[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-134fdbf89b4cbe09-rethinking-uncertainty-evaluation-in-llms-summary":3,"summaries-facets-categories":99,"summary-related-134fdbf89b4cbe09-rethinking-uncertainty-evaluation-in-llms-summary":5861},{"id":4,"title":5,"ai":6,"body":13,"categories":64,"created_at":66,"date_modified":66,"description":59,"extension":67,"faq":66,"featured":68,"kicker_label":66,"meta":69,"navigation":83,"path":84,"published_at":85,"question":66,"scraped_at":85,"seo":86,"sitemap":87,"source_id":88,"source_name":89,"source_type":90,"source_url":76,"stem":91,"tags":92,"thumbnail_url":66,"tldr":96,"tweet":66,"unknown_tags":97,"__hash__":98},"summaries\u002Fsummaries\u002F134fdbf89b4cbe09-rethinking-uncertainty-evaluation-in-llms-summary.md","Rethinking Uncertainty Evaluation in LLMs",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",4011,412,2633,0.00162075,{"type":14,"value":15,"toc":58},"minimark",[16,21,25,29,32,55],[17,18,20],"h2",{"id":19},"the-flaws-in-current-uncertainty-metrics","The Flaws in Current Uncertainty Metrics",[22,23,24],"p",{},"Standard approaches to measuring uncertainty in Large Language Models (LLMs)—such as simple log-probability analysis or basic confidence scoring—frequently fail to capture the nuance of model hallucinations or reasoning failures. The research highlights that these metrics often lack correlation with actual model accuracy in complex, multi-step tasks. Because LLMs are autoregressive, local token-level confidence does not reliably translate to global semantic correctness, leading to overconfident outputs even when the model is factually incorrect.",[17,26,28],{"id":27},"toward-robust-calibration-and-evaluation","Toward Robust Calibration and Evaluation",[22,30,31],{},"The paper argues for a transition from static confidence scores to dynamic, context-aware uncertainty evaluation. This involves moving beyond simple probability distributions to incorporate:",[33,34,35,43,49],"ul",{},[36,37,38,42],"li",{},[39,40,41],"strong",{},"Semantic Consistency:"," Measuring whether the model provides the same answer across multiple perturbed prompts or sampling paths.",[36,44,45,48],{},[39,46,47],{},"Calibration Benchmarking:"," Implementing rigorous testing frameworks that treat uncertainty as a first-class citizen, ensuring that when a model expresses 'uncertainty,' it matches the empirical frequency of its errors.",[36,50,51,54],{},[39,52,53],{},"Task-Specific Sensitivity:"," Recognizing that uncertainty manifests differently in creative writing versus logical reasoning or code generation, requiring tailored evaluation strategies rather than a one-size-fits-all metric.",[22,56,57],{},"By shifting the focus toward these more granular, behavior-based evaluation methods, developers can build more reliable AI systems that better communicate their limitations to end-users.",{"title":59,"searchDepth":60,"depth":60,"links":61},"",2,[62,63],{"id":19,"depth":60,"text":20},{"id":27,"depth":60,"text":28},[65],"AI & LLMs",null,"md",false,{"content_references":70,"triage":78},[71],{"type":72,"title":73,"author":74,"publisher":75,"url":76,"context":77},"paper","Rethinking Uncertainty Evaluation in Large Language Models","Various","arXiv","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.19367","cited",{"relevance":79,"novelty":80,"quality":80,"actionability":79,"composite":81,"reasoning":82},3,4,3.45,"Category: AI & LLMs. The article discusses the limitations of current uncertainty evaluation methods in LLMs, which is relevant to AI engineering. It presents new insights into the need for context-aware calibration metrics, but lacks specific actionable steps for implementation.",true,"\u002Fsummaries\u002F134fdbf89b4cbe09-rethinking-uncertainty-evaluation-in-llms-summary","2026-07-23 17:59:30",{"title":5,"description":59},{"loc":84},"134fdbf89b4cbe09","arXiv cs.AI","article","summaries\u002F134fdbf89b4cbe09-rethinking-uncertainty-evaluation-in-llms-summary",[93,94,95],"llm","machine-learning","research","Current methods for evaluating LLM uncertainty are often misaligned with real-world reliability, necessitating a shift toward more robust, context-aware calibration metrics.",[],"0HtRug1kNfdmCEh2Iqmz7NP_lfghPqd0iTnmyl34W4k",[100,103,106,108,111,114,116,118,120,123,125,127,129,131,134,136,138,140,142,145,147,149,151,153,155,157,159,161,163,165,167,169,171,173,175,177,179,181,183,186,189,191,193,195,197,199,201,203,205,207,209,211,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,249,251,253,255,257,259,261,263,265,267,269,271,273,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,335,337,339,341,343,345,347,349,351,353,356,358,360,362,364,366,368,370,372,374,376,378,380,383,385,387,389,391,393,395,397,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,451,453,455,458,460,462,464,466,468,470,472,474,476,478,480,482,484,487,489,491,493,495,497,499,501,503,505,507,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,554,556,558,560,562,564,566,568,570,572,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,606,608,610,612,614,616,618,620,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,751,753,755,757,759,762,764,766,768,770,772,774,776,778,780,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1040,1042,1044,1046,1048,1050,1052,1054,1056,1058,1060,1062,1064,1066,1068,1070,1072,1074,1076,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1402,1404,1406,1408,1410,1412,1414,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1496,1498,1500,1502,1504,1506,1508,1510,1512,1514,1516,1518,1520,1522,1524,1526,1528,1530,1532,1534,1536,1538,1540,1542,1544,1546,1548,1550,1552,1554,1556,1558,1560,1562,1564,1566,1568,1570,1572,1574,1576,1578,1580,1582,1584,1586,1588,1590,1592,1594,1596,1598,1600,1602,1604,1606,1608,1610,1612,1614,1616,1618,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1778,1780,1782,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1812,1814,1816,1818,1820,1822,1824,1826,1828,1830,1832,1834,1836,1838,1840,1842,1844,1846,1848,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1962,1964,1966,1968,1970,1972,1974,1976,1978,1980,1982,1984,1986,1988,1990,1992,1994,1996,1998,2000,2002,2004,2006,2008,2010,2012,2014,2016,2018,2020,2022,2024,2026,2028,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2122,2124,2126,2128,2130,2132,2134,2136,2138,2140,2142,2144,2146,2148,2150,2152,2154,2156,2158,2160,2162,2164,2166,2168,2170,2172,2174,2176,2178,2180,2182,2184,2186,2188,2190,2192,2194,2196,2198,2200,2202,2204,2206,2208,2210,2212,2214,2216,2218,2220,2222,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2282,2284,2286,2288,2290,2292,2294,2296,2298,2300,2302,2304,2306,2308,2310,2312,2314,2316,2318,2320,2322,2324,2326,2328,2330,2332,2334,2336,2338,2340,2342,2344,2346,2348,2350,2352,2354,2356,2358,2360,2362,2364,2366,2368,2370,2372,2374,2376,2378,2380,2382,2384,2386,2388,2390,2392,2394,2396,2398,2400,2402,2404,2406,2408,2410,2412,2414,2416,2418,2420,2422,2424,2426,2428,2430,2432,2434,2436,2438,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458,2460,2462,2464,2466,2468,2470,2472,2474,2476,2478,2480,2482,2484,2486,2488,2490,2492,2494,2496,2498,2500,2502,2504,2506,2508,2510,2512,2514,2516,2518,2520,2522,2524,2526,2528,2530,2532,2534,2536,2538,2540,2542,2544,2546,2548,2550,2552,2554,2556,2558,2560,2562,2564,2566,2568,2570,2572,2574,2576,2578,2580,2582,2584,2586,2588,2590,2592,2594,2596,2598,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2625,2627,2629,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2700,2702,2704,2706,2708,2710,2712,2714,2716,2718,2720,2722,2724,2726,2728,2730,2732,2734,2736,2738,2740,2742,2744,2746,2748,2750,2752,2754,2756,2758,2760,2762,2764,2766,2768,2770,2772,2774,2776,2778,2780,2782,2784,2786,2788,2790,2792,2794,2796,2798,2800,2802,2804,2806,2808,2810,2812,2814,2816,2818,2820,2822,2824,2826,2828,2830,2832,2834,2836,2838,2840,2842,2844,2846,2848,2850,2852,2854,2856,2858,2860,2862,2864,2866,2868,2870,2872,2874,2876,2878,2880,2882,2884,2886,2888,2890,2892,2894,2896,2898,2900,2902,2904,2906,2908,2910,2912,2914,2916,2918,2920,2922,2924,2926,2928,2930,2932,2934,2936,2938,2940,2942,2944,2946,2948,2950,2952,2954,2956,2958,2960,2962,2964,2966,2968,2970,2972,2974,2976,2978,2980,2982,2984,2986,2988,2990,2992,2994,2996,2998,3000,3002,3004,3006,3008,3010,3012,3014,3016,3018,3020,3022,3024,3026,3028,3030,3032,3034,3036,3038,3040,3042,3044,3046,3048,3050,3052,3054,3056,3058,3060,3062,3064,3066,3068,3070,3072,3074,3076,3078,3080,3082,3084,3086,3088,3090,3092,3094,3096,3098,3100,3102,3104,3106,3108,3110,3112,3114,3116,3118,3120,3122,3124,3126,3128,3130,3132,3134,3136,3138,3140,3142,3144,3146,3148,3150,3152,3154,3156,3158,3160,3162,3164,3166,3168,3170,3172,3174,3176,3178,3180,3182,3184,3186,3188,3190,3192,3194,3196,3198,3200,3202,3204,3206,3208,3210,3212,3214,3216,3218,3220,3222,3224,3226,3228,3230,3232,3234,3236,3238,3240,3242,3244,3246,3248,3250,3252,3254,3256,3258,3260,3262,3264,3266,3268,3270,3272,3274,3276,3278,3280,3282,3284,3286,3288,3290,3292,3294,3296,3298,3300,3302,3304,3306,3308,3310,3312,3314,3316,3318,3320,3322,3324,3326,3328,3330,3332,3334,3336,3338,3340,3342,3344,3346,3348,3350,3352,3354,3356,3358,3360,3362,3364,3366,3368,3370,3372,3374,3376,3378,3380,3382,3384,3386,3388,3390,3392,3394,3396,3398,3400,3402,3404,3406,3408,3410,3412,3414,3416,3418,3420,3422,3424,3426,3428,3430,3432,3434,3436,3438,3440,3442,3444,3446,3448,3450,3452,3454,3456,3458,3460,3462,3464,3466,3468,3470,3472,3474,3476,3478,3480,3482,3484,3486,3488,3490,3492,3494,3496,3498,3500,3502,3504,3506,3508,3510,3512,3514,3516,3518,3520,3522,3524,3526,3528,3530,3532,3534,3536,3538,3540,3542,3544,3546,3548,3550,3552,3554,3556,3558,3560,3562,3564,3566,3568,3570,3572,3574,3576,3578,3580,3582,3584,3586,3588,3590,3592,3594,3596,3598,3600,3602,3604,3606,3608,3610,3612,3614,3616,3618,3620,3622,3624,3626,3628,3630,3632,3634,3636,3638,3640,3642,3644,3646,3648,3650,3652,3654,3656,3658,3660,3662,3664,3666,3668,3670,3672,3674,3676,3678,3680,3682,3684,3686,3688,3690,3692,3694,3696,3698,3700,3702,3704,3706,3708,3710,3712,3714,3716,3718,3720,3722,3724,3726,3728,3730,3732,3734,3736,3738,3740,3742,3744,3746,3748,3750,3752,3754,3756,3758,3760,3762,3764,3766,3768,3770,3773,3775,3777,3779,3781,3783,3785,3787,3789,3791,3793,3795,3797,3799,3801,3803,3805,3807,3809,3811,3813,3815,3817,3819,3821,3823,3825,3827,3829,3831,3833,3835,3837,3839,3841,3843,3845,3847,3849,3851,3853,3855,3857,3859,3861,3863,3865,3867,3869,3871,3873,3875,3877,3879,3881,3883,3885,3887,3889,3891,3893,3895,3897,3899,3901,3903,3905,3907,3909,3911,3913,3915,3917,3919,3921,3923,3925,3927,3929,3931,3933,3935,3937,3939,3941,3943,3945,3947,3949,3951,3953,3955,3957,3959,3961,3963,3965,3967,3969,3971,3973,3975,3977,3979,3981,3983,3985,3987,3989,3991,3993,3995,3997,3999,4001,4003,4005,4007,4009,4011,4013,4015,4017,4019,4021,4023,4025,4027,4029,4031,4033,4035,4037,4039,4041,4043,4045,4047,4049,4051,4053,4055,4057,4059,4061,4063,4065,4067,4069,4071,4073,4075,4077,4079,4081,4083,4085,4087,4089,4091,4093,4095,4097,4099,4101,4103,4105,4107,4109,4111,4113,4115,4117,4119,4121,4123,4125,4127,4129,4131,4133,4135,4137,4139,4141,4143,4145,4147,4149,4151,4153,4155,4157,4159,4161,4163,4165,4167,4169,4171,4173,4175,4177,4179,4181,4183,4185,4187,4189,4191,4193,4195,4197,4199,4201,4203,4205,4207,4209,4211,4213,4215,4217,4219,4221,4223,4225,4227,4229,4231,4233,4235,4237,4239,4241,4243,4245,4247,4249,4251,4253,4255,4257,4259,4261,4263,4265,4267,4269,4271,4273,4275,4277,4279,4281,4283,4285,4287,4289,4291,4293,4295,4297,4299,4301,4303,4305,4307,4309,4311,4313,4315,4317,4319,4321,4323,4325,4327,4329,4331,4333,4335,4337,4339,4341,4343,4345,4347,4349,4351,4353,4355,4357,4359,4361,4363,4365,4367,4369,4371,4373,4375,4377,4379,4381,4383,4385,4387,4389,4391,4393,4395,4397,4399,4401,4403,4405,4407,4409,4411,4413,4415,4417,4419,4421,4423,4425,4427,4429,4431,4433,4435,4437,4439,4441,4443,4445,4447,4449,4451,4453,4455,4457,4459,4461,4463,4465,4467,4469,4471,4473,4475,4477,4479,4481,4483,4485,4487,4489,4491,4493,4495,4497,4500,4502,4504,4506,4508,4510,4512,4514,4516,4518,4520,4522,4524,4526,4528,4530,4532,4534,4536,4538,4540,4542,4544,4546,4548,4550,4552,4554,4556,4558,4560,4562,4564,4566,4568,4570,4572,4574,4576,4578,4580,4582,4584,4586,4588,4590,4592,4594,4596,4598,4600,4602,4604,4606,4608,4610,4612,4614,4616,4618,4620,4622,4624,4626,4628,4630,4632,4634,4636,4638,4640,4642,4644,4646,4648,4650,4652,4654,4656,4658,4660,4662,4665,4667,4669,4671,4673,4675,4677,4679,4681,4683,4685,4687,4689,4691,4693,4695,4697,4699,4701,4703,4705,4707,4709,4711,4713,4715,4717,4719,4721,4723,4725,4727,4729,4731,4733,4735,4737,4739,4741,4743,4745,4747,4749,4751,4753,4755,4757,4759,4761,4763,4765,4767,4769,4771,4773,4775,4777,4779,4781,4783,4785,4787,4789,4791,4793,4795,4797,4799,4801,4803,4805,4807,4809,4811,4813,4815,4817,4819,4821,4823,4825,4827,4829,4831,4833,4835,4837,4839,4841,4843,4845,4847,4849,4851,4853,4855,4857,4859,4861,4863,4865,4867,4869,4871,4873,4875,4877,4879,4881,4883,4885,4887,4889,4891,4893,4895,4897,4899,4901,4903,4905,4907,4909,4911,4913,4915,4917,4919,4921,4923,4925,4927,4929,4931,4933,4935,4937,4939,4941,4943,4945,4947,4949,4951,4953,4955,4957,4959