[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-a4cc3af3b10be34a-scalable-ai-evaluation-via-program-distillation-summary":3,"summaries-facets-categories":133,"summary-related-a4cc3af3b10be34a-scalable-ai-evaluation-via-program-distillation-summary":6551},{"id":4,"title":5,"ai":6,"body":13,"categories":98,"created_at":100,"date_modified":100,"description":91,"extension":101,"faq":100,"featured":102,"kicker_label":100,"meta":103,"navigation":115,"path":116,"published_at":117,"question":100,"scraped_at":117,"seo":118,"sitemap":119,"source_id":120,"source_name":121,"source_type":122,"source_url":123,"stem":124,"tags":125,"thumbnail_url":100,"tldr":130,"tweet":100,"unknown_tags":131,"__hash__":132},"summaries\u002Fsummaries\u002Fa4cc3af3b10be34a-scalable-ai-evaluation-via-program-distillation-summary.md","Scalable AI Evaluation via Program Distillation",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",6326,538,2617,0.0023885,{"type":14,"value":15,"toc":90},"minimark",[16,21,25,29,32,55,59,62,83,87],[17,18,20],"h2",{"id":19},"the-problem-with-llm-as-a-judge","The Problem with LLM-as-a-Judge",[22,23,24],"p",{},"Using LLMs to evaluate other models has become the industry standard, but it is fundamentally limited by high API costs, significant latency, and the 'black box' nature of LLM decisions. These factors make large-scale evaluation expensive and difficult to audit, as there is no clear logic behind why a specific score was assigned to a candidate output.",[17,26,28],{"id":27},"program-distillation-from-prompts-to-code","Program Distillation: From Prompts to Code",[22,30,31],{},"The authors propose 'program distillation' as a solution: extracting the decision-making logic of an LLM judge into a committee of executable programs. By converting an LLM's evaluation criteria into code, the system gains several advantages:",[33,34,35,43,49],"ul",{},[36,37,38,42],"li",{},[39,40,41],"strong",{},"Transparency:"," Programmatic judges are inherently inspectable and editable.",[36,44,45,48],{},[39,46,47],{},"Efficiency:"," They eliminate per-sample API costs, allowing for massive scaling of evaluation tasks.",[36,50,51,54],{},[39,52,53],{},"Performance:"," Across five datasets and four model families, these programmatic judges matched the performance of a 13B-parameter LLM judge.",[17,56,58],{"id":57},"the-pajama-system","The PAJAMA System",[22,60,61],{},"The authors introduce PAJAMA, a framework that manages this programmatic evaluation process. It functions through three core mechanisms:",[63,64,65,71,77],"ol",{},[36,66,67,70],{},[39,68,69],{},"Synthesis:"," It synthesizes a committee of programs to act as judges.",[36,72,73,76],{},[39,74,75],{},"Aggregation:"," It combines the outputs of these programs into a single, joint verdict.",[36,78,79,82],{},[39,80,81],{},"Selective Escalation:"," It includes a fallback mechanism that routes low-confidence cases to an LLM, ensuring that the system maintains high accuracy while keeping the majority of traffic on the cheaper, faster programmatic path.",[17,84,86],{"id":85},"beyond-evaluation-reward-signals","Beyond Evaluation: Reward Signals",[22,88,89],{},"Beyond simple evaluation, the authors demonstrate that these programmatic judges can generate high-quality, low-cost reward signals for training other models. On the RewardBench benchmark, a reward model trained on labels generated by these programs outperformed one trained on proprietary LLM labels, while operating at two orders of magnitude lower API cost.",{"title":91,"searchDepth":92,"depth":92,"links":93},"",2,[94,95,96,97],{"id":19,"depth":92,"text":20},{"id":27,"depth":92,"text":28},{"id":57,"depth":92,"text":58},{"id":85,"depth":92,"text":86},[99],"AI & LLMs",null,"md",false,{"content_references":104,"triage":109},[105],{"type":106,"title":107,"context":108},"other","RewardBench","mentioned",{"relevance":110,"novelty":111,"quality":111,"actionability":112,"composite":113,"reasoning":114},5,4,3,4.15,"Category: AI & LLMs. The article presents a novel approach to AI evaluation that addresses key pain points such as cost and transparency, which are critical for product builders. It introduces the PAJAMA system, which could be directly applicable for developers looking to implement efficient evaluation mechanisms in their AI products.",true,"\u002Fsummaries\u002Fa4cc3af3b10be34a-scalable-ai-evaluation-via-program-distillation-summary","2026-07-29 03:12:17",{"title":5,"description":91},{"loc":116},"a4cc3af3b10be34a","arXiv cs.AI","article","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.22561","summaries\u002Fa4cc3af3b10be34a-scalable-ai-evaluation-via-program-distillation-summary",[126,127,128,129],"automation","machine-learning","llm","ai-llms","PAJAMA replaces expensive LLM-as-a-judge systems with a committee of distilled programs, reducing costs while maintaining performance and increasing transparency.",[129],"WXIbXiLHvXWj0n970j8mnB2FnOzCghD6XOvTonA9Zjg",[134,136,139,141,144,146,149,152,154,156,158,161,163,165,167,169,172,174,176,178,180,183,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,229,231,233,235,237,239,241,243,245,247,249,251,253,255,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,295,297,299,301,303,305,307,309,311,313,315,317,319,321,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,387,389,391,393,395,397,399,401,403,405,407,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,443,445,447,449,451,453,455,457,459,461,463,466,468,470,472,474,476,478,480,482,484,486,488,490,492,494,496,498,500,502,504,506,508,510,512,514,516,518,521,523,525,528,530,532,534,536,538,540,542,544,546,548,550,552,554,557,559,561,563,565,567,569,571,573,575,577,579,582,584,586,588,590,592,594,596,598,600,602,604,606,608,610,612,614,616,618,620,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798,800,802,804,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,857,859,861,863,865,868,870,872,874,876,878,880,882,884,886,888,890,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1516,1518,1520,1522,1524,1526,1528,1530,1532,1534,1536,1538,1540,1542,1544,1546,1548,1550,1552,1554,1556,1558,1560,1562,1564,1566,1568,1570,1572,1574,1576,1578,1580,1582,1584,1586,1588,1590,1592,1594,1596,1598,1600,1602,1604,1606,1608,1610,1612,1614,1616,1618,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1778,1780,1782,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1812,1814,1816,1818,1820,1822,1824,1826,1828,1830,1832,1834,1836,1838,1840,1842,1844,1846,1848,1850,1852,1854,1856,1858,1860,1862,1864,1866,1868,1870,1872,1874,1876,1878,1880,1882,1884,1886,1888,1890,1892,1894,1896,1898,1900,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1962,1964,1966,1968,1970,1972,1974,1976,1978,1980,1982,1984,1986,1988,1990,1992,1994,1996,1998,2000,2002,2004,2006,2008,2010,2012,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032,2034,2036,2038,2040,2042,2044,2046,2048,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2206,2208,2210,2212,2214,2216,2218,2220,2222,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2282,2284,2286,2288,2290,2292,2294,2296,2298,2300,2302,2304,2306,2308,2310,2312,2314,2316,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2360,2362,2364,2366,2368,2370,2372,2374,2376,2378,2380,2382,2384,2386,2388,2390,2392,2394,2396,2398,2400,2402,2404,2406,2408,2410,2412,2414,2416,2418,2420,2422,2424,2426,2428,2430,2432,2434,2436,2438,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458,2460,2462,2464,2466,2468,2470,2472,2474,2476,2478,2480,2482,2484,2486,2488,2490,2492,2494,2496,2498,2500,2502,2504,2506,2508,2510,2512,2514,2516,2518,2520,2522,2524,2526,2528,2530,2532,2534,2536,2538,2540,2542,2544,2546,2548,2550,2552,2554,2556,2558,2560,2562,2564,2566,2568,2570,2572,2574,2576,2578,2580,2582,2584,2586,2588,2590,2592,2594,2596,2598,2600,2602,2604,2606,2608,2610,2612,2614,2616,2618,2620,2622,2624,2626,2628,2630,2632,2634,2636,2638,2640,2642,2644,2646,2648,2650,2652,2654,2656,2658,2660,2662,2664,2666,2668,2670,2672,2674,2676,2678,2680,2682,2684,2686,2688,2690,2692,2694,2696,2698,2700,2702,2704,2706,2708,2710,2712,2714,2716,2718,2720,2722,2724,2726,2728,2730,2732,2734,2736,2738,2740,2742,2744,2746,2748,2750,2752,2754,2756,2758,2760,2762,2764,2766,2768,2770,2772,2774,2776,2778,2780,2782,2784,2786,2788,2790,2792,2794,2796,2798,2800,2802,2804,2806,2808,2810,2812,2814,2816,2818,2820,2822,2824,2826,2828,2830,2832,2834,2836,2838,2840,2842,2844,2846,2848,2850,2852,2854,2856,2858,2860,2862,2864,2866,2868,2870,2872,2874,2876,2878,2880,2882,2884,2886,2888,2890,2892,2894,2896,2898,2900,2902,2904,2906,2908,2910,2912,2914,2916,2918,2920,2922,2924,2926,2928,2930,2932,2934,2936,2938,2940,2942,2944,2946,2948,2950,2952,2954,2956,2958,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3076,3078,3080,3082,3084,3086,3088,3090,3092,3094,3096,3098,3100,3102,3104,3106,3108,3110,3112,3114,3116,3118,3120,3122,3124,3126,3128,3130,3132,3134,3136,3138,3140,3142,3144,3146,3148,3150,3152,3154,3156,3158,3160,3162,3164,3166,3168,3170,3172,3174,3176,3178,3180,3182,3184,3186,3188,3190,3192,3194,3196,3198,3200,3202,3204,3206,3208,3210,3212,3214,3216,3218,3220,3222,3224,3226,3228,3230,3232,3234,3236,3238,3240,3242,3244,3246,3248,3250,3252,3254,3256,3258,3260,3262,3264,3266,3268,3270,3272,3274,3276,3278,3280,3282,3284,3286,3288,3290,3292,3294,3296,3298,3300,3302,3304,3306,3308,3310,3312,3314,3316,3318,3320,3322,3324,3326,3328,3330,3332,3334,3336,3338,3340,3342,3344,3346,3348,3350,3352,3354,3356,3358,3360,3362,3364,3366,3368,3370,3372,3374,3376,3378,3380,3382,3384,3386,3388,3390,3392,3394,3396,3398,3400,3402,3404,3406,3408,3410,3412,3414,3416,3418,3420,3422,3424,3426,3428,3430,3432,3434,3436,3438,3440,3442,3444,3446,3448,3450,3452,3454,3456,3458,3460,3462,3464,3466,3468,3470,3472,3474,3476,3478,3480,3482,3484,3486,3488,3490,3492,3494,3496,3498,3500,3502,3504,3506,3508,3510,3512,3514,3516,3518,3520,3522,3524,3526,3528,3530,3532,3534,3536,3538,3540,3542,3544,3546,3548,3550,3552,3554,3556,3558,3560,3562,3564,3566,3568,3570,3572,3574,3576,3578,3580,3582,3584,3586,3588,3590,3592,3594,3596,3598,3600,3602,3604,3606,3608,3610,3612,3614,3616,3618,3620,3622,3624,3626,3628,3630,3632,3634,3636,3638,3640,3642,3644,3646,3648,3650,3652,3654,3656,3658,3660,3662,3664,3666,3668,3670,3672,3674,3676,3678,3680,3682,3684,3686,3688,3690,3692,3694,3696,3698,3700,3702,3704,3706,3708,3710,3712,3714,3716,3718,3720,3722,3724,3726,3728,3730,3732,3734,3736,3738,3740,3742,3744,3746,3748,3750,3752,3754,3756,3758,3760,3762,3764,3766,3768,3770,3772,3774,3776,3778,3780,3782,3784,3786,3788,3790,3792,3794,3796,3798,3800,3802,3804,3806,3808,3810,3812,3814,3816,3818,3820,3822,3824,3826,3828,3830,3832,3834,3836,3838,3840,3842,3844,3846,3848,3850,3852,3854,3856,3858,3860,3862,3864,3866,3868,3870,3872,3874,3876,3878,3880,3882,3884,3886,3888,3890,3892,3894,3896,3898,3900,3902,3904,3906,3908,3910,3912,3914,3916,3918,3920,3922,3924,3926,3928,3930,3932,3934,3936,3938,3940,3942,3944,3946,3948,3950,3952,3954,3956,3958,3960,3962,3964,3966,3968,3970,3972,3974,3976,3978,3980,3982,3984,3986,3988,3990,3992,3994,3996,3998,4000,4002,4004,4006,4008,4010,4012,4014,4016,4018,4020,4022,4024,4026,4028,4030,4032,4034,4036,4038,4040,4042,4044,4046,4048,4050,4052,4054,4056,4058,4060,4062,4064,4066,4068,4070,4072,4074,4076,4078,4080,4082,4084,4086,4088,4090,4092,4094,4096,4098,4100,4102,4104,4106,4108,4110,4112,4114,4116,4118,4120,4122,4124,4126,4128,4130,4132,4134,4136,4138,4140,4142,4144,4146,4148,4150,4152,4154,4156,4158,4160,4162,4164,4166,4168,4170,4172,4174,4176,4178,4180,4182,4184,4186,4188,4190,4192,4194,4196,4198,4200,4202,4204,4206,4208,4210,4212,4214,4216,4218,4220,4222,4224,4226,4228,4230,4232,4234,4236,4238,4240,4242,4244,4246,4248,4250,4252,4254,4256,4258,4260,4263,4265,4267,4269,4271,4273,4275,4277,4279,4281,4283,4285,4287,4289,4291,4293,4295,4297,4299,4301,4303,4305,4307,4309,4311,4313,4315,4317,4319,4321,4323,4325,4327,4329,4331,4333,4335,4337,4339,4341,4343,4345,4347,4349,4351,4353,4355,4357,4359,4361,4363,4365,4367,4369,4371,4373,4375,4377,4379,4381,4383,4385,4387,4389,4391,4393,4395,4397,4399,4401,4403,4405,4407,4409,4411,4413,4415,4417,4419,4421,4423,4425,4427,4429,4431,4433,4435,4437,4439,4441,4443,4445,4447,4449,4451,4453,4455,4457,4459,4461,4463,4465,4467,4469,4471,4473,4475,4477,4479,4481,4483,4485,4487,4489,4491,4493,4495,4497,4499,4501,4503,4505,4507,4509,4511,4513,4515,4517,4519,4521,4523,4525,4527,4529,4531,4533,4535,4537,4539,4541,4543,4545,4547,4549,4551,4553,4555,4557,4559,4561,4563,4565,4567,4569,4571,4573,4575,4577,4579,4581,4583,4585,4587,4589,4591,4593,4595,4597,4599,4601,4603,4605,4607,4609,4611,4613,4615,4617,4619,4621,4623,4625,4627,4629,4631,4633,4635,4637,4639,4641,4643,4645,4647,4649,4651,4653,4655,4657,4659,4661,4663,4665,4667,4669,4671,4673,4675,4677,4679,4681,4683,4685,4687,4689,4691,4693,4695,4697,4699,4701,4703,4705,4707,4709,4711,4713,4715,4717,4719,4721,4723,4725,4727,4729,4731,4733,4735,4737,4739,4741,4743,4745,4747,4749,4751,4753,4755,4757,4759,4761,4763,4765,4767,4769,4771,4773,4775,4777,4779,4781,4783,4785,4787,4789,4791,4793,4795,4797,4799,4801,4803,4805,4807,4809,4811,4813,4815,4817,4819,4821,4823,4825,4827,4829,4831,4833,4835,4837,4839,4841,4843,4845,4847,4849,4851,4853,4855,4857,4859,4861,4863,4865,4867,4869,4871,4873,4875,4877,4879,4881,4883,4885,4887,4889,4891,4893,4895,4897,4899,4901,4903,4905,4907,4909,4911,4913,4915,4917,4919,4921,4923,4925,4927,4929,4931,4933,4935,4937,4939,4941,4943,4945,4947,4949,4951,4953,4955,4957,4959,4961,4963,4965,4967,4969,4971,4973,4975,4977,4979,4981,4983,4985,4987,4989,4991,4993,4995,4997,4999,5001,5003,5005,5007,5009,5011,5013,5015,5017,5019,5021,5023,5025,5027,5029,5031,5033,5035,5037,5039,5041,5043,5045,5047,5049,5051,5053,5055,5057,5060,5062,5064,5066,5068,5070,5072,5074,5076,5078,5080,5082,5084,5086,5088,5090,5092,5094,5096,5098,5100,5102,5104,5106,5108,5110,5112,5114,5116,5118,5120,5122,5124,5126,5128,5130,5132,5134,5136,5138,5140,5142,5144,5146,5148,5150,5152,5154,5156,5158,5160,5162,5164,5166,5168,5170,5172,5174,5176,5178,5180,5182,5184,5186,5188,5190,5192,5194,5196,5198,5200,5202,5204,5206,5208,5210,5212,5214,5216,5218,5220,5222,5224,5226,5228,5230,5232,5234,5236,5238,5240,5242,5244,5246,5248,5251,5253,5255,5257,5259,5261,5263,5265,5267,5269,5271,5273,5275,5277,5279,5281,5283,5285,5287,5289,5291,5293,5295,5297,5299,5301,5303,5305,5307,5309,5311,5313,5315,5317,5319,5321,5323,5325,5327,5329,5331,5333,5335,5337,5339,5341,5343,5345,5347,5349,5351,5353,5355,5357,5359,5361,5363,5365,5367,5369,5371,5373,5375,5377,5379,5381,5383,5385,5387,5389,5391,5393,5395,5397,5399,5401,5403,5405,5407,5409,5411,5413,5415,5417,5419,5421,5423,5425,5427,5429,5431,5433,5435,5437,5439,5441,5443,5445,5447,5449,5451,5453,5455,5457,5459,5461,5463,5465,5467,5469,5471,5473,5475,5477,5479,5481,5483,5485,5487,5489,5491,5493,5495,5497,5499,5501,5503,5505,5507,5509,5511,5513,5515,5517,5519,5521,5523,5525,5527,5529,5531,5533,5535,5537,5539,5541,5543,5545,5547,5549,5551,5553,5555,5557,5559,5561,5563,5565,5567,5569,5571,5573,5575,5577,5579,5581,5583,5585,5587,5589,5591,5593,5595,5597,5599,5601,5603,5605,5607,5609,5611,5613,5615,5617,5619,5621,5623,5625,5627,5629,5631,5633,5635,5637,5639,5641,5643,5645,5647,5649,5651,5653,5655,5657,5659,5661,5663,5665,5667,5669,5671,5673,5675,5677,5679,5681,5683,5685,5687,5689,5691,5693,5695,5697,5699,5701,5703,5705,5707,5709,5711,5713,5715,5717,5719,5721,5723,5725,5727,5729,5731,5733,5735,5737,5739,5741,5743,5745,5747,5749,5751,5753,5755,5757,5759,5761,5763,5765,5767,5769,5771,5773,5775,5777,5779,5781,5783,5785,5787,5789,5791,5793,5795,5797,5799,5801,5803,5805,5807,5809,5811,5813,5815,5817,5819,5821,5823,5825,5827,5829,5831,5833,5835,5837,5839,5841,5843,5845,5847,5849,5851,5853,5855,5857,5859,5861,5863,5865,5867,5869,5871,5873,5875,5877,5879,5881,5883,5885,5887,5889,5891,5893,5895,5897,5899,5901,5903,5905,5907,5909,5911,5913,5915,5917,5919,5921,5923,5925,5927,5929,5931,5933,5935,5937,5939,5941,5943,5945,5947,5949,5951,5953,5955,5957,5959,5961,5963,5965,5967,5969,5971,5973,5975,5977,5979,5981,5983,5985,5987,5989,5991,5993,5995,5997,5999,6001,6003,6005,6007,6009,6011,6013,6015,6017,6019,6021,6023,6025,6027,6029,6031,6033,6035,6037,6039,6041,6043,6045,6047,6049,6051,6053,6055,6057,6059,6061,6063,6065,6067,6069,6071,6073,6075,6077,6079,6081,6083,6085,6087,6089,6091,6093,6095,6097,6099,6101,6103,6105,6107,6109,6111,6113,6115,6117,6119,6121,6123,6125,6127,6129,6131,6133,6135,6137,6139,6141,6143,6145,6147,6149,6151,6153,6155,6157,6159,6161,6163,6165,6167,6169,6171,6173,6175,6177,6179,6181,6183,6185,6187,6189,6191,6193,6195,6197,6199,6201,6203,6205,6207,6209,6211,6213,6215,6217,6219,6221,6223,6225,6227,6229,6231,6233,6235,6237,6239,6241,6243,6245,6247,6249,6251,6253,6255,6257,6259,6261,6263,6265,6267,6269,6271,6273,6275,6277,6279,6281,6283,6285,6287,6289,6291,6293,6295,6297,6299,6301,6303,6305,6307,6309,6311,6313,6315,6317,6319,6321,6323,6325,6327,6329,6331,6333,6335,6337,6339,6341,6343,6345,6347,6349,6351,6353,6355,6357,6359,6361,6363,6365,6367,6369,6371,6373,6375,6377,6379,6381,6383,6385,6387,6389,6391,6393,6395,6397,6399,6401,6403,6405,6407,6409,6411,6413,6415,6417,6419,6421,6423,6425,6427,6429,6431,6433,6435,6437,6439,6441,6443,6445,6447,6449,6451,6453,6455,6457,6459,6461,6463,6465,6467,6469,6471,6473,6475,6477,6479,6481,6483,6485,6487,6489,6491,6493,6495,6497,6499,6501,6503,6505,6507,6509,6511,6513,6515,6517,6519,6521,6523,6525,6527,6529,6531,6533,6535,6537,6539,6541,6543,6545,6547,6549],{"categories":135},[99],{"categories":137},[138],"Developer