[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-995de8c39198301f-cohere-s-command-a-a-218b-sparse-moe-model-for-age-summary":3,"summaries-facets-categories":104,"summary-related-995de8c39198301f-cohere-s-command-a-a-218b-sparse-moe-model-for-age-summary":4121},{"id":4,"title":5,"ai":6,"body":13,"categories":67,"created_at":69,"date_modified":69,"description":62,"extension":70,"faq":69,"featured":71,"kicker_label":69,"meta":72,"navigation":85,"path":86,"published_at":87,"question":69,"scraped_at":88,"seo":89,"sitemap":90,"source_id":91,"source_name":92,"source_type":93,"source_url":94,"stem":95,"tags":96,"thumbnail_url":69,"tldr":101,"tweet":69,"unknown_tags":102,"__hash__":103},"summaries\u002Fsummaries\u002F995de8c39198301f-cohere-s-command-a-a-218b-sparse-moe-model-for-age-summary.md","Cohere's Command A+: A 218B Sparse MoE Model for Agentic Workflows",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",9439,596,2888,0.00325375,{"type":14,"value":15,"toc":61},"minimark",[16,21,25,28,32,35,58],[17,18,20],"h2",{"id":19},"architecture-and-efficiency","Architecture and Efficiency",[22,23,24],"p",{},"Command A+ is a decoder-only Sparse Mixture-of-Experts (MoE) model with 218B total parameters, utilizing 25B active parameters per token. It employs 128 experts, routing 8 experts per token alongside a shared expert. The model architecture features a 3:1 ratio of sliding-window attention to global attention layers, supporting a 128K input context and 64K generation length.",[22,26,27],{},"To optimize deployment, Cohere utilizes NVFP4 W4A4 quantization (4-bit weights and activations) for the MoE experts, while maintaining full precision for the attention path and KV cache. To mitigate quality loss from this aggressive quantization, the model undergoes Quantization-Aware Distillation (QAD), where the student model is trained to mirror the full-precision teacher's output distribution.",[17,29,31],{"id":30},"hardware-and-performance","Hardware and Performance",[22,33,34],{},"Command A+ is designed for high-performance enterprise agentic workflows, including RAG, multilingual processing, and multimodal document analysis. Hardware requirements scale by quantization level:",[36,37,38,46,52],"ul",{},[39,40,41,45],"li",{},[42,43,44],"strong",{},"BF16:"," 8x H100 or 4x B200",[39,47,48,51],{},[42,49,50],{},"FP8:"," 4x H100 or 2x B200",[39,53,54,57],{},[42,55,56],{},"W4A4:"," 2x H100 or 1x B200",[22,59,60],{},"Performance benchmarks show significant improvements over previous iterations, including a 20% increase in agentic QA accuracy and a 32% improvement in spreadsheet analysis. The model also demonstrates superior speed, with W4A4 quantization providing a 47% speed increase and 13% latency reduction. When combined with architecture-specific speculative decoding, inference speed increases by 1.5–1.6x.",{"title":62,"searchDepth":63,"depth":63,"links":64},"",2,[65,66],{"id":19,"depth":63,"text":20},{"id":30,"depth":63,"text":31},[68],"AI & LLMs",null,"md",false,{"content_references":73,"triage":79},[74],{"type":75,"title":76,"url":77,"context":78},"tool","Command A+","https:\u002F\u002Fhuggingface.co\u002FCohereLabs\u002Fcommand-a-plus-05-2026-w4a4","recommended",{"relevance":80,"novelty":81,"quality":81,"actionability":82,"composite":83,"reasoning":84},5,4,3,4.15,"Category: AI & LLMs. The article provides in-depth technical details about a new AI model specifically designed for agentic workflows, addressing the audience's need for practical applications of AI in product development. It discusses performance improvements and hardware requirements, which are actionable insights for developers looking to implement such models.",true,"\u002Fsummaries\u002F995de8c39198301f-cohere-s-command-a-a-218b-sparse-moe-model-for-age-summary","2026-05-21 21:47:00","2026-05-21 23:00:43",{"title":5,"description":62},{"loc":86},"995de8c39198301f","MarkTechPost","article","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F21\u002Fcohere-releases-command-a-a-218b-sparse-moe-model-for-agentic-workflows-that-runs-on-as-few-as-two-h100-gpus\u002F","summaries\u002F995de8c39198301f-cohere-s-command-a-a-218b-sparse-moe-model-for-age-summary",[97,98,99,100],"llm","agents","ai-tools","multimodal","Command A+ is a 218B parameter sparse MoE model designed for enterprise agentic tasks, featuring multimodal capabilities, a 128K context window, and efficient W4A4 quantization that allows it to run on as few as two H100 GPUs.",[100],"BzHICR17bdIGFf0VdGyuWT2NQcv0vuNxLneIlU9SJ00",[105,108,111,113,116,119,121,123,125,127,129,131,134,136,138,140,142,144,146,148,150,152,154,156,158,160,163,166,168,170,173,175,177,180,182,184,186,188,190,192,194,196,198,200,202,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2465,2467,2469,2471,2473,2475,2477,2479,2481,2483,2485,2487,2489,2491,2493,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2625,2627,2629,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671,3673,3675,3677,3679,3681,3683,3685,3687,3689,3691,3693,3695,3697,3699,3701,3703,3705,3707,3709,3711,3713,3715,3717,3719,3721,3723,3725,3727,3729,3731,3733,3735,3737,3739,3741,3743,3745,3747,3749,3751,3753,3755,3757,3759,3761,3763,3765,3767,3769,3771,3773,3775,3777,3779,3781,3783,3785,3787,3789,3791,3793,3795,3797,3799,3801,3803,3805,3807,3809,3811,3813,3815,3817,3819,3821,3823,3825,3827,3829,3831,3833,3835,3837,3839,3841,3843,3845,3847,3849,3851,3853,3855,3857,3859,3861,3863,3865,3867,3869,3871,3873,3875,3877,3879,3881,3883,3885,3887,3889,3891,3893,3895,3897,3899,3901,3903,3905,3907,3909,3911,3913,3915,3917,3919,3921,3923,3925,3927,3929,3931,3933,3935,3937,3939,3941,3943,3945,3947,3949,3951,3953,3955,3957,3959,3961,3963,3965,3967,3969,3971,3973,3975,3977,3979,3981,3983,3985,3987,3989,3991,3993,3995,3997,3999,4001,4003,4005,4007,4009,4011,4013,4015,4017,4019,4021,4023,4025,4027,4029,4031,4033,4035,4037,4039,4041,4043,4045,4047,4049,4051,4053,4055,4057,4059,4061,4063,4065,4067,4069,4071,4073,4075,4077,4079,4081,4083,4085,4087,4089,4091,4093,4095,4097,4099,4101,4103,4105,4107,4109,4111,4113,4115,4117,4119],{"categories":106},[107],"Developer Productivity",{"categories":109},[110],"Business & SaaS",{"categories":112},[68],{"categories":114},[115],"AI Automation",{"categories":117},[118],"Product Strategy",{"categories":120},[68],{"categories":122},[107],{"categories":124},[110],{"categories":126},[],{"categories":128},[68],{"categories":130},[],{"categories":132},[133],"AI News & Trends",{"categories":135},[115],{"categories":137},[115],{"categories":139},[133],{"categories":141},[115],{"categories":143},[115],{"categories":145},[115],{"categories":147},[68],{"categories":149},[68],{"categories":151},[68],{"categories":153},[133],{"categories":155},[68],{"categories":157},[68],{"categories":159},[],{"categories":161},[162],"Design & Frontend",{"categories":164},[165],"Data Science & Visualization",{"categories":167},[133],{"categories":169},[],{"categories":171},[172],"Software Engineering",{"categories":174},[68],{"categories":176},[115],{"categories":178},[179],"Marketing & Growth",{"categories":181},[162],{"categories":183},[68],{"categories":185},[115],{"categories":187},[],{"categories":189},[],{"categories":191},[162],{"categories":193},[115],{"categories":195},[107],{"categories":197},[172],{"categories":199},[162],{"categories":201},[68],{"categories":203},[204],"DevOps & Cloud",{"categories":206},[115],{"categories":208},[133],{"categories":210},[],{"categories":212},[],{"categories":214},[115],{"categories":216},[172],{"categories":218},[],{"categories":220},[110],{"categories":222},[],{"categories":224},[],{"categories":226},[115],{"categories":228},[68],{"categories":230},[68],{"categories":232},[115],{"categories":234},[68],{"categories":236},[68],{"categories":238},[],{"categories":240},[172],{"categories":242},[],{"categories":244},[],{"categories":246},[172],{"categories":248},[],{"categories":250},[172],{"categories":252},[68],{"categories":254},[68],{"categories":256},[179],{"categories":258},[162],{"categories":260},[162],{"categories":262},[68],{"categories":264},[115],{"categories":266},[172],{"categories":268},[68],{"categories":270},[68],{"categories":272},[115],{"categories":274},[115],{"categories":276},[165],{"categories":278},[133],{"categories":280},[115],{"categories":282},[115],{"categories":284},[179],{"categories":286},[115],{"categories":288},[118],{"categories":290},[172],{"categories":292},[],{"categories":294},[115],{"categories":296},[],{"categories":298},[115],{"categories":300},[172],{"categories":302},[204],{"categories":304},[162],{"categories":306},[68],{"categories":308},[],{"categories":310},[68],{"categories":312},[],{"categories":314},[115],{"categories":316},[],{"categories":318},[68],{"categories":320},[],{"categories":322},[107],{"categories":324},[172],{"categories":326},[110],{"categories":328},[68],{"categories":330},[68],{"categories":332},[133],{"categories":334},[68],{"categories":336},[],{"categories":338},[68],{"categories":340},[],{"categories":342},[172],{"categories":344},[165],{"categories":346},[],{"categories":348},[68],{"categories":350},[162],{"categories":352},[],{"categories":354},[162],{"categories":356},[115],{"categories":358},[],{"categories":360},[68],{"categories":362},[115],{"categories":364},[133],{"categories":366},[110],{"categories":368},[68],{"categories":370},[],{"categories":372},[115],{"categories":374},[68],{"categories":376},[118],{"categories":378},[],{"categories":380},[68],{"categories":382},[118],{"categories":384},[115],{"categories":386},[115],{"categories":388},[],{"categories":390},[165],{"categories":392},[68],{"categories":394},[],{"categories":396},[107],{"categories":398},[110],{"categories":400},[68],{"categories":402},[115],{"categories":404},[172],{"categories":406},[68],{"categories":408},[],{"categories":410},[],{"categories":412},[68],{"categories":414},[68],{"categories":416},[],{"categories":418},[162],{"categories":420},[],{"categories":422},[68],{"categories":424},[],{"categories":426},[115],{"categories":428},[68],{"categories":430},[162],{"categories":432},[],{"categories":434},[68],{"categories":436},[68],{"categories":438},[110],{"categories":440},[115],{"categories":442},[68],{"categories":444},[68],{"categories":446},[162],{"categories":448},[115],{"categories":450},[],{"categories":452},[],{"categories":454},[133],{"categories":456},[],{"categories":458},[68],{"categories":460},[110,179],{"categories":462},[],{"categories":464},[68],{"categories":466},[115],{"categories":468},[],{"categories":470},[],{"categories":472},[68],{"categories":474},[],{"categories":476},[68],{"categories":478},[204],{"categories":480},[],{"categories":482},[133],{"categories":484},[162],{"categories":486},[],{"categories":488},[133],{"categories":490},[115],{"categories":492},[133],{"categories":494},[68],{"categories":496},[179],{"categories":498},[],{"categories":500},[110],{"categories":502},[68],{"categories":504},[115],{"categories":506},[],{"categories":508},[68,204],{"categories":510},[68],{"categories":512},[68],{"categories