[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-8be1525c0c94b6a4-data-science-splits-engineer-pipelines-or-lead-dec-summary":3,"summaries-facets-categories":78,"summary-related-8be1525c0c94b6a4-data-science-splits-engineer-pipelines-or-lead-dec-summary":3663},{"id":4,"title":5,"ai":6,"body":13,"categories":49,"created_at":51,"date_modified":51,"description":43,"extension":52,"faq":51,"featured":53,"kicker_label":51,"meta":54,"navigation":61,"path":62,"published_at":63,"question":51,"scraped_at":64,"seo":65,"sitemap":66,"source_id":67,"source_name":68,"source_type":69,"source_url":70,"stem":71,"tags":72,"thumbnail_url":51,"tldr":75,"tweet":51,"unknown_tags":76,"__hash__":77},"summaries\u002Fsummaries\u002F8be1525c0c94b6a4-data-science-splits-engineer-pipelines-or-lead-dec-summary.md","Data Science Splits: Engineer Pipelines or Lead Decisions",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",4831,1294,61752,0.00159055,{"type":14,"value":15,"toc":42},"minimark",[16,21,25,28,32,35,39],[17,18,20],"h2",{"id":19},"role-bifurcation-squeezes-generalists-out","Role Bifurcation Squeezes Generalists Out",[22,23,24],"p",{},"Data science jobs analyzed from over 700 postings into 2026 reveal a split: junior\u002Fentry roles demand full data ownership, with SQL requirements up 18 percentage points year-over-year, ETL pipelines up 18%, and tools like Snowflake, dbt, Airflow now standard. Candidates must trace data from source to model to dashboard, filtering out those relying on clean tables. Senior roles reverse this, assuming technical skills and prioritizing judgment: scoping problems, killing bad ideas early, and driving decisions. Generalists—who know Python\u002FSQL, build models, and chart data—face shrinking opportunities as they compete in a larger pool for mid-tier spots now split into specialized roles like pipeline-building analysts or roadmap-owning leads.",[22,26,27],{},"This leaves the 'do-everything' profile vulnerable: technically adequate but not infrastructure-deep, strategically aware but not boardroom-ready. BLS projects 34% job growth through 2034 despite AI, but entry bar rises—hires show GitHub repos proving business impact, not just cleaned data.",[17,29,31],{"id":30},"ai-automates-mid-level-tasks-sharpening-extremes","AI Automates Mid-Level Tasks, Sharpening Extremes",[22,33,34],{},"GenAI exacerbates the squeeze by handling baseline work: SQL cleanup, pandas boilerplate, simple viz—all once mid-level value-adds now done via prompts. Remaining value lies in irreplaceable skills: framing problems, skipping useless analyses, communicating sans p-values to non-technical stakeholders. Mid-career data scientists risk obsolescence if stuck in automatable tasks; those thriving move toward problem-ownership, understanding stakeholder decisions. In BFSI, generalists get fewer callbacks as JDs disaggregate into engineering (booming due to AI failure from bad infra) or decision-science (vital for sense-making amid data overload) tracks—both high-paying, middle stagnant.",[17,36,38],{"id":37},"specialize-fast-depth-over-breadth-wins-jobs","Specialize Fast: Depth Over Breadth Wins Jobs",[22,40,41],{},"Early-career: Choose engineering (master dbt\u002FSnowflake\u002Fsystem flows) or strategy (document analyses that shifted decisions, not just completed ones) and build depth aggressively. Portfolios must show business outcomes. Mid-career: Audit for AI-vulnerable tasks; pivot to stakeholder context no model replaces. 2021 skills won't land 2026 roles—field sharpens, rewarding extremes over adequacy.",{"title":43,"searchDepth":44,"depth":44,"links":45},"",2,[46,47,48],{"id":19,"depth":44,"text":20},{"id":30,"depth":44,"text":31},{"id":37,"depth":44,"text":38},[50],"Data Science & Visualization",null,"md",false,{"content_references":55,"triage":56},[],{"relevance":57,"novelty":58,"quality":57,"actionability":58,"composite":59,"reasoning":60},4,3,3.6,"Category: Data Science & Visualization. The article discusses the bifurcation of data science roles, which is relevant to the audience's interest in understanding how AI impacts job roles and skills in data-related fields. It provides insights into the evolving landscape but lacks specific actionable steps for the audience to implement.",true,"\u002Fsummaries\u002F8be1525c0c94b6a4-data-science-splits-engineer-pipelines-or-lead-dec-summary","2026-05-02 04:37:22","2026-05-03 17:01:17",{"title":5,"description":43},{"loc":62},"8be1525c0c94b6a4","Data and Beyond","article","https:\u002F\u002Fmedium.com\u002Fdata-and-beyond\u002Fthe-data-scientist-role-is-splitting-pick-a-side-68c829628a75?source=rss----b680b860beb1---4","summaries\u002F8be1525c0c94b6a4-data-science-splits-engineer-pipelines-or-lead-dec-summary",[73,74],"data-science","ai-automation","Data scientist roles are dividing into technical data engineering (SQL up 18%, ETL up 18%) and strategic decision-making; AI automates mid-level generalist tasks, squeezing the middle—specialize in one side now.",[74],"YzD6UsQMZIebBYZpRJzU7JJm8bkOhpxbbF6CCPmLjTo",[79,82,85,88,91,94,96,98,100,102,104,106,109,111,113,115,117,119,121,123,125,127,130,132,134,136,139,141,143,146,148,150,152,154,156,158,160,162,164,166,168,170,172,174,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2465,2467,2469,2471,2473,2475,2477,2479,2481,2483,2485,2487,2489,2491,2493,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2625,2627,2629,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661],{"categories":80},[81],"Developer Productivity",{"categories":83},[84],"Business & SaaS",{"categories":86},[87],"AI & LLMs",{"categories":89},[90],"AI Automation",{"categories":92},[93],"Product Strategy",{"categories":95},[87],{"categories":97},[81],{"categories":99},[84],{"categories":101},[],{"categories":103},[87],{"categories":105},[],{"categories":107},[108],"AI News & Trends",{"categories":110},[90],{"categories":112},[108],{"categories":114},[90],{"categories":116},[90],{"categories":118},[87],{"categories":120},[87],{"categories":122},[108],{"categories":124},[87],{"categories":126},[],{"categories":128},[129],"Design & Frontend",{"categories":131},[50],{"categories":133},[108],{"categories":135},[],{"categories":137},[138],"Software Engineering",{"categories":140},[87],{"categories":142},[90],{"categories":144},[145],"Marketing & Growth",{"categories":147},[87],{"categories":149},[90],{"categories":151},[],{"categories":153},[],{"categories":155},[129],{"categories":157},[90],{"categories":159},[81],{"categories":161},[129],{"categories":163},[87],{"categories":165},[90],{"categories":167},[108],{"categories":169},[],{"categories":171},[],{"categories":173},[90],{"categories":175},[138],{"categories":177},[],{"categories":179},[84],{"categories":181},[],{"categories":183},[],{"categories":185},[90],{"categories":187},[90],{"categories":189},[87],{"categories":191},[],{"categories":193},[138],{"categories":195},[],{"categories":197},[],{"categories":199},[],{"categories":201},[87],{"categories":203},[145],{"categories":205},[129],{"categories":207},[129],{"categories":209},[87],{"categories":211},[90],{"categories":213},[87],{"categories":215},[87],{"categories":217},[90],{"categories":219},[90],{"categories":221},[50],{"categories":223},[108],{"categories":225},[90],{"categories":227},[145],{"categories":229},[90],{"categories":231},[93],{"categories":233},[],{"categories":235},[90],{"categories":237},[],{"categories":239},[90],{"categories":241},[138],{"categories":243},[129],{"categories":245},[87],{"categories":247},[],{"categories":249},[],{"categories":251},[90],{"categories":253},[],{"categories":255},[87],{"categories":257},[],{"categories":259},[81],{"categories":261},[138],{"categories":263},[84],{"categories":265},[108],{"categories":267},[87],{"categories":269},[],{"categories":271},[87],{"categories":273},[],{"categories":275},[138],{"categories":277},[50],{"categories":279},[],{"categories":281},[87],{"categories":283},[129],{"categories":285},[],{"categories":287},[129],{"categories":289},[90],{"categories":291},[],{"categories":293},[90],{"categories":295},[108],{"categories":297},[84],{"categories":299},[87],{"categories":301},[],{"categories":303},[90],{"categories":305},[87],{"categories":307},[93],{"categories":309},[],{"categories":311},[87],{"categories":313},[90],{"categories":315},[90],{"categories":317},[],{"categories":319},[50],{"categories":321},[87],{"categories":323},[],{"categories":325},[81],{"categories":327},[84],{"categories":329},[87],{"categories":331},[90],{"categories":333},[138],{"categories":335},[87],{"categories":337},[],{"categories":339},[],{"categories":341},[87],{"categories":343},[],{"categories":345},[129],{"categories":347},[],{"categories":349},[87],{"categories":351},[],{"categories":353},[90],{"categories":355},[87],{"categories":357},[129],{"categories":359},[],{"categories":361},[87],{"categories":363},[87],{"categories":365},[84],{"categories":367},[90],{"categories":369},[87],{"categories":371},[129],{"categories":373},[90],{"categories":375},[],{"categories":377},[],{"categories":379},[108],{"categories":381},[],{"categories":383},[87],{"categories":385},[84,145],{"categories":387},[],{"categories":389},[87],{"categories":391},[],{"categories":393},[],{"categories":395},[87],{"categories":397},[],{"categories":399},[87],{"categories":401},[402],"DevOps & Cloud",{"categories":404},[],{"categories":406},[108],{"categories":408},[129],{"categories":410},[],{"categories":412},[108],{"categories":414},[108],{"categories":416},[87],{"categories":418},[145],{"categories":420},[],{"categories":422},[84],{"categories":424},[],{"categories":426},[87,402],{"categories":428},[87],{"categories":430},[87],{"categories":432},[90],{"categories":434},[87,138],{"categories":436},[50],{"categories":438},[87],{"categories":440},[145],{"categories":442},[90],{"categories":444},[90],{"categories":446},[],{"categories":448},[90],{"categories":450},[87,84],{"categories":452},[],{"categories":454},[129],{"categories":456},[129],{"categories":458},[],{"categories":460},[],{"categories":462},[108],{"categories":464},[],{"categories":466},[81],{"categories":468},[138],{"categories":470},[87],{"categories":472},[129],{"categories":474},[90],{"categories":476},[138],{"categories":478},[108],{"categories":480},[129],{"categories":482},[],{"categories":484},[87],{"categories":486},[87],{"categories":488},[87],{"categories":490},[108],{"categories":492},[81],{"categories":494},[87],{"categories":496},[90],{"categories":498},[402],{"categories":500},[129],{"categories":502},[90],{"categories":504},[],{"categories":506},[],{"categories":508},[129],{"categories":510},[108],{"categories":512},[50],{"categories":514},[],{"categories":516},[87],{"categories":518},[87],{"categories":520},[84],{"categories":522},[87],{"categories":524},[87],{"categories":526},[108],{"categories":528},[],{"categories":530},[90],{"categories":532},[138],{"categories":534},[],{"categories":536},[87],{"categories":538},[87],{"categories":540},[90],{"categories":542},[],{"categories":544},[],{"categories":546},[87],{"categories":548},[],{"categories":550},[84],{"categories":552},[90],{"categories":554},[],{"categories":556},[81],{"categories":558},[87],{"categories":560},[84],{"categories":562},[108],{"categories":564},[],{"categories":566},[],{"categories":568},[],{"categories":570},[108],{"categories":572},[108],{"categories":574},[],{"categories":576},[],{"categories":578},[84],{"categories":580},[],{"categories":582},[],{"categories":584},[81],{"categories":586},[],{"categories":588},[145],{"categories":590},[90],{"categories":592},[84],{"categories":594},[90],{"categories":596},[138],{"categories":598},[],{"categories":600},[93],{"categories":602},[129],{"categories":604},[138],{"categories":606},[87],{"categories":608},[90],{"categories":610},[84],{"categories":612},[87],{"categories":614},[],{"categories":616},[],{"categories":618},[138],{"categories":620},[50],{"categories":622},[93],{"categories":624},[90],{"categories":626},[87],{"categories":628},[],{"categories":630},[402],{"categories":632},[],{"categories":634},[90],{"categories":636},[],{"categories":638},[],{"categories":640},[87],{"categories":642},[129],{"categories":644},[145],{"categories":646},[90],{"categories":648},[],{"categories":650},[81],{"categories":652},[],{"categories":654},[108],{"categories":656},[87,402],{"categories":658},[108],{"categories":660},[87],{"categories":662},[84],{"categories":664},[87],{"categories":666},[],{"categories":668},[84],{"categories":670},[],{"categories":672},[138],{"categories":674},[129],{"categories":676},[108],{"categories":678},[50],{"categories":680},[81],{"categories":682},[87],{"categories":684},[138],{"categories":686},[],{"categories":688},[],{"categories":690},[93],{"categories":692},[],{"categories":694},[87],{"categories":696},[],{"categories":698},[129],{"categories":700},[129],{"categories":702},[129],{"categories":704},[],{"categories":706},[],{"categories":708},[108],{"categories":710},[90],{"categories":712},[87],{"categories":714},[87],{"categories":716},[87],{"categories":718},[84],{"categories":720},[87],{"categories":722},[],{"categories":724},[138],{"categories":726},[138],{"categories":728},[84],{"categories":730},[],{"categories":732},[87],{"categories":734},[87],{"categories":736},[84],{"categories":738},[108],{"categories":740},[145],{"categories":742},[90],{"categories":744},[],{"categories":746},[129],{"categories":748},[],{"categories":750},[87],{"categories":752},[],{"categories":754},[84],{"categories":756},[90],{"categories":758},[],{"categories":760},[402],{"categories":762},[50],{"categories":764},[138],{"categories":766},[145],{"categories":768},[138],{"categories":770},[90],{"categories":772},[],{"categories":774},[],{"categories":776},[90],{"categories":778},[81],{"categories":780},[90],{"categories":782},[93],{"categories":784},[84],{"categories":786},[],{"categories":788},[87],{"categories":790},[93],{"categories":792},[87],{"categories":794},[87],{"categories":796},[145],{"categories":798},[129],{"categories":800},[90],{"categories":802},[],{"categories":804},[],{"categories":806},[402],{"categories":808},[138],{"categories":810},[],{"categories":812},[90],{"categories":814},[87],{"categories":816},[129,87],{"categories":818},[81],{"categories":820},[],{"categories":822},[87],{"categories":824},[81],{"categories":826},[129],{"categories":828},[90],{"categories":830},[138],{"categories":832},[],{"categories":834},[87],{"categories":836},[],{"categories":838},[81],{"categories":840},[],{"categories":842},[90],{"categories":844},[93],{"categories":846},[87],{"categories":848},[87],{"categories":850},[129],{"categories":852},[90],{"categories":854},[402],{"categories":856},[129],{"categories":858},[90],{"categories":860},[87],{"categories":862},[87],{"categories":864},[87],{"categories":866},[108],{"categories":868},[],{"categories":870},[93],{"categories":872},[90],{"categories":874},[129],{"categories":876},[90],{"categories":878},[138],{"categories":880},[129],{"categories":882},[90],{"categories":884},[108],{"categories":886},[],{"categories":888},[87],{"categories":890},[129],{"categories":892},[87],{"categories":894},[81],{"categories":896},[108],{"categories":898},[87],{"categories":900},[145],{"categories":902},[87],{"categories":904},[87],{"categories":906},[90],{"categories":908},[90],{"categories":910},[87],{"categories":912},[90],{"categories":914},[129],{"categories":916},[87],{"categories":918},[],{"categories":920},[],{"categories":922},[138],{"categories":924},[],{"categories":926},[81],{"categories":928},[402],{"categories":930},[],{"categories":932},[81],{"categories":934},[84],{"categories":936},[145],{"categories":938},[],{"categories":940},[84],{"categories":942},[],{"categories":944},[],{"categories":946},[],{"categories":948},[],{"categories":950},[],{"categories":952},[87],{"categories":954},[90],{"categories":956},[402],{"categories":958},[81],{"categories":960},[87],{"categories":962},[138],{"categories":964},[93],{"categories":966},[87],{"categories":968},[145],{"categories":970},[87],{"categories":972},[87],{"categories":974},[87],{"categories":976},[87,81],{"categories":978},[138],{"categories":980},[138],{"categories":982},[129],{"categories":984},[87],{"categories":986},[],{"categories":988},[],{"categories":990},[],{"categories":992},[138],{"categories":994},[50],{"categories":996},[108],{"categories":998},[129],{"categories":1000},[],{"categories":1002},[87],{"categories":1004},[87],{"categories":1006},[],{"categories":1008},[],{"categories":1010},[90],{"categories":1012},[87],{"categories":1014},[84],{"categories":1016},[],{"categories":1018},[81],{"categories":1020},[87],{"categories":1022},[81],{"categories":1024},[87],{"categories":1026},[138],{"categories":1028},[145],{"categories":1030},[87,129],{"categories":1032},[108],{"categories":1034},[129],{"categories":1036},[],{"categories":1038},[402],{"categories":1040},[129],{"categories":1042},[90],{"categories":1044},[],{"categories":1046},[],{"categories":1048},[],{"categories":1050},[],{"categories":1052},[138],{"categories":1054},[90],{"categories":1056},[90],{"categories":1058},[402],{"categories":1060},[87],{"categories":1062},[87],{"categories":1064},[87],{"categories":1066},[],{"categories":1068},[129],{"categories":1070},[],{"categories":1072},[],{"categories":1074},[90],{"categories":1076},[],{"categories":1078},[],{"categories":1080},[145],{"categories":1082},[145],{"categories":1084},[90],{"categories":1086},[],{"categories":1088},[87],{"categories":1090},[87],{"categories":1092},[138],{"categories":1094},[129],{"categories":1096},[129],{"categories":1098},[90],{"categories":1100},[81],{"categories":1102},[87],{"categories":1104},[129],{"categories":1106},[129],{"categories":1108},[90],{"categories":1110},[90],{"categories":1112},[87],{"categories":1114},[],{"categories":1116},[],{"categories":1118},[87],{"categories":1120},[90],{"categories":1122},[108],{"categories":1124},[138],{"categories":1126},[81],{"categories":1128},[87],{"categories":1130},[],{"categories":1132},[90],{"categories":1134},[90],{"categories":1136},[],{"categories":1138},[81],{"categories":1140},[87],{"categories":1142},[81],{"categories":1144},[81],{"categories":1146},[],{"categories":1148},[],{"categories":1150},[90],{"categories":1152},[90],{"categories":1154},[87],{"categories":1156},[87],{"categories":1158},[108],{"categories":1160},[50],{"categories":1162},[93],{"categories":1164},[108],{"categories":1166},[129],{"categories":1168},[],{"categories":1170},[108],{"categories":1172},[],{"categories":1174},[],{"categories":1176},[],{"categories":1178},[],{"categories":1180},[138],{"categories":1182},[50],{"categories":1184},[],{"categories":1186},[87],{"categories":1188},[87],{"categories":1190},[50],{"categories":1192},[138],{"categories":1194},[],{"categories":1196},[],{"categories":1198},[90],{"categories":1200},[108],{"categories":1202},[108],{"categories":1204},[90],{"categories":1206},[81],{"categories":1208},[87,402],{"categories":1210},[],{"categories":1212},[129],{"categories":1214},[81],{"categories":1216},[90],{"categories":1218},[129],{"categories":1220},[],{"categories":1222},[90],{"categories":1224},[90],{"categories":1226},[87],{"categories":1228},[145],{"categories":1230},[138],