[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-batched-l2-norm-layer-for-torch-neural-nets-summary":3,"summaries-facets-categories":147,"summary-related-batched-l2-norm-layer-for-torch-neural-nets-summary":7075},{"id":4,"title":5,"ai":6,"body":13,"categories":125,"created_at":127,"date_modified":127,"description":119,"extension":128,"faq":127,"featured":129,"kicker_label":127,"meta":130,"navigation":131,"path":132,"published_at":133,"question":127,"scraped_at":127,"seo":134,"sitemap":135,"source_id":136,"source_name":137,"source_type":138,"source_url":139,"stem":140,"tags":141,"thumbnail_url":127,"tldr":144,"tweet":127,"unknown_tags":145,"__hash__":146},"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":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",4617,1235,10447,0.0015184,{"type":14,"value":15,"toc":118},"minimark",[16,21,30,45,49,56,96,100],[17,18,20],"h2",{"id":19},"core-layer-design","Core Layer Design",[22,23,24,25,29],"p",{},"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 ",[26,27,28],"code",{},"local layer = nn.L2Normalize()",", then integrate into models like Sequential for end-to-end differentiability.",[22,31,32,33,36,37,40,41,44],{},"Forward pass (",[26,34,35],{},"updateOutput","): Computes per-row L2 norms squared via elementwise square and sum over dim 2 (",[26,38,39],{},"input:cmul(input):sum(2)","), takes sqrt, then elementwise divides input by expanded norms (",[26,42,43],{},"input:cdiv(buffer:expandAs(input))","). Avoids loops for batch efficiency; buffers reuse across calls.",[17,46,48],{"id":47},"gradient-computation","Gradient Computation",[22,50,51,52,55],{},"Backward pass (",[26,53,54],{},"updateGradInput",") derives local Jacobian of L2 transform for chain rule. Key steps:",[57,58,59,67,73,79,85],"ul",{},[60,61,62,63,66],"li",{},"Forms identity tensor repeated over batch (",[26,64,65],{},"torch.eye(d):repeatTensor(n,1):view(n,d,d)",").",[60,68,69,70,66],{},"Scales diagonal by norm squared (",[26,71,72],{},"cmul(eye, normSquared:view(n,1,1):expand(n,d,d))",[60,74,75,76,66],{},"Subtracts outer products (",[26,77,78],{},"-torch.bmm(input:view(n,d,1), input:view(n,1,d))",[60,80,81,82,66],{},"Divides by cubed norms (",[26,83,84],{},"cdiv(pow(buffer,3):expand(n,d,d))",[60,86,87,88,91,92,95],{},"Applies via batched matmul: ",[26,89,90],{},"bmm(diag, gradOutput:view(n,d,1)):resize(n,d)"," (fixed with ",[26,93,94],{},":squeeze()"," post-line 31).\nThis ensures correct gradients during backprop, critical for training stability in nets with normalization layers.",[17,97,99],{"id":98},"implementation-notes-and-fixes","Implementation Notes and Fixes",[22,101,102,103,106,107,110,111,113,114,117],{},"Code uses lazy buffer init (",[26,104,105],{},"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 ",[26,108,109],{},"torch.norm()"," in forward for simplicity; Karpathy confirmed feasibility. Atcold noted dimension mismatch in gradInput without ",[26,112,94],{}," after bmm resize—fixed by author. Soumith (Torch maintainer) provided additional pointers (unspecified). Thin gist from 2015; modern PyTorch has ",[26,115,116],{},"torch.nn.functional.normalize(p=2, dim=1)"," as built-in alternative.",{"title":119,"searchDepth":120,"depth":120,"links":121},"",2,[122,123,124],{"id":19,"depth":120,"text":20},{"id":47,"depth":120,"text":48},{"id":98,"depth":120,"text":99},[126],"Software Engineering",null,"md",false,{},true,"\u002Fsummaries\u002Fbatched-l2-norm-layer-for-torch-neural-nets-summary","2026-04-08 21:21:20",{"title":5,"description":119},{"loc":132},"07bd9d1a251cebe3","Andrej Karpathy Gists","article","https:\u002F\u002Funknown","summaries\u002Fbatched-l2-norm-layer-for-torch-neural-nets-summary",[142,143],"deep-learning","machine-learning","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",[148,151,154,156,159,161,164,167,169,171,173,175,177,179,181,183,185,188,190,192,194,196,199,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,248,251,253,255,257,259,261,263,265,267,269,271,273,275,277,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,321,323,325,327,329,331,333,335,337,339,341,343,345,347,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,415,417,419,421,423,425,427,429,431,433,435,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,471,473,475,477,479,481,483,485,487,489,491,494,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,555,557,559,562,564,566,568,570,572,574,576,578,580,582,584,586,588,590,592,594,596,598,601,603,605,607,609,611,613,615,617,619,621,623,625,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798,800,802,804,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,858,860,862,864,866,868,870,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,915,917,919,921,923,926,928,930,932,934,936,938,940,942,944,946,948,950,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1402,1404,1406,1408,1410,1412,1414,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1444,1446,1448,1450,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1842,1844,1846,1848,1850,1852,1854,1856,1858,1860,1862,1864,1866,1868,1870,1872,1874,1876,1878,1880,1882,1884,1886,1888,1890,1892,1894,1896,1898,1900,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1962,1964,1966,1968,1970,1972,1974,1976,1978,1980,1982,1984,1986,1988,1990,1992,1994,1996,1998,2000,2002,2004,2006,2008,2010,2012,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032,2034,2036,2038,2040,2042,2044,2046,2048,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2122,2124,2126,2128,2130,2132,2134,2136,2138,2140,2142,2144,2146,2148,2150,2152,2154,2156,2158,2160,2162,2164,2166,2168,2170,2172,2174,2176,2178,2180,2182,2184,2186,2188,2190,2192,2194,2196,2198,2200,2202,2204,2206,2208,2210,2212,2214,2216,2218,2220,2222,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2282,2284,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2380,2382,2384,2386,2388,2390,2392,2394,2396,2398,2400,2402,2404,2406,2408,2410,2412,2414,2416,2418,2420,2422,2424,2426,2428,2430,2432,2434,2436,2438,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458,2460,2462,2464,2466,2468,2470,2472,2474,2476,2478,2480,2482,2484,2486,2488,2490,2492,2494,2496,2498,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2544,2546,2548,2550,2552,2554,2556,2558,2560,2562,2564,2566,2568,2570,2572,2574,2576,2578,2580,2582,2584,2586,2588,2590,2592,2594,2596,2598,2600,2602,2604,2606,2608,2610,2612,2614,2616,2618,2620,2622,2624,2626,2628,2630,2632,2634,2636,2638,2640,2642,2644,2646,2648,2650,2652,2654,2656,2658,2660,2662,2664,2666,2668,2670,2672,2674,2676,2678,2680,2682,2684,2686,2688,2690,2692,2694,2696,2698,2700,2702,2704,2706,2708,2710,2712,2714,2716,2718,2720,2722,2724,2726,2728,2730,2732,2734,2736,2738,2740,2742,2744,2746,2748,2750,2752,2754,2756,2758,2760,2762,2764,2766,2768,2770,2772,2774,2776,2778,2780,2782,2784,2786,2788,2790,2792,2794,2796,2798,2800,2802,2804,2806,2808,2810,2812,2814,2816,2818,2820,2822,2824,2826,2828,2830,2832,2834,2836,2838,2840,2842,2844,2846,2848,2850,2852,2854,2856,2858,2860,2862,2864,2866,2868,2870,2872,2874,2876,2878,2880,2882,2884,2886,2888,2890,2892,2894,2896,2898,2900,2902,2904,2906,2908,2910,2912,2914,2916,2918,2920,2922,2924,2926,2928,2930,2932,2934,2936,2938,2940,2942,2944,2946,2948,2950,2952,2954,2956,2958,2960,2962,2964,2966,2968,2970,2972,2974,2976,2978,2980,2982,2984,2986,2988,2990,2992,2994,2996,2998,3000,3002,3004,3006,3008,3010,3012,3014,3016,3018,3020,3022,3024,3026,3028,3030,3032,3034,3036,3038,3040,3042,3044,3046,3048,3050,3052,3054,3056,3058,3060,3062,3064,3066,3068,3070,3072,3074,3076,3078,3080,3082,3084,3086,3088,3090,3092,3094,3096,3098,3100,3102,3104,3106,3108,3110,3112,3114,3116,3118,3120,3122,3124,3126,3128,3130,3132,3134,3136,3138,3140,3142,3144,3146,3148,3150,3152,3154,3156,3158,3160,3162,3164,3166,3168,3170,3172,3174,3176,3178,3180,3182,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3314,3316,3318,3320,3322,3324,3326,3328,3330,3332,3334,3336,3338,3340,3342,3344,3346,3348,3350,3352,3354,3356,3358,3360,3362,3364,3366,3368,3370,3372,3374,3376,3378,3380,3382,3384,3386,3388,3390,3392,3394,3396,3398,3400,3402,3404,3406,3408,3410,3412,3414,3416,3418,3420,3422,3424,3426,3428,3430,3432,3434,3436,3438,3440,3442,3444,3446,3448,3450,3452,3454,3456,3458,3460,3462,3464,3466,3468,3470,3472,3474,3476,3478,3480,3482,3484,3486,3488,3490,3492,3494,3496,3498,3500,3502,3504,3506,3508,3510,3512,3514,3516,3518,3520,3522,3524,3526,3528,3530,3532,3534,3536,3538,3540,3542,3544,3546,3548,3550,3552,3554,3556,3558,3560,3562,3564,3566,3568,3570,3572,3574,3576,3578,3580,3582,3584,3586,3588,3590,3592,3594,3596,3598,3600,3602,3604,3606,3608,3610,3612,3614,3616,3618,3620,3622,3624,3626,3628,3630,3632,3634,3636,3638,3640,3642,3644,3646,3648,3650,3652,3654,3656,3658,3660,3662,3664,3666,3668,3670,3672,3674,3676,3678,3680,3682,3684,3686,3688,3690,3692,3694,3696,3698,3700,3702,3704,3706,3708,3710,3712,3714,3716,3718,3720,3722,3724,3726,3728,3730,3732,3734,3736,3738,3740,3742,3744,3746,3748,3750,3752,3754,3756,3758,3760,3762,3764,3766,3768,3770,3772,3774,3776,3778,3780,3782,3784,3786,3788,3790,3792,3794,3796,3798,3800,3802,3804,3806,3808,3810,3812,3814,3816,3818,3820,3822,3824,3826,3828,3830,3832,3834,3836,3838,3840,3842,3844,3846,3848,3850,3852,3854,3856,3858,3860,3862,3864,3866,3868,3870,3872,3874,3876,3878,3880,3882,3884,3886,3888,3890,3892,3894,3896,3898,3900,3902,3904,3906,3908,3910,3912,3914,3916,3918,3920,3922,3924,3926,3928,3930,3932,3934,3936,3938,3940,3942,3944,3946,3948,3950,3952,3954,3956,3958,3960,3962,3964,3966,3968,3970,3972,3974,3976,3978,3980,3982,3984,3986,3988,3990,3992,3994,3996,3998,4000,4002,4004,4006,4008,4010,4012,4014,4016,4018,4020,4022,4024,4026,4028,4030,4032,4034,4036,4038,4040,4042,4044,4046,4048,4050,4052,4054,4056,4058,4060,4062,4064,4066,4068,4070,4072,4074,4076,4078,4080,4082,4084,4086,4088,4090,4092,4094,4096,4098,4100,4102,4104,4106,4108,4110,4112,4114,4116,4118,4120,4122,4124,4126,4128,4130,4132,4134,4136,4138,4140,4142,4144,4146,4148,4150,4152,4154,4156,4158,4160,4162,4164,4166,4168,4170,4172,4174,4176,4178,4180,4182,4184,4186,4188,4190,4192,4194,4196,4198,4200,4202,4204,4206,4208,4210,4212,4214,4216,4218,4220,4222,4224,4226,4228,4230,4232,4234,4236,4238,4240,4242,4244,4246,4248,4250,4252,4254,4256,4258,4260,4262,4264,4266,4268,4270,4272,4274,4276,4278,4280,4282,4284,4286,4288,4290,4292,4294,4296,4298,4300,4302,4304,4306,4308,4310,4312,4314,4316,4318,4320,4322,4324,4326,4328,4330,4332,4334,4336,4338,4340,4342,4344,4346,4348,4350,4352,4354,4356,4358,4360,4362,4364,4366,4368,4370,4372,4374,4376,4378,4380,4382,4384,4386,4388,4390,4392,4394,4396,4398,4400,4402,4404,4406,4408,4410,4412,4414,4416,4418,4420,4422,4424,4426,4428,4430,4432,4434,4436,4438,4440,4442,4444,4446,4448,4450,4452,4454,4456,4458,4460,4462,4464,4466,4468,4470,4472,4474,4476,4478,4480,4482,4484,4486,4488,4490,4492,4494,4496,4498,4500,4502,4504,4506,4508,4510,4512,4514,4516,4518,4520,4522,4524,4526,4528,4530,4532,4534,4536,4538,4540,4542,4544,4546,4548,4550,4552,4554,4556,4558,4560,4562,4564,4566,4568,4570,4572,4574,4576,4578,4580,4582,4584,4586,4588,4590,4592,4594,4596,4598,4600,4602,4605,4607,4609,4611,4613,4615,4617,4619,4621,4623,4625,4627,4629,4631,4633,4635,4637,4639,4641,4643,4645,4647,4649,4651,4653,4655,4657,4659,4661,4663,4665,4667,4669,4671,4673,4675,4677,4679,4681,4683,4685,4687,4689,4691,4693,4695,4697,4699,4701,4703,4705,4707,4709,4711,4713,4715,4717,4719,4721,4723,4725,4727,4729,4731,4733,4735,4737,4739,4741,4743,4745,4747,4749,4751,4753,4755,4757,4759,4761,4763,4765,4767,4769,4771,4773,4775,4777,4779,4781,4783,4785,4787,4789,4791,4793,4795,4797,4799,4801,4803,4805,4807,4809,4811,4813,4815,4817,4819,4821,4823,4825,4827,4829,4831,4833,4835,4837,4839,4841,4843,4845,4847,4849,4851,4853,4855,4857,4859,4861,4863,4865,4867,4869,4871,4873,4875,4877,4879,4881,4883,4885,4887,4889,4891,4893,4895,4897,4899,4901,4903,4905,4907,4909,4911,4913,4915,4917,4919,4921,4923,4925,4927,4929,4931,4933,4935,4937,4939,4941,4943,4945,4947,4949,4951,4953,4955,4957,4959,4961,4963,4965,4967,4969,4971,4973,4975,4977,4979,4981,4983,4985,4987,4989,4991,4993,4995,4997,4999,5001,5003,5005,5007,5009,5011,5013,5015,5017,5019,5021,5023,5025,5027,5029,5031,5033,5035,5037,5039,5041,5043,5045,5047,5049,5051,5053,5055,5057,5059,5061,5063,5065,5067,5069,5071,5073,5075,5077,5079,5081,5083,5085,5087,5089,5091,5093,5095,5097,5099,5101,5103,5105,5107,5109,5111,5113,5115,5117,5119,5121,5123,5125,5127,5129,5131,5133,5135,5137,5139,5141,5143,5145,5147,5149,5151,5153,5155,5157,5159,5161,5163,5165,5167,5169,5171,5173,5175,5177,5179,5181,5183,5185,5187,5189,5191,5193,5195,5197,5199,5201,5203,5205,5207,5209,5211,5213,5215,5217,5219,5221,5223,5225,5227,5229,5231,5233,5235,5237,5239,5241,5243,5245,5247,5249,5251,5253,5255,5257,5259,5261,5263,5265,5267,5269,5271,5273,5275,5277,5279,5281,5283,5285,5287,5289,5291,5293,5295,5297,5299,5301,5303,5305,5307,5309,5311,5313,5315,5317,5319,5321,5323,5325,5327,5329,5331,5333,5335,5337,5339,5341,5343,5345,5347,5349,5351,5353,5355,5357,5359,5361,5363,5365,5367,5369,5371,5373,5375,5377,5379,5381,5383,5385,5387,5389,5391,5393,5395,5397,5399,5401,5403,5405,5407,5409,5411,5413,5415,5417,5419,5421,5423,5425,5427,5429,5431,5433,5435,5437,5439,5441,5443,5445,5447,5449,5451,5453,5455,5457,5459,5462,5464,5466,5468,5470,5472,5474,5476,5478,5480,5482,5484,5486,5488,5490,5492,5494,5496,5498,5500,5502,5504,5506,5508,5510,5512,5514,5516,5518,5520,5522,5524,5526,5528,5530,5532,5534,5536,5538,5540,5542,5544,5546,5548,5550,5552,5554,5556,5558,5560,5562,5564,5566,5568,5570,5572,5574,5576,5578,5580,5582,5584,5586,5588,5590,5592,5594,5596,5598,5600,5602,5604,5606,5608,5610,5612,5614,5616,5618,5620,5622,5624,5626,5628,5630,5632,5634,5636,5638,5640,5642,5644,5646,5648,5650,5652,5654,5656,5658,5660,5662,5665,5667,5669,5671,5673,5675,5677,5679,5681,5683,5685,5687,5689,5691,5693,5695,5697,5699,5701,5703,5705,5707,5709,5711,5713,5715,5717,5719,5721,5723,5725,5727,5729,5731,5733,5735,5737,5739,5741,5743,5745,5747,5749,5751,5753,5755,5757,5759,5761,5763,5765,5767,5769,5771,5773,5775,5777,5779,5781,5783,5785,5787,5789,5791,5793,5795,5797,5799,5801,5803,5805,5807,5809,5811,5813,5815,5817,5819,5821,5823,5825,5827,5829,5831,5833,5835,5837,5839,5841,5843,5845,5847,5849,5851,5853,5855,5857,5859,5861,5863,5865,5867,5869,5871,5873,5875,5877,5879,5881,5883,5885,5887,5889,5891,5893,5895,5897,5899,5901,5903,5905,5907,5909,5911,5913,5915,5917,5919,5921,5923,5925,5927,5929,5931,5933,5935,5937,5939,5941,5943,5945,5947,5949,5951,5953,5955,5957,5959,5961,5963,5965,5967,5969,5971,5973,5975,5977,5979,5981,5983,5985,5987,5989,5991,5993,5995,5997,5999,6001,6003,6005,6007,6009,6011,6013,6015,6017,6019,6021,6023,6025,6027,6029,6031,6033,6035,6037,6039,6041,6043,6045,6047,6049,6051,6053,6055,6057,6059,6061,6063,6065,6067,6069,6071,6073,6075,6077,6079,6081,6083,6085,6087,6089,6091,6093,6095,6097,6099,6101,6103,6105,6107,6109,6111,6113,6115,6117,6119,6121,6123,6125,6127,6129,6131,6133,6135,6137,6139,6141,6143,6145,6147,6149,6151,6153,6155,6157,6159,6161,6163,6165,6167,6169,6171,6173,6175,6177,6179,6181,6183,6185,6187,6189,6191,6193,6195,6197,6199,6201,6203,6205,6207,6209,6211,6213,6215,6217,6219,6221,6223,6225,6227,6229,6231,6233,6235,6237,6239,6241,6243,6245,6247,6249,6251,6253,6255,6257,6259,6261,6263,6265,6267,6269,6271,6273,6275,6277,6279,6281,6283,6285,6287,6289,6291,6293,6295,6297,6299,6301,6303,6305,6307,6309,6311,6313,6315,6317,6319,6321,6323,6325,6327,6329,6331,6333,6335,6337,6339,6341,6343,6345,6347,6349,6351,6353,6355,6357,6359,6361,6363,6365,6367,6369,6371,6373,6375,6377,6379,6381,6383,6385,6387,6389,6391,6393,6395,6397,6399,6401,6403,6405,6407,6409,6411,6413,6415,6417,6419,6421,6423,6425,6427,6429,6431,6433,6435,6437,6439,6441,6443,6445,6447,6449,6451,6453,6455,6457,6459,6461,6463,6465,6467,6469,6471,6473,6475,6477,6479,6481,6483,6485,6487,6489,6491,6493,6495,6497,6499,6501,6503,6505,6507,6509,6511,6513,6515,6517,6519,6521,6523,6525,6527,6529,6531,6533,6535,6537,6539,6541,6543,6545,6547,6549,6551,6553,6555,6557,6559,6561,6563,6565,6567,6569,6571,6573,6575,6577,6579,6581,6583,6585,6587,6589,6591,6593,6595,6597,6599,6601,6603,6605,6607,6609,6611,6613,6615,6617,6619,6621,6623,6625,6627,6629,6631,6633,6635,6637,6639,6641,6643,6645,6647,6649,6651,6653,6655,6657,6659,6661,6663,6665,6667,6669,6671,6673,6675,6677,6679,6681,6683,6685,6687,6689,6691,6693,6695,6697,6699,6701,6703,6705,6707,6709,6711,6713,6715,6717,6719,6721,6723,6725,6727,6729,6731,6733,6735,6737,6739,6741,6743,6745,6747,6749,6751,6753,6755,6757,6759,6761,6763,6765,6767,6769,6771,6773,6775,6777,6779,6781,6783,6785,6787,6789,6791,6793,6795,6797,6799,6801,6803,6805,6807,6809,6811,6813,6815,6817,6819,6821,6823,6825,6827,6829,6831,6833,6835,6837,6839,6841,6843,6845,6847,6849,6851,6853,6855,6857,6859,6861,6863,6865,6867,6869,6871,6873,6875,6877,6879,6881,6883,6885,6887,6889,6891,6893,6895,6897,6899,6901,6903,6905,6907,6909,6911,6913,6915,6917,6919,6921,6923,6925,6927,6929,6931,6933,6935,6937,6939,6941,6943,6945,6947,6949,6951,6953,6955,6957,6959,6961,6963,6965,6967,6969,6971,6973,6975,6977,6979,6981,6983,6985,6987,6989,6991,6993,6995,6997,6999,7001,7003,7005,7007,7009,7011,7013,7015,7017,7019,7021,7023,7025,7027,7029,7031,7033,7035,7037,7039,7041,7043,7045,7047,7049,7051,7053,7055,7057,7059,7061,7063,7065,7067,7069,7071,7073],{"categories":149},[150],"AI & LLMs",{"categories":152},[153],"Developer Productivity",{"categories":155},[150],{"categories":157},[158],"Business & SaaS",{"categories":160},[150],{"categories":162},[163],"AI Automation",{"categories":165},[166],"Product Strategy",{"categories":168},[163],{"categories":170},[150],{"categories":172},[153],{"categories":174},[163],{"categories":176},[126],{"categories":178},[150],{"categories":180},[158],{"categories":182},[],{"categories":184},[150],{"categories":186},[187],"Inference & Serving",{"categories":189},[150],{"categories":191},[150],{"categories":193},[163],{"categories":195},[],{"categories":197},[198],"AI News & Trends",{"categories":200},[201],"Data Science & Visualization",{"categories":203},[163],{"categories":205},[150],{"categories":207},[150],{"categories":209},[158],{"categories":211},[153],{"categories":213},[150],{"categories":215},[163],{"categories":217},[198],{"categories":219},[150],{"categories":221},[163],{"categories":223},[163],{"categories":225},[150],{"categories":227},[150],{"categories":229},[163],{"categories":231},[150],{"categories":233},[150],{"categories":235},[150],{"categories":237},[163],{"categories":239},[198],{"categories":241},[150],{"categories":243},[150],{"categories":245},[150],{"categories":247},[],{"categories":249},[250],"Design & Frontend",{"categories":252},[201],{"categories":254},[198],{"categories":256},[150],{"categories":258},[150],{"categories":260},[150],{"categories":262},[],{"categories":264},[150],{"categories":266},[150],{"categories":268},[163],{"categories":270},[126],{"categories":272},[150],{"categories":274},[163],{"categories":276},[150],{"categories":278},[279],"Marketing & Growth",{"categories":281},[250],{"categories":283},[150],{"categories":285},[163],{"categories":287},[150],{"categories":289},[150],{"categories":291},[126],{"categories":293},[150],{"categories":295},[],{"categories":297},[],{"categories":299},[250],{"categories":301},[150],{"categories":303},[163],{"categories":305},[153],{"categories":307},[126],{"categories":309},[163],{"categories":311},[250],{"categories":313},[166],{"categories":315},[150],{"categories":317},[126],{"categories":319},[320],"DevOps & Cloud",{"categories":322},[163],{"categories":324},[166],{"categories":326},[198],{"categories":328},[150],{"categories":330},[],{"categories":332},[150],{"categories":334},[150],{"categories":336},[],{"categories":338},[163],{"categories":340},[126],{"categories":342},[],{"categories":344},[126],{"categories":346},[150],{"categories":348},[349],"Governance & Standards",{"categories":351},[158],{"categories":353},[],{"categories":355},[],{"categories":357},[150],{"categories":359},[150],{"categories":361},[163],{"categories":363},[150],{"categories":365},[150],{"categories":367},[163],{"categories":369},[150],{"categories":371},[150],{"categories":373},[150],{"categories":375},[],{"categories":377},[126],{"categories":379},[],{"categories":381},[],{"categories":383},[150],{"categories":385},[126],{"categories":387},[],{"categories":389},[126],{"categories":391},[150],{"categories":393},[150],{"categories":395},[279],{"categories":397},[150],{"categories":399},[150],{"categories":401},[150],{"categories":403},[250],{"categories":405},[250],{"categories":407},[150],{"categories":409},[126],{"categories":411},[163],{"categories":413},[414],"GovTech & Public-Sector Adoption",{"categories":416},[126],{"categories":418},[150],{"categories":420},[150],{"categories":422},[150],{"categories":424},[163],{"categories":426},[163],{"categories":428},[201],{"categories":430},[150],{"categories":432},[198],{"categories":434},[163],{"categories":436},[437],"Legal AI Tools",{"categories":439},[150],{"categories":441},[163],{"categories":443},[150],{"categories":445},[279],{"categories":447},[163],{"categories":449},[166],{"categories":451},[150],{"categories":453},[126],{"categories":455},[414],{"categories":457},[],{"categories":459},[163],{"categories":461},[],{"categories":463},[158],{"categories":465},[163],{"categories":467},[163],{"categories":469},[470],"RAG & Retrieval",{"categories":472},[158],{"categories":474},[150],{"categories":476},[126],{"categories":478},[126],{"categories":480},[320],{"categories":482},[250],{"categories":484},[163],{"categories":486},[150],{"categories":488},[150],{"categories":490},[],{"categories":492},[493],"Agents & Orchestration",{"categories":495},[126],{"categories":497},[150],{"categories":499},[],{"categories":501},[163],{"categories":503},[158],{"categories":505},[],{"categories":507},[150],{"categories":509},[],{"categories":511},[150],{"categories":513},[153],{"categories":515},[126],{"categories":517},[158],{"categories":519},[150],{"categories":521},[163],{"categories":523},[150],{"categories":525},[150],{"categories":527},[198],{"categories":529},[150],{"categories":531},[],{"categories":533},[150],{"categories":535},[],{"categories":537},[150],{"categories":539},[126],{"categories":541},[150],{"categories":543},[163],{"categories":545},[201],{"categories":547},[],{"categories":549},[150],{"categories":551},[250],{"categories":553},[554],"Models & Frontier Labs",{"categories":556},[],{"categories":558},[250],{"categories":560},[561],"Regulation & Governance of AI",{"categories":563},[166],{"categories":565},[163],{"categories":567},[],{"categories":569},[150],{"categories":571},[150],{"categories":573},[163],{"categories":575},[163],{"categories":577},[198],{"categories":579},[150],{"categories":581},[158],{"categories":583},[150],{"categories":585},[163],{"categories":587},[],{"categories":589},[126],{"categories":591},[163],{"categories":593},[150],{"categories":595},[166],{"categories":597},[150],{"categories":599},[600],"AI Policy & Regulation",{"categories":602},[],{"categories":604},[150],{"categories":606},[163],{"categories":608},[163],{"categories":610},[166],{"categories":612},[163],{"categories":614},[150],{"categories":616},[150],{"categories":618},[150],{"categories":620},[163],{"categories":622},[],{"categories":624},[201],{"categories":626},[627],"Evals & Reliability",{"categories":629},[150],{"categories":631},[150],{"categories":633},[],{"categories":635},[153],{"categories":637},[414],{"categories":639},[600],{"categories":641},[150],{"categories":643},[158],{"categories":645},[150],{"categories":647},[163],{"categories":649},[150],{"categories":651},[163],{"categories":653},[493],{"categories":655},[150],{"categories":657},[126],{"categories":659},[150],{"categories":661},[],{"categories":663},[250],{"categories":665},[],{"categories":667},[150],{"categories":669},[414],{"categories":671},[150],{"categories":673},[150],{"categories":675},[150],{"categories":677},[],{"categories":679},[150],{"categories":681},[250],{"categories":683},[126],{"categories":685},[],{"categories":687},[150],{"categories":689},[],{"categories":691},[163],{"categories":693},[150],{"categories":695},[250],{"categories":697},[],{"categories":699},[150],{"categories":701},[150],{"categories":703},[201],{"categories":705},[163],{"categories":707},[150],{"categories":709},[158],{"categories":711},[163],{"categories":713},[150],{"categories":715},[150],{"categories":717},[126],{"categories":719},[250],{"categories":721},[150],{"categories":723},[163],{"categories":725},[],{"categories":727},[126],{"categories":729},[163],{"categories":731},[201],{"categories":733},[],{"categories":735},[150],{"categories":737},[198],{"categories":739},[150],{"categories":741},[],{"categories":743},[150],{"categories":745},[150],{"categories":747},[150],{"categories":749},[158,279],{"categories":751},[],{"categories":753},[126],{"categories":755},[150],{"categories":757},[150],{"categories":759},[163],{"categories":761},[150],{"categories":763},[],{"categories":765},[],{"categories":767},[150],{"categories":769},[250],{"categories":771},[150],{"categories":773},[],{"categories":775},[150],{"categories":777},[320],{"categories":779},[],{"categories":781},[163],{"categories":783},[198],{"categories":785},[150],{"categories":787},[150],{"categories":789},[250],{"categories":791},[],{"categories":793},[198],{"categories":795},[150],{"categories":797},[187],{"categories":799},[150],{"categories":801},[150],{"categories":803},[163],{"categories":805},[198],{"categories":807},[554],{"categories":809},[150],{"categories":811},[279],{"categories":813},[],{"categories":815},[163],{"categories":817},[158],{"categories":819},[126],{"categories":821},[150],{"categories":823},[163],{"categories":825},[],{"categories":827},[150,320],{"categories":829},[150],{"categories":831},[150],{"categories":833},[150],{"categories":835},[163],{"categories":837},[150,126],{"categories":839},[201],{"categories":841},[150],{"categories":843},[150],{"categories":845},[150],{"categories":847},[126],{"categories":849},[150],{"categories":851},[163],{"categories":853},[163],{"categories":855},[600],{"categories":857},[279],{"categories":859},[150],{"categories":861},[163],{"categories":863},[150],{"categories":865},[150],{"categories":867},[163],{"categories":869},[],{"categories":871},[163],{"categories":873},[150],{"categories":875},[150],{"categories":877},[163],{"categories":879},[150],{"categories":881},[150,158],{"categories":883},[150],{"categories":885},[158],{"categories":887},[],{"categories":889},[250],{"categories":891},[250],{"categories":893},[150],{"categories":895},[],{"categories":897},[],{"categories":899},[150],{"categories":901},[198],{"categories":903},[],{"categories":905},[153],{"categories":907},[150],{"categories":909},[126],{"categories":911},[150],{"categories":913},[914],"Generative UI & Design-to-Code",{"categories":916},[150],{"categories":918},[150],{"categories":920},[250],{"categories":922},[150],{"categories":924},[925],"Algorithmic Accountability",{"categories":927},[163],{"categories":929},[126],{"categories":931},[198],{"categories":933},[250],{"categories":935},[150],{"categories":937},[],{"categories":939},[166],{"categories":941},[150],{"categories":943},[150],{"categories":945},[150],{"categories":947},[150],{"categories":949},[163],{"categories":951},[952],"MLOps & Infrastructure",{"categories":954},[150],{"categories":956},[150],{"categories":958},[150],{"categories":960},[150],{"categories":962},[150],{"categories":964},[126],{"categories":966},[198],{"categories":968},[150],{"categories":970},[166],{"categories":972},[153],{"categories":974},[150],{"categories":976},[163],{"categories":978},[320],{"categories":980},[150],{"categories":982},[158],{"categories":984},[150],{"categories":986},[250],{"categories":988},[150],{"categories":990},[150],{"categories":992},[163],{"categories":994},[],{"categories":996},[],{"categories":998},[150],{"categories":1000},[187],{"categories":1002},[250],{"categories":1004},[198],{"categories":1006},[201],{"categories":1008},[],{"categories":1010},[150],{"categories":1012},[150],{"categories":1014},[158],{"categories":1016},[163],{"categories":1018},[150],{"categories":1020},[150],{"categories":1022},[150],{"categories":1024},[150],{"categories":1026},[198],{"categories":1028},[187],{"categories":1030},[150],{"categories":1032},[250],{"categories":1034},[150],{"categories":1036},[],{"categories":1038},[163],{"categories":1040},[126],{"categories":1042},[],{"categories":1044},[150],{"categories":1046},[150],{"categories":1048},[163],{"categories":1050},[126],{"categories":1052},[150],{"categories":1054},[201],{"categories":1056},[250],{"categories":1058},[],{"categories":1060},[150],{"categories":1062},[],{"categories":1064},[150],{"categories":1066},[],{"categories":1068},[150],{"categories":1070},[150],{"categories":1072},[166],{"categories":1074},[158],{"categories":1076},[163],{"categories":1078},[163],{"categories":1080},[],{"categories":1082},[150],{"categories":1084},[153],{"categories":1086},[150],{"categories":1088},[150],{"categories":1090},[158],{"categories":1092},[198],{"categories":1094},[153],{"categories":1096},[],{"categories":1098},[150],{"categories":1100},[],{"categories":1102},[150],{"categories":1104},[],{"categories":1106},[198],{"categories":1108},[198],{"categories":1110},[],{"categories":1112},[493],{"categories":1114},[150],{"categories":1116},[250],{"categories":1118},[126],{"categories":1120},[],{"categories":1122},[437],{"categories":1124},[163],{"categories":1126},[158],{"categories":1128},[],{"categories":1130},[],{"categories":1132},[153],{"categories":1134},[201],{"categories":1136},[],{"categories":1138},[279],{"categories":1140},[163],{"categories":1142},[158],{"categories":1144},[163],{"categories":1146},[150],{"categories":1148},[158],{"categories":1150},[150],{"categories":1152},[126],{"categories":1154},[],{"categories":1156},[187],{"categories":1158},[166],{"categories":1160},[150],{"categories":1162},[250],{"categories":1164},[126],{"categories":1166},[158],{"categories":1168},[150],{"categories":1170},[126],{"categories":1172},[150],{"categories":1174},[163],{"categories":1176},[158],{"categories":1178},[150],{"categories":1180},[150],{"categories":1182},[150],{"categories":1184},[150],{"categories":1186},[150],{"categories":1188},[],{"categories":1190},[],{"categories":1192},[126],{"categories":1194},[201],{"categories":1196},[166],{"categories":1198},[150],{"categories":1200},[163],{"categories":1202},[126],{"categories":1204},[126],{"categories":1206},[150],{"categories":1208},[],{"categories":1210},[198],{"categories":1212},[166],{"categories":1214},[166],{"categories":1216},[126],{"categories":1218},[150],{"categories":1220},[627],{"categories":1222},[320],{"categories":1224},[],{"categories":1226},[163],{"categories":1228},[150],{"categories":1230},[],{"categories":1232},[153],{"categories":1234},[],{"categories":1236},[150],{"categories":1238},[150],{"categories":1240},[150],{"categories":1242},[250],{"categories":1244},[279],{"categories":1246},[150],{"categories":1248},[126],{"categories":1250},[150],{"categories":1252},[163],{"categories":1254},[],{"categories":1256},[126],{"categories":1258},[150],{"categories":1260},[153],{"categories":1262},[],{"categories":1264},[158],{"categories":1266},[150],{"categories":1268},[150],{"categories":1270},[198],{"categories":1272},[150,320],{"categories":1274},[150],{"categories":1276},[1277],"Design