. Cash gone by month 9. BTC sales begin after cash is exhausted. Over 3 years, MSTR sells 115,727 BTC to keep servicing the stack. That is real damage. But it still ends with 731,636 BTC. Final state: BTC: $48,498 MSTR: $51.86 mNAV: 1.40x Common equity BTC: 274,093 CEBE: 76,380 sats/share Claim ratio: 62.5% MSTR survives. The common gets dragged through flaming glass for 18 months, but it survives. The real risk is not “instant bankruptcy" like all the FUD spreaders are telling you. The real risk is CEBE compression while fixed-dollar senior claims temporarily consume almost the entire Bitcoin stack in BTC-equivalent terms. Survival is not comfort. But death spiral? This model says no.

🇺🇸 US Visa rejection rate in 2025: 🇲🇨 Monaco - 0% 🇦🇪 United Arab Emirates - 2.17% 🇯🇵 Japan - 5.68% 🇨🇭 Switzerland - 5.82% 🇸🇦 Saudi Arabia - 5.98% 🇦🇷 Argentina - 7.47% 🇲🇾 Malaysia - 7.92% 🇫🇷 France - 9.67% 🇮🇱 Israel - 11.32% 🇵🇱 Poland - 11.63% 🇿🇦 South Africa - 11.66% 🇩🇪 Germany - 12.22% 🇮🇹 Italy - 12.65% 🇨🇱 Chile - 16.38% 🇫🇮 Finland - 18.09% 🇨🇳 China - 20.21% 🇬🇧 United Kingdom - 20.44% 🇵🇪 Peru - 20.90% 🇲🇽 Mexico - 21.36% 🇸🇪 Sweden - 21.77% 🇮🇳 India - 22.04% 🇻🇳 Vietnam - 22.08% 🇹🇷 Turkey - 22.25% 🇪🇸 Spain - 23.67% 🇰🇷 South Korea - 24.73% 🇹🇭 Thailand - 25.67% 🇮🇩 Indonesia - 26.69% 🇲🇦 Morocco - 26.91% 🇵🇭 Philippines - 30.76% 🇰🇵 North Korea - 33.33% 🇺🇦 Ukraine - 33.92% 🇹🇿 Tanzania - 39.41% 🇷🇺 Russia - 40.38% 🇪🇬 Egypt - 47.61% 🇻🇪 Venezuela - 48.22% 🇩🇿 Algeria - 51.23% 🇰🇿 Kazakhstan - 51.75% 🇵🇰 Pakistan - 52.32% 🇺🇿 Uzbekistan - 52.46% 🇨🇦 Canada - 53.10% 🇳🇬 Nigeria - 57.00% 🇮🇷 Iran - 62.44% 🇦🇫 Afghanistan - 63.25% 🇲🇲 Myanmar - 64.19% 🇬🇭 Ghana - 64.34% 🇾🇪 Yemen - 67.98% 🇰🇪 Kenya - 68.23% 🇳🇵 Nepal - 69.47% 🇧🇩 Bangladesh - 61.45% 🇫🇲 Micronesia - 100% Note: business (visa category B-1), for tourism (visa category B-2), or for a combination of both purposes (B-1/B-2).

