[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-37ea158d3a7e0a74-python-conquers-ai-data-and-backend-via-libraries-summary":3,"summaries-facets-categories":77,"summary-related-37ea158d3a7e0a74-python-conquers-ai-data-and-backend-via-libraries-summary":3684},{"id":4,"title":5,"ai":6,"body":13,"categories":46,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":51,"navigation":58,"path":59,"published_at":60,"question":48,"scraped_at":61,"seo":62,"sitemap":63,"source_id":64,"source_name":65,"source_type":66,"source_url":67,"stem":68,"tags":69,"thumbnail_url":48,"tldr":74,"tweet":48,"unknown_tags":75,"__hash__":76},"summaries\u002Fsummaries\u002F37ea158d3a7e0a74-python-conquers-ai-data-and-backend-via-libraries-summary.md","Python Conquers AI, Data, and Backend via Libraries",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",4487,1297,19308,0.00152325,{"type":14,"value":15,"toc":39},"minimark",[16,21,25,29,32,36],[17,18,20],"h2",{"id":19},"pythons-shift-from-slow-to-essential","Python's Shift from 'Slow' to Essential",[22,23,24],"p",{},"Python overcame early criticisms of slow performance and high memory use compared to C++ or Java by prioritizing simplicity and ecosystem growth. This focus made it ideal for complex tasks: data scientists analyze patterns and build ML models faster with less code, avoiding hours of boilerplate. Backend devs deploy AI models via clean APIs. By 2026, Python powers advanced tech across industries, proving simplicity scales better than raw speed for most real-world problems—Instagram handles massive traffic with Django for this reason.",[17,26,28],{"id":27},"data-science-and-ai-libraries-for-rapid-prototyping","Data Science and AI Libraries for Rapid Prototyping",[22,30,31],{},"Use Pandas and NumPy for efficient data manipulation and numerical computing, Matplotlib and Seaborn for visualizing patterns, Scikit-learn for traditional ML models, and PyTorch for deep learning. These cut development time, letting you predict outcomes or build generative AI instead of wrestling syntax. For AI specifically, TensorFlow, Transformers (for NLP), and OpenCV (image recognition) handle massive datasets and algorithms in healthcare, finance, and cybersecurity. Deploy recommendation systems or chatbots quickly—Python's flexibility turns complex data into actionable insights without performance bottlenecks halting progress.",[17,33,35],{"id":34},"backend-and-data-engineering-frameworks-for-scale","Backend and Data Engineering Frameworks for Scale",[22,37,38],{},"Build scalable web apps with Django (Instagram's choice for high traffic), FastAPI for AI APIs, Flask for lightweight services, or Streamlit for ML dashboards and prototypes. These frameworks emphasize readability and speed, reducing code volume while managing databases, auth, and servers. In data engineering, integrate Python with Apache Spark and Hadoop for distributed processing, or Airflow for ETL pipelines and real-time streaming. Automate workflows on cloud infrastructure efficiently—Python's clean syntax simplifies massive data flows, making it indispensable for production systems.",{"title":40,"searchDepth":41,"depth":41,"links":42},"",2,[43,44,45],{"id":19,"depth":41,"text":20},{"id":27,"depth":41,"text":28},{"id":34,"depth":41,"text":35},[47],"Software Engineering",null,"md",false,{"content_references":52,"triage":53},[],{"relevance":54,"novelty":55,"quality":54,"actionability":54,"composite":56,"reasoning":57},4,3,3.8,"Category: Software Engineering. The article discusses Python's role in AI, data science, and backend development, addressing the audience's need for practical applications of AI tools and frameworks. It provides specific libraries and frameworks like Pandas, PyTorch, and Django, which are actionable for developers looking to integrate AI into their products.",true,"\u002Fsummaries\u002F37ea158d3a7e0a74-python-conquers-ai-data-and-backend-via-libraries-summary","2026-05-14 09:31:57","2026-05-14 11:00:22",{"title":5,"description":40},{"loc":59},"37ea158d3a7e0a74","Python in Plain English","article","https:\u002F\u002Fpython.plainenglish.io\u002Fpython-for-everything-5d889172897d?source=rss----78073def27b8---4","summaries\u002F37ea158d3a7e0a74-python-conquers-ai-data-and-backend-via-libraries-summary",[70,71,72,73],"python","data-science","machine-learning","ai-tools","Once mocked for slowness, Python now dominates data science, AI, backend development, and data engineering through libraries like Pandas, PyTorch, Django, and Airflow, enabling efficient analysis, model building, scalable apps, and pipelines.",[],"lsn69aHaiZQnUR0phjkMTCANxdbzUtdkDJ1l6zWyxhU",[78,81,84,87,90,93,95,97,99,101,103,105,108,110,112,114,116,118,120,122,124,126,129,132,134,136,138,140,142,145,147,149,151,153,155,157,159,161,163,165,167,169,171,173,175,177,179,181,183,185,187,189,191,193,195,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,494,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,554,556,558,560,562,564,566,568,570,572,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,606,608,610,612,614,616,618,620,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798,800,802,804,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,858,860,862,864,866,868,870,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,932,934,936,938,940,942,944,946,948,950,952,954,956,958,960,962,964,966,968,970,972,974,976,978,980,982,984,986,988,990,992,994,996,998,1000,1002,1004,1006,1008,1010,1012,1014,1016,1018,1020,1022,1024,1026,1028,1030,1032,1034,1036,1038,1040,1042,1044,1046,1048,1050,1052,1054,1056,1058,1060,1062,1064,1066,1068,1070,1072,1074,1076,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1402,1404,1406,1408,1410,1412,1414,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1444,1446,1448,1450,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1478,1480,1482,1484,1486,1488,1490,1492,1494,1496,1498,1500,1502,1504,1506,1508,1510,1512,1514,1516,1518,1520,1522,1524,1526,1528,1530,1532,1534,1536,1538,1540,1542,1544,1546,1548,1550,1552,1554,1556,1558,1560,1562,1564,1566,1568,1570,1572,1574,1576,1578,1580,1582,1584,1586,1588,1590,1592,1594,1596,1598,1600,1602,1604,1606,1608,1610,1612,1614,1616,1618,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1778,1780,1782,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1812,1814,1816,1818,1820,1822,1824,1826,1828,1830,1832,1834,1836,1838,1840,1842,1844,1846,1848,1850,1852,1854,1856,1858,1860,1862,1864,1866,1868,1870,1872,1874,1876,1878,1880,1882,1884,1886,1888,1890,1892,1894,1896,1898,1900,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1962,1964,1966,1968,1970,1972,1974,1976,1978,1980,1982,1984,1986,1988,1990,1992,1994,1996,1998,2000,2002,2004,2006,2008,2010,2012,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032,2034,2036,2038,2040,2042,2044,2046,2048,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2122,2124,2126,2128,2130,2132,2134,2136,2138,2140,2142,2144,2146,2148,2150,2152,2154,2156,2158,2160,2162,2164,2166,2168,2170,2172,2174,2176,2178,2180,2182,2184,2186,2188,2190,2192,2194,2196,2198,2200,2202,2204,2206,2208,2210,2212,2214,2216,2218,2220,2222,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2282,2284,2286,2288,2290,2292,2294,2296,2298,2300,2302,2304,2306,2308,2310,2312,2314,2316,2318,2320,2322,2324,2326,2328,2330,2332,2334,2336,2338,2340,2342,2344,2346,2348,2350,2352,2354,2356,2358,2360,2362,2364,2366,2368,2370,2372,2374,2376,2378,2380,2382,2384,2386,2388,2390,2392,2394,2396,2398,2400,2402,2404,2406,2408,2410,2412,2414,2416,2418,2420,2422,2424,2426,2428,2430,2432,2434,2436,2438,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458,2460,2462,2464,2466,2468,2470,2472,2474,2476,2478,2480,2482,2484,2486,2488,2490,2492,2494,2496,2498,2500,2502,2504,2506,2508,2510,2512,2514,2516,2518,2520,2522,2524,2526,2528,2530,2532,2534,2536,2538,2540,2542,2544,2546,2548,2550,2552,2554,2556,2558,2560,2562,2564,2566,2568,2570,2572,2574,2576,2578,2580,2582,2584,2586,2588,2590,2592,2594,2596,2598,2600,2602,2604,2606,2608,2610,2612,2614,2616,2618,2620,2622,2624,2626,2628,2630,2632,2634,2636,2638,2640,2642,2644,2646,2648,2650,2652,2654,2656,2658,2660,2662,2664,2666,2668,2670,2672,2674,2676,2678,2680,2682,2684,2686,2688,2690,2692,2694,2696,2698,2700,2702,2704,2706,2708,2710,2712,2714,2716,2718,2720,2722,2724,2726,2728,2730,2732,2734,2736,2738,2740,2742,2744,2746,2748,2750,2752,2754,2756,2758,2760,2762,2764,2766,2768,2770,2772,2774,2776,2778,2780,2782,2784,2786,2788,2790,2792,2794,2796,2798,2800,2802,2804,2806,2808,2810,2812,2814,2816,2818,2820,2822,2824,2826,2828,2830,2832,2834,2836,2838,2840,2842,2844,2846,2848,2850,2852,2854,2856,2858,2860,2862,2864,2866,2868,2870,2872,2874,2876,2878,2880,2882,2884,2886,2888,2890,2892,2894,2896,2898,2900,2902,2904,2906,2908,2910,2912,2914,2916,2918,2920,2922,2924,2926,2928,2930,2932,2934,2936,2938,2940,2942,2944,2946,2948,2950,2952,2954,2956,2958,2960,2962,2964,2966,2968,2970,2972,2974,2976,2978,2980,2982,2984,2986,2988,2990,2992,2994,2996,2998,3000,3002,3004,3006,3008,3010,3012,3014,3016,3018,3020,3022,3024,3026,3028,3030,3032,3034,3036,3038,3040,3042,3044,3046,3048,3050,3052,3054,3056,3058,3060,3062,3064,3066,3068,3070,3072,3074,3076,3078,3080,3082,3084,3086,3088,3090,3092,3094,3096,3098,3100,3102,3104,3106,3108,3110,3112,3114,3116,3118,3120,3122,3124,3126,3128,3130,3132,3134,3136,3138,3140,3142,3144,3146,3148,3150,3152,3154,3156,3158,3160,3162,3164,3166,3168,3170,3172,3174,3176,3178,3180,3182,3184,3186,3188,3190,3192,3194,3196,3198,3200,3202,3204,3206,3208,3210,3212,3214,3216,3218,3220,3222,3224,3226,3228,3230,3232,3234,3236,3238,3240,3242,3244,3246,3248,3250,3252,3254,3256,3258,3260,3262,3264,3266,3268,3270,3272,3274,3276,3278,3280,3282,3284,3286,3288,3290,3292,3294,3296,3298,3300,3302,3304,3306,3308,3310,3312,3314,3316,3318,3320,3322,3324,3326,3328,3330,3332,3334,3336,3338,3340,3342,3344,3346,3348,3350,3352,3354,3356,3358,3360,3362,3364,3366,3368,3370,3372,3374,3376,3378,3380,3382,3384,3386,3388,3390,3392,3394,3396,3398,3400,3402,3404,3406,3408,3410,3412,3414,3416,3418,3420,3422,3424,3426,3428,3430,3432,3434,3436,3438,3440,3442,3444,3446,3448,3450,3452,3454,3456,3458,3460,3462,3464,3466,3468,3470,3472,3474,3476,3478,3480,3482,3484,3486,3488,3490,3492,3494,3496,3498,3500,3502,3504,3506,3508,3510,3512,3514,3516,3518,3520,3522,3524,3526,3528,3530,3532,3534,3536,3538,3540,3542,3544,3546,3548,3550,3552,3554,3556,3558,3560,3562,3564,3566,3568,3570,3572,3574,3576,3578,3580,3582,3584,3586,3588,3590,3592,3594,3596,3598,3600,3602,3604,3606,3608,3610,3612,3614,3616,3618,3620,3622,3624,3626,3628,3630,3632,3634,3636,3638,3640,3642,3644,3646,3648,3650,3652,3654,3656,3658,3660,3662,3664,3666,3668,3670,3672,3674,3676,3678,3680,3682],{"categories":79},[80],"Developer