":514},[68],{"categories":516},[115],{"categories":518},[68,172],{"categories":520},[165],{"categories":522},[68],{"categories":524},[179],{"categories":526},[115],{"categories":528},[68],{"categories":530},[115],{"categories":532},[],{"categories":534},[115],{"categories":536},[68],{"categories":538},[68,110],{"categories":540},[],{"categories":542},[162],{"categories":544},[162],{"categories":546},[],{"categories":548},[],{"categories":550},[133],{"categories":552},[],{"categories":554},[107],{"categories":556},[172],{"categories":558},[68],{"categories":560},[162],{"categories":562},[115],{"categories":564},[172],{"categories":566},[133],{"categories":568},[162],{"categories":570},[],{"categories":572},[68],{"categories":574},[68],{"categories":576},[68],{"categories":578},[68],{"categories":580},[133],{"categories":582},[107],{"categories":584},[68],{"categories":586},[115],{"categories":588},[204],{"categories":590},[162],{"categories":592},[115],{"categories":594},[],{"categories":596},[],{"categories":598},[162],{"categories":600},[133],{"categories":602},[165],{"categories":604},[],{"categories":606},[68],{"categories":608},[68],{"categories":610},[110],{"categories":612},[68],{"categories":614},[68],{"categories":616},[68],{"categories":618},[133],{"categories":620},[],{"categories":622},[115],{"categories":624},[172],{"categories":626},[],{"categories":628},[68],{"categories":630},[68],{"categories":632},[115],{"categories":634},[],{"categories":636},[],{"categories":638},[68],{"categories":640},[],{"categories":642},[110],{"categories":644},[115],{"categories":646},[115],{"categories":648},[],{"categories":650},[107],{"categories":652},[68],{"categories":654},[110],{"categories":656},[133],{"categories":658},[107],{"categories":660},[],{"categories":662},[],{"categories":664},[],{"categories":666},[133],{"categories":668},[133],{"categories":670},[],{"categories":672},[172],{"categories":674},[],{"categories":676},[110],{"categories":678},[],{"categories":680},[],{"categories":682},[107],{"categories":684},[],{"categories":686},[179],{"categories":688},[115],{"categories":690},[110],{"categories":692},[115],{"categories":694},[172],{"categories":696},[],{"categories":698},[118],{"categories":700},[162],{"categories":702},[172],{"categories":704},[68],{"categories":706},[115],{"categories":708},[110],{"categories":710},[68],{"categories":712},[],{"categories":714},[],{"categories":716},[172],{"categories":718},[165],{"categories":720},[118],{"categories":722},[115],{"categories":724},[68],{"categories":726},[],{"categories":728},[204],{"categories":730},[],{"categories":732},[115],{"categories":734},[],{"categories":736},[107],{"categories":738},[],{"categories":740},[68],{"categories":742},[68],{"categories":744},[162],{"categories":746},[179],{"categories":748},[115],{"categories":750},[],{"categories":752},[172],{"categories":754},[107],{"categories":756},[],{"categories":758},[133],{"categories":760},[68,204],{"categories":762},[68],{"categories":764},[133],{"categories":766},[68],{"categories":768},[68],{"categories":770},[110],{"categories":772},[68],{"categories":774},[],{"categories":776},[68],{"categories":778},[110],{"categories":780},[],{"categories":782},[115],{"categories":784},[172],{"categories":786},[162],{"categories":788},[133],{"categories":790},[165],{"categories":792},[107],{"categories":794},[68],{"categories":796},[115],{"categories":798},[172],{"categories":800},[],{"categories":802},[],{"categories":804},[118],{"categories":806},[],{"categories":808},[68],{"categories":810},[],{"categories":812},[162],{"categories":814},[172],{"categories":816},[162],{"categories":818},[68],{"categories":820},[162],{"categories":822},[],{"categories":824},[],{"categories":826},[133],{"categories":828},[115],{"categories":830},[115],{"categories":832},[68],{"categories":834},[68],{"categories":836},[68],{"categories":838},[110],{"categories":840},[68],{"categories":842},[],{"categories":844},[172],{"categories":846},[172],{"categories":848},[110],{"categories":850},[],{"categories":852},[68],{"categories":854},[68],{"categories":856},[110],{"categories":858},[133],{"categories":860},[179],{"categories":862},[68],{"categories":864},[115],{"categories":866},[],{"categories":868},[162],{"categories":870},[],{"categories":872},[68],{"categories":874},[68],{"categories":876},[],{"categories":878},[110],{"categories":880},[115],{"categories":882},[],{"categories":884},[204],{"categories":886},[165],{"categories":888},[172],{"categories":890},[179],{"categories":892},[68],{"categories":894},[172],{"categories":896},[115],{"categories":898},[],{"categories":900},[],{"categories":902},[115],{"categories":904},[107],{"categories":906},[115],{"categories":908},[118],{"categories":910},[110],{"categories":912},[],{"categories":914},[68],{"categories":916},[118],{"categories":918},[68],{"categories":920},[68],{"categories":922},[179],{"categories":924},[68],{"categories":926},[162],{"categories":928},[115],{"categories":930},[],{"categories":932},[],{"categories":934},[204],{"categories":936},[172],{"categories":938},[],{"categories":940},[115],{"categories":942},[68],{"categories":944},[162,68],{"categories":946},[107],{"categories":948},[],{"categories":950},[68],{"categories":952},[107],{"categories":954},[162],{"categories":956},[115],{"categories":958},[172],{"categories":960},[],{"categories":962},[68],{"categories":964},[],{"categories":966},[],{"categories":968},[68],{"categories":970},[107],{"categories":972},[68],{"categories":974},[],{"categories":976},[115],{"categories":978},[118],{"categories":980},[68],{"categories":982},[68],{"categories":984},[68],{"categories":986},[162],{"categories":988},[115],{"categories":990},[204],{"categories":992},[162],{"categories":994},[115],{"categories":996},[68],{"categories":998},[68],{"categories":1000},[68],{"categories":1002},[172],{"categories":1004},[],{"categories":1006},[133],{"categories":1008},[],{"categories":1010},[118],{"categories":1012},[115],{"categories":1014},[162],{"categories":1016},[68],{"categories":1018},[115],{"categories":1020},[172],{"categories":1022},[162],{"categories":1024},[115],{"categories":1026},[133],{"categories":1028},[],{"categories":1030},[68],{"categories":1032},[162],{"categories":1034},[68],{"categories":1036},[107],{"categories":1038},[133],{"categories":1040},[68],{"categories":1042},[179],{"categories":1044},[68],{"categories":1046},[115],{"categories":1048},[115],{"categories":1050},[68],{"categories":1052},[115],{"categories":1054},[115],{"categories":1056},[68],{"categories":1058},[115],{"categories":1060},[162],{"categories":1062},[68],{"categories":1064},[],{"categories":1066},[],{"categories":1068},[172],{"categories":1070},[],{"categories":1072},[107],{"categories":1074},[204],{"categories":1076},[68],{"categories":1078},[],{"categories":1080},[107],{"categories":1082},[110],{"categories":1084},[179],{"categories":1086},[],{"categories":1088},[110],{"categories":1090},[],{"categories":1092},[68],{"categories":1094},[172],{"categories":1096},[],{"categories":1098},[],{"categories":1100},[],{"categories":1102},[],{"categories":1104},[68],{"categories":1106},[115],{"categories":1108},[204],{"categories":1110},[107],{"categories":1112},[172],{"categories":1114},[68],{"categories":1116},[172],{"categories":1118},[118],{"categories":1120},[68],{"categories":1122},[179],{"categories":1124},[110],{"categories":1126},[68],{"categories":1128},[68],{"categories":1130},[68],{"categories":1132},[68,107],{"categories":1134},[172],{"categories":1136},[172],{"categories":1138},[162],{"categories":1140},[115],{"categories":1142},[68],{"categories":1144},[],{"categories":1146},[],{"categories":1148},[],{"categories":1150},[172],{"categories":1152},[165],{"categories":1154},[133],{"categories":1156},[162],{"categories":1158},[172],{"categories":1160},[],{"categories":1162},[68],{"categories":1164},[68],{"categories":1166},[],{"categories":1168},[115],{"categories":1170},[68],{"categories":1172},[68],{"categories":1174},[],{"categories":1176},[115],{"categories":1178},[68],{"categories":1180},[110],{"categories":1182},[],{"categories":1184},[107],{"categories":1186},[68],{"categories":1188},[107],{"categories":1190},[68],{"categories":1192},[172],{"categories":1194},[179],{"categories":1196},[115],{"categories":1198},[68,162],{"categories":1200},[133],{"categories":1202},[68],{"categories":1204},[162],{"categories":1206},[],{"categories":1208},[172],{"categories":1210},[204],{"categories":1212},[162],{"categories":1214},[115],{"categories":1216},[],{"categories":1218},[],{"categories":1220},[],{"categories":1222},[],{"categories":1224},[172],{"categories":1226},[115],{"categories":1228},[115],{"categories":1230},[204],{"categories":1232},[68],{"categories":1234},[68],{"categories":1236},[115],{"categories":1238},[68],{"categories":1240},[68],{"categories":1242},[],{"categories":1244},[162],{"categories":1246},[],{"categories":1248},[],{"categories":1250},[115],{"categories":1252},[],{"categories":1254},[],{"categories":1256},[179],{"categories":1258},[179],{"categories":1260},[115],{"categories":1262},[172],{"categories":1264},[],{"categories":1266},[68],{"categories":1268},[68],{"categories":1270},[172],{"categories":1272},[162],{"categories":1274},[162],{"categories":1276},[115],{"categories":1278},[107],{"categories":1280},[68],{"categories":1282},[162],{"categories":1284},[162],{"categories":1286},[115],{"categories":1288},[115],{"categories":1290},[68],{"categories":1292},[],{"categories":1294},[68],{"categories":1296},[],{"categories":1298},[68],{"categories":1300},[115],{"categories":1302},[133],{"categories":1304},[172],{"categories":1306},[68],{"categories":1308},[107],{"categories":1310},[68],{"categories":1312},[],{"categories":1314},[115],{"categories":1316},[115],{"categories":1318},[],{"categories":1320},[68],{"categories":1322},[107],{"categories":1324},[68],{"categories":1326},[107],{"categories":1328},[107],{"categories":1330},[],{"categories":1332},[],{"categories":1334},[115],{"categories":1336},[133],{"categories":1338},[115],{"categories":1340},[68],{"categories":1342},[68],{"categories":1344},[133],{"categories":1346},[165],{"categories":1348},[118],{"categories":1350},[133],{"categories":1352},[162],{"categories":1354},[],{"categories":1356},[],{"categories":1358},[133],{"categories":1360},[],{"categories":1362},[],{"categories":1364},[],{"categories":1366},[],{"categories":1368},[172],{"categories":1370},[172],{"categories":1372},[165],{"categories":1374},[],{"categories":1376},[68],{"categories":1378},[68],{"categories":1380},[165],{"categories":1382},[172],{"categories":1384},[],{"categories":1386},[],{"categories":1388},[115],{"categories":1390},[115],{"categories":1392},[133],{"categories":1394},[133],{"categories":1396},[115],{"categories":1398},[115],{"categories":1400},[107],{"categories":1402},[68,204],{"categories":1404},[],{"categories":1406},[162],{"categories":1408},[107],{"categories":1410},[115],{"categories":1412},[162],{"categories":1414},[],{"categories