[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-fed2d5e03673ece0-integrating-gemini-intelligence-into-alloydb-via-a-summary":3,"summaries-facets-categories":138,"summary-related-fed2d5e03673ece0-integrating-gemini-intelligence-into-alloydb-via-a-summary":5712},{"id":4,"title":5,"ai":6,"body":13,"categories":96,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":101,"navigation":117,"path":118,"published_at":119,"question":98,"scraped_at":120,"seo":121,"sitemap":122,"source_id":123,"source_name":124,"source_type":125,"source_url":126,"stem":127,"tags":128,"thumbnail_url":133,"tldr":134,"tweet":135,"unknown_tags":136,"__hash__":137},"summaries\u002Fsummaries\u002Ffed2d5e03673ece0-integrating-gemini-intelligence-into-alloydb-via-a-summary.md","Integrating Gemini Intelligence into AlloyDB via AI Functions",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",4880,603,3107,0.0021245,{"type":14,"value":15,"toc":90},"minimark",[16,21,25,28,83,87],[17,18,20],"h2",{"id":19},"bringing-llm-intelligence-to-sql-workflows","Bringing LLM Intelligence to SQL Workflows",[22,23,24],"p",{},"AlloyDB AI functions bridge the gap between static database storage and generative AI by allowing developers to invoke foundation models like Gemini directly within SQL queries. This approach enables complex operations—such as reranking hybrid search results based on external knowledge, filtering transactions for fraud, or converting unstructured text into structured JSON—without needing to extract data to an external application layer.",[22,26,27],{},"Key generally available functions include:",[29,30,31,48,57,66],"ul",{},[32,33,34,38,39,43,44,47],"li",{},[35,36,37],"strong",{},"Ranking & Filtering:"," ",[40,41,42],"code",{},"ai.rank"," (including semantic ranking) and ",[40,45,46],{},"ai.if"," for intelligent, context-aware filtering.",[32,49,50,38,53,56],{},[35,51,52],{},"Generation:",[40,54,55],{},"ai.generate"," for transforming unstructured data into structured formats.",[32,58,59,38,62,65],{},[35,60,61],{},"Forecasting:",[40,63,64],{},"ai.forecast",", powered by the TimesFM model, for predictive analytics on historical data.",[32,67,68,38,71,74,75,78,79,82],{},[35,69,70],{},"Insights:",[40,72,73],{},"ai.analyze_sentiment",", ",[40,76,77],{},"ai.summarize",", and ",[40,80,81],{},"ai.agg_summarize"," for distilling large volumes of text or multi-row data into actionable insights.",[17,84,86],{"id":85},"optimizing-performance-and-cost","Optimizing Performance and Cost",[22,88,89],{},"A primary barrier to using LLMs in databases is the latency and cost of row-by-row API calls. AlloyDB addresses this through \"Optimized AI Functions.\" Instead of calling a remote LLM for every row, the system trains a local model on your specific embeddings and LLM outputs. When a query is executed, the database invokes this local model, which can process up to 100,000 rows per second. Benchmarks indicate this method is up to 23,000 times faster and 6,000 times cheaper than traditional row-at-a-time LLM calls, costing less than one-tenth of a cent per operation. Additional performance acceleration is achieved through asynchronous bulk prompting, AI function acceleration, and array-based processing.",{"title":91,"searchDepth":92,"depth":92,"links":93},"",2,[94,95],{"id":19,"depth":92,"text":20},{"id":85,"depth":92,"text":86},[97],"AI & LLMs",null,"md",false,{"content_references":102,"triage":112},[103,108],{"type":104,"title":105,"url":106,"context":107},"tool","AlloyDB for PostgreSQL","https:\u002F\u002Fcloud.google.com\u002Falloydb","recommended",{"type":109,"title":110,"context":111},"other","TimesFM","mentioned",{"relevance":113,"novelty":114,"quality":114,"actionability":113,"composite":115,"reasoning":116},5,4,4.55,"Category: AI & LLMs. The article provides a deep dive into integrating LLMs with SQL workflows using AlloyDB AI functions, addressing a specific pain point of performance and cost in AI integration. It offers concrete examples of functions that developers can implement immediately, making it highly actionable.",true,"\u002Fsummaries\u002Ffed2d5e03673ece0-integrating-gemini-intelligence-into-alloydb-via-a-summary","2026-06-22 15:35:46","2026-06-23 12:56:35",{"title":5,"description":91},{"loc":118},"fed2d5e03673ece0","Google Cloud Tech","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=PxbLWePxt40","summaries\u002Ffed2d5e03673ece0-integrating-gemini-intelligence-into-alloydb-via-a-summary",[129,130,131,132],"automation","data-science","ai-llms","postgresql","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FPxbLWePxt40\u002Fhqdefault.jpg","AlloyDB AI functions allow developers to execute LLM-powered tasks like ranking, summarization, and forecasting directly within SQL, using optimized local models to achieve massive performance gains and cost reductions over standard row-by-row LLM calls.","This video provides a technical overview of how to use integrated AI functions within [AlloyDB for PostgreSQL](https:\u002F\u002Fgoo.gle\u002F4d3cuSe) to perform tasks like reranking, sentiment analysis, and forecasting directly via SQL. It explains how the platform optimizes these calls to reduce latency and cost compared to standard row-by-row LLM requests.",[131,132],"NbXNzly9Z1e56UVHom9ADfMVUu4Fi3C9kuyM74ksmsQ",[139,142,145,147,150,153,155,157,160,162,164,166,168,171,173,175,177,179,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,219,222,224,226,228,230,232,234,236,238,240,242,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,276,278,280,282,284,286,288,290,292,294,296,298,300,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,362,364,366,368,370,372,374,376,378,380,383,385,387,389,391,393,395,397,399,401,403,405,408,410,412,414,416,418,420,422,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,474,476,478,481,483,485,487,489,491,493,495,497,499,501,503,505,507,510,512,514,516,518,520,522,524,526,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,768,770,772,774,777,779,781,783,785,787,789,791,793,795,798,800,802,804,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,858,860,862,864,866,868,870,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,932,934,936,938,940,942,944,946,948,950,952,954,956,958,960,962,964,966,968,970,972,974,976,978,980,982,984,986,988,990,992,994,996,998,1000,1002,1004,1006,1008,1010,1012,1014,1016,1018,1020,1022,1024,1026,1028,1030,1032,1034,1036,1038,1040,1042,1044,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1442,1444,1446,1448,1450,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1478,1480,1482,1484,1486,1488,1490,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1840,1842,1844,1846,1848,1850,1852,1854,1856,1858,1860,1862,1864,1866,1868,1870,1872,1874,1876,1878,1880,1882,1884,1886,1888,1890,1892,1894,1896,1898,1900,1902,1904,1906,1908,1910,1912,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032,2034,2036,2038,2040,2042,2044,2046,2048,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2465,2467,2469,2471,2473,2475,2477,2479,2481,2483,2485,2487,2489,2491,2493,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2572,2574,2576,2578,2580,2582,2584,2586,2588,2590,2592,2594,2596,2598,2600,2602,2604,2606,2608,2610,2612,2614,2616,2618,2620,2622,2624,2626,2628,2630,2632,2634,2636,2638,2640,2642,2644,2646,2648,2650,2652,2654,2656,2658,2660,2662,2664,2666,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671,3673,3675,3677,3679,3681,3683,3685,3687,3689,3691,3693,3695,3697,3699,3702,3704,3706,3708,3710,3712,3714,3716,3718,3720,3722,3724,3726,3728,3730,3732,3734,3736,3738,3740,3742,3744,3746,3748,3750,3752,3754,3756,3758,3760,3762,3764,3766,3768,3770,3772,3774,3776,3778,3780,3782,3784,3786,3788,3790,3792,3794,3796,3798,3800,3802,3804,3806,3808,3810,3812,3814,3816,3818,3820,3822,3824,3826,3828,3830,3832,3834,3836,3838,3840,3842,3844,3846,3848,3850,3852,3854,3856,3858,3860,3862,3864,3866,3868,3870,3872,3874,3876,3878,3880,3882,3884,3886,3888,3890,3892,3894,3896,3898,3900,3902,3904,3906,3908,3910,3912,3914,3916,3918,3920,3922,3924,3926,3928,3930,3932,3934,3936,3938,3940,3942,3944,3946,3948,3950,3952,3954,3956,3958,3960,3962,3964,3966,3968,3970,3972,3974,3976,3978,3980,3982,3984,3986,3988,3990,3992,3994,3996,3998,4000,4002,4004,4006,4008,4010,4012,4014,4016,4018,4020,4022,4024,4026,4028,4030,4032,4034,4036,4038,4040,4042,4044,4046,4048,4050,4052,4054,4056,4058,4060,4062,4064,4066,4068,4070,4072,4074,4076,4078,4080,4082,4084,4086,4088,4090,4092,4094,4096,4098,4100,4102,4104,4106,4108,4110,4112,4114,4116,4118,4120,4122,4124,4126,4128,4130,4132,4134,4136,4138,4140,4142,4144,4146,4148,4150,4152,4154,4156,4158,4160,4162,4164,4166,4168,4170,4172,4174,4176,4178,4180,4182,4184,4186,4188,4190,4192,4194,4196,4198,4200,4202,4204,4206,4208,4210,4212,4214,4216,4218,4220,4222,4224,4226,4228,4230,4232,4234,4236,4238,4240,4242,4244,4246,4248,4250,4252,4254,4256,4258,4260,4262,4264,4266,4268,4270,4272,4274,4276,4278,4280,4282,4284,4286,4288,4290,4292,4294,4296,4298,4300,4302,4304,4306,4308,4310,4312,4314,4316,4318,4320,4322,4324,4326,4328,4330,4332,4334,4336,4338,4340,4342,4344,4346,4348,4350,4352,4354,4356,4358,4360,4362,4364,4366,4368,4370,4372,4374,4376,4378,4380,4382,4384,4386,4388,4390,4392,4394,4396,4399,4401,4403,4405,4407,4409,4411,4413,4415,4417,4419,4421,4423,4425,4427,4429,4431,4433,4435,4437,4439,4441,4443,4445,4447,4449,4451,4453,4455,4457,4459,4461,4463,4465,4467,4469,4471,4473,4475,4477,4479,4481,4483,4485,4487,4489,4491,4493,4495,4497,4499,4501,4503,4505,4507,4509,4511,4513,4515,4517,4519,4521,4523,4525,4527,4529,4531,4533,4535,4537,4539,4541,4543,4545,4547,4549,4551,4553,4555,4557,4559,4562,4564,4566,4568,4570,4572,4574,4576,4578,4580,4582,4584,4586,4588,4590,4592,4594,4596,4598,4600,4602,4604,4606,4608,4610,4612,4614,4616,4618,4620,4622,4624,4626,4628,4630,4632,4634,4636,4638,4640,4642,4644,4646,4648,4650,4652,4654,4656,4658,4660,4662,4664,4666,4668,4670,4672,4674,4676,4678,4680,4682,4684,4686,4688,4690,4692,4694,4696,4698,4700,4702,4704,4706,4708,4710,4712,4714,4716,4718,4720,4722,4724,4726,4728,4730,4732,4734,4736,4738,4740,4742,4744,4746,4748,4750,4752,4754,4756,4758,4760,4762,4764,4766,4768,4770,4772,4774,4776,4778,4780,4782,4784,4786,4788,4790,4792,4794,4796,4798,4800,4802,4804,4806,4808,4810,4812,4814,4816,4818,4820,4822,4824,4826,4828,4830,4832,4834,4836,4838,4840,4842,4844,4846,4848,4850,4852,4854,4856,4858,4860,4862,4864,4866,4868,4870,4872,4874,4876,4878,4880,4882,4884,4886,4888,4890,4892,4894,4896,4898,4900,4902,4904,4906,4908,4910,4912,4914,4916,4918,4920,4922,4924,4926,4928,4930,4932,4934,4936,4938,4940,4942,4944,4946,4948,4950,4952,4954,4956,4958,4960,4962,4964,4966,4968,4970,4972,4974,4976,4978,4980,4982,4984,4986,4988,4990,4992,4994,4996,4998,5000,5002,5004,5006,5008,5010,5012,5014,5016,5018,5020,5022,5024,5026,5028,5030,5032,5034,5036,5038,5040,5042,5044,5046,5048,5050,5052,5054,5056,5058,5060,5062,5064,5066,5068,5070,5072,5074,5076,5078,5080,5082,5084,5086,5088,5090,5092,5094,5096,5098,5100,5102,5104,5106,5108,5110,5112,5114,5116,5118,5120,5122,5124,5126,5128,5130,5132,5134,5136,5138,5140,5142,5144,5146,5148,5150,5152,5154,5156,5158,5160,5162,5164,5166,5168,5170,5172,5174,5176,5178,5180,5182,5184,5186,5188,5190,5192,5194,5196,5198,5200,5202,5204,5206,5208,5210,5212,5214,5216,5218,5220,5222,5224,5226,5228,5230,5232,5234,5236,5238,5240,5242,5244,5246,5248,5250,5252,5254,5256,5258,5260,5262,5264,5266,5268,5270,5272,5274,5276,5278,5280,5282,5284,5286,5288,5290,5292,5294,5296,5298,5300,5302,5304,5306,5308,5310,5312,5314,5316,5318,5320,5322,5324,5326,5328,5330,5332,5334,5336,5338,5340,5342,5344,5346,5348,5350,5352,5354,5356,5358,5360,5362,5364,5366,5368,5370,5372,5374,5376,5378,5380,5382,5384,5386,5388,5390,5392,5394,5396,5398,5400,5402,5404,5406,5408,5410,5412,5414,5416,5418,5420,5422,5424,5426,5428,5430,5432,5434,5436,5438,5440,5442,5444,5446,5448,5450,5452,5454,5456,5458,5460,5462,5464,5466,5468,5470,5472,5474,5476,5478,5480,5482,5484,5486,5488,5490,5492,5494,5496,5498,5500,5502,5504,5506,5508,5510,5512,5514,5516,5518,5520,5522,5524,5526,5528,5530,5532,5534,5536,5538,5540,5542,5544,5546,5548,5550,5552,5554,5556,5558,5560,5562,5564,5566,5568,5570,5572,5574,5576,5578,5580,5582,5584,5586,5588,5590,5592,5594,5596,5598,5600,5602,5604,5606,5608,5610,5612,5614,5616,5618,5620,5622,5624,5626,5628,5630,5632,5634,5636,5638,5640,5642,5644,5646,5648,5650,5652,5654,5656,5658,5660,5662,5664,5666,5668,5670,5672,5674,5676,5678,5680,5682,5684,5686,5688,5690,5692,5694,5696,5698,5700,5702,5704,5706,5708,5710],{"categories":140},[141],"Developer Productivity",{"categories":143},[144],"Business & SaaS",{"categories":146},[97],{"categories":148},[149],"AI