[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-28dfe10dc0220a86-duckdb-python-fast-in-process-analytics-db-summary":3,"summaries-facets-categories":234,"summary-related-28dfe10dc0220a86-duckdb-python-fast-in-process-analytics-db-summary":7162},{"id":4,"title":5,"ai":6,"body":13,"categories":192,"created_at":194,"date_modified":194,"description":64,"extension":195,"faq":194,"featured":196,"kicker_label":194,"meta":197,"navigation":219,"path":220,"published_at":194,"question":194,"scraped_at":221,"seo":222,"sitemap":223,"source_id":224,"source_name":225,"source_type":226,"source_url":227,"stem":228,"tags":229,"thumbnail_url":194,"tldr":231,"tweet":194,"unknown_tags":232,"__hash__":233},"summaries\u002Fsummaries\u002F28dfe10dc0220a86-duckdb-python-fast-in-process-analytics-db-summary.md","DuckDB Python: Fast In-Process Analytics DB",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",12461,2682,17233,0.0038107,{"type":14,"value":15,"toc":185},"minimark",[16,21,25,41,44,48,55,58,87,94,97,100,104,107,110,113,117,120,123,126,129,133,181],[17,18,20],"h2",{"id":19},"serverless-analytical-queries-in-python","Serverless Analytical Queries in Python",[22,23,24],"p",{},"DuckDB delivers a complete analytical database engine embedded within your Python application—no external server, no network overhead, zero configuration. Designed for OLAP workloads, it processes complex SQL queries over large datasets with vectorized execution and columnar storage, outperforming traditional tools like Pandas for aggregations and joins on GB-scale data. As an open-source project, it prioritizes portability across platforms while maintaining high performance through hand-optimized query plans and parallel execution.",[22,26,27,28,32,33,36,37,40],{},"The Python client binds directly to this engine, allowing seamless SQL execution via ",[29,30,31],"code",{},"duckdb.query()"," or integration with Pandas via ",[29,34,35],{},"df.sql()",". This eliminates data movement costs: load CSVs, Parquet files, or remote HTTP sources, then run analytics in-memory or persisted to ",[29,38,39],{},".duckdb"," files. Trade-off: excels at read-heavy analytics but lacks full transactional OLTP ACID guarantees of client-server DBs like Postgres.",[22,42,43],{},"\"DuckDB: A Fast, In-Process, Portable, Open Source, Analytical Database System\"",[17,45,47],{"id":46},"frictionless-setup-and-extensibility","Frictionless Setup and Extensibility",[22,49,50,51,54],{},"Installation is a single pip command: ",[29,52,53],{},"pip install duckdb",", pulling the latest stable release (1.5.2 as of April 2026) with all optional dependencies for formats like Parquet, JSON, and HTTP. No Docker, no JVM, no extensions to compile—runs natively on CPython 3.11+.",[22,56,57],{},"Post-install, connect in three lines:",[59,60,65],"pre",{"className":61,"code":62,"language":63,"meta":64,"style":64},"language-python shiki shiki-themes github-light github-dark","import duckdb\ncon = duckdb.connect(':memory:')  # or 'mydb.duckdb'\nresult = con.execute('SELECT * FROM read_csv_auto(\"data.csv\")').fetchall()\n","python","",[29,66,67,75,81],{"__ignoreMap":64},[68,69,72],"span",{"class":70,"line":71},"line",1,[68,73,74],{},"import duckdb\n",[68,76,78],{"class":70,"line":77},2,[68,79,80],{},"con = duckdb.connect(':memory:')  # or 'mydb.duckdb'\n",[68,82,84],{"class":70,"line":83},3,[68,85,86],{},"result = con.execute('SELECT * FROM read_csv_auto(\"data.csv\")').fetchall()\n",[22,88,89,90,93],{},"For production, persist connections and leverage extensions via ",[29,91,92],{},"INSTALL httpfs; LOAD httpfs;"," to query S3 or web data directly. Integrates with Polars, Arrow, and NumPy for zero-copy data exchange, accelerating ETL pipelines.",[22,95,96],{},"Official resources point to structured starting points: DuckDB.org for core docs, Python User Guide for setup nuances, and API reference for advanced bindings. Community support via Discord accelerates troubleshooting.",[22,98,99],{},"\"Install the latest release of DuckDB directly from PyPI\"",[17,101,103],{"id":102},"sustained-momentum-in-development","Sustained Momentum in Development",[22,105,106],{},"DuckDB's Python package mirrors the core project's rapid iteration: over 100 releases since 2019, with 1.5.x hitting stable in early 2026 after dozens of dev builds. Recent cadence—weekly pre-releases, bi-weekly stables—signals reliability for production use, fixing bugs and adding features like ARM64 optimizations and Python 3.14 wheels.",[22,108,109],{},"Maintainers include core contributors (hfmuehleisen, likely project lead Mark Mühleisen; Mytherin; duckdb_admin), ensuring vested interest in Python ecosystem fit. GitHub stats (implied via badges) and CONTRIBUTING.md invite extensions, with focus on embeddability over bloat.",[22,111,112],{},"This velocity beats many data tools: from 0.1.0 (2019) to 1.5.2 (2026), incorporating community feedback into query optimizer improvements and format readers. Pre-releases like 1.6.0.dev12 allow early access without risking stability.",[17,114,116],{"id":115},"cross-platform-reliability-at-scale","Cross-Platform Reliability at Scale",[22,118,119],{},"Wheels cover every modern stack: CPython 3.11-3.14 on Windows (x86-64, ARM64), macOS (10.13+ x86-64, 11.0+ ARM64, universal2), and Linux (manylinux glibc 2.26\u002F2.28 x86-64\u002FARM64). Source distributions enable custom builds.",[22,121,122],{},"This universality suits data notebooks (Jupyter), scripts, or serverless functions—deploy anywhere without platform shims. Files uploaded April 13, 2026, for 1.5.2 confirm freshness, with sizes optimized for quick pulls.",[22,124,125],{},"Trade-off: In-process limits concurrency to single-threaded apps unless using multiprocessing; for distributed needs, pair with Ray or Dask.",[22,127,128],{},"\"Install with all optional dependencies\"",[17,130,132],{"id":131},"key-takeaways","Key Takeaways",[134,135,136,143,156,166,169,172,175,178],"ul",{},[137,138,139,140,142],"li",{},"Run ",[29,141,53],{}," to embed a full analytical DB—no servers, instant queries on Parquet\u002FCSV\u002FJSON.",[137,144,145,146,149,150,152,153,155],{},"Use ",[29,147,148],{},":memory:"," for ephemeral analysis or ",[29,151,39],{}," files for persistence; query Pandas DataFrames directly with ",[29,154,35],{},".",[137,157,158,159,162,163,155],{},"Leverage extensions like ",[29,160,161],{},"httpfs"," for remote data: ",[29,164,165],{},"SELECT * FROM 's3:\u002F\u002Fbucket\u002Fdata.parquet'",[137,167,168],{},"Expect top-tier performance on aggregations\u002Fjoins; benchmark against Pandas for your workloads (often 10-100x faster).",[137,170,171],{},"Track releases on PyPI for cutting-edge features; join Discord for real-world patterns.",[137,173,174],{},"Build pipelines with Arrow\u002FPolars interop to skip serialization overhead.",[137,176,177],{},"For contrib, follow CONTRIBUTING.md—focus on Python-specific extensions.",[137,179,180],{},"Test on target platforms via provided wheels; source for edge cases.",[182,183,184],"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":64,"searchDepth":77,"depth":77,"links":186},[187,188,189,190,191],{"id":19,"depth":77,"text":20},{"id":46,"depth":77,"text":47},{"id":102,"depth":77,"text":103},{"id":115,"depth":77,"text":116},{"id":131,"depth":77,"text":132},[193],"Data Science & Visualization",null,"md",false,{"content_references":198,"triage":215},[199,204,209,212],{"type":200,"title":201,"url":202,"context":203},"tool","DuckDB","https:\u002F\u002Fduckdb.org","mentioned",{"type":205,"title":206,"url":207,"context":208},"other","User Guide (Python)","https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fstable\u002Fguides\u002Fpython\u002Finstall","recommended",{"type":205,"title":210,"url":211,"context":208},"API Docs (Python)","https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fstable\u002Fclients\u002Fpython\u002Foverview",{"type":205,"title":213,"url":214,"context":203},"DuckDB Discord","https:\u002F\u002Fdiscord.gg\u002FtcvwpjfnZx",{"relevance":216,"novelty":83,"quality":216,"actionability":216,"composite":217,"reasoning":218},4,3.8,"Category: Data Science & Visualization. The article provides a detailed overview of DuckDB, an analytical database that integrates with Python, addressing the audience's need for efficient data processing tools. It includes practical installation instructions and code examples, making it actionable for developers looking to implement it in their projects.",true,"\u002Fsummaries\u002F28dfe10dc0220a86-duckdb-python-fast-in-process-analytics-db-summary","2026-04-15 15:32:48",{"title":5,"description":64},{"loc":220},"28dfe10dc0220a86","__oneoff__","article","https:\u002F\u002Fpypi.org\u002Fproject\u002Fduckdb\u002F","summaries\u002F28dfe10dc0220a86-duckdb-python-fast-in-process-analytics-db-summary",[63,230],"data-science","pip install duckdb for a portable, serverless OLAP database that runs analytical SQL queries at high speed directly in Python processes.",[],"x1VIvaRuvvzrpz2JsgM89t1ieCLwV6ftbHT96KjpJ0Q",[235,238,241,243,246,248,251,254,256,258,260,262,265,267,269,271,273,276,278,280,282,284,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,338,340,342,344,346,348,350,352,354,356,358,360,362,364,367,369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,401,403,405,408,410,412,414,416,418,420,422,424,426,428,430,432,434,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,502,504,506,508,510,512,514,516,518,520,522,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,558,560,562,564,566,568,570,572,574,576,578,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,642,644,646,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,688,690,692,694,696,698,700,702,704,706,708,710,712,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1002,1004,1006,1008,1010,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1040,1042,1044,1046,1048,1050,1052,1054,1056,1058,1060,1062,1064,1066,1068,1070,1072,1074,1076,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1566,1568,1570,1572,1574,1576,1578,1580,1582,1584,1586,1588,1590,1592,1594,1596,1598,1600,1602,1604,1606,1608,1610,1612,1614,1616,1618,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1866,1868,1870,1872,1874,1876,1878,1880,1882,1884,1886,1888,1890,1892,1894,1896,1898,1900,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2374,2376,2378,2380,2382,2384,2386,2388,2390,2392,2394,2396,2398,2400,2402,2404,2406,2408,2410,2412,2414,2416,2418,2420,2422,2424,2426,2428,2430,2432,2434,2436,2438,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458,2460,2462,2464,2467,2469,2471,2473,2475,2477,2479,2481,2483,2485,2487,2489,2491,2493,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2588,2590,2592,2594,2596,2598,2600,2602,2604,2606,2608,2610,2612,2614,2616,2618,2620,2622,2624,2626,2628,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3272,3274,3276,3278,3280,3282,3284,3286,3288,3290,3292,3294,3296,3298,3300,3302,3304,3306,3308,3310,3312,3314,3316,3318,3320,3322,3324,3326,3328,3330,3332,3334,3336,3338,3340,3342,3344,3346,3348,3350,3352,3354,3356,3358,3360,3362,3364,3366,3368,3370,3372,3374,3376,3378,3380,3382,3384,3386,3388,3390,3392,3394,3396,3398,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671,3673,3675,3677,3679,3681,3683,3685,3687,3689,3691,3693,3695,3697,3699,3701,3703,3705,3707,3709,3711,3713,3715,3717,3719,3721,3723,3725,3727,3729,3731,3733,3735,3737,3739,3741,3743,3745,3747,3749,3751,3753,3755,3757,3759,3761,3763,3765,3767,3769,3771,3773,3775,3777,3779,3781,3783,3785,3787,3789,3791,3793,3795,3797,3799,3801,3803,3805,3807,3809,3811,3813,3815,3817,3819,3821,3823,3825,3827,3829,3831,3833,3835,3837,3839,3841,3843,3845,3847,3849,3851,3853,3855,3857,3859,3861,3863,3865,3867,3869,3871,3873,3875,3877,3879,3881,3883,3885,3887,3889,3891,3893,3895,3897,3899,3901,3903,3905,3907,3909,3911,3913,3915,3917,3919,3921,3923,3925,3927,3929,3931,3933,3935,3937,3939,3941,3943,3945,3947,3949,3951,3953,3955,3957,3959,3961,3963,3965,3967,3969,3971,3973,3975,3977,3979,3981,3983,3985,3987,3989,3991,3993,3995,3997,3999,4001,4003,4005,4007,4009,4011,4013,4015,4017,4019,4021,4023,4025,4027,4029,4031,4033,4035,4037,4039,4041,4043,4045,4047,4049,4051,4053,4055,4057,4059,4061,4063,4065,4067,4069,4071,4073,4075,4077,4079,4081,4083,4085,4087,4089,4091,4093,4095,4097,4099,4101,4103,4105,4107,4109,4111,4113,4115,4117,4119,4121,4123,4125,4127,4129,4131,4133,4135,4137,4139,4141,4143,4145,4147,4149,4151,4153,4155,4157,4159,4161,4163,4165,4167,4169,4171,4173,4175,4177,4179,4181,4183,4185,4187,4189,4191,4193,4195,4197,4199,4201,4203,4205,4207,4209,4211,4213,4215,4217,4219,4221,4223,4225,4227,4229,4231,4233,4235,4237,4239,4241,4243,4245,4247,4249,4251,4253,4255,4257,4259,4261,4263,4265,4267,4269,4271,4273,4275,4277,4279,4281,4283,4285,4287,4289,4291,4293,4295,4297,4299,4301,4303,4305,4307,4309,4311,4313,4315,4317,4319,4321,4323,4325,4327,4329,4331,4333,4335,4337,4339,4341,4343,4345,4347,4349,4351,4353,4355,4357,4359,4361,4363,4365,4367,4369,4371,4373,4375,4377,4379,4381,4383,4385,4387,4389,4391,4393,4395,4397,4399,4401,4403,4405,4407,4409,4411,4413,4415,4417,4419,4421,4423,4425,4427,4429,4431,4433,4435,4437,4439,4441,4443,4445,4447,4449,4451,4453,4455,4457,4459,4461,4463,4465,4467,4469,4471,4473,4475,4477,4479,4481,4483,4485,4487,4489,4491,4493,4495,4497,4499,4501,4503,4505,4507,4509,4511,4513,4515,4517,4519,4521,4523,4525,4527,4529,4531,4533,4535,4537,4539,4541,4543,4545,4547,4549,4551,4553,4555,4557,4559,4561,4563,4565,4567,4569,4571,4573,4575,4577,4579,4581,4583,4585,4587,4589,4591,4593,4595,4597,4599,4601,4603,4605,4607,4609,4611,4613,4615,4617,4619,4621,4623,4625,4627,4629,4631,4633,4635,4637,4639,4641,4643,4645,4647,4649,4651,4653,4655,4657,4659,4661,4663,4665,4667,4669,4671,4673,4675,4677,4679,4681,4683,4685,4687,4689,4692,4694,4696,4698,4700,4702,4704,4706,4708,4710,4712,4714,4716,4718,4720,4722,4724,4726,4728,4730,4732,4734,4736,4738,4740,4742,4744,4746,4748,4750,4752,4754,4756,4758,4760,4762,4764,4766,4768,4770,4772,4774,4776,4778,4780,4782,4784,4786,4788,4790,4792,4794,4796,4798,4800,4802,4804,4806,4808,4810,4812,4814,4816,4818,4820,4822,4824,4826,4828,4830,4832,4834,4836,4838,4840,4842,4844,4846,4848,4850,4852,4854,4856,4858,4860,4862,4864,4866,4868,4870,4872,4874,4876,4878,4880,4882,4884,4886,4888,4890,4892,4894,4896,4898,4900,4902,4904,4906,4908,4910,4912,4914,4916,4918,4920,4922,4924,4926,4928,4930,4932,4934,4936,4938,4940,4942,4944,4946,4948,4950,4952,4954,4956,4958,4960,4962,4964,4966,4968,4970,4972,4974,4976,4978,4980,4982,4984,4986,4988,4990,4992,4994,4996,4998,5000,5002,5004,5006,5008,5010,5012,5014,5016,5018,5020,5022,5024,5026,5028,5030,5032,5034,5036,5038,5040,5042,5044,5046,5048,5050,5052,5054,5056,5058,5060,5062,5064,5066,5068,5070,5072,5074,5076,5078,5080,5082,5084,5086,5088,5090,5092,5094,5096,5098,5100,5102,5104,5106,5108,5110,5112,5114,5116,5118,5120,5122,5124,5126,5128,5130,5132,5134,5136,5138,5140,5142,5144,5146,5148,5150,5152,5154,5156,5158,5160,5162,5164,5166,5168,5170,5172,5174,5176,5178,5180,5182,5184,5186,5188,5190,5192,5194,5196,5198,5200,5202,5204,5206,5208,5210,5212,5214,5216,5218,5220,5222,5224,5226,5228,5230,5232,5234,5236,5238,5240,5242,5244,5246,5248,5250,5252,5254,5256,5258,5260,5262,5264,5266,5268,5270,5272,5274,5276,5278,5280,5282,5284,5286,5288,5290,5292,5294,5296,5298,5300,5302,5304,5306,5308,5310,5312,5314,5316,5318,5320,5322,5324,5326,5328,5330,5332,5334,5336,5338,5340,5342,5344,5346,5348,5350,5352,5354,5356,5358,5360,5362,5364,5366,5368,5370,5372,5374,5376,5378,5380,5382,5384,5386,5388,5390,5392,5394,5396,5398,5400,5402,5404,5406,5408,5410,5412,5414,5416,5418,5420,5422,5424,5426,5428,5430,5432,5434,5436,5438,5440,5442,5444,5446,5448,5450,5452,5454,5456,5458,5460,5462,5464,5466,5468,5470,5472,5474,5476,5478,5480,5482,5484