":1416},[115],{"categories":1418},[115],{"categories":1420},[68],{"categories":1422},[179],{"categories":1424},[172],{"categories":1426},[162],{"categories":1428},[],{"categories":1430},[115],{"categories":1432},[68],{"categories":1434},[115],{"categories":1436},[115],{"categories":1438},[115],{"categories":1440},[179],{"categories":1442},[68],{"categories":1444},[115],{"categories":1446},[68],{"categories":1448},[],{"categories":1450},[179],{"categories":1452},[133],{"categories":1454},[172],{"categories":1456},[68],{"categories":1458},[115],{"categories":1460},[],{"categories":1462},[],{"categories":1464},[68],{"categories":1466},[115],{"categories":1468},[133],{"categories":1470},[115],{"categories":1472},[115],{"categories":1474},[],{"categories":1476},[68],{"categories":1478},[],{"categories":1480},[],{"categories":1482},[115],{"categories":1484},[],{"categories":1486},[],{"categories":1488},[165],{"categories":1490},[68],{"categories":1492},[165],{"categories":1494},[133],{"categories":1496},[68],{"categories":1498},[68],{"categories":1500},[115],{"categories":1502},[68],{"categories":1504},[],{"categories":1506},[],{"categories":1508},[204],{"categories":1510},[68],{"categories":1512},[],{"categories":1514},[],{"categories":1516},[107],{"categories":1518},[],{"categories":1520},[],{"categories":1522},[68],{"categories":1524},[],{"categories":1526},[],{"categories":1528},[172],{"categories":1530},[133],{"categories":1532},[179],{"categories":1534},[110],{"categories":1536},[68],{"categories":1538},[68],{"categories":1540},[110],{"categories":1542},[],{"categories":1544},[162],{"categories":1546},[115],{"categories":1548},[110],{"categories":1550},[68],{"categories":1552},[68],{"categories":1554},[107],{"categories":1556},[68],{"categories":1558},[],{"categories":1560},[107],{"categories":1562},[68],{"categories":1564},[179],{"categories":1566},[115],{"categories":1568},[133],{"categories":1570},[110],{"categories":1572},[68],{"categories":1574},[68],{"categories":1576},[115],{"categories":1578},[],{"categories":1580},[68],{"categories":1582},[107],{"categories":1584},[68],{"categories":1586},[68],{"categories":1588},[],{"categories":1590},[133],{"categories":1592},[68],{"categories":1594},[68],{"categories":1596},[],{"categories":1598},[110],{"categories":1600},[110],{"categories":1602},[68],{"categories":1604},[68],{"categories":1606},[],{"categories":1608},[],{"categories":1610},[],{"categories":1612},[68],{"categories":1614},[133],{"categories":1616},[],{"categories":1618},[204],{"categories":1620},[68],{"categories":1622},[68],{"categories":1624},[],{"categories":1626},[68],{"categories":1628},[68],{"categories":1630},[68],{"categories":1632},[68,204],{"categories":1634},[68],{"categories":1636},[68],{"categories":1638},[162],{"categories":1640},[115],{"categories":1642},[],{"categories":1644},[115],{"categories":1646},[115],{"categories":1648},[68],{"categories":1650},[68],{"categories":1652},[68],{"categories":1654},[107],{"categories":1656},[107],{"categories":1658},[172],{"categories":1660},[162],{"categories":1662},[115],{"categories":1664},[],{"categories":1666},[68],{"categories":1668},[133],{"categories":1670},[68],{"categories":1672},[68],{"categories":1674},[110],{"categories":1676},[],{"categories":1678},[204],{"categories":1680},[162],{"categories":1682},[162],{"categories":1684},[115],{"categories":1686},[133],{"categories":1688},[115],{"categories":1690},[68],{"categories":1692},[],{"categories":1694},[68],{"categories":1696},[],{"categories":1698},[],{"categories":1700},[68],{"categories":1702},[68],{"categories":1704},[68],{"categories":1706},[115],{"categories":1708},[68],{"categories":1710},[68],{"categories":1712},[],{"categories":1714},[165],{"categories":1716},[115],{"categories":1718},[],{"categories":1720},[],{"categories":1722},[68],{"categories":1724},[68],{"categories":1726},[68],{"categories":1728},[133],{"categories":1730},[],{"categories":1732},[162],{"categories":1734},[204],{"categories":1736},[133],{"categories":1738},[172],{"categories":1740},[172],{"categories":1742},[133],{"categories":1744},[133],{"categories":1746},[204],{"categories":1748},[],{"categories":1750},[133],{"categories":1752},[68],{"categories":1754},[107],{"categories":1756},[68],{"categories":1758},[133],{"categories":1760},[],{"categories":1762},[172],{"categories":1764},[165],{"categories":1766},[68],{"categories":1768},[133],{"categories":1770},[172],{"categories":1772},[115],{"categories":1774},[133],{"categories":1776},[204],{"categories":1778},[115],{"categories":1780},[68],{"categories":1782},[68],{"categories":1784},[68],{"categories":1786},[],{"categories":1788},[110],{"categories":1790},[],{"categories":1792},[],{"categories":1794},[68],{"categories":1796},[68],{"categories":1798},[68],{"categories":1800},[68],{"categories":1802},[],{"categories":1804},[165],{"categories":1806},[107],{"categories":1808},[],{"categories":1810},[68],{"categories":1812},[68],{"categories":1814},[204],{"categories":1816},[204],{"categories":1818},[],{"categories":1820},[115],{"categories":1822},[133],{"categories":1824},[133],{"categories":1826},[68],{"categories":1828},[115],{"categories":1830},[],{"categories":1832},[162],{"categories":1834},[68],{"categories":1836},[68],{"categories":1838},[],{"categories":1840},[68],{"categories":1842},[],{"categories":1844},[172],{"categories":1846},[204],{"categories":1848},[68],{"categories":1850},[172],{"categories":1852},[110],{"categories":1854},[68],{"categories":1856},[],{"categories":1858},[115],{"categories":1860},[107],{"categories":1862},[107],{"categories":1864},[],{"categories":1866},[68],{"categories":1868},[162],{"categories":1870},[115],{"categories":1872},[],{"categories":1874},[68],{"categories":1876},[68],{"categories":1878},[115],{"categories":1880},[],{"categories":1882},[115],{"categories":1884},[172],{"categories":1886},[],{"categories":1888},[68],{"categories":1890},[],{"categories":1892},[68],{"categories":1894},[],{"categories":1896},[68],{"categories":1898},[68],{"categories":1900},[],{"categories":1902},[68],{"categories":1904},[133],{"categories":1906},[68],{"categories":1908},[68],{"categories":1910},[107],{"categories":1912},[68],{"categories":1914},[133],{"categories":1916},[115],{"categories":1918},[],{"categories":1920},[68],{"categories":1922},[162],{"categories":1924},[179],{"categories":1926},[68],{"categories":1928},[],{"categories":1930},[],{"categories":1932},[],{"categories":1934},[107],{"categories":1936},[133],{"categories":1938},[115],{"categories":1940},[68],{"categories":1942},[162],{"categories":1944},[115],{"categories":1946},[],{"categories":1948},[115],{"categories":1950},[],{"categories":1952},[68],{"categories":1954},[115],{"categories":1956},[68],{"categories":1958},[],{"categories":1960},[68],{"categories":1962},[68],{"categories":1964},[133],{"categories":1966},[162],{"categories":1968},[115],{"categories":1970},[162],{"categories":1972},[110],{"categories":1974},[],{"categories":1976},[],{"categories":1978},[68],{"categories":1980},[107],{"categories":1982},[133],{"categories":1984},[],{"categories":1986},[162],{"categories":1988},[],{"categories":1990},[172],{"categories":1992},[172],{"categories":1994},[162],{"categories":1996},[],{"categories":1998},[68],{"categories":2000},[],{"categories":2002},[179],{"categories":2004},[68],{"categories":2006},[204],{"categories":2008},[172],{"categories":2010},[],{"categories":2012},[115],{"categories":2014},[68],{"categories":2016},[107],{"categories":2018},[115],{"categories":2020},[115],{"categories":2022},[68],{"categories":2024},[],{"categories":2026},[107],{"categories":2028},[68],{"categories":2030},[110],{"categories":2032},[172],{"categories":2034},[162],{"categories":2036},[],{"categories":2038},[],{"categories":2040},[],{"categories":2042},[115],{"categories":2044},[172],{"categories":2046},[162],{"categories":2048},[133],{"categories":2050},[68],{"categories":2052},[133],{"categories":2054},[162],{"categories":2056},[],{"categories":2058},[162],{"categories":2060},[133],{"categories":2062},[110],{"categories":2064},[172],{"categories":2066},[68],{"categories":2068},[133],{"categories":2070},[179],{"categories":2072},[],{"categories":2074},[],{"categories":2076},[165],{"categories":2078},[68,172],{"categories":2080},[133],{"categories":2082},[68],{"categories":2084},[115],{"categories":2086},[68],{"categories":2088},[115],{"categories":2090},[68],{"categories":2092},[68],{"categories":2094},[],{"categories":2096},[172],{"categories":2098},[68],{"categories":2100},[165],{"categories":2102},[115],{"categories":2104},[179],{"categories":2106},[204],{"categories":2108},[],{"categories":2110},[107],{"categories":2112},[115],{"categories":2114},[115],{"categories":2116},[172],{"categories":2118},[68],{"categories":2120},[68],{"categories":2122},[],{"categories":2124},[],{"categories":2126},[],{"categories":2128},[204],{"categories":2130},[133],{"categories":2132},[68],{"categories":2134},[68],{"categories":2136},[68],{"categories":2138},[],{"categories":2140},[165],{"categories":2142},[110],{"categories":2144},[],{"categories":2146},[68],{"categories":2148},[115],{"categories":2150},[204],{"categories":2152},[],{"categories":2154},[162],{"categories":2156},[162],{"categories":2158},[],{"categories":2160},[172],{"categories":2162},[68],{"categories":2164},[162],{"categories":2166},[68],{"categories":2168},[],{"categories":2170},[133],{"categories":2172},[68],{"categories":2174},[68],{"categories":2176},[162],{"categories":2178},[115],{"categories":2180},[133],{"categories":2182},[],{"categories":2184},[115],{"categories":2186},[162],{"categories":2188},[68],{"categories":2190},[],{"categories":2192},[68],{"categories":2194},[68],{"categories":2196},[204],{"categories":2198},[133],{"categories":2200},[165],{"categories":2202},[165],{"categories":2204},[],{"categories":2206},[],{"categories":2208},[],{"categories":2210},[115],{"categories":2212},[172],{"categories":2214},[172],{"categories":2216},[68],{"categories":2218},[68],{"categories":2220},[],{"categories":2222},[],{"categories":2224},[68],{"categories":2226},[],{"categories":2228},[115],{"categories":2230},[68],{"categories":2232},[],{"categories":2234},[68],{"categories":2236},[110],{"categories":2238},[68],{"categories":2240},[179],{"categories":2242},[115],{"categories":2244},[68],{"categories":2246},[68],{"categories":2248},[68],{"categories":2250},[172],{"categories":2252},[],{"categories":2254},[133],{"categories":2256},[115],{"categories":2258},[],{"categories":2260},[133],{"categories":2262},[115],{"categories":2264},[68],{"categories":2266},[115],{"categories":2268},[],{"categories":2270},[110],{"categories":2272},[115],{"categories":2274},[],{"categories":2276},[172],{"categories":2278},[68],{"categories":2280},[107],{"categories":2282},[133],{"categories":2284},[204],{"categories":2286},[115],{"categories":2288},[115],{"categories":2290},[107],{"categories":2292},[],{"categories":2294},[68],{"categories":2296},[],{"categories":2298},[],{"categories":2300},[162],{"categories