Automation",{"categories":151},[152],"Product Strategy",{"categories":154},[97],{"categories":156},[141],{"categories":158},[159],"Software Engineering",{"categories":161},[97],{"categories":163},[144],{"categories":165},[],{"categories":167},[97],{"categories":169},[170],"Inference & Serving",{"categories":172},[97],{"categories":174},[97],{"categories":176},[149],{"categories":178},[],{"categories":180},[181],"AI News & Trends",{"categories":183},[149],{"categories":185},[97],{"categories":187},[144],{"categories":189},[97],{"categories":191},[149],{"categories":193},[181],{"categories":195},[149],{"categories":197},[149],{"categories":199},[97],{"categories":201},[149],{"categories":203},[97],{"categories":205},[97],{"categories":207},[97],{"categories":209},[181],{"categories":211},[97],{"categories":213},[97],{"categories":215},[],{"categories":217},[218],"Design & Frontend",{"categories":220},[221],"Data Science & Visualization",{"categories":223},[181],{"categories":225},[97],{"categories":227},[97],{"categories":229},[],{"categories":231},[97],{"categories":233},[149],{"categories":235},[159],{"categories":237},[97],{"categories":239},[149],{"categories":241},[97],{"categories":243},[244],"Marketing & Growth",{"categories":246},[218],{"categories":248},[97],{"categories":250},[149],{"categories":252},[97],{"categories":254},[],{"categories":256},[],{"categories":258},[218],{"categories":260},[97],{"categories":262},[149],{"categories":264},[141],{"categories":266},[159],{"categories":268},[218],{"categories":270},[97],{"categories":272},[159],{"categories":274},[275],"DevOps & Cloud",{"categories":277},[149],{"categories":279},[152],{"categories":281},[181],{"categories":283},[97],{"categories":285},[],{"categories":287},[97],{"categories":289},[],{"categories":291},[149],{"categories":293},[159],{"categories":295},[],{"categories":297},[159],{"categories":299},[97],{"categories":301},[302],"Governance & Standards",{"categories":304},[144],{"categories":306},[],{"categories":308},[],{"categories":310},[97],{"categories":312},[97],{"categories":314},[149],{"categories":316},[97],{"categories":318},[97],{"categories":320},[149],{"categories":322},[97],{"categories":324},[97],{"categories":326},[97],{"categories":328},[],{"categories":330},[159],{"categories":332},[],{"categories":334},[],{"categories":336},[159],{"categories":338},[],{"categories":340},[159],{"categories":342},[97],{"categories":344},[97],{"categories":346},[244],{"categories":348},[97],{"categories":350},[218],{"categories":352},[218],{"categories":354},[97],{"categories":356},[159],{"categories":358},[149],{"categories":360},[361],"GovTech & Public-Sector Adoption",{"categories":363},[159],{"categories":365},[97],{"categories":367},[97],{"categories":369},[149],{"categories":371},[149],{"categories":373},[221],{"categories":375},[97],{"categories":377},[181],{"categories":379},[149],{"categories":381},[382],"Legal AI Tools",{"categories":384},[149],{"categories":386},[244],{"categories":388},[149],{"categories":390},[152],{"categories":392},[159],{"categories":394},[361],{"categories":396},[],{"categories":398},[149],{"categories":400},[],{"categories":402},[149],{"categories":404},[149],{"categories":406},[407],"RAG & Retrieval",{"categories":409},[144],{"categories":411},[97],{"categories":413},[159],{"categories":415},[275],{"categories":417},[218],{"categories":419},[97],{"categories":421},[],{"categories":423},[424],"Agents & Orchestration",{"categories":426},[159],{"categories":428},[97],{"categories":430},[],{"categories":432},[149],{"categories":434},[144],{"categories":436},[],{"categories":438},[97],{"categories":440},[],{"categories":442},[141],{"categories":444},[159],{"categories":446},[144],{"categories":448},[97],{"categories":450},[97],{"categories":452},[181],{"categories":454},[97],{"categories":456},[],{"categories":458},[97],{"categories":460},[],{"categories":462},[159],{"categories":464},[221],{"categories":466},[],{"categories":468},[97],{"categories":470},[218],{"categories":472},[473],"Models & Frontier Labs",{"categories":475},[],{"categories":477},[218],{"categories":479},[480],"Regulation & Governance of AI",{"categories":482},[149],{"categories":484},[],{"categories":486},[97],{"categories":488},[97],{"categories":490},[149],{"categories":492},[181],{"categories":494},[144],{"categories":496},[97],{"categories":498},[],{"categories":500},[159],{"categories":502},[149],{"categories":504},[97],{"categories":506},[152],{"categories":508},[509],"AI Policy & Regulation",{"categories":511},[],{"categories":513},[97],{"categories":515},[152],{"categories":517},[149],{"categories":519},[97],{"categories":521},[149],{"categories":523},[],{"categories":525},[221],{"categories":527},[528],"Evals & Reliability",{"categories":530},[97],{"categories":532},[],{"categories":534},[141],{"categories":536},[361],{"categories":538},[509],{"categories":540},[97],{"categories":542},[144],{"categories":544},[97],{"categories":546},[149],{"categories":548},[97],{"categories":550},[149],{"categories":552},[424],{"categories":554},[97],{"categories":556},[159],{"categories":558},[97],{"categories":560},[],{"categories":562},[],{"categories":564},[97],{"categories":566},[361],{"categories":568},[97],{"categories":570},[97],{"categories":572},[],{"categories":574},[218],{"categories":576},[],{"categories":578},[97],{"categories":580},[],{"categories":582},[149],{"categories":584},[97],{"categories":586},[218],{"categories":588},[],{"categories":590},[97],{"categories":592},[149],{"categories":594},[97],{"categories":596},[144],{"categories":598},[149],{"categories":600},[97],{"categories":602},[97],{"categories":604},[159],{"categories":606},[218],{"categories":608},[149],{"categories":610},[],{"categories":612},[159],{"categories":614},[149],{"categories":616},[],{"categories":618},[181],{"categories":620},[],{"categories":622},[97],{"categories":624},[97],{"categories":626},[144,244],{"categories":628},[],{"categories":630},[97],{"categories":632},[97],{"categories":634},[149],{"categories":636},[],{"categories":638},[],{"categories":640},[97],{"categories":642},[218],{"categories":644},[97],{"categories":646},[],{"categories":648},[97],{"categories":650},[275],{"categories":652},[],{"categories":654},[149],{"categories":656},[181],{"categories":658},[97],{"categories":660},[218],{"categories":662},[],{"categories":664},[181],{"categories":666},[97],{"categories":668},[170],{"categories":670},[97],{"categories":672},[149],{"categories":674},[181],{"categories":676},[473],{"categories":678},[97],{"categories":680},[244],{"categories":682},[],{"categories":684},[149],{"categories":686},[144],{"categories":688},[159],{"categories":690},[97],{"categories":692},[149],{"categories":694},[],{"categories":696},[97,275],{"categories":698},[97],{"categories":700},[97],{"categories":702},[97],{"categories":704},[149],{"categories":706},[97,159],{"categories":708},[221],{"categories":710},[97],{"categories":712},[97],{"categories":714},[159],{"categories":716},[149],{"categories":718},[509],{"categories":720},[244],{"categories":722},[97],{"categories":724},[149],{"categories":726},[97],{"categories":728},[97],{"categories":730},[149],{"categories":732},[],{"categories":734},[97],{"categories":736},[149],{"categories":738},[97],{"categories":740},[97,144],{"categories":742},[144],{"categories":744},[],{"categories":746},[218],{"categories":748},[218],{"categories":750},[97],{"categories":752},[],{"categories":754},[],{"categories":756},[181],{"categories":758},[],{"categories":760},[141],{"categories":762},[97],{"categories":764},[159],{"categories":766},[767],"Generative UI & Design-to-Code",{"categories":769},[97],{"categories":771},[218],{"categories":773},[97],{"categories":775},[776],"Algorithmic Accountability",{"categories":778},[149],{"categories":780},[159],{"categories":782},[181],{"categories":784},[218],{"categories":786},[],{"categories":788},[97],{"categories":790},[97],{"categories":792},[97],{"categories":794},[149],{"categories":796},[797],"MLOps & Infrastructure",{"categories":799},[97],{"categories":801},[97],{"categories":803},[97],{"categories":805},[97],{"categories":807},[181],{"categories":809},[141],{"categories":811},[97],{"categories":813},[149],{"categories":815},[275],{"categories":817},[97],{"categories":819},[218],{"categories":821},[97],{"categories":823},[149],{"categories":825},[],{"categories":827},[],{"categories":829},[170],{"categories":831},[218],{"categories":833},[181],{"categories":835},[221],{"categories":837},[],{"categories":839},[97],{"categories":841},[97],{"categories":843},[144],{"categories":845},[97],{"categories":847},[97],{"categories":849},[97],{"categories":851},[181],{"categories":853},[170],{"categories":855},[97],{"categories":857},[218],{"categories":859},[],{"categories":861},[149],{"categories":863},[159],{"categories":865},[],{"categories":867},[97],{"categories":869},[97],{"categories":871},[149],{"categories":873},[159],{"categories":875},[97],{"categories":877},[221],{"categories":879},[],{"categories":881},[97],{"categories":883},[],{"categories":885},[97],{"categories":887},[],{"categories":889},[152],{"categories":891},[144],{"categories":893},[149],{"categories":895},[149],{"categories":897},[],{"categories":899},[141],{"categories":901},[97],{"categories":903},[144],{"categories":905},[181],{"categories":907},[141],{"categories":909},[],{"categories":911},[97],{"categories":913},[],{"categories":915},[],{"categories":917},[181],{"categories":919},[181],{"categories":921},[],{"categories":923},[424],{"categories":925},[97],{"categories":927},[218],{"categories":929},[159],{"categories":931},[],{"categories":933},[382],{"categories":935},[144],{"categories":937},[],{"categories":939},[],{"categories":941},[141],{"categories":943},[221],{"categories":945},[],{"categories":947},[244],{"categories":949},[149],{"categories":951},[144],{"categories":953},[149],{"categories":955},[144],{"categories":957},[159],{"categories":959},[],{"categories":961},[170],{"categories":963},[152],{"categories":965},[97],{"categories":967},[218],{"categories":969},[159],{"categories":971},[144],{"categories":973},[97],{"categories":975},[149],{"categories":977},[144],{"categories":979},[97],{"categories":981},[97],{"categories":983},[],{"categories":985},[],{"categories":987},[159],{"categories":989},[221],{"categories":991},[152],{"categories":993},[97],{"categories":995},[149],{"categories":997},[97],{"categories":999},[],{"categories":1001},[181],{"categories":1003},[152],{"categories":1005},[97],{"categories":1007},[528],{"categories":1009},[275],{"categories":1011},[],{"categories":1013},[149],{"categories":1015},[],{"categories":1017},[141],{"categories":1019},[],{"categories":1021},[97],{"categories":1023},[97],{"categories":1025},[218],{"categories":1027},[244],{"categories":1029},[159],{"categories":1031},[149],{"categories":1033},[],{"categories":1035},[159],{"categories":1037},[141],{"categories":1039},[],{"categories":1041},[181],{"categories":1043},[97,275],{"categories":1045},[1046],"Design Systems for AI",{"categories":1048},[97],{"categories":1050},[181],{"categories":1052},[97],{"categories":1054},[97],{"categories":1056},[144],{"categories":1058},[97],{"categories":1060},[],{"categories":1062},[97],{"categories":1064},[97],{"categories":1066},[144],{"categories":1068},[97],{"categories":1070},[],{"categories":1072},[149],{"categories":1074},[159],{"categories":1076},[159],{"categories":1078},[218],{"categories":1080},[181],{"categories":1082},[221],{"categories":1084},[97],{"categories":1086},[141],{"categories":1088},[509],{"categories":1090},[97],{"categories":1092},[149],{"categories":1094},[97],{"categories":1096},[159],{"categories":1098},[159],{"categories":1100},[],{"categories":1102},[],{"categories":1104},[149],{"categories":1106},[152],{"categories":1108},[],{"categories":1110},[97],{"categories":1112},[],{"categories":1114},[218],{"categories":1116},[149],{"categories":1118},[159],{"categories":1120},[218],{"categories":1122},[97],{"categories":1124},[218],{"categories":1126},[],{"categories":1128},[],{"categories":1130},[181],{"categories":1132},[149],{"categories":1134},[149],{"categories":1136},[97],{"categories":1138},[97],{"categories":1140},[97],{"categories":1142},[144],{"categories":1144},[97],{"categories":1146},[97],{"categories":1148},[],{"categories":1150},[159],{"categories":1152},[159],{"categories":1154},[97],{"categories":1156},[159],{"categories":1158},[144],{"categories":1160},[],{"categories":1162},[97],{"categories":1164},[97],{"categories":1166},[97],{"categories":1168},[149],{"categories":1170},[141],{"categories":1172},[144],{"categories":1174},[181],{"categories":1176},[149],{"categories":1178},[170],{"categories":1180},[244],{"categories":1182},[97],{"categories":1184},[149],{"categories":1186},[],{"categories":1188},[218],{"categories":1190},[],{"categories":1192},[97],{"categories":1194},[97],{"categories":1196},[],{"categories":1198},[159],{"categories":1200},[144],{"categories":1202},[1203],"Visual & Generative Media",{"categories":1205},[149],{"categories":1207},[],{"categories":1209},[97],{"categories":1211},[97],{"categories":1213},[275],{"categories":1215},[221],{"categories":1217},[509],{"categories":1219},[159],{"categories":1221},[244],{"categories":1223},[97],{"categories":1225},[218],{"categories":1227},[97],{"categories":1229},[159],{"categories":1231},[149],{"categories":1233},[],{"categories":1235},[],{"categories":1237},[149],{"categories":1239},[141],{"categories":1241},[149],{"categories":1243},[473],{"categories":1245},[97],{"categories":1247},[152],{"categories":1249},[144],{"categories":1251},[],{"categories":1253},[97],{"categories":1255},[152],{"categories":1257},[97],{"categories":1259},[97],{"categories":1261},[97],{"categories":1263},[97],{"categories":1265},[97],{"categories":1267},[244],{"categories":1269},[97],{"categories":1271},[424],{"categories":1273},[97],{"categories":1275},[97],{"categories":1277},[97],{"categories":1279},[97],{"categories":1281},[97],{"categories":1283},[218],{"categories":1285},[149],{"categories":1287},[],{"categories":1289},[149],{"categories":1291},[],{"categories":1293},[275],{"categories":1295},[159],{"categories":1297},[],{"categories":1299},[473],{"categories":1301},[149],{"categories":1303},[97],{"categories":1305},[218,97],{"categories":1307},[141],{"categories":1309},[],{"categories":1311},[97],{"categories":1313},[141],{"categories":1315},[1316],"Medical Imaging & Radiology",{"categories":1318},[218],{"categories":1320},[149],{"categories":1322},[159],{"categories":1324},[],{"categories":1326},[97],{"categories":1328},[97],{"categories":1330},[97],{"categories":1332},[],{"categories":1334},[],{"categories":1336},[97],{"categories":1338},[424],{"categories":1340},[97],{"categories":1342},[141],{"categories":1344},[97],{"categories":1346},[97],{"categories":1348},[],{"categories":1350},[149],{"categories":1352},[97],{"categories":1354},[152],{"categories":1356},[159],{"categories":1358},[97],{"categories":1360},[424],{"categories":1362},[97],{"categories":1364},[149],{"categories":1366},[97],{"categories":1368},[218],{"categories":1370},[149],{"categories":1372},[275],{"categories":1374},[218],{"categories":1376},[144],{"categories":1378},[149],{"categories":1380},[97],{"categories":1382},[97],{"categories":1384},[97],{"categories":1386},[97],{"categories":1388},[97],{"categories":1390},[149],{"categories":1392},[159],{"categories":1394},[97],{"categories":1396},[152],{"categories":1398},[],{"categories":1400},[181],{"categories":1402},[],{"categories":1404},[152],{"categories":1406},[149],{"categories":1408},[1046],{"categories":1410},[1046],{"categories":1412},[218],{"categories":1414},[97],{"categories":1416},[97],{"categories":1418},[149],{"categories":1420},[159],{"categories":1422},[218],{"categories":1424},[149],{"categories":1426},[181],{"categories":1428},[],{"categories":1430},[97],{"categories":1432},[],{"categories":1434},[97],{"categories":1436},[97],{"categories":1438},[97],{"categories":1440},[1441],"Contract Review & E-Discovery",{"categories":1443},[218],{"categories":1445},[97],{"categories":1447},[141],{"categories":1449},[181],{"categories":1451},[97],{"categories":1453},[97],{"categories":1455},[244],{"categories":1457},[159],{"categories":1459},[97],{"categories":1461},[97],{"categories":1463},[149],{"categories":1465},[149],{"categories":1467},[776],{"categories":1469},[149],{"categories":1471},[149],{"categories":1473},[97],{"categories":1475},[97],{"categories":1477},[149],{"categories":1479},[97],{"categories":1481},[424],{"categories":1483},[407],{"categories":1485},[97],{"categories":1487},[149],{"categories":1489},[97],{"categories":1491},[1492],"Law-Firm Practice & Adoption",{"categories":1494},[97],{"categories":1496},[149],{"categories":1498},[218],{"categories":1500},[97],{"categories":1502},[97],{"categories":1504},[],{"categories":1506},[],{"categories":1508},[159],{"categories":1510},[],{"categories":1512},[141],{"categories":1514},[275],{"categories":1516},[97],{"categories":1518},[],{"categories":1520},[141],{"categories":1522},[144],{"categories":1524},[97],{"categories":1526},[244],{"categories":1528},[],{"categories":1530},[144],{"categories":1532},[144],{"categories":1534},[],{"categories":1536},[97],{"categories":1538},[97],{"categories":1540},[159],{"categories":1542},[],{"categories":1544},[],{"categories":1546},[],{"categories":1548},[],{"categories":1550},[97],{"categories":1552},[149],{"categories":1554},[275],{"categories":1556},[97],{"categories":1558},[141],{"categories":1560},[159],{"categories":1562},[97],{"categories":1564},[97],{"categories":1566},[159],{"categories":1568},[152],{"categories":1570},[97],{"categories":1572},[797],{"categories":1574},[97],{"categories":1576},[244],{"categories":1578},[159],{"categories":1580},[144],{"categories":1582},[97],{"categories":1584},[97],{"categories":1586},[218],{"categories":1588},[97],{"categories":1590},[97],{"categories":1592},[97],{"categories":1594},[149],{"categories":1596},[97,141],{"categories":1598},[424],{"categories":1600},[97],{"categories":1602},[159],{"categories":1604},[159],{"categories":1606},[218],{"categories":1608},[149],{"categories":1610},[159],{"categories":1612},[97],{"categories":1614},[97],{"categories":1616},[],{"categories":1618},[],{"categories":1620},[97],{"categories":1622},[],{"categories":1624},[97],{"categories":1626},[159],{"categories":1628},[221],{"categories":1630},[181],{"categories":1632},[218],{"categories":1634},[97],{"categories":1636},[159],{"categories":1638},[],{"categories":1640},[149],{"categories":1642},[97],{"categories":1644},[97],{"categories":1646},[97],{"categories":1648},[97],{"categories":1650},[],{"categories":1652},[149],{"categories":1654},[97],{"categories":1656},[97],{"categories":1658},[],{"categories":1660},[149],{"categories":1662},[97],{"categories":1664},[97],{"categories":1666},[144],{"categories":1668},[97],{"categories":1670},[],{"categories":1672},[141],{"categories":1674},[97],{"categories":1676},[218],{"categories":1678},[159],{"categories":1680},[97],{"categories":1682},[141],{"categories":1684},[97],{"categories":1686},[159],{"categories":1688},[244],{"categories":1690},[149],{"categories":1692},[149],{"categories":1694},[97,218],{"categories":1696},[97],{"categories":1698},[181],{"categories":1700},[97],{"categories":1702},[181],{"categories":1704},[149],{"categories":1706},[218],{"categories":1708},[],{"categories":1710},[159],{"categories":1712},[275],{"categories":1714},[218],{"categories":1716},[159],{"categories":1718},[97],{"categories":1720},[152],{"categories":1722},[97],{"categories":1724},[149],{"categories":1726},[],{"categories":1728},[],{"categories":1730},[97],{"categories":1732},[],{"categories":1734},[],{"categories":1736},[152],{"categories":1738},[159],{"categories":1740},[97],{"categories":1742},[149],{"categories":1744},[149],{"categories":1746},[144],{"categories":1748},[149],{"categories":1750},[275],{"categories":1752},[97],{"categories":1754},[97],{"categories":1756},[170],{"categories":1758},[97],{"categories":1760},[97],{"categories":1762},[149],{"categories":1764},[97],{"categories":1766},[97],{"categories":1768},[382],{"categories":1770},[776],{"categories":1772},[],{"categories":1774},[218],{"categories":1776},[1492],{"categories":1778},[159],{"categories":1780},[],{"categories":1782},[],{"categories":1784},[149],{"categories":1786},[],{"categories":1788},[],{"categories":1790},[244],{"categories":1792},[244],{"categories":1794},[149],{"categories":1796},[159],{"categories":1798},[],{"categories":1800},[97],{"categories":1802},[97],{"categories":1804},[159],{"categories":1806},[1441],{"categories":1808},[218],{"categories":1810},[218],{"categories":1812},[97],{"categories":1814},[149],{"categories":1816},[141],{"categories":1818},[97],{"categories":1820},[97],{"categories":1822},[218],{"categories":1824},[218],{"categories":1826},[149],{"categories":1828},[149],{"categories":1830},[97],{"categories":1832},[],{"categories":1834},[97],{"categories":1836},[],{"categories":1838},[1839],"Interaction & Product Design",{"categories":1841},[97],{"categories":1843},[149],{"categories":1845},[302],{"categories":1847},[181],{"categories":1849},[159],{"categories":1851},[97],{"categories":1853},[97],{"categories":1855},[159],{"categories":1857},[141],{"categories":1859},[97],{"categories":1861},[],{"categories":1863},[149],{"categories":1865},[149],{"categories":1867},[],{"categories":1869},[159],{"categories":1871},[97],{"categories":1873},[141],{"categories":1875},[1839],{"categories":1877},[97],{"categories":1879},[141],{"categories":1881},[141],{"categories":1883},[],{"categories":1885},[159],{"categories":1887},[],{"categories":1889},[149],{"categories":1891},[181],{"categories":1893},[97],{"categories":1895},[149],{"categories":1897},[97],{"categories":1899},[149],{"categories":1901},[97],{"categories":1903},[181],{"categories":1905},[221],{"categories":1907},[97],{"categories":1909},[152],{"categories":1911},[159],{"categories":1913},[1914],"Coding Agents & Dev Productivity",{"categories":1916},[181],{"categories":1918},[218],{"categories":1920},[],{"categories":1922},[97],{"categories":1924},[776],{"categories":1926},[],{"categories":1928},[97],{"categories":1930},[97],{"categories":1932},[181],{"categories":1934},[],{"categories":1936},[],{"categories":1938},[97],{"categories":1940},[],{"categories":1942},[149],{"categories":1944},[97],{"categories":1946},[],{"categories":1948},[159],{"categories":1950},[159],{"categories":1952},[97],{"categories":1954},[221],{"categories":1956},[],{"categories":1958},[97],{"categories":1960},[97],{"categories":1962},[97],{"categories":1964},[221],{"categories":1966},[159],{"categories":1968},[],{"categories":1970},[],{"categories":1972},[149],{"categories":1974},[149],{"categories":1976},[361],{"categories":1978},[159],{"categories":1980},[159],{"categories":1982},[149],{"categories":1984},[181],{"categories":1986},[181],{"categories":1988},[149],{"categories":1990},[149],{"categories":1992},[97],{"categories":1994},[141],{"categories":1996},[1839],{"categories":1998},[97,275],{"categories":2000},[],{"categories":2002},[218],{"categories":2004},[159],{"categories":2006},[141],{"categories":2008},[97],{"categories":2010},[149],{"categories":2012},[2013],"The Designer's Role & Craft",{"categories":2015},[218],{"categories":2017},[],{"categories":2019},[149],{"categories":2021},[97],{"categories":2023},[149],{"categories":2025},[149],{"categories":2027},[97],{"categories":2029},[244],{"categories":2031},[97],{"categories":2033},[159],{"categories":2035},[218],{"categories":2037},[97],{"categories":2039},[],{"categories":2041},[149],{"categories":2043},[218],{"categories":2045},[97],{"categories":2047},[97],{"categories":2049},[2050],"AI UX