[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-23f811571aca670f-gemma-4-elite-open-performance-at-31b-params-summary":3,"summaries-facets-categories":78,"summary-related-23f811571aca670f-gemma-4-elite-open-performance-at-31b-params-summary":3647},{"id":4,"title":5,"ai":6,"body":13,"categories":52,"created_at":54,"date_modified":54,"description":55,"extension":56,"faq":54,"featured":57,"kicker_label":54,"meta":58,"navigation":59,"path":60,"published_at":61,"question":54,"scraped_at":62,"seo":63,"sitemap":64,"source_id":65,"source_name":66,"source_type":67,"source_url":68,"stem":69,"tags":70,"thumbnail_url":54,"tldr":75,"tweet":54,"unknown_tags":76,"__hash__":77},"summaries\u002Fsummaries\u002F23f811571aca670f-gemma-4-elite-open-performance-at-31b-params-summary.md","Gemma 4: Elite Open Performance at 31B Params",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",5099,1362,11964,0.00167815,{"type":14,"value":15,"toc":45},"minimark",[16,21,25,28,32,35,38,42],[17,18,20],"h2",{"id":19},"unmatched-efficiency-high-elo-and-arena-ranking-in-small-models","Unmatched Efficiency: High ELO and Arena Ranking in Small Models",[22,23,24],"p",{},"Gemma 4 delivers top-tier intelligence per parameter, plotting high on ELO charts (y-axis) while staying left on the x-axis (total params in billions). The 31B dense model and 26B MoE (4B active params) rival Qwen 3.5's 397B (17B active) and outperform DeepSeek V3.2 or GPT-O OSS, despite being runnable on medium-high consumer GPUs like those without GB300-scale hardware. This shift—smaller, faster open models—supports hybrid setups: frontier hosted models for toughest tasks, edge compute for most workloads. On Arena text leaderboard, 31B ranks #3 worldwide (1452 score), trailing only massive GLM-5 and Kimi K2.5 but enabling local runs where giants fail.",[22,26,27],{},"Benchmarks reinforce this: MMLU 85.2%, AIME 2026 89% (frontier nears 100%), LiveCodeBench 80%, T2Bench 86%, GPQA Diamond 84.3%. ToolCall-15 scores perfectly for 31B, proving reliable function calling for agents.",[17,29,31],{"id":30},"edge-optimized-variants-for-multimodal-agents","Edge-Optimized Variants for Multimodal Agents",[22,33,34],{},"Four sizes target diverse hardware: effective 2B (E2B) and 4B (E4B) use per-layer embeddings (PLE)—small token-specific tables for fast lookups—yielding tiny inference footprints for mobile (phones, Raspberry Pi, Nvidia Jetson, Orin Nano). Developed with Pixel, Qualcomm, MediaTek teams, they run offline with zero latency, handling native audio, video\u002Fimages (variable res, OCR, charts). Larger 26B MoE\u002F31B dense add 256K context (edge at 128K), native function calling, JSON outputs, system prompts for autonomous agents interacting with tools\u002FAPIs.",[22,36,37],{},"Supports code gen as local assistant (though hosted like GPT-5\u002Fo1 best for serious coding), multi-step reasoning, math, instructions. Apache 2.0 license permits commercial use; available on Hugging Face, vLLM, llama.cpp, MLX, Ollama, Nvidia NIMs, LM Studio, Unsloth for fine-tuning\u002Finference.",[17,39,41],{"id":40},"trade-offs-strong-on-agents-short-on-context","Trade-offs: Strong on Agents, Short on Context",[22,43,44],{},"Gemma 4 excels in agentic workflows and logic but lags context—128K\u002F256K vs. desired longer windows—limiting long-doc tasks. Prioritize for on-device agents over chat; pair with hosted models for code\u002Fmath peaks. Download and test: 31B matches trillion-param giants on consumer hardware, accelerating local-first AI products.",{"title":46,"searchDepth":47,"depth":47,"links":48},"",2,[49,50,51],{"id":19,"depth":47,"text":20},{"id":30,"depth":47,"text":31},{"id":40,"depth":47,"text":41},[53],"AI & LLMs",null,"Try Recraft V4 today! Start creating with professional-grade AI at https:\u002F\u002Fgo.recraft.ai\u002Fcreate-with-berman\n\nDownload The 25 OpenClaw Use Cases eBook 👇🏼\nhttps:\u002F\u002Fbit.ly\u002F4aBQwo1\n\nDownload The Subtle Art of Not Being Replaced 👇🏼\nhttp:\u002F\u002Fbit.ly\u002F3WLNzdV\n\nDownload Humanities Last Prompt Engineering Guide 👇🏼\nhttps:\u002F\u002Fbit.ly\u002F4kFhajz\n\nJoin My Newsletter for Regular AI Updates 👇🏼\nhttps:\u002F\u002Fforwardfuture.ai\n\nDiscover The Best AI Tools👇🏼\nhttps:\u002F\u002Ftools.forwardfuture.ai\n\nMy Links 🔗\n👉🏻 X: https:\u002F\u002Fx.com\u002Fmatthewberman\n👉🏻 Forward Future X: https:\u002F\u002Fx.com\u002Fforwardfuture\n👉🏻 Instagram: https:\u002F\u002Fwww.instagram.com\u002Fmatthewberman_ai\n👉🏻 TikTok: https:\u002F\u002Fwww.tiktok.com\u002F@matthewberman_ai\n👉🏻 Spotify: https:\u002F\u002Fopen.spotify.com\u002Fshow\u002F6dBxDwxtHl1hpqHhfoXmy8\n\nMedia\u002FSponsorship Inquiries ✅ \nhttps:\u002F\u002Fbit.ly\u002F44TC45V","md",false,{},true,"\u002Fsummaries\u002F23f811571aca670f-gemma-4-elite-open-performance-at-31b-params-summary","2026-04-03 00:13:51","2026-04-03 21:18:38",{"title":5,"description":55},{"loc":60},"23f811571aca670f","Matthew Berman","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=BrJdGP21B5g","summaries\u002F23f811571aca670f-gemma-4-elite-open-performance-at-31b-params-summary",[71,72,73,74],"llm","open-source","agents","ai-news","Google's Gemma 4 31B dense model ranks #3 on Arena leaderboard (ELO ~1452), matching Qwen 3.5's intelligence in 1\u002F10th the size—runs on consumer GPUs for agents and edge devices.",[74],"TWo79UwZkb-XbIbcz7NR5B-vcXcH49GIXDSfCenKKYI",[79,82,85,87,90,93,95,97,99,101,103,105,108,110,112,114,116,118,120,122,124,126,129,132,134,136,139,141,143,146,148,150,152,154,156,158,160,162,164,166,168,170,172,174,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2465,2467,2469,2471,2473,2475,2477,2479,2481,2483,2485,2487,2489,2491,2493,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2625,2627,2629,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645],{"categories":80},[81],"Developer Productivity",{"categories":83},[84],"Business & SaaS",{"categories":86},[53],{"categories":88},[89],"AI Automation",{"categories":91},[92],"Product Strategy",{"categories":94},[53],{"categories":96},[81],{"categories":98},[84],{"categories":100},[],{"categories":102},[53],{"categories":104},[],{"categories":106},[107],"AI News & Trends",{"categories":109},[89],{"categories":111},[107],{"categories":113},[89],{"categories":115},[89],{"categories":117},[53],{"categories":119},[53],{"categories":121},[107],{"categories":123},[53],{"categories":125},[],{"categories":127},[128],"Design & Frontend",{"categories":130},[131],"Data Science & Visualization",{"categories":133},[107],{"categories":135},[],{"categories":137},[138],"Software Engineering",{"categories":140},[53],{"categories":142},[89],{"categories":144},[145],"Marketing & Growth",{"categories":147},[53],{"categories":149},[89],{"categories":151},[],{"categories":153},[],{"categories":155},[128],{"categories":157},[89],{"categories":159},[81],{"categories":161},[128],{"categories":163},[53],{"categories":165},[89],{"categories":167},[107],{"categories":169},[],{"categories":171},[],{"categories":173},[89],{"categories":175},[138],{"categories":177},[],{"categories":179},[84],{"categories":181},[],{"categories":183},[],{"categories":185},[89],{"categories":187},[89],{"categories":189},[53],{"categories":191},[],{"categories":193},[138],{"categories":195},[],{"categories":197},[],{"categories":199},[],{"categories":201},[53],{"categories":203},[145],{"categories":205},[128],{"categories":207},[128],{"categories":209},[53],{"categories":211},[89],{"categories":213},[53],{"categories":215},[53],{"categories":217},[89],{"categories":219},[89],{"categories":221},[131],{"categories":223},[107],{"categories":225},[89],{"categories":227},[145],{"categories":229},[89],{"categories":231},[92],{"categories":233},[],{"categories":235},[89],{"categories":237},[],{"categories":239},[89],{"categories":241},[138],{"categories":243},[128],{"categories":245},[53],{"categories":247},[],{"categories":249},[],{"categories":251},[89],{"categories":253},[],{"categories":255},[53],{"categories":257},[],{"categories":259},[81],{"categories":261},[138],{"categories":263},[84],{"categories":265},[107],{"categories":267},[53],{"categories":269},[],{"categories":271},[53],{"categories":273},[],{"categories":275},[138],{"categories":277},[131],{"categories":279},[],{"categories":281},[53],{"categories":283},[128],{"categories":285},[],{"categories":287},[128],{"categories":289},[89],{"categories":291},[],{"categories":293},[89],{"categories":295},[107],{"categories":297},[53],{"categories":299},[],{"categories":301},[89],{"categories":303},[53],{"categories":305},[92],{"categories":307},[],{"categories":309},[53],{"categories":311},[89],{"categories":313},[89],{"categories":315},[],{"categories":317},[131],{"categories":319},[53],{"categories":321},[],{"categories":323},[81],{"categories":325},[84],{"categories":327},[53],{"categories":329},[89],{"categories":331},[138],{"categories":333},[53],{"categories":335},[],{"categories":337},[],{"categories":339},[53],{"categories":341},[],{"categories":343},[128],{"categories":345},[],{"categories":347},[53],{"categories":349},[],{"categories":351},[89],{"categories":353},[53],{"categories":355},[128],{"categories":357},[],{"categories":359},[53],{"categories":361},[53],{"categories":363},[84],{"categories":365},[89],{"categories":367},[53],{"categories":369},[128],{"categories":371},[89],{"categories":373},[],{"categories":375},[],{"categories":377},[107],{"categories":379},[],{"categories":381},[53],{"categories":383},[84,145],{"categories":385},[],{"categories":387},[53],{"categories":389},[],{"categories":391},[],{"categories":393},[53],{"categories":395},[],{"categories":397},[53],{"categories":399},[400],"DevOps & Cloud",{"categories":402},[],{"categories":404},[107],{"categories":406},[128],{"categories":408},[],{"categories":410},[107],{"categories":412},[107],{"categories":414},[53],{"categories":416},[145],{"categories":418},[],{"categories":420},[84],{"categories":422},[],{"categories":424},[53,400],{"categories":426},[53],{"categories":428},[53],{"categories":430},[89],{"categories":432},[53,138],{"categories":434},[131],{"categories":436},[53],{"categories":438},[145],{"categories":440},[89],{"categories":442},[89],{"categories":444},[],{"categories":446},[89],{"categories":448},[53,84],{"categories":450},[],{"categories":452},[128],{"categories":454},[128],{"categories":456},[],{"categories":458},[],{"categories":460},[107],{"categories":462},[],{"categories":464},[81],{"categories":466},[138],{"categories":468},[53],{"categories":470},[128],{"categories":472},[89],{"categories":474},[138],{"categories":476},[107],{"categories":478},[128],{"categories":480},[],{"categories":482},[53],{"categories":484},[53],{"categories":486},[53],{"categories":488},[107],{"categories":490},[81],{"categories":492},[53],{"categories":494},[89],{"categories":496},[400],{"categories":498},[128],{"categories":500},[89],{"categories":502},[],{"categories":504},[],{"categories":506},[128],{"categories":508},[107],{"categories":510},[131],{"categories":512},[],{"categories":514},[53],{"categories":516},[53],{"categories":518},[84],{"categories":520},[53],{"categories":522},[53],{"categories":524},[107],{"categories":526},[],{"categories":528},[89],{"categories":530},[138],{"categories":532},[],{"categories":534},[53],{"categories":536},[53],{"categories":538},[89],{"categories":540},[],{"categories":542},[],{"categories":544},[53],{"categories":546},[],{"categories":548},[84],{"categories":550},[89],{"categories":552},[],{"categories":554},[81],{"categories":556},[53],{"categories":558},[84],{"categories":560},[107],{"categories":562},[],{"categories":564},[],{"categories":566},[],{"categories":568},[107],{"categories":570},[107],{"categories":572},[],{"categories":574},[],{"categories":576},[84],{"categories":578},[],{"categories":580},[],{"categories":582},[81],{"categories":584},[],{"categories":586},[145],{"categories":588},[89],{"categories":590},[84],{"categories":592},[89],{"categories":594},[],{"categories":596},[92],{"categories":598},[128],{"categories":600},[138],{"categories":602},[53],{"categories":604},[89],{"categories":606},[84],{"categories":608},[53],{"categories":610},[],{"categories":612},[],{"categories":614},[138],{"categories":616},[131],{"categories":618},[92],{"categories":620},[89],{"categories":622},[53],{"categories":624},[],{"categories":626},[400],{"categories":628},[],{"categories":630},[89],{"categories":632},[],{"categories":634},[],{"categories":636},[53],{"categories":638},[128],{"categories":640},[145],{"categories":642},[89],{"categories":644},[],{"categories":646},[81],{"categories":648},[],{"categories":650},[107],{"categories":652},[53,400],{"categories":654},[107],{"categories":656},[53],{"categories":658},[84],{"categories":660},[53],{"categories":662},[],{"categories":664},[84],{"categories":666},[],{"categories":668},[138],{"categories":670},[128],{"categories":672},[107],{"categories":674},[131],{"categories":676},[81],{"categories":678},[53],{"categories":680},[138],{"categories":682},[],{"categories":684},[],{"categories":686},[92],{"categories":688},[],{"categories":690},[53],{"categories":692},[],{"categories":694},[128],{"categories":696},[128],{"categories":698},[128],{"categories":700},[],{"categories":702},[],{"categories":704},[107],{"categories":706},[89],{"categories":708},[53],{"categories":710},[53],{"categories":712},[53],{"categories":714},[84],{"categories":716},[53],{"categories":718},[],{"categories":720},[138],{"categories":722},[138],{"categories":724},[84],{"categories":726},[],{"categories":728},[53],{"categories":730},[53],{"categories":732},[84],{"categories":734},[107],{"categories":736},[145],{"categories":738},[89],{"categories":740},[],{"categories":742},[128],{"categories":744},[],{"categories":746},[53],{"categories":748},[],{"categories":750},[84],{"categories":752},[89],{"categories":754},[],{"categories":756},[400],{"categories":758},[131],{"categories":760},[138],{"categories":762},[145],{"categories":764},[138],{"categories":766},[89],{"categories":768},[],{"categories":770},[],{"categories":772},[89],{"categories":774},[81],{"categories":776},[89],{"categories":778},[92],{"categories":780},[84],{"categories":782},[],{"categories":784},[53],{"categories":786},[92],{"categories":788},[53],{"categories":790},[53],{"categories":792},[145],{"categories":794},[128],{"categories":796},[89],{"categories":798},[],{"categories":800},[],{"categories":802},[400],{"categories":804},[138],{"categories":806},[],{"categories":808},[89],{"categories":810},[53],{"categories":812},[128,53],{"categories":814},[81],{"categories":816},[],{"categories":818},[53],{"categories":820},[81],{"categories":822},[128],{"categories":824},[89],{"categories":826},[138],{"categories":828},[],{"categories":830},[53],{"categories":832},[],{"categories":834},[81],{"categories":836},[],{"categories":838},[89],{"categories":840},[92],{"categories":842},[53],{"categories":844},[53],{"categories":846},[128],{"categories":848},[89],{"categories":850},[400],{"categories":852},[128],{"categories":854},[89],{"categories":856},[53],{"categories":858},[53],{"categories":860},[53],{"categories":862},[107],{"categories":864},[],{"categories":866},[92],{"categories":868},[89],{"categories":870},[128],{"categories":872},[89],{"categories":874},[138],{"categories":876},[128],{"categories":878},[89],{"categories":880},[107],{"categories":882},[],{"categories":884},[53],{"categories":886},[128],{"categories":888},[53],{"categories":890},[81],{"categories":892},[107],{"categories":894},[53],{"categories":896},[145],{"categories":898},[53],{"categories":900},[53],{"categories":902},[89],{"categories":904},[89],{"categories":906},[53],{"categories":908},[89],{"categories":910},[128],{"categories":912},[53],{"categories":914},[],{"categories":916},[],{"categories":918},[138],{"categories":920},[],{"categories":922},[81],{"categories":924},[400],{"categories":926},[],{"categories":928},[81],{"categories":930},[84],{"categories":932},[145],{"categories":934},[],{"categories":936},[84],{"categories":938},[],{"categories":940},[],{"categories":942},[],{"categories":944},[],{"categories":946},[],{"categories":948},[53],{"categories":950},[89],{"categories":952},[400],{"categories":954},[81],{"categories":956},[53],{"categories":958},[138],{"categories":960},[92],{"categories":962},[53],{"categories":964},[145],{"categories":966},[53],{"categories":968},[53],{"categories":970},[53],{"categories":972},[53,81],{"categories":974},[138],{"categories":976},[138],{"categories":978},[128],{"categories":980},[53],{"categories":982},[],{"categories":984},[],{"categories":986},[],{"categories":988},[138],{"categories":990},[131],{"categories":992},[107],{"categories":994},[128],{"categories":996},[],{"categories":998},[53],{"categories":1000},[53],{"categories":1002},[],{"categories":1004},[],{"categories":1006},[89],{"categories":1008},[53],{"categories":1010},[84],{"categories":1012},[],{"categories":1014},[81],{"categories":1016},[53],{"categories":1018},[81],{"categories":1020},[53],{"categories":1022},[138],{"categories":1024},[145],{"categories":1026},[53,128],{"categories":1028},[107],{"categories":1030},[128],{"categories":1032},[],{"categories":1034},[400],{"categories":1036},[128],{"categories":1038},[89],{"categories":1040},[],{"categories":1042},[],{"categories":1044},[],{"categories":1046},[],{"categories":1048},[138],{"categories":1050},[89],{"categories":1052},[89],{"categories":1054},[53],{"categories":1056},[53],{"categories":1058},[],{"categories":1060},[128],{"categories":1062},[],{"categories":1064},[],{"categories":1066},[89],{"categories":1068},[],{"categories":1070},[],{"categories":1072},[145],{"categories":1074},[145],{"categories":1076},[89],{"categories":1078},[],{"categories":1080},[53],{"categories":1082},[53],{"categories":1084},[138],{"categories":1086},[128],{"categories":1088},[128],{"categories":1090},[89],{"categories":1092},[81],{"categories":1094},[53],{"categories":1096},[128],{"categories":1098},[128],{"categories":1100},[89],{"categories":1102},[89],{"categories":1104},[53],{"categories":1106},[],{"categories":1108},[],{"categories":1110},[53],{"categories":1112},[89],{"categories":1114},[107],{"categories":1116},[138],{"categories":1118},[81],{"categories":1120},[53],{"categories":1122},[],{"categories":1124},[89],{"categories":1126},[89],{"categories":1128},[],{"categories":1130},[81],{"categories":1132},[53],{"categories":1134},[81],{"categories":1136},[81],{"categories":1138},[],{"categories":1140},[],{"categories":1142},[89],{"categories":1144},[89],{"categories":1146},[53],{"categories":1148},[53],{"categories":1150},[107],{"categories":1152},[131],{"categories":1154},[92],{"categories":1156},[107],{"categories":1158},[128],{"categories":1160},[],{"categories":1162},[107],{"categories":1164},[],{"categories":1166},[],{"categories":1168},[],{"categories":1170},[],{"categories":1172},[138],{"categories":1174},[131],{"categories":1176},[],{"categories":1178},[53],{"categories":1180},[53],{"categories":1182},[131],{"categories":1184},[138],{"categories":1186},[],{"categories":1188},[],{"categories":1190},[89],{"categories":1192},[107],{"categories":1194},[107],{"categories":1196},[89],{"categories":1198},[81],{"categories":1200},[53,400],{"categories":1202},[],{"categories":1204},[128],{"categories":1206},[81],{"categories":1208},[89],{"categories":1210},[128],{"categories":1212},[],{"categories":1214},[89],{"categories":1216},[89],{"categories":1218},[53],{"categories":1220},[145],{"categories":1222},[138],{"categories":1224},[128],{