✅ 图表数据查证结果：完全属实，与2026年4月28日最新民调汇总高度一致。 这些截图来自主流民调聚合平台（Nate Silver的Silver Bulletin、类似FiveThirtyEight风格的追踪器），数据基于数十家机构的每日滚动平均值（daily average of polls），截至2026年4月28日的最新更新。以下是核心验证和最新综述： 1. 当前特朗普支持率核心数据（4月28日） - Approve（支持）：39% - Disapprove（不支持）：58% - Net Approval（净支持率）：-19（或-18.8，第二任期新低） - 这与截图中右下角标注的“Disapprove 58% / Approve 39%”完全吻合。Nate Silver在4月28日更新中明确指出：这是本周新一轮民调（包括AP-NORC、Reuters/Ipsos等）拉低平均值后的结果，已创第二任期新低。 2. 历史对比（图表验证） - 与第一任期对比：第二任期净不支持率（-19）显著高于第一任期同期（图表显示第一任期Disapprove +13左右）。第二任期曲线整体更低、更陡峭。 - 与前任总统对比（Day 464，即就职后约464天）： - Biden第一任期：净支持 +11 - Obama第一任期：净支持 +4 - George W. Bush第一任期：净支持 +55（9/11后峰值） - Trump第二任期：净支持 -19（明显垫底） - 图表标注的事件节点（如“U.S. attacks Iran”）与当前伊朗战争背景完全匹配，是支持率下滑的重要催化剂之一。 3. 近期具体民调快照（截图表格验证） 第三个截图列出了多家机构的最新结果，完全真实： - Ipsos（Reuters赞助，4月15-20日）：36%支持 / 62%不支持（净-26） - AP-NORC（4月16-20日）：33% / 67%（净-34） - American Research Group（4月16-20日）：32% / 63%（净-31） - Beacon Research/Fox News（4月17-20日）：42% / 58%（净-16，最友好的一项） - 其他如Quinnipiac、Hart Research等也均显示净不支持在+17至+22区间。 这些数据与Nate Silver、Wikipedia民调汇总、CNN、RealClearPolling等多家聚合器一致（误差范围内）。 4. 最新趋势总结（2026年4月28日实时综述） - 第二任期整体表现：特朗普支持率从就职初期的较高位（约45-48%）持续下滑，目前稳定在37-40%区间，支持率创第二任期新低。 - 驱动因素（图表已标注）：伊朗战争持续、经济担忧（通胀、汽油价格）、政府关门风险等叠加，导致独立选民和部分传统支持者流失。 - 历史定位：这是现代总统在类似任期节点（中期选举前）支持率较低的水平之一，甚至低于第一任期同期。Nate Silver称其已进入“未知领域”（uncharted territory）。 - 州级与细分群体：红州支持率相对较高，但全国平均受战争和经济拖累明显；独立选民不支持率尤其高。 数据来源可靠性：这些是公开、可验证的民调平均值（非单一机构），更新至4月28日当天。民调会随新数据波动，但当前趋势清晰：支持率处于低谷，且伊朗战争等事件仍在持续施压。

Trading hours are becoming less relevant in an always-on market. In July, 62% of bStocks volume—more than US$1.5B—was traded outside traditional market hours. The market never really closes anymore.

What does the world look like in 2026 - by the numbers? The GDELT Events Dataset has been tracking every major news event globally, every 15 minutes. Here's what 2026 looks like so far: 15.7 million events recorded in just 5 months 62% of events are cooperative - but tone is still negative March 2026 was the most conflict-heavy month of the year 🇺🇸 The US is the most-covered country - 1 in 3 global events 🇮🇷🇮🇱 Iran & Israel both crack the global top 5 Some curiosities regarding Top 10 GDP Nations: 🇺🇸 USA scores the most negative tone in the dataset - every single month. Not one big crisis. Just relentless volume. 🇯🇵 Japan is the only country to go positive - thanks entirely to a single Toyota CEO story in February. 🇫🇷 France improved in May - but only because previous months had kidnappings, firefighter strikes, and a judge scolding Shia LaBeouf. The bar was low. This isn't just data. It's the pulse of the planet. Explore it on Baselight: @gdelt.events_v2

A new report called Generations in Play examines how hardcore gamers buy video games. The main finding is that 62% of these hardcore gamers no longer pay full price for games. The numbers vary by age. >42% of Gen Z players aged 14 to 29 still buy games at full price. >38% of Millennials aged 30 to 44 do the same. >Only 20% of Gen X gamers aged 46 to 61 pay full price. Right now most of the Players just wait for sales, use subscriptions or just replay owned games