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1 + 4 strongest repos for weather trading on Polymarket I found over the last few weeks. They pull $250k+ per season combined. All free. All open source.https://t.co/qNhC2foPdb -> replaces Bloomberg Weather Terminal 31-member GFS ensemble forecasts. Trades weather temperature markets on both Kalshi (KXHIGH series) and Polymarket simultaneously. Kelly criterion sizing. Signal calibration with Brier score. React dashboard. All data sources free.https://t.co/nKbsqithuQ -> replaces "city-center" for
1:29
1 + 4 strongest repos for weather trading on Polymarket I found ove…
17.9K views2 months ago
x.comzostaff
要約累計1億7280万フレーム(中間48時間)の境界断面をジャストで通過し、背景の暗号割込みハンドラ(PID: 0x05)が自動シャットシール(Day 2封印マニフェストダンプ)を完全無人執行。前日の署名を親とする不可逆な暗号ハッシュチェインバイナリが生成され、不揮発性NVRAMへのパージを完了した。直後に移行したDay 3初期周回フェーズにおいて、HUD 3軸ゲージマトリクス $[R_{mask}, \gamma, \alpha_{norm}]^T$ が設計不動点座標 $[0.7241, 0.0876, 4.9312]$ へ極小の分散($\Delta R \le 10^{-5}$)のまま完全クランプされ続けていることを連続自動検証した。結論時間多様体チェインの固着成功: 48時間断面における親ハッシュカスケード演算およびNVRAMへのライトバックは、メインの1,000 Hz並列重畳ループに対してオーバーヘッドを1クロックもリークさせずに $860\,\text{ns}$ でアトミックに完遂。画面右上のインジケータ DAY-2 CHAIN SEALED が鮮明な緑色へとリアルタイム同期
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要約累計1億7280万フレーム(中間48時間)の境界断面をジャストで …
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x.comOMUXΩ∞KUT-ASI Junki Kanamori
XGRIDS Lixel K2Handheld 3D scanner, 1 cm accuracy + 3D Gaussian Splatting in one portable device.⚖️ Weight: 2.6 lbs (1.2 kg)🎯 Accuracy: 1 cm (0.39370 in) relative accuracy RMSE, ~3 cm (1.1811 in) absolute accuracy.📡 LiDAR: 200,000 pts/s, 131 - 328 ft (40-100m)🧭 Features: Multi-SLAM + RTK, IP54 rating, 512 GB storage.🔋 Battery: 1.5 hours📦 Outputs: Point cloud, mesh, and 3D Gaussian Splatting.
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XGRIDS Lixel K2Handheld 3D scanner, 1 cm accuracy + 3D Gaus…
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x.comMark
Boring part, skip if you don't understand 🐶Methodology in V3:1. Atlas raw inputs, no bias correction. Atlas's series projected through the model hit Abelardo's certified May 31 result within 0.3pp. With that benchmark in hand, V3 treats Atlas's runoff series as well-calibrated and applies zero house effects, the data is its own validation.2. SES (Simple Exponential Smoothing) on the log-odds of the valid share. Level-only, no trend extrapolation. Correct for runoffs because post-R1 voter realig
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Boring part, skip if you don't understand 🐶Methodology in V3:1. …
4.8K views1 month ago
x.comBasenji
Delitos Cibernéticos más comunes - Muro Educativo
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Delitos Cibernéticos más comunes - Muro Educativo
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YouTubeMuro Educativo
【MATLAB代码演示】USBL(超短基线)误差补偿,使用LSTM(长短期记忆),带补偿前后的定位结果对比
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【MATLAB代码演示】USBL(超短基线)误差补偿,使用LSTM(长短 …
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bilibiliMATLAB卡尔曼滤波
Case Study on ANN Model - Financial Machine Learning | SRM
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