Patterns",{"categories":2052},[149],{"categories":2054},[149],{"categories":2056},[149],{"categories":2058},[149],{"categories":2060},[244],{"categories":2062},[221],{"categories":2064},[97],{"categories":2066},[149],{"categories":2068},[97],{"categories":2070},[1046],{"categories":2072},[],{"categories":2074},[244],{"categories":2076},[181],{"categories":2078},[159],{"categories":2080},[97],{"categories":2082},[149],{"categories":2084},[],{"categories":2086},[],{"categories":2088},[97],{"categories":2090},[149],{"categories":2092},[97],{"categories":2094},[149],{"categories":2096},[361],{"categories":2098},[218],{"categories":2100},[181],{"categories":2102},[159],{"categories":2104},[97],{"categories":2106},[149],{"categories":2108},[149],{"categories":2110},[],{"categories":2112},[97],{"categories":2114},[],{"categories":2116},[],{"categories":2118},[97],{"categories":2120},[97],{"categories":2122},[149],{"categories":2124},[159],{"categories":2126},[],{"categories":2128},[],{"categories":2130},[221],{"categories":2132},[170],{"categories":2134},[97],{"categories":2136},[221],{"categories":2138},[181],{"categories":2140},[97],{"categories":2142},[97],{"categories":2144},[149],{"categories":2146},[149],{"categories":2148},[97],{"categories":2150},[149],{"categories":2152},[],{"categories":2154},[],{"categories":2156},[97],{"categories":2158},[275],{"categories":2160},[97],{"categories":2162},[],{"categories":2164},[],{"categories":2166},[218],{"categories":2168},[797],{"categories":2170},[149],{"categories":2172},[141],{"categories":2174},[2013],{"categories":2176},[],{"categories":2178},[],{"categories":2180},[97],{"categories":2182},[],{"categories":2184},[],{"categories":2186},[159],{"categories":2188},[181],{"categories":2190},[244],{"categories":2192},[144],{"categories":2194},[97],{"categories":2196},[97],{"categories":2198},[144],{"categories":2200},[],{"categories":2202},[218],{"categories":2204},[97],{"categories":2206},[149],{"categories":2208},[144],{"categories":2210},[97],{"categories":2212},[97],{"categories":2214},[141],{"categories":2216},[97],{"categories":2218},[],{"categories":2220},[141],{"categories":2222},[97],{"categories":2224},[244],{"categories":2226},[149],{"categories":2228},[181],{"categories":2230},[97],{"categories":2232},[144],{"categories":2234},[97],{"categories":2236},[97],{"categories":2238},[97],{"categories":2240},[149],{"categories":2242},[],{"categories":2244},[97],{"categories":2246},[159],{"categories":2248},[141],{"categories":2250},[97],{"categories":2252},[97],{"categories":2254},[],{"categories":2256},[424],{"categories":2258},[181],{"categories":2260},[97],{"categories":2262},[97],{"categories":2264},[],{"categories":2266},[144],{"categories":2268},[144],{"categories":2270},[97],{"categories":2272},[97],{"categories":2274},[152],{"categories":2276},[97],{"categories":2278},[97],{"categories":2280},[159],{"categories":2282},[159],{"categories":2284},[97],{"categories":2286},[],{"categories":2288},[159],{"categories":2290},[97],{"categories":2292},[159],{"categories":2294},[509],{"categories":2296},[],{"categories":2298},[],{"categories":2300},[97],{"categories":2302},[181],{"categories":2304},[],{"categories":2306},[275],{"categories":2308},[97],{"categories":2310},[97],{"categories":2312},[218],{"categories":2314},[767],{"categories":2316},[],{"categories":2318},[97],{"categories":2320},[97],{"categories":2322},[159],{"categories":2324},[97],{"categories":2326},[97],{"categories":2328},[97,275],{"categories":2330},[97],{"categories":2332},[97],{"categories":2334},[218],{"categories":2336},[149],{"categories":2338},[],{"categories":2340},[149],{"categories":2342},[149],{"categories":2344},[97],{"categories":2346},[97],{"categories":2348},[97],{"categories":2350},[221],{"categories":2352},[97],{"categories":2354},[2050],{"categories":2356},[141],{"categories":2358},[221],{"categories":2360},[141],{"categories":2362},[159],{"categories":2364},[218],{"categories":2366},[149],{"categories":2368},[97],{"categories":2370},[],{"categories":2372},[97],{"categories":2374},[181],{"categories":2376},[97],{"categories":2378},[149],{"categories":2380},[97],{"categories":2382},[97],{"categories":2384},[144],{"categories":2386},[],{"categories":2388},[275],{"categories":2390},[97],{"categories":2392},[361],{"categories":2394},[218],{"categories":2396},[218],{"categories":2398},[159],{"categories":2400},[149],{"categories":2402},[97],{"categories":2404},[144],{"categories":2406},[181],{"categories":2408},[97],{"categories":2410},[218],{"categories":2412},[149],{"categories":2414},[97],{"categories":2416},[97],{"categories":2418},[473],{"categories":2420},[],{"categories":2422},[97],{"categories":2424},[97],{"categories":2426},[97],{"categories":2428},[],{"categories":2430},[],{"categories":2432},[97],{"categories":2434},[97],{"categories":2436},[97],{"categories":2438},[97],{"categories":2440},[159],{"categories":2442},[97],{"categories":2444},[97],{"categories":2446},[149],{"categories":2448},[97],{"categories":2450},[97],{"categories":2452},[97],{"categories":2454},[97],{"categories":2456},[],{"categories":2458},[159],{"categories":2460},[221],{"categories":2462},[97],{"categories":2464},[149],{"categories":2466},[97],{"categories":2468},[],{"categories":2470},[],{"categories":2472},[97],{"categories":2474},[97],{"categories":2476},[97],{"categories":2478},[181],{"categories":2480},[],{"categories":2482},[97],{"categories":2484},[218],{"categories":2486},[97],{"categories":2488},[275],{"categories":2490},[1492],{"categories":2492},[181],{"categories":2494},[159],{"categories":2496},[159],{"categories":2498},[159],{"categories":2500},[181],{"categories":2502},[181],{"categories":2504},[275],{"categories":2506},[],{"categories":2508},[181],{"categories":2510},[97],{"categories":2512},[141],{"categories":2514},[159],{"categories":2516},[97],{"categories":2518},[181],{"categories":2520},[],{"categories":2522},[97],{"categories":2524},[159],{"categories":2526},[221],{"categories":2528},[97],{"categories":2530},[181],{"categories":2532},[97],{"categories":2534},[159],{"categories":2536},[149],{"categories":2538},[181],{"categories":2540},[149],{"categories":2542},[275],{"categories":2544},[149],{"categories":2546},[97],{"categories":2548},[97],{"categories":2550},[159],{"categories":2552},[97],{"categories":2554},[],{"categories":2556},[144],{"categories":2558},[159],{"categories":2560},[],{"categories":2562},[],{"categories":2564},[97],{"categories":2566},[149],{"categories":2568},[97],{"categories":2570},[2571],"Frameworks & Tooling",{"categories":2573},[97],{"categories":2575},[97],{"categories":2577},[159],{"categories":2579},[97],{"categories":2581},[97],{"categories":2583},[],{"categories":2585},[221],{"categories":2587},[221],{"categories":2589},[141],{"categories":2591},[149],{"categories":2593},[218],{"categories":2595},[],{"categories":2597},[1492],{"categories":2599},[97],{"categories":2601},[159],{"categories":2603},[97],{"categories":2605},[275],{"categories":2607},[275],{"categories":2609},[],{"categories":2611},[149],{"categories":2613},[181],{"categories":2615},[181],{"categories":2617},[97],{"categories":2619},[149],{"categories":2621},[],{"categories":2623},[218],{"categories":2625},[97],{"categories":2627},[97],{"categories":2629},[],{"categories":2631},[97],{"categories":2633},[],{"categories":2635},[159],{"categories":2637},[97],{"categories":2639},[159],{"categories":2641},[275],{"categories":2643},[97],{"categories":2645},[159],{"categories":2647},[144],{"categories":2649},[97],{"categories":2651},[1492],{"categories":2653},[],{"categories":2655},[149],{"categories":2657},[141],{"categories":2659},[141],{"categories":2661},[],{"categories":2663},[149],{"categories":2665},[97],{"categories":2667},[2668],"AI Design Tooling",{"categories":2670},[218],{"categories":2672},[97],{"categories":2674},[97],{"categories":2676},[159],{"categories":2678},[218],{"categories":2680},[97],{"categories":2682},[159],{"categories":2684},[181],{"categories":2686},[152],{"categories":2688},[159],{"categories":2690},[149],{"categories":2692},[],{"categories":2694},[97],{"categories":2696},[97],{"categories":2698},[149],{"categories":2700},[97],{"categories":2702},[97],{"categories":2704},[],{"categories":2706},[149],{"categories":2708},[2571],{"categories":2710},[97],{"categories":2712},[149],{"categories":2714},[149],{"categories":2716},[159],{"categories":2718},[159],{"categories":2720},[],{"categories":2722},[159],{"categories":2724},[97],{"categories":2726},[97],{"categories":2728},[149],{"categories":2730},[144],{"categories":2732},[97],{"categories":2734},[],{"categories":2736},[97],{"categories":2738},[1839],{"categories":2740},[],{"categories":2742},[97],{"categories":2744},[97],{"categories":2746},[],{"categories":2748},[97],{"categories":2750},[97],{"categories":2752},[97],{"categories":2754},[244],{"categories":2756},[181],{"categories":2758},[97],{"categories":2760},[97],{"categories":2762},[1492],{"categories":2764},[141],{"categories":2766},[97],{"categories":2768},[97],{"categories":2770},[221],{"categories":2772},[97],{"categories":2774},[181],{"categories":2776},[149],{"categories":2778},[],{"categories":2780},[97],{"categories":2782},[218],{"categories":2784},[97],{"categories":2786},[244],{"categories":2788},[97],{"categories":2790},[149],{"categories":2792},[],{"categories":2794},[],{"categories":2796},[],{"categories":2798},[141],{"categories":2800},[181],{"categories":2802},[149],{"categories":2804},[97],{"categories":2806},[97],{"categories":2808},[97],{"categories":2810},[382],{"categories":2812},[218],{"categories":2814},[149],{"categories":2816},[97],{"categories":2818},[],{"categories":2820},[149],{"categories":2822},[149],{"categories":2824},[],{"categories":2826},[97],{"categories":2828},[149],{"categories":2830},[97],{"categories":2832},[],{"categories":2834},[97],{"categories":2836},[97],{"categories":2838},[181],{"categories":2840},[218],{"categories":2842},[149],{"categories":2844},[218],{"categories":2846},[149],{"categories":2848},[144],{"categories":2850},[],{"categories":2852},[],{"categories":2854},[97],{"categories":2856},[97],{"categories":2858},[141],{"categories":2860},[149],{"categories":2862},[181],{"categories":2864},[],{"categories":2866},[218],{"categories":2868},[],{"categories":2870},[159],{"categories":2872},[159],{"categories":2874},[218],{"categories":2876},[159],{"categories":2878},[97],{"categories":2880},[],{"categories":2882},[97],{"categories":2884},[97],{"categories":2886},[],{"categories":2888},[244],{"categories":2890},[97],{"categories":2892},[275],{"categories":2894},[159],{"categories":2896},[],{"categories":2898},[149],{"categories":2900},[97],{"categories":2902},[141],{"categories":2904},[473],{"categories":2906},[149],{"categories":2908},[149],{"categories":2910},[97],{"categories":2912},[97],{"categories":2914},[],{"categories":2916},[141],{"categories":2918},[97],{"categories":2920},[144],{"categories":2922},[159],{"categories":2924},[218],{"categories":2926},[],{"categories":2928},[],{"categories":2930},[],{"categories":2932},[149],{"categories":2934},[159],{"categories":2936},[218],{"categories":2938},[181],{"categories":2940},[97],{"categories":2942},[181],{"categories":2944},[149],{"categories":2946},[218],{"categories":2948},[97],{"categories":2950},[],{"categories":2952},[97],{"categories":2954},[170],{"categories":2956},[149],{"categories":2958},[218],{"categories":2960},[181],{"categories":2962},[144],{"categories":2964},[159],{"categories":2966},[97],{"categories":2968},[181],{"categories":2970},[244],{"categories":2972},[],{"categories":2974},[],{"categories":2976},[221],{"categories":2978},[424],{"categories":2980},[97],{"categories":2982},[149],{"categories":2984},[97,159],{"categories":2986},[181],{"categories":2988},[97],{"categories":2990},[97],{"categories":2992},[149],{"categories":2994},[97],{"categories":2996},[149],{"categories":2998},[97],{"categories":3000},[97],{"categories":3002},[],{"categories":3004},[1046],{"categories":3006},[159],{"categories":3008},[218],{"categories":3010},[97],{"categories":3012},[97],{"categories":3014},[97],{"categories":3016},[221],{"categories":3018},[149],{"categories":3020},[244],{"categories":3022},[275],{"categories":3024},[],{"categories":3026},[97],{"categories":3028},[144],{"categories":3030},[149],{"categories":3032},[141],{"categories":3034},[149],{"categories":3036},[97],{"categories":3038},[149],{"categories":3040},[152],{"categories":3042},[159],{"categories":3044},[97],{"categories":3046},[97],{"categories":3048},[],{"categories":3050},[],{"categories":3052},[],{"categories":3054},[275],{"categories":3056},[97],{"categories":3058},[181],{"categories":3060},[97],{"categories":3062},[97],{"categories":3064},[97],{"categories":3066},[97],{"categories":3068},[],{"categories":3070},[221],{"categories":3072},[144],{"categories":3074},[149],{"categories":3076},[97],{"categories":3078},[],{"categories":3080},[97],{"categories":3082},[149],{"categories":3084},[97],{"categories":3086},[275],{"categories":3088},[],{"categories":3090},[218],{"categories":3092},[218],{"categories":3094},[],{"categories":3096},[159],{"categories":3098},[97],{"categories":3100},[218],{"categories":3102},[97],{"categories":3104},[144],{"categories":3106},[149],{"categories":3108},[97],{"categories":3110},[],{"categories":3112},[181],{"categories":3114},[97],{"categories":3116},[97],{"categories":3118},[97],{"categories":3120},[218],{"categories":3122},[149],{"categories":3124},[181],{"categories":3126},[],{"categories":3128},[149],{"categories":3130},[149],{"categories":3132},[218],{"categories":3134},[97],{"categories":3136},[97],{"categories":3138},[97],{"categories":3140},[424],{"categories":3142},[97],{"categories":3144},[],{"categories":3146},[97],{"categories":3148},[97],{"categories":3150},[275],{"categories":3152},[181],{"categories":3154},[221],{"categories":3156