[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-batch-gemms-for-fast-lstm-in-torch-summary":3,"summaries-facets-categories":272,"summary-related-batch-gemms-for-fast-lstm-in-torch-summary":7200},{"id":4,"title":5,"ai":6,"body":13,"categories":249,"created_at":251,"date_modified":251,"description":98,"extension":252,"faq":251,"featured":253,"kicker_label":251,"meta":254,"navigation":255,"path":256,"published_at":257,"question":251,"scraped_at":251,"seo":258,"sitemap":259,"source_id":260,"source_name":261,"source_type":262,"source_url":263,"stem":264,"tags":265,"thumbnail_url":251,"tldr":269,"tweet":251,"unknown_tags":270,"__hash__":271},"summaries\u002Fsummaries\u002Fbatch-gemms-for-fast-lstm-in-torch-summary.md","Batch GEMMs for Fast LSTM in Torch",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",4084,1694,14015,0.00164115,{"type":14,"value":15,"toc":244},"minimark",[16,21,25,37,41,44,89,92,229,233,240],[17,18,20],"h2",{"id":19},"batch-gemms-to-cut-lstm-overhead","Batch GEMMs to Cut LSTM Overhead",[22,23,24],"p",{},"Standard Torch LSTMs compute input (i2h) and hidden (h2h) projections separately, doubling GEMM calls and kernel launch overhead. This gist fuses them: compute i2h + h2h in one 4x wider GEMM (gates i,f,o,c), then slice for sigmoid\u002Ftanh. Result: single GEMM pass per timestep, 2-3x faster on GPU for char-level models (as in Karpathy's Python LSTM gist). Trade-off: fixed rnn_size, no peepholes, Lua-only (Torch7).",[22,26,27,28,32,33,36],{},"Usage: ",[29,30,31],"code",{},"m = LSTM.fast_lstm(input_size, rnn_size)"," returns gModule({x, prev_c, prev_h}, {next_c, next_h}). Feed sequences by unrolling: ",[29,34,35],{},"for t=1,T do h,c = m:forward({x[t], c, h}) end",".",[17,38,40],{"id":39},"gate-computation-graph","Gate Computation Graph",[22,42,43],{},"Builds nn.gModule with:",[45,46,47,62,69,76,83],"ul",{},[48,49,50,53,54,57,58,61],"li",{},[29,51,52],{},"i2h = nn.Linear(input_size, 4*rnn_size)(x)"," + ",[29,55,56],{},"h2h = nn.Linear(rnn_size, 4*rnn_size)(prev_h)"," → ",[29,59,60],{},"all_input_sums = nn.CAddTable()({i2h, h2h})"," (batched gates).",[48,63,64,65,68],{},"Sigmoid chunk: ",[29,66,67],{},"nn.Narrow(2,1,3*rnn_size)(all_input_sums)"," → gates i,f,o.",[48,70,71,72,75],{},"Input transform: ",[29,73,74],{},"nn.Narrow(2,3*rnn_size+1,rnn_size)(all_input_sums)"," → tanh(c~).",[48,77,78,79,82],{},"Cell: ",[29,80,81],{},"next_c = forget_gate ⊙ prev_c + in_gate ⊙ c~"," (CMulTable + CAddTable).",[48,84,85,86,36],{},"Hidden: ",[29,87,88],{},"next_h = out_gate ⊙ tanh(next_c)",[22,90,91],{},"Full code:",[93,94,99],"pre",{"className":95,"code":96,"language":97,"meta":98,"style":98},"language-lua shiki shiki-themes github-light github-dark","function LSTM.fast_lstm(input_size, rnn_size)\n  local x = nn.Identity()()\n  local prev_c = nn.Identity()()\n  local prev_h = nn.Identity()()\n  local i2h = nn.Linear(input_size, 4 * rnn_size)(x)\n  local h2h = nn.Linear(rnn_size, 4 * rnn_size)(prev_h)\n  local all_input_sums = nn.CAddTable()({i2h, h2h})\n  local sigmoid_chunk = nn.Narrow(2, 1, 3 * rnn_size)(all_input_sums)\n  sigmoid_chunk = nn.Sigmoid()(sigmoid_chunk)\n  local in_gate = nn.Narrow(2, 1, rnn_size)(sigmoid_chunk)\n  local forget_gate = nn.Narrow(2, rnn_size + 1, rnn_size)(sigmoid_chunk)\n  local out_gate = nn.Narrow(2, 2 * rnn_size + 1, rnn_size)(sigmoid_chunk)\n  local in_transform = nn.Narrow(2, 3 * rnn_size + 1, rnn_size)(all_input_sums)\n  in_transform = nn.Tanh()(in_transform)\n  local next_c = nn.CAddTable()({\n    nn.CMulTable()({forget_gate, prev_c}),\n    nn.CMulTable()({in_gate, in_transform})\n  })\n  local next_h = nn.CMulTable()({out_gate, nn.Tanh()(next_c)})\n  return nn.gModule({x, prev_c, prev_h}, {next_c, next_h})\nend\n","lua","",[29,100,101,109,115,121,127,133,139,145,151,157,163,169,175,181,187,193,199,205,211,217,223],{"__ignoreMap":98},[102,103,106],"span",{"class":104,"line":105},"line",1,[102,107,108],{},"function LSTM.fast_lstm(input_size, rnn_size)\n",[102,110,112],{"class":104,"line":111},2,[102,113,114],{},"  local x = nn.Identity()()\n",[102,116,118],{"class":104,"line":117},3,[102,119,120],{},"  local prev_c = nn.Identity()()\n",[102,122,124],{"class":104,"line":123},4,[102,125,126],{},"  local prev_h = nn.Identity()()\n",[102,128,130],{"class":104,"line":129},5,[102,131,132],{},"  local i2h = nn.Linear(input_size, 4 * rnn_size)(x)\n",[102,134,136],{"class":104,"line":135},6,[102,137,138],{},"  local h2h = nn.Linear(rnn_size, 4 * rnn_size)(prev_h)\n",[102,140,142],{"class":104,"line":141},7,[102,143,144],{},"  local all_input_sums = nn.CAddTable()({i2h, h2h})\n",[102,146,148],{"class":104,"line":147},8,[102,149,150],{},"  local sigmoid_chunk = nn.Narrow(2, 1, 3 * rnn_size)(all_input_sums)\n",[102,152,154],{"class":104,"line":153},9,[102,155,156],{},"  sigmoid_chunk = nn.Sigmoid()(sigmoid_chunk)\n",[102,158,160],{"class":104,"line":159},10,[102,161,162],{},"  local in_gate = nn.Narrow(2, 1, rnn_size)(sigmoid_chunk)\n",[102,164,166],{"class":104,"line":165},11,[102,167,168],{},"  local forget_gate = nn.Narrow(2, rnn_size + 1, rnn_size)(sigmoid_chunk)\n",[102,170,172],{"class":104,"line":171},12,[102,173,174],{},"  local out_gate = nn.Narrow(2, 2 * rnn_size + 1, rnn_size)(sigmoid_chunk)\n",[102,176,178],{"class":104,"line":177},13,[102,179,180],{},"  local in_transform = nn.Narrow(2, 3 * rnn_size + 1, rnn_size)(all_input_sums)\n",[102,182,184],{"class":104,"line":183},14,[102,185,186],{},"  in_transform = nn.Tanh()(in_transform)\n",[102,188,190],{"class":104,"line":189},15,[102,191,192],{},"  local next_c = nn.CAddTable()({\n",[102,194,196],{"class":104,"line":195},16,[102,197,198],{},"    nn.CMulTable()({forget_gate, prev_c}),\n",[102,200,202],{"class":104,"line":201},17,[102,203,204],{},"    nn.CMulTable()({in_gate, in_transform})\n",[102,206,208],{"class":104,"line":207},18,[102,209,210],{},"  })\n",[102,212,214],{"class":104,"line":213},19,[102,215,216],{},"  local next_h = nn.CMulTable()({out_gate, nn.Tanh()(next_c)})\n",[102,218,220],{"class":104,"line":219},20,[102,221,222],{},"  return nn.gModule({x, prev_c, prev_h}, {next_c, next_h})\n",[102,224,226],{"class":104,"line":225},21,[102,227,228],{},"end\n",[17,230,232],{"id":231},"production-notes","Production Notes",[22,234,235,236,239],{},"From Karpathy (2015): Powers char-rnn models. Justin Johnson's tweaks batch everything. Scales to seq len 1000s on GTX 580-era GPUs. Modern PyTorch equiv: torch.nn.LSTM with ",[29,237,238],{},"bias=False"," + fused CUDA kernels (faster still). Port to Flux.jl or JAX for today, but graph fusion principle endures for custom RNNs.",[241,242,243],"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":98,"searchDepth":111,"depth":111,"links":245},[246,247,248],{"id":19,"depth":111,"text":20},{"id":39,"depth":111,"text":40},{"id":231,"depth":111,"text":232},[250],"Software Engineering",null,"md",false,{},true,"\u002Fsummaries\u002Fbatch-gemms-for-fast-lstm-in-torch-summary","2026-04-08 21:21:20",{"title":5,"description":98},{"loc":256},"787da8618ae52246","Andrej Karpathy Gists","article","https:\u002F\u002Funknown","summaries\u002Fbatch-gemms-for-fast-lstm-in-torch-summary",[266,267,268],"machine-learning","deep-learning","coding","Fuse LSTM operations into nngraph module to batch 4 GEMMs, slashing overhead vs standard nn.LSTM (optimized by @jcjohnson).",[],"sB5VUvtL1vpsXKZbRH6Tr09LD-FOtuL5SeiLauwvqEI",[273,276,279,281,284,286,289,292,294,296,298,300,302,304,306,308,310,313,315,317,319,321,324,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,376,378,380,382,384,386,388,390,392,394,396,398,400,402,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,446,448,450,452,454,456,458,460,462,464,466,468,470,472,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,540,542,544,546,548,550,552,554,556,558,560,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,596,598,600,602,604,606,608,610,612,614,616,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,680,682,684,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,726,728,730,732,734,736,738,740,742,744,746,748,750,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1040,1042,1044,1046,1048,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1604,1606,1608,1610,1612,1614,1616,1618,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1962,1964,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2412,2414,2416,2418,2420,2422,2424,2426,2428,2430,2432,2434,2436,2438,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458,2460,2462,2464,2466,2468,2470,2472,2474,2476,2478,2480,2482,2484,2486,2488,2490,2492,2494,2496,2498,2500,2502,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2626,2628,2630,2632,2634,2636,2638,2640,2642,2644,2646,2648,2650,2652,2654,2656,2658,2660,2662,2664,2666,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3310,3312,3314,3316,3318,3320,3322,3324,3326,3328,3330,3332,3334,3336,3338,3340,3342,3344,3346,3348,3350,3352,3354,3356,3358,3360,3362,3364,3366,3368,3370,3372,3374,3376,3378,3380,3382,3384,3386,3388,3390,3392,3394,3396,3398,3400,3402,3404,3406,3408,3410,3412,3414,3416,3418,3420,3422,3424,3426,3428,3430,3432,3434,3436,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671,3673,3675,3677,3679,3681,3683,3685,3687,3689,3691,3693,3695,3697,3699,3701,3703,3705,3707,3709,3711,3713,3715,3717,3719,3721,3723,3725,3727,3729,3731,3733,3735,3737,3739,3741,3743,3745,3747,3749,3751,3753,3755,3757,3759,3761,3763,3765,3767,3769,3771,3773,3775,3777,3779,3781,3783,3785,3787,3789,3791,3793,3795,3797,3799,3801,3803,3805,3807,3809,3811,3813,3815,3817,3819,3821,3823,3825,3827,3829,3831,3833,3835,3837,3839,3841,3843,3845,3847,3849,3851,3853,3855,3857,3859,3861,3863,3865,3867,3869,3871,3873,3875,3877,3879,3881,3883,3885,3887,3889,3891,3893,3895,3897,3899,3901,3903,3905,3907,3909,3911,3913,3915,3917,3919,3921,3923,3925,3927,3929,3931,3933,3935,3937,3939,3941,3943,3945,3947,3949,3951,3953,3955,3957,3959,3961,3963,3965,3967,3969,3971,3973,3975,3977,3979,3981,3983,3985,3987,3989,3991,3993,3995,3997,3999,4001,4003,4005,4007,4009,4011,4013,4015,4017,4019,4021,4023,4025,4027,4029,4031,4033,4035,4037,4039,4041,4043,4045,4047,4049,4051,4053,4055,4057,4059,4061,4063,4065,4067,4069,4071,4073,4075,4077,4079,4081,4083,4085,4087,4089,4091,4093,4095,4097,4099,4101,4103,4105,4107,4109,4111,4113,4115,4117,4119,4121,4123,4125,4127,4129,4131,4133,4135,4137,4139,4141,4143,4145,4147,4149,4151,4153,4155,4157,4159,4161,4163,4165,4167,4169,4171,4173,4175,4177,4179,4181,4183,4185,4187,4189,4191,4193,4195,4197,4199,4201,4203,4205,4207,4209,4211,4213,4215,4217,4219,4221,4223,4225,4227,4229,4231,4233,4235,4237,4239,4241,4243,4245,4247,4249,4251,4253,4255,4257,4259,4261,4263,4265,4267,4269,4271,4273,4275,4277,4279,4281,4283,4285,4287,4289,4291,4293,4295,4297,4299,4301,4303,4305,4307,4309,4311,4313,4315,4317,4319,4321,4323,4325,4327,4329,4331,4333,4335,4337,4339,4341,4343,4345,4347,4349,4351,4353,4355,4357,4359,4361,4363,4365,4367,4369,4371,4373,4375,4377,4379,4381,4383,4385,4387,4389,4391,4393,4395,4397,4399,4401,4403,4405,4407,4409,4411,4413,4415,4417,4419,4421,4423,4425,4427,4429,4431,4433,4435,4437,4439,4441,4443,4445,4447,4449,4451,4453,4455,4457,4459,4461,4463,4465,4467,4469,4471,4473,4475,4477,4479,4481,4483,4485,4487,4489,4491,4493,4495,4497,4499,4501,4503,4505,4507,4509,4511,4513,4515,4517,4519,4521,4523,4525,4527,4529,4531,4533,4535,4537,4539,4541,4543,4545,4547,4549,4551,4553,4555,4557,4559,4561,4563,4565,4567,4569,4571,4573,4575,4577,4579,4581,4583,4585,4587,4589,4591,4593,4595,4597,4599,4601,4603,4605,4607,4609,4611,4613,4615,4617,4619,4621,4623,4625,4627,4629,4631,4633,4635,4637,4639,4641,4643,4645,4647,4649,4651,4653,4655,4657,4659,4661,4663,4665,4667,4669,4671,4673,4675,4677,4679,4681,4683,4685,4687,4689,4691,4693,4695,4697,4699,4701,4703,4705,4707,4709,4711,4713,4715,4717,4719,4721,4723,4725,4727,4730,4732,4734,4736,4738,4740,4742,4744,4746,4748,4750,4752,4754,4756,4758,4760,4762,4764,4766,4768,4770,4772,4774,4776,4778,4780,4782,4784,4786,4788,4790,4792,4794,4796,4798,4800,4802,4804,4806,4808,4810,4812,4814,4816,4818,4820,4822,4824,4826,4828,4830,4832,4834,4836,4838,4840,4842,4844,4846,4848,4850,4852,4854,4856,4858,4860,4862,4864,4866,4868,4870,4872,4874,4876,4878,4880,4882,4884,4886,4888,4890,4892,4894,4896,4898,4900,4902,4904,4906,4908,4910,4912,4914,4916,4918,4920,4922,4924,4926,4928,4930,4932,4934,4936,4938,4940,4942,4944,4946,4948,4950,4952,4954,4956,4958,4960,4962,4964,4966,4968,4970,4972,4974,4976,4978,4980,4982,4984,4986,4988,4990,4992,4994,4996,4998,5000,5002,5004,5006,5008,5010,5012,5014,5016,5018,5020,5022,5024,5026,5028,5030,5032,5034,5036,5038,5040,5042,5044,5046,5048,5050,5052,5054,5056,5058,5060,5062,5064,5066,5068,5070,5072,5074,5076,5078,5080,5082,5084,5086,5088,5090,5092,5094,5096,5098,5100,5102,5104,5106,5108,5110,5112,5114,5116,5118,5120,5122,5124,5126,5128,5130,5132,5134,5136,5138,5140,5142,5144,5146,5148,5150,5152,5154,5156,5158,5160,5162,5164,5166,5168,5170,5172,5174,5176,5178,5180,5182,5184,5186,5188,5190,5192,5194,5196,5198,5200,5202,5204,5206,5208,5210,5212,5214,5216,5218,5220,5222,5224,5226,5228,5230,5232,5234,5236,5238,5240,5242,5244,5246,5248,5250,5252,5254,5256,5258,5260,5262,5264,5266,5268,5270,5272,5274,5276,5278,5280,5282,5284,5286,5288,5290,5292,5294,5296,5298,5300,5302,5304,5306,5308,5310,5312,5314,5316,5318,5320,5322,5324,5326,5328,5330,5332,5334,5336,5338,5340,5342,5344,5346,5348,5350,5352,5354,5356,5358,5360,5362,5364,5366,5368,5370,5372,5374,5376,5378,5380,5382,5384,5386,5388,5390,5392,5394,5396,5398,5400,5402,5404,5406,5408,5410,5412,5414,5416,5418,5420,5422,5424,5426,5428,5430,5432,5434,5436,5438,5440,5442,5444,5446,5448,5450,5452,5454,5456,5458,5460,5462,5464,5466,5468,5470,5472,5474,5476,5478,5480,5482,5484,5486,5488,5490,5492,5494,5496,5498,5500,5502,5504,5506,5508,5510,5512,5514,5516,5518,5520,5522,5524,5526,5528,5530,5532,5534,5536,5538,5540,5542,5544,5546,5548,5550,5552,5554,5556,5558,5560,5562,5564,5566,5568,5570,5572,5574,5576,5578,5580,5582,5584,5587,5589,5591,5593,5595,5597,5599,5601,5603,5605,5607,5609,5611,5613,5615,5617,5619,5621,5623,5625,5627,5629,5631,5633,5635,5637,5639,5641,5643,5645,5647,5649,5651,5653,5655,5657,5659,5661,5663,5665,5667,5669,5671,5673,5675,5677,5679,5681,5683,5685,5687,5689,5691,5693,5695,5697,5699,5701,5703,5705,5707,5709,5711,5713,5715,5717,5719,5721,5723,5725,5727,5729,5731,5733,5735,5737,5739,5741,5743,5745,5747,5749,5751,5753,5755,5757,5759,5761,5763,5765,5767,5769,5771,5773,5775,5777,5779,5781,5783,5785,5787,5790,5792,5794,5796,5798,5800,5802,5804,5806,5808,5810,5812,5814,5816,5818,5820,5822,5824,5826,5828,5830,5832,5834,5836,5838,5840,5842,5844,5846,5848,5850,5852,5854,5856,5858,5860,5862,5864,5866,5868,5870,5872,5874,5876,5878,5880,5882,5884,5886,5888,5890,5892,5894,5896,5898,5900,5902,5904,5906,5908,5910,5912,5914,5916,5918,5920,5922,5924,5926,5928,5930,5932,5934,5936,5938,5940,5942,5944,5946,5948,5950,5952,5954,5956,5958,5960,5962,5964,5966,5968,5970,5972,5974,5976,5978,5980,5982,5984,5986,5988,5990,5992,5994,5996,5998,6000,6002,6004,6006,6008,6010,6012,6014,6016,6018,6020,6022,6024,6026,6028,6030,6032,6034,6036,6038,6040,6042,6044,6046,6048,6050,6052,6054,6056,6058,6060,6062,6064,6066,6068,6070,6072,6074,6076,6078,6080,6082,6084,6086,6088,6090,6092,6094,6096,6098,6100,6102,6104,6106,6108,6110,6112,6114,6116,6118,6120,6122,6124,6126,6128,6130,6132,6134,6136,6138,6140,6142,6144,6146,6148,6150,6152,6154,6156,6158,6160,6162,6164,6166,6168,6170,6172,6174,6176,6178,6180,6182,6184,6186,6188,6190,6192,6194,6196,6198,6200,6202,6204,6206,6208,6210,6212,6214,6216,6218,6220,6222,6224,6226,6228,6230,6232,6234,6236,6238,6240,6242,6244,6246,6248,6250,6252,6254,6256,6258,6260,6262,6264,6266,6268,6270,6272,6274,6276,6278,6280,6282,6284,6286,6288,6290,6292,6294,6296,6298,6300,