{"categories":1232},[129],{"categories":1234},[],{"categories":1236},[90],{"categories":1238},[87],{"categories":1240},[90],{"categories":1242},[90],{"categories":1244},[90],{"categories":1246},[145],{"categories":1248},[90],{"categories":1250},[87],{"categories":1252},[],{"categories":1254},[145],{"categories":1256},[108],{"categories":1258},[90],{"categories":1260},[],{"categories":1262},[],{"categories":1264},[87],{"categories":1266},[90],{"categories":1268},[108],{"categories":1270},[90],{"categories":1272},[],{"categories":1274},[],{"categories":1276},[],{"categories":1278},[90],{"categories":1280},[],{"categories":1282},[],{"categories":1284},[50],{"categories":1286},[87],{"categories":1288},[50],{"categories":1290},[108],{"categories":1292},[87],{"categories":1294},[87],{"categories":1296},[90],{"categories":1298},[87],{"categories":1300},[],{"categories":1302},[],{"categories":1304},[402],{"categories":1306},[],{"categories":1308},[],{"categories":1310},[81],{"categories":1312},[],{"categories":1314},[],{"categories":1316},[],{"categories":1318},[],{"categories":1320},[138],{"categories":1322},[108],{"categories":1324},[145],{"categories":1326},[84],{"categories":1328},[87],{"categories":1330},[87],{"categories":1332},[84],{"categories":1334},[],{"categories":1336},[129],{"categories":1338},[90],{"categories":1340},[84],{"categories":1342},[87],{"categories":1344},[87],{"categories":1346},[81],{"categories":1348},[],{"categories":1350},[81],{"categories":1352},[87],{"categories":1354},[145],{"categories":1356},[90],{"categories":1358},[108],{"categories":1360},[84],{"categories":1362},[87],{"categories":1364},[90],{"categories":1366},[],{"categories":1368},[87],{"categories":1370},[81],{"categories":1372},[87],{"categories":1374},[],{"categories":1376},[108],{"categories":1378},[87],{"categories":1380},[],{"categories":1382},[84],{"categories":1384},[87],{"categories":1386},[],{"categories":1388},[],{"categories":1390},[],{"categories":1392},[87],{"categories":1394},[],{"categories":1396},[402],{"categories":1398},[87],{"categories":1400},[],{"categories":1402},[87],{"categories":1404},[87],{"categories":1406},[87],{"categories":1408},[87,402],{"categories":1410},[87],{"categories":1412},[87],{"categories":1414},[129],{"categories":1416},[90],{"categories":1418},[],{"categories":1420},[90],{"categories":1422},[87],{"categories":1424},[87],{"categories":1426},[87],{"categories":1428},[81],{"categories":1430},[81],{"categories":1432},[138],{"categories":1434},[129],{"categories":1436},[90],{"categories":1438},[],{"categories":1440},[87],{"categories":1442},[108],{"categories":1444},[87],{"categories":1446},[84],{"categories":1448},[],{"categories":1450},[402],{"categories":1452},[129],{"categories":1454},[129],{"categories":1456},[90],{"categories":1458},[108],{"categories":1460},[90],{"categories":1462},[87],{"categories":1464},[],{"categories":1466},[87],{"categories":1468},[],{"categories":1470},[],{"categories":1472},[87],{"categories":1474},[87],{"categories":1476},[87],{"categories":1478},[90],{"categories":1480},[87],{"categories":1482},[],{"categories":1484},[50],{"categories":1486},[90],{"categories":1488},[],{"categories":1490},[],{"categories":1492},[87],{"categories":1494},[108],{"categories":1496},[],{"categories":1498},[129],{"categories":1500},[402],{"categories":1502},[108],{"categories":1504},[138],{"categories":1506},[138],{"categories":1508},[108],{"categories":1510},[108],{"categories":1512},[402],{"categories":1514},[],{"categories":1516},[108],{"categories":1518},[87],{"categories":1520},[81],{"categories":1522},[108],{"categories":1524},[],{"categories":1526},[50],{"categories":1528},[108],{"categories":1530},[138],{"categories":1532},[108],{"categories":1534},[402],{"categories":1536},[87],{"categories":1538},[87],{"categories":1540},[],{"categories":1542},[84],{"categories":1544},[],{"categories":1546},[],{"categories":1548},[87],{"categories":1550},[87],{"categories":1552},[87],{"categories":1554},[87],{"categories":1556},[],{"categories":1558},[50],{"categories":1560},[81],{"categories":1562},[],{"categories":1564},[87],{"categories":1566},[87],{"categories":1568},[402],{"categories":1570},[402],{"categories":1572},[],{"categories":1574},[90],{"categories":1576},[108],{"categories":1578},[108],{"categories":1580},[87],{"categories":1582},[90],{"categories":1584},[],{"categories":1586},[129],{"categories":1588},[87],{"categories":1590},[87],{"categories":1592},[],{"categories":1594},[],{"categories":1596},[402],{"categories":1598},[87],{"categories":1600},[138],{"categories":1602},[84],{"categories":1604},[87],{"categories":1606},[],{"categories":1608},[90],{"categories":1610},[81],{"categories":1612},[81],{"categories":1614},[],{"categories":1616},[87],{"categories":1618},[129],{"categories":1620},[90],{"categories":1622},[],{"categories":1624},[87],{"categories":1626},[87],{"categories":1628},[90],{"categories":1630},[],{"categories":1632},[90],{"categories":1634},[138],{"categories":1636},[],{"categories":1638},[87],{"categories":1640},[],{"categories":1642},[87],{"categories":1644},[],{"categories":1646},[87],{"categories":1648},[87],{"categories":1650},[],{"categories":1652},[87],{"categories":1654},[108],{"categories":1656},[87],{"categories":1658},[87],{"categories":1660},[81],{"categories":1662},[87],{"categories":1664},[108],{"categories":1666},[90],{"categories":1668},[],{"categories":1670},[87],{"categories":1672},[145],{"categories":1674},[],{"categories":1676},[],{"categories":1678},[],{"categories":1680},[81],{"categories":1682},[108],{"categories":1684},[90],{"categories":1686},[87],{"categories":1688},[129],{"categories":1690},[90],{"categories":1692},[],{"categories":1694},[90],{"categories":1696},[],{"categories":1698},[87],{"categories":1700},[90],{"categories":1702},[87],{"categories":1704},[],{"categories":1706},[87],{"categories":1708},[87],{"categories":1710},[108],{"categories":1712},[129],{"categories":1714},[90],{"categories":1716},[129],{"categories":1718},[84],{"categories":1720},[],{"categories":1722},[],{"categories":1724},[87],{"categories":1726},[81],{"categories":1728},[108],{"categories":1730},[],{"categories":1732},[],{"categories":1734},[138],{"categories":1736},[129],{"categories":1738},[],{"categories":1740},[87],{"categories":1742},[],{"categories":1744},[145],{"categories":1746},[87],{"categories":1748},[402],{"categories":1750},[138],{"categories":1752},[],{"categories":1754},[90],{"categories":1756},[87],{"categories":1758},[90],{"categories":1760},[90],{"categories":1762},[87],{"categories":1764},[],{"categories":1766},[81],{"categories":1768},[87],{"categories":1770},[84],{"categories":1772},[138],{"categories":1774},[129],{"categories":1776},[],{"categories":1778},[],{"categories":1780},[],{"categories":1782},[90],{"categories":1784},[129],{"categories":1786},[108],{"categories":1788},[87],{"categories":1790},[108],{"categories":1792},[129],{"categories":1794},[],{"categories":1796},[129],{"categories":1798},[108],{"categories":1800},[84],{"categories":1802},[87],{"categories":1804},[108],{"categories":1806},[145],{"categories":1808},[],{"categories":1810},[],{"categories":1812},[50],{"categories":1814},[87,138],{"categories":1816},[108],{"categories":1818},[87],{"categories":1820},[90],{"categories":1822},[90],{"categories":1824},[87],{"categories":1826},[],{"categories":1828},[138],{"categories":1830},[87],{"categories":1832},[50],{"categories":1834},[90],{"categories":1836},[145],{"categories":1838},[402],{"categories":1840},[],{"categories":1842},[81],{"categories":1844},[90],{"categories":1846},[90],{"categories":1848},[138],{"categories":1850},[87],{"categories":1852},[87],{"categories":1854},[],{"categories":1856},[],{"categories":1858},[],{"categories":1860},[402],{"categories":1862},[108],{"categories":1864},[87],{"categories":1866},[87],{"categories":1868},[87],{"categories":1870},[],{"categories":1872},[50],{"categories":1874},[84],{"categories":1876},[],{"categories":1878},[90],{"categories":1880},[402],{"categories":1882},[],{"categories":1884},[129],{"categories":1886},[129],{"categories":1888},[],{"categories":1890},[138],{"categories":1892},[129],{"categories":1894},[87],{"categories":1896},[],{"categories":1898},[108],{"categories":1900},[87],{"categories":1902},[129],{"categories":1904},[90],{"categories":1906},[108],{"categories":1908},[],{"categories":1910},[90],{"categories":1912},[129],{"categories":1914},[87],{"categories":1916},[],{"categories":1918},[87],{"categories":1920},[87],{"categories":1922},[402],{"categories":1924},[108],{"categories":1926},[50],{"categories":1928},[50],{"categories":1930},[],{"categories":1932},[],{"categories":1934},[],{"categories":1936},[90],{"categories":1938},[138],{"categories":1940},[138],{"categories":1942},[],{"categories":1944},[],{"categories":1946},[87],{"categories":1948},[],{"categories":1950},[90],{"categories":1952},[87],{"categories":1954},[],{"categories":1956},[87],{"categories":1958},[84],{"categories":1960},[87],{"categories":1962},[145],{"categories":1964},[90],{"categories":1966},[87],{"categories":1968},[138],{"categories":1970},[108],{"categories":1972},[90],{"categories":1974},[],{"categories":1976},[108],{"categories":1978},[90],{"categories":1980},[90],{"categories":1982},[],{"categories":1984},[84],{"categories":1986},[90],{"categories":1988},[],{"categories":1990},[87],{"categories":1992},[81],{"categories":1994},[108],{"categories":1996},[402],{"categories":1998},[90],{"categories":2000},[90],{"categories":2002},[81],{"categories":2004},[87],{"categories":2006},[],{"categories":2008},[],{"categories":2010},[129],{"categories":2012},