,5486,5488,5490,5492,5494,5496,5498,5500,5502,5504,5506,5508,5510,5512,5514,5516,5518,5520,5522,5524,5526,5528,5530,5532,5534,5536,5538,5540,5542,5544,5546,5549,5551,5553,5555,5557,5559,5561,5563,5565,5567,5569,5571,5573,5575,5577,5579,5581,5583,5585,5587,5589,5591,5593,5595,5597,5599,5601,5603,5605,5607,5609,5611,5613,5615,5617,5619,5621,5623,5625,5627,5629,5631,5633,5635,5637,5639,5641,5643,5645,5647,5649,5651,5653,5655,5657,5659,5661,5663,5665,5667,5669,5671,5673,5675,5677,5679,5681,5683,5685,5687,5689,5691,5693,5695,5697,5699,5701,5703,5705,5707,5709,5711,5713,5715,5717,5719,5721,5723,5725,5727,5729,5731,5733,5735,5737,5739,5741,5743,5745,5747,5749,5752,5754,5756,5758,5760,5762,5764,5766,5768,5770,5772,5774,5776,5778,5780,5782,5784,5786,5788,5790,5792,5794,5796,5798,5800,5802,5804,5806,5808,5810,5812,5814,5816,5818,5820,5822,5824,5826,5828,5830,5832,5834,5836,5838,5840,5842,5844,5846,5848,5850,5852,5854,5856,5858,5860,5862,5864,5866,5868,5870,5872,5874,5876,5878,5880,5882,5884,5886,5888,5890,5892,5894,5896,5898,5900,5902,5904,5906,5908,5910,5912,5914,5916,5918,5920,5922,5924,5926,5928,5930,5932,5934,5936,5938,5940,5942,5944,5946,5948,5950,5952,5954,5956,5958,5960,5962,5964,5966,5968,5970,5972,5974,5976,5978,5980,5982,5984,5986,5988,5990,5992,5994,5996,5998,6000,6002,6004,6006,6008,6010,6012,6014,6016,6018,6020,6022,6024,6026,6028,6030,6032,6034,6036,6038,6040,6042,6044,6046,6048,6050,6052,6054,6056,6058,6060,6062,6064,6066,6068,6070,6072,6074,6076,6078,6080,6082,6084,6086,6088,6090,6092,6094,6096,6098,6100,6102,6104,6106,6108,6110,6112,6114,6116,6118,6120,6122,6124,6126,6128,6130,6132,6134,6136,6138,6140,6142,6144,6146,6148,6150,6152,6154,6156,6158,6160,6162,6164,6166,6168,6170,6172,6174,6176,6178,6180,6182,6184,6186,6188,6190,6192,6194,6196,6198,6200,6202,6204,6206,6208,6210,6212,6214,6216,6218,6220,6222,6224,6226,6228,6230,6232,6234,6236,6238,6240,6242,6244,6246,6248,6250,6252,6254,6256,6258,6260,6262,6264,6266,6268,6270,6272,6274,6276,6278,6280,6282,6284,6286,6288,6290,6292,6294,6296,6298,6300,6302,6304,6306,6308,6310,6312,6314,6316,6318,6320,6322,6324,6326,6328,6330,6332,6334,6336,6338,6340,6342,6344,6346,6348,6350,6352,6354,6356,6358,6360,6362,6364,6366,6368,6370,6372,6374,6376,6378,6380,6382,6384,6386,6388,6390,6392,6394,6396,6398,6400,6402,6404,6406,6408,6410,6412,6414,6416,6418,6420,6422,6424,6426,6428,6430,6432,6434,6436,6438,6440,6442,6444,6446,6448,6450,6452,6454,6456,6458,6460,6462,6464,6466,6468,6470,6472,6474,6476,6478,6480,6482,6484,6486,6488,6490,6492,6494,6496,6498,6500,6502,6504,6506,6508,6510,6512,6514,6516,6518,6520,6522,6524,6526,6528,6530,6532,6534,6536,6538,6540,6542,6544,6546,6548,6550,6552,6554,6556,6558,6560,6562,6564,6566,6568,6570,6572,6574,6576,6578,6580,6582,6584,6586,6588,6590,6592,6594,6596,6598,6600,6602,6604,6606,6608,6610,6612,6614,6616,6618,6620,6622,6624,6626,6628,6630,6632,6634,6636,6638,6640,6642,6644,6646,6648,6650,6652,6654,6656,6658,6660,6662,6664,6666,6668,6670,6672,6674,6676,6678,6680,6682,6684,6686,6688,6690,6692,6694,6696,6698,6700,6702,6704,6706,6708,6710,6712,6714,6716,6718,6720,6722,6724,6726,6728,6730,6732,6734,6736,6738,6740,6742,6744,6746,6748,6750,6752,6754,6756,6758,6760,6762,6764,6766,6768,6770,6772,6774,6776,6778,6780,6782,6784,6786,6788,6790,6792,6794,6796,6798,6800,6802,6804,6806,6808,6810,6812,6814,6816,6818,6820,6822,6824,6826,6828,6830,6832,6834,6836,6838,6840,6842,6844,6846,6848,6850,6852,6854,6856,6858,6860,6862,6864,6866,6868,6870,6872,6874,6876,6878,6880,6882,6884,6886,6888,6890,6892,6894,6896,6898,6900,6902,6904,6906,6908,6910,6912,6914,6916,6918,6920,6922,6924,6926,6928,6930,6932,6934,6936,6938,6940,6942,6944,6946,6948,6950,6952,6954,6956,6958,6960,6962,6964,6966,6968,6970,6972,6974,6976,6978,6980,6982,6984,6986,6988,6990,6992,6994,6996,6998,7000,7002,7004,7006,7008,7010,7012,7014,7016,7018,7020,7022,7024,7026,7028,7030,7032,7034,7036,7038,7040,7042,7044,7046,7048,7050,7052,7054,7056,7058,7060,7062,7064,7066,7068,7070,7072,7074,7076,7078,7080,7082,7084,7086,7088,7090,7092,7094,7096,7098,7100,7102,7104,7106,7108,7110,7112,7114,7116,7118,7120,7122,7124,7126,7128,7130,7132,7134,7136,7138,7140,7142,7144,7146,7148,7150,7152,7154,7156,7158,7160],{"categories":236},[237],"AI & LLMs",{"categories":239},[240],"Developer Productivity",{"categories":242},[237],{"categories":244},[245],"Business & SaaS",{"categories":247},[237],{"categories":249},[250],"AI Automation",{"categories":252},[253],"Product Strategy",{"categories":255},[250],{"categories":257},[237],{"categories":259},[240],{"categories":261},[250],{"categories":263},[264],"Software Engineering",{"categories":266},[237],{"categories":268},[245],{"categories":270},[],{"categories":272},[237],{"categories":274},[275],"Inference & Serving",{"categories":277},[237],{"categories":279},[237],{"categories":281},[250],{"categories":283},[],{"categories":285},[286],"AI News & Trends",{"categories":288},[193],{"categories":290},[250],{"categories":292},[237],{"categories":294},[237],{"categories":296},[245],{"categories":298},[240],{"categories":300},[237],{"categories":302},[250],{"categories":304},[286],{"categories":306},[237],{"categories":308},[250],{"categories":310},[250],{"categories":312},[237],{"categories":314},[237],{"categories":316},[250],{"categories":318},[237],{"categories":320},[237],{"categories":322},[237],{"categories":324},[250],{"categories":326},[286],{"categories":328},[237],{"categories":330},[237],{"categories":332},[237],{"categories":334},[],{"categories":336},[337],"Design & Frontend",{"categories":339},[193],{"categories":341},[286],{"categories":343},[237],{"categories":345},[237],{"categories":347},[237],{"categories":349},[],{"categories":351},[237],{"categories":353},[237],{"categories":355},[250],{"categories":357},[264],{"categories":359},[237],{"categories":361},[250],{"categories":363},[237],{"categories":365},[366],"Marketing & Growth",{"categories":368},[337],{"categories":370},[237],{"categories":372},[250],{"categories":374},[237],{"categories":376},[237],{"categories":378},[264],{"categories":380},[237],{"categories":382},[],{"categories":384},[],{"categories":386},[337],{"categories":388},[237],{"categories":390},[250],{"categories":392},[240],{"categories":394},[264],{"categories":396},[250],{"categories":398},[337],{"categories":400},[253],{"categories":402},[237],{"categories":404},[264],{"categories":406},[407],"DevOps & Cloud",{"categories":409},[250],{"categories":411},[253],{"categories":413},[286],{"categories":415},[237],{"categories":417},[],{"categories":419},[237],{"categories":421},[237],{"categories":423},[],{"categories":425},[250],{"categories":427},[264],{"categories":429},[],{"categories":431},[264],{"categories":433},[237],{"categories":435},[436],"Governance & Standards",{"categories":438},[245],{"categories":440},[],{"categories":442},[],{"categories":444},[237],{"categories":446},[237],{"categories":448},[250],{"categories":450},[237],{"categories":452},[237],{"categories":454},[250],{"categories":456},[237],{"categories":458},[237],{"categories":460},[237],{"categories":462},[],{"categories":464},[264],{"categories":466},[],{"categories":468},[],{"categories":470},[237],{"categories":472},[264],{"categories":474},[],{"categories":476},[264],{"categories":478},[237],{"categories":480},[237],{"categories":482},[366],{"categories":484},[237],{"categories":486},[237],{"categories":488},[237],{"categories":490},[337],{"categories":492},[337],{"categories":494},[237],{"categories":496},[264],{"categories":498},[250],{"categories":500},[501],"GovTech & Public-Sector Adoption",{"categories":503},[264],{"categories":505},[237],{"categories":507},[237],{"categories":509},[237],{"categories":511},[250],{"categories":513},[250],{"categories":515},[193],{"categories":517},[237],{"categories":519},[286],{"categories":521},[250],{"categories":523},[524],"Legal AI Tools",{"categories":526},[237],{"categories":528},[250],{"categories":530},[237],{"categories":532},[366],{"categories":534},[250],{"categories":536},[253],{"categories":538},[237],{"categories":540},[264],{"categories":542},[501],{"categories":544},[],{"categories":546},[250],{"categories":548},[],{"categories":550},[245],{"categories":552},[250],{"categories":554},[250],{"categories":556},[557],"RAG & Retrieval",{"categories":559},[245],{"categories":561},[237],{"categories":563},[264],{"categories":565},[264],{"categories":567},[407],{"categories":569},[337],{"categories":571},[250],{"categories":573},[237],{"categories":575},[237],{"categories":577},[],{"categories":579},[580],"Agents & Orchestration",{"categories":582},[264],{"categories":584},[237],{"categories":586},[],{"categories":588},[250],{"categories":590},[245],{"categories":592},[],{"categories":594},[237],{"categories":596},[],{"categories":598},[237],{"categories":600},[240],{"categories":602},[264],{"categories":604},[245],{"categories":606},[237],{"categories":608},[250],{"categories":610},[237],{"categories":612},[237],{"categories":614},[286],{"categories":616},[237],{"categories":618},[],{"categories":620},[237],{"categories":622},[],{"categories":624},[237],{"categories":626},[264],{"categories":628},[237],{"categories":630},[250],{"categories":632},[193],{"categories":634},[],{"categories":636},[237],{"categories":638},[337],{"categories":640},[641],"Models & Frontier Labs",{"categories":643},[],{"categories":645},[337],{"categories":647},[648],"Regulation & Governance of AI",{"categories":650},[253],{"categories":652},[250],{"categories":654},[],{"categories":656},[237],{"categories":658},[237],{"categories":660},[250],{"categories":662},[250],{"categories":664},[286],{"categories":666},[237],{"categories":668},[245],{"categories":670},[237],{"categories":672},[250],{"categories":674},[],{"categories":676},[264],{"categories":678},[250],{"categories":680},[237],{"categories":682},[253],{"categories":684},[237],{"categories":686},[687],"AI Policy & Regulation",{"categories":689},[],{"categories":691},[237],{"categories":693},[250],{"categories":695},[250],{"categories":697},[253],{"categories":699},[250],{"categories":701},[237],{"categories":703},[237],{"categories":705},[237],{"categories":707},[250],{"categories":709},[],{"categories":711},[193],{"categories":713},[714],"Evals & Reliability",{"categories":716},[237],{"categories":718},[237],{"categories":720},[],{"categories":722},[240],{"categories":724},[501],{"categories":726},[687],{"categories":728},[237],{"categories":730},[245],{"categories":732},[237],{"categories":734},[250],{"categories":736},[237],{"categories":738},[250],{"categories":740},[580],{"categories":742},[237],{"categories":744},[264],{"categories":746},[237],{"categories":748},[],{"categories":750},[337],{"categories":752},[],{"categories":754},[237],{"categories":756},[501],{"categories":758},[237],{"categories":760},[237],{"categories":762},[237],{"categories":764},[],{"categories":766},[237],{"categories":768},[337],{"categories":770},[264],{"categories":772},[],{"categories":774},[237],{"categories":776},[],{"categories":778},[250],{"categories":780},[237],{"categories":782},[337],{"categories":784},[],{"categories":786},[237],{"categories":788},[237],{"categories":790},[193],{"categories":792},[250],{"categories":794},[237],{"categories":796},[245],{"categories":798},[250],{"categories":800},[237],{"categories":802},[237],{"categories":804},[264],{"categories":806},[337],{"categories":808},[237],{"categories":810},[250],{"categories":812},[],{"categories":814},[264],{"categories":816},[250],{"categories":818},[193],{"categories":820},[],{"categories":822},[237],{"categories":824},[286],{"categories":826},[237],{"categories":828},[],{"categories":830},[237],{"categories":832},[237],{"categories":834},[237],{"categories":836},[245,366],{"categories":838},[],{"categories":840},[264],{"categories":842},[237],{"categories":844},[237],{"categories":846},[250],{"categories":848},[237],{"categories":850},[],{"categories":852},[],{"categories":854},[237],{"categories":856},[337],{"categories":858},[237],{"categories":860},[],{"categories":862},[237],{"categories":864},[407],{"categories":866},[],{"categories":868},[250],{"categories":870},[286],{"categories":872},[237],{"categories":874},[237],{"categories":876},[337],{"categories":878},[],{"categories":880},[286],{"categories":882},[237],{"categories":884},[275],{"categories":886},[237],{"categories":888},[237],{"categories":890},[250],{"categories":892},[286],{"categories":894},[641],{"categories":896},[237],{"categories":898},[366],{"categories":900},[],{"categories":902},[250],{"categories":904},[245],{"categories":906},[264],{"categories":908},[237],{"categories":910},[250],{"categories":912},[],{"categories":914},[237,407],{"categories":916},[237],{"categories":918},[237],{"categories":920},[237],{"categories":922},[250],{"categories":924},[237,264],{"categories":926},[193],{"categories":928},[237],{"categories":930},[237],{"categories":932},[237],{"categories":934},[264],{"categories":936},[237],{"categories":938},[250],{"categories":940},[250],{"categories":942},[687],{"categories":944},[366],{"categories":946},[237],{"categories":948},[250],{"categories":950},[237],{"categories":952},[237],{"categories":954},[250],{"categories":956},[],{"categories":958},[250],{"categories":960},[237],{"categories":962},[237],{"categories":964},[250],{"categories":966},[237],{"categories":968},[237,245],{"categories":970},[237],{"categories":972},[245],{"categories":974},[],{"categories":976},[337],{"categories":978},[337],{"categories":980},[237],{"categories":982},[],{"categories":984},[],{"categories":986},[237],{"categories":988},[286],{"categories":990},[],{"categories":992},[240],{"categories":994},[237],{"categories":996},[264],{"categories":998},[237],{"categories":1000},[1001],"Generative UI & Design-to-Code",{"categories":1003},[237],{"categories":1005},[237],{"categories":1007},[337],{"categories":1009},[237],{"categories":1011},[1012],"Algorithmic Accountability",{"categories":1014},[250],{"categories":1016},[264],{"categories":1018},[286],{"categories":1020},[337],{"categories":1022},[237],{"categories":1024},[],{"categories":1026},[253],{"categories":1028},[237],{"categories":1030},[237],{"categories":1032},[237],{"categories":1034},[237],{"categories":1036},[250],{"categories":1038},[1039],"MLOps & Infrastructure",{"categories":1041},[237],{"categories":1043},[237],{"categories":1045},[237],{"categories":1047},[237],{"categories":1049},[237],{"categories":1051},[264],{"categories":1053},[286],{"categories":1055},[237],{"categories":1057},[253],{"categories":1059},[240],{"categories":1061},[237],{"categories":1063},[250],{"categories":1065},[407],{"categories":1067},[237],{"categories":1069},[245],{"categories":1071},[237],{"categories":1073},[337],{"categories":1075},[237],{"categories":1077},[237],{"categories":1079},[250],{"categories":1081},[],{"categories":1083},[],{"categories":1085},[237],{"categories":1087},[275],{"categories":1089},[337],{"categories":1091},[286],{"categories":1093},[193],{"categories":1095},[],{"categories":1097},[237],{"categories":1099},[237],{"categories":1101},[245],{"categories":1103},[250],{"categories":1105},[237],{"categories":1107},[237],{"categories":1109},[237],{"categories":1111},[237],{"categories":1113},[286],{"categories":1115},[275],{"categories":1117},[237],{"categories":1119},[337],{"categories":1121},[237],{"categories":1123},[],{"categories":1125},[250],{"categories":1127},[264],{"categories":1129},[],{"categories":1131},[237],{"categories":1133},[237],{"categories":1135},[250],{"categories":1137},[264],{"categories":1139},[237],{"categories":1141},[193],{"categories":1143},[337],{"categories":1145},[],{"categories":1147},[237],{"categories":1149},[],{"categories":1151},[237],{"categories":1153},[],{"categories":1155},[237],{"categories":1157},[237],{"categories":1159},[253],{"categories":1161},[245],{"categories":1163},[250],{"categories":1165},[250],{"categories":1167},[],{"categories":1169},[237],{"categories":1171},[240],{"categories":1173},[237],{"categories":1175},[237],{"categories":1177},[245],{"categories":1179},[286],{"categories":1181},[240],{"categories":1183},[],{"categories":1185},[237],{"categories":1187},[],{"categories":1189},[237],{"categories":1191},[],{"categories":1193},[286],{"categories":1195},[286],{"categories":1197},[],{"categories":1199},[580],{"categories":1201},[237],{"categories":1203},[337],{"categories":1205},[264],{"categories":1207},[],{"categories":1209},[524],{"categories":1211},[250],{"categories":1213},[245],{"categories":1215},[],{"categories":1217},[],{"categories":1219},[240],{"categories":1221},[193],{"categories":1223},[],{"categories":1225},[366],{"categories":1227},[250],{"categories":1229},[245],{"categories":1231},[250],{"categories":1233},[237],{"categories":1235},[245],{"categories":1237},[237],{"categories":1239},[264],{"categories":1241},[],{"categories":1243},[275],{"categories":1245},[253],{"categories":1247},[237],{"categories":1249},[337],{"categories":1251},[264],{"categories":1253},[245],{"categories":1255},[237],{"categories":1257},[264],{"categories":1259},[237],{"categories":1261},[250],{"categories":1263},[245],{"categories":1265},[237],{"categories":1267},[237],{"categories":1269},[237],{"categories":1271},[237],{"categories":1273},[237],{"categories":1275},[],{"categories":1277},[],{"categories":1279},[264],{"categories":1281},[193],{"categories":1283},[253],{"categories":1285},[237],{"categories":1287},[250],{"categories":1289},[264],{"categories":1291},[264],{"categories":1293},[237],{"categories":1295},[],{"categories":1297},[286],{"categories":1299},[253],{"categories":1301},[253],{"categories":1303},[264],{"categories":1305},[237],{"categories":1307},[714],{"categories":1309},[407],{"categories":1311},[],{"categories":1313},[250],{"categories":1315},[237],{"categories":1317},[],{"categories":1319},[240],{"categories":1321},[],{"categories":1323},[237],{"categories":1325},[237],{"categories":1327},[237],{"categories":1329},[337],{"categories":1331},[366],{"categories":1333},[237],{"categories":1335},[264],{"categories":1337},[237],{"categories":1339},[250],{"categories":1341},[],{"categories":1343},[264],{"categories":1345},[237],{"categories":1347},[240],{"categories":1349},[],{"categories":1351},[245],{"categories":1353},[237],{"categories":1355},[237],{"categories":1357},[286],{"categories":1359},[237,407],{"categories":1361},[237],{"categories":1363},[1364],"Design Systems for AI",{"categories":1366},[237],{"categories":1368},[237],{"categories":1370},[286],{"categories":1372},[237],{"categories":1374},[237],{"categories":1376},[237],{"categories":1378},[245],{"categories":1380},[237],{"categories":1382},[237],{"categories":1384},[237],{"categories":1386},[],{"categories":1388},[237],{"categories":1390},[237],{"categories":1392},[245],{"categories":1394},[237],{"categories":1396},[],{"categories":1398},[250],{"categories":1400},[250],{"categories":1402},[264],{"categories":1404},[286],{"categories":1406},[264],{"categories":1408},[237],{"categories":1410},[337],{"categories":1412},[286],{"categories":1414},[193],{"categories":1416},[237],{"categories":1418},[237],{"categories":1420},[250],{"categories":1422},[240],{"categories":1424},[687],{"categories":1426},[237],{"categories":1428},[250],{"categories":1430},[237],{"categories":1432},[264],{"categories":1434},[264],{"categories":1436},[],{"categories":1438},[],{"categories":1440},[237],{"categories":1442},[250],{"categories":1444},[253],{"categories":1446},[],{"categories":1448},[245],{"categories":1450},[237],{"categories":1452},[],{"categories":1454},[337],{"categories":1456},[264],{"categories":1458},[250],{"categories":1460},[264],{"categories":1462},[337],{"categories":1464},[237],{"categories":1466},[237],{"categories":1468},[337],{"categories":1470},[],{"categories":1472},[],{"categories":1474},[286],{"categories":1476},[250],{"categories":1478},[250],{"categories":1480},[237],{"categories":1482},[237],{"categories":1484},[237],{"categories":1486},[237],{"categories":1488},[245],{"categories":1490},[237],{"categories":1492},[237],{"categories":1494},[],{"categories":1496},[264],{"categories":1498},[264],{"categories":1500},[237],{"categories":1502},[264],{"categories":1504},[245],{"categories":1506},[],{"categories":1508},[237],{"categories":1510},[237],{"categories":1512},[237],{"categories":1514},[237],{"categories":1516},[237],{"categories":1518},[250],{"categories":1520},[240],{"categories":1522},[245],{"categories":1524},[237],{"categories":1526},[250],{"categories":1528},[286],{"categories":1530},[250],{"categories":1532},[275],{"categories":1534},[366],{"categories":1536},[237],{"categories":1538},[250],{"categories":1540},[237],{"categories":1542},[237],{"categories":1544},[237],{"categories":1546},[],{"categories":1548},[337],{"categories":1550},[],{"categories":1552},[237],{"categories":1554},[237],{"categories":1556},[],{"categories":1558},[237],{"categories":1560},[264],{"categories":1562},[245],{"categories":1564},[1565],"Visual & Generative Media",{"categories":1567},[250],{"categories":1569},[],{"categories":1571},[237],{"categories":1573},[237],{"categories":1575},[264],{"categories":1577},[407],{"categories":1579},[237],{"categories":1581},[193],{"categories":1583},[687],{"categories":1585},[264],{"categories":1587},[366],{"categories":1589},[237],{"categories":1591},[337],{"categories":1593},[237],{"categories":1595},[237],{"categories":1597},[264],{"categories":1599},[250],{"categories":1601},[237],{"categories":1603},[],{"categories":1605},[],{"categories":1607},[250],{"categories":1609},[264],{"categories":1611},[240],{"categories":1613},[250],{"categories":1615},[641],{"categories":1617},[237],{"categories":1619},[253],{"categories":1621},[237],{"categories":1623},[245],{"categories":1625},[],{"categories":1627},[237],{"categories":1629},[253],{"categories":1631},[237],{"categories":1633},[237],{"categories":1635},[237],{"categories":1637},[253],{"categories":1639},[237],{"categories":1641},[237],{"categories":1643},[366],{"categories":1645},[237],{"categories":1647},[580],{"categories":1649},[237],{"categories":1651},[250],{"categories":1653},[237],{"categories":1655},[237],{"categories":1657},[250],{"categories":1659},[237],{"categories":1661},[237],{"categories":1663},[337],{"categories":1665},[250],{"categories":1667},[],{"categories":1669},[250],{"categories":1671},[],{"categories":1673},[407],{"categories":1675},[264],{"categories":1677},[],{"categories":1679},[641],{"categories":1681},[237],{"categories":1683},[250],{"categories":1685},[250],{"categories":1687},[237],{"categories":1689},[337,237],{"categories":1691},[240],{"categories":1693},[237],{"categories":1695},[337],{"categories":1697},[],{"categories":1699},[237],{"categories":1701},[240],{"categories":1703},[237],{"categories":1705},[1706],"Medical Imaging & Radiology",{"categories":1708},[237],{"categories":1710},[237],{"categories":1712},[237],{"categories":1714},[337],{"categories":1716},[250],{"categories":1718},[264],{"categories":1720},[],{"categories":1722},[237],{"categories":1724},[237],{"categories":1726},[237],{"categories":1728},[],{"categories":1730},[],{"categories":1732},[237],{"categories":1734},[237],{"categories":1736},[580],{"categories":1738},[237],{"categories":1740},[240],{"categories":1742},[237],{"categories":1744},[237],{"categories":1746},[],{"categories":1748},[250],{"categories":1750},[237],{"categories":1752},[253],{"categories":1754},[264],{"categories":1756},[237],{"categories":1758},[250],{"categories":1760},[580],{"categories":1762},[237],{"categories":1764},[250],{"categories":1766},[237],{"categories":1768},[237],{"categories":1770},[237],{"categories":1772},[337],{"categories":1774},[250],{"categories":1776},[407],{"categories":1778},[337],{"categories":1780},[245],{"categories":1782},[250],{"categories":1784},[286],{"categories":1786},[237],{"categories":1788},[237],{"categories":1790},[253],{"categories":1792},[237],{"categories":1794},[237],{"categories":1796},[237],{"categories":1798},[237],{"categories":1800},[250],{"categories":1802},[237],{"categories":1804},[264],{"categories":1806},[264],{"categories":1808},[237],{"categories":1810},[253],{"categories":1812},[],{"categories":1814},[286],{"categories":1816},[],{"categories":1818},[253],{"categories":1820},[250],{"categories":1822},[237],{"categories":1824},[250],{"categories":1826},[1364],{"categories":1828},[1364],{"categories":1830},[337],{"categories":1832},[237],{"categories":1834},[237],{"categories":1836},[237],{"categories":1838},[250],{"categories":1840},[264],{"categories":1842},[337],{"categories":1844},[250],{"categories":1846},[286],{"categories":1848},[],{"categories":1850},[237],{"categories":1852},[],{"categories":1854},[237],{"categories":1856},[237],{"categories":1858},[237],{"categories":1860},[237],{"categories":1862},[250],{"categories":1864},[1865],"Contract Review & E-Discovery",{"categories":1867},[237],{"categories":1869},[337],{"categories":1871},[237],{"categories":1873},[240],{"categories":1875},[237],{"categories":1877},[286],{"categories":1879},[237],{"categories":1881},[237],{"categories":1883},[366],{"categories":1885},[264],{"categories":1887},[237],{"categories":1889},[237],{"categories":1891},[250],{"categories":1893},[250],{"categories":1895},[1012],{"categories":1897},[237],{"categories":1899},[237],{"categories":1901},[250],{"categories":1903},[250],{"categories":1905},[237],{"categories":1907},[237],{"categories":1909},[237],{"categories":1911},[250],{"categories":1913},[237],{"categories":1915},[237],{"categories":1917},[580],{"categories":1919},[557],{"categories":1921},[237],{"categories":1923},[250],{"categories":1925},[237],{"categories":1927},[1928],"Law-Firm Practice & Adoption",{"categories":1930},[237],{"categories":1932},[250],{"categories":1934},[337],{"categories":1936},[237],{"categories":1938},[237],{"categories":1940},[237],{"categories":1942},[],{"categories":1944},[264],{"categories":1946},[],{"categories":1948},[264],{"categories":1950},[237],{"categories":1952},[],{"categories":1954},[250],{"categories":1956},[240],{"categories":1958},[407],{"categories":1960},[237],{"categories":1962},[],{"categories":1964},[240],{"categories":1966},[245],{"categories":1968},[237],{"categories":1970},[366],{"categories":1972},[],{"categories":1974},[245],{"categories":1976},[250],{"categories":1978},[245],{"categories":1980},[],{"categories":1982},[237],{"categories":1984},[253],{"categories":1986},[237],{"categories":1988},[264],{"categories":1990},[],{"categories":1992},[],{"categories":1994},[],{"categories":1996},[],{"categories":1998},[237],{"categories":2000},[253],{"categories":2002},[250],{"categories":2004},[407],{"categories":2006},[237],{"categories":2008},[240],{"categories":2010},[264],{"categories":2012},[237],{"categories":2014},[237],{"categories":2016},[264],{"categories":2018},[253],{"categories":2020},[237],{"categories":2022},[237],{"categories":2024},[237],{"categories":2026},[1039],{"categories":2028},[237],{"categories":2030},[264],{"categories":2032},[237],{"categories":2034},[366],{"categories":2036},[264],{"categories":2038},[245],{"categories":2040},[237],{"categories":2042},[237],{"categories":2044},[237],{"categories":2046},[337],{"categories":2048},[237],{"categories":2050},[237],{"categories":2052},[237],{"categories":2054},[237],{"categories":2056},[245],{"categories":2058},[250],{"categories":2060},[237,240],{"categories":2062},[580],{"categories":2064},[237],{"categories":2066},[237],{"categories":2068},[264],{"categories":2070},[264],{"categories":2072},[337],{"categories":2074},[250],{"categories":2076},[250],{"categories":2078},[264],{"categories":2080},[237],{"categories":2082},[237],{"categories":2084},[237],{"categories":2086},[],{"categories":2088},[],{"categories":2090},[237],{"categories":2092},[193],{"categories":2094},[237],{"categories":2096},[337],{"categories":2098},[250],{"categories":2100},[],{"categories":2102},[237],{"categories":2104},[237],{"categories":2106},[264],{"categories":2108},[193],{"categories":2110},[286],{"categories":2112},[337],{"categories":2114},[237],{"categories":2116},[250],{"categories":2118},[237],{"categories":2120},[264],{"categories":2122},[],{"categories":2124},[250],{"categories":2126},[237],{"categories":2128},[237],{"categories":2130},[237],{"categories":2132},[237],{"categories":2134},[],{"categories":2136},[250],{"categories":2138},[237],{"categories":2140},[237],{"categories":2142},[237],{"categories":2144},[],{"categories":2146},[250],{"categories":2148},[237],{"categories":2150},[237],{"categories":2152},[245],{"categories":2154},[237],{"categories":2156},[237],{"categories":2158},[],{"categories":2160},[240],{"categories":2162},[237],{"categories":2164},[237],{"categories":2166},[237],{"categories":2168},[337],{"categories":2170},[237],{"categories":2172},[264],{"categories":2174},[237],{"categories":2176},[240],{"categories":2178},[237],{"categories":2180},[264],{"categories":2182},[366],{"categories":2184},[250],{"categories":2186},[250],{"categories":2188},[237],{"categories":2190},[237],{"categories":2192},[237,337],{"categories":2194},[237],{"categories":2196},[250],{"categories":2198},[286],{"categories":2200},[237],{"categories":2202},[286],{"categories":2204},[250],{"categories":2206},[337],{"categories":2208},[237],{"categories":2210},[],{"categories":2212},[264],{"categories":2214},[407],{"categories":2216},[337],{"categories":2218},[264],{"categories":2220},[237],{"categories":2222},[253],{"categories":2224},[237],{"categories":2226},[237],{"categories":2228},[250],{"categories":2230},[],{"categories":2232},[],{"categories":2234},[237],{"categories":2236},[],{"categories":2238},[],{"categories":2240},[253],{"categories":2242},[264],{"categories":2244},[237],{"categories":2246},[250],{"categories":2248},[250],{"categories":2250},[245],{"categories":2252},[250],{"categories":2254},[407],{"categories":2256},[237],{"categories":2258},[237],{"categories":2260},[237],{"categories":2262},[275],{"categories":2264},[237],{"categories":2266},[237],{"categories":2268},[237],{"categories":2270},[264],{"categories":2272},[250],{"categories":2274},[237],{"categories":2276},[237],{"categories":2278},[264],{"categories":2280},[524],{"categories":2282},[250],{"categories":2284},[1012],{"categories":2286},[],{"categories":2288},[337],{"categories":2290},[1928],{"categories":2292},[264],{"categories":2294},[],{"categories":2296},[],{"categories":2298},[237],{"categories":2300},[250],{"categories":2302},[],{"categories":2304},[],{"categories":2306},[237],{"categories":2308},[366],{"categories":2310},[237],{"categories":2312},[366],{"categories":2314},[250],{"categories":2316},[237],{"categories":2318},[237],{"categories":2320},[264],{"categories":2322},[253],{"categories":2324},[],{"categories":2326},[237],{"categories":2328},[237],{"categories":2330},[264],{"categories":2332},[1865],{"categories":2334},[337],{"categories":2336},[337],{"categories":2338},[237],{"categories":2340},[250],{"categories":2342},[240],{"categories":2344},[237],{"categories":2346},[237],{"categories":2348},[237],{"categories":2350},[237],{"categories":2352},[337],{"categories":2354},[337],{"categories":2356},[250],{"categories":2358},[250],{"categories":2360},[250],{"categories":2362},[237],{"categories":2364},[237],{"categories":2366},[],{"categories":2368},[237],{"categories":2370},[],{"categories":2372},[2373],"Interaction & Product Design",{"categories":2375},[237],{"categories":2377},[250],{"categories":2379},[264],{"categories":2381},[436],{"categories":2383},[286],{"categories":2385},[264],{"categories":2387},[237],{"categories":2389},[237],{"categories":2391},[237],{"categories":2393},[264],{"categories":2395},[237],{"categories":2397},[240],{"categories":2399},[250],{"categories":2401},[237],{"categories":2403},[],{"categories":2405},[250],{"categories":2407},[250],{"categories":2409},[250],{"categories":2411},[],{"categories":2413},[264],{"categories":2415},[237],{"categories":2417},[250],{"categories":2419},[240],{"categories":2421},[2373],{"categories":2423},[237],{"categories":2425},[240],{"categories":2427},[240],{"categories":2429},[],{"categories":2431},[250],{"categories":2433},[264],{"categories":2435},[],{"categories":2437},[250],{"categories":2439},[286],{"categories":2441},[237],{"categories":2443},[250],{"categories":2445},[237],{"categories":2447},[250],{"categories":2449},[250],{"categories":2451},[237],{"categories":2453},[237],{"categories":2455},[286],{"categories":2457},[193],{"categories":2459},[237],{"categories":2461},[253],{"categories":2463},[264],{"categories":2465},[2466],"Coding Agents & Dev Productivity",{"categories":2468},[286],{"categories":2470},[337],{"categories":2472},[237],{"categories":2474},[237],{"categories":2476},[],{"categories":2478},[237],{"categories":2480},[1012],{"categories":2482},[],{"categories":2484},[237],{"categories":2486},[237],{"categories":2488},[407],{"categories":2490},[237],{"categories":2492},[286],{"categories":2494},[],{"categories":2496},[],{"categories":2498},[237],{"categories":2500},[],{"categories":2502},[250],{"categories":2504},[237],{"categories":2506},[],{"categories":2508},[264],{"categories":2510},[264],{"categories":2512},[237],{"categories":2514},[193],{"categories":2516},[],{"categories":2518},[237],{"categories":2520},[237],{"categories":2522},[237],{"categories":2524},[193],{"categories":2526},[264],{"categories":2528},[250],{"categories":2530},[],{"categories":2532},[],{"categories":2534},[237],{"categories":2536},[237],{"categories":2538},[250],{"categories":2540},[250],{"categories":2542},[501],{"categories":2544},[264],{"categories":2546},[253],{"categories":2548},[264],{"categories":2550},[250],{"categories":2552},[286],{"categories":2554},[286],{"categories":2556},[250],{"categories":2558},[250],{"categories":2560},[237],{"categories":2562},[240],{"categories":2564},[2373],{"categories":2566},[253],{"categories":2568},[237,407],{"categories":2570},[193],{"categories":2572},[],{"categories":2574},[337],{"categories":2576},[250],{"categories":2578},[264],{"categories":2580},[240],{"categories":2582},[237],{"categories":2584},[250],{"categories":2586},[2587],"The Designer's Role & Craft",{"categories":2589},[337],{"categories":2591},[],{"categories":2593},[250],{"categories":2595},[237],{"categories":2597},[250],{"categories":2599},[250],{"categories":2601},[237],{"categories":2603},[366],{"categories":2605},[237],{"categories":2607},[264],{"categories":2609},[237],{"categories":2611},[337],{"categories":2613},[237],{"categories":2615},[],{"categories":2617},[250],{"categories":2619},[337],{"categories":2621},[253],{"categories":2623},[237],{"categories":2625},[237],{"categories":2627},[237],{"categories":2629},[2630],"AI UX Patterns",{"categories":2632},[250],{"categories":2634},[250],{"categories":2636},[250],{"categories":2638},[250],{"categories":2640},[366],{"categories":2642},[193],{"categories":2644},[237],{"categories":2646},[250],{"categories":2648},[237],{"categories":2650},[1364],{"categories":2652},[],{"categories":2654},[366],{"categories":2656},[250],{"categories":2658},[286],{"categories":2660},[264],{"categories":2662},[237],{"categories":2664},[250],{"categories":2666},[],{"categories":2668},[],{"categories":2670},[237],{"categories":2672},[237],{"categories":2674},[250],{"categories":2676},[237],{"categories":2678},[250],{"categories":2680},[501],{"categories":2682},[337],{"categories":2684},[237],{"categories":2686},[286],{"categories":2688},[264],{"categories":2690},[237],{"categories":2692},[250],{"categories":2694},[250],{"categories":2696},[],{"categories":2698},[237],{"categories":2700},[],{"categories":2702},[237],{"categories":2704},[],{"categories":2706},[237],{"categories":2708},[237],{"categories":2710},[237],{"categories":2712},[250],{"categories":2714},[264],{"categories":2716},[],{"categories":2718},[],{"categories":2720},[193],{"categories":2722},[275],{"categories":2724},[237],{"categories":2726},[237],{"categories":2728},[237],{"categories":2730},[193],{"categories":2732},[237],{"categories":2734},[237],{"categories":2736},[286],{"categories":2738},[237],{"categories":2740},[237],{"categories":2742},[237],{"categories":2744},[250],{"categories":2746},[237],{"categories":2748},[250],{"categories":2750},[237],{"categories":2752},[237],{"categories":2754},[237],{"categories":2756},[250],{"categories":2758},[],{"categories":2760},[237],{"categories":2762},[],{"categories":2764},[237],{"categories":2766},[237],{"categories":2768},[407],{"categories":2770},[237],{"categories":2772},[],{"categories":2774},[],{"categories":2776},[337],{"categories":2778},[1039],{"categories":2780},[250],{"categories":2782},[240],{"categories":2784},[2587],{"categories":2786},[],{"categories":2788},[],{"categories":2790},[237],{"categories":2792},[],{"categories":2794},[],{"categories":2796},[264],{"categories":2798},[286],{"categories":2800},[366],{"categories":2802},[250],{"categories":2804},[245],{"categories":2806},[237],{"categories":2808},[237],{"categories":2810},[245],{"categories":2812},[],{"categories":2814},[337],{"categories":2816},[253],{"categories":2818},[237],{"categories":2820},[237],{"categories":2822},[250],{"categories":2824},[245],{"categories":2826},[237],{"categories":2828},[237],{"categories":2830},[240],{"categories":2832},[237],{"categories":2834},[237],{"categories":2836},[],{"categories":2838},[240],{"categories":2840},[237],{"categories":2842},[366],{"categories":2844},[250],{"categories":2846},[286],{"categories":2848},[237],{"categories":2850},[264],{"categories":2852},[237],{"categories":2854},[237],{"categories":2856},[245],{"categories":2858},[237],{"categories":2860},[237],{"categories":2862},[237],{"categories":2864},[250],{"categories":2866},[237],{"categories":2868},[],{"categories":2870},[237],{"categories":2872},[264],{"categories":2874},[240],{"categories":2876},[237],{"categories":2878},[237],{"categories":2880},[237],{"categories":2882},[],{"categories":2884},[237],{"categories":2886},[580],{"categories":2888},[250],{"categories":2890},[245],{"categories":2892},[286],{"categories":2894},[237],{"categories":2896},[237],{"categories":2898},[],{"categories":2900},[245],{"categories":2902},[245],{"categories":2904},[237],{"categories":2906},[237],{"categories":2908},[253],{"categories":2910},[237],{"categories":2912},[237],{"categories":2914},[237],{"categories":2916},[237],{"categories":2918},[264],{"categories":2920},[264],{"categories":2922},[237],{"categories":2924},[],{"categories":2926},[264],{"categories":2928},[237],{"categories":2930},[264],{"categories":2932},[250],{"categories":2934},[687],{"categories":2936},[],{"categories":2938},[],{"categories":2940},[237],{"categories":2942},[286],{"categories":2944},[],{"categories":2946},[407],{"categories":2948},[237],{"categories":2950},[237],{"categories":2952},[237],{"categories":2954},[337],{"categories":2956},[1001],{"categories":2958},[],{"categories":2960},[237],{"categories":2962},[237],{"categories":2964},[237],{"categories":2966},[264],{"categories":2968},[237],{"categories":2970},[237],{"categories":2972},[237,407],{"categories":2974},[237],{"categories":2976},[237],{"categories":2978},[337],{"categories":2980},[250],{"categories":2982},[],{"categories":2984},[250],{"categories":2986},[250],{"categories":2988},[237],{"categories":2990},[237],{"categories":2992},[237],{"categories":2994},[237],{"categories":2996},[193],{"categories":2998},[237],{"categories":3000},[2630],{"categories":3002},[240],{"categories":3004},[193],{"categories":3006},[240],{"categories":3008},[264],{"categories":3010},[337],{"categories":3012},[250],{"categories":3014},[237],{"categories":3016},[],{"categories":3018},[245],{"categories":3020},[237],{"categories":3022},[237],{"categories":3024},[286],{"categories":3026},[237],{"categories":3028},[237],{"categories":3030},[237],{"categories":3032},[250],{"categories":3034},[237],{"categories":3036},[237],{"categories":3038},[237],{"categories":3040},[245],{"categories":3042},[],{"categories":3044},[407],{"categories":3046},[237],{"categories":3048},[501],{"categories":3050},[337],{"categories":3052},[337],{"categories":3054},[264],{"categories":3056},[250],{"categories":3058},[237],{"categories":3060},[245],{"categories":3062},[286],{"categories":3064},[237],{"categories":3066},[237],{"categories":3068},[237],{"categories":3070},[337],{"categories":3072},[250],{"categories":3074},[250],{"categories":3076},[237],{"categories":3078},[237],{"categories":3080},[641],{"categories":3082},[250],{"categories":3084},[],{"categories":3086},[237],{"categories":3088},[237],{"categories":3090},[237],{"categories":3092},[],{"categories":3094},[],{"categories":3096},[237],{"categories":3098},[237],{"categories":3100},[250],{"categories":3102},[237],{"categories":3104},[237],{"categories":3106},[237],{"categories":3108},[264],{"categories":3110},[237],{"categories":3112},[237],{"categories":3114},[250],{"categories":3116},[237],{"categories":3118},[237],{"categories":3120},[237],{"categories":3122},[237],{"categories":3124},[237],{"categories":3126},[],{"categories":3128},[264],{"categories":3130},[193],{"categories":3132},[237],{"categories":3134},[250],{"categories":3136},[250],{"categories":3138},[237],{"categories":3140},[237],{"categories":3142},[],{"categories":3144},[],{"categories":3146},[237],{"categories":3148},[237],{"categories":3150},[237],{"categories":3152},[286],{"categories":3154},[193],{"categories":3156},[],{"categories":3158},[237],{"categories":3160},[337],{"categories":3162},[237],{"categories":3164},[407],{"categories":3166},[1928],{"categories":3168},[286],{"categories":3170},[264],{"categories":3172},[237],{"categories":3174},[264],{"categories":3176},[264],{"categories":3178},[237],{"categories":3180},[237],{"categories":3182},[264],{"categories":3184},[286],{"categories":3186},[286],{"categories":3188},[407],{"categories":3190},[250],{"categories":3192},[],{"categories":3194},[286],{"categories":3196},[237],{"categories":3198},[250],{"categories":3200},[240],{"categories":3202},[264],{"categories":3204},[237],{"categories":3206},[286],{"categories":3208},[],{"categories":3210},[237],{"categories":3212},[264],{"categories":3214},[264],{"categories":3216},[193],{"categories":3218},[237],{"categories":3220},[286],{"categories":3222},[237],{"categories":3224},[264],{"categories":3226},[250],{"categories":3228},[250],{"categories":3230},[286],{"categories":3232},[250],{"categories":3234},[407],{"categories":3236},[250],{"categories":3238},[237],{"categories":3240},[237],{"categories":3242},[237],{"categories":3244},[237],{"categories":3246},[264],{"categories":3248},[237],{"categories":3250},[],{"categories":3252},[250],{"categories":3254},[245],{"categories":3256},[264],{"categories":3258},[],{"categories":3260},[],{"categories":3262},[237],{"categories":3264},[250],{"categories":3266},[237],{"categories":3268},[237],{"categories":3270},[3271],"Frameworks & Tooling",{"categories":3273},[237],{"categories":3275},[237],{"categories":3277},[264],{"categories":3279},[237],{"categories":3281},[237],{"categories":3283},[],{"categories":3285},[193],{"categories":3287},[193],{"categories":3289},[240],{"categories":3291},[237],{"categories":3293},[250],{"categories":3295},[237],{"categories":3297},[337],{"categories":3299},[],{"categories":3301},[1928],{"categories":3303},[237],{"categories":3305},[264],{"categories":3307},[237],{"categories":3309},[407],{"categories":3311},[407],{"categories":3313},[],{"categories":3315},[250],{"categories":3317},[250],{"categories":3319},[237],{"categories":3321},[237],{"categories":3323},[286],{"categories":3325},[250],{"categories":3327},[286],{"categories":3329},[237],{"categories":3331},[250],{"categories":3333},[],{"categories":3335},[337],{"categories":3337},[237],{"categories":3339},[237],{"categories":3341},[],{"categories":3343},[237],{"categories":3345},[250],{"categories":3347},[237],{"categories":3349},[237],{"categories":3351},[237],{"categories":3353},[],{"categories":3355},[245],{"categories":3357},[264],{"categories":3359},[237],{"categories":3361},[264],{"categories":3363},[407],{"categories":3365},[237],{"categories":3367},[237],{"categories":3369},[237],{"categories":3371},[264],{"categories":3373},[245],{"categories":3375},[237],{"categories":3377},[1928],{"categories":3379},[],{"categories":3381},[250],{"categories":3383},[240],{"categories":3385},[237],{"categories":3387},[240],{"categories":3389},[237],{"categories":3391},[],{"categories":3393},[250],{"categories":3395},[237],{"categories":3397},[237],{"categories":3399},[3400],"AI Design Tooling",{"categories":3402},[337],{"categories":3404},[237],{"categories":3406},[237],{"categories":3408},[264],{"categories":3410},[337],{"categories":3412},[237],{"categories":3414},[237],{"categories":3416},[264],{"categories":3418},[286],{"categories":3420},[253],{"categories":3422},[264],{"categories":3424},[237],{"categories":3426},[237],{"categories":3428},[237],{"categories":3430},[250],{"categories":3432},[237],{"categories":3434},[],{"categories":3436},[250],{"categories":3438},[237],{"categories":3440},[237],{"categories":3442},[250],{"categories":3444},[237],{"categories":3446},[237],{"categories":3448},[237],{"categories":3450},[250],{"categories":3452},[],{"categories":3454},[250],{"categories":3456},[3271],{"categories":3458},[237],{"categories":3460},[237],{"categories":3462},[250],{"categories":3464},[250],{"categories":3466},[264],{"categories":3468},[264],{"categories":3470},[237],{"categories":3472},[],{"categories":3474},[264],{"categories":3476},[237],{"categories":3478},[237],{"categories":3480},[250],{"categories":3482},[245],{"categories":3484},[237],{"categories":3486},[],{"categories":3488},[237],{"categories":3490},[237],{"categories":3492},[2373],{"categories":3494},[],{"categories":3496},[237],{"categories":3498},[237],{"categories":3500},[237],{"categories":3502},[237],{"categories":3504},[337],{"categories":3506},[237],{"categories":3508},[],{"categories":3510},[237],{"categories":3512},[237],{"categories":3514},[237],{"categories":3516},[237],{"categories":3518},[366],{"categories":3520},[286],{"categories":3522},[237],{"categories":3524},[237],{"categories":3526},[1928],{"categories":3528},[240],{"categories":3530},[237],{"categories":3532},[237],{"categories":3534},[193],{"categories":3536},[237],{"categories":3538},[237],{"categories":3540},[286],{"categories":3542},[250],{"categories":3544},[],{"categories":3546},[237],{"categories":3548},[237],{"categories":3550},[337],{"categories":3552},[237],{"categories":3554},[366],{"categories":3556},[250],{"categories":3558},[237],{"categories":3560},[250],{"categories":3562},[],{"categories":3564},[],{"categories":3566},[],{"categories":3568},[240],{"categories":3570},[286],{"categories":3572},[250],{"categories":3574},[237],{"categories":3576},[237],{"categories":3578},[237],{"categories":3580},[237],{"categories":3582},[524],{"categories":3584},[337],{"categories":3586},[250],{"categories":3588},[237],{"categories":3590},[],{"categories":3592},[250],{"categories":3594},[250],{"categories":3596},[],{"categories":3598},[237],{"categories":3600},[250],{"categories":3602},[237],{"categories":3604},[],{"categories":3606},[237],{"categories":3608},[237],{"categories":3610},[237],{"categories":3612},[286],{"categories":3614},[337],{"categories":3616},[250],{"categories":3618},[337],{"categories":3620},[250],{"categories":3622},[237],{"categories":3624},[245],{"categories":3626},[],{"categories":3628},[],{"categories":3630},[237],{"categories":3632},[237],{"categories":3634},[237],{"categories":3636},[240],{"categories":3638},[250],{"categories":3640},[286],{"categories":3642},[],{"categories":3644},[337],{"categories":3646},[],{"categories":3648},[264],{"categories":3650},[237],{"categories":3652},[264],{"categories":3654},[337],{"categories":3656},[264],{"categories":3658},[237],{"categories":3660},[],{"categories":3662},[237],{"categories":3664},[237],{"categories":3666},[],{"categories":3668},[237],{"categories":3670},[237],{"categories":3672},[366],{"categories":3674},[237],{"categories":3676},[237],{"categories":3678},[407],{"categories":3680},[264],{"categories":3682},[237],{"categories":3684},[],{"categories":3686},[250],{"categories":3688},[237],{"categories":3690},[240],{"categories":3692},[641],{"categories":3694},[237],{"categories":3696},[237],{"categories":3698},[250],{"categories":3700},[237],{"categories":3702},[250],{"categories":3704},[237],{"categories":3706},[237],{"categories":3708},[237],{"categories":3710},[237],{"categories":3712},[],{"categories":3714},[237],{"categories":3716},[240],{"categories":3718},[237],{"categories":3720},[245],{"categories":3722},[264],{"categories":3724},[337],{"categories":3726},[],{"categories":3728},[237],{"categories":3730},[],{"categories":3732},[250],{"categories":3734},[237],{"categories":3736},[],{"categories":3738},[250],{"categories":3740},[237],{"categories":3742},[264],{"categories":3744},[337],{"categories":3746},[286],{"categories":3748},[237],{"categories":3750},[286],{"categories":3752},[250],{"categories":3754},[337],{"categories":3756},[237],{"categories":3758},[],{"categories":3760},[237],{"categories":3762},[275],{"categories":3764},[250],{"categories":3766},[237],{"categories":3768},[337],{"categories":3770},[286],{"categories":3772},[245],{"categories":3774},[264],{"categories":3776},[237],{"categories":3778},[237],{"categories":3780},[237],{"categories":3782},[237],{"categories":3784},[286],{"categories":3786},[366],{"categories":3788},[],{"categories":3790},[],{"categories":3792},[193],{"categories":3794},[580],{"categories":3796},[237],{"categories":3798},[250],{"categories":3800},[237,264],{"categories":3802},[286],{"categories":3804},[237],{"categories":3806},[237],{"categories":3808},[237],{"categories":3810},[237],{"categories":3812},[237],{"categories":3814},[237],{"categories":3816},[237],{"categories":3818},[250],{"categories":3820},[237],{"categories":3822},[250],{"categories":3824},[237],{"categories":3826},[237],{"categories":3828},[237],{"categories":3830},[],{"categories":3832},[237],{"categories":3834},[1364],{"categories":3836},[264],{"categories":3838},[337],{"categories":3840},[237],{"categories":3842},[237],{"categories":3844},[237],{"categories":3846},[193],{"categories":3848},[250],{"categories":3850},[366],{"categories":3852},[407],{"categories":3854},[],{"categories":3856},[264],{"categories":3858},[237],{"categories":3860},[245],{"categories":3862},[250],{"categories":3864},[240],{"categories":3866},[250],{"categories":3868},[237],{"categories":3870},[250],{"categories":3872},[250],{"categories":3874},[253],{"categories":3876},[264],{"categories":3878},[237],{"categories":3880},[237],{"categories":3882},[],{"categories":3884},[],{"categories":3886},[],{"categories":3888},[407],{"categories":3890},[237],{"categories":3892},[286],{"categories":3894},[237],{"categories":3896},[237],{"categories":3898},[237],{"categories":3900},[237],{"categories":3902},[],{"categories":3904},[237],{"categories":3906},[193],{"categories":3908},[245],{"categories":3910},[250],{"categories":3912},[237],{"categories":3914},[],{"categories":3916},[237],{"categories":3918},[250],{"categories":3920},[264],{"categories":3922},[237],{"categories":3924},[407],{"categories":3926},[],{"categories":3928},[337],{"categories":3930},[337],{"categories":3932},[237],{"categories":3934},[250],{"categories":3936},[],{"categories":3938},[264],{"categories":3940},[237],{"categories":3942},[337],{"categories":3944},[237],{"categories":3946},[245],{"categories":3948},[250],{"categories":3950},[237],{"categories":3952},[],{"categories":3954},[286],{"categories":3956},[237],{"categories":3958},[237],{"categories":3960},[237],{"categories":3962},[337],{"categories":3964},[250],{"categories":3966},[286],{"categories":3968},[],{"categories":3970},[250],{"categories":3972},[245],{"categories":3974},[250],{"categories":3976},[337],{"categories":3978},[237],{"categories":3980},[237],{"categories":3982},[237],{"categories":3984},[580],{"categories":3986},[237],{"categories":3988},[250],{"categories":3990},[],{"categories":3992},[237],{"categories":3994},[237],{"categories":3996},[407],{"categories":3998},[286],{"categories":4000},[193],{"categories":4002},[687],{"categories":4004},[193],{"categories":4006},[193],{"categories":4008},[237],{"categories":4010},[],{"categories":4012},[],{"categories":4014},[],{"categories":4016},[250],{"categories":4018},[237],{"categories":4020},[250],{"categories":4022},[250],{"categories":4024},[264],{"categories":4026},[237],{"categories":4028},[557],{"categories":4030},[264],{"categories":4032},[250],{"categories":4034},[237],{"categories":4036},[237],{"categories":4038},[237],{"categories":4040},[237],{"categories":4042},[237],{"categories":4044},[250],{"categories":4046},[237],{"categories":4048},[],{"categories":4050},[],{"categories":4052},[237],{"categories":4054},[],{"categories":4056},[237],{"categories":4058},[250],{"categories":4060},[337],{"categories":4062},[237],{"categories":4064},[237],{"categories":4066},[],{"categories":4068},[250],{"categories":4070},[237],{"categories":4072},[237],{"categories":4074},[253],{"categories":4076},[237],{"categories":4078},[337],{"categories":4080},[237],{"categories":4082},[250],{"categories":4084},[245],{"categories":4086},[237],{"categories":4088},[237],{"categories":4090},[366],{"categories":4092},[250],{"categories":4094},[237],{"categories":4096},[237],{"categories":4098},[1001],{"categories":4100},[237],{"categories":4102},[250],{"categories":4104},[237],{"categories":4106},[264],{"categories":4108},[237],{"categories":4110},[641],{"categories":4112},[337],{"categories":4114},[],{"categories":4116},[237],{"categories":4118},[237],{"categories":4120},[286],{"categories":4122},[580],{"categories":4124},[250],{"categories":4126},[237],{"categories":4128},[],{"categories":4130},[286],{"categories":4132},[501],{"categories":4134},[250],{"categories":4136},[250],{"categories":4138},[250],{"categories":4140},[237],{"categories":4142},[237],{"categories":4144},[250],{"categories":4146},[],{"categories":4148},[245],{"categories":4150},[237],{"categories":4152},[245],{"categories":4154},[250],{"categories":4156},[],{"categories":4158},[264],{"categories":4160},[237],{"categories":4162},[237],{"categories":4164},[240],{"categories":4166},[237],{"categories":4168},[286],{"categories":4170},[407],{"categories":4172},[275],{"categories":4174},[250],{"categories":4176},[250],{"categories":4178},[237],{"categories":4180},[237],{"categories":4182},[250],{"categories":4184},[237],{"categories":4186},[240],{"categories":4188},[],{"categories":4190},[250],{"categories":4192},[237],{"categories":4194},[237],{"categories":4196},[237],{"categories":4198},[250],{"categories":4200},[237],{"categories":4202},[],{"categories":4204},[237],{"categories":4206},[],{"categories":4208},[337],{"categories":4210},[250],{"categories":4212},[237,245],{"categories":4214},[250],{"categories":4216},[237],{"categories":4218},[],{"categories":4220},[240],{"categories":4222},[193],{"categories":4224},[245],{"categories":4226},[237],{"categories":4228},[264],{"categories":4230},[237],{"categories":4232},[237],{"categories":4234},[250],{"categories":4236},[237],{"categories":4238},[237],{"categories":4240},[237],{"categories":4242},[286],{"categories":4244},[1364],{"categories":4246},[250],{"categories":4248},[237],{"categories":4250},[],{"categories":4252},[],{"categories":4254},[237],{"categories":4256},[250],{"categories":4258},[237],{"categories":4260},[237],{"categories":4262},[407],{"categories":4264},[],{"categories":4266},[237],{"categories":4268},[250],{"categories":4270},[275],{"categories":4272},[250],{"categories":4274},[580],{"categories":4276},[],{"categories":4278},[524],{"categories":4280},[250],{"categories":4282},[237],{"categories":4284},[237],{"categories":4286},[366],{"categories":4288},[250],{"categories":4290},[237],{"categories":4292},[193],{"categories":4294},[253],{"categories":4296},[250],{"categories":4298},[237],{"categories":4300},[580],{"categories":4302},[237],{"categories":4304},[407],{"categories":4306},[245],{"categories":4308},[],{"categories":4310},[237],{"categories":4312},[237],{"categories":4314},[366],{"categories":4316},[337],{"categories":4318},[237],{"categories":4320},[237],{"categories":4322},[237],{"categories":4324},[],{"categories":4326},[366],{"categories":4328},[286],{"categories":4330},[237],{"categories":4332},[237],{"categories":4334},[237],{"categories":4336},[687],{"categories":4338},[240],{"categories":4340},[237],{"categories":4342},[253],{"categories":4344},[237],{"categories":4346},[],{"categories":4348},[],{"categories":4350},[337],{"categories":4352},[237],{"categories":4354},[193],{"categories":4356},[366],{"categories":4358},[250],{"categories":4360},[237],{"categories":4362},[237],{"categories":4364},[366],{"categories":4366},[286],{"categories":4368},[237],{"categories":4370},[],{"categories":4372},[237],{"categories":4374},[237],{"categories":4376},[],{"categories":4378},[237],{"categories":4380},[237],{"categories":4382},[714],{"categories":4384},[237],{"categories":4386},[237],{"categories":4388},[250],{"categories":4390},[264],{"categories":4392},[580],{"categories":4394},[237],{"categories":4396},[237],{"categories":4398},[237],{"categories":4400},[],{"categories":4402},[237,264],{"categories":4404},[286],{"categories":4406},[250],{"categories":4408},[264],{"categories":4410},[250],{"categories":4412},[1039],{"categories":4414},[264],{"categories":4416},[264],{"categories":4418},[250],{"categories":4420},[237],{"categories":4422},[240],{"categories":4424},[],{"categories":4426},[],{"categories":4428},[250],{"categories":4430},[237],{"categories":4432},[264],{"categories":4434},[237],{"categories":4436},[240],{"categories":4438},[264],{"categories":4440},[264],{"categories":4442},[237],{"categories":4444},[366],{"categories":4446},[237],{"categories":4448},[264],{"categories":4450},[237],{"categories":4452},[],{"categories":4454},[237],{"categories":4456},[237],{"categories":4458},[337,237],{"categories":4460},[407],{"categories":4462},[240],{"categories":4464},[237],{"categories":4466},[],{"categories":4468},[237],{"categories":4470},[237],{"categories":4472},[245],{"categories":4474},[237],{"categories":4476},[245],{"categories":4478},[237],{"categories":4480},[237],{"categories":4482},[501],{"categories":4484},[237],{"categories":4486},[245],{"categories":4488},[264],{"categories":4490},[193],{"categories":4492},[250],{"categories":4494},[237],{"categories":4496},[264],{"categories":4498},[237],{"categories":4500},[237],{"categories":4502},[286],{"categories":4504},[366],{"categories":4506},[337],{"categories":4508},[237],{"categories":4510},[237],{"categories":4512},[237],{"categories":4514},[237],{"categories":4516},[240],{"categories":4518},[237],{"categories":4520},[250],{"categories":4522},[250],{"categories":4524},[264],{"categories":4526},[286],{"categories":4528},[264],{"categories":4530},[264],{"categories":4532},[237],{"categories":4534},[237],{"categories":4536},[],{"categories":4538},[],{"categories":4540},[193],{"categories":4542},[237],{"categories":4544},[264],{"categories":4546},[237],{"categories":4548},[337],{"categories":4550},[580],{"categories":4552},[524],{"categories":4554},[501],{"categories":4556},[237],{"categories":4558},[237],{"categories":4560},[237],{"categories":4562},[193],{"categories":4564},[237],{"categories":4566},[237],{"categories":4568},[237],{"categories":4570},[237],{"categories":4572},[237],{"categories":4574},[237],{"categories":4576},[237],{"categories":4578},[250],{"categories":4580},[240],{"categories":4582},[250],{"categories":4584},[237,245],{"categories":4586},[],{"categories":4588},[337],{"categories":4590},[],{"categories":4592},[253],{"categories":4594},[237],{"categories":4596},[286],{"categories":4598},[240],{"categories":4600},[237],{"categories":4602},[240],{"categories":4604},[250],{"categories":4606},[193],{"categories":4608},[250],{"categories":4610},[253],{"categories":4612},[250],{"categories":4614},[237],{"categories":4616},[237],{"categories":4618},[237],{"categories":4620},[245],{"categories":4622},[250],{"categories":4624},[264],{"categories":4626},[366],{"categories":4628},[237],{"categories":4630},[237],{"categories":4632},[],{"categories":4634},[286],{"categories":4636},[237],{"categories":4638},[237],{"categories":4640},[237],{"categories":4642},[237],{"categories":4644},[237],{"categories":4646},[237],{"categories":4648},[264],{"categories":4650},[286],{"categories":4652},[264],{"categories":4654},[264],{"categories":4656},[237],{"categories":4658},[237],{"categories":4660},[237],{"categories":4662},[237],{"categories":4664},[524],{"categories":4666},[237],{"categories":4668},[250],{"categories":4670},[250],{"categories":4672},[286],{"categories":4674},[237],{"categories":4676},[237],{"categories":4678},[237],{"categories":4680},[250],{"categories":4682},[237],{"categories":4684},[237],{"categories":4686},[237],{"categories":4688},[3271],{"categories":4690},[4691],"Clinical AI",{"categories":4693},[337],{"categories":4695},[237],{"categories":4697},[237],{"categories":4699},[237],{"categories":4701},[237],{"categories":4703},[407],{"categories":4705},[2630],{"categories":4707},[237],{"categories":4709},[253],{"categories":4711},[337],{"categories":4713},[237],{"categories":4715},[250],{"categories":4717},[237],{"categories":4719},[237],{"categories":4721},[286],{"categories":4723},[237],{"categories":4725},[250],{"categories":4727},[264],{"categories":4729},[366],{"categories":4731},[237],{"categories":4733},[237],{"categories":4735},[245],{"categories":4737},[237],{"categories":4739},[237],{"categories":4741},[641],{"categories":4743},[237],{"categories":4745},[],{"categories":4747},[250],{"categories":4749},[237],{"categories":4751},[264],{"categories":4753},[240],{"categories":4755},[237],{"categories":4757},[],{"categories":4759},[],{"categories":4761},[237],{"categories":4763},[],{"categories":4765},[245],{"categories":4767},[237],{"categories":4769},[237],{"categories":4771},[250],{"categories":4773},[237],{"categories":4775},[286],{"categories":4777},[286],{"categories":4779},[286],{"categories":4781},[286],{"categories":4783},[],{"categories":4785},[240],{"categories":4787},[250],{"categories":4789},[286],{"categories":4791},[237],{"categories":4793},[714],{"categories":4795},[253],{"categories":4797},[250],{"categories":4799},[237],{"categories":4801},[240],{"categories":4803},[237],{"categories":4805},[250],{"categories":4807},[237],{"categories":4809},[237],{"categories":4811},[237],{"categories":4813},[237,250],{"categories":4815},[250],{"categories":4817},[407],{"categories":4819},[286],{"categories":4821},[250],{"categories":4823},[286],{"categories":4825},[250],{"categories":4827},[237],{"categories":4829},[],{"categories":4831},[286],{"categories":4833},[366],{"categories":4835},[240],{"categories":4837},[237],{"categories":4839},[237],{"categories":4841},[],{"categories":4843},[264],{"categories":4845},[],{"categories":4847},[240],{"categories":4849},[250],{"categories":4851},[286],{"categories":4853},[237],{"categories":4855},[286],{"categories":4857},[240],{"categories":4859},[286],{"categories":4861},[286],{"categories":4863},[],{"categories":4865},[245],{"categories":4867},[250],{"categories":4869},[286],{"categories":4871},[286],{"categories":4873},[286],{"categories":4875},[286],{"categories":4877},[286],{"categories":4879},[286],{"categories":4881},[286],{"categories":4883},[286],{"categories":4885},[286],{"categories":4887},[286],{"categories":4889},[193],{"categories":4891},[240],{"categories":4893},[237],{"categories":4895},[237],{"categories":4897},[250],{"categories":4899},[250],{"categories":4901},[],{"categories":4903},[237],{"categories":4905},[237,240],{"categories":4907},[],{"categories":4909},[250],{"categories":4911},[237],{"categories":4913},[286],{"categories":4915},[250],{"categories":4917},[1039],{"categories":4919},[237],{"categories":4921},[237],{"categories":4923},[237],{"categories":4925},[237],{"categories":4927},[237],{"categories":4929},[501],{"categories":4931},[237],{"categories":4933},[237],{"categories":4935},[250],{"categories":4937},[237],{"categories":4939},[237],{"categories":4941},[245],{"categories":4943},[253],{"categories":4945},[250],{"categories":4947},[250],{"categories":4949},[],{"categories":4951},[250],{"categories":4953},[337],{"categories":4955},[286],{"categories":4957},[237],{"categories":4959},[],{"categories":4961},[253],{"categories":4963},[],{"categories":4965},[264],{"categories":4967},[237],{"categories":4969},[250],{"categories":4971},[337],{"categories":4973},[237],{"categories":4975},[],{"categories":4977},[237],{"categories":4979},[237],{"categories":4981},[],{"categories":4983},[366],{"categories":4985},[237],{"categories":4987},[250],{"categories":4989},[],{"categories":4991},[],{"categories":4993},[286],{"categories":4995},[240],{"categories":4997},[237],{"categories":4999},[237],{"categories":5001},[245],{"categories":5003},[237],{"categories":5005},[237],{"categories":5007},[250],{"categories":5009},[237],{"categories":5011},[245],{"categories":5013},[245],{"categories":5015},[337],{"categories":5017},[],{"categories":5019},[237],{"categories":5021},[286],{"categories":5023},[],{"categories":5025},[237],{"categories":5027},[237],{"categories":5029},[337],{"categories":5031},[237],{"categories":5033},[237],{"categories":5035},[366],{"categories":5037},[237],{"categories":5039},[407],{"categories":5041},[],{"categories":5043},[250],{"categories":5045},[237],{"categories":5047},[366],{"categories":5049},[264],{"categories":5051},[],{"categories":5053},[237],{"categories":5055},[],{"categories":5057},[250],{"categories":5059},[337],{"categories":5061},[264],{"categories":5063},[],{"categories":5065},[3271],{"categories":5067},[245],{"categories":5069},[240],{"categories":5071},[237],{"categories":5073},[193],{"categories":5075},[250],{"categories":5077},[337],{"categories":5079},[237],{"categories":5081},[264],{"categories":5083},[],{"categories":5085},[],{"categories":5087},[237],{"categories":5089},[240],{"categories":5091},[237],{"categories":5093},[366],{"categories":5095},[],{"categories":5097},[250],{"categories":5099},[250],{"categories":5101},[237],{"categories":5103},[250],{"categories":5105},[237],{"categories":5107},[286],{"categories":5109},[264],{"categories":5111},[237],{"categories":5113},[250],{"categories":5115},[253],{"categories":5117},[237],{"categories":5119},[237],{"categories":5121},[237],{"categories":5123},[250],{"categories":5125},[237],{"categories":5127},[253],{"categories":5129},[366],{"categories":5131},[286],{"categories":5133},[],{"categories":5135},[366],{"categories":5137},[237],{"categories":5139},[],{"categories":5141},[264],{"categories":5143},[250],{"categories":5145},[],{"categories":5147},[237],{"categories":5149},[237],{"categories":5151},[237],{"categories":5153},[237],{"categories":5155},[237],{"categories":5157},[250],{"categories":5159},[245],{"categories":5161},[240],{"categories":5163},[250],{"categories":5165},[237],{"categories":5167},[337],{"categories":5169},[264],{"categories":5171},[264],{"categories":5173},[237],{"categories":5175},[193],{"categories":5177},[250],{"categories":5179},[237],{"categories":5181},[237],{"categories":5183},[250],{"categories":5185},[237],{"categories":5187},[237],{"categories":5189},[250],{"categories":5191},[245],{"categories":5193},[237],{"categories":5195},[337],{"categories":5197},[264],{"categories":5199},[250],{"categories":5201},[237],{"categories":5203},[253],{"categories":5205},[237],{"categories":5207},[250],{"categories":5209},[237],{"categories":5211},[237],{"categories":5213},[286],{"categories":5215},[237],{"categories":5217},[],{"categories":5219},[240],{"categories":5221},[237],{"categories":5223},[237],{"categories":5225},[237],{"categories":5227},[264],{"categories":5229},[264],{"categories":5231},[237],{"categories":5233},[264],{"categories":5235},[237],{"categories":5237},[250],{"categories":5239},[237],{"categories":5241},[237],{"categories":5243},[237],{"categories":5245},[237],{"categories":5247},[237],{"categories":5249},[],{"categories":5251},[237],{"categories":5253},[337],{"categories":5255},[250],{"categories":5257},[245],{"categories":5259},[286],{"categories":5261},[237],{"categories":5263},[250],{"categories":5265},[237],{"categories":5267},[250],{"categories":5269},[237],{"categories":5271},[237],{"categories":5273},[337],{"categories":5275},[250],{"categories":5277},[237],{"categories":5279},[366],{"categories":5281},[237],{"categories":5283},[193],{"categories":5285},[237],{"categories":5287},[237],{"categories":5289},[286],{"categories":5291},[237],{"categories":5293},[237],{"categories":5295},[237],{"categories":5297},[237],{"categories":5299},[250],{"categories":5301},[407],{"categories":5303},[237],{"categories":5305},[264],{"categories":5307},[250],{"categories":5309},[193],{"categories":5311},[],{"categories":5313},[250],{"categories":5315},[264],{"categories":5317},[237],{"categories":5319},[237],{"categories":5321},[2466],{"categories":5323},[337],{"categories":5325},[436],{"categories":5327},[237],{"categories":5329},[237],{"categories":5331},[237],{"categories":5333},[237],{"categories":5335},[240],{"categories":5337},[237],{"categories":5339},[237],{"categories":5341},[264],{"categories":5343},[245],{"categories":5345},[237],{"categories":5347},[264],{"categories":5349},[237],{"categories":5351},[],{"categories":5353},[250],{"categories":5355},[250],{"categories":5357},[237],{"categories":5359},[237],{"categories":5361},[237],{"categories":5363},[193],{"categories":5365},[],{"categories":5367},[286],{"categories":5369},[],{"categories":5371},[286],{"categories":5373},[237],{"categories":5375},[237],{"categories":5377},[250],{"categories":5379},[237],{"categories":5381},[250],{"categories":5383},[250],{"categories":5385},[],{"categories":5387},[237],{"categories":5389},[286],{"categories":5391},[237],{"categories":5393},[],{"categories":5395},[237],{"categories":5397},[237],{"categories":5399},[],{"categories":5401},[237],{"categories":5403},[237],{"categories":5405},[337],{"categories":5407},[264],{"categories":5409},[250],{"categories":5411},[237],{"categories":5413},[237],{"categories":5415},[237],{"categories":5417},[237],{"categories":5419},[366],{"categories":5421},[237],{"categories":5423},[237],{"categories":5425},[237],{"categories":5427},[240],{"categories":5429},[237],{"categories":5431},[237],{"categories":5433},[],{"categories":5435},[237],{"categories":5437},[237],{"categories":5439},[237],{"categories":5441},[],{"categories":5443},[240],{"categories":5445},[237],{"categories":5447},[237],{"categories":5449},[286],{"categories":5451},[264],{"categories":5453},[253],{"categories":5455},[250],{"categories":5457},[580],{"categories":5459},[237],{"categories":5461},[237],{"categories":5463},[237],{"categories":5465},[264],{"categories":5467},[286],{"categories":5469},[337],{"categories":5471},[237],{"categories":5473},[237],{"categories":5475},[237],{"categories":5477},[237],{"categories":5479},[286],{"categories":5481},[237],{"categories":5483},[337],{"categories":5485},[237],{"categories":5487},[237],{"categories":5489},[286],{"categories":5491},[337],{"categories":5493},[237],{"categories":5495},[286],{"categories":5497},[237],{"categories":5499},[250],{"categories":5501},[250],{"categories":5503},[250],{"categories":5505},[264],{"categories":5507},[286],{"categories":5509},[250],{"categories":5511},[250],{"categories":5513},[237],{"categories":5515},[264],{"categories":5517},[337],{"categories":5519},[237],{"categories":5521},[237],{"categories":5523},[250],{"categories":5525},[237],{"categories":5527},[],{"categories":5529},[250],{"categories":5531},[],{"categories":5533},[237],{"categories":5535},[237],{"categories":5537},[],{"categories":5539},[],{"categories":5541},[250],{"categories":5543},[245],{"categories":5545},[250],{"categories":5547},[5548],"Liability & Ethics",{"categories":5550},[237],{"categories":5552},[237],{"categories":5554},[237],{"categories":5556},[250],{"categories":5558},[240],{"categories":5560},[250],{"categories":5562},[245],{"categories":5564},[366],{"categories":5566},[250],{"categories":5568},[237],{"categories":5570},[237],{"categories":5572},[],{"categories":5574},[687],{"categories":5576},[250],{"categories":5578},[],{"categories":5580},[237],{"categories":5582},[240],{"categories":5584},[250],{"categories":5586},[],{"categories":5588},[250],{"categories":5590},[237],{"categories":5592},[237],{"categories":5594},[264],{"categories":5596},[237],{"categories":5598},[286],{"categories":5600},[237],{"categories":5602},[237],{"categories":5604},[253],{"categories":5606},[250],{"categories":5608},[237],{"categories":5610},[237],{"categories":5612},[237],{"categories":5614},[286],{"categories":5616},[250],{"categories":5618},[264],{"categories":5620},[337],{"categories":5622},[240],{"categories":5624},[237],{"categories":5626},[237],{"categories":5628},[237],{"categories":5630},[],{"categories":5632},[250],{"categories":5634},[250],{"categories":5636},[250],{"categories":5638},[580],{"categories":5640},[337],{"categories":5642},[250],{"categories":5644},[407],{"categories":5646},[264],{"categories":5648},[286],{"categories":5650},[237],{"categories":5652},[337],{"categories":5654},[237],{"categories":5656},[240],{"categories":5658},[],{"categories":5660},[250],{"categories":5662},[237],{"categories":5664},[237],{"categories":5666},[237],{"categories":5668},[237],{"categories":5670},[250],{"categories":5672},[237],{"categories":5674},[237],{"categories":5676},[337],{"categories":5678},[],{"categories":5680},[250],{"categories":5682},[253],{"categories":5684},[286],{"categories":5686},[250],{"categories":5688},[245],{"categories":5690},[],{"categories":5692},[237],{"categories":5694},[237],{"categories":5696},[253],{"categories":5698},[237],{"categories":5700},[250],{"categories":5702},[286],{"categories":5704},[240],{"categories":5706},[407],{"categories":5708},[237],{"categories":5710},[237],{"categories":5712},[237],{"categories":5714},[286],{"categories":5716},[245],{"categories":5718},[237],{"categories":5720},[337],{"categories":5722},[286],{"categories":5724},[407],{"categories":5726},[237],{"categories":5728},[250],{"categories":5730},[],{"categories":5732},[641],{"categories":5734},[],{"categories":5736},[237],{"categories":5738},[407],{"categories":5740},[237],{"categories":5742},[193],{"categories":5744},[237],{"categories":5746},[250],{"categories":5748},[250],{"categories":5750},[5751],"Design News & Tools",{"categories":5753},[237],{"categories":5755},[237],{"categories":5757},[286],{"categories":5759},[237],{"categories":5761},[237],{"categories":5763},[240],{"categories":5765},[250],{"categories":5767},[237],{"categories":5769},[337],{"categories":5771},[250],{"categories":5773},[250],{"categories":5775},[337],{"categories":5777},[237],{"categories":5779},[237],{"categories":5781},[580],{"categories":5783},[250],{"categories":5785},[237],{"categories":5787},[237],{"categories":5789},[580],{"categories":5791},[237],{"categories":5793},[366],{"categories":5795},[237],{"categories":5797},[250],{"categories":5799},[],{"categories":5801},[237],{"categories":5803},[237],{"categories":5805},[237],{"categories":5807},[286],{"categories":5809},[237],{"categories":5811},[240],{"categories":5813},[],{"categories":5815},[237],{"categories":5817},[237],{"categories":5819},[237],{"categories":5821},[264],{"categories":5823},[714],{"categories":5825},[264],{"categories":5827},[337],{"categories":5829},[237],{"categories":5831},[237,250],{"categories":5833},[366,245],{"categories":5835},[264],{"categories":5837},[237],{"categories":5839},[237],{"categories":5841},[237],{"categories":5843},[237],{"categories":5845},[],{"categories":5847},[250],{"categories":5849},[237],{"categories":5851},[],{"categories":5853},[237],{"categories":5855},[264],{"categories":5857},[237],{"categories":5859},[264],{"categories":5861},[],{"categories":5863},[250],{"categories":5865},[237],{"categories":5867},[245],{"categories":5869},[237],{"categories":5871},[286],{"categories":5873},[237],{"categories":5875},[],{"categories":5877},[250],{"categories":5879},[237],{"categories":5881},[],{"categories":5883},[337],{"categories":5885},[237],{"categories":5887},[237],{"categories":5889},[250],{"categories":5891},[237],{"categories":5893},[237],{"categories":5895},[240],{"categories":5897},[250],{"categories":5899},[237],{"categories":5901},[],{"categories":5903},[237],{"categories":5905},[407],{"categories":5907},[366],{"categories":5909},[245],{"categories":5911},[245],{"categories":5913},[237],{"categories":5915},[240],{"categories":5917},[240],{"categories":5919},[237],{"categories":5921},[250],{"categories":5923},[237],{"categories":5925},[237],{"categories":5927},[237],{"categories":5929},[237],{"categories":5931},[264],{"categories":5933},[237],{"categories":5935},[240],{"categories":5937},[237],{"categories":5939},[237],{"categories":5941},[250],{"categories":5943},[237],{"categories":5945},[366],{"categories":5947},[237],{"categories":5949},[286],{"categories":5951},[237],{"categories":5953},[237],{"categories":5955},[250],{"categories":5957},[253],{"categories":5959},[237],{"categories":5961},[237],{"categories":5963},[250],{"categories":5965},[],{"categories":5967},[264],{"categories":5969},[],{"categories":5971},[264],{"categories":5973},[250],{"categories":5975},[240],{"categories":5977},[237],{"categories":5979},[],{"categories":5981},[193],{"categories":5983},[407],{"categories":5985},[237],{"categories":5987},[264],{"categories":5989},[237],{"categories":5991},[],{"categories":5993},[286],{"categories":5995},[250],{"categories":5997},[264],{"categories":5999},[337],{"categories":6001},[245],{"categories":6003},[237],{"categories":6005},[237],{"categories":6007},[250],{"categories":6009},[264],{"categories":6011},[250],{"categories":6013},[286],{"categories":6015},[237],{"categories":6017},[253],{"categories":6019},[240],{"categories":6021},[253],{"categories":6023},[286],{"categories":6025},[237],{"categories":6027},[264],{"categories":6029},[237],{"categories":6031},[337],{"categories":6033},[245],{"categories":6035},[237],{"categories":6037},[237],{"categories":6039},[237],{"categories":6041},[237],{"categories":6043},[237],{"categories