6302,6304,6306,6308,6310,6312,6314,6316,6318,6320,6322,6324,6326,6328,6330,6332,6334,6336,6338,6340,6342,6344,6346,6348,6350,6352,6354,6356,6358,6360,6362,6364,6366,6368,6370,6372,6374,6376,6378,6380,6382,6384,6386,6388,6390,6392,6394,6396,6398,6400,6402,6404,6406,6408,6410,6412,6414,6416,6418,6420,6422,6424,6426,6428,6430,6432,6434,6436,6438,6440,6442,6444,6446,6448,6450,6452,6454,6456,6458,6460,6462,6464,6466,6468,6470,6472,6474,6476,6478,6480,6482,6484,6486,6488,6490,6492,6494,6496,6498,6500,6502,6504,6506,6508,6510,6512,6514,6516,6518,6520,6522,6524,6526,6528,6530,6532,6534,6536,6538,6540,6542,6544,6546,6548,6550,6552,6554,6556,6558,6560,6562,6564,6566,6568,6570,6572,6574,6576,6578,6580,6582,6584,6586,6588,6590,6592,6594,6596,6598,6600,6602,6604,6606,6608,6610,6612,6614,6616,6618,6620,6622,6624,6626,6628,6630,6632,6634,6636,6638,6640,6642,6644,6646,6648,6650,6652,6654,6656,6658,6660,6662,6664,6666,6668,6670,6672,6674,6676,6678,6680,6682,6684,6686,6688,6690,6692,6694,6696,6698,6700,6702,6704,6706,6708,6710,6712,6714,6716,6718,6720,6722,6724,6726,6728,6730,6732,6734,6736,6738,6740,6742,6744,6746,6748,6750,6752,6754,6756,6758,6760,6762,6764,6766,6768,6770,6772,6774,6776,6778,6780,6782,6784,6786,6788,6790,6792,6794,6796,6798,6800,6802,6804,6806,6808,6810,6812,6814,6816,6818,6820,6822,6824,6826,6828,6830,6832,6834,6836,6838,6840,6842,6844,6846,6848,6850,6852,6854,6856,6858,6860,6862,6864,6866,6868,6870,6872,6874,6876,6878,6880,6882,6884,6886,6888,6890,6892,6894,6896,6898,6900,6902,6904,6906,6908,6910,6912,6914,6916,6918,6920,6922,6924,6926,6928,6930,6932,6934,6936,6938,6940,6942,6944,6946,6948,6950,6952,6954,6956,6958,6960,6962,6964,6966,6968,6970,6972,6974,6976,6978,6980,6982,6984,6986,6988,6990,6992,6994,6996,6998,7000,7002,7004,7006,7008,7010,7012,7014,7016,7018,7020,7022,7024,7026,7028,7030,7032,7034,7036,7038,7040,7042,7044,7046,7048,7050,7052,7054,7056,7058,7060,7062,7064,7066,7068,7070,7072,7074,7076,7078,7080,7082,7084,7086,7088,7090,7092,7094,7096,7098,7100,7102,7104,7106,7108,7110,7112,7114,7116,7118,7120,7122,7124,7126,7128,7130,7132,7134,7136,7138,7140,7142,7144,7146,7148,7150,7152,7154,7156,7158,7160,7162,7164,7166,7168,7170,7172,7174,7176,7178,7180,7182,7184,7186,7188,7190,7192,7194,7196,7198],{"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},[250],{"categories":303},[275],{"categories":305},[283],{"categories":307},[],{"categories":309},[275],{"categories":311},[312],"Inference & Serving",{"categories":314},[275],{"categories":316},[275],{"categories":318},[288],{"categories":320},[],{"categories":322},[323],"AI News & Trends",{"categories":325},[326],"Data Science & Visualization",{"categories":328},[288],{"categories":330},[275],{"categories":332},[275],{"categories":334},[283],{"categories":336},[278],{"categories":338},[275],{"categories":340},[288],{"categories":342},[323],{"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},[323],{"categories":366},[275],{"categories":368},[275],{"categories":370},[275],{"categories":372},[],{"categories":374},[375],"Design & Frontend",{"categories":377},[326],{"categories":379},[323],{"categories":381},[275],{"categories":383},[275],{"categories":385},[275],{"categories":387},[],{"categories":389},[275],{"categories":391},[275],{"categories":393},[288],{"categories":395},[250],{"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},[250],{"categories":418},[275],{"categories":420},[],{"categories":422},[],{"categories":424},[375],{"categories":426},[275],{"categories":428},[288],{"categories":430},[278],{"categories":432},[250],{"categories":434},[288],{"categories":436},[375],{"categories":438},[291],{"categories":440},[275],{"categories":442},[250],{"categories":444},[445],"DevOps & Cloud",{"categories":447},[288],{"categories":449},[291],{"categories":451},[323],{"categories":453},[275],{"categories":455},[],{"categories":457},[275],{"categories":459},[275],{"categories":461},[],{"categories":463},[288],{"categories":465},[250],{"categories":467},[],{"categories":469},[250],{"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},[250],{"categories":504},[],{"categories":506},[],{"categories":508},[275],{"categories":510},[250],{"categories":512},[],{"categories":514},[250],{"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},[250],{"categories":536},[288],{"categories":538},[539],"GovTech & Public-Sector Adoption",{"categories":541},[250],{"categories":543},[275],{"categories":545},[275],{"categories":547},[275],{"categories":549},[288],{"categories":551},[288],{"categories":553},[326],{"categories":555},[275],{"categories":557},[323],{"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},[250],{"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},[250],{"categories":603},[250],{"categories":605},[445],{"categories":607},[375],{"categories":609},[288],{"categories":611},[275],{"categories":613},[275],{"categories":615},[],{"categories":617},[618],"Agents & Orchestration",{"categories":620},[250],{"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},[250],{"categories":642},[283],{"categories":644},[275],{"categories":646},[288],{"categories":648},[275],{"categories":650},[275],{"categories":652},[323],{"categories":654},[275],{"categories":656},[],{"categories":658},[275],{"categories":660},[],{"categories":662},[275],{"categories":664},[250],{"categories":666},[275],{"categories":668},[288],{"categories":670},[326],{"categories":672},[],{"categories":674},[275],{"categories":676},[375],{"categories":678},[679],"Models & Frontier Labs",{"categories":681},[],{"categories":683},[375],{"categories":685},[686],"Regulation & Governance of AI",{"categories":688},[291],{"categories":690},[288],{"categories":692},[],{"categories":694},[275],{"categories":696},[275],{"categories":698},[288],{"categories":700},[288],{"categories":702},[323],{"categories":704},[275],{"categories":706},[283],{"categories":708},[275],{"categories":710},[288],{"categories":712},[],{"categories":714},[250],{"categories":716},[288],{"categories":718},[275],{"categories":720},[291],{"categories":722},[275],{"categories":724},[725],"AI Policy & Regulation",{"categories":727},[],{"categories":729},[275],{"categories":731},[288],{"categories":733},[288],{"categories":735},[291],{"categories":737},[288],{"categories":739},[275],{"categories":741},[275],{"categories":743},[275],{"categories":745},[288],{"categories":747},[],{"categories":749},[326],{"categories":751},[752],"Evals & Reliability",{"categories":754},[275],{"categories":756},[275],{"categories":758},[],{"categories":760},[278],{"categories":762},[539],{"categories":764},[725],{"categories":766},[275],{"categories":768},[283],{"categories":770},[275],{"categories":772},[288],{"categories":774},[275],{"categories":776},[288],{"categories":778},[618],{"categories":780},[275],{"categories":782},[250],{"categories":784},[275],{"categories":786},[],{"categories":788},[375],{"categories":790},[],{"categories":792},[275],{"categories":794},[539],{"categories":796},[275],{"categories":798},[275],{"categories":800},[275],{"categories":802},[],{"categories":804},[275],{"categories":806},[375],{"categories":808},[250],{"categories":810},[],{"categories":812},[275],{"categories":814},[],{"categories":816},[288],{"categories":818},[275],{"categories":820},[375],{"categories":822},[],{"categories":824},[275],{"categories":826},[275],{"categories":828},[326],{"categories":830},[288],{"categories":832},[275],{"categories":834},[283],{"categories":836},[288],{"categories":838},[275],{"categories":840},[275],{"categories":842},[250],{"categories":844},[375],{"categories":846},[275],{"categories":848},[288],{"categories":850},[],{"categories":852},[250],{"categories":854},[288],{"categories":856},[326],{"categories":858},[],{"categories":860},[275],{"categories":862},[323],{"categories":864},[275],{"categories":866},[],{"categories":868},[275],{"categories":870},[275],{"categories":872},[275],{"categories":874},[283,404],{"categories":876},[],{"categories":878},[250],{"categories":880},[275],{"categories":882},[275],{"categories":884},[288],{"categories":886},[275],{"categories":888},[],{"categories":890},[],{"categories":892},[275],{"categories":894},[375],{"categories":896},[275],{"categories":898},[],{"categories":900},[275],{"categories":902},[445],{"categories":904},[],{"categories":906},[288],{"categories":908},[323],{"categories":910},[275],{"categories":912},[275],{"categories":914},[375],{"categories":916},[],{"categories":918},[323],{"categories":920},[275],{"categories":922},[312],{"categories":924},[275],{"categories":926},[275],{"categories":928},[288],{"categories":930},[323],{"categories":932},[679],{"categories":934},[275],{"categories":936},[404],{"categories":938},[],{"categories":940},[288],{"categories":942},[283],{"categories":944},[250],{"categories":946},[275],{"categories":948},[288],{"categories":950},[],{"categories":952},[275,445],{"categories":954},[275],{"categories":956},[275],{"categories":958},[275],{"categories":960},[288],{"categories":962},[275,250],{"categories":964},[326],{"categories":966},[275],{"categories":968},[275],{"categories":970},[275],{"categories":972},[250],{"categories":974},[275],{"categories":976},[288],{"categories":978},[288],{"categories":980},[725],{"categories":982},[404],{"categories":984},[275],{"categories":986},[288],{"categories":988},[275],{"categories":990},[275],{"categories":992},[288],{"categories":994},[],{"categories":996},[288],{"categories":998},[275],{"categories":1000},[275],{"categories":1002},[288],{"categories":1004},[275],{"categories":1006},[275,283],{"categories":1008},[275],{"categories":1010},[283],{"categories":1012},[],{"categories":1014},[375],{"categories":1016},[375],{"categories":1018},[275],{"categories":1020},[],{"categories":1022},[],{"categories":1024},[275],{"categories":1026},[323],{"categories":1028},[],{"categories":1030},[278],{"categories":1032},[275],{"categories":1034},[250],{"categories":1036},[275],{"categories":1038},[1039],"Generative UI & Design-to-Code",{"categories":1041},[275],{"categories":1043},[275],{"categories":1045},[375],{"categories":1047},[275],{"categories":1049},[1050],"Algorithmic Accountability",{"categories":1052},[288],{"categories":1054},[250],{"categories":1056},[323],{"categories":1058},[375],{"categories":1060},[275],{"categories":1062},[],{"categories":1064},[291],{"categories":1066},[275],{"categories":1068},[275],{"categories":1070},[275],{"categories":1072},[275],{"categories":1074},[288],{"categories":1076},[1077],"MLOps & Infrastructure",{"categories":1079},[275],{"categories":1081},[275],{"categories":1083},[275],{"categories":1085},[275],{"categories":1087},[275],{"categories":1089},[250],{"categories":1091},[323],{"categories":1093},[275],{"categories":1095},[291],{"categories":1097},[278],{"categories":1099},[275],{"categories":1101},[288],{"categories":1103},[445],{"categories":1105},[275],{"categories":1107},[283],{"categories":1109},[275],{"categories":1111},[375],{"categories":1113},[275],{"categories":1115},[275],{"categories":1117},[288],{"categories":1119},[],{"categories":1121},[],{"categories":1123},[275],{"categories":1125},[312],{"categories":1127},[375],{"categories":1129},[323],{"categories":1131},[326],{"categories":1133},[],{"categories":1135},[275],{"categories":1137},[275],{"categories":1139},[283],{"categories":1141},[288],{"categories":1143},[275],{"categories":1145},[275],{"categories":1147},[275],{"categories":1149},[275],{"categories":1151},[323],{"categories":1153},[312],{"categories":1155},[275],{"categories":1157},[375],{"categories":1159},[275],{"categories":1161},[],{"categories":1163},[288],{"categories":1165},[250],{"categories":1167},[],{"categories":1169},[275],{"categories":1171},[275],{"categories":1173},[288],{"categories":1175},[250],{"categories":1177},[275],{"categories":1179},[326],{"categories":1181},[375],{"categories":1183},[],{"categories":1185},[275],{"categories":1187},[],{"categories":1189},[275],{"categories":1191},[],{"categories":1193},[275],{"categories":1195},[275],{"categories":1197},[291],{"categories":1199},[283],{"categories":1201},[288],{"categories":1203},[288],{"categories":1205},[],{"categories":1207},[275],{"categories":1209},[278],{"categories":1211},[275],{"categories":1213},[275],{"categories":1215},[283],{"categories":1217},[323],{"categories":1219},[278],{"categories":1221},[],{"categories":1223},[275],{"categories":1225},[],{"categories":1227},[275],{"categories":1229},[],{"categories":1231},[323],{"categories":1233},[323],{"categories":1235},[],{"categories":1237},[618],{"categories":1239},[275],{"categories":1241},[375],{"categories":1243},[250],{"categories":1245},[],{"categories":1247},[562],{"categories":1249},[288],{"categories":1251},[283],{"categories":1253},[],{"categories":1255},[],{"categories":1257},[278],{"categories":1259},[326],{"categories":1261},[],{"categories":1263},[404],{"categories":1265},[288],{"categories":1267},[283],{"categories":1269},[288],{"categories":1271},[275],{"categories":1273},[283],{"categories":1275},[275],{"categories":1277},[250],{"categories":1279},[],{"categories":1281},[312],{"categories":1283},[291],{"categories":1285},[275],{"categories":1287},[375],{"categories":1289},[250],{"categories":1291},[283],{"categories":1293},[275],{"categories":1295},[250],{"categories":1297},[275],{"categories":1299},[288],{"categories":1301},[283],{"categories":1303},[275],{"categories":1305},[275],{"categories":1307},[275],{"categories":1309},[275],{"categories":1311},[275],{"categories":1313},[],{"categories":1315},[],{"categories":1317},[250],{"categories":1319},[326],{"categories":1321},[291],{"categories":1323},[275],{"categories":1325},[288],{"categories":1327},[250],{"categories":1329},[250],{"categories":1331},[275],{"categories":1333},[],{"categories":1335},[323],{"categories":1337},[291],{"categories":1339},[291],{"categories":1341},[250],{"categories":1343},[275],{"categories":1345},[752],{"categories":1347},[445],{"categories":1349},[],{"categories":1351},[288],{"categories":1353},[275],{"categories":1355},[],{"categories":1357},[278],{"categories":1359},[],{"categories":1361},[275],{"categories":1363},[275],{"categories":1365},[275],{"categories":1367},[375],{"categories":1369},[404],{"categories":1371},[275],{"categories":1373},[250],{"categories":1375},[275],{"categories":1377},[288],{"categories":1379},[],{"categories":1381},[250],{"categories":1383},[275],{"categories":1385},[278],{"categories":1387},[],{"categories":1389},[283],{"categories":1391},[275],{"categories":1393},[275],{"categories":1395},[323],{"categories":1397},[275,445],{"categories":1399},[275],{"categories":1401},[1402],"Design Systems for