":2302},[68,110],{"categories":2304},[68],{"categories":2306},[],{"categories":2308},[107],{"categories":2310},[165],{"categories":2312},[68],{"categories":2314},[172],{"categories":2316},[68],{"categories":2318},[115],{"categories":2320},[68],{"categories":2322},[68],{"categories":2324},[68],{"categories":2326},[133],{"categories":2328},[115],{"categories":2330},[68],{"categories":2332},[],{"categories":2334},[],{"categories":2336},[115],{"categories":2338},[68],{"categories":2340},[204],{"categories":2342},[],{"categories":2344},[68],{"categories":2346},[115],{"categories":2348},[],{"categories":2350},[115],{"categories":2352},[68],{"categories":2354},[179],{"categories":2356},[165],{"categories":2358},[115],{"categories":2360},[68],{"categories":2362},[204],{"categories":2364},[],{"categories":2366},[68],{"categories":2368},[179],{"categories":2370},[162],{"categories":2372},[68],{"categories":2374},[68],{"categories":2376},[],{"categories":2378},[179],{"categories":2380},[133],{"categories":2382},[68],{"categories":2384},[68],{"categories":2386},[107],{"categories":2388},[],{"categories":2390},[],{"categories":2392},[162],{"categories":2394},[68],{"categories":2396},[165],{"categories":2398},[179],{"categories":2400},[115],{"categories":2402},[179],{"categories":2404},[133],{"categories":2406},[],{"categories":2408},[],{"categories":2410},[68],{"categories":2412},[115],{"categories":2414},[68],{"categories":2416},[68],{"categories":2418},[],{"categories":2420},[68,172],{"categories":2422},[133],{"categories":2424},[115],{"categories":2426},[172],{"categories":2428},[68],{"categories":2430},[107],{"categories":2432},[],{"categories":2434},[],{"categories":2436},[107],{"categories":2438},[172],{"categories":2440},[179],{"categories":2442},[68],{"categories":2444},[172],{"categories":2446},[],{"categories":2448},[162,68],{"categories":2450},[204],{"categories":2452},[107],{"categories":2454},[],{"categories":2456},[110],{"categories":2458},[110],{"categories":2460},[68],{"categories":2462},[68],{"categories":2464},[172],{"categories":2466},[115],{"categories":2468},[133],{"categories":2470},[179],{"categories":2472},[162],{"categories":2474},[68],{"categories":2476},[68],{"categories":2478},[68],{"categories":2480},[107],{"categories":2482},[68],{"categories":2484},[115],{"categories":2486},[133],{"categories":2488},[],{"categories":2490},[],{"categories":2492},[165],{"categories":2494},[172],{"categories":2496},[68],{"categories":2498},[162],{"categories":2500},[68],{"categories":2502},[165],{"categories":2504},[68],{"categories":2506},[68],{"categories":2508},[68],{"categories":2510},[115],{"categories":2512},[115],{"categories":2514},[68,110],{"categories":2516},[],{"categories":2518},[162],{"categories":2520},[],{"categories":2522},[68],{"categories":2524},[133],{"categories":2526},[107],{"categories":2528},[107],{"categories":2530},[115],{"categories":2532},[68],{"categories":2534},[68],{"categories":2536},[110],{"categories":2538},[172],{"categories":2540},[179],{"categories":2542},[68],{"categories":2544},[],{"categories":2546},[133],{"categories":2548},[68],{"categories":2550},[68],{"categories":2552},[68],{"categories":2554},[68],{"categories":2556},[68],{"categories":2558},[172],{"categories":2560},[133],{"categories":2562},[172],{"categories":2564},[172],{"categories":2566},[68],{"categories":2568},[68],{"categories":2570},[115],{"categories":2572},[133],{"categories":2574},[68],{"categories":2576},[162],{"categories":2578},[68],{"categories":2580},[68],{"categories":2582},[204],{"categories":2584},[68],{"categories":2586},[118],{"categories":2588},[115],{"categories":2590},[68],{"categories":2592},[133],{"categories":2594},[115],{"categories":2596},[179],{"categories":2598},[68],{"categories":2600},[],{"categories":2602},[68],{"categories":2604},[68],{"categories":2606},[],{"categories":2608},[],{"categories":2610},[],{"categories":2612},[110],{"categories":2614},[68],{"categories":2616},[115],{"categories":2618},[133],{"categories":2620},[133],{"categories":2622},[133],{"categories":2624},[133],{"categories":2626},[],{"categories":2628},[107],{"categories":2630},[115],{"categories":2632},[133],{"categories":2634},[68],{"categories":2636},[107],{"categories":2638},[115],{"categories":2640},[68],{"categories":2642},[68,115],{"categories":2644},[115],{"categories":2646},[204],{"categories":2648},[133],{"categories":2650},[133],{"categories":2652},[115],{"categories":2654},[68],{"categories":2656},[],{"categories":2658},[133],{"categories":2660},[179],{"categories":2662},[107],{"categories":2664},[68],{"categories":2666},[68],{"categories":2668},[],{"categories":2670},[172],{"categories":2672},[],{"categories":2674},[107],{"categories":2676},[115],{"categories":2678},[133],{"categories":2680},[68],{"categories":2682},[133],{"categories":2684},[107],{"categories":2686},[133],{"categories":2688},[133],{"categories":2690},[],{"categories":2692},[110],{"categories":2694},[115],{"categories":2696},[133],{"categories":2698},[133],{"categories":2700},[133],{"categories":2702},[133],{"categories":2704},[133],{"categories":2706},[133],{"categories":2708},[133],{"categories":2710},[133],{"categories":2712},[133],{"categories":2714},[133],{"categories":2716},[165],{"categories":2718},[107],{"categories":2720},[68],{"categories":2722},[68],{"categories":2724},[115],{"categories":2726},[],{"categories":2728},[68,107],{"categories":2730},[],{"categories":2732},[115],{"categories":2734},[133],{"categories":2736},[115],{"categories":2738},[68],{"categories":2740},[68],{"categories":2742},[68],{"categories":2744},[68],{"categories":2746},[68],{"categories":2748},[115],{"categories":2750},[110],{"categories":2752},[],{"categories":2754},[162],{"categories":2756},[133],{"categories":2758},[68],{"categories":2760},[],{"categories":2762},[],{"categories":2764},[115],{"categories":2766},[162],{"categories":2768},[68],{"categories":2770},[],{"categories":2772},[68],{"categories":2774},[],{"categories":2776},[179],{"categories":2778},[68],{"categories":2780},[],{"categories":2782},[],{"categories":2784},[133],{"categories":2786},[107],{"categories":2788},[68],{"categories":2790},[110],{"categories":2792},[68],{"categories":2794},[110],{"categories":2796},[162],{"categories":2798},[],{"categories":2800},[133],{"categories":2802},[],{"categories":2804},[162],{"categories":2806},[68],{"categories":2808},[179],{"categories":2810},[],{"categories":2812},[179],{"categories":2814},[],{"categories":2816},[],{"categories":2818},[115],{"categories":2820},[],{"categories":2822},[110],{"categories":2824},[107],{"categories":2826},[162],{"categories":2828},[172],{"categories":2830},[],{"categories":2832},[],{"categories":2834},[68],{"categories":2836},[107],{"categories":2838},[179],{"categories":2840},[],{"categories":2842},[115],{"categories":2844},[115],{"categories":2846},[133],{"categories":2848},[172],{"categories":2850},[68],{"categories":2852},[115],{"categories":2854},[68],{"categories":2856},[115],{"categories":2858},[68],{"categories":2860},[118],{"categories":2862},[179],{"categories":2864},[133],{"categories":2866},[],{"categories":2868},[179],{"categories":2870},[],{"categories":2872},[172],{"categories":2874},[115],{"categories":2876},[],{"categories":2878},[68],{"categories":2880},[115],{"categories":2882},[110],{"categories":2884},[107],{"categories":2886},[68],{"categories":2888},[162],{"categories":2890},[172],{"categories":2892},[172],{"categories":2894},[68],{"categories":2896},[165],{"categories":2898},[68],{"categories":2900},[115],{"categories":2902},[110],{"categories":2904},[162],{"categories":2906},[115],{"categories":2908},[68],{"categories":2910},[68],{"categories":2912},[115],{"categories":2914},[133],{"categories":2916},[],{"categories":2918},[107],{"categories":2920},[68],{"categories":2922},[68],{"categories":2924},[115],{"categories":2926},[68],{"categories":2928},[68],{"categories":2930},[],{"categories":2932},[162],{"categories":2934},[110],{"categories":2936},[133],{"categories":2938},[68],{"categories":2940},[68],{"categories":2942},[162],{"categories":2944},[68],{"categories":2946},[179],{"categories":2948},[165],{"categories":2950},[68],{"categories":2952},[133],{"categories":2954},[68],{"categories":2956},[115],{"categories":2958},[204],{"categories":2960},[68],{"categories":2962},[115],{"categories":2964},[165],{"categories":2966},[],{"categories":2968},[115],{"categories":2970},[172],{"categories":2972},[162],{"categories":2974},[68],{"categories":2976},[107],{"categories":2978},[172],{"categories":2980},[110],{"categories":2982},[172],{"categories":2984},[68],{"categories":2986},[],{"categories":2988},[115],{"categories":2990},[115],{"categories":2992},[68],{"categories":2994},[165],{"categories":2996},[],{"categories":2998},[133],{"categories":3000},[],{"categories":3002},[133],{"categories":3004},[68],{"categories":3006},[68],{"categories":3008},[115],{"categories":3010},[115],{"categories":3012},[115],{"categories":3014},[],{"categories":3016},[133],{"categories":3018},[],{"categories":3020},[68],{"categories":3022},[68],{"categories":3024},[],{"categories":3026},[162],{"categories":3028},[115],{"categories":3030},[179],{"categories":3032},[107],{"categories":3034},[],{"categories":3036},[68],{"categories":3038},[],{"categories":3040},[107],{"categories":3042},[133],{"categories":3044},[172],{"categories":3046},[68],{"categories":3048},[68],{"categories":3050},[68],{"categories":3052},[172],{"categories":3054},[133],{"categories":3056},[162],{"categories":3058},[68],{"categories":3060},[68],{"categories":3062},[68],{"categories":3064},[133],{"categories":3066},[68],{"categories":3068},[133],{"categories":3070},[133],{"categories":3072},[115],{"categories":3074},[115],{"categories":3076},[172],{"categories":3078},[133],{"categories":3080},[115],{"categories":3082},[68],{"categories":3084},[172],{"categories":3086},[162],{"categories":3088},[],{"categories":3090},[115],{"categories":3092},[],{"categories":3094},[],{"categories":3096},[],{"categories":3098},[110],{"categories":3100},[115],{"categories":3102},[68],{"categories":3104},[115],{"categories":3106},[107],{"categories":3108},[115],{"categories":3110},[179],{"categories":3112},[],{"categories":3114},[115],{"categories":3116},[],{"categories":3118},[107],{"categories":3120},[115],{"categories":3122},[],{"categories":3124},[115],{"categories":3126},[68],{"categories":3128},[133],{"categories":3130},[68],{"categories":3132},[115],{"categories":3134},[133],{"categories":3136},[115],{"categories":3138},[172],{"categories":3140},[162],{"categories":3142},[107],{"categories":3144},[],{"categories":3146},[115],{"categories":3148},[162],{"categories":3150},[204],{"categories":3152},[133],{"categories":3154},[68],{"categories":3156},[162],{"categories":3158},[107],{"categories":3160},[],{"categories":3162},[115],{"categories":3164},[68],{"categories":3166},[115],{"categories":3168},[68],{"categories":3170},[162],{"categories":3172},[],{"categories":3174},[115],{"categories":3176},[118],{"categories":3178},[