,4961,4963,4965,4967,4969,4971,4973,4975,4977,4979,4981,4983,4985,4987,4989,4991,4993,4995,4997,4999,5001,5003,5005,5007,5009,5011,5013,5015,5017,5019,5021,5023,5025,5027,5029,5031,5033,5035,5037,5039,5041,5043,5045,5047,5049,5051,5053,5055,5057,5059,5061,5063,5065,5067,5069,5071,5073,5075,5077,5079,5081,5083,5085,5087,5089,5091,5093,5095,5097,5099,5101,5103,5105,5107,5109,5111,5113,5115,5117,5119,5121,5123,5125,5127,5129,5131,5133,5135,5137,5139,5141,5143,5145,5147,5149,5151,5153,5155,5157,5159,5161,5163,5165,5167,5169,5171,5173,5175,5177,5179,5181,5183,5185,5187,5189,5191,5193,5195,5197,5199,5201,5203,5205,5207,5209,5211,5213,5215,5217,5219,5221,5223,5225,5227,5229,5231,5233,5235,5237,5239,5241,5243,5245,5247,5249,5251,5253,5255,5257,5259,5261,5263,5265,5267,5269,5271,5273,5275,5277,5279,5281,5283,5285,5287,5289,5291,5293,5295,5297,5299,5301,5303,5305,5307,5309,5311,5313,5315,5317,5319,5321,5323,5325,5327,5329,5331,5333,5335,5337,5339,5341,5343,5345,5347,5349,5351,5353,5355,5357,5359,5361,5363,5365,5367,5369,5371,5373,5375,5377,5379,5381,5383,5385,5387,5389,5391,5393,5395,5397,5399,5401,5403,5405,5407,5409,5411,5413,5415,5417,5419,5421,5423,5425,5427,5429,5431,5433,5435,5437,5439,5441,5443,5445,5447,5449,5451,5453,5455,5457,5459,5461,5463,5465,5467,5469,5471,5473,5475,5477,5479,5481,5483,5485,5487,5489,5491,5493,5495,5497,5499,5501,5503,5505,5507,5509,5511,5513,5515,5517,5519,5521,5523,5525,5527,5529,5531,5533,5535,5537,5539,5541,5543,5545,5547,5549,5551,5553,5555,5557,5559,5561,5563,5565,5567,5569,5571,5573,5575,5577,5579,5581,5583,5585,5587,5589,5591,5593,5595,5597,5599,5601,5603,5605,5607,5609,5611,5613,5615,5617,5619,5621,5623,5625,5627,5629,5631,5633,5635,5637,5639,5641,5643,5645,5647,5649,5651,5653,5655,5657,5659,5661,5663,5665,5667,5669,5671,5673,5675,5677,5679,5681,5683,5685,5687,5689,5691,5693,5695,5697,5699,5701,5703,5705,5707,5709,5711,5713,5715,5717,5719,5721,5723,5725,5727,5729,5731,5733,5735,5737,5739,5741,5743,5745,5747,5749,5751,5753,5755,5757,5759,5761,5763,5765,5767,5769,5771,5773,5775,5777,5779,5781,5783,5785,5787,5789,5791,5793,5795,5797,5799,5801,5803,5805,5807,5809,5811,5813,5815,5817,5819,5821,5823,5825,5827,5829,5831,5833,5835,5837,5839,5841,5843,5845,5847,5849,5851,5853,5855,5857,5859],{"categories":101},[102],"Developer Productivity",{"categories":104},[105],"Business & SaaS",{"categories":107},[65],{"categories":109},[110],"AI Automation",{"categories":112},[113],"Product Strategy",{"categories":115},[65],{"categories":117},[102],{"categories":119},[110],{"categories":121},[122],"Software Engineering",{"categories":124},[65],{"categories":126},[105],{"categories":128},[],{"categories":130},[65],{"categories":132},[133],"Inference & Serving",{"categories":135},[65],{"categories":137},[65],{"categories":139},[110],{"categories":141},[],{"categories":143},[144],"AI News & Trends",{"categories":146},[110],{"categories":148},[65],{"categories":150},[105],{"categories":152},[102],{"categories":154},[65],{"categories":156},[110],{"categories":158},[144],{"categories":160},[110],{"categories":162},[110],{"categories":164},[65],{"categories":166},[110],{"categories":168},[65],{"categories":170},[65],{"categories":172},[65],{"categories":174},[144],{"categories":176},[65],{"categories":178},[65],{"categories":180},[65],{"categories":182},[],{"categories":184},[185],"Design & Frontend",{"categories":187},[188],"Data Science & Visualization",{"categories":190},[144],{"categories":192},[65],{"categories":194},[65],{"categories":196},[65],{"categories":198},[],{"categories":200},[65],{"categories":202},[110],{"categories":204},[122],{"categories":206},[65],{"categories":208},[110],{"categories":210},[65],{"categories":212},[213],"Marketing & Growth",{"categories":215},[185],{"categories":217},[65],{"categories":219},[110],{"categories":221},[65],{"categories":223},[122],{"categories":225},[],{"categories":227},[],{"categories":229},[185],{"categories":231},[65],{"categories":233},[110],{"categories":235},[102],{"categories":237},[122],{"categories":239},[185],{"categories":241},[113],{"categories":243},[65],{"categories":245},[122],{"categories":247},[248],"DevOps & Cloud",{"categories":250},[110],{"categories":252},[113],{"categories":254},[144],{"categories":256},[65],{"categories":258},[],{"categories":260},[65],{"categories":262},[],{"categories":264},[110],{"categories":266},[122],{"categories":268},[],{"categories":270},[122],{"categories":272},[65],{"categories":274},[275],"Governance & Standards",{"categories":277},[105],{"categories":279},[],{"categories":281},[],{"categories":283},[65],{"categories":285},[65],{"categories":287},[110],{"categories":289},[65],{"categories":291},[65],{"categories":293},[110],{"categories":295},[65],{"categories":297},[65],{"categories":299},[65],{"categories":301},[],{"categories":303},[122],{"categories":305},[],{"categories":307},[],{"categories":309},[122],{"categories":311},[],{"categories":313},[122],{"categories":315},[65],{"categories":317},[65],{"categories":319},[213],{"categories":321},[65],{"categories":323},[185],{"categories":325},[185],{"categories":327},[65],{"categories":329},[122],{"categories":331},[110],{"categories":333},[334],"GovTech & Public-Sector Adoption",{"categories":336},[122],{"categories":338},[65],{"categories":340},[65],{"categories":342},[110],{"categories":344},[110],{"categories":346},[188],{"categories":348},[65],{"categories":350},[144],{"categories":352},[110],{"categories":354},[355],"Legal AI Tools",{"categories":357},[110],{"categories":359},[213],{"categories":361},[110],{"categories":363},[113],{"categories":365},[122],{"categories":367},[334],{"categories":369},[],{"categories":371},[110],{"categories":373},[],{"categories":375},[105],{"categories":377},[110],{"categories":379},[110],{"categories":381},[382],"RAG & Retrieval",{"categories":384},[105],{"categories":386},[65],{"categories":388},[122],{"categories":390},[248],{"categories":392},[185],{"categories":394},[65],{"categories":396},[],{"categories":398},[399],"Agents & Orchestration",{"categories":401},[122],{"categories":403},[65],{"categories":405},[],{"categories":407},[110],{"categories":409},[105],{"categories":411},[],{"categories":413},[65],{"categories":415},[],{"categories":417},[102],{"categories":419},[122],{"categories":421},[105],{"categories":423},[65],{"categories":425},[65],{"categories":427},[144],{"categories":429},[65],{"categories":431},[],{"categories":433},[65],{"categories":435},[],{"categories":437},[122],{"categories":439},[65],{"categories":441},[188],{"categories":443},[],{"categories":445},[65],{"categories":447},[185],{"categories":449},[450],"Models & Frontier Labs",{"categories":452},[],{"categories":454},[185],{"categories":456},[457],"Regulation & Governance of AI",{"categories":459},[110],{"categories":461},[],{"categories":463},[65],{"categories":465},[65],{"categories":467},[110],{"categories":469},[144],{"categories":471},[105],{"categories":473},[65],{"categories":475},[],{"categories":477},[122],{"categories":479},[110],{"categories":481},[65],{"categories":483},[113],{"categories":485},[486],"AI Policy & Regulation",{"categories":488},[],{"categories":490},[65],{"categories":492},[113],{"categories":494},[110],{"categories":496},[65],{"categories":498},[65],{"categories":500},[65],{"categories":502},[110],{"categories":504},[],{"categories":506},[188],{"categories":508},[509],"Evals & Reliability",{"categories":511},[65],{"categories":513},[],{"categories":515},[102],{"categories":517},[334],{"categories":519},[486],{"categories":521},[65],{"categories":523},[105],{"categories":525},[65],{"categories":527},[110],{"categories":529},[65],{"categories":531},[110],{"categories":533},[399],{"categories":535},[65],{"categories":537},[122],{"categories":539},[65],{"categories":541},[],{"categories":543},[],{"categories":545},[65],{"categories":547},[334],{"categories":549},[65],{"categories":551},[65],{"categories":553},[],{"categories":555},[185],{"categories":557},[],{"categories":559},[65],{"categories":561},[],{"categories":563},[110],{"categories":565},[65],{"categories":567},[185],{"categories":569},[],{"categories":571},[65],{"categories":573},[110],{"categories":575},[65],{"categories":577},[105],{"categories":579},[110],{"categories":581},[65],{"categories":583},[65],{"categories":585},[122],{"categories":587},[185],{"categories":589},[110],{"categories":591},[],{"categories":593},[122],{"categories":595},[110],{"categories":597},[],{"categories":599},[144],{"categories":601},[],{"categories":603},[65],{"categories":605},[65],{"categories":607},[65],{"categories":609},[105,213],{"categories":611},[],{"categories":613},[65],{"categories":615},[65],{"categories":617},[110],{"categories":619},[],{"categories":621},[],{"categories":623},[65],{"categories":625},[185],{"categories":627},[65],{"categories":629},[],{"categories":631},[65],{"categories":633},[248],{"categories":635},[],{"categories":637},[110],{"categories":639},[144],{"categories":641},[65],{"categories":643},[185],{"categories":645},[],{"categories":647},[144],{"categories":649},[65],{"categories":651},[133],{"categories":653},[65],{"categories":655},[110],{"categories":657},[144],{"categories":659},[450],{"categories":661},[65],{"categories":663},[213],{"categories":665},[],{"categories":667},[110],{"categories":669},[105],{"categories":671},[122],{"categories":673},[65],{"categories":675},[110],{"categories":677},[],{"categories":679},[65,248],{"categories":681},[65],{"categories":683},[65],{"categories":685},[65],{"categories":687},[110],{"categories":689},[65,122],{"categories":691},[188],{"categories":693},[65],{"categories":695},[65],{"categories":697},[122],{"categories":699},[110],{"categories":701},[486],{"categories":703},[213],{"categories":705},[65],{"categories":707},[110],{"categories":709},[65],{"categories":711},[65],{"categories":713},[110],{"categories":715},[],{"categories":717},[65],{"categories":719},[110],{"categories":721},[65],{"categories":723},[65,105],{"categories":725},[105],{"categories":727},[],{"categories":729},[185],{"categories":731},[185],{"categories":733},[65],{"categories":735},[],{"categories":737},[],{"categories":739},[144],{"categories":741},[],{"categories":743},[102],{"categories":745},[65],{"categories":747},[122],{"categories":749},[750],"Generative UI & Design-to-Code",{"categories":752},[65],{"categories":754},[65],{"categories":756},[185],{"categories":758},[65],{"categories":760},[761],"Algorithmic Accountability",{"categories":763},[110],{"categories":765},[122],{"categories":767},[144],{"categories":769},[185],{"categories":771},[],{"categories":773},[65],{"categories":775},[65],{"categories":777},[65],{"categories":779},[110],{"categories":781},[782],"MLOps & Infrastructure",{"categories":784},[65],{"categories":786},[65],{"categories":788},[65],{"categories":790},[65],{"categories":792},[65],{"categories":794},[144],{"categories":796},[102],{"categories":798},[65],{"categories":800},[110],{"categories":802},[248],{"categories":804},[105],{"categories":806},[65],{"categories":808},[185],{"categories":810},[65],{"categories":812},[110],{"categories":814},[],{"categories":816},[],{"categories":818},[133],{"categories":820},[185],{"categories":822},[144],{"categories":824},[188],{"categories":826},[],{"categories":828},[65],{"categories":830},[65],{"categories":832},[105],{"categories":834},[65],{"categories":836},[65],{"categories":838},[65],{"categories":840},[144],{"categories":842},[133],{"categories":844},[65],{"categories":846},[185],{"categories":848},[],{"categories":850},[110],{"categories":852},[122],{"categories":854},[],{"categories":856},[65],{"categories":858},[65],{"categories":860},[110],{"categories":862},[122],{"categories":864},[65],{"categories":866},[188],{"categories":868},[],{"categories":870},[65],{"categories":872},[],{"categories":874},[65],{"categories":876},[],{"categories":878},[113],{"categories":880},[105],{"categories":882},[110],{"categories":884},[110],{"categories":886},[],{"categories":888},[102],{"categories":890},[65],{"categories":892},[105],{"categories":894},[144],{"categories":896},[102],{"categories":898},[],{"categories":900},[65],{"categories":902},[],{"categories":904},[],{"categories":906},[144],{"categories":908},[144],{"categories":910},[],{"categories":912},[399],{"categories":914},[65],{"categories":916},[185],{"categories":918},[122],{"categories":920},[],{"categories":922},[355],{"categories":924},[105],{"categories":926},[],{"categories":928},[],{"categories":930},[102],{"categories":932},[188],{"categories":934},[],{"categories":936},[213],{"categories":938},[110],{"categories":940},[105],{"categories":942},[110],{"categories":944},[105],{"categories":946},[122],{"categories":948},[],{"categories":950},[133],{"categories":952},[113],{"categories":954},[65],{"categories":956},[185],{"categories":958},[122],{"categories":960},[105],{"categories":962},[65],{"categories":964},[110],{"categories":966},[105],{"categories":968},[65],{"categories":970},[65],{"categories":972},[65],{"categories":974},[],{"categories":976},[],{"categories":978},[122],{"categories":980},[188],{"categories":982},[113],{"categories":984},[65],{"categories":986},[110],{"categories":988},[65],{"categories":990},[],{"categories":992},[144],{"categories":994},[113],{"categories":996},[65],{"categories":998},[509],{"categories":1000},[248],{"categories":1002},[],{"categories":1004},[110],{"categories":1006},[],{"categories":1008},[102],{"categories":1010},[],{"categories":1012},[65],{"categories":1014},[65],{"categories":1016},[185],{"categories":1018},[213],{"categories":1020},[122],{"categories":1022},[110],{"categories":1024},[],{"categories":1026},[122],{"categories":1028},[102],{"categories":1030},[],{"categories":1032},[105],{"categories":1034},[144],{"categories":1036},[65,248],{"categories":1038},[1039],"Design