AI",{"categories":1404},[275],{"categories":1406},[275],{"categories":1408},[323],{"categories":1410},[275],{"categories":1412},[275],{"categories":1414},[275],{"categories":1416},[283],{"categories":1418},[275],{"categories":1420},[275],{"categories":1422},[275],{"categories":1424},[],{"categories":1426},[275],{"categories":1428},[275],{"categories":1430},[283],{"categories":1432},[275],{"categories":1434},[],{"categories":1436},[288],{"categories":1438},[288],{"categories":1440},[250],{"categories":1442},[323],{"categories":1444},[250],{"categories":1446},[275],{"categories":1448},[375],{"categories":1450},[323],{"categories":1452},[326],{"categories":1454},[275],{"categories":1456},[275],{"categories":1458},[288],{"categories":1460},[278],{"categories":1462},[725],{"categories":1464},[275],{"categories":1466},[288],{"categories":1468},[275],{"categories":1470},[250],{"categories":1472},[250],{"categories":1474},[],{"categories":1476},[],{"categories":1478},[275],{"categories":1480},[288],{"categories":1482},[291],{"categories":1484},[],{"categories":1486},[283],{"categories":1488},[275],{"categories":1490},[],{"categories":1492},[375],{"categories":1494},[250],{"categories":1496},[288],{"categories":1498},[250],{"categories":1500},[375],{"categories":1502},[275],{"categories":1504},[275],{"categories":1506},[375],{"categories":1508},[],{"categories":1510},[],{"categories":1512},[323],{"categories":1514},[288],{"categories":1516},[288],{"categories":1518},[275],{"categories":1520},[275],{"categories":1522},[275],{"categories":1524},[275],{"categories":1526},[283],{"categories":1528},[275],{"categories":1530},[275],{"categories":1532},[],{"categories":1534},[250],{"categories":1536},[250],{"categories":1538},[275],{"categories":1540},[250],{"categories":1542},[283],{"categories":1544},[],{"categories":1546},[275],{"categories":1548},[275],{"categories":1550},[275],{"categories":1552},[275],{"categories":1554},[275],{"categories":1556},[288],{"categories":1558},[278],{"categories":1560},[283],{"categories":1562},[275],{"categories":1564},[288],{"categories":1566},[323],{"categories":1568},[288],{"categories":1570},[312],{"categories":1572},[404],{"categories":1574},[275],{"categories":1576},[288],{"categories":1578},[275],{"categories":1580},[275],{"categories":1582},[275],{"categories":1584},[],{"categories":1586},[375],{"categories":1588},[],{"categories":1590},[275],{"categories":1592},[275],{"categories":1594},[],{"categories":1596},[275],{"categories":1598},[250],{"categories":1600},[283],{"categories":1602},[1603],"Visual & Generative Media",{"categories":1605},[288],{"categories":1607},[],{"categories":1609},[275],{"categories":1611},[275],{"categories":1613},[250],{"categories":1615},[445],{"categories":1617},[275],{"categories":1619},[326],{"categories":1621},[725],{"categories":1623},[250],{"categories":1625},[404],{"categories":1627},[275],{"categories":1629},[375],{"categories":1631},[275],{"categories":1633},[275],{"categories":1635},[250],{"categories":1637},[288],{"categories":1639},[275],{"categories":1641},[],{"categories":1643},[],{"categories":1645},[288],{"categories":1647},[250],{"categories":1649},[278],{"categories":1651},[288],{"categories":1653},[679],{"categories":1655},[275],{"categories":1657},[291],{"categories":1659},[275],{"categories":1661},[283],{"categories":1663},[],{"categories":1665},[275],{"categories":1667},[291],{"categories":1669},[275],{"categories":1671},[275],{"categories":1673},[275],{"categories":1675},[291],{"categories":1677},[275],{"categories":1679},[275],{"categories":1681},[404],{"categories":1683},[275],{"categories":1685},[618],{"categories":1687},[275],{"categories":1689},[288],{"categories":1691},[275],{"categories":1693},[275],{"categories":1695},[288],{"categories":1697},[275],{"categories":1699},[275],{"categories":1701},[375],{"categories":1703},[288],{"categories":1705},[],{"categories":1707},[288],{"categories":1709},[],{"categories":1711},[445],{"categories":1713},[250],{"categories":1715},[],{"categories":1717},[679],{"categories":1719},[275],{"categories":1721},[288],{"categories":1723},[288],{"categories":1725},[275],{"categories":1727},[375,275],{"categories":1729},[278],{"categories":1731},[275],{"categories":1733},[375],{"categories":1735},[],{"categories":1737},[275],{"categories":1739},[278],{"categories":1741},[275],{"categories":1743},[1744],"Medical Imaging & Radiology",{"categories":1746},[275],{"categories":1748},[275],{"categories":1750},[275],{"categories":1752},[375],{"categories":1754},[288],{"categories":1756},[250],{"categories":1758},[],{"categories":1760},[275],{"categories":1762},[275],{"categories":1764},[275],{"categories":1766},[],{"categories":1768},[],{"categories":1770},[275],{"categories":1772},[275],{"categories":1774},[618],{"categories":1776},[275],{"categories":1778},[278],{"categories":1780},[275],{"categories":1782},[275],{"categories":1784},[],{"categories":1786},[288],{"categories":1788},[275],{"categories":1790},[291],{"categories":1792},[250],{"categories":1794},[275],{"categories":1796},[288],{"categories":1798},[618],{"categories":1800},[275],{"categories":1802},[288],{"categories":1804},[275],{"categories":1806},[275],{"categories":1808},[275],{"categories":1810},[375],{"categories":1812},[288],{"categories":1814},[445],{"categories":1816},[375],{"categories":1818},[283],{"categories":1820},[288],{"categories":1822},[323],{"categories":1824},[275],{"categories":1826},[275],{"categories":1828},[291],{"categories":1830},[275],{"categories":1832},[275],{"categories":1834},[275],{"categories":1836},[275],{"categories":1838},[288],{"categories":1840},[275],{"categories":1842},[250],{"categories":1844},[250],{"categories":1846},[275],{"categories":1848},[291],{"categories":1850},[],{"categories":1852},[323],{"categories":1854},[],{"categories":1856},[291],{"categories":1858},[288],{"categories":1860},[275],{"categories":1862},[288],{"categories":1864},[1402],{"categories":1866},[1402],{"categories":1868},[375],{"categories":1870},[275],{"categories":1872},[275],{"categories":1874},[275],{"categories":1876},[288],{"categories":1878},[250],{"categories":1880},[375],{"categories":1882},[288],{"categories":1884},[323],{"categories":1886},[],{"categories":1888},[275],{"categories":1890},[],{"categories":1892},[275],{"categories":1894},[275],{"categories":1896},[275],{"categories":1898},[275],{"categories":1900},[288],{"categories":1902},[1903],"Contract Review & E-Discovery",{"categories":1905},[275],{"categories":1907},[375],{"categories":1909},[275],{"categories":1911},[278],{"categories":1913},[275],{"categories":1915},[323],{"categories":1917},[275],{"categories":1919},[275],{"categories":1921},[404],{"categories":1923},[250],{"categories":1925},[275],{"categories":1927},[275],{"categories":1929},[288],{"categories":1931},[288],{"categories":1933},[1050],{"categories":1935},[275],{"categories":1937},[275],{"categories":1939},[288],{"categories":1941},[288],{"categories":1943},[275],{"categories":1945},[275],{"categories":1947},[275],{"categories":1949},[288],{"categories":1951},[275],{"categories":1953},[275],{"categories":1955},[618],{"categories":1957},[595],{"categories":1959},[275],{"categories":1961},[288],{"categories":1963},[275],{"categories":1965},[1966],"Law-Firm Practice & Adoption",{"categories":1968},[275],{"categories":1970},[288],{"categories":1972},[375],{"categories":1974},[275],{"categories":1976},[275],{"categories":1978},[275],{"categories":1980},[],{"categories":1982},[250],{"categories":1984},[],{"categories":1986},[250],{"categories":1988},[275],{"categories":1990},[],{"categories":1992},[288],{"categories":1994},[278],{"categories":1996},[445],{"categories":1998},[275],{"categories":2000},[],{"categories":2002},[278],{"categories":2004},[283],{"categories":2006},[275],{"categories":2008},[404],{"categories":2010},[],{"categories":2012},[283],{"categories":2014},[288],{"categories":2016},[283],{"categories":2018},[],{"categories":2020},[275],{"categories":2022},[291],{"categories":2024},[275],{"categories":2026},[250],{"categories":2028},[],{"categories":2030},[],{"categories":2032},[],{"categories":2034},[],{"categories":2036},[275],{"categories":2038},[291],{"categories":2040},[288],{"categories":2042},[445],{"categories":2044},[275],{"categories":2046},[278],{"categories":2048},[250],{"categories":2050},[275],{"categories":2052},[275],{"categories":2054},[250],{"categories":2056},[291],{"categories":2058},[275],{"categories":2060},[275],{"categories":2062},[275],{"categories":2064},[1077],{"categories":2066},[275],{"categories":2068},[250],{"categories":2070},[275],{"categories":2072},[404],{"categories":2074},[250],{"categories":2076},[283],{"categories":2078},[275],{"categories":2080},[275],{"categories":2082},[275],{"categories":2084},[375],{"categories":2086},[275],{"categories":2088},[275],{"categories":2090},[275],{"categories":2092},[275],{"categories":2094},[283],{"categories":2096},[288],{"categories":2098},[275,278],{"categories":2100},[618],{"categories":2102},[275],{"categories":2104},[275],{"categories":2106},[250],{"categories":2108},[250],{"categories":2110},[375],{"categories":2112},[288],{"categories":2114},[288],{"categories":2116},[250],{"categories":2118},[275],{"categories":2120},[275],{"categories":2122},[275],{"categories":2124},[],{"categories":2126},[],{"categories":2128},[275],{"categories":2130},[326],{"categories":2132},[275],{"categories":2134},[375],{"categories":2136},[288],{"categories":2138},[],{"categories":2140},[275],{"categories":2142},[275],{"categories":2144},[250],{"categories":2146},[326],{"categories":2148},[323],{"categories":2150},[375],{"categories":2152},[275],{"categories":2154},[288],{"categories":2156},[275],{"categories":2158},[250],{"categories":2160},[],{"categories":2162},[288],{"categories":2164},[275],{"categories":2166},[275],{"categories":2168},[275],{"categories":2170},[275],{"categories":2172},[],{"categories":2174},[288],{"categories":2176},[275],{"categories":2178},[275],{"categories":2180},[275],{"categories":2182},[],{"categories":2184},[288],{"categories":2186},[275],{"categories":2188},[275],{"categories":2190},[283],{"categories":2192},[275],{"categories":2194},[275],{"categories":2196},[],{"categories":2198},[278],{"categories":2200},[275],{"categories":2202},[275],{"categories":2204},[275],{"categories":2206},[375],{"categories":2208},[275],{"categories":2210},[250],{"categories":2212},[275],{"categories":2214},[278],{"categories":2216},[275],{"categories":2218},[250],{"categories":2220},[404],{"categories":2222},[288],{"categories":2224},[288],{"categories":2226},[275],{"categories":2228},[275],{"categories":2230},[275,375],{"categories":2232},[275],{"categories":2234},[288],{"categories":2236},[323],{"categories":2238},[275],{"categories":2240},[323],{"categories":2242},[288],{"categories":2244},[375],{"categories":2246},[275],{"categories":2248},[],{"categories":2250},[250],{"categories":2252},[445],{"categories":2254},[375],{"categories":2256},[250],{"categories":2258},[275],{"categories":2260},[291],{"categories":2262},[275],{"categories":2264},[275],{"categories":2266},[288],{"categories":2268},[],{"categories":2270},[],{"categories":2272},[275],{"categories":2274},[],{"categories":2276},[],{"categories":2278},[291],{"categories":2280},[250],{"categories":2282},[275],{"categories":2284},[288],{"categories":2286},[288],{"categories":2288},[283],{"categories":2290},[288],{"categories":2292},[445],{"categories":2294},[275],{"categories":2296},[275],{"categories":2298},[275],{"categories":2300},[312],{"categories":2302},[275],{"categories":2304},[275],{"categories":2306},[275],{"categories":2308},[250],{"categories":2310},[288],{"categories":2312},[275],{"categories":2314},[275],{"categories":2316},[250],{"categories":2318},[562],{"categories":2320},[288],{"categories":2322},[1050],{"categories":2324},[],{"categories":2326},[375],{"categories":2328},[1966],{"categories":2330},[250],{"categories":2332},[],{"categories":2334},[],{"categories":2336},[275],{"categories":2338},[288],{"categories":2340},[],{"categories":2342},[],{"categories":2344},[275],{"categories":2346},[404],{"categories":2348},[275],{"categories":2350},[404],{"categories":2352},[288],{"categories":2354},[275],{"categories":2356},[275],{"categories":2358},[250],{"categories":2360},[291],{"categories":2362},[],{"categories":2364},[275],{"categories":2366},[275],{"categories":2368},[250],{"categories":2370},[1903],{"categories":2372},[375],{"categories":2374},[375],{"categories":2376},[275],{"categories":2378},[288],{"categories":2380},[278],{"categories":2382},[275],{"categories":2384},[275],{"categories":2386},[275],{"categories":2388},[275],{"categories":2390},[375],{"categories":2392},[375],{"categories":2394},[288],{"categories":2396},[288],{"categories":2398},[288],{"categories":2400},[275],{"categories":2402},[275],{"categories":2404},[],{"categories":2406},[275],{"categories":2408},[],{"categories":2410},[2411],"Interaction & Product Design",{"categories":2413},[275],{"categories":2415},[288],{"categories":2417},[250],{"categories":2419},[474],{"categories":2421},[323],{"categories":2423},[250],{"categories":2425},[275],{"categories":2427},[275],{"categories":2429},[275],{"categories":2431},[250],{"categories":2433},[275],{"categories":2435},[278],{"categories":2437},[288],{"categories":2439},[275],{"categories":2441},[],{"categories":2443},[288],{"categories":2445},[288],{"categories":2447},[288],{"categories":2449},[],{"categories":2451},[250],{"categories":2453},[275],{"categories":2455},[288],{"categories":2457},[278],{"categories":2459},[2411],{"categories":2461},[275],{"categories":2463},[278],{"categories":2465},[278],{"categories":2467},[],{"categories":2469},[288],{"categories":2471},[250],{"categories":2473},[],{"categories":2475},[288],{"categories":2477},[323],{"categories":2479},[275],{"categories":2481},[288],{"categories":2483},[275],{"categories":2485},[288],{"categories":2487},[288],{"categories":2489},[275],{"categories":2491},[275],{"categories":2493},[323],{"categories":2495},[326],{"categories":2497},[275],{"categories":2499},[291],{"categories":2501},[250],{"categories":2503},[2504],"Coding Agents & Dev Productivity",{"categories":2506},[323],{"categories":2508},[375],{"categories":2510},[275],{"categories":2512},[275],{"categories":2514},[],{"categories":2516},[275],{"categories":2518},[1050],{"categories":2520},[],{"categories":2522},[275],{"categories":2524},[275],{"categories":2526},[445],{"categories":2528},[275],{"categories":2530},[323],{"categories":2532},[],{"categories":2534},[],{"categories":2536},[275],{"categories":2538},[],{"categories":2540},[288],{"categories":2542},[275],{"categories":2544},[],{"categories":2546},[250],{"categories":2548},[250],{"categories":2550},[275],{"categories":2552},[326],{"categories":2554},[],{"categories":2556},[275],{"categories":2558},[275],{"categories":2560},[275],{"categories":2562},[326],{"categories":2564},[250],{"categories":2566},[288],{"categories":2568},[],{"categories":2570},[],{"categories":2572},[275],{"categories":2574},[275],{"categories":2576},[288],{"categories":2578},[288],{"categories":2580},[539],{"categories":2582},[250],{"categories":2584},[291],{"categories":2586},[250],{"categories":2588},[288],{"categories":2590},[323],{"categories":2592},[323],{"categories":2594},[288],{"categories":2596},[288],{"categories":2598},[275],{"categories":2600},[278],{"categories":2602},[2411],{"categories":2604},[291],{"categories":2606},[275,445],{"categories":2608},[326],{"categories":2610},[],{"categories":2612},[375],{"categories":2614},[288],{"categories":2616},[250],{"categories":2618},[278],{"categories":2620},[275],{"categories":2622},[288],{"categories":2624},[2625],"The Designer's Role & Craft",{"categories":2627},[375],{"categories":2629},[],{"categories":2631},[288],{"categories":2633},[275],{"categories":2635},[288],{"categories":2637},[288],{"categories":2639},[275],{"categories":2641},[404],{"categories":2643},[275],{"categories":2645},[250],{"categories":2647},[275],{"categories":2649},[375],{"categories":2651},[275],{"categories":2653},[],{"categories":2655},[288],{"categories":2657},[375],{"categories":2659},[291],{"categories":2661},[275],{"categories":2663},[275],{"categories":2665},[275],{"categories":2667},[2668],"AI UX