133],{"categories":3180},[115],{"categories":3182},[110],{"categories":3184},[],{"categories":3186},[68],{"categories":3188},[118],{"categories":3190},[68],{"categories":3192},[115],{"categories":3194},[133],{"categories":3196},[107],{"categories":3198},[204],{"categories":3200},[68],{"categories":3202},[68],{"categories":3204},[68],{"categories":3206},[133],{"categories":3208},[110],{"categories":3210},[68],{"categories":3212},[162],{"categories":3214},[133],{"categories":3216},[204],{"categories":3218},[68],{"categories":3220},[],{"categories":3222},[],{"categories":3224},[68],{"categories":3226},[204],{"categories":3228},[165],{"categories":3230},[115],{"categories":3232},[115],{"categories":3234},[133],{"categories":3236},[68],{"categories":3238},[107],{"categories":3240},[162],{"categories":3242},[115],{"categories":3244},[115],{"categories":3246},[68],{"categories":3248},[179],{"categories":3250},[68],{"categories":3252},[115],{"categories":3254},[],{"categories":3256},[68],{"categories":3258},[68],{"categories":3260},[133],{"categories":3262},[107],{"categories":3264},[],{"categories":3266},[68],{"categories":3268},[68],{"categories":3270},[172],{"categories":3272},[162],{"categories":3274},[68,115],{"categories":3276},[179,110],{"categories":3278},[68],{"categories":3280},[],{"categories":3282},[115],{"categories":3284},[],{"categories":3286},[172],{"categories":3288},[68],{"categories":3290},[],{"categories":3292},[68],{"categories":3294},[133],{"categories":3296},[],{"categories":3298},[115],{"categories":3300},[68],{"categories":3302},[],{"categories":3304},[162],{"categories":3306},[115],{"categories":3308},[68],{"categories":3310},[107],{"categories":3312},[115],{"categories":3314},[68],{"categories":3316},[],{"categories":3318},[204],{"categories":3320},[179],{"categories":3322},[110],{"categories":3324},[110],{"categories":3326},[107],{"categories":3328},[107],{"categories":3330},[68],{"categories":3332},[115],{"categories":3334},[68],{"categories":3336},[68],{"categories":3338},[107],{"categories":3340},[68],{"categories":3342},[179],{"categories":3344},[133],{"categories":3346},[68],{"categories":3348},[115],{"categories":3350},[68],{"categories":3352},[],{"categories":3354},[172],{"categories":3356},[],{"categories":3358},[172],{"categories":3360},[115],{"categories":3362},[107],{"categories":3364},[],{"categories":3366},[204],{"categories":3368},[68],{"categories":3370},[],{"categories":3372},[133],{"categories":3374},[115],{"categories":3376},[172],{"categories":3378},[68],{"categories":3380},[115],{"categories":3382},[172],{"categories":3384},[115],{"categories":3386},[133],{"categories":3388},[107],{"categories":3390},[133],{"categories":3392},[172],{"categories":3394},[68],{"categories":3396},[162],{"categories":3398},[68],{"categories":3400},[68],{"categories":3402},[68],{"categories":3404},[68],{"categories":3406},[68],{"categories":3408},[115],{"categories":3410},[68],{"categories":3412},[115],{"categories":3414},[68],{"categories":3416},[107],{"categories":3418},[68],{"categories":3420},[115],{"categories":3422},[162],{"categories":3424},[107],{"categories":3426},[115],{"categories":3428},[162],{"categories":3430},[],{"categories":3432},[68],{"categories":3434},[68],{"categories":3436},[68],{"categories":3438},[172],{"categories":3440},[],{"categories":3442},[115],{"categories":3444},[179],{"categories":3446},[68],{"categories":3448},[133],{"categories":3450},[179],{"categories":3452},[115],{"categories":3454},[110],{"categories":3456},[110],{"categories":3458},[68],{"categories":3460},[68],{"categories":3462},[107],{"categories":3464},[],{"categories":3466},[115],{"categories":3468},[68],{"categories":3470},[],{"categories":3472},[107],{"categories":3474},[68],{"categories":3476},[115],{"categories":3478},[115],{"categories":3480},[],{"categories":3482},[172],{"categories":3484},[172],{"categories":3486},[179],{"categories":3488},[162],{"categories":3490},[],{"categories":3492},[68],{"categories":3494},[115],{"categories":3496},[107],{"categories":3498},[68],{"categories":3500},[172],{"categories":3502},[107],{"categories":3504},[133],{"categories":3506},[133],{"categories":3508},[],{"categories":3510},[133],{"categories":3512},[115],{"categories":3514},[162],{"categories":3516},[165],{"categories":3518},[68],{"categories":3520},[],{"categories":3522},[133],{"categories":3524},[172],{"categories":3526},[110],{"categories":3528},[68],{"categories":3530},[107],{"categories":3532},[204],{"categories":3534},[107],{"categories":3536},[],{"categories":3538},[],{"categories":3540},[133],{"categories":3542},[],{"categories":3544},[115],{"categories":3546},[115],{"categories":3548},[115],{"categories":3550},[],{"categories":3552},[68],{"categories":3554},[],{"categories":3556},[133],{"categories":3558},[107],{"categories":3560},[162],{"categories":3562},[68],{"categories":3564},[133],{"categories":3566},[133],{"categories":3568},[],{"categories":3570},[133],{"categories":3572},[107],{"categories":3574},[115],{"categories":3576},[68],{"categories":3578},[],{"categories":3580},[115],{"categories":3582},[115],{"categories":3584},[107],{"categories":3586},[],{"categories":3588},[],{"categories":3590},[],{"categories":3592},[162],{"categories":3594},[115],{"categories":3596},[68],{"categories":3598},[],{"categories":3600},[],{"categories":3602},[],{"categories":3604},[162],{"categories":3606},[],{"categories":3608},[68],{"categories":3610},[107],{"categories":3612},[],{"categories":3614},[],{"categories":3616},[162],{"categories":3618},[68],{"categories":3620},[133],{"categories":3622},[],{"categories":3624},[179],{"categories":3626},[133],{"categories":3628},[179],{"categories":3630},[165],{"categories":3632},[68],{"categories":3634},[68],{"categories":3636},[],{"categories":3638},[],{"categories":3640},[115],{"categories":3642},[],{"categories":3644},[],{"categories":3646},[115],{"categories":3648},[68],{"categories":3650},[],{"categories":3652},[115],{"categories":3654},[133],{"categories":3656},[68],{"categories":3658},[179],{"categories":3660},[68],{"categories":3662},[165],{"categories":3664},[115],{"categories":3666},[115],{"categories":3668},[],{"categories":3670},[],{"categories":3672},[],{"categories":3674},[133],{"categories":3676},[],{"categories":3678},[],{"categories":3680},[162],{"categories":3682},[107],{"categories":3684},[],{"categories":3686},[110],{"categories":3688},[179],{"categories":3690},[68],{"categories":3692},[172],{"categories":3694},[107],{"categories":3696},[165],{"categories":3698},[110],{"categories":3700},[172],{"categories":3702},[172],{"categories":3704},[],{"categories":3706},[],{"categories":3708},[115],{"categories":3710},[107],{"categories":3712},[162],{"categories":3714},[107],{"categories":3716},[115],{"categories":3718},[204],{"categories":3720},[68],{"categories":3722},[107],{"categories":3724},[115],{"categories":3726},[],{"categories":3728},[68],{"categories":3730},[133],{"categories":3732},[172],{"categories":3734},[],{"categories":3736},[162],{"categories":3738},[133],{"categories":3740},[107],{"categories":3742},[115],{"categories":3744},[68],{"categories":3746},[110],{"categories":3748},[115,204],{"categories":3750},[115],{"categories":3752},[172],{"categories":3754},[68],{"categories":3756},[68],{"categories":3758},[165],{"categories":3760},[179],{"categories":3762},[115],{"categories":3764},[],{"categories":3766},[115],{"categories":3768},[68],{"categories":3770},[110],{"categories":3772},[],{"categories":3774},[],{"categories":3776},[68],{"categories":3778},[165],{"categories":3780},[68],{"categories":3782},[],{"categories":3784},[133],{"categories":3786},[],{"categories":3788},[133],{"categories":3790},[172],{"categories":3792},[107],{"categories":3794},[172],{"categories":3796},[68],{"categories":3798},[115],{"categories":3800},[68],{"categories":3802},[68],{"categories":3804},[179],{"categories":3806},[172],{"categories":3808},[],{"categories":3810},[133],{"categories":3812},[68],{"categories":3814},[],{"categories":3816},[68],{"categories":3818},[68],{"categories":3820},[115],{"categories":3822},[68],{"categories":3824},[115],{"categories":3826},[68],{"categories":3828},[68],{"categories":3830},[68],{"categories":3832},[68],{"categories":3834},[110],{"categories":3836},[],{"categories":3838},[118],{"categories":3840},[133],{"categories":3842},[115],{"categories":3844},[68],{"categories":3846},[],{"categories":3848},[172],{"categories":3850},[68],{"categories":3852},[68],{"categories":3854},[68],{"categories":3856},[115],{"categories":3858},[133],{"categories":3860},[68],{"categories":3862},[68],{"categories":3864},[68],{"categories":3866},[110],{"categories":3868},[68],{"categories":3870},[115],{"categories":3872},[162],{"categories":3874},[],{"categories":3876},[165],{"categories":3878},[68],{"categories":3880},[],{"categories":3882},[133],{"categories":3884},[179],{"categories":3886},[],{"categories":3888},[],{"categories":3890},[133],{"categories":3892},[133],{"categories":3894},[68],{"categories":3896},[179],{"categories":3898},[107],{"categories":3900},[115],{"categories":3902},[68],{"categories":3904},[115],{"categories":3906},[68],{"categories":3908},[110],{"categories":3910},[],{"categories":3912},[165],{"categories":3914},[],{"categories":3916},[133],{"categories":3918},[165],{"categories":3920},[172],{"categories":3922},[115],{"categories":3924},[162],{"categories":3926},[165],{"categories":3928},[165],{"categories":3930},[],{"categories":3932},[133],{"categories":3934},[68],{"categories":3936},[68],{"categories":3938},[172],{"categories":3940},[],{"categories":3942},[133],{"categories":3944},[133],{"categories":3946},[133],{"categories":3948},[],{"categories":3950},[115],{"categories":3952},[68],{"categories":3954},[],{"categories":3956},[107],{"categories":3958},[110],{"categories":3960},[],{"categories":3962},[68],{"categories":3964},[68],{"categories":3966},[],{"categories":3968},[172],{"categories":3970},[],{"categories":3972},[],{"categories":3974},[],{"categories":3976},[],{"categories":3978},[68],{"categories":3980},[133],{"categories":3982},[],{"categories":3984},[],{"categories":3986},[68],{"categories":3988},[68],{"categories":3990},[68],{"categories":3992},[165],{"categories":3994},[68],{"categories":3996},[165],{"categories":3998},[],{"categories":4000},[165],{"categories":4002},[165],{"categories":4004},[204],{"categories":4006},[115],{"categories":4008},[172],{"categories":4010},[],{"categories":4012},[],{"categories":4014},[165],{"categories":4016},[172],{"categories":4018},[172],{"categories":4020},[172],{"categories":4022},[],{"categories":4024},[107],{"categories":4026},[172],{"categories":4028},[172],{"categories":4030},[107],{"categories":4032},[172],{"categories":4034},[110],{"categories":4036},[172],{"categories":4038},[172],{"categories":4040},[172],{"categories":4042},[165],{"categories":4044},[133],{"categories":4046},[133],{"categories":4048},[68],{"categories":4050},[172],{"categories":4052},[165],{"categories":4054},[204],{"categories":4056},[165],{"categories":4058},[165],{"categories":4060},[165],{"categories":4062},[],{"categories":4064},[110],{"categories":4066},[],{"categories":4068},[204],{"categories":4070},[172],{"categories":4072},[172],{"categories":4074},[172],{"categories":4076},[115],{"categories":4078},[133,110],{"categories":4080},[165],{"categories":4082},[],{"categories":4084},[],{"categories":4086},[165],{"categories":4088},[],{"categories":4090},[165],{"categories":4092},[133],{"categories":4094},[115],{"categories":4096},[],{"categories":4098},[172],{"categories":4100},[68],{"categories":4102},[162],{"categories":4104},[],{"categories":4106},[68],{"categories":4108},[],{"categories":4110},[133],{"categories":4112},[107],{"categories":4114},[165],{"categories":4116},[],{"categories":4118},[172],{"categories":4120},[133],[4122,4293,4372,4451],{"id":4123,"title":4124,"ai":4125,"body":4130,"categories":4265,"created_at":69,"date_modified":69,"description":62,"extension":70,"faq":69,"featured":71,"kicker_label":69,"meta":4266,"navigation":85,"path":4277,"published_at":4278,"question":69,"scraped_at":4279,"seo":4280,"sitemap":4281,"source_id":4282,"source_name":4283,"source_type":4284,"source_url":4285,"stem":4286,"tags":4287,"thumbnail_url":4288,"tldr":4289,"tweet":4290,"unknown_tags":4291,"__hash__":4292},"summaries\u002Fsummaries\u002Fc227a71ab9afbe29-next-gen-agentic-architecture-gemini-3-5-adk-summary.md","Next-Gen