[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-a023db718b391a31-building-native-multimodal-agents-with-gemini-summary":3,"summaries-facets-categories":143,"summary-related-a023db718b391a31-building-native-multimodal-agents-with-gemini-summary":4022},{"id":4,"title":5,"ai":6,"body":13,"categories":97,"created_at":99,"date_modified":99,"description":91,"extension":100,"faq":99,"featured":101,"kicker_label":99,"meta":102,"navigation":122,"path":123,"published_at":124,"question":99,"scraped_at":125,"seo":126,"sitemap":127,"source_id":128,"source_name":129,"source_type":130,"source_url":131,"stem":132,"tags":133,"thumbnail_url":138,"tldr":139,"tweet":140,"unknown_tags":141,"__hash__":142},"summaries\u002Fsummaries\u002Fa023db718b391a31-building-native-multimodal-agents-with-gemini-summary.md","Building Native Multimodal Agents with Gemini",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",6418,795,5044,0.002797,{"type":14,"value":15,"toc":90},"minimark",[16,21,25,28,32,35,63,67,70],[17,18,20],"h2",{"id":19},"the-any-to-any-architecture","The Any-to-Any Architecture",[22,23,24],"p",{},"Building modern multimodal agents requires moving away from cascaded pipelines toward a unified, reasoning-based architecture. The \"any-to-any\" approach leverages Gemini as a central reasoning engine that understands diverse inputs (PDFs, video, audio, code) and orchestrates specialized generation models (for images, speech, and video) via function calling.",[22,26,27],{},"Instead of hardcoding workflows, developers should implement an agentic loop where the model evaluates the current state of a project and decides which modalities are required to enhance the output. For example, a research agent can ingest a multi-hour lecture, summarize it, and then autonomously decide to generate an infographic for complex concepts or a podcast-style audio summary for key takeaways.",[17,29,31],{"id":30},"multimodal-understanding-and-context","Multimodal Understanding and Context",[22,33,34],{},"Gemini’s native multimodal capabilities allow for massive context windows—up to 1 million tokens. This enables the ingestion of over nine hours of audio or one hour of video in a single prompt.",[36,37,38,46,52],"ul",{},[39,40,41,45],"li",{},[42,43,44],"strong",{},"Efficiency:"," Use context caching to reduce costs by up to 90% when performing repeated queries on large files.",[39,47,48,51],{},[42,49,50],{},"Granularity:"," You can target specific timestamps (e.g., analyze only minutes 5-15) to manage token usage and focus the model's attention.",[39,53,54,57,58,62],{},[42,55,56],{},"Integration:"," The File API simplifies uploading diverse assets, and the Gemini SDK allows for seamless combination of these sources into a single ",[59,60,61],"code",{},"contents"," list for analysis.",[17,64,66],{"id":65},"agentic-generation-and-live-interaction","Agentic Generation and Live Interaction",[22,68,69],{},"Native generation models (such as the Nano Banana series) are built on the same foundation as the main Gemini models, meaning they possess \"world understanding\" rather than just pixel-matching capabilities. This allows for nuanced outputs, such as generating accurate diagrams based on hand-drawn maps or correcting math homework with visual annotations.",[36,71,72,78,84],{},[39,73,74,77],{},[42,75,76],{},"Function Calling:"," To build an agent, define function declarations for image and speech generation tools. Provide the model with a clear system prompt that defines its role (e.g., \"Research Agent\") and the criteria for when to trigger specific modalities.",[39,79,80,83],{},[42,81,82],{},"Live API:"," The latest iteration of the Live API uses a single architecture where audio goes in and audio comes out. This eliminates the latency and quality loss associated with traditional speech-to-text-to-speech pipelines, enabling natural, real-time conversational agents.",[39,85,86,89],{},[42,87,88],{},"Unified Embeddings:"," New multimodal embedding models allow for mapping different modalities into a single vector space, enabling advanced use cases like cross-modal search (e.g., searching video content using text or images).",{"title":91,"searchDepth":92,"depth":92,"links":93},"",2,[94,95,96],{"id":19,"depth":92,"text":20},{"id":30,"depth":92,"text":31},{"id":65,"depth":92,"text":66},[98],"AI & LLMs",null,"md",false,{"content_references":103,"triage":117},[104,109,113],{"type":105,"title":106,"url":107,"context":108},"tool","Gemini API","https:\u002F\u002Faistudio.google.com\u002F","recommended",{"type":105,"title":110,"url":111,"context":112},"NotebookLM","https:\u002F\u002Fnotebooklm.google.com\u002F","mentioned",{"type":114,"title":115,"author":116,"context":112},"paper","Attention Is All You Need","Vaswani et al.",{"relevance":118,"novelty":119,"quality":119,"actionability":119,"composite":120,"reasoning":121},5,4,4.35,"Category: AI & LLMs. The article provides a deep dive into building multimodal agents using Gemini, addressing the audience's need for practical applications in AI integration. It discusses specific techniques like the 'any-to-any' architecture and context caching, which are actionable for developers looking to implement these concepts.",true,"\u002Fsummaries\u002Fa023db718b391a31-building-native-multimodal-agents-with-gemini-summary","2026-05-20 17:00:07","2026-05-20 19:00:18",{"title":5,"description":91},{"loc":123},"a023db718b391a31","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GIRpQEfYf3U","summaries\u002Fa023db718b391a31-building-native-multimodal-agents-with-gemini-summary",[134,135,136,137],"llm","agents","python","multimodal","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FGIRpQEfYf3U\u002Fhqdefault.jpg","Learn to build agentic, multimodal applications using Gemini's native understanding and generation capabilities, moving beyond hardcoded pipelines to reasoning-based agent loops.","This is a technical walkthrough of how to use the [Gemini API](https:\u002F\u002Fai.google.dev\u002F) to build an agentic pipeline that orchestrates multimodal tasks. The speaker demonstrates how to use Gemini as a reasoning engine to trigger specialized tools for image and speech generation, rather than relying on hardcoded workflows.",[137],"RbemtAVLuWMzx3-GZMaGhRKVihVDP0I7-2UDFTy6k8A",[144,147,150,152,155,158,160,162,164,166,168,170,173,175,177,179,181,183,185,187,189,191,193,195,197,200,203,205,207,210,212,214,217,219,221,223,225,227,229,231,233,235,237,239,242,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,494,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,554,556,558,560,562,564,566,568,570,572,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,606,608,610,612,614,616,618,620,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798,800,802,804,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,858,860,862,864,866,868,870,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,932,934,936,938,940,942,944,946,948,950,952,954,956,958,960,962,964,966,968,970,972,974,976,978,980,982,984,986,988,990,992,994,996,998,1000,1002,1004,1006,1008,1010,1012,1014,1016,1018,1020,1022,1024,1026,1028,1030,1032,1034,1036,1038,1040,1042,1044,1046,1048,1050,1052,1054,1056,1058,1060,1062,1064,1066,1068,1070,1072,1074,1076,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1402,1404,1406,1408,1410,1412,1414,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1444,1446,1448,1450,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1478,1480,1482,1484,1486,1488,1490,1492,1494,1496,1498,1500,1502,1504,1506,1508,1510,1512,1514,1516,1518,1520,1522,1524,1526,1528,1530,1532,1534,1536,1538,1540,1542,1544,1546,1548,1550,1552,1554,1556,1558,1560,1562,1564,1566,1568,1570,1572,1574,1576,1578,1580,1582,1584,1586,1588,1590,1592,1594,1596,1598,1600,1602,1604,1606,1608,1610,1612,1614,1616,1618,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1778,1780,1782,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1812,1814,1816,1818,1820,1822,1824,1826,1828,1830,1832,1834,1836,1838,1840,1842,1844,1846,1848,1850,1852,1854,1856,1858,1860,1862,1864,1866,1868,1870,1872,1874,1876,1878,1880,1882,1884,1886,1888,1890,1892,1894,1896,1898,1900,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1962,1964,1966,1968,1970,1972,1974,1976,1978,1980,1982,1984,1986,1988,1990,1992,1994,1996,1998,2000,2002,2004,2006,2008,2010,2012,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032,2034,2036,2038,2040,2042,2044,2046,2048,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2122,2124,2126,2128,2130,2132,2134,2136,2138,2140,2142,2144,2146,2148,2150,2152,2154,2156,2158,2160,2162,2164,2166,2168,2170,2172,2174,2176,2178,2180,2182,2184,2186,2188,2190,2192,2194,2196,2198,2200,2202,2204,2206,2208,2210,2212,2214,2216,2218,2220,2222,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2282,2284,2286,2288,2290,2292,2294,2296,2298,2300,2302,2304,2306,2308,2310,2312,2314,2316,2318,2320,2322,2324,2326,2328,2330,2332,2334,2336,2338,2340,2342,2344,2346,2348,2350,2352,2354,2356,2358,2360,2362,2364,2366,2368,2370,2372,2374,2376,2378,2380,2382,2384,2386,2388,2390,2392,2394,2396,2398,2400,2402,2404,2406,2408,2410,2412,2414,2416,2418,2420,2422,2424,2426,2428,2430,2432,2434,2436,2438,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458,2460,2462,2464,2466,2468,2470,2472,2474,2476,2478,2480,2482,2484,2486,2488,2490,2492,2494,2496,2498,2500,2502,2504,2506,2508,2510,2512,2514,2516,2518,2520,2522,2524,2526,2528,2530,2532,2534,2536,2538,2540,2542,2544,2546,2548,2550,2552,2554,2556,2558,2560,2562,2564,2566,2568,2570,2572,2574,2576,2578,2580,2582,2584,2586,2588,2590,2592,2594,2596,2598,2600,2602,2604,2606,2608,2610,2612,2614,2616,2618,2620,2622,2624,2626,2628,2630,2632,2634,2636,2638,2640,2642,2644,2646,2648,2650,2652,2654,2656,2658,2660,2662,2664,2666,2668,2670,2672,2674,2676,2678,2680,2682,2684,2686,2688,2690,2692,2694,2696,2698,2700,2702,2704,2706,2708,2710,2712,2714,2716,2718,2720,2722,2724,2726,2728,2730,2732,2734,2736,2738,2740,2742,2744,2746,2748,2750,2752,2754,2756,2758,2760,2762,2764,2766,2768,2770,2772,2774,2776,2778,2780,2782,2784,2786,2788,2790,2792,2794,2796,2798,2800,2802,2804,2806,2808,2810,2812,2814,2816,2818,2820,2822,2824,2826,2828,2830,2832,2834,2836,2838,2840,2842,2844,2846,2848,2850,2852,2854,2856,2858,2860,2862,2864,2866,2868,2870,2872,2874,2876,2878,2880,2882,2884,2886,2888,2890,2892,2894,2896,2898,2900,2902,2904,2906,2908,2910,2912,2914,2916,2918,2920,2922,2924,2926,2928,2930,2932,2934,2936,2938,2940,2942,2944,2946,2948,2950,2952,2954,2956,2958,2960,2962,2964,2966,2968,2970,2972,2974,2976,2978,2980,2982,2984,2986,2988,2990,2992,2994,2996,2998,3000,3002,3004,3006,3008,3010,3012,3014,3016,3018,3020,3022,3024,3026,3028,3030,3032,3034,3036,3038,3040,3042,3044,3046,3048,3050,3052,3054,3056,3058,3060,3062,3064,3066,3068,3070,3072,3074,3076,3078,3080,3082,3084,3086,3088,3090,3092,3094,3096,3098,3100,3102,3104,3106,3108,3110,3112,3114,3116,3118,3120,3122,3124,3126,3128,3130,3132,3134,3136,3138,3140,3142,3144,3146,3148,3150,3152,3154,3156,3158,3160,3162,3164,3166,3168,3170,3172,3174,3176,3178,3180,3182,3184,3186,3188,3190,