},[509],{"categories":3158},[221],{"categories":3160},[],{"categories":3162},[],{"categories":3164},[],{"categories":3166},[149],{"categories":3168},[149],{"categories":3170},[159],{"categories":3172},[97],{"categories":3174},[407],{"categories":3176},[159],{"categories":3178},[97],{"categories":3180},[97],{"categories":3182},[97],{"categories":3184},[97],{"categories":3186},[149],{"categories":3188},[],{"categories":3190},[],{"categories":3192},[97],{"categories":3194},[],{"categories":3196},[97],{"categories":3198},[149],{"categories":3200},[218],{"categories":3202},[97],{"categories":3204},[97],{"categories":3206},[],{"categories":3208},[152],{"categories":3210},[97],{"categories":3212},[218],{"categories":3214},[97],{"categories":3216},[149],{"categories":3218},[144],{"categories":3220},[97],{"categories":3222},[244],{"categories":3224},[149],{"categories":3226},[97],{"categories":3228},[767],{"categories":3230},[97],{"categories":3232},[149],{"categories":3234},[97],{"categories":3236},[159],{"categories":3238},[97],{"categories":3240},[473],{"categories":3242},[218],{"categories":3244},[],{"categories":3246},[181],{"categories":3248},[424],{"categories":3250},[149],{"categories":3252},[97],{"categories":3254},[],{"categories":3256},[181],{"categories":3258},[361],{"categories":3260},[149],{"categories":3262},[149],{"categories":3264},[97],{"categories":3266},[97],{"categories":3268},[149],{"categories":3270},[],{"categories":3272},[144],{"categories":3274},[149],{"categories":3276},[],{"categories":3278},[159],{"categories":3280},[97],{"categories":3282},[141],{"categories":3284},[181],{"categories":3286},[275],{"categories":3288},[170],{"categories":3290},[149],{"categories":3292},[149],{"categories":3294},[97],{"categories":3296},[149],{"categories":3298},[141],{"categories":3300},[],{"categories":3302},[97],{"categories":3304},[97],{"categories":3306},[],{"categories":3308},[],{"categories":3310},[218],{"categories":3312},[97,144],{"categories":3314},[149],{"categories":3316},[97],{"categories":3318},[],{"categories":3320},[141],{"categories":3322},[221],{"categories":3324},[144],{"categories":3326},[97],{"categories":3328},[159],{"categories":3330},[97],{"categories":3332},[149],{"categories":3334},[97],{"categories":3336},[97],{"categories":3338},[97],{"categories":3340},[181],{"categories":3342},[1046],{"categories":3344},[149],{"categories":3346},[97],{"categories":3348},[],{"categories":3350},[],{"categories":3352},[149],{"categories":3354},[97],{"categories":3356},[275],{"categories":3358},[],{"categories":3360},[97],{"categories":3362},[149],{"categories":3364},[170],{"categories":3366},[149],{"categories":3368},[424],{"categories":3370},[],{"categories":3372},[382],{"categories":3374},[149],{"categories":3376},[97],{"categories":3378},[244],{"categories":3380},[97],{"categories":3382},[221],{"categories":3384},[149],{"categories":3386},[97],{"categories":3388},[424],{"categories":3390},[97],{"categories":3392},[275],{"categories":3394},[],{"categories":3396},[97],{"categories":3398},[244],{"categories":3400},[218],{"categories":3402},[97],{"categories":3404},[97],{"categories":3406},[],{"categories":3408},[244],{"categories":3410},[181],{"categories":3412},[97],{"categories":3414},[97],{"categories":3416},[509],{"categories":3418},[141],{"categories":3420},[97],{"categories":3422},[],{"categories":3424},[],{"categories":3426},[218],{"categories":3428},[97],{"categories":3430},[221],{"categories":3432},[244],{"categories":3434},[149],{"categories":3436},[244],{"categories":3438},[181],{"categories":3440},[],{"categories":3442},[97],{"categories":3444},[],{"categories":3446},[97],{"categories":3448},[528],{"categories":3450},[97],{"categories":3452},[97],{"categories":3454},[149],{"categories":3456},[424],{"categories":3458},[97],{"categories":3460},[97],{"categories":3462},[97],{"categories":3464},[],{"categories":3466},[97,159],{"categories":3468},[181],{"categories":3470},[149],{"categories":3472},[159],{"categories":3474},[149],{"categories":3476},[797],{"categories":3478},[159],{"categories":3480},[97],{"categories":3482},[141],{"categories":3484},[],{"categories":3486},[],{"categories":3488},[149],{"categories":3490},[97],{"categories":3492},[159],{"categories":3494},[141],{"categories":3496},[159],{"categories":3498},[159],{"categories":3500},[97],{"categories":3502},[244],{"categories":3504},[97],{"categories":3506},[159],{"categories":3508},[],{"categories":3510},[97],{"categories":3512},[218,97],{"categories":3514},[275],{"categories":3516},[141],{"categories":3518},[],{"categories":3520},[97],{"categories":3522},[97],{"categories":3524},[144],{"categories":3526},[144],{"categories":3528},[97],{"categories":3530},[97],{"categories":3532},[361],{"categories":3534},[97],{"categories":3536},[159],{"categories":3538},[221],{"categories":3540},[149],{"categories":3542},[97],{"categories":3544},[97],{"categories":3546},[181],{"categories":3548},[244],{"categories":3550},[218],{"categories":3552},[97],{"categories":3554},[97],{"categories":3556},[97],{"categories":3558},[97],{"categories":3560},[141],{"categories":3562},[97],{"categories":3564},[149],{"categories":3566},[149],{"categories":3568},[159],{"categories":3570},[181],{"categories":3572},[159],{"categories":3574},[],{"categories":3576},[],{"categories":3578},[221],{"categories":3580},[97],{"categories":3582},[159],{"categories":3584},[97],{"categories":3586},[218],{"categories":3588},[424],{"categories":3590},[382],{"categories":3592},[361],{"categories":3594},[97],{"categories":3596},[97],{"categories":3598},[97],{"categories":3600},[221],{"categories":3602},[97],{"categories":3604},[97],{"categories":3606},[97],{"categories":3608},[149],{"categories":3610},[141],{"categories":3612},[149],{"categories":3614},[97,144],{"categories":3616},[],{"categories":3618},[218],{"categories":3620},[],{"categories":3622},[152],{"categories":3624},[97],{"categories":3626},[181],{"categories":3628},[141],{"categories":3630},[141],{"categories":3632},[149],{"categories":3634},[149],{"categories":3636},[149],{"categories":3638},[97],{"categories":3640},[97],{"categories":3642},[144],{"categories":3644},[159],{"categories":3646},[244],{"categories":3648},[97],{"categories":3650},[],{"categories":3652},[181],{"categories":3654},[97],{"categories":3656},[97],{"categories":3658},[97],{"categories":3660},[97],{"categories":3662},[97],{"categories":3664},[159],{"categories":3666},[181],{"categories":3668},[159],{"categories":3670},[159],{"categories":3672},[97],{"categories":3674},[97],{"categories":3676},[382],{"categories":3678},[97],{"categories":3680},[149],{"categories":3682},[181],{"categories":3684},[97],{"categories":3686},[97],{"categories":3688},[97],{"categories":3690},[149],{"categories":3692},[97],{"categories":3694},[97],{"categories":3696},[97],{"categories":3698},[2571],{"categories":3700},[3701],"Clinical AI",{"categories":3703},[218],{"categories":3705},[97],{"categories":3707},[97],{"categories":3709},[97],{"categories":3711},[275],{"categories":3713},[2050],{"categories":3715},[97],{"categories":3717},[152],{"categories":3719},[97],{"categories":3721},[149],{"categories":3723},[97],{"categories":3725},[97],{"categories":3727},[181],{"categories":3729},[97],{"categories":3731},[149],{"categories":3733},[244],{"categories":3735},[97],{"categories":3737},[97],{"categories":3739},[144],{"categories":3741},[97],{"categories":3743},[473],{"categories":3745},[97],{"categories":3747},[],{"categories":3749},[97],{"categories":3751},[159],{"categories":3753},[97],{"categories":3755},[],{"categories":3757},[],{"categories":3759},[97],{"categories":3761},[],{"categories":3763},[144],{"categories":3765},[97],{"categories":3767},[149],{"categories":3769},[181],{"categories":3771},[181],{"categories":3773},[181],{"categories":3775},[181],{"categories":3777},[],{"categories":3779},[141],{"categories":3781},[149],{"categories":3783},[181],{"categories":3785},[97],{"categories":3787},[528],{"categories":3789},[152],{"categories":3791},[97],{"categories":3793},[141],{"categories":3795},[149],{"categories":3797},[97],{"categories":3799},[97],{"categories":3801},[97,149],{"categories":3803},[149],{"categories":3805},[275],{"categories":3807},[181],{"categories":3809},[149],{"categories":3811},[181],{"categories":3813},[149],{"categories":3815},[97],{"categories":3817},[],{"categories":3819},[181],{"categories":3821},[244],{"categories":3823},[141],{"categories":3825},[97],{"categories":3827},[97],{"categories":3829},[],{"categories":3831},[159],{"categories":3833},[],{"categories":3835},[141],{"categories":3837},[149],{"categories":3839},[181],{"categories":3841},[97],{"categories":3843},[181],{"categories":3845},[141],{"categories":3847},[181],{"categories":3849},[181],{"categories":3851},[],{"categories":3853},[144],{"categories":3855},[149],{"categories":3857},[181],{"categories":3859},[181],{"categories":3861},[181],{"categories":3863},[181],{"categories":3865},[181],{"categories":3867},[181],{"categories":3869},[181],{"categories":3871},[181],{"categories":3873},[181],{"categories":3875},[181],{"categories":3877},[221],{"categories":3879},[141],{"categories":3881},[97],{"categories":3883},[97],{"categories":3885},[149],{"categories":3887},[149],{"categories":3889},[],{"categories":3891},[97,141],{"categories":3893},[],{"categories":3895},[149],{"categories":3897},[181],{"categories":3899},[149],{"categories":3901},[797],{"categories":3903},[97],{"categories":3905},[97],{"categories":3907},[97],{"categories":3909},[97],{"categories":3911},[361],{"categories":3913},[97],{"categories":3915},[149],{"categories":3917},[144],{"categories":3919},[149],{"categories":3921},[149],{"categories":3923},[],{"categories":3925},[149],{"categories":3927},[218],{"categories":3929},[181],{"categories":3931},[97],{"categories":3933},[],{"categories":3935},[],{"categories":3937},[149],{"categories":3939},[218],{"categories":3941},[97],{"categories":3943},[],{"categories":3945},[97],{"categories":3947},[],{"categories":3949},[244],{"categories":3951},[97],{"categories":3953},[],{"categories":3955},[],{"categories":3957},[181],{"categories":3959},[141],{"categories":3961},[97],{"categories":3963},[97],{"categories":3965},[144],{"categories":3967},[97],{"categories":3969},[97],{"categories":3971},[97],{"categories":3973},[144],{"categories":3975},[218],{"categories":3977},[],{"categories":3979},[97],{"categories":3981},[181],{"categories":3983},[],{"categories":3985},[97],{"categories":3987},[97],{"categories":3989},[218],{"categories":3991},[97],{"categories":3993},[244],{"categories":3995},[97],{"categories":3997},[275],{"categories":3999},[],{"categories":4001},[149],{"categories":4003},[244],{"categories":4005},[159],{"categories":4007},[],{"categories":4009},[97],{"categories":4011},[],{"categories":4013},[149],{"categories":4015},[218],{"categories":4017},[159],{"categories":4019},[],{"categories":4021},[2571],{"categories":4023},[144],{"categories":4025},[141],{"categories":4027},[221],{"categories":4029},[149],{"categories":4031},[218],{"categories":4033},[159],{"categories":4035},[],{"categories":4037},[],{"categories":4039},[97],{"categories":4041},[141],{"categories":4043},[97],{"categories":4045},[244],{"categories":4047},[],{"categories":4049},[149],{"categories":4051},[149],{"categories":4053},[149],{"categories":4055},[97],{"categories":4057},[181],{"categories":4059},[159],{"categories":4061},[97],{"categories":4063},[149],{"categories":4065},[152],{"categories":4067},[97],{"categories":4069},[149],{"categories":4071},[97],{"categories":4073},[152],{"categories":4075},[244],{"categories":4077},[181],{"categories":4079},[],{"categories":4081},[244],{"categories":4083},[],{"categories":4085},[159],{"categories":4087},[149],{"categories":4089},[],{"categories":4091},[97],{"categories":4093},[97],{"categories":4095},[97],{"categories":4097},[97],{"categories":4099},[149],{"categories":4101},[144],{"categories":4103},[141],{"categories":4105},[97],{"categories":4107},[218],{"categories":4109},[159],{"categories":4111},[159],{"categories":4113},[97],{"categories":4115},[221],{"categories":4117},[149],{"categories":4119},[97],{"categories":4121},[149],{"categories":4123},[97],{"categories":4125},[144],{"categories":4127},[218],{"categories":4129},[159],{"categories":4131},[149],{"categories":4133},[97],{"categories":4135},[152],{"categories":4137},[97],{"categories":4139},[149],{"categories":4141},[97],{"categories":4143},[181],{"categories":4145},[],{"categories":4147},[141],{"categories":4149},[97],{"categories":4151},[97],{"categories":4153},[97],{