":6045},[237],{"categories":6047},[250],{"categories":6049},[237],{"categories":6051},[250],{"categories":6053},[237],{"categories":6055},[237],{"categories":6057},[240],{"categories":6059},[237],{"categories":6061},[250],{"categories":6063},[250],{"categories":6065},[337],{"categories":6067},[250],{"categories":6069},[250],{"categories":6071},[237],{"categories":6073},[240],{"categories":6075},[250],{"categories":6077},[337],{"categories":6079},[],{"categories":6081},[237],{"categories":6083},[193],{"categories":6085},[580],{"categories":6087},[237],{"categories":6089},[250],{"categories":6091},[237],{"categories":6093},[237],{"categories":6095},[264],{"categories":6097},[237],{"categories":6099},[],{"categories":6101},[237],{"categories":6103},[250],{"categories":6105},[237],{"categories":6107},[366],{"categories":6109},[237],{"categories":6111},[264],{"categories":6113},[237],{"categories":6115},[286],{"categories":6117},[250],{"categories":6119},[237],{"categories":6121},[366],{"categories":6123},[250],{"categories":6125},[245],{"categories":6127},[245],{"categories":6129},[237],{"categories":6131},[237],{"categories":6133},[237],{"categories":6135},[237],{"categories":6137},[237],{"categories":6139},[237],{"categories":6141},[240],{"categories":6143},[],{"categories":6145},[237],{"categories":6147},[237],{"categories":6149},[250],{"categories":6151},[237],{"categories":6153},[250],{"categories":6155},[237],{"categories":6157},[237],{"categories":6159},[237],{"categories":6161},[237],{"categories":6163},[237],{"categories":6165},[264],{"categories":6167},[],{"categories":6169},[240],{"categories":6171},[237],{"categories":6173},[237],{"categories":6175},[250],{"categories":6177},[250],{"categories":6179},[],{"categories":6181},[264],{"categories":6183},[264],{"categories":6185},[237],{"categories":6187},[366],{"categories":6189},[245],{"categories":6191},[337],{"categories":6193},[],{"categories":6195},[237],{"categories":6197},[250],{"categories":6199},[240],{"categories":6201},[237],{"categories":6203},[237],{"categories":6205},[264],{"categories":6207},[240],{"categories":6209},[237],{"categories":6211},[237],{"categories":6213},[286],{"categories":6215},[193],{"categories":6217},[237],{"categories":6219},[286],{"categories":6221},[250],{"categories":6223},[237],{"categories":6225},[],{"categories":6227},[286],{"categories":6229},[250],{"categories":6231},[337],{"categories":6233},[193],{"categories":6235},[237],{"categories":6237},[237],{"categories":6239},[],{"categories":6241},[250],{"categories":6243},[250],{"categories":6245},[250],{"categories":6247},[3271],{"categories":6249},[286],{"categories":6251},[237],{"categories":6253},[264],{"categories":6255},[237],{"categories":6257},[237],{"categories":6259},[237],{"categories":6261},[237],{"categories":6263},[237],{"categories":6265},[245],{"categories":6267},[237],{"categories":6269},[240],{"categories":6271},[1928],{"categories":6273},[407],{"categories":6275},[240],{"categories":6277},[],{"categories":6279},[237],{"categories":6281},[],{"categories":6283},[286],{"categories":6285},[250],{"categories":6287},[337],{"categories":6289},[237],{"categories":6291},[237],{"categories":6293},[237],{"categories":6295},[286],{"categories":6297},[],{"categories":6299},[250],{"categories":6301},[237],{"categories":6303},[250],{"categories":6305},[250],{"categories":6307},[],{"categories":6309},[237],{"categories":6311},[],{"categories":6313},[286],{"categories":6315},[240],{"categories":6317},[337],{"categories":6319},[237],{"categories":6321},[250],{"categories":6323},[286],{"categories":6325},[237],{"categories":6327},[286],{"categories":6329},[],{"categories":6331},[286],{"categories":6333},[237],{"categories":6335},[240],{"categories":6337},[580],{"categories":6339},[250],{"categories":6341},[237],{"categories":6343},[],{"categories":6345},[264],{"categories":6347},[250],{"categories":6349},[253],{"categories":6351},[250],{"categories":6353},[240],{"categories":6355},[237],{"categories":6357},[237],{"categories":6359},[],{"categories":6361},[],{"categories":6363},[],{"categories":6365},[337],{"categories":6367},[237],{"categories":6369},[250],{"categories":6371},[237],{"categories":6373},[237],{"categories":6375},[],{"categories":6377},[],{"categories":6379},[],{"categories":6381},[237],{"categories":6383},[250],{"categories":6385},[337],{"categories":6387},[237],{"categories":6389},[],{"categories":6391},[250],{"categories":6393},[237],{"categories":6395},[237],{"categories":6397},[240],{"categories":6399},[],{"categories":6401},[],{"categories":6403},[237],{"categories":6405},[237],{"categories":6407},[250],{"categories":6409},[337],{"categories":6411},[237],{"categories":6413},[286],{"categories":6415},[],{"categories":6417},[237],{"categories":6419},[237],{"categories":6421},[366],{"categories":6423},[286],{"categories":6425},[366],{"categories":6427},[193],{"categories":6429},[237],{"categories":6431},[237],{"categories":6433},[],{"categories":6435},[],{"categories":6437},[250],{"categories":6439},[],{"categories":6441},[237],{"categories":6443},[580],{"categories":6445},[237],{"categories":6447},[237],{"categories":6449},[237],{"categories":6451},[237],{"categories":6453},[],{"categories":6455},[250],{"categories":6457},[237],{"categories":6459},[237],{"categories":6461},[],{"categories":6463},[250],{"categories":6465},[237],{"categories":6467},[286],{"categories":6469},[237],{"categories":6471},[366],{"categories":6473},[245],{"categories":6475},[253],{"categories":6477},[237],{"categories":6479},[237],{"categories":6481},[250],{"categories":6483},[193],{"categories":6485},[250],{"categories":6487},[250],{"categories":6489},[],{"categories":6491},[237],{"categories":6493},[250],{"categories":6495},[],{"categories":6497},[237],{"categories":6499},[],{"categories":6501},[286],{"categories":6503},[245],{"categories":6505},[],{"categories":6507},[237],{"categories":6509},[237],{"categories":6511},[237],{"categories":6513},[],{"categories":6515},[250],{"categories":6517},[337],{"categories":6519},[240],{"categories":6521},[237],{"categories":6523},[],{"categories":6525},[245],{"categories":6527},[366],{"categories":6529},[237],{"categories":6531},[264],{"categories":6533},[240],{"categories":6535},[193],{"categories":6537},[245],{"categories":6539},[264],{"categories":6541},[250],{"categories":6543},[264],{"categories":6545},[],{"categories":6547},[237],{"categories":6549},[253],{"categories":6551},[237],{"categories":6553},[],{"categories":6555},[250],{"categories":6557},[240],{"categories":6559},[337],{"categories":6561},[237],{"categories":6563},[240],{"categories":6565},[250],{"categories":6567},[407],{"categories":6569},[237],{"categories":6571},[237],{"categories":6573},[237],{"categories":6575},[237],{"categories":6577},[237],{"categories":6579},[240],{"categories":6581},[237],{"categories":6583},[264],{"categories":6585},[193],{"categories":6587},[250],{"categories":6589},[],{"categories":6591},[237],{"categories":6593},[237],{"categories":6595},[237],{"categories":6597},[264],{"categories":6599},[250],{"categories":6601},[286],{"categories":6603},[264],{"categories":6605},[237],{"categories":6607},[253],{"categories":6609},[],{"categories":6611},[337],{"categories":6613},[264],{"categories":6615},[286],{"categories":6617},[237],{"categories":6619},[240],{"categories":6621},[250],{"categories":6623},[237],{"categories":6625},[237],{"categories":6627},[250],{"categories":6629},[253],{"categories":6631},[237],{"categories":6633},[250],{"categories":6635},[237],{"categories":6637},[245],{"categories":6639},[250],{"categories":6641},[250,407],{"categories":6643},[237],{"categories":6645},[237],{"categories":6647},[250],{"categories":6649},[264],{"categories":6651},[237],{"categories":6653},[237],{"categories":6655},[193],{"categories":6657},[250],{"categories":6659},[366],{"categories":6661},[250],{"categories":6663},[245],{"categories":6665},[],{"categories":6667},[250],{"categories":6669},[237],{"categories":6671},[245],{"categories":6673},[],{"categories":6675},[],{"categories":6677},[264],{"categories":6679},[237],{"categories":6681},[237],{"categories":6683},[250],{"categories":6685},[193],{"categories":6687},[366],{"categories":6689},[237],{"categories":6691},[237],{"categories":6693},[237],{"categories":6695},[250],{"categories":6697},[],{"categories":6699},[250],{"categories":6701},[286],{"categories":6703},[237],{"categories":6705},[250],{"categories":6707},[250],{"categories":6709},[237],{"categories":6711},[],{"categories":6713},[286],{"categories":6715},[264],{"categories":6717},[3271],{"categories":6719},[240],{"categories":6721},[264],{"categories":6723},[237],{"categories":6725},[250],{"categories":6727},[237],{"categories":6729},[237],{"categories":6731},[366],{"categories":6733},[264],{"categories":6735},[193],{"categories":6737},[],{"categories":6739},[286],{"categories":6741},[237],{"categories":6743},[237],{"categories":6745},[],{"categories":6747},[250],{"categories":6749},[237],{"categories":6751},[237],{"categories":6753},[237],{"categories":6755},[237],{"categories":6757},[250],{"categories":6759},[237],{"categories":6761},[237],{"categories":6763},[237],{"categories":6765},[253],{"categories":6767},[237],{"categories":6769},[250],{"categories":6771},[237],{"categories":6773},[237],{"categories":6775},[237],{"categories":6777},[237],{"categories":6779},[237],{"categories":6781},[237],{"categories":6783},[237],{"categories":6785},[245],{"categories":6787},[],{"categories":6789},[253],{"categories":6791},[286],{"categories":6793},[250],{"categories":6795},[237],{"categories":6797},[264],{"categories":6799},[],{"categories":6801},[264],{"categories":6803},[264],{"categories":6805},[250],{"categories":6807},[264],{"categories":6809},[237],{"categories":6811},[237],{"categories":6813},[237],{"categories":6815},[250],{"categories":6817},[264],{"categories":6819},[237],{"categories":6821},[237],{"categories":6823},[237],{"categories":6825},[250],{"categories":6827},[286],{"categories":6829},[237],{"categories":6831},[237],{"categories":6833},[237],{"categories":6835},[245],{"categories":6837},[237],{"categories":6839},[250],{"categories":6841},[337],{"categories":6843},[],{"categories":6845},[237],{"categories":6847},[193],{"categories":6849},[237],{"categories":6851},[250],{"categories":6853},[237],{"categories":6855},[237],{"categories":6857},[],{"categories":6859},[237],{"categories":6861},[237],{"categories":6863},[286],{"categories":6865},[237],{"categories":6867},[237],{"categories":6869},[250],{"categories":6871},[366],{"categories":6873},[],{"categories":6875},[],{"categories":6877},[264],{"categories":6879},[237],{"categories":6881},[237],{"categories":6883},[286],{"categories":6885},[237],{"categories":6887},[264],{"categories":6889},[286],{"categories":6891},[237],{"categories":6893},[237],{"categories":6895},[366],{"categories":6897},[193],{"categories":6899},[237],{"categories":6901},[237],{"categories":6903},[240],{"categories":6905},[250],{"categories":6907},[237],{"categories":6909},[237],{"categories":6911},[250],{"categories":6913},[245],{"categories":6915},[250],{"categories":6917},[264],{"categories":6919},[237],{"categories":6921},[245],{"categories":6923},[],{"categories":6925},[237],{"categories":6927},[193],{"categories":6929},[237],{"categories":6931},[237],{"categories":6933},[],{"categories":6935},[286],{"categories":6937},[237],{"categories":6939},[250],{"categories":6941},[193],{"categories":6943},[237],{"categories":6945},[264],{"categories":6947},[264],{"categories":6949},[264],{"categories":6951},[237],{"categories":6953},[250],{"categories":6955},[250],{"categories":6957},[237],{"categories":6959},[250],{"categories":6961},[237],{"categories":6963},[237],{"categories":6965},[337],{"categories":6967},[193],{"categories":6969},[193],{"categories":6971},[],{"categories":6973},[286],{"categories":6975},[237],{"categories":6977},[237],{"categories":6979},[264],{"categories":6981},[],{"categories":6983},[286],{"categories":6985},[286],{"categories":6987},[286],{"categories":6989},[],{"categories":6991},[250],{"categories":6993},[237],{"categories":6995},[],{"categories":6997},[240],{"categories":6999},[245],{"categories":7001},[],{"categories":7003},[237],{"categories":7005},[237],{"categories":7007},[],{"categories":7009},[264],{"categories":7011},[],{"categories":7013},[],{"categories":7015},[],{"categories":7017},[],{"categories":7019},[237],{"categories":7021},[286],{"categories":7023},[],{"categories":7025},[],{"categories":7027},[237],{"categories":7029},[237],{"categories":7031},[237],{"categories":7033},[193],{"categories":7035},[237],{"categories":7037},[193],{"categories":7039},[],{"categories":7041},[193],{"categories":7043},[193],{"categories":7045},[407],{"categories":7047},[250],{"categories":7049},[264],{"categories":7051},[],{"categories":7053},[],{"categories":7055},[193],{"categories":7057},[264],{"categories":7059},[264],{"categories":7061},[264],{"categories":7063},[],{"categories":7065},[240],{"categories":7067},[264],{"categories":7069},[264],{"categories":7071},[240],{"categories":7073},[264],{"categories":7075},[245],{"categories":7077},[264],{"categories":7079},[264],{"categories":7081},[264],{"categories":7083},[193],{"categories":7085},[286],{"categories":7087},[286],{"categories":7089},[237],{"categories":7091},[264],{"categories":7093},[193],{"categories":7095},[407],{"categories":7097},[193],{"categories":7099},[193],{"categories":7101},[193],{"categories":7103},[],{"categories":7105},[245],{"categories":7107},[],{"categories":7109},[407],{"categories":7111},[264],{"categories":7113},[264],{"categories":7115},[264],{"categories":7117},[250],{"categories":7119},[286,245],{"categories":7121},[193],{"categories":7123},[],{"categories":7125},[],{"categories":7127},[193],{"categories":7129},[],{"categories":7131},[193],{"categories":7133},[286],{"categories":7135},[250],{"categories":7137},[],{"categories":7139},[264],{"categories":7141},[237],{"categories":7143},[337],{"categories":7145},[],{"categories":7147},[237],{"categories":7149},[],{"categories":7151},[286],{"categories":7153},[240],{"categories":7155},[193],{"categories":7157},[],{"categories":7159},[264],{"categories":7161},[286],[7163,7213,7286,7512],{"id":7164,"title":7165,"ai":7166,"body":7171,"categories":7199,"created_at":194,"date_modified":194,"description":64,"extension":195,"faq":194,"featured":196,"kicker_label":194,"meta":7200,"navigation":219,"path":7201,"published_at":7202,"question":194,"scraped_at":194,"seo":7203,"sitemap":7204,"source_id":7205,"source_name":7206,"source_type":226,"source_url":7207,"stem":7208,"tags":7209,"thumbnail_url":194,"tldr":7210,"tweet":194,"unknown_tags":7211,"__hash__":7212},"summaries\u002Fsummaries\u002Fpractical-oop-python-data-quality-toolkit-summary.md","Practical OOP: Python Data Quality Toolkit",{"provider":7,"model":8,"input_tokens":7167,"output_tokens":7168,"processing_time_ms":7169,"cost_usd":7170},3380,809,8486,0.00061355,{"type":14,"value":7172,"toc":7194},[7173,7177,7180,7184,7187,7191],[17,7174,7176],{"id":7175},"from-toy-examples-to-real-world-oop","From Toy Examples to Real-World OOP",[22,7178,7179],{},"Generic OOP tutorials often use abstract classes like animals or shapes that don't solve actual problems. Instead, apply OOP to create a data quality toolkit that checks datasets for issues like missing values, duplicates, and schema mismatches—directly usable in data pipelines.",[17,7181,7183],{"id":7182},"core-oop-structure-for-data-validators","Core OOP Structure for Data Validators",[22,7185,7186],{},"Define abstract base classes for validators (e.g., BaseValidator with validate() and report() methods). Extend with