3192,3194,3196,3198,3200,3202,3204,3206,3208,3210,3212,3214,3216,3218,3220,3222,3224,3226,3228,3230,3232,3234,3236,3238,3240,3242,3244,3246,3248,3250,3252,3254,3256,3258,3260,3262,3264,3266,3268,3270,3272,3274,3276,3278,3280,3282,3284,3286,3288,3290,3292,3294,3296,3298,3300,3302,3304,3306,3308,3310,3312,3314,3316,3318,3320,3322,3324,3326,3328,3330,3332,3334,3336,3338,3340,3342,3344,3346,3348,3350,3352,3354,3356,3358,3360,3362,3364,3366,3368,3370,3372,3374,3376,3378,3380,3382,3384,3386,3388,3390,3392,3394,3396,3398,3400,3402,3404,3406,3408,3410,3412,3414,3416,3418,3420,3422,3424,3426,3428,3430,3432,3434,3436,3438,3440,3442,3444,3446,3448,3450,3452,3454,3456,3458,3460,3462,3464,3466,3468,3470,3472,3474,3476,3478,3480,3482,3484,3486,3488,3490,3492,3494,3496,3498,3500,3502,3504,3506,3508,3510,3512,3514,3516,3518,3520,3522,3524,3526,3528,3530,3532,3534,3536,3538,3540,3542,3544,3546,3548,3550,3552,3554,3556,3558,3560,3562,3564,3566,3568,3570,3572,3574,3576,3578,3580,3582,3584,3586,3588,3590,3592,3594,3596,3598,3600,3602,3604,3606,3608,3610,3612,3614,3616,3618,3620,3622,3624,3626,3628,3630,3632,3634,3636,3638,3640,3642,3644,3646,3648,3650,3652,3654,3656,3658,3660,3662,3664,3666,3668,3670,3672,3674,3676,3678,3680,3682,3684,3686,3688,3690,3692,3694,3696,3698,3700,3702,3704,3706,3708,3710,3712,3714,3716,3718,3720,3722,3724,3726,3728,3730,3732,3734,3736,3738,3740,3742,3744,3746,3748,3750,3752,3754,3756,3758,3760,3762,3764,3766,3768,3770,3772,3774,3776,3778,3780,3782,3784,3786,3788,3790,3792,3794,3796,3798,3800,3802,3804,3806,3808,3810,3812,3814,3816,3818,3820,3822,3824,3826,3828,3830,3832,3834,3836,3838,3840,3842,3844,3846,3848,3850,3852,3854,3856,3858,3860,3862,3864,3866,3868,3870,3872,3874,3876,3878,3880,3882,3884,3886,3888,3890,3892,3894,3896,3898,3900,3902,3904,3906,3908,3910,3912,3914,3916,3918,3920,3922,3924,3926,3928,3930,3932,3934,3936,3938,3940,3942,3944,3946,3948,3950,3952,3954,3956,3958,3960,3962,3964,3966,3968,3970,3972,3974,3976,3978,3980,3982,3984,3986,3988,3990,3992,3994,3996,3998,4000,4002,4004,4006,4008,4010,4012,4014,4016,4018,4020],{"categories":145},[146],"Developer Productivity",{"categories":148},[149],"Business & SaaS",{"categories":151},[98],{"categories":153},[154],"AI Automation",{"categories":156},[157],"Product Strategy",{"categories":159},[98],{"categories":161},[146],{"categories":163},[149],{"categories":165},[],{"categories":167},[98],{"categories":169},[],{"categories":171},[172],"AI News & Trends",{"categories":174},[154],{"categories":176},[154],{"categories":178},[172],{"categories":180},[154],{"categories":182},[154],{"categories":184},[98],{"categories":186},[98],{"categories":188},[98],{"categories":190},[172],{"categories":192},[98],{"categories":194},[98],{"categories":196},[],{"categories":198},[199],"Design & Frontend",{"categories":201},[202],"Data Science & Visualization",{"categories":204},[172],{"categories":206},[],{"categories":208},[209],"Software Engineering",{"categories":211},[98],{"categories":213},[154],{"categories":215},[216],"Marketing & Growth",{"categories":218},[199],{"categories":220},[98],{"categories":222},[154],{"categories":224},[],{"categories":226},[],{"categories":228},[199],{"categories":230},[154],{"categories":232},[146],{"categories":234},[209],{"categories":236},[199],{"categories":238},[98],{"categories":240},[241],"DevOps & Cloud",{"categories":243},[154],{"categories":245},[172],{"categories":247},[],{"categories":249},[],{"categories":251},[154],{"categories":253},[209],{"categories":255},[],{"categories":257},[149],{"categories":259},[],{"categories":261},[],{"categories":263},[154],{"categories":265},[98],{"categories":267},[154],{"categories":269},[98],{"categories":271},[98],{"categories":273},[],{"categories":275},[209],{"categories":277},[],{"categories":279},[],{"categories":281},[209],{"categories":283},[],{"categories":285},[209],{"categories":287},[98],{"categories":289},[98],{"categories":291},[216],{"categories":293},[199],{"categories":295},[199],{"categories":297},[98],{"categories":299},[154],{"categories":301},[209],{"categories":303},[98],{"categories":305},[98],{"categories":307},[154],{"categories":309},[154],{"categories":311},[202],{"categories":313},[172],{"categories":315},[154],{"categories":317},[216],{"categories":319},[154],{"categories":321},[157],{"categories":323},[209],{"categories":325},[],{"categories":327},[154],{"categories":329},[],{"categories":331},[154],{"categories":333},[209],{"categories":335},[241],{"categories":337},[199],{"categories":339},[98],{"categories":341},[],{"categories":343},[],{"categories":345},[154],{"categories":347},[],{"categories":349},[98],{"categories":351},[],{"categories":353},[146],{"categories":355},[209],{"categories":357},[149],{"categories":359},[98],{"categories":361},[172],{"categories":363},[98],{"categories":365},[],{"categories":367},[98],{"categories":369},[],{"categories":371},[209],{"categories":373},[202],{"categories":375},[],{"categories":377},[98],{"categories":379},[199],{"categories":381},[],{"categories":383},[199],{"categories":385},[154],{"categories":387},[],{"categories":389},[98],{"categories":391},[154],{"categories":393},[172],{"categories":395},[149],{"categories":397},[98],{"categories":399},[],{"categories":401},[154],{"categories":403},[98],{"categories":405},[157],{"categories":407},[],{"categories":409},[98],{"categories":411},[154],{"categories":413},[154],{"categories":415},[],{"categories":417},[202],{"categories":419},[98],{"categories":421},[],{"categories":423},[146],{"categories":425},[149],{"categories":427},[98],{"categories":429},[154],{"categories":431},[209],{"categories":433},[98],{"categories":435},[],{"categories":437},[],{"categories":439},[98],{"categories":441},[98],{"categories":443},[],{"categories":445},[199],{"categories":447},[],{"categories":449},[98],{"categories":451},[],{"categories":453},[154],{"categories":455},[98],{"categories":457},[199],{"categories":459},[],{"categories":461},[98],{"categories":463},[98],{"categories":465},[149],{"categories":467},[154],{"categories":469},[98],{"categories":471},[199],{"categories":473},[154],{"categories":475},[],{"categories":477},[],{"categories":479},[172],{"categories":481},[],{"categories":483},[98],{"categories":485},[149,216],{"categories":487},[],{"categories":489},[98],{"categories":491},[154],{"categories":493},[],{"categories":495},[],{"categories":497},[98],{"categories":499},[],{"categories":501},[98],{"categories":503},[241],{"categories":505},[],{"categories":507},[172],{"categories":509},[199],{"categories":511},[],{"categories":513},[172],{"categories":515},[172],{"categories":517},[98],{"categories":519},[216],{"categories":521},[],{"categories":523},[149],{"categories":525},[154],{"categories":527},[],{"categories":529},[98,241],{"categories":531},[98],{"categories":533},[98],{"categories":535},[98],{"categories":537},[154],{"categories":539},[98,209],{"categories":541},[202],{"categories":543},[98],{"categories":545},[216],{"categories":547},[154],{"categories":549},[154],{"categories":551},[],{"categories":553},[154],{"categories":555},[98],{"categories":557},[98,149],{"categories":559},[],{"categories":561},[199],{"categories":563},[199],{"categories":565},[],{"categories":567},[],{"categories":569},[172],{"categories":571},[],{"categories":573},[146],{"categories":575},[209],{"categories":577},[98],{"categories":579},[199],{"categories":581},[154],{"categories":583},[209],{"categories":585},[172],{"categories":587},[199],{"categories":589},[],{"categories":591},[98],{"categories":593},[98],{"categories":595},[98],{"categories":597},[98],{"categories":599},[172],{"categories":601},[146],{"categories":603},[98],{"categories":605},[154],{"categories":607},[241],{"categories":609},[199],{"categories":611},[154],{"categories":613},[],{"categories":615},[],{"categories":617},[199],{"categories":619},[172],{"categories":621},[202],{"categories":623},[],{"categories":625},[98],{"categories":627},[98],{"categories":629},[149],{"categories":631},[98],{"categories":633},[98],{"categories":635},[172],{"categories":637},[],{"categories":639},[154],{"categories":641},[209],{"categories":643},[],{"categories":645},[98],{"categories":647},[98],{"categories":649},[154],{"categories":651},[],{"categories":653},[],{"categories":655},[98],{"categories":657},[],{"categories":659},[149],{"categories":661},[154],{"categories":663},[154],{"categories":665},[],{"categories":667},[146],{"categories":669},[98],{"categories":671},[149],{"categories":673},[172],{"categories":675},[146],{"categories":677},[],{"categories":679},[],{"categories":681},[],{"categories":683},[172],{"categories":685},[172],{"categories":687},[],{"categories":689},[],{"categories":691},[149],{"categories":693},[],{"categories":695},[],{"categories":697},[146],{"categories":699},[],{"categories":701},[216],{"categories":703},[154],{"categories":705},[149],{"categories":707},[154],{"categories":709},[209],{"categories":711},[],{"categories":713},[157],{"categories":715},[199],{"categories":717},[209],{"categories":719},[98],{"categories":721},[154],{"categories":723},[149],{"categories":725},[98],{"categories":727},[],{"categories":729},[],{"categories":731},[209],{"categories":733},[202],{"categories":735},[157],{"categories":737},[154],{"categories":739},[98],{"categories":741},[],{"categories":743},[241],{"categories":745},[],{"categories":747},[154],{"categories":749},[],{"categories":751},[146],{"categories":753},[],{"categories":755},[98],{"categories":757},[98],{"categories":759},[199],{"categories":761},[216],{"categories":763},[154],{"categories":765},[],{"categories":767},[146],{"categories":769},[],{"categories":771},[172],{"categories":773},[98,241],{"categories":775},[98],{"categories":777},[172],{"categories":779},[98],{"categories":781},[149],{"categories":783},[98],{"categories":785},[],{"categories":787},[98],{"categories":789},[149],{"categories":791},[],{"categories":793},[209],{"categories":795},[199],{"categories":797},[172],{"categories":799},[202],{"categories":801},[146],{"categories":803},[98],{"categories":805},[154],{"categories":807},[209],{"categories":809},[],{"categories":811},[],{"categories":813},[157],{"categories":815},[],{"categories":817},[98],{"categories":819},[],{"categories":821},[199],{"categories":823},[209],{"categories":825},[199],{"categories":827},[98],{"categories":829},[199],{"categories":831},[],{"categories":833},[],{"categories":835},[172],{"categories":837},[154],{"categories":839},[98],{"categories":841},[98],{"categories":843},[98],{"categories":845},[149],{"categories":847},[98],{"categories":849},[],{"categories":851},[209],{"categories":853},[209],{"categories":855},[149],{"categories":857},[],{"categories":859},[98],{"categories":861},[98],{"categories":863},[149],{"categories":865},[172],{"categories":867},[216],{"categories":869},[98],{"categories":871},[154],{"categories":873},[],{"categories":875},[199],{"categories":877},[],{"categories":879},[98],{"categories":881},[98],{"categories":883},[],{"categories":885},[149],{"categories":887},[154],{"categories":889},[],{"categories":891},[241],{"categories":893},[202],{"categories":895},[209],{"categories":897},[216],{"categories":899},[98],{"categories":901},[209],{"categories":903},[154],{"categories":905},[],{"categories":907},[],{"categories":909},[154],{"categories":911},[146],{"categories":913},[154],{"categories":915},[157],{"categories":917},[149],{"categories":919},[],{"categories":921},[98],{"categories":923},[157],{"categories":925},[98],{"categories":927},[98],{"categories":929},[216],{"categories":931},[98],{"categories":933},[199],{"categories":935},[154],{"categories":937},[],{"categories":939},[],{"categories":941},[241],{"categories":943},[209],{"categories":945},[],{"categories":947},[154],{"categories":949},[98],{"categories":951},[199,98],{"categories":953},[146],{"categories":955},[],{"categories":957},[98],{"categories":959},[146],{"categories":961},[199],{"categories":963},[154],{"categories":965},[209],{"categories":967},[],{"categories":969},[98],{"categories":971},[],{"categories":973},[],{"categories":975},[98],{"categories":977},[146],{"categories":979},[],{"categories":981},[154],{"categories":983},[157],{"categories":985},[98],{"categories":987},[98],{"categories":989},[98],{"categories":991},[199],{"categories":993},[154],{"categories":995},[241],{"categories":997},[199],{"categories":999},[154],{"categories":1001},[98],{"categories":1003},[98],{"categories":1005},[98],{"categories":1007},[209],{"categories":1009},[],{"categories":1011},[172],{"categories":1013},[],{"categories":1015},[157],{"categories":1017},[154],{"categories":1019},[199],{"categories":1021},[98],{"categories":1023},[154],{"categories":1025},[209],{"categories":1027},[199],{"categories":1029},[154],{"categories":1031},[172],{"categories":1033},[],{"categories":1035},[98],{"categories":1037},[199],{"categories":1039},[98],{"categories":1041},[146],{"categories":1043},[172],{"categories":1045},[98],{"categories":1047},[216],{"categories":1049},[98],{"categories":1051},[154],{"categories":1053},[98],{"categories":1055},[154],{"categories":1057},[154],{"categories":1059},[98],{"categories":1061},[154],{"categories":1063},[199],{"categories":1065},[98],{"categories":1067},[],{"categories":1069},[],{"categories":1071},[209],{"categories":1073},[],{"categories":1075},[146],{"categories":1077},[241],{"categories":1079},[98],{"categories":1081},[],{"categories":1083},[146],{"categories":1085},[149],{"categories":1087},[216],{"categories":1089},[],{"categories":1091},[149],{"categories":1093},[],{"categories":1095},[98],{"categories":1097},[],{"categories":1099},[],{"categories":1101},[],{"categories":1103},[],{"categories":1105},[98],{"categories":1107},[154],{"categories":1109},[241],{"categories":1111},[146],{"categories":1113},[209],{"categories":1115},[98],{"categories":1117},[209],{"categories":1119},[157],{"categories":1121},[98],{"categories":1123},[216],{"categories":1125},[149],{"categories":1127},[98],{"categories":1129},[98],{"categories":1131},[98],{"categories":1133},[98,146],{"categories":1135},[209],{"categories":1137},[209],{"categories":1139},[199],{"categories":1141},[98],{"categories":1143},[],{"categories":1145},[],{"categories":1147},[],{"categories":1149},[209],{"categories":1151},[202],{"categories":1153},[172],{"categories":1155},[199],{"categories":1157},[],{"categories":1159},[98],{"categories":1161},[98],{"categories":1163},[],{"categories":1165},[154],{"categories":1167},[98],{"categories":1169},[],{"categories":1171},[154],{"categories":1173},[98],{"categories":1175},[149],{"categories":1177},[],{"categories":1179},[146],{"categories":1181},[98],{"categories":1183},[146],{"categories":1185},[98],{"categories":1187},[209],{"categories":1189},[216],{"categories":1191},[154],{"categories":1193},[98,199],{"categories":1195},[172],{"categories":1197},[98],{"categories":1199},[199],{"categories":1201},[],{"categories":1203},[209],{"categories":1205},[241],{"categories":1207},[199],{"categories":1209},[154],{"categories":1211},[],{"categories":1213},[],{"categories":1215},[],{"categories":1217},[],{"categories":1219},[209],{"categories":1221},[154],{"categories":1223},[154],{"categories":1225},[241],{"categories":1227},[98],{"categories":1229},[98],{"categories":1231},[154],{"categories":1233},[98],{"categories":1235},[98],{"categories":1237},[],{"categories":1239},[199],{"categories":1241},[],{"categories":1243},[],{"categories":1245},[154],{"categories":1247},[],{"categories":1249},[],{"categories":1251},[216],{"categories":1253},[216],{"categories":1255},[154],{"categories":1257},[209],{"categories":1259},[],{"categories":1261},[98],{"categories":1263},[98],{"categories":1265},[209],{"categories":1267},[199],{"categories":1269},[199],{"categories":1271},[154],{"categories":1273},[146],{"categories":1275},[98],{"categories":1277},[199],{"categories":1279},[199],{"categories":1281},[154],{"categories":1283},[154],{"categories":1285},[98],{"categories":1287},[],{"categories":1289},[],{"categories":1291},[98],{"categories":1293},[154],{"categories":1295},[172],{"categories":1297},[209],{"categories":1299},[98],{"categories":1301},[146],{"categories":1303},[98],{"categories":1305},[],{"categories":1307},[154],{"categories":1309},[154],{"categories":1311},[],{"categories":1313},[98],{"categories":1315},[146],{"categories":1317},[98],{"categories":1319},[146],{"categories":1321},[146],{"categories":1323},[],{"categories":1325},[],{"categories":1327},[154],{"categories":1329},[172],{"categories":1331},[154],{"categories":1333},[98],{"categories":1335},[98],{"categories":1337},[172],{"categories":1339},[202],{"categories":1341},[157],{"categories":1343},[172],{"categories":1345},[199],{"categories":1347},[],{"categories":1349},[],{"categories":1351},[172],{"categories":1353},[],{"categories":1355},[],{"categories":1357},[],{"categories":1359},[],{"categories":1361},[209],{"categories":1363},[202],{"categories":1365},[],{"categories":1367},[98],{"categories":1369},[98],{"categories":1371},[202],{"categories":1373},[209],{"categories":1375},[],{"categories":1377},[],{"categories":1379},[154],{"categories":1381},[172],{"categories":1383},[172],{"categories":1385},[154],{"categories":1387},[146],{"categories":1389},[98,241],{"categories":1391},[],{"categories":1393},[199],{"categories":1395},[146],{"categories":1397},[154],{"categories":1399},[199],{"categories":1401},[],{"categories":1403},[154],{"categories":1405},[154],{"categories":1407},[98],{"categories":1409},[216],{"categories":1411},[209],{"categories":1413},[199],{"categories":1415},[],{"categories":1417},[154],{"categories":1419},[98],{"categories":1421},[154],{"categories":1423},[154],{"categories":1425},[154],{"categories":1427},[216],{"categories":1429},[98],{"categories":1431},[154],{"categories":1433},[98],{"categories":1435},[],{"categories":1437},[216],{"categories":1439},[172],{"categories":1441},[154],{"categories":1443},[],{"categories":1445},[],{"categories":1447},[98],{"categories":1449},[154],{"categories":1451},[172],{"categories":1453},[154],{"categories":1455},[154],{"categories":1457},[],{"categories":1459},[98],{"categories":1461},[],{"categories":1463},[],{"categories":1465},[154],{"categories":1467},[],{"categories":1469},[],{"categories":1471},[202],{"categories":1473},[98],{"categories":1475},[202],{"categories":1477},[172],{"categories":1479},[98],{"categories":1481},[98],{"categories":1483},[154],{"categories":1485},[98],{"categories":1487},[],{"categories":1489},[],{"categories":1491},[241],{"categories":1493},[98],{"categories":1495},[],{"categories":1497},[],{"categories":1499},[146],{"categories":1501},[],{"categories":1503},[],{"categories":1505},[98],{"categories":1507},[],{"categories":1509},[],{"categories":1511},[209],{"categories":1513},[172],{"categories":1515},[216],{"categories":1517},[149],{"categories":1519},[98],{"categories":1521},[98],{"categories":1523},[149],{"categories":1525},[],{"categories":1527},[199],{"categories":1529},[154],{"categories":1531},[149],{"categories":1533},[98],{"categories":1535},[98],{"categories":1537},[146],{"categories":1539},[],{"categories":1541},[146],{"categories":1543},[98],{"categories":1545},[216],{"categories":1547},[154],{"categories":1549},[172],{"categories":1551},[149],{"categories":1553},[98],{"categories":1555},[98],{"categories":1557},[154],{"categories":1559},[],{"categories":1561},[98],{"categories":1563},[146],{"categories":1565},[98],{"categories":1567},[98],{"categories":1569},[],{"categories":1571},[172],{"categories":1573},[98],{"categories":1575},[],{"categories":1577},[149],{"categories":1579},[149],{"categories":1581},[98],{"categories":1583},[],{"categories":1585},[],{"categories":1587},[],{"categories":1589},[98],{"categories":1591},[172],{"categories":1593},[],{"categories":1595},[241],{"categories":1597},[98],{"categories":1599},[],{"categories":1601},[98],{"categories":1603},[98],{"categories":1605},[98],{"categories":1607},[98,241],{"categories":1609},[98],{"categories":1611},[98],{"categories":1613},[199],{"categories":1615},[154],{"categories":1617},[],{"categories":1619},[154],{"categories":1621},[154],{"categories":1623},[98],{"categories":1625},[98],{"categories":1627},[98],{"categories":1629},[146],{"categories":1631},[146],{"categories":1633},[209],{"categories":1635},[199],{"categories":1637},[154],{"categories":1639},[],{"categories":1641},[98],{"categories":1643},[172],{"categories":1645},[98],{"categories":1647},[149],{"categories":1649},[],{"categories":1651},[241],{"categories":1653},[199],{"categories":1655},[199],{"categories":1657},[154],{"categories":1659},[172],{"categories":1661},[154],{"categories":1663},[98],{"categories":1665},[],{"categories":1667},[98],{"categories":1669},[],{"categories":1671},[],{"categories":1673},[98],{"categories":1675},[98],{"categories":1677},[98],{"categories":1679},[154],{"categories":1681},[98],{"categories":1683},[98],{"categories":1685},[],{"categories":1687},[202],{"categories":1689},[154],{"categories":1691},[],{"categories":1693},[],{"categories":1695},[98],{"categories":1697},[172],{"categories":1699},[],{"categories":1701},[199],{"categories":1703},[241],{"categories":1705},[172],{"categories":1707},[209],{"categories":1709},[209],{"categories":1711},[172],{"categories":1713},[172],{"categories":1715},[241],{"categories":1717},[],{"categories":1719},[172],{"categories":1721},[98],{"categories":1723},[146],{"categories":1725},[98],{"categories":1727},[172],{"categories":1729},[],{"categories":1731},[209],{"categories":1733},[202],{"categories":1735},[98],{"categories":1737},[172],{"categories":1739},[209],{"categories":1741},[154],{"categories":1743},[172],{"categories":1745},[241],{"categories":1747},[154],{"categories":1749},[98],{"categories":1751},[98],{"categories":1753},[98],{"categories":1755},[],{"categories":1757},[149],{"categories":1759},[],{"categories":1761},[],{"categories":1763},[98],{"categories":1765},[98],{"categories":1767},[98],{"categories":1769},[98],{"categories":1771},[],{"categories":1773},[202],{"categories":1775},[146],{"categories":1777},[],{"categories":1779},[98],{"categories":1781},[98],{"categories":1783},[241],{"categories":1785},[241],{"categories":1787},[],{"categories":1789},[154],{"categories":1791},[172],{"categories":1793},[172],{"categories":1795},[98],{"categories":1797},[154],{"categories":1799},[],{"categories":1801},[199],{"categories":1803},[98],{"categories":1805},[98],{"categories":1807},[],{"categories":1809},[98],{"categories":1811},[],{"categories":1813},[209],{"categories":1815},[241],{"categories":1817},[98],{"categories":1819},[209],{"categories":1821},[149],{"categories":1823},[98],{"categories":1825},[],{"categories":1827},[154],{"categories":1829},[146],{"categories":1831},[146],{"categories":1833},[],{"categories":1835},[98],{"categories":1837},[199],{"categories":1839},[154],{"categories":1841},[],{"categories":1843},[98],{"categories":1845},[98],{"categories":1847},[154],{"categories":1849},[],{"categories":1851},[154],{"categories":1853},[209],{"categories":1855},[],{"categories":1857},[98],{"categories":1859},[],{"categories":1861},[98],{"categories":1863},[],{"categories":1865},[98],{"categories":1867},[98],{"categories":1869},[],{"categories":1871},[98],{"categories":1873},[172],{"categories":1875},[98],{"categories":1877},[98],{"categories":1879},[146],{"categories":1881},[98],{"categories":1883},[172],{"categories":1885},[154],{"categories":1887},[],{"categories":1889},[98],{"categories":1891},[199],{"categories":1893},[216],{"categories":1895},[98],{"categories":1897},[],{"categories":1899},[],{"categories":1901},[],{"categories":1903},[146],{"categories":1905},[172],{"categories":1907},[154],{"categories":1909},[98],{"categories":1911},[199],{"categories":1913},[154],{"categories":1915},[],{"categories":1917},[154],{"categories":1919},[],{"categories":1921},[98],{"categories":1923},[154],{"categories":1925},[98],{"categories":1927},[],{"categories":1929},[98],{"categories":1931},[98],{"categories":1933},[172],{"categories":1935},[199],{"categories":1937},[154],{"categories":1939},[199],{"categories":1941},[149],{"categories":1943},[],{"categories":1945},[],{"categories":1947},[98],{"categories":1949},[146],{"categories":1951},[172],{"categories":1953},[],{"categories":1955},[199],{"categories":1957},[],{"categories":1959},[209],{"categories":1961},[209],{"categories":1963},[199],{"categories":1965},[],{"categories":1967},[98],{"categories":1969},[],{"categories":1971},[216],{"categories":1973},[98],{"categories":1975},[241],{"categories":1977},[209],{"categories":1979},[],{"categories":1981},[154],{"categories":1983},[98],{"categories":1985},[146],{"categories":1987},[154],{"categories":1989},[154],{"categories":1991},[98],{"categories":1993},[],{"categories":1995},[146],{"categories":1997},[98],{"categories":1999},[149],{"categories":2001},[209],{"categories":2003},[199],{"categories":2005},[],{"categories":2007},[],{"categories":2009},[],{"categories":2011},[154],{"categories":2013},[199],{"categories":2015},[172],{"categories":2017},[98],{"categories":2019},[172],{"categories":2021},[199],{"categories":2023},[],{"categories":2025},[199],{"categories":2027},[172],{"categories":2029},[149],{"categories":2031},[209],{"categories":2033},[98],{"categories":2035},[172],{"categories":2037},[216],{"categories":2039},[],{"categories":2041},[],{"categories":2043},[202],{"categories":2045},[98,209],{"categories":2047},[172],{"categories":2049},[98],{"categories":2051},[154],{"categories":2053},[98],{"categories":2055},[154],{"categories":2057},[98],{"categories":2059},[98],{"categories":2061},[],{"categories":2063},[209],{"categories":2065},[98],{"categories":2067},[202],{"categories":2069},[154],{"categories":2071},[216],{"categories":2073},[241],{"categories":2075},[],{"categories":2077},[146],{"categories":2079},[154],{"categories":2081},[154],{"categories":2083},[209],{"categories":2085},[98],{"categories":2087},[98],{"categories":2089},[],{"categories":2091},[],{"categories":2093},[],{"categories":2095},[241],{"categories":2097},[172],{"categories":2099},[98],{"categories":2101},[98],{"categories":2103},[98],{"categories":2105},[],{"categories":2107},[202],{"categories":2109},[149],{"categories":2111},[],{"categories":2113},[154],{"categories":2115},[241],{"categories":2117},[],{"categories":2119},[199],{"categories":2121},[199],{"categories":2123},[],{"categories":2125},[209],{"categories":2127},[98],{"categories":2129},[199],{"categories":2131},[98],{"categories":2133},[],{"categories":2135},[172],{"categories":2137},[98],{"categories":2139},[98],{"categories":2141},[199],{"categories":2143},[154],{"categories":2145},[172],{"categories":2147},[],{"categories":2149},[154],{"categories":2151},[199],{"categories":2153},[98],{"categories":2155},[],{"categories":2157},[98],{"categories":2159},[98],{"categories":2161},[241],{"categories":2163},[172],{"categories":2165},[202],{"categories":2167},[202],{"categories":2169},[],{"categories":2171},[],{"categories":2173},[],{"categories":2175},[154],{"categories":2177},[209],{"categories":2179},[209],{"categories":2181},[98],{"categories":2183},[],{"categories":2185},[],{"categories":2187},[98],{"categories":2189},[],{"categories":2191},[154],{"categories":2193},[98],{"categories":2195},[],{"categories":2197},[98],{"categories":2199},[149],{"categories":2201},[98],{"categories":2203},[216],{"categories":2205},[154],{"categories":2207},[98],{"categories":2209},[98],{"categories":2211},[98],{"categories":2213},[209],{"categories":2215},[],{"categories":2217},[172],{"categories":2219},[154],{"categories":2221},[],{"categories":2223},[172],{"categories":2225},[154],{"categories":2227},[154],{"categories":2229},[],{"categories":2231},[149],{"categories":2233},[154],{"categories":2235},[],{"categories":2237},[98],{"categories":2239},[146],{"categories":2241},[172],{"categories":2243},[241],{"categories":2245},[154],{"categories":2247},[154],{"categories":2249},[146],{"categories":2251},[],{"categories":2253},[98],{"categories":2255},[],{"categories":2257},[],{"categories":2259},[199],{"categories":2261},[98,149],{"categories":2263},[98],{"categories":2265},[],{"categories":2267},[146],{"categories":2269},[202],{"categories":2271},[98],{"categories":2273},[209],{"categories":2275},[98],{"categories":2277},[154],{"categories":2279},[98],{"categories":2281},[98],{"categories":2283},[172],{"categories":2285},[154],{"categories":2287},[],{"categories":2289},[],{"categories":2291},[154],{"categories":2293},[98],{"categories":2295},[241],{"categories":2297},[],{"categories":2299},[98],{"categories":2301},[154],{"categories":2303},[],{"categories":2305},[154],{"categories":2307},[98],{"categories":2309},[216],{"categories":2311},[202],{"categories":2313},[154],{"categories":2315},[98],{"categories":2317},[241],{"categories":2319},[],{"categories":2321},[98],{"categories":2323},[216],{"categories":2325},[199],{"categories":2327},[98],{"categories":2329},[98],{"categories":2331},[],{"categories":2333},[216],{"categories":2335},[172],{"categories":2337},[98],{"categories":2339},[98],{"categories":2341},[146],{"categories":2343},[],{"categories":2345},[],{"categories":2347},[199],{"categories":2349},[98],{"categories":2351},[202],{"categories":2353},[216],{"categories":2355},[216],{"categories":2357},[172],{"categories":2359},[],{"categories":2361},[],{"categories":2363},[98],{"categories":2365},[98],{"categories":2367},[98],{"categories":2369},[],{"categories":2371},[98,209],{"categories":2373},[172],{"categories":2375},[154],{"categories":2377},[209],{"categories":2379},[98],{"categories":2381},[146],{"categories":2383},[],{"categories":2385},[],{"categories":2387},[146],{"categories":2389},[209],{"categories":2391},[216],{"categories":2393},[98],{"categories":2395},[],{"categories":2397},[199,98],{"categories":2399},[241],{"categories":2401},[146],{"categories":2403},[],{"categories":2405},[149],{"categories":2407},[149],{"categories":2409},[98],{"categories":2411},[98],{"categories":2413},[209],{"categories":2415},[154],{"categories":2417},[172],{"categories":2419},[216],{"categories":2421},[199],{"categories":2423},[98],{"categories":2425},[98],{"categories":2427},[98],{"categories":2429},[146],{"categories":2431},[98],{"categories":2433},[154],{"categories":2435},[172],{"categories":2437},[],{"categories":2439},[],{"categories":2441},[202],{"categories":2443},[209],{"categories":2445},[98],{"categories":2447},[199],{"categories":2449},[98],{"categories":2451},[202],{"categories":2453},[98],{"categories":2455},[98],{"categories":2457},[98],{"categories":2459},[154],{"categories":2461},[154],{"categories":2463},[98,149],{"categories":2465},[],{"categories":2467},[199],{"categories":2469},[],{"categories":2471},[98],{"categories":2473},[172],{"categories":2475},[146],{"categories":2477},[146],{"categories":2479},[154],{"categories":2481},[98],{"categories":2483},[98],{"categories":2485},[149],{"categories":2487},[209],{"categories":2489},[216],{"categories":2491},[98],{"categories":2493},[],{"categories":2495},[172],{"categories":2497},[98],{"categories":2499},[98],{"categories":2501},[98],{"categories":2503},[98],{"categories":2505},[172],{"categories":2507},[209],{"categories":2509},[209],{"categories":2511},[98],{"categories":2513},[98],{"categories":2515},[154],{"categories":2517},[172],{"categories":2519},[98],{"categories":2521},[199],{"categories":2523},[98],{"categories":2525},[98],{"categories":2527},[241],{"categories":2529},[98],{"categories":2531},[157],{"categories":2533},[154],{"categories":2535},[98],{"categories":2537},[172],{"categories":2539},[154],{"categories":2541},[216],{"categories":2543},[98],{"categories":2545},[],{"categories":2547},[98],{"categories":2549},[],{"categories":2551},[],{"categories":2553},[],{"categories":2555},[149],{"categories":2557},[98],{"categories":2559},[154],{"categories":2561},[172],{"categories":2563},[172],{"categories":2565},[172],{"categories":2567},[172],{"categories":2569},[],{"categories":2571},[146],{"categories":2573},[154],{"categories":2575},[172],{"categories":2577},[98],{"categories":2579},[146],{"categories":2581},[154],{"categories":2583},[98],{"categories":2585},[98,154],{"categories":2587},[154],{"categories":2589},[241],{"categories":2591},[172],{"categories":2593},[172],{"categories":2595},[154],{"categories":2597},[98],{"categories":2599},[],{"categories":2601},[172],{"categories":2603},[216],{"categories":2605},[146],{"categories":2607},[98],{"categories":2609},[98],{"categories":2611},[],{"categories":2613},[209],{"categories":2615},[],{"categories":2617},[146],{"categories":2619},[154],{"categories":2621},[172],{"categories":2623},[98],{"categories":2625},[172],{"categories":2627},[146],{"categories":2629},[172],{"categories":2631},[172],{"categories":2633},[],{"categories":2635},[149],{"categories":2637},[154],{"categories":2639},[172],{"categories":2641},[172],{"categories":2643},[172],{"categories":2645},[172],{"categories":2647},[172],{"categories":2649},[172],{"categories":2651},[172],{"categories":2653},[172],{"categories