"categories":1226},[],{"categories":1228},[89],{"categories":1230},[53],{"categories":1232},[89],{"categories":1234},[89],{"categories":1236},[89],{"categories":1238},[145],{"categories":1240},[89],{"categories":1242},[53],{"categories":1244},[],{"categories":1246},[145],{"categories":1248},[107],{"categories":1250},[89],{"categories":1252},[],{"categories":1254},[],{"categories":1256},[53],{"categories":1258},[89],{"categories":1260},[107],{"categories":1262},[89],{"categories":1264},[],{"categories":1266},[],{"categories":1268},[],{"categories":1270},[89],{"categories":1272},[],{"categories":1274},[],{"categories":1276},[131],{"categories":1278},[53],{"categories":1280},[131],{"categories":1282},[107],{"categories":1284},[53],{"categories":1286},[53],{"categories":1288},[89],{"categories":1290},[53],{"categories":1292},[],{"categories":1294},[],{"categories":1296},[400],{"categories":1298},[],{"categories":1300},[],{"categories":1302},[81],{"categories":1304},[],{"categories":1306},[],{"categories":1308},[],{"categories":1310},[],{"categories":1312},[138],{"categories":1314},[107],{"categories":1316},[145],{"categories":1318},[84],{"categories":1320},[53],{"categories":1322},[53],{"categories":1324},[84],{"categories":1326},[],{"categories":1328},[128],{"categories":1330},[89],{"categories":1332},[84],{"categories":1334},[53],{"categories":1336},[53],{"categories":1338},[81],{"categories":1340},[],{"categories":1342},[81],{"categories":1344},[53],{"categories":1346},[145],{"categories":1348},[89],{"categories":1350},[107],{"categories":1352},[84],{"categories":1354},[53],{"categories":1356},[89],{"categories":1358},[],{"categories":1360},[53],{"categories":1362},[81],{"categories":1364},[53],{"categories":1366},[],{"categories":1368},[107],{"categories":1370},[53],{"categories":1372},[],{"categories":1374},[84],{"categories":1376},[53],{"categories":1378},[],{"categories":1380},[],{"categories":1382},[],{"categories":1384},[53],{"categories":1386},[],{"categories":1388},[400],{"categories":1390},[53],{"categories":1392},[],{"categories":1394},[53],{"categories":1396},[53],{"categories":1398},[53],{"categories":1400},[53,400],{"categories":1402},[53],{"categories":1404},[53],{"categories":1406},[128],{"categories":1408},[89],{"categories":1410},[],{"categories":1412},[89],{"categories":1414},[53],{"categories":1416},[53],{"categories":1418},[53],{"categories":1420},[81],{"categories":1422},[81],{"categories":1424},[138],{"categories":1426},[128],{"categories":1428},[89],{"categories":1430},[],{"categories":1432},[53],{"categories":1434},[107],{"categories":1436},[53],{"categories":1438},[84],{"categories":1440},[],{"categories":1442},[400],{"categories":1444},[128],{"categories":1446},[128],{"categories":1448},[89],{"categories":1450},[107],{"categories":1452},[89],{"categories":1454},[53],{"categories":1456},[],{"categories":1458},[53],{"categories":1460},[],{"categories":1462},[],{"categories":1464},[53],{"categories":1466},[53],{"categories":1468},[53],{"categories":1470},[89],{"categories":1472},[53],{"categories":1474},[],{"categories":1476},[131],{"categories":1478},[89],{"categories":1480},[],{"categories":1482},[53],{"categories":1484},[107],{"categories":1486},[],{"categories":1488},[128],{"categories":1490},[400],{"categories":1492},[107],{"categories":1494},[138],{"categories":1496},[138],{"categories":1498},[107],{"categories":1500},[107],{"categories":1502},[400],{"categories":1504},[],{"categories":1506},[107],{"categories":1508},[53],{"categories":1510},[81],{"categories":1512},[107],{"categories":1514},[],{"categories":1516},[131],{"categories":1518},[107],{"categories":1520},[138],{"categories":1522},[107],{"categories":1524},[400],{"categories":1526},[53],{"categories":1528},[53],{"categories":1530},[],{"categories":1532},[84],{"categories":1534},[],{"categories":1536},[],{"categories":1538},[53],{"categories":1540},[53],{"categories":1542},[53],{"categories":1544},[53],{"categories":1546},[],{"categories":1548},[131],{"categories":1550},[81],{"categories":1552},[],{"categories":1554},[53],{"categories":1556},[53],{"categories":1558},[400],{"categories":1560},[400],{"categories":1562},[],{"categories":1564},[89],{"categories":1566},[107],{"categories":1568},[107],{"categories":1570},[53],{"categories":1572},[89],{"categories":1574},[],{"categories":1576},[128],{"categories":1578},[53],{"categories":1580},[53],{"categories":1582},[],{"categories":1584},[],{"categories":1586},[400],{"categories":1588},[53],{"categories":1590},[138],{"categories":1592},[84],{"categories":1594},[53],{"categories":1596},[],{"categories":1598},[89],{"categories":1600},[81],{"categories":1602},[81],{"categories":1604},[],{"categories":1606},[53],{"categories":1608},[128],{"categories":1610},[89],{"categories":1612},[],{"categories":1614},[53],{"categories":1616},[53],{"categories":1618},[89],{"categories":1620},[],{"categories":1622},[89],{"categories":1624},[138],{"categories":1626},[],{"categories":1628},[53],{"categories":1630},[],{"categories":1632},[53],{"categories":1634},[],{"categories":1636},[53],{"categories":1638},[53],{"categories":1640},[],{"categories":1642},[53],{"categories":1644},[107],{"categories":1646},[53],{"categories":1648},[53],{"categories":1650},[81],{"categories":1652},[53],{"categories":1654},[107],{"categories":1656},[89],{"categories":1658},[],{"categories":1660},[53],{"categories":1662},[145],{"categories":1664},[],{"categories":1666},[],{"categories":1668},[],{"categories":1670},[81],{"categories":1672},[107],{"categories":1674},[89],{"categories":1676},[53],{"categories":1678},[128],{"categories":1680},[89],{"categories":1682},[],{"categories":1684},[89],{"categories":1686},[],{"categories":1688},[53],{"categories":1690},[89],{"categories":1692},[53],{"categories":1694},[],{"categories":1696},[53],{"categories":1698},[53],{"categories":1700},[107],{"categories":1702},[128],{"categories":1704},[89],{"categories":1706},[128],{"categories":1708},[84],{"categories":1710},[],{"categories":1712},[],{"categories":1714},[53],{"categories":1716},[81],{"categories":1718},[107],{"categories":1720},[],{"categories":1722},[],{"categories":1724},[138],{"categories":1726},[128],{"categories":1728},[],{"categories":1730},[53],{"categories":1732},[],{"categories":1734},[145],{"categories":1736},[53],{"categories":1738},[400],{"categories":1740},[138],{"categories":1742},[],{"categories":1744},[89],{"categories":1746},[53],{"categories":1748},[89],{"categories":1750},[89],{"categories":1752},[53],{"categories":1754},[],{"categories":1756},[81],{"categories":1758},[53],{"categories":1760},[84],{"categories":1762},[138],{"categories":1764},[128],{"categories":1766},[],{"categories":1768},[],{"categories":1770},[],{"categories":1772},[89],{"categories":1774},[128],{"categories":1776},[107],{"categories":1778},[53],{"categories":1780},[107],{"categories":1782},[128],{"categories":1784},[],{"categories":1786},[128],{"categories":1788},[107],{"categories":1790},[84],{"categories":1792},[53],{"categories":1794},[107],{"categories":1796},[145],{"categories":1798},[],{"categories":1800},[],{"categories":1802},[131],{"categories":1804},[53,138],{"categories":1806},[107],{"categories":1808},[53],{"categories":1810},[89],{"categories":1812},[89],{"categories":1814},[53],{"categories":1816},[],{"categories":1818},[138],{"categories":1820},[53],{"categories":1822},[131],{"categories":1824},[89],{"categories":1826},[145],{"categories":1828},[400],{"categories":1830},[],{"categories":1832},[81],{"categories":1834},[89],{"categories":1836},[89],{"categories":1838},[138],{"categories":1840},[53],{"categories":1842},[53],{"categories":1844},[],{"categories":1846},[],{"categories":1848},[],{"categories":1850},[400],{"categories":1852},[107],{"categories":1854},[53],{"categories":1856},[53],{"categories":1858},[53],{"categories":1860},[],{"categories":1862},[131],{"categories":1864},[84],{"categories":1866},[],{"categories":1868},[89],{"categories":1870},[400],{"categories":1872},[],{"categories":1874},[128],{"categories":1876},[128],{"categories":1878},[],{"categories":1880},[138],{"categories":1882},[128],{"categories":1884},[53],{"categories":1886},[],{"categories":1888},[107],{"categories":1890},[53],{"categories":1892},[128],{"categories":1894},[89],{"categories":1896},[107],{"categories":1898},[],{"categories":1900},[89],{"categories":1902},[128],{"categories":1904},[53],{"categories":1906},[],{"categories":1908},[53],{"categories":1910},[53],{"categories":1912},[400],{"categories":1914},[107],{"categories":1916},[131],{"categories":1918},[131],{"categories":1920},[],{"categories":1922},[],{"categories":1924},[],{"categories":1926},[89],{"categories":1928},[138],{"categories":1930},[138],{"categories":1932},[],{"categories":1934},[],{"categories":1936},[53],{"categories":1938},[],{"categories":1940},[89],{"categories":1942},[53],{"categories":1944},[],{"categories":1946},[53],{"categories":1948},[84],{"categories":1950},[53],{"categories":1952},[145],{"categories":1954},[89],{"categories":1956},[53],{"categories":1958},[138],{"categories":1960},[107],{"categories":1962},[89],{"categories":1964},[],{"categories":1966},[107],{"categories":1968},[89],{"categories":1970},[89],{"categories":1972},[],{"categories":1974},[84],{"categories":1976},[89],{"categories":1978},[],{"categories":1980},[53],{"categories":1982},[81],{"categories":1984},[107],{"categories":1986},[400],{"categories":1988},[89],{"categories":1990},[89],{"categories":1992},[81],{"categories":1994},[53],{"categories":1996},[],{"categories":1998},[],{"categories":2000},[128],{"categories":2002},[53,84],{"categories":2004},[],{"categories":2006},[81],{"categories":2008},[131],{"categories":2010},[53],{"categories":2012},[138],{"categories":2014},[53],{"categories":2016},[89],{"categories":2018},[53],{"categories":2020},[53],{"categories":2022},[107],{"categories":2024},[89],{"categories":2026},[],{"categories":2028},[],{"categories":2030},[89],{"categories":2032},[53],{"categories":2034},[400],{"categories":2036},[],{"categories":2038},[53],{"categories":2040},[89],{"categories":2042},[],{"categories":2044},[53],{"categories":2046},[145],{"categories":2048},[131],{"categories":2050},[89],{"categories":2052},[53],{"categories":2054},[400],{"categories":2056},[],{"categories":2058},[53],{"categories":2060},[145],{"categories":2062},[128],{"categories":2064},[53],{"categories":2066},[],{"categories":2068},[145],{"categories":2070},[107],{"categories":2072},[53],{"categories":2074},[53],{"categories":2076},[81],{"categories":2078},[],{"categories":2080},[],{"categories":2082},[128],{"categories":2084},[53],{"categories":2086},[131],{"categories":2088},[145],{"categories":2090},[145],{"categories":2092},[107],{"categories":2094},[],{"categories":2096},[],{"categories":2098},[53],{"categories":2100},[],{"categories":2102},[53,138],{"categories":2104},[107],{"categories":2106},[89],{"categories":2108},[138],{"categories":2110},[53],{"categories":2112},[81],{"categories":2114},[],{"categories":2116},[],{"categories":2118},[81],{"categories":2120},[145],{"categories":2122},[53],{"categories":2124},[],{"categories":2126},[128,53],{"categories":2128},[400],{"categories":2130},[81],{"categories":2132},[],{"categories":2134},[84],{"categories":2136},[84],{"categories":2138},[53],{"categories":2140},[138],{"categories":2142},[89],{"categories":2144},[107],{"categories":2146},[145],{"categories":2148},[128],{"categories":2150},[53],{"categories":2152},[53],{"categories":2154},[53],{"categories":2156},[81],{"categories":2158},[53],{"categories":2160},[89],{"categories":2162},[107],{"categories":2164},[],{"categories":2166},[],{"categories":2168},[131],{"categories":2170},[138],{"categories":2172},[53],{"categories":2174},[128],{"categories":2176},[131],{"categories":2178},[53],{"categories":2180},[53],{"categories":2182},[89],{"categories":2184},[89],{"categories":2186},[53,84],{"categories":2188},[],{"categories":2190},[128],{"categories":2192},[],{"categories":2194},[53],{"categories":2196},[107],{"categories":2198},[81],{"categories":2200},[81],{"categories":2202},[89],{"categories":2204},[53],{"categories":2206},[84],{"categories":2208},[138],{"categories":2210},[145],{"categories":2212},[],{"categories":2214},[107],{"categories":2216},[53],{"categories":2218},[53],{"categories":2220},[107],{"categories":2222},[138],{"categories":2224},[53],{"categories":2226},[89],{"categories":2228},[107],{"categories":2230},[53],{"categories":2232},[128],{"categories":2234},[53],{"categories":2236},[53],{"categories":2238},[400],{"categories":2240},[92],{"categories":2242},[89],{"categories":2244},[53],{"categories":2246},[107],{"categories":2248},[89],{"categories":2250},[145],{"categories":2252},[53],{"categories":2254},[],{"categories":2256},[53],{"categories":2258},[],{"categories":2260},[],{"categories":2262},[],{"categories":2264},[84],{"categories":2266},[53],{"categories":2268},[89],{"categories":2270},[107],{"categories":2272},[107],{"categories":2274},[107],{"categories":2276},[107],{"categories":2278},[],{"categories":2280},[81],{"categories":2282},[89],{"categories":2284},[107],{"categories":2286},[81],{"categories":2288},[89],{"categories":2290},[53],{"categories":2292},[53,89],{"categories":2294},[89],{"categories":2296},[400],{"categories":2298},[107],{"categories":2300},[107],{"categories":2302},[89],{"categories":2304},[53],{"categories":2306},[],{"categories":2308},[107],{"categories":2310},[145],{"categories":2312},[81],{"categories":2314},[53],{"categories":2316},[53],{"categories":2318},[],{"categories":2320},[138],{"categories":2322},[],{"categories":2324},[81],{"categories":2326},[89],{"categories":2328},[107],{"categories":2330},[53],{"categories":2332},[107],{"categories":2334},[81],{"categories":2336},[107],{"categories":2338},[107],{"categories":2340},[],{"categories":2342},[84],{"categories":2344},[89],{"categories":2346},[107],{"categories":2348},[107],{"categories":2350},[107],{"categories":2352},[107],{"categories":2354},[107],{"categories":2356},[107],{"categories":2358},[107],{"categories":2360},[107],{"categories":2362},[107],{"categories":2364},[107],{"categories":2366},[131],{"categories":2368},[81],{"categories":2370},[53],{"categories":2372},[53],{"categories":2374},[],{"categories":2376},[53,81],{"categories":2378},[],{"categories":2380},[89],{"categories":2382},[107],{"categories":2384},[89],{"categories":2386},[53],{"categories":2388},[53],{"categories":2390},[53],{"categories":2392},[53],{"categories":2394},[53],{"categories":2396},[89],{"categories":2398},[84],{"categories":2400},[128],{"categories":2402},[107],{"categories":2404},[53],{"categories":2406},[],{"categories":2408},[],{"categories":2410},[89],{"categories":2412},[128],{"categories":2414},[53],{"categories":2416},[],{"categories":2418},[],{"categories":2420},[145],{"categories":2422},[53],{"categories":2424},[],{"categories":2426},[],{"categories":2428},[81],{"categories":2430},[84],{"categories":2432},[53],{"categories":2434},[84],{"categories":2436},[128],{"categories":2438},[],{"categories":2440},[107],{"categories":2442},[],{"categories":2444},[128],{"categories":2446},[53],{"categories":2448},[145],{"categories":2450},[],{"categories":2452},[145],{"categories":2454},[],{"categories":2456},[],{"categories":2458},[89],{"categories":2460},[],{"categories":2462},[84],{"categories":2464},[81],{"categories":2466},[128],{"categories":2468},[138],{"categories":2470},[],{"categories":2472},[],{"categories":2474},[53],{"categories":2476},[81],{"categories":2478},[145],{"categories":2480},[],{"categories":2482},[89],{"categories":2484},[89],{"categories":2486},[107],{"categories":2488},[53],{"categories":2490},[89],{"categories":2492},[53],{"categories":2494},[89],{"categories":2496},[53],{"categories":2498},[92],{"categories":2500},[107],{"categories":2502},[],{"categories":2504},[145],{"categories":2506},[138],{"categories":2508},[89],{"categories":2510},[],{"categories":2512},[53],{"categories":2514},[89],{"categories":2516},[84],{"categories":2518},[81],{"categories":2520},[53],{"categories":2522},[128],{"categories":2524},[138],{"categories":2526},[138],{"categories":2528},[53],{"categories":2530},[131],{"categories":2532},[53],{"categories":2534},[89],{"categories":2536},[84],{"categories":2538},[89],{"categories":2540},[53],{"categories":2542},[53],{"categories":2544},[89],{"categories":2546},[107],{"categories":2548},[],{"categories":2550},[81],{"categories":2552},[53],{"categories":2554},[89],{"categories":2556},[53],{"categories":2558},[53],{"categories":2560},[],{"categories":2562},[128],{"categories":2564},[84],{"categories":2566},[107],{"categories":2568},[53],{"categories":2570},[53],{"categories":2572},[128],{"categories":2574},[145],{"categories":2576},[131],{"categories":2578},[53],{"categories":2580},[107],{"categories":2582},[53],{"categories":2584},[89],{"categories":2586},[400],{"categories":2588},[53],{"categories":2590},[89],{"categories":2592},[131],{"categories":2594},[],{"categories":2596},[89],{"categories":2598},[138],{"categories":2600},[128],{"categories":2602},[53],{"categories":2604},[81],{"categories":2606},[84],{"categories":2608},[138],{"categories":2610},[],{"categories":2612},[89],{"categories":2614},[53],{"categories":2616},[],{"categories":2618},[107],{"categories":2620},[],{"categories":2622},[107],{"categories":2624},[53],{"categories":2626},[89],{"categories":2628},[89],{"categories":2630},[89],{"categories":2632},[],{"categories":2634},[],{"categories":2636},[53],{"categories":2638},[53],{"categories":2640},[],{"categories":2642},[128],{"categories":2644},[89],{"categories":2646},[145],{"categories":2648},[81],{"categories":2650},[],{"categories":2652},[],{"categories":2654},[107],{"categories":2656},[138],{"categories":2658},[53],{"categories":2660},[53],{"categories":2662},[53],{"categories":2664},[138],{"categories":2666},[107],{"categories":2668},[128],{"categories":2670},[53],{"categories":2672},[53],{"categories":2674},[53],{"categories":2676},[107],{"categories":2678},[53],{"categories":2680},[107],{"categories":2682},[89],{"categories":2684},[89],{"categories":2686},[138],{"categories":2688},[89],{"categories":2690},[53],{"categories":2692},[138],{"categories":2694},[128],{"categories":2696},[],{"categories":2698},[89],{"categories":2700},[],{"categories":2702},[],{"categories":2704},[84],{"categories":2706},[53],{"categories":2708},[89],{"categories":2710},[81],{"categories":2712},[89],{"categories":2714},[145],{"categories":2716},[],{"categories":2718},[89],{"categories":2720},[],{"categories":2722},[81],{"categories":2724},[89],{"categories":2726},[],{"categories":2728},[89],{"categories":2730},[53],{"categories":2732},[107],{"categories":2734},[53],{"categories":2736},[89],{"categories":2738},[107],{"categories":2740},[89],{"categories":2742},[138],{"categories":2744},[128],{"categories":2746},[81],{"categories":2748},[],{"categories":2750},[89],{"categories":2752},[128],{"categories":2754},[107],{"categories":2756},[53],{"categories":2758},[128],{"categories":2760},[81],{"categories":2762},[],{"categories":2764},[89],{"categories":2766},[89],{"categories":2768},[53],{"categories":2770},[],{"categories":2772},[89],{"categories":2774},[92],{"categories":2776},[107],{"categories":2778},[89],{"categories":2780},[84],{"categories":2782},[],{"categories":2784},[53],{"categories":2786},[92],{"categories":2788},[53],{"categories":2790},[89],{"categories":2792},[107],{"categories":2794},[81],{"categories":2796},[400],{"categories":2798},[53],{"categories":2800},[53],{"categories":2802},[53],{"