Agentic Architecture: Gemini 3.5 & ADK",{"provider":7,"model":8,"input_tokens":4126,"output_tokens":4127,"processing_time_ms":4128,"cost_usd":4129},8876,1107,5250,0.0038795,{"type":14,"value":4131,"toc":4257},[4132,4136,4139,4143,4146,4166,4170,4173,4177,4180,4206,4210,4242,4246],[17,4133,4135],{"id":4134},"the-gemini-enterprise-agent-platform","The Gemini Enterprise Agent Platform",[22,4137,4138],{},"Google’s agentic strategy is built on four pillars: Build, Scale, Govern, and Optimize. While developers often focus exclusively on the 'build' phase, the platform is designed to handle the entire lifecycle. The Agent Development Kit (ADK) serves as the primary framework for creating complex agents, while the Agent Registry and runtime environments provide the necessary infrastructure for scaling and security. A critical, often overlooked component is automated evaluation, which ensures that agents remain performant and aligned with organizational standards as models and requirements evolve.",[17,4140,4142],{"id":4141},"gemini-35-flash-performance-at-scale","Gemini 3.5 Flash: Performance at Scale",[22,4144,4145],{},"Gemini 3.5 Flash represents a shift in how developers should think about model selection. By delivering near state-of-the-art results at a fraction of the cost of previous 'Pro' models, it enables high-performance agentic workflows that were previously cost-prohibitive. Key improvements include:",[36,4147,4148,4154,4160],{},[39,4149,4150,4153],{},[42,4151,4152],{},"Coding & Tool Calling:"," Significant gains in benchmarks like 'Terminal Bench' and 'MCP Atlas,' making the model highly effective for autonomous coding tasks.",[39,4155,4156,4159],{},[42,4157,4158],{},"Latency:"," The model is optimized for speed, providing high tokens-per-second, which is essential for real-time agentic interactions.",[39,4161,4162,4165],{},[42,4163,4164],{},"Agentic Workflows:"," The model is specifically trained to handle complex tool-calling sequences, allowing it to act as the 'brain' for agents that need to interact with external APIs and CLI tools.",[17,4167,4169],{"id":4168},"gemini-omni-multimodal-generation","Gemini Omni: Multimodal Generation",[22,4171,4172],{},"Gemini Omni introduces 'any-to-any' multimodal capabilities, allowing users to generate video from various inputs, including text, images, and other videos. The model demonstrates a deep understanding of physics, branding, and temporal consistency. A standout feature is its ability to maintain character and object consistency across video generations, which is a major unlock for marketing and creative workflows. Users can perform iterative video editing through natural language, allowing for precise control over scene composition and style without needing traditional video editing software.",[17,4174,4176],{"id":4175},"from-idea-to-implementation-the-adk-workflow","From Idea to Implementation: The ADK Workflow",[22,4178,4179],{},"Lavi Nigam demonstrated how the Agent Development Kit (ADK) and the Antigravity CLI accelerate the development lifecycle. By providing a natural language prompt, the system can:",[36,4181,4182,4188,4194,4200],{},[39,4183,4184,4187],{},[42,4185,4186],{},"Scaffold Projects:"," Automatically generate the necessary directory structure, test files, and configuration.",[39,4189,4190,4193],{},[42,4191,4192],{},"Plan Implementation:"," Create a step-by-step task list based on the user's requirements.",[39,4195,4196,4199],{},[42,4197,4198],{},"Integrate Tools:"," Automatically identify and call necessary MCP (Model Context Protocol) tools to fulfill the agent's objectives.",[39,4201,4202,4205],{},[42,4203,4204],{},"Collaborate:"," Allow for human-in-the-loop feedback via comments, ensuring the agent's logic aligns with developer intent before deployment.",[17,4207,4209],{"id":4208},"key-takeaways","Key Takeaways",[36,4211,4212,4218,4224,4230,4236],{},[39,4213,4214,4217],{},[42,4215,4216],{},"Prioritize the Lifecycle:"," Don't just focus on building; integrate evaluation and governance early to ensure your agents remain reliable as they scale.",[39,4219,4220,4223],{},[42,4221,4222],{},"Leverage Flash Models:"," Use Gemini 3.5 Flash for agentic tasks where cost-efficiency and high-speed tool calling are more important than the raw parameter count of a 'Pro' model.",[39,4225,4226,4229],{},[42,4227,4228],{},"Multimodal Consistency:"," Use Gemini Omni’s ability to maintain context across frames to create consistent branded content or complex visual narratives.",[39,4231,4232,4235],{},[42,4233,4234],{},"Automate Scaffolding:"," Use the Antigravity CLI and ADK to move from an idea to a functional, deployed agent by letting the model handle the boilerplate and tool-calling logic.",[39,4237,4238,4241],{},[42,4239,4240],{},"Iterative Refinement:"," Treat agent development as a conversational process; use natural language to refine agent behavior and video outputs iteratively.",[17,4243,4245],{"id":4244},"notable-quotes","Notable Quotes",[36,4247,4248,4251,4254],{},[39,4249,4250],{},"\"The moment you sort of just put a query and it's really fast and that's the kind of thing that we are aiming with the flash models which is you get the best performance in the cost.\" — Lavi Nigam",[39,4252,4253],{},"\"The real unlock of this model is you're able to do iterative video editing through natural language.\" — Katie Nguyen",[39,4255,4256],{},"\"Unless they're evaluated and the organization sort of agrees and aligns with that, there's no point of doing that.\" — Lavi Nigam",{"title":62,"searchDepth":63,"depth":63,"links":4258},[4259,4260,4261,4262,4263,4264],{"id":4134,"depth":63,"text":4135},{"id":4141,"depth":63,"text":4142},{"id":4168,"depth":63,"text":4169},{"id":4175,"depth":63,"text":4176},{"id":4208,"depth":63,"text":4209},{"id":4244,"depth":63,"text":4245},[68],{"content_references":4267,"triage":4274},[4268,4270,4272],{"type":75,"title":4269,"context":78},"Gemini Enterprise Agent Platform",{"type":75,"title":4271,"context":78},"Agent Development Kit (ADK)",{"type":75,"title":4273,"context":78},"Antigravity CLI",{"relevance":81,"novelty":82,"quality":81,"actionability":82,"composite":4275,"reasoning":4276},3.6,"Category: AI & LLMs. The article discusses Google's Gemini 3.5 and the Agent Development Kit (ADK), which are relevant to AI engineering and product development. It addresses the lifecycle of building AI agents, a specific pain point for developers looking to integrate AI into their products.","\u002Fsummaries\u002Fc227a71ab9afbe29-next-gen-agentic-architecture-gemini-3-5-adk-summary","2026-05-22 16:00:37","2026-05-22 19:00:35",{"title":4124,"description":62},{"loc":4277},"c227a71ab9afbe29","Google Cloud Tech","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XZJk_S82Gpk","summaries\u002Fc227a71ab9afbe29-next-gen-agentic-architecture-gemini-3-5-adk-summary",[97,98,99,100],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FXZJk_S82Gpk\u002Fhqdefault.jpg","Google's Gemini 3.5 Flash and Gemini Omni introduce higher intelligence, lower costs, and advanced multimodal capabilities, while the Agent Development Kit (ADK) streamlines the lifecycle of building, scaling, and governing AI agents.","This is a promotional interview from Google I\u002FO 2026 covering the release of Gemini 3.5 Flash and Gemini Omni. The speakers provide a high-level overview of the [Gemini Enterprise Agent Platform](https:\u002F\u002Fcloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Flearn\u002Foverview), focusing on how the [Agent Development Kit](https:\u002F\u002Fcloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Flearn\u002Foverview) and [Antigravity CLI](https:\u002F\u002Fcloud.google.com\u002Fvertex-ai\u002Fgenerative-ai\u002Fdocs\u002Flearn\u002Foverview) are intended to streamline the lifecycle of building, scaling, and evaluating AI agents.",[100],"D-9n7dFTbYt1WfbXOmNRk7NcxvuseGUd49KrQgBjTcU",{"id":4294,"title":4295,"ai":4296,"body":4301,"categories":4349,"created_at":69,"date_modified":69,"description":62,"extension":70,"faq":69,"featured":71,"kicker_label":69,"meta":4350,"navigation":85,"path":4359,"published_at":4360,"question":69,"scraped_at":4361,"seo":4362,"sitemap":4363,"source_id":4364,"source_name":4365,"source_type":93,"source_url":4366,"stem":4367,"tags":4368,"thumbnail_url":69,"tldr":4369,"tweet":69,"unknown_tags":4370,"__hash__":4371},"summaries\u002Fsummaries\u002Fa31dee5fd39d09d2-google-transforms-gemini-app-into-an-agentic-ai-hu-summary.md","Google Transforms Gemini App into an Agentic AI Hub",{"provider":7,"model":8,"input_tokens":4297,"output_tokens":4298,"processing_time_ms":4299,"cost_usd":4300},6188,537,2872,0.0023525,{"type":14,"value":4302,"toc":4344},[4303,4307,4310,4314,4317,4320,4324,4327,4341],[17,4304,4306],{"id":4305},"from-chatbot-to-proactive-agent","From Chatbot to Proactive Agent",[22,4308,4309],{},"Google is shifting the Gemini app's architecture to function as an all-purpose AI hub rather than a simple conversational interface. The update introduces \"Gemini Spark,\" a 24\u002F7 cloud-based agent designed to manage digital workflows in the background, even when the user's device is locked. This moves the platform toward an agentic model where the AI performs active work across Google services like Gmail and Calendar rather than just responding to prompts.",[17,4311,4313],{"id":4312},"personalized-context-and-multimodal-output","Personalized Context and Multimodal Output",[22,4315,4316],{},"To improve utility, Google introduced \"Daily Brief,\" a feature that aggregates information from a user's inbox, calendar, and task lists. Unlike a standard summary, it prioritizes tasks and suggests actionable next steps.",[22,4318,4319],{},"Additionally, the app is integrating \"Gemini Omni,\" a new video-generation model. This tool allows users to generate consistent, high-quality video content from text, audio, or image prompts, positioning Google to compete directly in the multimodal content creation space. These outputs are being integrated into Google Flow and YouTube Shorts.",[17,4321,4323],{"id":4322},"interface-redesign-for-information-density","Interface Redesign for Information Density",[22,4325,4326],{},"Google has overhauled the app's UI with a design language called \"Neural Expressive.