Productivity",{"categories":82},[83],"Business & SaaS",{"categories":85},[86],"AI & LLMs",{"categories":88},[89],"AI Automation",{"categories":91},[92],"Product Strategy",{"categories":94},[86],{"categories":96},[80],{"categories":98},[83],{"categories":100},[],{"categories":102},[86],{"categories":104},[],{"categories":106},[107],"AI News & Trends",{"categories":109},[89],{"categories":111},[107],{"categories":113},[89],{"categories":115},[89],{"categories":117},[86],{"categories":119},[86],{"categories":121},[107],{"categories":123},[86],{"categories":125},[],{"categories":127},[128],"Design & Frontend",{"categories":130},[131],"Data Science & Visualization",{"categories":133},[107],{"categories":135},[],{"categories":137},[47],{"categories":139},[86],{"categories":141},[89],{"categories":143},[144],"Marketing & Growth",{"categories":146},[86],{"categories":148},[89],{"categories":150},[],{"categories":152},[],{"categories":154},[128],{"categories":156},[89],{"categories":158},[80],{"categories":160},[128],{"categories":162},[86],{"categories":164},[89],{"categories":166},[107],{"categories":168},[],{"categories":170},[],{"categories":172},[89],{"categories":174},[47],{"categories":176},[],{"categories":178},[83],{"categories":180},[],{"categories":182},[],{"categories":184},[89],{"categories":186},[89],{"categories":188},[86],{"categories":190},[],{"categories":192},[47],{"categories":194},[],{"categories":196},[],{"categories":198},[],{"categories":200},[86],{"categories":202},[144],{"categories":204},[128],{"categories":206},[128],{"categories":208},[86],{"categories":210},[89],{"categories":212},[86],{"categories":214},[86],{"categories":216},[89],{"categories":218},[89],{"categories":220},[131],{"categories":222},[107],{"categories":224},[89],{"categories":226},[144],{"categories":228},[89],{"categories":230},[92],{"categories":232},[],{"categories":234},[89],{"categories":236},[],{"categories":238},[89],{"categories":240},[47],{"categories":242},[243],"DevOps & Cloud",{"categories":245},[128],{"categories":247},[86],{"categories":249},[],{"categories":251},[],{"categories":253},[89],{"categories":255},[],{"categories":257},[86],{"categories":259},[],{"categories":261},[80],{"categories":263},[47],{"categories":265},[83],{"categories":267},[107],{"categories":269},[86],{"categories":271},[],{"categories":273},[86],{"categories":275},[],{"categories":277},[47],{"categories":279},[131],{"categories":281},[],{"categories":283},[86],{"categories":285},[128],{"categories":287},[],{"categories":289},[128],{"categories":291},[89],{"categories":293},[],{"categories":295},[89],{"categories":297},[107],{"categories":299},[83],{"categories":301},[86],{"categories":303},[],{"categories":305},[89],{"categories":307},[86],{"categories":309},[92],{"categories":311},[],{"categories":313},[86],{"categories":315},[89],{"categories":317},[89],{"categories":319},[],{"categories":321},[131],{"categories":323},[86],{"categories":325},[],{"categories":327},[80],{"categories":329},[83],{"categories":331},[86],{"categories":333},[89],{"categories":335},[47],{"categories":337},[86],{"categories":339},[],{"categories":341},[],{"categories":343},[86],{"categories":345},[],{"categories":347},[128],{"categories":349},[],{"categories":351},[86],{"categories":353},[],{"categories":355},[89],{"categories":357},[86],{"categories":359},[128],{"categories":361},[],{"categories":363},[86],{"categories":365},[86],{"categories":367},[83],{"categories":369},[89],{"categories":371},[86],{"categories":373},[128],{"categories":375},[89],{"categories":377},[],{"categories":379},[],{"categories":381},[107],{"categories":383},[],{"categories":385},[86],{"categories":387},[83,144],{"categories":389},[],{"categories":391},[86],{"categories":393},[89],{"categories":395},[],{"categories":397},[],{"categories":399},[86],{"categories":401},[],{"categories":403},[86],{"categories":405},[243],{"categories":407},[],{"categories":409},[107],{"categories":411},[128],{"categories":413},[],{"categories":415},[107],{"categories":417},[107],{"categories":419},[86],{"categories":421},[144],{"categories":423},[],{"categories":425},[83],{"categories":427},[],{"categories":429},[86,243],{"categories":431},[86],{"categories":433},[86],{"categories":435},[89],{"categories":437},[86,47],{"categories":439},[131],{"categories":441},[86],{"categories":443},[144],{"categories":445},[89],{"categories":447},[89],{"categories":449},[],{"categories":451},[89],{"categories":453},[86,83],{"categories":455},[],{"categories":457},[128],{"categories":459},[128],{"categories":461},[],{"categories":463},[],{"categories":465},[107],{"categories":467},[],{"categories":469},[80],{"categories":471},[47],{"categories":473},[86],{"categories":475},[128],{"categories":477},[89],{"categories":479},[47],{"categories":481},[107],{"categories":483},[128],{"categories":485},[],{"categories":487},[86],{"categories":489},[86],{"categories":491},[86],{"categories":493},[107],{"categories":495},[80],{"categories":497},[86],{"categories":499},[89],{"categories":501},[243],{"categories":503},[128],{"categories":505},[89],{"categories":507},[],{"categories":509},[],{"categories":511},[128],{"categories":513},[107],{"categories":515},[131],{"categories":517},[],{"categories":519},[86],{"categories":521},[86],{"categories":523},[83],{"categories":525},[86],{"categories":527},[86],{"categories":529},[107],{"categories":531},[],{"categories":533},[89],{"categories":535},[47],{"categories":537},[],{"categories":539},[86],{"categories":541},[86],{"categories":543},[89],{"categories":545},[],{"categories":547},[],{"categories":549},[86],{"categories":551},[],{"categories":553},[83],{"categories":555},[89],{"categories":557},[],{"categories":559},[80],{"categories":561},[86],{"categories":563},[83],{"categories":565},[107],{"categories":567},[80],{"categories":569},[],{"categories":571},[],{"categories":573},[],{"categories":575},[107],{"categories":577},[107],{"categories":579},[],{"categories":581},[],{"categories":583},[83],{"categories":585},[],{"categories":587},[],{"categories":589},[80],{"categories":591},[],{"categories":593},[144],{"categories":595},[89],{"categories":597},[83],{"categories":599},[89],{"categories":601},[47],{"categories":603},[],{"categories":605},[92],{"categories":607},[128],{"categories":609},[47],{"categories":611},[86],{"categories":613},[89],{"categories":615},[83],{"categories":617},[86],{"categories":619},[],{"categories":621},[],{"categories":623},[47],{"categories":625},[131],{"categories":627},[92],{"categories":629},[89],{"categories":631},[86],{"categories":633},[],{"categories":635},[243],{"categories":637},[],{"categories":639},[89],{"categories":641},[],{"categories":643},[],{"categories":645},[86],{"categories":647},[128],{"categories":649},[144],{"categories":651},[89],{"categories":653},[],{"categories":655},[80],{"categories":657},[],{"categories":659},[107],{"categories":661},[86,243],{"categories":663},[107],{"categories":665},[86],{"categories":667},[83],{"categories":669},[86],{"categories":671},[],{"categories":673},[83],{"categories":675},[],{"categories":677},[47],{"categories":679},[128],{"categories":681},[107],{"categories":683},[131],{"categories":685},[80],{"categories":687},[86],{"categories":689},[47],{"categories":691},[],{"categories":693},[],{"categories":695},[92],{"categories":697},[],{"categories":699},[86],{"categories":701},[],{"categories":703},[128],{"categories":705},[128],{"categories":707},[128],{"categories":709},[],{"categories":711},[],{"categories":713},[107],{"categories":715},[89],{"categories":717},[86],{"categories":719},[86],{"categories":721},[86],{"categories":723},[83],{"categories":725},[86],{"categories":727},[],{"categories":729},[47],{"categories":731},[47],{"categories":733},[83],{"categories":735},[],{"categories":737},[86],{"categories":739},[86],{"categories":741},[83],{"categories":743},[107],{"categories":745},[144],{"categories":747},[89],{"categories":749},[],{"categories":751},[128],{"categories":753},[],{"categories":755},[86],{"categories":757},[],{"categories":759},[83],{"categories":761},[89],{"categories":763},[],{"categories":765},[243],{"categories":767},[131],{"categories":769},[47],{"categories":771},[144],{"categories":773},[47],{"categories":775},[89],{"categories":777},[],{"categories":779},[],{"categories":781},[89],{"categories":783},[80],{"categories":785},[89],{"categories":787},[92],{"categories":789},[83],{"categories":791},[],{"categories":793},[86],{"categories":795},[92],{"categories":797},[86],{"categories":799},[86],{"categories":801},[144],{"categories":803},[128],{"categories":805},[89],{"categories":807},[],{"categories":809},[],{"categories":811},[243],{"categories":813},[47],{"categories":815},[],{"categories":817},[89],{"categories":819},[86],{"categories":821},[128,86],{"categories":823},[80],{"categories":825},[],{"categories":827},[86],{"categories":829},[80],{"categories":831},[128],{"categories":833},[89],{"categories":835},[47],{"categories":837},[],{"categories":839},[86],{"categories":841},[],{"categories":843},[],{"categories":845},[80],{"categories":847},[],{"categories":849},[89],{"categories":851},[92],{"categories":853},[86],{"categories":855},[86],{"categories":857},[128],{"categories":859},[89],{"categories":861},[243],{"categories":863},[