":2655},[172],{"categories":2657},[172],{"categories":2659},[202],{"categories":2661},[146],{"categories":2663},[98],{"categories":2665},[98],{"categories":2667},[],{"categories":2669},[98,146],{"categories":2671},[],{"categories":2673},[154],{"categories":2675},[172],{"categories":2677},[154],{"categories":2679},[98],{"categories":2681},[98],{"categories":2683},[98],{"categories":2685},[98],{"categories":2687},[98],{"categories":2689},[154],{"categories":2691},[149],{"categories":2693},[],{"categories":2695},[199],{"categories":2697},[172],{"categories":2699},[98],{"categories":2701},[],{"categories":2703},[],{"categories":2705},[154],{"categories":2707},[199],{"categories":2709},[98],{"categories":2711},[],{"categories":2713},[98],{"categories":2715},[],{"categories":2717},[216],{"categories":2719},[98],{"categories":2721},[],{"categories":2723},[],{"categories":2725},[172],{"categories":2727},[146],{"categories":2729},[98],{"categories":2731},[149],{"categories":2733},[98],{"categories":2735},[149],{"categories":2737},[199],{"categories":2739},[],{"categories":2741},[172],{"categories":2743},[],{"categories":2745},[199],{"categories":2747},[98],{"categories":2749},[216],{"categories":2751},[],{"categories":2753},[216],{"categories":2755},[],{"categories":2757},[],{"categories":2759},[154],{"categories":2761},[],{"categories":2763},[149],{"categories":2765},[146],{"categories":2767},[199],{"categories":2769},[209],{"categories":2771},[],{"categories":2773},[],{"categories":2775},[98],{"categories":2777},[146],{"categories":2779},[216],{"categories":2781},[],{"categories":2783},[154],{"categories":2785},[154],{"categories":2787},[172],{"categories":2789},[209],{"categories":2791},[98],{"categories":2793},[154],{"categories":2795},[98],{"categories":2797},[154],{"categories":2799},[98],{"categories":2801},[157],{"categories":2803},[172],{"categories":2805},[],{"categories":2807},[216],{"categories":2809},[],{"categories":2811},[209],{"categories":2813},[154],{"categories":2815},[],{"categories":2817},[98],{"categories":2819},[154],{"categories":2821},[149],{"categories":2823},[146],{"categories":2825},[98],{"categories":2827},[199],{"categories":2829},[209],{"categories":2831},[209],{"categories":2833},[98],{"categories":2835},[202],{"categories":2837},[98],{"categories":2839},[154],{"categories":2841},[149],{"categories":2843},[199],{"categories":2845},[154],{"categories":2847},[98],{"categories":2849},[98],{"categories":2851},[154],{"categories":2853},[172],{"categories":2855},[],{"categories":2857},[146],{"categories":2859},[98],{"categories":2861},[154],{"categories":2863},[98],{"categories":2865},[98],{"categories":2867},[],{"categories":2869},[199],{"categories":2871},[149],{"categories":2873},[172],{"categories":2875},[98],{"categories":2877},[98],{"categories":2879},[199],{"categories":2881},[98],{"categories":2883},[216],{"categories":2885},[202],{"categories":2887},[98],{"categories":2889},[172],{"categories":2891},[98],{"categories":2893},[154],{"categories":2895},[241],{"categories":2897},[98],{"categories":2899},[154],{"categories":2901},[202],{"categories":2903},[],{"categories":2905},[154],{"categories":2907},[209],{"categories":2909},[199],{"categories":2911},[98],{"categories":2913},[146],{"categories":2915},[149],{"categories":2917},[209],{"categories":2919},[98],{"categories":2921},[],{"categories":2923},[154],{"categories":2925},[154],{"categories":2927},[98],{"categories":2929},[202],{"categories":2931},[],{"categories":2933},[172],{"categories":2935},[],{"categories":2937},[172],{"categories":2939},[98],{"categories":2941},[154],{"categories":2943},[154],{"categories":2945},[154],{"categories":2947},[],{"categories":2949},[172],{"categories":2951},[],{"categories":2953},[98],{"categories":2955},[98],{"categories":2957},[],{"categories":2959},[199],{"categories":2961},[154],{"categories":2963},[216],{"categories":2965},[146],{"categories":2967},[],{"categories":2969},[98],{"categories":2971},[],{"categories":2973},[146],{"categories":2975},[172],{"categories":2977},[209],{"categories":2979},[98],{"categories":2981},[98],{"categories":2983},[98],{"categories":2985},[209],{"categories":2987},[172],{"categories":2989},[199],{"categories":2991},[98],{"categories":2993},[98],{"categories":2995},[98],{"categories":2997},[172],{"categories":2999},[98],{"categories":3001},[172],{"categories":3003},[172],{"categories":3005},[154],{"categories":3007},[154],{"categories":3009},[209],{"categories":3011},[172],{"categories":3013},[154],{"categories":3015},[98],{"categories":3017},[209],{"categories":3019},[199],{"categories":3021},[],{"categories":3023},[154],{"categories":3025},[],{"categories":3027},[],{"categories":3029},[],{"categories":3031},[149],{"categories":3033},[98],{"categories":3035},[154],{"categories":3037},[146],{"categories":3039},[154],{"categories":3041},[216],{"categories":3043},[],{"categories":3045},[154],{"categories":3047},[],{"categories":3049},[146],{"categories":3051},[154],{"categories":3053},[],{"categories":3055},[154],{"categories":3057},[98],{"categories":3059},[172],{"categories":3061},[98],{"categories":3063},[154],{"categories":3065},[172],{"categories":3067},[154],{"categories":3069},[209],{"categories":3071},[199],{"categories":3073},[146],{"categories":3075},[],{"categories":3077},[154],{"categories":3079},[199],{"categories":3081},[241],{"categories":3083},[172],{"categories":3085},[98],{"categories":3087},[199],{"categories":3089},[146],{"categories":3091},[],{"categories":3093},[154],{"categories":3095},[98],{"categories":3097},[154],{"categories":3099},[98],{"categories":3101},[],{"categories":3103},[154],{"categories":3105},[157],{"categories":3107},[172],{"categories":3109},[154],{"categories":3111},[149],{"categories":3113},[],{"categories":3115},[98],{"categories":3117},[157],{"categories":3119},[98],{"categories":3121},[154],{"categories":3123},[172],{"categories":3125},[146],{"categories":3127},[241],{"categories":3129},[98],{"categories":3131},[98],{"categories":3133},[98],{"categories":3135},[172],{"categories":3137},[149],{"categories":3139},[98],{"categories":3141},[199],{"categories":3143},[172],{"categories":3145},[241],{"categories":3147},[98],{"categories":3149},[],{"categories":3151},[],{"categories":3153},[98],{"categories":3155},[241],{"categories":3157},[202],{"categories":3159},[154],{"categories":3161},[154],{"categories":3163},[172],{"categories":3165},[98],{"categories":3167},[146],{"categories":3169},[199],{"categories":3171},[154],{"categories":3173},[98],{"categories":3175},[216],{"categories":3177},[98],{"categories":3179},[154],{"categories":3181},[],{"categories":3183},[98],{"categories":3185},[98],{"categories":3187},[172],{"categories":3189},[146],{"categories":3191},[],{"categories":3193},[98],{"categories":3195},[98],{"categories":3197},[209],{"categories":3199},[199],{"categories":3201},[98,154],{"categories":3203},[216,149],{"categories":3205},[98],{"categories":3207},[],{"categories":3209},[154],{"categories":3211},[],{"categories":3213},[209],{"categories":3215},[98],{"categories":3217},[],{"categories":3219},[98],{"categories":3221},[172],{"categories":3223},[],{"categories":3225},[154],{"categories":3227},[98],{"categories":3229},[],{"categories":3231},[199],{"categories":3233},[154],{"categories":3235},[98],{"categories":3237},[146],{"categories":3239},[154],{"categories":3241},[98],{"categories":3243},[],{"categories":3245},[241],{"categories":3247},[216],{"categories":3249},[149],{"categories":3251},[149],{"categories":3253},[146],{"categories":3255},[146],{"categories":3257},[98],{"categories":3259},[154],{"categories":3261},[98],{"categories":3263},[98],{"categories":3265},[146],{"categories":3267},[98],{"categories":3269},[216],{"categories":3271},[172],{"categories":3273},[98],{"categories":3275},[154],{"categories":3277},[98],{"categories":3279},[],{"categories":3281},[209],{"categories":3283},[],{"categories":3285},[209],{"categories":3287},[154],{"categories":3289},[146],{"categories":3291},[],{"categories":3293},[241],{"categories":3295},[98],{"categories":3297},[],{"categories":3299},[172],{"categories":3301},[154],{"categories":3303},[209],{"categories":3305},[98],{"categories":3307},[154],{"categories":3309},[209],{"categories":3311},[154],{"categories":3313},[172],{"categories":3315},[146],{"categories":3317},[172],{"categories":3319},[209],{"categories":3321},[98],{"categories":3323},[199],{"categories":3325},[98],{"categories":3327},[98],{"categories":3329},[98],{"categories":3331},[98],{"categories":3333},[98],{"categories":3335},[154],{"categories":3337},[98],{"categories":3339},[154],{"categories":3341},[98],{"categories":3343},[146],{"categories":3345},[98],{"categories":3347},[154],{"categories":3349},[199],{"categories":3351},[146],{"categories":3353},[154],{"categories":3355},[199],{"categories":3357},[],{"categories":3359},[98],{"categories":3361},[98],{"categories":3363},[209],{"categories":3365},[],{"categories":3367},[154],{"categories":3369},[216],{"categories":3371},[98],{"categories":3373},[172],{"categories":3375},[216],{"categories":3377},[154],{"categories":3379},[149],{"categories":3381},[149],{"categories":3383},[98],{"categories":3385},[146],{"categories":3387},[],{"categories":3389},[154],{"categories":3391},[98],{"categories":3393},[],{"categories":3395},[146],{"categories":3397},[98],{"categories":3399},[154],{"categories":3401},[154],{"categories":3403},[],{"categories":3405},[209],{"categories":3407},[209],{"categories":3409},[216],{"categories":3411},[199],{"categories":3413},[],{"categories":3415},[98],{"categories":3417},[154],{"categories":3419},[146],{"categories":3421},[98],{"categories":3423},[209],{"categories":3425},[146],{"categories":3427},[172],{"categories":3429},[172],{"categories":3431},[],{"categories":3433},[172],{"categories":3435},[154],{"categories":3437},[199],{"categories":3439},[202],{"categories":3441},[98],{"categories":3443},[],{"categories":3445},[172],{"categories":3447},[209],{"categories":3449},[149],{"categories":3451},[98],{"categories":3453},[146],{"categories":3455},[241],{"categories":3457},[146],{"categories":3459},[],{"categories":3461},[],{"categories":3463},[172],{"categories":3465},[],{"categories":3467},[154],{"categories":3469},[154],{"categories":3471},[154],{"categories":3473},[],{"categories":3475},[98],{"categories":3477},[],{"categories":3479},[172],{"categories":3481},[146],{"categories":3483},[199],{"categories":3485},[98],{"categories":3487},[172],{"categories":3489},[172],{"categories":3491},[],{"categories":3493},[172],{"categories":3495},[146],{"categories":3497},[98],{"categories":3499},[],{"categories":3501},[154],{"categories":3503},[154],{"categories":3505},[146],{"categories":3507},[],{"categories":3509},[],{"categories":3511},[],{"categories":3513},[199],{"categories":3515},[154],{"categories":3517},[98],{"categories":3519},[],{"categories":3521},[],{"categories":3523},[],{"categories":3525},[199],{"categories":3527},[],{"categories":3529},[98],{"categories":3531},[146],{"categories