\" The new interface moves away from the traditional \"wall of text\" format common in most LLM chat interfaces. Instead, it uses:",[36,4328,4329,4335],{},[39,4330,4331,4334],{},[42,4332,4333],{},"Hierarchical display:"," Key information is bolded at the top, with supplementary details, images, and timelines revealed as the user scrolls.",[39,4336,4337,4340],{},[42,4338,4339],{},"Fluid interactions:"," The app incorporates haptic feedback and fluid animations to improve the tactile feel of the interface.",[22,4342,4343],{},"These changes reflect a broader strategy to retain the app's 900 million monthly users by increasing the density and accessibility of information provided in each response.",{"title":62,"searchDepth":63,"depth":63,"links":4345},[4346,4347,4348],{"id":4305,"depth":63,"text":4306},{"id":4312,"depth":63,"text":4313},{"id":4322,"depth":63,"text":4323},[68],{"content_references":4351,"triage":4357},[4352,4355],{"type":75,"title":4353,"context":4354},"Gemini Omni","mentioned",{"type":75,"title":4356,"context":4354},"Gemini Spark",{"relevance":81,"novelty":82,"quality":81,"actionability":82,"composite":4275,"reasoning":4358},"Category: AI & LLMs. The article discusses the transformation of the Gemini app into a proactive AI hub, addressing the audience's interest in AI tools and agents. It provides insights into new features like 'Daily Brief' and 'Gemini Omni,' which could inspire product builders, though it lacks detailed actionable steps for implementation.","\u002Fsummaries\u002Fa31dee5fd39d09d2-google-transforms-gemini-app-into-an-agentic-ai-hu-summary","2026-05-19 17:45:00","2026-05-19 19:00:41",{"title":4295,"description":62},{"loc":4359},"a31dee5fd39d09d2","TechCrunch — AI","https:\u002F\u002Ftechcrunch.com\u002F2026\u002F05\u002F19\u002Fgoogle-updates-its-gemini-app-to-take-on-chatgpt-and-claude\u002F","summaries\u002Fa31dee5fd39d09d2-google-transforms-gemini-app-into-an-agentic-ai-hu-summary",[99,98,100],"Google is pivoting the Gemini app from a static chatbot to a proactive, agentic hub featuring personalized daily briefings, background task automation, and native video generation.",[100],"iIrb4ufdNyBNzn55bbG2oR_oLaSv1P6O4JCdeF_F4fM",{"id":4373,"title":4374,"ai":4375,"body":4381,"categories":4427,"created_at":69,"date_modified":69,"description":62,"extension":70,"faq":69,"featured":71,"kicker_label":69,"meta":4428,"navigation":85,"path":4438,"published_at":4439,"question":69,"scraped_at":4440,"seo":4441,"sitemap":4442,"source_id":4443,"source_name":4444,"source_type":93,"source_url":4445,"stem":4446,"tags":4447,"thumbnail_url":69,"tldr":4448,"tweet":69,"unknown_tags":4449,"__hash__":4450},"summaries\u002Fsummaries\u002Fa012b47e318fcbff-claude-dreaming-6x-agent-boost-via-memory-cron-job-summary.md","Claude Dreaming: 6x Agent Boost via Memory Cron Jobs",{"provider":7,"model":4376,"input_tokens":4377,"output_tokens":4378,"processing_time_ms":4379,"cost_usd":4380},"x-ai\u002Fgrok-4.1-fast",3920,1637,27309,0.00158,{"type":14,"value":4382,"toc":4422},[4383,4387,4390,4393,4397,4400,4403,4407],[17,4384,4386],{"id":4385},"memory-optimization-mechanics-deliver-cross-session-intelligence","Memory Optimization Mechanics Deliver Cross-Session Intelligence",[22,4388,4389],{},"Anthropic's Dreaming feature, launched as a research preview at Code w\u002F Claude 2026 on May 6, operates as a scheduled cron job that processes your Claude agent's memory file offline. Between user sessions, it scans all prior interactions, eliminates duplicate entries to reduce bloat, resolves factual contradictions by prioritizing consistent patterns, and extracts multi-session insights—like user preferences or recurring tasks—that no single conversation could detect. This isn't a context window expansion or model upgrade; it's targeted file editing that keeps memory lean and coherent, enabling agents to maintain state across disjointed interactions without hallucinating from noisy data.",[22,4391,4392],{},"To implement a similar system yourself, use the public open-source replica: run a nightly script that ingests session logs, applies deduplication via similarity clustering (e.g., embedding-based cosine thresholds >0.9), merges conflicting facts with confidence scoring, and appends synthesized summaries. This approach scales memory indefinitely without token limits, as the job outputs a compact, structured file Claude reloads on next use.",[17,4394,4396],{"id":4395},"harveys-6x-completion-rate-proves-production-value","Harvey's 6x Completion Rate Proves Production Value",[22,4398,4399],{},"Legal AI startup Harvey reported roughly 6x higher agent task completion rates in internal tests after enabling Dreaming. Agents previously stalled on long-running legal research or multi-step drafting due to memory overload—duplicates caused loops, contradictions led to errors. Post-Dreaming, optimized memory let agents chain 10x more steps reliably, surfacing patterns like \"user prefers concise briefs\" from weeks of chats. This validates Dreaming for production agents: expect 4-8x gains in workflows with >50 sessions, but only if your memory format supports structured edits (JSONL with metadata timestamps works best).",[22,4401,4402],{},"Replicate Harvey's setup by logging sessions to a vector store, then cron the optimization—test on 100-sample legal datasets shows completion jumps from 15% to 85% on chained queries.",[17,4404,4406],{"id":4405},"three-under-discussed-risks-in-auto-dreaming-defaults","Three Under-discussed Risks in Auto-Dreaming Defaults",[22,4408,4409,4410,4413,4414,4417,4418,4421],{},"Despite gains, Dreaming introduces pitfalls: (1) ",[42,4411,4412],{},"Over-pruning erodes nuance","—aggressive duplicate removal can strip context-specific details, like evolving client instructions; tune similarity thresholds to 0.85 max. (2) ",[42,4415,4416],{},"Contradiction resolution biases toward recency",", potentially overwriting valid early facts; add user-voted weights or manual review queues. (3) ",[42,4419,4420],{},"GDPR compliance gaps in defaults","—Auto Dream processes all session data without explicit consent logging, risking fines under EU data minimization rules; implement opt-in flags and anonymization before cron runs. Avoid blindly enabling; audit memory diffs post-job to catch drifts early.",{"title":62,"searchDepth":63,"depth":63,"links":4423},[4424,4425,4426],{"id":4385,"depth":63,"text":4386},{"id":4395,"depth":63,"text":4396},{"id":4405,"depth":63,"text":4406},[68],{"content_references":4429,"triage":4435},[4430,4432],{"type":75,"title":4431,"context":4354},"Harvey",{"type":4433,"title":4434,"context":4354},"event","Code w\u002F Claude 2026",{"relevance":80,"novelty":81,"quality":81,"actionability":80,"composite":4436,"reasoning":4437},4.55,"Category: AI & LLMs. The article provides a detailed explanation of Anthropic's Dreaming feature, which directly addresses the audience's need for practical AI tooling and optimization strategies. It includes actionable steps for implementing a similar memory optimization system, making it highly relevant and immediately applicable.","\u002Fsummaries\u002Fa012b47e318fcbff-claude-dreaming-6x-agent-boost-via-memory-cron-job-summary","2026-05-15 14:29:17","2026-05-15 15:00:28",{"title":4374,"description":62},{"loc":4438},"a012b47e318fcbff","Level Up Coding","https:\u002F\u002Flevelup.gitconnected.com\u002Fclaude-dreaming-anthropic-memory-explained-a038f17f7d13?source=rss----5517fd7b58a6---4","summaries\u002Fa012b47e318fcbff-claude-dreaming-6x-agent-boost-via-memory-cron-job-summary",[97,98,99],"Anthropic's Dreaming runs a cron job between sessions to prune duplicates, resolve contradictions, and surface patterns in Claude's memory file, delivering 6x higher agent completion rates per Harvey's tests.",[],"A-5IJXRSzmkq11VgJ5B1H0z16Gdg3AXxtIjUwAyGXQc",{"id":4452,"title":4453,"ai":4454,"body":4459,"categories":4736,"created_at":69,"date_modified":69,"description":62,"extension":70,"faq":69,"featured":71,"kicker_label":69,"meta":4737,"navigation":85,"path":4748,"published_at":4749,"question":69,"scraped_at":4750,"seo":4751,"sitemap":4752,"source_id":4753,"source_name":4754,"source_type":4284,"source_url":4755,"stem":4756,"tags":4757,"thumbnail_url":4758,"tldr":4759,"tweet":4760,"unknown_tags":4761,"__hash__":4762},"summaries\u002Fsummaries\u002F172b79615a38a463-build-agent-evals-traces-to-experiments-summary.md","Build Agent Evals: Traces to Experiments",{"provider":7,"model":4376,"input_tokens":4455,"output_tokens":4456,"processing_time_ms":4457,"cost_usd":4458},8665,2929,36004,0.00317485,{"type":14,"value":4460,"toc":4729},[4461,4465,4468,4471,4477,4481,4489,4492,4498,4503,4507,4510,4516,4558,4565,4579,4585,4620,4623,4629,4634,4638,4645,4665,4668,4674,4680,4686,4692,4697,4699,4725],[17,4462,4464],{"id":4463},"replace-vibes-testing-with-systematic-evals-to-catch-regressions","Replace Vibes Testing with Systematic Evals to Catch Regressions",[22,4466,4467],{},"Agents fail silently on untested inputs like adversarial queries, edge cases, or simplistic user phrasing because traditional unit tests break on non-deterministic outputs—same prompt yields varying but potentially correct text. Human review doesn't scale, misses regressions in CI, and can't validate model switches or prompt tweaks without retesting everything. Evals solve this by treating traces (nested JSON logs of LLM\u002Ftool calls with inputs, outputs, metadata like tokens\u002Ftiming) as test data. Run evals in CI to ensure prompt fixes don't hallucinate features or alter tone adversely. Real teams like Decrypt, Bolt, and Anthropic iterated from vibes to evals for production agents.",[22,4469,4470],{},"Agents amplify issues via cascading failures: wrong tool choice, bad parameters, misparsed tool output, or multi-agent routing errors compound into disasters like confusing Tesla (car) with Nikola Tesla in reports. Evals must handle non-prescriptive paths—agents evolve with model upgrades, finding clever shortcuts that break rigid tests. Distinguish capability evals (hard tasks to benchmark improvement) from regression evals (ensure baselines hold). Eval outputs include score, label, and LLM explanations revealing patterns like systematic prompt flaws vs. one-offs.",[22,4472,4473,4476],{},[42,4474,4475],{},"Quote:"," \"The usual fix unit test doesn't work here... because the same prompt will produce different text on every single run, but those outputs might all be correct.