Systems for AI",{"categories":1279},[150],{"categories":1281},[150],{"categories":1283},[198],{"categories":1285},[150],{"categories":1287},[150],{"categories":1289},[150],{"categories":1291},[158],{"categories":1293},[150],{"categories":1295},[150],{"categories":1297},[150],{"categories":1299},[],{"categories":1301},[150],{"categories":1303},[150],{"categories":1305},[158],{"categories":1307},[150],{"categories":1309},[],{"categories":1311},[163],{"categories":1313},[163],{"categories":1315},[126],{"categories":1317},[198],{"categories":1319},[126],{"categories":1321},[150],{"categories":1323},[250],{"categories":1325},[198],{"categories":1327},[201],{"categories":1329},[150],{"categories":1331},[150],{"categories":1333},[163],{"categories":1335},[153],{"categories":1337},[600],{"categories":1339},[150],{"categories":1341},[163],{"categories":1343},[150],{"categories":1345},[126],{"categories":1347},[126],{"categories":1349},[],{"categories":1351},[],{"categories":1353},[150],{"categories":1355},[163],{"categories":1357},[166],{"categories":1359},[],{"categories":1361},[158],{"categories":1363},[150],{"categories":1365},[],{"categories":1367},[250],{"categories":1369},[126],{"categories":1371},[163],{"categories":1373},[126],{"categories":1375},[250],{"categories":1377},[150],{"categories":1379},[150],{"categories":1381},[250],{"categories":1383},[],{"categories":1385},[],{"categories":1387},[198],{"categories":1389},[163],{"categories":1391},[163],{"categories":1393},[150],{"categories":1395},[150],{"categories":1397},[150],{"categories":1399},[150],{"categories":1401},[158],{"categories":1403},[150],{"categories":1405},[150],{"categories":1407},[],{"categories":1409},[126],{"categories":1411},[126],{"categories":1413},[150],{"categories":1415},[126],{"categories":1417},[158],{"categories":1419},[],{"categories":1421},[150],{"categories":1423},[150],{"categories":1425},[150],{"categories":1427},[150],{"categories":1429},[150],{"categories":1431},[163],{"categories":1433},[153],{"categories":1435},[158],{"categories":1437},[150],{"categories":1439},[163],{"categories":1441},[198],{"categories":1443},[163],{"categories":1445},[187],{"categories":1447},[279],{"categories":1449},[150],{"categories":1451},[163],{"categories":1453},[150],{"categories":1455},[150],{"categories":1457},[150],{"categories":1459},[],{"categories":1461},[250],{"categories":1463},[],{"categories":1465},[150],{"categories":1467},[150],{"categories":1469},[],{"categories":1471},[150],{"categories":1473},[126],{"categories":1475},[158],{"categories":1477},[1478],"Visual & Generative Media",{"categories":1480},[163],{"categories":1482},[],{"categories":1484},[150],{"categories":1486},[150],{"categories":1488},[126],{"categories":1490},[320],{"categories":1492},[150],{"categories":1494},[201],{"categories":1496},[600],{"categories":1498},[126],{"categories":1500},[279],{"categories":1502},[150],{"categories":1504},[250],{"categories":1506},[150],{"categories":1508},[150],{"categories":1510},[126],{"categories":1512},[163],{"categories":1514},[150],{"categories":1516},[],{"categories":1518},[],{"categories":1520},[163],{"categories":1522},[126],{"categories":1524},[153],{"categories":1526},[163],{"categories":1528},[554],{"categories":1530},[150],{"categories":1532},[166],{"categories":1534},[150],{"categories":1536},[158],{"categories":1538},[],{"categories":1540},[150],{"categories":1542},[166],{"categories":1544},[150],{"categories":1546},[150],{"categories":1548},[150],{"categories":1550},[166],{"categories":1552},[150],{"categories":1554},[150],{"categories":1556},[279],{"categories":1558},[150],{"categories":1560},[493],{"categories":1562},[150],{"categories":1564},[163],{"categories":1566},[150],{"categories":1568},[150],{"categories":1570},[163],{"categories":1572},[150],{"categories":1574},[150],{"categories":1576},[250],{"categories":1578},[163],{"categories":1580},[],{"categories":1582},[163],{"categories":1584},[],{"categories":1586},[320],{"categories":1588},[126],{"categories":1590},[],{"categories":1592},[554],{"categories":1594},[150],{"categories":1596},[163],{"categories":1598},[163],{"categories":1600},[150],{"categories":1602},[250,150],{"categories":1604},[153],{"categories":1606},[150],{"categories":1608},[250],{"categories":1610},[],{"categories":1612},[150],{"categories":1614},[153],{"categories":1616},[150],{"categories":1618},[1619],"Medical Imaging & Radiology",{"categories":1621},[150],{"categories":1623},[150],{"categories":1625},[150],{"categories":1627},[250],{"categories":1629},[163],{"categories":1631},[126],{"categories":1633},[],{"categories":1635},[150],{"categories":1637},[150],{"categories":1639},[150],{"categories":1641},[],{"categories":1643},[],{"categories":1645},[150],{"categories":1647},[150],{"categories":1649},[493],{"categories":1651},[150],{"categories":1653},[153],{"categories":1655},[150],{"categories":1657},[150],{"categories":1659},[],{"categories":1661},[163],{"categories":1663},[150],{"categories":1665},[166],{"categories":1667},[126],{"categories":1669},[150],{"categories":1671},[163],{"categories":1673},[493],{"categories":1675},[150],{"categories":1677},[163],{"categories":1679},[150],{"categories":1681},[150],{"categories":1683},[150],{"categories":1685},[250],{"categories":1687},[163],{"categories":1689},[320],{"categories":1691},[250],{"categories":1693},[158],{"categories":1695},[163],{"categories":1697},[198],{"categories":1699},[150],{"categories":1701},[150],{"categories":1703},[166],{"categories":1705},[150],{"categories":1707},[150],{"categories":1709},[150],{"categories":1711},[150],{"categories":1713},[163],{"categories":1715},[150],{"categories":1717},[126],{"categories":1719},[126],{"categories":1721},[150],{"categories":1723},[166],{"categories":1725},[],{"categories":1727},[198],{"categories":1729},[],{"categories":1731},[166],{"categories":1733},[163],{"categories":1735},[150],{"categories":1737},[163],{"categories":1739},[1277],{"categories":1741},[1277],{"categories":1743},[250],{"categories":1745},[150],{"categories":1747},[150],{"categories":1749},[150],{"categories":1751},[163],{"categories":1753},[126],{"categories":1755},[250],{"categories":1757},[163],{"categories":1759},[198],{"categories":1761},[],{"categories":1763},[150],{"categories":1765},[],{"categories":1767},[150],{"categories":1769},[150],{"categories":1771},[150],{"categories":1773},[150],{"categories":1775},[163],{"categories":1777},[1778],"Contract Review & E-Discovery",{"categories":1780},[150],{"categories":1782},[250],{"categories":1784},[150],{"categories":1786},[153],{"categories":1788},[150],{"categories":1790},[198],{"categories":1792},[150],{"categories":1794},[150],{"categories":1796},[279],{"categories":1798},[126],{"categories":1800},[150],{"categories":1802},[150],{"categories":1804},[163],{"categories":1806},[163],{"categories":1808},[925],{"categories":1810},[150],{"categories":1812},[150],{"categories":1814},[163],{"categories":1816},[163],{"categories":1818},[150],{"categories":1820},[150],{"categories":1822},[150],{"categories":1824},[163],{"categories":1826},[150],{"categories":1828},[150],{"categories":1830},[493],{"categories":1832},[470],{"categories":1834},[150],{"categories":1836},[163],{"categories":1838},[150],{"categories":1840},[1841],"Law-Firm Practice & Adoption",{"categories":1843},[150],{"categories":1845},[163],{"categories":1847},[250],{"categories":1849},[150],{"categories":1851},[150],{"categories":1853},[150],{"categories":1855},[],{"categories":1857},[126],{"categories":1859},[],{"categories":1861},[126],{"categories":1863},[150],{"categories":1865},[],{"categories":1867},[163],{"categories":1869},[153],{"categories":1871},[320],{"categories":1873},[150],{"categories":1875},[],{"categories":1877},[153],{"categories":1879},[158],{"categories":1881},[150],{"categories":1883},[279],{"categories":1885},[],{"categories":1887},[158],{"categories":1889},[163],{"categories":1891},[158],{"categories":1893},[],{"categories":1895},[150],{"categories":1897},[166],{"categories":1899},[150],{"categories":1901},[126],{"categories":1903},[],{"categories":1905},[],{"categories":1907},[],{"categories":1909},[],{"categories":1911},[150],{"categories":1913},[166],{"categories":1915},[163],{"categories":1917},[320],{"categories":1919},[150],{"categories":1921},[153],{"categories":1923},[126],{"categories":1925},[150],{"categories":1927},[150],{"categories":1929},[126],{"categories":1931},[166],{"categories":1933},[150],{"categories":1935},[150],{"categories":1937},[150],{"categories":1939},[952],{"categories":1941},[150],{"categories":1943},[126],{"categories":1945},[150],{"categories":1947},[279],{"categories":1949},[126],{"categories":1951},[158],{"categories":1953},[150],{"categories":1955},[150],{"categories":1957},[150],{"categories":1959},[250],{"categories":1961},[150],{"categories":1963},[150],{"categories":1965},[150],{"categories":1967},[150],{"categories":1969},[158],{"categories":1971},[163],{"categories":1973},[150,153],{"categories":1975},[493],{"categories":1977},[150],{"categories":1979},[150],{"categories":1981},[126],{"categories":1983},[126],{"categories":1985},[250],{"categories":1987},[163],{"categories":1989},[163],{"categories":1991},[126],{"categories":1993},[150],{"categories":1995},[150],{"categories":1997},[150],{"categories":1999},[],{"categories":2001},[],{"categories":2003},[150],{"categories":2005},[201],{"categories":2007},[150],{"categories":2009},[250],{"categories":2011},[163],{"categories":2013},[],{"categories":2015},[150],{"categories":2017},[150],{"categories":2019},[126],{"categories":2021},[201],{"categories":2023},[198],{"categories":2025},[250],{"categories":2027},[150],{"categories":2029},[163],{"categories":2031},[150],{"categories":2033},[126],{"categories":2035},[],{"categories":2037},[163],{"categories":2039},[150],{"categories":2041},[150],{"categories":2043},[150],{"categories":2045},[150],{"categories":2047},[],{"categories":2049},[163],{"categories":2051},[150],{"categories":2053},[150],{"categories":2055},[150],{"categories":2057},[],{"categories":2059},[163],{"categories":2061},[150],{"categories":2063},[150],{"categories":2065},[158],{"categories":2067},[150],{"categories":2069},[150],{"categories":2071},[],{"categories":2073},[153],{"categories":2075},[150],{"categories":2077},[150],{"categories":2079},[150],{"categories":2081},[250],{"categories":2083},[150],{"categories":2085},[126],{"categories":2087},[150],{"categories":2089},[153],{"categories":2091},[150],{"categories":2093},[126],{"categories":2095},[279],{"categories":2097},[163],{"categories":2099},[163],{"categories":2101},[150],{"categories":2103},[150],{"categories":2105},[150,250],{"categories":2107},[150],{"categories":2109},[163],{"categories":2111},[198],{"categories":2113},[150],{"categories":2115},[198],{"categories":2117},[163],{"categories":2119},[250],{"categories":2121},[150],{"categories":2123},[],{"categories":2125},[126],{"categories":2127},[320],{"categories":2129},[250],{"categories":2131},[126],{"categories":2133},[150],{"categories":2135},[166],{"categories":2137},[150],{"categories":2139},[150],{"categories":2141},[163],{"categories":2143},[],{"categories":2145},[],{"categories":2147},[150],{"categories":2149},[],{"categories":2151},[],{"categories":2153},[166],{"categories":2155},[126],{"categories":2157},[150],{"categories":2159},[163],{"categories":2161},[163],{"categories":2163},[158],{"categories":2165},[163],{"categories":2167},[320],{"categories":2169},[150],{"categories":2171},[150],{"categories":2173},[150],{"categories":2175},[187],{"categories":2177},[150],{"categories":2179},[150],{"categories":2181},[150],{"categories":2183},[126],{"categories":2185},[163],{"categories":2187},[150],{"categories":2189},[150],{"categories":2191},[126],{"categories":2193},[437],{"categories":2195},[163],{"categories":2197},[925],{"categories":2199},[],{"categories":2201},[250],{"categories":2203},[1841],{"categories":2205},[126],{"categories":2207},[],{"categories":2209},[],{"categories":2211},[150],{"categories":2213},[163],{"categories":2215},[],{"categories":2217},[],{"categories":2219},[150],{"categories":2221},[279],{"categories":2223},[150],{"categories":2225},[279],{"categories":2227},[163],{"categories":2229},[150],{"categories":2231},[150],{"categories":2233},[126],{"categories":2235},[166],{"categories":2237},[],{"categories":2239},[150],{"categories":2241},[150],{"categories":2243},[126],{"categories":2245},[1778],{"categories":2247},[250],{"categories":2249},[250],{"categories":2251},[150],{"categories":2253},[163],{"categories":2255},[153],{"categories":2257},[150],{"categories":2259},[150],{"categories":2261},[150],{"categories":2263},[150],{"categories":2265},[250],{"categories":2267},[250],{"categories":2269},[163],{"categories":2271},[163],{"categories":2273},[163],{"categories":2275},[150],{"categories":2277},[150],{"categories":2279},[],{"categories":2281},[150],{"categories":2283},[],{"categories":2285},[2286],"Interaction & Product Design",{"categories":2288},[150],{"categories":2290},[163],{"categories":2292},[126],{"categories":2294},[349],{"categories":2296},[198],{"categories":2298},[126],{"categories":2300},[150],{"categories":2302},[150],{"categories":2304},[150],{"categories":2306},[126],{"categories":2308},[150],{"categories":2310},[153],{"categories":2312},[163],{"categories":2314},[150],{"categories":2316},[],{"categories":2318},[163],{"categories":2320},[163],{"categories":2322},[163],{"categories":2324},[],{"categories":2326},[126],{"categories":2328},[150],{"categories":2330},[163],{"categories":2332},[153],{"categories":2334},[2286],{"categories":2336},[150],{"categories":2338},[153],{"categories":2340},[153],{"categories":2342},[],{"categories":2344},[163],{"categories":2346},[126],{"categories":2348},[],{"categories":2350},[163],{"categories":2352},[198],{"categories":2354},[150],{"categories":2356},[163],{"categories":2358},[150],{"categories":2360},[163],{"categories":2362},[163],{"categories":2364},[150],{"categories":2366},[150],{"categories":2368},[198],{"categories":2370},[201],{"categories":2372},[150],{"categories":2374},[166],{"categories":2376},[126],{"categories":2378},[2379],"Coding