128],{"categories":865},[89],{"categories":867},[86],{"categories":869},[86],{"categories":871},[86],{"categories":873},[47],{"categories":875},[107],{"categories":877},[],{"categories":879},[92],{"categories":881},[89],{"categories":883},[128],{"categories":885},[89],{"categories":887},[47],{"categories":889},[128],{"categories":891},[89],{"categories":893},[107],{"categories":895},[],{"categories":897},[86],{"categories":899},[128],{"categories":901},[86],{"categories":903},[80],{"categories":905},[107],{"categories":907},[86],{"categories":909},[144],{"categories":911},[86],{"categories":913},[86],{"categories":915},[89],{"categories":917},[89],{"categories":919},[86],{"categories":921},[89],{"categories":923},[128],{"categories":925},[86],{"categories":927},[],{"categories":929},[],{"categories":931},[47],{"categories":933},[],{"categories":935},[80],{"categories":937},[243],{"categories":939},[],{"categories":941},[80],{"categories":943},[83],{"categories":945},[144],{"categories":947},[],{"categories":949},[83],{"categories":951},[],{"categories":953},[],{"categories":955},[],{"categories":957},[],{"categories":959},[],{"categories":961},[86],{"categories":963},[89],{"categories":965},[243],{"categories":967},[80],{"categories":969},[86],{"categories":971},[47],{"categories":973},[92],{"categories":975},[86],{"categories":977},[144],{"categories":979},[86],{"categories":981},[86],{"categories":983},[86],{"categories":985},[86,80],{"categories":987},[47],{"categories":989},[47],{"categories":991},[128],{"categories":993},[86],{"categories":995},[],{"categories":997},[],{"categories":999},[],{"categories":1001},[47],{"categories":1003},[131],{"categories":1005},[107],{"categories":1007},[128],{"categories":1009},[],{"categories":1011},[86],{"categories":1013},[86],{"categories":1015},[],{"categories":1017},[],{"categories":1019},[89],{"categories":1021},[86],{"categories":1023},[83],{"categories":1025},[],{"categories":1027},[80],{"categories":1029},[86],{"categories":1031},[80],{"categories":1033},[86],{"categories":1035},[47],{"categories":1037},[144],{"categories":1039},[86,128],{"categories":1041},[107],{"categories":1043},[128],{"categories":1045},[],{"categories":1047},[243],{"categories":1049},[128],{"categories":1051},[89],{"categories":1053},[],{"categories":1055},[],{"categories":1057},[],{"categories":1059},[],{"categories":1061},[47],{"categories":1063},[89],{"categories":1065},[89],{"categories":1067},[243],{"categories":1069},[86],{"categories":1071},[86],{"categories":1073},[86],{"categories":1075},[],{"categories":1077},[128],{"categories":1079},[],{"categories":1081},[],{"categories":1083},[89],{"categories":1085},[],{"categories":1087},[],{"categories":1089},[144],{"categories":1091},[144],{"categories":1093},[89],{"categories":1095},[],{"categories":1097},[86],{"categories":1099},[86],{"categories":1101},[47],{"categories":1103},[128],{"categories":1105},[128],{"categories":1107},[89],{"categories":1109},[80],{"categories":1111},[86],{"categories":1113},[128],{"categories":1115},[128],{"categories":1117},[89],{"categories":1119},[89],{"categories":1121},[86],{"categories":1123},[],{"categories":1125},[],{"categories":1127},[86],{"categories":1129},[89],{"categories":1131},[107],{"categories":1133},[47],{"categories":1135},[80],{"categories":1137},[86],{"categories":1139},[],{"categories":1141},[89],{"categories":1143},[89],{"categories":1145},[],{"categories":1147},[80],{"categories":1149},[86],{"categories":1151},[80],{"categories":1153},[80],{"categories":1155},[],{"categories":1157},[],{"categories":1159},[89],{"categories":1161},[89],{"categories":1163},[86],{"categories":1165},[86],{"categories":1167},[107],{"categories":1169},[131],{"categories":1171},[92],{"categories":1173},[107],{"categories":1175},[128],{"categories":1177},[],{"categories":1179},[107],{"categories":1181},[],{"categories":1183},[],{"categories":1185},[],{"categories":1187},[],{"categories":1189},[47],{"categories":1191},[131],{"categories":1193},[],{"categories":1195},[86],{"categories":1197},[86],{"categories":1199},[131],{"categories":1201},[47],{"categories":1203},[],{"categories":1205},[],{"categories":1207},[89],{"categories":1209},[107],{"categories":1211},[107],{"categories":1213},[89],{"categories":1215},[80],{"categories":1217},[86,243],{"categories":1219},[],{"categories":1221},[128],{"categories":1223},[80],{"categories":1225},[89],{"categories":1227},[128],{"categories":1229},[],{"categories":1231},[89],{"categories":1233},[89],{"categories":1235},[86],{"categories":1237},[144],{"categories":1239},[47],{"categories":1241},[128],{"categories":1243},[],{"categories":1245},[89],{"categories":1247},[86],{"categories":1249},[89],{"categories":1251},[89],{"categories":1253},[89],{"categories":1255},[144],{"categories":1257},[89],{"categories":1259},[86],{"categories":1261},[],{"categories":1263},[144],{"categories":1265},[107],{"categories":1267},[89],{"categories":1269},[],{"categories":1271},[],{"categories":1273},[86],{"categories":1275},[89],{"categories":1277},[107],{"categories":1279},[89],{"categories":1281},[],{"categories":1283},[],{"categories":1285},[],{"categories":1287},[89],{"categories":1289},[],{"categories":1291},[],{"categories":1293},[131],{"categories":1295},[86],{"categories":1297},[131],{"categories":1299},[107],{"categories":1301},[86],{"categories":1303},[86],{"categories":1305},[89],{"categories":1307},[86],{"categories":1309},[],{"categories":1311},[],{"categories":1313},[243],{"categories":1315},[],{"categories":1317},[],{"categories":1319},[80],{"categories":1321},[],{"categories":1323},[],{"categories":1325},[],{"categories":1327},[],{"categories":1329},[47],{"categories":1331},[107],{"categories":1333},[144],{"categories":1335},[83],{"categories":1337},[86],{"categories":1339},[86],{"categories":1341},[83],{"categories":1343},[],{"categories":1345},[128],{"categories":1347},[89],{"categories":1349},[83],{"categories":1351},[86],{"categories":1353},[86],{"categories":1355},[80],{"categories":1357},[],{"categories":1359},[80],{"categories":1361},[86],{"categories":1363},[144],{"categories":1365},[89],{"categories":1367},[107],{"categories":1369},[83],{"categories":1371},[86],{"categories":1373},[89],{"categories":1375},[],{"categories":1377},[86],{"categories":1379},[80],{"categories":1381},[86],{"categories":1383},[],{"categories":1385},[107],{"categories":1387},[86],{"categories":1389},[],{"categories":1391},[83],{"categories":1393},[86],{"categories":1395},[],{"categories":1397},[],{"categories":1399},[],{"categories":1401},[86],{"categories":1403},[],{"categories":1405},[243],{"categories":1407},[86],{"categories":1409},[],{"categories":1411},[86],{"categories":1413},[86],{"categories":1415},[86],{"categories":1417},[86,243],{"categories":1419},[86],{"categories":1421},[86],{"categories":1423},[128],{"categories":1425},[89],{"categories":1427},[],{"categories":1429},[89],{"categories":1431},[86],{"categories":1433},[86],{"categories":1435},[86],{"categories":1437},[80],{"categories":1439},[80],{"categories":1441},[47],{"categories":1443},[128],{"categories":1445},[89],{"categories":1447},[],{"categories":1449},[86],{"categories":1451},[107],{"categories":1453},[86],{"categories":1455},[83],{"categories":1457},[],{"categories":1459},[243],{"categories":1461},[128],{"categories":1463},[128],{"categories":1465},[89],{"categories":1467},[107],{"categories":1469},[89],{"categories":1471},[86],{"categories":1473},[],{"categories":1475},[86],{"categories":1477},[],{"categories":1479},[],{"categories":1481},[86],{"categories":1483},[86],{"categories":1485},[86],{"categories":1487},[89],{"categories":1489},[86],{"categories":1491},[],{"categories":1493},[131],{"categories":1495},[89],{"categories":1497},[],{"categories":1499},[],{"categories":1501},[86],{"categories":1503},[107],{"categories":1505},[],{"categories":1507},[128],{"categories":1509},[243],{"categories":1511},[107],{"categories":1513},[47],{"categories":1515},[47],{"categories":1517},[107],{"categories":1519},[107],{"categories":1521},[243],{"categories":1523},[],{"categories":1525},[107],{"categories":1527},[86],{"categories":1529},[80],{"categories":1531},[107],{"categories":1533},[],{"categories":1535},[131],{"categories":1537},[107],{"categories":1539},[47],{"categories":1541},[107],{"categories":1543},[243],{"categories":1545},[86],{"categories":1547},[86],{"categories":1549},[],{"categories":1551},[83],{"categories":1553},[],{"categories":1555},[],{"categories":1557},[86],{"categories":1559},[86],{"categories":1561},[86],{"categories":1563},[86],{"categories":1565},[],{"categories":1567},[131],{"categories":1569},[80],{"categories":1571},[],{"categories":1573},[86],{"categories":1575},[86],{"categories":1577},[243],{"categories":1579},[243],{"categories":1581},[],{"categories":1583},[89],{"categories":1585},[107],{"categories":1587},[107],{"categories":1589},[86],{"categories":1591},[89],{"categories":1593},[],{"categories":1595},[128],{"categories":1597},[86],{"categories":1599},[86],{"categories":1601},[],{"categories":1603},[],{"categories":1605},[243],{"categories":1607},[86],{"categories":1609},[47],{"categories":1611},[83],{"categories":1613},[86],{"categories":1615},[],{"categories":1617},[89],{"categories":1619},[80],{"categories":1621},[80],{"categories":1623},[],{"categories":1625},[86],{"categories":1627},[128],{"categories":1629},[89],{"categories":1631},[],{"categories":1633},[86],{"categories":1635},[86],{"categories":1637},[89],{"categories":1639},[],{"categories":1641},[89],{"categories":1643},[47],{"categories":1645},[],{"categories":1647},[86],{"categories":1649},[],{"categories":1651},[86],{"categories":1653},[],{"categories":1655},[86],{"categories":1657},[86],{"categories":1659},[],{"categories":1661},[86],{"categories":1663},[107],{"categories":1665},[86],{"categories":1667},[86],{"categories":1669},[80],{"categories":1671},[86],{"categories":1673},[107],{"categories":1675},[89],{"categories":1677},[],{"categories":1679},[86],{"categories