":3533},[],{"categories":3535},[],{"categories":3537},[199],{"categories":3539},[98],{"categories":3541},[172],{"categories":3543},[],{"categories":3545},[216],{"categories":3547},[172],{"categories":3549},[216],{"categories":3551},[98],{"categories":3553},[],{"categories":3555},[],{"categories":3557},[154],{"categories":3559},[],{"categories":3561},[],{"categories":3563},[154],{"categories":3565},[98],{"categories":3567},[],{"categories":3569},[154],{"categories":3571},[172],{"categories":3573},[98],{"categories":3575},[216],{"categories":3577},[202],{"categories":3579},[154],{"categories":3581},[154],{"categories":3583},[],{"categories":3585},[],{"categories":3587},[],{"categories":3589},[172],{"categories":3591},[],{"categories":3593},[],{"categories":3595},[199],{"categories":3597},[146],{"categories":3599},[],{"categories":3601},[149],{"categories":3603},[216],{"categories":3605},[98],{"categories":3607},[209],{"categories":3609},[146],{"categories":3611},[202],{"categories":3613},[149],{"categories":3615},[209],{"categories":3617},[209],{"categories":3619},[],{"categories":3621},[],{"categories":3623},[154],{"categories":3625},[146],{"categories":3627},[199],{"categories":3629},[146],{"categories":3631},[154],{"categories":3633},[241],{"categories":3635},[98],{"categories":3637},[146],{"categories":3639},[154],{"categories":3641},[],{"categories":3643},[98],{"categories":3645},[172],{"categories":3647},[209],{"categories":3649},[],{"categories":3651},[199],{"categories":3653},[172],{"categories":3655},[146],{"categories":3657},[154],{"categories":3659},[98],{"categories":3661},[149],{"categories":3663},[154,241],{"categories":3665},[154],{"categories":3667},[209],{"categories":3669},[98],{"categories":3671},[98],{"categories":3673},[202],{"categories":3675},[216],{"categories":3677},[154],{"categories":3679},[],{"categories":3681},[154],{"categories":3683},[98],{"categories":3685},[149],{"categories":3687},[],{"categories":3689},[],{"categories":3691},[98],{"categories":3693},[202],{"categories":3695},[98],{"categories":3697},[],{"categories":3699},[172],{"categories":3701},[],{"categories":3703},[172],{"categories":3705},[146],{"categories":3707},[209],{"categories":3709},[98],{"categories":3711},[154],{"categories":3713},[98],{"categories":3715},[98],{"categories":3717},[216],{"categories":3719},[209],{"categories":3721},[],{"categories":3723},[172],{"categories":3725},[98],{"categories":3727},[],{"categories":3729},[98],{"categories":3731},[154],{"categories":3733},[98],{"categories":3735},[154],{"categories":3737},[98],{"categories":3739},[98],{"categories":3741},[98],{"categories":3743},[98],{"categories":3745},[149],{"categories":3747},[],{"categories":3749},[157],{"categories":3751},[172],{"categories":3753},[98],{"categories":3755},[],{"categories":3757},[209],{"categories":3759},[98],{"categories":3761},[98],{"categories":3763},[98],{"categories":3765},[154],{"categories":3767},[172],{"categories":3769},[98],{"categories":3771},[98],{"categories":3773},[98],{"categories":3775},[149],{"categories":3777},[154],{"categories":3779},[199],{"categories":3781},[],{"categories":3783},[202],{"categories":3785},[98],{"categories":3787},[],{"categories":3789},[172],{"categories":3791},[216],{"categories":3793},[],{"categories":3795},[],{"categories":3797},[172],{"categories":3799},[172],{"categories":3801},[216],{"categories":3803},[146],{"categories":3805},[154],{"categories":3807},[154],{"categories":3809},[98],{"categories":3811},[149],{"categories":3813},[],{"categories":3815},[],{"categories":3817},[172],{"categories":3819},[202],{"categories":3821},[209],{"categories":3823},[154],{"categories":3825},[199],{"categories":3827},[202],{"categories":3829},[202],{"categories":3831},[],{"categories":3833},[172],{"categories":3835},[98],{"categories":3837},[98],{"categories":3839},[209],{"categories":3841},[],{"categories":3843},[172],{"categories":3845},[172],{"categories":3847},[172],{"categories":3849},[],{"categories":3851},[154],{"categories":3853},[98],{"categories":3855},[],{"categories":3857},[146],{"categories":3859},[149],{"categories":3861},[],{"categories":3863},[98],{"categories":3865},[98],{"categories":3867},[],{"categories":3869},[209],{"categories":3871},[],{"categories":3873},[],{"categories":3875},[],{"categories":3877},[],{"categories":3879},[98],{"categories":3881},[172],{"categories":3883},[],{"categories":3885},[],{"categories":3887},[98],{"categories":3889},[98],{"categories":3891},[98],{"categories":3893},[202],{"categories":3895},[98],{"categories":3897},[202],{"categories":3899},[],{"categories":3901},[202],{"categories":3903},[202],{"categories":3905},[241],{"categories":3907},[154],{"categories":3909},[209],{"categories":3911},[],{"categories":3913},[],{"categories":3915},[202],{"categories":3917},[209],{"categories":3919},[209],{"categories":3921},[209],{"categories":3923},[],{"categories":3925},[146],{"categories":3927},[209],{"categories":3929},[209],{"categories":3931},[146],{"categories":3933},[209],{"categories":3935},[149],{"categories":3937},[209],{"categories":3939},[209],{"categories":3941},[209],{"categories":3943},[202],{"categories":3945},[172],{"categories":3947},[172],{"categories":3949},[98],{"categories":3951},[209],{"categories":3953},[202],{"categories":3955},[241],{"categories":3957},[202],{"categories":3959},[202],{"categories":3961},[202],{"categories":3963},[],{"categories":3965},[149],{"categories":3967},[],{"categories":3969},[241],{"categories":3971},[209],{"categories":3973},[209],{"categories":3975},[209],{"categories":3977},[154],{"categories":3979},[172,149],{"categories":3981},[202],{"categories":3983},[],{"categories":3985},[],{"categories":3987},[202],{"categories":3989},[],{"categories":3991},[202],{"categories":3993},[172],{"categories":3995},[154],{"categories":3997},[],{"categories":3999},[209],{"categories":4001},[98],{"categories":4003},[199],{"categories":4005},[],{"categories":4007},[98],{"categories":4009},[],{"categories":4011},[172],{"categories":4013},[146],{"categories":4015},[202],{"categories":4017},[],{"categories":4019},[209],{"categories":4021},[172],[4023,4171,4322,4477],{"id":4024,"title":4025,"ai":4026,"body":4032,"categories":4130,"created_at":99,"date_modified":99,"description":91,"extension":100,"faq":99,"featured":101,"kicker_label":99,"meta":4131,"navigation":122,"path":4158,"published_at":99,"question":99,"scraped_at":4159,"seo":4160,"sitemap":4161,"source_id":4162,"source_name":4163,"source_type":4164,"source_url":4165,"stem":4166,"tags":4167,"thumbnail_url":99,"tldr":4168,"tweet":99,"unknown_tags":4169,"__hash__":4170},"summaries\u002Fsummaries\u002Fd5c7b26fc3a6353b-qwen3-coder-next-coding-llm-for-agents-with-tool-c-summary.md","Qwen3-Coder-Next: Coding LLM for Agents with Tool Calling",{"provider":7,"model":4027,"input_tokens":4028,"output_tokens":4029,"processing_time_ms":4030,"cost_usd":4031},"x-ai\u002Fgrok-4.1-fast",5328,1737,10140,0.00191145,{"type":14,"value":4033,"toc":4125},[4034,4038,4057,4060,4064,4095,4099],[17,4035,4037],{"id":4036},"core-features-and-quick-inference","Core Features and Quick Inference",[22,4039,4040,4041,4044,4045,4048,4049,4052,4053,4056],{},"Qwen3-Coder-Next runs in non-thinking mode without generating ",[59,4042,4043],{},"\u003Cthink>\u003C\u002Fthink>"," blocks, simplifying outputs for coding tasks. Load it via ",[59,4046,4047],{},"transformers"," (latest version) with ",[59,4050,4051],{},"torch_dtype=\"auto\""," and ",[59,4054,4055],{},"device_map=\"auto\""," for automatic hardware placement. Use chat template for prompts like \"Write a quick sort algorithm,\" generating up to 65,536 new tokens. To avoid OOM errors, cap context at 32,768 tokens. Local apps like Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers support it out-of-the-box, enabling fast prototyping without cloud dependency.",[22,4058,4059],{},"Benchmarks (via images) show top performance on coding evals like SWE-Bench Verified, positioning it for agentic coding over general models.",[17,4061,4063],{"id":4062},"efficient-deployment-for-production","Efficient Deployment for Production",[22,4065,4066,4067,4070,4071,4074,4075,4078,4079,4085,4086,4089,4090,4094],{},"Serve with OpenAI-compatible APIs using SGLang (>=v0.5.8, ",[59,4068,4069],{},"pip install 'sglang[app]>=v0.5.8'",") or vLLM (>=0.15.0, ",[59,4072,4073],{},"pip install 'vllm>=0.15.0'","). For SGLang: ",[59,4076,4077],{},"python -m sglang.launch_server --model Qwen\u002FQwen3-Coder-Next --port 30000 --tp-size 2 --tool-call-parser qwen3_coder"," starts at ",[4080,4081,4082],"a",{"href":4082,"rel":4083},"http:\u002F\u002Flocalhost:30000\u002Fv1",[4084],"nofollow"," with 256K context on 2 GPUs (tensor parallel). vLLM: ",[59,4087,4088],{},"vllm serve Qwen\u002FQwen3-Coder-Next --port 8000 --tensor-parallel-size 2 --enable-auto-tool-choice --tool-call-parser qwen3_coder"," at ",[4080,4091,4092],{"href":4092,"rel":4093},"http:\u002F\u002Flocalhost:8000\u002Fv1",[4084],". Reduce to 32,768 context if startup fails due to memory limits, trading length for reliability on smaller hardware.",[17,4096,4098],{"id":4097},"agentic-workflows-and-optimization","Agentic Workflows and Optimization",[22,4100,4101,4102,4105,4106,4109,4110,4113,4114,4117,4118,4117,4121,4124],{},"Define JSON tools (e.g., ",[59,4103,4104],{},"square_the_number"," function taking ",[59,4107,4108],{},"input_num: number",") and call via OpenAI client against local endpoint: ",[59,4111,4112],{},"client.chat.completions.create(..., tools=tools)",". Model handles function calling natively without thinking tokens. For best results, sample at ",[59,4115,4116],{},"temperature=1.0",", ",[59,4119,4120],{},"top_p=0.95",[59,4122,4123],{},"top_k=40"," to balance creativity and focus in code generation. Full details in linked blog, GitHub, and docs; cite the Qwen3-Coder-Next tech report for production use.",{"title":91,"searchDepth":92,"depth":92,"links":4126},[4127,4128,4129],{"id":4036,"depth":92,"text":4037},{"id":4062,"depth":92,"text":4063},{"id":4097,"depth":92,"text":4098},[],{"content_references":4132,"triage":4155},[4133,4139,4143,4146,4149,4152],{"type":4134,"title":4135,"author":4136,"url":4137,"context":4138},"report","Qwen3-Coder-Next Technical Report","Qwen Team","https:\u002F\u002Fgithub.com\u002FQwenLM\u002FQwen3-Coder\u002Fblob\u002Fmain\u002Fqwen3_coder_next_tech_report.pdf","cited",{"type":4140,"title":4141,"url":4142,"context":112},"other","Qwen3-Coder-Next blog","https:\u002F\u002Fqwen.ai\u002Fblog?id=qwen3-coder-next",{"type":4140,"title":4144,"url":4145,"context":112},"Qwen3-Coder GitHub","https:\u002F\u002Fgithub.com\u002FQwenLM\u002FQwen3-Coder",{"type":4140,"title":4147,"url":4148,"context":112},"Qwen Documentation","https:\u002F\u002Fqwen.readthedocs.io\u002Fen\u002Flatest\u002F",{"type":105,"title":4150,"url":4151,"context":108},"SGLang","https:\u002F\u002Fgithub.com\u002Fsgl-project\u002Fsglang",{"type":105,"title":4153,"url":4154,"context":108},"vLLM","https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm",{"relevance":118,"novelty":119,"quality":119,"actionability":118,"composite":4156,"reasoning":4157},4.55,"Category: AI & LLMs. The article provides in-depth technical details about the Qwen3-Coder-Next model, including its deployment and usage for coding agents, which directly addresses the needs of developers looking to integrate AI into their products. It offers actionable steps for deployment and optimization, making it highly relevant and practical for the target audience.","\u002Fsummaries\u002Fd5c7b26fc3a6353b-qwen3-coder-next-coding-llm-for-agents-with-tool-c-summary","2026-04-15 15:35:14",{"title":4025,"description":91},{"loc":4158},"d5c7b26fc3a6353b","__oneoff__","article","https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen3-Coder-Next","summaries\u002Fd5c7b26fc3a6353b-qwen3-coder-next-coding-llm-for-agents-with-tool-c-summary",[134,135,136],"Qwen3-Coder-Next is an open-weight model optimized for coding agents, featuring non-thinking mode, 256K context, strong benchmarks, and easy deployment via transformers, SGLang, or vLLM for local dev and tool use.",[],"-Kmf7Ahy-Hq9bPYNqqWxKfn6saDo7KGeACSG7NKydSg",{"id":4172,"title":4173,"ai":4174,"body":4179,"categories":4300,"created_at":99,"date_modified":99,"description":91,"extension":100,"faq":99,"featured":101,"kicker_label":99,"meta":4301,"navigation":122,"path":4308,"published_at":4309,"question":99,"scraped_at":4310,"seo":4311,"sitemap":4312,"source_id":4313,"source_name":4314,"source_type":4164,"source_url":4315,"stem":4316,"tags":4317,"thumbnail_url":99,"tldr":4319,"tweet":99,"unknown_tags":4320,"__hash__":4321},"summaries\u002Fsummaries\u002Fd61f6d790ee8e894-building-a-modular-agentic-ai-pipeline-with-openai-summary.md","Building a Modular Agentic AI Pipeline with OpenAI",{"provider":7,"model":8,"input_tokens":4175,"output_tokens":4176,"processing_time_ms":4177,"cost_usd":4178},10445,653,3680,0.00359075,{"type":14,"value":4180,"toc":4295},[4181,4185,4188,4208,4212,4215,4261,4265],[17,4182,4184],{"id":4183},"modular-agent-architecture","Modular Agent Architecture",[22,4186,4187],{},"To move beyond simple chat interactions, structure your agent as a pipeline of specialized roles. This separation of concerns improves reliability and debuggability:",[36,4189,4190,4196,4202],{},[39,4191,4192,4195],{},[42,4193,4194],{},"Planner:"," Responsible for high-level strategy. It takes the user goal and outputs a structured JSON object containing the objective, a list of sequential steps, and potential tool checkpoints.",[39,4197,4198,4201],{},[42,4199,4200],{},"Executor:"," The engine that performs the work. It operates in a loop, calling tools as needed and maintaining a trace of all actions. It keeps intermediate notes to ensure the model stays grounded in the current task.",[39,4203,4204,4207],{},[42,4205,4206],{},"Critic:"," The quality control layer. It reviews the executor's draft against the original goal and the execution trace, identifying issues and generating a polished final output.",[17,4209,4211],{"id":4210},"tooling-and-state-management","Tooling and State Management",[22,4213,4214],{},"For an agent to be useful, it must interact with the environment reliably. Use structured tool definitions and machine-readable outputs to minimize errors:",[36,4216,4217,4233,4251],{},[39,4218,4219,4222,4223,4117,4226,4117,4229,4232],{},[42,4220,4221],{},"Tool Schema:"," Define clear function schemas (e.g., ",[59,4224,4225],{},"calc",[59,4227,4228],{},"kb_search",[59,4230,4231],{},"write_file",") so the model can reliably invoke Python functions.",[39,4234,4235,4238,4239,4242,4243,4246,4247,4250],{},[42,4236,4237],{},"State Tracking:"," Use a ",[59,4240,4241],{},"dataclass"," to maintain the ",[59,4244,4245],{},"AgentState",", which stores the goal, memory, and a full ",[59,4248,4249],{},"trace"," of tool calls. This trace is critical for debugging and allows the critic to understand exactly how the agent arrived at its draft.",[39,4252,4253,4256,4257,4260],{},[42,4254,4255],{},"Structured Outputs:"," Ensure tools return dictionaries with an ",[59,4258,4259],{},"ok"," status and relevant data. This prevents the agent from hallucinating tool results and makes it easier to handle errors gracefully.",[17,4262,4264],{"id":4263},"implementation-workflow","Implementation Workflow",[4266,4267,4268,4277,4283,4289],"ol",{},[39,4269,4270,4273,4274,4276],{},[42,4271,4272],{},"Initialization:"," Set up the OpenAI client and define a persistent ",[59,4275,4245],{},".",[39,4278,4279,4282],{},[42,4280,4281],{},"Planning:"," Prompt the model to generate a structured plan in JSON format. If parsing fails, provide a fallback mechanism to proceed directly.",[39,4284,4285,4288],{},[42,4286,4287],{},"Execution Loop:"," Run the executor for a fixed number of iterations (e.g., 12). In each step, check for tool calls, execute them in Python, and append the results back to the message history so the model can adjust its next move.",[39,4290,4291,4294],{},[42,4292,4293],{},"Critique & Finalization:"," Pass the draft and the execution trace to the critic to generate the final deliverable. This ensures that even if the executor makes minor errors, the critic can catch and fix them before the user sees the result.",{"title":91,"searchDepth":92,"depth":92,"links":4296},[4297,4298,4299],{"id":4183,"depth":92,"text":4184},{"id":4210,"depth":92,"text":4211},{"id":4263,"depth":92,"text":4264},[98],{"content_references":4302,"triage":4306},[4303],{"type":105,"title":4304,"url":4305,"context":108},"OpenAI API","https:\u002F\u002Fplatform.openai.com\u002F",{"relevance":118,"novelty":119,"quality":119,"actionability":118,"composite":4156,"reasoning":4307},"Category: AI & LLMs. The article provides a detailed framework for building a modular agentic AI pipeline, addressing specific pain points such as reliability and debuggability in AI systems. It offers actionable steps for implementation, including defining roles and structured outputs, making it highly relevant and practical for developers looking to integrate AI into their products.","\u002Fsummaries\u002Fd61f6d790ee8e894-building-a-modular-agentic-ai-pipeline-with-openai-summary","2026-05-19 05:29:40","2026-05-19 07:00:53",{"title":4173,"description":91},{"loc":4308},"d61f6d790ee8e894","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F18\u002Fhow-to-build-an-advanced-agentic-ai-system-with-planning-tool-calling-memory-and-self-critique-using-openai-api\u002F","summaries\u002Fd61f6d790ee8e894-building-a-modular-agentic-ai-pipeline-with-openai-summary",[134,135,136,4318],"ai-tools","Implement a robust agentic system by decoupling strategy, execution, and quality control into three specialized roles: a planner, a tool-using executor, and a critic.",[],"he0LoeA-Q8WyV39XxilqPy49h7y55SbdH5YRykiXAo0",{"id":4323,"title":4324,"ai":4325,"body":4330,"categories":4442,"created_at":99,"date_modified":99,"description":91,"extension":100,"faq":99,"featured":101,"kicker_label":99,"meta":4443,"navigation":122,"path":4462,"published_at":4463,"question":99,"scraped_at":4464,"seo":4465,"sitemap":4466,"source_id":4467,"source_name":4451,"source_type":130,"source_url":4468,"stem":4469,"tags":4470,"thumbnail_url":4472,"tldr":4473,"tweet":4474,"unknown_tags":4475,"__hash__":4476},"summaries\u002Fsummaries\u002F47f6e1401b14afd4-melia-secures-ai-skills-openai-pivots-to-consultin-summary.md","Melia Secures AI Skills, OpenAI Pivots to Consulting, AI Zero-Days",{"provider":7,"model":4027,"input_tokens":4326,"output_tokens":4327,"processing_time_ms":4328,"cost_usd":4329},8198,2554,33042,0.00289395,{"type":14,"value":4331,"toc":4436},[4332,4336,4339,4342,4345,4349,4352,4355,4358,4362,4365,4368,4371,4375,4378,4383,4409,4414],[17,4333,4335],{"id":4334},"compiling-natural-language-skills-into-secure-python-programs","Compiling Natural Language Skills into Secure Python Programs",[22,4337,4338],{},"IBM Research's Melia skills compiler addresses the chaos in AI agent skills marketplaces like OpenClaw by transforming narrative .mmd files into verifiable Python programs. Kush Varshney explains it as part of 'generative computing,' blending deterministic code for control flow with targeted LLM calls for context handling. The pipeline adds safety hooks, security checks, and guardian functions, enabling execution in harnesses like command-line or agent frameworks. Panelists agree this reverses the 'narrative dream' toward determinism for enterprise deployment, acting like a traditional compiler: easy authoring in natural language, hardened output for reliability.",[22,4340,4341],{},"Aaron Baughman highlights practical benefits in IBM's field work, such as schema validation, tool safety (e.g., CDN protection against high-scale hits), versioning prompts, and defense against prompt injection. It enables reusable 'digital workers' with predictable multi-step behavior, compatible with protocols like A2A. Kush envisions expansion to OS-level generative tasks, drawing from 80+ years of computer science. All panelists see it taming agentic 'havoc,' with Aaron noting auto-prompt creation flows could integrate Melia for trust and idempotency.",[22,4343,4344],{},"Trade-offs: Narrative excels for authoring but fails security; compilation adds robustness at the cost of flexibility. Access via melia.ai and GitHub for experimentation.",[17,4346,4348],{"id":4347},"openai-deployment-company-validates-ai-integration-over-pure-models","OpenAI Deployment Company Validates AI Integration Over Pure Models",[22,4350,4351],{},"OpenAI's new $10B consulting venture, partnering with McKinsey, Capgemini, and Bain, shifts focus from commoditizing models to enterprise integration services. Aaron predicts a 'merging of software and consulting,' with virtual workers (badged in tech like Python or domains like finance) amplifying humans to solve complex problems unattainable before. Kush notes models commoditize (echoing Sam Altman), making integration the moat; Anthropic's similar JV reinforces this.",[22,4353,4354],{},"Divergences emerge on model agnosticism: Aaron questions if OpenAI will support non-OpenAI stacks (e.g., Granite, Bedrock, AWS\u002FAzure), suggesting ecosystem partnerships. Kush advocates 'sovereign' approaches meeting customers' existing ML\u002Fcloud\u002Fmodels, avoiding opinionated pushes. Only ~1\u002F3 of firms scale AI enterprise-wide, per stats, creating real demand but hype around 'transformation.' Tim Hwang posits consulting as 'AI-proof,' evolving jobs rather than replacing them.",[22,4356,4357],{},"Predictions: Hybrid human-AI consulting accelerates; OpenAI's 150 forward-deployed engineers likely use their models symbiotically. Risks: Vertical integration biases; competition intensifies between tech-enabled consultancies.",[17,4359,4361],{"id":4360},"ai-shifts-cybersecurity-toward-offense-with-zero-day-exploits","AI Shifts Cybersecurity Toward Offense with Zero-Day Exploits",[22,4363,4364],{},"Google's disclosure of AI-discovered and exploited zero-days alarms panelists on offense-defense imbalance. Dustin Haywood (Evil Mog, IBM X-Force) joins to unpack how AI automates