"categories":4155},[159],{"categories":4157},[97],{"categories":4159},[159],{"categories":4161},[97],{"categories":4163},[149],{"categories":4165},[97],{"categories":4167},[97],{"categories":4169},[97],{"categories":4171},[97],{"categories":4173},[],{"categories":4175},[97],{"categories":4177},[218],{"categories":4179},[144],{"categories":4181},[181],{"categories":4183},[149],{"categories":4185},[97],{"categories":4187},[97],{"categories":4189},[218],{"categories":4191},[149],{"categories":4193},[97],{"categories":4195},[244],{"categories":4197},[97],{"categories":4199},[221],{"categories":4201},[97],{"categories":4203},[97],{"categories":4205},[181],{"categories":4207},[97],{"categories":4209},[97],{"categories":4211},[149],{"categories":4213},[275],{"categories":4215},[97],{"categories":4217},[159],{"categories":4219},[149],{"categories":4221},[221],{"categories":4223},[],{"categories":4225},[149],{"categories":4227},[159],{"categories":4229},[97],{"categories":4231},[1914],{"categories":4233},[218],{"categories":4235},[302],{"categories":4237},[97],{"categories":4239},[141],{"categories":4241},[159],{"categories":4243},[144],{"categories":4245},[159],{"categories":4247},[97],{"categories":4249},[],{"categories":4251},[149],{"categories":4253},[149],{"categories":4255},[97],{"categories":4257},[97],{"categories":4259},[221],{"categories":4261},[],{"categories":4263},[181],{"categories":4265},[],{"categories":4267},[181],{"categories":4269},[97],{"categories":4271},[97],{"categories":4273},[149],{"categories":4275},[149],{"categories":4277},[149],{"categories":4279},[],{"categories":4281},[181],{"categories":4283},[97],{"categories":4285},[],{"categories":4287},[97],{"categories":4289},[97],{"categories":4291},[],{"categories":4293},[218],{"categories":4295},[159],{"categories":4297},[149],{"categories":4299},[97],{"categories":4301},[97],{"categories":4303},[244],{"categories":4305},[97],{"categories":4307},[97],{"categories":4309},[141],{"categories":4311},[],{"categories":4313},[97],{"categories":4315},[97],{"categories":4317},[],{"categories":4319},[141],{"categories":4321},[181],{"categories":4323},[159],{"categories":4325},[424],{"categories":4327},[97],{"categories":4329},[97],{"categories":4331},[97],{"categories":4333},[159],{"categories":4335},[181],{"categories":4337},[218],{"categories":4339},[97],{"categories":4341},[97],{"categories":4343},[97],{"categories":4345},[181],{"categories":4347},[218],{"categories":4349},[97],{"categories":4351},[181],{"categories":4353},[218],{"categories":4355},[97],{"categories":4357},[181],{"categories":4359},[149],{"categories":4361},[149],{"categories":4363},[149],{"categories":4365},[159],{"categories":4367},[181],{"categories":4369},[149],{"categories":4371},[149],{"categories":4373},[97],{"categories":4375},[159],{"categories":4377},[218],{"categories":4379},[97],{"categories":4381},[],{"categories":4383},[149],{"categories":4385},[],{"categories":4387},[],{"categories":4389},[],{"categories":4391},[149],{"categories":4393},[144],{"categories":4395},[149],{"categories":4397},[4398],"Liability & Ethics",{"categories":4400},[97],{"categories":4402},[149],{"categories":4404},[141],{"categories":4406},[149],{"categories":4408},[144],{"categories":4410},[244],{"categories":4412},[149],{"categories":4414},[],{"categories":4416},[509],{"categories":4418},[149],{"categories":4420},[],{"categories":4422},[141],{"categories":4424},[149],{"categories":4426},[],{"categories":4428},[149],{"categories":4430},[97],{"categories":4432},[97],{"categories":4434},[181],{"categories":4436},[97],{"categories":4438},[97],{"categories":4440},[149],{"categories":4442},[97],{"categories":4444},[97],{"categories":4446},[181],{"categories":4448},[149],{"categories":4450},[159],{"categories":4452},[218],{"categories":4454},[141],{"categories":4456},[97],{"categories":4458},[],{"categories":4460},[149],{"categories":4462},[149],{"categories":4464},[424],{"categories":4466},[218],{"categories":4468},[275],{"categories":4470},[181],{"categories":4472},[97],{"categories":4474},[218],{"categories":4476},[97],{"categories":4478},[141],{"categories":4480},[],{"categories":4482},[149],{"categories":4484},[97],{"categories":4486},[97],{"categories":4488},[149],{"categories":4490},[97],{"categories":4492},[218],{"categories":4494},[],{"categories":4496},[149],{"categories":4498},[152],{"categories":4500},[181],{"categories":4502},[149],{"categories":4504},[144],{"categories":4506},[],{"categories":4508},[97],{"categories":4510},[152],{"categories":4512},[97],{"categories":4514},[149],{"categories":4516},[181],{"categories":4518},[141],{"categories":4520},[275],{"categories":4522},[97],{"categories":4524},[97],{"categories":4526},[97],{"categories":4528},[181],{"categories":4530},[144],{"categories":4532},[97],{"categories":4534},[218],{"categories":4536},[181],{"categories":4538},[275],{"categories":4540},[97],{"categories":4542},[149],{"categories":4544},[],{"categories":4546},[473],{"categories":4548},[],{"categories":4550},[97],{"categories":4552},[275],{"categories":4554},[221],{"categories":4556},[149],{"categories":4558},[149],{"categories":4560},[4561],"Design News & Tools",{"categories":4563},[97],{"categories":4565},[181],{"categories":4567},[97],{"categories":4569},[141],{"categories":4571},[97],{"categories":4573},[218],{"categories":4575},[149],{"categories":4577},[149],{"categories":4579},[97],{"categories":4581},[424],{"categories":4583},[97],{"categories":4585},[424],{"categories":4587},[244],{"categories":4589},[97],{"categories":4591},[149],{"categories":4593},[],{"categories":4595},[97],{"categories":4597},[97],{"categories":4599},[97],{"categories":4601},[181],{"categories":4603},[141],{"categories":4605},[],{"categories":4607},[97],{"categories":4609},[97],{"categories":4611},[159],{"categories":4613},[528],{"categories":4615},[159],{"categories":4617},[218],{"categories":4619},[97],{"categories":4621},[97,149],{"categories":4623},[244,144],{"categories":4625},[97],{"categories":4627},[97],{"categories":4629},[97],{"categories":4631},[],{"categories":4633},[149],{"categories":4635},[],{"categories":4637},[159],{"categories":4639},[97],{"categories":4641},[159],{"categories":4643},[],{"categories":4645},[149],{"categories":4647},[97],{"categories":4649},[181],{"categories":4651},[97],{"categories":4653},[],{"categories":4655},[149],{"categories":4657},[97],{"categories":4659},[],{"categories":4661},[218],{"categories":4663},[97],{"categories":4665},[149],{"categories":4667},[97],{"categories":4669},[97],{"categories":4671},[141],{"categories":4673},[149],{"categories":4675},[97],{"categories":4677},[],{"categories":4679},[275],{"categories":4681},[244],{"categories":4683},[144],{"categories":4685},[144],{"categories":4687},[97],{"categories":4689},[141],{"categories":4691},[141],{"categories":4693},[97],{"categories":4695},[149],{"categories":4697},[97],{"categories":4699},[97],{"categories":4701},[97],{"categories":4703},[159],{"categories":4705},[97],{"categories":4707},[141],{"categories":4709},[149],{"categories":4711},[97],{"categories":4713},[244],{"categories":4715},[97],{"categories":4717},[181],{"categories":4719},[97],{"categories":4721},[97],{"categories":4723},[149],{"categories":4725},[97],{"categories":4727},[],{"categories":4729},[159],{"categories":4731},[],{"categories":4733},[159],{"categories":4735},[149],{"categories":4737},[141],{"categories":4739},[],{"categories":4741},[221],{"categories":4743},[275],{"categories":4745},[97],{"categories":4747},[159],{"categories":4749},[97],{"categories":4751},[],{"categories":4753},[181],{"categories":4755},[149],{"categories":4757},[159],{"categories":4759},[218],{"categories":4761},[97],{"categories":4763},[149],{"categories":4765},[159],{"categories":4767},[149],{"categories":4769},[181],{"categories":4771},[97],{"categories":4773},[141],{"categories":4775},[181],{"categories":4777},[159],{"categories":4779},[97],{"categories":4781},[218],{"categories":4783},[144],{"categories":4785},[97],{"categories":4787},[97],{"categories":4789},[97],{"categories":4791},[97],{"categories":4793},[97],{"categories":4795},[149],{"categories":4797},[97],{"categories":4799},[149],{"categories":4801},[97],{"categories":4803},[97],{"categories":4805},[141],{"categories":4807},[97],{"categories":4809},[149],{"categories":4811},[149],{"categories":4813},[218],{"categories":4815},[149],{"categories":4817},[149],{"categories":4819},[141],{"categories":4821},[149],{"categories":4823},[218],{"categories":4825},[],{"categories":4827},[97],{"categories":4829},[221],{"categories":4831},[424],{"categories":4833},[97],{"categories":4835},[97],{"categories":4837},[97],{"categories":4839},[159],{"categories":4841},[],{"categories":4843},[149],{"categories":4845},[244],{"categories":4847},[97],{"categories":4849},[181],{"categories":4851},[149],{"categories":4853},[97],{"categories":4855},[244],{"categories":4857},[149],{"categories":4859},[144],{"categories":4861},[144],{"categories":4863},[97],{"categories":4865},[97],{"categories":4867},[97],{"categories":4869},[141],{"categories":4871},[],{"categories":4873},[97],{"categories":4875},[149],{"categories":4877},[149],{"categories":4879},[97],{"categories":4881},[97],{"categories":4883},[97],{"categories":4885},[159],{"categories":4887},[],{"categories":4889},[141],{"categories":4891},[97],{"categories":4893},[97],{"categories":4895},[149],{"categories":4897},[149],{"categories":4899},[],{"categories":4901},[159],{"categories":4903},[159],{"categories":4905},[97],{"categories":4907},[244],{"categories":4909},[144],{"categories":4911},[218],{"categories":4913},[],{"categories":4915},[97],{"categories":4917},[149],{"categories":4919},[141],{"categories":4921},[97],{"categories":4923},[159],{"categories":4925},[141],{"categories":4927},[181],{"categories":4929},[221],{"categories":4931},[181],{"categories":4933},[149],{"categories":4935},[],{"categories":4937},[181],{"categories":4939},[149],{"categories":4941},[218],{"categories":4943},[221],{"categories":4945},[97],{"categories":4947},[],{"categories":4949},[149],{"categories":4951},[2571],{"categories":4953},[181],{"categories":4955},[159],{"categories":4957},[97],{"categories":4959},[97],{"categories":4961},[144],{"categories":4963},[97],{"categories":4965},[141],{"categories":4967},[1492],{"categories":4969},[275],{"categories":4971},[141],{"categories":4973},[],{"categories":4975},[],{"categories":4977},[181],{"categories":4979},[149],{"categories":4981},[181],{"categories":4983},[],{"categories":4985},[149],{"categories":4987},[149],{"categories":4989},[149],{"categories":4991},[],{"categories":4993},[97],{"categories":4995},[],{"categories":4997},[181],{"categories":4999},[141],{"categories":5001},[218],{"categories":5003},[97],{"categories":5005},[149],{"categories":5007},[181],{"categories":5009},[97],{"categories":5011},[181],{"categories":5013},[],{"categories":5015},[181],{"categories":5017},[141],{"categories":5019},[424],{"categories":5021},[149],{"categories":5023},[97],{"categories":5025},[],{"categories":5027},[159],{"categories":5029},[149],{"categories":5031},[152],{"categories":5033},[149],{"categories":5035},[141],{"categories":5037},[],{"categories":5039},[],{"categories":5041},[],{"categories":5043},[218],{"categories":5045},[149],{"categories":5047},[97],{"categories":5049},[97],{"categories":5051},[],{"categories":5053},[],{"categories":5055},[],{"categories":5057},[218],{"categories":5059},[97],{"categories":5061},[],{"categories":5063},[149],{"categories":5065},[97],{"categories":5067},[141],{"categories":5069},[],{"categories":5071},[],{"categories":5073},[218],{"categories":5075},[97],{"categories":5077},[181],{"categories":5079},[],{"categories":5081},[244],{"categories":5083},[181],{"categories":5085},[244],{"categories":5087},[221],{"categories":5089},[97],{"categories":5091},[97],{"categories":5093},[],{"categories":5095},[],{"categories":5097},[149],{"categories":5099},[],{"categories":5101},[97],{"categories":5103},[424],{"categories":5105},[97],{"categories":5107},[97],{"categories":5109},[97],{"categories":5111},[],{"categories":5113},[149],{"categories":5115},[97],{"categories":5117},[97],{"categories":5119},[],{"categories":5121},[149],{"categories":5123},[97],{"categories":5125},[181],{"categories":5127},[97],{"categories":5129},[244],{"categories":5131},[144],{"categories":5133},[97],{"categories":5135},[97],{"categories":5137},[149],{"categories":5139},[221],{"categories":5141},[149],{"categories":5143},[149],{"categories":5145},[],{"categories":5147},[],{"categories":5149},[97],{"categories":5151},