":1681},[144],{"categories":1683},[],{"categories":1685},[],{"categories":1687},[],{"categories":1689},[80],{"categories":1691},[107],{"categories":1693},[89],{"categories":1695},[86],{"categories":1697},[128],{"categories":1699},[89],{"categories":1701},[],{"categories":1703},[89],{"categories":1705},[],{"categories":1707},[86],{"categories":1709},[89],{"categories":1711},[86],{"categories":1713},[],{"categories":1715},[86],{"categories":1717},[86],{"categories":1719},[107],{"categories":1721},[128],{"categories":1723},[89],{"categories":1725},[128],{"categories":1727},[83],{"categories":1729},[],{"categories":1731},[],{"categories":1733},[86],{"categories":1735},[80],{"categories":1737},[107],{"categories":1739},[],{"categories":1741},[],{"categories":1743},[47],{"categories":1745},[128],{"categories":1747},[],{"categories":1749},[86],{"categories":1751},[],{"categories":1753},[144],{"categories":1755},[86],{"categories":1757},[243],{"categories":1759},[47],{"categories":1761},[],{"categories":1763},[89],{"categories":1765},[86],{"categories":1767},[89],{"categories":1769},[89],{"categories":1771},[86],{"categories":1773},[],{"categories":1775},[80],{"categories":1777},[86],{"categories":1779},[83],{"categories":1781},[47],{"categories":1783},[128],{"categories":1785},[],{"categories":1787},[],{"categories":1789},[],{"categories":1791},[89],{"categories":1793},[128],{"categories":1795},[107],{"categories":1797},[86],{"categories":1799},[107],{"categories":1801},[128],{"categories":1803},[],{"categories":1805},[128],{"categories":1807},[107],{"categories":1809},[83],{"categories":1811},[86],{"categories":1813},[107],{"categories":1815},[144],{"categories":1817},[],{"categories":1819},[],{"categories":1821},[131],{"categories":1823},[86,47],{"categories":1825},[107],{"categories":1827},[86],{"categories":1829},[89],{"categories":1831},[89],{"categories":1833},[86],{"categories":1835},[],{"categories":1837},[47],{"categories":1839},[86],{"categories":1841},[131],{"categories":1843},[89],{"categories":1845},[144],{"categories":1847},[243],{"categories":1849},[],{"categories":1851},[80],{"categories":1853},[89],{"categories":1855},[89],{"categories":1857},[47],{"categories":1859},[86],{"categories":1861},[86],{"categories":1863},[],{"categories":1865},[],{"categories":1867},[],{"categories":1869},[243],{"categories":1871},[107],{"categories":1873},[86],{"categories":1875},[86],{"categories":1877},[86],{"categories":1879},[],{"categories":1881},[131],{"categories":1883},[83],{"categories":1885},[],{"categories":1887},[89],{"categories":1889},[243],{"categories":1891},[],{"categories":1893},[128],{"categories":1895},[128],{"categories":1897},[],{"categories":1899},[47],{"categories":1901},[128],{"categories":1903},[86],{"categories":1905},[],{"categories":1907},[107],{"categories":1909},[86],{"categories":1911},[128],{"categories":1913},[89],{"categories":1915},[107],{"categories":1917},[],{"categories":1919},[89],{"categories":1921},[128],{"categories":1923},[86],{"categories":1925},[],{"categories":1927},[86],{"categories":1929},[86],{"categories":1931},[243],{"categories":1933},[107],{"categories":1935},[131],{"categories":1937},[131],{"categories":1939},[],{"categories":1941},[],{"categories":1943},[],{"categories":1945},[89],{"categories":1947},[47],{"categories":1949},[47],{"categories":1951},[],{"categories":1953},[],{"categories":1955},[86],{"categories":1957},[],{"categories":1959},[89],{"categories":1961},[86],{"categories":1963},[],{"categories":1965},[86],{"categories":1967},[83],{"categories":1969},[86],{"categories":1971},[144],{"categories":1973},[89],{"categories":1975},[86],{"categories":1977},[47],{"categories":1979},[],{"categories":1981},[107],{"categories":1983},[89],{"categories":1985},[],{"categories":1987},[107],{"categories":1989},[89],{"categories":1991},[89],{"categories":1993},[],{"categories":1995},[83],{"categories":1997},[89],{"categories":1999},[],{"categories":2001},[86],{"categories":2003},[80],{"categories":2005},[107],{"categories":2007},[243],{"categories":2009},[89],{"categories":2011},[89],{"categories":2013},[80],{"categories":2015},[86],{"categories":2017},[],{"categories":2019},[],{"categories":2021},[128],{"categories":2023},[86,83],{"categories":2025},[],{"categories":2027},[80],{"categories":2029},[131],{"categories":2031},[86],{"categories":2033},[47],{"categories":2035},[86],{"categories":2037},[89],{"categories":2039},[86],{"categories":2041},[86],{"categories":2043},[107],{"categories":2045},[89],{"categories":2047},[],{"categories":2049},[],{"categories":2051},[89],{"categories":2053},[86],{"categories":2055},[243],{"categories":2057},[],{"categories":2059},[86],{"categories":2061},[89],{"categories":2063},[],{"categories":2065},[86],{"categories":2067},[144],{"categories":2069},[131],{"categories":2071},[89],{"categories":2073},[86],{"categories":2075},[243],{"categories":2077},[],{"categories":2079},[86],{"categories":2081},[144],{"categories":2083},[128],{"categories":2085},[86],{"categories":2087},[],{"categories":2089},[144],{"categories":2091},[107],{"categories":2093},[86],{"categories":2095},[86],{"categories":2097},[80],{"categories":2099},[],{"categories":2101},[],{"categories":2103},[128],{"categories":2105},[86],{"categories":2107},[131],{"categories":2109},[144],{"categories":2111},[144],{"categories":2113},[107],{"categories":2115},[],{"categories":2117},[],{"categories":2119},[86],{"categories":2121},[],{"categories":2123},[86,47],{"categories":2125},[107],{"categories":2127},[89],{"categories":2129},[47],{"categories":2131},[86],{"categories":2133},[80],{"categories":2135},[],{"categories":2137},[],{"categories":2139},[80],{"categories":2141},[144],{"categories":2143},[86],{"categories":2145},[],{"categories":2147},[128,86],{"categories":2149},[243],{"categories":2151},[80],{"categories":2153},[],{"categories":2155},[83],{"categories":2157},[83],{"categories":2159},[86],{"categories":2161},[47],{"categories":2163},[89],{"categories":2165},[107],{"categories":2167},[144],{"categories":2169},[128],{"categories":2171},[86],{"categories":2173},[86],{"categories":2175},[86],{"categories":2177},[80],{"categories":2179},[86],{"categories":2181},[89],{"categories":2183},[107],{"categories":2185},[],{"categories":2187},[],{"categories":2189},[131],{"categories":2191},[47],{"categories":2193},[86],{"categories":2195},[128],{"categories":2197},[131],{"categories":2199},[86],{"categories":2201},[86],{"categories":2203},[89],{"categories":2205},[89],{"categories":2207},[86,83],{"categories":2209},[],{"categories":2211},[128],{"categories":2213},[],{"categories":2215},[86],{"categories":2217},[107],{"categories":2219},[80],{"categories":2221},[80],{"categories":2223},[89],{"categories":2225},[86],{"categories":2227},[83],{"categories":2229},[47],{"categories":2231},[144],{"categories":2233},[86],{"categories":2235},[],{"categories":2237},[107],{"categories":2239},[86],{"categories":2241},[86],{"categories":2243},[107],{"categories":2245},[47],{"categories":2247},[86],{"categories":2249},[89],{"categories":2251},[107],{"categories":2253},[86],{"categories":2255},[128],{"categories":2257},[86],{"categories":2259},[86],{"categories":2261},[243],{"categories":2263},[92],{"categories":2265},[89],{"categories":2267},[86],{"categories":2269},[107],{"categories":2271},[89],{"categories":2273},[144],{"categories":2275},[86],{"categories":2277},[],{"categories":2279},[86],{"categories":2281},[],{"categories":2283},[],{"categories":2285},[],{"categories":2287},[83],{"categories":2289},[86],{"categories":2291},[89],{"categories":2293},[107],{"categories":2295},[107],{"categories":2297},[107],{"categories":2299},[107],{"categories":2301},[],{"categories":2303},[80],{"categories":2305},[89],{"categories":2307},[107],{"categories":2309},[80],{"categories":2311},[89],{"categories":2313},[86],{"categories":2315},[86,89],{"categories":2317},[89],{"categories":2319},[243],{"categories":2321},[107],{"categories":2323},[107],{"categories":2325},[89],{"categories":2327},[86],{"categories":2329},[],{"categories":2331},[107],{"categories":2333},[144],{"categories":2335},[80],{"categories":2337},[86],{"categories":2339},[86],{"categories":2341},[],{"categories":2343},[47],{"categories":2345},[],{"categories":2347},[80],{"categories":2349},[89],{"categories":2351},[107],{"categories":2353},[86],{"categories":2355},[107],{"categories":2357},[80],{"categories":2359},[107],{"categories":2361},[107],{"categories":2363},[],{"categories":2365},[83],{"categories":2367},[89],{"categories":2369},[107],{"categories":2371},[107],{"categories":2373},[107],{"categories":2375},[107],{"categories":2377},[107],{"categories":2379},[107],{"categories":2381},[107],{"categories":2383},[107],{"categories":2385},[107],{"categories":2387},[107],{"categories":2389},[131],{"categories":2391},[80],{"categories":2393},[86],{"categories":2395},[86],{"categories":2397},[],{"categories":2399},[86,80],{"categories":2401},[],{"categories":2403},[89],{"categories":2405},[107],{"categories":2407},[89],{"categories":2409},[86],{"categories":2411},[86],{"categories":2413},[86],{"categories":2415},[86],{"categories":2417},[86],{"categories":2419},[89],{"categories":2421},[83],{"categories":2423},[128],{"categories":2425},[107],{"categories":2427},[86],{"categories":2429},[],{"categories":2431},[],{"categories":2433},[89],{"categories":2435},[128],{"categories":2437},[86],{"categories":2439},[],{"categories":2441},[],{"categories":2443},[144],{"categories":2445},[86],{"categories":2447},[],{"categories":2449},[],{"categories":2451},[80],{"categories":2453},[83],{"categories":2455},[86],{"categories":2457},[83],{"categories":2459},[128],{"categories":2461},[],{"categories":2463},[107],{"categories":2465},[],{"categories":2467},[128],{"categories":2469},[86],{"categories":2471},[144],{"categories":2473},[],{"categories":2475},[144],{"categories":2477},[],{"categories":2479},[],{"categories":2481},[89],{"categories":2483},[],{"categories":2485},[83],{"categories":2487},[80