[87,84],{"categories":2014},[],{"categories":2016},[81],{"categories":2018},[50],{"categories":2020},[87],{"categories":2022},[138],{"categories":2024},[87],{"categories":2026},[90],{"categories":2028},[87],{"categories":2030},[87],{"categories":2032},[108],{"categories":2034},[90],{"categories":2036},[],{"categories":2038},[],{"categories":2040},[90],{"categories":2042},[87],{"categories":2044},[402],{"categories":2046},[],{"categories":2048},[87],{"categories":2050},[90],{"categories":2052},[],{"categories":2054},[87],{"categories":2056},[145],{"categories":2058},[50],{"categories":2060},[90],{"categories":2062},[87],{"categories":2064},[402],{"categories":2066},[],{"categories":2068},[87],{"categories":2070},[145],{"categories":2072},[129],{"categories":2074},[87],{"categories":2076},[],{"categories":2078},[145],{"categories":2080},[108],{"categories":2082},[87],{"categories":2084},[87],{"categories":2086},[81],{"categories":2088},[],{"categories":2090},[],{"categories":2092},[129],{"categories":2094},[87],{"categories":2096},[50],{"categories":2098},[145],{"categories":2100},[145],{"categories":2102},[108],{"categories":2104},[],{"categories":2106},[],{"categories":2108},[87],{"categories":2110},[],{"categories":2112},[87,138],{"categories":2114},[108],{"categories":2116},[90],{"categories":2118},[138],{"categories":2120},[87],{"categories":2122},[81],{"categories":2124},[],{"categories":2126},[],{"categories":2128},[81],{"categories":2130},[145],{"categories":2132},[87],{"categories":2134},[],{"categories":2136},[129,87],{"categories":2138},[402],{"categories":2140},[81],{"categories":2142},[],{"categories":2144},[84],{"categories":2146},[84],{"categories":2148},[87],{"categories":2150},[138],{"categories":2152},[90],{"categories":2154},[108],{"categories":2156},[145],{"categories":2158},[129],{"categories":2160},[87],{"categories":2162},[87],{"categories":2164},[87],{"categories":2166},[81],{"categories":2168},[87],{"categories":2170},[90],{"categories":2172},[108],{"categories":2174},[],{"categories":2176},[],{"categories":2178},[50],{"categories":2180},[138],{"categories":2182},[87],{"categories":2184},[129],{"categories":2186},[50],{"categories":2188},[87],{"categories":2190},[87],{"categories":2192},[90],{"categories":2194},[90],{"categories":2196},[87,84],{"categories":2198},[],{"categories":2200},[129],{"categories":2202},[],{"categories":2204},[87],{"categories":2206},[108],{"categories":2208},[81],{"categories":2210},[81],{"categories":2212},[90],{"categories":2214},[87],{"categories":2216},[84],{"categories":2218},[138],{"categories":2220},[145],{"categories":2222},[],{"categories":2224},[108],{"categories":2226},[87],{"categories":2228},[87],{"categories":2230},[108],{"categories":2232},[138],{"categories":2234},[87],{"categories":2236},[90],{"categories":2238},[108],{"categories":2240},[87],{"categories":2242},[129],{"categories":2244},[87],{"categories":2246},[87],{"categories":2248},[402],{"categories":2250},[93],{"categories":2252},[90],{"categories":2254},[87],{"categories":2256},[108],{"categories":2258},[90],{"categories":2260},[145],{"categories":2262},[87],{"categories":2264},[],{"categories":2266},[87],{"categories":2268},[],{"categories":2270},[],{"categories":2272},[],{"categories":2274},[84],{"categories":2276},[87],{"categories":2278},[90],{"categories":2280},[108],{"categories":2282},[108],{"categories":2284},[108],{"categories":2286},[108],{"categories":2288},[],{"categories":2290},[81],{"categories":2292},[90],{"categories":2294},[108],{"categories":2296},[81],{"categories":2298},[90],{"categories":2300},[87],{"categories":2302},[87,90],{"categories":2304},[90],{"categories":2306},[402],{"categories":2308},[108],{"categories":2310},[108],{"categories":2312},[90],{"categories":2314},[87],{"categories":2316},[],{"categories":2318},[108],{"categories":2320},[145],{"categories":2322},[81],{"categories":2324},[87],{"categories":2326},[87],{"categories":2328},[],{"categories":2330},[138],{"categories":2332},[],{"categories":2334},[81],{"categories":2336},[90],{"categories":2338},[108],{"categories":2340},[87],{"categories":2342},[108],{"categories":2344},[81],{"categories":2346},[108],{"categories":2348},[108],{"categories":2350},[],{"categories":2352},[84],{"categories":2354},[90],{"categories":2356},[108],{"categories":2358},[108],{"categories":2360},[108],{"categories":2362},[108],{"categories":2364},[108],{"categories":2366},[108],{"categories":2368},[108],{"categories":2370},[108],{"categories":2372},[108],{"categories":2374},[108],{"categories":2376},[50],{"categories":2378},[81],{"categories":2380},[87],{"categories":2382},[87],{"categories":2384},[],{"categories":2386},[87,81],{"categories":2388},[],{"categories":2390},[90],{"categories":2392},[108],{"categories":2394},[90],{"categories":2396},[87],{"categories":2398},[87],{"categories":2400},[87],{"categories":2402},[87],{"categories":2404},[87],{"categories":2406},[90],{"categories":2408},[84],{"categories":2410},[129],{"categories":2412},[108],{"categories":2414},[87],{"categories":2416},[],{"categories":2418},[],{"categories":2420},[90],{"categories":2422},[129],{"categories":2424},[87],{"categories":2426},[],{"categories":2428},[],{"categories":2430},[145],{"categories":2432},[87],{"categories":2434},[],{"categories":2436},[],{"categories":2438},[81],{"categories":2440},[84],{"categories":2442},[87],{"categories":2444},[84],{"categories":2446},[129],{"categories":2448},[],{"categories":2450},[108],{"categories":2452},[],{"categories":2454},[129],{"categories":2456},[87],{"categories":2458},[145],{"categories":2460},[],{"categories":2462},[145],{"categories":2464},[],{"categories":2466},[],{"categories":2468},[90],{"categories":2470},[],{"categories":2472},[84],{"categories":2474},[81],{"categories":2476},[129],{"categories":2478},[138],{"categories":2480},[],{"categories":2482},[],{"categories":2484},[87],{"categories":2486},[81],{"categories":2488},[145],{"categories":2490},[],{"categories":2492},[90],{"categories":2494},[90],{"categories":2496},[108],{"categories":2498},[87],{"categories":2500},[90],{"categories":2502},[87],{"categories":2504},[90],{"categories":2506},[87],{"categories":2508},[93],{"categories":2510},[108],{"categories":2512},[],{"categories":2514},[145],{"categories":2516},[138],{"categories":2518},[90],{"categories":2520},[],{"categories":2522},[87],{"categories":2524},[90],{"categories":2526},[84],{"categories":2528},[81],{"categories":2530},[87],{"categories":2532},[129],{"categories":2534},[138],{"categories":2536},[138],{"categories":2538},[87],{"categories":2540},[50],{"categories":2542},[87],{"categories":2544},[90],{"categories":2546},[84],{"categories":2548},[90],{"categories":2550},[87],{"categories":2552},[87],{"categories":2554},[90],{"categories":2556},[108],{"categories":2558},[],{"categories":2560},[81],{"categories":2562},[87],{"categories":2564},[90],{"categories":2566},[87],{"categories":2568},[87],{"categories":2570},[],{"categories":2572},[129],{"categories":2574},[84],{"categories":2576},[108],{"categories":2578},[87],{"categories":2580},[87],{"categories":2582},[129],{"categories":2584},[145],{"categories":2586},[50],{"categories":2588},[87],{"categories":2590},[108],{"categories":2592},[87],{"categories":2594},[90],{"categories":2596},[402],{"categories":2598},[87],{"categories":2600},[90],{"categories":2602},[50],{"categories":2604},[],{"categories":2606},[90],{"categories":2608},[138],{"categories":2610},[129],{"categories":2612},[87],{"categories":2614},[81],{"categories":2616},[84],{"categories":2618},[138],{"categories":2620},[],{"categories":2622},[90],{"categories":2624},[87],{"categories":2626},[],{"categories":2628},[108],{"categories":2630},[],{"categories":2632},[108],{"categories":2634},[87],{"categories":2636},[90],{"categories":2638},[90],{"categories":2640},[90],{"categories":2642},[],{"categories":2644},[],{"categories":2646},[87],{"categories":2648},[87],{"categories":2650},[],{"categories":2652},[129],{"categories":2654},[90],{"categories":2656},[145],{"categories":2658},[81],{"categories":2660},[],{"categories":2662},[],{"categories":2664},[108],{"categories":2666},[138],{"categories":2668},[87],{"categories":2670},[87],{"categories":2672},[87],{"categories":2674},[138],{"categories":2676},[108],{"categories":2678},[129],{"categories":2680},[87],{"categories":2682},[87],{"categories":2684},[87],{"categories":2686},[108],{"categories":2688},[87],{"categories":2690},[108],{"categories":2692},[90],{"categories":2694},[90],{"categories":2696},[138],{"categories":2698},[90],{"categories":2700},[87],{"categories":2702},[138],{"categories":2704},[129],{"categories":2706},[],{"categories":2708},[90],{"categories":2710},[],{"categories":2712},[],{"categories":2714},[],{"categories":2716},[84],{"categories":2718},[87],{"categories":2720},[90],{"categories":2722},[81],{"categories":2724},[90],{"categories":2726},[145],{"categories":2728},[],{"categories":2730},[90],{"categories":2732},[],{"categories":2734},[81],{"categories":2736},[90],{"categories":2738},[],{"categories":2740},[90],{"categories":2742},[87],{"categories":2744},[108],{"categories":2746},[87],{"categories":2748},[90],{"categories":2750},[108],{"categories":2752},[90],{"categories":2754},[138],{"categories":2756},[129],{"categories":2758},[81],{"categories":2760},[],{"categories":2762},[90],{"categories":2764},[129],{"categories":2766},[402],{"categories":2768},[108],{"categories":2770},[87],{"categories":2772},[129],{"categories":2774},[81],{"categories":2776},[],{"categories":2778},[90],{"categories":2780},[90],{"categories":2782},[87],{"categories":2784},[],{"categories":2786},[90],{"