[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-a59df2d47dafe018-scanpy-pipeline-for-pbmc-scrna-seq-clustering-traj-summary":3,"summaries-facets-categories":272,"summary-related-a59df2d47dafe018-scanpy-pipeline-for-pbmc-scrna-seq-clustering-traj-summary":7176},{"id":4,"title":5,"ai":6,"body":13,"categories":227,"created_at":229,"date_modified":229,"description":220,"extension":230,"faq":229,"featured":231,"kicker_label":229,"meta":232,"navigation":254,"path":255,"published_at":256,"question":229,"scraped_at":257,"seo":258,"sitemap":259,"source_id":260,"source_name":261,"source_type":262,"source_url":263,"stem":264,"tags":265,"thumbnail_url":229,"tldr":269,"tweet":229,"unknown_tags":270,"__hash__":271},"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":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",9209,2235,26831,0.0029368,{"type":14,"value":15,"toc":219},"minimark",[16,21,56,82,86,109,125,129,152,170,174,205],[17,18,20],"h2",{"id":19},"rigorous-qc-and-filtering-removes-noise-for-reliable-downstream-analysis","Rigorous QC and Filtering Removes Noise for Reliable Downstream Analysis",[22,23,24,25,29,30,33,34,37,38,41,42,45,46,49,50,49,53,55],"p",{},"Load PBMC-3k via ",[26,27,28],"code",{},"sc.datasets.pbmc3k()"," (2700 cells, ~2k genes\u002Fcell). Compute QC metrics for mitochondrial (",[26,31,32],{},"MT-"," prefix, filter \u003C5% ",[26,35,36],{},"pct_counts_mt",") and ribosomal (",[26,39,40],{},"RPS\u002FRPL",") genes using ",[26,43,44],{},"sc.pp.calculate_qc_metrics",". Visualize with violin plots (",[26,47,48],{},"n_genes_by_counts",", ",[26,51,52],{},"total_counts",[26,54,36],{},") and scatters to spot outliers.",[22,57,58,59,49,62,65,66,69,70,73,74,77,78,81],{},"Filter: ",[26,60,61],{},"min_genes=200",[26,63,64],{},"min_cells=3",", upper ",[26,67,68],{},"n_genes_by_counts \u003C2500",". Detect doublets via ",[26,71,72],{},"sc.pp.scrublet"," (removes ~sum of ",[26,75,76],{},"predicted_doublet","). Preserve raw in ",[26,79,80],{},"layers[\"counts\"]",". This yields cleaner data, preventing artifacts in clustering.",[17,83,85],{"id":84},"normalization-hvgs-and-cell-cycle-correction-focus-on-biological-signal","Normalization, HVGs, and Cell-Cycle Correction Focus on Biological Signal",[22,87,88,89,92,93,96,97,100,101,104,105,108],{},"Normalize to 10k counts (",[26,90,91],{},"sc.pp.normalize_total(target_sum=1e4)","), log-transform (",[26,94,95],{},"sc.pp.log1p","). Identify highly variable genes (",[26,98,99],{},"sc.pp.highly_variable_genes(min_mean=0.0125, max_mean=3, min_disp=0.5)","), subset to them (",[26,102,103],{},"adata = adata[:, adata.var.highly_variable]","). Store raw in ",[26,106,107],{},"adata.raw",".",[22,110,111,112,49,114,116,117,120,121,124],{},"Score S\u002FG2M phases with 40+ predefined markers (e.g., S: MCM5,PCNA; G2M: HMGB2,CDK1, filter to dataset genes). Regress out ",[26,113,52],{},[26,115,36],{}," (",[26,118,119],{},"sc.pp.regress_out","). Scale (",[26,122,123],{},"sc.pp.scale(max_value=10)","). These steps isolate biological variance, regressing technical noise for accurate modeling.",[17,126,128],{"id":127},"dimensionality-reduction-leiden-clustering-and-marker-based-annotation-reveals-cell-types","Dimensionality Reduction, Leiden Clustering, and Marker-Based Annotation Reveals Cell Types",[22,130,131,132,135,136,139,140,143,144,147,148,151],{},"PCA (",[26,133,134],{},"sc.tl.pca(svd_solver=\"arpack\")",", check ",[26,137,138],{},"n_pcs=50"," variance). Neighbors (",[26,141,142],{},"sc.pp.neighbors(n_neighbors=10, n_pcs=40)","). Embeddings: UMAP (",[26,145,146],{},"sc.tl.umap","), t-SNE (",[26,149,150],{},"sc.tl.tsne(n_pcs=40)",").",[22,153,154,155,158,159,162,163,49,166,169],{},"Cluster with Leiden (",[26,156,157],{},"sc.tl.leiden(resolution=0.5, flavor=\"igraph\", n_iterations=2)","). Rank markers (",[26,160,161],{},"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 ",[26,164,165],{},"sc.pl.dotplot",[26,167,168],{},"sc.pl.stacked_violin(groupby=\"leiden\")",". Visualizes 8-9 clusters matching immune subsets.",[17,171,173],{"id":172},"paga-trajectories-pseudotime-and-custom-scores-enable-developmental-insights","PAGA Trajectories, Pseudotime, and Custom Scores Enable Developmental Insights",[22,175,176,177,180,181,184,185,188,189,192,193,196,197,200,201,204],{},"Graph-based trajectories: ",[26,178,179],{},"sc.tl.paga(groups=\"leiden\")",", threshold=0.1, init UMAP (",[26,182,183],{},"sc.tl.umap(init_pos=\"paga\")","). Diffusion maps (",[26,186,187],{},"sc.tl.diffmap","), recompute neighbors on ",[26,190,191],{},"X_diffmap",", root at cluster 0 (",[26,194,195],{},"adata.uns[\"iroot\"]","), pseudotime (",[26,198,199],{},"sc.tl.dpt","). Plot ",[26,202,203],{},"dpt_pseudotime"," on UMAP.",[22,206,207,208,49,211,214,215,218],{},"Custom score: IFN-response genes (ISG15,IFI6,IFIT1,IFIT3,MX1,OAS1,STAT1,IRF7) via ",[26,209,210],{},"sc.tl.score_genes(score_name=\"IFN_score\")",[26,212,213],{},"cmap=\"viridis\"",". Save full AnnData (",[26,216,217],{},"adata.write(\"pbmc3k_analyzed.h5ad\")",") with embeddings, clusters, scores for reuse. Extends basic clustering to infer progression and response states.",{"title":220,"searchDepth":221,"depth":221,"links":222},"",2,[223,224,225,226],{"id":19,"depth":221,"text":20},{"id":84,"depth":221,"text":85},{"id":127,"depth":221,"text":128},{"id":172,"depth":221,"text":173},[228],"Data Science & Visualization",null,"md",false,{"content_references":233,"triage":249},[234,239,242,244],{"type":235,"title":236,"url":237,"context":238},"tool","Scanpy","https:\u002F\u002Fgithub.com\u002Fscverse\u002Fscanpy","mentioned",{"type":240,"title":241,"context":238},"dataset","PBMC-3k",{"type":235,"title":243,"context":238},"Scrublet",{"type":245,"title":246,"url":247,"context":248},"other","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":250,"novelty":221,"quality":251,"actionability":250,"composite":252,"reasoning":253},3,4,3.05,"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.",true,"\u002Fsummaries\u002Fa59df2d47dafe018-scanpy-pipeline-for-pbmc-scrna-seq-clustering-traj-summary","2026-05-08 21:32:12","2026-05-09 15:37:24",{"title":5,"description":220},{"loc":255},"a59df2d47dafe018","MarkTechPost","article","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",[266,267,268],"data-science","machine-learning","python","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",[273,276,279,281,284,286,289,292,294,296,298,300,303,305,307,309,311,314,316,318,320,322,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,376,378,380,382,384,386,388,390,392,394,396,398,400,402,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,446,448,450,452,454,456,458,460,462,464,466,468,470,472,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,540,542,544,546,548,550,552,554,556,558,560,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,596,598,600,602,604,606,608,610,612,614,616,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,678,680,682,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,724,726,728,730,732,734,736,738,740,742,744,746,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1036,1038,1040,1042,1044,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1074,1076,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1600,1602,1604,1606,1608,1610,1612,1614,1616,1618,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1898,1900,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2402,2404,2406,2408,2410,2412,2414,2416,2418,2420,2422,2424,2426,2428,2430,2432,2434,2436,2438,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458,2460,2462,2464,2466,2468,2470,2472,2474,2476,2478,2480,2482,2484,2486,2488,2490,2492,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2614,2616,2618,2620,2622,2624,2626,2628,2630,2632,2634,2636,2638,2640,2642,2644,2646,2648,2650,2652,2654,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3298,3300,3302,3304,3306,3308,3310,3312,3314,3316,3318,3320,3322,3324,3326,3328,3330,3332,3334,3336,3338,3340,3342,3344,3346,3348,3350,3352,3354,3356,3358,3360,3362,3364,3366,3368,3370,3372,3374,3376,3378,3380,3382,3384,3386,3388,3390,3392,3394,3396,3398,3400,3402,3404,3406,3408,3410,3412,3414,3416,3418,3420,3422,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671,3673,3675,3677,3679,3681,3683,3685,3687,3689,3691,3693,3695,3697,3699,3701,3703,3705,3707,3709,3711,3713,3715,3717,3719,3721,3723,3725,3727,3729,3731,3733,3735,3737,3739,3741,3743,3745,3747,3749,3751,3753,3755,3757,3759,3761,3763,3765,3767,3769,3771,3773,3775,3777,3779,3781,3783,3785,3787,3789,3791,3793,3795,3797,3799,3801,3803,3805,3807,3809,3811,3813,3815,3817,3819,3821,3823,3825,3827,3829,3831,3833,3835,3837,3839,3841,3843,3845,3847,3849,3851,3853,3855,3857,3859,3861,3863,3865,3867,3869,3871,3873,3875,3877,3879,3881,3883,3885,3887,3889,3891,3893,3895,3897,3899,3901,3903,3905,3907,3909,3911,3913,3915,3917,3919,3921,3923,3925,3927,3929,3931,3933,3935,3937,3939,3941,3943,3945,3947,3949,3951,3953,3955,3957,3959,3961,3963,3965,3967,3969,3971,3973,3975,3977,3979,3981,3983,3985,3987,3989,3991,3993,3995,3997,3999,4001,4003,4005,4007,4009,4011,4013,4015,4017,4019,4021,4023,4025,4027,4029,4031,4033,4035,4037,4039,4041,4043,4045,4047,4049,4051,4053,4055,4057,4059,4061,4063,4065,4067,4069,4071,4073,4075,4077,4079,4081,4083,4085,4087,4089,4091,4093,4095,4097,4099,4101,4103,4105,4107,4109,4111,4113,4115,4117,4119,4121,4123,4125,4127,4129,4131,4133,4135,4137,4139,4141,4143,4145,4147,4149,4151,4153,4155,4157,4159,4161,4163,4165,4167,4169,4171,4173,4175,4177,4179,4181,4183,4185,4187,4189,4191,4193,4195,4197,4199,4201,4203,4205,4207,4209,4211,4213,4215,4217,4219,4221,4223,4225,4227,4229,4231,4233,4235,4237,4239,4241,4243,4245,4247,4249,4251,4253,4255,4257,4259,4261,4263,4265,4267,4269,4271,4273,4275,4277,4279,4281,4283,4285,4287,4289,4291,4293,4295,4297,4299,4301,4303,4305,4307,4309,4311,4313,4315,4317,4319,4321,4323,4325,4327,4329,4331,4333,4335,4337,4339,4341,4343,4345,4347,4349,4351,4353,4355,4357,4359,4361,4363,4365,4367,4369,4371,4373,4375,4377,4379,4381,4383,4385,4387,4389,4391,4393,4395,4397,4399,4401,4403,4405,4407,4409,4411,4413,4415,4417,4419,4421,4423,4425,4427,4429,4431,4433,4435,4437,4439,4441,4443,4445,4447,4449,4451,4453,4455,4457,4459,4461,4463,4465,4467,4469,4471,4473,4475,4477,4479,4481,4483,4485,4487,4489,4491,4493,4495,4497,4499,4501,4503,4505,4507,4509,4511,4513,4515,4517,4519,4521,4523,4525,4527,4529,4531,4533,4535,4537,4539,4541,4543,4545,4547,4549,4551,4553,4555,4557,4559,4561,4563,4565,4567,4569,4571,4573,4575,4577,4579,4581,4583,4585,4587,4589,4591,4593,4595,4597,4599,4601,4603,4605,4607,4609,4611,4613,4615,4617,4619,4621,4623,4625,4627,4629,4631,4633,4635,4637,4639,4641,4643,4645,4647,4649,4651,4653,4655,4657,4659,4661,4663,4665,4667,4669,4671,4673,4675,4677,4679,4681,4683,4685,4687,4689,4691,4693,4695,4697,4699,4701,4703,4705,4707,4710,4712,4714,4716,4718,4720,4722,4724,4726,4728,4730,4732,4734,4736,4738,4740,4742,4744,4746,4748,4750,4752,4754,4756,4758,4760,4762,4764,4766,4768,4770,4772,4774,4776,4778,4780,4782,4784,4786,4788,4790,4792,4794,4796,4798,4800,4802,4804,4806,4808,4810,4812,4814,4816,4818,4820,4822,4824,4826,4828,4830,4832,4834,4836,4838,4840,4842,4844,4846,4848,4850,4852,4854,4856,4858,4860,4862,4864,4866,4868,4870,4872,4874,4876,4878,4880,4882,4884,4886,4888,4890,4892,4894,4896,4898,4900,4902,4904,4906,4908,4910,4912,4914,4916,4918,4920,4922,4924,4926,4928,4930,4932,4934,4936,4938,4940,4942,4944,4946,4948,4950,4952,4954,4956,4958,4960,4962,4964,4966,4968,4970,4972,4974,4976,4978,4980,4982,4984,4986,4988,4990,4992,4994,4996,4998,5000,5002,5004,5006,5008,5010,5012,5014,5016,5018,5020,5022,5024,5026,5028,5030,5032,5034,5036,5038,5040,5042,5044,5046,5048,5050,5052,5054,5056,5058,5060,5062,5064,5066,5068,5070,5072,5074,5076,5078,5080,5082,5084,5086,5088,5090,5092,5094,5096,5098,5100,5102,5104,5106,5108,5110,5112,5114,5116,5118,5120,5122,5124,5126,5128,5130,5132,5134,5136,5138,5140,5142,5144,5146,5148,5150,5152,5154,5156,5158,5160,5162,5164,5166,5168,5170,5172,5174,5176,5178,5180,5182,5184,5186,5188,5190,5192,5194,5196,5198,5200,5202,5204,5206,5208,5210,5212,5214,5216,5218,5220,5222,5224,5226,5228,5230,5232,5234,5236,5238,5240,5242,5244,5246,5248,5250,5252,5254,5256,5258,5260,5262,5264,5266,5268,5270,5272,5274,5276,5278,5280,5282,5284,5286,5288,5290,5292,5294,5296,5298,5300,5302,5304,5306,5308,5310,5312,5314,5316,5318,5320,5322,5324,5326,5328,5330,5332,5334,5336,5338,5340,5342,5344,5346,5348,5350,5352,5354,5356,5358,5360,5362,5364,5366,5368,5370,5372,5374,5376,5378,5380,5382,5384,5386,5388,5390,5392,5394,5396,5398,5400,5402,5404,5406,5408,5410,5412,5414,5416,5418,5420,5422,5424,5426,5428,5430,5432,5434,5436,5438,5440,5442,5444,5446,5448,5450,5452,5454,5456,5458,5460,5462,5464,5466,5468,5470,5472,5474,5476,5478,5480,5482,5484,5486,5488,5490,5492,5494,5496,5498,5500,5502,5504,5506,5508,5510,5512,5514,5516,5518,5520,5522,5524,5526,5528,5530,5532,5534,5536,5538,5540,5542,5544,5546,5548,5550,5552,5554,5556,5558,5560,5562,5564,5567,5569,5571,5573,5575,5577,5579,5581,5583,5585,5587,5589,5591,5593,5595,5597,5599,5601,5603,5605,5607,5609,5611,5613,5615,5617,5619,5621,5623,5625,5627,5629,5631,5633,5635,5637,5639,5641,5643,5645,5647,5649,5651,5653,5655,5657,5659,5661,5663,5665,5667,5669,5671,5673,5675,5677,5679,5681,5683,5685,5687,5689,5691,5693,5695,5697,5699,5701,5703,5705,5707,5709,5711,5713,5715,5717,5719,5721,5723,5725,5727,5729,5731,5733,5735,5737,5739,5741,5743,5745,5747,5749,5751,5753,5755,5757,5759,5761,5763,5765,5767,5770,5772,5774,5776,5778,5780,5782,5784,5786,5788,5790,5792,5794,5796,5798,5800,5802,5804,5806,5808,5810,5812,5814,5816,5818,5820,5822,5824,5826,5828,5830,5832,5834,5836,5838,5840,5842,5844,5846,5848,5850,5852,5854,5856,5858,5860,5862,5864,5866,5868,5870,5872,5874,5876,5878,5880,5882,5884,5886,5888,5890,5892,5894,5896,5898,5900,5902,5904,5906,5908,5910,5912,5914,5916,5918,5920,5922,5924,5926,5928,5930,5932,5934,5936,5938,5940,5942,5944,5946,5948,5950,5952,5954,5956,5958,5960,5962,5964,5966,5968,5970,5972,5974,5976,5978,5980,5982,5984,5986,5988,5990,5992,5994,5996,5998,6000,6002,6004,6006,6008,6010,6012,6014,6016,6018,6020,6022,6024,6026,6028,6030,6032,6034,6036,6038,6040,6042,6044,6046,6048,6050,6052,6054,6056,6058,6060,6062,6064,6066,6068,6070,6072,6074,6076,6078,6080,6082,6084,6086,6088,6090,6092,6094,6096,6098,6100,6102,6104,6106,6108,6110,6112,6114,6116,6118,6120,6122,6124,6126,6128,6130,6132,6134,6136,6138,6140,6142,6144,6146,6148,6150,6152,6154,6156,6158,6160,6162,6164,6166,6168,6170,6172,6174,6176,6178,6180,6182,6184,6186,6188,6190,6192,6194,6196,6198,6200,6202,6204,6206,6208,6210,6212,6214,6216,6218,6220,6222,6224,6226,6228,6230,6232,6234,6236,6238,6240,6242,6244,6246,6248,6250,6252,6254,6256,6258,6260,6262,6264,6266,6268,6270,6272,6274,6276,6278,6280,6282,6284,6286,6288,6290,6292,6294,6296,6298,6300,6302,6304,6306,6308,6310,6312,6314,6316,6318,6320,6322,6324,6326,6328,6330,6332,6334,6336,6338,6340,6342,6344,6346,6348,6350,6352,6354,6356,6358,6360,6362,6364,6366,6368,6370,6372,6374,6376,6378,6380,6382,6384,6386,6388,6390,6392,6394,6396,6398,6400,6402,6404,6406,6408,6410,6412,6414,6416,6418,6420,6422,6424,6426,6428,6430,6432,6434,6436,6438,6440,6442,6444,6446,6448,6450,6452,6454,6456,6458,6460,6462,6464,6466,6468,6470,6472,6474,6476,6478,6480,6482,6484,6486,6488,6490,6492,6494,6496,6498,6500,6502,6504,6506,6508,6510,6512,6514,6516,6518,6520,6522,6524,6526,6528,6530,6532,6534,6536,6538,6540,6542,6544,6546,6548,6550,6552,6554,6556,6558,6560,6562,6564,6566,6568,6570,6572,6574,6576,6578,6580,6582,6584,6586,6588,6590,6592,6594,6596,6598,6600,6602,6604,6606,6608,6610,6612,6614,6616,6618,6620,6622,6624,6626,6628,6630,6632,6634,6636,6638,6640,6642,6644,6646,6648,6650,6652,6654,6656,6658,6660,6662,6664,6666,6668,6670,6672,6674,6676,6678,6680,6682,6684,6686,6688,6690,6692,6694,6696,6698,6700,6702,6704,6706,6708,6710,6712,6714,6716,6718,6720,6722,6724,6726,6728,6730,6732,6734,6736,6738,6740,6742,6744,6746,6748,6750,6752,6754,6756,6758,6760,6762,6764,6766,6768,6770,6772,6774,6776,6778,6780,6782,6784,6786,6788,6790,6792,6794,6796,6798,6800,6802,6804,6806,6808,6810,6812,6814,6816,6818,6820,6822,6824,6826,6828,6830,6832,6834,6836,6838,6840,6842,6844,6846,6848,6850,6852,6854,6856,6858,6860,6862,6864,6866,6868,6870,6872,6874,6876,6878,6880,6882,6884,6886,6888,6890,6892,6894,6896,6898,6900,6902,6904,6906,6908,6910,6912,6914,6916,6918,6920,6922,6924,6926,6928,6930,6932,6934,6936,6938,6940,6942,6944,6946,6948,6950,6952,6954,6956,6958,6960,6962,6964,6966,6968,6970,6972,6974,6976,6978,6980,6982,6984,6986,6988,6990,6992,6994,6996,6998,7000,7002,7004,7006,7008,7010,7012,7014,7016,7018,7020,7022,7024,7026,7028,7030,7032,7034,7036,7038,7040,7042,7044,7046,7048,7050,7052,7054,7056,7058,7060,7062,7064,7066,7068,7070,7072,7074,7076,7078,7080,7082,7084,7086,7088,7090,7092,7094,7096,7098,7100,7102,7104,7106,7108,7110,7112,7114,7116,7118,7120,7122,7124,7126,7128,7130,7132,7134,7136,7138,7140,7142,7144,7146,7148,7150,7152,7154,7156,7158,7160,7162,7164,7166,7168,7170,7172,7174],{"categories":274},[275],"AI & LLMs",{"categories":277},[278],"Developer Productivity",{"categories":280},[275],{"categories":282},[283],"Business & SaaS",{"categories":285},[275],{"categories":287},[288],"AI Automation",{"categories":290},[291],"Product Strategy",{"categories":293},[288],{"categories":295},[275],{"categories":297},[278],{"categories":299},[288],{"categories":301},[302],"Software Engineering",{"categories":304},[275],{"categories":306},[283],{"categories":308},[],{"categories":310},[275],{"categories":312},[313],"Inference & Serving",{"categories":315},[275],{"categories":317},[275],{"categories":319},[288],{"categories":321},[],{"categories":323},[324],"AI News & Trends",{"categories":326},[228],{"categories":328},[288],{"categories":330},[275],{"categories":332},[275],{"categories":334},[283],{"categories":336},[278],{"categories":338},[275],{"categories":340},[288],{"categories":342},[324],{"categories":344},[275],{"categories":346},[288],{"categories":348},[288],{"categories":350},[275],{"categories":352},[275],{"categories":354},[288],{"categories":356},[275],{"categories":358},[275],{"categories":360},[275],{"categories":362},[288],{"categories":364},[324],{"categories":366},[275],{"categories":368},[275],{"categories":370},[275],{"categories":372},[],{"categories":374},[375],"Design & Frontend",{"categories":377},[228],{"categories":379},[324],{"categories":381},[275],{"categories":383},[275],{"categories":385},[275],{"categories":387},[],{"categories":389},[275],{"categories":391},[275],{"categories":393},[288],{"categories":395},[302],{"categories":397},[275],{"categories":399},[288],{"categories":401},[275],{"categories":403},[404],"Marketing & Growth",{"categories":406},[375],{"categories":408},[275],{"categories":410},[288],{"categories":412},[275],{"categories":414},[275],{"categories":416},[302],{"categories":418},[275],{"categories":420},[],{"categories":422},[],{"categories":424},[375],{"categories":426},[275],{"categories":428},[288],{"categories":430},[278],{"categories":432},[302],{"categories":434},[288],{"categories":436},[375],{"categories":438},[291],{"categories":440},[275],{"categories":442},[302],{"categories":444},[445],"DevOps & Cloud",{"categories":447},[288],{"categories":449},[291],{"categories":451},[324],{"categories":453},[275],{"categories":455},[],{"categories":457},[275],{"categories":459},[275],{"categories":461},[],{"categories":463},[288],{"categories":465},[302],{"categories":467},[],{"categories":469},[302],{"categories":471},[275],{"categories":473},[474],"Governance & Standards",{"categories":476},[283],{"categories":478},[],{"categories":480},[],{"categories":482},[275],{"categories":484},[275],{"categories":486},[288],{"categories":488},[275],{"categories":490},[275],{"categories":492},[288],{"categories":494},[275],{"categories":496},[275],{"categories":498},[275],{"categories":500},[],{"categories":502},[302],{"categories":504},[],{"categories":506},[],{"categories":508},[275],{"categories":510},[302],{"categories":512},[],{"categories":514},[302],{"categories":516},[275],{"categories":518},[275],{"categories":520},[404],{"categories":522},[275],{"categories":524},[275],{"categories":526},[275],{"categories":528},[375],{"categories":530},[375],{"categories":532},[275],{"categories":534},[302],{"categories":536},[288],{"categories":538},[539],"GovTech & Public-Sector Adoption",{"categories":541},[302],{"categories":543},[275],{"categories":545},[275],{"categories":547},[275],{"categories":549},[288],{"categories":551},[288],{"categories":553},[228],{"categories":555},[275],{"categories":557},[324],{"categories":559},[288],{"categories":561},[562],"Legal AI Tools",{"categories":564},[275],{"categories":566},[288],{"categories":568},[275],{"categories":570},[404],{"categories":572},[288],{"categories":574},[291],{"categories":576},[275],{"categories":578},[302],{"categories":580},[539],{"categories":582},[],{"categories":584},[288],{"categories":586},[],{"categories":588},[283],{"categories":590},[288],{"categories":592},[288],{"categories":594},[595],"RAG & Retrieval",{"categories":597},[283],{"categories":599},[275],{"categories":601},[302],{"categories":603},[302],{"categories":605},[445],{"categories":607},[375],{"categories":609},[288],{"categories":611},[275],{"categories":613},[275],{"categories":615},[],{"categories":617},[618],"Agents & Orchestration",{"categories":620},[302],{"categories":622},[275],{"categories":624},[],{"categories":626},[288],{"categories":628},[283],{"categories":630},[],{"categories":632},[275],{"categories":634},[],{"categories":636},[275],{"categories":638},[278],{"categories":640},[302],{"categories":642},[283],{"categories":644},[275],{"categories":646},[288],{"categories":648},[275],{"categories":650},[324],{"categories":652},[275],{"categories":654},[],{"categories":656},[275],{"categories":658},[],{"categories":660},[275],{"categories":662},[302],{"categories":664},[275],{"categories":666},[288],{"categories":668},[228],{"categories":670},[],{"categories":672},[275],{"categories":674},[375],{"categories":676},[677],"Models & Frontier Labs",{"categories":679},[],{"categories":681},[375],{"categories":683},[684],"Regulation & Governance of AI",{"categories":686},[291],{"categories":688},[288],{"categories":690},[],{"categories":692},[275],{"categories":694},[275],{"categories":696},[288],{"categories":698},[288],{"categories":700},[324],{"categories":702},[275],{"categories":704},[283],{"categories":706},[275],{"categories":708},[288],{"categories":710},[],{"categories":712},[302],{"categories":714},[288],{"categories":716},[275],{"categories":718},[291],{"categories":720},[275],{"categories":722},[723],"AI Policy & Regulation",{"categories":725},[],{"categories":727},[275],{"categories":729},[288],{"categories":731},[291],{"categories":733},[288],{"categories":735},[275],{"categories":737},[275],{"categories":739},[275],{"categories":741},[288],{"categories":743},[],{"categories":745},[228],{"categories":747},[748],"Evals & Reliability",{"categories":750},[275],{"categories":752},[275],{"categories":754},[],{"categories":756},[278],{"categories":758},[539],{"categories":760},[723],{"categories":762},[275],{"categories":764},[283],{"categories":766},[275],{"categories":768},[288],{"categories":770},[275],{"categories":772},[288],{"categories":774},[618],{"categories":776},[275],{"categories":778},[302],{"categories":780},[275],{"categories":782},[],{"categories":784},[375],{"categories":786},[],{"categories":788},[275],{"categories":790},[539],{"categories":792},[275],{"categories":794},[275],{"categories":796},[275],{"categories":798},[],{"categories":800},[275],{"categories":802},[375],{"categories":804},[302],{"categories":806},[],{"categories":808},[275],{"categories":810},[],{"categories":812},[288],{"categories":814},[275],{"categories":816},[375],{"categories":818},[],{"categories":820},[275],{"categories":822},[275],{"categories":824},[228],{"categories":826},[288],{"categories":828},[275],{"categories":830},[283],{"categories":832},[288],{"categories":834},[275],{"categories":836},[275],{"categories":838},[302],{"categories":840},[375],{"categories":842},[275],{"categories":844},[288],{"categories":846},[],{"categories":848},[302],{"categories":850},[288],{"categories":852},[228],{"categories":854},[],{"categories":856},[275],{"categories":858},[324],{"categories":860},[275],{"categories":862},[],{"categories":864},[275],{"categories":866},[275],{"categories":868},[275],{"categories":870},[283,404],{"categories":872},[],{"categories":874},[302],{"categories":876},[275],{"categories":878},[275],{"categories":880},[288],{"categories":882},[275],{"categories":884},[],{"categories":886},[],{"categories":888},[275],{"categories":890},[375],{"categories":892},[275],{"categories":894},[],{"categories":896},[275],{"categories":898},[445],{"categories":900},[],{"categories":902},[288],{"categories":904},[324],{"categories":906},[275],{"categories":908},[275],{"categories":910},[375],{"categories":912},[],{"categories":914},[324],{"categories":916},[275],{"categories":918},[313],{"categories":920},[275],{"categories":922},[275],{"categories":924},[288],{"categories":926},[324],{"categories":928},[677],{"categories":930},[275],{"categories":932},[404],{"categories":934},[],{"categories":936},[288],{"categories":938},[283],{"categories":940},[302],{"categories":942},[275],{"categories":944},[288],{"categories":946},[],{"categories":948},[275,445],{"categories":950},[275],{"categories":952},[275],{"categories":954},[275],{"categories":956},[288],{"categories":958},[275,302],{"categories":960},[228],{"categories":962},[275],{"categories":964},[275],{"categories":966},[275],{"categories":968},[302],{"categories":970},[275],{"categories":972},[288],{"categories":974},[288],{"categories":976},[723],{"categories":978},[404],{"categories":980},[275],{"categories":982},[288],{"categories":984},[275],{"categories":986},[275],{"categories":988},[288],{"categories":990},[],{"categories":992},[288],{"categories":994},[275],{"categories":996},[275],{"categories":998},[288],{"categories":1000},[275],{"categories":1002},[275,283],{"categories":1004},[275],{"categories":1006},[283],{"categories":1008},[],{"categories":1010},[375],{"categories":1012},[375],{"categories":1014},[275],{"categories":1016},[],{"categories":1018},[],{"categories":1020},[275],{"categories":1022},[324],{"categories":1024},[],{"categories":1026},[278],{"categories":1028},[275],{"categories":1030},[302],{"categories":1032},[275],{"categories":1034},[1035],"Generative UI & Design-to-Code",{"categories":1037},[275],{"categories":1039},[275],{"categories":1041},[375],{"categories":1043},[275],{"categories":1045},[1046],"Algorithmic Accountability",{"categories":1048},[288],{"categories":1050},[302],{"categories":1052},[324],{"categories":1054},[375],{"categories":1056},[275],{"categories":1058},[],{"categories":1060},[291],{"categories":1062},[275],{"categories":1064},[275],{"categories":1066},[275],{"categories":1068},[275],{"categories":1070},[288],{"categories":1072},[1073],"MLOps & Infrastructure",{"categories":1075},[275],{"categories":1077},[275],{"categories":1079},[275],{"categories":1081},[275],{"categories":1083},[275],{"categories":1085},[302],{"categories":1087},[324],{"categories":1089},[275],{"categories":1091},[291],{"categories":1093},[278],{"categories":1095},[275],{"categories":1097},[288],{"categories":1099},[445],{"categories":1101},[275],{"categories":1103},[283],{"categories":1105},[275],{"categories":1107},[375],{"categories":1109},[275],{"categories":1111},[275],{"categories":1113},[288],{"categories":1115},[],{"categories":1117},[],{"categories":1119},[275],{"categories":1121},[313],{"categories":1123},[375],{"categories":1125},[324],{"categories":1127},[228],{"categories":1129},[],{"categories":1131},[275],{"categories":1133},[275],{"categories":1135},[283],{"categories":1137},[288],{"categories":1139},[275],{"categories":1141},[275],{"categories":1143},[275],{"categories":1145},[275],{"categories":1147},[324],{"categories":1149},[313],{"categories":1151},[275],{"categories":1153},[375],{"categories":1155},[275],{"categories":1157},[],{"categories":1159},[288],{"categories":1161},[302],{"categories":1163},[],{"categories":1165},[275],{"categories":1167},[275],{"categories":1169},[288],{"categories":1171},[302],{"categories":1173},[275],{"categories":1175},[228],{"categories":1177},[375],{"categories":1179},[],{"categories":1181},[275],{"categories":1183},[],{"categories":1185},[275],{"categories":1187},[],{"categories":1189},[275],{"categories":1191},[275],{"categories":1193},[291],{"categories":1195},[283],{"categories":1197},[288],{"categories":1199},[288],{"categories":1201},[],{"categories":1203},[275],{"categories":1205},[278],{"categories":1207},[275],{"categories":1209},[275],{"categories":1211},[283],{"categories":1213},[324],{"categories":1215},[278],{"categories":1217},[],{"categories":1219},[275],{"categories":1221},[],{"categories":1223},[275],{"categories":1225},[],{"categories":1227},[324],{"categories":1229},[324],{"categories":1231},[],{"categories":1233},[618],{"categories":1235},[275],{"categories":1237},[375],{"categories":1239},[302],{"categories":1241},[],{"categories":1243},[562],{"categories":1245},[288],{"categories":1247},[283],{"categories":1249},[],{"categories":1251},[],{"categories":1253},[278],{"categories":1255},[228],{"categories":1257},[],{"categories":1259},[404],{"categories":1261},[288],{"categories":1263},[283],{"categories":1265},[288],{"categories":1267},[275],{"categories":1269},[283],{"categories":1271},[275],{"categories":1273},[302],{"categories":1275},[],{"categories":1277},[313],{"categories":1279},[291],{"categories":1281},[275],{"categories":1283},[375],{"categories":1285},[302],{"categories":1287},[283],{"categories":1289},[275],{"categories":1291},[302],{"categories":1293},[275],{"categories":1295},[288],{"categories":1297},[283],{"categories":1299},[275],{"categories":1301},[275],{"categories":1303},[275],{"categories":1305},[275],{"categories":1307},[275],{"categories":1309},[],{"categories":1311},[],{"categories":1313},[302],{"categories":1315},[228],{"categories":1317},[291],{"categories":1319},[275],{"categories":1321},[288],{"categories":1323},[302],{"categories":1325},[302],{"categories":1327},[275],{"categories":1329},[],{"categories":1331},[324],{"categories":1333},[291],{"categories":1335},[291],{"categories":1337},[302],{"categories":1339},[275],{"categories":1341},[748],{"categories":1343},[445],{"categories":1345},[],{"categories":1347},[288],{"categories":1349},[275],{"categories":1351},[],{"categories":1353},[278],{"categories":1355},[],{"categories":1357},[275],{"categories":1359},[275],{"categories":1361},[275],{"categories":1363},[375],{"categories":1365},[404],{"categories":1367},[275],{"categories":1369},[302],{"categories":1371},[275],{"categories":1373},[288],{"categories":1375},[],{"categories":1377},[302],{"categories":1379},[275],{"categories":1381},[278],{"categories":1383},[],{"categories":1385},[283],{"categories":1387},[275],{"categories":1389},[275],{"categories":1391},[324],{"categories":1393},[275,445],{"categories":1395},[275],{"categories":1397},[1398],"Design Systems for AI",{"categories":1400},[275],{"categories":1402},[275],{"categories":1404},[324],{"categories":1406},[275],{"categories":1408},[275],{"categories":1410},[275],{"categories":1412},[283],{"categories":1414},[275],{"categories":1416},[275],{"categories":1418},[275],{"categories":1420},[],{"categories":1422},[275],{"categories":1424},[275],{"categories":1426},[283],{"categories":1428},[275],{"categories":1430},[],{"categories":1432},[288],{"categories":1434},[288],{"categories":1436},[302],{"categories":1438},[324],{"categories":1440},[302],{"categories":1442},[275],{"categories":1444},[375],{"categories":1446},[324],{"categories":1448},[228],{"categories":1450},[275],{"categories":1452},[275],{"categories":1454},[288],{"categories":1456},[278],{"categories":1458},[723],{"categories":1460},[275],{"categories":1462},[288],{"categories":1464},[275],{"categories":1466},[302],{"categories":1468},[302],{"categories":1470},[],{"categories":1472},[],{"categories":1474},[275],{"categories":1476},[288],{"categories":1478},[291],{"categories":1480},[],{"categories":1482},[283],{"categories":1484},[275],{"categories":1486},[],{"categories":1488},[375],{"categories":1490},[302],{"categories":1492},[288],{"categories":1494},[302],{"categories":1496},[375],{"categories":1498},[275],{"categories":1500},[275],{"categories":1502},[375],{"categories":1504},[],{"categories":1506},[],{"categories":1508},[324],{"categories":1510},[288],{"categories":1512},[288],{"categories":1514},[275],{"categories":1516},[275],{"categories":1518},[275],{"categories":1520},[275],{"categories":1522},[283],{"categories":1524},[275],{"categories":1526},[275],{"categories":1528},[],{"categories":1530},[302],{"categories":1532},[302],{"categories":1534},[275],{"categories":1536},[302],{"categories":1538},[283],{"categories":1540},[],{"categories":1542},[275],{"categories":1544},[275],{"categories":1546},[275],{"categories":1548},[275],{"categories":1550},[275],{"categories":1552},[288],{"categories":1554},[278],{"categories":1556},[283],{"categories":1558},[275],{"categories":1560},[288],{"categories":1562},[324],{"categories":1564},[288],{"categories":1566},[313],{"categories":1568},[404],{"categories":1570},[275],{"categories":1572},[288],{"categories":1574},[275],{"categories":1576},[275],{"categories":1578},[275],{"categories":1580},[],{"categories":1582},[375],{"categories":1584},[],{"categories":1586},[275],{"categories":1588},[275],{"categories":1590},[],{"categories":1592},[275],{"categories":1594},[302],{"categories":1596},[283],{"categories":1598},[1599],"Visual & Generative Media",{"categories":1601},[288],{"categories":1603},[],{"categories":1605},[275],{"categories":1607},[275],{"categories":1609},[302],{"categories":1611},[445],{"categories":1613},[275],{"categories":1615},[228],{"categories":1617},[723],{"categories":1619},[302],{"categories":1621},[404],{"categories":1623},[275],{"categories":1625},[375],{"categories":1627},[275],{"categories":1629},[275],{"categories":1631},[302],{"categories":1633},[288],{"categories":1635},[275],{"categories":1637},[],{"categories":1639},[],{"categories":1641},[288],{"categories":1643},[302],{"categories":1645},[278],{"categories":1647},[288],{"categories":1649},[677],{"categories":1651},[275],{"categories":1653},[291],{"categories":1655},[275],{"categories":1657},[283],{"categories":1659},[],{"categories":1661},[275],{"categories":1663},[291],{"categories":1665},[275],{"categories":1667},[275],{"categories":1669},[275],{"categories":1671},[291],{"categories":1673},[275],{"categories":1675},[275],{"categories":1677},[404],{"categories":1679},[275],{"categories":1681},[618],{"categories":1683},[275],{"categories":1685},[288],{"categories":1687},[275],{"categories":1689},[275],{"categories":1691},[275],{"categories":1693},[275],{"categories":1695},[375],{"categories":1697},[288],{"categories":1699},[],{"categories":1701},[288],{"categories":1703},[],{"categories":1705},[445],{"categories":1707},[302],{"categories":1709},[],{"categories":1711},[677],{"categories":1713},[275],{"categories":1715},[288],{"categories":1717},[288],{"categories":1719},[275],{"categories":1721},[375,275],{"categories":1723},[278],{"categories":1725},[275],{"categories":1727},[375],{"categories":1729},[],{"categories":1731},[275],{"categories":1733},[278],{"categories":1735},[275],{"categories":1737},[1738],"Medical Imaging & Radiology",{"categories":1740},[275],{"categories":1742},[275],{"categories":1744},[275],{"categories":1746},[375],{"categories":1748},[288],{"categories":1750},[302],{"categories":1752},[],{"categories":1754},[275],{"categories":1756},[275],{"categories":1758},[275],{"categories":1760},[],{"categories":1762},[],{"categories":1764},[275],{"categories":1766},[275],{"categories":1768},[618],{"categories":1770},[275],{"categories":1772},[278],{"categories":1774},[275],{"categories":1776},[275],{"categories":1778},[],{"categories":1780},[288],{"categories":1782},[275],{"categories":1784},[291],{"categories":1786},[302],{"categories":1788},[275],{"categories":1790},[288],{"categories":1792},[618],{"categories":1794},[275],{"categories":1796},[288],{"categories":1798},[275],{"categories":1800},[275],{"categories":1802},[275],{"categories":1804},[375],{"categories":1806},[288],{"categories":1808},[445],{"categories":1810},[375],{"categories":1812},[283],{"categories":1814},[288],{"categories":1816},[324],{"categories":1818},[275],{"categories":1820},[275],{"categories":1822},[291],{"categories":1824},[275],{"categories":1826},[275],{"categories":1828},[275],{"categories":1830},[275],{"categories":1832},[288],{"categories":1834},[275],{"categories":1836},[302],{"categories":1838},[302],{"categories":1840},[275],{"categories":1842},[291],{"categories":1844},[],{"categories":1846},[324],{"categories":1848},[],{"categories":1850},[291],{"categories":1852},[288],{"categories":1854},[275],{"categories":1856},[288],{"categories":1858},[1398],{"categories":1860},[1398],{"categories":1862},[375],{"categories":1864},[275],{"categories":1866},[275],{"categories":1868},[275],{"categories":1870},[288],{"categories":1872},[302],{"categories":1874},[375],{"categories":1876},[288],{"categories":1878},[324],{"categories":1880},[],{"categories":1882},[275],{"categories":1884},[],{"categories":1886},[275],{"categories":1888},[275],{"categories":1890},[275],{"categories":1892},[275],{"categories":1894},[288],{"categories":1896},[1897],"Contract Review & E-Discovery",{"categories":1899},[275],{"categories":1901},[375],{"categories":1903},[275],{"categories":1905},[278],{"categories":1907},[275],{"categories":1909},[324],{"categories":1911},[275],{"categories":1913},[275],{"categories":1915},[404],{"categories":1917},[302],{"categories":1919},[275],{"categories":1921},[275],{"categories":1923},[288],{"categories":1925},[288],{"categories":1927},[1046],{"categories":1929},[275],{"categories":1931},[275],{"categories":1933},[288],{"categories":1935},[288],{"categories":1937},[275],{"categories":1939},[275],{"categories":1941},[275],{"categories":1943},[288],{"categories":1945},[275],{"categories":1947},[275],{"categories":1949},[618],{"categories":1951},[595],{"categories":1953},[275],{"categories":1955},[288],{"categories":1957},[275],{"categories":1959},[1960],"Law-Firm Practice & Adoption",{"categories":1962},[275],{"categories":1964},[288],{"categories":1966},[375],{"categories":1968},[275],{"categories":1970},[275],{"categories":1972},[275],{"categories":1974},[],{"categories":1976},[],{"categories":1978},[302],{"categories":1980},[275],{"categories":1982},[],{"categories":1984},[288],{"categories":1986},[278],{"categories":1988},[445],{"categories":1990},[275],{"categories":1992},[],{"categories":1994},[278],{"categories":1996},[283],{"categories":1998},[275],{"categories":2000},[404],{"categories":2002},[],{"categories":2004},[283],{"categories":2006},[288],{"categories":2008},[283],{"categories":2010},[],{"categories":2012},[275],{"categories":2014},[291],{"categories":2016},[275],{"categories":2018},[302],{"categories":2020},[],{"categories":2022},[],{"categories":2024},[],{"categories":2026},[],{"categories":2028},[275],{"categories":2030},[291],{"categories":2032},[288],{"categories":2034},[445],{"categories":2036},[275],{"categories":2038},[278],{"categories":2040},[302],{"categories":2042},[275],{"categories":2044},[275],{"categories":2046},[302],{"categories":2048},[291],{"categories":2050},[275],{"categories":2052},[275],{"categories":2054},[275],{"categories":2056},[1073],{"categories":2058},[275],{"categories":2060},[302],{"categories":2062},[275],{"categories":2064},[404],{"categories":2066},[302],{"categories":2068},[283],{"categories":2070},[275],{"categories":2072},[275],{"categories":2074},[275],{"categories":2076},[375],{"categories":2078},[275],{"categories":2080},[275],{"categories":2082},[275],{"categories":2084},[275],{"categories":2086},[283],{"categories":2088},[288],{"categories":2090},[275,278],{"categories":2092},[618],{"categories":2094},[275],{"categories":2096},[275],{"categories":2098},[302],{"categories":2100},[302],{"categories":2102},[375],{"categories":2104},[288],{"categories":2106},[288],{"categories":2108},[302],{"categories":2110},[275],{"categories":2112},[275],{"categories":2114},[275],{"categories":2116},[],{"categories":2118},[],{"categories":2120},[275],{"categories":2122},[228],{"categories":2124},[275],{"categories":2126},[288],{"categories":2128},[],{"categories":2130},[275],{"categories":2132},[275],{"categories":2134},[302],{"categories":2136},[228],{"categories":2138},[324],{"categories":2140},[375],{"categories":2142},[275],{"categories":2144},[288],{"categories":2146},[275],{"categories":2148},[302],{"categories":2150},[],{"categories":2152},[288],{"categories":2154},[275],{"categories":2156},[275],{"categories":2158},[275],{"categories":2160},[275],{"categories":2162},[],{"categories":2164},[288],{"categories":2166},[275],{"categories":2168},[275],{"categories":2170},[275],{"categories":2172},[],{"categories":2174},[288],{"categories":2176},[275],{"categories":2178},[275],{"categories":2180},[283],{"categories":2182},[275],{"categories":2184},[275],{"categories":2186},[],{"categories":2188},[278],{"categories":2190},[275],{"categories":2192},[275],{"categories":2194},[275],{"categories":2196},[375],{"categories":2198},[275],{"categories":2200},[302],{"categories":2202},[275],{"categories":2204},[278],{"categories":2206},[275],{"categories":2208},[302],{"categories":2210},[404],{"categories":2212},[288],{"categories":2214},[288],{"categories":2216},[275],{"categories":2218},[275],{"categories":2220},[275,375],{"categories":2222},[275],{"categories":2224},[288],{"categories":2226},[324],{"categories":2228},[275],{"categories":2230},[324],{"categories":2232},[288],{"categories":2234},[375],{"categories":2236},[275],{"categories":2238},[],{"categories":2240},[302],{"categories":2242},[445],{"categories":2244},[375],{"categories":2246},[302],{"categories":2248},[275],{"categories":2250},[291],{"categories":2252},[275],{"categories":2254},[275],{"categories":2256},[288],{"categories":2258},[],{"categories":2260},[],{"categories":2262},[275],{"categories":2264},[],{"categories":2266},[],{"categories":2268},[291],{"categories":2270},[302],{"categories":2272},[275],{"categories":2274},[288],{"categories":2276},[288],{"categories":2278},[283],{"categories":2280},[288],{"categories":2282},[445],{"categories":2284},[275],{"categories":2286},[275],{"categories":2288},[275],{"categories":2290},[313],{"categories":2292},[275],{"categories":2294},[275],{"categories":2296},[275],{"categories":2298},[302],{"categories":2300},[288],{"categories":2302},[275],{"categories":2304},[275],{"categories":2306},[302],{"categories":2308},[562],{"categories":2310},[288],{"categories":2312},[1046],{"categories":2314},[],{"categories":2316},[375],{"categories":2318},[1960],{"categories":2320},[302],{"categories":2322},[],{"categories":2324},[],{"categories":2326},[275],{"categories":2328},[288],{"categories":2330},[],{"categories":2332},[],{"categories":2334},[275],{"categories":2336},[404],{"categories":2338},[275],{"categories":2340},[404],{"categories":2342},[288],{"categories":2344},[275],{"categories":2346},[275],{"categories":2348},[302],{"categories":2350},[291],{"categories":2352},[],{"categories":2354},[275],{"categories":2356},[275],{"categories":2358},[302],{"categories":2360},[1897],{"categories":2362},[375],{"categories":2364},[375],{"categories":2366},[275],{"categories":2368},[288],{"categories":2370},[278],{"categories":2372},[275],{"categories":2374},[275],{"categories":2376},[275],{"categories":2378},[275],{"categories":2380},[375],{"categories":2382},[375],{"categories":2384},[288],{"categories":2386},[288],{"categories":2388},[288],{"categories":2390},[275],{"categories":2392},[275],{"categories":2394},[],{"categories":2396},[275],{"categories":2398},[],{"categories":2400},[2401],"Interaction & Product Design",{"categories":2403},[275],{"categories":2405},[288],{"categories":2407},[302],{"categories":2409},[474],{"categories":2411},[324],{"categories":2413},[302],{"categories":2415},[275],{"categories":2417},[275],{"categories":2419},[275],{"categories":2421},[302],{"categories":2423},[275],{"categories":2425},[278],{"categories":2427},[288],{"categories":2429},[275],{"categories":2431},[],{"categories":2433},[288],{"categories":2435},[288],{"categories":2437},[288],{"categories":2439},[],{"categories":2441},[302],{"categories":2443},[275],{"categories":2445},[288],{"categories":2447},[278],{"categories":2449},[2401],{"categories":2451},[275],{"categories":2453},[278],{"categories":2455},[278],{"categories":2457},[],{"categories":2459},[288],{"categories":2461},[302],{"categories":2463},[],{"categories":2465},[288],{"categories":2467},[324],{"categories":2469},[275],{"categories":2471},[288],{"categories":2473},[275],{"categories":2475},[288],{"categories":2477},[288],{"categories":2479},[275],{"categories":2481},[275],{"categories":2483},[324],{"categories":2485},[228],{"categories":2487},[275],{"categories":2489},[291],{"categories":2491},[302],{"categories":2493},[2494],"Coding Agents & Dev Productivity",{"categories":2496},[324],{"categories":2498},[375],{"categories":2500},[275],{"categories":2502},[275],{"categories":2504},[],{"categories":2506},[275],{"categories":2508},[1046],{"categories":2510},[],{"categories":2512},[275],{"categories":2514},[275],{"categories":2516},[445],{"categories":2518},[275],{"categories":2520},[324],{"categories":2522},[],{"categories":2524},[],{"categories":2526},[275],{"categories":2528},[],{"categories":2530},[288],{"categories":2532},[275],{"categories":2534},[],{"categories":2536},[302],{"categories":2538},[302],{"categories":2540},[275],{"categories":2542},[228],{"categories":2544},[],{"categories":2546},[275],{"categories":2548},[275],{"categories":2550},[275],{"categories":2552},[228],{"categories":2554},[302],{"categories":2556},[288],{"categories":2558},[],{"categories":2560},[],{"categories":2562},[275],{"categories":2564},[275],{"categories":2566},[288],{"categories":2568},[288],{"categories":2570},[539],{"categories":2572},[302],{"categories":2574},[302],{"categories":2576},[288],{"categories":2578},[324],{"categories":2580},[324],{"categories":2582},[288],{"categories":2584},[288],{"categories":2586},[275],{"categories":2588},[278],{"categories":2590},[2401],{"categories":2592},[291],{"categories":2594},[275,445],{"categories":2596},[228],{"categories":2598},[],{"categories":2600},[375],{"categories":2602},[288],{"categories":2604},[302],{"categories":2606},[278],{"categories":2608},[275],{"categories":2610},[288],{"categories":2612},[2613],"The Designer's Role & Craft",{"categories":2615},[375],{"categories":2617},[],{"categories":2619},[288],{"categories":2621},[275],{"categories":2623},[288],{"categories":2625},[288],{"categories":2627},[275],{"categories":2629},[404],{"categories":2631},[275],{"categories":2633},[302],{"categories":2635},[275],{"categories":2637},[375],{"categories":2639},[275],{"categories":2641},[],{"categories":2643},[288],{"categories":2645},[375],{"categories":2647},[291],{"categories":2649},[275],{"categories":2651},[275],{"categories":2653},[275],{"categories":2655},[2656],"AI UX