],{"categories":2489},[128],{"categories":2491},[47],{"categories":2493},[],{"categories":2495},[],{"categories":2497},[86],{"categories":2499},[80],{"categories":2501},[144],{"categories":2503},[],{"categories":2505},[89],{"categories":2507},[89],{"categories":2509},[107],{"categories":2511},[86],{"categories":2513},[89],{"categories":2515},[86],{"categories":2517},[89],{"categories":2519},[86],{"categories":2521},[92],{"categories":2523},[107],{"categories":2525},[],{"categories":2527},[144],{"categories":2529},[47],{"categories":2531},[89],{"categories":2533},[],{"categories":2535},[86],{"categories":2537},[89],{"categories":2539},[83],{"categories":2541},[80],{"categories":2543},[86],{"categories":2545},[128],{"categories":2547},[47],{"categories":2549},[47],{"categories":2551},[86],{"categories":2553},[131],{"categories":2555},[86],{"categories":2557},[89],{"categories":2559},[83],{"categories":2561},[89],{"categories":2563},[86],{"categories":2565},[86],{"categories":2567},[89],{"categories":2569},[107],{"categories":2571},[],{"categories":2573},[80],{"categories":2575},[86],{"categories":2577},[89],{"categories":2579},[86],{"categories":2581},[86],{"categories":2583},[],{"categories":2585},[128],{"categories":2587},[83],{"categories":2589},[107],{"categories":2591},[86],{"categories":2593},[86],{"categories":2595},[128],{"categories":2597},[144],{"categories":2599},[131],{"categories":2601},[86],{"categories":2603},[107],{"categories":2605},[86],{"categories":2607},[89],{"categories":2609},[243],{"categories":2611},[86],{"categories":2613},[89],{"categories":2615},[131],{"categories":2617},[],{"categories":2619},[89],{"categories":2621},[47],{"categories":2623},[128],{"categories":2625},[86],{"categories":2627},[80],{"categories":2629},[83],{"categories":2631},[47],{"categories":2633},[86],{"categories":2635},[],{"categories":2637},[89],{"categories":2639},[86],{"categories":2641},[],{"categories":2643},[107],{"categories":2645},[],{"categories":2647},[107],{"categories":2649},[86],{"categories":2651},[89],{"categories":2653},[89],{"categories":2655},[89],{"categories":2657},[],{"categories":2659},[],{"categories":2661},[86],{"categories":2663},[86],{"categories":2665},[],{"categories":2667},[128],{"categories":2669},[89],{"categories":2671},[144],{"categories":2673},[80],{"categories":2675},[],{"categories":2677},[],{"categories":2679},[107],{"categories":2681},[47],{"categories":2683},[86],{"categories":2685},[86],{"categories":2687},[86],{"categories":2689},[47],{"categories":2691},[107],{"categories":2693},[128],{"categories":2695},[86],{"categories":2697},[86],{"categories":2699},[86],{"categories":2701},[107],{"categories":2703},[86],{"categories":2705},[107],{"categories":2707},[107],{"categories":2709},[89],{"categories":2711},[89],{"categories":2713},[47],{"categories":2715},[89],{"categories":2717},[86],{"categories":2719},[47],{"categories":2721},[128],{"categories":2723},[],{"categories":2725},[89],{"categories":2727},[],{"categories":2729},[],{"categories":2731},[],{"categories":2733},[83],{"categories":2735},[86],{"categories":2737},[89],{"categories":2739},[80],{"categories":2741},[89],{"categories":2743},[144],{"categories":2745},[],{"categories":2747},[89],{"categories":2749},[],{"categories":2751},[80],{"categories":2753},[89],{"categories":2755},[],{"categories":2757},[89],{"categories":2759},[86],{"categories":2761},[107],{"categories":2763},[86],{"categories":2765},[89],{"categories":2767},[107],{"categories":2769},[89],{"categories":2771},[47],{"categories":2773},[128],{"categories":2775},[80],{"categories":2777},[],{"categories":2779},[89],{"categories":2781},[128],{"categories":2783},[243],{"categories":2785},[107],{"categories":2787},[86],{"categories":2789},[128],{"categories":2791},[80],{"categories":2793},[],{"categories":2795},[89],{"categories":2797},[89],{"categories":2799},[86],{"categories":2801},[],{"categories":2803},[89],{"categories":2805},[92],{"categories":2807},[107],{"categories":2809},[89],{"categories":2811},[83],{"categories":2813},[],{"categories":2815},[86],{"categories":2817},[92],{"categories":2819},[86],{"categories":2821},[89],{"categories":2823},[107],{"categories":2825},[80],{"categories":2827},[243],{"categories":2829},[86],{"categories":2831},[86],{"categories":2833},[86],{"categories":2835},[107],{"categories":2837},[83],{"categories":2839},[86],{"categories":2841},[128],{"categories":2843},[107],{"categories":2845},[243],{"categories":2847},[86],{"categories":2849},[],{"categories":2851},[],{"categories":2853},[243],{"categories":2855},[131],{"categories":2857},[89],{"categories":2859},[89],{"categories":2861},[107],{"categories":2863},[86],{"categories":2865},[80],{"categories":2867},[128],{"categories":2869},[89],{"categories":2871},[86],{"categories":2873},[144],{"categories":2875},[86],{"categories":2877},[89],{"categories":2879},[],{"categories":2881},[86],{"categories":2883},[86],{"categories":2885},[107],{"categories":2887},[80],{"categories":2889},[],{"categories":2891},[86],{"categories":2893},[86],{"categories":2895},[47],{"categories":2897},[128],{"categories":2899},[86,89],{"categories":2901},[144,83],{"categories":2903},[86],{"categories":2905},[],{"categories":2907},[89],{"categories":2909},[],{"categories":2911},[47],{"categories":2913},[],{"categories":2915},[86],{"categories":2917},[107],{"categories":2919},[],{"categories":2921},[89],{"categories":2923},[],{"categories":2925},[128],{"categories":2927},[89],{"categories":2929},[80],{"categories":2931},[89],{"categories":2933},[86],{"categories":2935},[243],{"categories":2937},[144],{"categories":2939},[83],{"categories":2941},[83],{"categories":2943},[80],{"categories":2945},[80],{"categories":2947},[86],{"categories":2949},[89],{"categories":2951},[86],{"categories":2953},[86],{"categories":2955},[80],{"categories":2957},[86],{"categories":2959},[144],{"categories":2961},[107],{"categories":2963},[86],{"categories":2965},[89],{"categories":2967},[86],{"categories":2969},[],{"categories":2971},[47],{"categories":2973},[],{"categories":2975},[89],{"categories":2977},[80],{"categories":2979},[],{"categories":2981},[243],{"categories":2983},[86],{"categories":2985},[],{"categories":2987},[107],{"categories":2989},[89],{"categories":2991},[47],{"categories":2993},[86],{"categories":2995},[89],{"categories":2997},[47],{"categories":2999},[89],{"categories":3001},[107],{"categories":3003},[80],{"categories":3005},[107],{"categories":3007},[47],{"categories":3009},[86],{"categories":3011},[128],{"categories":3013},[86],{"categories":3015},[86],{"categories":3017},[86],{"categories":3019},[86],{"categories":3021},[89],{"categories":3023},[86],{"categories":3025},[89],{"categories":3027},[86],{"categories":3029},[80],{"categories":3031},[86],{"categories":3033},[89],{"categories":3035},[128],{"categories":3037},[80],{"categories":3039},[89],{"categories":3041},[128],{"categories":3043},[],{"categories":3045},[86],{"categories":3047},[86],{"categories":3049},[47],{"categories":3051},[],{"categories":3053},[89],{"categories":3055},[144],{"categories":3057},[86],{"categories":3059},[107],{"categories":3061},[144],{"categories":3063},[89],{"categories":3065},[83],{"categories":3067},[83],{"categories":3069},[86],{"categories":3071},[80],{"categories":3073},[],{"categories":3075},[86],{"categories":3077},[],{"categories":3079},[80],{"categories":3081},[86],{"categories":3083},[89],{"categories":3085},[89],{"categories":3087},[],{"categories":3089},[47],{"categories":3091},[47],{"categories":3093},[144],{"categories":3095},[128],{"categories":3097},[],{"categories":3099},[86],{"categories":3101},[80],{"categories":3103},[86],{"categories":3105},[47],{"categories":3107},[80],{"categories":3109},[107],{"categories":3111},[107],{"categories":3113},[],{"categories":3115},[107],{"categories":3117},[89],{"categories":3119},[128],{"categories":3121},[131],{"categories":3123},[86],{"categories":3125},[],{"categories":3127},[107],{"categories":3129},[47],{"categories":3131},[83],{"categories":3133},[86],{"categories":3135},[80],{"categories":3137},[243],{"categories":3139},[80],{"categories":3141},[],{"categories":3143},[],{"categories":3145},[107],{"categories":3147},[],{"categories":3149},[89],{"categories":3151},[89],{"categories":3153},[89],{"categories":3155},[],{"categories":3157},[86],{"categories":3159},[],{"categories":3161},[107],{"categories":3163},[80],{"categories":3165},[128],{"categories":3167},[86],{"categories":3169},[107],{"categories":3171},[107],{"categories":3173},[],{"categories":3175},[107],{"categories":3177},[80],{"categories":3179},[86],{"categories":3181},[],{"categories":3183},[89],{"categories":3185},[89],{"categories":3187},[80],{"categories":3189},[],{"categories":3191},[],{"categories":3193},[],{"categories":3195},[128],{"categories":3197},[89],{"categories":3199},[86],{"categories":3201},[],{"categories":3203},[],{"categories":3205},[],{"categories":3207},[128],{"categories":3209},[],{"categories":3211},[80],{"categories":3213},[],{"categories":3215},[],{"categories":3217},[128],{"categories":3219},[86],{"categories":3221},[107],{"categories":3223},[],{"categories":3225},[144],{"categories":3227},[107],{"categories":3229},[144],{"categories":3231},[86],{"categories":3233},[],{"categories":3235},[],{"categories":3237},[89],{"categories":3239},[],{"categories":3241},[],{"categories":3243},[89],{"categories":3245},[86],{"categories":3247},[],{"categories":3249},[89],{"categories":3251},[107],{"categories":3253},[144],{"categories":3255},[131],{"categories":3257},[89],{"categories":3259},[89],{"categories":3261},[],{"categories":3263},[],{"categories":3265},[],{"categories":3267},[107],{"categories":3269},[],{"categories":3271},[],{"categories":3273},[128],{"categories":3275},[80],{"categories":3277},[],{"categories":3279},[83],{"categories":3281},[144],{"categories":3283},[86],{"categories":3285},[47],{"categories":3287},[80],{"categories":3289},[131],{"categories":3291},[83],{"categories":3293},[47],{"categories":3295},