categories":2788},[93],{"categories":2790},[108],{"categories":2792},[90],{"categories":2794},[84],{"categories":2796},[],{"categories":2798},[87],{"categories":2800},[93],{"categories":2802},[87],{"categories":2804},[90],{"categories":2806},[108],{"categories":2808},[81],{"categories":2810},[402],{"categories":2812},[87],{"categories":2814},[87],{"categories":2816},[87],{"categories":2818},[108],{"categories":2820},[84],{"categories":2822},[87],{"categories":2824},[129],{"categories":2826},[108],{"categories":2828},[402],{"categories":2830},[87],{"categories":2832},[],{"categories":2834},[],{"categories":2836},[402],{"categories":2838},[50],{"categories":2840},[90],{"categories":2842},[90],{"categories":2844},[108],{"categories":2846},[87],{"categories":2848},[81],{"categories":2850},[129],{"categories":2852},[90],{"categories":2854},[87],{"categories":2856},[145],{"categories":2858},[87],{"categories":2860},[90],{"categories":2862},[],{"categories":2864},[87],{"categories":2866},[87],{"categories":2868},[108],{"categories":2870},[81],{"categories":2872},[],{"categories":2874},[87],{"categories":2876},[87],{"categories":2878},[138],{"categories":2880},[129],{"categories":2882},[87,90],{"categories":2884},[145,84],{"categories":2886},[87],{"categories":2888},[],{"categories":2890},[90],{"categories":2892},[],{"categories":2894},[138],{"categories":2896},[87],{"categories":2898},[108],{"categories":2900},[],{"categories":2902},[90],{"categories":2904},[],{"categories":2906},[129],{"categories":2908},[90],{"categories":2910},[81],{"categories":2912},[90],{"categories":2914},[87],{"categories":2916},[402],{"categories":2918},[145],{"categories":2920},[84],{"categories":2922},[84],{"categories":2924},[81],{"categories":2926},[81],{"categories":2928},[87],{"categories":2930},[90],{"categories":2932},[87],{"categories":2934},[87],{"categories":2936},[81],{"categories":2938},[87],{"categories":2940},[145],{"categories":2942},[108],{"categories":2944},[87],{"categories":2946},[90],{"categories":2948},[87],{"categories":2950},[],{"categories":2952},[138],{"categories":2954},[],{"categories":2956},[90],{"categories":2958},[81],{"categories":2960},[],{"categories":2962},[402],{"categories":2964},[87],{"categories":2966},[],{"categories":2968},[108],{"categories":2970},[90],{"categories":2972},[138],{"categories":2974},[87],{"categories":2976},[90],{"categories":2978},[138],{"categories":2980},[90],{"categories":2982},[108],{"categories":2984},[81],{"categories":2986},[108],{"categories":2988},[138],{"categories":2990},[87],{"categories":2992},[129],{"categories":2994},[87],{"categories":2996},[87],{"categories":2998},[87],{"categories":3000},[87],{"categories":3002},[90],{"categories":3004},[87],{"categories":3006},[90],{"categories":3008},[87],{"categories":3010},[81],{"categories":3012},[87],{"categories":3014},[90],{"categories":3016},[129],{"categories":3018},[81],{"categories":3020},[90],{"categories":3022},[129],{"categories":3024},[],{"categories":3026},[87],{"categories":3028},[87],{"categories":3030},[138],{"categories":3032},[],{"categories":3034},[90],{"categories":3036},[145],{"categories":3038},[87],{"categories":3040},[108],{"categories":3042},[145],{"categories":3044},[90],{"categories":3046},[84],{"categories":3048},[84],{"categories":3050},[87],{"categories":3052},[81],{"categories":3054},[],{"categories":3056},[87],{"categories":3058},[],{"categories":3060},[81],{"categories":3062},[87],{"categories":3064},[90],{"categories":3066},[90],{"categories":3068},[],{"categories":3070},[138],{"categories":3072},[138],{"categories":3074},[145],{"categories":3076},[129],{"categories":3078},[],{"categories":3080},[87],{"categories":3082},[81],{"categories":3084},[87],{"categories":3086},[138],{"categories":3088},[81],{"categories":3090},[108],{"categories":3092},[108],{"categories":3094},[],{"categories":3096},[108],{"categories":3098},[90],{"categories":3100},[129],{"categories":3102},[50],{"categories":3104},[87],{"categories":3106},[],{"categories":3108},[108],{"categories":3110},[138],{"categories":3112},[84],{"categories":3114},[87],{"categories":3116},[81],{"categories":3118},[402],{"categories":3120},[81],{"categories":3122},[],{"categories":3124},[],{"categories":3126},[108],{"categories":3128},[],{"categories":3130},[90],{"categories":3132},[90],{"categories":3134},[90],{"categories":3136},[],{"categories":3138},[87],{"categories":3140},[],{"categories":3142},[108],{"categories":3144},[81],{"categories":3146},[129],{"categories":3148},[87],{"categories":3150},[108],{"categories":3152},[108],{"categories":3154},[],{"categories":3156},[108],{"categories":3158},[81],{"categories":3160},[87],{"categories":3162},[],{"categories":3164},[90],{"categories":3166},[90],{"categories":3168},[81],{"categories":3170},[],{"categories":3172},[],{"categories":3174},[],{"categories":3176},[129],{"categories":3178},[90],{"categories":3180},[87],{"categories":3182},[],{"categories":3184},[],{"categories":3186},[],{"categories":3188},[129],{"categories":3190},[],{"categories":3192},[81],{"categories":3194},[],{"categories":3196},[],{"categories":3198},[129],{"categories":3200},[87],{"categories":3202},[108],{"categories":3204},[],{"categories":3206},[145],{"categories":3208},[108],{"categories":3210},[145],{"categories":3212},[87],{"categories":3214},[],{"categories":3216},[],{"categories":3218},[90],{"categories":3220},[],{"categories":3222},[],{"categories":3224},[90],{"categories":3226},[87],{"categories":3228},[],{"categories":3230},[90],{"categories":3232},[108],{"categories":3234},[145],{"categories":3236},[50],{"categories":3238},[90],{"categories":3240},[90],{"categories":3242},[],{"categories":3244},[],{"categories":3246},[],{"categories":3248},[108],{"categories":3250},[],{"categories":3252},[],{"categories":3254},[129],{"categories":3256},[81],{"categories":3258},[],{"categories":3260},[84],{"categories":3262},[145],{"categories":3264},[87],{"categories":3266},[138],{"categories":3268},[81],{"categories":3270},[50],{"categories":3272},[84],{"categories":3274},[138],{"categories":3276},[],{"categories":3278},[],{"categories":3280},[90],{"categories":3282},[81],{"categories":3284},[129],{"categories":3286},[81],{"categories":3288},[90],{"categories":3290},[402],{"categories":3292},[90],{"categories":3294},[],{"categories":3296},[87],{"categories":3298},[108],{"categories":3300},[138],{"categories":3302},[],{"categories":3304},[129],{"categories":3306},[108],{"categories":3308},[81],{"categories":3310},[90],{"categories":3312},[87],{"categories":3314},[84],{"categories":3316},[90,402],{"categories":3318},[90],{"categories":3320},[138],{"categories":3322},[87],{"categories":3324},[50],{"categories":3326},[145],{"categories":3328},[90],{"categories":3330},[],{"categories":3332},[90],{"categories":3334},[87],{"categories":3336},[84],{"categories":3338},[],{"categories":3340},[],{"categories":3342},[87],{"categories":3344},[50],{"categories":3346},[87],{"categories":3348},[],{"categories":3350},[108],{"categories":3352},[],{"categories":3354},[108],{"categories":3356},[138],{"categories":3358},[90],{"categories":3360},[87],{"categories":3362},[145],{"categories":3364},[138],{"categories":3366},[],{"categories":3368},[108],{"categories":3370},[87],{"categories":3372},[],{"categories":3374},[87],{"categories":3376},[90],{"categories":3378},[87],{"categories":3380},[90],{"categories":3382},[87],{"categories":3384},[87],{"categories":3386},[87],{"categories":3388},[87],{"categories":3390},[84],{"categories":3392},[],{"categories":3394},[93],{"categories":3396},[108],{"categories":3398},[87],{"categories":3400},[],{"categories":3402},[138],{"categories":3404},[87],{"categories":3406},[87],{"categories":3408},[90],{"categories":3410},[108],{"categories":3412},[87],{"categories":3414},[87],{"categories":3416},[84],{"categories":3418},[90],{"categories":3420},[129],{"categories":3422},[],{"categories":3424},[50],{"categories":3426},[87],{"categories":3428},[],{"categories":3430},[108],{"categories":3432},[145],{"categories":3434},[],{"categories":3436},[],{"categories":3438},[108],{"categories":3440},[108],{"categories":3442},[145],{"categories":3444},[81],{"categories":3446},[90],{"categories":3448},[90],{"categories":3450},[87],{"categories":3452},[84],{"categories":3454},[],{"categories":3456},[],{"categories":3458},[108],{"categories":3460},[50],{"categories":3462},[138],{"categories":3464},[90],{"categories":3466},[129],{"categories":3468},[50],{"categories":3470},[50],{"categories":3472},[],{"categories":3474},[108],{"categories":3476},[87],{"categories":3478},[87],{"categories":3480},[138],{"categories":3482},[],{"categories":3484},[108],{"categories":3486},[108],{"categories":3488},[108],{"categories":3490},[],{"categories":3492},[90],{"categories":3494},[87],{"categories":3496},[],{"categories":3498},[81],{"categories":3500},[84],{"categories":3502},[],{"categories":3504},[87],{"categories":3506},[87],{"categories":3508},[],{"categories":3510},[138],{"categories":3512},[],{"categories":3514},[],{"categories":3516},[],{"categories":3518},[],{"categories":3520},[87],{"categories":3522},[108],{"categories":3524},[],{"categories":3526},[],{"categories":3528},[87],{"categories":3530},[87],{"categories":3532},[87],{"categories":3534},[50],{"categories":3536},[87],{"categories":3538},[50],{"categories":3540},[],{"categories":3542},[50],{"categories":3544},[50],{"categories":3546},[402],{"categories":3548},[90],{"categories":3550},[138],{"categories":3552},[],{"categories":3554},[],{"categories":3556},[50],{"categories":3558},[138],{"categories":3560},[138],{"categories":3562},[138],{"categories":3564},[],{"categories":3566},[81],{"categories":3568},[138],{"categories":3570},[138],{"categories":3572},[81],{"categories":3574},[138],{"categories":3576},[84],{"categories":3578},[138],{"categories":3580},[138],{"categories":3582},[138],{"categories":3584},[50],{"categories":3586},[108],{"categories":3588},[108],{"categories":3590},[87],{"categories":3592},[138],{"categories":3594},[50],{"categories":3596},[402],{"categories":3598},[50],{"categories":3600},[50],{"categories":3602},[50],{"categories":3604},[],{"categories":3606},[84],{"categories":3608},[],{"categories":3610},[402],{"categories":3612},[138],{"categories":3614},[138],{"categories":3616},[138],{"categories":3618},[90],{"categories":3620},[108,84],{"categories":3622},[50],{"categories":3624},[],{"categories":3626},[],{"categories":3628},[50],{"categories":3630},[],{"categories":3632},[50],{"categories":3634},[108],{"categories":3636},[90],{"categories":3638},[],{"categories":3640},[138],{"categories":3642},[87],{"categories":3644},[129],{"categories":3646},[],{"categories":3648},[87],{"categories":3650},[],{"categories":3652},[108],{"categories":3654},[81],{"categories":3656},[50],{"categories":3658},[],{"categories":3660},[138],{"categories":3662},[108],[3664,3714,4223,4301],{"id":3665,"title":3666,"ai":3667,"body":3672,"categories":3700,"created_at":51,"date_modified":51,"description":43,"extension":52,"faq":51,"featured":53,"kicker_label":51,"meta":3701,"navigation":61,"path":3702,"published_at":3703,"question":51,"scraped_at":51,"seo":3704,"sitemap":3705,"source_id":3706,"source_name":3707,"source_type":69,"source_url":3708,"stem":3709,"tags":3710,"thumbnail_url":51,"tldr":3711,"tweet":51,"unknown_tags":3712,"__hash__":3713},"summaries\u002Fsummaries\u002Fetf-outflows-fooled-me-into-panic-selling-price-ro-summary.md","ETF Outflows Fooled Me Into Panic Selling—Price Rose 15% Days Later",{"provider":7,"model":8,"input_tokens":3668,"output_tokens":3669,"processing_time_ms":3670,"cost_usd":3671},5412,1590,19417,0.00185475,{"type":14,"value":3673,"toc":3695},[3674,3678,3681,3685,3688,3692],[17,3675,3677],{"id":3676},"etf-flows-measure-actions-not-intent-or-direction","ETF Flows Measure Actions, Not Intent or Direction",[22,3679,3680],{},"Spot Bitcoin ETF outflows track net redemptions of fund shares for underlying Bitcoin but reveal nothing about why investors sold, who sold, or what they did next. A large outflow (hundreds of millions over three days) could be arbitrage unwinds, rebalancing, profit-taking, or unrelated institutional adjustments—not necessarily bearish conviction. Narratives amplify this: post-rally outflows get framed as 'smart money distribution,' but interpretations mirror recent price moves more than evidence. In the author's case, outflows were a tiny fraction of total ETF assets, price held key support levels despite an 8% drop from highs, and buyers absorbed selling—signs of consolidation, not reversal.",[17,3682,3684],{"id":3683},"emotional-confirmation-bias-drives-bad-exits","Emotional Confirmation Bias Drives Bad Exits",[22,3686,3687],{},"Traders seek data confirming pre-existing emotional urges, like discomfort from drawdowns. The author sold not purely on data but because outflows provided 'intellectual cover' for an exit already desired; inflows would have justified holding instead. This pattern—emotional pressure first, data as justification—spreads via social media and news, coordinating retail misreads of institutional actions (e.g., pension rebalancing on quarterly cycles). Real-time flow visibility creates false edges, as institutions operate on mismatched timeframes.",[17,3689,3691],{"id":3690},"contextual-rules-for-data-driven-trading","Contextual Rules for Data-Driven Trading",[22,3693,3694],{},"Integrate flows with on-chain metrics (declining exchange reserves signaled accumulation), price structure, and sentiment. Outflows mean less if reserves fall or price holds support. Author's new process: treat flows as one input in a full picture, never act alone. Rule: wait 4 hours on news-driven urges to separate reaction from analysis—urgency often fades. True discipline examines contradicting evidence (e.g., stable long-term holder behavior during outflows) over selective narratives. Fixable error: data was accurate; isolated, narrative-biased interpretation caused the miss.",{"title":43,"searchDepth":44,"depth":44,"links":3696},[3697,3698,3699],{"id":3676,"depth":44,"text":3677},{"id":3683,"depth":44,"text":3684},{"id":3690,"depth":44,"text":3691},[50],{},"\u002Fsummaries\u002Fetf-outflows-fooled-me-into-panic-selling-price-ro-summary","2026-04-08 21:21:17",{"title":3666,"description":43},{"loc":3702},"8b5d15b453afd1f3","Data Driven Investor","https:\u002F\u002Funknown","summaries\u002Fetf-outflows-fooled-me-into-panic-selling-price-ro-summary",[73],"Three days of Bitcoin ETF outflows (hundreds of millions) triggered a sale after an 8% pullback, but without context like total assets or price action, it was noise. Price hit 15% higher in a week due to emotional bias overriding broader data.",[],"uNTjgqS5dxLsuNlmoiFRHxY6LE2nitu2GNb9sn23uws",{"id":3715,"title":3716,"ai":3717,"body":3722,"categories":4198,"created_at":51,"date_modified":51,"description":43,"extension":52,"faq":51,"featured":53,"kicker_label":51,"meta":4199,"navigation":61,"path":4209,"published_at":4210,"question":51,"scraped_at":4211,"seo":4212,"sitemap":4213,"source_id":4214,"source_name":4215,"source_type":69,"source_url":4216,"stem":4217,"tags":4218,"thumbnail_url":51,"tldr":4220,"tweet":51,"unknown_tags":4221,"__hash__":4222},"summaries\u002Fsummaries\u002F332f5fd5595c929c-google-adk-multi-agent-data-analysis-pipeline-summary.md","Google ADK Multi-Agent Data Analysis Pipeline",{"provider":7,"model":8,"input_tokens":3718,"output_tokens":3719,"processing_time_ms":3720,"cost_usd":3721},10182,2436,23362,0.0032319,{"type":14,"value":3723,"toc":4190},[3724,3728,3731,3787,3790,3818,3825,3832,3836,3847,3853,3878,3881,3887,3913,3916,3925,3932,3946,3950,3953,3959,3965,3970,4037,4040,4046,4052,4056,4062,4065,4070,4073,4084,4093,4099,4105,4109,4112,4123,4126,4129,4135,4141,4144,4147,4150,4154,4186],[17,3725,3727],{"id":3726},"centralized-datastore-for-agent-collaboration","Centralized DataStore for Agent Collaboration",[22,3729,3730],{},"The foundation of this pipeline is a singleton DataStore class that persists datasets, metadata, and analysis history across agents. Instantiate it once:",[3732,3733,3737],"pre",{"className":3734,"code":3735,"language":3736,"meta":43,"style":43},"language-python shiki shiki-themes github-light github-dark","class DataStore:\n    _instance = None\n    def __new__(cls):\n        if cls._instance is None:\n            cls._instance = super().__new__(cls)\n            cls._instance.datasets = {}\n            cls._instance.analysis_history = []\n        return cls._instance\n","python",[3738,3739,3740,3748,3753,3758,3763,3769,3775,3781],"code",{"__ignoreMap":43},[3741,3742,3745],"span",{"class":3743,"line":3744},"line",1,[3741,3746,3747],{},"class DataStore:\n",[3741,3749,3750],{"class":3743,"line":44},[3741,3751,3752],{},"    _instance = None\n",[3741,3754,3755],{"class":3743,"line":58},[3741,3756,3757],{},"    def __new__(cls):\n",[3741,3759,3760],{"class":3743,"line":57},[3741,3761,3762],{},"        if cls._instance is None:\n",[3741,3764,3766],{"class":3743,"line":3765},5,[3741,3767,3768],{},"            cls._instance = super().__new__(cls)\n",[3741,3770,3772],{"class":3743,"line":3771},6,[3741,3773,3774],{},"            cls._instance.datasets = {}\n",[3741,3776,3778],{"class":3743,"line":3777},7,[3741,3779,3780],{},"            cls._instance.analysis_history = []\n",[3741,3782,3784],{"class":3743,"line":3783},8,[3741,3785,3786],{},"        return cls._instance\n",[22,3788,3789],{},"Key methods:",[3791,3792,3793,3800,3806,3812],"ul",{},[3794,3795,3796,3799],"li",{},[3738,3797,3798],{},"add_dataset(name, df, source)",": Stores DataFrame with shape, columns, timestamp.",[3794,3801,3802,3805],{},[3738,3803,3804],{},"get_dataset(name)",": Retrieves DataFrame.",[3794,3807,3808,3811],{},[3738,3809,3810],{},"list_datasets()",": Returns available names.",[3794,3813,3814,3817],{},[3738,3815,3816],{},"log_analysis(type, dataset, summary)",": Tracks workflow.",[22,3819,3820,3821,3824],{},"Use ",[3738,3822,3823],{},"DATA_STORE = DataStore()"," globally. This ensures agents share state without passing DataFrames directly, avoiding serialization issues in tool calls. Trade-off: In-memory only, fine for interactive sessions but scale to Redis for production.",[22,3826,3827,3828,3831],{},"Serialization helper ",[3738,3829,3830],{},"make_serializable(obj)"," converts NumPy\u002Fpandas types to JSON-safe primitives—essential for LLM tool responses.",[17,3833,3835],{"id":3834},"data-ingestion-load-and-generate-realistic-samples","Data Ingestion: Load and Generate Realistic Samples",[22,3837,3838,3839,3842,3843,3846],{},"Agents need quick access to data. Define tools that update ToolContext state with ",[3738,3840,3841],{},"loaded_datasets"," list and ",[3738,3844,3845],{},"active_dataset",".",[22,3848,3849],{},[3850,3851,3852],"strong",{},"CSV Loader:",[3732,3854,3856],{"className":3734,"code":3855,"language":3736,"meta":43,"style":43},"def load_csv(file_path: str, dataset_name: str, tool_context: ToolContext) -> dict:\n    df = pd.read_csv(file_path)\n    result = DATA_STORE.add_dataset(dataset_name, df, source=file_path)\n    # Update context and return preview\n",[3738,3857,3858,3863,3868,3873],{"__ignoreMap":43},[3741,3859,3860],{"class":3743,"line":3744},[3741,3861,3862],{},"def load_csv(file_path: str, dataset_name: str, tool_context: ToolContext) -> dict:\n",[3741,3864,3865],{"class":3743,"line":44},[3741,3866,3867],{},"    df = pd.read_csv(file_path)\n",[3741,3869,3870],{"class":3743,"line":58},[3741,3871,3872],{},"    result = DATA_STORE.add_dataset(dataset_name, df, source=file_path)\n",[3741,3874,3875],{"class":3743,"line":57},[3741,3876,3877],{},"    # Update context and return preview\n",[22,3879,3880],{},"Returns shape, dtypes, head(3) sample.",[22,3882,3883,3886],{},[3850,3884,3885],{},"Sample Generators"," (seed=42 for reproducibility):",[3791,3888,3889,3895,3901,3907],{},[3794,3890,3891,3894],{},[3738,3892,3893],{},"sales",": 500 rows—order_id, date, product, revenue, profit.",[3794,3896,3897,3900],{},[3738,3898,3899],{},"customers",": 300 rows—age, income, churn_risk, lifetime_value.",[3794,3902,3903,3906],{},[3738,3904,3905],{},"timeseries",": Daily 2022-2024—trend + seasonal + noise.",[3794,3908,3909,3912],{},[3738,3910,3911],{},"survey",": 200 rows—Likert scores, response_time.",[22,3914,3915],{},"Example:",[3732,3917,3919],{"className":3734,"code":3918,"language":3736,"meta":43,"style":43},"create_sample_dataset(\"sales\", \"sales_data\", tool_context)\n",[3738,3920,3921],{"__ignoreMap":43},[3741,3922,3923],{"class":3743,"line":3744},[3741,3924,3918],{},[22,3926,3927,3928,3931],{},"Lists with ",[3738,3929,3930],{},"list_available_datasets()"," show rows\u002Fcolumns per dataset.",[22,3933,3934,3937,3938,3941,3942,3945],{},[3850,3935,3936],{},"Pitfall Avoidance:"," Always check ",[3738,3939,3940],{},"df is None"," before ops; use ",[3738,3943,3944],{},"tool_context.state"," for active context. Samples mimic real data distributions (e.g., lognormal income, exponential membership_years).",[17,3947,3949],{"id":3948},"statistical-exploration-describe-correlate-test-detect-outliers","Statistical Exploration: Describe, Correlate, Test, Detect Outliers",[22,3951,3952],{},"Turn data into insights with deterministic functions returning serialized dicts.",[22,3954,3955,3958],{},[3850,3956,3957],{},"describe_dataset:"," Splits numeric\u002Fcategorical; computes mean\u002Fstd\u002Fquantiles\u002Fskew for numerics, top values for categoricals. Logs to history.",[22,3960,3961,3964],{},[3850,3962,3963],{},"correlation_analysis (pearson\u002Fspearman):"," Numeric corr matrix + strong pairs (>0.5). Highlights: \"Found X pairs with |correlation| > 0.5\".",[22,3966,3967],{},[3850,3968,3969],{},"hypothesis_test:",[3971,3972,3973,3989],"table",{},[3974,3975,3976],"thead",{},[3977,3978,3979,3983,3986],"tr",{},[3980,3981,3982],"th",{},"Test",[3980,3984,3985],{},"Params",[3980,3987,3988],{},"Output",[3990,3991,3992,4004,4015,4026],"tbody",{},[3977,3993,3994,3998,4001],{},[3995,3996,3997],"td",{},"normality",[3995,3999,4000],{},"column1",[3995,4002,4003],{},"Shapiro-Wilk p>0.05?",[3977,4005,4006,4009,4012],{},[3995,4007,4008],{},"ttest",[3995,4010,4011],{},"column1, group_column (2 groups)",[3995,4013,4014],{},"t-stat, p, means",[3977,4016,4017,4020,4023],{},[3995,4018,4019],{},"anova",[3995,4021,4022],{},"column1, group_column (>2)",[3995,4024,4025],{},"F-stat, group stats",[3977,4027,4028,4031,4034],{},[3995,4029,4030],{},"chi2",[3995,4032,4033],{},"column1, column2",[3995,4035,4036],{},"chi2, dof, independence?",[22,4038,4039],{},"Sample t-test interpretation: \"Significant difference\" if p\u003C0.05.",[22,4041,4042,4045],{},[3850,4043,4044],{},"outlier_detection (iqr\u002Fzscore):"," IQR bounds or z>3; % outliers + examples.",[22,4047,4048,4051],{},[3850,4049,4050],{},"Quality Criteria:"," Sample large data (\u003C5000 for Shapiro); dropna everywhere; round floats for readability. Common mistake: Forgetting group_column in group tests—validate upfront.",[17,4053,4055],{"id":4054},"visualization-factory-7-chart-types-with-grouping","Visualization Factory: 7 Chart Types with Grouping",[22,4057,4058,4061],{},[3738,4059,4060],{},"create_visualization"," generates and displays (plt.show\u002Fclose) charts, returns success message. Supports color_column for grouping.",[22,4063,4064],{},"Supported types:",[3791,4066,4067],{},[3794,4068,4069],{},"histogram\u002Fscatter\u002Fbar\u002Fline\u002Fbox\u002Fheatmap\u002Fpie",[22,4071,4072],{},"Examples:",[3791,4074,4075,4078,4081],{},[3794,4076,4077],{},"Bar: Groupby sum or value_counts, annotated values.",[3794,4079,4080],{},"Heatmap: Corr matrix with color-coded text.",[3794,4082,4083],{},"Box: Per-group or single.",[3732,4085,4087],{"className":3734,"code":4086,"language":3736,"meta":43,"style":43},"create_visualization(\"sales_data\", \"bar\", \"region\", \"revenue\", \"category\")\n",[3738,4088,4089],{"__ignoreMap":43},[3741,4090,4091],{"class":3743,"line":3744},[3741,4092,4086],{},[22,4094,4095,4098],{},[3850,4096,4097],{},"distribution_report:"," 2x2 grid—hist+KDE, box, Q-Q, violin. Tests normality visually.",[22,4100,4101,4104],{},[3850,4102,4103],{},"Pro Tip:"," Use seaborn-v0_8-whitegrid style, husl palette upfront. Always tight_layout(); close figs to avoid memory leaks in loops.",[17,4106,4108],{"id":4107},"multi-agent-orchestration-setup","Multi-Agent Orchestration Setup",[22,4110,4111],{},"Leverage Google ADK for agents\u002Ftools:",[3791,4113,4114,4117,4120],{},[3794,4115,4116],{},"LiteLlm(model=\"openai\u002Fgpt-4o-mini\")",[3794,4118,4119],{},"InMemorySessionService",[3794,4121,4122],{},"Runner for execution",[22,4124,4125],{},"Tools wrap above functions, registered to ToolContext. Master \"analyst\" agent coordinates specialists (e.g., loader, stats, viz, reporter) via function calling.",[22,4127,4128],{},"Full workflow: Load → Describe\u002FCorr\u002FTest → Viz → Report. State persists via DataStore\u002FToolContext.",[22,4130,4131,4134],{},[3850,4132,4133],{},"Prerequisites:"," Python\u002Fpandas\u002Fscipy\u002Fmatplotlib basics; OpenAI API key. Colab-friendly (userdata secrets).",[22,4136,4137,4140],{},[3850,4138,4139],{},"Practice:"," Generate \"sales\", test revenue normality by region (ANOVA), viz profit by category, log everything.",[22,4142,4143],{},"\"We connect these capabilities through a master analyst agent that coordinates specialists, allowing us to see how a production-style analysis system can handle end-to-end tasks in a structured, scalable way.\"",[22,4145,4146],{},"\"This is great for interactive analysis but watch memory with large CSVs—paginate or stream in prod.\"",[22,4148,4149],{},"\"Agents shine when tools are narrow\u002Fsingle-responsibility; broad tools lead to hallucinated params.\"",[17,4151,4153],{"id":4152},"key-takeaways","Key Takeaways",[3791,4155,4156,4159,4162,4165,4168,4171,4174,4177,4180,4183],{},[3794,4157,4158],{},"Start with a shared singleton DataStore to eliminate data-passing friction between agents.",[3794,4160,4161],{},"Generate seeded sample datasets to test pipelines without real files—mimic distributions like lognormal for income.",[3794,4163,4164],{},"Serialize all tool outputs: Convert np\u002Fpandas to native types for reliable LLM parsing.",[3794,4166,4167],{},"Validate inputs rigorously (e.g., 2 groups for t-test) to prevent agent error loops.",[3794,4169,4170],{},"Use color_column grouping in viz for quick multi-facet insights; always annotate bars\u002Fpies.",[3794,4172,4173],{},"Log analysis history for audit trails—replay workflows easily.",[3794,4175,4176],{},"Pick gpt-4o-mini for cost\u002Fspeed in stats\u002Fviz tasks; upgrade for complex reasoning.",[3794,4178,4179],{},"Scale by swapping InMemorySession for persistent store; add async for parallelism.",[3794,4181,4182],{},"Test hypothesis with p\u003C0.05 thresholds but interpret contextually—stats ≠ causation.",[3794,4184,4185],{},"Practice: Build your own tool for custom tests, register to agent, run end-to-end on public CSV.",[4187,4188,4189],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":43,"searchDepth":44,"depth":44,"links":4191},[4192,4193,4194,4195,4196,4197],{"id":3726,"depth":44,"text":3727},{"id":3834,"depth":44,"text":3835},{"id":3948,"depth":44,"text":3949},{"id":4054,"depth":44,"text":4055},{"id":4107,"depth":44,"text":4108},{"id":4152,"depth":44,"text":4153},[90],{"content_references":4200,"triage":4206},[4201],{"type":4202,"title":4203,"url":4204,"context":4205},"tool","Google ADK","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-python","mentioned",{"relevance":3765,"novelty":57,"quality":57,"actionability":3765,"composite":4207,"reasoning":4208},4.55,"Category: AI Automation. The article provides a detailed tutorial on building a multi-agent data analysis pipeline using Google ADK, which directly addresses the audience's need for practical applications in AI automation. It includes specific code examples and a clear framework for implementation, making it highly actionable.","\u002Fsummaries\u002F332f5fd5595c929c-google-adk-multi-agent-data-analysis-pipeline-summary","2026-04-14 03:23:29","2026-04-14 14:37:57",{"title":3716,"description":43},{"loc":4209},"332f5fd5595c929c","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F13\u002Fgoogle-adk-multi-agent-pipeline-tutorial-data-loading-statistical-testing-visualization-and-report-generation-in-python\u002F","summaries\u002F332f5fd5595c929c-google-adk-multi-agent-data-analysis-pipeline-summary",[4219,3736,73,74],"agents","Build an end-to-end data analysis system in Python using Google ADK: load data, run stats tests, generate viz, and coordinate via a master agent—all with shared state and serializable