[],{"categories":5153},[181],{"categories":5155},[144],{"categories":5157},[],{"categories":5159},[],{"categories":5161},[218],{"categories":5163},[141],{"categories":5165},[],{"categories":5167},[144],{"categories":5169},[244],{"categories":5171},[97],{"categories":5173},[159],{"categories":5175},[141],{"categories":5177},[221],{"categories":5179},[144],{"categories":5181},[159],{"categories":5183},[159],{"categories":5185},[],{"categories":5187},[97],{"categories":5189},[],{"categories":5191},[149],{"categories":5193},[141],{"categories":5195},[218],{"categories":5197},[97],{"categories":5199},[141],{"categories":5201},[149],{"categories":5203},[275],{"categories":5205},[97],{"categories":5207},[97],{"categories":5209},[97],{"categories":5211},[141],{"categories":5213},[221],{"categories":5215},[149],{"categories":5217},[],{"categories":5219},[97],{"categories":5221},[159],{"categories":5223},[181],{"categories":5225},[159],{"categories":5227},[97],{"categories":5229},[152],{"categories":5231},[],{"categories":5233},[218],{"categories":5235},[181],{"categories":5237},[141],{"categories":5239},[149],{"categories":5241},[97],{"categories":5243},[97],{"categories":5245},[149],{"categories":5247},[97],{"categories":5249},[97],{"categories":5251},[144],{"categories":5253},[149],{"categories":5255},[149,275],{"categories":5257},[149],{"categories":5259},[159],{"categories":5261},[97],{"categories":5263},[97],{"categories":5265},[221],{"categories":5267},[149],{"categories":5269},[244],{"categories":5271},[149],{"categories":5273},[144],{"categories":5275},[],{"categories":5277},[149],{"categories":5279},[97],{"categories":5281},[144],{"categories":5283},[],{"categories":5285},[],{"categories":5287},[159],{"categories":5289},[97],{"categories":5291},[97],{"categories":5293},[149],{"categories":5295},[221],{"categories":5297},[244],{"categories":5299},[97],{"categories":5301},[97],{"categories":5303},[149],{"categories":5305},[],{"categories":5307},[149],{"categories":5309},[181],{"categories":5311},[149],{"categories":5313},[],{"categories":5315},[181],{"categories":5317},[159],{"categories":5319},[2571],{"categories":5321},[141],{"categories":5323},[159],{"categories":5325},[97],{"categories":5327},[149],{"categories":5329},[97],{"categories":5331},[97],{"categories":5333},[244],{"categories":5335},[159],{"categories":5337},[],{"categories":5339},[181],{"categories":5341},[97],{"categories":5343},[],{"categories":5345},[149],{"categories":5347},[97],{"categories":5349},[97],{"categories":5351},[97],{"categories":5353},[149],{"categories":5355},[97],{"categories":5357},[97],{"categories":5359},[152],{"categories":5361},[149],{"categories":5363},[97],{"categories":5365},[97],{"categories":5367},[97],{"categories":5369},[97],{"categories":5371},[97],{"categories":5373},[97],{"categories":5375},[144],{"categories":5377},[],{"categories":5379},[152],{"categories":5381},[181],{"categories":5383},[149],{"categories":5385},[97],{"categories":5387},[159],{"categories":5389},[],{"categories":5391},[159],{"categories":5393},[159],{"categories":5395},[149],{"categories":5397},[159],{"categories":5399},[97],{"categories":5401},[97],{"categories":5403},[159],{"categories":5405},[97],{"categories":5407},[149],{"categories":5409},[181],{"categories":5411},[97],{"categories":5413},[97],{"categories":5415},[97],{"categories":5417},[144],{"categories":5419},[97],{"categories":5421},[149],{"categories":5423},[218],{"categories":5425},[],{"categories":5427},[97],{"categories":5429},[221],{"categories":5431},[149],{"categories":5433},[97],{"categories":5435},[],{"categories":5437},[97],{"categories":5439},[97],{"categories":5441},[181],{"categories":5443},[97],{"categories":5445},[97],{"categories":5447},[149],{"categories":5449},[244],{"categories":5451},[],{"categories":5453},[],{"categories":5455},[159],{"categories":5457},[181],{"categories":5459},[159],{"categories":5461},[181],{"categories":5463},[97],{"categories":5465},[244],{"categories":5467},[97],{"categories":5469},[141],{"categories":5471},[149],{"categories":5473},[97],{"categories":5475},[149],{"categories":5477},[149],{"categories":5479},[97],{"categories":5481},[144],{"categories":5483},[],{"categories":5485},[221],{"categories":5487},[97],{"categories":5489},[],{"categories":5491},[181],{"categories":5493},[97],{"categories":5495},[221],{"categories":5497},[97],{"categories":5499},[159],{"categories":5501},[159],{"categories":5503},[159],{"categories":5505},[149],{"categories":5507},[149],{"categories":5509},[149],{"categories":5511},[97],{"categories":5513},[97],{"categories":5515},[218],{"categories":5517},[221],{"categories":5519},[221],{"categories":5521},[],{"categories":5523},[181],{"categories":5525},[97],{"categories":5527},[97],{"categories":5529},[159],{"categories":5531},[],{"categories":5533},[181],{"categories":5535},[181],{"categories":5537},[181],{"categories":5539},[],{"categories":5541},[149],{"categories":5543},[97],{"categories":5545},[],{"categories":5547},[141],{"categories":5549},[144],{"categories":5551},[],{"categories":5553},[97],{"categories":5555},[97],{"categories":5557},[],{"categories":5559},[159],{"categories":5561},[],{"categories":5563},[],{"categories":5565},[],{"categories":5567},[],{"categories":5569},[97],{"categories":5571},[181],{"categories":5573},[],{"categories":5575},[],{"categories":5577},[97],{"categories":5579},[97],{"categories":5581},[97],{"categories":5583},[221],{"categories":5585},[97],{"categories":5587},[221],{"categories":5589},[],{"categories":5591},[221],{"categories":5593},[221],{"categories":5595},[275],{"categories":5597},[149],{"categories":5599},[159],{"categories":5601},[],{"categories":5603},[],{"categories":5605},[221],{"categories":5607},[159],{"categories":5609},[159],{"categories":5611},[159],{"categories":5613},[],{"categories":5615},[141],{"categories":5617},[159],{"categories":5619},[159],{"categories":5621},[141],{"categories":5623},[159],{"categories":5625},[144],{"categories":5627},[159],{"categories":5629},[159],{"categories":5631},[159],{"categories":5633},[221],{"categories":5635},[181],{"categories":5637},[181],{"categories":5639},[97],{"categories":5641},[159],{"categories":5643},[221],{"categories":5645},[275],{"categories":5647},[221],{"categories":5649},[221],{"categories":5651},[221],{"categories":5653},[],{"categories":5655},[144],{"categories":5657},[],{"categories":5659},[275],{"categories":5661},[159],{"categories":5663},[159],{"categories":5665},[159],{"categories":5667},[149],{"categories":5669},[181,144],{"categories":5671},[221],{"categories":5673},[],{"categories":5675},[],{"categories":5677},[221],{"categories":5679},[],{"categories":5681},[221],{"categories":5683},[181],{"categories":5685},[149],{"categories":5687},[],{"categories":5689},[159],{"categories":5691},[97],{"categories":5693},[218],{"categories":5695},[],{"categories":5697},[97],{"categories":5699},[],{"categories":5701},[181],{"categories":5703},[141],{"categories":5705},[221],{"categories":5707},[],{"categories":5709},[159],{"categories":5711},[181],[5713,5793,5900,5956],{"id":5714,"title":5715,"ai":5716,"body":5721,"categories":5772,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":5773,"navigation":117,"path":5779,"published_at":5780,"question":98,"scraped_at":5780,"seo":5781,"sitemap":5782,"source_id":5783,"source_name":5784,"source_type":5785,"source_url":5786,"stem":5787,"tags":5788,"thumbnail_url":98,"tldr":5790,"tweet":98,"unknown_tags":5791,"__hash__":5792},"summaries\u002Fsummaries\u002Fadc718e7c1350c24-evaluating-llm-agents-in-high-stakes-energy-analyt-summary.md","Evaluating LLM Agents in High-Stakes Energy Analytics",{"provider":7,"model":8,"input_tokens":5717,"output_tokens":5718,"processing_time_ms":5719,"cost_usd":5720},6041,462,3132,0.00220325,{"type":14,"value":5722,"toc":5767},[5723,5727,5730,5734,5737,5757,5760,5764],[17,5724,5726],{"id":5725},"bridging-the-gap-in-domain-specific-agent-benchmarking","Bridging the Gap in Domain-Specific Agent Benchmarking",[22,5728,5729],{},"Most existing agent benchmarks focus on general-purpose tasks or static knowledge recall. This research addresses a critical void in the energy sector, which demands high-stakes, real-world capabilities: live data retrieval, complex regulatory interpretation, and multi-step quantitative reasoning. The authors introduce a new evaluation environment comprising 243 expert-curated problems designed to test how LLM agents perform when equipped with specialized domain tools.",[17,5731,5733],{"id":5732},"the-evaluation-framework","The Evaluation Framework",[22,5735,5736],{},"The benchmark categorizes tasks into three distinct domains to stress-test agentic reasoning:",[29,5738,5739,5745,5751],{},[32,5740,5741,5744],{},[35,5742,5743],{},"Market Data Retrieval and Analysis:"," Involves interacting with live electricity market APIs from major U.S. Independent System Operators (ISOs).",[32,5746,5747,5750],{},[35,5748,5749],{},"Knowledge Retrieval and Interpretation:"," Focuses on navigating regulatory dockets and utility tariff databases using retrieval-augmented generation (RAG).",[32,5752,5753,5756],{},[35,5754,5755],{},"Advanced Quantitative Modeling:"," Requires agents to perform asset revenue estimation, hedging strategy analysis, and optimization modeling.",[22,5758,5759],{},"To ensure rigorous assessment, the authors employ a multi-dimensional protocol that scores agents based on approach correctness, answer accuracy, attribute alignment, and source validity. The framework utilizes category-aware routing, ensuring that the scoring criteria are tailored to the specific nature of the task (e.g., quantitative vs. qualitative).",[17,5761,5763],{"id":5762},"insights-on-tool-augmented-performance","Insights on Tool-Augmented Performance",[22,5765,5766],{},"The study provides a comparative analysis of both closed-source and open-source models, specifically examining the interaction between model reasoning capabilities and domain-specific tooling. By releasing the benchmark and its associated artifacts, the authors aim to establish a reproducible standard for evaluating AI agents in professional, high-stakes environments where accuracy and source reliability are paramount.",{"title":91,"searchDepth":92,"depth":92,"links":5768},[5769,5770,5771],{"id":5725,"depth":92,"text":5726},{"id":5732,"depth":92,"text":5733},{"id":5762,"depth":92,"text":5763},[97],{"content_references":5774,"triage":5775},[],{"relevance":113,"novelty":114,"quality":114,"actionability":5776,"composite":5777,"reasoning":5778},3,4.15,"Category: AI & LLMs. The article presents a new benchmark for evaluating LLM agents in the energy sector, addressing a specific audience pain point regarding the application of AI in high-stakes environments. It offers insights into the evaluation framework and performance metrics, which can inform product builders about the capabilities of AI agents in specialized domains.","\u002Fsummaries\u002Fadc718e7c1350c24-evaluating-llm-agents-in-high-stakes-energy-analyt-summary","2026-06-26 12:58:19",{"title":5715,"description":91},{"loc":5779},"adc718e7c1350c24","arXiv cs.AI","article","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.26346","summaries\u002Fadc718e7c1350c24-evaluating-llm-agents-in-high-stakes-energy-analyt-summary",[5789,130,129,131],"agents","A new benchmark of 243 expert-curated energy tasks reveals how tool-augmented LLM agents handle live data, regulatory knowledge, and quantitative modeling in professional energy markets.",[131],"VDI34L-g_eZ2d8zUgITK4_q10cI79IQ98SWVq_0tGqc",{"id":5794,"title":5795,"ai":5796,"body":5801,"categories":5875,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":5876,"navigation":117,"path":5887,"published_at":5888,"question":98,"scraped_at":5888,"seo":5889,"sitemap":5890,"source_id":5891,"source_name":5892,"source_type":5785,"source_url":5893,"stem":5894,"tags":5895,"thumbnail_url":98,"tldr":5897,"tweet":98,"unknown_tags":5898,"__hash__":5899},"summaries\u002Fsummaries\u002F7b59cd8e4387736f-hands-on-guide-to-fineweb-corpus-processing-and-an-summary.md","Hands-On Guide to FineWeb Corpus Processing and Analytics",{"provider":7,"model":8,"input_tokens":5797,"output_tokens":5798,"processing_time_ms":5799,"cost_usd":5800},11152,651,3432,0.0037645,{"type":14,"value":5802,"toc":5871},[5803,5807,5822,5825,5845,5849,5856],[17,5804,5806],{"id":5805},"efficient-corpus-handling-and-quality-filtering","Efficient Corpus Handling and Quality Filtering",[22,5808,5809,5810,5813,5814,5817,5818,5821],{},"To manage multi-terabyte datasets without local storage constraints, use the Hugging