[],{"categories":3297},[],{"categories":3299},[89],{"categories":3301},[80],{"categories":3303},[128],{"categories":3305},[80],{"categories":3307},[89],{"categories":3309},[243],{"categories":3311},[80],{"categories":3313},[89],{"categories":3315},[],{"categories":3317},[86],{"categories":3319},[107],{"categories":3321},[47],{"categories":3323},[],{"categories":3325},[128],{"categories":3327},[107],{"categories":3329},[80],{"categories":3331},[89],{"categories":3333},[86],{"categories":3335},[83],{"categories":3337},[89,243],{"categories":3339},[89],{"categories":3341},[47],{"categories":3343},[86],{"categories":3345},[131],{"categories":3347},[144],{"categories":3349},[89],{"categories":3351},[],{"categories":3353},[89],{"categories":3355},[86],{"categories":3357},[83],{"categories":3359},[],{"categories":3361},[],{"categories":3363},[86],{"categories":3365},[131],{"categories":3367},[86],{"categories":3369},[],{"categories":3371},[107],{"categories":3373},[],{"categories":3375},[107],{"categories":3377},[47],{"categories":3379},[89],{"categories":3381},[86],{"categories":3383},[144],{"categories":3385},[47],{"categories":3387},[],{"categories":3389},[107],{"categories":3391},[86],{"categories":3393},[],{"categories":3395},[86],{"categories":3397},[89],{"categories":3399},[86],{"categories":3401},[89],{"categories":3403},[86],{"categories":3405},[86],{"categories":3407},[86],{"categories":3409},[86],{"categories":3411},[83],{"categories":3413},[],{"categories":3415},[92],{"categories":3417},[107],{"categories":3419},[86],{"categories":3421},[],{"categories":3423},[47],{"categories":3425},[86],{"categories":3427},[86],{"categories":3429},[89],{"categories":3431},[107],{"categories":3433},[86],{"categories":3435},[86],{"categories":3437},[83],{"categories":3439},[89],{"categories":3441},[128],{"categories":3443},[],{"categories":3445},[131],{"categories":3447},[86],{"categories":3449},[],{"categories":3451},[107],{"categories":3453},[144],{"categories":3455},[],{"categories":3457},[],{"categories":3459},[107],{"categories":3461},[107],{"categories":3463},[144],{"categories":3465},[80],{"categories":3467},[89],{"categories":3469},[89],{"categories":3471},[86],{"categories":3473},[83],{"categories":3475},[],{"categories":3477},[],{"categories":3479},[107],{"categories":3481},[131],{"categories":3483},[47],{"categories":3485},[89],{"categories":3487},[128],{"categories":3489},[131],{"categories":3491},[131],{"categories":3493},[],{"categories":3495},[107],{"categories":3497},[86],{"categories":3499},[86],{"categories":3501},[47],{"categories":3503},[],{"categories":3505},[107],{"categories":3507},[107],{"categories":3509},[107],{"categories":3511},[],{"categories":3513},[89],{"categories":3515},[86],{"categories":3517},[],{"categories":3519},[80],{"categories":3521},[83],{"categories":3523},[],{"categories":3525},[86],{"categories":3527},[86],{"categories":3529},[],{"categories":3531},[47],{"categories":3533},[],{"categories":3535},[],{"categories":3537},[],{"categories":3539},[],{"categories":3541},[86],{"categories":3543},[107],{"categories":3545},[],{"categories":3547},[],{"categories":3549},[86],{"categories":3551},[86],{"categories":3553},[86],{"categories":3555},[131],{"categories":3557},[86],{"categories":3559},[131],{"categories":3561},[],{"categories":3563},[131],{"categories":3565},[131],{"categories":3567},[243],{"categories":3569},[89],{"categories":3571},[47],{"categories":3573},[],{"categories":3575},[],{"categories":3577},[131],{"categories":3579},[47],{"categories":3581},[47],{"categories":3583},[47],{"categories":3585},[],{"categories":3587},[80],{"categories":3589},[47],{"categories":3591},[47],{"categories":3593},[80],{"categories":3595},[47],{"categories":3597},[83],{"categories":3599},[47],{"categories":3601},[47],{"categories":3603},[47],{"categories":3605},[131],{"categories":3607},[107],{"categories":3609},[107],{"categories":3611},[86],{"categories":3613},[47],{"categories":3615},[131],{"categories":3617},[243],{"categories":3619},[131],{"categories":3621},[131],{"categories":3623},[131],{"categories":3625},[],{"categories":3627},[83],{"categories":3629},[],{"categories":3631},[243],{"categories":3633},[47],{"categories":3635},[47],{"categories":3637},[47],{"categories":3639},[89],{"categories":3641},[107,83],{"categories":3643},[131],{"categories":3645},[],{"categories":3647},[],{"categories":3649},[131],{"categories":3651},[],{"categories":3653},[131],{"categories":3655},[107],{"categories":3657},[89],{"categories":3659},[],{"categories":3661},[47],{"categories":3663},[86],{"categories":3665},[128],{"categories":3667},[],{"categories":3669},[86],{"categories":3671},[],{"categories":3673},[107],{"categories":3675},[80],{"categories":3677},[131],{"categories":3679},[],{"categories":3681},[47],{"categories":3683},[107],[3685,3735,3966,4211],{"id":3686,"title":3687,"ai":3688,"body":3693,"categories":3721,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":3722,"navigation":58,"path":3723,"published_at":3724,"question":48,"scraped_at":48,"seo":3725,"sitemap":3726,"source_id":3727,"source_name":3728,"source_type":66,"source_url":3729,"stem":3730,"tags":3731,"thumbnail_url":48,"tldr":3732,"tweet":48,"unknown_tags":3733,"__hash__":3734},"summaries\u002Fsummaries\u002Fgenerate-videos-by-slerp-walking-stable-diffusion--summary.md","Generate Videos by Slerp-Walking Stable Diffusion Latents",{"provider":7,"model":8,"input_tokens":3689,"output_tokens":3690,"processing_time_ms":3691,"cost_usd":3692},10775,1430,16123,0.00284735,{"type":14,"value":3694,"toc":3716},[3695,3699,3702,3706,3709,3713],[17,3696,3698],{"id":3697},"latent-space-walking-creates-hypnotic-videos","Latent Space Walking Creates Hypnotic Videos",[22,3700,3701],{},"Sample two random latents (shape 1x4x64x64 for 512x512 images), then use spherical linear interpolation (slerp) across 200 steps from init1 to init2. For each interpolated latent, run diffusion conditioned on a fixed text prompt (e.g., \"blueberry spaghetti\") with classifier-free guidance: concatenate unconditional and conditional embeddings, predict noise with UNet, apply guidance_scale=7.5, and denoise over num_inference_steps=50 using LMSDiscreteScheduler. Decode final latents via VAE to produce one frame per step. Repeat pairs up to max_frames=10000, saving JPEGs at 90% quality. Stitch with ffmpeg -r 10 -f image2 -s 512x512 -i frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p output.mp4. This random walk yields surreal, morphing visuals without prompt changes.",[17,3703,3705],{"id":3704},"custom-diffuse-handles-guidance-and-schedulers","Custom Diffuse Handles Guidance and Schedulers",[22,3707,3708],{},"Bypass pipeline for fine control: compute unconditional embeddings from empty prompt, cat with conditional (1x77x768). Set timesteps with offset=1 if supported, eta=0.0 for DDIM compatibility. For each timestep, double latents for CFG, predict noise_pred, scale as uncond + guidance_scale*(text - uncond), step scheduler to prev_sample. Scale latents by 1\u002F0.18215 before VAE decode, clamp\u002Fpost-process to uint8 numpy. Supports LMSDiscreteScheduler (multiplies latents by sigmas initially, divides model input by sqrt(sigma^2 +1)). Slerp avoids straight-line artifacts in high-D latent space using arccos(dot) for theta, blending with sin terms if dot \u003C 0.9995.",[17,3710,3712],{"id":3711},"setup-params-and-optimizations","Setup, Params, and Optimizations",[22,3714,3715],{},"Requires Hugging Face access token for CompVis\u002Fstable-diffusion-v1-3-diffusers (or v1-4), diffusers library, torch, einops, PIL, fire (pip install fire), ~10GB VRAM for 512x512. Run: python stablediffusionwalk.py --prompt \"blueberry spaghetti\" --name outdir --num_steps 200 --num_inference_steps 50 --guidance_scale 7.5 --seed 1337 --max_frames 10000. Wrap diffuse in torch.autocast('cuda') for half-precision speedup. Higher inference steps (100-200) improve quality; guidance 3-10 tunes adherence. Users extended to prompt interpolation, fp16 models (fix dtype mismatches by upgrading diffusers\u002Ftransformers\u002Fscipy), or pipeline simplifications (pipe(prompt, latents=init, ...)).",{"title":40,"searchDepth":41,"depth":41,"links":3717},[3718,3719,3720],{"id":3697,"depth":41,"text":3698},{"id":3704,"depth":41,"text":3705},{"id":3711,"depth":41,"text":3712},[47],{},"\u002Fsummaries\u002Fgenerate-videos-by-slerp-walking-stable-diffusion-summary","2026-04-08 21:21:20",{"title":3687,"description":40},{"loc":3723},"9fd1fce56d7f77a1","Andrej Karpathy Gists","https:\u002F\u002Funknown","summaries\u002Fgenerate-videos-by-slerp-walking-stable-diffusion--summary",[70,73,72],"Interpolate random latents with slerp under a fixed prompt to create smooth, hypnotic videos from Stable Diffusion frames (50 inference steps, 7.5 guidance, 200 steps per pair).",[],"VddoAG9zJ0Akb8dH2o3dDgU_wO7ggV90n9VzfWlSvPE",{"id":3736,"title":3737,"ai":3738,"body":3743,"categories":3938,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":3939,"navigation":58,"path":3953,"published_at":3954,"question":48,"scraped_at":3955,"seo":3956,"sitemap":3957,"source_id":3958,"source_name":3959,"source_type":66,"source_url":3960,"stem":3961,"tags":3962,"thumbnail_url":48,"tldr":3963,"tweet":48,"unknown_tags":3964,"__hash__":3965},"summaries\u002Fsummaries\u002Fff126f8e0954389e-skfolio-build-tune-portfolio-optimizers-in-python-summary.md","skfolio: Build & Tune Portfolio Optimizers in Python",{"provider":7,"model":8,"input_tokens":3739,"output_tokens":3740,"processing_time_ms":3741,"cost_usd":3742},9292,2519,30098,0.00309525,{"type":14,"value":3744,"toc":3932},[3745,3749,3781,3785,3834,3838,3903,3907],[17,3746,3748],{"id":3747},"data-prep-and-baseline-benchmarks-deliver-quick-wins","Data Prep and Baseline Benchmarks Deliver Quick Wins",[22,3750,3751,3752,3756,3757,3760,3761,3764,3765,3768,3769,3772,3773,3776,3777,3780],{},"Load S&P 500 prices via ",[3753,3754,3755],"code",{},"skfolio.datasets.load_sp500_dataset()",", convert to returns with ",[3753,3758,3759],{},"prices_to_returns()",", and