categories":2804},[107],{"categories":2806},[84],{"categories":2808},[53],{"categories":2810},[128],{"categories":2812},[107],{"categories":2814},[400],{"categories":2816},[53],{"categories":2818},[],{"categories":2820},[],{"categories":2822},[400],{"categories":2824},[131],{"categories":2826},[89],{"categories":2828},[89],{"categories":2830},[107],{"categories":2832},[53],{"categories":2834},[81],{"categories":2836},[128],{"categories":2838},[89],{"categories":2840},[53],{"categories":2842},[145],{"categories":2844},[53],{"categories":2846},[89],{"categories":2848},[],{"categories":2850},[53],{"categories":2852},[53],{"categories":2854},[107],{"categories":2856},[81],{"categories":2858},[],{"categories":2860},[53],{"categories":2862},[53],{"categories":2864},[138],{"categories":2866},[128],{"categories":2868},[53,89],{"categories":2870},[145,84],{"categories":2872},[53],{"categories":2874},[],{"categories":2876},[89],{"categories":2878},[],{"categories":2880},[138],{"categories":2882},[53],{"categories":2884},[107],{"categories":2886},[],{"categories":2888},[89],{"categories":2890},[],{"categories":2892},[89],{"categories":2894},[81],{"categories":2896},[89],{"categories":2898},[53],{"categories":2900},[400],{"categories":2902},[145],{"categories":2904},[84],{"categories":2906},[84],{"categories":2908},[81],{"categories":2910},[81],{"categories":2912},[53],{"categories":2914},[89],{"categories":2916},[53],{"categories":2918},[53],{"categories":2920},[81],{"categories":2922},[53],{"categories":2924},[145],{"categories":2926},[107],{"categories":2928},[53],{"categories":2930},[89],{"categories":2932},[53],{"categories":2934},[],{"categories":2936},[138],{"categories":2938},[],{"categories":2940},[89],{"categories":2942},[81],{"categories":2944},[],{"categories":2946},[400],{"categories":2948},[53],{"categories":2950},[],{"categories":2952},[107],{"categories":2954},[89],{"categories":2956},[138],{"categories":2958},[53],{"categories":2960},[89],{"categories":2962},[138],{"categories":2964},[89],{"categories":2966},[107],{"categories":2968},[81],{"categories":2970},[107],{"categories":2972},[138],{"categories":2974},[53],{"categories":2976},[128],{"categories":2978},[53],{"categories":2980},[53],{"categories":2982},[53],{"categories":2984},[53],{"categories":2986},[89],{"categories":2988},[53],{"categories":2990},[89],{"categories":2992},[53],{"categories":2994},[81],{"categories":2996},[53],{"categories":2998},[89],{"categories":3000},[128],{"categories":3002},[81],{"categories":3004},[89],{"categories":3006},[128],{"categories":3008},[],{"categories":3010},[53],{"categories":3012},[53],{"categories":3014},[138],{"categories":3016},[],{"categories":3018},[89],{"categories":3020},[145],{"categories":3022},[53],{"categories":3024},[107],{"categories":3026},[145],{"categories":3028},[89],{"categories":3030},[84],{"categories":3032},[84],{"categories":3034},[53],{"categories":3036},[81],{"categories":3038},[],{"categories":3040},[53],{"categories":3042},[],{"categories":3044},[81],{"categories":3046},[53],{"categories":3048},[89],{"categories":3050},[89],{"categories":3052},[],{"categories":3054},[138],{"categories":3056},[138],{"categories":3058},[145],{"categories":3060},[128],{"categories":3062},[],{"categories":3064},[53],{"categories":3066},[81],{"categories":3068},[53],{"categories":3070},[138],{"categories":3072},[81],{"categories":3074},[107],{"categories":3076},[107],{"categories":3078},[],{"categories":3080},[107],{"categories":3082},[89],{"categories":3084},[128],{"categories":3086},[131],{"categories":3088},[53],{"categories":3090},[],{"categories":3092},[107],{"categories":3094},[138],{"categories":3096},[84],{"categories":3098},[53],{"categories":3100},[81],{"categories":3102},[400],{"categories":3104},[81],{"categories":3106},[],{"categories":3108},[],{"categories":3110},[107],{"categories":3112},[],{"categories":3114},[89],{"categories":3116},[89],{"categories":3118},[89],{"categories":3120},[],{"categories":3122},[53],{"categories":3124},[],{"categories":3126},[107],{"categories":3128},[81],{"categories":3130},[128],{"categories":3132},[53],{"categories":3134},[107],{"categories":3136},[107],{"categories":3138},[],{"categories":3140},[107],{"categories":3142},[81],{"categories":3144},[53],{"categories":3146},[],{"categories":3148},[89],{"categories":3150},[89],{"categories":3152},[81],{"categories":3154},[],{"categories":3156},[],{"categories":3158},[],{"categories":3160},[128],{"categories":3162},[89],{"categories":3164},[53],{"categories":3166},[],{"categories":3168},[],{"categories":3170},[],{"categories":3172},[128],{"categories":3174},[],{"categories":3176},[81],{"categories":3178},[],{"categories":3180},[],{"categories":3182},[128],{"categories":3184},[53],{"categories":3186},[107],{"categories":3188},[],{"categories":3190},[145],{"categories":3192},[107],{"categories":3194},[145],{"categories":3196},[53],{"categories":3198},[],{"categories":3200},[],{"categories":3202},[89],{"categories":3204},[],{"categories":3206},[],{"categories":3208},[89],{"categories":3210},[53],{"categories":3212},[],{"categories":3214},[89],{"categories":3216},[107],{"categories":3218},[145],{"categories":3220},[131],{"categories":3222},[89],{"categories":3224},[89],{"categories":3226},[],{"categories":3228},[],{"categories":3230},[],{"categories":3232},[107],{"categories":3234},[],{"categories":3236},[],{"categories":3238},[128],{"categories":3240},[81],{"categories":3242},[],{"categories":3244},[84],{"categories":3246},[145],{"categories":3248},[53],{"categories":3250},[138],{"categories":3252},[81],{"categories":3254},[131],{"categories":3256},[84],{"categories":3258},[138],{"categories":3260},[],{"categories":3262},[],{"categories":3264},[89],{"categories":3266},[81],{"categories":3268},[128],{"categories":3270},[81],{"categories":3272},[89],{"categories":3274},[400],{"categories":3276},[89],{"categories":3278},[],{"categories":3280},[53],{"categories":3282},[107],{"categories":3284},[138],{"categories":3286},[],{"categories":3288},[128],{"categories":3290},[107],{"categories":3292},[81],{"categories":3294},[89],{"categories":3296},[53],{"categories":3298},[84],{"categories":3300},[89,400],{"categories":3302},[89],{"categories":3304},[138],{"categories":3306},[53],{"categories":3308},[131],{"categories":3310},[145],{"categories":3312},[89],{"categories":3314},[],{"categories":3316},[89],{"categories":3318},[53],{"categories":3320},[84],{"categories":3322},[],{"categories":3324},[],{"categories":3326},[53],{"categories":3328},[131],{"categories":3330},[53],{"categories":3332},[],{"categories":3334},[107],{"categories":3336},[],{"categories":3338},[107],{"categories":3340},[138],{"categories":3342},[89],{"categories":3344},[53],{"categories":3346},[145],{"categories":3348},[138],{"categories":3350},[],{"categories":3352},[107],{"categories":3354},[53],{"categories":3356},[],{"categories":3358},[53],{"categories":3360},[89],{"categories":3362},[53],{"categories":3364},[89],{"categories":3366},[53],{"categories":3368},[53],{"categories":3370},[53],{"categories":3372},[53],{"categories":3374},[84],{"categories":3376},[],{"categories":3378},[92],{"categories":3380},[107],{"categories":3382},[53],{"categories":3384},[],{"categories":3386},[138],{"categories":3388},[53],{"categories":3390},[53],{"categories":3392},[89],{"categories":3394},[107],{"categories":3396},[53],{"categories":3398},[53],{"categories":3400},[84],{"categories":3402},[89],{"categories":3404},[128],{"categories":3406},[],{"categories":3408},[131],{"categories":3410},[53],{"categories":3412},[],{"categories":3414},[107],{"categories":3416},[145],{"categories":3418},[],{"categories":3420},[],{"categories":3422},[107],{"categories":3424},[107],{"categories":3426},[145],{"categories":3428},[81],{"categories":3430},[89],{"categories":3432},[89],{"categories":3434},[53],{"categories":3436},[84],{"categories":3438},[],{"categories":3440},[],{"categories":3442},[107],{"categories":3444},[131],{"categories":3446},[138],{"categories":3448},[89],{"categories":3450},[128],{"categories":3452},[131],{"categories":3454},[131],{"categories":3456},[],{"categories":3458},[107],{"categories":3460},[53],{"categories":3462},[53],{"categories":3464},[138],{"categories":3466},[],{"categories":3468},[107],{"categories":3470},[107],{"categories":3472},[107],{"categories":3474},[],{"categories":3476},[89],{"categories":3478},[53],{"categories":3480},[],{"categories":3482},[81],{"categories":3484},[84],{"categories":3486},[],{"categories":3488},[53],{"categories":3490},[53],{"categories":3492},[],{"categories":3494},[138],{"categories":3496},[],{"categories":3498},[],{"categories":3500},[],{"categories":3502},[],{"categories":3504},[53],{"categories":3506},[107],{"categories":3508},[],{"categories":3510},[],{"categories":3512},[53],{"categories":3514},[53],{"categories":3516},[53],{"categories":3518},[131],{"categories":3520},[53],{"categories":3522},[131],{"categories":3524},[],{"categories":3526},[131],{"categories":3528},[131],{"categories":3530},[400],{"categories":3532},[89],{"categories":3534},[138],{"categories":3536},[],{"categories":3538},[],{"categories":3540},[131],{"categories":3542},[138],{"categories":3544},[138],{"categories":3546},[138],{"categories":3548},[],{"categories":3550},[81],{"categories":3552},[138],{"categories":3554},[138],{"categories":3556},[81],{"categories":3558},[138],{"categories":3560},[84],{"categories":3562},[138],{"categories":3564},[138],{"categories":3566},[138],{"categories":3568},[131],{"categories":3570},[107],{"categories":3572},[107],{"categories":3574},[53],{"categories":3576},[138],{"categories":3578},[131],{"categories":3580},[400],{"categories":3582},[131],{"categories":3584},[131],{"categories":3586},[131],{"categories":3588},[],{"categories":3590},[84],{"categories":3592},[],{"categories":3594},[400],{"categories":3596},[138],{"categories":3598},[138],{"categories":3600},[138],{"categories":3602},[89],{"categories":3604},[107,84],{"categories":3606},[131],{"categories":3608},[],{"categories":3610},[],{"categories":3612},[131],{"categories":3614},[],{"categories":3616},[131],{"categories":3618},[107],{"categories":3620},[89],{"categories":3622},[],{"categories":3624},[138],{"categories":3626},[53],{"categories":3628},[128],{"categories":3630},[],{"categories":3632},[53],{"categories":3634},[],{"categories":3636},[107],{"categories":3638},[81],{"categories":3640},[131],{"categories":3642},[],{"categories":3644},[138],{"categories":3646},[107],[3648,3707,3830,3897],{"id":3649,"title":3650,"ai":3651,"body":3656,"categories":3692,"created_at":54,"date_modified":54,"description":46,"extension":56,"faq":54,"featured":57,"kicker_label":54,"meta":3693,"navigation":59,"path":3694,"published_at":3695,"question":54,"scraped_at":54,"seo":3696,"sitemap":3697,"source_id":3698,"source_name":3699,"source_type":3700,"source_url":3701,"stem":3702,"tags":3703,"thumbnail_url":54,"tldr":3704,"tweet":54,"unknown_tags":3705,"__hash__":3706},"summaries\u002Fsummaries\u002Fgemma-4-delivers-top-tier-reasoning-in-open-models-summary.md","Gemma 4 Delivers Top-Tier Reasoning in Open Models",{"provider":7,"model":8,"input_tokens":3652,"output_tokens":3653,"processing_time_ms":3654,"cost_usd":3655},5873,1116,13484,0.0017101,{"type":14,"value":3657,"toc":3686},[3658,3662,3665,3669,3672,3676,3679,3683],[17,3659,3661],{"id":3660},"open-models-core-weaknesses-exposed","Open Models' Core Weaknesses Exposed",[22,3663,3664],{},"Current open LLMs excel at single-turn text generation but fail on multi-step reasoning essential for AI agents. They struggle to parse implied intent, plan sequential tasks, connect disparate data, or self-correct—leading to endless loops, incorrect outputs, and brittle workflows. For instance, an agent handling dynamic data analysis with conditional logic often requires constant retries, burning cloud credits and developer time. Deployment compounds this: even capable open models demand multiple NVIDIA B200 GPUs or heavy cloud instances for inference, negating cost savings through high operational expenses. Quantization reduces footprint but degrades performance, trapping projects in prototypes. Result: developers default to pricey proprietary APIs for genuine intelligence, sacrificing flexibility and ownership.",[17,3666,3668],{"id":3667},"gemma-4s-architectural-edge-for-practical-ai","Gemma 4's Architectural Edge for Practical AI",[22,3670,3671],{},"Gemma 4, from Google DeepMind, imports Gemini's innovations into efficient open models, maximizing intelligence per byte. It handles complex instructions, maintains coherence in long interactions, and excels at agentic planning—turning fragile chains-of-thought into reliable multi-stage executions. Efficiency lets it run sophisticated inference on lighter hardware, cutting AWS\u002FAzure\u002FGCP bills and enabling edge or on-device deployment. Fine-tune it on proprietary data for private solutions in healthcare or finance, keeping control without third-party APIs. Builders gain Gemini-level reasoning for autonomous agents that synthesize API data, analyze trends, and output actionable decisions, reducing human oversight.",[17,3673,3675],{"id":3674},"builder-use-cases-and-direct-wins","Builder Use Cases and Direct Wins",[22,3677,3678],{},"Target agentic workflows: financial agents pulling multi-API data, applying rules, and suggesting trades now succeed without synthesis failures. Cost-conscious teams deploy at scale without infrastructure overhauls. Custom needs like domain-specific fine-tuning create bespoke agents under full control. Impact: faster iteration, lower bills, and production-ready intelligence previously locked behind proprietary walls—democratizing advanced AI for startups and small teams.",[17,3680,3682],{"id":3681},"trade-offs-no-free-lunch","Trade-offs: No Free Lunch",[22,3684,3685],{},"Gemma 4 isn't AGI; it shines on trained tasks but lacks human intuition and can hallucinate on ambiguous prompts or out-of-distribution data—always validate outputs, especially high-stakes. Larger variants still need compute, demanding fine-tuning and optimization skills. Human-in-the-loop persists for reliability. Despite this, it shrinks the proprietary gap, sparking open-source innovation in edge AI and enterprise agents.",{"title":46,"searchDepth":47,"depth":47,"links":3687},[3688,3689,3690,3691],{"id":3660,"depth":47,"text":3661},{"id":3667,"depth":47,"text":3668},{"id":3674,"depth":47,"text":3675},{"id":3681,"depth":47,"text":3682},[],{},"\u002Fsummaries\u002Fgemma-4-delivers-top-tier-reasoning-in-open-models-summary","2026-04-08 21:21:20",{"title":3650,"description":46},{"loc":3694},"8cb911b0435f5d2f","Towards AI","article","https:\u002F\u002Funknown","summaries\u002Fgemma-4-delivers-top-tier-reasoning-in-open-models-summary",[71,73,72,74],"Gemma 4 matches proprietary models like Gemini on advanced reasoning and agent workflows while slashing compute costs, enabling developers to build robust, customizable AI agents without vendor lock-in.",[74],"kFlUZWwgFy9YEsIk5Fkhr-JTpu_rCjKPCaCsHx46pGQ",{"id":3708,"title":3709,"ai":3710,"body":3715,"categories":3815,"created_at":54,"date_modified":54,"description":3816,"extension":56,"faq":54,"featured":57,"kicker_label":54,"meta":3817,"navigation":59,"path":3818,"published_at":3819,"question":54,"scraped_at":3820,"seo":3821,"sitemap":3822,"source_id":3823,"source_name":66,"source_type":67,"source_url":3824,"stem":3825,"tags":3826,"thumbnail_url":54,"tldr":3827,"tweet":54,"unknown_tags":3828,"__hash__":3829},"summaries\u002Fsummaries\u002F9062a105b98e6072-gemma-4-crushes-benchmarks-open-source-edges-front-summary.md","Gemma 4 Crushes Benchmarks: Open Source Edges Frontier",{"provider":7,"model":8,"input_tokens":3711,"output_tokens":3712,"processing_time_ms":3713,"cost_usd":3714},8757,2125,22339,0.00279125,{"type":14,"value":3716,"toc":3807},[3717,3721,3724,3727,3731,3734,3738,3741,3745,3748,3752,3755,3761,3777,3781],[17,3718,3720],{"id":3719},"open-source-models-close-the-gap-on-frontier-capabilities","Open Source Models Close the Gap on Frontier Capabilities",[22,3722,3723],{},"Hosts Matt Berman and Nick Wince highlight Google's Gemma 4 family—E2B, E4B, 26B, and 31B dense models—as a breakthrough in open-weights AI. Released under a commercially permissive license on Hugging Face, Kaggle, and Ollama, these models punch above their size: the 31B ranks #3 on Arena AI's global open leaderboard, 26B at #6. The 4B variant beats Anthropic's Sonnet 4.6 on graduate-level reasoning while running on a 24GB Mac Mini at high speeds. Matt notes 100 tokens\u002Fsecond on a DGX Station, praising their edge viability for phones and local use. Both agree this accelerates a hybrid future: frontier hosted models for cutting-edge tasks like new knowledge discovery, open source for everyday workflows. Tradeoff: Gemma skips 1M context windows to prioritize speed and size, unlike competitors.",[22,3725,3726],{},"Alibaba's Qwen 3.6+ (not open source yet, promised soon) complements this with multimodal agentic coding—task breakdown, screenshot-to-code, design drafts, iterative debugging. It matches Claude Opus 4.5 benchmarks, offers 1M context by default, and costs 29¢\u002FM input tokens. Qwen's user base exploded from 31M to 200M monthly actives (Jan-Mar), fueling rapid iterations despite key researcher departures. Matt favors ~30B dense Qwens (e.g., 3.5 27B) in his OpenClaw production setup for reliability. Nick and Matt concur: open source like Qwen and Gemma lets enterprises fine-tune cheaply, bypassing pricey hosted frontiers—though data sovereignty concerns limit hosted Chinese models.",[17,3728,3730],{"id":3729},"edge-ai-unlocks-adoption-but-integration-is-key","Edge AI Unlocks Adoption, But Integration Is Key",[22,3732,3733],{},"Debate centers on barriers to mass AI use. Matt argues edge deployment isn't the blocker; people underestimate capabilities beyond Q&A\u002Fsearch supplements. Nick agrees, predicting unlocks via seamless consumer integration by Apple\u002FGoogle—proactive, workflow-embedded AI in Docs, Sheets, Gmail. Current Workspace tools feel 'bolted on,' not intuitive. Their scale (millions of users) enables tight feedback loops for refinement; Meta trails here despite consumer apps. Matt envisions edge for 'so much else' post-frontier tasks, citing Gemma's phone-ready variants. Both excited by Qwen's scale signaling global open source momentum, but wish for more U.S. models—Gemma and RC's thinking version fill the gap.",[17,3735,3737],{"id":3736},"agentic-coding-interfaces-converge-on-simplicity","Agentic Coding Interfaces Converge on Simplicity",[22,3739,3740],{},"Cursor 3's launch emphasizes agents: parallel agents in isolated work trees via a new agent window, marketplace, and MCP layer. Matt loves ditching multi-window hopping; Nick, with early access, notes convergence on chat-over-code UIs—simpler interfaces surfacing context for decisions. Cursor evolves into a dev OS, moating against Claude Code via agentic workflows. Matt prefers IDEs over terminals for agents; both value Cursor's free tokens boosting coding volume. Ties to Anthropic's enterprise flywheel: top coding models generate revenue while self-improving next gens via recursive loops.",[17,3742,3744],{"id":3743},"frontier-labs-agi-paths-diverge-by-revenue-flywheels","Frontier Labs' AGI Paths Diverge by Revenue Flywheels",[22,3746,3747],{},"Matt ranks Meta lowest for AGI odds among Google, OpenAI, Anthropic, xAI, Meta. Anthropic pioneered coding-model focus, selling to enterprise for revenue that funds scaling—coding aids research\u002Fmath\u002Fscience, creating self-improvement. OpenAI followed suit. Meta's consumer bent (FB\u002FIG\u002FWA) misaligns: models optimized for consumers, not enterprise coding revenue. No equivalent flywheel; integration into social apps doesn't drive frontier R&D like Anthropic's does. Consensus: labs must productize research into money-printers; Alibaba's Qwen shifts from lab to commercialization, echoing DeepMind\u002FOpenAI transitions amid departures.",[17,3749,3751],{"id":3750},"science-inspiration-amid-ai-hype","Science Inspiration Amid AI Hype",[22,3753,3754],{},"Brief detour to NASA's Artemis I success—inspiring Matt's son to build rocket models from toilet rolls. Test flight validates Orion for 2028 lunar landing (Artemis 3). Hosts tie to broader innovation: 'science is back,' alongside Project Hail Mary film. Right-place window views underscore rarity\u002Fexcitement.",[22,3756,3757],{},[3758,3759,3760],"strong",{},"Notable Quotes:",[3762,3763,3764,3768,3771,3774],"ul",{},[3765,3766,3767],"li",{},"\"These are phenomenal open source open weights models, commercially viable, commercially permissive, and they're also relatively small. But for their size, they're incredibly good.