Patterns",{"categories":2658},[288],{"categories":2660},[288],{"categories":2662},[288],{"categories":2664},[288],{"categories":2666},[404],{"categories":2668},[228],{"categories":2670},[275],{"categories":2672},[288],{"categories":2674},[275],{"categories":2676},[1398],{"categories":2678},[],{"categories":2680},[404],{"categories":2682},[288],{"categories":2684},[324],{"categories":2686},[302],{"categories":2688},[275],{"categories":2690},[288],{"categories":2692},[],{"categories":2694},[],{"categories":2696},[275],{"categories":2698},[275],{"categories":2700},[288],{"categories":2702},[275],{"categories":2704},[288],{"categories":2706},[539],{"categories":2708},[375],{"categories":2710},[275],{"categories":2712},[324],{"categories":2714},[302],{"categories":2716},[275],{"categories":2718},[288],{"categories":2720},[288],{"categories":2722},[],{"categories":2724},[275],{"categories":2726},[],{"categories":2728},[275],{"categories":2730},[],{"categories":2732},[275],{"categories":2734},[275],{"categories":2736},[275],{"categories":2738},[288],{"categories":2740},[302],{"categories":2742},[],{"categories":2744},[],{"categories":2746},[228],{"categories":2748},[313],{"categories":2750},[275],{"categories":2752},[275],{"categories":2754},[275],{"categories":2756},[228],{"categories":2758},[275],{"categories":2760},[275],{"categories":2762},[324],{"categories":2764},[275],{"categories":2766},[275],{"categories":2768},[275],{"categories":2770},[288],{"categories":2772},[275],{"categories":2774},[288],{"categories":2776},[275],{"categories":2778},[275],{"categories":2780},[275],{"categories":2782},[288],{"categories":2784},[],{"categories":2786},[275],{"categories":2788},[],{"categories":2790},[275],{"categories":2792},[275],{"categories":2794},[445],{"categories":2796},[275],{"categories":2798},[],{"categories":2800},[],{"categories":2802},[375],{"categories":2804},[1073],{"categories":2806},[288],{"categories":2808},[278],{"categories":2810},[2613],{"categories":2812},[],{"categories":2814},[],{"categories":2816},[275],{"categories":2818},[],{"categories":2820},[],{"categories":2822},[302],{"categories":2824},[324],{"categories":2826},[404],{"categories":2828},[288],{"categories":2830},[283],{"categories":2832},[275],{"categories":2834},[275],{"categories":2836},[283],{"categories":2838},[],{"categories":2840},[375],{"categories":2842},[291],{"categories":2844},[275],{"categories":2846},[275],{"categories":2848},[288],{"categories":2850},[283],{"categories":2852},[275],{"categories":2854},[275],{"categories":2856},[278],{"categories":2858},[275],{"categories":2860},[275],{"categories":2862},[],{"categories":2864},[278],{"categories":2866},[275],{"categories":2868},[404],{"categories":2870},[288],{"categories":2872},[324],{"categories":2874},[275],{"categories":2876},[302],{"categories":2878},[275],{"categories":2880},[275],{"categories":2882},[283],{"categories":2884},[275],{"categories":2886},[275],{"categories":2888},[275],{"categories":2890},[288],{"categories":2892},[275],{"categories":2894},[],{"categories":2896},[275],{"categories":2898},[302],{"categories":2900},[278],{"categories":2902},[275],{"categories":2904},[275],{"categories":2906},[275],{"categories":2908},[],{"categories":2910},[275],{"categories":2912},[618],{"categories":2914},[288],{"categories":2916},[283],{"categories":2918},[324],{"categories":2920},[275],{"categories":2922},[275],{"categories":2924},[],{"categories":2926},[283],{"categories":2928},[283],{"categories":2930},[275],{"categories":2932},[275],{"categories":2934},[291],{"categories":2936},[275],{"categories":2938},[275],{"categories":2940},[275],{"categories":2942},[275],{"categories":2944},[302],{"categories":2946},[302],{"categories":2948},[275],{"categories":2950},[],{"categories":2952},[302],{"categories":2954},[275],{"categories":2956},[302],{"categories":2958},[288],{"categories":2960},[723],{"categories":2962},[],{"categories":2964},[],{"categories":2966},[275],{"categories":2968},[324],{"categories":2970},[],{"categories":2972},[445],{"categories":2974},[275],{"categories":2976},[275],{"categories":2978},[275],{"categories":2980},[375],{"categories":2982},[1035],{"categories":2984},[],{"categories":2986},[275],{"categories":2988},[275],{"categories":2990},[275],{"categories":2992},[302],{"categories":2994},[275],{"categories":2996},[275],{"categories":2998},[275,445],{"categories":3000},[275],{"categories":3002},[275],{"categories":3004},[375],{"categories":3006},[288],{"categories":3008},[],{"categories":3010},[288],{"categories":3012},[288],{"categories":3014},[275],{"categories":3016},[275],{"categories":3018},[275],{"categories":3020},[275],{"categories":3022},[228],{"categories":3024},[275],{"categories":3026},[2656],{"categories":3028},[278],{"categories":3030},[228],{"categories":3032},[278],{"categories":3034},[302],{"categories":3036},[375],{"categories":3038},[288],{"categories":3040},[275],{"categories":3042},[],{"categories":3044},[283],{"categories":3046},[275],{"categories":3048},[275],{"categories":3050},[324],{"categories":3052},[275],{"categories":3054},[275],{"categories":3056},[275],{"categories":3058},[288],{"categories":3060},[275],{"categories":3062},[275],{"categories":3064},[275],{"categories":3066},[283],{"categories":3068},[],{"categories":3070},[445],{"categories":3072},[275],{"categories":3074},[539],{"categories":3076},[375],{"categories":3078},[375],{"categories":3080},[302],{"categories":3082},[288],{"categories":3084},[275],{"categories":3086},[283],{"categories":3088},[324],{"categories":3090},[275],{"categories":3092},[275],{"categories":3094},[275],{"categories":3096},[375],{"categories":3098},[288],{"categories":3100},[288],{"categories":3102},[275],{"categories":3104},[275],{"categories":3106},[677],{"categories":3108},[288],{"categories":3110},[],{"categories":3112},[275],{"categories":3114},[275],{"categories":3116},[275],{"categories":3118},[],{"categories":3120},[],{"categories":3122},[275],{"categories":3124},[275],{"categories":3126},[288],{"categories":3128},[275],{"categories":3130},[275],{"categories":3132},[275],{"categories":3134},[302],{"categories":3136},[275],{"categories":3138},[275],{"categories":3140},[288],{"categories":3142},[275],{"categories":3144},[275],{"categories":3146},[275],{"categories":3148},[275],{"categories":3150},[275],{"categories":3152},[],{"categories":3154},[302],{"categories":3156},[228],{"categories":3158},[275],{"categories":3160},[288],{"categories":3162},[288],{"categories":3164},[275],{"categories":3166},[275],{"categories":3168},[],{"categories":3170},[],{"categories":3172},[275],{"categories":3174},[275],{"categories":3176},[275],{"categories":3178},[324],{"categories":3180},[228],{"categories":3182},[],{"categories":3184},[275],{"categories":3186},[375],{"categories":3188},[275],{"categories":3190},[445],{"categories":3192},[1960],{"categories":3194},[324],{"categories":3196},[302],{"categories":3198},[275],{"categories":3200},[302],{"categories":3202},[302],{"categories":3204},[275],{"categories":3206},[275],{"categories":3208},[302],{"categories":3210},[324],{"categories":3212},[324],{"categories":3214},[445],{"categories":3216},[288],{"categories":3218},[],{"categories":3220},[324],{"categories":3222},[275],{"categories":3224},[288],{"categories":3226},[278],{"categories":3228},[302],{"categories":3230},[275],{"categories":3232},[324],{"categories":3234},[],{"categories":3236},[275],{"categories":3238},[302],{"categories":3240},[302],{"categories":3242},[228],{"categories":3244},[275],{"categories":3246},[324],{"categories":3248},[275],{"categories":3250},[302],{"categories":3252},[288],{"categories":3254},[288],{"categories":3256},[324],{"categories":3258},[288],{"categories":3260},[445],{"categories":3262},[288],{"categories":3264},[275],{"categories":3266},[275],{"categories":3268},[275],{"categories":3270},[275],{"categories":3272},[302],{"categories":3274},[275],{"categories":3276},[],{"categories":3278},[288],{"categories":3280},[283],{"categories":3282},[302],{"categories":3284},[],{"categories":3286},[],{"categories":3288},[275],{"categories":3290},[288],{"categories":3292},[275],{"categories":3294},[275],{"categories":3296},[3297],"Frameworks & Tooling",{"categories":3299},[275],{"categories":3301},[275],{"categories":3303},[302],{"categories":3305},[275],{"categories":3307},[275],{"categories":3309},[],{"categories":3311},[228],{"categories":3313},[228],{"categories":3315},[278],{"categories":3317},[275],{"categories":3319},[288],{"categories":3321},[275],{"categories":3323},[375],{"categories":3325},[],{"categories":3327},[1960],{"categories":3329},[275],{"categories":3331},[302],{"categories":3333},[275],{"categories":3335},[445],{"categories":3337},[445],{"categories":3339},[],{"categories":3341},[288],{"categories":3343},[288],{"categories":3345},[275],{"categories":3347},[275],{"categories":3349},[324],{"categories":3351},[288],{"categories":3353},[324],{"categories":3355},[275],{"categories":3357},[288],{"categories":3359},[],{"categories":3361},[375],{"categories":3363},[275],{"categories":3365},[275],{"categories":3367},[],{"categories":3369},[275],{"categories":3371},[288],{"categories":3373},[275],{"categories":3375},[275],{"categories":3377},[275],{"categories":3379},[],{"categories":3381},[302],{"categories":3383},[275],{"categories":3385},[302],{"categories":3387},[445],{"categories":3389},[275],{"categories":3391},[275],{"categories":3393},[275],{"categories":3395},[302],{"categories":3397},[283],{"categories":3399},[275],{"categories":3401},[1960],{"categories":3403},[],{"categories":3405},[288],{"categories":3407},[278],{"categories":3409},[275],{"categories":3411},[278],{"categories":3413},[275],{"categories":3415},[],{"categories":3417},[288],{"categories":3419},[275],{"categories":3421},[275],{"categories":3423},[3424],"AI Design Tooling",{"categories":3426},[375],{"categories":3428},[275],{"categories":3430},[275],{"categories":3432},[302],{"categories":3434},[375],{"categories":3436},[275],{"categories":3438},[275],{"categories":3440},[302],{"categories":3442},[324],{"categories":3444},[291],{"categories":3446},[302],{"categories":3448},[275],{"categories":3450},[275],{"categories":3452},[275],{"categories":3454},[288],{"categories":3456},[275],{"categories":3458},[],{"categories":3460},[288],{"categories":3462},[275],{"categories":3464},[275],{"categories":3466},[288],{"categories":3468},[275],{"categories":3470},[275],{"categories":3472},[275],{"categories":3474},[288],{"categories":3476},[],{"categories":3478},[288],{"categories":3480},[3297],{"categories":3482},[275],{"categories":3484},[275],{"categories":3486},[288],{"categories":3488},[288],{"categories":3490},[302],{"categories":3492},[302],{"categories":3494},[275],{"categories":3496},[],{"categories":3498},[302],{"categories":3500},[275],{"categories":3502},[275],{"categories":3504},[288],{"categories":3506},[283],{"categories":3508},[275],{"categories":3510},[],{"categories":3512},[275],{"categories":3514},[275],{"categories":3516},[2401],{"categories":3518},[],{"categories":3520},[275],{"categories":3522},[275],{"categories":3524},[275],{"categories":3526},[275],{"categories":3528},[375],{"categories":3530},[275],{"categories":3532},[],{"categories":3534},[275],{"categories":3536},[275],{"categories":3538},[275],{"categories":3540},[275],{"categories":3542},[404],{"categories":3544},[324],{"categories":3546},[275],{"categories":3548},[275],{"categories":3550},[1960],{"categories":3552},[278],{"categories":3554},[275],{"categories":3556},[275],{"categories":3558},[228],{"categories":3560},[275],{"categories":3562},[275],{"categories":3564},[324],{"categories":3566},[288],{"categories":3568},[],{"categories":3570},[275],{"categories":3572},[275],{"categories":3574},[375],{"categories":3576},[275],{"categories":3578},[404],{"categories":3580},[288],{"categories":3582},[275],{"categories":3584},[288],{"categories":3586},[],{"categories":3588},[],{"categories":3590},[],{"categories":3592},[278],{"categories":3594},[324],{"categories":3596},[288],{"categories":3598},[275],{"categories":3600},[275],{"categories":3602},[275],{"categories":3604},[275],{"categories":3606},[562],{"categories":3608},[375],{"categories":3610},[288],{"categories":3612},[275],{"categories":3614},[],{"categories":3616},[288],{"categories":3618},[288],{"categories":3620},[],{"categories":3622},[275],{"categories":3624},[288],{"categories":3626},[275],{"categories":3628},[],{"categories":3630},[275],{"categories":3632},[275],{"categories":3634},[275],{"categories":3636},[324],{"categories":3638},[375],{"categories":3640},[288],{"categories":3642},[375],{"categories":3644},[288],{"categories":3646},[275],{"categories":3648},[283],{"categories":3650},[],{"categories":3652},[],{"categories":3654},[275],{"categories":3656},[275],{"categories":3658},[275],{"categories":3660},[278],{"categories":3662},[288],{"categories":3664},[324],{"categories":3666},[],{"categories":3668},[375],{"categories":3670},[],{"categories":3672},[302],{"categories":3674},[275],{"categories":3676},[302],{"categories":3678},[375],{"categories":3680},[302],{"categories":3682},[275],{"categories":3684},[],{"categories":3686},[275],{"categories":3688},[275],{"categories":3690},[],{"categories":3692},[275],{"categories":3694},[275],{"categories":3696},[404],{"categories":3698},[275],{"categories":3700},[275],{"categories":3702},[445],{"categories":3704},[302],{"categories":3706},[275],{"categories":3708},[],{"categories":3710},[288],{"categories":3712},[275],{"categories":3714},[278],{"categories":3716},[677],{"categories":3718},[275],{"categories":3720},[275],{"categories":3722},[288],{"categories":3724},[275],{"categories":3726},[288],{"categories":3728},[275],{"categories":3730},[275],{"categories":3732},[275],{"categories":3734},[275],{"categories":3736},[],{"categories":3738},[275],{"categories":3740},[278],{"categories":3742},[275],{"categories":3744},[283],{"categories":3746},[302],{"categories":3748},[375],{"categories":3750},[],{"categories":3752},[275],{"categories":3754},[],{"categories":3756},[288],{"categories":3758},[275],{"categories":3760},[],{"categories":3762},[288],{"categories":3764},[275],{"categories":3766},[302],{"categories":3768},[375],{"categories":3770},[324],{"categories":3772},[275],{"categories":3774},[324],{"categories":3776},[288],{"categories":3778},[375],{"categories":3780},[275],{"categories":3782},[],{"categories":3784},[275],{"categories":3786},[313],{"categories":3788},[288],{"categories":3790},[275],{"categories":3792},[375],{"categories":3794},[324],{"categories":3796},[283],{"categories":3798},[302],{"categories":3800},[275],{"categories":3802},[275],{"categories":3804},[275],{"categories":3806},[275],{"categories":3808},[324],{"categories":3810},[404],{"categories":3812},[],{"categories":3814},[],{"categories":3816},[228],{"categories":3818},[618],{"categories":3820},[275],{"categories":3822},[288],{"categories":3824},[275,302],{"categories":3826},[324],{"categories":3828},[275],{"categories":3830},[275],{"categories":3832},[275],{"categories":3834},[275],{"categories":3836},[275],{"categories":3838},[275],{"categories":3840},[275],{"categories":3842},[288],{"categories":3844},[275],{"categories":3846},[288],{"categories":3848},[275],{"categories":3850},[275],{"categories":3852},[275],{"categories":3854},[],{"categories":3856},[275],{"categories":3858},[1398],{"categories":3860},[302],{"categories":3862},[375],{"categories":3864},[275],{"categories":3866},[275],{"categories":3868},[275],{"categories":3870},[228],{"categories":3872},[288],{"categories":3874},[404],{"categories":3876},[445],{"categories":3878},[],{"categories":3880},[302],{"categories":3882},[275],{"categories":3884},[283],{"categories":3886},[288],{"categories":3888},[278],{"categories":3890},[288],{"categories":3892},[275],{"categories":3894},[288],{"categories":3896},[288],{"categories":3898},[291],{"categories":3900},[302],{"categories":3902},[275],{"categories":3904},[275],{"categories":3906},[],{"categories":3908},[],{"categories":3910},[],{"categories":3912},[445],{"categories":3914},[275],{"categories":3916},[324],{"categories":3918},[275],{"categories":3920},[275],{"categories":3922},[275],{"categories":3924},[275],{"categories":3926},[],{"categories":3928},[275],{"categories":3930},[228],{"categories":3932},[283],{"categories":3934},[288],{"categories":3936},[275],{"categories":3938},[],{"categories":3940},[275],{"categories":3942},[288],{"categories":3944},[275],{"categories":3946},[445],{"categories":3948},[],{"categories":3950},[375],{"categories":3952},[375],{"categories":3954},[275],{"categories":3956},[288],{"categories":3958},[],{"categories":3960},[302],{"categories":3962},[275],{"categories":3964},[375],{"categories":3966},[275],{"categories":3968},[283],{"categories":3970},[288],{"categories":3972},[275],{"categories":3974},[],{"categories":3976},[324],{"categories":3978},[275],{"categories":3980},[275],{"categories":3982},[275],{"categories":3984},[375],{"categories":3986},[288],{"categories":3988},[324],{"categories":3990},[],{"categories":3992},[288],{"categories":3994},[283],{"categories":3996},[288],{"categories":3998},[375],{"categories":4000},[275],{"categories":4002},[275],{"categories":4004},[275],{"categories":4006},[618],{"categories":4008},[275],{"categories":4010},[288],{"categories":4012},[],{"categories":4014},[275],{"categories":4016},[275],{"categories":4018},[445],{"categories":4020},[324],{"categories":4022},[228],{"categories":4024},[723],{"categories":4026},[228],{"categories":4028},[228],{"categories":4030},[275],{"categories":4032},[],{"categories