Face ",[40,5811,5812],{},"datasets"," library in ",[40,5815,5816],{},"streaming=True"," mode. This allows for iterative processing of samples, such as the ",[40,5819,5820],{},"sample-10BT"," subset of FineWeb.",[22,5823,5824],{},"Quality filtering is essential for LLM training. You can reproduce production-grade pipelines by applying three categories of heuristics:",[29,5826,5827,5833,5839],{},[32,5828,5829,5832],{},[35,5830,5831],{},"Gopher-style:"," Filters based on word count (50–100k range), mean word length (3–10 characters), symbol density, bullet point frequency, and stopword presence.",[32,5834,5835,5838],{},[35,5836,5837],{},"C4-style:"," Removes boilerplate text (e.g., \"lorem ipsum\", \"javascript is disabled\") and excessive brace usage.",[32,5840,5841,5844],{},[35,5842,5843],{},"Custom Logic:"," Evaluates line-level quality by calculating duplicate line fractions (rejecting if > 30%) and identifying list-heavy content (rejecting if > 67% of lines are short).",[17,5846,5848],{"id":5847},"deduplication-and-tokenization-validation","Deduplication and Tokenization Validation",[22,5850,5851,5852,5855],{},"Near-duplicate detection is critical to prevent data leakage and model memorization. Using ",[40,5853,5854],{},"datasketch",", you can implement a MinHash-based LSH (Locality Sensitive Hashing) pipeline. By converting text into word shingles (k=5) and generating MinHash signatures, you can identify document pairs with a Jaccard similarity above a set threshold (e.g., 0.7).",[22,5857,5858,5859,5862,5863,5866,5867,5870],{},"Validating metadata is equally important. By recomputing token counts using ",[40,5860,5861],{},"tiktoken"," (specifically the ",[40,5864,5865],{},"gpt2"," encoding), you can verify the integrity of stored ",[40,5868,5869],{},"token_count"," fields. Small discrepancies are expected due to tokenizer versioning, but consistent results confirm the dataset's reliability. Calculating the \"characters per token\" ratio provides a useful metric for assessing tokenizer compression efficiency across different domains.",{"title":91,"searchDepth":92,"depth":92,"links":5872},[5873,5874],{"id":5805,"depth":92,"text":5806},{"id":5847,"depth":92,"text":5848},[97],{"content_references":5877,"triage":5885},[5878,5881,5883],{"type":104,"title":5879,"url":5880,"context":107},"FineWeb Dataset","https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FHuggingFaceFW\u002Ffineweb",{"type":104,"title":5854,"url":5882,"context":107},"https:\u002F\u002Fekzhu.github.io\u002Fdatasketch\u002F",{"type":104,"title":5861,"url":5884,"context":107},"https:\u002F\u002Fgithub.com\u002Fopenai\u002Ftiktoken",{"relevance":113,"novelty":114,"quality":114,"actionability":113,"composite":115,"reasoning":5886},"Category: AI & LLMs. The article provides a hands-on guide for processing and analyzing large-scale web datasets, which is directly relevant to building AI-powered products. It includes specific techniques for quality filtering and deduplication that can be immediately applied in LLM training workflows.","\u002Fsummaries\u002F7b59cd8e4387736f-hands-on-guide-to-fineweb-corpus-processing-and-an-summary","2026-06-15 12:56:59",{"title":5795,"description":91},{"loc":5887},"7b59cd8e4387736f","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F06\u002F14\u002Fa-coding-hands-on-on-fineweb-for-streaming-filtering-deduplication-tokenization-and-large-scale-web-corpus-analytics\u002F","summaries\u002F7b59cd8e4387736f-hands-on-guide-to-fineweb-corpus-processing-and-an-summary",[5896,130,129,131],"python","Learn to stream, filter, deduplicate, and analyze large-scale web datasets like FineWeb using Python, MinHash, and tiktoken to prepare high-quality data for LLM training.",[131],"daAnSA8_x9HlX8ghCmH4Z83DgjXLdbAemd6jWliqFn4",{"id":5901,"title":5902,"ai":5903,"body":5909,"categories":5937,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":5938,"navigation":117,"path":5943,"published_at":5944,"question":98,"scraped_at":5945,"seo":5946,"sitemap":5947,"source_id":5948,"source_name":5949,"source_type":5785,"source_url":5950,"stem":5951,"tags":5952,"thumbnail_url":98,"tldr":5953,"tweet":98,"unknown_tags":5954,"__hash__":5955},"summaries\u002Fsummaries\u002F6c1cdcac335f19f8-ai-amplifies-bad-data-fix-it-first-summary.md","AI Amplifies Bad Data—Fix It First",{"provider":7,"model":5904,"input_tokens":5905,"output_tokens":5906,"processing_time_ms":5907,"cost_usd":5908},"x-ai\u002Fgrok-4.1-fast",5216,1258,13939,0.0016497,{"type":14,"value":5910,"toc":5932},[5911,5915,5918,5922,5925,5929],[17,5912,5914],{"id":5913},"data-quality-drives-85-of-ai-failures","Data Quality Drives 85% of AI Failures",[22,5916,5917],{},"Organizations rushing into AI overlook that 77% report data quality as \"average at best\" (up from 66% last year), only 15% of large enterprise executives believe their data suffices for goals, 26% of enterprise data is \"dirty,\" 94% suspect inaccurate customer data, and 85% of AI projects fail due to poor data. No company lacks data quality issues. AI operationalizes these flaws: messy lending data leads to approving bad loans, duplicative sales data misprioritizes customers, and broken metrics optimize flawed processes. Trusting confident but wrong AI outputs industrializes bad decisions that stayed contained in traditional reports and dashboards.",[17,5919,5921],{"id":5920},"ais-semantic-processing-exposes-data-costs","AI's Semantic Processing Exposes Data Costs",[22,5923,5924],{},"Unlike cheap, deterministic SQL queries scanning 10,000 rows in milliseconds with near-zero marginal cost, AI uses GPU-heavy semantic search: it embeds data into vectors, performs matrix multiplications for inference, and synthesizes proactive insights like spotting outliers, seasonal spikes, or correlations without explicit queries. This makes AI 10x more energy-intensive per query, billing for cognition—tokens processed, context maintained, reasoning performed—scaling like tireless labor. Dirty data forces repeated heavy processing in evolving conversations, shifting economics from FinOps-style cost reduction (store, query, pay per run) to usage → output → value, where data quality determines real returns.",[17,5926,5928],{"id":5927},"reframe-ai-management-around-data-not-symptoms","Reframe AI Management Around Data, Not Symptoms",[22,5930,5931],{},"Fears of AI costs, skills gaps, and security mask root data problems; rising costs signal inefficient processing of messy data, variable outputs reveal inaccuracies, and slowed adoption ignores symptoms. Traditional IT models (cost → efficiency → reduction) fail for probabilistic, consumption-based AI fueled by imperfect data. Leaders must prioritize data cleaning to avoid AI confidently recommending actions like shutting profitable lines based on flawed inputs. AI acts as an unavoidable mirror: fix data to capture its value, or scale mistakes.",{"title":91,"searchDepth":92,"depth":92,"links":5933},[5934,5935,5936],{"id":5913,"depth":92,"text":5914},{"id":5920,"depth":92,"text":5921},{"id":5927,"depth":92,"text":5928},[],{"content_references":5939,"triage":5940},[],{"relevance":114,"novelty":5776,"quality":114,"actionability":5776,"composite":5941,"reasoning":5942},3.6,"Category: Data Science & Visualization. The article discusses the critical importance of data quality in AI implementations, addressing a specific pain point for product builders who need to ensure their data is clean before deploying AI solutions. It provides insights into the consequences of poor data but lacks detailed actionable steps for improving data quality.","\u002Fsummaries\u002F6c1cdcac335f19f8-ai-amplifies-bad-data-fix-it-first-summary","2026-04-20 16:23:11","2026-04-21 15:26:26",{"title":5902,"description":91},{"loc":5943},"6c1cdcac335f19f8","Data Driven Investor","https:\u002F\u002Fmedium.datadriveninvestor.com\u002Fdont-be-afraid-of-ai-be-terrified-of-your-data-97569858b42f?source=rss----32881626c9c9---4","summaries\u002F6c1cdcac335f19f8-ai-amplifies-bad-data-fix-it-first-summary",[130,131],"AI doesn't fix poor data quality; it scales the errors, leading to wrong decisions like approving bad loans or prioritizing wrong customers. 85% of AI failures stem from bad data, so clean data before adopting AI.",[131],"pXNZvNlaf4m9AWQ5hD6eh2a7NtGiLwpEY1JU9QQSt6c",{"id":5957,"title":5958,"ai":5959,"body":5964,"categories":6007,"created_at":98,"date_modified":98,"description":91,"extension":99,"faq":98,"featured":100,"kicker_label":98,"meta":6008,"navigation":117,"path":6015,"published_at":6016,"question":98,"scraped_at":6016,"seo":6017,"sitemap":6018,"source_id":6019,"source_name":5892,"source_type":5785,"source_url":6020,"stem":6021,"tags":6022,"thumbnail_url":98,"tldr":6024,"tweet":98,"unknown_tags":6025,"__hash__":6026},"summaries\u002Fsummaries\u002F3dd2b79848ef9684-microsoft-s-mai-transcribe-1-5-production-ready-sp-summary.md","Microsoft's MAI-Transcribe-1.5: Production-Ready Speech Recognition",{"provider":7,"model":8,"input_tokens":5960,"output_tokens":5961,"processing_time_ms":5962,"cost_usd":5963},8424,503,3080,0.0028605,{"type":14,"value":5965,"toc":6003},[5966,5970,5973,5976,5980,5983],[17,5967,5969],{"id":5968},"performance-and-efficiency-gains","Performance and Efficiency Gains",[22,5971,5972],{},"Microsoft's MAI-Transcribe-1.5 represents a significant iteration in their in-house speech-to-text stack, focusing on production-grade performance. The model achieves a 2.4% Word-Error-Rate (WER) on the Artificial Analysis leaderboard, positioning it as a competitive option for high-accuracy transcription.",[22,5974,5975],{},"Efficiency is the model's primary differentiator, particularly for long-form audio. Microsoft reports that the model is up to 5x faster than competitors like Gemini 3.1 and GPT-4o-Transcribe, and 5.7x faster than its predecessor, MAI-Transcribe-1. An hour of audio can now be processed in under 15 seconds, a critical improvement for batch-processing large archives.",[17,5977,5979],{"id":5978},"enterprise-focused-features","Enterprise-Focused Features",[22,5981,5982],{},"Beyond raw speed, the model introduces features designed to solve common enterprise transcription failures:",[29,5984,5985,5991,5997],{},[32,5986,5987,5990],{},[35,5988,5989],{},"Entity Biasing:"," Users can provide up to 200 domain-specific keywords (names, medical terms, internal acronyms). The model uses contextual awareness to apply these biases, rather than forcing matches blindly. This has been shown to reduce WER by 30% on the FLEURS benchmark.",[32,5992,5993,5996],{},[35,5994,5995],{},"Expanded Language Support:"," The model now supports 43 languages, up from 25. This includes 10 new South Asian languages and 8 European languages, all integrated into a single system.",[32,5998,5999,6002],{},[35,6000,6001],{},"Automatic Language Identification:"," The model can now detect the input language without requiring manual configuration, simplifying deployment in global contact centers and multi-language meeting environments.",{"title":91,"searchDepth":92,"depth":92,"links":6004},[6005,6006],{"id":5968,"depth":92,"text":5969},{"id":5978,"depth":92,"text":5979},[97],{"content_references":6009,"triage":6013},[6010],{"type":104,"title":6011,"url":6012,"context":107},"MAI-Transcribe-1.5","https:\u002F\u002Fai.azure.com\u002Fcatalog\u002Fmodels\u002FMAI-Transcribe-1.5",{"relevance":113,"novelty":5776,"quality":114,"actionability":114,"composite":5777,"reasoning":6014},"Category: AI & LLMs. The article provides in-depth information about Microsoft's MAI-Transcribe-1.5, a production-ready speech recognition tool, which is highly relevant for developers looking to integrate AI-powered transcription features into their products. It discusses specific features like entity biasing and automatic language identification that can be directly applied in real-world applications.","\u002Fsummaries\u002F3dd2b79848ef9684-microsoft-s-mai-transcribe-1-5-production-ready-sp-summary","2026-06-08 12:56:50",{"title":5958,"description":91},{"loc":6015},"3dd2b79848ef9684","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F06\u002F08\u002Fmicrosoft-ai-introduces-mai-transcribe-1-5-2-4-wer-on-artificial-analysis-best-in-class-fleurs-accuracy-and-up-to-5x-faster-long-audio-transcription\u002F","summaries\u002F3dd2b79848ef9684-microsoft-s-mai-transcribe-1-5-production-ready-sp-summary",[129,131,6023],"speech-recognition","Microsoft's MAI-Transcribe-1.5 improves speech-to-text with 43-language support, 5x faster long-form inference, and entity-aware keyword biasing for enterprise accuracy.",[131,6023],"EzpbPexvXrF0mZ5jmNu6ZPioWBkBMTVSl5bvMf8iCmc"]