Agents & Dev Productivity",{"categories":2381},[198],{"categories":2383},[250],{"categories":2385},[150],{"categories":2387},[150],{"categories":2389},[],{"categories":2391},[150],{"categories":2393},[925],{"categories":2395},[],{"categories":2397},[150],{"categories":2399},[150],{"categories":2401},[320],{"categories":2403},[150],{"categories":2405},[198],{"categories":2407},[],{"categories":2409},[],{"categories":2411},[150],{"categories":2413},[],{"categories":2415},[163],{"categories":2417},[150],{"categories":2419},[],{"categories":2421},[126],{"categories":2423},[126],{"categories":2425},[150],{"categories":2427},[201],{"categories":2429},[],{"categories":2431},[150],{"categories":2433},[150],{"categories":2435},[150],{"categories":2437},[201],{"categories":2439},[126],{"categories":2441},[163],{"categories":2443},[],{"categories":2445},[],{"categories":2447},[150],{"categories":2449},[150],{"categories":2451},[163],{"categories":2453},[163],{"categories":2455},[414],{"categories":2457},[126],{"categories":2459},[166],{"categories":2461},[126],{"categories":2463},[163],{"categories":2465},[198],{"categories":2467},[198],{"categories":2469},[163],{"categories":2471},[163],{"categories":2473},[150],{"categories":2475},[153],{"categories":2477},[2286],{"categories":2479},[166],{"categories":2481},[150,320],{"categories":2483},[201],{"categories":2485},[],{"categories":2487},[250],{"categories":2489},[163],{"categories":2491},[126],{"categories":2493},[153],{"categories":2495},[150],{"categories":2497},[163],{"categories":2499},[2500],"The Designer's Role & Craft",{"categories":2502},[250],{"categories":2504},[],{"categories":2506},[163],{"categories":2508},[150],{"categories":2510},[163],{"categories":2512},[163],{"categories":2514},[150],{"categories":2516},[279],{"categories":2518},[150],{"categories":2520},[126],{"categories":2522},[150],{"categories":2524},[250],{"categories":2526},[150],{"categories":2528},[],{"categories":2530},[163],{"categories":2532},[250],{"categories":2534},[166],{"categories":2536},[150],{"categories":2538},[150],{"categories":2540},[150],{"categories":2542},[2543],"AI UX Patterns",{"categories":2545},[163],{"categories":2547},[163],{"categories":2549},[163],{"categories":2551},[163],{"categories":2553},[279],{"categories":2555},[201],{"categories":2557},[150],{"categories":2559},[163],{"categories":2561},[150],{"categories":2563},[1277],{"categories":2565},[],{"categories":2567},[279],{"categories":2569},[163],{"categories":2571},[198],{"categories":2573},[126],{"categories":2575},[150],{"categories":2577},[163],{"categories":2579},[],{"categories":2581},[],{"categories":2583},[150],{"categories":2585},[150],{"categories":2587},[163],{"categories":2589},[150],{"categories":2591},[163],{"categories":2593},[414],{"categories":2595},[250],{"categories":2597},[150],{"categories":2599},[198],{"categories":2601},[126],{"categories":2603},[150],{"categories":2605},[163],{"categories":2607},[163],{"categories":2609},[],{"categories":2611},[150],{"categories":2613},[],{"categories":2615},[150],{"categories":2617},[],{"categories":2619},[150],{"categories":2621},[150],{"categories":2623},[150],{"categories":2625},[163],{"categories":2627},[126],{"categories":2629},[],{"categories":2631},[],{"categories":2633},[201],{"categories":2635},[187],{"categories":2637},[150],{"categories":2639},[150],{"categories":2641},[150],{"categories":2643},[201],{"categories":2645},[150],{"categories":2647},[150],{"categories":2649},[198],{"categories":2651},[150],{"categories":2653},[150],{"categories":2655},[150],{"categories":2657},[163],{"categories":2659},[150],{"categories":2661},[163],{"categories":2663},[150],{"categories":2665},[150],{"categories":2667},[150],{"categories":2669},[163],{"categories":2671},[],{"categories":2673},[150],{"categories":2675},[],{"categories":2677},[150],{"categories":2679},[150],{"categories":2681},[320],{"categories":2683},[150],{"categories":2685},[],{"categories":2687},[],{"categories":2689},[250],{"categories":2691},[952],{"categories":2693},[163],{"categories":2695},[153],{"categories":2697},[2500],{"categories":2699},[],{"categories":2701},[],{"categories":2703},[150],{"categories":2705},[],{"categories":2707},[],{"categories":2709},[126],{"categories":2711},[198],{"categories":2713},[279],{"categories":2715},[163],{"categories":2717},[158],{"categories":2719},[150],{"categories":2721},[150],{"categories":2723},[158],{"categories":2725},[],{"categories":2727},[250],{"categories":2729},[166],{"categories":2731},[150],{"categories":2733},[150],{"categories":2735},[163],{"categories":2737},[158],{"categories":2739},[150],{"categories":2741},[150],{"categories":2743},[153],{"categories":2745},[150],{"categories":2747},[150],{"categories":2749},[],{"categories":2751},[153],{"categories":2753},[150],{"categories":2755},[279],{"categories":2757},[163],{"categories":2759},[198],{"categories":2761},[150],{"categories":2763},[126],{"categories":2765},[150],{"categories":2767},[150],{"categories":2769},[158],{"categories":2771},[150],{"categories":2773},[150],{"categories":2775},[150],{"categories":2777},[163],{"categories":2779},[150],{"categories":2781},[],{"categories":2783},[150],{"categories":2785},[126],{"categories":2787},[153],{"categories":2789},[150],{"categories":2791},[150],{"categories":2793},[150],{"categories":2795},[],{"categories":2797},[150],{"categories":2799},[493],{"categories":2801},[163],{"categories":2803},[158],{"categories":2805},[198],{"categories":2807},[150],{"categories":2809},[150],{"categories":2811},[],{"categories":2813},[158],{"categories":2815},[158],{"categories":2817},[150],{"categories":2819},[150],{"categories":2821},[166],{"categories":2823},[150],{"categories":2825},[150],{"categories":2827},[150],{"categories":2829},[150],{"categories":2831},[126],{"categories":2833},[126],{"categories":2835},[150],{"categories":2837},[],{"categories":2839},[126],{"categories":2841},[150],{"categories":2843},[126],{"categories":2845},[163],{"categories":2847},[600],{"categories":2849},[],{"categories":2851},[],{"categories":2853},[150],{"categories":2855},[198],{"categories":2857},[],{"categories":2859},[320],{"categories":2861},[150],{"categories":2863},[150],{"categories":2865},[150],{"categories":2867},[250],{"categories":2869},[914],{"categories":2871},[],{"categories":2873},[150],{"categories":2875},[150],{"categories":2877},[150],{"categories":2879},[126],{"categories":2881},[150],{"categories":2883},[150],{"categories":2885},[150,320],{"categories":2887},[150],{"categories":2889},[150],{"categories":2891},[250],{"categories":2893},[163],{"categories":2895},[],{"categories":2897},[163],{"categories":2899},[163],{"categories":2901},[150],{"categories":2903},[150],{"categories":2905},[150],{"categories":2907},[150],{"categories":2909},[201],{"categories":2911},[150],{"categories":2913},[2543],{"categories":2915},[153],{"categories":2917},[201],{"categories":2919},[153],{"categories":2921},[126],{"categories":2923},[250],{"categories":2925},[163],{"categories":2927},[150],{"categories":2929},[],{"categories":2931},[158],{"categories":2933},[150],{"categories":2935},[150],{"categories":2937},[198],{"categories":2939},[150],{"categories":2941},[150],{"categories":2943},[150],{"categories":2945},[163],{"categories":2947},[150],{"categories":2949},[150],{"categories":2951},[150],{"categories":2953},[158],{"categories":2955},[],{"categories":2957},[320],{"categories":2959},[150],{"categories":2961},[414],{"categories":2963},[250],{"categories":2965},[250],{"categories":2967},[126],{"categories":2969},[163],{"categories":2971},[150],{"categories":2973},[158],{"categories":2975},[198],{"categories":2977},[150],{"categories":2979},[150],{"categories":2981},[150],{"categories":2983},[250],{"categories":2985},[163],{"categories":2987},[163],{"categories":2989},[150],{"categories":2991},[150],{"categories":2993},[554],{"categories":2995},[163],{"categories":2997},[],{"categories":2999},[150],{"categories":3001},[150],{"categories":3003},[150],{"categories":3005},[],{"categories":3007},[],{"categories":3009},[150],{"categories":3011},[150],{"categories":3013},[163],{"categories":3015},[150],{"categories":3017},[150],{"categories":3019},[150],{"categories":3021},[126],{"categories":3023},[150],{"categories":3025},[150],{"categories":3027},[163],{"categories":3029},[150],{"categories":3031},[150],{"categories":3033},[150],{"categories":3035},[150],{"categories":3037},[150],{"categories":3039},[],{"categories":3041},[126],{"categories":3043},[201],{"categories":3045},[150],{"categories":3047},[163],{"categories":3049},[163],{"categories":3051},[150],{"categories":3053},[150],{"categories":3055},[],{"categories":3057},[],{"categories":3059},[150],{"categories":3061},[150],{"categories":3063},[150],{"categories":3065},[198],{"categories":3067},[201],{"categories":3069},[],{"categories":3071},[150],{"categories":3073},[250],{"categories":3075},[150],{"categories":3077},[320],{"categories":3079},[1841],{"categories":3081},[198],{"categories":3083},[126],{"categories":3085},[150],{"categories":3087},[126],{"categories":3089},[126],{"categories":3091},[150],{"categories":3093},[150],{"categories":3095},[126],{"categories":3097},[198],{"categories":3099},[198],{"categories":3101},[320],{"categories":3103},[163],{"categories":3105},[],{"categories":3107},[198],{"categories":3109},[150],{"categories":3111},[163],{"categories":3113},[153],{"categories":3115},[126],{"categories":3117},[150],{"categories":3119},[198],{"categories":3121},[],{"categories":3123},[150],{"categories":3125},[126],{"categories":3127},[126],{"categories":3129},[201],{"categories":3131},[150],{"categories":3133},[198],{"categories":3135},[150],{"categories":3137},[126],{"categories":3139},[163],{"categories":3141},[163],{"categories":3143},[198],{"categories":3145},[163],{"categories":3147},[320],{"categories":3149},[163],{"categories":3151},[150],{"categories":3153},[150],{"categories":3155},[150],{"categories":3157},[150],{"categories":3159},[126],{"categories":3161},[150],{"categories":3163},[],{"categories":3165},[163],{"categories":3167},[158],{"categories":3169},[126],{"categories":3171},[],{"categories":3173},[],{"categories":3175},[150],{"categories":3177},[163],{"categories":3179},[150],{"categories":3181},[150],{"categories":3183},[3184],"Frameworks & Tooling",{"categories":3186},[150],{"categories":3188},[150],{"categories":3190},[126],{"categories":3192},[150],{"categories":3194},[150],{"categories":3196},[],{"categories":3198},[201],{"categories":3200},[201],{"categories":3202},[153],{"categories":3204},[150],{"categories":3206},[163],{"categories":3208},[150],{"categories":3210},[250],{"categories":3212},[],{"categories":3214},[1841],{"categories":3216},[150],{"categories":3218},[126],{"categories":3220},[150],{"categories":3222},[320],{"categories":3224},[320],{"categories":3226},[],{"categories":3228},[163],{"categories":3230},[163],{"categories":3232},[150],{"categories":3234},[150],{"categories":3236},[198],{"categories":3238},[163],{"categories":3240},[198],{"categories":3242},[150],{"categories":3244},[163],{"categories":3246},[],{"categories":3248},[250],{"categories":3250},[150],{"categories":3252},[150],{"categories":3254},[],{"categories":3256},[150],{"categories":3258},[163],{"categories":3260},[150],{"categories":3262},[150],{"categories":3264},[150],{"categories":3266},[],{"categories":3268},[158],{"categories":3270},[126],{"categories":3272},[150],{"categories":3274},[126],{"categories":3276},[320],{"categories":3278},[150],{"categories":3280},[150],{"categories":3282},[150],{"categories":3284},[126],{"categories":3286},[158],{"categories":3288},[150],{"categories":3290},[1841],{"categories":3292},[],{"categories":3294},[163],{"categories":3296},[153],{"categories":3298},[150],{"categories":3300},[153],{"categories":3302},[150],{"categories":3304},[],{"categories":3306},[163],{"categories":3308},[150],{"categories":3310},[150],{"categories":3312},[3313],"AI Design Tooling",{"categories":3315},[250],{"categories":3317},[150],{"categories":3319},[150],{"categories":3321},[126],{"categories":3323},[250],{"categories":3325},[150],{"categories":3327},[150],{"categories":3329},[126],{"categories":3331},[198],{"categories":3333},[166],{"categories":3335},[126],{"categories":3337},[150],{"categories":3339},[150],{"categories":3341},[150],{"categories":3343},[163],{"categories":3345},[150],{"categories":3347},[],{"categories":3349},[163],{"categories":3351},[150],{"categories":3353},[150],{"categories":3355},[163],{"categories":3357},[150],{"categories":3359},[150],{"categories":3361},[150],{"categories":3363},[163],{"categories":3365},[],{"categories":3367},[163],{"categories":3369},[3184],{"categories":3371},[150],{"categories":3373},[150],{"categories":3375},[163],{"categories":3377},[163],{"categories":3379},[126],{"categories":3381},[126],{"categories":3383},[150],{"categories":3385},[],{"categories":3387},[126],{"categories":3389},[150],{"categories":3391},[150],{"categories":3393},[163],{"categories":3395},[158],{"categories":3397},[150],{"categories":3399},[],{"categories":3401},[150],{"categories":3403},[150],{"categories":3405},[2286],{"categories":3407},[],{"categories":3409},[150],{"categories":3411},[150],{"categories":3413},[150],{"categories":3415},[150],{"categories":3417},[250],{"categories":3419},[150],{"categories":3421},[],{"categories":3423},[150],{"categories":3425},[150],{"categories":3427},[150],{"categories":3429},[150],{"categories":3431},[279],{"categories":3433},[198],{"categories":3435},[150],{"categories":3437},[150],{"categories":3439},[1841],{"categories":3441},[153],{"categories":3443},[150],{"categories":3445},[150],{"categories":3447},[201],{"categories":3449},[150],{"categories":3451},[150],{"categories":3453},[198],{"categories":3455},[163],{"categories":3457},[],{"categories":3459},[150],{"categories":3461},[150],{"categories":3463},[250],{"categories":3465},[150],{"categories":3467},[279],{"categories":3469},[163],{"categories":3471},[150],{"categories":3473},[163],{"categories":3475},[],{"categories":3477},[],{"categories":3479},[],{"categories":3481},[153],{"categories":3483},[198],{"categories":3485},[163],{"categories":3487},[150],{"categories":3489},[150],{"categories":3491},[150],{"categories":3493},[150],{"categories":3495},[437],{"categories":3497},[250],{"categories":3499},[163],{"categories":3501},[150],{"categories":3503},[],{"categories":3505},[163],{"categories":3507},[163],{"categories":3509},[],{"categories":3511},[150],{"categories":3513},[163],{"categories":3515},[150],{"categories":3517},[],{"categories":3519},[150],{"categories":3521},[150],{"categories":3523},[150],{"categories":3525},[198],{"categories":3527},[250],{"categories":3529},[163],{"categories":3531},[250],{"categories":3533},[163],{"categories":3535},[150],{"categories":3537},[158],{"categories":3539},[],{"categories":3541},[],{"categories":3543},[150],{