":4034},[],{"categories":4036},[],{"categories":4038},[288],{"categories":4040},[275],{"categories":4042},[288],{"categories":4044},[288],{"categories":4046},[302],{"categories":4048},[275],{"categories":4050},[595],{"categories":4052},[302],{"categories":4054},[288],{"categories":4056},[275],{"categories":4058},[275],{"categories":4060},[275],{"categories":4062},[275],{"categories":4064},[275],{"categories":4066},[288],{"categories":4068},[275],{"categories":4070},[],{"categories":4072},[],{"categories":4074},[275],{"categories":4076},[],{"categories":4078},[275],{"categories":4080},[288],{"categories":4082},[375],{"categories":4084},[275],{"categories":4086},[275],{"categories":4088},[],{"categories":4090},[288],{"categories":4092},[275],{"categories":4094},[275],{"categories":4096},[291],{"categories":4098},[275],{"categories":4100},[375],{"categories":4102},[275],{"categories":4104},[288],{"categories":4106},[283],{"categories":4108},[275],{"categories":4110},[275],{"categories":4112},[404],{"categories":4114},[288],{"categories":4116},[275],{"categories":4118},[275],{"categories":4120},[1035],{"categories":4122},[275],{"categories":4124},[288],{"categories":4126},[275],{"categories":4128},[302],{"categories":4130},[275],{"categories":4132},[677],{"categories":4134},[375],{"categories":4136},[],{"categories":4138},[275],{"categories":4140},[275],{"categories":4142},[324],{"categories":4144},[618],{"categories":4146},[288],{"categories":4148},[275],{"categories":4150},[],{"categories":4152},[324],{"categories":4154},[539],{"categories":4156},[288],{"categories":4158},[288],{"categories":4160},[288],{"categories":4162},[275],{"categories":4164},[275],{"categories":4166},[288],{"categories":4168},[],{"categories":4170},[283],{"categories":4172},[275],{"categories":4174},[283],{"categories":4176},[288],{"categories":4178},[],{"categories":4180},[302],{"categories":4182},[275],{"categories":4184},[275],{"categories":4186},[278],{"categories":4188},[275],{"categories":4190},[324],{"categories":4192},[445],{"categories":4194},[313],{"categories":4196},[288],{"categories":4198},[288],{"categories":4200},[275],{"categories":4202},[275],{"categories":4204},[288],{"categories":4206},[275],{"categories":4208},[278],{"categories":4210},[],{"categories":4212},[288],{"categories":4214},[275],{"categories":4216},[275],{"categories":4218},[275],{"categories":4220},[288],{"categories":4222},[275],{"categories":4224},[],{"categories":4226},[275],{"categories":4228},[],{"categories":4230},[375],{"categories":4232},[288],{"categories":4234},[275,283],{"categories":4236},[288],{"categories":4238},[275],{"categories":4240},[],{"categories":4242},[278],{"categories":4244},[228],{"categories":4246},[283],{"categories":4248},[275],{"categories":4250},[302],{"categories":4252},[275],{"categories":4254},[275],{"categories":4256},[288],{"categories":4258},[275],{"categories":4260},[275],{"categories":4262},[275],{"categories":4264},[324],{"categories":4266},[1398],{"categories":4268},[288],{"categories":4270},[275],{"categories":4272},[],{"categories":4274},[],{"categories":4276},[275],{"categories":4278},[288],{"categories":4280},[275],{"categories":4282},[275],{"categories":4284},[445],{"categories":4286},[],{"categories":4288},[275],{"categories":4290},[288],{"categories":4292},[313],{"categories":4294},[288],{"categories":4296},[618],{"categories":4298},[],{"categories":4300},[562],{"categories":4302},[288],{"categories":4304},[275],{"categories":4306},[275],{"categories":4308},[404],{"categories":4310},[288],{"categories":4312},[275],{"categories":4314},[228],{"categories":4316},[291],{"categories":4318},[288],{"categories":4320},[275],{"categories":4322},[618],{"categories":4324},[275],{"categories":4326},[445],{"categories":4328},[283],{"categories":4330},[],{"categories":4332},[275],{"categories":4334},[275],{"categories":4336},[404],{"categories":4338},[375],{"categories":4340},[275],{"categories":4342},[275],{"categories":4344},[275],{"categories":4346},[],{"categories":4348},[404],{"categories":4350},[324],{"categories":4352},[275],{"categories":4354},[275],{"categories":4356},[275],{"categories":4358},[723],{"categories":4360},[278],{"categories":4362},[275],{"categories":4364},[291],{"categories":4366},[275],{"categories":4368},[],{"categories":4370},[],{"categories":4372},[375],{"categories":4374},[275],{"categories":4376},[228],{"categories":4378},[404],{"categories":4380},[288],{"categories":4382},[275],{"categories":4384},[275],{"categories":4386},[404],{"categories":4388},[324],{"categories":4390},[275],{"categories":4392},[],{"categories":4394},[275],{"categories":4396},[275],{"categories":4398},[],{"categories":4400},[275],{"categories":4402},[275],{"categories":4404},[748],{"categories":4406},[275],{"categories":4408},[275],{"categories":4410},[288],{"categories":4412},[302],{"categories":4414},[618],{"categories":4416},[275],{"categories":4418},[275],{"categories":4420},[275],{"categories":4422},[],{"categories":4424},[275,302],{"categories":4426},[324],{"categories":4428},[288],{"categories":4430},[302],{"categories":4432},[288],{"categories":4434},[1073],{"categories":4436},[302],{"categories":4438},[302],{"categories":4440},[288],{"categories":4442},[275],{"categories":4444},[278],{"categories":4446},[],{"categories":4448},[],{"categories":4450},[288],{"categories":4452},[275],{"categories":4454},[302],{"categories":4456},[275],{"categories":4458},[278],{"categories":4460},[302],{"categories":4462},[302],{"categories":4464},[275],{"categories":4466},[404],{"categories":4468},[275],{"categories":4470},[302],{"categories":4472},[275],{"categories":4474},[],{"categories":4476},[275],{"categories":4478},[275],{"categories":4480},[375,275],{"categories":4482},[445],{"categories":4484},[278],{"categories":4486},[275],{"categories":4488},[],{"categories":4490},[275],{"categories":4492},[275],{"categories":4494},[283],{"categories":4496},[275],{"categories":4498},[283],{"categories":4500},[275],{"categories":4502},[275],{"categories":4504},[539],{"categories":4506},[275],{"categories":4508},[283],{"categories":4510},[302],{"categories":4512},[228],{"categories":4514},[288],{"categories":4516},[275],{"categories":4518},[302],{"categories":4520},[275],{"categories":4522},[275],{"categories":4524},[324],{"categories":4526},[404],{"categories":4528},[375],{"categories":4530},[275],{"categories":4532},[275],{"categories":4534},[275],{"categories":4536},[275],{"categories":4538},[278],{"categories":4540},[275],{"categories":4542},[288],{"categories":4544},[288],{"categories":4546},[302],{"categories":4548},[324],{"categories":4550},[302],{"categories":4552},[302],{"categories":4554},[275],{"categories":4556},[275],{"categories":4558},[],{"categories":4560},[],{"categories":4562},[228],{"categories":4564},[275],{"categories":4566},[302],{"categories":4568},[275],{"categories":4570},[375],{"categories":4572},[618],{"categories":4574},[562],{"categories":4576},[539],{"categories":4578},[275],{"categories":4580},[275],{"categories":4582},[275],{"categories":4584},[228],{"categories":4586},[275],{"categories":4588},[275],{"categories":4590},[275],{"categories":4592},[275],{"categories":4594},[275],{"categories":4596},[275],{"categories":4598},[275],{"categories":4600},[288],{"categories":4602},[278],{"categories":4604},[288],{"categories":4606},[275,283],{"categories":4608},[],{"categories":4610},[375],{"categories":4612},[],{"categories":4614},[291],{"categories":4616},[275],{"categories":4618},[324],{"categories":4620},[278],{"categories":4622},[275],{"categories":4624},[278],{"categories":4626},[288],{"categories":4628},[228],{"categories":4630},[288],{"categories":4632},[291],{"categories":4634},[288],{"categories":4636},[275],{"categories":4638},[275],{"categories":4640},[283],{"categories":4642},[288],{"categories":4644},[302],{"categories":4646},[404],{"categories":4648},[275],{"categories":4650},[275],{"categories":4652},[],{"categories":4654},[324],{"categories":4656},[275],{"categories":4658},[275],{"categories":4660},[275],{"categories":4662},[275],{"categories":4664},[275],{"categories":4666},[275],{"categories":4668},[302],{"categories":4670},[324],{"categories":4672},[302],{"categories":4674},[302],{"categories":4676},[275],{"categories":4678},[275],{"categories":4680},[275],{"categories":4682},[275],{"categories":4684},[562],{"categories":4686},[275],{"categories":4688},[288],{"categories":4690},[324],{"categories":4692},[275],{"categories":4694},[275],{"categories":4696},[275],{"categories":4698},[288],{"categories":4700},[275],{"categories":4702},[275],{"categories":4704},[275],{"categories":4706},[3297],{"categories":4708},[4709],"Clinical AI",{"categories":4711},[375],{"categories":4713},[275],{"categories":4715},[275],{"categories":4717},[275],{"categories":4719},[275],{"categories":4721},[445],{"categories":4723},[2656],{"categories":4725},[275],{"categories":4727},[291],{"categories":4729},[375],{"categories":4731},[275],{"categories":4733},[288],{"categories":4735},[275],{"categories":4737},[275],{"categories":4739},[324],{"categories":4741},[275],{"categories":4743},[288],{"categories":4745},[302],{"categories":4747},[404],{"categories":4749},[275],{"categories":4751},[275],{"categories":4753},[283],{"categories":4755},[275],{"categories":4757},[275],{"categories":4759},[677],{"categories":4761},[275],{"categories":4763},[],{"categories":4765},[288],{"categories":4767},[275],{"categories":4769},[302],{"categories":4771},[278],{"categories":4773},[275],{"categories":4775},[],{"categories":4777},[],{"categories":4779},[275],{"categories":4781},[],{"categories":4783},[283],{"categories":4785},[275],{"categories":4787},[275],{"categories":4789},[288],{"categories":4791},[275],{"categories":4793},[324],{"categories":4795},[324],{"categories":4797},[324],{"categories":4799},[324],{"categories":4801},[],{"categories":4803},[278],{"categories":4805},[288],{"categories":4807},[324],{"categories":4809},[275],{"categories":4811},[748],{"categories":4813},[291],{"categories":4815},[288],{"categories":4817},[275],{"categories":4819},[278],{"categories":4821},[275],{"categories":4823},[288],{"categories":4825},[275],{"categories":4827},[275],{"categories":4829},[275],{"categories":4831},[275,288],{"categories":4833},[288],{"categories":4835},[445],{"categories":4837},[324],{"categories":4839},[288],{"categories":4841},[324],{"categories":4843},[288],{"categories":4845},[275],{"categories":4847},[],{"categories":4849},[324],{"categories":4851},[404],{"categories":4853},[278],{"categories":4855},[275],{"categories":4857},[275],{"categories":4859},[],{"categories":4861},[302],{"categories":4863},[],{"categories":4865},[278],{"categories":4867},[288],{"categories":4869},[324],{"categories":4871},[275],{"categories":4873},[324],{"categories":4875},[278],{"categories":4877},[324],{"categories":4879},[324],{"categories":4881},[],{"categories":4883},[283],{"categories":4885},[288],{"categories":4887},[324],{"categories":4889},[324],{"categories":4891},[324],{"categories":4893},[324],{"categories":4895},[324],{"categories":4897},[324],{"categories":4899},[324],{"categories":4901},[324],{"categories":4903},[324],{"categories":4905},[324],{"categories":4907},[228],{"categories":4909},[278],{"categories":4911},[275],{"categories":4913},[275],{"categories":4915},[288],{"categories":4917},[288],{"categories":4919},[],{"categories":4921},[275],{"categories":4923},[275,278],{"categories":4925},[],{"categories":4927},[288],{"categories":4929},[275],{"categories":4931},[324],{"categories":4933},[288],{"categories":4935},[1073],{"categories":4937},[275],{"categories":4939},[275],{"categories":4941},[275],{"categories":4943},[275],{"categories":4945},[275],{"categories":4947},[539],{"categories":4949},[275],{"categories":4951},[275],{"categories":4953},[288],{"categories":4955},[275],{"categories":4957},[275],{"categories":4959},[283],{"categories":4961},[291],{"categories":4963},[288],{"categories":4965},[288],{"categories":4967},[],{"categories":4969},[288],{"categories":4971},[375],{"categories":4973},[324],{"categories":4975},[275],{"categories":4977},[],{"categories":4979},[291],{"categories":4981},[],{"categories":4983},[302],{"categories":4985},[275],{"categories":4987},[288],{"categories":4989},[375],{"categories":4991},[275],{"categories":4993},[],{"categories":4995},[275],{"categories":4997},[275],{"categories":4999},[],{"categories":5001},[404],{"categories":5003},[275],{"categories":5005},[288],{"categories":5007},[],{"categories":5009},[],{"categories":5011},[324],{"categories":5013},[278],{"categories":5015},[275],{"categories":5017},[275],{"categories":5019},[283],{"categories":5021},[275],{"categories":5023},[275],{"categories":5025},[288],{"categories":5027},[275],{"categories":5029},[283],{"categories":5031},[283],{"categories":5033},[375],{"categories":5035},[],{"categories":5037},[275],{"categories":5039},[324],{"categories":5041},[],{"categories":5043},[275],{"categories":5045},[275],{"categories":5047},[375],{"categories":5049},[275],{"categories":5051},[275],{"categories":5053},[404],{"categories":5055},[275],{"categories":5057},[445],{"categories":5059},[],{"categories":5061},[288],{"categories":5063},[275],{"categories":5065},[404],{"categories":5067},[302],{"categories":5069},[],{"categories":5071},[275],{"categories":5073},[],{"categories":5075},[288],{"categories":5077},[375],{"categories":5079},[302],{"categories":5081},[],{"categories":5083},[3297],{"categories":5085},[283],{"categories":5087},[278],{"categories":5089},[275],{"categories":5091},[228],{"categories":5093},[288],{"categories":5095},[375],{"categories":5097},[275],{"categories":5099},[302],{"categories":5101},[],{"categories":5103},[],{"categories":5105},[275],{"categories":5107},[278],{"categories":5109},[275],{"categories":5111},[404],{"categories":5113},[],{"categories":5115},[288],{"categories":5117},[288],{"categories":5119},[275],{"categories":5121},[288],{"categories":5123},[275],{"categories":5125},[324],{"categories":5127},[302],{"categories":5129},[275],{"categories":5131},[288],{"categories":5133},[291],{"categories":5135},[275],{"categories":5137},[275],{"categories":5139},[275],{"categories":5141},[288],{"categories":5143},[275],{"categories":5145},[291],{"categories":5147},[404],{"categories":5149},[324],{"categories":5151},[],{"categories":5153},[404],{"categories":5155},[275],{"categories":5157},[],{"categories":5159},[302],{"categories":5161},[288],{"categories":5163},[],{"categories":5165},[275],{"categories":5167},[275],{"categories":5169},[275],{"categories":5171},[275],{"categories":5173},[275],{"categories":5175},[288],{"categories":5177},[283],{"categories":5179},[278],{"categories":5181},[288],{"categories":5183},[275],{"categories":5185},[375],{"categories":5187},[302],{"categories":5189},[302],{"categories":5191},[275],{"categories":5193},[228],{"categories":5195},[288],{"categories":5197},[275],{"categories":5199},[275],{"categories":5201},[288],{"categories":5203},[275],{"categories":5205},[275],{"categories":5207},[288],{"categories":5209},[283],{"categories":5211},[275],{"categories":5213},[375],{"categories":5215},[302],{"categories":5217},[288],{"categories":5219},[275],{"categories":5221},[291],{"categories":5223},[275],{"categories":5225},[288],{"categories":5227},[275],{"categories":5229},[275],{"categories":5231},[324],{"categories":5233},[275],{"categories":5235},[],{"categories":5237},[278],{"categories":5239},[275],{"categories":5241},[275],{"categories":5243},[275],{"categories":5245},[302],{"categories":5247},[302],{"categories":5249},[275],{"categories":5251},[302],{"categories":5253},[275],{"categories":5255},[288],{"categories":5257},[275],{"categories":5259},[275],{"categories":5261},[275],{"categories":5263},[275],{"categories":5265},[275],{"categories":5267},[],{"categories":5269},[275],{"categories":5271},[375],{"categories":5273},[288],{"categories":5275},[283],{"categories":5277},[324],{"categories":5279},[275],{"categories":5281},[288],{"categories":5283},[275],{"categories":5285},[288],{"categories":5287},[275],{"categories":5289},[275],{"categories":5291},[375],{"categories":5293},[288],{"categories":5295},[275],{"categories":5297},[404],{"categories":5299},[275],{"categories":5301},[228],{"categories":5303},[275],{"categories":5305},[275],{"categories":5307},[324],{"categories":5309},[275],{"categories":5311},[275],{"categories":5313},[275],{"categories":5315},[275],{"categories":5317},[288],{"categories":5319},[445],{"categories":5321},[275],{"categories":5323},[302],{"categories":5325},[288],{"categories":5327},[228],{"categories":5329},[],{"categories":5331},[288],{"categories":5333},[302],{"categories":5335},[275],{"categories":5337},[275],{"categories":5339},[2494],{"categories":5341},[375],{"categories":5343},[474],{"categories":5345},[275],{"categories":5347},[275],{"categories":5349},[275],{"categories":5351},[275],{"categories":5353},[278],{"categories":5355},[275],{"categories":5357},[275],{"categories":5359},[302],{"categories":5361},[283],{"categories":5363},[275],{"categories":5365},[302],{"categories":5367},[275],{"categories":5369},[],{"categories":5371},[288],{"categories":5373},[288],{"categories":5375},[275],{"categories":5377},[275],{"categories":5379},[275],{"categories":5381},[228],{"categories":5383},[],{"categories":5385},[324],{"categories":5387},[],{"categories":5389},[324],{"categories":5391},[275],{"categories":5393},[275],{"categories":5395},[288],{"categories":5397},[275],{"categories":5399},[288],{"categories":5401},[288],{"categories":5403},[],{"categories":5405},[275],{"categories":5407},[324],{"categories":5409},[275],{"categories":5411},[],{"categories":5413},[275],{"categories":5415},[275],{"categories":5417},[],{"categories":5419},[275],{"categories":5421},[275],{"categories":5423},[375],{"categories":5425},[302],{"categories":5427},[288],{"categories":5429},[275],{"categories":5431},[275],{"categories":5433},[275],{"categories":5435},[275],{"categories":5437},[404],{"categories":5439},[275],{"categories":5441},[275],{"categories":5443},[275],{"categories":5445},[278],{"categories":5447},[275],{"categories":5449},[275],{"categories":5451},[],{"categories":5453},[275],{"categories":5455},[275],{"categories":5457},[275],{"categories":5459},[],{"categories":5461},[278],{"categories":5463},[275],{"categories":5465},[275],{"categories":5467},[324],{"categories":5469},[302],{"categories":5471},[291],{"categories":5473},[288],{"categories":5475},[618],{"categories":5477},[275],{"categories":5479},[275],{"categories":5481},[275],{"categories":5483},[302],{"categories":5485},[324],{"categories":5487},[375],{"categories":5489},[275],{"categories":5491},[275],{"categories":5493},[275],{"categories":5495},[275],{"categories":5497},[324],{"categories":5499},[275],{"categories":5501},[375],{"categories":5503},[275],{"categories":5505},[275],{"categories":5507},[324],{"categories":5509},[375],{"categories":5511},[275],{"categories":5513},[324],{"categories":5515},[275],{"categories":5517},[288],{"categories":5519},[288],{"categories":5521},[288],{"categories":5523},[302],{"categories":5525},[324],{"categories":5527},[288],{"categories":5529},[288],{"categories":5531},[275],{"categories":5533},[302],{"categories":5535},[375],{"categories":5537},[275],{"categories":5539},[275],{"categories":5541},[288],{"categories":5543},[275],{"categories":5545},[],{"categories":5547},[288],{"categories":5549},[],{"categories":5551},[275],{"categories":5553},[275],{"categories":5555},[],{"categories":5557},[],{"categories":5559},[288],{"categories":5561},[283],{"categories":5563},[288],{"categories":5565},[5566],"Liability & Ethics",{"categories":5568},[275],{"categories":5570},[275],{"categories":5572},[275],{"categories":5574},[288],{"categories":5576},[278],{"categories":5578},[288],{"categories":5580},[283],{"categories":5582},[404],{"categories":5584},[288],{"categories":5586},[275],{"categories":5588},[275],{"categories":5590},[],{"categories":5592},[723],{"categories":5594},[288],{"categories":5596},[],{"categories":5598},[275],{"categories":5600},[278],{"categories":5602},[288],{"categories":5604},[],{"categories":5606},[288],{"categories":5608},[275],{"categories":5610},[275],{"categories":5612},[302],{"categories":5614},[275],{"categories":5616},[324],{"categories":5618},[275],{"categories":5620},[275],{"categories":5622},[291],{"categories":5624},[288],{"categories":5626},[275],{"categories":5628},[275],{"categories":5630},[275],{"categories":5632},[324],{"categories":5634},[288],{"categories":5636},[302],{"categories":5638},[375],{"categories":5640},[278],{"categories":5642},[275],{"categories":5644},[275],{"categories":5646},[275],{"categories":5648},[],{"categories":5650},[288],{"categories":5652},[288],{"categories":5654},[288],{"categories":5656},[618],{"categories":5658},[375],{"categories":5660},[288],{"categories":5662},[445],{"categories":5664},[302],{"categories":5666},[324],{"categories":5668},[275],{"categories":5670},[375],{"categories":5672},[275],{"categories":5674},[278],{"categories":5676},[],{"categories":5678},[288],{"categories":5680},[275],{"categories":5682},[275],{"categories":5684},[275],{"categories":5686},[275],{"categories":5688},[288],{"categories":5690},[275],{"categories":5692},[275],{"categories":5694},[375],{"categories":5696},[],{"categories":5698},[288],{"categories":5700},[291],{"categories":5702},[324],{"categories":5704},[288],{"categories":5706},[283],{"categories":5708},[],{"categories":5710},[275],{"categories":5712},[275],{"categories":5714},[291],{"categories":5716},[275],{"categories":5718},[288],{"categories":5720},[324],{"categories":5722},[278],{"categories":5724},[445],{"categories":5726},[275],{"categories":5728},[275],{"categories":5730},[275],{"categories":5732},[324],{"categories":5734},[283],{"categories":5736},[275],{"categories":5738},[375],{"categories":5740},[324],{"categories":5742},[445],{"categories":5744},[275],{"categories":5746},[288],{"categories":5748},[],{"categories":5750},[677],{"categories":5752},[],{"categories":5754},[275],{"categories":5756},[445],{"categories":5758},[275],{"categories":5760},[228],{"categories":5762},[275],{"categories":5764},[288],{"categories":5766},[288],{"categories":5768},[5769],"Design News & Tools",{"categories":5771},[275],{"categories":5773},[275],{"categories":5775},[324],{"categories":5777},[275],{"categories":5779},[275],{"categories":5781},[278],{"categories":5783},[288],{"categories":5785},[275],{"categories":5787},[375],{"categories":5789},[288],{"categories":5791},[288],{"categories":5793},[375],{"categories":5795},[275],{"categories":5797},[275],{"categories":5799},[618],{"categories":5801},[288],{"categories":5803},[275],{"categories":5805},[275],{"categories":5807},[618],{"categories":5809},[275],{"categories":5811},[404],{"categories":5813},[275],{"categories":5815},[288],{"categories":5817},[],{"categories":5819},[275],{"categories":5821},[275],{"categories":5823},[275],{"categories":5825},[324],{"categories":5827},[275],{"categories":5829},[278],{"categories":5831},[],{"categories":5833},[275],{"categories":5835},[275],{"categories":5837},[275],{"categories":5839},[302],{"categories":5841},[748],{"categories":5843},[302],{"categories":5845},[375],{"categories":5847},[275],{"categories":5849},[275,288],{"categories":5851},[404,283],{"categories":5853},[302],{"categories":5855},[275],{"categories":5857},[275],{"categories":5859},[275],{"categories":5861},[275],{"categories":5863},[],{"categories":5865},[288],{"categories":5867},[275],{"categories":5869},[],{"categories":5871},[275],{"categories":5873},[302],{"categories":5875},[275],{"categories":5877},[302],{"categories":5879},[],{"categories":5881},[288],{"categories":5883},[275],{"categories":5885},[283],{"categories":5887},[275],{"categories":5889},[324],{"categories":5891},[275],{"categories":5893},[],{"categories":5895},[288],{"categories":5897},[275],{"categories":5899},[],{"categories":5901},[375],{"categories":5903},[275],{"categories":5905},[275],{"categories":5907},[288],{"categories":5909},[275],{"categories":5911},[275],{"categories":5913},[278],{"categories":5915},[288],{"categories":5917},[275],{"categories":5919},[],{"categories":5921},[275],{"categories":5923},[445],{"categories":5925},[404],{"categories":5927},[283],{"categories":5929},[283],{"categories":5931},[275],{"categories":5933},[278],{"categories":5935},[278],{"categories":5937},[275],{"categories":5939},[288],{"categories":5941},[275],{"categories":5943},[275],{"categories":5945},[275],{"categories":5947},[275],{"categories":5949},[302],{"categories":5951},[275],{"categories":5953},[278],{"categories":5955},[275],{"categories":5957},[275],{"categories":5959},[288],{"categories":5961},[275],{"categories":5963},[404],{"categories":5965},[275],{"categories":5967},[324],{"categories":5969},[275],{"categories":5971},[275],{"categories":5973},[288],{"categories":5975},[291],{"categories":5977},[275],{"categories":5979},[275],{"categories":5981},[288],{"categories":5983},[],{"categories":5985},[302],{"categories":5987},[],{"categories":5989},[302],{"categories":5991},[288],{"categories":5993},[278],{"categories":5995},[275],{"categories":5997},[],{"categories":5999},[228],{"categories":6001},[445],{"categories":6003},[275],{"categories":6005},[302],{"categories":6007},[275],{"categories":6009},[],{"categories":6011},[324],{"categories":6013},[288],{"categories":6015},[302],{"categories":6017},[375],{"categories":6019},[283],{"categories":6021},[275],{"categories":6023},[275],{"categories":6025},[288],{"categories":6027},[302],{"categories":6029},[288],{"categories":6031},[324],{"categories":6033},[275],{"categories":6035},[291],{"categories":6037},[278],{"categories":6039},[291],{"categories":6041},[324],{"categories":6043},[275],{"categories":6045},[302],{"categories":6047},[275],{"categories":6049},[375],{"categories":6051},[283],{"categories":6053},[275],{"categories":6055},[275],{"categories":6057},[275],{"categories":6059},[275],{"categories":6061},[275],{"categories":6063},[275],{"categories":6065},[288],{"categories":6067},[275],{"categories":6069},[288],{"categories":6071},[275],{"categories":6073},[275],{"categories":6075},[278],{"categories":6077},[275],{"categories":6079},[288],{"categories":6081},[288],{"categories":6083},[375],{"categories":6085},[288],{"categories":6087},[288],{"categories":6089},[275],{"categories":6091},[278],{"categories":6093},[288],{"categories":6095},[375],{"categories":6097},[],{"categories":6099},[275],{"categories":6101},[228],{"categories":6103},[618],{"categories":6105},[275],{"categories":6107},[288],{"categories":6109},[275],{"categories":6111},[275],{"categories":6113},[302],{"categories":6115},[275],{"categories":6117},[],{"categories":6119},[275],{"categories":6121},[288],{"categories":6123},[275],{"categories":6125},[404],{"categories":6127},[275],{"categories":6129},[302],{"categories":6131},[275],{"categories":6133},[324],{"categories":6135},[288],{"categories":6137},[275],{"categories":6139},[404],{"categories":6141},[288],{"categories":6143},[283],{"categories":6145},[283],{"categories":6147},[275],{"categories":6149},[275],{"categories":6151},[275],{"categories":6153},[275],{"categories":6155},[275],{"categories":6157},[275],{"categories":6159},[278],{"categories":6161},[],{"categories":6163},[275],{"categories":6165},[275],{"categories":6167},[288],{"categories":6169},[288],{"categories":6171},[275],{"categories":6173},[275],{"categories":6175},[275],{"categories":6177},[275],{"categories":6179},[275],{"categories":6181},[302],{"categories":6183},[],{"categories":6185},[278],{"categories":6187},[275],{"categories":6189},[275],{"categories":6191},[288],{"categories":6193},[288],{"categories":6195},[],{"categories":6197},[302],{"categories":6199},[302],{"categories":6201},[275],{"categories":6203},[404],{"categories":6205},[283],{"categories":6207},[375],{"categories":6209},[],{"categories":6211},[275],{"categories":6213},[288],{"categories":6215},[278],{"categories":6217},[275],{"categories":6219},[275],{"categories":6221},[302],{"categories":6223},[278],{"categories":6225},[275],{"categories":6227},[275],{"categories":6229},[324],{"categories":6231},[228],{"categories":6233},[275],{"categories":6235},[324],{"categories":6237},[288],{"categories":6239},[275],{"categories":6241},[],{"categories":6243},[324],{"categories":6245},[288],{"categories":6247},[375],{"categories":6249},[228],{"categories":6251},[275],{"categories":6253},[275],{"categories":6255},[],{"categories":6257},[288],{"categories":6259},[288],{"categories":6261},[288],{"categories":6263},[3297],{"categories":6265},[324],{"categories":6267},[275],{"categories":6269},[302],{"categories":6271},[275],{"categories":6273},[275],{"categories":6275},[275],{"categories":6277},[275],{"categories":6279},[275],{"categories":6281},[283],{"categories":6283},[275],{"categories":6285},[278],{"categories":6287},[1960],{"categories":6289},[445],{"categories":6291},[278],{"categories":6293},[],{"categories":6295},[275],{"categories":6297},[],{"categories":6299},[324],{"categories":6301},[288],{"categories":6303},[375],{"categories":6305},[275],{"categories":6307},[275],{"categories":6309},[275],{"categories":6311},[324],{"categories":6313},[],{"categories":6315},[288],{"categories":6317},[275],{"categories":6319},[288],{"categories":6321},[288],{"categories":6323},[],{"categories":6325},[275],{"categories":6327},[],{"categories":6329},[324],{"categories":6331},[278],{"categories":6333},[375],{"categories":6335},[275],{"categories":6337},[288],{"categories":6339},[324],{"categories":6341},[275],{"categories":6343},[324],{"categories":6345},[],{"categories":6347},[324],{"categories":6349},[275],{"categories":6351},[278],{"categories":6353},[618],{"categories":6355},[288],{"categories":6357},[275],{"categories":6359},[],{"categories":6361},[302],{"categories":6363},[288],{"categories":6365},[291],{"categories":6367},[288],{"categories":6369},[278],{"categories":6371},[275],{"categories":6373},[275],{"categories":6375},[],{"categories":6377},[],{"categories":6379},[],{"categories":6381},[375],{"categories":6383},[275],{"categories":6385},[288],{"categories":6387},[275],{"categories":6389},[275],{"categories":6391},[],{"categories":6393},[],{"categories":6395},[],{"categories":6397},[275],{"categories":6399},[288],{"categories":6401},[375],{"categories":6403},[275],{"categories":6405},[],{"categories":6407},[288],{"categories":6409},[275],{"categories":6411},[275],{"categories":6413},[278],{"categories":6415},[],{"categories":6417},[],{"categories":6419},[275],{"categories":6421},[275],{"categories":6423},[288],{"categories":6425},[375],{"categories":6427},[275],{"categories":6429},[324],{"categories":6431},[],{"categories":6433},[275],{"categories":6435},[275],{"categories":6437},[404],{"categories":6439},[324],{"categories":6441},[404],{"categories":6443},[228],{"categories":6445},[275],{"categories":6447},[275],{"categories":6449},[],{"categories":6451},[],{"categories":6453},[288],{"categories":6455},[],{"categories":6457},[275],{"categories":6459},[618],{"categories":6461},[275],{"categories":6463},[275],{"categories":6465},[275],{"categories":6467},[275],{"categories":6469},[],{"categories":6471},[288],{"categories":6473},[275],{"categories":6475},[275],{"categories":6477},[],{"categories":6479},[288],{"categories":6481},[275],{"categories":6483},[324],{"categories":6485},[275],{"categories":6487},[404],{"categories":6489},[283],{"categories":6491},[291],{"categories":6493},[275],{"categories":6495},[275],{"categories":6497},[288],{"categories":6499},[228],{"categories":6501},[288],{"categories":6503},[288],{"categories":6505},[],{"categories":6507},[275],{"categories":6509},[288],{"categories":6511},[],{"categories":6513},[275],{"categories":6515},[],{"categories":6517},[324],{"categories":6519},[283],{"categories":6521},[],{"categories":6523},[275],{"categories":6525},[275],{"categories":6527},[275],{"categories":6529},[],{"categories":6531},[288],{"categories":6533},[375],{"categories":6535},[278],{"categories":6537},[275],{"categories":6539},[],{"categories":6541},[283],{"categories":6543},[404],{"categories":6545},[275],{"categories":6547},[302],{"categories":6549},[278],{"categories":6551},[228],{"categories":6553},[283],{"categories":6555},[302],{"categories":6557},[288],{"categories":6559},[302],{"categories":6561},[],{"categories":6563},[275],{"categories":6565},[291],{"categories":6567},[275],{"categories":6569},[],{"