Productivity",{"categories":140},[99],{"categories":142},[143],"Business & SaaS",{"categories":145},[99],{"categories":147},[148],"AI Automation",{"categories":150},[151],"Product Strategy",{"categories":153},[99],{"categories":155},[138],{"categories":157},[148],{"categories":159},[160],"Software Engineering",{"categories":162},[99],{"categories":164},[143],{"categories":166},[],{"categories":168},[99],{"categories":170},[171],"Inference & Serving",{"categories":173},[99],{"categories":175},[99],{"categories":177},[148],{"categories":179},[],{"categories":181},[182],"AI News & Trends",{"categories":184},[185],"Data Science & Visualization",{"categories":187},[148],{"categories":189},[99],{"categories":191},[99],{"categories":193},[143],{"categories":195},[138],{"categories":197},[99],{"categories":199},[148],{"categories":201},[182],{"categories":203},[148],{"categories":205},[148],{"categories":207},[99],{"categories":209},[148],{"categories":211},[99],{"categories":213},[99],{"categories":215},[99],{"categories":217},[182],{"categories":219},[99],{"categories":221},[99],{"categories":223},[99],{"categories":225},[],{"categories":227},[228],"Design & Frontend",{"categories":230},[185],{"categories":232},[182],{"categories":234},[99],{"categories":236},[99],{"categories":238},[99],{"categories":240},[],{"categories":242},[99],{"categories":244},[99],{"categories":246},[148],{"categories":248},[160],{"categories":250},[99],{"categories":252},[148],{"categories":254},[99],{"categories":256},[257],"Marketing & Growth",{"categories":259},[228],{"categories":261},[99],{"categories":263},[148],{"categories":265},[99],{"categories":267},[160],{"categories":269},[],{"categories":271},[],{"categories":273},[228],{"categories":275},[99],{"categories":277},[148],{"categories":279},[138],{"categories":281},[160],{"categories":283},[148],{"categories":285},[228],{"categories":287},[151],{"categories":289},[99],{"categories":291},[160],{"categories":293},[294],"DevOps & Cloud",{"categories":296},[148],{"categories":298},[151],{"categories":300},[182],{"categories":302},[99],{"categories":304},[],{"categories":306},[99],{"categories":308},[99],{"categories":310},[],{"categories":312},[148],{"categories":314},[160],{"categories":316},[],{"categories":318},[160],{"categories":320},[99],{"categories":322},[323],"Governance & Standards",{"categories":325},[143],{"categories":327},[],{"categories":329},[],{"categories":331},[99],{"categories":333},[99],{"categories":335},[148],{"categories":337},[99],{"categories":339},[99],{"categories":341},[148],{"categories":343},[99],{"categories":345},[99],{"categories":347},[99],{"categories":349},[],{"categories":351},[160],{"categories":353},[],{"categories":355},[],{"categories":357},[99],{"categories":359},[160],{"categories":361},[],{"categories":363},[160],{"categories":365},[99],{"categories":367},[99],{"categories":369},[257],{"categories":371},[99],{"categories":373},[99],{"categories":375},[228],{"categories":377},[228],{"categories":379},[99],{"categories":381},[160],{"categories":383},[148],{"categories":385},[386],"GovTech & Public-Sector Adoption",{"categories":388},[160],{"categories":390},[99],{"categories":392},[99],{"categories":394},[99],{"categories":396},[148],{"categories":398},[148],{"categories":400},[185],{"categories":402},[99],{"categories":404},[182],{"categories":406},[148],{"categories":408},[409],"Legal AI Tools",{"categories":411},[99],{"categories":413},[148],{"categories":415},[99],{"categories":417},[257],{"categories":419},[148],{"categories":421},[151],{"categories":423},[99],{"categories":425},[160],{"categories":427},[386],{"categories":429},[],{"categories":431},[148],{"categories":433},[],{"categories":435},[143],{"categories":437},[148],{"categories":439},[148],{"categories":441},[442],"RAG & Retrieval",{"categories":444},[143],{"categories":446},[99],{"categories":448},[160],{"categories":450},[160],{"categories":452},[294],{"categories":454},[228],{"categories":456},[148],{"categories":458},[99],{"categories":460},[99],{"categories":462},[],{"categories":464},[465],"Agents & Orchestration",{"categories":467},[160],{"categories":469},[99],{"categories":471},[],{"categories":473},[148],{"categories":475},[143],{"categories":477},[],{"categories":479},[99],{"categories":481},[],{"categories":483},[99],{"categories":485},[138],{"categories":487},[160],{"categories":489},[143],{"categories":491},[99],{"categories":493},[148],{"categories":495},[99],{"categories":497},[182],{"categories":499},[99],{"categories":501},[],{"categories":503},[99],{"categories":505},[],{"categories":507},[160],{"categories":509},[99],{"categories":511},[185],{"categories":513},[],{"categories":515},[99],{"categories":517},[228],{"categories":519},[520],"Models & Frontier Labs",{"categories":522},[],{"categories":524},[228],{"categories":526},[527],"Regulation & Governance of AI",{"categories":529},[148],{"categories":531},[],{"categories":533},[99],{"categories":535},[99],{"categories":537},[148],{"categories":539},[182],{"categories":541},[143],{"categories":543},[99],{"categories":545},[],{"categories":547},[160],{"categories":549},[148],{"categories":551},[99],{"categories":553},[151],{"categories":555},[556],"AI Policy & Regulation",{"categories":558},[],{"categories":560},[99],{"categories":562},[148],{"categories":564},[151],{"categories":566},[148],{"categories":568},[99],{"categories":570},[99],{"categories":572},[99],{"categories":574},[148],{"categories":576},[],{"categories":578},[185],{"categories":580},[581],"Evals & Reliability",{"categories":583},[99],{"categories":585},[99],{"categories":587},[],{"categories":589},[138],{"categories":591},[386],{"categories":593},[556],{"categories":595},[99],{"categories":597},[143],{"categories":599},[99],{"categories":601},[148],{"categories":603},[99],{"categories":605},[148],{"categories":607},[465],{"categories":609},[99],{"categories":611},[160],{"categories":613},[99],{"categories":615},[],{"categories":617},[],{"categories":619},[99],{"categories":621},[386],{"categories":623},[99],{"categories":625},[99],{"categories":627},[99],{"categories":629},[],{"categories":631},[228],{"categories":633},[],{"categories":635},[99],{"categories":637},[],{"categories":639},[148],{"categories":641},[99],{"categories":643},[228],{"categories":645},[],{"categories":647},[99],{"categories":649},[99],{"categories":651},[185],{"categories":653},[148],{"categories":655},[99],{"categories":657},[143],{"categories":659},[148],{"categories":661},[99],{"categories":663},[99],{"categories":665},[160],{"categories":667},[228],{"categories":669},[99],{"categories":671},[148],{"categories":673},[],{"categories":675},[160],{"categories":677},[148],{"categories":679},[185],{"categories":681},[],{"categories":683},[99],{"categories":685},[182],{"categories":687},[99],{"categories":689},[],{"categories":691},[99],{"categories":693},[99],{"categories":695},[99],{"categories":697},[143,257],{"categories":699},[],{"categories":701},[99],{"categories":703},[99],{"categories":705},[148],{"categories":707},[99],{"categories":709},[],{"categories":711},[],{"categories":713},[99],{"categories":715},[228],{"categories":717},[99],{"categories":719},[],{"categories":721},[99],{"categories":723},[294],{"categories":725},[],{"categories":727},[148],{"categories":729},[182],{"categories":731},[99],{"categories":733},[99],{"categories":735},[228],{"categories":737},[],{"categories":739},[182],{"categories":741},[99],{"categories":743},[171],{"categories":745},[99],{"categories":747},[99],{"categories":749},[148],{"categories":751},[182],{"categories":753},[520],{"categories":755},[99],{"categories":757},[257],{"categories":759},[],{"categories":761},[148],{"categories":763},[143],{"categories":765},[160],{"categories":767},[99],{"categories":769},[148],{"categories":771},[],{"categories":773},[99,294],{"categories":775},[99],{"categories":777},[99],{"categories":779},[99],{"categories":781},[148],{"categories":783},[99,160],{"categories":785},[185],{"categories":787},[99],{"categories":789},[99],{"categories":791},[160],{"categories":793},[99],{"categories":795},[148],{"categories":797},[556],{"categories":799},[257],{"categories":801},[99],{"categories":803},[148],{"categories":805},[99],{"categories":807},[99],{"categories":809},[148],{"categories":811},[],{"categories":813},[148],{"categories":815},[99],{"categories":817},[99],{"categories":819},[148],{"categories":821},[99],{"categories":823},[99,143],{"categories":825},[99],{"categories":827},[143],{"categories":829},[],{"categories":831},[228],{"categories":833},[228],{"categories":835},[99],{"categories":837},[],{"categories":839},[],{"categories":841},[99],{"categories":843},[182],{"categories":845},[],{"categories":847},[138],{"categories":849},[99],{"categories":851},[160],{"categories":853},[99],{"categories":855},[856],"Generative UI & Design-to-Code",{"categories":858},[99],{"categories":860},[99],{"categories":862},[228],{"categories":864},[99],{"categories":866},[867],"Algorithmic Accountability",{"categories":869},[148],{"categories":871},[160],{"categories":873},[182],{"categories":875},[228],{"categories":877},[99],{"categories":879},[],{"categories":881},[151],{"categories":883},[99],{"categories":885},[99],{"categories":887},[99],{"categories":889},[148],{"categories":891},[892],"MLOps & Infrastructure",{"categories":894},[99],{"categories":896},[99],{"categories":898},[99],{"categories":900},[99],{"categories":902},[99],{"categories":904},[182],{"categories":906},[151],{"categories":908},[138],{"categories":910},[99],{"categories":912},[148],{"categories":914},[294],{"categories":916},[99],{"categories":918},[143],{"categories":920},[99],{"categories":922},[228],{"categories":924},[99],{"categories":926},[99],{"categories":928},[148],{"categories":930},[],{"categories":932},[],{"categories":934},[99],{"categories":936},[171],{"categories":938},[228],{"categories":940},[182],{"categories":942},[185],{"categories":944},[],{"categories":946},[99],{"categories":948},[99],{"categories":950},[143],{"categories":952},[148],{"categories":954},[99],{"categories":956},[99],{"categories":958},[99],{"categories":960},[99],{"categories":962},[182],{"categories":964},[171],{"categories":966},[99],{"categories":968},[228],{"categories":970},[99],{"categories":972},[],{"categories":974},[148],{"categories":976},[160],{"categories":978},[],{"categories":980},[99],{"categories":982},[99],{"categories":984},[148],{"categories":986},[160],{"categories":988},[99],{"categories":990},[185],{"categories":992},[],{"categories":994},[99],{"categories":996},[],{"categories":998},[99],{"categories":1000},[],{"categories":1002},[151],{"categories":1004},[143],{"categories":1006},[148],{"categories":1008},[148],{"categories":1010},[],{"categories":1012},[138],{"categories":1014},[99],{"categories":1016},[99],{"categories":1018},[143],{"categories":1020},[182],{"categories":1022},[138],{"categories":1024},[],{"categories":1026},[99],{"categories":1028},[],{"categories":1030},[],{"categories":1032},[182],{"categories":1034},[182],{"categories":1036},[],{"categories":1038},[465],{"categories":1040},[99],{"categories":1042},[228],{"categories":1044},[160],{"categories":1046},[],{"categories":1048},[409],{"categories":1050},[148],{"categories":1052},[143],{"categories":1054},[],{"categories":1056},[],{"categories":1058},[138],{"categories":1060},[185],{"categories":1062},[],{"categories":1064},[257],{"categories":1066},[148],{"categories":1068},[143],{"categories":1070},[148],{"categories":1072},[143],{"categories":1074},[99],{"categories":1076},[160],{"categories":1078},[],{"categories":1080},[171],{"categories":1082},[151],{"categories":1084},[99],{"categories":1086},[228],{"categories":1088},[160],{"categories":1090},[143],{"categories":1092},[99],{"categories":1094},[148],{"categories":1096},[143],{"categories":1098},[99],{"categories":1100},[99],{"categories":1102},[99],{"categories":1104},[99],{"categories":1106},[99],{"categories":1108},[],{"categories":1110},[],{"categories":1112},[160],{"categories":1114},[185],{"categories":1116},[151],{"categories":1118},[99],{"categories":1120},[148],{"categories":1122},[160],{"categories":1124},[99],{"categories":1126},[],{"categories":1128},[182],{"categories":1130},[151],{"categories":1132},[160],{"categories":1134},[99],{"categories":1136},[581],{"categories":1138},[294],{"categories":1140},[],{"categories":1142},[148],{"categories":1144},[99],{"categories":1146},[],{"categories":1148},[138],{"categories":1150},[],{"categories":1152},[99],{"categories":1154},[99],{"categories":1156},[99],{"categories":1158},[228],{"categories":1160},[257],{"categories":1162},[99],{"categories":1164},[160],{"categories":1166},[99],{"categories":1168},[148],{"categories":1170},[],{"categories":1172},[160],{"categories":1174},[99],{"categories":1176},[138],{"categories":1178},[],{"categories":1180},[143],{"categories":1182},[99],{"categories":1184},[182],{"categories":1186},[99,294],{"categories":1188},[99],{"categories":1190},[1191],"Design Systems for