"categories":3545},[150],{"categories":3547},[150],{"categories":3549},[153],{"categories":3551},[163],{"categories":3553},[198],{"categories":3555},[],{"categories":3557},[250],{"categories":3559},[],{"categories":3561},[126],{"categories":3563},[150],{"categories":3565},[126],{"categories":3567},[250],{"categories":3569},[126],{"categories":3571},[150],{"categories":3573},[],{"categories":3575},[150],{"categories":3577},[150],{"categories":3579},[],{"categories":3581},[150],{"categories":3583},[150],{"categories":3585},[279],{"categories":3587},[150],{"categories":3589},[150],{"categories":3591},[320],{"categories":3593},[126],{"categories":3595},[150],{"categories":3597},[],{"categories":3599},[163],{"categories":3601},[150],{"categories":3603},[153],{"categories":3605},[554],{"categories":3607},[150],{"categories":3609},[150],{"categories":3611},[163],{"categories":3613},[150],{"categories":3615},[163],{"categories":3617},[150],{"categories":3619},[150],{"categories":3621},[150],{"categories":3623},[150],{"categories":3625},[],{"categories":3627},[150],{"categories":3629},[153],{"categories":3631},[150],{"categories":3633},[158],{"categories":3635},[126],{"categories":3637},[250],{"categories":3639},[],{"categories":3641},[150],{"categories":3643},[],{"categories":3645},[163],{"categories":3647},[150],{"categories":3649},[],{"categories":3651},[163],{"categories":3653},[150],{"categories":3655},[126],{"categories":3657},[250],{"categories":3659},[198],{"categories":3661},[150],{"categories":3663},[198],{"categories":3665},[163],{"categories":3667},[250],{"categories":3669},[150],{"categories":3671},[],{"categories":3673},[150],{"categories":3675},[187],{"categories":3677},[163],{"categories":3679},[150],{"categories":3681},[250],{"categories":3683},[198],{"categories":3685},[158],{"categories":3687},[126],{"categories":3689},[150],{"categories":3691},[150],{"categories":3693},[150],{"categories":3695},[150],{"categories":3697},[198],{"categories":3699},[279],{"categories":3701},[],{"categories":3703},[],{"categories":3705},[201],{"categories":3707},[493],{"categories":3709},[150],{"categories":3711},[163],{"categories":3713},[150,126],{"categories":3715},[198],{"categories":3717},[150],{"categories":3719},[150],{"categories":3721},[150],{"categories":3723},[150],{"categories":3725},[150],{"categories":3727},[150],{"categories":3729},[150],{"categories":3731},[163],{"categories":3733},[150],{"categories":3735},[163],{"categories":3737},[150],{"categories":3739},[150],{"categories":3741},[150],{"categories":3743},[],{"categories":3745},[150],{"categories":3747},[1277],{"categories":3749},[126],{"categories":3751},[250],{"categories":3753},[150],{"categories":3755},[150],{"categories":3757},[150],{"categories":3759},[201],{"categories":3761},[163],{"categories":3763},[279],{"categories":3765},[320],{"categories":3767},[],{"categories":3769},[126],{"categories":3771},[150],{"categories":3773},[158],{"categories":3775},[163],{"categories":3777},[153],{"categories":3779},[163],{"categories":3781},[150],{"categories":3783},[163],{"categories":3785},[163],{"categories":3787},[166],{"categories":3789},[126],{"categories":3791},[150],{"categories":3793},[150],{"categories":3795},[],{"categories":3797},[],{"categories":3799},[],{"categories":3801},[320],{"categories":3803},[150],{"categories":3805},[198],{"categories":3807},[150],{"categories":3809},[150],{"categories":3811},[150],{"categories":3813},[150],{"categories":3815},[],{"categories":3817},[150],{"categories":3819},[201],{"categories":3821},[158],{"categories":3823},[163],{"categories":3825},[150],{"categories":3827},[],{"categories":3829},[150],{"categories":3831},[163],{"categories":3833},[126],{"categories":3835},[150],{"categories":3837},[320],{"categories":3839},[],{"categories":3841},[250],{"categories":3843},[250],{"categories":3845},[150],{"categories":3847},[163],{"categories":3849},[],{"categories":3851},[126],{"categories":3853},[150],{"categories":3855},[250],{"categories":3857},[150],{"categories":3859},[158],{"categories":3861},[163],{"categories":3863},[150],{"categories":3865},[],{"categories":3867},[198],{"categories":3869},[150],{"categories":3871},[150],{"categories":3873},[150],{"categories":3875},[250],{"categories":3877},[163],{"categories":3879},[198],{"categories":3881},[],{"categories":3883},[163],{"categories":3885},[158],{"categories":3887},[163],{"categories":3889},[250],{"categories":3891},[150],{"categories":3893},[150],{"categories":3895},[150],{"categories":3897},[493],{"categories":3899},[150],{"categories":3901},[163],{"categories":3903},[],{"categories":3905},[150],{"categories":3907},[150],{"categories":3909},[320],{"categories":3911},[198],{"categories":3913},[201],{"categories":3915},[600],{"categories":3917},[201],{"categories":3919},[201],{"categories":3921},[150],{"categories":3923},[],{"categories":3925},[],{"categories":3927},[],{"categories":3929},[163],{"categories":3931},[150],{"categories":3933},[163],{"categories":3935},[163],{"categories":3937},[126],{"categories":3939},[150],{"categories":3941},[470],{"categories":3943},[126],{"categories":3945},[163],{"categories":3947},[150],{"categories":3949},[150],{"categories":3951},[150],{"categories":3953},[150],{"categories":3955},[150],{"categories":3957},[163],{"categories":3959},[150],{"categories":3961},[],{"categories":3963},[],{"categories":3965},[150],{"categories":3967},[],{"categories":3969},[150],{"categories":3971},[163],{"categories":3973},[250],{"categories":3975},[150],{"categories":3977},[150],{"categories":3979},[],{"categories":3981},[163],{"categories":3983},[150],{"categories":3985},[150],{"categories":3987},[166],{"categories":3989},[150],{"categories":3991},[250],{"categories":3993},[150],{"categories":3995},[163],{"categories":3997},[158],{"categories":3999},[150],{"categories":4001},[150],{"categories":4003},[279],{"categories":4005},[163],{"categories":4007},[150],{"categories":4009},[150],{"categories":4011},[914],{"categories":4013},[150],{"categories":4015},[163],{"categories":4017},[150],{"categories":4019},[126],{"categories":4021},[150],{"categories":4023},[554],{"categories":4025},[250],{"categories":4027},[],{"categories":4029},[150],{"categories":4031},[150],{"categories":4033},[198],{"categories":4035},[493],{"categories":4037},[163],{"categories":4039},[150],{"categories":4041},[],{"categories":4043},[198],{"categories":4045},[414],{"categories":4047},[163],{"categories":4049},[163],{"categories":4051},[163],{"categories":4053},[150],{"categories":4055},[150],{"categories":4057},[163],{"categories":4059},[],{"categories":4061},[158],{"categories":4063},[150],{"categories":4065},[158],{"categories":4067},[163],{"categories":4069},[],{"categories":4071},[126],{"categories":4073},[150],{"categories":4075},[150],{"categories":4077},[153],{"categories":4079},[150],{"categories":4081},[198],{"categories":4083},[320],{"categories":4085},[187],{"categories":4087},[163],{"categories":4089},[163],{"categories":4091},[150],{"categories":4093},[150],{"categories":4095},[163],{"categories":4097},[150],{"categories":4099},[153],{"categories":4101},[],{"categories":4103},[163],{"categories":4105},[150],{"categories":4107},[150],{"categories":4109},[150],{"categories":4111},[163],{"categories":4113},[150],{"categories":4115},[],{"categories":4117},[150],{"categories":4119},[],{"categories":4121},[250],{"categories":4123},[163],{"categories":4125},[150,158],{"categories":4127},[163],{"categories":4129},[150],{"categories":4131},[],{"categories":4133},[153],{"categories":4135},[201],{"categories":4137},[158],{"categories":4139},[150],{"categories":4141},[126],{"categories":4143},[150],{"categories":4145},[150],{"categories":4147},[163],{"categories":4149},[150],{"categories":4151},[150],{"categories":4153},[150],{"categories":4155},[198],{"categories":4157},[1277],{"categories":4159},[163],{"categories":4161},[150],{"categories":4163},[],{"categories":4165},[],{"categories":4167},[150],{"categories":4169},[163],{"categories":4171},[150],{"categories":4173},[150],{"categories":4175},[320],{"categories":4177},[],{"categories":4179},[150],{"categories":4181},[163],{"categories":4183},[187],{"categories":4185},[163],{"categories":4187},[493],{"categories":4189},[],{"categories":4191},[437],{"categories":4193},[163],{"categories":4195},[150],{"categories":4197},[150],{"categories":4199},[279],{"categories":4201},[163],{"categories":4203},[150],{"categories":4205},[201],{"categories":4207},[166],{"categories":4209},[163],{"categories":4211},[150],{"categories":4213},[493],{"categories":4215},[150],{"categories":4217},[320],{"categories":4219},[158],{"categories":4221},[],{"categories":4223},[150],{"categories":4225},[150],{"categories":4227},[279],{"categories":4229},[250],{"categories":4231},[150],{"categories":4233},[150],{"categories":4235},[150],{"categories":4237},[],{"categories":4239},[279],{"categories":4241},[198],{"categories":4243},[150],{"categories":4245},[150],{"categories":4247},[150],{"categories":4249},[600],{"categories":4251},[153],{"categories":4253},[150],{"categories":4255},[166],{"categories":4257},[150],{"categories":4259},[],{"categories":4261},[],{"categories":4263},[250],{"categories":4265},[150],{"categories":4267},[201],{"categories":4269},[279],{"categories":4271},[163],{"categories":4273},[150],{"categories":4275},[150],{"categories":4277},[279],{"categories":4279},[198],{"categories":4281},[150],{"categories":4283},[],{"categories":4285},[150],{"categories":4287},[150],{"categories":4289},[],{"categories":4291},[150],{"categories":4293},[150],{"categories":4295},[627],{"categories":4297},[150],{"categories":4299},[150],{"categories":4301},[163],{"categories":4303},[126],{"categories":4305},[493],{"categories":4307},[150],{"categories":4309},[150],{"categories":4311},[150],{"categories":4313},[],{"categories":4315},[150,126],{"categories":4317},[198],{"categories":4319},[163],{"categories":4321},[126],{"categories":4323},[163],{"categories":4325},[952],{"categories":4327},[126],{"categories":4329},[126],{"categories":4331},[163],{"categories":4333},[150],{"categories":4335},[153],{"categories":4337},[],{"categories":4339},[],{"categories":4341},[163],{"categories":4343},[150],{"categories":4345},[126],{"categories":4347},[150],{"categories":4349},[153],{"categories":4351},[126],{"categories":4353},[126],{"categories":4355},[150],{"categories":4357},[279],{"categories":4359},[150],{"categories":4361},[126],{"categories":4363},[150],{"categories":4365},[],{"categories":4367},[150],{"categories":4369},[150],{"categories":4371},[250,150],{"categories":4373},[320],{"categories":4375},[153],{"categories":4377},[150],{"categories":4379},[],{"categories":4381},[150],{"categories":4383},[150],{"categories":4385},[158],{"categories":4387},[150],{"categories":4389},[158],{"categories":4391},[150],{"categories":4393},[150],{"categories":4395},[414],{"categories":4397},[150],{"categories":4399},[158],{"categories":4401},[126],{"categories":4403},[201],{"categories":4405},[163],{"categories":4407},[150],{"categories":4409},[126],{"categories":4411},[150],{"categories":4413},[150],{"categories":4415},[198],{"categories":4417},[279],{"categories":4419},[250],{"categories":4421},[150],{"categories":4423},[150],{"categories":4425},[150],{"categories":4427},[150],{"categories":4429},[153],{"categories":4431},[150],{"categories":4433},[163],{"categories":4435},[163],{"categories":4437},[126],{"categories":4439},[198],{"categories":4441},[126],{"categories":4443},[126],{"categories":4445},[150],{"categories":4447},[150],{"categories":4449},[],{"categories":4451},[],{"categories":4453},[201],{"categories":4455},[150],{"categories":4457},[126],{"categories":4459},[150],{"categories":4461},[250],{"categories":4463},[493],{"categories":4465},[437],{"categories":4467},[414],{"categories":4469},[150],{"categories":4471},[150],{"categories":4473},[150],{"categories":4475},[201],{"categories":4477},[150],{"categories":4479},[150],{"categories":4481},[150],{"categories":4483},[150],{"categories":4485},[150],{"categories":4487},[150],{"categories":4489},[150],{"categories":4491},[163],{"categories":4493},[153],{"categories":4495},[163],{"categories":4497},[150,158],{"categories":4499},[],{"categories":4501},[250],{"categories":4503},[],{"categories":4505},[166],{"categories":4507},[150],{"categories":4509},[198],{"categories":4511},[153],{"categories":4513},[150],{"categories":4515},[153],{"categories":4517},[163],{"categories":4519},[201],{"categories":4521},[163],{"categories":4523},[166],{"categories":4525},[163],{"categories":4527},[150],{"categories":4529},[150],{"categories":4531},[150],{"categories":4533},[158],{"categories":4535},[163],{"categories":4537},[126],{"categories":4539},[279],{"categories":4541},[150],{"categories":4543},[150],{"categories":4545},[],{"categories":4547},[198],{"categories":4549},[150],{"categories":4551},[150],{"categories":4553},[150],{"categories":4555},[150],{"categories":4557},[150],{"categories":4559},[150],{"categories":4561},[126],{"categories":4563},[198],{"categories":4565},[126],{"categories":4567},[126],{"categories":4569},[150],{"categories":4571},[150],{"categories":4573},[150],{"categories":4575},[150],{"categories":4577},[437],{"categories":4579},[150],{"categories":4581},[163],{"categories":4583},[163],{"categories":4585},[198],{"categories":4587},[150],{"categories":4589},[150],{"categories":4591},[150],{"categories":4593},[163],{"categories":4595},[150],{"categories":4597},[150],{"categories":4599},[150],{"categories":4601},[3184],{"categories":4603},[4604],"Clinical