\"",[17,4478,4480],{"id":4479},"trace-first-then-diagnose-failures-before-writing-evals","Trace First, Then Diagnose Failures Before Writing Evals",[22,4482,4483,4484,4488],{},"Start every pipeline with instrumentation: use Phoenix (Arize's open-source observability) to capture spans (LLM\u002Ftool steps) without local install via Phoenix Cloud (free account, API key). Install ",[4485,4486,4487],"code",{},"pip install arize-phoenix[crewai] claude-agent-sdk"," (assumes Claude API key; adaptable to OpenAI\u002FGemini). Run pre-built financial analysis agent (Claude-powered, fetches Yahoo Finance data, generates reports) on 13 test queries, auto-tracing to Phoenix UI.",[22,4490,4491],{},"Inspect traces in Phoenix before evals: filter by spans, view inputs\u002Foutputs, identify failure modes manually. Categorize root causes—e.g., model unaware of current year fails forward-looking data; tool param errors; hallucinated facts. Use LLM to auto-categorize eval explanations at scale (LLMs all the way down). This data-driven step skips most tutorials' mistake: writing evals blind, measuring wrong metrics. Example: correctness eval scores 0\u002F13 (can't verify future data), but faithfulness (sticks to sources) scores 13\u002F13—proves eval choice > tuning.",[22,4493,4494,4497],{},[42,4495,4496],{},"Key principle:"," Read traces to define rubrics—what's \"good\"? For financial agent: accurate tool use, source fidelity, complete reports without extras. Avoid prescriptive evals (e.g., exact tool sequence) that fail smarter agents. Humans build golden datasets for novel failures; code\u002FLLM evals handle volume.",[22,4499,4500,4502],{},[42,4501,4475],{}," \"We're going to do something that most of the tutorials skip. We're going to actually look at the data. We're going to read our traces, categorize what went wrong, and figure out what to measure before we write a single eval.\"",[17,4504,4506],{"id":4505},"layer-code-built-in-and-custom-llm-evals-for-comprehensive-coverage","Layer Code, Built-in, and Custom LLM Evals for Comprehensive Coverage",[22,4508,4509],{},"Build evals post-tracing, complementary: code for deterministic checks (fast\u002Fcheap), LLM-as-judge for semantics (flexible but costly\u002Fnondeterministic).",[22,4511,4512,4515],{},[42,4513,4514],{},"Code evals (Python functions):"," Validate JSON output, token limits (\u003C500), required fields, forbidden phrases, keyword presence. Example:",[4517,4518,4522],"pre",{"className":4519,"code":4520,"language":4521,"meta":62,"style":62},"language-python shiki shiki-themes github-light github-dark","def json_eval(output: str) -> dict:\n    try:\n        json.loads(output)\n        return {\"score\": 1.0, \"label\": \"valid\", \"reason\": \"Parses as JSON\"}\n    except:\n        return {\"score\": 0.0, \"label\": \"invalid\", \"reason\": \"JSON parse error\"}\n","python",[4485,4523,4524,4532,4537,4542,4547,4552],{"__ignoreMap":62},[4525,4526,4529],"span",{"class":4527,"line":4528},"line",1,[4525,4530,4531],{},"def json_eval(output: str) -> dict:\n",[4525,4533,4534],{"class":4527,"line":63},[4525,4535,4536],{},"    try:\n",[4525,4538,4539],{"class":4527,"line":82},[4525,4540,4541],{},"        json.loads(output)\n",[4525,4543,4544],{"class":4527,"line":81},[4525,4545,4546],{},"        return {\"score\": 1.0, \"label\": \"valid\", \"reason\": \"Parses as JSON\"}\n",[4525,4548,4549],{"class":4527,"line":80},[4525,4550,4551],{},"    except:\n",[4525,4553,4555],{"class":4527,"line":4554},6,[4525,4556,4557],{},"        return {\"score\": 0.0, \"label\": \"invalid\", \"reason\": \"JSON parse error\"}\n",[22,4559,4560,4561,4564],{},"Run via Phoenix: ",[4485,4562,4563],{},"evaluate(pnx.Eval(name=\"json\") .with_code(json_eval), dataset)","—milliseconds, reproducible.",[22,4566,4567,4570,4571,4574,4575,4578],{},[42,4568,4569],{},"Built-in LLM evals (Arize Phoenix):"," ",[4485,4572,4573],{},"pnx.qa_correctness",", ",[4485,4576,4577],{},"pnx.answer_relevancy","—prompt powerful LLM (e.g., Claude-3.5-Sonnet) vs. agent output\u002Freference. Scores 0-1 with explanations.",[22,4580,4581,4584],{},[42,4582,4583],{},"Custom LLM rubric evals:"," Define rules in prompt, add few-shot examples from traces. For faithfulness:",[4517,4586,4588],{"className":4519,"code":4587,"language":4521,"meta":62,"style":62},"faithfulness_eval = pnx.LLMEval(\n    name=\"faithfulness\",\n    prompt_template=\"\"\"Judge if {output} is faithful to {sources}...\"\"\",\n    examples=[{\"input\": ..., \"output\": ..., \"reference\": ..., \"score\": 1.0, \"explanation\": ...}],\n    model=\"claude-3-5-sonnet-20240620\"\n)\n",[4485,4589,4590,4595,4600,4605,4610,4615],{"__ignoreMap":62},[4525,4591,4592],{"class":4527,"line":4528},[4525,4593,4594],{},"faithfulness_eval = pnx.LLMEval(\n",[4525,4596,4597],{"class":4527,"line":63},[4525,4598,4599],{},"    name=\"faithfulness\",\n",[4525,4601,4602],{"class":4527,"line":82},[4525,4603,4604],{},"    prompt_template=\"\"\"Judge if {output} is faithful to {sources}...\"\"\",\n",[4525,4606,4607],{"class":4527,"line":81},[4525,4608,4609],{},"    examples=[{\"input\": ..., \"output\": ..., \"reference\": ..., \"score\": 1.0, \"explanation\": ...}],\n",[4525,4611,4612],{"class":4527,"line":80},[4525,4613,4614],{},"    model=\"claude-3-5-sonnet-20240620\"\n",[4525,4616,4617],{"class":4527,"line":4554},[4525,4618,4619],{},")\n",[22,4621,4622],{},"Meta-evaluate judges: run golden dataset through your eval, score agreement (e.g., 90%+ reliable). Use stronger model for judging.",[22,4624,4625,4628],{},[42,4626,4627],{},"When to use:"," Code for format\u002Flength; LLM for accuracy, faithfulness, tone. Agents need end-to-end: tool selection, params, output parsing.",[22,4630,4631,4633],{},[42,4632,4475],{}," \"Choosing the right eval matters more than tuning it. A correctness eval scored 0 out of 13 on the same agent that a faithfulness eval scored 13 out of 13.\"",[17,4635,4637],{"id":4636},"datasets-and-experiments-prove-iterations-work","Datasets and Experiments: Prove Iterations Work",[22,4639,4640,4641,4644],{},"Create datasets from traces: ",[4485,4642,4643],{},"dataset = pnx.Dataset.from_pandas(traces_df)"," or golden sets (human-labeled). Run experiments: baseline vs. prompt variants.",[4517,4646,4648],{"className":4519,"code":4647,"language":4521,"meta":62,"style":62},"exp = pnx.Experiment(name=\"prompt-v2\", trace_dataset=dataset)\nexp.log_evals([json_eval, faithfulness_eval], variant=\"v2_prompt\")\nexp.compare()  # Tables\u002Fcharts: scores, spans, explanations\n",[4485,4649,4650,4655,4660],{"__ignoreMap":62},[4525,4651,4652],{"class":4527,"line":4528},[4525,4653,4654],{},"exp = pnx.Experiment(name=\"prompt-v2\", trace_dataset=dataset)\n",[4525,4656,4657],{"class":4527,"line":63},[4525,4658,4659],{},"exp.log_evals([json_eval, faithfulness_eval], variant=\"v2_prompt\")\n",[4525,4661,4662],{"class":4527,"line":82},[4525,4663,4664],{},"exp.compare()  # Tables\u002Fcharts: scores, spans, explanations\n",[22,4666,4667],{},"Visualize regressions, filter low-scorers, iterate prompts. Scale to thousands: patterns emerge (e.g., budget query misses costs).",[22,4669,4670,4673],{},[42,4671,4672],{},"Advanced frameworks:"," Impact hierarchy (prioritize high-failure evals); data flywheel (evals → insights → prompts → better traces); pairwise (A\u002FB outputs); reliability scoring (judge variance).",[22,4675,4676,4679],{},[42,4677,4678],{},"Common pitfalls:"," Overly brittle code evals; unaligned LLM judges (meta-eval fixes); ignoring cascades\u002Fmulti-agents; prescriptive tests.",[22,4681,4682,4685],{},[42,4683,4684],{},"Quality criteria:"," 100% regression suite; explanations actionable; CI-runnable; humans validate outliers.",[22,4687,4688,4691],{},[42,4689,4690],{},"Practice:"," Fork speaker's notebook (GitHub\u002Fseldo), trace your agent, build 3 evals, experiment on 50+ traces. Prerequisites: Python, LLM API, basic agents (2+ years dev exp).",[22,4693,4694,4696],{},[42,4695,4475],{}," \"Without evals you can't change your system prompt to fix a tone issue because the tone might get better but suddenly the bot might be hallucinating product features.\"",[17,4698,4209],{"id":4208},[36,4700,4701,4704,4707,4710,4713,4716,4719,4722],{},[39,4702,4703],{},"Instrument with Phoenix traces before any evals—inspect spans to pinpoint failures like wrong tools or date ignorance.",[39,4705,4706],{},"Layer evals: code for deterministic (JSON, length), LLM for semantic (faithfulness > correctness for sourced tasks).",[39,4708,4709],{},"Meta-evaluate LLM judges on golden data to ensure reliability >90%.",[39,4711,4712],{},"Use capability evals for new skills, convert to regressions; run experiments to validate changes, not eyeballing.",[39,4714,4715],{},"Categorize failures from explanations to fix systematic prompt issues at scale.",[39,4717,4718],{},"Avoid prescriptive tests—agents optimize paths; focus non-brittle metrics.",[39,4720,4721],{},"Humans for golden sets\u002Foutliers only; evals scale CI for model\u002Fprompt upgrades.",[39,4723,4724],{},"Start simple: 13-query financial agent → full pipeline in notebook.",[4726,4727,4728],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":62,"searchDepth":63,"depth":63,"links":4730},[4731,4732,4733,4734,4735],{"id":4463,"depth":63,"text":4464},{"id":4479,"depth":63,"text":4480},{"id":4505,"depth":63,"text":4506},{"id":4636,"depth":63,"text":4637},{"id":4208,"depth":63,"text":4209},[68],{"content_references":4738,"triage":4745},[4739,4742],{"type":75,"title":4740,"url":4741,"context":78},"Arize Phoenix","https:\u002F\u002Fphoenix.arize.com\u002F",{"type":75,"title":4743,"author":4744,"context":78},"Claude","Anthropic",{"relevance":80,"novelty":81,"quality":81,"actionability":81,"composite":4746,"reasoning":4747},4.35,"Category: AI & LLMs. The article provides a detailed framework for implementing systematic evaluations of AI agents, addressing a key pain point for developers who struggle with traditional testing methods. It offers actionable steps, such as using Phoenix for instrumentation, which can be directly applied to improve testing processes.","\u002Fsummaries\u002F172b79615a38a463-build-agent-evals-traces-to-experiments-summary","2026-05-14 18:00:06","2026-05-14 23:00:14",{"title":4453,"description":62},{"loc":4748},"172b79615a38a463","AI Engineer","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Xfl50508LZM","summaries\u002F172b79615a38a463-build-agent-evals-traces-to-experiments-summary",[98,97,99],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FXfl50508LZM\u002Fhqdefault.jpg","Replace vibes-based testing with a full eval pipeline: trace agent runs with Phoenix, categorize failures from data, build code\u002FLLM evals, run experiments to validate prompt changes on a financial agent.","Hands-on workshop by [Laurie Voss](https:\u002F\u002Fx.com\u002Fseldo) where you code along building an eval pipeline for a financial analysis agent: Phoenix tracing, failure categorization, code evals, LLM-as-judge (Arize-built and custom), and experiments to test prompt changes.",[],"nQ3a3NVfZARLm-VZlTYTQNTtmNfMM2erqwuLoai8SRk"]