categories":6571},[288],{"categories":6573},[278],{"categories":6575},[375],{"categories":6577},[275],{"categories":6579},[278],{"categories":6581},[288],{"categories":6583},[445],{"categories":6585},[275],{"categories":6587},[275],{"categories":6589},[275],{"categories":6591},[275],{"categories":6593},[275],{"categories":6595},[278],{"categories":6597},[275],{"categories":6599},[302],{"categories":6601},[228],{"categories":6603},[288],{"categories":6605},[],{"categories":6607},[275],{"categories":6609},[275],{"categories":6611},[275],{"categories":6613},[302],{"categories":6615},[288],{"categories":6617},[324],{"categories":6619},[302],{"categories":6621},[275],{"categories":6623},[291],{"categories":6625},[],{"categories":6627},[375],{"categories":6629},[302],{"categories":6631},[324],{"categories":6633},[275],{"categories":6635},[278],{"categories":6637},[288],{"categories":6639},[275],{"categories":6641},[275],{"categories":6643},[288],{"categories":6645},[291],{"categories":6647},[275],{"categories":6649},[288],{"categories":6651},[275],{"categories":6653},[283],{"categories":6655},[288],{"categories":6657},[288,445],{"categories":6659},[275],{"categories":6661},[275],{"categories":6663},[288],{"categories":6665},[302],{"categories":6667},[275],{"categories":6669},[275],{"categories":6671},[228],{"categories":6673},[288],{"categories":6675},[404],{"categories":6677},[288],{"categories":6679},[283],{"categories":6681},[],{"categories":6683},[288],{"categories":6685},[275],{"categories":6687},[283],{"categories":6689},[],{"categories":6691},[],{"categories":6693},[302],{"categories":6695},[275],{"categories":6697},[275],{"categories":6699},[288],{"categories":6701},[228],{"categories":6703},[404],{"categories":6705},[275],{"categories":6707},[275],{"categories":6709},[275],{"categories":6711},[288],{"categories":6713},[],{"categories":6715},[288],{"categories":6717},[324],{"categories":6719},[275],{"categories":6721},[288],{"categories":6723},[288],{"categories":6725},[275],{"categories":6727},[],{"categories":6729},[324],{"categories":6731},[302],{"categories":6733},[3297],{"categories":6735},[278],{"categories":6737},[302],{"categories":6739},[275],{"categories":6741},[288],{"categories":6743},[275],{"categories":6745},[275],{"categories":6747},[404],{"categories":6749},[302],{"categories":6751},[228],{"categories":6753},[],{"categories":6755},[324],{"categories":6757},[275],{"categories":6759},[275],{"categories":6761},[],{"categories":6763},[288],{"categories":6765},[275],{"categories":6767},[275],{"categories":6769},[275],{"categories":6771},[275],{"categories":6773},[288],{"categories":6775},[275],{"categories":6777},[275],{"categories":6779},[275],{"categories":6781},[291],{"categories":6783},[275],{"categories":6785},[288],{"categories":6787},[275],{"categories":6789},[275],{"categories":6791},[275],{"categories":6793},[275],{"categories":6795},[275],{"categories":6797},[275],{"categories":6799},[275],{"categories":6801},[283],{"categories":6803},[],{"categories":6805},[291],{"categories":6807},[324],{"categories":6809},[288],{"categories":6811},[275],{"categories":6813},[302],{"categories":6815},[],{"categories":6817},[302],{"categories":6819},[302],{"categories":6821},[288],{"categories":6823},[302],{"categories":6825},[275],{"categories":6827},[275],{"categories":6829},[275],{"categories":6831},[288],{"categories":6833},[302],{"categories":6835},[275],{"categories":6837},[275],{"categories":6839},[275],{"categories":6841},[288],{"categories":6843},[324],{"categories":6845},[275],{"categories":6847},[275],{"categories":6849},[275],{"categories":6851},[283],{"categories":6853},[275],{"categories":6855},[288],{"categories":6857},[375],{"categories":6859},[],{"categories":6861},[275],{"categories":6863},[228],{"categories":6865},[288],{"categories":6867},[275],{"categories":6869},[275],{"categories":6871},[],{"categories":6873},[275],{"categories":6875},[275],{"categories":6877},[324],{"categories":6879},[275],{"categories":6881},[275],{"categories":6883},[288],{"categories":6885},[404],{"categories":6887},[],{"categories":6889},[],{"categories":6891},[302],{"categories":6893},[275],{"categories":6895},[275],{"categories":6897},[324],{"categories":6899},[275],{"categories":6901},[302],{"categories":6903},[324],{"categories":6905},[275],{"categories":6907},[275],{"categories":6909},[404],{"categories":6911},[228],{"categories":6913},[275],{"categories":6915},[275],{"categories":6917},[278],{"categories":6919},[288],{"categories":6921},[275],{"categories":6923},[275],{"categories":6925},[288],{"categories":6927},[283],{"categories":6929},[288],{"categories":6931},[302],{"categories":6933},[275],{"categories":6935},[283],{"categories":6937},[],{"categories":6939},[275],{"categories":6941},[228],{"categories":6943},[275],{"categories":6945},[275],{"categories":6947},[],{"categories":6949},[324],{"categories":6951},[275],{"categories":6953},[288],{"categories":6955},[228],{"categories":6957},[275],{"categories":6959},[302],{"categories":6961},[302],{"categories":6963},[302],{"categories":6965},[275],{"categories":6967},[288],{"categories":6969},[288],{"categories":6971},[275],{"categories":6973},[288],{"categories":6975},[275],{"categories":6977},[275],{"categories":6979},[375],{"categories":6981},[228],{"categories":6983},[228],{"categories":6985},[],{"categories":6987},[324],{"categories":6989},[275],{"categories":6991},[275],{"categories":6993},[302],{"categories":6995},[],{"categories":6997},[324],{"categories":6999},[324],{"categories":7001},[324],{"categories":7003},[],{"categories":7005},[288],{"categories":7007},[275],{"categories":7009},[],{"categories":7011},[278],{"categories":7013},[283],{"categories":7015},[],{"categories":7017},[275],{"categories":7019},[275],{"categories":7021},[],{"categories":7023},[302],{"categories":7025},[],{"categories":7027},[],{"categories":7029},[],{"categories":7031},[],{"categories":7033},[275],{"categories":7035},[324],{"categories":7037},[],{"categories":7039},[],{"categories":7041},[275],{"categories":7043},[275],{"categories":7045},[275],{"categories":7047},[228],{"categories":7049},[275],{"categories":7051},[228],{"categories":7053},[],{"categories":7055},[228],{"categories":7057},[228],{"categories":7059},[445],{"categories":7061},[288],{"categories":7063},[302],{"categories":7065},[],{"categories":7067},[],{"categories":7069},[228],{"categories":7071},[302],{"categories":7073},[302],{"categories":7075},[302],{"categories":7077},[],{"categories":7079},[278],{"categories":7081},[302],{"categories":7083},[302],{"categories":7085},[278],{"categories":7087},[302],{"categories":7089},[283],{"categories":7091},[302],{"categories":7093},[302],{"categories":7095},[302],{"categories":7097},[228],{"categories":7099},[324],{"categories":7101},[324],{"categories":7103},[275],{"categories":7105},[302],{"categories":7107},[228],{"categories":7109},[445],{"categories":7111},[228],{"categories":7113},[228],{"categories":7115},[228],{"categories":7117},[],{"categories":7119},[283],{"categories":7121},[],{"categories":7123},[445],{"categories":7125},[302],{"categories":7127},[302],{"categories":7129},[302],{"categories":7131},[288],{"categories":7133},[324,283],{"categories":7135},[228],{"categories":7137},[],{"categories":7139},[],{"categories":7141},[228],{"categories":7143},[],{"categories":7145},[228],{"categories":7147},[324],{"categories":7149},[288],{"categories":7151},[],{"categories":7153},[302],{"categories":7155},[275],{"categories":7157},[375],{"categories":7159},[],{"categories":7161},[275],{"categories":7163},[],{"categories":7165},[324],{"categories":7167},[278],{"categories":7169},[228],{"categories":7171},[],{"categories":7173},[302],{"categories":7175},[324],[7177,7402,7535,7909],{"id":7178,"title":7179,"ai":7180,"body":7185,"categories":7378,"created_at":229,"date_modified":229,"description":220,"extension":230,"faq":229,"featured":231,"kicker_label":229,"meta":7379,"navigation":254,"path":7390,"published_at":7391,"question":229,"scraped_at":7392,"seo":7393,"sitemap":7394,"source_id":7395,"source_name":261,"source_type":262,"source_url":7396,"stem":7397,"tags":7398,"thumbnail_url":229,"tldr":7399,"tweet":229,"unknown_tags":7400,"__hash__":7401},"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":7181,"output_tokens":7182,"processing_time_ms":7183,"cost_usd":7184},9292,2519,30098,0.00309525,{"type":14,"value":7186,"toc":7372},[7187,7191,7221,7225,7274,7278,7343,7347],[17,7188,7190],{"id":7189},"data-prep-and-baseline-benchmarks-deliver-quick-wins","Data Prep and Baseline Benchmarks Deliver Quick Wins",[22,7192,7193,7194,7197,7198,7201,7202,7205,7206,49,7209,7212,7213,7216,7217,7220],{},"Load S&P 500 prices via ",[26,7195,7196],{},"skfolio.datasets.load_sp500_dataset()",", convert to returns with ",[26,7199,7200],{},"prices_to_returns()",", and split chronologically (",[26,7203,7204],{},"train_test_split(shuffle=False, test_size=0.33)",") to prevent look-ahead bias—training spans ~67% historical days, testing the rest. Baselines like ",[26,7207,7208],{},"EqualWeighted()",[26,7210,7211],{},"InverseVolatility()",", and ",[26,7214,7215],{},"Random()"," fit on train, predict on test, yielding metrics like annualized Sharpe (printed via ",[26,7218,7219],{},"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.",[17,7222,7224],{"id":7223},"mean-variance-risk-measures-and-clustering-beat-baselines","Mean-Variance, Risk Measures, and Clustering Beat Baselines",[22,7226,7227,7230,7231,7234,7235,7238,7239,7242,7243,49,7246,7249,7250,7253,7254,7257,7258,7261,7262,7265,7266,7269,7270,7273],{},[26,7228,7229],{},"MeanRisk(risk_measure=RiskMeasure.VARIANCE)"," minimizes variance or maximizes Sharpe (",[26,7232,7233],{},"ObjectiveFunction.MAXIMIZE_RATIO","), generating efficient frontiers (",[26,7236,7237],{},"efficient_frontier_size=20",") plotted by risk vs. Sharpe. Swap risks to ",[26,7240,7241],{},"CVaR"," (95%), ",[26,7244,7245],{},"SEMI_VARIANCE",[26,7247,7248],{},"CDAR",", or ",[26,7251,7252],{},"MAX_DRAWDOWN"," for tail-focused portfolios that cut CVaR@95% and max drawdown vs. variance. ",[26,7255,7256],{},"RiskBudgeting()"," equalizes contributions (variance or CVaR). Hierarchical methods shine: ",[26,7259,7260],{},"HierarchicalRiskParity()"," clusters assets via dendrograms for stable weights; ",[26,7263,7264],{},"NestedClustersOptimization()"," nests ",[26,7267,7268],{},"MeanRisk(CVAR)"," inside ",[26,7271,7272],{},"RiskBudgeting(VARIANCE)"," with 5-fold CV, capturing correlations without covariance pitfalls.",[17,7275,7277],{"id":7276},"robust-priors-constraints-and-views-stabilize-real-world-use","Robust Priors, Constraints, and Views Stabilize Real-World Use",[22,7279,7280,7281,7284,7285,7288,7289,49,7292,49,7295,7249,7298,7301,7302,7305,7306,49,7309,49,7312,49,7315,7318,7319,7322,7323,7326,7327,7330,7331,7334,7335,7338,7339,7342],{},"Replace ",[26,7282,7283],{},"EmpiricalCovariance()","\u002F",[26,7286,7287],{},"EmpiricalMu()"," with ",[26,7290,7291],{},"DenoiseCovariance()",[26,7293,7294],{},"ShrunkMu()",[26,7296,7297],{},"GerberCovariance()",[26,7299,7300],{},"EWMu(alpha=0.1)"," in ",[26,7303,7304],{},"EmpiricalPrior()"," for max-Sharpe portfolios resilient to estimation error. Add realism via ",[26,7307,7308],{},"min_weights=0.0",[26,7310,7311],{},"max_weights=0.20",[26,7313,7314],{},"transaction_costs=0.0005",[26,7316,7317],{},"groups"," (e.g., GroupA \u003C=0.6, GroupB>=0.2), ",[26,7320,7321],{},"l2_coef=0.01",". ",[26,7324,7325],{},"BlackLitterman(views=[\"AAPL == 0.0008\", \"JPM - BAC == 0.0002\"])"," blends market priors with views. ",[26,7328,7329],{},"FactorModel()"," on ",[26,7332,7333],{},"load_factors_dataset()"," explains returns via external factors, boosting Sharpe. Pipelines like ",[26,7336,7337],{},"SelectKExtremes(k=8)"," + ",[26,7340,7341],{},"MeanRisk()"," prune to top performers.",[17,7344,7346],{"id":7345},"walk-forward-cv-and-tuning-ensure-out-of-sample-performance","Walk-Forward CV and Tuning Ensure Out-of-Sample Performance",[22,7348,7349,7288,7352,7355,7356,7359,7360,7363,7364,7367,7368,7371],{},[26,7350,7351],{},"cross_val_predict()",[26,7353,7354],{},"WalkForward(train_size=252*2, test_size=63)"," simulates rolling 2-year trains\u002F3-month tests, computing portfolio Sharpe\u002FCalmar. ",[26,7357,7358],{},"GridSearchCV()"," tunes ",[26,7361,7362],{},"l2_coef=[0.0,0.01,0.1]"," and ",[26,7365,7366],{},"mu_estimator__alpha=[0.05,0.1,0.2,0.5]"," on max-Sharpe, selecting best CV Sharpe. Final ",[26,7369,7370],{},"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":220,"searchDepth":221,"depth":221,"links":7373},[7374,7375,7376,7377],{"id":7189,"depth":221,"text":7190},{"id":7223,"depth":221,"text":7224},{"id":7276,"depth":221,"text":7277},{"id":7345,"depth":221,"text":7346},[228],{"content_references":7380,"triage":7387},[7381,7384],{"type":235,"title":7382,"url":7383,"context":238},"skfolio","https:\u002F\u002Fgithub.com\u002Fskfolio\u002Fskfolio",{"type":245,"title":7385,"url":7386,"context":238},"Full Codes","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FData%20Science\u002Fportfolio_optimization_with_skfolio_Marktechpost.ipynb",{"relevance":250,"novelty":250,"quality":251,"actionability":251,"composite":7388,"reasoning":7389},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":7179,"description":220},{"loc":7390},"ff126f8e0954389e","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",[268,266,267],"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":7403,"title":7404,"ai":7405,"body":7410,"categories":7512,"created_at":229,"date_modified":229,"description":220,"extension":230,"faq":229,"featured":231,"kicker_label":229,"meta":7513,"navigation":254,"path":7523,"published_at":7524,"question":229,"scraped_at":7525,"seo":7526,"sitemap":7527,"source_id":7528,"source_name":261,"source_type":262,"source_url":7529,"stem":7530,"tags":7531,"thumbnail_url":229,"tldr":7532,"tweet":229,"unknown_tags":7533,"__hash__":7534},"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":7406,"output_tokens":7407,"processing_time_ms":7408,"cost_usd":7409},9215,1914,16447,0.00277735,{"type":14,"value":7411,"toc":7507},[7412,7416,7419,7430,7460,7463,7467,7470,7493,7496,7500,7503],[17,7413,7415],{"id":7414},"tabpfns-pretraining-enables-direct-inference-on-tabular-tasks","TabPFN's Pretraining Enables Direct Inference on Tabular Tasks",[22,7417,7418],{},"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.",[22,7420,7421,7422,7425,7426,7429],{},"To implement, install via ",[26,7423,7424],{},"pip install tabpfn-client scikit-learn catboost",", set ",[26,7427,7428],{},"TABPFN_TOKEN"," from priorlabs.ai, then:",[7431,7432,7435],"pre",{"className":7433,"code":7434,"language":268,"meta":220,"style":220},"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",[26,7436,7437,7445,7450,7455],{"__ignoreMap":220},[7438,7439,7442],"span",{"class":7440,"line":7441},"line",1,[7438,7443,7444],{},"from tabpfn_client import TabPFNClassifier\n",[7438,7446,7447],{"class":7440,"line":221},[7438,7448,7449],{},"tabpfn = TabPFNClassifier()\n",[7438,7451,7452],{"class":7440,"line":250},[7438,7453,7454],{},"tabpfn.fit(X_train, y_train)  # Loads weights\n",[7438,7456,7457],{"class":7440,"line":251},[7438,7458,7459],{},"tabpfn_preds = tabpfn.predict(X_test)\n",[22,7461,7462],{},"This shifts computation from training to inference, ideal for rapid prototyping where setup speed trumps everything.",[17,7464,7466],{"id":7465},"quantified-wins-over-tree-based-baselines","Quantified Wins Over Tree-Based Baselines",[22,7468,7469],{},"Tested on scikit-learn's synthetic binary classification: 5,000 samples, 20 features (10 informative, 5 redundant), 80\u002F20 train\u002Ftest split.",[7471,7472,7473,7481,7487],"ul",{},[7474,7475,7476,7480],"li",{},[7477,7478,7479],"strong",{},"Random Forest"," (200 trees): 95.5% accuracy, 9.56s train, 0.0627s infer. Robust bagging handles noise but plateaus on complex interactions.",[7474,7482,7483,7486],{},[7477,7484,7485],{},"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.",[7474,7488,7489,7492],{},[7477,7490,7491],{},"TabPFN",": 98.8% accuracy, 0.47s fit, 2.21s infer. Gains 2.1-3.3% accuracy by leveraging pretrained priors on noisy features.",[22,7494,7495],{},"TabPFN wins on accuracy and setup for small-to-medium