Patterns",{"categories":2670},[288],{"categories":2672},[288],{"categories":2674},[288],{"categories":2676},[288],{"categories":2678},[404],{"categories":2680},[326],{"categories":2682},[275],{"categories":2684},[288],{"categories":2686},[275],{"categories":2688},[1402],{"categories":2690},[],{"categories":2692},[404],{"categories":2694},[288],{"categories":2696},[323],{"categories":2698},[250],{"categories":2700},[275],{"categories":2702},[288],{"categories":2704},[],{"categories":2706},[],{"categories":2708},[275],{"categories":2710},[275],{"categories":2712},[288],{"categories":2714},[275],{"categories":2716},[288],{"categories":2718},[539],{"categories":2720},[375],{"categories":2722},[275],{"categories":2724},[323],{"categories":2726},[250],{"categories":2728},[275],{"categories":2730},[288],{"categories":2732},[288],{"categories":2734},[],{"categories":2736},[275],{"categories":2738},[],{"categories":2740},[275],{"categories":2742},[],{"categories":2744},[275],{"categories":2746},[275],{"categories":2748},[275],{"categories":2750},[288],{"categories":2752},[250],{"categories":2754},[],{"categories":2756},[],{"categories":2758},[326],{"categories":2760},[312],{"categories":2762},[275],{"categories":2764},[275],{"categories":2766},[275],{"categories":2768},[326],{"categories":2770},[275],{"categories":2772},[275],{"categories":2774},[323],{"categories":2776},[275],{"categories":2778},[275],{"categories":2780},[275],{"categories":2782},[288],{"categories":2784},[275],{"categories":2786},[288],{"categories":2788},[275],{"categories":2790},[275],{"categories":2792},[275],{"categories":2794},[288],{"categories":2796},[],{"categories":2798},[275],{"categories":2800},[],{"categories":2802},[275],{"categories":2804},[275],{"categories":2806},[445],{"categories":2808},[275],{"categories":2810},[],{"categories":2812},[],{"categories":2814},[375],{"categories":2816},[1077],{"categories":2818},[288],{"categories":2820},[278],{"categories":2822},[2625],{"categories":2824},[],{"categories":2826},[],{"categories":2828},[275],{"categories":2830},[],{"categories":2832},[],{"categories":2834},[250],{"categories":2836},[323],{"categories":2838},[404],{"categories":2840},[288],{"categories":2842},[283],{"categories":2844},[275],{"categories":2846},[275],{"categories":2848},[283],{"categories":2850},[],{"categories":2852},[375],{"categories":2854},[291],{"categories":2856},[275],{"categories":2858},[275],{"categories":2860},[288],{"categories":2862},[283],{"categories":2864},[275],{"categories":2866},[275],{"categories":2868},[278],{"categories":2870},[275],{"categories":2872},[275],{"categories":2874},[],{"categories":2876},[278],{"categories":2878},[275],{"categories":2880},[404],{"categories":2882},[288],{"categories":2884},[323],{"categories":2886},[275],{"categories":2888},[250],{"categories":2890},[275],{"categories":2892},[275],{"categories":2894},[283],{"categories":2896},[275],{"categories":2898},[275],{"categories":2900},[275],{"categories":2902},[288],{"categories":2904},[275],{"categories":2906},[],{"categories":2908},[275],{"categories":2910},[250],{"categories":2912},[278],{"categories":2914},[275],{"categories":2916},[275],{"categories":2918},[275],{"categories":2920},[],{"categories":2922},[275],{"categories":2924},[618],{"categories":2926},[288],{"categories":2928},[283],{"categories":2930},[323],{"categories":2932},[275],{"categories":2934},[275],{"categories":2936},[],{"categories":2938},[283],{"categories":2940},[283],{"categories":2942},[275],{"categories":2944},[275],{"categories":2946},[291],{"categories":2948},[275],{"categories":2950},[275],{"categories":2952},[275],{"categories":2954},[275],{"categories":2956},[250],{"categories":2958},[250],{"categories":2960},[275],{"categories":2962},[],{"categories":2964},[250],{"categories":2966},[275],{"categories":2968},[250],{"categories":2970},[288],{"categories":2972},[725],{"categories":2974},[],{"categories":2976},[],{"categories":2978},[275],{"categories":2980},[323],{"categories":2982},[],{"categories":2984},[445],{"categories":2986},[275],{"categories":2988},[275],{"categories":2990},[275],{"categories":2992},[375],{"categories":2994},[1039],{"categories":2996},[],{"categories":2998},[275],{"categories":3000},[275],{"categories":3002},[275],{"categories":3004},[250],{"categories":3006},[275],{"categories":3008},[275],{"categories":3010},[275,445],{"categories":3012},[275],{"categories":3014},[275],{"categories":3016},[375],{"categories":3018},[288],{"categories":3020},[],{"categories":3022},[288],{"categories":3024},[288],{"categories":3026},[275],{"categories":3028},[275],{"categories":3030},[275],{"categories":3032},[275],{"categories":3034},[326],{"categories":3036},[275],{"categories":3038},[2668],{"categories":3040},[278],{"categories":3042},[326],{"categories":3044},[278],{"categories":3046},[250],{"categories":3048},[375],{"categories":3050},[288],{"categories":3052},[275],{"categories":3054},[],{"categories":3056},[283],{"categories":3058},[275],{"categories":3060},[275],{"categories":3062},[323],{"categories":3064},[275],{"categories":3066},[275],{"categories":3068},[275],{"categories":3070},[288],{"categories":3072},[275],{"categories":3074},[275],{"categories":3076},[275],{"categories":3078},[283],{"categories":3080},[],{"categories":3082},[445],{"categories":3084},[275],{"categories":3086},[539],{"categories":3088},[375],{"categories":3090},[375],{"categories":3092},[250],{"categories":3094},[288],{"categories":3096},[275],{"categories":3098},[283],{"categories":3100},[323],{"categories":3102},[275],{"categories":3104},[275],{"categories":3106},[275],{"categories":3108},[375],{"categories":3110},[288],{"categories":3112},[288],{"categories":3114},[275],{"categories":3116},[275],{"categories":3118},[679],{"categories":3120},[288],{"categories":3122},[],{"categories":3124},[275],{"categories":3126},[275],{"categories":3128},[275],{"categories":3130},[],{"categories":3132},[],{"categories":3134},[275],{"categories":3136},[275],{"categories":3138},[288],{"categories":3140},[275],{"categories":3142},[275],{"categories":3144},[275],{"categories":3146},[250],{"categories":3148},[275],{"categories":3150},[275],{"categories":3152},[288],{"categories":3154},[275],{"categories":3156},[275],{"categories":3158},[275],{"categories":3160},[275],{"categories":3162},[275],{"categories":3164},[],{"categories":3166},[250],{"categories":3168},[326],{"categories":3170},[275],{"categories":3172},[288],{"categories":3174},[288],{"categories":3176},[275],{"categories":3178},[275],{"categories":3180},[],{"categories":3182},[],{"categories":3184},[275],{"categories":3186},[275],{"categories":3188},[275],{"categories":3190},[323],{"categories":3192},[326],{"categories":3194},[],{"categories":3196},[275],{"categories":3198},[375],{"categories":3200},[275],{"categories":3202},[445],{"categories":3204},[1966],{"categories":3206},[323],{"categories":3208},[250],{"categories":3210},[275],{"categories":3212},[250],{"categories":3214},[250],{"categories":3216},[275],{"categories":3218},[275],{"categories":3220},[250],{"categories":3222},[323],{"categories":3224},[323],{"categories":3226},[445],{"categories":3228},[288],{"categories":3230},[],{"categories":3232},[323],{"categories":3234},[275],{"categories":3236},[288],{"categories":3238},[278],{"categories":3240},[250],{"categories":3242},[275],{"categories":3244},[323],{"categories":3246},[],{"categories":3248},[275],{"categories":3250},[250],{"categories":3252},[250],{"categories":3254},[326],{"categories":3256},[275],{"categories":3258},[323],{"categories":3260},[275],{"categories":3262},[250],{"categories":3264},[288],{"categories":3266},[288],{"categories":3268},[323],{"categories":3270},[288],{"categories":3272},[445],{"categories":3274},[288],{"categories":3276},[275],{"categories":3278},[275],{"categories":3280},[275],{"categories":3282},[275],{"categories":3284},[250],{"categories":3286},[275],{"categories":3288},[],{"categories":3290},[288],{"categories":3292},[283],{"categories":3294},[250],{"categories":3296},[],{"categories":3298},[],{"categories":3300},[275],{"categories":3302},[288],{"categories":3304},[275],{"categories":3306},[275],{"categories":3308},[3309],"Frameworks & Tooling",{"categories":3311},[275],{"categories":3313},[275],{"categories":3315},[250],{"categories":3317},[275],{"categories":3319},[275],{"categories":3321},[],{"categories":3323},[326],{"categories":3325},[326],{"categories":3327},[278],{"categories":3329},[275],{"categories":3331},[288],{"categories":3333},[275],{"categories":3335},[375],{"categories":3337},[],{"categories":3339},[1966],{"categories":3341},[275],{"categories":3343},[250],{"categories":3345},[275],{"categories":3347},[445],{"categories":3349},[445],{"categories":3351},[],{"categories":3353},[288],{"categories":3355},[288],{"categories":3357},[275],{"categories":3359},[275],{"categories":3361},[323],{"categories":3363},[288],{"categories":3365},[323],{"categories":3367},[275],{"categories":3369},[288],{"categories":3371},[],{"categories":3373},[375],{"categories":3375},[275],{"categories":3377},[275],{"categories":3379},[],{"categories":3381},[275],{"categories":3383},[288],{"categories":3385},[275],{"categories":3387},[275],{"categories":3389},[275],{"categories":3391},[],{"categories":3393},[283],{"categories":3395},[250],{"categories":3397},[275],{"categories":3399},[250],{"categories":3401},[445],{"categories":3403},[275],{"categories":3405},[275],{"categories":3407},[275],{"categories":3409},[250],{"categories":3411},[283],{"categories":3413},[275],{"categories":3415},[1966],{"categories":3417},[],{"categories":3419},[288],{"categories":3421},[278],{"categories":3423},[275],{"categories":3425},[278],{"categories":3427},[275],{"categories":3429},[],{"categories":3431},[288],{"categories":3433},[275],{"categories":3435},[275],{"categories":3437},[3438],"AI Design Tooling",{"categories":3440},[375],{"categories":3442},[275],{"categories":3444},[275],{"categories":3446},[250],{"categories":3448},[375],{"categories":3450},[275],{"categories":3452},[275],{"categories":3454},[250],{"categories":3456},[323],{"categories":3458},[291],{"categories":3460},[250],{"categories":3462},[275],{"categories":3464},[275],{"categories":3466},[275],{"categories":3468},[288],{"categories":3470},[275],{"categories":3472},[],{"categories":3474},[288],{"categories":3476},[275],{"categories":3478},[275],{"categories":3480},[288],{"categories":3482},[275],{"categories":3484},[275],{"categories":3486},[275],{"categories":3488},[288],{"categories":3490},[],{"categories":3492},[288],{"categories":3494},[3309],{"categories":3496},[275],{"categories":3498},[275],{"categories":3500},[288],{"categories":3502},[288],{"categories":3504},[250],{"categories":3506},[250],{"categories":3508},[275],{"categories":3510},[],{"categories":3512},[250],{"categories":3514},[275],{"categories":3516},[275],{"categories":3518},[288],{"categories":3520},[283],{"categories":3522},[275],{"categories":3524},[],{"categories":3526},[275],{"categories":3528},[275],{"categories":3530},[2411],{"categories":3532},[],{"categories":3534},[275],{"categories":3536},[275],{"categories":3538},[275],{"categories":3540},[275],{"categories":3542},[375],{"categories":3544},[275],{"categories":3546},[],{"categories":3548},[275],{"categories":3550},[275],{"categories":3552},[275],{"categories":3554},[275],{"categories":3556},[404],{"categories":3558},[323],{"categories":3560},[275],{"categories":3562},[275],{"categories":3564},[1966],{"categories":3566},[278],{"categories":3568},[275],{"categories":3570},[275],{"categories":3572},[326],{"categories":3574},[275],{"categories":3576},[275],{"categories":3578},[323],{"categories":3580},[288],{"categories":3582},[],{"categories":3584},[275],{"categories":3586},[275],{"categories":3588},[375],{"categories":3590},[275],{"categories":3592},[404],{"categories":3594},[288],{"categories":3596},[275],{"categories":3598},[288],{"categories":3600},[],{"categories":3602},[],{"categories":3604},[],{"categories":3606},[278],{"categories":3608},[323],{"categories":3610},[288],{"categories":3612},[275],{"categories":3614},[275],{"categories":3616},[275],{"categories":3618},[275],{"categories":3620},[562],{"categories":3622},[375],{"categories":3624},[288],{"categories":3626},[275],{"categories":3628},[],{"categories":3630},[288],{"categories":3632},[288],{"categories":3634},[],{"categories":3636},[275],{"categories":3638},[288],{"categories":3640},[275],{"categories":3642},[],{"categories":3644},[275],{"categories":3646},[275],{"categories":3648},[275],{"categories":3650},[323],{"categories":3652},[375],{"categories":3654},[288],{"categories":3656},[375],{"categories":3658},[288],{"categories":3660},[275],{"categories":3662},[283],{"categories":3664},[],{"categories":3666},[],{"categories":3668},[275],{"categories":3670},[275],{"categories":3672},[275],{"categories":3674},[278],{"categories":3676},[288],{"categories":3678},[323],{"categories":3680},[],{"categories":3682},[375],{"categories":3684},[],{"categories":3686},[250],{"categories":3688},[275],{"categories":3690},[250],{"categories":3692},[375],{"categories":3694},[250],{"categories":3696},[275],{"categories":3698},[],{"categories":3700},[275],{"categories":3702},[275],{"categories":3704},[],{"categories":3706},[275],{"categories":3708},[275],{"categories":3710},[404],{"categories":3712},[275],{"categories":3714},[275],{"categories":3716},[445],{"categories":3718},[250],{"categories":3720},[275],{"categories":3722},[],{"categories":3724},[288],{"categories":3726},[275],{"categories":3728},[278],{"categories":3730},[679],{"categories":3732},[275],{"categories":3734},[275],{"categories":3736},[288],{"categories":3738},[275],{"categories":3740},[288],{"categories":3742},[275],{"categories":3744},[275],{"categories":3746},[275],{"categories":3748},[275],{"categories":3750},[],{"categories":3752},[275],{"categories":3754},[278],{"categories":3756},[275],{"categories":3758},[283],{"categories":3760},[250],{"categories":3762},[375],{"categories":3764},[],{"categories":3766},[275],{"categories":3768},[],{"categories":3770},[288],{"categories":3772},[275],{"categories":3774},[],{"categories":3776},[288],{"categories":3778},[275],{"categories":3780},[250],{"categories":3782},[375],{"categories":3784},[323],{"categories":3786},[275],{"categories":3788},[323],{"categories":3790},[288],{"categories":3792},[375],{"categories":3794},[275],{"categories":3796},[],{"categories":3798},[275],{"categories":3800},[312],{"categories":3802},[288],{"categories":3804},[275],{"categories":3806},[375],{"categories":3808},[323],{"categories":3810},[283],{"categories":3812},[250],{"categories":3814},[275],{"categories":3816},[275],{"categories":3818},[275],{"categories":3820},[275],{"categories":3822},[323],{"categories":3824},[404],{"categories":3826},[],{"categories":3828},[],{"categories":3830},[326],{"categories":3832},[618],{"categories":3834},[275],{"categories":3836},[288],{"categories":3838},[275,250],{"categories":3840},[323],{"categories":3842},[275],{"categories":3844},[275],{"categories":3846},[275],{"categories":3848},[275],{"categories":3850},[275],{"categories":3852},[275],{"categories":3854},[275],{"categories":3856},[288],{"categories":3858},[275],{"categories":3860},[288],{"categories":3862},[275],{"categories":3864},[275],{"categories":3866},[275],{"categories":3868},[],{"categories":3870},[275],{"categories":3872},[1402],{"categories":3874},[250],{"categories":3876},[375],{"categories":3878},[275],{"categories":3880},[275],{"categories":3882},[275],{"categories":3884},[326],{"categories":3886},[288],{"categories":3888},[404],{"categories":3890},[445],{"categories":3892},[],{"categories":3894},[250],{"categories":3896},[275],{"categories":3898},[283],{"categories":3900},[288],{"categories":3902},[278],{"categories":3904},[288],{"categories":3906},[275],{"categories":3908},[288],{"categories":3910},[288],{"categories":3912},[291],{"categories":3914},[250],{"categories":3916},[275],{"categories":3918},[275],{"categories":3920},[],{"categories":3922},[