\" — Matt Berman on Gemma 4, emphasizing edge potential.",[3765,3769,3770],{},"\"I think it'll take a company like Apple or like Google to build it deeply into the products and services that consumers use, and make it so seamless that it just works without the consumer having to think about what they're actually going to do.\" — Nick Wince on adoption unlock.",[3765,3772,3773],{},"\"You're building these incredible coding models and then you sell the incredible coding models to enterprise, thus driving a ton of revenue... it's this incredible self-improving, recursive self-improving loop.\" — Matt Berman on Anthropic's flywheel.",[3765,3775,3776],{},"\"Open source is getting better, faster, smaller, and we're going to have this hybrid architecture in the future.\" — Matt Berman forecasting AI stacks.",[17,3778,3780],{"id":3779},"key-takeaways","Key Takeaways",[3762,3782,3783,3786,3789,3792,3795,3798,3801,3804],{},[3765,3784,3785],{},"Prioritize Gemma 4's 4B-31B variants for local\u002Fedge coding\u002Freasoning; beats Sonnet 4.6 benchmarks on modest hardware—test via Ollama\u002FHugging Face.",[3765,3787,3788],{},"For agentic coding, adopt Cursor 3's parallel agents in isolated trees; favors chat UIs over terminals for production workflows.",[3765,3790,3791],{},"Build hybrid stacks: hosted frontiers (Opus\u002FSonnet) for complex tasks, open source (Gemma\u002FQwen) for cost-sensitive, local runs.",[3765,3793,3794],{},"Watch enterprise flywheels—coding revenue funds AGI progress; Meta lags without one.",[3765,3796,3797],{},"Drive adoption via seamless product integration, not just edge compute; emulate Google\u002FApple scale.",[3765,3799,3800],{},"Fine-tune Qwen 3.5\u002F3.6 ~30B models locally post-open-sourcing for cheap, high-perf alternatives to U.S. frontiers.",[3765,3802,3803],{},"Benchmark new releases immediately: Arena leaderboards + real workflows > hype.",[3765,3805,3806],{},"Inspire via real science (e.g., Artemis) to counter AI echo chambers.",{"title":46,"searchDepth":47,"depth":47,"links":3808},[3809,3810,3811,3812,3813,3814],{"id":3719,"depth":47,"text":3720},{"id":3729,"depth":47,"text":3730},{"id":3736,"depth":47,"text":3737},{"id":3743,"depth":47,"text":3744},{"id":3750,"depth":47,"text":3751},{"id":3779,"depth":47,"text":3780},[107],"Guests:\nBen Pouladian, CEO, BEP Holdings & BEP Research\nhttps:\u002F\u002Fwww.bepholdings.com\nhttps:\u002F\u002Fbepresearch.substack.com\nhttps:\u002F\u002Fx.com\u002Fbenitoz\n\nWayne Nelms, Co-Founder & CTO, Ornn\nhttps:\u002F\u002Fwww.ornnai.com\nhttps:\u002F\u002Fx.com\u002FOrnnExchange\nhttps:\u002F\u002Fx.com\u002Fwayne_nelmz\n\nJayden Clark, Founder & Host, MOTS Podcast\nhttps:\u002F\u002Fx.com\u002Fcreatine_cycle\nhttps:\u002F\u002Fx.com\u002Fmots_pod\n\nDownload Humanities Last Prompt Engineering Guide (free) 👇🏼\nhttps:\u002F\u002Fbit.ly\u002F4kFhajz\n\nDownload The Matthew Berman Vibe Coding Playbook (free) 👇🏼\nhttps:\u002F\u002Fbit.ly\u002F3I2J0YQ\n\nJoin My Newsletter for Regular AI Updates 👇🏼\nhttps:\u002F\u002Fforwardfuture.ai\n\nDiscover The Best AI Tools👇🏼\nhttps:\u002F\u002Ftools.forwardfuture.ai\n\nMy Links 🔗\n👉🏻 X: https:\u002F\u002Fx.com\u002Fmatthewberman\n👉🏻 Forward Future X: https:\u002F\u002Fx.com\u002Fforward_future_\n👉🏻 Instagram: https:\u002F\u002Fwww.instagram.com\u002Fmatthewberman_ai\n👉🏻 Discord: https:\u002F\u002Fdiscord.gg\u002FxxysSXBxFW\n👉🏻 TikTok: https:\u002F\u002Fwww.tiktok.com\u002F@matthewberman_ai\n\nMedia\u002FSponsorship Inquiries ✅ \nhttps:\u002F\u002Fbit.ly\u002F44TC45V",{},"\u002Fsummaries\u002F9062a105b98e6072-gemma-4-crushes-benchmarks-open-source-edges-front-summary","2026-04-04 07:21:06","2026-04-05 16:14:30",{"title":3709,"description":3816},{"loc":3818},"9062a105b98e6072","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MKl4KVMDrqc","summaries\u002F9062a105b98e6072-gemma-4-crushes-benchmarks-open-source-edges-front-summary",[71,72,73,74],"Google's Gemma 4 open-weights models deliver elite performance at small sizes, runnable on edge devices, beating Sonnet 4.6 on reasoning—pushing hybrid AI architectures where open source handles most tasks locally.",[74],"sU1zIOSm7XsoJSPjZm2gCsTmy2pa385L4GdtTo47EwI",{"id":3831,"title":3832,"ai":3833,"body":3838,"categories":3866,"created_at":54,"date_modified":54,"description":46,"extension":56,"faq":54,"featured":57,"kicker_label":54,"meta":3867,"navigation":59,"path":3884,"published_at":3885,"question":54,"scraped_at":3886,"seo":3887,"sitemap":3888,"source_id":3889,"source_name":3890,"source_type":3700,"source_url":3891,"stem":3892,"tags":3893,"thumbnail_url":54,"tldr":3894,"tweet":54,"unknown_tags":3895,"__hash__":3896},"summaries\u002Fsummaries\u002F138f159d6a0dc547-tokenspeed-beats-tensorrt-llm-9-11-on-agentic-codi-summary.md","TokenSpeed Beats TensorRT-LLM 9-11% on Agentic Coding Inference",{"provider":7,"model":8,"input_tokens":3834,"output_tokens":3835,"processing_time_ms":3836,"cost_usd":3837},6300,1652,21300,0.00157915,{"type":14,"value":3839,"toc":3861},[3840,3844,3847,3851,3854,3858],[17,3841,3843],{"id":3842},"tackling-agentic-inference-bottlenecks","Tackling Agentic Inference Bottlenecks",[22,3845,3846],{},"Agentic coding systems like Claude Code, Codex, and Cursor push inference engines with contexts over 50K tokens across dozens of turns, stressing per-GPU tokens-per-minute (TPM) for multi-user scaling and per-user tokens-per-second (TPS) for responsiveness (target floor: 70 TPS, up to 200+ TPS). Public benchmarks miss this dual pressure, so TokenSpeed (MIT-licensed preview from LightSeek Foundation) prioritizes both metrics via specialized architecture, avoiding generic chat optimizations.",[17,3848,3850],{"id":3849},"architectural-edges-for-speed-and-safety","Architectural Edges for Speed and Safety",[22,3852,3853],{},"TokenSpeed builds on five subsystems: (1) Compiler-backed SPMD modeling auto-generates collective ops from I\u002FO annotations, skipping manual comms code. (2) Scheduler splits C++ control plane (FSM with type-enforced KV cache ownership\u002Ftransfers for compile-time safety) from Python execution plane (fast iteration). (3) Pluggable kernel layer with registry supports heterogeneous accelerators; its MLA kernel (grouping q_seqlen\u002Fnum_heads for Tensor Core fill, tuned binary prefill softmax) beats TensorRT-LLM decode\u002Fprefill, adopted by vLLM. (4) Safe KV reuse restrictions. (5) SMG for low-overhead CPU-GPU handoff. These cut KV errors (common pitfall) and enable modular accel support beyond NVIDIA.",[17,3855,3857],{"id":3856},"benchmark-dominance-on-real-workloads","Benchmark Dominance on Real Workloads",[22,3859,3860],{},"On NVIDIA B200 with SWE-smith traces (production-like coding agent traffic) and Kimi K2.5 model, TokenSpeed in Attention TP4 + MoE TP4 config tops TensorRT-LLM Pareto: 9% faster at batch=1 min-latency (>70 TPS\u002Fuser), 11% higher throughput at ~100 TPS\u002Fuser. Decode MLA folds query-seq into head axis for better BMM tile fill; binary prefill tunes softmax. With speculative decoding + long prefix KV at batches 4\u002F8\u002F16, latency nearly halves vs. TensorRT-LLM. Single-node only for now; PD disagg coming.",{"title":46,"searchDepth":47,"depth":47,"links":3862},[3863,3864,3865],{"id":3842,"depth":47,"text":3843},{"id":3849,"depth":47,"text":3850},{"id":3856,"depth":47,"text":3857},[53],{"content_references":3868,"triage":3879},[3869,3874],{"type":3870,"title":3871,"url":3872,"context":3873},"tool","TokenSpeed","https:\u002F\u002Fgithub.com\u002Flightseekorg\u002Ftokenspeed","mentioned",{"type":3875,"title":3876,"url":3877,"context":3878},"other","LightSeek TokenSpeed Technical Details","https:\u002F\u002Flightseek.org\u002Fblog\u002Flightseek-tokenspeed.html","recommended",{"relevance":3880,"novelty":3881,"quality":3880,"actionability":3881,"composite":3882,"reasoning":3883},4,3,3.6,"Category: AI & LLMs. The article discusses a new open-source LLM inference engine, TokenSpeed, which addresses specific performance issues in agentic workloads, directly relevant to AI engineers and developers. It provides insights into architectural improvements and benchmarks, but lacks detailed implementation guidance for practical application.","\u002Fsummaries\u002F138f159d6a0dc547-tokenspeed-beats-tensorrt-llm-9-11-on-agentic-codi-summary","2026-05-07 22:03:47","2026-05-08 11:28:23",{"title":3832,"description":46},{"loc":3884},"138f159d6a0dc547","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F07\u002Flightseek-foundation-releases-tokenspeed-an-open-source-llm-inference-engine-targeting-tensorrt-llm-level-performance-for-agentic-workloads\u002F","summaries\u002F138f159d6a0dc547-tokenspeed-beats-tensorrt-llm-9-11-on-agentic-codi-summary",[71,73,72],"TokenSpeed