AI",{"categories":4606},[250],{"categories":4608},[150],{"categories":4610},[150],{"categories":4612},[150],{"categories":4614},[150],{"categories":4616},[320],{"categories":4618},[2543],{"categories":4620},[150],{"categories":4622},[166],{"categories":4624},[250],{"categories":4626},[150],{"categories":4628},[163],{"categories":4630},[150],{"categories":4632},[150],{"categories":4634},[198],{"categories":4636},[150],{"categories":4638},[163],{"categories":4640},[126],{"categories":4642},[279],{"categories":4644},[150],{"categories":4646},[150],{"categories":4648},[158],{"categories":4650},[150],{"categories":4652},[150],{"categories":4654},[554],{"categories":4656},[150],{"categories":4658},[],{"categories":4660},[163],{"categories":4662},[150],{"categories":4664},[126],{"categories":4666},[153],{"categories":4668},[150],{"categories":4670},[],{"categories":4672},[],{"categories":4674},[150],{"categories":4676},[],{"categories":4678},[158],{"categories":4680},[150],{"categories":4682},[150],{"categories":4684},[163],{"categories":4686},[150],{"categories":4688},[198],{"categories":4690},[198],{"categories":4692},[198],{"categories":4694},[198],{"categories":4696},[],{"categories":4698},[153],{"categories":4700},[163],{"categories":4702},[198],{"categories":4704},[150],{"categories":4706},[627],{"categories":4708},[166],{"categories":4710},[163],{"categories":4712},[150],{"categories":4714},[153],{"categories":4716},[150],{"categories":4718},[163],{"categories":4720},[150],{"categories":4722},[150],{"categories":4724},[150],{"categories":4726},[150,163],{"categories":4728},[163],{"categories":4730},[320],{"categories":4732},[198],{"categories":4734},[163],{"categories":4736},[198],{"categories":4738},[163],{"categories":4740},[150],{"categories":4742},[],{"categories":4744},[198],{"categories":4746},[279],{"categories":4748},[153],{"categories":4750},[150],{"categories":4752},[150],{"categories":4754},[],{"categories":4756},[126],{"categories":4758},[],{"categories":4760},[153],{"categories":4762},[163],{"categories":4764},[198],{"categories":4766},[150],{"categories":4768},[198],{"categories":4770},[153],{"categories":4772},[198],{"categories":4774},[198],{"categories":4776},[],{"categories":4778},[158],{"categories":4780},[163],{"categories":4782},[198],{"categories":4784},[198],{"categories":4786},[198],{"categories":4788},[198],{"categories":4790},[198],{"categories":4792},[198],{"categories":4794},[198],{"categories":4796},[198],{"categories":4798},[198],{"categories":4800},[198],{"categories":4802},[201],{"categories":4804},[153],{"categories":4806},[150],{"categories":4808},[150],{"categories":4810},[163],{"categories":4812},[163],{"categories":4814},[],{"categories":4816},[150],{"categories":4818},[150,153],{"categories":4820},[],{"categories":4822},[163],{"categories":4824},[150],{"categories":4826},[198],{"categories":4828},[163],{"categories":4830},[952],{"categories":4832},[150],{"categories":4834},[150],{"categories":4836},[150],{"categories":4838},[150],{"categories":4840},[150],{"categories":4842},[414],{"categories":4844},[150],{"categories":4846},[150],{"categories":4848},[163],{"categories":4850},[150],{"categories":4852},[150],{"categories":4854},[158],{"categories":4856},[166],{"categories":4858},[163],{"categories":4860},[163],{"categories":4862},[],{"categories":4864},[163],{"categories":4866},[250],{"categories":4868},[198],{"categories":4870},[150],{"categories":4872},[],{"categories":4874},[166],{"categories":4876},[],{"categories":4878},[126],{"categories":4880},[150],{"categories":4882},[163],{"categories":4884},[250],{"categories":4886},[150],{"categories":4888},[],{"categories":4890},[150],{"categories":4892},[150],{"categories":4894},[],{"categories":4896},[279],{"categories":4898},[150],{"categories":4900},[163],{"categories":4902},[],{"categories":4904},[],{"categories":4906},[198],{"categories":4908},[153],{"categories":4910},[150],{"categories":4912},[150],{"categories":4914},[158],{"categories":4916},[150],{"categories":4918},[150],{"categories":4920},[163],{"categories":4922},[150],{"categories":4924},[158],{"categories":4926},[158],{"categories":4928},[250],{"categories":4930},[],{"categories":4932},[150],{"categories":4934},[198],{"categories":4936},[],{"categories":4938},[150],{"categories":4940},[150],{"categories":4942},[250],{"categories":4944},[150],{"categories":4946},[150],{"categories":4948},[279],{"categories":4950},[150],{"categories":4952},[320],{"categories":4954},[],{"categories":4956},[163],{"categories":4958},[150],{"categories":4960},[279],{"categories":4962},[126],{"categories":4964},[],{"categories":4966},[150],{"categories":4968},[],{"categories":4970},[163],{"categories":4972},[250],{"categories":4974},[126],{"categories":4976},[],{"categories":4978},[3184],{"categories":4980},[158],{"categories":4982},[153],{"categories":4984},[150],{"categories":4986},[201],{"categories":4988},[163],{"categories":4990},[250],{"categories":4992},[150],{"categories":4994},[126],{"categories":4996},[],{"categories":4998},[],{"categories":5000},[150],{"categories":5002},[153],{"categories":5004},[150],{"categories":5006},[279],{"categories":5008},[],{"categories":5010},[163],{"categories":5012},[163],{"categories":5014},[150],{"categories":5016},[163],{"categories":5018},[150],{"categories":5020},[198],{"categories":5022},[126],{"categories":5024},[150],{"categories":5026},[163],{"categories":5028},[166],{"categories":5030},[150],{"categories":5032},[150],{"categories":5034},[150],{"categories":5036},[163],{"categories":5038},[150],{"categories":5040},[166],{"categories":5042},[279],{"categories":5044},[198],{"categories":5046},[],{"categories":5048},[279],{"categories":5050},[150],{"categories":5052},[],{"categories":5054},[126],{"categories":5056},[163],{"categories":5058},[],{"categories":5060},[150],{"categories":5062},[150],{"categories":5064},[150],{"categories":5066},[150],{"categories":5068},[150],{"categories":5070},[163],{"categories":5072},[158],{"categories":5074},[153],{"categories":5076},[163],{"categories":5078},[150],{"categories":5080},[250],{"categories":5082},[126],{"categories":5084},[126],{"categories":5086},[150],{"categories":5088},[201],{"categories":5090},[163],{"categories":5092},[150],{"categories":5094},[150],{"categories":5096},[163],{"categories":5098},[150],{"categories":5100},[150],{"categories":5102},[163],{"categories":5104},[158],{"categories":5106},[150],{"categories":5108},[250],{"categories":5110},[126],{"categories":5112},[163],{"categories":5114},[150],{"categories":5116},[166],{"categories":5118},[150],{"categories":5120},[163],{"categories":5122},[150],{"categories":5124},[150],{"categories":5126},[198],{"categories":5128},[150],{"categories":5130},[],{"categories":5132},[153],{"categories":5134},[150],{"categories":5136},[150],{"categories":5138},[150],{"categories":5140},[126],{"categories":5142},[126],{"categories":5144},[150],{"categories":5146},[126],{"categories":5148},[150],{"categories":5150},[163],{"categories":5152},[150],{"categories":5154},[150],{"categories":5156},[150],{"categories":5158},[150],{"categories":5160},[150],{"categories":5162},[],{"categories":5164},[150],{"categories":5166},[250],{"categories":5168},[163],{"categories":5170},[158],{"categories":5172},[198],{"categories":5174},[150],{"categories":5176},[163],{"categories":5178},[150],{"categories":5180},[163],{"categories":5182},[150],{"categories":5184},[150],{"categories":5186},[250],{"categories":5188},[163],{"categories":5190},[150],{"categories":5192},[279],{"categories":5194},[150],{"categories":5196},[201],{"categories":5198},[150],{"categories":5200},[150],{"categories":5202},[198],{"categories":5204},[150],{"categories":5206},[150],{"categories":5208},[150],{"categories":5210},[150],{"categories":5212},[163],{"categories":5214},[320],{"categories":5216},[150],{"categories":5218},[126],{"categories":5220},[163],{"categories":5222},[201],{"categories":5224},[],{"categories":5226},[163],{"categories":5228},[126],{"categories":5230},[150],{"categories":5232},[150],{"categories":5234},[2379],{"categories":5236},[250],{"categories":5238},[349],{"categories":5240},[150],{"categories":5242},[150],{"categories":5244},[150],{"categories":5246},[150],{"categories":5248},[153],{"categories":5250},[150],{"categories":5252},[150],{"categories":5254},[126],{"categories":5256},[158],{"categories":5258},[150],{"categories":5260},[126],{"categories":5262},[150],{"categories":5264},[],{"categories":5266},[163],{"categories":5268},[163],{"categories":5270},[150],{"categories":5272},[150],{"categories":5274},[150],{"categories":5276},[201],{"categories":5278},[],{"categories":5280},[198],{"categories":5282},[],{"categories":5284},[198],{"categories":5286},[150],{"categories":5288},[150],{"categories":5290},[163],{"categories":5292},[150],{"categories":5294},[163],{"categories":5296},[163],{"categories":5298},[],{"categories":5300},[150],{"categories":5302},[198],{"categories":5304},[150],{"categories":5306},[],{"categories":5308},[150],{"categories":5310},[150],{"categories":5312},[],{"categories":5314},[150],{"categories":5316},[150],{"categories":5318},[250],{"categories":5320},[126],{"categories":5322},[163],{"categories":5324},[150],{"categories":5326},[150],{"categories":5328},[150],{"categories":5330},[150],{"categories":5332},[279],{"categories":5334},[150],{"categories":5336},[150],{"categories":5338},[150],{"categories":5340},[153],{"categories":5342},[150],{"categories":5344},[150],{"categories":5346},[],{"categories":5348},[150],{"categories":5350},[150],{"categories":5352},[150],{"categories":5354},[],{"categories":5356},[153],{"categories":5358},[150],{"categories":5360},[150],{"categories":5362},[198],{"categories":5364},[126],{"categories":5366},[166],{"categories":5368},[163],{"categories":5370},[493],{"categories":5372},[150],{"categories":5374},[150],{"categories":5376},[150],{"categories":5378},[126],{"categories":5380},[198],{"categories":5382},[250],{"categories":5384},[150],{"categories":5386},[150],{"categories":5388},[150],{"categories":5390},[150],{"categories":5392},[198],{"categories":5394},[150],{"categories":5396},[250],{"categories":5398},[150],{"categories":5400},[150],{"categories":5402},[198],{"categories":5404},[250],{"categories":5406},[150],{"categories":5408},[198],{"categories":5410},[150],{"categories":5412},[163],{"categories":5414},[163],{"categories":5416},[163],{"categories":5418},[126],{"categories":5420},[198],{"categories":5422},[163],{"categories":5424},[163],{"categories":5426},[150],{"categories":5428},[126],{"categories":5430},[250],{"categories":5432},[150],{"categories":5434},[150],{"categories":5436},[163],{"categories":5438},[150],{"categories":5440},[],{"categories":5442},[163],{"categories":5444},[],{"categories":5446},[150],{"categories":5448},[150],{"categories":5450},[],{"categories":5452},[],{"categories":5454},[163],{"categories":5456},[158],{"categories":5458},[163],{"categories":5460},[5461],"Liability & Ethics",{"categories":5463},[150],{"categories":5465},[150],{"categories":5467},[150],{"categories":5469},[163],{"categories":5471},[153],{"categories":5473},[163],{"categories":5475},[158],{"categories":5477},[279],{"categories":5479},[163],{"categories":5481},[150],{"categories":5483},[150],{"categories":5485},[],{"categories":5487},[600],{"categories":5489},[163],{"categories":5491},[],{"categories":5493},[150],{"categories":5495},[153],{"categories":5497},[163],{"categories":5499},[],{"categories":5501},[163],{"categories":5503},[150],{"categories":5505},[150],{"categories":5507},[126],{"categories":5509},[150],{"categories":5511},[198],{"categories":5513},[150],{"categories":5515},[150],{"categories":5517},[166],{"categories":5519},[163],{"categories":5521},[150],{"categories":5523},[150],{"categories":5525},[150],{"categories":5527},[198],{"categories":5529},[163],{"categories":5531},[126],{"categories":5533},[250],{"categories":5535},[153],{"categories":5537},[150],{"categories":5539},[150],{"categories":5541},[150],{"categories":5543},[],{"categories":5545},[163],{"categories":5547},[163],{"categories":5549},[163],{"categories":5551},[493],{"categories":5553},[250],{"categories":5555},[163],{"categories":5557},[320],{"categories":5559},[126],{"categories":5561},[198],{"categories":5563},[150],{"categories":5565},[250],{"categories":5567},[150],{"categories":5569},[153],{"categories":5571},[],{"categories":5573},[163],{"categories":5575},[150],{"categories":5577},[150],{"categories":5579},[150],{"categories":5581},[150],{"categories":5583},[163],{"categories":5585},[150],{"categories":5587},[150],{"categories":5589},[250],{"categories":5591},[],{"categories":5593},[163],{"categories":5595},[166],{"categories":5597},[198],{"categories":5599},[163],{"categories":5601},[158],{"categories":5603},[],{"categories":5605},[150],{"categories":5607},[150],{"categories":5609},[166],{"categories":5611},[150],{"categories":5613},[163],{"categories":5615},[198],{"categories":5617},[153],{"categories":5619},[320],{"categories":5621},[150],{"categories":5623},[150],{"categories":5625},[150],{"categories":5627},[198],{"categories":5629},[158],{"categories":5631},[150],{"categories":5633},[250],{"categories":5635},[198],{"categories":5637},[320],{"categories":5639},[150],{"categories":5641},[163],{"categories":5643},[],{"categories":5645},[554],{"categories":5647},[],{"categories":5649},[150],{"categories":5651},[320],{"categories":5653},[150],{"categories":5655},[201],{"categories":5657},[150],{"categories":5659},[163],{"categories":5661},[163],{"categories":5663},[5664],"Design News & Tools",{"categories":5666},[150],{"categories":5668},[150],{"categories":5670},[198],{"categories":5672},[150],{"categories":5674},[150],{"categories":5676},[153],{"categories":5678},[163],{"categories":5680},[150],{"categories":5682},[250],{"categories":5684},[163],{"categories":5686},[163],{"categories":5688},[250],{"categories":5690},[150],{"categories":5692},[150],{"categories":5694},[493],{"categories":5696},[163],{"categories":5698},[150],{"categories":5700},[150],{"categories":5702},[493],{"categories":5704},[150],{"categories":5706},[279],{"categories":5708},[150],{"categories":5710},[163],{"categories":5712},[],{"categories":5714},[150],{"categories":5716},[150],{"categories":5718},[150],{"categories":5720},[198],{"categories":5722},[150],{"categories":5724},[153],{"categories":5726},[],{"categories":5728},[150],{"categories":5730},[150],{"categories":5732},[150],{"categories":5734},[126],{"categories":5736},[627],{"categories":5738},[126],{"categories":5740},[250],{"categories":5742},[150],{"categories":5744},[150,163],{"categories":5746},[279,158],{"categories":5748},[126],{"categories":5750},[150],{"categories":5752},[150],{"categories":5754},[150],{"categories":5756},[150],{"categories":5758},[],{"categories":5760},[163],{"categories":5762},[150],{"categories":5764},[],{"categories":5766},[150],{"categories":5768},[126],{"categories":5770},[150],{"categories":5772},[126],{"categories":5774},[],{"categories":5776},[163],{"categories":5778},[150],{"categories":5780},[158],{"categories":5782},[150],{"categories":5784},[198],{"categories":5786},[150],{"categories":5788},[],{"categories":5790},[163],{"categories":5792},[150],{"categories":5794},[],{"categories":5796},[250],{"categories":5798},[150],{"categories":5800},[150],{"categories":5802},[163],{"categories":5804},[150],{"categories":5806},[150],{"categories":5808},[153],{"categories":5810},[163],{"categories":5812},[150],{"categories":5814},[],{"categories":5816},[150],{"categories":5818},[320],{"categories":5820},[279],{"categories":5822},[158],{"categories":5824},[158],{"categories":5826},[150],{"categories":5828},[153],{"categories":5830},[153],{"categories":5832},[150],{"categories":5834},[163],{"categories":5836},[150],{"categories":5838},[150],{"categories":5840},[150],{"categories":5842},[150],{"categories":5844},[126],{"categories":5846},[150],{"categories":5848},[153],{"categories":5850},[150],{"categories":5852},[150],{"categories":5854},[163],{"categories":5856},[150],{"categories":5858},[279],{"categories":5860},[150],{"categories":5862},[198],{"categories":5864},[150],{"categories":5866},[150],{"categories":5868},[163],{"categories":5870},[166],{"categories":5872},[150],{"categories":5874},[150],{"categories":5876},[163],{"categories":5878},[],{"categories":5880},[126],{"categories":5882},[],{"categories":5884},[126],{"categories":5886},[163],{"categories":5888},[153],{"categories":5890},[150],{"categories":5892},[],{"categories":5894},[201],{"categories":5896},[320],{"categories":5898},[150],{"categories":5900},[126],{"categories":5902},[150],{"categories":5904},[],{"categories":5906},[198