AI",{"categories":1193},[99],{"categories":1195},[99],{"categories":1197},[182],{"categories":1199},[99],{"categories":1201},[99],{"categories":1203},[99],{"categories":1205},[143],{"categories":1207},[99],{"categories":1209},[99],{"categories":1211},[99],{"categories":1213},[],{"categories":1215},[99],{"categories":1217},[99],{"categories":1219},[143],{"categories":1221},[99],{"categories":1223},[],{"categories":1225},[148],{"categories":1227},[160],{"categories":1229},[182],{"categories":1231},[160],{"categories":1233},[99],{"categories":1235},[228],{"categories":1237},[182],{"categories":1239},[185],{"categories":1241},[99],{"categories":1243},[99],{"categories":1245},[148],{"categories":1247},[138],{"categories":1249},[556],{"categories":1251},[99],{"categories":1253},[148],{"categories":1255},[99],{"categories":1257},[160],{"categories":1259},[160],{"categories":1261},[],{"categories":1263},[],{"categories":1265},[148],{"categories":1267},[151],{"categories":1269},[],{"categories":1271},[143],{"categories":1273},[99],{"categories":1275},[],{"categories":1277},[228],{"categories":1279},[148],{"categories":1281},[160],{"categories":1283},[228],{"categories":1285},[99],{"categories":1287},[99],{"categories":1289},[228],{"categories":1291},[],{"categories":1293},[],{"categories":1295},[182],{"categories":1297},[148],{"categories":1299},[148],{"categories":1301},[99],{"categories":1303},[99],{"categories":1305},[99],{"categories":1307},[99],{"categories":1309},[143],{"categories":1311},[99],{"categories":1313},[99],{"categories":1315},[],{"categories":1317},[160],{"categories":1319},[160],{"categories":1321},[99],{"categories":1323},[160],{"categories":1325},[143],{"categories":1327},[],{"categories":1329},[99],{"categories":1331},[99],{"categories":1333},[99],{"categories":1335},[99],{"categories":1337},[99],{"categories":1339},[148],{"categories":1341},[138],{"categories":1343},[143],{"categories":1345},[99],{"categories":1347},[148],{"categories":1349},[182],{"categories":1351},[148],{"categories":1353},[171],{"categories":1355},[257],{"categories":1357},[99],{"categories":1359},[148],{"categories":1361},[99],{"categories":1363},[99],{"categories":1365},[],{"categories":1367},[228],{"categories":1369},[],{"categories":1371},[99],{"categories":1373},[99],{"categories":1375},[],{"categories":1377},[160],{"categories":1379},[143],{"categories":1381},[1382],"Visual & Generative Media",{"categories":1384},[148],{"categories":1386},[],{"categories":1388},[99],{"categories":1390},[99],{"categories":1392},[160],{"categories":1394},[294],{"categories":1396},[185],{"categories":1398},[556],{"categories":1400},[160],{"categories":1402},[257],{"categories":1404},[99],{"categories":1406},[228],{"categories":1408},[99],{"categories":1410},[99],{"categories":1412},[160],{"categories":1414},[148],{"categories":1416},[99],{"categories":1418},[],{"categories":1420},[],{"categories":1422},[148],{"categories":1424},[160],{"categories":1426},[138],{"categories":1428},[148],{"categories":1430},[520],{"categories":1432},[99],{"categories":1434},[151],{"categories":1436},[99],{"categories":1438},[143],{"categories":1440},[],{"categories":1442},[99],{"categories":1444},[151],{"categories":1446},[99],{"categories":1448},[99],{"categories":1450},[99],{"categories":1452},[151],{"categories":1454},[99],{"categories":1456},[99],{"categories":1458},[257],{"categories":1460},[99],{"categories":1462},[465],{"categories":1464},[99],{"categories":1466},[148],{"categories":1468},[99],{"categories":1470},[99],{"categories":1472},[99],{"categories":1474},[99],{"categories":1476},[228],{"categories":1478},[148],{"categories":1480},[],{"categories":1482},[148],{"categories":1484},[],{"categories":1486},[294],{"categories":1488},[160],{"categories":1490},[],{"categories":1492},[520],{"categories":1494},[99],{"categories":1496},[148],{"categories":1498},[99],{"categories":1500},[228,99],{"categories":1502},[138],{"categories":1504},[99],{"categories":1506},[228],{"categories":1508},[],{"categories":1510},[99],{"categories":1512},[138],{"categories":1514},[1515],"Medical Imaging & Radiology",{"categories":1517},[99],{"categories":1519},[228],{"categories":1521},[148],{"categories":1523},[160],{"categories":1525},[],{"categories":1527},[99],{"categories":1529},[99],{"categories":1531},[99],{"categories":1533},[],{"categories":1535},[],{"categories":1537},[99],{"categories":1539},[465],{"categories":1541},[99],{"categories":1543},[138],{"categories":1545},[99],{"categories":1547},[99],{"categories":1549},[],{"categories":1551},[148],{"categories":1553},[99],{"categories":1555},[151],{"categories":1557},[160],{"categories":1559},[99],{"categories":1561},[465],{"categories":1563},[99],{"categories":1565},[148],{"categories":1567},[99],{"categories":1569},[99],{"categories":1571},[99],{"categories":1573},[228],{"categories":1575},[148],{"categories":1577},[294],{"categories":1579},[228],{"categories":1581},[143],{"categories":1583},[148],{"categories":1585},[182],{"categories":1587},[99],{"categories":1589},[99],{"categories":1591},[151],{"categories":1593},[99],{"categories":1595},[99],{"categories":1597},[99],{"categories":1599},[148],{"categories":1601},[160],{"categories":1603},[160],{"categories":1605},[99],{"categories":1607},[151],{"categories":1609},[],{"categories":1611},[182],{"categories":1613},[],{"categories":1615},[151],{"categories":1617},[148],{"categories":1619},[99],{"categories":1621},[148],{"categories":1623},[1191],{"categories":1625},[1191],{"categories":1627},[228],{"categories":1629},[99],{"categories":1631},[99],{"categories":1633},[148],{"categories":1635},[160],{"categories":1637},[228],{"categories":1639},[148],{"categories":1641},[182],{"categories":1643},[],{"categories":1645},[99],{"categories":1647},[],{"categories":1649},[99],{"categories":1651},[99],{"categories":1653},[99],{"categories":1655},[99],{"categories":1657},[148],{"categories":1659},[1660],"Contract Review & E-Discovery",{"categories":1662},[99],{"categories":1664},[228],{"categories":1666},[99],{"categories":1668},[138],{"categories":1670},[99],{"categories":1672},[182],{"categories":1674},[99],{"categories":1676},[99],{"categories":1678},[257],{"categories":1680},[160],{"categories":1682},[99],{"categories":1684},[99],{"categories":1686},[148],{"categories":1688},[148],{"categories":1690},[867],{"categories":1692},[99],{"categories":1694},[99],{"categories":1696},[148],{"categories":1698},[148],{"categories":1700},[99],{"categories":1702},[99],{"categories":1704},[148],{"categories":1706},[99],{"categories":1708},[99],{"categories":1710},[465],{"categories":1712},[442],{"categories":1714},[99],{"categories":1716},[148],{"categories":1718},[99],{"categories":1720},[1721],"Law-Firm Practice & Adoption",{"categories":1723},[99],{"categories":1725},[148],{"categories":1727},[228],{"categories":1729},[99],{"categories":1731},[99],{"categories":1733},[99],{"categories":1735},[],{"categories":1737},[],{"categories":1739},[160],{"categories":1741},[99],{"categories":1743},[],{"categories":1745},[148],{"categories":1747},[138],{"categories":1749},[294],{"categories":1751},[99],{"categories":1753},[],{"categories":1755},[138],{"categories":1757},[143],{"categories":1759},[99],{"categories":1761},[257],{"categories":1763},[],{"categories":1765},[143],{"categories":1767},[143],{"categories":1769},[],{"categories":1771},[99],{"categories":1773},[151],{"categories":1775},[99],{"categories":1777},[160],{"categories":1779},[],{"categories":1781},[],{"categories":1783},[],{"categories":1785},[],{"categories":1787},[99],{"categories":1789},[148],{"categories":1791},[294],{"categories":1793},[99],{"categories":1795},[138],{"categories":1797},[160],{"categories":1799},[99],{"categories":1801},[99],{"categories":1803},[160],{"categories":1805},[151],{"categories":1807},[99],{"categories":1809},[99],{"categories":1811},[99],{"categories":1813},[892],{"categories":1815},[99],{"categories":1817},[99],{"categories":1819},[257],{"categories":1821},[160],{"categories":1823},[143],{"categories":1825},[99],{"categories":1827},[99],{"categories":1829},[228],{"categories":1831},[99],{"categories":1833},[99],{"categories":1835},[99],{"categories":1837},[99],{"categories":1839},[148],{"categories":1841},[99,138],{"categories":1843},[465],{"categories":1845},[99],{"categories":1847},[99],{"categories":1849},[160],{"categories":1851},[160],{"categories":1853},[228],{"categories":1855},[148],{"categories":1857},[160],{"categories":1859},[99],{"categories":1861},[99],{"categories":1863},[],{"categories":1865},[],{"categories":1867},[99],{"categories":1869},[148],{"categories":1871},[],{"categories":1873},[99],{"categories":1875},[160],{"categories":1877},[185],{"categories":1879},[182],{"categories":1881},[228],{"categories":1883},[99],{"categories":1885},[99],{"categories":1887},[160],{"categories":1889},[],{"categories":1891},[148],{"categories":1893},[99],{"categories":1895},[99],{"categories":1897},[99],{"categories":1899},[99],{"categories":1901},[],{"categories":1903},[148],{"categories":1905},[99],{"categories":1907},[99],{"categories":1909},[],{"categories":1911},[148],{"categories":1913},[99],{"categories":1915},[99],{"categories":1917},[143],{"categories":1919},[99],{"categories":1921},[99],{"categories":1923},[],{"categories":1925},[138],{"categories":1927},[99],{"categories":1929},[99],{"categories":1931},[228],{"categories":1933},[160],{"categories":1935},[99],{"categories":1937},[138],{"categories":1939},[99],{"categories":1941},[160],{"categories":1943},[257],{"categories":1945},[148],{"categories":1947},[148],{"categories":1949},[99],{"categories":1951},[99],{"categories":1953},[99,228],{"categories":1955},[99],{"categories":1957},[148],{"categories":1959},[182],{"categories":1961},[99],{"categories":1963},[182],{"categories":1965},[148],{"categories":1967},[228],{"categories":1969},[99],{"categories":1971},[],{"categories":1973},[160],{"categories":1975},[294],{"categories":1977},[228],{"categories":1979},[160],{"categories":1981},[99],{"categories":1983},[151],{"categories":1985},[99],{"categories":1987},[99],{"categories":1989},[148],{"categories":1991},[],{"categories":1993},[],{"categories":1995},[99],{"categories":1997},[],{"categories":1999},[],{"categories":2001},[151],{"categories":2003},[160],{"categories":2005},[99],{"categories":2007},[148],{"categories":2009},[148],{"categories":2011},[143],{"categories":2013},[148],{"categories":2015},[294],{"categories":2017},[99],{"categories":2019},[99],{"categories":2021},[99],{"categories":2023},[171],{"categories":2025},[99],{"categories":2027},[99],{"categories":2029},[160],{"categories":2031},[148],{"categories":2033},[99],{"categories":2035},[99],{"categories":2037},[409],{"categories":2039},[867],{"categories":2041},[],{"categories":2043},[228],{"categories":2045},[1721],{"categories":2047},[160],{"categories":2049},[],{"categories":2051},[],{"categories":2053},[148],{"categories":2055},[],{"categories":2057},[],{"categories":2059},[99],{"categories":2061},[257],{"categories":2063},[99],{"categories":2065},[257],{"categories":2067},[148],{"categories":2069},[99],{"categories":2071},[160],{"categories":2073},[151],{"categories":2075},[],{"categories":2077},[99],{"categories":2079},[99],{"categories":2081},[160],{"categories":2083},[1660],{"categories":2085},[228],{"categories":2087},[228],{"categories":2089},[99],{"categories":2091},[148],{"categories":2093},[138],{"categories":2095},[99],{"categories":2097},[99],{"categories":2099},[99],{"categories":2101},[228],{"categories":2103},[228],{"categories":2105},[148],{"categories":2107},[148],{"categories":2109},[148],{"categories":2111},[99],{"categories":2113},[99],{"categories":2115},[],{"categories":2117},[99],{"categories":2119},[],{"categories":2121},[2122],"Interaction & Product Design",{"categories":2124},[99],{"categories":2126},[148],{"categories":2128},[160],{"categories":2130},[323],{"categories":2132},[182],{"categories":2134},[160],{"categories":2136},[99],{"categories":2138},[99],{"categories":2140},[160],{"categories":2142},[138],{"categories":2144},[148],{"categories":2146},[99],{"categories":2148},[],{"categories":2150},[148],{"categories":2152},[148],{"categories":2154},[],{"categories":2156},[160],{"categories":2158},[99],{"categories":2160},[138],{"categories":2162},[2122],{"categories":2164},[99],{"categories":2166},[138],{"categories":2168},[138],{"categories":2170},[],{"categories":2172},[148],{"categories":2174},[160],{"categories":2176},[],{"categories":2178},[148],{"categories":2180},[182],{"categories":2182},[99],{"categories":2184},[148],{"categories":2186},[99],{"categories":2188},[148],{"categories":2190},[99],{"categories":2192},[99],{"categories":2194},[182],{"categories":2196},[185],{"categories":2198},[99],{"categories":2200},[151],{"categories":2202},[160],{"categories":2204},[2205],"Coding Agents & Dev Productivity",{"categories":2207},[182],{"categories":2209},[228],{"categories":2211},[99],{"categories":2213},[99],{"categories":2215},[],{"categories":2217},[99],{"categories":2219},[867],{"categories":2221},[],{"categories":2223},[99],{"categories":2225},[294],{"categories":2227},[99],{"categories":2229},[182],{"categories":2231},[],{"categories":2233},[],{"categories":2235},[99],{"categories":2237},[],{"categories":2239},[148],{"categories":2241},[99],{"categories":2243},[],{"categories":2245},[160],{"categories":2247},[160],{"categories":2249},[99],{"categories":2251},[185],{"categories":2253},[],{"categories":2255},[99],{"categories":2257},[99],{"categories":2259},[99],{"categories":2261},[185],{"categories":2263},[160],{"categories":2265},[148],{"categories":2267},[],{"categories":2269},[],{"categories":2271},[99],{"categories":2273},[99],{"categories":2275},[148],{"categories":2277},[148],{"categories":2279},[386],{"categories":2281},[160],{"categories":2283},[160],{"categories":2285},[148],{"categories":2287},[182],{"categories":2289},[182],{"categories":2291},[148],{"categories":2293},[148],{"categories":2295},[99],{"categories":2297},[138],{"categories":2299},[2122],{"categories":2301},[99,294],{"categories":2303},[185],{"categories":2305},[],{"categories":2307},[228],{"categories":2309},[160],{"categories":2311},[138],{"categories":2313},[99],{"categories":2315},[148],{"categories":2317},[2318],"The Designer's Role & Craft",{"categories":2320},[228],{"categories":2322},[],{"categories":2324},[148],{"categories":2326},[99],{"categories":2328},[148],{"categories":2330},[148],{"categories":2332},[99],{"categories":2334},[257],{"categories":2336},[99],{"categories":2338},[160],{"categories":2340},[99],{"categories":2342},[228],{"categories":2344},[99],{"categories":2346},[],{"categories":2348},[148],{"categories":2350},[228],{"categories":2352},[99],{"categories":2354},[99],{"categories":2356},[99],{"categories":2358},[2359],"AI UX