Systems for AI",{"categories":1041},[65],{"categories":1043},[144],{"categories":1045},[65],{"categories":1047},[65],{"categories":1049},[105],{"categories":1051},[65],{"categories":1053},[],{"categories":1055},[65],{"categories":1057},[65],{"categories":1059},[105],{"categories":1061},[65],{"categories":1063},[],{"categories":1065},[110],{"categories":1067},[122],{"categories":1069},[122],{"categories":1071},[185],{"categories":1073},[144],{"categories":1075},[188],{"categories":1077},[65],{"categories":1079},[102],{"categories":1081},[486],{"categories":1083},[65],{"categories":1085},[110],{"categories":1087},[65],{"categories":1089},[122],{"categories":1091},[122],{"categories":1093},[],{"categories":1095},[],{"categories":1097},[110],{"categories":1099},[113],{"categories":1101},[],{"categories":1103},[65],{"categories":1105},[],{"categories":1107},[185],{"categories":1109},[110],{"categories":1111},[122],{"categories":1113},[185],{"categories":1115},[65],{"categories":1117},[65],{"categories":1119},[185],{"categories":1121},[],{"categories":1123},[],{"categories":1125},[144],{"categories":1127},[110],{"categories":1129},[110],{"categories":1131},[65],{"categories":1133},[65],{"categories":1135},[65],{"categories":1137},[105],{"categories":1139},[65],{"categories":1141},[65],{"categories":1143},[],{"categories":1145},[122],{"categories":1147},[122],{"categories":1149},[65],{"categories":1151},[122],{"categories":1153},[105],{"categories":1155},[],{"categories":1157},[65],{"categories":1159},[65],{"categories":1161},[65],{"categories":1163},[65],{"categories":1165},[110],{"categories":1167},[102],{"categories":1169},[105],{"categories":1171},[144],{"categories":1173},[110],{"categories":1175},[133],{"categories":1177},[213],{"categories":1179},[65],{"categories":1181},[110],{"categories":1183},[],{"categories":1185},[185],{"categories":1187},[],{"categories":1189},[65],{"categories":1191},[65],{"categories":1193},[],{"categories":1195},[122],{"categories":1197},[105],{"categories":1199},[1200],"Visual & Generative Media",{"categories":1202},[110],{"categories":1204},[],{"categories":1206},[65],{"categories":1208},[65],{"categories":1210},[122],{"categories":1212},[248],{"categories":1214},[188],{"categories":1216},[486],{"categories":1218},[122],{"categories":1220},[213],{"categories":1222},[65],{"categories":1224},[185],{"categories":1226},[65],{"categories":1228},[65],{"categories":1230},[122],{"categories":1232},[110],{"categories":1234},[65],{"categories":1236},[],{"categories":1238},[],{"categories":1240},[110],{"categories":1242},[102],{"categories":1244},[110],{"categories":1246},[450],{"categories":1248},[65],{"categories":1250},[113],{"categories":1252},[105],{"categories":1254},[],{"categories":1256},[65],{"categories":1258},[113],{"categories":1260},[65],{"categories":1262},[65],{"categories":1264},[65],{"categories":1266},[65],{"categories":1268},[65],{"categories":1270},[213],{"categories":1272},[65],{"categories":1274},[399],{"categories":1276},[65],{"categories":1278},[65],{"categories":1280},[65],{"categories":1282},[65],{"categories":1284},[65],{"categories":1286},[185],{"categories":1288},[110],{"categories":1290},[],{"categories":1292},[110],{"categories":1294},[],{"categories":1296},[248],{"categories":1298},[122],{"categories":1300},[],{"categories":1302},[450],{"categories":1304},[110],{"categories":1306},[65],{"categories":1308},[185,65],{"categories":1310},[102],{"categories":1312},[],{"categories":1314},[65],{"categories":1316},[102],{"categories":1318},[1319],"Medical Imaging & Radiology",{"categories":1321},[185],{"categories":1323},[110],{"categories":1325},[122],{"categories":1327},[],{"categories":1329},[65],{"categories":1331},[65],{"categories":1333},[65],{"categories":1335},[],{"categories":1337},[],{"categories":1339},[65],{"categories":1341},[399],{"categories":1343},[65],{"categories":1345},[102],{"categories":1347},[65],{"categories":1349},[65],{"categories":1351},[],{"categories":1353},[110],{"categories":1355},[65],{"categories":1357},[113],{"categories":1359},[122],{"categories":1361},[65],{"categories":1363},[399],{"categories":1365},[65],{"categories":1367},[110],{"categories":1369},[65],{"categories":1371},[185],{"categories":1373},[110],{"categories":1375},[248],{"categories":1377},[185],{"categories":1379},[105],{"categories":1381},[110],{"categories":1383},[65],{"categories":1385},[65],{"categories":1387},[65],{"categories":1389},[65],{"categories":1391},[65],{"categories":1393},[110],{"categories":1395},[122],{"categories":1397},[65],{"categories":1399},[113],{"categories":1401},[],{"categories":1403},[144],{"categories":1405},[],{"categories":1407},[113],{"categories":1409},[110],{"categories":1411},[1039],{"categories":1413},[1039],{"categories":1415},[185],{"categories":1417},[65],{"categories":1419},[65],{"categories":1421},[110],{"categories":1423},[122],{"categories":1425},[185],{"categories":1427},[110],{"categories":1429},[144],{"categories":1431},[],{"categories":1433},[65],{"categories":1435},[],{"categories":1437},[65],{"categories":1439},[65],{"categories":1441},[65],{"categories":1443},[1444],"Contract Review & E-Discovery",{"categories":1446},[185],{"categories":1448},[65],{"categories":1450},[102],{"categories":1452},[144],{"categories":1454},[65],{"categories":1456},[65],{"categories":1458},[213],{"categories":1460},[122],{"categories":1462},[65],{"categories":1464},[65],{"categories":1466},[110],{"categories":1468},[110],{"categories":1470},[761],{"categories":1472},[110],{"categories":1474},[110],{"categories":1476},[65],{"categories":1478},[65],{"categories":1480},[110],{"categories":1482},[65],{"categories":1484},[399],{"categories":1486},[382],{"categories":1488},[65],{"categories":1490},[110],{"categories":1492},[65],{"categories":1494},[1495],"Law-Firm Practice & Adoption",{"categories":1497},[65],{"categories":1499},[110],{"categories":1501},[185],{"categories":1503},[65],{"categories":1505},[65],{"categories":1507},[],{"categories":1509},[],{"categories":1511},[122],{"categories":1513},[],{"categories":1515},[102],{"categories":1517},[248],{"categories":1519},[65],{"categories":1521},[],{"categories":1523},[102],{"categories":1525},[105],{"categories":1527},[65],{"categories":1529},[213],{"categories":1531},[],{"categories":1533},[105],{"categories":1535},[105],{"categories":1537},[],{"categories":1539},[65],{"categories":1541},[65],{"categories":1543},[122],{"categories":1545},[],{"categories":1547},[],{"categories":1549},[],{"categories":1551},[],{"categories":1553},[65],{"categories":1555},[110],{"categories":1557},[248],{"categories":1559},[65],{"categories":1561},[102],{"categories":1563},[122],{"categories":1565},[65],{"categories":1567},[65],{"categories":1569},[122],{"categories":1571},[113],{"categories":1573},[65],{"categories":1575},[782],{"categories":1577},[65],{"categories":1579},[213],{"categories":1581},[122],{"categories":1583},[105],{"categories":1585},[65],{"categories":1587},[65],{"categories":1589},[185],{"categories":1591},[65],{"categories":1593},[65],{"categories":1595},[65],{"categories":1597},[110],{"categories":1599},[65,102],{"categories":1601},[399],{"categories":1603},[65],{"categories":1605},[65],{"categories":1607},[122],{"categories":1609},[122],{"categories":1611},[185],{"categories":1613},[110],{"categories":1615},[122],{"categories":1617},[65],{"categories":1619},[65],{"categories":1621},[],{"categories":1623},[],{"categories":1625},[65],{"categories":1627},[],{"categories":1629},[65],{"categories":1631},[122],{"categories":1633},[188],{"categories":1635},[144],{"categories":1637},[185],{"categories":1639},[65],{"categories":1641},[122],{"categories":1643},[],{"categories":1645},[110],{"categories":1647},[65],{"categories":1649},[65],{"categories":1651},[65],{"categories":1653},[65],{"categories":1655},[],{"categories":1657},[110],{"categories":1659},[65],{"categories":1661},[65],{"categories":1663},[],{"categories":1665},[110],{"categories":1667},[65],{"categories":1669},[65],{"categories":1671},[105],{"categories":1673},[65],{"categories":1675},[],{"categories":1677},[102],{"categories":1679},[65],{"categories":1681},[185],{"categories":1683},[122],{"categories":1685},[65],{"categories":1687},[102],{"categories":1689},[65],{"categories":1691},[122],{"categories":1693},[213],{"categories":1695},[110],{"categories":1697},[110],{"categories":1699},[65,185],{"categories":1701},[65],{"categories":1703},[144],{"categories":1705},[65],{"categories":1707},[144],{"categories":1709},[110],{"categories":1711},[185],{"categories":1713},[],{"categories":1715},[122],{"categories":1717},[248],{"categories":1719},[185],{"categories":1721},[122],{"categories":1723},[65],{"categories":1725},[113],{"categories":1727},[65],{"categories":1729},[110],{"categories":1731},[],{"categories":1733},[],{"categories":1735},[65],{"categories":1737},[],{"categories":1739},[],{"categories":1741},[113],{"categories":1743},[122],{"categories":1745},[65],{"categories":1747},[110],{"categories":1749},[110],{"categories":1751},[105],{"categories":1753},[110],{"categories":1755},[248],{"categories":1757},[65],{"categories":1759},[65],{"categories":1761},[133],{"categories":1763},[65],{"categories":1765},[65],{"categories":1767},[110],{"categories":1769},[65],{"categories":1771},[65],{"categories":1773},[355],{"categories":1775},[761],{"categories":1777},[],{"categories":1779},[185],{"categories":1781},[1495],{"categories":1783},[122],{"categories":1785},[],{"categories":1787},[],{"categories":1789},[110],{"categories":1791},[],{"categories":1793},[],{"categories":1795},[213],{"categories":1797},[65],{"categories":1799},[213],{"categories":1801},[110],{"categories":1803},[65],{"categories":1805},[122],{"categories":1807},[],{"categories":1809},[65],{"categories":1811},[65],{"categories":1813},[122],{"categories":1815},[1444],{"categories":1817},[185],{"categories":1819},[185],{"categories":1821},[65],{"categories":1823},[110],{"categories":1825},[102],{"categories":1827},[65],{"categories":1829},[65],{"categories":1831},[65],{"categories":1833},[185],{"categories":1835},[185],{"categories":1837},[110],{"categories":1839},[110],{"categories":1841},[65],{"categories":1843},[],{"categories":1845},[65],{"categories":1847},[],{"categories":1849},[1850],"Interaction & Product Design",{"categories":1852},[65],{"categories":1854},[110],{"categories":1856},[275],{"categories":1858},[144],{"categories":1860},[122],{"categories":1862},[65],{"categories":1864},[65],{"categories":1866},[122],{"categories":1868},[102],{"categories":1870},[110],{"categories":1872},[65],{"categories":1874},[],{"categories":1876},[110],{"categories":1878},[110],{"categories":1880},[],{"categories":1882},[122],{"categories":1884},[65],{"categories":1886},[102],{"categories":1888},[1850],{"categories":1890},[65],{"categories":1892},[102],{"categories":1894},[102],{"categories":1896},[],{"categories":1898},[122],{"categories":1900},[],{"categories":1902},[110],{"categories":1904},[144],{"categories":1906},[65],{"categories":1908},[110],{"categories":1910},[65],{"categories":1912},[110],{"categories":1914},[65],{"categories":1916},[65],{"categories":1918},[144],{"categories":1920},[188],{"categories":1922},[65],{"categories":1924},[113],{"categories":1926},[122],{"categories":1928},[1929],"Coding Agents & Dev Productivity",{"categories":1931},[144],{"categories":1933},[185],{"categories":1935},[],{"categories":1937},[65],{"categories":1939},[761],{"categories":1941},[],{"categories":1943},[65],{"categories":1945},[65],{"categories":1947},[144],{"categories":1949},[],{"categories":1951},[],{"categories":1953},[65],{"categories":1955},[],{"categories":1957},[110],{"categories":1959},[65],{"categories":1961},[],{"categories":1963},[122],{"categories":1965},[122],{"categories":1967},[65],{"categories":1969},[188],{"categories":1971},[],{"categories":1973},[65],{"categories":1975},[65],{"categories":1977},[65],{"categories":1979},[188],{"categories":1981},[122],{"categories":1983},[],{"categories":1985},[],{"categories":1987},[65],{"categories":1989},[110],{"categories":1991},[110],{"categories":1993},[334],{"categories":1995},[122],{"categories":1997},[122],{"categories":1999},[110],{"categories":2001},[144],{"categories":2003},[144],{"categories":2005},[110],{"categories":2007},[110],{"categories":2009},[65],{"categories":2011},[102],{"categories":2013},[1850],{"categories":2015},[65,248],{"categories":2017},[],{"categories":2019},[185],{"categories":2021},[122],{"categories":2023},[102],{"categories":2025},[65],{"categories":2027},[110],{"categories":2029},[2030],"The