],{"categories":5908},[163],{"categories":5910},[126],{"categories":5912},[250],{"categories":5914},[158],{"categories":5916},[150],{"categories":5918},[150],{"categories":5920},[163],{"categories":5922},[126],{"categories":5924},[163],{"categories":5926},[198],{"categories":5928},[150],{"categories":5930},[166],{"categories":5932},[153],{"categories":5934},[166],{"categories":5936},[198],{"categories":5938},[150],{"categories":5940},[126],{"categories":5942},[150],{"categories":5944},[250],{"categories":5946},[158],{"categories":5948},[150],{"categories":5950},[150],{"categories":5952},[150],{"categories":5954},[150],{"categories":5956},[150],{"categories":5958},[150],{"categories":5960},[163],{"categories":5962},[150],{"categories":5964},[163],{"categories":5966},[150],{"categories":5968},[150],{"categories":5970},[153],{"categories":5972},[150],{"categories":5974},[163],{"categories":5976},[163],{"categories":5978},[250],{"categories":5980},[163],{"categories":5982},[163],{"categories":5984},[150],{"categories":5986},[153],{"categories":5988},[163],{"categories":5990},[250],{"categories":5992},[],{"categories":5994},[150],{"categories":5996},[201],{"categories":5998},[493],{"categories":6000},[150],{"categories":6002},[163],{"categories":6004},[150],{"categories":6006},[150],{"categories":6008},[126],{"categories":6010},[150],{"categories":6012},[],{"categories":6014},[150],{"categories":6016},[163],{"categories":6018},[150],{"categories":6020},[279],{"categories":6022},[150],{"categories":6024},[126],{"categories":6026},[150],{"categories":6028},[198],{"categories":6030},[163],{"categories":6032},[150],{"categories":6034},[279],{"categories":6036},[163],{"categories":6038},[158],{"categories":6040},[158],{"categories":6042},[150],{"categories":6044},[150],{"categories":6046},[150],{"categories":6048},[150],{"categories":6050},[150],{"categories":6052},[150],{"categories":6054},[153],{"categories":6056},[],{"categories":6058},[150],{"categories":6060},[150],{"categories":6062},[163],{"categories":6064},[150],{"categories":6066},[163],{"categories":6068},[150],{"categories":6070},[150],{"categories":6072},[150],{"categories":6074},[150],{"categories":6076},[150],{"categories":6078},[126],{"categories":6080},[],{"categories":6082},[153],{"categories":6084},[150],{"categories":6086},[150],{"categories":6088},[163],{"categories":6090},[163],{"categories":6092},[],{"categories":6094},[126],{"categories":6096},[126],{"categories":6098},[150],{"categories":6100},[279],{"categories":6102},[158],{"categories":6104},[250],{"categories":6106},[],{"categories":6108},[150],{"categories":6110},[163],{"categories":6112},[153],{"categories":6114},[150],{"categories":6116},[150],{"categories":6118},[126],{"categories":6120},[153],{"categories":6122},[150],{"categories":6124},[150],{"categories":6126},[198],{"categories":6128},[201],{"categories":6130},[150],{"categories":6132},[198],{"categories":6134},[163],{"categories":6136},[150],{"categories":6138},[],{"categories":6140},[198],{"categories":6142},[163],{"categories":6144},[250],{"categories":6146},[201],{"categories":6148},[150],{"categories":6150},[150],{"categories":6152},[],{"categories":6154},[163],{"categories":6156},[163],{"categories":6158},[163],{"categories":6160},[3184],{"categories":6162},[198],{"categories":6164},[150],{"categories":6166},[126],{"categories":6168},[150],{"categories":6170},[150],{"categories":6172},[150],{"categories":6174},[150],{"categories":6176},[150],{"categories":6178},[158],{"categories":6180},[150],{"categories":6182},[153],{"categories":6184},[1841],{"categories":6186},[320],{"categories":6188},[153],{"categories":6190},[],{"categories":6192},[150],{"categories":6194},[],{"categories":6196},[198],{"categories":6198},[163],{"categories":6200},[250],{"categories":6202},[150],{"categories":6204},[150],{"categories":6206},[150],{"categories":6208},[198],{"categories":6210},[],{"categories":6212},[163],{"categories":6214},[150],{"categories":6216},[163],{"categories":6218},[163],{"categories":6220},[],{"categories":6222},[150],{"categories":6224},[],{"categories":6226},[198],{"categories":6228},[153],{"categories":6230},[250],{"categories":6232},[150],{"categories":6234},[163],{"categories":6236},[198],{"categories":6238},[150],{"categories":6240},[198],{"categories":6242},[],{"categories":6244},[198],{"categories":6246},[150],{"categories":6248},[153],{"categories":6250},[493],{"categories":6252},[163],{"categories":6254},[150],{"categories":6256},[],{"categories":6258},[126],{"categories":6260},[163],{"categories":6262},[166],{"categories":6264},[163],{"categories":6266},[153],{"categories":6268},[150],{"categories":6270},[150],{"categories":6272},[],{"categories":6274},[],{"categories":6276},[],{"categories":6278},[250],{"categories":6280},[150],{"categories":6282},[163],{"categories":6284},[150],{"categories":6286},[150],{"categories":6288},[],{"categories":6290},[],{"categories":6292},[],{"categories":6294},[150],{"categories":6296},[163],{"categories":6298},[250],{"categories":6300},[150],{"categories":6302},[],{"categories":6304},[163],{"categories":6306},[150],{"categories":6308},[150],{"categories":6310},[153],{"categories":6312},[],{"categories":6314},[],{"categories":6316},[150],{"categories":6318},[150],{"categories":6320},[163],{"categories":6322},[250],{"categories":6324},[150],{"categories":6326},[198],{"categories":6328},[],{"categories":6330},[150],{"categories":6332},[150],{"categories":6334},[279],{"categories":6336},[198],{"categories":6338},[279],{"categories":6340},[201],{"categories":6342},[150],{"categories":6344},[150],{"categories":6346},[],{"categories":6348},[],{"categories":6350},[163],{"categories":6352},[],{"categories":6354},[150],{"categories":6356},[493],{"categories":6358},[150],{"categories":6360},[150],{"categories":6362},[150],{"categories":6364},[150],{"categories":6366},[],{"categories":6368},[163],{"categories":6370},[150],{"categories":6372},[150],{"categories":6374},[],{"categories":6376},[163],{"categories":6378},[150],{"categories":6380},[198],{"categories":6382},[150],{"categories":6384},[279],{"categories":6386},[158],{"categories":6388},[166],{"categories":6390},[150],{"categories":6392},[150],{"categories":6394},[163],{"categories":6396},[201],{"categories":6398},[163],{"categories":6400},[163],{"categories":6402},[],{"categories":6404},[150],{"categories":6406},[163],{"categories":6408},[],{"categories":6410},[150],{"categories":6412},[],{"categories":6414},[198],{"categories":6416},[158],{"categories":6418},[],{"categories":6420},[150],{"categories":6422},[150],{"categories":6424},[150],{"categories":6426},[],{"categories":6428},[163],{"categories":6430},[250],{"categories":6432},[153],{"categories":6434},[150],{"categories":6436},[],{"categories":6438},[158],{"categories":6440},[279],{"categories":6442},[150],{"categories":6444},[126],{"categories":6446},[153],{"categories":6448},[201],{"categories":6450},[158],{"categories":6452},[126],{"categories":6454},[163],{"categories":6456},[126],{"categories":6458},[],{"categories":6460},[150],{"categories":6462},[166],{"categories":6464},[150],{"categories":6466},[],{"categories":6468},[163],{"categories":6470},[153],{"categories":6472},[250],{"categories":6474},[150],{"categories":6476},[153],{"categories":6478},[163],{"categories":6480},[320],{"categories":6482},[150],{"categories":6484},[150],{"categories":6486},[150],{"categories":6488},[150],{"categories":6490},[150],{"categories":6492},[153],{"categories":6494},[150],{"categories":6496},[126],{"categories":6498},[201],{"categories":6500},[163],{"categories":6502},[],{"categories":6504},[150],{"categories":6506},[150],{"categories":6508},[150],{"categories":6510},[126],{"categories":6512},[163],{"categories":6514},[198],{"categories":6516},[126],{"categories":6518},[150],{"categories":6520},[166],{"categories":6522},[],{"categories":6524},[250],{"categories":6526},[126],{"categories":6528},[198],{"categories":6530},[150],{"categories":6532},[153],{"categories":6534},[163],{"categories":6536},[150],{"categories":6538},[150],{"categories":6540},[163],{"categories":6542},[166],{"categories":6544},[150],{"categories":6546},[163],{"categories":6548},[150],{"categories":6550},[158],{"categories":6552},[163],{"categories":6554},[163,320],{"categories":6556},[150],{"categories":6558},[150],{"categories":6560},[163],{"categories":6562},[126],{"categories":6564},[150],{"categories":6566},[150],{"categories":6568},[201],{"categories":6570},[163],{"categories":6572},[279],{"categories":6574},[163],{"categories":6576},[158],{"categories":6578},[],{"categories":6580},[163],{"categories":6582},[150],{"categories":6584},[158],{"categories":6586},[],{"categories":6588},[],{"categories":6590},[126],{"categories":6592},[150],{"categories":6594},[150],{"categories":6596},[163],{"categories":6598},[201],{"categories":6600},[279],{"categories":6602},[150],{"categories":6604},[150],{"categories":6606},[150],{"categories":6608},[163],{"categories":6610},[],{"categories":6612},[163],{"categories":6614},[198],{"categories":6616},[150],{"categories":6618},[163],{"categories":6620},[163],{"categories":6622},[150],{"categories":6624},[],{"categories":6626},[198],{"categories":6628},[126],{"categories":6630},[3184],{"categories":6632},[153],{"categories":6634},[126],{"categories":6636},[150],{"categories":6638},[163],{"categories":6640},[150],{"categories":6642},[150],{"categories":6644},[279],{"categories":6646},[126],{"categories":6648},[201],{"categories":6650},[],{"categories":6652},[198],{"categories":6654},[150],{"categories":6656},[150],{"categories":6658},[],{"categories":6660},[163],{"categories":6662},[150],{"categories":6664},[150],{"categories":6666},[150],{"categories":6668},[150],{"categories":6670},[163],{"categories":6672},[150],{"categories":6674},[150],{"categories":6676},[150],{"categories":6678},[166],{"categories":6680},[150],{"categories":6682},[163],{"categories":6684},[150],{"categories":6686},[150],{"categories":6688},[150],{"categories":6690},[150],{"categories":6692},[150],{"categories":6694},[150],{"categories":6696},[150],{"categories":6698},[158],{"categories":6700},[],{"categories":6702},[166],{"categories":6704},[198],{"categories":6706},[163],{"categories":6708},[150],{"categories":6710},[126],{"categories":6712},[],{"categories":6714},[126],{"categories":6716},[126],{"categories":6718},[163],{"categories":6720},[126],{"categories":6722},[150],{"categories":6724},[150],{"categories":6726},[150],{"categories":6728},[163],{"categories":6730},[126],{"categories":6732},[150],{"categories":6734},[150],{"categories":6736},[150],{"categories":6738},[163],{"categories":6740},[198],{"categories":6742},[150],{"categories":6744},[150],{"categories":6746},[150],{"categories":6748},[158],{"categories":6750},[150],{"categories":6752},[163],{"categories":6754},[250],{"categories":6756},[],{"categories":6758},[150],{"categories":6760},[201],{"categories":6762},[150],{"categories":6764},[163],{"categories":6766},[150],{"categories":6768},[150],{"categories":6770},[],{"categories":6772},[150],{"categories":6774},[150],{"categories":6776},[198],{"categories":6778},[150],{"categories":6780},[150],{"categories":6782},[163],{"categories":6784},[279],{"categories":6786},[],{"categories":6788},[],{"categories":6790},[126],{"categories":6792},[150],{"categories":6794},[150],{"categories":6796},[198],{"categories":6798},[150],{"categories":6800},[126],{"categories":6802},[198],{"categories":6804},[150],{"categories":6806},[150],{"categories":6808},[279],{"categories":6810},[201],{"categories":6812},[150],{"categories":6814},[150],{"categories":6816},[153],{"categories":6818},[163],{"categories":6820},[150],{"categories":6822},[150],{"categories":6824},[163],{"categories":6826},[158],{"categories":6828},[163],{"categories":6830},[126],{"categories":6832},[150],{"categories":6834},[158],{"categories":6836},[],{"categories":6838},[150],{"categories":6840},[201],{"categories":6842},[150],{"categories":6844},[150],{"categories":6846},[],{"categories":6848},[198],{"categories":6850},[150],{"categories":6852},[163],{"categories":6854},[201],{"categories":6856},[150],{"categories":6858},[126],{"categories":6860},[126],{"categories":6862},[126],{"categories":6864},[150],{"categories":6866},[163],{"categories":6868},[163],{"categories":6870},[150],{"categories":6872},[163],{"categories":6874},[150],{"categories":6876},[150],{"categories":6878},[250],{"categories":6880},[201],{"categories":6882},[201],{"categories":6884},[],{"categories":6886},[198],{"categories":6888},[150],{"categories":6890},[150],{"categories":6892},[126],{"categories":6894},[],{"categories":6896},[198],{"categories":6898},[198],{"categories":6900},[198],{"categories":6902},[],{"categories":6904},[163],{"categories":6906},[150],{"categories":6908},[],{"categories":6910},[153],{"categories":6912},[158],{"categories":6914},[],{"categories":6916},[150],{"categories":6918},[150],{"categories":6920},[],{"categories":6922},[126],{"categories":6924},[],{"categories":6926},[],{"categories":6928},[],{"categories":6930},[],{"categories":6932},[150],{"categories":6934},[198],{"categories":6936},[],{"categories":6938},[],{"categories":6940},[150],{"categories":6942},[150],{"categories":6944},[150],{"categories":6946},[201],{"categories":6948},[150],{"categories":6950},[201],{"categories":6952},[],{"categories":6954},[201],{"categories":6956},[201],{"categories":6958},[320],{"categories":6960},[163],{"categories":6962},[126],{"categories":6964},[],{"categories":6966},[],{"categories":6968},[201],{"categories":6970},[126],{"categories":6972},[126],{"categories":6974},[126],{"categories":6976},[],{"categories":6978},[153],{"categories":6980},[126],{"categories":6982},[126],{"categories":6984},[153],{"categories":6986},[126],{"categories":6988},[158],{"categories":6990},[126],{"categories":6992},[126],{"categories":6994},[126],{"categories":6996},[201],{"categories":6998},[198],{"categories":7000},[198],{"categories":7002},[150],{"categories":7004},[126],{"categories":7006},[201],{"categories":7008},[320],{"categories":7010},[201],{"categories":7012},[201],{"categories":7014},[201],{"categories":7016},[],{"categories":7018},[158],{"categories":7020},[],{"categories":7022},[320],{"categories":7024},[126],{"categories":7026},[126],{"categories":7028},[126],{"categories":7030},[163],{"categories":7032},[198,158],{"categories":7034},[201],{"categories":7036},[],{"categories":7038},[],{"categories":7040},[201],{"categories":7042},[],{"categories":7044},[201],{"categories":7046},[198],{"categories":7048},[163],{"categories":7050},[],{"categories":7052},[126],{"categories":7054},[150],{"categories":7056},[250],{"categories":7058},[],{"categories":7060},[150],{"categories":7062},[],{"categories":7064},[198],{"categories":7066},[153],{"categories":7068},[201],{"categories":7070},[],{"categories":7072},[126],{"categories":7074},[198],[7076,7148,7247,7491],{"id":7077,"title":7078,"ai":7079,"body":7084,"categories":7113,"created_at":127,"date_modified":127,"description":119,"extension":128,"faq":127,"featured":129,"kicker_label":127,"meta":7114,"navigation":131,"path":7135,"published_at":7136,"question":127,"scraped_at":7137,"seo":7138,"sitemap":7139,"source_id":7140,"source_name":7141,"source_type":138,"source_url":7142,"stem":7143,"tags":7144,"thumbnail_url":127,"tldr":7145,"tweet":127,"unknown_tags":7146,"__hash__":7147},"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":7080,"output_tokens":7081,"processing_time_ms":7082,"cost_usd":7083},8371,1988,22871,0.00264555,{"type":14,"value":7085,"toc":7109},[7086,7090,7093,7096,7099,7103,7106],[17,7087,7089],{"id":7088},"throughput-design-hides-latency-with-massive-parallelism","Throughput