data (\u003C10k rows), eliminating tuning that tree models demand.",[17,7497,7499],{"id":7498},"inference-cost-and-distillation-for-production","Inference Cost and Distillation for Production",[22,7501,7502],{},"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.",[7504,7505,7506],"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":220,"searchDepth":221,"depth":221,"links":7508},[7509,7510,7511],{"id":7414,"depth":221,"text":7415},{"id":7465,"depth":221,"text":7466},{"id":7498,"depth":221,"text":7499},[228],{"content_references":7514,"triage":7519},[7515,7517],{"type":235,"title":7491,"url":7516,"context":238},"https:\u002F\u002Fux.priorlabs.ai\u002Fhome",{"type":245,"title":246,"url":7518,"context":238},"https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FData%20Science\u002FTabPFN.ipynb",{"relevance":7520,"novelty":251,"quality":251,"actionability":251,"composite":7521,"reasoning":7522},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":7404,"description":220},{"loc":7523},"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",[267,266,268],"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.",[],"9KrCooHF7vR_dcuIczpeQ-ZAJA2-GbybMn_JX6dybVY",{"id":7536,"title":7537,"ai":7538,"body":7543,"categories":7895,"created_at":229,"date_modified":229,"description":220,"extension":230,"faq":229,"featured":231,"kicker_label":229,"meta":7896,"navigation":254,"path":7897,"published_at":7898,"question":229,"scraped_at":229,"seo":7899,"sitemap":7900,"source_id":7901,"source_name":7902,"source_type":262,"source_url":7903,"stem":7904,"tags":7905,"thumbnail_url":229,"tldr":7906,"tweet":229,"unknown_tags":7907,"__hash__":7908},"summaries\u002Fsummaries\u002Fsynthetically-label-sparse-bequest-donors-realisti-summary.md","Synthetically Label Sparse Bequest Donors Realistically",{"provider":7,"model":8,"input_tokens":7539,"output_tokens":7540,"processing_time_ms":7541,"cost_usd":7542},9589,2408,16814,0.00309915,{"type":14,"value":7544,"toc":7889},[7545,7549,7556,7559,7563,7574,7620,7674,7705,7714,7718,7721,7854,7864,7868,7887],[17,7546,7548],{"id":7547},"tackle-imbalanced-bequest-data-with-synthetic-targets","Tackle Imbalanced Bequest Data with Synthetic Targets",[22,7550,7551,7552,7555],{},"Charity databases have \u003C1% confirmed bequest donors—those formally notifying intent—despite >50% of gifts coming from lifetime strangers. Build a realistic target ",[26,7553,7554],{},"bequest_status"," ('Confirmed' or NA) using a propensity formula on RFMT (recency\u002Ffrequency\u002Fmonetary\u002Ftenure), age groups, and regular giving (RG) status. Add controlled randomness via Bernoulli sampling on propensity probability to mimic human variability and block model 'cheating'—where deterministic labels let algorithms rediscover the exact formula, creating an echo chamber.",[22,7557,7558],{},"Max propensity normalizes to ~357 (sum of peak scores: r=5,f=10,m=3,t=10,age=10x2=20 * rg=1.2), yielding probs like 0.089 for high scorers. This forces models to extract true signals amid noise, mirroring real sparse data.",[17,7560,7562],{"id":7561},"engineer-rfmt-age-and-rg-features-from-transactions","Engineer RFMT, Age, and RG Features from Transactions",[22,7564,7565,7566,7569,7570,7573],{},"Start with ",[26,7567,7568],{},"df_opps"," (opportunities) and ",[26,7571,7572],{},"df_contacts",":",[7471,7575,7576],{},[7474,7577,7578,7581,7582,7585,7586,7589,7590,7593,7594,7597,7598,7601,7602,7593,7605,7608,7609,7611,7612,7615,7616,7619],{},[7477,7579,7580],{},"RFMT",": Group by ",[26,7583,7584],{},"contact_id","; compute ",[26,7587,7588],{},"last_gift_date"," (max ",[26,7591,7592],{},"close_date","), ",[26,7595,7596],{},"first_gift_date"," (min), ",[26,7599,7600],{},"frequency"," (count ",[26,7603,7604],{},"amount",[26,7606,7607],{},"monetary_value"," (sum ",[26,7610,7604],{},"). Then ",[26,7613,7614],{},"recency"," = months since end_date (2025-12-31); ",[26,7617,7618],{},"tenure"," = months between first\u002Flast gift.",[7431,7621,7623],{"className":7433,"code":7622,"language":268,"meta":220,"style":220},"def generate_rfmt(data):\n    df = data.groupby('contact_id').agg({\n        'close_date': ['max', 'min'],\n        'amount': ['count', 'sum']\n    })\n    df.columns = ['last_gift_date', 'first_gift_date', 'frequency', 'monetary_value']\n    # Convert to date, compute recency\u002Ftenure with relativedelta\n    # ...\n    return df.reset_index()\n",[26,7624,7625,7630,7635,7640,7645,7650,7656,7662,7668],{"__ignoreMap":220},[7438,7626,7627],{"class":7440,"line":7441},[7438,7628,7629],{},"def generate_rfmt(data):\n",[7438,7631,7632],{"class":7440,"line":221},[7438,7633,7634],{},"    df = data.groupby('contact_id').agg({\n",[7438,7636,7637],{"class":7440,"line":250},[7438,7638,7639],{},"        'close_date': ['max', 'min'],\n",[7438,7641,7642],{"class":7440,"line":251},[7438,7643,7644],{},"        'amount': ['count', 'sum']\n",[7438,7646,7647],{"class":7440,"line":7520},[7438,7648,7649],{},"    })\n",[7438,7651,7653],{"class":7440,"line":7652},6,[7438,7654,7655],{},"    df.columns = ['last_gift_date', 'first_gift_date', 'frequency', 'monetary_value']\n",[7438,7657,7659],{"class":7440,"line":7658},7,[7438,7660,7661],{},"    # Convert to date, compute recency\u002Ftenure with relativedelta\n",[7438,7663,7665],{"class":7440,"line":7664},8,[7438,7666,7667],{},"    # ...\n",[7438,7669,7671],{"class":7440,"line":7670},9,[7438,7672,7673],{},"    return df.reset_index()\n",[7471,7675,7676,7685],{},[7474,7677,7678,7681,7682,108],{},[7477,7679,7680],{},"Age groups",": ",[26,7683,7684],{},"pd.cut(age, bins=[0,39,49,59,69,90], labels=['under_40','40-49','50-59','60-69','70_or_over'])",[7474,7686,7687,7690,7691,7694,7695,7284,7698,7701,7702,7704],{},[7477,7688,7689],{},"RG status",": Filter ",[26,7692,7693],{},"df_opps[type=='Regular']","; get ",[26,7696,7697],{},"first_rg_date",[26,7699,7700],{},"last_rg_date"," per ID. If ",[26,7703,7700],{}," in 2025-12: 'Active'; else 'Cancelled'. No RG → 'No RG' post-merge.",[22,7706,7707,7708,7284,7711,108],{},"Merge right on RFMT (drop no-history contacts), left on RG; fillna 'No RG'; drop extras like ",[26,7709,7710],{},"name",[26,7712,7713],{},"gender",[17,7715,7717],{"id":7716},"sector-tailored-scores-capture-counterintuitive-patterns","Sector-Tailored Scores Capture Counterintuitive Patterns",[22,7719,7720],{},"Assign 0-10 scores per feature, weighted for legacy giving realities (e.g., retired lapsed donors outscore active; mid-value > high-value):",[7722,7723,7724,7743],"table",{},[7725,7726,7727],"thead",{},[7728,7729,7730,7734,7737,7740],"tr",{},[7731,7732,7733],"th",{},"Feature",[7731,7735,7736],{},"Bins\u002FLogic",[7731,7738,7739],{},"Labels",[7731,7741,7742],{},"Rationale",[7744,7745,7746,7768,7788,7808,7826,7840],"tbody",{},[7728,7747,7748,7752,7757,7762],{},[7749,7750,7751],"td",{},"Recency",[7749,7753,7754],{},[26,7755,7756],{},"[-1,18,42,84,1000]",[7749,7758,7759],{},[7438,7760,7761],{},"4,5,2,1",[7749,7763,7764,7765,108],{},"18-42mo 'sweet spot' for retired lapsed (highest); recent active lower; long dormant still viable. ",[26,7766,7767],{},"pd.cut",[7728,7769,7770,7773,7778,7783],{},[7749,7771,7772],{},"Frequency",[7749,7774,7775],{},[26,7776,7777],{},"[-1,2,9,49,99,10000]",[7749,7779,7780],{},[7438,7781,7782],{},"0,1,4,7,10",[7749,7784,7785,7786,108],{},"Frequency > value; 100+ 'Revolutionary'=10. ",[26,7787,7767],{},[7728,7789,7790,7793,7802,7805],{},[7749,7791,7792],{},"Monetary (quintiles)",[7749,7794,7795,7798,7799],{},[26,7796,7797],{},"pd.qcut(q=5, labels=[1,2,3,4,5])"," → map ",[26,7800,7801],{},"{1:0,2:2,3:3,4:3,5:1}",[7749,7803,7804],{},"Peak mid-quintiles",[7749,7806,7807],{},"Mid-value (40-80%) most generous legacies; top 20% less confirmatory.",[7728,7809,7810,7813,7818,7823],{},[7749,7811,7812],{},"Tenure",[7749,7814,7815],{},[26,7816,7817],{},"pd.cut(bins=5)",[7749,7819,7820],{},[7438,7821,7822],{},"0,1,3,6,10",[7749,7824,7825],{},"Long tenure >> short; steep curve for loyalty.",[7728,7827,7828,7831,7834,7837],{},[7749,7829,7830],{},"Age",[7749,7832,7833],{},"Map groups",[7749,7835,7836],{},"{'under_40':0,'40-49':1,'50-59':3,'60-69':7,'70+':10}",[7749,7838,7839],{},"Exponential post-60; doubled in formula, not gated.",[7728,7841,7842,7845,7848,7851],{},[7749,7843,7844],{},"RG Weight (multiplier)",[7749,7846,7847],{},"Map",[7749,7849,7850],{},"{'Cancelled':1.2,'Active':1.0,'No RG':0.5}",[7749,7852,7853],{},"Lapsed RG strong signal of estate shift.",[22,7855,7856,7859,7860,7863],{},[7477,7857,7858],{},"Raw propensity"," = ",[26,7861,7862],{},"(r_score + f_score + m_score + t_score + 2*age_score) * rg_weight",". E.g., high-freq recent-lapsed 70+: ~31.8 (prob 0.089); low everything: ~1 (prob 0.003).",[17,7865,7867],{"id":7866},"stochastic-assignment-mimics-real-donor-behavior","Stochastic Assignment Mimics Real Donor Behavior",[22,7869,7870,7871,7874,7875,7878,7879,7882,7883,7886],{},"Convert ",[26,7872,7873],{},"raw_propensity"," to ",[26,7876,7877],{},"assignment_prob"," (e.g., ",[26,7880,7881],{},"\u002F357"," for 0-1 scale), then ",[26,7884,7885],{},"bequest_status = np.random.binomial(1, prob)"," → 'Confirmed' if 1. This injects noise: perfect scorers sometimes miss, low scorers occasionally confirm—breaking determinism so downstream classifiers learn generalizable patterns, not the formula.",[7504,7888,7506],{},{"title":220,"searchDepth":221,"depth":221,"links":7890},[7891,7892,7893,7894],{"id":7547,"depth":221,"text":7548},{"id":7561,"depth":221,"text":7562},{"id":7716,"depth":221,"text":7717},{"id":7866,"depth":221,"text":7867},[228],{},"\u002Fsummaries\u002Fsynthetically-label-sparse-bequest-donors-realisti-summary","2026-04-08 21:21:18",{"title":7537,"description":220},{"loc":7897},"e0225ec94060d95d","Data and Beyond","https:\u002F\u002Funknown","summaries\u002Fsynthetically-label-sparse-bequest-donors-realisti-summary",[268,266,267],"Engineer RFMT-age-RG propensity scores with sector-specific bins (e.g., recency sweet spot 18-42mo=5pts) and stochastic noise to create 'Confirmed' labels, preventing models from overfitting formulas in \u003C1% positive charity data.",[],"Y2cIR1YxXNmF6nVq7KUQn_Jk5dp8tvzxIL29SZ2yDmA",{"id":7910,"title":7911,"ai":7912,"body":7918,"categories":8037,"created_at":229,"date_modified":229,"description":220,"extension":230,"faq":229,"featured":231,"kicker_label":229,"meta":8038,"navigation":254,"path":8063,"published_at":8064,"question":229,"scraped_at":8065,"seo":8066,"sitemap":8067,"source_id":8068,"source_name":8069,"source_type":262,"source_url":8070,"stem":8071,"tags":8072,"thumbnail_url":229,"tldr":8074,"tweet":229,"unknown_tags":8075,"__hash__":8076},"summaries\u002Fsummaries\u002F70f39582f8d6feb6-mastering-probability-distributions-for-machine-le-summary.md","Mastering Probability Distributions for Machine Learning",{"provider":7,"model":7913,"input_tokens":7914,"output_tokens":7915,"processing_time_ms":7916,"cost_usd":7917},"google\u002Fgemini-3.1-flash-lite",9946,792,3147,0.0036745,{"type":14,"value":7919,"toc":8032},[7920,7924,7927,7931,8004,8008,8011],[17,7921,7923],{"id":7922},"the-role-of-distributions-in-data-science","The Role of Distributions in Data Science",[22,7925,7926],{},"A probability distribution is a map of how likely different outcomes are. Understanding the 'shape' of your data is critical because machine learning algorithms make implicit assumptions about these shapes. Ignoring these assumptions leads to underperforming models and unreliable predictions. Distributions are categorized into discrete (countable outcomes) and continuous (any value in a range) types.",[17,7928,7930],{"id":7929},"the-nine-essential-distributions","The Nine Essential Distributions",[7471,7932,7933,7939,7945,7956,7966,7972,7982,7992,7998],{},[7474,7934,7935,7938],{},[7477,7936,7937],{},"Normal (Bell Curve):"," Symmetric distribution defined by mean (μ) and standard deviation (σ). Governed by the 68-95-99.7 rule.",[7474,7940,7941,7944],{},[7477,7942,7943],{},"Bernoulli:"," A single trial with two outcomes (success\u002Ffailure). The building block for more complex models.",[7474,7946,7947,7950,7951,7955],{},[7477,7948,7949],{},"Binomial:"," The result of repeating Bernoulli trials ",[7952,7953,7954],"em",{},"n"," times. Useful for counting total successes.",[7474,7957,7958,7961,7962,7965],{},[7477,7959,7960],{},"Geometric:"," Models the number of trials required to achieve the ",[7952,7963,7964],{},"first"," success.",[7474,7967,7968,7971],{},[7477,7969,7970],{},"Poisson:"," Models the number of rare events occurring in a fixed interval (e.g., support tickets per hour).",[7474,7973,7974,7977,7978,7981],{},[7477,7975,7976],{},"Exponential:"," Models the time ",[7952,7979,7980],{},"between"," consecutive Poisson events. Features the 'memoryless' property.",[7474,7983,7984,7987,7988,7991],{},[7477,7985,7986],{},"Gamma:"," Extends the Exponential distribution to model the time until the ",[7952,7989,7990],{},"k","-th event.",[7474,7993,7994,7997],{},[7477,7995,7996],{},"Beta:"," Designed for proportions and probabilities (0 to 1). Essential for Bayesian inference to update beliefs with new evidence.",[7474,7999,8000,8003],{},[7477,8001,8002],{},"Uniform:"," Represents complete neutrality where all outcomes are equally likely.",[17,8005,8007],{"id":8006},"practical-application-in-ml-pipelines","Practical Application in ML Pipelines",[22,8009,8010],{},"Understanding distributions provides three specific superpowers:",[8012,8013,8014,8020,8026],"ol",{},[7474,8015,8016,8019],{},[7477,8017,8018],{},"Model Selection:"," Matching the model to the data type (e.g., using Poisson regression for count data rather than linear regression).",[7474,8021,8022,8025],{},[7477,8023,8024],{},"Feature Engineering:"," Applying transformations (like log-transforms) to skewed features (Exponential\u002FGamma) to make them more Normal, which improves performance for many algorithms.",[7474,8027,8028,8031],{},[7477,8029,8030],{},"Uncertainty Quantification:"," Using Bayesian priors (Beta distribution) to provide confidence intervals rather than just point estimates, which is critical for safety-critical applications.",{"title":220,"searchDepth":221,"depth":221,"links":8033},[8034,8035,8036],{"id":7922,"depth":221,"text":7923},{"id":7929,"depth":221,"text":7930},{"id":8006,"depth":221,"text":8007},[228],{"content_references":8039,"triage":8060},[8040,8045,8048,8051,8054,8057],{"type":8041,"title":8042,"author":8043,"context":8044},"book","Introductory Statistics with R","Peter Dalgaard","cited",{"type":8041,"title":8046,"author":8047,"context":8044},"Introduction to Probability Models","Sheldon Ross",{"type":235,"title":8049,"url":8050,"context":238},"NumPy","https:\u002F\u002Fnumpy.org\u002F",{"type":235,"title":8052,"url":8053,"context":238},"SciPy","https:\u002F\u002Fscipy.org\u002F",{"type":235,"title":8055,"url":8056,"context":238},"scikit-learn","https:\u002F\u002Fscikit-learn.org\u002F",{"type":235,"title":8058,"url":8059,"context":238},"statsmodels","https:\u002F\u002Fwww.statsmodels.org\u002F",{"relevance":251,"novelty":250,"quality":251,"actionability":251,"composite":8061,"reasoning":8062},3.8,"Category: Data Science & Visualization. The article discusses how understanding probability distributions can enhance model selection and feature engineering in machine learning, addressing a specific pain point for builders looking to improve their AI products. It provides practical applications, such as using Poisson regression for count data, which can be directly applied in production.","\u002Fsummaries\u002F70f39582f8d6feb6-mastering-probability-distributions-for-machine-le-summary","2026-06-28 19:37:46","2026-06-29 12:57:23",{"title":7911,"description":220},{"loc":8063},"70f39582f8d6feb6","Python in Plain English","https:\u002F\u002Fpython.plainenglish.io\u002Ffrom-bell-curves-to-rare-events-mastering-probability-distributions-like-a-pro-33b25ce8cc4b?source=rss----78073def27b8---4","summaries\u002F70f39582f8d6feb6-mastering-probability-distributions-for-machine-le-summary",[267,266,268,8073],"statistics","Probability distributions are maps of data behavior. Understanding them allows you to select better models, engineer features effectively, and quantify uncertainty in production pipelines.",[8073],"7Qe3nIDAGFGGm_FOPEpc6iOM5hWgQZuFmPxR93I3waQ"]