Patterns",{"categories":2361},[148],{"categories":2363},[148],{"categories":2365},[148],{"categories":2367},[148],{"categories":2369},[257],{"categories":2371},[185],{"categories":2373},[99],{"categories":2375},[148],{"categories":2377},[99],{"categories":2379},[1191],{"categories":2381},[],{"categories":2383},[257],{"categories":2385},[148],{"categories":2387},[182],{"categories":2389},[160],{"categories":2391},[99],{"categories":2393},[148],{"categories":2395},[],{"categories":2397},[],{"categories":2399},[99],{"categories":2401},[148],{"categories":2403},[99],{"categories":2405},[148],{"categories":2407},[386],{"categories":2409},[228],{"categories":2411},[99],{"categories":2413},[182],{"categories":2415},[160],{"categories":2417},[99],{"categories":2419},[148],{"categories":2421},[148],{"categories":2423},[],{"categories":2425},[99],{"categories":2427},[],{"categories":2429},[],{"categories":2431},[99],{"categories":2433},[99],{"categories":2435},[99],{"categories":2437},[148],{"categories":2439},[160],{"categories":2441},[],{"categories":2443},[],{"categories":2445},[185],{"categories":2447},[171],{"categories":2449},[99],{"categories":2451},[99],{"categories":2453},[185],{"categories":2455},[99],{"categories":2457},[182],{"categories":2459},[99],{"categories":2461},[99],{"categories":2463},[99],{"categories":2465},[148],{"categories":2467},[99],{"categories":2469},[148],{"categories":2471},[99],{"categories":2473},[99],{"categories":2475},[148],{"categories":2477},[],{"categories":2479},[],{"categories":2481},[99],{"categories":2483},[294],{"categories":2485},[99],{"categories":2487},[],{"categories":2489},[],{"categories":2491},[228],{"categories":2493},[892],{"categories":2495},[148],{"categories":2497},[138],{"categories":2499},[2318],{"categories":2501},[],{"categories":2503},[],{"categories":2505},[99],{"categories":2507},[],{"categories":2509},[],{"categories":2511},[160],{"categories":2513},[182],{"categories":2515},[257],{"categories":2517},[143],{"categories":2519},[99],{"categories":2521},[99],{"categories":2523},[143],{"categories":2525},[],{"categories":2527},[228],{"categories":2529},[151],{"categories":2531},[99],{"categories":2533},[99],{"categories":2535},[148],{"categories":2537},[143],{"categories":2539},[99],{"categories":2541},[99],{"categories":2543},[138],{"categories":2545},[99],{"categories":2547},[],{"categories":2549},[138],{"categories":2551},[99],{"categories":2553},[257],{"categories":2555},[148],{"categories":2557},[182],{"categories":2559},[99],{"categories":2561},[160],{"categories":2563},[99],{"categories":2565},[99],{"categories":2567},[143],{"categories":2569},[99],{"categories":2571},[99],{"categories":2573},[99],{"categories":2575},[148],{"categories":2577},[],{"categories":2579},[99],{"categories":2581},[160],{"categories":2583},[138],{"categories":2585},[99],{"categories":2587},[99],{"categories":2589},[99],{"categories":2591},[],{"categories":2593},[99],{"categories":2595},[465],{"categories":2597},[148],{"categories":2599},[143],{"categories":2601},[182],{"categories":2603},[99],{"categories":2605},[99],{"categories":2607},[],{"categories":2609},[143],{"categories":2611},[143],{"categories":2613},[99],{"categories":2615},[99],{"categories":2617},[151],{"categories":2619},[99],{"categories":2621},[99],{"categories":2623},[99],{"categories":2625},[99],{"categories":2627},[160],{"categories":2629},[160],{"categories":2631},[99],{"categories":2633},[],{"categories":2635},[160],{"categories":2637},[99],{"categories":2639},[160],{"categories":2641},[148],{"categories":2643},[556],{"categories":2645},[],{"categories":2647},[],{"categories":2649},[99],{"categories":2651},[182],{"categories":2653},[],{"categories":2655},[294],{"categories":2657},[99],{"categories":2659},[99],{"categories":2661},[228],{"categories":2663},[856],{"categories":2665},[],{"categories":2667},[99],{"categories":2669},[99],{"categories":2671},[99],{"categories":2673},[160],{"categories":2675},[99],{"categories":2677},[99],{"categories":2679},[99,294],{"categories":2681},[99],{"categories":2683},[99],{"categories":2685},[228],{"categories":2687},[148],{"categories":2689},[],{"categories":2691},[148],{"categories":2693},[148],{"categories":2695},[99],{"categories":2697},[99],{"categories":2699},[99],{"categories":2701},[99],{"categories":2703},[185],{"categories":2705},[99],{"categories":2707},[2359],{"categories":2709},[138],{"categories":2711},[185],{"categories":2713},[138],{"categories":2715},[160],{"categories":2717},[228],{"categories":2719},[148],{"categories":2721},[99],{"categories":2723},[],{"categories":2725},[143],{"categories":2727},[99],{"categories":2729},[99],{"categories":2731},[182],{"categories":2733},[99],{"categories":2735},[99],{"categories":2737},[148],{"categories":2739},[99],{"categories":2741},[99],{"categories":2743},[99],{"categories":2745},[143],{"categories":2747},[],{"categories":2749},[294],{"categories":2751},[99],{"categories":2753},[386],{"categories":2755},[228],{"categories":2757},[228],{"categories":2759},[160],{"categories":2761},[148],{"categories":2763},[99],{"categories":2765},[143],{"categories":2767},[182],{"categories":2769},[99],{"categories":2771},[99],{"categories":2773},[228],{"categories":2775},[148],{"categories":2777},[148],{"categories":2779},[99],{"categories":2781},[99],{"categories":2783},[520],{"categories":2785},[],{"categories":2787},[99],{"categories":2789},[99],{"categories":2791},[99],{"categories":2793},[],{"categories":2795},[],{"categories":2797},[99],{"categories":2799},[99],{"categories":2801},[148],{"categories":2803},[99],{"categories":2805},[99],{"categories":2807},[99],{"categories":2809},[160],{"categories":2811},[99],{"categories":2813},[99],{"categories":2815},[148],{"categories":2817},[99],{"categories":2819},[99],{"categories":2821},[99],{"categories":2823},[99],{"categories":2825},[99],{"categories":2827},[],{"categories":2829},[160],{"categories":2831},[185],{"categories":2833},[99],{"categories":2835},[148],{"categories":2837},[99],{"categories":2839},[],{"categories":2841},[],{"categories":2843},[99],{"categories":2845},[99],{"categories":2847},[99],{"categories":2849},[182],{"categories":2851},[185],{"categories":2853},[],{"categories":2855},[99],{"categories":2857},[228],{"categories":2859},[99],{"categories":2861},[294],{"categories":2863},[1721],{"categories":2865},[182],{"categories":2867},[160],{"categories":2869},[160],{"categories":2871},[160],{"categories":2873},[99],{"categories":2875},[99],{"categories":2877},[182],{"categories":2879},[182],{"categories":2881},[294],{"categories":2883},[148],{"categories":2885},[],{"categories":2887},[182],{"categories":2889},[99],{"categories":2891},[138],{"categories":2893},[160],{"categories":2895},[99],{"categories":2897},[182],{"categories":2899},[],{"categories":2901},[99],{"categories":2903},[160],{"categories":2905},[160],{"categories":2907},[185],{"categories":2909},[99],{"categories":2911},[182],{"categories":2913},[99],{"categories":2915},[160],{"categories":2917},[148],{"categories":2919},[182],{"categories":2921},[148],{"categories":2923},[294],{"categories":2925},[148],{"categories":2927},[99],{"categories":2929},[99],{"categories":2931},[99],{"categories":2933},[99],{"categories":2935},[160],{"categories":2937},[99],{"categories":2939},[],{"categories":2941},[148],{"categories":2943},[143],{"categories":2945},[160],{"categories":2947},[],{"categories":2949},[],{"categories":2951},[99],{"categories":2953},[148],{"categories":2955},[99],{"categories":2957},[99],{"categories":2959},[2960],"Frameworks & Tooling",{"categories":2962},[99],{"categories":2964},[99],{"categories":2966},[160],{"categories":2968},[99],{"categories":2970},[99],{"categories":2972},[],{"categories":2974},[185],{"categories":2976},[185],{"categories":2978},[138],{"categories":2980},[99],{"categories":2982},[148],{"categories":2984},[99],{"categories":2986},[228],{"categories":2988},[],{"categories":2990},[1721],{"categories":2992},[99],{"categories":2994},[160],{"categories":2996},[99],{"categories":2998},[294],{"categories":3000},[294],{"categories":3002},[],{"categories":3004},[148],{"categories":3006},[99],{"categories":3008},[182],{"categories":3010},[182],{"categories":3012},[99],{"categories":3014},[148],{"categories":3016},[],{"categories":3018},[228],{"categories":3020},[99],{"categories":3022},[99],{"categories":3024},[],{"categories":3026},[99],{"categories":3028},[148],{"categories":3030},[99],{"categories":3032},[99],{"categories":3034},[],{"categories":3036},[160],{"categories":3038},[99],{"categories":3040},[160],{"categories":3042},[294],{"categories":3044},[99],{"categories":3046},[99],{"categories":3048},[160],{"categories":3050},[143],{"categories":3052},[99],{"categories":3054},[1721],{"categories":3056},[],{"categories":3058},[148],{"categories":3060},[138],{"categories":3062},[99],{"categories":3064},[138],{"categories":3066},[99],{"categories":3068},[],{"categories":3070},[148],{"categories":3072},[99],{"categories":3074},[3075],"AI Design Tooling",{"categories":3077},[228],{"categories":3079},[99],{"categories":3081},[99],{"categories":3083},[160],{"categories":3085},[228],{"categories":3087},[99],{"categories":3089},[160],{"categories":3091},[182],{"categories":3093},[151],{"categories":3095},[160],{"categories":3097},[99],{"categories":3099},[99],{"categories":3101},[148],{"categories":3103},[99],{"categories":3105},[],{"categories":3107},[99],{"categories":3109},[99],{"categories":3111},[148],{"categories":3113},[99],{"categories":3115},[99],{"categories":3117},[99],{"categories":3119},[148],{"categories":3121},[],{"categories":3123},[148],{"categories":3125},[2960],{"categories":3127},[99],{"categories":3129},[99],{"categories":3131},[148],{"categories":3133},[148],{"categories":3135},[160],{"categories":3137},[160],{"categories":3139},[],{"categories":3141},[160],{"categories":3143},[99],{"categories":3145},[99],{"categories":3147},[148],{"categories":3149},[143],{"categories":3151},[99],{"categories":3153},[],{"categories":3155},[99],{"categories":3157},[99],{"categories":3159},[2122],{"categories":3161},[],{"categories":3163},[99],{"categories":3165},[99],{"categories":3167},[99],{"categories":3169},[99],{"categories":3171},[228],{"categories":3173},[99],{"categories":3175},[],{"categories":3177},[99],{"categories":3179},[99],{"categories":3181},[99],{"categories":3183},[257],{"categories":3185},[182],{"categories":3187},[99],{"categories":3189},[99],{"categories":3191},[1721],{"categories":3193},[138],{"categories":3195},[99],{"categories":3197},[99],{"categories":3199},[185],{"categories":3201},[99],{"categories":3203},[99],{"categories":3205},[182],{"categories":3207},[148],{"categories":3209},[],{"categories":3211},[99],{"categories":3213},[99],{"categories":3215},[228],{"categories":3217},[99],{"categories":3219},[257],{"categories":3221},[148],{"categories":3223},[99],{"categories":3225},[148],{"categories":3227},[],{"categories":3229},[],{"categories":3231},[],{"categories":3233},[138],{"categories":3235},[182],{"categories":3237},[148],{"categories":3239},[99],{"categories":3241},[99],{"categories":3243},[99],{"categories":3245},[99],{"categories":3247},[409],{"categories":3249},[228],{"categories":3251},[148],{"categories":3253},[99],{"categories":3255},[],{"categories":3257},[148],{"categories":3259},[148],{"categories":3261},[],{"categories":3263},[99],{"categories":3265},[148],{"categories":3267},[99],{"categories":3269},[],{"categories":3271},[99],{"categories":3273},[99],{"categories":3275},[182],{"categories":3277},[228],{"categories":3279},[148],{"categories":3281},[228],{"categories":3283},[148],{"categories":3285},[99],{"categories":3287},[143],{"categories":3289},[],{"categories":3291},[],{"categories":3293},[99],{"categories":3295},[99],{"categories":3297},[138],{"categories":3299},[148],{"categories":3301},[182],{"categories":3303},[],{"categories":3305},[228],{"categories":3307},[],{"categories":3309},[160],{"categories":3311},[99],{"categories":3313},[160],{"categories":3315},[228],{"categories":3317},[160],{"categories":3319},[99],{"categories":3321},[],{"categories":3323},[99],{"categories":3325},[99],{"categories":3327},[],{"categories":3329},[99],{"categories":3331},[257],{"categories":3333},[99],{"categories":3335},[294],{"categories":3337},[160],{"categories":3339},[99],{"categories":3341},[],{"categories":3343},[148],{"categories":3345},[99],{"categories":3347},[138],{"categories":3349},[520],{"categories":3351},[99],{"categories":3353},[148],{"categories":3355},[99],{"categories":3357},[148],{"categories":3359},[99],{"categories":3361},[99],{"categories":3363},[99],{"categories":3365},[],{"categories":3367},[99],{"categories":3369},[138],{"categories":3371},[99],{"categories":3373},[143],{"categories":3375},[160],{"categories":3377},[228],{"categories":3379},[],{"categories":3381},[99],{"categories":3383},[],{"categories":3385},[],{"categories":3387},[148],{"categories":3389},[99],{"categories":3391},[160],{"categories":3393},[228],{"categories":3395},[182],{"categories":3397},[99],{"categories":3399},[182],{"categories":3401},[148],{"categories":3403},[228],{"categories":3405},[99],{"categories":3407},[],{"categories":3409},[99],{"categories":3411},[171],{"categories":3413},[148],{"categories":3415},[228],{"categories":3417},[182],{"categories":3419},[143],{"categories":3421},[160],{"categories":3423},[99],{"categories":3425},[99],{"categories":3427},[182],{"categories":3429},[257],{"categories":3431},[],{"categories":3433},[],{"categories":3435},[185],{"categories":3437},[465],{"categories":3439},[99],{"categories":3441},[148],{"categories":3443},[99,160],{"categories":3445},[182],{"categories":3447},[99],{"categories":3449},[99],{"categories":3451},[99],{"categories":3453},[99],{"categories":3455},[148],{"categories":3457},[99],{"categories":3459},[148],{"categories":3461},[99],{"categories":3463},[99],{"categories":3465},[99],{"categories":3467},[],{"categories":3469},[99],{"categories":3471},[1191],{"categories":3473},[160],{"categories":3475},[228],{"categories":3477},[99],{"categories":3479},[99],{"categories":3481},[99],{"categories":3483},[185],{"categories":3485},[148],{"categories":3487},[257],{"categories":3489},[294],{"categories":3491},[],{"categories":3493},[99],{"categories":3495},[143],{"categories":3497},[148],{"categories":3499},[138],{"categories":3501},[148],{"categories":3503},[99],{"categories":3505},[148],{"categories":3507},[148],{"categories":3509},[151],{"categories":3511},[160],{"categories":3513},[99],{"categories":3515},[99],{"categories":3517},[],{"categories":3519},[],{"categories":3521},[],{"categories":3523},[294],{"categories":3525},[99],{"categories":3527},[182],{"categories":3529},[99],{"categories":3531},[99],{"categories":3533},[99],{"categories":3535},[99],{"categories":3537},[],{"categories":3539},[99],{"categories":3541},[185],{"categories":3543},[143],{"categories":3545},[148],{"categories":3547},[99],{"categories":3549},[],{"categories":3551},[99],{"categories":3553},[148],{"categories":3555},[99],{"categories":3557},[294],{"categories":3559},[],{"categories":3561},[228],{"categories":3563},[228],{"categories":3565},[],{"categories":3567},[160],{"categories":3569},[99],{"categories":3571},[228],{"categories":3573},[99],{"categories":3575},[143],{"categories":3577},[148],{"categories":3579},[99],{"categories":3581},[],{"categories":3583},[182],{"categories":3585},[99],{"categories":3587},[99],{"categories":3589},[99],{"categories":3591},[228],{"categories":3593},[148],{"categories":3595},[182],{"categories":3597},[],{"categories":3599},[148],{"categories":3601},[143],{"categories":3603},[148],{"categories":3605},[228],{"categories":3607},[99],{"categories":3609},[99],{"categories":3611},[99],{"categories":3613},[465],{"categories":3615},[99],{"categories":3617},[],{"categories":3619},[99],{"categories":3621},[99],{"categories":3623},[294],{"categories":3625},[182],{"categories":3627},[185],{"categories":3629},[556],{"categories":3631},[185],{"categories":3633},[99],{"categories":3635},[],{"categories":3637},[],{"categories":3639},[],{"categories":3641},[148],{"categories":3643},[148],{"categories":3645},[160],{"categories":3647},[99],{"categories":3649},[442],{"categories":3651},[160],{"categories":3653},[99],{"categories":3655},[99],{"categories":3657},[99],{"categories":3659},[99],{"categories":3661},[148],{"categories":3663},[],{"categories":3665},[],{"categories":3667},[99],{"categories":3669},[],{"categories":3671},[99],{"categories":3673},[148],{"categories":3675},[228],{"categories":3677},[99],{"categories":3679},[99],{"categories":3681},[],{"categories":3683},[148],{"categories":3685},[151],{"categories":3687},[99],{"categories":3689},[228],{"categories":3691},[99],{"categories":3693},[148],{"categories":3695},[143],{"categories":3697},[99],{"categories":3699},[257],{"categories":3701},[148],{"categories":3703},[99],{"categories":3705},[99],{"categories":3707},[856],{"categories":3709},[99],{"categories":3711},[148],{"categories":3713},[99],{"categories":3715},[160],{"categories":3717},[99],{"categories":3719},[520],{"categories":3721},[228],{"categories":3723},[],{"categories":3725},[99],{"categories":3727},[182],{"categories":3729},[465],{"categories":3731},[148],{"categories":3733},[99],{"categories":3735},[],{"categories":3737},[182],{"categories":3739},[386],{"categories":3741},[148],{"categories":3743},[148],{"categories":3745},[148],{"categories":3747},[99],{"categories":3749},[99],{"categories":3751},[148],{"categories":3753},[],{"categories":3755},[143],{"categories":3757},[99],{"categories":3759},[143],{"categories":3761},[148],{"categories":3763},[],{"categories":3765},[160],{"categories":3767},[99],{"categories":3769},[99],{"categories":3771},[138],{"categories":3773},[182],{"categories":3775},[294],{"categories":3777},[171],{"categories":3779},[148],{"categories":3781},[148],{"categories":3783},[99],{"categories":3785},[148],{"categories":3787},[99],{"categories":3789},[138],{"categories":3791},[],{"categories":3793},[99],{"categories":3795},[99],{"categories":3797},[99],{"categories":3799},[148],{"categories":3801},[99],{"categories":3803},[],{"categories":3805},[],{"categories":3807},[228],{"categories":3809},[148],{"categories":3811},[99,143],{"categories":3813},[148],{"categories":3815},[99],{"categories":3817},[],{"categories":3819},[138],{"categories":3821},[185],{"categories":3823},[143],{"categories":3825},[99],{"categories":3827},[160],{"categories":3829},[99],{"categories":3831},[99],{"categories":3833},[148],{"categories":3835},[99],{"categories":3837},[99],{"categories":3839},[99],{"categories":3841},[182],{"categories":3843},[1191],{"categories":3845},[148],{"categories":3847},[99],{"categories":3849},[],{"categories":3851},[],{"categories":3853},[99],{"categories":3855},[148],{"categories":3857},[99],{"categories":3859},[99],{"categories":3861},[294],{"categories":3863},[],{"categories":3865},[99],{"categories":3867},[148],{"categories":3869},[171],{"categories":3871},[148],{"categories":3873},[465],{"categories":3875},[],{"categories":3877},[409],{"categories":3879},[148],{"categories":3881},[99],{"categories":3883},[257],{"categories":3885},[148],{"categories":3887},[99],{"categories":3889},[185],{"categories":3891},[151],{"categories":3893},[148],{"categories":3895},[99],{"categories":3897},[465],{"categories":3899},[99],{"categories":3901},[294],{"categories":3903},[],{"categories":3905},[99],{"categories":3907},[257],{"categories":3909},[228],{"categories":3911},[99],{"categories":3913},[99],{"categories":3915},[99],{"categories":3917},[],{"categories":3919},[257],{"categories":3921},[182],{"categories":3923},[99],{"categories":3925},[99],{"categories":3927},[99],{"categories":3929},[556],{"categories":3931},[138],{"categories":3933},[99],{"categories":3935},[99],{"categories":3937},[],{"categories":3939},[],{"categories":3941},[228],{"categories":3943},[99],{"categories":3945},[185],{"categories":3947},[257],{"categories":3949},[148],{"categories":3951},[99],{"categories":3953},[99],{"categories":3955},[257],{"categories":3957},[182],{"categories":3959},[],{"categories":3961},[99],{"categories":3963},[99],{"categories":3965},[],{"categories":3967},[99],{"categories":3969},[99],{"categories":3971},[581],{"categories":3973},[99],{"categories":3975},[99],{"categories":3977},[148],{"categories":3979},[160],{"categories":3981},[465],{"categories":3983},[99],{"categories":3985},[99],{"categories":3987},[99],{"categories":3989},[],{"categories":3991},[99,160],{"categories":3993},[182],{"categories":3995},[148],{"categories":3997},[160],{"categories":3999},[148],{"categories":4001},[892],{"categories":4003},[160],{"categories":4005},[148],{"categories":4007},[99],{"categories":4009},[138],{"categories":4011},[],{"categories":4013},[],{"categories":4015},[148],{"categories":4017},[99],{"categories":4019},[160],{"categories":4021},[99],{"categories":4023},[138],{"categories":4025},[160],{"categories":4027},[160],{"categories":4029},[99],{"categories":4031},[257],{"categories":4033},[99],{"categories":4035},[160],{"categories":4037},[99],{"categories":4039},[],{"categories":4041},[99],{"categories":4043},[228,99],{"categories":4045},[294],{"categories":4047},[138],{"categories":4049},[99],{"categories":4051},[],{"categories":4053},[99],{"categories":4055},[99],{"categories":4057},[143],{"categories":4059},[99],{"categories":4061},[143],{"categories":4063},[99],{"categories":4065},[99],{"categories":4067},[386],{"categories":4069},[99],{"categories":4071},[143],{"categories":4073},[160],{"categories":4075},[185],{"categories":4077},[148],{"categories":4079},[160],{"categories":4081},[99],{"categories":4083},[99],{"categories":4085},[182],{"categories":4087},[257],{"categories":4089},[228],{"categories":4091},[99],{"categories":4093},[99],{"categories":4095},[99],{"categories":4097},[99],{"categories":4099},[138],{"categories":4101},[99],{"categories":4103},[148],{"categories":4105},[148],{"categories":4107},[160],{"categories":4109},[182],{"categories":4111},[160],{"categories":4113},[160],{"categories":4115},[99],{"categories":4117},[99],{"categories":4119},[],{"categories":4121},[],{"categories":4123},[185],{"categories":4125},[99],{"categories":4127},[160],{"categories":4129},[99],{"categories":4131},[228],{"categories":4133},[465],{"categories":4135},[409],{"categories":4137},[386],{"categories":4139},[99],{"categories":4141},[99],{"categories":4143},[99],{"categories":4145},[185],{"categories":4147},[99],{"categories":4149},[99],{"categories":4151},[99],{"categories":4153},[99],{"categories":4155},[99],{"categories":4157},[99],{"categories":4159},[148],{"categories":4161},[138],{"categories":4163},[148],{"categories":4165},[99,143],{"categories":4167},[],{"categories":4169},[228],{"categories":4171},[],{"categories":4173},[151],{"categories":4175},[99],{"categories":4177},[182],{"categories":4179},[138],{"categories":4181},[99],{"categories":4183},[138],{"categories":4185},[148],{"categories":4187},[185],{"categories":4189},[148],{"categories":4191},[148],{"categories":4193},[99],{"categories":4195},[99],{"categories":4197},[143],{"categories":4199},[148],{"categories":4201},[160],{"categories":4203},[257],{"categories":4205},[99],{"categories":4207},[],{"categories":4209},[182],{"categories":4211},[99],{"categories":4213},[99],{"categories":4215},[99],{"categories":4217},[99],{"categories":4219},[99],{"categories":4221},[99],{"categories":4223},[160],{"categories":4225},[182],{"categories":4227},[160],{"categories":4229},[160],{"categories":4231},[99],{"categories":4233},[99],{"categories":4235},[99],{"categories":4237},[409],{"categories":4239},[99],{"categories":4241},[148],{"categories":4243},[182],{"categories":4245},[99],{"categories":4247},[99],{"categories":4249},[99],{"categories":4251},[148],{"categories":4253},[99],{"categories":4255},[99],{"categories":4257},[99],{"categories":4259},[2960],{"categories":4261},[4262],"Clinical AI",{"categories":4264},[228],{"categories":4266},[99],{"categories":4268},[99],{"categories":4270},[99],{"categories":4272},[294],{"categories":4274},[2359],{"categories":4276},[99],{"categories":4278},[151],{"categories":4280},[99],{"categories":4282},[148],{"categories":4284},[99],{"categories":4286},[99],{"categories":4288},[182],{"categories":4290},[99],{"categories":4292},[148],{"categories":4294},[160],{"categories":4296},[257],{"categories":4298},[99],{"categories":4300},[99],{"categories":4302},[143],{"categories":4304},[99],{"categories":4306},[99],{"categories":4308},[520],{"categories":4310},[99],{"categories":4312},[],{"categories":4314},[99],{"categories":4316},[160],{"categories":4318},[138],{"categories":4320},[99],{"categories":4322},[],{"categories":4324},[],{"categories":4326},[99],{"categories":4328},[],{"categories":4330},[143],{"categories":4332},[99],{"categories":4334},[99],{"categories":4336},[148],{"categories":4338},[182],{"categories":4340},[182],{"categories":4342},[182],{"categories":4344},[182],{"categories":4346},[],{"categories":4348},[138],{"categories":4350},[148],{"categories":4352},[182],{"categories":4354},[99],{"categories":4356},[581],{"categories":4358},[151],{"categories":4360},[99],{"categories":4362},[138],{"categories":4364},[99],{"categories":4366},[148],{"categories":4368},[99],{"categories":4370},[99],{"categories":4372},[99,148],{"categories":4374},[148],{"categories":4376},[294],{"categories":4378},[182],{"categories":4380},[148],{"categories":4382},[182],{"categories":4384},[148],{"categories":4386},[99],{"categories":4388},[],{"categories":4390},[182],{"categories":4392},[257],{"categories":4394},[138],{"categories":4396},[99],{"categories":4398},[99],{"categories":4400},[],{"categories":4402},[160],{"categories":4404},[],{"categories":4406},[138],{"categories":4408},[148],{"categories":4410},[182],{"categories":4412},[99],{"categories":4414},[182],{"categories":4416},[138],{"categories":4418},[182],{"categories":4420},[182],{"categories":4422},[],{"categories":4424},[143],{"categories":4426},[148],{"categories":4428},[182],{"categories":4430},[182],{"categories":4432},[182],{"categories":4434},[182],{"categories":4436},[182],{"categories":4438},[182],{"categories":4440},[182],{"categories":4442},[182],{"categories":4444},[182],{"categories":4446},[182],{"categories":4448},[185],{"categories":4450},[138],{"categories":4452},[99],{"categories":4454},[99],{"categories":4456},[148],{"categories":4458},[148],{"categories":4460},[],{"categories":4462},[99],{"categories":4464},[99,138],{"categories":4466},[],{"categories":4468},[148],{"categories":4470},[99],{"categories":4472},[182],{"categories":4474},[148],{"categories":4476},[892],{"categories":4478},[99],{"categories":4480},[99],{"categories":4482},[99],{"categories":4484},[99],{"categories":4486},[99],{"categories":4488},[386],{"categories":4490},[99],{"categories":4492},[99],{"categories":4494},[148],{"categories":4496},[99],{"categories":4498},[143],{"categories":4500},[151],{"categories":4502},[148],{"categories":4504},[148],{"categories":4506},[],{"categories":4508},[148],{"categories":4510},[228],{"categories":4512},[182],{"categories":4514},[99],{"categories":4516},[],{"categories":4518},[151],{"categories":4520},[],{"categories":4522},[160],{"categories":4524},[148],{"categories":4526},[228],{"categories":4528},[99],{"categories":4530},[],{"categories":4532},[99],{"categories":4534},[],{"categories":4536},[257],{"categories":4538},[99],{"categories":4540},[],{"categories":4542},[],{"categories":4544},[182],{"categories":4546},[138],{"categories":4548},[99],{"categories":4550},[99],{"categories":4552},[143],{"categories":4554},[99],{"categories":4556},[99],{"categories":4558},[99],{"categories":4560},[143],{"categories":4562},[143],{"categories":4564},[228],{"categories":4566},[],{"categories":4568},[99],{"categories":4570},[182],{"categories":4572},[],{"categories":4574},[99],{"categories":4576},[99],{"categories":4578},[228],{"categories":4580},[99],{"categories":4582},[257],{"categories":4584},[99],{"categories":4586},[294],{"categories":4588},[],{"categories":4590},[148],{"categories":4592},[257],{"categories":4594},[160],{"categories":4596},[],{"categories":4598},[99],{"categories":4600},[],{"categories":4602},[148],{"categories":4604},[228],{"categories":4606},[160],{"categories":4608},[],{"categories":4610},[2960],{"categories":4612},[143],{"categories":4614},[138],{"categories":4616},[99],{"categories":4618},[185],{"categories":4620},[148],{"categories":4622},[228],{"categories":4624},[160],{"categories":4626},[],{"categories":4628},[],{"categories":4630},[99],{"categories":4632},[138],{"categories":4634},[99],{"categories":4636},[257],{"categories":4638},[],{"categories":4640},[148],{"categories":4642},[148],{"categories":4644},[99],{"categories":4646},[148],{"categories":4648},[99],{"categories":4650},[182],{"categories":4652},[160],{"categories":4654},[99],{"categories":4656},[148],{"categories":4658},[151],{"categories":4660},[99],{"categories":4662},[99],{"categories":4664},[148],{"categories":4666},[99],{"categories":4668},[151],{"categories":4670},[257],{"categories":4672},[182],{"categories":4674},[],{"categories":4676},[257],{"categories":4678},[99],{"categories":4680},[],{"categories":4682},[160],{"categories":4684},[148],{"categories":4686},[],{"categories":4688},[99],{"categories":4690},[99],{"categories":4692},[99],{"categories":4694},[99],{"categories":4696},[99],{"categories":4698},[148],{"categories":4700},[143],{"categories":4702},[138],{"categories":4704},[99],{"categories":4706},[228],{"categories":4708},[160],{"categories":4710},[160],{"categories":4712},[99],{"categories":4714},[185],{"categories":4716},[148],{"categories":4718},[99],{"categories":4720},[99],{"categories":4722},[148],{"categories":4724},[99],{"categories":4726},[143],{"categories":4728},[228],{"categories":4730},[160],{"categories":4732},[148],{"categories":4734},[99],{"categories":4736},[151],{"categories":4738},[99],{"categories":4740},[148],{"categories":4742},[99],{"categories":4744},[99],{"categories":4746},[182],{"categories":4748},[99],{"categories":4750},[],{"categories":4752},[138],{"categories":4754},[99],{"categories":4756},[99],{"categories":4758},[99],{"categories":4760},[160],{"categories":4762},[160],{"categories":4764},[99],{"categories":4766},[160],{"categories":4768},[99],{"categories":4770},[148],{"categories":4772},[99],{"categories":4774},[99],{"categories":4776},[99],{"categories":4778},[99],{"categories":4780},[99],{"categories":4782},[],{"categories":4784},[99],{"categories":4786},[228],{"categories":4788},[143],{"categories":4790},[182],{"categories":4792},[99],{"categories":4794},[148],{"categories":4796},[99],{"categories":4798},[148],{"categories":4800},[99],{"categories":4802},[99],{"categories":4804},[228],{"categories":4806},[148],{"categories":4808},[99],{"categories":4810},[257],{"categories":4812},[99],{"categories":4814},[185],{"categories":4816},[99],{"categories":4818},[99],{"categories":4820},[182],{"categories":4822},[99],{"categories":4824},[99],{"categories":4826},[99],{"categories":4828},[99],{"categories":4830},[148],{"categories":4832},[294],{"categories":4834},[99],{"categories":4836},[160],{"categories":4838},[148],{"categories":4840},[185],{"categories":4842},[],{"categories":4844},[148],{"categories":4846},[160],{"categories":4848},[99],{"categories":4850},[99],{"categories":4852},[2205],{"categories":4854},[228],{"categories":4856},[323],{"categories":4858},[99],{"categories":4860},[99],{"categories":4862},[99],{"categories":4864},[99],{"categories":4866},[138],{"categories":4868},[99],{"categories":4870},[99],{"categories":4872},[160],{"categories":4874},[143],{"categories":4876},[160],{"categories":4878},[99],{"categories":4880},[],{"categories":4882},[148],{"categories":4884},[148],{"categories":4886},[99],{"categories":4888},[99],{"categories":4890},[185],{"categories":4892},[],{"categories":4894},[182],{"categories":4896},[],{"categories":4898},[182],{"categories":4900},[99],{"categories":4902},[99],{"categories":4904},[148],{"categories":4906},[99],{"categories":4908},[148],{"categories":4910},[148],{"categories":4912},[],{"categories":4914},[182],{"categories":4916},[99],{"categories":4918},[],{"categories":4920},[99],{"categories":4922},[99],{"categories":4924},[],{"categories":4926},[99],{"categories":4928},[228],{"categories":4930},[160],{"categories":4932},[148],{"categories":4934},[99],{"categories":4936},[99],{"categories":4938},[99],{"categories":4940},[257],{"categories":4942},[99],{"categories":4944},[99],{"categories":4946},[99],{"categories":4948},[138],{"categories":4950},[99],{"categories":4952},[99],{"categories":4954},[],{"categories":4956},[99],{"categories":4958},[99],{"categories":4960},[],{"categories":4962},[138],{"categories":4964},[99],{"categories":4966},[182],{"categories":4968},[160],{"categories":4970},[151],{"categories":4972},[465],{"categories":4974},[99],{"categories":4976},[99],{"categories":4978},[99],{"categories":4980},[160],{"categories":4982},[182],{"categories":4984},[228],{"categories":4986},[99],{"categories":4988},[99],{"categories":4990},[99],{"categories":4992},[99],{"categories":4994},[182],{"categories":4996},[228],{"categories":4998},[99],{"categories":5000},[99],{"categories":5002},[182],{"categories":5004},[228],{"categories":5006},[99],{"categories":5008},[182],{"categories":5010},[99],{"categories":5012},[148],{"categories":5014},[148],{"categories":5016},[148],{"categories":5018},[160],{"categories":5020},[182],{"categories":5022},[148],{"categories":5024},[148],{"categories":5026},[99],{"categories":5028},[160],{"categories":5030},[228],{"categories":5032},[99],{"categories":5034},[99],{"categories":5036},[99],{"categories":5038},[],{"categories":5040},[148],{"categories":5042},[],{"categories":5044},[99],{"categories":5046},[99],{"categories":5048},[],{"categories":5050},[],{"categories":5052},[148],{"categories":5054},[143],{"categories":5056},[148],{"categories":5058},[5059],"Liability & Ethics",{"categories":5061},[99],{"categories":5063},[99],{"categories":5065},[148],{"categories":5067},[138],{"categories":5069},[148],{"categories":5071},[143],{"categories":5073},[257],{"categories":5075},[148],{"categories":5077},[99],{"categories":5079},[99],{"categories":5081},[],{"categories":5083},[556],{"categories":5085},[148],{"categories":5087},[],{"categories":5089},[99],{"categories":5091},[138],{"categories":5093},[148],{"categories":5095},[],{"categories":5097},[148],{"categories":5099},[99],{"categories":5101},[99],{"categories":5103},[160],{"categories":5105},[99],{"categories":5107},[182],{"categories":5109},[99],{"categories":5111},[99],{"categories":5113},[148],{"categories":5115},[99],{"categories":5117},[99],{"categories":5119},[99],{"categories":5121},[182],{"categories":5123},[148],{"categories":5125},[160],{"categories":5127},[228],{"categories":5129},[138],{"categories":5131},[99],{"categories":5133},[99],{"categories":5135},[],{"categories":5137},[148],{"categories":5139},[148],{"categories":5141},[148],{"categories":5143},[465],{"categories":5145},[228],{"categories":5147},[148],{"categories":5149},[294],{"categories":5151},[160],{"categories":5153},[182],{"categories":5155},[99],{"categories":5157},[228],{"categories":5159},[99],{"categories":5161},[138],{"categories":5163},[],{"categories":5165},[148],{"categories":5167},[99],{"categories":5169},[99],{"categories":5171},[99],{"categories":5173},[148],{"categories":5175},[99],{"categories":5177},[228],{"categories":5179},[],{"categories":5181},[148],{"categories":5183},[151],{"categories":5185},[182],{"categories":5187},[148],{"categories":5189},[143],{"categories":5191},[],{"categories":5193},[99],{"categories":5195},[99],{"categories":5197},[151],{"categories":5199},[99],{"categories":5201},[148],{"categories":5203},[182],{"categories":5205},[138],{"categories":5207},[294],{"categories":5209},[99],{"categories":5211},[99],{"categories":5213},[99],{"categories":5215},[182],{"categories":5217},[143],{"categories":5219},[99],{"categories":5221},[228],{"categories":5223},[182],{"categories":5225},[294],{"categories":5227},[99],{"categories":5229},[148],{"categories":5231},[],{"categories":5233},[520],{"categories":5235},[],{"categories":5237},[99],{"categories":5239},[294],{"categories":5241},[99],{"categories":5243},[185],{"categories":5245},[148],{"categories":5247},[148],{"categories":5249},[5250],"Design News & Tools",{"categories":5252},[99],{"categories":5254},[182],{"categories":5256},[99],{"categories":5258},[99],{"categories":5260},[138],{"categories":5262},[99],{"categories":5264},[228],{"categories":5266},[148],{"categories":5268},[148],{"categories":5270},[228],{"categories":5272},[99],{"categories":5274},[465],{"categories":5276},[148],{"categories":5278},[99],{"categories":5280},[99],{"categories":5282},[465],{"categories":5284},[99],{"categories":5286},[257],{"categories":5288},[99],{"categories":5290},[148],{"categories":5292},[],{"categories":5294},[99],{"categories":5296},[99],{"categories":5298},[99],{"categories":5300},[182],{"categories":5302},[138],{"categories":5304},[],{"categories":5306},[99],{"categories":5308},[99],{"categories":5310},[99],{"categories":5312},[160],{"categories":5314},[581],{"categories":5316},[160],{"categories":5318},[228],{"categories":5320},[99],{"categories":5322},[99,148],{"categories":5324},[257,143],{"categories":5326},[160],{"categories":5328},[99],{"categories":5330},[99],{"categories":5332},[99],{"categories":5334},[99],{"categories":5336},[],{"categories":5338},[148],{"categories":5340},[99],{"categories":5342},[],{"categories":5344},[99],{"categories":5346},[160],{"categories":5348},[99],{"categories":5350},[160],{"categories":5352},[],{"categories":5354},[148],{"categories":5356},[99],{"categories":5358},[143],{"categories":5360},[99],{"categories":5362},[182],{"categories":5364},[99],{"categories":5366},[],{"categories":5368},[148],{"categories":5370},[99],{"categories":5372},[],{"categories":5374},[228],{"categories":5376},[99],{"categories":5378},[99],{"categories":5380},[148],{"categories":5382},[99],{"categories":5384},[99],{"categories":5386},[138],{"categories":5388},[148],{"categories":5390},[99],{"categories":5392},[],{"categories":5394},[294],{"categories":5396},[257],{"categories":5398},[143],{"categories":5400},[143],{"categories":5402},[99],{"categories":5404},[138],{"categories":5406},[138],{"categories":5408},[99],{"categories":5410},[148],{"categories":5412},[99],{"categories":5414},[99],{"categories":5416},[99],{"categories":5418},[99],{"categories":5420},[160],{"categories":5422},[99],{"categories":5424},[138],{"categories":5426},[99],{"categories":5428},[148],{"categories":5430},[99],{"categories":5432},[257],{"categories":5434},[99],{"categories":5436},[182],{"categories":5438},[99],{"categories":5440},[99],{"categories":5442},[148],{"categories":5444},[99],{"categories":5446},[148],{"categories":5448},[],{"categories":5450},[160],{"categories":5452},[],{"categories":5454},[160],{"categories":5456},[148],{"categories":5458},[138],{"categories":5460},[],{"categories":5462},[185],{"categories":5464},[294],{"categories":5466},[99],{"categories":5468},[160],{"categories":5470},[99],{"categories":5472},[],{"categories":5474},[182],{"categories":5476},[148],{"categories":5478},[160],{"categories":5480},[228],{"categories":5482},[99],{"categories":5484},[99],{"categories":5486},[148],{"categories":5488},[160],{"categories":5490},[148],{"categories":5492},[182],{"categories":5494},[99],{"categories":5496},[151],{"categories":5498},[138],{"categories":5500},[151],{"categories":5502},[182],{"categories":5504},[99],{"categories":5506},[160],{"categories":5508},[99],{"categories":5510},[228],{"categories":5512},[143],{"categories":5514},[99],{"categories":5516},[99],{"categories":5518},[99],{"categories":5520},[99],{"categories":5522},[99],{"categories":5524},[99],{"categories":5526},[148],{"categories":5528},[99],{"categories":5530},[148],{"categories":5532},[99],{"categories":5534},[99],{"categories":5536},[138],{"categories":5538},[99],{"categories":5540},[148],{"categories":5542},[148],{"categories":5544},[228],{"categories":5546},[148],{"categories":5548},[148],{"categories":5550},[138],{"categories":5552},[148],{"categories":5554},[228],{"categories":5556},[],{"categories":5558},[99],{"categories":5560},[185],{"categories":5562},[465],{"categories":5564},[99],{"categories":5566},[99],{"categories":5568},[99],{"categories":5570},[160],{"categories":5572},[99],{"categories":5574},[],{"categories":5576},[99],{"categories":5578},[148],{"categories":5580},[99],{"categories":5582},[257],{"categories":5584},[99],{"categories":5586},[182],{"categories":5588},[148],{"categories":5590},[99],{"categories":5592},[257],{"categories":5594},[148],{"categories":5596},[143],{"categories":5598},[143],{"categories":5600},[99],{"categories":5602},[99],{"categories":5604},[99],{"categories":5606},[99],{"categories":5608},[99],{"categories":5610},[138],{"categories":5612},[],{"categories":5614},[99],{"categories":5616},[99],{"categories":5618},[148],{"categories":5620},[148],{"categories":5622},[99],{"categories":5624},[99],{"categories":5626},[99],{"categories":5628},[160],{"categories":5630},[],{"categories":5632},[138],{"categories":5634},[99],{"categories":5636},[99],{"categories":5638},[148],{"categories":5640},[148],{"categories":5642},[],{"categories":5644},[160],{"categories":5646},[160],{"categories":5648},[99],{"categories":5650},[257],{"categories":5652},[143],{"categories":5654},[228],{"categories":5656},[],{"categories":5658},[99],{"categories":5660},[148],{"categories":5662},[138],{"categories":5664},[99],{"categories":5666},[99],{"categories":5668},[160],{"categories":5670},[138],{"categories":5672},[99],{"categories":5674},[99],{"categories":5676},[182],{"categories":5678},[185],{"categories":5680},[182],{"categories":5682},[148],{"categories":5684},[99],{"categories":5686},[],{"categories":5688},[182],{"categories":5690},[148],{"categories":5692},[228],{"categories":5694},[185],{"categories":5696},[99],{"categories":5698},[99],{"categories":5700},[],{"categories":5702},[148],{"categories":5704},[148],{"categories":5706},[148],{"categories":5708},[2960],{"categories":5710},[182],{"categories":5712},[99],{"categories":5714},[160],{"categories":5716},[99],{"categories":5718},[99],{"categories":5720},[99],{"categories":5722},[99],{"categories":5724},[143],{"categories":5726},[99],{"categories":5728},[138],{"categories":5730},[1721],{"categories":5732},[294],{"categories":5734},[138],{"categories":5736},[],{"categories":5738},[99],{"categories":5740},[],{"categories":5742},[182],{"categories":5744},[148],{"categories":5746},[228],{"categories":5748},[99],{"categories":5750},[99],{"categories":5752},[182],{"categories":5754},[],{"categories":5756},[148],{"categories":5758},[148],{"categories":5760},[148],{"categories":5762},[],{"categories":5764},[99],{"categories":5766},[],{"categories":5768},[182],{"categories":5770},[138],{"categories":5772},[228],{"categories":5774},[99],{"categories":5776},[148],{"categories":5778},[182],{"categories":5780},[99],{"categories":5782},[182],{"categories":5784},[],{"categories":5786},[182],{"categories":5788},[138],{"categories":5790},[465],{"categories":5792},[148],{"categories":5794},[99],{"categories":5796},[],{"categories":5798},[160],{"categories":5800},[148],{"categories":5802},[151],{"categories":5804},[148],{"categories":5806},[138],{"categories":5808},[],{"categories":5810},[],{"categories":5812},[],{"categories":5814},[228],{"categories":5816},[148],{"categories":5818},[99],{"categories":5820},[99],{"categories":5822},[],{"categories":5824},[],{"categories":5826},[],{"categories":5828},[99],{"categories":5830},[228],{"categories":5832},[99],{"categories":5834},[],{"categories":5836},[148],{"categories":5838},[99],{"categories":5840},[99],{"categories":5842},[138],{"categories":5844},[],{"categories":5846},[],{"categories":5848},[99],{"categories":5850},[228],{"categories":5852},[99],{"categories":5854},[182],{"categories":5856},[],{"categories":5858},[99],{"categories":5860},[257],{"categories":5862},[182],{"categories":5864},[257],{"categories":5866},[185],{"categories":5868},[99],{"categories":5870},[99],{"categories":5872},[],{"categories":5874},[],{"categories":5876},[148],{"categories":5878},[],{"categories":5880},[99],{"categories":5882},[465],{"categories":5884},[99],{"categories":5886},[99],{"categories":5888},[99],{"categories":5890},[99],{"categories":5892},[],{"categories":5894},[148],{"categories":5896},[99],{"categories":5898},[99],{"categories":5900},[],{"categories":5902},[148],{"categories":5904},[99],{"categories":5906},[182],{"categories":5908},[99],{"categories":5910},[257],{"categories":5912},[143],{"categories":5914},[99],{"categories":5916},[99],{"categories":5918},[148],{"categories":5920},[185],{"categories":5922},[148],{"categories":5924},[148],{"categories":5926},[],{"categories":5928},[148],{"categories":5930},[],{"categories":5932},[99],{"categories":5934},[],{"categories":5936},[182],{"categories":5938},[143],{"categories":5940},[],{"categories":5942},[99],{"categories":5944},[99],{"categories":5946},[],{"categories":5948},[228],{"categories":5950},[138],{"categories":5952},[],{"categories":5954},[143],{"categories":5956},[257],{"categories":5958},[99],{"categories":5960},[160],{"categories":5962},[138],{"categories":5964},[185],{"categories":5966},[143],{"categories":5968},[160],{"categories":5970},[148],{"categories":5972},[160],{"categories":5974},[],{"categories":5976},[99],{"categories":5978},[151],{"categories":5980},[99],{"categories":5982},[],{"categories":5984},[148],{"categories":5986},[138],{"categories":5988},[228],{"categories":5990},[99],{"categories":5992},[138],{"categories":5994},[148],{"categories":5996},[294],{"categories":5998},[99],{"categories":6000},[99],{"categories":6002},[99],{"categories":6004},[138],{"categories":6006},[185],{"categories":6008},[148],{"categories":6010},[],{"categories":6012},[99],{"categories":6014},[99],{"categories":6016},[99],{"categories":6018},[160],{"categories":6020},[148],{"categories":6022},[182],{"categories":6024},[160],{"categories":6026},[99],{"categories":6028},[151],{"categories":6030},[],{"categories":6032},[228],{"categories":6034},[160],{"categories":6036},[182],{"categories":6038},[138],{"categories":6040},[148],{"categories":6042},[99],{"categories":6044},[99],{"categories":6046},[148],{"categories":6048},[151],{"categories":6050},[99],{"categories":6052},[148],{"categories":6054},[99],{"categories":6056},[143],{"categories":6058},[148],{"categories":6060},[148,294],{"categories":6062},[99],{"categories":6064},[99],{"categories":6066},[148],{"categories":6068},[160],{"categories":6070},[99],{"categories":6072},[99],{"categories":6074},[185],{"categories":6076},[148],{"categories":6078},[257],{"categories":6080},[148],{"categories":6082},[143],{"categories":6084},[],{"categories":6086},[148],{"categories":6088},[99],{"categories":6090},[143],{"categories":6092},[],{"categories":6094},[],{"categories":6096},[160],{"categories":6098},[99],{"categories":6100},[99],{"categories":6102},[148],{"categories":6104},[185],{"categories":6106},[257],{"categories":6108},[99],{"categories":6110},[99],{"categories":6112},[148],{"categories":6114},[],{"categories":6116},[148],{"categories":6118},[182],{"categories":6120},[148],{"categories":6122},[99],{"categories":6124},[],{"categories":6126},[182],{"categories":6128},[160],{"categories":6130},[2960],{"categories":6132},[138],{"categories":6134},[160],{"categories":6136},[99],{"categories":6138},[148],{"categories":6140},[99],{"categories":6142},[99],{"categories":6144},[257],{"categories":6146},[160],{"categories":6148},[],{"categories":6150},[182],{"categories":6152},[99],{"categories":6154},[],{"categories":6156},[148],{"categories":6158},[99],{"categories":6160},[99],{"categories":6162},[99],{"categories":6164},[99],{"categories":6166},[148],{"categories":6168},[99],{"categories":6170},[99],{"categories":6172},[151],{"categories":6174},[99],{"categories":6176},[148],{"categories":6178},[99],{"categories":6180},[99],{"categories":6182},[99],{"categories":6184},[99],{"categories":6186},[99],{"categories":6188},[99],{"categories":6190},[99],{"categories":6192},[143],{"categories":6194},[],{"categories":6196},[151],{"categories":6198},[182],{"categories":6200},[148],{"categories":6202},[99],{"categories":6204},[160],{"categories":6206},[],{"categories":6208},[160],{"categories":6210},[160],{"categories":6212},[148],{"categories":6214},[160],{"categories":6216},[99],{"categories":6218},[99],{"categories":6220},[99],{"categories":6222},[148],{"categories":6224},[160],{"categories":6226},[99],{"categories":6228},[99],{"categories":6230},[99],{"categories":6232},[148],{"categories":6234},[182],{"categories":6236},[99],{"categories":6238},[99],{"categories":6240},[99],{"categories":6242},[143],{"categories":6244},[99],{"categories":6246},[148],{"categories":6248},[228],{"categories":6250},[],{"categories":6252},[99],{"categories":6254},[185],{"categories":6256},[148],{"categories":6258},[99],{"categories":6260},[99],{"categories":6262},[],{"categories":6264},[99],{"categories":6266},[99],{"categories":6268},[182],{"categories":6270},[99],{"categories":6272},