Design Hides Latency with Massive Parallelism",[22,7091,7092],{},"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.",[22,7094,7095],{},"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).",[22,7097,7098],{},"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%.",[17,7100,7102],{"id":7101},"six-tier-memory-hierarchy-sets-bandwidth-bounds","Six-Tier Memory Hierarchy Sets Bandwidth Bounds",[22,7104,7105],{},"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.",[22,7107,7108],{},"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":119,"searchDepth":120,"depth":120,"links":7110},[7111,7112],{"id":7088,"depth":120,"text":7089},{"id":7101,"depth":120,"text":7102},[150],{"content_references":7115,"triage":7130},[7116,7121,7125],{"type":7117,"title":7118,"author":7119,"context":7120},"paper","FlashAttention-3","Shah et al.","cited",{"type":7117,"title":7122,"author":7123,"publisher":7124,"context":7120},"Microbenchmarks of the Hopper architecture","Luo et al.","2025",{"type":7126,"title":7127,"author":7128,"context":7129},"other","NVIDIA’s Hopper architecture documentation","NVIDIA","mentioned",{"relevance":7131,"novelty":7131,"quality":7132,"actionability":120,"composite":7133,"reasoning":7134},3,4,3.05,"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":7078,"description":119},{"loc":7135},"0d1957d00ad6e7e2","Towards AI","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",[143,142],"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":7149,"title":7150,"ai":7151,"body":7156,"categories":7235,"created_at":127,"date_modified":127,"description":119,"extension":128,"faq":127,"featured":129,"kicker_label":127,"meta":7236,"navigation":131,"path":7237,"published_at":7238,"question":127,"scraped_at":127,"seo":7239,"sitemap":7240,"source_id":7241,"source_name":7141,"source_type":138,"source_url":139,"stem":7242,"tags":7243,"thumbnail_url":127,"tldr":7244,"tweet":127,"unknown_tags":7245,"__hash__":7246},"summaries\u002Fsummaries\u002Fword2vec-turning-word-neighborhoods-into-embedding-summary.md","Word2Vec: Turning Word Neighborhoods into Embeddings",{"provider":7,"model":8,"input_tokens":7152,"output_tokens":7153,"processing_time_ms":7154,"cost_usd":7155},8588,1873,21956,0.0026316,{"type":14,"value":7157,"toc":7229},[7158,7162,7178,7181,7185,7192,7195,7206,7210,7213,7216,7219,7223,7226],[17,7159,7161],{"id":7160},"shift-from-isolated-ids-to-relational-embeddings","Shift from Isolated IDs to Relational Embeddings",[22,7163,7164,7165,7169,7170,7173,7174,7177],{},"Before Word2Vec, words were treated as unique IDs or one-hot vectors (e.g., cat → ",[7166,7167,7168],"span",{},"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 ",[7166,7171,7172],{},"0.82, 0.21, -0.05"," and dog ",[7166,7175,7176],{},"0.79, 0.25, -0.03"," into nearby regions, enabling geometric analogies like king - man + woman ≈ queen.",[22,7179,7180],{},"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.",[17,7182,7184],{"id":7183},"cbow-vs-skip-gram-dual-paths-to-context-prediction","CBOW vs Skip-gram: Dual Paths to Context Prediction",[22,7186,7187,7188,7191],{},"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 ",[7166,7189,7190],{},"0.11, -0.08, 0.05",") gets refined.",[22,7193,7194],{},"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.",[22,7196,7197,7198,7201,7202,7205],{},"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 ",[7166,7199,7200],{},"0.10, 0.88, -0.12"," clusters near water ",[7166,7203,7204],{},"0.07, 0.84, -0.10",".",[17,7207,7209],{"id":7208},"training-mechanics-gradients-sculpt-the-space","Training Mechanics: Gradients Sculpt the Space",[22,7211,7212],{},"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'.",[22,7214,7215],{},"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.",[22,7217,7218],{},"Outcome: random initials become relational map. Training builds it via 'enormous tiny corrections'; full process turns prediction errors into stable positions.",[17,7220,7222],{"id":7221},"inference-and-limitations-in-modern-context","Inference and Limitations in Modern Context",[22,7224,7225],{},"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.",[22,7227,7228],{},"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":119,"searchDepth":120,"depth":120,"links":7230},[7231,7232,7233,7234],{"id":7160,"depth":120,"text":7161},{"id":7183,"depth":120,"text":7184},{"id":7208,"depth":120,"text":7209},{"id":7221,"depth":120,"text":7222},[],{},"\u002Fsummaries\u002Fword2vec-turning-word-neighborhoods-into-embedding-summary","2026-04-08 21:21:21",{"title":7150,"description":119},{"loc":7237},"2165d09f4254bef0","summaries\u002Fword2vec-turning-word-neighborhoods-into-embedding-summary",[143,142],"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",{"id":7248,"title":7249,"ai":7250,"body":7255,"categories":7479,"created_at":127,"date_modified":127,"description":119,"extension":128,"faq":127,"featured":129,"kicker_label":127,"meta":7480,"navigation":131,"path":7481,"published_at":133,"question":127,"scraped_at":127,"seo":7482,"sitemap":7483,"source_id":7484,"source_name":137,"source_type":138,"source_url":139,"stem":7485,"tags":7486,"thumbnail_url":127,"tldr":7488,"tweet":127,"unknown_tags":7489,"__hash__":7490},"summaries\u002Fsummaries\u002Fbatch-gemms-for-fast-lstm-in-torch-summary.md","Batch GEMMs for Fast LSTM in Torch",{"provider":7,"model":8,"input_tokens":7251,"output_tokens":7252,"processing_time_ms":7253,"cost_usd":7254},4084,1694,14015,0.00164115,{"type":14,"value":7256,"toc":7474},[7257,7261,7264,7274,7278,7281,7324,7327,7459,7463,7470],[17,7258,7260],{"id":7259},"batch-gemms-to-cut-lstm-overhead","Batch GEMMs to Cut LSTM Overhead",[22,7262,7263],{},"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).",[22,7265,7266,7267,7270,7271,7205],{},"Usage: ",[26,7268,7269],{},"m = LSTM.fast_lstm(input_size, rnn_size)"," returns gModule({x, prev_c, prev_h}, {next_c, next_h}). Feed sequences by unrolling: ",[26,7272,7273],{},"for t=1,T do h,c = m:forward({x[t], c, h}) end",[17,7275,7277],{"id":7276},"gate-computation-graph","Gate Computation Graph",[22,7279,7280],{},"Builds nn.gModule with:",[57,7282,7283,7297,7304,7311,7318],{},[60,7284,7285,7288,7289,7292,7293,7296],{},[26,7286,7287],{},"i2h = nn.Linear(input_size, 4*rnn_size)(x)"," + ",[26,7290,7291],{},"h2h = nn.Linear(rnn_size, 4*rnn_size)(prev_h)"," → ",[26,7294,7295],{},"all_input_sums = nn.CAddTable()({i2h, h2h})"," (batched gates).",[60,7298,7299,7300,7303],{},"Sigmoid chunk: ",[26,7301,7302],{},"nn.Narrow(2,1,3*rnn_size)(all_input_sums)"," → gates i,f,o.",[60,7305,7306,7307,7310],{},"Input transform: ",[26,7308,7309],{},"nn.Narrow(2,3*rnn_size+1,rnn_size)(all_input_sums)"," → tanh(c~).",[60,7312,7313,7314,7317],{},"Cell: ",[26,7315,7316],{},"next_c = forget_gate ⊙ prev_c + in_gate ⊙ c~"," (CMulTable + CAddTable).",[60,7319,7320,7321,7205],{},"Hidden: ",[26,7322,7323],{},"next_h = out_gate ⊙ tanh(next_c)",[22,7325,7326],{},"Full code:",[7328,7329,7333],"pre",{"className":7330,"code":7331,"language":7332,"meta":119,"style":119},"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",[26,7334,7335,7342,7347,7352,7357,7363,7369,7375,7381,7387,7393,7399,7405,7411,7417,7423,7429,7435,7441,7447,7453],{"__ignoreMap":119},[7166,7336,7339],{"class":7337,"line":7338},"line",1,[7166,7340,7341],{},"function LSTM.fast_lstm(input_size, rnn_size)\n",[7166,7343,7344],{"class":7337,"line":120},[7166,7345,7346],{},"  local x = nn.Identity()()\n",[7166,7348,7349],{"class":7337,"line":7131},[7166,7350,7351],{},"  local prev_c = nn.Identity()()\n",[7166,7353,7354],{"class":7337,"line":7132},[7166,7355,7356],{},"  local prev_h = nn.Identity()()\n",[7166,7358,7360],{"class":7337,"line":7359},5,[7166,7361,7362],{},"  local i2h = nn.Linear(input_size, 4 * rnn_size)(x)\n",[7166,7364,7366],{"class":7337,"line":7365},6,[7166,7367,7368],{},"  local h2h = nn.Linear(rnn_size, 4 * rnn_size)(prev_h)\n",[7166,7370,7372],{"class":7337,"line":7371},7,[7166,7373,7374],{},"  local all_input_sums = nn.CAddTable()({i2h, h2h})\n",[7166,7376,7378],{"class":7337,"line":7377},8,[7166,7379,7380],{},"  local sigmoid_chunk = nn.Narrow(2, 1, 3 * rnn_size)(all_input_sums)\n",[7166,7382,7384],{"class":7337,"line":7383},9,[7166,7385,7386],{},"  sigmoid_chunk = nn.Sigmoid()(sigmoid_chunk)\n",[7166,7388,7390],{"class":7337,"line":7389},10,[7166,7391,7392],{},"  local in_gate = nn.Narrow(2, 1, rnn_size)(sigmoid_chunk)\n",[7166,7394,7396],{"class":7337,"line":7395},11,[7166,7397,7398],{},"  local forget_gate = nn.Narrow(2, rnn_size + 1, rnn_size)(sigmoid_chunk)\n",[7166,7400,7402],{"class":7337,"line":7401},12,[7166,7403,7404],{},"  local out_gate = nn.Narrow(2, 2 * rnn_size + 1, rnn_size)(sigmoid_chunk)\n",[7166,7406,7408],{"class":7337,"line":7407},13,[7166,7409,7410],{},"  local in_transform = nn.Narrow(2, 3 * rnn_size + 1, rnn_size)(all_input_sums)\n",[7166,7412,7414],{"class":7337,"line":7413},14,[7166,7415,7416],{},"  in_transform = nn.Tanh()(in_transform)\n",[7166,7418,7420],{"class":7337,"line":7419},15,[7166,7421,7422],{},"  local next_c = nn.CAddTable()({\n",[7166,7424,7426],{"class":7337,"line":7425},16,[7166,7427,7428],{},"    nn.CMulTable()({forget_gate, prev_c}),\n",[7166,7430,7432],{"class":7337,"line":7431},17,[7166,7433,7434],{},"    nn.CMulTable()({in_gate, in_transform})\n",[7166,7436,7438],{"class":7337,"line":7437},18,[7166,7439,7440],{},"  })\n",[7166,7442,7444],{"class":7337,"line":7443},19,[7166,7445,7446],{},"  local next_h = nn.CMulTable()({out_gate, nn.Tanh()(next_c)})\n",[7166,7448,7450],{"class":7337,"line":7449},20,[7166,7451,7452],{},"  return nn.gModule({x, prev_c, prev_h}, {next_c, next_h})\n",[7166,7454,7456],{"class":7337,"line":7455},21,[7166,7457,7458],{},"end\n",[17,7460,7462],{"id":7461},"production-notes","Production Notes",[22,7464,7465,7466,7469],{},"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 ",[26,7467,7468],{},"bias=False"," + fused CUDA kernels (faster still). Port to Flux.jl or JAX for today, but graph fusion principle endures for custom RNNs.",[7471,7472,7473],"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":119,"searchDepth":120,"depth":120,"links":7475},[7476,7477,7478],{"id":7259,"depth":120,"text":7260},{"id":7276,"depth":120,"text":7277},{"id":7461,"depth":120,"text":7462},[126],{},"\u002Fsummaries\u002Fbatch-gemms-for-fast-lstm-in-torch-summary",{"title":7249,"description":119},{"loc":7481},"787da8618ae52246","summaries\u002Fbatch-gemms-for-fast-lstm-in-torch-summary",[143,142,7487],"coding","Fuse LSTM operations into nngraph module to batch 4 GEMMs, slashing overhead vs standard nn.LSTM (optimized by @jcjohnson).",[],"sB5VUvtL1vpsXKZbRH6Tr09LD-FOtuL5SeiLauwvqEI",{"id":7492,"title":7493,"ai":7494,"body":7499,"categories":7549,"created_at":127,"date_modified":127,"description":119,"extension":128,"faq":127,"featured":129,"kicker_label":127,"meta":7550,"navigation":131,"path":7551,"published_at":133,"question":127,"scraped_at":127,"seo":7552,"sitemap":7553,"source_id":7554,"source_name":137,"source_type":138,"source_url":139,"stem":7555,"tags":7556,"thumbnail_url":127,"tldr":7558,"tweet":127,"unknown_tags":7559,"__hash__":7560},"summaries\u002Fsummaries\u002Fpolicy-gradients-for-pong-100-line-rl-agent-summary.md","Policy Gradients for Pong: 100-Line RL Agent",{"provider":7,"model":8,"input_tokens":7495,"output_tokens":7496,"processing_time_ms":7497,"cost_usd":7498},12952,1480,13868,0.00286,{"type":14,"value":7500,"toc":7543},[7501,7505,7508,7511,7515,7522,7526,7533,7537,7540],[17,7502,7504],{"id":7503},"network-architecture-and-forwardbackward-passes","Network Architecture and Forward\u002FBackward Passes",[22,7506,7507],{},"Build a fully connected policy network with 200 ReLU hidden units: input is 80x80=6400D (binary diff frame), W1 (200x6400 Xavier init), ReLU, W2 (200x1), sigmoid for P(UP=action 2). Forward: h = ReLU(W1 @ x), p = sigmoid(W2 @ h). Sample action stochastically: UP if uniform() \u003C p else DOWN.",[22,7509,7510],{},"Backward computes policy gradient analytically. For episode: stack epx (inputs), eph (hiddens), epdlogp (y - p where y=1 for UP). dW2 = eph.T @ epdlogp. dh = epdlogp.outer(W2), zero ReLU grads (eph\u003C=0), dW1 = dh.T @ epx. Accumulate into grad_buffer over batch_size=10 episodes.",[17,7512,7514],{"id":7513},"image-preprocessing-for-atari-pong","Image Preprocessing for Atari Pong",[22,7516,7517,7518,7521],{},"Transform 210x160x3 uint8 frame: crop top\u002Fbottom to 160x80 (35:195), downsample 2x to 80x80 grayscale (I",[7166,7519,7520],{},"::2,::2,0","), binarize (set bg 144\u002F109=0, else=1), flatten to 6400D float. Use difference frames x = cur_x - prev_x (motion highlights ball\u002Fpaddles, zeros static bg). This reduces noise, enables end-to-end from pixels.",[17,7523,7525],{"id":7524},"reward-discounting-and-advantage-normalization","Reward Discounting and Advantage Normalization",[22,7527,7528,7529,7532],{},"Pong rewards: +1 win, -1 lose (sparse, at episode end). For trajectory drs: discount backwards with gamma=0.99, reset running sum at r",[7166,7530,7531],{},"t","!=0 (game boundaries). Standardize discounted_epr to mean=0, std=1 (controls gradient variance). Modulate: epdlogp *= discounted_epr (REINFORCE: grad log pi(a|s) * advantage).",[17,7534,7536],{"id":7535},"training-loop-and-optimization","Training Loop and Optimization",[22,7538,7539],{},"OpenAI Gym Pong-v0. Loop: prepro obs, forward policy, sample\u002Fact, record x\u002Fh\u002Fdlogp\u002Fr. On done: compute discounted\u002Fcentered advantages, backward, add to grad_buffer. Every 10 eps: RMSProp update (decay=0.99, lr=1e-4): g \u002F (sqrt(rms_cache) + 1e-5), reset buffer. Track running_reward (EWMA 0.99), save model every 100 eps. Render optional. Resume from save.p.",[22,7541,7542],{},"Prints episode rewards; agent learns to beat random policy quickly, human-level after ~1-2hr CPU (per blog link in comments).",{"title":119,"searchDepth":120,"depth":120,"links":7544},[7545,7546,7547,7548],{"id":7503,"depth":120,"text":7504},{"id":7513,"depth":120,"text":7514},{"id":7524,"depth":120,"text":7525},{"id":7535,"depth":120,"text":7536},[126],{},"\u002Fsummaries\u002Fpolicy-gradients-for-pong-100-line-rl-agent-summary",{"title":7493,"description":119},{"loc":7551},"7c1c3951efe2f58d","summaries\u002Fpolicy-gradients-for-pong-100-line-rl-agent-summary",[7557,143,142],"python","Train a 2-layer NN to play Atari Pong from raw pixels using REINFORCE policy gradients. Uses 80x80 binary diff frames, discounts rewards with gamma=0.99, standardizes advantages, RMSProp updates every 10 episodes. Converges on CPU in hours.",[],"6XMa-na9tAra5BBDuY7gL83XBVHrWa_0VpHOibbLrNQ"]