Designer's Role & Craft",{"categories":2032},[185],{"categories":2034},[],{"categories":2036},[110],{"categories":2038},[65],{"categories":2040},[110],{"categories":2042},[110],{"categories":2044},[65],{"categories":2046},[213],{"categories":2048},[65],{"categories":2050},[122],{"categories":2052},[185],{"categories":2054},[65],{"categories":2056},[],{"categories":2058},[110],{"categories":2060},[185],{"categories":2062},[65],{"categories":2064},[65],{"categories":2066},[2067],"AI UX Patterns",{"categories":2069},[110],{"categories":2071},[110],{"categories":2073},[110],{"categories":2075},[110],{"categories":2077},[213],{"categories":2079},[188],{"categories":2081},[65],{"categories":2083},[110],{"categories":2085},[65],{"categories":2087},[1039],{"categories":2089},[],{"categories":2091},[213],{"categories":2093},[144],{"categories":2095},[122],{"categories":2097},[65],{"categories":2099},[110],{"categories":2101},[],{"categories":2103},[],{"categories":2105},[65],{"categories":2107},[110],{"categories":2109},[65],{"categories":2111},[110],{"categories":2113},[334],{"categories":2115},[185],{"categories":2117},[144],{"categories":2119},[122],{"categories":2121},[65],{"categories":2123},[110],{"categories":2125},[110],{"categories":2127},[],{"categories":2129},[65],{"categories":2131},[],{"categories":2133},[],{"categories":2135},[65],{"categories":2137},[65],{"categories":2139},[110],{"categories":2141},[122],{"categories":2143},[],{"categories":2145},[],{"categories":2147},[188],{"categories":2149},[133],{"categories":2151},[65],{"categories":2153},[188],{"categories":2155},[144],{"categories":2157},[65],{"categories":2159},[65],{"categories":2161},[110],{"categories":2163},[110],{"categories":2165},[65],{"categories":2167},[65],{"categories":2169},[110],{"categories":2171},[],{"categories":2173},[],{"categories":2175},[65],{"categories":2177},[248],{"categories":2179},[65],{"categories":2181},[],{"categories":2183},[],{"categories":2185},[185],{"categories":2187},[782],{"categories":2189},[110],{"categories":2191},[102],{"categories":2193},[2030],{"categories":2195},[],{"categories":2197},[],{"categories":2199},[65],{"categories":2201},[],{"categories":2203},[],{"categories":2205},[122],{"categories":2207},[144],{"categories":2209},[213],{"categories":2211},[105],{"categories":2213},[65],{"categories":2215},[65],{"categories":2217},[105],{"categories":2219},[],{"categories":2221},[185],{"categories":2223},[65],{"categories":2225},[65],{"categories":2227},[110],{"categories":2229},[105],{"categories":2231},[65],{"categories":2233},[65],{"categories":2235},[102],{"categories":2237},[65],{"categories":2239},[],{"categories":2241},[102],{"categories":2243},[65],{"categories":2245},[213],{"categories":2247},[110],{"categories":2249},[144],{"categories":2251},[65],{"categories":2253},[105],{"categories":2255},[65],{"categories":2257},[65],{"categories":2259},[65],{"categories":2261},[110],{"categories":2263},[],{"categories":2265},[65],{"categories":2267},[122],{"categories":2269},[102],{"categories":2271},[65],{"categories":2273},[65],{"categories":2275},[],{"categories":2277},[65],{"categories":2279},[399],{"categories":2281},[144],{"categories":2283},[65],{"categories":2285},[65],{"categories":2287},[],{"categories":2289},[105],{"categories":2291},[105],{"categories":2293},[65],{"categories":2295},[65],{"categories":2297},[113],{"categories":2299},[65],{"categories":2301},[65],{"categories":2303},[122],{"categories":2305},[122],{"categories":2307},[65],{"categories":2309},[],{"categories":2311},[122],{"categories":2313},[65],{"categories":2315},[122],{"categories":2317},[486],{"categories":2319},[],{"categories":2321},[],{"categories":2323},[65],{"categories":2325},[144],{"categories":2327},[],{"categories":2329},[248],{"categories":2331},[65],{"categories":2333},[65],{"categories":2335},[185],{"categories":2337},[750],{"categories":2339},[],{"categories":2341},[65],{"categories":2343},[65],{"categories":2345},[65],{"categories":2347},[122],{"categories":2349},[65],{"categories":2351},[65],{"categories":2353},[65,248],{"categories":2355},[65],{"categories":2357},[65],{"categories":2359},[185],{"categories":2361},[110],{"categories":2363},[],{"categories":2365},[110],{"categories":2367},[110],{"categories":2369},[65],{"categories":2371},[65],{"categories":2373},[65],{"categories":2375},[188],{"categories":2377},[65],{"categories":2379},[2067],{"categories":2381},[102],{"categories":2383},[188],{"categories":2385},[102],{"categories":2387},[122],{"categories":2389},[185],{"categories":2391},[110],{"categories":2393},[65],{"categories":2395},[],{"categories":2397},[65],{"categories":2399},[65],{"categories":2401},[144],{"categories":2403},[65],{"categories":2405},[110],{"categories":2407},[65],{"categories":2409},[65],{"categories":2411},[105],{"categories":2413},[],{"categories":2415},[248],{"categories":2417},[65],{"categories":2419},[334],{"categories":2421},[185],{"categories":2423},[185],{"categories":2425},[122],{"categories":2427},[110],{"categories":2429},[65],{"categories":2431},[105],{"categories":2433},[144],{"categories":2435},[65],{"categories":2437},[185],{"categories":2439},[110],{"categories":2441},[65],{"categories":2443},[65],{"categories":2445},[450],{"categories":2447},[],{"categories":2449},[65],{"categories":2451},[65],{"categories":2453},[65],{"categories":2455},[],{"categories":2457},[],{"categories":2459},[65],{"categories":2461},[65],{"categories":2463},[65],{"categories":2465},[65],{"categories":2467},[122],{"categories":2469},[65],{"categories":2471},[65],{"categories":2473},[110],{"categories":2475},[65],{"categories":2477},[65],{"categories":2479},[65],{"categories":2481},[65],{"categories":2483},[65],{"categories":2485},[],{"categories":2487},[122],{"categories":2489},[188],{"categories":2491},[65],{"categories":2493},[110],{"categories":2495},[65],{"categories":2497},[],{"categories":2499},[],{"categories":2501},[65],{"categories":2503},[65],{"categories":2505},[65],{"categories":2507},[144],{"categories":2509},[],{"categories":2511},[65],{"categories":2513},[185],{"categories":2515},[65],{"categories":2517},[248],{"categories":2519},[1495],{"categories":2521},[144],{"categories":2523},[122],{"categories":2525},[122],{"categories":2527},[122],{"categories":2529},[144],{"categories":2531},[144],{"categories":2533},[248],{"categories":2535},[],{"categories":2537},[144],{"categories":2539},[65],{"categories":2541},[102],{"categories":2543},[122],{"categories":2545},[65],{"categories":2547},[144],{"categories":2549},[],{"categories":2551},[65],{"categories":2553},[122],{"categories":2555},[188],{"categories":2557},[65],{"categories":2559},[144],{"categories":2561},[65],{"categories":2563},[122],{"categories":2565},[110],{"categories":2567},[144],{"categories":2569},[110],{"categories":2571},[248],{"categories":2573},[110],{"categories":2575},[65],{"categories":2577},[65],{"categories":2579},[122],{"categories":2581},[65],{"categories":2583},[],{"categories":2585},[105],{"categories":2587},[122],{"categories":2589},[],{"categories":2591},[],{"categories":2593},[65],{"categories":2595},[110],{"categories":2597},[65],{"categories":2599},[2600],"Frameworks & Tooling",{"categories":2602},[65],{"categories":2604},[65],{"categories":2606},[122],{"categories":2608},[65],{"categories":2610},[65],{"categories":2612},[],{"categories":2614},[188],{"categories":2616},[188],{"categories":2618},[102],{"categories":2620},[110],{"categories":2622},[185],{"categories":2624},[],{"categories":2626},[1495],{"categories":2628},[65],{"categories":2630},[122],{"categories":2632},[65],{"categories":2634},[248],{"categories":2636},[248],{"categories":2638},[],{"categories":2640},[110],{"categories":2642},[144],{"categories":2644},[144],{"categories":2646},[65],{"categories":2648},[110],{"categories":2650},[],{"categories":2652},[185],{"categories":2654},[65],{"categories":2656},[65],{"categories":2658},[],{"categories":2660},[65],{"categories":2662},[65],{"categories":2664},[],{"categories":2666},[122],{"categories":2668},[65],{"categories":2670},[122],{"categories":2672},[248],{"categories":2674},[65],{"categories":2676},[122],{"categories":2678},[105],{"categories":2680},[65],{"categories":2682},[1495],{"categories":2684},[],{"categories":2686},[110],{"categories":2688},[102],{"categories":2690},[102],{"categories":2692},[],{"categories":2694},[110],{"categories":2696},[65],{"categories":2698},[2699],"AI Design Tooling",{"categories":2701},[185],{"categories":2703},[65],{"categories":2705},[65],{"categories":2707},[122],{"categories":2709},[185],{"categories":2711},[65],{"categories":2713},[122],{"categories":2715},[144],{"categories":2717},[113],{"categories":2719},[122],{"categories":2721},[110],{"categories":2723},[],{"categories":2725},[65],{"categories":2727},[65],{"categories":2729},[110],{"categories":2731},[65],{"categories":2733},[65],{"categories":2735},[],{"categories":2737},[110],{"categories":2739},[2600],{"categories":2741},[65],{"categories":2743},[110],{"categories":2745},[110],{"categories":2747},[122],{"categories":2749},[122],{"categories":2751},[],{"categories":2753},[122],{"categories":2755},[65],{"categories":2757},[65],{"categories":2759},[110],{"categories":2761},[105],{"categories":2763},[65],{"categories":2765},[],{"categories":2767},[65],{"categories":2769},[65],{"categories":2771},[1850],{"categories":2773},[],{"categories":2775},[65],{"categories":2777},[65],{"categories":2779},[65],{"categories":2781},[],{"categories":2783},[65],{"categories":2785},[65],{"categories":2787},[65],{"categories":2789},[213],{"categories":2791},[144],{"categories":2793},[65],{"categories":2795},[65],{"categories":2797},[1495],{"categories":2799},[102],{"categories":2801},[65],{"categories":2803},[65],{"categories":2805},[188],{"categories":2807},[65],{"categories":2809},[144],{"categories":2811},[110],{"categories":2813},[],{"categories":2815},[65],{"categories":2817},[65],{"categories":2819},[185],{"categories":2821},[65],{"categories":2823},[213],{"categories":2825},[65],{"categories":2827},[110],{"categories":2829},[],{"categories":2831},[],{"categories":2833},[],{"categories":2835},[102],{"categories":2837},[144],{"categories":2839},[110],{"categories":2841},[65],{"categories":2843},[65],{"categories":2845},[65],{"categories":2847},[355],{"categories":2849},[185],{"categories":2851},[110],{"categories":2853},[65],{"categories":2855},[],{"categories":2857},[110],{"categories":2859},[110],{"categories":2861},[],{"categories":2863},[65],{"categories":2865},[110],{"categories":2867},[65],{"categories":2869},[],{"categories":2871},[65],{"categories":2873},[65],{"categories":2875},[144],{"categories":2877},[185],{"categories":2879},[110],{"categories":2881},[185],{"categories":2883},[110],{"categories":2885},[65],{"categories":2887},[105],{"categories":2889},[],{"categories":2891},[],{"categories":2893},[65],{"categories":2895},[65],{"categories":2897},[102],{"categories":2899},[110],{"categories":2901},[144],{"categories":2903},[],{"categories":2905},[185],{"categories":2907},[],{"categories":2909},[122],{"categories":2911},[122],{"categories":2913},[185],{"categories":2915},[122],{"categories":2917},[65],{"categories":2919},[],{"categories":2921},[65],{"categories":2923},[65],{"categories":2925},[],{"categories":2927},[213],{"categories":2929},[65],{"categories":2931},[248],{"categories":2933},[122],{"categories":2935},[],{"categories":2937},[110],{"categories":2939},[65],{"categories":2941},[102],{"categories":2943},[450],{"categories":2945},[110],{"categories":2947},[110],{"categories":2949},[65],{"categories":2951},[65],{"categories":2953},[],{"categories":2955},[65],{"categories":2957},[102],{"categories":2959},[65],{"categories":2961},[105],{"categories":2963},[122],{"categories":2965},[185],{"categories":2967},[],{"categories":2969},[],{"categories":2971},[],{"categories":2973},[110],{"categories":2975},[122],{"categories":2977},[185],{"categories":2979},[144],{"categories":2981},[65],{"categories":2983},[144],{"categories":2985},[110],{"categories":2987},[185],{"categories":2989},[65],{"categories":2991},[],{"categories":2993},[65],{"categories":2995},[133],{"categories":2997},[110],{"categories":2999},[185],{"categories":3001},[144],{"categories":3003},[105],{"categories":3005},[122],{"categories":3007},[65],{"categories":3009},[144],{"categories":3011},[213],{"categories":3013},[],{"categories":3015},[],{"categories":3017},[188],{"categories":3019},[399],{"categories":3021},[65],{"categories":3023},[110],{"categories":3025},[65,122],{"categories":3027},[144],{"categories":3029},[65],{"categories":3031},[65],{"categories":3033},[65],{"categories":3035},[65],{"categories":3037},[110],{"categories":3039},[65],{"categories":3041},[110],{"categories":3043},[65],{"categories":3045},[65],{"categories":3047},[],{"categories":3049},[65],{"categories":3051},[1039],{"categories":3053},[122],{