[99],{"categories":6274},[148],{"categories":6276},[257],{"categories":6278},[],{"categories":6280},[],{"categories":6282},[160],{"categories":6284},[99],{"categories":6286},[182],{"categories":6288},[99],{"categories":6290},[160],{"categories":6292},[182],{"categories":6294},[99],{"categories":6296},[257],{"categories":6298},[185],{"categories":6300},[99],{"categories":6302},[99],{"categories":6304},[138],{"categories":6306},[148],{"categories":6308},[99],{"categories":6310},[99],{"categories":6312},[148],{"categories":6314},[148],{"categories":6316},[99],{"categories":6318},[143],{"categories":6320},[],{"categories":6322},[185],{"categories":6324},[99],{"categories":6326},[],{"categories":6328},[182],{"categories":6330},[99],{"categories":6332},[185],{"categories":6334},[99],{"categories":6336},[160],{"categories":6338},[160],{"categories":6340},[160],{"categories":6342},[148],{"categories":6344},[148],{"categories":6346},[99],{"categories":6348},[148],{"categories":6350},[99],{"categories":6352},[99],{"categories":6354},[228],{"categories":6356},[185],{"categories":6358},[185],{"categories":6360},[],{"categories":6362},[182],{"categories":6364},[99],{"categories":6366},[99],{"categories":6368},[160],{"categories":6370},[],{"categories":6372},[182],{"categories":6374},[182],{"categories":6376},[182],{"categories":6378},[],{"categories":6380},[148],{"categories":6382},[99],{"categories":6384},[],{"categories":6386},[138],{"categories":6388},[143],{"categories":6390},[],{"categories":6392},[99],{"categories":6394},[99],{"categories":6396},[],{"categories":6398},[160],{"categories":6400},[],{"categories":6402},[],{"categories":6404},[],{"categories":6406},[],{"categories":6408},[99],{"categories":6410},[182],{"categories":6412},[],{"categories":6414},[],{"categories":6416},[99],{"categories":6418},[99],{"categories":6420},[99],{"categories":6422},[185],{"categories":6424},[99],{"categories":6426},[185],{"categories":6428},[],{"categories":6430},[185],{"categories":6432},[185],{"categories":6434},[294],{"categories":6436},[148],{"categories":6438},[160],{"categories":6440},[],{"categories":6442},[],{"categories":6444},[185],{"categories":6446},[160],{"categories":6448},[160],{"categories":6450},[160],{"categories":6452},[],{"categories":6454},[138],{"categories":6456},[160],{"categories":6458},[160],{"categories":6460},[138],{"categories":6462},[160],{"categories":6464},[143],{"categories":6466},[160],{"categories":6468},[160],{"categories":6470},[160],{"categories":6472},[185],{"categories":6474},[182],{"categories":6476},[182],{"categories":6478},[99],{"categories":6480},[160],{"categories":6482},[185],{"categories":6484},[294],{"categories":6486},[185],{"categories":6488},[185],{"categories":6490},[185],{"categories":6492},[],{"categories":6494},[143],{"categories":6496},[],{"categories":6498},[294],{"categories":6500},[160],{"categories":6502},[160],{"categories":6504},[160],{"categories":6506},[148],{"categories":6508},[182,143],{"categories":6510},[185],{"categories":6512},[],{"categories":6514},[],{"categories":6516},[185],{"categories":6518},[],{"categories":6520},[185],{"categories":6522},[182],{"categories":6524},[148],{"categories":6526},[],{"categories":6528},[160],{"categories":6530},[99],{"categories":6532},[228],{"categories":6534},[],{"categories":6536},[99],{"categories":6538},[],{"categories":6540},[182],{"categories":6542},[138],{"categories":6544},[185],{"categories":6546},[],{"categories":6548},[160],{"categories":6550},[182],[6552,6692,6816,6938],{"id":6553,"title":6554,"ai":6555,"body":6561,"categories":6666,"created_at":100,"date_modified":100,"description":91,"extension":101,"faq":100,"featured":102,"kicker_label":100,"meta":6667,"navigation":115,"path":6679,"published_at":6680,"question":100,"scraped_at":6681,"seo":6682,"sitemap":6683,"source_id":6684,"source_name":6685,"source_type":122,"source_url":6686,"stem":6687,"tags":6688,"thumbnail_url":100,"tldr":6689,"tweet":100,"unknown_tags":6690,"__hash__":6691},"summaries\u002Fsummaries\u002F9aa350456b8c67ba-eve-bodnia-ebms-fix-what-llms-can-t-for-critical-t-summary.md","Eve Bodnia: EBMs Fix What LLMs Can't for Critical Tasks",{"provider":7,"model":6556,"input_tokens":6557,"output_tokens":6558,"processing_time_ms":6559,"cost_usd":6560},"x-ai\u002Fgrok-4.1-fast",8762,2251,23722,0.00285525,{"type":14,"value":6562,"toc":6658},[6563,6567,6570,6573,6576,6580,6583,6586,6589,6592,6596,6599,6602,6605,6608,6612,6615,6618,6622,6625,6628,6632],[17,6564,6566],{"id":6565},"llms-fatal-flaws-for-mission-critical-systems","LLMs' Fatal Flaws for Mission-Critical Systems",[22,6568,6569],{},"Eve Bodnia argues that transformer-based LLMs, dominant in AI today, are fundamentally unreliable for high-stakes applications like chip design, financial analysis, or aviation controls. Their autoregressive nature—generating output token-by-token without mid-process inspection—leads to hallucinations, where the model commits to errors without correction. \"Imagine there's AI driving a car and you're in that car and that car is an LLM and someone tells you like, you know, 20% of the time it's going to hallucinate and you might end up like in in like a wrong place,\" Bodnia warns, contrasting Dan Shipper's more experimental curiosity about such risks.",[22,6571,6572],{},"LLMs act as black boxes: you can't peek inside during generation to assess confidence or reasoning. Even with external verifiers like Lean 4—a machine-verifiable proof language—attached post-generation, the core issue persists. Token prediction remains a costly \"guessing game,\" expensive in compute and unreliable for determinism. Shipper pushes back, noting LLMs excel at generating useful output verifiable via tests, but Bodnia counters that this \"guess and check\" is inefficient and doesn't guarantee internals align with outputs.",[22,6574,6575],{},"Mission-critical industries haven't widely adopted LLMs precisely because of this gap. Bodnia sees Logical Intelligence filling it by prioritizing \"deterministic AI, verifiable AI,\" starting with software\u002Fhardware correctness.",[17,6577,6579],{"id":6578},"energy-based-models-physics-inspired-alternatives","Energy-Based Models: Physics-Inspired Alternatives",[22,6581,6582],{},"Bodnia's solution is energy-based models (EBMs), rooted in physics' energy minimization principle—think Lagrangians deriving equations of motion from kinetic and potential energy terms. EBMs are non-autoregressive and token-free, mapping all possible outcomes onto an \"energy landscape\": probable states settle in low-energy \"valleys,\" improbable ones on high-energy \"peaks.\"",[22,6584,6585],{},"Unlike LLMs' sequential navigation (like a left-brain pathfinder taking wrong turns without backtracking), EBMs survey the entire map upfront. \"EBM going to have the first view all the time. So if you see there's a hole, you're going to choose a different route,\" Bodnia explains with a navigation metaphor. Her team's model, dubbed Kona (energy-based reasoning model with latent variables), constructs these landscapes from data, enabling real-time inspection and self-alignment during training.",[22,6587,6588],{},"Shipper tests the concept: modeling his post-podcast behavior (ending on the couch). An LLM might predict via token probabilities from vast text data, but EBMs directly map observed states (tiredness, house geometry) to the landscape without language mediation. This yields inspectable confidence scores pre-output, plus external verifiers for double assurance.",[22,6590,6591],{},"EBMs are cheaper—no tokens mean no guessing compute—and controllable: \"You control the training. It's no longer black box for you.\" Bodnia envisions hybrid use: prototype on LLMs, plug in EBMs for production.",[17,6593,6595],{"id":6594},"beyond-language-true-data-understanding","Beyond Language: True Data Understanding",[22,6597,6598],{},"A core critique: LLMs force all intelligence through language, distorting non-verbal tasks. Human reasoning is abstract, multilingual, and language-independent; LLMs' token chains vary by training language, yielding inconsistent processes. Driving a car or navigating a house relies on visual-spatial data, not word prediction—yet LLMs embed it into language space first.",[22,6600,6601],{},"\"Intelligence which is language-dependent... feels really wrong,\" Bodnia asserts. \"When you drive a car, when you walk around your house, how much language you actually use? Are you trying to predict next word...? Probably not.\"",[22,6603,6604],{},"EBMs process raw data modally, constructing landscapes that reveal underlying \"laws\" (e.g., conservation principles). Shipper suggests sequence modeling via movement tokens; Bodnia agrees it's viable but unnecessary—EBMs handle it natively, without language crutches.",[22,6606,6607],{},"This enables \"understanding\" as structural insight, not statistical correlation. Observing Shipper repeatedly, an EBM learns his \"equation of motion\": tired → couch (lowest valley), gym as secondary low point.",[17,6609,6611],{"id":6610},"verifiable-code-from-plain-english","Verifiable Code from Plain English",[22,6613,6614],{},"EBMs tackle \"vibe coding\"—LLM-generated code that feels right but fails scrutiny. By enabling formal verification in plain English (no C++ needed), they produce certifiably correct outputs. Internal verifiers assess solution quality mid-process; landscapes quantify confidence.",[22,6616,6617],{},"Logical Intelligence targets code gen and chip design, where LLMs falter. Bodnia predicts EBMs bridge the adoption gap in banking, aviation, and beyond, automating without risk.",[17,6619,6621],{"id":6620},"signs-of-llm-plateau-and-ebm-momentum","Signs of LLM Plateau and EBM Momentum",[22,6623,6624],{},"Bodnia observes LLM progress stalling: scaling laws yield diminishing returns as language ceilings hit. Non-language tasks expose limits; mission-critical sectors demand alternatives.",[22,6626,6627],{},"\"LLM progress is plateauing,\" she states at 00:43:21 timestamp context. EBMs, inspectable and efficient, position Logical Intelligence as a foundational player. Shipper probes trade-offs, but Bodnia emphasizes EBMs' universality for verifiable AI everywhere.",[17,6629,6631],{"id":6630},"key-takeaways","Key Takeaways",[33,6633,6634,6637,6640,6643,6646,6649,6652,6655],{},[36,6635,6636],{},"Prioritize internal verifiers in AI architecture for mission-critical tasks; LLMs' black-box token generation can't self-correct hallucinations.",[36,6638,6639],{},"Build energy landscapes to model data: map states to valleys\u002Fpeaks for probabilistic navigation without sequences.",[36,6641,6642],{},"Ditch language dependency—process visual\u002Fspatial data natively to avoid embedding distortions in non-verbal reasoning.",[36,6644,6645],{},"Combine EBM self-alignment with external tools like Lean 4 for double verification, slashing compute costs.",[36,6647,6648],{},"Prototype on LLMs, deploy EBMs: hybrids accelerate verifiable code gen and chip design from plain English.",[36,6650,6651],{},"Watch LLM scaling plateau; physics-based models like EBMs unlock deterministic AI for aviation, finance, and automation.",[36,6653,6654],{},"Inspect models in real-time during training to control outcomes—EBMs make AI transparent, not a post-hoc guess.",[36,6656,6657],{},"For behavior prediction (e.g., post-work routines), observe states directly; energy minimization reveals 'laws' like tired → relax.",{"title":91,"searchDepth":92,"depth":92,"links":6659},[6660,6661,6662,6663,6664,6665],{"id":6565,"depth":92,"text":6566},{"id":6578,"depth":92,"text":6579},{"id":6594,"depth":92,"text":6595},{"id":6610,"depth":92,"text":6611},{"id":6620,"depth":92,"text":6621},{"id":6630,"depth":92,"text":6631},[],{"content_references":6668,"triage":6676},[6669,6672],{"type":6670,"title":6671,"context":108},"tool","Lean 4",{"type":6670,"title":6673,"url":6674,"context":6675},"Granola","http:\u002F\u002Fgranola.ai\u002Fevery","recommended",{"relevance":111,"novelty":111,"quality":111,"actionability":112,"composite":6677,"reasoning":6678},3.8,"Category: AI & LLMs. The article critiques LLMs for critical applications and introduces energy-based models as a solution, addressing a specific pain point regarding reliability in mission-critical systems. It provides insights into the limitations of LLMs and presents a novel alternative, making it relevant and actionable for those exploring AI integration.","\u002Fsummaries\u002F9aa350456b8c67ba-eve-bodnia-ebms-fix-what-llms-can-t-for-critical-t-summary","2026-04-15 15:00:53","2026-04-19 03:30:59",{"title":6554,"description":91},{"loc":6679},"9aa350456b8c67ba","Every","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Q-i8ZSUCtIc","summaries\u002F9aa350456b8c67ba-eve-bodnia-ebms-fix-what-llms-can-t-for-critical-t-summary",[127,128,129],"Eve Bodnia critiques LLMs' hallucinations and language bias for mission-critical uses like chip design; her energy-based models (EBMs) enable verifiable AI via physics-inspired energy landscapes, inspectable reasoning, and token-free processing.",[129],"8ssLLHnCnWq5KGfNVVUm-fei4Wo6G5e8Z45nrsuxziA",{"id":6693,"title":6694,"ai":6695,"body":6700,"categories":6788,"created_at":100,"date_modified":100,"description":91,"extension":101,"faq":100,"featured":102,"kicker_label":100,"meta":6789,"navigation":115,"path":6800,"published_at":6801,"question":100,"scraped_at":6802,"seo":6803,"sitemap":6804,"source_id":6805,"source_name":6806,"source_type":6807,"source_url":6808,"stem":6809,"tags":6810,"thumbnail_url":100,"tldr":6812,"tweet":6813,"unknown_tags":6814,"__hash__":6815},"summaries\u002Fsummaries\u002F95337a4c838a40af-continuously-improving-ai-agents-via-trace-data-mi-summary.md","Continuously Improving AI Agents via Trace Data Mining",{"provider":7,"model":8,"input_tokens":6696,"output_tokens":6697,"processing_time_ms":6698,"cost_usd":6699},7525,657,3492,0.00286675,{"type":14,"value":6701,"toc":6782},[6702,6706,6713,6717,6720,6740,6744,6755,6759,6762],[17,6703,6705],{"id":6704},"the-shift-from-determinism-to-autonomous-traces","The Shift from Determinism to Autonomous Traces",[22,6707,6708,6709,6712],{},"Modern AI agents have traded code-level determinism for autonomy, making it difficult for developers to reason about behavior through static code analysis. Because agents operate in dynamic environments using prompts, tools, and middleware, the only way to understand their performance is through ",[39,6710,6711],{},"trace data",". This data—which includes tool calls, API interactions, and output messages—is the primary substrate for improvement. As agents scale, the volume of this data will soon exceed the total historical data produced by humans, necessitating automated systems to mine these logs.",[17,6714,6716],{"id":6715},"the-agent-improvement-loop","The Agent Improvement Loop",[22,6718,6719],{},"Improving agents requires a cyclical process analogous to classical machine learning:",[63,6721,6722,6728,6734],{},[36,6723,6724,6727],{},[39,6725,6726],{},"Ship and Trace",": Deploy the agent to collect real-world execution data.",[36,6729,6730,6733],{},[39,6731,6732],{},"Mine and Analyze",": Use other agents to query these traces. This allows you to identify successful vs. failed interactions, detect performance degradation (e.g., after context compaction), and perform counterfactual testing (e.g., comparing different model versions on the same task).",[36,6735,6736,6739],{},[39,6737,6738],{},"Iterate",": Use the insights to refine the agent's \"harness\" (prompts and orchestration) or fine-tune the underlying model.",[17,6741,6743],{"id":6742},"harness-engineering-vs-fine-tuning","Harness Engineering vs. Fine-Tuning",[22,6745,6746,6747,6750,6751,6754],{},"There is a clear hierarchy for optimization. Start with ",[39,6748,6749],{},"harness engineering"," (prompt and tool adjustments) because it provides rapid feedback, often within minutes. Once you hit the performance ceiling of your prompt architecture, move to ",[39,6752,6753],{},"fine-tuning",". By training base models on domain-specific, high-quality traces, you can often match or exceed the performance of larger frontier models at a fraction of the cost. For high-inference workloads, shifting from token-based pricing to dedicated hardware clusters can further improve the economics of these systems.",[17,6756,6758],{"id":6757},"the-future-of-continual-learning","The Future of Continual Learning",[22,6760,6761],{},"True continual learning for agents involves three axes:",[33,6763,6764,6770,6776],{},[36,6765,6766,6769],{},[39,6767,6768],{},"Observational Data",": Using agent actions to build training sets.",[36,6771,6772,6775],{},[39,6773,6774],{},"Harness Evolution",": Updating prompts and tools based on real-world task performance.",[36,6777,6778,6781],{},[39,6779,6780],{},"Memory Management",": Moving beyond simple append-only logs to systems that perform \"sleep-time compute\"—periodically processing and distilling historical traces to update the agent's internal state and knowledge, rather than just stuffing more data into context windows.",{"title":91,"searchDepth":92,"depth":92,"links":6783},[6784,6785,6786,6787],{"id":6704,"depth":92,"text":6705},{"id":6715,"depth":92,"text":6716},{"id":6742,"depth":92,"text":6743},{"id":6757,"depth":92,"text":6758},[99],{"content_references":6790,"triage":6797},[6791,6794],{"type":6670,"title":6792,"url":6793,"context":108},"LangChain","https:\u002F\u002Fwww.langchain.com\u002F",{"type":6670,"title":6795,"url":6796,"context":108},"scikit-learn","https:\u002F\u002Fscikit-learn.org\u002F",{"relevance":110,"novelty":111,"quality":111,"actionability":111,"composite":6798,"reasoning":6799},4.35,"Category: AI & LLMs. The article provides a detailed framework for improving AI agents through trace data mining, directly addressing the audience's need for practical applications in AI integration. It outlines a clear iterative process for refining agent performance, which is actionable for developers looking to implement these strategies.","\u002Fsummaries\u002F95337a4c838a40af-continuously-improving-ai-agents-via-trace-data-mi-summary","2026-08-12 19:00:01","2026-08-13 03:24:33",{"title":6694,"description":91},{"loc":6800},"95337a4c838a40af","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CvRngaQZQ3Y","summaries\u002F95337a4c838a40af-continuously-improving-ai-agents-via-trace-data-mi-summary",[6811,128,126,127],"agents","To improve autonomous agents, treat them like machine learning models: collect execution traces, mine them for feedback, and use that data to iteratively refine prompts, fine-tune models, and update agent state.","A talk from a LangChain researcher on the necessity of \"trace mining\"—using LLMs to analyze the logs of your own autonomous agents to identify failure points, distill successful behaviors into smaller models, and automate the creation of evaluation datasets. It serves as a conceptual pitch for [LangSmith](https:\u002F\u002Fsmith.langchain.com), framing observability as the prerequisite for any serious attempt at agentic continuous learning.",[],"ELP4cFJ-GLDxDYBHuAYSkX36tuv1j_PzGQlcWSxrE9I",{"id":6817,"title":6818,"ai":6819,"body":6824,"categories":6913,"created_at":100,"date_modified":100,"description":91,"extension":101,"faq":100,"featured":102,"kicker_label":100,"meta":6914,"navigation":115,"path":6925,"published_at":6926,"question":100,"scraped_at":6927,"seo":6928,"sitemap":6929,"source_id":6930,"source_name":6806,"source_type":6807,"source_url":6931,"stem":6932,"tags":6933,"thumbnail_url":100,"tldr":6934,"tweet":6935,"unknown_tags":6936,"__hash__":6937},"summaries\u002Fsummaries\u002F19867c4b686fadbd-continual-learning-via-distillation-a-2x2-taxonomy-summary.md","Continual Learning via Distillation: A 2x2 Taxonomy",{"provider":7,"model":8,"input_tokens":6820,"output_tokens":6821,"processing_time_ms":6822,"cost_usd":6823},7283,821,91455,0.00305225,{"type":14,"value":6825,"toc":6905},[6826,6830,6833,6859,6863,6866,6871,6874,6878,6881,6885],[17,6827,6829],{"id":6828},"the-distillation-spectrum","The Distillation Spectrum",[22,6831,6832],{},"To implement continual learning, Applied Compute categorizes distillation into a 2x2 grid based on two axes: the source of the production traces (offline vs. online) and the source of the hints (offline vs. online).",[33,6834,6835,6841,6847,6853],{},[36,6836,6837,6840],{},[39,6838,6839],{},"Offline Traces:"," Uses static batches of historical production data. This is the entry point for enterprises to improve agents without needing a live, replayable environment.",[36,6842,6843,6846],{},[39,6844,6845],{},"Online Traces:"," Integrates inference and training into a unified engine, where the model learns from its own live rollouts. This represents the \"holy grail\" of continual learning.",[36,6848,6849,6852],{},[39,6850,6851],{},"Offline Hints:"," Uses static rubrics or general priors (e.g., \"don't give refunds so easily\") to guide the model.",[36,6854,6855,6858],{},[39,6856,6857],{},"Online Hints:"," Dynamically constructs hints based on the specific behavior observed in the current online rollout.",[17,6860,6862],{"id":6861},"implementation-strategies","Implementation Strategies",[22,6864,6865],{},"Applied Compute focuses on two primary quadrants to drive value:",[6867,6868,6870],"h3",{"id":6869},"quadrant-1-offline-traces-offline-hints","Quadrant 1: Offline Traces + Offline Hints",[22,6872,6873],{},"This approach allows for immediate value by taking existing production logs and nudging the model toward specific behaviors. For example, in SWEBench, the team successfully trained an agent to call a 'submit' tool before turn 40 by using a hint that warned the model about its turn limit. Crucially, this improved the task completion rate (from 22% to 60%) without degrading the base test pass rate, even though the original traces lacked the specific reasoning path for the tool call.",[6867,6875,6877],{"id":6876},"quadrant-4-online-traces-online-hints","Quadrant 4: Online Traces + Online Hints",[22,6879,6880],{},"This is the most scalable solution for raising performance ceilings. By generating hints dynamically based on the model's live performance, the system can adapt to specific edge cases. For instance, when teaching an agent to use a specific, out-of-distribution hyperlink format, online hinting improved accuracy from 15% to 80%, significantly outperforming offline hinting methods which struggled to adapt to the nuance of the harness.",[17,6882,6884],{"id":6883},"tips-for-effective-distillation","Tips for Effective Distillation",[33,6886,6887,6893,6899],{},[36,6888,6889,6892],{},[39,6890,6891],{},"Per-step Hinting:"," Rather than applying a hint to an entire rollout, use an LLM judge to identify the specific moment in time where the teacher should intervene. Distillation is most effective on the immediate next steps following the hint.",[36,6894,6895,6898],{},[39,6896,6897],{},"Relevance Masking:"," Use an LLM judge to sample and select which tokens to learn from. This prevents the student model from picking up irrelevant stylistic preferences (like connector words) from the teacher model, which helps avoid catastrophic forgetting or degradation of base capabilities.",[36,6900,6901,6904],{},[39,6902,6903],{},"Avoid 'Golden' Rubrics:"," The most effective continual learning systems do not rely on having a perfect, pre-existing answer key for every task. Instead, they focus on nudging behavior through targeted hints.",{"title":91,"searchDepth":92,"depth":92,"links":6906},[6907,6908,6912],{"id":6828,"depth":92,"text":6829},{"id":6861,"depth":92,"text":6862,"children":6909},[6910,6911],{"id":6869,"depth":112,"text":6870},{"id":6876,"depth":112,"text":6877},{"id":6883,"depth":92,"text":6884},[99],{"content_references":6915,"triage":6923},[6916,6919],{"type":6670,"title":6917,"url":6918,"context":108},"SWEBench","https:\u002F\u002Fwww.swebench.com\u002F",{"type":106,"title":6920,"publisher":6921,"url":6922,"context":6675},"Relevance Mass Self-Distillation Blog Post","Applied Compute","https:\u002F\u002Fappliedcompute.com",{"relevance":110,"novelty":111,"quality":111,"actionability":111,"composite":6798,"reasoning":6924},"Category: AI & LLMs. The article provides a detailed framework for implementing continual learning through distillation, addressing a specific pain point for AI developers looking to improve model performance. It includes concrete examples of how different quadrants of the 2x2 grid can be applied in practice, making it actionable for the audience.","\u002Fsummaries\u002F19867c4b686fadbd-continual-learning-via-distillation-a-2x2-taxonomy-summary","2026-08-12 17:30:06","2026-08-13 03:24:51",{"title":6818,"description":91},{"loc":6925},"19867c4b686fadbd","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZTA0GwpAUak","summaries\u002F19867c4b686fadbd-continual-learning-via-distillation-a-2x2-taxonomy-summary",[128,6811,127,126],"Enterprises can implement continual learning by mapping distillation tasks across a 2x2 grid of offline\u002Fonline traces and hints, allowing for immediate performance improvements without requiring 'golden' datasets.","This is a technical presentation on building continual learning systems for enterprise AI agents. The speaker categorizes distillation methods into a 2x2 grid based on whether the data and \"hints\" (guidance) are sourced offline or online, focusing on how to improve model performance without requiring a \"golden\" ground-truth dataset.",[],"lVT9nJlC-oZPILWUydjWeoZ7CkqIPkcrjfH3gx2x2pE",{"id":6939,"title":6940,"ai":6941,"body":6946,"categories":7001,"created_at":100,"date_modified":100,"description":91,"extension":101,"faq":100,"featured":102,"kicker_label":100,"meta":7002,"navigation":115,"path":7013,"published_at":7014,"question":100,"scraped_at":7015,"seo":7016,"sitemap":7017,"source_id":7018,"source_name":6806,"source_type":6807,"source_url":7019,"stem":7020,"tags":7021,"thumbnail_url":100,"tldr":7022,"tweet":7023,"unknown_tags":7024,"__hash__":7025},"summaries\u002Fsummaries\u002Fe5d89401665344eb-democratizing-frontier-ai-automating-discovery-and-summary.md","Democratizing Frontier AI: Automating Discovery and Scaling",{"provider":7,"model":8,"input_tokens":6942,"output_tokens":6943,"processing_time_ms":6944,"cost_usd":6945},7547,677,3499,0.00290225,{"type":14,"value":6947,"toc":6996},[6948,6952,6955,6962,6966,6969,6989,6993],[17,6949,6951],{"id":6950},"the-shift-from-monolithic-scaling-to-adaptive-intelligence","The Shift from Monolithic Scaling to Adaptive Intelligence",[22,6953,6954],{},"Modern AI research has historically been constrained by an \"unreasonably narrow path\"—requiring access to elite labs, massive compute budgets, and specific academic pedigrees. This created a bottleneck where only a few organizations could contribute to the frontier. However, the paradigm is shifting. We are reaching a saturation point in model architecture where simply increasing pre-training size no longer yields the same step-wise performance gains.",[22,6956,6957,6958,6961],{},"Instead, the most significant returns are now found in the ",[39,6959,6960],{},"broader action space","—specifically in how models interact with their environment and how they are customized post-training. This transition moves the field away from monolithic, one-size-fits-all models toward adaptive intelligence that can be tailored to specific domains like medicine, law, and science.",[17,6963,6965],{"id":6964},"automating-the-research-loop","Automating the Research Loop",[22,6967,6968],{},"To democratize access to frontier-level intelligence, we must automate the training process itself. The author introduces \"Auto Scientist,\" a system designed to co-optimize the entire training loop—from data curation to model alignment. Key insights include:",[33,6970,6971,6977,6983],{},[36,6972,6973,6976],{},[39,6974,6975],{},"Data-Model Co-optimization:"," Performance gains are not achieved by agents alone; they require tight integration between data quality and model architecture. Controlling the data flow is as critical as the model parameters themselves.",[36,6978,6979,6982],{},[39,6980,6981],{},"Exploiting the Search Space:"," By automating hyperparameter tuning and architecture selection, systems can outperform human research staff, who are often biased toward familiar configurations. This allows for massive exploitation of the search space with greater predictability.",[36,6984,6985,6988],{},[39,6986,6987],{},"Reducing Compute Barriers:"," By shifting the focus to post-training and agentic compute, the reliance on massive, centralized GPU clusters is reduced. This makes it possible for smaller teams to build high-performing, domain-specific models without needing thousands of GPUs.",[17,6990,6992],{"id":6991},"the-future-of-frontier-discovery","The Future of Frontier Discovery",[22,6994,6995],{},"We are moving toward an era where the \"recipe\" and the research question matter more than the raw volume of compute. As pre-training becomes less of a differentiator, the ability to rapidly iterate and customize models becomes the primary driver of innovation. This shift lowers the barrier to entry, allowing builders to focus on answering specific, high-impact questions rather than spending years learning the mechanics of model training. The next frontier involves making test-time compute adaptive, ensuring that the resources spent on a task are proportional to its complexity, further optimizing the efficiency of AI systems.",{"title":91,"searchDepth":92,"depth":92,"links":6997},[6998,6999,7000],{"id":6950,"depth":92,"text":6951},{"id":6964,"depth":92,"text":6965},{"id":6991,"depth":92,"text":6992},[99],{"content_references":7003,"triage":7011},[7004,7006,7009],{"type":6670,"title":7005,"context":108},"Auto Scientist",{"type":106,"title":7007,"author":7008,"context":108},"Slow Death of Scaling","Unknown",{"type":106,"title":7010,"context":108},"Open LLM Leaderboard",{"relevance":110,"novelty":111,"quality":111,"actionability":111,"composite":6798,"reasoning":7012},"Category: AI & LLMs. The article discusses the shift from monolithic AI models to adaptive intelligence, addressing a key pain point for builders regarding the accessibility of AI tools. It provides insights on automating the training process and optimizing data flow, which are actionable strategies for developers looking to implement AI in their products.","\u002Fsummaries\u002Fe5d89401665344eb-democratizing-frontier-ai-automating-discovery-and-summary","2026-08-12 16:30:19","2026-08-13 03:25:01",{"title":6940,"description":91},{"loc":7013},"e5d89401665344eb","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XEd_SRVHBgU","summaries\u002Fe5d89401665344eb-democratizing-frontier-ai-automating-discovery-and-summary",[6811,126,127,129],"The era of massive, monolithic pre-training is hitting a ceiling. By automating model training and data optimization, we can shift the focus from compute-heavy scaling to domain-specific innovation, allowing more builders to participate at the frontier.","This is a talk by an AI researcher arguing that the current \"narrow path\" of frontier AI development—dominated by a few labs and massive compute—is shifting toward decentralized, domain-specific model training. The speaker introduces their project, [Auto Scientist](https:\u002F\u002Fgithub.com\u002FSakanaAI\u002FAI-Scientist), which automates the model training loop by co-optimizing data and architecture to allow for more accessible, efficient, and specialized AI development.",[129],"mpm9rGR3hkfb2dTRdoeQkXWMhW5e2pV-sFgiE1pzLNg"]