open-source engine optimizes agentic workloads with long contexts (>50K tokens) and multi-turn convos, delivering 9% lower latency and 11% higher throughput than TensorRT-LLM at 70-100 TPS\u002Fuser on NVIDIA B200.",[],"iEELj0BdlJHttBF4chi4fvtEa11ZZu5n0Lg0DMzVIIg",{"id":3898,"title":3899,"ai":3900,"body":3905,"categories":4005,"created_at":54,"date_modified":54,"description":46,"extension":56,"faq":54,"featured":57,"kicker_label":54,"meta":4006,"navigation":59,"path":4020,"published_at":4021,"question":54,"scraped_at":4022,"seo":4023,"sitemap":4024,"source_id":4025,"source_name":4015,"source_type":3700,"source_url":4026,"stem":4027,"tags":4028,"thumbnail_url":54,"tldr":4029,"tweet":54,"unknown_tags":4030,"__hash__":4031},"summaries\u002Fsummaries\u002F9cf4eabf30c8f73e-open-source-ai-innovation-engine-or-security-risk-summary.md","Open Source AI: Innovation Engine or Security Risk?",{"provider":7,"model":8,"input_tokens":3901,"output_tokens":3902,"processing_time_ms":3903,"cost_usd":3904},8403,2068,28390,0.00242375,{"type":14,"value":3906,"toc":3998},[3907,3911,3914,3917,3921,3924,3927,3930,3934,3937,3940,3943,3947,3950,3953,3956,3973,3975],[17,3908,3910],{"id":3909},"open-source-as-ais-innovation-backbone","Open Source as AI's Innovation Backbone",[22,3912,3913],{},"Gabe Goodhart champions open source as the core of AI progress, arguing that AI's science—math and tensors—is unusually close to usable products, unlike past tech waves. This tight coupling means open source hosts most real innovation, with examples like linear attention mechanisms enabling better long-context models through collaborative tweaks (e.g., hybridizing recurrent linear layers with attention). Martin Keen reinforces this, noting open source underpins even closed frontier models via standards like Anthropic's Model Context Protocol (MCP), donated to Linux Foundation, and agent skill specs like skill.md. These allow open-weight models (Mistral, Llama, Deepseek) or closed ones to invoke services uniformly. All panelists concur: open source democratizes access, accelerates catch-up to frontier capabilities, and fosters architectures anyone can run on their hardware, bypassing lab gatekeeping.",[22,3915,3916],{},"Jeff Crume, the security skeptic, doesn't dispute innovation benefits but tempers hype. He invokes Kerckhoffs' principle from cryptography: only keys should be secret, not algorithms, as scrutiny strengthens systems. Yet he cautions Linux's history—once claimed malware-proof—shows open source isn't secure by default. Consensus emerges: open source thrives on 'a thousand eyes' for innovation, but scale (billions of parameters) overwhelms full vetting.",[17,3918,3920],{"id":3919},"secure-vs-securable-core-security-distinction","Secure vs. Securable: Core Security Distinction",[22,3922,3923],{},"Jeff Crume draws a sharp line: open source AI is 'securable' (design allows fixes) but not 'secure' without deliberate controls. Proprietary claims of inherent security fare no better; security stems from implementation, not source status. Transparency builds trust, essential for AI, but latent bugs in decades-old open code prove even crowds miss flaws. AI itself aids detection—scanning source or reverse-engineering binaries via LLMs\u002Fdecompilers, a capability predating gen AI but now amplified.",[22,3925,3926],{},"Gabe echoes: AI stacks mirror Linux\u002FKubernetes—composable open projects with attack surfaces needing updates and policies. Open code enables fixes, but poor projects emit 'vibe code Spidey sense' (unmanaged security). Martin highlights hybrid realities: closed models rely on open foundations, blurring lines. Divergence: Gabe sees open weights accelerating science (e.g., attention innovations), while Jeff notes proprietary guardrails (pre\u002Fpost-model filters) block misuse—open weights invite 'obliteration' of safety layers via embedding tweaks.",[22,3928,3929],{},"Panel agrees on trust: opacity breeds blind faith, not security. Open source invites scrutiny, but demands proactive policy.",[17,3931,3933],{"id":3932},"model-access-bad-actors-and-emerging-threats","Model Access, Bad Actors, and Emerging Threats",[22,3935,3936],{},"Debate heats on access: frontier models (e.g., latest from labs) gatekeep via approvals\u002Fconsortia, while open models on Hugging Face run anywhere. Martin predicts open source will close gaps quickly. Jeff worries bad actors access simultaneously—security through obscurity fails, as leaks inevitable. Yet open weights expose more: attackers strip refusals, unleashing unfiltered capabilities.",[22,3938,3939],{},"Gabe ties to agents: autonomy turns agent loops into code interpreters where 'the internet is your untrusted code.' Textual inputs, once filtered by humans\u002Fprograms, now trigger actions via tools—massive attack surface. OpenClaw-like systems exemplify chaos. Jeff nods to AI's dual role: vulnerability scanner and exploit amplifier (reverse-engineering binaries). Martin\u002F Gabe stress context layers (beyond models\u002Fsoftware) compound risks.",[22,3941,3942],{},"Strongest arguments: Pro-open (Gabe\u002FMartin)—stifling access hampers progress; security (Jeff)—scale defeats 'many eyes,' demands controls. No one-size-fits-all; mitigate via guardrails, sandboxes, updates.",[17,3944,3946],{"id":3945},"using-ai-to-secure-ai-and-forward-outlook","Using AI to Secure AI and Forward Outlook",[22,3948,3949],{},"Jeff sees AI securing itself as nuanced: LLMs find code vulns faster than humans, even in proprietary binaries (decompile → scan). Not new—pre-gen AI tools existed—but gen AI scales it. Gabe warns of net-new agent risks, urging trust-boundary rethinking.",[22,3951,3952],{},"Predictions: Open models catch frontiers; innovation via open science\u002Farchitectures unstoppable. Recommendations: Vet projects rigorously; sandbox agents; stay updated; blend open\u002Fclosed (e.g., open standards with closed models). Tradeoffs: Open weights boost utility\u002Finnovation but heighten misuse; closed offers controls at velocity cost.",[22,3954,3955],{},"Notable quotes:",[3762,3957,3958,3961,3964,3967,3970],{},[3765,3959,3960],{},"Gabe Goodhart: \"Open-source relative to most other innovation waves is where the vast majority of the actual innovation is happening because science by its very nature is open.\"",[3765,3962,3963],{},"Jeff Crume: \"Linux is a good example of a system that is securable, but in and of itself is not necessarily secure.\"",[3765,3965,3966],{},"Gabe Goodhart: \"The agent loop is essentially a code interpreter and the code is literally any text you pass through it... now the internet is your untrusted code.\"",[3765,3968,3969],{},"Jeff Crume: \"Security through obscurity is not an effective model... the only thing about a crypto system that should be secret are the keys.\"",[3765,3971,3972],{},"Martin Keen: \"Open source is foundational to everything in AI now even if we're talking about models that were actually frontier closed models.\"",[17,3974,3780],{"id":3779},[3762,3976,3977,3980,3983,3986,3989,3992,3995],{},[3765,3978,3979],{},"Prioritize 'securable' open source projects with strong security contribution policies and update cadences—avoid vibe-check fails.",[3765,3981,3982],{},"Distinguish open code (innovation accelerator) from open weights (misuse risk)—use guardrails for models, sandboxes for agents.",[3765,3984,3985],{},"Leverage AI for vuln scanning on open or closed code; reverse-engineering erodes proprietary edges.",[3765,3987,3988],{},"Blend approaches: Open standards (MCP, skill.md) enhance closed models; run open weights on your infra for flexibility.",[3765,3990,3991],{},"Build trust via transparency and controls, not secrecy—bad actors leak anyway; focus on implementation.",[3765,3993,3994],{},"For agents, treat all inputs as untrusted code—rethink textual data assumptions.",[3765,3996,3997],{},"Expect open models to trail but catch frontier capabilities quickly via collaborative innovation.",{"title":46,"searchDepth":47,"depth":47,"links":3999},[4000,4001,4002,4003,4004],{"id":3909,"depth":47,"text":3910},{"id":3919,"depth":47,"text":3920},{"id":3932,"depth":47,"text":3933},{"id":3945,"depth":47,"text":3946},{"id":3779,"depth":47,"text":3780},[53],{"content_references":4007,"triage":4017},[4008,4013],{"type":4009,"title":4010,"author":4011,"url":4012,"context":3873},"podcast","Security Intelligence x Mixture of Experts Crossover Episode","IBM Technology (Matt Kazinski host)","https:\u002F\u002Fibm.biz\u002F~sTfk9xICA",{"type":4009,"title":4014,"author":4015,"url":4016,"context":3878},"Mixture of Experts","IBM Technology","https:\u002F\u002Fibm.biz\u002F~SMOMF0sqx",{"relevance":3881,"novelty":3881,"quality":3880,"actionability":47,"composite":4018,"reasoning":4019},3.05,"Category: AI & LLMs. The article discusses the role of open source in AI innovation and security, which aligns with the AI & LLMs category. While it presents some new perspectives on the security risks associated with open source AI, it lacks specific actionable steps for the audience to implement in their projects.","\u002Fsummaries\u002F9cf4eabf30c8f73e-open-source-ai-innovation-engine-or-security-risk-summary","2026-04-29 10:00:42","2026-05-03 16:43:49",{"title":3899,"description":46},{"loc":4020},"e15ac1bd93fe2101","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=PJGIWDW_W2A","summaries\u002F9cf4eabf30c8f73e-open-source-ai-innovation-engine-or-security-risk-summary",[72,71,73],"Panelists agree open source drives AI breakthroughs but warn it's 'securable' not 'secure'—needs rigorous practices to mitigate risks like model tampering and agent exploits.",[],"k2Bl5Kceueq5fD2_72bTo7FXkyKIWmwztjNZnjDyot8"]