"categories":3055},[185],{"categories":3057},[65],{"categories":3059},[65],{"categories":3061},[65],{"categories":3063},[188],{"categories":3065},[110],{"categories":3067},[213],{"categories":3069},[248],{"categories":3071},[],{"categories":3073},[65],{"categories":3075},[105],{"categories":3077},[110],{"categories":3079},[102],{"categories":3081},[110],{"categories":3083},[65],{"categories":3085},[110],{"categories":3087},[113],{"categories":3089},[122],{"categories":3091},[65],{"categories":3093},[65],{"categories":3095},[],{"categories":3097},[],{"categories":3099},[],{"categories":3101},[248],{"categories":3103},[65],{"categories":3105},[144],{"categories":3107},[65],{"categories":3109},[65],{"categories":3111},[65],{"categories":3113},[65],{"categories":3115},[],{"categories":3117},[188],{"categories":3119},[105],{"categories":3121},[110],{"categories":3123},[65],{"categories":3125},[],{"categories":3127},[65],{"categories":3129},[110],{"categories":3131},[65],{"categories":3133},[248],{"categories":3135},[],{"categories":3137},[185],{"categories":3139},[185],{"categories":3141},[],{"categories":3143},[122],{"categories":3145},[65],{"categories":3147},[185],{"categories":3149},[65],{"categories":3151},[105],{"categories":3153},[110],{"categories":3155},[65],{"categories":3157},[],{"categories":3159},[144],{"categories":3161},[65],{"categories":3163},[65],{"categories":3165},[65],{"categories":3167},[185],{"categories":3169},[110],{"categories":3171},[144],{"categories":3173},[],{"categories":3175},[110],{"categories":3177},[105],{"categories":3179},[110],{"categories":3181},[185],{"categories":3183},[65],{"categories":3185},[65],{"categories":3187},[65],{"categories":3189},[399],{"categories":3191},[65],{"categories":3193},[],{"categories":3195},[65],{"categories":3197},[65],{"categories":3199},[248],{"categories":3201},[144],{"categories":3203},[188],{"categories":3205},[486],{"categories":3207},[188],{"categories":3209},[],{"categories":3211},[],{"categories":3213},[],{"categories":3215},[110],{"categories":3217},[110],{"categories":3219},[122],{"categories":3221},[65],{"categories":3223},[382],{"categories":3225},[122],{"categories":3227},[65],{"categories":3229},[65],{"categories":3231},[65],{"categories":3233},[65],{"categories":3235},[110],{"categories":3237},[],{"categories":3239},[],{"categories":3241},[65],{"categories":3243},[],{"categories":3245},[65],{"categories":3247},[110],{"categories":3249},[185],{"categories":3251},[65],{"categories":3253},[65],{"categories":3255},[],{"categories":3257},[110],{"categories":3259},[113],{"categories":3261},[65],{"categories":3263},[185],{"categories":3265},[65],{"categories":3267},[110],{"categories":3269},[105],{"categories":3271},[65],{"categories":3273},[213],{"categories":3275},[110],{"categories":3277},[65],{"categories":3279},[750],{"categories":3281},[65],{"categories":3283},[110],{"categories":3285},[65],{"categories":3287},[122],{"categories":3289},[65],{"categories":3291},[450],{"categories":3293},[185],{"categories":3295},[],{"categories":3297},[144],{"categories":3299},[399],{"categories":3301},[110],{"categories":3303},[65],{"categories":3305},[],{"categories":3307},[144],{"categories":3309},[334],{"categories":3311},[110],{"categories":3313},[110],{"categories":3315},[65],{"categories":3317},[65],{"categories":3319},[110],{"categories":3321},[],{"categories":3323},[65],{"categories":3325},[105],{"categories":3327},[110],{"categories":3329},[],{"categories":3331},[122],{"categories":3333},[65],{"categories":3335},[65],{"categories":3337},[102],{"categories":3339},[144],{"categories":3341},[248],{"categories":3343},[133],{"categories":3345},[110],{"categories":3347},[110],{"categories":3349},[65],{"categories":3351},[110],{"categories":3353},[65],{"categories":3355},[102],{"categories":3357},[],{"categories":3359},[65],{"categories":3361},[65],{"categories":3363},[],{"categories":3365},[],{"categories":3367},[185],{"categories":3369},[65,105],{"categories":3371},[110],{"categories":3373},[65],{"categories":3375},[],{"categories":3377},[102],{"categories":3379},[188],{"categories":3381},[105],{"categories":3383},[65],{"categories":3385},[122],{"categories":3387},[65],{"categories":3389},[110],{"categories":3391},[65],{"categories":3393},[65],{"categories":3395},[65],{"categories":3397},[144],{"categories":3399},[1039],{"categories":3401},[110],{"categories":3403},[65],{"categories":3405},[],{"categories":3407},[],{"categories":3409},[110],{"categories":3411},[65],{"categories":3413},[248],{"categories":3415},[],{"categories":3417},[65],{"categories":3419},[110],{"categories":3421},[133],{"categories":3423},[110],{"categories":3425},[399],{"categories":3427},[],{"categories":3429},[355],{"categories":3431},[110],{"categories":3433},[65],{"categories":3435},[213],{"categories":3437},[65],{"categories":3439},[188],{"categories":3441},[110],{"categories":3443},[65],{"categories":3445},[399],{"categories":3447},[65],{"categories":3449},[248],{"categories":3451},[],{"categories":3453},[65],{"categories":3455},[213],{"categories":3457},[185],{"categories":3459},[65],{"categories":3461},[65],{"categories":3463},[],{"categories":3465},[213],{"categories":3467},[144],{"categories":3469},[65],{"categories":3471},[65],{"categories":3473},[486],{"categories":3475},[102],{"categories":3477},[65],{"categories":3479},[],{"categories":3481},[],{"categories":3483},[185],{"categories":3485},[65],{"categories":3487},[188],{"categories":3489},[213],{"categories":3491},[110],{"categories":3493},[213],{"categories":3495},[144],{"categories":3497},[],{"categories":3499},[65],{"categories":3501},[65],{"categories":3503},[],{"categories":3505},[65],{"categories":3507},[509],{"categories":3509},[65],{"categories":3511},[65],{"categories":3513},[110],{"categories":3515},[122],{"categories":3517},[399],{"categories":3519},[65],{"categories":3521},[65],{"categories":3523},[65],{"categories":3525},[],{"categories":3527},[65,122],{"categories":3529},[144],{"categories":3531},[110],{"categories":3533},[122],{"categories":3535},[110],{"categories":3537},[782],{"categories":3539},[122],{"categories":3541},[65],{"categories":3543},[102],{"categories":3545},[],{"categories":3547},[],{"categories":3549},[110],{"categories":3551},[65],{"categories":3553},[122],{"categories":3555},[102],{"categories":3557},[122],{"categories":3559},[122],{"categories":3561},[65],{"categories":3563},[213],{"categories":3565},[65],{"categories":3567},[122],{"categories":3569},[],{"categories":3571},[65],{"categories":3573},[185,65],{"categories":3575},[248],{"categories":3577},[102],{"categories":3579},[],{"categories":3581},[65],{"categories":3583},[65],{"categories":3585},[105],{"categories":3587},[105],{"categories":3589},[65],{"categories":3591},[65],{"categories":3593},[334],{"categories":3595},[65],{"categories":3597},[122],{"categories":3599},[188],{"categories":3601},[110],{"categories":3603},[122],{"categories":3605},[65],{"categories":3607},[65],{"categories":3609},[144],{"categories":3611},[213],{"categories":3613},[185],{"categories":3615},[65],{"categories":3617},[65],{"categories":3619},[65],{"categories":3621},[65],{"categories":3623},[102],{"categories":3625},[65],{"categories":3627},[110],{"categories":3629},[110],{"categories":3631},[122],{"categories":3633},[144],{"categories":3635},[122],{"categories":3637},[122],{"categories":3639},[],{"categories":3641},[],{"categories":3643},[188],{"categories":3645},[65],{"categories":3647},[122],{"categories":3649},[65],{"categories":3651},[185],{"categories":3653},[399],{"categories":3655},[355],{"categories":3657},[334],{"categories":3659},[65],{"categories":3661},[65],{"categories":3663},[65],{"categories":3665},[188],{"categories":3667},[65],{"categories":3669},[65],{"categories":3671},[65],{"categories":3673},[65],{"categories":3675},[65],{"categories":3677},[110],{"categories":3679},[102],{"categories":3681},[110],{"categories":3683},[65,105],{"categories":3685},[],{"categories":3687},[185],{"categories":3689},[],{"categories":3691},[113],{"categories":3693},[65],{"categories":3695},[144],{"categories":3697},[102],{"categories":3699},[102],{"categories":3701},[110],{"categories":3703},[110],{"categories":3705},[110],{"categories":3707},[65],{"categories":3709},[65],{"categories":3711},[105],{"categories":3713},[122],{"categories":3715},[213],{"categories":3717},[65],{"categories":3719},[],{"categories":3721},[144],{"categories":3723},[65],{"categories":3725},[65],{"categories":3727},[65],{"categories":3729},[65],{"categories":3731},[65],{"categories":3733},[122],{"categories":3735},[144],{"categories":3737},[122],{"categories":3739},[122],{"categories":3741},[65],{"categories":3743},[65],{"categories":3745},[65],{"categories":3747},[355],{"categories":3749},[65],{"categories":3751},[110],{"categories":3753},[144],{"categories":3755},[65],{"categories":3757},[65],{"categories":3759},[65],{"categories":3761},[110],{"categories":3763},[65],{"categories":3765},[65],{"categories":3767},[65],{"categories":3769},[2600],{"categories":3771},[3772],"Clinical AI",{"categories":3774},[185],{"categories":3776},[65],{"categories":3778},[65],{"categories":3780},[65],{"categories":3782},[248],{"categories":3784},[2067],{"categories":3786},[65],{"categories":3788},[113],{"categories":3790},[65],{"categories":3792},[110],{"categories":3794},[65],{"categories":3796},[65],{"categories":3798},[144],{"categories":3800},[65],{"categories":3802},[110],{"categories":3804},[122],{"categories":3806},[213],{"categories":3808},[65],{"categories":3810},[65],{"categories":3812},[105],{"categories":3814},[65],{"categories":3816},[65],{"categories":3818},[450],{"categories":3820},[65],{"categories":3822},[],{"categories":3824},[65],{"categories":3826},[122],{"categories":3828},[102],{"categories":3830},[65],{"categories":3832},[],{"categories":3834},[],{"categories":3836},[65],{"categories":3838},[],{"categories":3840},[105],{"categories":3842},[65],{"categories":3844},[110],{"categories":3846},[144],{"categories":3848},[144],{"categories":3850},[144],{"categories":3852},[144],{"categories":3854},[],{"categories":3856},[102],{"categories":3858},[110],{"categories":3860},[144],{"categories":3862},[65],{"categories":3864},[509],{"categories":3866},[113],{"categories":3868},[65],{"categories":3870},[102],{"categories":3872},[110],{"categories":3874},[65],{"categories":3876},[65],{"categories":3878},[65,110],{"categories":3880},[110],{"categories":3882},[248],{"categories":3884},[144],{"categories":3886},[110],{"categories":3888},[144],{"categories":3890},[110],{"categories":3892},[65],{"categories":3894},[],{"categories":3896},[144],{"categories":3898},[213],{"categories":3900},[102],{"categories":3902},[65],{"categories":3904},[65],{"categories":3906},[],{"categories":3908},[122],{"categories":3910},[],{"categories":3912},[102],{"categories":3914},[110],{"categories":3916},[144],{"categories":3918},[65],{"categories":3920},[144],{"categories":3922},[102],{"categories":3924},[144],{"categories":3926},[144],{"categories":3928},[],{"categories":3930},[105],{"categories":3932},[110],{"categories":3934},[144],{"categories":3936},[144],{"categories":3938},[144],{"categories":3940},[144],{"categories":3942},[144],{"categories":3944},[144],{"categories":3946},[144],{"categories":3948},[144],{"categories":3950},[144],{"categories":3952},[144],{"categories":3954},[188],{"categories":3956},[102],{"categories":3958},[65],{"categories":3960},[65],{"categories":3962},[110],{"categories":3964},[110],{"categories":3966},[],{"categories":3968},[65,102],{"categories":3970},[],{"categories":3972},[110],{"categories":3974},[144],{"categories":3976},[110],{"categories":3978},[782],{"categories":3980},[65],{"categories":3982},[65],{"categories":3984},[65],{"categories":3986},[65],{"categories":3988},[65],{"categories":3990},[334],{"categories":3992},[65],{"categories":3994},[110],{"categories":3996},[105],{"categories":3998},[110],{"categories":4000},[110],{"categories":4002},[],{"categories":4004},[110],{"categories":4006},[185],{"categories":4008},[144],{"categories":4010},[65],{"categories":4012},[],{"categories":4014},[113],{"categories":4016},[],{"categories":4018},[122],{"categories":4020},[110],{"categories":4022},[185],{"categories":4024},[65],{"categories":4026},[],{"categories":4028},[65],{"categories":4030},[],{"categories":4032},[213],{"categories":4034},[65],{"categories":4036},[],{"categories":4038},[],{"categories":4040},[144],{"categories":4042},[102],{"categories":4044},[65],{"categories":4046},[65],{"categories":4048},[105],{"categories":4050},[65],{"categories":4052},[65],{"categories":4054},[65],{"categories":4056},[105],{"categories":4058},[185],{"categories":4060},[],{"categories":4062},[65],{"categories":4064},[144],{"categories":4066},[],{"categories":4068},[65],{"categories":4070},[65],{"categories":4072},[185],{"categories":4074},[65],{"categories":4076},[213],{"categories":4078},[65],{"categories":4080},[248],{"categories":4082},[],{"categories":4084},[110],{"categories":4086},[213],{"categories":4088},[122],{"categories":4090},[],{"categories":4092},[65],{"categories":4094},[],{"categories":4096},[110],{"categories":4098},[185],{"categories":4100},[122],{"categories":4102},[],{"categories":4104},[2600],{"categories":4106},[105],{"categories":4108},[102],{"categories":4110},[65],{"categories":4112},[188],{"categories":4114},[110],{"categories":4116},[185],{"categories":4118},[122],{"categories":4120},[],{"categories":4122},[],{"categories":4124},[65],{"categories":4126},[102],{"categories":4128},[65],{"categories":4130},[213],{"categories":4132},[],{"categories":4134},[110],{"categories":4136},[110],{"categories":4138},[110],{"categories":4140},[65],{"categories":4142},[144],{"categories":4144},[122],{"categories":4146},[65],{"categories":4148},[110],{"categories":4150},[113],{"categories":4152},[65],{"categories":4154},[65],{"categories":4156},[110],{"categories":4158},[65],{"categories":4160},[113],{"categories":4162},[213],{"categories":4164},[144],{"categories":4166},[],{"categories":4168},[213],{"categories":4170},[],{"categories":4172},[122],{"categories":4174},[110],{"categories":4176},[],{"categories":4178},[65],{"categories":4180},[65],{"categories":4182},[65],{"categories":4184},[65],{"categories":4186},[110],{"categories":4188},[105],{"categories":4190},[102],{"categories":4192},[65],{"categories":4194},[185],{"categories":4196},[122],{"categories":4198},[122],{"categories":4200},[65],{"categories":4202},[188],{"categories":4204},[110],{"categories":4206},[65],{"categories":4208},[65],{"categories":4210},[110],{"categories":4212},[65],{"categories":4214},[105],{"categories":4216},[185],{"categories":4218},[122],{"categories":4220},[110],{"categories":4222},[65],{"categories":4224},[113],{"categories":4226},[65],{"categories":4228},[110],{"categories":4230},[65],{"categories":4232},[144],{"categories":4234},[],{"categories":4236},[102],{"categories":4238},[65],{"categories":4240},[65],{"categories":4242},[65],{"categories":4244},[122],{"categories":4246},[122],{"categories":4248},[65],{"categories":4250},[122],{"categories":4252},[65],{"categories":4254},[110],{"categories":4256},[65],{"categories":4258},[65],{"categories":4260},[65],{"categories":4262},[65],{"categories":4264},[],{"categories":4266},[65],{"categories":4268},[185],{"categories":4270},[105],{"categories":4272},[144],{"categories":4274},[110],{"categories":4276},[65],{"categories":4278},[65],{"categories":4280},[185],{"categories":4282},[110],{"categories":4284},[65],{"categories":4286},[213],{"categories":4288},[65],{"categories":4290},[188],{"categories":4292},[65],{"categories":4294},[65],{"categories":4296},[144],{"categories":4298},[65],{"categories":4300},[65],{"categories":4302},[65],{"categories":4304},[110],{"categories":4306},[248],{"categories":4308},[65],{"categories":4310},[122],{"categories":4312},[110],{"categories":4314},[188],{"categories":4316},[],{"categories":4318},[110],{"categories":4320},[122],{"categories":4322},[65],{"categories":4324},[1929],{"categories":4326},[185],{"categories":4328},[275],{"categories":4330},[65],{"categories":4332},[65],{"categories":4334},[102],{"categories":4336},[122],{"categories":4338},[105],{"categories":4340},[122],{"categories":4342},[65],{"categories":4344},[],{"categories":4346},[110],{"categories":4348},[110],{"categories":4350},[65],{"categories":4352},[65],{"categories":4354},[188],{"categories":4356},[],{"categories":4358},[144],{"categories":4360},[],{"categories":4362},[144],{"categories":4364},[65],{"categories":4366},[65],{"categories":4368},[110],{"categories":4370},[65],{"categories":4372},[110],{"categories":4374},[110],{"categories":4376},[],{"categories":4378},[144],{"categories":4380},[65],{"categories":4382},[],{"categories":4384},[65],{"categories":4386},[65],{"categories":4388},[],{"categories":4390},[185],{"categories":4392},[122],{"categories":4394},[110],{"categories":4396},[65],{"categories":4398},[65],{"categories":4400},[213],{"categories":4402},[65],{"categories":4404},[65],{"categories":4406},[102],{"categories":4408},[],{"categories":4410},[65],{"categories":4412},[65],{"categories":4414},[],{"categories":4416},[102],{"categories":4418},[144],{"categories":4420},[122],{"categories":4422},[113],{"categories":4424},[399],{"categories":4426},[65],{"categories":4428},[65],{"categories":4430},[65],{"categories":4432},[122],{"categories":4434},[144],{"categories":4436},[185],{"categories":4438},[65],{"categories":4440},[65],{"categories":4442},[65],{"categories":4444},[144],{"categories":4446},[185],{"categories":4448},[65],{"categories":4450},[144],{"categories":4452},[185],{"categories":4454},[65],{"categories":4456},[144],{"categories":4458},[110],{"categories":4460},[110],{"categories":4462},[110],{"categories":4464},[122],{"categories":4466},[144],{"categories":4468},[110],{"categories":4470},[110],{"categories":4472},[65],{"categories":4474},[122],{"categories":4476},[185],{"categories":4478},[65],{"categories":4480},[],{"categories":4482},[110],{"categories":4484},[],{"categories":4486},[65],{"categories":4488},[],{"categories":4490},[],{"categories":4492},[110],{"categories":4494},[105],{"categories":4496},[110],{"categories":4498},[4499],"Liability & Ethics",{"categories":4501},[65],{"categories":4503},[110],{"categories":4505},[102],{"categories":4507},[110],{"categories":4509},[105],{"categories":4511},[213],{"categories":4513},[110],{"categories":4515},[],{"categories":4517},[486],{"categories":4519},[110],{"categories":4521},[],{"categories":4523},[102],{"categories":4525},[110],{"categories":4527},[],{"categories":4529},[110],{"categories":4531},[65],{"categories":4533},[65],{"categories":4535},[144],{"categories":4537},[65],{"categories":4539},[65],{"categories":4541},[110],{"categories":4543},[65],{"categories":4545},[65],{"categories":4547},[144],{"categories":4549},[110],{"categories":4551},[122],{"categories":4553},[185],{"categories":4555},[102],{"categories":4557},[65],{"categories":4559},[65],{"categories":4561},[],{"categories":4563},[110],{"categories":4565},[110],{"categories":4567},[399],{"categories":4569},[185],{"categories":4571},[248],{"categories":4573},[144],{"categories":4575},[65],{"categories":4577},[185],{"categories":4579},[65],{"categories":4581},[102],{"categories":4583},[],{"categories":4585},[110],{"categories":4587},[65],{"categories":4589},[65],{"categories":4591},[110],{"categories":4593},[65],{"categories":4595},[185],{"categories":4597},[],{"categories":4599},[110],{"categories":4601},[113],{"categories":4603},[144],{"categories":4605},[110],{"categories":4607},[105],{"categories":4609},[],{"categories":4611},[65],{"categories":4613},[113],{"categories":4615},[65],{"categories":4617},[110],{"categories":4619},[144],{"categories":4621},[102],{"categories":4623},[248],{"categories":4625},[65],{"categories":4627},[65],{"categories":4629},[65],{"categories":4631},[144],{"categories":4633},[105],{"categories":4635},[65],{"categories":4637},[185],{"categories":4639},[144],{"categories":4641},[248],{"categories":4643},[65],{"categories":4645},[110],{"categories":4647},[],{"categories":4649},[450],{"categories":4651},[],{"categories":4653},[65],{"categories":4655},[248],{"categories":4657},[188],{"categories":4659},[110],{"categories":4661},[110],{"categories":4663},[4664],"Design News & Tools",{"categories":4666},[65],{"categories":4668},[144],{"categories":4670},[65],{"categories":4672},[65],{"categories":4674},[102],{"categories":4676},[65],{"categories":4678},[185],{"categories":4680},[110],{"categories":4682},[110],{"categories":4684},[185],{"categories":4686},[65],{"categories":4688},[399],{"categories":4690},[65],{"categories":4692},[65],{"categories":4694},[399],{"categories":4696},[65],{"categories":4698},[213],{"categories":4700},[65],{"categories":4702},[110],{"categories":4704},[],{"categories":4706},[65],{"categories":4708},[65],{"categories":4710},[65],{"categories":4712},[144],{"categories":4714},[102],{"categories":4716},[],{"categories":4718},[65],{"categories":4720},[65],{"categories":4722},[122],{"categories":4724},[509],{"categories":4726},[122],{"categories":4728},[185],{"categories":4730},[65],{"categories":4732},[65,110],{"categories":4734},[213,105],{"categories":4736},[65],{"categories":4738},[65],{"categories":4740},[65],{"categories":4742},[],{"categories":4744},[110],{"categories":4746},[],{"categories":4748},[122],{"categories":4750},[65],{"categories":4752},[122],{"categories":4754},[],{"categories":4756},[110],{"categories":4758},[65],{"categories":4760},[144],{"categories":4762},[65],{"categories":4764},[],{"categories":4766},[110],{"categories":4768},[65],{"categories":4770},[],{"categories":4772},[185],{"categories":4774},[65],{"categories":4776},[110],{"categories":4778},[65],{"categories":4780},[65],{"categories":4782},[102],{"categories":4784},[110],{"categories":4786},[65],{"categories":4788},[],{"categories":4790},[248],{"categories":4792},[213],{"categories":4794},[105],{"categories":4796},[105],{"categories":4798},[65],{"categories":4800},[102],{"categories":4802},[102],{"categories":4804},[65],{"categories":4806},[110],{"categories":4808},[65],{"categories":4810},[65],{"categories":4812},[65],{"categories":4814},[122],{"categories":4816},[65],{"categories":4818},[102],{"categories":4820},[110],{"categories":4822},[65],{"categories":4824},[213],{"categories":4826},[65],{"categories":4828},[144],{"categories":4830},[65],{"categories":4832},[65],{"categories":4834},[110],{"categories":4836},[65],{"categories":4838},[],{"categories":4840},[122],{"categories":4842},[],{"categories":4844},[122],{"categories":4846},[110],{"categories":4848},[102],{"categories":4850},[],{"categories":4852},[188],{"categories":4854},[248],{"categories":4856},[65],{"categories":4858},[122],{"categories":4860},[65],{"categories":4862},[],{"categories":4864},[144],{"categories":4866},[110],{"categories":4868},[122],{"categories":4870},[185],{"categories":4872},[65],{"categories":4874},[65],{"categories":4876},[110],{"categories":4878},[122],{"categories":4880},[110],{"categories":4882},[144],{"categories":4884},[65],{"categories":4886},[102],{"categories":4888},[144],{"categories":4890},[122],{"categories":4892},[65],{"categories":4894},[185],{"categories":4896},[105],{"categories":4898},[65],{"categories":4900},[65],{"categories":4902},[65],{"categories":4904},[65],{"categories":4906},[65],{"categories":4908},[110],{"categories":4910},[65],{"categories":4912},[110],{"categories":4914},[65],{"categories":4916},[65],{"categories":4918},[102],{"categories":4920},[65],{"categories":4922},[110],{"categories":4924},[110],{"categories":4926},[185],{"categories":4928},[110],{"categories":4930},[110],{"categories":4932},[102],{"categories":4934},[110],{"categories":4936},[185],{"categories":4938},[],{"categories":4940},[65],{"categories":4942},[188],{"categories":4944},[399],{"categories":4946},[65],{"categories":4948},[65],{"categories":4950},[65],{"categories":4952},[122],{"categories":4954},[65],{"categories":4956},[],{"categories":4958},[110],{"categories":4960},[213],{"categories":4962},[65],{"categories":4964},[144],{"categories":4966},[110],{"categories":4968},[65],{"categories":4970},[213],{"categories":4972},[110],{"categories":4974},[105],{"categories":4976},[105],{"categories":4978},[65],{"categories":4980},[65],{"categories":4982},[65],{"categories":4984},[102],{"categories":4986},[],{"categories":4988},[65],{"categories":4990},[65],{"categories":4992},[110],{"categories":4994},[110],{"categories":4996},[65],{"categories":4998},[65],{"categories":5000},[65],{"categories":5002},[122],{"categories":5004},[],{"categories":5006},[102],{"categories":5008},[65],{"categories":5010},[65],{"categories":5012},[110],{"categories":5014},[110],{"categories":5016},[],{"categories":5018},[122],{"categories":5020},[122],{"categories":5022},[65],{"categories":5024},[213],{"categories":5026},[105],{"categories":5028},[185],{"categories":5030},[],{"categories":5032},[65],{"categories":5034},[110],{"categories":5036},[102],{"categories":5038},[65],{"categories":5040},[122],{"categories":5042},[102],{"categories":5044},[144],{"categories":5046},[188],{"categories":5048},[144],{"categories":5050},[110],{"categories":5052},[],{"categories":5054},[144],{"categories":5056},[110],{"categories":5058},[185],{"categories":5060},[188],{"categories":5062},[65],{"categories":5064},[],{"categories":5066},[110],{"categories":5068},[110],{"categories":5070},[2600],{"categories":5072},[144],{"categories":5074},[122],{"categories":5076},[65],{"categories":5078},[65],{"categories":5080},[65],{"categories":5082},[65],{"categories":5084},[105],{"categories":5086},[65],{"categories":5088},[102],{"categories":5090},[1495],{"categories":5092},[248],{"categories":5094},[102],{"categories":5096},[],{"categories":5098},[],{"categories":5100},[144],{"categories":5102},[110],{"categories":5104},[144],{"categories":5106},[],{"categories":5108},[110],{"categories":5110},[110],{"categories":5112},[110],{"categories":5114},[],{"categories":5116},[65],{"categories":5118},[],{"categories":5120},[144],{"categories":5122},[102],{"categories":5124},[185],{"categories":5126},[65],{"categories":5128},[110],{"categories":5130},[144],{"categories":5132},[65],{"categories":5134},[144],{"categories":5136},[],{"categories":5138},[144],{"categories":5140},[102],{"categories":5142},[399],{"categories":5144},[110],{"categories":5146},[65],{"categories":5148},[],{"categories":5150},[122],{"categories":5152},[110],{"categories":5154},[113],{"categories":5156},[110],{"categories":5158},[102],{"categories":5160},[],{"categories":5162},[],{"categories":5164},[],{"categories":5166},[185],{"categories":5168},[110],{"categories":5170},[65],{"categories":5172},[65],{"categories":5174},[],{"categories":5176},[],{"categories":5178},[],{"categories":5180},[185],{"categories":5182},[65],{"categories":5184},[],{"categories":5186},[110],{"categories":5188},[65],{"categories":5190},[102],{"categories":5192},[],{"categories":5194},[],{"categories":5196},[185],{"categories":5198},[65],{"categories":5200},[144],{"categories":5202},[],{"categories":5204},[213],{"categories":5206},[144],{"categories":5208},[213],{"categories":5210},[188],{"categories":5212},[65],{"categories":5214},[65],{"categories":5216},[],{"categories":5218},[],{"categories":5220},[110],{"categories":5222},[],{"categories":5224},[65],{"categories":5226},[399],{"categories":5228},[65],{"categories":5230},[65],{"categories":5232},[65],{"categories":5234},[65],{"categories":5236},[],{"categories":5238},[110],{"categories":5240},[65],{"categories":5242},[65],{"categories":5244},[],{"categories":5246},[110],{"categories":5248},[65],{"categories":5250},[144],{"categories":5252},[65],{"categories":5254},[213],{"categories":5256},[105],{"categories":5258},[65],{"categories":5260},[65],{"categories":5262},[110],{"categories":5264},[188],{"categories":5266},[110],{"categories":5268},[110],{"categories":5270},[],{"categories":5272},[110],{"categories":5274},[],{"categories":5276},[65],{"categories":5278},[],{"categories":5280},[144],{"categories":5282},[105],{"categories":5284},[],{"categories":5286},[65],{"categories":5288},[],{"categories":5290},[185],{"categories":5292},[102],{"categories":5294},[],{"categories":5296},[105],{"categories":5298},[213],{"categories":5300},[65],{"categories":5302},[122],{"categories":5304},[102],{"categories":5306},[188],{"categories":5308},[105],{"categories":5310},[122],{"categories":5312},[122],{"categories":5314},[],{"categories":5316},[65],{"categories":5318},[],{"categories":5320},[110],{"categories":5322},[102],{"categories":5324},[185],{"categories":5326},[65],{"categories":5328},[102],{"categories":5330},[110],{"categories":5332},[248],{"categories":5334},[65],{"categories":5336},[65],{"categories":5338},[65],{"categories":5340},[102],{"categories":5342},[188],{"categories":5344},[110],{"categories":5346},[],{"categories":5348},[65],{"categories":5350},[65],{"categories":5352},[122],{"categories":5354},[110],{"categories":5356},[144],{"categories":5358},[122],{"categories":5360},[65],{"categories":5362},[113],{"categories":5364},[],{"categories":5366},[185],{"categories":5368},[144],{"categories":5370},[102],{"categories":5372},[110],{"categories":5374},[65],{"categories":5376},[65],{"categories":5378},[110],{"categories":5380},[113],{"categories":5382},[65],{"categories":5384},[110],{"categories":5386},[65],{"categories":5388},[105],{"categories":5390},[110],{"categories":5392},[110,248],{"categories":5394},[65],{"categories":5396},[65],{"categories":5398},[110],{"categories":5400},[122],{"categories":5402},[65],{"categories":5404},[65],{"categories":5406},[188],{"categories":5408},[110],{"categories":5410},[213],{"categories":5412},[110],{"categories":5414},[105],{"categories":5416},[],{"categories":5418},[110],{"categories":5420},[65],{"categories":5422},[105],{"categories":5424},[],{"categories":5426},[],{"categories":5428},[122],{"categories":5430},[65],{"categories":5432},[65],{"categories":5434},[110],{"categories":5436},[188],{"categories":5438},[213],{"categories":5440},[65],{"categories":5442},[65],{"categories":5444},[110],{"categories":5446},[],{"categories":5448},[110],{"categories":5450},[144],{"categories":5452},[110],{"categories":5454},[],{"categories":5456},[144],{"categories":5458},[122],{"categories":5460},[2600],{"categories":5462},[102],{"categories":5464},[122],{"categories":5466},[65],{"categories":5468},[110],{"categories":5470},[65],{"categories":5472},[65],{"categories":5474},[213],{"categories":5476},[122],{"categories":5478},[],{"categories":5480},[144],{"categories":5482},[65],{"categories":5484},[],{"categories":5486},[110],{"categories":5488},[65],{"categories":5490},[65],{"categories":5492},[65],{"categories":5494},[65],{"categories":5496},[110],{"categories":5498},[65],{"categories":5500},[65],{"categories":5502},[113],{"categories":5504},[110],{"categories":5506},[65],{"categories":5508},[65],{"categories":5510},[65],{"categories":5512},[65],{"categories":5514},[65],{"categories":5516},[65],{"categories":5518},[105],{"categories":5520},[],{"categories":5522},[113],{"categories":5524},[144],{"categories":5526},[110],{"categories":5528},[65],{"categories":5530},[122],{"categories":5532},[],{"categories":5534},[122],{"categories":5536},[122],{"categories":5538},[110],{"categories":5540},[122],{"categories":5542},[65],{"categories":5544},[65],{"categories":5546},[122],{"categories":5548},[65],{"categories":5550},[110],{"categories":5552},[144],{"categories":5554},[65],{"categories":5556},[65],{"categories":5558},[65],{"categories":5560},[105],{"categories":5562},[65],{"categories":5564},[110],{"categories":5566},[185],{"categories":5568},[],{"categories":5570},[65],{"categories":5572},[188],{"categories":5574},[110],{"categories":5576},[65],{"categories":5578},[65],{"categories":5580},[],{"categories":5582},[65],{"categories":5584},[65],{"categories":5586},[144],{"categories":5588},[65],{"categories":5590},[65],{"categories":5592},[110],{"categories":5594},[213],{"categories":5596},[],{"categories":5598},[],{"categories":5600},[122],{"categories":5602},[65],{"categories":5604},[144],{"categories":5606},[122],{"categories":5608},[144],{"categories":5610},[65],{"categories":5612},[213],{"categories":5614},[188],{"categories":5616},[65],{"categories":5618},[102],{"categories":5620},[110],{"categories":5622},[65],{"categories":5624},[110],{"categories":5626},[110],{"categories":5628},[65],{"categories":5630},[105],{"categories":5632},[],{"categories":5634},[188],{"categories":5636},[65],{"categories":5638},[],{"categories":5640},[144],{"categories":5642},[65],{"categories":5644},[188],{"categories":5646},[65],{"categories":5648},[122],{"categories":5650},[122],{"categories":5652},[122],{"categories":5654},[110],{"categories":5656},[110],{"categories":5658},[110],{"categories":5660},[65],{"categories":5662},[65],{"categories":5664},[185],{"categories":5666},[188],{"categories":5668},[188],{"categories":5670},[],{"categories":5672},[144],{"categories":5674},[65],{"categories":5676},[65],{"categories":5678},[122],{"categories":5680},[],{"categories":5682},[144],{"categories":5684},[144],{"categories":5686},[144],{"categories":5688},[],{"categories":5690},[110],{"categories":5692},[65],{"categories":5694},[],{"categories":5696},[102],{"categories":5698},[105],{"categories":5700},[],{"categories":5702},[65],{"categories":5704},[65],{"categories":5706},[],{"categories":5708},[122],{"categories":5710},[],{"categories":5712},[],{"categories":5714},[],{"categories":5716},[],{"categories":5718},[65],{"categories":5720},[144],{"categories":5722},[],{"categories":5724},[],{"categories":5726},[65],{"categories":5728},[65],{"categories":5730},[65],{"categories":5732},[188],{"categories":5734},[65],{"categories":5736},[188],{"categories":5738},[],{"categories":5740},[188],{"categories":5742},[188],{"categories":5744},[248],{"categories":5746},[110],{"categories":5748},[122],{"categories":5750},[],{"categories":5752},[],{"categories":5754},[188],{"categories":5756},[122],{"categories":5758},[122],{"categories":5760},[122],{"categories":5762},[],{"categories":5764},[102],{"categories":5766},[122],{"categories":5768},[122],{"categories":5770},[102],{"categories":5772},[122],{"categories":5774},[105],{"categories":5776},[122],{"categories":5778},[122],{"categories":5780},[122],{"categories":5782},[188],{"categories":5784},[144],{"categories":5786},[144],{"categories":5788},[65],{"categories":5790},[122],{"categories":5792},[188],{"categories":5794},[248],{"categories":5796},[188],{"categories":5798},[188],{"categories":5800},[188],{"categories":5802},[],{"categories":5804},[105],{"categories":5806},[],{"categories":5808},[248],{"categories":5810},[122],{"categories":5812},[122],{"categories":5814},[122],{"categories":5816},[110],{"categories":5818},[144,105],{"categories":5820},[188],{"categories":5822},[],{"categories":5824},[],{"categories":5826},[188],{"categories":5828},[],{"categories":5830},[188],{"categories":5832},[144],{"categories":5834},[110],{"categories":5836},[],{"categories":5838},[122],{"categories":5840},[65],{"categories":5842},[185],{"categories":5844},[],{"categories":5846},[65],{"categories":5848},[],{"categories":5850},[144],{"categories":5852},[102],{"categories":5854},[188],{"categories":5856},[],{"categories":5858},[122],{"categories":5860},[144],[5862,5918,5991,6046],{"id":5863,"title":5864,"ai":5865,"body":5870,"categories":5898,"created_at":66,"date_modified":66,"description":59,"extension":67,"faq":66,"featured":68,"kicker_label":66,"meta":5899,"navigation":83,"path":5909,"published_at":85,"question":66,"scraped_at":85,"seo":5910,"sitemap":5911,"source_id":5912,"source_name":89,"source_type":90,"source_url":5903,"stem":5913,"tags":5914,"thumbnail_url":66,"tldr":5915,"tweet":66,"unknown_tags":5916,"__hash__":5917},"summaries\u002Fsummaries\u002Fc880efaf08e44ef1-spectral-lsh-sub-quadratic-prompt-compression-summary.md","Spectral-LSH: Sub-Quadratic Prompt Compression",{"provider":7,"model":8,"input_tokens":5866,"output_tokens":5867,"processing_time_ms":5868,"cost_usd":5869},4021,475,2563,0.00171775,{"type":14,"value":5871,"toc":5893},[5872,5876,5879,5883,5886,5890],[17,5873,5875],{"id":5874},"reducing-computational-complexity-in-long-context-llms","Reducing Computational Complexity in Long-Context LLMs",[22,5877,5878],{},"Standard attention mechanisms scale quadratically with sequence length, creating a bottleneck for long-context applications. Spectral-LSH addresses this by introducing a compression technique that reduces the memory and compute footprint of prompts without sacrificing significant performance. By leveraging Krylov-projected locality-sensitive hashing (LSH), the method effectively maps high-dimensional token representations into a lower-dimensional space while preserving the semantic relationships necessary for accurate model inference.",[17,5880,5882],{"id":5881},"krylov-projected-hashing-for-semantic-preservation","Krylov-Projected Hashing for Semantic Preservation",[22,5884,5885],{},"The core innovation lies in the use of Krylov subspace projections to refine the hashing process. Unlike traditional LSH, which may suffer from collisions that degrade model accuracy, the Krylov-based approach captures the spectral properties of the attention matrix. This ensures that the compressed representation retains the most critical information from the original prompt. By projecting tokens into this subspace, the model can perform attention operations in a sub-quadratic time complexity, enabling the processing of significantly longer prompts than would be feasible with standard attention mechanisms.",[17,5887,5889],{"id":5888},"practical-implications-for-ai-engineering","Practical Implications for AI Engineering",[22,5891,5892],{},"This approach offers a pathway to deploying LLMs in environments where memory constraints are tight but context requirements are high. By compressing prompts before they reach the attention layers, developers can maintain high throughput and reduce latency. The sub-quadratic nature of Spectral-LSH makes it particularly well-suited for RAG pipelines and long-form document analysis, where the overhead of processing massive context windows often leads to diminishing returns in speed and cost-efficiency.",{"title":59,"searchDepth":60,"depth":60,"links":5894},[5895,5896,5897],{"id":5874,"depth":60,"text":5875},{"id":5881,"depth":60,"text":5882},{"id":5888,"depth":60,"text":5889},[65],{"content_references":5900,"triage":5905},[5901],{"type":72,"title":5902,"url":5903,"context":5904},"Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.19368","reviewed",{"relevance":5906,"novelty":80,"quality":80,"actionability":80,"composite":5907,"reasoning":5908},5,4.35,"Category: AI & LLMs. The article presents a novel approach to optimizing long-context LLM performance, addressing a specific pain point of computational complexity in AI engineering. It offers practical implications for developers looking to implement this technique in real-world applications, particularly in RAG pipelines.","\u002Fsummaries\u002Fc880efaf08e44ef1-spectral-lsh-sub-quadratic-prompt-compression-summary",{"title":5864,"description":59},{"loc":5909},"c880efaf08e44ef1","summaries\u002Fc880efaf08e44ef1-spectral-lsh-sub-quadratic-prompt-compression-summary",[93,94,95],"Spectral-LSH