vulnerability hunting, chaining exploits faster than human red teams. Consensus: AI lowers barriers for attackers, amplifying threats; defenders lag as patching cycles outpace discovery.",[22,4366,4367],{},"Dustin argues AI excels at pattern recognition in codebases, generating payloads autonomously—Google's case proves real-world efficacy. Aaron ties to Melia: Verifiable skills prevent agent misuse in security tools. Kush warns generative skills marketplaces enable malicious agents. Trade-offs: AI boosts blue-team automation (e.g., anomaly detection) but red-team gains outpace, per Dustin's hacker perspective.",[22,4369,4370],{},"Predictions: Expect AI-driven zero-day surges; enterprises need compiled, guarded agents. Brianna Frank (Red Hat VP) adds from Summit: Culture trumps tech—AI transformation fails without change management.",[17,4372,4374],{"id":4373},"enterprise-ai-adoption-culture-first-tech-second","Enterprise AI Adoption: Culture First, Tech Second",[22,4376,4377],{},"Brianna emphasizes Red Hat Summit insights: AI succeeds via cultural shifts, not just tools. Agreements: Tech is 10-50% of challenge; integration\u002Fchange management dominates. Ties to Melia\u002FOpenAI: Secure skills and consulting address operational gaps.",[22,4379,4380],{},[42,4381,4382],{},"Key Takeaways",[36,4384,4385,4388,4391,4394,4397,4400,4403,4406],{},[39,4386,4387],{},"Compile AI skills from natural language to Python with Melia for security, versioning, and reuse—check melia.ai to start.",[39,4389,4390],{},"Use deterministic code + targeted LLM calls in generative computing to balance flexibility and safety.",[39,4392,4393],{},"OpenAI's consulting pivot proves integration > models; evaluate hybrid human-AI teams for your stack.",[39,4395,4396],{},"Prioritize model-agnostic sovereignty in enterprise AI to avoid vendor lock-in.",[39,4398,4399],{},"AI tilts cyber offense ahead—harden agents against injection, automate defenses with verified skills.",[39,4401,4402],{},"Scale AI enterprise-wide requires culture change; only 1\u002F3 succeed today.",[39,4404,4405],{},"Version prompts as code libraries for large teams; protect tools with CDNs and permissions.",[39,4407,4408],{},"Watch Anthropic\u002FIBM for competing consulting models blending virtual workers.",[22,4410,4411],{},[42,4412,4413],{},"Notable Quotes",[36,4415,4416,4419,4422,4430,4433],{},[39,4417,4418],{},"Kush Varshney: \"Just like in any other compiler... you make things easier for the programmer... but then in the back end you compile it into something that's hardened that's more robust.\" (On Melia's value for enterprise deployment.)",[39,4420,4421],{},"Aaron Baughman: \"It's going to change jobs... make consulting... able to solve problems that they otherwise would not have been able to. But I don't think it's going to be the traditional consulting.\" (Predicting AI-human merger in services.)",[39,4423,4424,4425,4429],{},"Dustin Haywood: ",[4426,4427,4428],"span",{},"Transcript truncated, but context implies on zero-days: AI chains exploits faster than humans."," (Note: Full quote unavailable due to truncation; panel stresses AI's red-team speed.)",[39,4431,4432],{},"Tim Hwang: \"The skills ecosystem is pretty scary right now.\" (Framing OpenClaw chaos Melia solves.)",[39,4434,4435],{},"Brianna Frank: Enterprise AI is \"a culture challenge first, technology quest second.\" (From Red Hat Summit segment.)",{"title":91,"searchDepth":92,"depth":92,"links":4437},[4438,4439,4440,4441],{"id":4334,"depth":92,"text":4335},{"id":4347,"depth":92,"text":4348},{"id":4360,"depth":92,"text":4361},{"id":4373,"depth":92,"text":4374},[98],{"content_references":4444,"triage":4458},[4445,4448,4453,4456],{"type":105,"title":4446,"url":4447,"context":108},"Melia Skills Compiler","https:\u002F\u002Fmelia.ai",{"type":4449,"title":4450,"author":4451,"url":4452,"context":112},"podcast","Mixture of Experts","IBM Technology","https:\u002F\u002Fibm.biz\u002F~LKPrxFIbD",{"type":4140,"title":4454,"url":4455,"context":112},"IBM AI Newsletter","https:\u002F\u002Fibm.biz\u002F~HwGb4NJ13",{"type":105,"title":4457,"context":4138},"OpenClaw",{"relevance":119,"novelty":4459,"quality":119,"actionability":4459,"composite":4460,"reasoning":4461},3,3.6,"Category: AI & LLMs. The article discusses IBM's Melia compiler, which transforms natural language into secure Python programs, addressing a specific pain point of integrating AI into production environments. While it provides some practical insights, the content is more focused on high-level concepts rather than detailed actionable steps.","\u002Fsummaries\u002F47f6e1401b14afd4-melia-secures-ai-skills-openai-pivots-to-consultin-summary","2026-05-15 10:01:01","2026-05-15 11:00:16",{"title":4324,"description":91},{"loc":4462},"47f6e1401b14afd4","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=YCWwh70FZtQ","summaries\u002F47f6e1401b14afd4-melia-secures-ai-skills-openai-pivots-to-consultin-summary",[135,134,136,4471],"ai-automation","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FYCWwh70FZtQ\u002Fhqdefault.jpg","IBM's Melia compiles natural language AI skills into secure Python for enterprise safety; OpenAI's $10B consulting arm signals integration as AI's real business; Google AI exploits zero-days, tilting cyber offense-defense balance.","Panel discussion on IBM's [MELLEA](https:\u002F\u002Fibm.biz\u002F~LKPrxFIbD) skills compiler (turns natural language agent skills into verifiable Python), OpenAI's $10B consulting venture, Google AI-discovered zero-days, and enterprise AI culture shifts from Red Hat Summit.",[4471],"roKZNhkCKL7YVngbovpQ0fvI7a68zHxIiz2-TQXA5sg",{"id":4478,"title":4479,"ai":4480,"body":4485,"categories":4525,"created_at":99,"date_modified":99,"description":91,"extension":100,"faq":99,"featured":101,"kicker_label":99,"meta":4526,"navigation":122,"path":4533,"published_at":4534,"question":99,"scraped_at":4535,"seo":4536,"sitemap":4537,"source_id":4538,"source_name":4314,"source_type":4164,"source_url":4539,"stem":4540,"tags":4541,"thumbnail_url":99,"tldr":4542,"tweet":99,"unknown_tags":4543,"__hash__":4544},"summaries\u002Fsummaries\u002F3b2f08fbb5006360-modular-hybrid-memory-agent-with-openai-tools-summary.md","Modular Hybrid-Memory Agent with OpenAI Tools",{"provider":7,"model":4027,"input_tokens":4481,"output_tokens":4482,"processing_time_ms":4483,"cost_usd":4484},9343,1485,22683,0.0025886,{"type":14,"value":4486,"toc":4520},[4487,4491,4494,4497,4501,4504,4507,4510,4514,4517],[17,4488,4490],{"id":4489},"hybrid-memory-combines-vector-and-keyword-search-via-rrf","Hybrid Memory Combines Vector and Keyword Search via RRF",[22,4492,4493],{},"Store facts as embedded chunks with metadata (e.g., category: 'user_pref') using OpenAI's text-embedding-3-small, normalized to unit vectors. Maintain a live BM25Okapi index on tokenized text (lowercase alphanum only). Retrieve top_k=5 by computing cosine similarities for semantics and BM25 scores for keywords, then fuse ranks with Reciprocal Rank Fusion: score = 1\u002F(60 + vec_rank) + 1\u002F(60 + kw_rank). This handles exact matches missed by embeddings (e.g., \"order 4821\" retrieves via BM25 despite low cosine) and semantic queries (e.g., \"consensus algorithm\" pulls Raft via vectors). Results include id, text, metadata, rrf_score, cosine, and bm25 for transparency. Dump all memories or search directly for inspection.",[22,4495,4496],{},"Trade-off: In-memory only, rebuilds BM25 on every store (fine for \u003C1000 chunks); scales by swapping MemoryBackend impl.",[17,4498,4500],{"id":4499},"autonomous-loop-with-persona-driven-tool-dispatch","Autonomous Loop with Persona-Driven Tool Dispatch",[22,4502,4503],{},"Agent owns history, memory, tools dict, and LLM (gpt-4o-mini, temp=0.2). Per user message: search memory top_k=3, inject as context into persona's system prompt (compiles traits like \"Methodical\", goals like \"Use tools proactively\", forbids \"I cannot\"). Loop up to 8 rounds: call LLM with tool schemas (OpenAI function spec), parse tool_calls, execute (e.g., memory_store, calculator with safe eval on math funcs, mock web_search), append tool results by id. Stops on text reply.",[22,4505,4506],{},"Tools auto-register schemas with params (e.g., memory_search: query str, top_k int). Persona ensures consistency: reason step-by-step, quote memory IDs, stay concise. Hot-swap tools at runtime (e.g., upgrade web_search KB with \"lsm-tree\" snippet) via register_tool—no restart needed.",[22,4508,4509],{},"Interfaces (ABC: MemoryBackend, LLMProvider, Tool) enable swaps: plug Anthropic for LLM or Pinecone for memory without agent changes.",[17,4511,4513],{"id":4512},"demos-prove-recall-reasoning-and-persistence","Demos Prove Recall, Reasoning, and Persistence",[22,4515,4516],{},"Pre-seed 7 facts (e.g., \"VelocityDB uses Raft\", deadline March 31). Query \"What consensus algorithm does VelocityDB use?\" yields mem_0003 (cosine=0.847, bm25=1.23, rrf=0.03328). Agent chats recall project\u002Fdeadline\u002FRaft, finds order #4821 (32GB RAM), computes 22 days * 6.5h = 143h left (via calculator: safe eval on math lib). Stores new facts autonomously (e.g., switch to B-tree), recalls them next turn, explains B-tree fit via upgraded tool (read-optimized vs LSM write-heavy). Full dump verifies 8 chunks persisted across turns.",[22,4518,4519],{},"This modular design persists state, reasons over history+memory, acts via tools, and extends without core rewrites—ready for prod with vector DB swap.",{"title":91,"searchDepth":92,"depth":92,"links":4521},[4522,4523,4524],{"id":4489,"depth":92,"text":4490},{"id":4499,"depth":92,"text":4500},{"id":4512,"depth":92,"text":4513},[98],{"content_references":4527,"triage":4531},[4528],{"type":4140,"title":4529,"url":4530,"context":108},"Full Codes with Notebook","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fhybrid_memory_autonomous_agent_Marktechpost.ipynb",{"relevance":118,"novelty":119,"quality":119,"actionability":118,"composite":4156,"reasoning":4532},"Category: AI & LLMs. The article provides a detailed guide on building a hybrid-memory autonomous agent using OpenAI tools, addressing practical applications for developers looking to implement AI features. It includes specific techniques like using RRF for memory management and modular tool dispatch, making it highly actionable.","\u002Fsummaries\u002F3b2f08fbb5006360-modular-hybrid-memory-agent-with-openai-tools-summary","2026-05-12 21:55:57","2026-05-13 12:00:56",{"title":4479,"description":91},{"loc":4533},"3b2f08fbb5006360","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F12\u002Fbuild-a-hybrid-memory-autonomous-agent-with-modular-architecture-and-tool-dispatch-using-openai\u002F","summaries\u002F3b2f08fbb5006360-modular-hybrid-memory-agent-with-openai-tools-summary",[135,134,136,4471],"Build a production-ready autonomous agent in Python using hybrid vector+BM25 memory fused by RRF (K=60), modular tool dispatch, and a self-managing loop limited to 8 tool rounds for reliable reasoning and action.",[4471],"yvRrpH4xRSccwcw181arJwfSPBH9-_EPx2atVPP8tIs"]