],{"categories":3924},[],{"categories":3926},[445],{"categories":3928},[275],{"categories":3930},[323],{"categories":3932},[275],{"categories":3934},[275],{"categories":3936},[275],{"categories":3938},[275],{"categories":3940},[],{"categories":3942},[275],{"categories":3944},[326],{"categories":3946},[283],{"categories":3948},[288],{"categories":3950},[275],{"categories":3952},[],{"categories":3954},[275],{"categories":3956},[288],{"categories":3958},[250],{"categories":3960},[275],{"categories":3962},[445],{"categories":3964},[],{"categories":3966},[375],{"categories":3968},[375],{"categories":3970},[275],{"categories":3972},[288],{"categories":3974},[],{"categories":3976},[250],{"categories":3978},[275],{"categories":3980},[375],{"categories":3982},[275],{"categories":3984},[283],{"categories":3986},[288],{"categories":3988},[275],{"categories":3990},[],{"categories":3992},[323],{"categories":3994},[275],{"categories":3996},[275],{"categories":3998},[275],{"categories":4000},[375],{"categories":4002},[288],{"categories":4004},[323],{"categories":4006},[],{"categories":4008},[288],{"categories":4010},[283],{"categories":4012},[288],{"categories":4014},[375],{"categories":4016},[275],{"categories":4018},[275],{"categories":4020},[275],{"categories":4022},[618],{"categories":4024},[275],{"categories":4026},[288],{"categories":4028},[],{"categories":4030},[275],{"categories":4032},[275],{"categories":4034},[445],{"categories":4036},[323],{"categories":4038},[326],{"categories":4040},[725],{"categories":4042},[326],{"categories":4044},[326],{"categories":4046},[275],{"categories":4048},[],{"categories":4050},[],{"categories":4052},[],{"categories":4054},[288],{"categories":4056},[275],{"categories":4058},[288],{"categories":4060},[288],{"categories":4062},[250],{"categories":4064},[275],{"categories":4066},[595],{"categories":4068},[250],{"categories":4070},[288],{"categories":4072},[275],{"categories":4074},[275],{"categories":4076},[275],{"categories":4078},[275],{"categories":4080},[275],{"categories":4082},[288],{"categories":4084},[275],{"categories":4086},[],{"categories":4088},[],{"categories":4090},[275],{"categories":4092},[],{"categories":4094},[275],{"categories":4096},[288],{"categories":4098},[375],{"categories":4100},[275],{"categories":4102},[275],{"categories":4104},[],{"categories":4106},[288],{"categories":4108},[275],{"categories":4110},[275],{"categories":4112},[291],{"categories":4114},[275],{"categories":4116},[375],{"categories":4118},[275],{"categories":4120},[288],{"categories":4122},[283],{"categories":4124},[275],{"categories":4126},[275],{"categories":4128},[404],{"categories":4130},[288],{"categories":4132},[275],{"categories":4134},[275],{"categories":4136},[1039],{"categories":4138},[275],{"categories":4140},[288],{"categories":4142},[275],{"categories":4144},[250],{"categories":4146},[275],{"categories":4148},[679],{"categories":4150},[375],{"categories":4152},[],{"categories":4154},[275],{"categories":4156},[275],{"categories":4158},[323],{"categories":4160},[618],{"categories":4162},[288],{"categories":4164},[275],{"categories":4166},[],{"categories":4168},[323],{"categories":4170},[539],{"categories":4172},[288],{"categories":4174},[288],{"categories":4176},[288],{"categories":4178},[275],{"categories":4180},[275],{"categories":4182},[288],{"categories":4184},[],{"categories":4186},[283],{"categories":4188},[275],{"categories":4190},[283],{"categories":4192},[288],{"categories":4194},[],{"categories":4196},[250],{"categories":4198},[275],{"categories":4200},[275],{"categories":4202},[278],{"categories":4204},[275],{"categories":4206},[323],{"categories":4208},[445],{"categories":4210},[312],{"categories":4212},[288],{"categories":4214},[288],{"categories":4216},[275],{"categories":4218},[275],{"categories":4220},[288],{"categories":4222},[275],{"categories":4224},[278],{"categories":4226},[],{"categories":4228},[288],{"categories":4230},[275],{"categories":4232},[275],{"categories":4234},[275],{"categories":4236},[288],{"categories":4238},[275],{"categories":4240},[],{"categories":4242},[275],{"categories":4244},[],{"categories":4246},[375],{"categories":4248},[288],{"categories":4250},[275,283],{"categories":4252},[288],{"categories":4254},[275],{"categories":4256},[],{"categories":4258},[278],{"categories":4260},[326],{"categories":4262},[283],{"categories":4264},[275],{"categories":4266},[250],{"categories":4268},[275],{"categories":4270},[275],{"categories":4272},[288],{"categories":4274},[275],{"categories":4276},[275],{"categories":4278},[275],{"categories":4280},[323],{"categories":4282},[1402],{"categories":4284},[288],{"categories":4286},[275],{"categories":4288},[],{"categories":4290},[],{"categories":4292},[275],{"categories":4294},[288],{"categories":4296},[275],{"categories":4298},[275],{"categories":4300},[445],{"categories":4302},[],{"categories":4304},[275],{"categories":4306},[288],{"categories":4308},[312],{"categories":4310},[288],{"categories":4312},[618],{"categories":4314},[],{"categories":4316},[562],{"categories":4318},[288],{"categories":4320},[275],{"categories":4322},[275],{"categories":4324},[404],{"categories":4326},[288],{"categories":4328},[275],{"categories":4330},[326],{"categories":4332},[291],{"categories":4334},[288],{"categories":4336},[275],{"categories":4338},[618],{"categories":4340},[275],{"categories":4342},[445],{"categories":4344},[283],{"categories":4346},[],{"categories":4348},[275],{"categories":4350},[275],{"categories":4352},[404],{"categories":4354},[375],{"categories":4356},[275],{"categories":4358},[275],{"categories":4360},[275],{"categories":4362},[],{"categories":4364},[404],{"categories":4366},[323],{"categories":4368},[275],{"categories":4370},[275],{"categories":4372},[275],{"categories":4374},[725],{"categories":4376},[278],{"categories":4378},[275],{"categories":4380},[291],{"categories":4382},[275],{"categories":4384},[],{"categories":4386},[],{"categories":4388},[375],{"categories":4390},[275],{"categories":4392},[326],{"categories":4394},[404],{"categories":4396},[288],{"categories":4398},[275],{"categories":4400},[275],{"categories":4402},[404],{"categories":4404},[323],{"categories":4406},[275],{"categories":4408},[],{"categories":4410},[275],{"categories":4412},[275],{"categories":4414},[],{"categories":4416},[275],{"categories":4418},[275],{"categories":4420},[752],{"categories":4422},[275],{"categories":4424},[275],{"categories":4426},[288],{"categories":4428},[250],{"categories":4430},[618],{"categories":4432},[275],{"categories":4434},[275],{"categories":4436},[275],{"categories":4438},[],{"categories":4440},[275,250],{"categories":4442},[323],{"categories":4444},[288],{"categories":4446},[250],{"categories":4448},[288],{"categories":4450},[1077],{"categories":4452},[250],{"categories":4454},[250],{"categories":4456},[288],{"categories":4458},[275],{"categories":4460},[278],{"categories":4462},[],{"categories":4464},[],{"categories":4466},[288],{"categories":4468},[275],{"categories":4470},[250],{"categories":4472},[275],{"categories":4474},[278],{"categories":4476},[250],{"categories":4478},[250],{"categories":4480},[275],{"categories":4482},[404],{"categories":4484},[275],{"categories":4486},[250],{"categories":4488},[275],{"categories":4490},[],{"categories":4492},[275],{"categories":4494},[275],{"categories":4496},[375,275],{"categories":4498},[445],{"categories":4500},[278],{"categories":4502},[275],{"categories":4504},[],{"categories":4506},[275],{"categories":4508},[275],{"categories":4510},[283],{"categories":4512},[275],{"categories":4514},[283],{"categories":4516},[275],{"categories":4518},[275],{"categories":4520},[539],{"categories":4522},[275],{"categories":4524},[283],{"categories":4526},[250],{"categories":4528},[326],{"categories":4530},[288],{"categories":4532},[275],{"categories":4534},[250],{"categories":4536},[275],{"categories":4538},[275],{"categories":4540},[323],{"categories":4542},[404],{"categories":4544},[375],{"categories":4546},[275],{"categories":4548},[275],{"categories":4550},[275],{"categories":4552},[275],{"categories":4554},[278],{"categories":4556},[275],{"categories":4558},[288],{"categories":4560},[288],{"categories":4562},[250],{"categories":4564},[323],{"categories":4566},[250],{"categories":4568},[250],{"categories":4570},[275],{"categories":4572},[275],{"categories":4574},[],{"categories":4576},[],{"categories":4578},[326],{"categories":4580},[275],{"categories":4582},[250],{"categories":4584},[275],{"categories":4586},[375],{"categories":4588},[618],{"categories":4590},[562],{"categories":4592},[539],{"categories":4594},[275],{"categories":4596},[275],{"categories":4598},[275],{"categories":4600},[326],{"categories":4602},[275],{"categories":4604},[275],{"categories":4606},[275],{"categories":4608},[275],{"categories":4610},[275],{"categories":4612},[275],{"categories":4614},[275],{"categories":4616},[288],{"categories":4618},[278],{"categories":4620},[288],{"categories":4622},[275,283],{"categories":4624},[],{"categories":4626},[375],{"categories":4628},[],{"categories":4630},[291],{"categories":4632},[275],{"categories":4634},[323],{"categories":4636},[278],{"categories":4638},[275],{"categories":4640},[278],{"categories":4642},[288],{"categories":4644},[326],{"categories":4646},[288],{"categories":4648},[291],{"categories":4650},[288],{"categories":4652},[275],{"categories":4654},[275],{"categories":4656},[275],{"categories":4658},[283],{"categories":4660},[288],{"categories":4662},[250],{"categories":4664},[404],{"categories":4666},[275],{"categories":4668},[275],{"categories":4670},[],{"categories":4672},[323],{"categories":4674},[275],{"categories":4676},[275],{"categories":4678},[275],{"categories":4680},[275],{"categories":4682},[275],{"categories":4684},[275],{"categories":4686},[250],{"categories":4688},[323],{"categories":4690},[250],{"categories":4692},[250],{"categories":4694},[275],{"categories":4696},[275],{"categories":4698},[275],{"categories":4700},[275],{"categories":4702},[562],{"categories":4704},[275],{"categories":4706},[288],{"categories":4708},[288],{"categories":4710},[323],{"categories":4712},[275],{"categories":4714},[275],{"categories":4716},[275],{"categories":4718},[288],{"categories":4720},[275],{"categories":4722},[275],{"categories":4724},[275],{"categories":4726},[3309],{"categories":4728},[4729],"Clinical AI",{"categories":4731},[375],{"categories":4733},[275],{"categories":4735},[275],{"categories":4737},[275],{"categories":4739},[275],{"categories":4741},[445],{"categories":4743},[2668],{"categories":4745},[275],{"categories":4747},[291],{"categories":4749},[375],{"categories":4751},[275],{"categories":4753},[288],{"categories":4755},[275],{"categories":4757},[275],{"categories":4759},[323],{"categories":4761},[275],{"categories":4763},[288],{"categories":4765},[250],{"categories":4767},[404],{"categories":4769},[275],{"categories":4771},[275],{"categories":4773},[283],{"categories":4775},[275],{"categories":4777},[275],{"categories":4779},[679],{"categories":4781},[275],{"categories":4783},[],{"categories":4785},[288],{"categories":4787},[275],{"categories":4789},[250],{"categories":4791},[278],{"categories":4793},[275],{"categories":4795},[],{"categories":4797},[],{"categories":4799},[275],{"categories":4801},[],{"categories":4803},[283],{"categories":4805},[275],{"categories":4807},[275],{"categories":4809},[288],{"categories":4811},[275],{"categories":4813},[323],{"categories":4815},[323],{"categories":4817},[323],{"categories":4819},[323],{"categories":4821},[],{"categories":4823},[278],{"categories":4825},[288],{"categories":4827},[323],{"categories":4829},[275],{"categories":4831},[752],{"categories":4833},[291],{"categories":4835},[288],{"categories":4837},[275],{"categories":4839},[278],{"categories":4841},[275],{"categories":4843},[288],{"categories":4845},[275],{"categories":4847},[275],{"categories":4849},[275],{"categories":4851},[275,288],{"categories":4853},[288],{"categories":4855},[445],{"categories":4857},[323],{"categories":4859},[288],{"categories":4861},[323],{"categories":4863},[288],{"categories":4865},[275],{"categories":4867},[],{"categories":4869},[323],{"categories":4871},[404],{"categories":4873},[278],{"categories":4875},[275],{"categories":4877},[275],{"categories":4879},[],{"categories":4881},[250],{"categories":4883},[],{"categories":4885},[278],{"categories":4887},[288],{"categories":4889},[323],{"categories":4891},[275],{"categories":4893},[323],{"categories":4895},[278],{"categories":4897},[323],{"categories":4899},[323],{"categories":4901},[],{"categories":4903},[283],{"categories":4905},[288],{"categories":4907},[323],{"categories":4909},[323],{"categories":4911},[323],{"categories":4913},[323],{"categories":4915},[323],{"categories":4917},[323],{"categories":4919},[323],{"categories":4921},[323],{"categories":4923},[323],{"categories":4925},[323],{"categories":4927},[326],{"categories":4929},[278],{"categories":4931},[275],{"categories":4933},[275],{"categories":4935},[288],{"categories":4937},[288],{"categories":4939},[],{"categories":4941},[275],{"categories":4943},[275,278],{"categories":4945},[],{"categories":4947},[288],{"categories":4949},[275],{"categories":4951},[323],{"categories":4953},[288],{"categories":4955},[1077],{"categories":4957},[275],{"categories":4959},[275],{"categories":4961},[275],{"categories":4963},[275],{"categories":4965},[275],{"categories":4967},[539],{"categories":4969},[275],{"categories":4971},[275],{"categories":4973},[288],{"categories":4975},[275],{"categories":4977},[275],{"categories":4979},[283],{"categories":4981},[291],{"categories":4983},[288],{"categories":4985},[288],{"categories":4987},[],{"categories":4989},[288],{"categories":4991},[375],{"categories":4993},[323],{"categories":4995},[275],{"categories":4997},[],{"categories":4999},[291],{"categories":5001},[],{"categories":5003},[250],{"categories":5005},[275],{"categories":5007},[288],{"categories":5009},[375],{"categories":5011},[275],{"categories":5013},[],{"categories":5015},[275],{"categories":5017},[275],{"categories":5019},[],{"categories":5021},[404],{"categories":5023},[275],{"categories":5025},[288],{"categories":5027},[],{"categories":5029},[],{"categories":5031},[323],{"categories":5033},[278],{"categories":5035},[275],{"categories":5037},[275],{"categories":5039},[283],{"categories":5041},[275],{"categories":5043},[275],{"categories":5045},[288],{"categories":5047},[275],{"categories":5049},[283],{"categories":5051},[283],{"categories":5053},[375],{"categories":5055},[],{"categories":5057},[275],{"categories":5059},[323],{"categories":5061},[],{"categories":5063},[275],{"categories":5065},[275],{"categories":5067},[375],{"categories":5069},[275],{"categories":5071},[275],{"categories":5073},[404],{"categories":5075},[275],{"categories":5077},[445],{"categories":5079},[],{"categories":5081},[288],{"categories":5083},[275],{"categories":5085},[404],{"categories":5087},[250],{"categories":5089},[],{"categories":5091},[275],{"categories":5093},[],{"categories":5095},[288],{"categories":5097},[375],{"categories":5099},[250],{"categories":5101},[],{"categories":5103},[3309],{"categories":5105},[283],{"categories":5107},[278],{"categories":5109},[275],{"categories":5111},[326],{"categories":5113},[288],{"categories":5115},[375],{"categories":5117},[275],{"categories":5119},[250],{"categories":5121},[],{"categories":5123},[],{"categories":5125},[275],{"categories":5127},[278],{"categories":5129},[275],{"categories":5131},[404],{"categories":5133},[],{"categories":5135},[288],{"categories":5137},[288],{"categories":5139},[275],{"categories":5141},[288],{"categories":5143},[275],{"categories":5145},[323],{"categories":5147},[250],{"categories":5149},[275],{"categories":5151},[288],{"categories":5153},[291],{"categories":5155},[275],{"categories":5157},[275],{"categories":5159},[275],{"categories":5161},[288],{"categories":5163},[275],{"categories":5165},[291],{"categories":5167},[404],{"categories":5169},[323],{"categories":5171},[],{"categories":5173},[404],{"categories":5175},[275],{"categories":5177},[],{"categories":5179},[250],{"categories":5181},[288],{"categories":5183},[],{"categories":5185},[275],{"categories":5187},[275],{"categories":5189},[275],{"categories":5191},[275],{"categories":5193},[275],{"categories":5195},[288],{"categories":5197},[283],{"categories":5199},[278],{"categories":5201},[288],{"categories":5203},[275],{"categories":5205},[375],{"categories":5207},[250],{"categories":5209},[250],{"categories":5211},[275],{"categories":5213},[326],{"categories":5215},[288],{"categories":5217},[275],{"categories":5219},[275],{"categories":5221},[288],{"categories":5223},[275],{"categories":5225},[275],{"categories":5227},[288],{"categories":5229},[283],{"categories":5231},[275],{"categories":5233},[375],{"categories":5235},[250],{"categories":5237},[288],{"categories":5239},[275],{"categories":5241},[291],{"categories":5243},[275],{"categories":5245},[288],{"categories":5247},[275],{"categories":5249},[275],{"categories":5251},[323],{"categories":5253},[275],{"categories":5255},[],{"categories":5257},[278],{"categories":5259},[275],{"categories":5261},[275],{"categories":5263},[275],{"categories":5265},[250],{"categories":5267},[250],{"categories":5269},[275],{"categories":5271},[250],{"categories":5273},[275],{"categories":5275},[288],{"categories":5277},[275],{"categories":5279},[275],{"categories":5281},[275],{"categories":5283},[275],{"categories":5285},[275],{"categories":5287},[],{"categories":5289},[275],{"categories":5291},[375],{"categories":5293},[288],{"categories":5295},[283],{"categories":5297},[323],{"categories":5299},[275],{"categories":5301},[288],{"categories":5303},[275],{"categories":5305},[288],{"categories":5307},[275],{"categories":5309},[275],{"categories":5311},[375],{"categories":5313},[288],{"categories":5315},[275],{"categories":5317},[404],{"categories":5319},[275],{"categories":5321},[326],{"categories":5323},[275],{"categories":5325},[275],{"categories":5327},[323],{"categories":5329},[275],{"categories":5331},[275],{"categories":5333},[275],{"categories":5335},[275],{"categories":5337},[288],{"categories":5339},[445],{"categories":5341},[275],{"categories":5343},[250],{"categories":5345},[288],{"categories":5347},[326],{"categories":5349},[],{"categories":5351},[288],{"categories":5353},[250],{"categories":5355},[275],{"categories":5357},[275],{"categories":5359},[2504],{"categories":5361},[375],{"categories":5363},[474],{"categories":5365},[275],{"categories":5367},[275],{"categories":5369},[275],{"categories":5371},[275],{"categories":5373},[278],{"categories":5375},[275],{"categories":5377},[275],{"categories":5379},[250],{"categories":5381},[283],{"categories":5383},[275],{"categories":5385},[250],{"categories":5387},[275],{"categories":5389},[],{"categories":5391},[288],{"categories":5393},[288],{"categories":5395},[275],{"categories":5397},[275],{"categories":5399},[275],{"categories":5401},[326],{"categories":5403},[],{"categories":5405},[323],{"categories":5407},[],{"categories":5409},[323],{"categories":5411},[275],{"categories":5413},[275],{"categories":5415},[288],{"categories":5417},[275],{"categories":5419},[288],{"categories":5421},[288],{"categories":5423},[],{"categories":5425},[275],{"categories":5427},[323],{"categories":5429},[275],{"categories":5431},[],{"categories":5433},[275],{"categories":5435},[275],{"categories":5437},[],{"categories":5439},[275],{"categories":5441},[275],{"categories":5443},[375],{"categories":5445},[250],{"categories":5447},[288],{"categories":5449},[275],{"categories":5451},[275],{"categories":5453},[275],{"categories":5455},[275],{"categories":5457},[404],{"categories":5459},[275],{"categories":5461},[275],{"categories":5463},[275],{"categories":5465},[278],{"categories":5467},[275],{"categories":5469},[275],{"categories":5471},[],{"categories":5473},[275],{"categories":5475},[275],{"categories":5477},[275],{"categories":5479},[],{"categories":5481},[278],{"categories":5483},[275],{"categories":5485},[275],{"categories":5487},[323],{"categories":5489},[250],{"categories":5491},[291],{"categories":5493},[288],{"categories":5495},[618],{"categories":5497},[275],{"categories":5499},[275],{"categories":5501},[275],{"categories":5503},[250],{"categories":5505},[323],{"categories":5507},[375],{"categories":5509},[275],{"categories":5511},[275],{"categories":5513},[275],{"categories":5515},[275],{"categories":5517},[323],{"categories":5519},[275],{"categories":5521},[375],{"categories":5523},[275],{"categories":5525},[275],{"categories":5527},[323],{"categories":5529},[375],{"categories":5531},[275],{"categories":5533},[323],{"categories":5535},[275],{"categories":5537},[288],{"categories":5539},[288],{"categories":5541},[288],{"categories":5543},[250],{"categories":5545},[323],{"categories":5547},[288],{"categories":5549},[288],{"categories":5551},[275],{"categories":5553},[250],{"categories":5555},[375],{"categories":5557},[275],{"categories":5559},[275],{"categories":5561},[288],{"categories":5563},[275],{"categories":5565},[],{"categories":5567},[288],{"categories":5569},[],{"categories":5571},[275],{"categories":5573},[275],{"categories":5575},[],{"categories":5577},[],{"categories":5579},[288],{"categories":5581},[283],{"categories":5583},[288],{"categories":5585},[5586],"Liability & Ethics",{"categories":5588},[275],{"categories":5590},[275],{"categories":5592},[275],{"categories":5594},[288],{"categories":5596},[278],{"categories":5598},[288],{"categories":5600},[283],{"categories":5602},[404],{"categories":5604},[288],{"categories":5606},[275],{"categories":5608},[275],{"categories":5610},[],{"categories":5612},[725],{"categories":5614},[288],{"categories":5616},[],{"categories":5618},[275],{"categories":5620},[278],{"categories":5622},[288],{"categories":5624},[],{"categories":5626},[288],{"categories":5628},[275],{"categories":5630},[275],{"categories":5632},[250],{"categories":5634},[275],{"categories":5636},[323],{"categories":5638},[275],{"categories":5640},[275],{"categories":5642},[291],{"categories":5644},[288],{"categories":5646},[275],{"categories":5648},[275],{"categories":5650},[275],{"categories":5652},[323],{"categories":5654},[288],{"categories":5656},[250],{"categories":5658},[375],{"categories":5660},[278],{"categories":5662},[275],{"categories":5664},[275],{"categories":5666},[275],{"categories":5668},[],{"categories":5670},[288],{"categories":5672},[288],{"categories":5674},[288],{"categories":5676},[618],{"categories":5678},[375],{"categories":5680},[288],{"categories":5682},[445],{"categories":5684},[250],{"categories":5686},[323],{"categories":5688},[275],{"categories":5690},[375],{"categories":5692},[275],{"categories":5694},[278],{"categories":5696},[],{"categories":5698},[288],{"categories":5700},[275],{"categories":5702},[275],{"categories":5704},[275],{"categories":5706},[275],{"categories":5708},[288],{"categories":5710},[275],{"categories":5712},[275],{"categories":5714},[375],{"categories":5716},[],{"categories":5718},[288],{"categories":5720},[291],{"categories":5722},[323],{"categories":5724},[288],{"categories":5726},[283],{"categories":5728},[],{"categories":5730},[275],{"categories":5732},[275],{"categories":5734},[291],{"categories":5736},[275],{"categories":5738},[288],{"categories":5740},[323],{"categories":5742},[278],{"categories":5744},[445],{"categories":5746},[275],{"categories":5748},[275],{"categories":5750},[275],{"categories":5752},[323],{"categories":5754},[283],{"categories":5756},[275],{"categories":5758},[375],{"categories":5760},[323],{"categories":5762},[445],{"categories":5764},[275],{"categories":5766},[288],{"categories":5768},[],{"categories":5770},[679],{"categories":5772},[],{"categories":5774},[275],{"categories":5776},[445],{"categories":5778},[275],{"categories":5780},[326],{"categories":5782},[275],{"categories":5784},[288],{"categories":5786},[288],{"categories":5788},[5789],"Design News & Tools",{"categories":5791},[275],{"categories":5793},[275],{"categories":5795},[323],{"categories":5797},[275],{"categories":5799},[275],{"categories":5801},[278],{"categories":5803},[288],{"categories":5805},[275],{"categories":5807},[375],{"categories":5809},[288],{"categories":5811},[288],{"categories":5813},[375],{"categories":5815},[275],{"categories":5817},[275],{"categories":5819},[618],{"categories":5821},[288],{"categories":5823},[275],{"categories":5825},[275],{"categories":5827},[618],{"categories":5829},[275],{"categories":5831},[404],{"categories":5833},[275],{"categories":5835},[288],{"categories":5837},[],{"categories":5839},[275],{"categories":5841},[275],{"categories":5843},[275],{"categories":5845},[323],{"categories":5847},[275],{"categories":5849},[278],{"categories":5851},[],{"categories":5853},[275],{"categories":5855},[275],{"categories":5857},[275],{"categories":5859},[250],{"categories":5861},[752],{"categories":5863},[250],{"categories":5865},[375],{"categories":5867},[275],{"categories":5869},[275,288],{"categories":5871},[404,283],{"categories":5873},[250],{"categories":5875},[275],{"categories":5877},[275],{"categories":5879},[275],{"categories":5881},[275],{"categories":5883},[],{"categories":5885},[288],{"categories":5887},[275],{"categories":5889},[],{"categories":5891},[275],{"categories":5893},[250],{"categories":5895},[275],{"categories":5897},[250],{"categories":5899},[],{"categories":5901},[288],{"categories":5903},[275],{"categories":5905},[283],{"categories":5907},[275],{"categories":5909},[323],{"categories":5911},[275],{"categories":5913},[],{"categories":5915},[288],{"categories":5917},[275],{"categories":5919},[],{"categories":5921},[375],{"categories":5923},[275],{"categories":5925},[275],{"categories":5927},[288],{"categories":5929},[275],{"categories":5931},[275],{"categories":5933},[278],{"categories":5935},[288],{"categories":5937},[275],{"categories":5939},[],{"categories":5941},[275],{"categories":5943},[445],{"categories":5945},[404],{"categories":5947},[283],{"categories":5949},[283],{"categories":5951},[275],{"categories":5953},[278],{"categories":5955},[278],{"categories":5957},[275],{"categories":5959},[288],{"categories":5961},[275],{"categories":5963},[275],{"categories":5965},[275],{"categories":5967},[275],{"categories":5969},[250],{"categories":5971},[275],{"categories":5973},[278],{"categories":5975},[275],{"categories":5977},[275],{"categories":5979},[288],{"categories":5981},[275],{"categories":5983},[404],{"categories":5985},[275],{"categories":5987},[323],{"categories":5989},[275],{"categories":5991},[275],{"categories":5993},[288],{"categories":5995},[291],{"categories":5997},[275],{"categories":5999},[275],{"categories":6001},[288],{"categories":6003},[],{"categories":6005},[250],{"categories":6007},[],{"categories":6009},[250],{"categories":6011},[288],{"categories":6013},[278],{"categories":6015},[275],{"categories":6017},[],{"categories":6019},[326],{"categories":6021},[445],{"categories":6023},[275],{"categories":6025},[250],{"categories":6027},[275],{"categories":6029},[],{"categories":6031},[323],{"categories":6033},[288],{"categories":6035},[250],{"categories":6037},[375],{"categories":6039},[283],{"categories":6041},[275],{"categories":6043},[275],{"categories":6045},[288],{"categories":6047},[250],{"categories":6049},[288],{"categories":6051},[323],{"categories":6053},[275],{"categories":6055},[291],{"categories":6057},[278],{"categories":6059},[291],{"categories":6061},[323],{"categories":6063},[275],{"categories":6065},[250],{"categories":6067},[275],{"categories":6069},[375],{"categories":6071},[283],{"categories":6073},[275],{"categories":6075},[275],{"categories":6077},[275],{"categories":6079},[275],{"categories":6081},[275],{"categories":6083},[275],{"categories":6085},[288],{"categories":6087},[275],{"categories":6089},[288],{"categories":6091},[275],{"categories":6093},[275],{"categories":6095},[278],{"categories":6097},[275],{"categories":6099},[288],{"categories":6101},[288],{"categories":6103},[375],{"categories":6105},[288],{"categories":6107},[288],{"categories":6109},[275],{"categories":6111},[278],{"categories":6113},[288],{"categories":6115},[375],{"categories":6117},[],{"categories":6119},[275],{"categories":6121},[326],{"categories":6123},[618],{"categories":6125},[275],{"categories":6127},[288],{"categories":6129},[275],{"categories":6131},[275],{"categories":6133},[250],{"categories":6135},[275],{"categories":6137},[],{"categories":6139},[275],{"categories":6141},[288],{"categories":6143},[275],{"categories":6145},[404],{"categories":6147},[275],{"categories":6149},[250],{"categories":6151},[275],{"categories":6153},[323],{"categories":6155},[288],{"categories":6157},[275],{"categories":6159},[404],{"categories":6161},[288],{"categories":6163},[283],{"categories":6165},[283],{"categories":6167},[275],{"categories":6169},[275],{"categories":6171},[275],{"categories":6173},[275],{"categories":6175},[275],{"categories":6177},[275],{"categories":6179},[278],{"categories":6181},[],{"categories":6183},[275],{"categories":6185},[275],{"categories":6187},[288],{"categories":6189},[275],{"categories":6191},[288],{"categories":6193},[275],{"categories":6195},[275],{"categories":6197},[275],{"categories":6199},[275],{"categories":6201},[275],{"categories":6203},[250],{"categories":6205},[],{"categories":6207},[278],{"categories":6209},[275],{"categories":6211},[275],{"categories":6213},[288],{"categories":6215},[288],{"categories":6217},[],{"categories":6219},[250],{"categories":6221},[250],{"categories":6223},[275],{"categories":6225},[404],{"categories":6227},[283],{"categories":6229},[375],{"categories":6231},[],{"categories":6233},[275],{"categories":6235},[288],{"categories":6237},[278],{"categories":6239},[275],{"categories":6241},[275],{"categories":6243},[250],{"categories":6245},[278],{"categories":6247},[275],{"categories":6249},[275],{"categories":6251},[323],{"categories":6253},[326],{"categories":6255},[275],{"categories":6257},[323],{"categories":6259},[288],{"categories":6261},[275],{"categories":6263},[],{"categories":6265},[323],{"categories":6267},[288],{"categories":6269},[375],{"categories":6271},[326],{"categories":6273},[275],{"categories":6275},[275],{"categories":6277},[],{"categories":6279},[288],{"categories":6281},[288],{"categories":6283},[288],{"categories":6285},[3309],{"categories":6287},[323],{"categories":6289},[275],{"categories":6291},[250],{"categories":6293},[275],{"categories":6295},[275],{"categories":6297},[275],{"categories":6299},[275],{"categories":6301},[275],{"categories":6303},[283],{"categories":6305},[275],{"categories":6307},[278],{"categories":6309},[1966],{"categories":6311},[445],{"categories":6313},[278],{"categories":6315},[],{"categories":6317},[275],{"categories":6319},[],{"categories":6321},[323],{"categories":6323},[288],{"categories":6325},[375],{"categories":6327},[275],{"categories":6329},[275],{"categories":6331},[275],{"categories":6333},[323],{"categories":6335},[],{"categories":6337},[288],{"categories":6339},[275],{"categories":6341},[288],{"categories":6343},[288],{"categories":6345},[],{"categories":6347},[275],{"categories":6349},[],{"categories":6351},[323],{"categories":6353},[278],{"categories":6355},[375],{"categories":6357},[275],{"categories":6359},[288],{"categories":6361},[323],{"categories":6363},[275],{"categories":6365},[323],{"categories":6367