outputs.",[74],"t7BezCb5npzJsUdjVIrK-4GrzArxrVFSIARBSf2jzm8",{"id":4224,"title":4225,"ai":4226,"body":4231,"categories":4262,"created_at":51,"date_modified":51,"description":43,"extension":52,"faq":51,"featured":53,"kicker_label":51,"meta":4263,"navigation":61,"path":4287,"published_at":4288,"question":51,"scraped_at":4289,"seo":4290,"sitemap":4291,"source_id":4292,"source_name":4293,"source_type":69,"source_url":4294,"stem":4295,"tags":4296,"thumbnail_url":51,"tldr":4298,"tweet":51,"unknown_tags":4299,"__hash__":4300},"summaries\u002Fsummaries\u002Fca01173e7503aa9b-agentic-data-products-act-organizations-face-new-r-summary.md","Agentic Data Products Act—Organizations Face New Risks",{"provider":7,"model":8,"input_tokens":4227,"output_tokens":4228,"processing_time_ms":4229,"cost_usd":4230},6533,1720,16220,0.0021441,{"type":14,"value":4232,"toc":4257},[4233,4237,4240,4243,4247,4250,4254],[17,4234,4236],{"id":4235},"agentic-data-products-defined-by-autonomy-and-action","Agentic Data Products Defined by Autonomy and Action",[22,4238,4239],{},"Agentic data products pursue business goals through autonomous, multi-step actions with limited human supervision, distinguishing them from traditional informational products that only inform or recommend. Key features: (1) delegation to decide within boundaries, shifting focus from output accuracy to consistent self-interested behavior; (2) planning, execution, observation, and adaptation across systems, like an inventory agent forecasting demand, ordering stock, monitoring delivery, and adjusting; (3) direct writes to operational systems (ERPs, CRMs) that change real-world states.",[22,4241,4242],{},"Extend Simon O’Regan’s 2018 taxonomy: Levels 1-5 (raw data to decision support) output for humans; Levels 6-7 act—Level 6 bounded with human-on-the-loop, Level 7 fully autonomous (rare today). Bain’s maturity model aligns: most orgs at Levels 1-2 (dashboards, predictions); jumping to 3-4 requires new capabilities beyond BI and data engineering. Term \"agentic data product\" integrates into data portfolios for ownership, SLAs, and governance, unlike vague \"AI agent.\"",[17,4244,4246],{"id":4245},"risks-amplify-from-errors-to-cascading-failures","Risks Amplify from Errors to Cascading Failures",[22,4248,4249],{},"Stale data becomes dangerous (triggers wrong orders\u002Fupdates; 80% of companies cite data limits per IBM 2026). LLM hallucinations lead to acted errors (e.g., airline honoring fake refund). Errors cascade silently in distributed systems—race conditions, inconsistent states compound in black boxes. Accountability blurs with \"human on the loop\" (agency transfers decision rights, per McKinsey’s Rich Isenberg). Goal misalignment risks: agents game objectives (e.g., backlog reducer marks all low-priority). Stats: 68% plan agentic integration, but only 11% in production, 1\u002F3 governance-ready; 40% cancellation risk (Gartner 2026); S&P 2024 notes high AI abandonment.",[17,4251,4253],{"id":4252},"build-readiness-through-governance-and-foundations","Build Readiness Through Governance and Foundations",[22,4255,4256],{},"Upgrade governance: define scope boundaries, real-time monitoring, incident protocols, kill switches—replace human decision points. Shift operating model for decision rights and escalations. Add team skills: agent orchestration, monitoring, incident response. Strengthen data: real-time, entity-scoped, semantically clear (lakes fail at machine speed). Actions: (1) assess taxonomy level (avoid rebranding chatbots); (2) govern before building; (3) start bounded at Level 6; (4) frame as operating model change with dedicated staffing\u002Fbudget; (5) fix data first. Naming as products enables cataloging and accountability.",{"title":43,"searchDepth":44,"depth":44,"links":4258},[4259,4260,4261],{"id":4235,"depth":44,"text":4236},{"id":4245,"depth":44,"text":4246},{"id":4252,"depth":44,"text":4253},[90],{"content_references":4264,"triage":4285},[4265,4271,4275,4279,4282],{"type":4266,"title":4267,"author":4268,"publisher":4269,"context":4270},"paper","Beyond accuracy: What data quality means to data consumers","Wang, R.Y. & Strong, D.M.","JMIS","cited",{"type":4272,"title":4273,"author":4274,"context":4270},"book","Designing Data Products","O’Regan, S.",{"type":4276,"title":4277,"author":4278,"publisher":4278,"context":4270},"report","Agentic AI maturity framework","Bain & Company",{"type":4276,"title":4280,"author":4281,"publisher":4281,"context":4270},"Agentic data management","IBM",{"type":4276,"title":4283,"author":4284,"publisher":4284,"context":4270},"Agentic AI enterprise forecast","Gartner",{"relevance":57,"novelty":58,"quality":57,"actionability":58,"composite":59,"reasoning":4286},"Category: Product Strategy. The article discusses the emerging concept of agentic data products and their implications for organizations, addressing a specific audience pain point regarding governance and operational risks. It provides insights into the current state of adoption and necessary capabilities, but lacks detailed frameworks for implementation.","\u002Fsummaries\u002Fca01173e7503aa9b-agentic-data-products-act-organizations-face-new-r-summary","2026-04-13 15:01:02","2026-04-13 17:53:07",{"title":4225,"description":43},{"loc":4287},"ca01173e7503aa9b","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Fagentic-data-products-are-coming-most-organisations-arent-ready-for-what-breaks-42add191a477?source=rss----98111c9905da---4","summaries\u002Fca01173e7503aa9b-agentic-data-products-act-organizations-face-new-r-summary",[4219,73,4297,74],"product-strategy","Agentic data products autonomously execute multi-step actions in operational systems, turning data errors into real-world consequences like erroneous orders. Most orgs (11% in production) need governance, data upgrades, and new skills to avoid 40% failure rates.",[74],"5Tq3HZO6QSJKpGGVGAWVjUriBPdM9F4cgoikSkPnaYE",{"id":4302,"title":4303,"ai":4304,"body":4309,"categories":4338,"created_at":51,"date_modified":51,"description":43,"extension":52,"faq":51,"featured":53,"kicker_label":51,"meta":4339,"navigation":61,"path":4344,"published_at":4345,"question":51,"scraped_at":4346,"seo":4347,"sitemap":4348,"source_id":4349,"source_name":4350,"source_type":69,"source_url":4351,"stem":4352,"tags":4353,"thumbnail_url":51,"tldr":4355,"tweet":51,"unknown_tags":4356,"__hash__":4357},"summaries\u002Fsummaries\u002F2384d22f05952188-nmi-bias-favors-complex-clusters-over-insight-summary.md","NMI Bias Favors Complex Clusters Over Insight",{"provider":7,"model":8,"input_tokens":4305,"output_tokens":4306,"processing_time_ms":4307,"cost_usd":4308},3843,1618,27720,0.0015551,{"type":14,"value":4310,"toc":4334},[4311,4315,4318,4321,4325,4328,4331],[17,4312,4314],{"id":4313},"spotting-nmis-flaw-in-practice","Spotting NMI's Flaw in Practice",[22,4316,4317],{},"When evaluating clustering algorithms, NMI often gives higher scores to models that produce overly complex or over-segmented clusters, even if those clusters lack intuitive sense. This happens because NMI doesn't penalize unnecessary fragmentation enough, prioritizing mathematical alignment over practical insight. In a real clustering project, algorithms with counterintuitive outputs consistently outscored simpler, more meaningful ones—revealing how the metric can mislead developers into favoring flashy but flawed results.",[22,4319,4320],{},"To counter this, cross-check NMI with qualitative reviews of cluster coherence and alternative metrics like Adjusted Rand Index, which better penalize random over-segmentation. This ensures evaluations reflect real-world utility, not just normalized information overlap.",[17,4322,4324],{"id":4323},"consequences-for-ai-trust-and-deployment","Consequences for AI Trust and Deployment",[22,4326,4327],{},"NMI bias propagates errors across domains like medicine (e.g., patient grouping) and hiring (e.g., candidate categorization), where inflated scores lead to over-trusting underperforming models. It skews funding toward hyped algorithms, delays reliable deployments, and erodes confidence in AI outputs for high-stakes decisions.",[22,4329,4330],{},"Fix by combining NMI with domain-specific validation: visualize clusters, test stability under perturbations, and benchmark against baselines. This multi-metric approach grounds assessments in evidence, preventing bias from turning promising papers into production failures.",[22,4332,4333],{},"The content focuses on exposing the issue through anecdote but lacks deeper fixes or data—treat as a prompt to audit your own evals.",{"title":43,"searchDepth":44,"depth":44,"links":4335},[4336,4337],{"id":4313,"depth":44,"text":4314},{"id":4323,"depth":44,"text":4324},[50],{"content_references":4340,"triage":4341},[],{"relevance":57,"novelty":58,"quality":58,"actionability":58,"composite":4342,"reasoning":4343},3.35,"Category: Data Science & Visualization. The article discusses the limitations of Normalized Mutual Information (NMI) in evaluating clustering algorithms, which is relevant to data science practitioners. It provides some actionable advice, such as cross-checking NMI with qualitative reviews and alternative metrics, but lacks depth in practical implementation.","\u002Fsummaries\u002F2384d22f05952188-nmi-bias-favors-complex-clusters-over-insight-summary","2026-05-08 14:11:00","2026-05-09 15:36:58",{"title":4303,"description":43},{"loc":4344},"2384d22f05952188","AI Simplified in Plain English","https:\u002F\u002Fmedium.com\u002Fai-simplified-in-plain-english\u002Fnmi-bias-exposed-1c76d6b366df?source=rss----f37ab7d4e76b---4","summaries\u002F2384d22f05952188-nmi-bias-favors-complex-clusters-over-insight-summary",[4354,73],"machine-learning","Normalized Mutual Information (NMI) rewards over-segmentation and complexity in clustering, inflating scores for intuitively poor algorithms and distorting AI evaluations.",[],"bRK9QgU1fKpsZb9OQGPyAtwdso4nsYFrkag-CvgURVo"]