split chronologically (",[3753,3762,3763],{},"train_test_split(shuffle=False, test_size=0.33)",") to prevent look-ahead bias—training spans ~67% historical days, testing the rest. Baselines like ",[3753,3766,3767],{},"EqualWeighted()",", ",[3753,3770,3771],{},"InverseVolatility()",", and ",[3753,3774,3775],{},"Random()"," fit on train, predict on test, yielding metrics like annualized Sharpe (printed via ",[3753,3778,3779],{},"ptf.annualized_sharpe_ratio","), mean return, and volatility. These expose naive strategies' flaws: equal-weight ignores volatility, random adds noise—use them to benchmark any optimizer.",[17,3782,3784],{"id":3783},"mean-variance-risk-measures-and-clustering-beat-baselines","Mean-Variance, Risk Measures, and Clustering Beat Baselines",[22,3786,3787,3790,3791,3794,3795,3798,3799,3802,3803,3768,3806,3809,3810,3813,3814,3817,3818,3821,3822,3825,3826,3829,3830,3833],{},[3753,3788,3789],{},"MeanRisk(risk_measure=RiskMeasure.VARIANCE)"," minimizes variance or maximizes Sharpe (",[3753,3792,3793],{},"ObjectiveFunction.MAXIMIZE_RATIO","), generating efficient frontiers (",[3753,3796,3797],{},"efficient_frontier_size=20",") plotted by risk vs. Sharpe. Swap risks to ",[3753,3800,3801],{},"CVaR"," (95%), ",[3753,3804,3805],{},"SEMI_VARIANCE",[3753,3807,3808],{},"CDAR",", or ",[3753,3811,3812],{},"MAX_DRAWDOWN"," for tail-focused portfolios that cut CVaR@95% and max drawdown vs. variance. ",[3753,3815,3816],{},"RiskBudgeting()"," equalizes contributions (variance or CVaR). Hierarchical methods shine: ",[3753,3819,3820],{},"HierarchicalRiskParity()"," clusters assets via dendrograms for stable weights; ",[3753,3823,3824],{},"NestedClustersOptimization()"," nests ",[3753,3827,3828],{},"MeanRisk(CVAR)"," inside ",[3753,3831,3832],{},"RiskBudgeting(VARIANCE)"," with 5-fold CV, capturing correlations without covariance pitfalls.",[17,3835,3837],{"id":3836},"robust-priors-constraints-and-views-stabilize-real-world-use","Robust Priors, Constraints, and Views Stabilize Real-World Use",[22,3839,3840,3841,3844,3845,3848,3849,3768,3852,3768,3855,3809,3858,3861,3862,3865,3866,3768,3869,3768,3872,3768,3875,3878,3879,3882,3883,3886,3887,3890,3891,3894,3895,3898,3899,3902],{},"Replace ",[3753,3842,3843],{},"EmpiricalCovariance()","\u002F",[3753,3846,3847],{},"EmpiricalMu()"," with ",[3753,3850,3851],{},"DenoiseCovariance()",[3753,3853,3854],{},"ShrunkMu()",[3753,3856,3857],{},"GerberCovariance()",[3753,3859,3860],{},"EWMu(alpha=0.1)"," in ",[3753,3863,3864],{},"EmpiricalPrior()"," for max-Sharpe portfolios resilient to estimation error. Add realism via ",[3753,3867,3868],{},"min_weights=0.0",[3753,3870,3871],{},"max_weights=0.20",[3753,3873,3874],{},"transaction_costs=0.0005",[3753,3876,3877],{},"groups"," (e.g., GroupA \u003C=0.6, GroupB>=0.2), ",[3753,3880,3881],{},"l2_coef=0.01",". ",[3753,3884,3885],{},"BlackLitterman(views=[\"AAPL == 0.0008\", \"JPM - BAC == 0.0002\"])"," blends market priors with views. ",[3753,3888,3889],{},"FactorModel()"," on ",[3753,3892,3893],{},"load_factors_dataset()"," explains returns via external factors, boosting Sharpe. Pipelines like ",[3753,3896,3897],{},"SelectKExtremes(k=8)"," + ",[3753,3900,3901],{},"MeanRisk()"," prune to top performers.",[17,3904,3906],{"id":3905},"walk-forward-cv-and-tuning-ensure-out-of-sample-performance","Walk-Forward CV and Tuning Ensure Out-of-Sample Performance",[22,3908,3909,3848,3912,3915,3916,3919,3920,3923,3924,3927,3928,3931],{},[3753,3910,3911],{},"cross_val_predict()",[3753,3913,3914],{},"WalkForward(train_size=252*2, test_size=63)"," simulates rolling 2-year trains\u002F3-month tests, computing portfolio Sharpe\u002FCalmar. ",[3753,3917,3918],{},"GridSearchCV()"," tunes ",[3753,3921,3922],{},"l2_coef=[0.0,0.01,0.1]"," and ",[3753,3925,3926],{},"mu_estimator__alpha=[0.05,0.1,0.2,0.5]"," on max-Sharpe, selecting best CV Sharpe. Final ",[3753,3929,3930],{},"Population()"," of 18 strategies compares annualized mean\u002Fvol\u002FSharpe\u002FSortino\u002FCVaR@95%\u002Fdrawdowns (sorted by test Sharpe), with plots for cumulative returns, weights, risk contributions—revealing hierarchical\u002Frisk-parity often top variance-based in stability.",{"title":40,"searchDepth":41,"depth":41,"links":3933},[3934,3935,3936,3937],{"id":3747,"depth":41,"text":3748},{"id":3783,"depth":41,"text":3784},{"id":3836,"depth":41,"text":3837},{"id":3905,"depth":41,"text":3906},[131],{"content_references":3940,"triage":3950},[3941,3946],{"type":3942,"title":3943,"url":3944,"context":3945},"tool","skfolio","https:\u002F\u002Fgithub.com\u002Fskfolio\u002Fskfolio","mentioned",{"type":3947,"title":3948,"url":3949,"context":3945},"other","Full Codes","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FData%20Science\u002Fportfolio_optimization_with_skfolio_Marktechpost.ipynb",{"relevance":55,"novelty":55,"quality":54,"actionability":54,"composite":3951,"reasoning":3952},3.45,"Category: Data Science & Visualization. The article provides a practical guide on using the skfolio library for portfolio optimization, which aligns with the audience's interest in actionable AI and data science tools. It includes specific code examples and methodologies that can be directly applied, making it useful for developers looking to implement AI in financial products.","\u002Fsummaries\u002Fff126f8e0954389e-skfolio-build-tune-portfolio-optimizers-in-python-summary","2026-05-12 07:05:02","2026-05-12 15:01:25",{"title":3737,"description":40},{"loc":3953},"ff126f8e0954389e","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F12\u002Fa-coding-implementation-to-portfolio-optimization-with-skfolio-for-building-testing-tuning-and-comparing-modern-investment-strategies\u002F","summaries\u002Fff126f8e0954389e-skfolio-build-tune-portfolio-optimizers-in-python-summary",[70,71,72],"skfolio's scikit-learn API lets you construct, validate, and compare 18+ portfolio strategies—from baselines to HRP, Black-Litterman, factors, and tuned models—on S&P 500 returns with walk-forward CV and GridSearchCV.",[],"s9QUFNF_HWzNZV61Dh6PEETN3C3-K3FsZalb0rd3HRQ",{"id":3967,"title":3968,"ai":3969,"body":3974,"categories":4181,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":4182,"navigation":58,"path":4199,"published_at":4200,"question":48,"scraped_at":4201,"seo":4202,"sitemap":4203,"source_id":4204,"source_name":3959,"source_type":66,"source_url":4205,"stem":4206,"tags":4207,"thumbnail_url":48,"tldr":4208,"tweet":48,"unknown_tags":4209,"__hash__":4210},"summaries\u002Fsummaries\u002Fa59df2d47dafe018-scanpy-pipeline-for-pbmc-scrna-seq-clustering-traj-summary.md","Scanpy Pipeline for PBMC scRNA-seq Clustering & Trajectories",{"provider":7,"model":8,"input_tokens":3970,"output_tokens":3971,"processing_time_ms":3972,"cost_usd":3973},9209,2235,26831,0.0029368,{"type":14,"value":3975,"toc":4175},[3976,3980,4012,4038,4042,4065,4081,4085,4108,4126,4130,4161],[17,3977,3979],{"id":3978},"rigorous-qc-and-filtering-removes-noise-for-reliable-downstream-analysis","Rigorous QC and Filtering Removes Noise for Reliable Downstream Analysis",[22,3981,3982,3983,3986,3987,3990,3991,3994,3995,3998,3999,4002,4003,3768,4006,3768,4009,4011],{},"Load PBMC-3k via ",[3753,3984,3985],{},"sc.datasets.pbmc3k()"," (2700 cells, ~2k genes\u002Fcell). Compute QC metrics for mitochondrial (",[3753,3988,3989],{},"MT-"," prefix, filter \u003C5% ",[3753,3992,3993],{},"pct_counts_mt",") and ribosomal (",[3753,3996,3997],{},"RPS\u002FRPL",") genes using ",[3753,4000,4001],{},"sc.pp.calculate_qc_metrics",". Visualize with violin plots (",[3753,4004,4005],{},"n_genes_by_counts",[3753,4007,4008],{},"total_counts",[3753,4010,3993],{},") and scatters to spot outliers.",[22,4013,4014,4015,3768,4018,4021,4022,4025,4026,4029,4030,4033,4034,4037],{},"Filter: ",[3753,4016,4017],{},"min_genes=200",[3753,4019,4020],{},"min_cells=3",", upper ",[3753,4023,4024],{},"n_genes_by_counts \u003C2500",". Detect doublets via ",[3753,4027,4028],{},"sc.pp.scrublet"," (removes ~sum of ",[3753,4031,4032],{},"predicted_doublet","). Preserve raw in ",[3753,4035,4036],{},"layers[\"counts\"]",". This yields cleaner data, preventing artifacts in clustering.",[17,4039,4041],{"id":4040},"normalization-hvgs-and-cell-cycle-correction-focus-on-biological-signal","Normalization, HVGs, and Cell-Cycle Correction Focus on Biological Signal",[22,4043,4044,4045,4048,4049,4052,4053,4056,4057,4060,4061,4064],{},"Normalize to 10k counts (",[3753,4046,4047],{},"sc.pp.normalize_total(target_sum=1e4)","), log-transform (",[3753,4050,4051],{},"sc.pp.log1p","). Identify highly variable genes (",[3753,4054,4055],{},"sc.pp.highly_variable_genes(min_mean=0.0125, max_mean=3, min_disp=0.5)","), subset to them (",[3753,4058,4059],{},"adata = adata[:, adata.var.highly_variable]","). Store raw in ",[3753,4062,4063],{},"adata.raw",".",[22,4066,4067,4068,3768,4070,4072,4073,4076,4077,4080],{},"Score S\u002FG2M phases with 40+ predefined markers (e.g., S: MCM5,PCNA; G2M: HMGB2,CDK1, filter to dataset genes). Regress out ",[3753,4069,4008],{},[3753,4071,3993],{}," (",[3753,4074,4075],{},"sc.pp.regress_out","). Scale (",[3753,4078,4079],{},"sc.pp.scale(max_value=10)","). These steps isolate biological variance, regressing technical noise for accurate modeling.",[17,4082,4084],{"id":4083},"dimensionality-reduction-leiden-clustering-and-marker-based-annotation-reveals-cell-types","Dimensionality Reduction, Leiden Clustering, and Marker-Based Annotation Reveals Cell Types",[22,4086,4087,4088,4091,4092,4095,4096,4099,4100,4103,4104,4107],{},"PCA (",[3753,4089,4090],{},"sc.tl.pca(svd_solver=\"arpack\")",", check ",[3753,4093,4094],{},"n_pcs=50"," variance). Neighbors (",[3753,4097,4098],{},"sc.pp.neighbors(n_neighbors=10, n_pcs=40)","). Embeddings: UMAP (",[3753,4101,4102],{},"sc.tl.umap","), t-SNE (",[3753,4105,4106],{},"sc.tl.tsne(n_pcs=40)",").",[22,4109,4110,4111,4114,4115,4118,4119,3768,4122,4125],{},"Cluster with Leiden (",[3753,4112,4113],{},"sc.tl.leiden(resolution=0.5, flavor=\"igraph\", n_iterations=2)","). Rank markers (",[3753,4116,4117],{},"sc.tl.rank_genes_groups(method=\"wilcoxon\")",", top 10\u002Fcluster via Wilcoxon). Annotate using PBMC markers: B-cell (CD79A,MS4A1), CD8 T (CD8A,CD8B), CD4 T (IL7R,CD4), NK (GNLY,NKG7), CD14 Mono (CD14,LYZ), FCGR3A Mono (FCGR3A,MS4A7), Dendritic (FCER1A,CST3), Mega (PPBP). Confirm via ",[3753,4120,4121],{},"sc.pl.dotplot",[3753,4123,4124],{},"sc.pl.stacked_violin(groupby=\"leiden\")",". Visualizes 8-9 clusters matching immune subsets.",[17,4127,4129],{"id":4128},"paga-trajectories-pseudotime-and-custom-scores-enable-developmental-insights","PAGA Trajectories, Pseudotime, and Custom Scores Enable Developmental Insights",[22,4131,4132,4133,4136,4137,4140,4141,4144,4145,4148,4149,4152,4153,4156,4157,4160],{},"Graph-based trajectories: ",[3753,4134,4135],{},"sc.tl.paga(groups=\"leiden\")",", threshold=0.1, init UMAP (",[3753,4138,4139],{},"sc.tl.umap(init_pos=\"paga\")","). Diffusion maps (",[3753,4142,4143],{},"sc.tl.diffmap","), recompute neighbors on ",[3753,4146,4147],{},"X_diffmap",", root at cluster 0 (",[3753,4150,4151],{},"adata.uns[\"iroot\"]","), pseudotime (",[3753,4154,4155],{},"sc.tl.dpt","). Plot ",[3753,4158,4159],{},"dpt_pseudotime"," on UMAP.",[22,4162,4163,4164,3768,4167,4170,4171,4174],{},"Custom score: IFN-response genes (ISG15,IFI6,IFIT1,IFIT3,MX1,OAS1,STAT1,IRF7) via ",[3753,4165,4166],{},"sc.tl.score_genes(score_name=\"IFN_score\")",[3753,4168,4169],{},"cmap=\"viridis\"",". Save full AnnData (",[3753,4172,4173],{},"adata.write(\"pbmc3k_analyzed.h5ad\")",") with embeddings, clusters, scores for reuse. Extends basic clustering to infer progression and response states.",{"title":40,"searchDepth":41,"depth":41,"links":4176},[4177,4178,4179,4180],{"id":3978,"depth":41,"text":3979},{"id":4040,"depth":41,"text":4041},{"id":4083,"depth":41,"text":4084},{"id":4128,"depth":41,"text":4129},[131],{"content_references":4183,"triage":4196},[4184,4187,4190,4192],{"type":3942,"title":4185,"url":4186,"context":3945},"Scanpy","https:\u002F\u002Fgithub.com\u002Fscverse\u002Fscanpy",{"type":4188,"title":4189,"context":3945},"dataset","PBMC-3k",{"type":3942,"title":4191,"context":3945},"Scrublet",{"type":3947,"title":4193,"url":4194,"context":4195},"Full Codes with Notebook","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FData%20Science\u002Fscanpy_pbmc3k_single_cell_rnaseq_analysis_Marktechpost.ipynb","recommended",{"relevance":55,"novelty":41,"quality":54,"actionability":55,"composite":4197,"reasoning":4198},3.05,"Category: Data Science & Visualization. The article provides a detailed overview of building a single-cell RNA-seq analysis pipeline using Scanpy, which is relevant for data scientists working with biological data. However, it primarily focuses on a specific use case without broader implications or insights that could apply to a wider audience.","\u002Fsummaries\u002Fa59df2d47dafe018-scanpy-pipeline-for-pbmc-scrna-seq-clustering-traj-summary","2026-05-08 21:32:12","2026-05-09 15:37:24",{"title":3968,"description":40},{"loc":4199},"a59df2d47dafe018","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F08\u002Fhow-to-build-a-single-cell-rna-seq-analysis-pipeline-with-scanpy-for-pbmc-clustering-annotation-and-trajectory-discovery\u002F","summaries\u002Fa59df2d47dafe018-scanpy-pipeline-for-pbmc-scrna-seq-clustering-traj-summary",[71,72,70],"Process PBMC-3k data with Scanpy: filter cells (min 200 genes, \u003C2500 genes, \u003C5% mt), remove Scrublet doublets, select HVGs (min_mean=0.0125, max_mean=3, min_disp=0.5), Leiden cluster at res=0.5, annotate via markers, infer PAGA\u002FDPT trajectories, score IFN response.",[],"jTCku7xsp8M-LiBcwiNLzHzB68G5RjE-UBMIb_cET-c",{"id":4212,"title":4213,"ai":4214,"body":4219,"categories":4321,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":4322,"navigation":58,"path":4332,"published_at":4333,"question":48,"scraped_at":4334,"seo":4335,"sitemap":4336,"source_id":4337,"source_name":3959,"source_type":66,"source_url":4338,"stem":4339,"tags":4340,"thumbnail_url":48,"tldr":4341,"tweet":48,"unknown_tags":4342,"__hash__":4343},"summaries\u002Fsummaries\u002Fa50c8b812151a371-tabpfn-beats-tree-models-on-tabular-accuracy-with--summary.md","TabPFN Beats Tree Models on Tabular Accuracy with Zero Training",{"provider":7,"model":8,"input_tokens":4215,"output_tokens":4216,"processing_time_ms":4217,"cost_usd":4218},9215,1914,16447,0.00277735,{"type":14,"value":4220,"toc":4316},[4221,4225,4228,4239,4269,4272,4276,4279,4302,4305,4309,4312],[17,4222,4224],{"id":4223},"tabpfns-pretraining-enables-direct-inference-on-tabular-tasks","TabPFN's Pretraining Enables Direct Inference on Tabular Tasks",[22,4226,4227],{},"TabPFN is a foundation model pretrained on millions of synthetic tabular datasets from causal processes, allowing it to perform supervised classification without dataset-specific training. Provide your training data during the .fit() call, which loads pretrained weights in 0.47 seconds—no hyperparameter tuning or iterative optimization needed. Predictions use in-context learning: the model conditions on your full training set (e.g., 4,000 samples) alongside test inputs at inference time, mimicking LLM prompting but for structured data. TabPFN-2.5 extends this to larger datasets up to millions of rows, outperforming tuned XGBoost, CatBoost, and ensembles like AutoGluon on benchmarks by capturing general tabular patterns.",[22,4229,4230,4231,4234,4235,4238],{},"To implement, install via ",[3753,4232,4233],{},"pip install tabpfn-client scikit-learn catboost",", set ",[3753,4236,4237],{},"TABPFN_TOKEN"," from priorlabs.ai, then:",[4240,4241,4244],"pre",{"className":4242,"code":4243,"language":70,"meta":40,"style":40},"language-python shiki shiki-themes github-light github-dark","from tabpfn_client import TabPFNClassifier\ntabpfn = TabPFNClassifier()\ntabpfn.fit(X_train, y_train)  # Loads weights\ntabpfn_preds = tabpfn.predict(X_test)\n",[3753,4245,4246,4254,4259,4264],{"__ignoreMap":40},[4247,4248,4251],"span",{"class":4249,"line":4250},"line",1,[4247,4252,4253],{},"from tabpfn_client import TabPFNClassifier\n",[4247,4255,4256],{"class":4249,"line":41},[4247,4257,4258],{},"tabpfn = TabPFNClassifier()\n",[4247,4260,4261],{"class":4249,"line":55},[4247,4262,4263],{},"tabpfn.fit(X_train, y_train)  # Loads weights\n",[4247,4265,4266],{"class":4249,"line":54},[4247,4267,4268],{},"tabpfn_preds = tabpfn.predict(X_test)\n",[22,4270,4271],{},"This shifts computation from training to inference, ideal for rapid prototyping where setup speed trumps everything.",[17,4273,4275],{"id":4274},"quantified-wins-over-tree-based-baselines","Quantified Wins Over Tree-Based Baselines",[22,4277,4278],{},"Tested on scikit-learn's synthetic binary classification: 5,000 samples, 20 features (10 informative, 5 redundant), 80\u002F20 train\u002Ftest split.",[4280,4281,4282,4290,4296],"ul",{},[4283,4284,4285,4289],"li",{},[4286,4287,4288],"strong",{},"Random Forest"," (200 trees): 95.5% accuracy, 9.56s train, 0.0627s infer. Robust bagging handles noise but plateaus on complex interactions.",[4283,4291,4292,4295],{},[4286,4293,4294],{},"CatBoost"," (500 iterations, depth=6, lr=0.1): 96.7% accuracy, 8.15s train, 0.0119s infer. Boosting edges out RF via error correction, excels in low-latency production.",[4283,4297,4298,4301],{},[4286,4299,4300],{},"TabPFN",": 98.8% accuracy, 0.47s fit, 2.21s infer. Gains 2.1-3.3% accuracy by leveraging pretrained priors on noisy features.",[22,4303,4304],{},"TabPFN wins on accuracy and setup for small-to-medium data (\u003C10k rows), eliminating tuning that tree models demand.",[17,4306,4308],{"id":4307},"inference-cost-and-distillation-for-production","Inference Cost and Distillation for Production",[22,4310,4311],{},"TabPFN's 2.21s inference (vs \u003C0.1s for trees) arises from joint processing of train+test data—scales with training set size, unsuitable for real-time apps or huge datasets without tweaks. Solution: distillation engine converts predictions to compact neural nets or tree ensembles, preserving ~98% of accuracy while slashing inference to milliseconds. Use for offline analysis, A\u002FB tests, or batch scoring; distill for deployment. Best for dev speed on tabular tasks where trees fall short, like healthcare\u002Ffinance with mixed types—no preprocessing grind required.",[4313,4314,4315],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":40,"searchDepth":41,"depth":41,"links":4317},[4318,4319,4320],{"id":4223,"depth":41,"text":4224},{"id":4274,"depth":41,"text":4275},{"id":4307,"depth":41,"text":4308},[131],{"content_references":4323,"triage":4328},[4324,4326],{"type":3942,"title":4300,"url":4325,"context":3945},"https:\u002F\u002Fux.priorlabs.ai\u002Fhome",{"type":3947,"title":4193,"url":4327,"context":3945},"https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FData%20Science\u002FTabPFN.ipynb",{"relevance":4329,"novelty":54,"quality":54,"actionability":54,"composite":4330,"reasoning":4331},5,4.35,"Category: AI & LLMs. The article provides a detailed comparison of TabPFN with traditional tree models, addressing the audience's need for practical AI applications in product development. It includes specific implementation steps for using TabPFN, making it actionable for developers looking to integrate this model into their workflows.","\u002Fsummaries\u002Fa50c8b812151a371-tabpfn-beats-tree-models-on-tabular-accuracy-with-summary","2026-04-19 19:11:03","2026-04-21 15:26:59",{"title":4213,"description":40},{"loc":4332},"a50c8b812151a371","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F19\u002Fhow-tabpfn-leverages-in-context-learning-to-achieve-superior-accuracy-on-tabular-datasets-compared-to-random-forest-and-catboost\u002F","summaries\u002Fa50c8b812151a371-tabpfn-beats-tree-models-on-tabular-accuracy-with--summary",[72,71,70],"On a 5k-sample tabular dataset, TabPFN hits 98.8% accuracy vs CatBoost's 96.7% and Random Forest's 95.5%, with 0.47s setup but 2.21s inference due to in-context learning at predict time.",[],"ib8Gsg5sdpFcbFssj_HpjZUdd84YjROCIhNcU90X7HE"]