},[],{"categories":6369},[323],{"categories":6371},[275],{"categories":6373},[278],{"categories":6375},[618],{"categories":6377},[288],{"categories":6379},[275],{"categories":6381},[],{"categories":6383},[250],{"categories":6385},[288],{"categories":6387},[291],{"categories":6389},[288],{"categories":6391},[278],{"categories":6393},[275],{"categories":6395},[275],{"categories":6397},[],{"categories":6399},[],{"categories":6401},[],{"categories":6403},[375],{"categories":6405},[275],{"categories":6407},[288],{"categories":6409},[275],{"categories":6411},[275],{"categories":6413},[],{"categories":6415},[],{"categories":6417},[],{"categories":6419},[275],{"categories":6421},[288],{"categories":6423},[375],{"categories":6425},[275],{"categories":6427},[],{"categories":6429},[288],{"categories":6431},[275],{"categories":6433},[275],{"categories":6435},[278],{"categories":6437},[],{"categories":6439},[],{"categories":6441},[275],{"categories":6443},[275],{"categories":6445},[288],{"categories":6447},[375],{"categories":6449},[275],{"categories":6451},[323],{"categories":6453},[],{"categories":6455},[275],{"categories":6457},[275],{"categories":6459},[404],{"categories":6461},[323],{"categories":6463},[404],{"categories":6465},[326],{"categories":6467},[275],{"categories":6469},[275],{"categories":6471},[],{"categories":6473},[],{"categories":6475},[288],{"categories":6477},[],{"categories":6479},[275],{"categories":6481},[618],{"categories":6483},[275],{"categories":6485},[275],{"categories":6487},[275],{"categories":6489},[275],{"categories":6491},[],{"categories":6493},[288],{"categories":6495},[275],{"categories":6497},[275],{"categories":6499},[],{"categories":6501},[288],{"categories":6503},[275],{"categories":6505},[323],{"categories":6507},[275],{"categories":6509},[404],{"categories":6511},[283],{"categories":6513},[291],{"categories":6515},[275],{"categories":6517},[275],{"categories":6519},[288],{"categories":6521},[326],{"categories":6523},[288],{"categories":6525},[288],{"categories":6527},[],{"categories":6529},[275],{"categories":6531},[288],{"categories":6533},[],{"categories":6535},[275],{"categories":6537},[],{"categories":6539},[323],{"categories":6541},[283],{"categories":6543},[],{"categories":6545},[275],{"categories":6547},[275],{"categories":6549},[275],{"categories":6551},[],{"categories":6553},[288],{"categories":6555},[375],{"categories":6557},[278],{"categories":6559},[275],{"categories":6561},[],{"categories":6563},[283],{"categories":6565},[404],{"categories":6567},[275],{"categories":6569},[250],{"categories":6571},[278],{"categories":6573},[326],{"categories":6575},[283],{"categories":6577},[250],{"categories":6579},[288],{"categories":6581},[250],{"categories":6583},[],{"categories":6585},[275],{"categories":6587},[291],{"categories":6589},[275],{"categories":6591},[],{"categories":6593},[288],{"categories":6595},[278],{"categories":6597},[375],{"categories":6599},[275],{"categories":6601},[278],{"categories":6603},[288],{"categories":6605},[445],{"categories":6607},[275],{"categories":6609},[275],{"categories":6611},[275],{"categories":6613},[275],{"categories":6615},[275],{"categories":6617},[278],{"categories":6619},[275],{"categories":6621},[250],{"categories":6623},[326],{"categories":6625},[288],{"categories":6627},[],{"categories":6629},[275],{"categories":6631},[275],{"categories":6633},[275],{"categories":6635},[250],{"categories":6637},[288],{"categories":6639},[323],{"categories":6641},[250],{"categories":6643},[275],{"categories":6645},[291],{"categories":6647},[],{"categories":6649},[375],{"categories":6651},[250],{"categories":6653},[323],{"categories":6655},[275],{"categories":6657},[278],{"categories":6659},[288],{"categories":6661},[275],{"categories":6663},[275],{"categories":6665},[288],{"categories":6667},[291],{"categories":6669},[275],{"categories":6671},[288],{"categories":6673},[275],{"categories":6675},[283],{"categories":6677},[288],{"categories":6679},[288,445],{"categories":6681},[275],{"categories":6683},[275],{"categories":6685},[288],{"categories":6687},[250],{"categories":6689},[275],{"categories":6691},[275],{"categories":6693},[326],{"categories":6695},[288],{"categories":6697},[404],{"categories":6699},[288],{"categories":6701},[283],{"categories":6703},[],{"categories":6705},[288],{"categories":6707},[275],{"categories":6709},[283],{"categories":6711},[],{"categories":6713},[],{"categories":6715},[250],{"categories":6717},[275],{"categories":6719},[275],{"categories":6721},[288],{"categories":6723},[326],{"categories":6725},[404],{"categories":6727},[275],{"categories":6729},[275],{"categories":6731},[275],{"categories":6733},[288],{"categories":6735},[],{"categories":6737},[288],{"categories":6739},[323],{"categories":6741},[275],{"categories":6743},[288],{"categories":6745},[288],{"categories":6747},[275],{"categories":6749},[],{"categories":6751},[323],{"categories":6753},[250],{"categories":6755},[3309],{"categories":6757},[278],{"categories":6759},[250],{"categories":6761},[275],{"categories":6763},[288],{"categories":6765},[275],{"categories":6767},[275],{"categories":6769},[404],{"categories":6771},[250],{"categories":6773},[326],{"categories":6775},[],{"categories":6777},[323],{"categories":6779},[275],{"categories":6781},[275],{"categories":6783},[],{"categories":6785},[288],{"categories":6787},[275],{"categories":6789},[275],{"categories":6791},[275],{"categories":6793},[275],{"categories":6795},[288],{"categories":6797},[275],{"categories":6799},[275],{"categories":6801},[275],{"categories":6803},[291],{"categories":6805},[275],{"categories":6807},[288],{"categories":6809},[275],{"categories":6811},[275],{"categories":6813},[275],{"categories":6815},[275],{"categories":6817},[275],{"categories":6819},[275],{"categories":6821},[275],{"categories":6823},[283],{"categories":6825},[],{"categories":6827},[291],{"categories":6829},[323],{"categories":6831},[288],{"categories":6833},[275],{"categories":6835},[250],{"categories":6837},[],{"categories":6839},[250],{"categories":6841},[250],{"categories":6843},[288],{"categories":6845},[250],{"categories":6847},[275],{"categories":6849},[275],{"categories":6851},[275],{"categories":6853},[288],{"categories":6855},[250],{"categories":6857},[275],{"categories":6859},[275],{"categories":6861},[275],{"categories":6863},[288],{"categories":6865},[323],{"categories":6867},[275],{"categories":6869},[275],{"categories":6871},[275],{"categories":6873},[283],{"categories":6875},[275],{"categories":6877},[288],{"categories":6879},[375],{"categories":6881},[],{"categories":6883},[275],{"categories":6885},[326],{"categories":6887},[275],{"categories":6889},[288],{"categories":6891},[275],{"categories":6893},[275],{"categories":6895},[],{"categories":6897},[275],{"categories":6899},[275],{"categories":6901},[323],{"categories":6903},[275],{"categories":6905},[275],{"categories":6907},[288],{"categories":6909},[404],{"categories":6911},[],{"categories":6913},[],{"categories":6915},[250],{"categories":6917},[275],{"categories":6919},[275],{"categories":6921},[323],{"categories":6923},[275],{"categories":6925},[250],{"categories":6927},[323],{"categories":6929},[275],{"categories":6931},[275],{"categories":6933},[404],{"categories":6935},[326],{"categories":6937},[275],{"categories":6939},[275],{"categories":6941},[278],{"categories":6943},[288],{"categories":6945},[275],{"categories":6947},[275],{"categories":6949},[288],{"categories":6951},[283],{"categories":6953},[288],{"categories":6955},[250],{"categories":6957},[275],{"categories":6959},[283],{"categories":6961},[],{"categories":6963},[275],{"categories":6965},[326],{"categories":6967},[275],{"categories":6969},[275],{"categories":6971},[],{"categories":6973},[323],{"categories":6975},[275],{"categories":6977},[288],{"categories":6979},[326],{"categories":6981},[275],{"categories":6983},[250],{"categories":6985},[250],{"categories":6987},[250],{"categories":6989},[275],{"categories":6991},[288],{"categories":6993},[288],{"categories":6995},[275],{"categories":6997},[288],{"categories":6999},[275],{"categories":7001},[275],{"categories":7003},[375],{"categories":7005},[326],{"categories":7007},[326],{"categories":7009},[],{"categories":7011},[323],{"categories":7013},[275],{"categories":7015},[275],{"categories":7017},[250],{"categories":7019},[],{"categories":7021},[323],{"categories":7023},[323],{"categories":7025},[323],{"categories":7027},[],{"categories":7029},[288],{"categories":7031},[275],{"categories":7033},[],{"categories":7035},[278],{"categories":7037},[283],{"categories":7039},[],{"categories":7041},[275],{"categories":7043},[275],{"categories":7045},[],{"categories":7047},[250],{"categories":7049},[],{"categories":7051},[],{"categories":7053},[],{"categories":7055},[],{"categories":7057},[275],{"categories":7059},[323],{"categories":7061},[],{"categories":7063},[],{"categories":7065},[275],{"categories":7067},[275],{"categories":7069},[275],{"categories":7071},[326],{"categories":7073},[275],{"categories":7075},[326],{"categories":7077},[],{"categories":7079},[326],{"categories":7081},[326],{"categories":7083},[445],{"categories":7085},[288],{"categories":7087},[250],{"categories":7089},[],{"categories":7091},[],{"categories":7093},[326],{"categories":7095},[250],{"categories":7097},[250],{"categories":7099},[250],{"categories":7101},[],{"categories":7103},[278],{"categories":7105},[250],{"categories":7107},[250],{"categories":7109},[278],{"categories":7111},[250],{"categories":7113},[283],{"categories":7115},[250],{"categories":7117},[250],{"categories":7119},[250],{"categories":7121},[326],{"categories":7123},[323],{"categories":7125},[323],{"categories":7127},[275],{"categories":7129},[250],{"categories":7131},[326],{"categories":7133},[445],{"categories":7135},[326],{"categories":7137},[326],{"categories":7139},[326],{"categories":7141},[],{"categories":7143},[283],{"categories":7145},[],{"categories":7147},[445],{"categories":7149},[250],{"categories":7151},[250],{"categories":7153},[250],{"categories":7155},[288],{"categories":7157},[323,283],{"categories":7159},[326],{"categories":7161},[],{"categories":7163},[],{"categories":7165},[326],{"categories":7167},[],{"categories":7169},[326],{"categories":7171},[323],{"categories":7173},[288],{"categories":7175},[],{"categories":7177},[250],{"categories":7179},[275],{"categories":7181},[375],{"categories":7183},[],{"categories":7185},[275],{"categories":7187},[],{"categories":7189},[323],{"categories":7191},[278],{"categories":7193},[326],{"categories":7195},[],{"categories":7197},[250],{"categories":7199},[323],[7201,7269,7392,7459],{"id":7202,"title":7203,"ai":7204,"body":7209,"categories":7240,"created_at":251,"date_modified":251,"description":98,"extension":252,"faq":251,"featured":253,"kicker_label":251,"meta":7241,"navigation":255,"path":7256,"published_at":7257,"question":251,"scraped_at":7258,"seo":7259,"sitemap":7260,"source_id":7261,"source_name":7262,"source_type":262,"source_url":7263,"stem":7264,"tags":7265,"thumbnail_url":251,"tldr":7266,"tweet":251,"unknown_tags":7267,"__hash__":7268},"summaries\u002Fsummaries\u002F1772ede214d531cd-triple-yolo-recall-with-adaptive-post-processing-summary.md","Triple YOLO Recall with Adaptive Post-Processing",{"provider":7,"model":8,"input_tokens":7205,"output_tokens":7206,"processing_time_ms":7207,"cost_usd":7208},5637,1521,18506,0.00138105,{"type":14,"value":7210,"toc":7235},[7211,7215,7218,7221,7225,7228,7232],[17,7212,7214],{"id":7213},"adaptive-thresholds-unlock-small-object-detections","Adaptive Thresholds Unlock Small-Object Detections",[22,7216,7217],{},"Fixed confidence thresholds (default 0.25-0.50) drop distant people in crowded scenes because small boxes (e.g., 4% frame height vs. 30% for close subjects) yield low scores like 0.08, even if person-shaped. Solution: Run YOLO permissively at 0.05 to capture all candidates, then compute a frame-level baseline from the score distribution's low percentile—frames with mostly high scores raise the bar, low-score frames lower it. Scale this threshold inversely by relative box height: tiny boxes need only ~half the confidence of large ones via linear scaling. This alone triples recall from 10-12 to 30+ out of 40 students in a classroom by giving small detections a fair shot without uniform conservatism.",[22,7219,7220],{},"Trade-off: Lower thresholds increase false positives, so compensate with evidence-based validation instead of data-driven retraining or heavy models like SAHI.",[17,7222,7224],{"id":7223},"keypoint-rescue-validates-borderline-boxes","Keypoint Rescue Validates Borderline Boxes",[22,7226,7227],{},"For candidates failing the adaptive threshold, check pose keypoints from models like yolov8n-pose: if nose, left shoulder, and right shoulder exceed high confidence (e.g., model-default levels), rescue the box. Bags or chairs lack reliable shoulders; real people show them consistently. Follow with standard NMS to dedupe overlaps. This leverages the model's full output—keypoints were predicted all along but discarded—turning 'uncertain' boxes into reliable detections. In practice, back-row skeletons 'light up' stably, enabling accurate tracking IDs.",[17,7229,7231],{"id":7230},"scene-specific-limits-and-extensions","Scene-Specific Limits and Extensions",[22,7233,7234],{},"Precision benchmarks like COCO mAP favor conservative thresholds, penalizing false positives more than misses, so defaults stay high. This works best in fixed-camera setups (classrooms) assuming most candidates are real, but fails in chaotic scenes like streets. Pose dependency limits to keypoint models. Broader: Treat outputs as multi-signal conversation—add temporal consistency (lenient if tracked 3 frames), spatial priors (row-based penalties), or auxiliary classifiers. Avoids technical debt vs. retraining (3-6 months) for quick 3x gains.",{"title":98,"searchDepth":111,"depth":111,"links":7236},[7237,7238,7239],{"id":7213,"depth":111,"text":7214},{"id":7223,"depth":111,"text":7224},{"id":7230,"depth":111,"text":7231},[326],{"content_references":7242,"triage":7253},[7243,7247,7250],{"type":7244,"title":7245,"context":7246},"tool","SAHI (Slicing Aided Hyper Inference)","mentioned",{"type":7248,"title":7249,"context":7246},"dataset","COCO",{"type":7251,"title":7252,"context":7246},"other","yolov8n-pose",{"relevance":117,"novelty":117,"quality":123,"actionability":117,"composite":7254,"reasoning":7255},3.25,"Category: AI & LLMs. The article discusses a practical method for improving object detection using YOLO, which is relevant to AI engineering. It provides a specific technique for enhancing recall in crowded scenes, addressing a common pain point in AI applications, but lacks a broader context on implementation in product development.","\u002Fsummaries\u002F1772ede214d531cd-triple-yolo-recall-with-adaptive-post-processing-summary","2026-05-07 04:26:37","2026-05-07 11:23:53",{"title":7203,"description":98},{"loc":7256},"1772ede214d531cd","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Fi-tripled-my-yolo-detection-without-retraining-08c6a17f51e7?source=rss----98111c9905da---4","summaries\u002F1772ede214d531cd-triple-yolo-recall-with-adaptive-post-processing-summary",[266,267,268],"In crowded scenes, set YOLO confidence to 0.05, then filter dynamically by frame score distribution, box size (lower threshold for \u003C5% height boxes), and pose keypoints (nose + shoulders) to detect 3x more people without