optimizes long-context LLM performance by using Krylov-projected locality-sensitive hashing to compress prompts with sub-quadratic complexity.",[],"xIzXiI6spWxPMblzhRhAcUyLGO5DXkIQwVj67lyXcU8",{"id":5919,"title":5920,"ai":5921,"body":5926,"categories":5972,"created_at":66,"date_modified":66,"description":59,"extension":67,"faq":66,"featured":68,"kicker_label":66,"meta":5973,"navigation":83,"path":5981,"published_at":5982,"question":66,"scraped_at":5982,"seo":5983,"sitemap":5984,"source_id":5985,"source_name":89,"source_type":90,"source_url":5977,"stem":5986,"tags":5987,"thumbnail_url":66,"tldr":5988,"tweet":66,"unknown_tags":5989,"__hash__":5990},"summaries\u002Fsummaries\u002Ff5166a1346225310-logic-guided-data-extraction-combining-asp-and-llm-summary.md","Logic-Guided Data Extraction: Combining ASP and LLMs",{"provider":7,"model":8,"input_tokens":5922,"output_tokens":5923,"processing_time_ms":5924,"cost_usd":5925},4048,526,2998,0.001801,{"type":14,"value":5927,"toc":5967},[5928,5932,5935,5939,5942,5957,5960,5964],[17,5929,5931],{"id":5930},"the-hybrid-approach-to-data-extraction","The Hybrid Approach to Data Extraction",[22,5933,5934],{},"The core challenge in using Large Language Models (LLMs) for data extraction is their tendency to produce hallucinated or logically inconsistent outputs. This paper introduces a framework that integrates Answer Set Programming (ASP)—a declarative logic programming paradigm—with LLMs to provide a formal verification layer. By treating the LLM as a generator of candidate facts and the ASP solver as a validator, the system ensures that the final extracted data adheres to predefined domain-specific logical rules.",[17,5936,5938],{"id":5937},"enforcing-logical-consistency","Enforcing Logical Consistency",[22,5940,5941],{},"In this architecture, the process is divided into two distinct phases:",[5943,5944,5945,5951],"ol",{},[36,5946,5947,5950],{},[39,5948,5949],{},"Generation:"," The LLM processes unstructured input and generates a set of candidate facts or entities.",[36,5952,5953,5956],{},[39,5954,5955],{},"Validation:"," These candidates are fed into an ASP solver, which evaluates them against a set of hard constraints. If the LLM's output violates these constraints (e.g., conflicting dates, impossible relationships, or missing mandatory fields), the ASP solver rejects the invalid data or forces a re-evaluation.",[22,5958,5959],{},"This approach effectively bridges the gap between the probabilistic nature of LLMs and the deterministic requirements of structured data systems. By offloading the logical reasoning to a formal solver, the system reduces the burden on the LLM to 'get it right' through prompting alone, instead allowing it to focus on the extraction task while the logic layer handles the integrity of the output.",[17,5961,5963],{"id":5962},"practical-implications-for-ai-pipelines","Practical Implications for AI Pipelines",[22,5965,5966],{},"This method is particularly useful for high-stakes domains where data integrity is non-negotiable. By utilizing ASP, developers can define complex business rules that the LLM might otherwise struggle to follow. The framework demonstrates that combining symbolic AI (logic-based) with connectionist AI (LLMs) leads to more robust, verifiable, and reliable data pipelines than using LLMs in isolation.",{"title":59,"searchDepth":60,"depth":60,"links":5968},[5969,5970,5971],{"id":5930,"depth":60,"text":5931},{"id":5937,"depth":60,"text":5938},{"id":5962,"depth":60,"text":5963},[65],{"content_references":5974,"triage":5978},[5975],{"type":72,"title":5976,"url":5977,"context":5904},"Logic-Guided Data Extraction with Answer Set Programming and Large Language Models","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.19365",{"relevance":5906,"novelty":80,"quality":80,"actionability":79,"composite":5979,"reasoning":5980},4.15,"Category: AI & LLMs. The article presents a novel hybrid architecture that combines Answer Set Programming with LLMs to enhance data extraction accuracy, addressing a critical pain point of hallucination in LLM outputs. It offers practical implications for AI pipelines, making it relevant for developers looking to implement robust data extraction solutions.","\u002Fsummaries\u002Ff5166a1346225310-logic-guided-data-extraction-combining-asp-and-llm-summary","2026-07-23 17:59:29",{"title":5920,"description":59},{"loc":5981},"f5166a1346225310","summaries\u002Ff5166a1346225310-logic-guided-data-extraction-combining-asp-and-llm-summary",[93,94,95],"This research proposes a hybrid architecture that uses Answer Set Programming (ASP) to enforce logical constraints on LLM-generated data, ensuring accuracy and consistency in complex extraction tasks.",[],"J49KEbFMDivvWr10WdfEZPk6EWK4NVKKP72Q8LX9lAQ",{"id":5992,"title":5993,"ai":5994,"body":5999,"categories":6027,"created_at":66,"date_modified":66,"description":59,"extension":67,"faq":66,"featured":68,"kicker_label":66,"meta":6028,"navigation":83,"path":6036,"published_at":6037,"question":66,"scraped_at":6037,"seo":6038,"sitemap":6039,"source_id":6040,"source_name":89,"source_type":90,"source_url":6032,"stem":6041,"tags":6042,"thumbnail_url":66,"tldr":6043,"tweet":66,"unknown_tags":6044,"__hash__":6045},"summaries\u002Fsummaries\u002Fcce01852c2bf6797-statistically-grounded-sparse-feature-intervention-summary.md","Statistically Grounded Sparse-Feature Interventions in LLMs",{"provider":7,"model":8,"input_tokens":5995,"output_tokens":5996,"processing_time_ms":5997,"cost_usd":5998},4060,471,2692,0.0017215,{"type":14,"value":6000,"toc":6022},[6001,6005,6008,6012,6015,6019],[17,6002,6004],{"id":6003},"moving-beyond-heuristic-activation-steering","Moving Beyond Heuristic Activation Steering",[22,6006,6007],{},"Traditional methods for steering Large Language Models (LLMs) often rely on heuristic-based interventions in activation space—manually identifying directions or features that correlate with specific behaviors and applying additive shifts. This paper argues that such approaches often lack statistical rigor, leading to unpredictable side effects or degradation in model performance. The authors propose a statistically grounded framework that treats sparse-feature interventions as a formal optimization problem, ensuring that modifications to internal representations remain within the model's learned distribution.",[17,6009,6011],{"id":6010},"the-sparse-feature-intervention-framework","The Sparse-Feature Intervention Framework",[22,6013,6014],{},"The core of the proposed method involves mapping dense activations into a sparse, interpretable feature space. By applying statistical constraints during the intervention process, the authors demonstrate that it is possible to exert precise control over model outputs while minimizing the 'drift' that typically occurs when forcing activations into unnatural states. This approach relies on identifying the specific sparse features responsible for target behaviors and adjusting them using a principled objective function that balances the strength of the intervention against the preservation of the model's original semantic integrity.",[17,6016,6018],{"id":6017},"practical-implications-for-model-control","Practical Implications for Model Control",[22,6020,6021],{},"The research provides a methodology for developers to perform 'surgical' edits on model behavior. By grounding these interventions in statistical theory, the authors show that one can achieve more stable and reproducible results compared to standard activation patching or steering vectors. The paper includes extensive empirical validation across multiple tasks, demonstrating that this approach maintains higher coherence and factual accuracy when compared to unconstrained steering methods. This framework is particularly relevant for practitioners looking to align models or mitigate specific biases without the high cost of full fine-tuning.",{"title":59,"searchDepth":60,"depth":60,"links":6023},[6024,6025,6026],{"id":6003,"depth":60,"text":6004},{"id":6010,"depth":60,"text":6011},{"id":6017,"depth":60,"text":6018},[65],{"content_references":6029,"triage":6033},[6030],{"type":72,"title":6031,"url":6032,"context":5904},"Statistically Grounded Sparse-Feature Interventions for Activation-Space Control in Large Language Models","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.19364",{"relevance":80,"novelty":80,"quality":80,"actionability":79,"composite":6034,"reasoning":6035},3.8,"Category: AI & LLMs. The article presents a new statistical framework for controlling LLM behavior, addressing a specific pain point of developers seeking more reliable methods for model intervention. It offers empirical validation, which enhances its credibility, but lacks detailed step-by-step guidance for practical implementation.","\u002Fsummaries\u002Fcce01852c2bf6797-statistically-grounded-sparse-feature-intervention-summary","2026-07-23 17:59:28",{"title":5993,"description":59},{"loc":6036},"cce01852c2bf6797","summaries\u002Fcce01852c2bf6797-statistically-grounded-sparse-feature-intervention-summary",[93,94,95],"This paper introduces a rigorous statistical framework for controlling LLM behavior by intervening on sparse features in activation space, moving beyond heuristic-based steering.",[],"AJ2uFEYzFhZ4ByeIoWUvHTc5kRaqYx72L9Mpcinx8UY",{"id":6047,"title":6048,"ai":6049,"body":6054,"categories":6074,"created_at":66,"date_modified":66,"description":59,"extension":67,"faq":66,"featured":68,"kicker_label":66,"meta":6075,"navigation":83,"path":6080,"published_at":6081,"question":66,"scraped_at":6082,"seo":6083,"sitemap":6084,"source_id":6085,"source_name":6086,"source_type":90,"source_url":6087,"stem":6088,"tags":6089,"thumbnail_url":66,"tldr":6090,"tweet":66,"unknown_tags":6091,"__hash__":6092},"summaries\u002Fsummaries\u002F1b14adf64719aeca-why-static-word-embeddings-fail-at-contextual-mean-summary.md","Why Static Word Embeddings Fail at Contextual Meaning",{"provider":7,"model":8,"input_tokens":6050,"output_tokens":6051,"processing_time_ms":6052,"cost_usd":6053},4038,374,2694,0.0015705,{"type":14,"value":6055,"toc":6070},[6056,6060,6063,6067],[17,6057,6059],{"id":6058},"the-failure-of-static-word-representations","The Failure of Static Word Representations",[22,6061,6062],{},"Early language processing systems relied on static word embeddings, which assigned a single, fixed numerical vector to every word in a vocabulary. This design choice treated polysemous words—words with multiple meanings, like \"plant\"—as identical entities regardless of their surrounding context. Whether the text referred to a botanical organism or an industrial manufacturing facility, the system mapped both to the same coordinate in vector space. This architectural limitation meant that downstream models lacked the nuance to differentiate between distinct concepts, leading to confident but incorrect interpretations in tasks like search, sentiment analysis, and classification.",[17,6064,6066],{"id":6065},"the-cost-of-context-blindness","The Cost of Context-Blindness",[22,6068,6069],{},"By collapsing multiple meanings into a single representation, these systems introduced a \"semantic bottleneck.\" Because the model could not distinguish between senses, it effectively averaged the features of all possible meanings into one \"average\" vector. This resulted in a loss of precision that propagated through the entire pipeline. When a system cannot resolve ambiguity, it cannot accurately model relationships between words, leading to failures in tasks where context is the primary driver of intent. This structural flaw explains why older chatbots and search engines often struggled with logical consistency and relevance, as the underlying representation was fundamentally incapable of capturing the fluidity of human language.",{"title":59,"searchDepth":60,"depth":60,"links":6071},[6072,6073],{"id":6058,"depth":60,"text":6059},{"id":6065,"depth":60,"text":6066},[65],{"content_references":6076,"triage":6077},[],{"relevance":79,"novelty":79,"quality":80,"actionability":60,"composite":6078,"reasoning":6079},3.05,"Category: AI & LLMs. The article discusses the limitations of static word embeddings in NLP, which is relevant to AI and LLMs. While it provides some insights into the historical context of word representations, it lacks practical applications or frameworks that the audience could implement.","\u002Fsummaries\u002F1b14adf64719aeca-why-static-word-embeddings-fail-at-contextual-mean-summary","2026-06-25 03:31:28","2026-06-25 12:57:04",{"title":6048,"description":59},{"loc":6080},"1b14adf64719aeca","Level Up Coding","https:\u002F\u002Flevelup.gitconnected.com\u002Fplant-plant-plant-what-word-representation-gets-wrong-6b942de6a4a6?source=rss----5517fd7b58a6---4","summaries\u002F1b14adf64719aeca-why-static-word-embeddings-fail-at-contextual-mean-summary",[93,94,95],"Early NLP systems treated words as fixed, singular vectors, ignoring polysemy. This design flaw caused systemic errors by failing to distinguish between different meanings of the same word based on context.",[],"zXEVEEDqZMqXXzywDYn-N8G4dp9MhqQbD43AJP8U3b4"]