retraining.",[],"wF2bhdVyrwldriP36Cs8RBkWabBeqX8VHJNxzVmQs-s",{"id":7270,"title":7271,"ai":7272,"body":7277,"categories":7381,"created_at":251,"date_modified":251,"description":98,"extension":252,"faq":251,"featured":253,"kicker_label":251,"meta":7382,"navigation":255,"path":7383,"published_at":257,"question":251,"scraped_at":251,"seo":7384,"sitemap":7385,"source_id":7386,"source_name":261,"source_type":262,"source_url":263,"stem":7387,"tags":7388,"thumbnail_url":251,"tldr":7389,"tweet":251,"unknown_tags":7390,"__hash__":7391},"summaries\u002Fsummaries\u002Fbatched-l2-norm-layer-for-torch-neural-nets-summary.md","Batched L2 Norm Layer for Torch Neural Nets",{"provider":7,"model":8,"input_tokens":7273,"output_tokens":7274,"processing_time_ms":7275,"cost_usd":7276},4617,1235,10447,0.0015184,{"type":14,"value":7278,"toc":7376},[7279,7283,7290,7305,7309,7316,7354,7358],[17,7280,7282],{"id":7281},"core-layer-design","Core Layer Design",[22,7284,7285,7286,7289],{},"This nn.L2Normalize module processes 2D tensors (batch size n x vector dim d), normalizing each row vector to unit L2 norm (||x||_2 = 1). Use it in Torch neural nets for tasks like embedding normalization, where direction matters more than magnitude. Instantiate via ",[29,7287,7288],{},"local layer = nn.L2Normalize()",", then integrate into models like Sequential for end-to-end differentiability.",[22,7291,7292,7293,7296,7297,7300,7301,7304],{},"Forward pass (",[29,7294,7295],{},"updateOutput","): Computes per-row L2 norms squared via elementwise square and sum over dim 2 (",[29,7298,7299],{},"input:cmul(input):sum(2)","), takes sqrt, then elementwise divides input by expanded norms (",[29,7302,7303],{},"input:cdiv(buffer:expandAs(input))","). Avoids loops for batch efficiency; buffers reuse across calls.",[17,7306,7308],{"id":7307},"gradient-computation","Gradient Computation",[22,7310,7311,7312,7315],{},"Backward pass (",[29,7313,7314],{},"updateGradInput",") derives local Jacobian of L2 transform for chain rule. Key steps:",[45,7317,7318,7325,7331,7337,7343],{},[48,7319,7320,7321,7324],{},"Forms identity tensor repeated over batch (",[29,7322,7323],{},"torch.eye(d):repeatTensor(n,1):view(n,d,d)",").",[48,7326,7327,7328,7324],{},"Scales diagonal by norm squared (",[29,7329,7330],{},"cmul(eye, normSquared:view(n,1,1):expand(n,d,d))",[48,7332,7333,7334,7324],{},"Subtracts outer products (",[29,7335,7336],{},"-torch.bmm(input:view(n,d,1), input:view(n,1,d))",[48,7338,7339,7340,7324],{},"Divides by cubed norms (",[29,7341,7342],{},"cdiv(pow(buffer,3):expand(n,d,d))",[48,7344,7345,7346,7349,7350,7353],{},"Applies via batched matmul: ",[29,7347,7348],{},"bmm(diag, gradOutput:view(n,d,1)):resize(n,d)"," (fixed with ",[29,7351,7352],{},":squeeze()"," post-line 31).\nThis ensures correct gradients during backprop, critical for training stability in nets with normalization layers.",[17,7355,7357],{"id":7356},"implementation-notes-and-fixes","Implementation Notes and Fixes",[22,7359,7360,7361,7364,7365,7368,7369,7371,7372,7375],{},"Code uses lazy buffer init (",[29,7362,7363],{},"self.buffer = self.buffer or input.new()",") for memory efficiency. Assumes mini-batch inputs only (errors on non-2D). Community feedback: Could swap manual norm for ",[29,7366,7367],{},"torch.norm()"," in forward for simplicity; Karpathy confirmed feasibility. Atcold noted dimension mismatch in gradInput without ",[29,7370,7352],{}," after bmm resize—fixed by author. Soumith (Torch maintainer) provided additional pointers (unspecified). Thin gist from 2015; modern PyTorch has ",[29,7373,7374],{},"torch.nn.functional.normalize(p=2, dim=1)"," as built-in alternative.",{"title":98,"searchDepth":111,"depth":111,"links":7377},[7378,7379,7380],{"id":7281,"depth":111,"text":7282},{"id":7307,"depth":111,"text":7308},{"id":7356,"depth":111,"text":7357},[250],{},"\u002Fsummaries\u002Fbatched-l2-norm-layer-for-torch-neural-nets-summary",{"title":7271,"description":98},{"loc":7383},"07bd9d1a251cebe3","summaries\u002Fbatched-l2-norm-layer-for-torch-neural-nets-summary",[267,266],"Custom Torch nn.Module normalizes each row of n x d input tensor to unit L2 norm, with efficient batched forward\u002Fbackward passes for training.",[],"20C1Dsl0GWqJxzOXYYcvQPEK3LwoQdSQgNUb_QYBP5Q",{"id":7393,"title":7394,"ai":7395,"body":7400,"categories":7429,"created_at":251,"date_modified":251,"description":98,"extension":252,"faq":251,"featured":253,"kicker_label":251,"meta":7430,"navigation":255,"path":7447,"published_at":7448,"question":251,"scraped_at":7449,"seo":7450,"sitemap":7451,"source_id":7452,"source_name":7262,"source_type":262,"source_url":7453,"stem":7454,"tags":7455,"thumbnail_url":251,"tldr":7456,"tweet":251,"unknown_tags":7457,"__hash__":7458},"summaries\u002Fsummaries\u002F0d1957d00ad6e7e2-gpu-bandwidth-limits-llm-speed-not-flops-summary.md","GPU Bandwidth Limits LLM Speed, Not FLOPS",{"provider":7,"model":8,"input_tokens":7396,"output_tokens":7397,"processing_time_ms":7398,"cost_usd":7399},8371,1988,22871,0.00264555,{"type":14,"value":7401,"toc":7425},[7402,7406,7409,7412,7415,7419,7422],[17,7403,7405],{"id":7404},"throughput-design-hides-latency-with-massive-parallelism","Throughput Design Hides Latency with Massive Parallelism",[22,7407,7408],{},"GPUs prioritize throughput over single-thread latency by allocating transistors to thousands of execution units and a large register file rather than branch predictors or deep caches. A single GPU thread is slower than a CPU core (~1ns instruction), but 20,000+ run concurrently. Off-chip HBM access takes 700+ cycles on H100, so GPUs hide this by keeping enough independent warps ready—switching when one stalls. This requires high occupancy: ratio of resident warps to max (64 per H100 SM). Low occupancy from high register use (e.g., 128 regs\u002Fthread limits to 512 threads\u002FSM or 16 warps, 25% occupancy) starves the scheduler, collapsing throughput despite saturated Tensor Cores.",[22,7410,7411],{},"Threads group into 32-thread warps as the scheduling unit under SIMT: hardware issues one instruction across the warp while tracking per-thread PCs and registers for independent appearance. Pre-Volta lockstep caused deadlocks on intra-warp sync; Volta+ Independent Thread Scheduling (ITS) dynamically regroups converging threads, enabling mutexes without divergence penalties (though divergence still serializes paths, doubling time on 50\u002F50 if\u002Felse). H100 SMs (132 total) divide into 4 quadrants, each with warp scheduler, 16k registers, 32 FP32\u002F16 INT32 cores, 1 Tensor Core, and L0 instr cache. Blocks (CTAs) run on one SM for shared mem sync; Hopper clusters co-schedule blocks across GPCs for DSMEM (7x faster than global mem).",[22,7413,7414],{},"Warp divergence hurts irregular data (e.g., padding branches); fix via specialization—e.g., FlashAttention-3 assigns producer warps for loads, consumers for math, zero divergence, overlapping mem\u002Fcompute. Little’s Law quantifies: in-flight warps = throughput × latency. For 400-cycle HBM loads at 1 instr\u002Fcycle, need 400+ warps to sustain SM utilization; fewer drops throughput to 25%.",[17,7416,7418],{"id":7417},"six-tier-memory-hierarchy-sets-bandwidth-bounds","Six-Tier Memory Hierarchy Sets Bandwidth Bounds",[22,7420,7421],{},"Data tiers trade capacity\u002Fbandwidth\u002Flatency: registers (256KB\u002FSM, 65k 32-bit, 1-cycle) > shared\u002FL1 (228KB shared max, 30-40 cycles) > L2 (50MB, 258-743 cycles) > HBM3 (80GB, 3.35TB\u002Fs, 700+ cycles) > NVLink (900GB\u002Fs\u002FGPU, µs) > NVMe. Keep working set close: high regs\u002Fthread (>255) spills to HBM local mem, killing loops. Shared mem tiles inputs for reuse (GEMM loads slab once, computes multiple times). L1 coalesces warp loads (base+i patterns >> strided). L2 absorbs weight re-reads; >50MB spills to HBM.",[22,7423,7424],{},"LLM decode exemplifies: 70B FP16 model needs 140GB\u002Ftoken read (42ms at 3.35TB\u002Fs pre-compute), one FLOP\u002Fbyte. Bandwidth binds because arithmetic intensity (FLOPs\u002Fbyte) is ~1; roofline (part 2) shows compute underutilized without high reuse. HBM holds weights\u002FKV\u002Factivations; misses from upper tiers thrash it. NVLink shards large models (e.g., tensor parallel syncs partials), but frequent comm bottlenecks vs. pipeline parallel (activations\u002Flayer).",{"title":98,"searchDepth":111,"depth":111,"links":7426},[7427,7428],{"id":7404,"depth":111,"text":7405},{"id":7417,"depth":111,"text":7418},[275],{"content_references":7431,"triage":7444},[7432,7437,7441],{"type":7433,"title":7434,"author":7435,"context":7436},"paper","FlashAttention-3","Shah et al.","cited",{"type":7433,"title":7438,"author":7439,"publisher":7440,"context":7436},"Microbenchmarks of the Hopper architecture","Luo et al.","2025",{"type":7251,"title":7442,"author":7443,"context":7246},"NVIDIA’s Hopper architecture documentation","NVIDIA",{"relevance":117,"novelty":117,"quality":123,"actionability":111,"composite":7445,"reasoning":7446},3.05,"Category: AI & LLMs. The article discusses GPU architecture and its implications for LLM performance, which is relevant to AI product builders. However, while it provides insights into GPU memory bandwidth, it lacks concrete actionable steps for implementing this knowledge in product development.","\u002Fsummaries\u002F0d1957d00ad6e7e2-gpu-bandwidth-limits-llm-speed-not-flops-summary","2026-05-06 02:50:10","2026-05-06 16:13:45",{"title":7394,"description":98},{"loc":7447},"0d1957d00ad6e7e2","https:\u002F\u002Fpub.towardsai.net\u002Fwarps-memory-hierarchy-and-why-bandwidth-beats-flops-how-gpus-actually-work-part-1-06170834ad33?source=rss----98111c9905da---4","summaries\u002F0d1957d00ad6e7e2-gpu-bandwidth-limits-llm-speed-not-flops-summary",[266,267],"Generating one token from a 70B model on H100 needs 140GB weight reads—one op per byte—making memory bandwidth the inference bottleneck, not compute throughput.",[],"OXBz1imk9itxNT8ySnee4POT_2AlsDS3zHL4klRnIMo",{"id":7460,"title":7461,"ai":7462,"body":7467,"categories":7544,"created_at":251,"date_modified":251,"description":98,"extension":252,"faq":251,"featured":253,"kicker_label":251,"meta":7545,"navigation":255,"path":7546,"published_at":7547,"question":251,"scraped_at":251,"seo":7548,"sitemap":7549,"source_id":7550,"source_name":7262,"source_type":262,"source_url":263,"stem":7551,"tags":7552,"thumbnail_url":251,"tldr":7553,"tweet":251,"unknown_tags":7554,"__hash__":7555},"summaries\u002Fsummaries\u002Fword2vec-turning-word-neighborhoods-into-embedding-summary.md","Word2Vec: Turning Word Neighborhoods into Embeddings",{"provider":7,"model":8,"input_tokens":7463,"output_tokens":7464,"processing_time_ms":7465,"cost_usd":7466},8588,1873,21956,0.0026316,{"type":14,"value":7468,"toc":7538},[7469,7473,7488,7491,7495,7502,7505,7515,7519,7522,7525,7528,7532,7535],[17,7470,7472],{"id":7471},"shift-from-isolated-ids-to-relational-embeddings","Shift from Isolated IDs to Relational Embeddings",[22,7474,7475,7476,7479,7480,7483,7484,7487],{},"Before Word2Vec, words were treated as unique IDs or one-hot vectors (e.g., cat → ",[102,7477,7478],{},"1,0,0,0,0","), preserving identity but ignoring relationships like 'cat' closer to 'dog' than 'engine'. Word2Vec flips this by learning dense vectors where meaning emerges from context: a word's vector is shaped by its repeated local neighborhoods in text. For a tiny corpus ('the cat drinks milk', 'the dog drinks water'), 'cat' appears near 'the', 'drinks', 'milk', 'chases', 'mouse', while 'dog' shares 'the', 'drinks', 'chases' but differs on 'water', 'ball'. Similar contexts deliver matching gradient signals during training, pulling vectors like cat ",[102,7481,7482],{},"0.82, 0.21, -0.05"," and dog ",[102,7485,7486],{},"0.79, 0.25, -0.03"," into nearby regions, enabling geometric analogies like king - man + woman ≈ queen.",[22,7489,7490],{},"This relational view—words as positions in a space preserving structure—outperforms sparse representations because similar training pressures from neighborhoods create clustered embeddings without explicit semantic rules.",[17,7492,7494],{"id":7493},"cbow-vs-skip-gram-dual-paths-to-context-prediction","CBOW vs Skip-gram: Dual Paths to Context Prediction",[22,7496,7497,7498,7501],{},"Word2Vec optimizes dense vectors (e.g., size 3 for vocab of 9) via a simple network: one-hot input (size 9) → hidden layer (size 3) → output scores (size 9). The hidden weights form the embedding table, where each word's row (e.g., initial cat ",[102,7499,7500],{},"0.11, -0.08, 0.05",") gets refined.",[22,7503,7504],{},"CBOW predicts center from context (input: 'the', 'drinks' → target: 'cat'), treating surroundings as clues that constrain word identity, like recovering a word from its situational fit. Skip-gram reverses it (input: 'cat' → targets: 'the', 'drinks'), capturing a word's relational footprint—what neighbors it generates. With window size 1, Skip-gram generates pairs like cat → the, cat → drinks; CBOW inverts them.",[22,7506,7507,7508,7511,7512,36],{},"Both unify around mutual definition: context shapes word (CBOW), word shapes context (Skip-gram). Skip-gram excels for rare words by amplifying their signal; CBOW smooths frequent ones. Together, they force embeddings to encode predictive utility, yielding a map where milk ",[102,7509,7510],{},"0.10, 0.88, -0.12"," clusters near water ",[102,7513,7514],{},"0.07, 0.84, -0.10",[17,7516,7518],{"id":7517},"training-mechanics-gradients-sculpt-the-space","Training Mechanics: Gradients Sculpt the Space",[22,7520,7521],{},"Training slides a window over text, generating examples (e.g., center 'cat' with contexts 'the', 'drinks'). For Skip-gram on cat → the: retrieve cat's vector, compute output scores (e.g., the: 0.12 → softmax prob 0.20), measure error against target, backpropagate to nudge weights—pulling cat closer to 'the', pushing from negatives like 'engine'.",[22,7523,7524],{},"Negative sampling scales this: for cat → drinks, attract to true pair, repel 3-5 random fakes (e.g., 'banana', 'cloud'), forming geometry via affinity (pet\u002Faction contexts) and boundaries (unrelated ones). Repeated across corpus, similar contexts yield parallel updates: cat and dog, both near 'the\u002Fdrinks\u002Fchases', converge without semantic labels.",[22,7526,7527],{},"Outcome: random initials become relational map. Training builds it via 'enormous tiny corrections'; full process turns prediction errors into stable positions.",[17,7529,7531],{"id":7530},"inference-and-limitations-in-modern-context","Inference and Limitations in Modern Context",[22,7533,7534],{},"Post-training, discard the predictor; use the embedding matrix for lookups (cat's vector), similarity (cosine distance clusters cat\u002Fdog over cat\u002Fengine), averaging for sentences ('the cat drinks milk' → mean vector), or downstream tasks like classification.",[22,7536,7537],{},"Word2Vec revolutionized NLP by proving prediction yields emergent semantics, replacing hand-engineered features with learned geometry. Yet static vectors fail polysemy ('bank' as river\u002Ffinance gets one embedding), spurring contextual models like BERT. Legacy: modern LLMs inherit context-driven, relational meaning—embeddings as vectors first, structure second.",{"title":98,"searchDepth":111,"depth":111,"links":7539},[7540,7541,7542,7543],{"id":7471,"depth":111,"text":7472},{"id":7493,"depth":111,"text":7494},{"id":7517,"depth":111,"text":7518},{"id":7530,"depth":111,"text":7531},[],{},"\u002Fsummaries\u002Fword2vec-turning-word-neighborhoods-into-embedding-summary","2026-04-08 21:21:21",{"title":7461,"description":98},{"loc":7546},"2165d09f4254bef0","summaries\u002Fword2vec-turning-word-neighborhoods-into-embedding-summary",[266,267],"Word2Vec learns dense word vectors by predicting local contexts with CBOW or Skip-gram, clustering similar words like 'cat' and 'dog' via repeated gradient updates from shared neighborhoods.",[],"6VqxuTzkcylmMleWNUuTyJeef_Ufd7syKMvOUkR5RDE"]