concrete classes like MissingValueValidator or DuplicateValidator. Each handles specific checks: MissingValueValidator scans for NaNs and computes percentages; DuplicateValidator identifies and counts repeats. This inheritance ensures consistent interfaces while customizing logic per rule.",[17,7188,7190],{"id":7189},"benefits-and-usage","Benefits and Usage",[22,7192,7193],{},"Encapsulate checks into a QualityChecker class that composes multiple validators, runs them on DataFrames, and aggregates reports into JSON or HTML. Trade-offs: Adds abstraction overhead but improves modularity, testability, and extensibility for growing validation needs. Integrate via simple API: checker = QualityChecker(validators); results = checker.validate(df). Content is thin RSS teaser; full article details code on Medium.",{"title":64,"searchDepth":77,"depth":77,"links":7195},[7196,7197,7198],{"id":7175,"depth":77,"text":7176},{"id":7182,"depth":77,"text":7183},{"id":7189,"depth":77,"text":7190},[264],{},"\u002Fsummaries\u002Fpractical-oop-python-data-quality-toolkit-summary","2026-04-08 21:21:17",{"title":7165,"description":64},{"loc":7201},"3bc99baf3e1a274b","Learning Data","https:\u002F\u002Funknown","summaries\u002Fpractical-oop-python-data-quality-toolkit-summary",[63,230],"Use OOP to build a reusable data quality toolkit in Python that validates real datasets, ditching toy examples for production-ready code.",[],"jJTXnZGT0inxfzWez5pDC3MXsSZ1ffUVqikWuQEyX8o",{"id":7214,"title":7215,"ai":7216,"body":7222,"categories":7264,"created_at":194,"date_modified":194,"description":64,"extension":195,"faq":194,"featured":196,"kicker_label":194,"meta":7265,"navigation":219,"path":7272,"published_at":7273,"question":194,"scraped_at":7274,"seo":7275,"sitemap":7276,"source_id":7277,"source_name":7278,"source_type":226,"source_url":7279,"stem":7280,"tags":7281,"thumbnail_url":194,"tldr":7283,"tweet":194,"unknown_tags":7284,"__hash__":7285},"summaries\u002Fsummaries\u002F67dbbade0cd2aa6f-essential-numpy-concepts-for-practical-data-scienc-summary.md","Essential NumPy Concepts for Practical Data Science",{"provider":7,"model":7217,"input_tokens":7218,"output_tokens":7219,"processing_time_ms":7220,"cost_usd":7221},"google\u002Fgemini-3.1-flash-lite",3989,410,2411,0.00161225,{"type":14,"value":7223,"toc":7260},[7224,7228,7231,7234,7238,7245],[17,7225,7227],{"id":7226},"mastering-vectorization-and-broadcasting","Mastering Vectorization and Broadcasting",[22,7229,7230],{},"Vectorization is the primary mechanism that makes NumPy faster than standard Python loops. By applying operations to entire arrays at once rather than iterating through individual elements, you leverage highly optimized C code. This shift in thinking is essential for performance-critical data pipelines.",[22,7232,7233],{},"Broadcasting complements vectorization by allowing NumPy to perform arithmetic operations on arrays of different shapes. Instead of manually resizing arrays to match dimensions, NumPy automatically expands the smaller array to align with the larger one, provided they are compatible. This eliminates redundant memory allocation and simplifies code for element-wise operations.",[17,7235,7237],{"id":7236},"efficient-data-manipulation-and-indexing","Efficient Data Manipulation and Indexing",[22,7239,7240,7241,7244],{},"Practical data science relies on sophisticated indexing techniques to extract and transform subsets of data. Beyond basic slicing, Boolean indexing allows you to filter data based on specific conditions (e.g., ",[29,7242,7243],{},"arr[arr > 5]","), which is a cornerstone of data cleaning and exploratory analysis.",[22,7246,7247,7248,7251,7252,7255,7256,7259],{},"Additionally, understanding array reshaping and stacking is vital for preparing data for machine learning models. Functions like ",[29,7249,7250],{},"reshape",", ",[29,7253,7254],{},"vstack",", and ",[29,7257,7258],{},"hstack"," allow you to reorganize data structures without altering the underlying data, ensuring compatibility with various library requirements. Mastering these techniques reduces the overhead of data preprocessing and ensures that your data structures are optimized for the specific algorithms you are deploying.",{"title":64,"searchDepth":77,"depth":77,"links":7261},[7262,7263],{"id":7226,"depth":77,"text":7227},{"id":7236,"depth":77,"text":7237},[193],{"content_references":7266,"triage":7270},[7267],{"type":200,"title":7268,"url":7269,"context":208},"NumPy","https:\u002F\u002Fnumpy.org\u002F",{"relevance":216,"novelty":83,"quality":216,"actionability":216,"composite":217,"reasoning":7271},"Category: Data Science & Visualization. The article provides practical insights into essential NumPy concepts that are directly applicable to data science tasks, addressing the audience's need for actionable content. It discusses techniques like vectorization and broadcasting, which are crucial for optimizing data pipelines.","\u002Fsummaries\u002F67dbbade0cd2aa6f-essential-numpy-concepts-for-practical-data-scienc-summary","2026-06-04 12:44:26","2026-06-06 16:11:40",{"title":7215,"description":64},{"loc":7272},"67dbbade0cd2aa6f","Python in Plain English","https:\u002F\u002Fpython.plainenglish.io\u002F8-numpy-concepts-that-do-the-heavy-lifting-in-real-data-science-ded8eb572c2d?source=rss----78073def27b8---4","summaries\u002F67dbbade0cd2aa6f-essential-numpy-concepts-for-practical-data-scienc-summary",[63,230,7282],"numpy","Mastering eight core NumPy concepts—from vectorization to broadcasting—provides the foundation for 80% of daily data science tasks in Python.",[7282],"wkQjYyvg-JsTuoUWjq2BZQDPWT96nIuXlnUp93CV8Eo",{"id":7287,"title":7288,"ai":7289,"body":7294,"categories":7486,"created_at":194,"date_modified":194,"description":64,"extension":195,"faq":194,"featured":196,"kicker_label":194,"meta":7487,"navigation":219,"path":7498,"published_at":7499,"question":194,"scraped_at":7500,"seo":7501,"sitemap":7502,"source_id":7503,"source_name":7504,"source_type":226,"source_url":7505,"stem":7506,"tags":7507,"thumbnail_url":194,"tldr":7509,"tweet":194,"unknown_tags":7510,"__hash__":7511},"summaries\u002Fsummaries\u002Fff126f8e0954389e-skfolio-build-tune-portfolio-optimizers-in-python-summary.md","skfolio: Build & Tune Portfolio Optimizers in Python",{"provider":7,"model":8,"input_tokens":7290,"output_tokens":7291,"processing_time_ms":7292,"cost_usd":7293},9292,2519,30098,0.00309525,{"type":14,"value":7295,"toc":7480},[7296,7300,7329,7333,7382,7386,7451,7455],[17,7297,7299],{"id":7298},"data-prep-and-baseline-benchmarks-deliver-quick-wins","Data Prep and Baseline Benchmarks Deliver Quick Wins",[22,7301,7302,7303,7306,7307,7310,7311,7314,7315,7251,7318,7255,7321,7324,7325,7328],{},"Load S&P 500 prices via ",[29,7304,7305],{},"skfolio.datasets.load_sp500_dataset()",", convert to returns with ",[29,7308,7309],{},"prices_to_returns()",", and split chronologically (",[29,7312,7313],{},"train_test_split(shuffle=False, test_size=0.33)",") to prevent look-ahead bias—training spans ~67% historical days, testing the rest. Baselines like ",[29,7316,7317],{},"EqualWeighted()",[29,7319,7320],{},"InverseVolatility()",[29,7322,7323],{},"Random()"," fit on train, predict on test, yielding metrics like annualized Sharpe (printed via ",[29,7326,7327],{},"ptf.annualized_sharpe_ratio","), mean return, and volatility. These expose naive strategies' flaws: equal-weight ignores volatility, random adds noise—use them to benchmark any optimizer.",[17,7330,7332],{"id":7331},"mean-variance-risk-measures-and-clustering-beat-baselines","Mean-Variance, Risk Measures, and Clustering Beat Baselines",[22,7334,7335,7338,7339,7342,7343,7346,7347,7350,7351,7251,7354,7357,7358,7361,7362,7365,7366,7369,7370,7373,7374,7377,7378,7381],{},[29,7336,7337],{},"MeanRisk(risk_measure=RiskMeasure.VARIANCE)"," minimizes variance or maximizes Sharpe (",[29,7340,7341],{},"ObjectiveFunction.MAXIMIZE_RATIO","), generating efficient frontiers (",[29,7344,7345],{},"efficient_frontier_size=20",") plotted by risk vs. Sharpe. Swap risks to ",[29,7348,7349],{},"CVaR"," (95%), ",[29,7352,7353],{},"SEMI_VARIANCE",[29,7355,7356],{},"CDAR",", or ",[29,7359,7360],{},"MAX_DRAWDOWN"," for tail-focused portfolios that cut CVaR@95% and max drawdown vs. variance. ",[29,7363,7364],{},"RiskBudgeting()"," equalizes contributions (variance or CVaR). Hierarchical methods shine: ",[29,7367,7368],{},"HierarchicalRiskParity()"," clusters assets via dendrograms for stable weights; ",[29,7371,7372],{},"NestedClustersOptimization()"," nests ",[29,7375,7376],{},"MeanRisk(CVAR)"," inside ",[29,7379,7380],{},"RiskBudgeting(VARIANCE)"," with 5-fold CV, capturing correlations without covariance pitfalls.",[17,7383,7385],{"id":7384},"robust-priors-constraints-and-views-stabilize-real-world-use","Robust Priors, Constraints, and Views Stabilize Real-World Use",[22,7387,7388,7389,7392,7393,7396,7397,7251,7400,7251,7403,7357,7406,7409,7410,7413,7414,7251,7417,7251,7420,7251,7423,7426,7427,7430,7431,7434,7435,7438,7439,7442,7443,7446,7447,7450],{},"Replace ",[29,7390,7391],{},"EmpiricalCovariance()","\u002F",[29,7394,7395],{},"EmpiricalMu()"," with ",[29,7398,7399],{},"DenoiseCovariance()",[29,7401,7402],{},"ShrunkMu()",[29,7404,7405],{},"GerberCovariance()",[29,7407,7408],{},"EWMu(alpha=0.1)"," in ",[29,7411,7412],{},"EmpiricalPrior()"," for max-Sharpe portfolios resilient to estimation error. Add realism via ",[29,7415,7416],{},"min_weights=0.0",[29,7418,7419],{},"max_weights=0.20",[29,7421,7422],{},"transaction_costs=0.0005",[29,7424,7425],{},"groups"," (e.g., GroupA \u003C=0.6, GroupB>=0.2), ",[29,7428,7429],{},"l2_coef=0.01",". ",[29,7432,7433],{},"BlackLitterman(views=[\"AAPL == 0.0008\", \"JPM - BAC == 0.0002\"])"," blends market priors with views. ",[29,7436,7437],{},"FactorModel()"," on ",[29,7440,7441],{},"load_factors_dataset()"," explains returns via external factors, boosting Sharpe. Pipelines like ",[29,7444,7445],{},"SelectKExtremes(k=8)"," + ",[29,7448,7449],{},"MeanRisk()"," prune to top performers.",[17,7452,7454],{"id":7453},"walk-forward-cv-and-tuning-ensure-out-of-sample-performance","Walk-Forward CV and Tuning Ensure Out-of-Sample Performance",[22,7456,7457,7396,7460,7463,7464,7467,7468,7471,7472,7475,7476,7479],{},[29,7458,7459],{},"cross_val_predict()",[29,7461,7462],{},"WalkForward(train_size=252*2, test_size=63)"," simulates rolling 2-year trains\u002F3-month tests, computing portfolio Sharpe\u002FCalmar. ",[29,7465,7466],{},"GridSearchCV()"," tunes ",[29,7469,7470],{},"l2_coef=[0.0,0.01,0.1]"," and ",[29,7473,7474],{},"mu_estimator__alpha=[0.05,0.1,0.2,0.5]"," on max-Sharpe, selecting best CV Sharpe. Final ",[29,7477,7478],{},"Population()"," of 18 strategies compares annualized mean\u002Fvol\u002FSharpe\u002FSortino\u002FCVaR@95%\u002Fdrawdowns (sorted by test Sharpe), with plots for cumulative returns, weights, risk contributions—revealing hierarchical\u002Frisk-parity often top variance-based in stability.",{"title":64,"searchDepth":77,"depth":77,"links":7481},[7482,7483,7484,7485],{"id":7298,"depth":77,"text":7299},{"id":7331,"depth":77,"text":7332},{"id":7384,"depth":77,"text":7385},{"id":7453,"depth":77,"text":7454},[193],{"content_references":7488,"triage":7495},[7489,7492],{"type":200,"title":7490,"url":7491,"context":203},"skfolio","https:\u002F\u002Fgithub.com\u002Fskfolio\u002Fskfolio",{"type":205,"title":7493,"url":7494,"context":203},"Full Codes","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FData%20Science\u002Fportfolio_optimization_with_skfolio_Marktechpost.ipynb",{"relevance":83,"novelty":83,"quality":216,"actionability":216,"composite":7496,"reasoning":7497},3.45,"Category: Data Science & Visualization. The article provides a practical guide on using the skfolio library for portfolio optimization, which aligns with the audience's interest in actionable AI and data science tools. It includes specific code examples and methodologies that can be directly applied, making it useful for developers looking to implement AI in financial products.","\u002Fsummaries\u002Fff126f8e0954389e-skfolio-build-tune-portfolio-optimizers-in-python-summary","2026-05-12 07:05:02","2026-05-12 15:01:25",{"title":7288,"description":64},{"loc":7498},"ff126f8e0954389e","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F12\u002Fa-coding-implementation-to-portfolio-optimization-with-skfolio-for-building-testing-tuning-and-comparing-modern-investment-strategies\u002F","summaries\u002Fff126f8e0954389e-skfolio-build-tune-portfolio-optimizers-in-python-summary",[63,230,7508],"machine-learning","skfolio's scikit-learn API lets you construct, validate, and compare 18+ portfolio strategies—from baselines to HRP, Black-Litterman, factors, and tuned models—on S&P 500 returns with walk-forward CV and GridSearchCV.",[],"s9QUFNF_HWzNZV61Dh6PEETN3C3-K3FsZalb0rd3HRQ",{"id":7513,"title":7514,"ai":7515,"body":7520,"categories":7726,"created_at":194,"date_modified":194,"description":64,"extension":195,"faq":194,"featured":196,"kicker_label":194,"meta":7727,"navigation":219,"path":7743,"published_at":7744,"question":194,"scraped_at":7745,"seo":7746,"sitemap":7747,"source_id":7748,"source_name":7504,"source_type":226,"source_url":7749,"stem":7750,"tags":7751,"thumbnail_url":194,"tldr":7752,"tweet":194,"unknown_tags":7753,"__hash__":7754},"summaries\u002Fsummaries\u002Fa59df2d47dafe018-scanpy-pipeline-for-pbmc-scrna-seq-clustering-traj-summary.md","Scanpy Pipeline for PBMC scRNA-seq Clustering & Trajectories",{"provider":7,"model":8,"input_tokens":7516,"output_tokens":7517,"processing_time_ms":7518,"cost_usd":7519},9209,2235,26831,0.0029368,{"type":14,"value":7521,"toc":7720},[7522,7526,7558,7584,7588,7610,7626,7630,7653,7671,7675,7706],[17,7523,7525],{"id":7524},"rigorous-qc-and-filtering-removes-noise-for-reliable-downstream-analysis","Rigorous QC and Filtering Removes Noise for Reliable Downstream Analysis",[22,7527,7528,7529,7532,7533,7536,7537,7540,7541,7544,7545,7548,7549,7251,7552,7251,7555,7557],{},"Load PBMC-3k via ",[29,7530,7531],{},"sc.datasets.pbmc3k()"," (2700 cells, ~2k genes\u002Fcell). Compute QC metrics for mitochondrial (",[29,7534,7535],{},"MT-"," prefix, filter \u003C5% ",[29,7538,7539],{},"pct_counts_mt",") and ribosomal (",[29,7542,7543],{},"RPS\u002FRPL",") genes using ",[29,7546,7547],{},"sc.pp.calculate_qc_metrics",". Visualize with violin plots (",[29,7550,7551],{},"n_genes_by_counts",[29,7553,7554],{},"total_counts",[29,7556,7539],{},") and scatters to spot outliers.",[22,7559,7560,7561,7251,7564,7567,7568,7571,7572,7575,7576,7579,7580,7583],{},"Filter: ",[29,7562,7563],{},"min_genes=200",[29,7565,7566],{},"min_cells=3",", upper ",[29,7569,7570],{},"n_genes_by_counts \u003C2500",". Detect doublets via ",[29,7573,7574],{},"sc.pp.scrublet"," (removes ~sum of ",[29,7577,7578],{},"predicted_doublet","). Preserve raw in ",[29,7581,7582],{},"layers[\"counts\"]",". This yields cleaner data, preventing artifacts in clustering.",[17,7585,7587],{"id":7586},"normalization-hvgs-and-cell-cycle-correction-focus-on-biological-signal","Normalization, HVGs, and Cell-Cycle Correction Focus on Biological Signal",[22,7589,7590,7591,7594,7595,7598,7599,7602,7603,7606,7607,155],{},"Normalize to 10k counts (",[29,7592,7593],{},"sc.pp.normalize_total(target_sum=1e4)","), log-transform (",[29,7596,7597],{},"sc.pp.log1p","). Identify highly variable genes (",[29,7600,7601],{},"sc.pp.highly_variable_genes(min_mean=0.0125, max_mean=3, min_disp=0.5)","), subset to them (",[29,7604,7605],{},"adata = adata[:, adata.var.highly_variable]","). Store raw in ",[29,7608,7609],{},"adata.raw",[22,7611,7612,7613,7251,7615,7617,7618,7621,7622,7625],{},"Score S\u002FG2M phases with 40+ predefined markers (e.g., S: MCM5,PCNA; G2M: HMGB2,CDK1, filter to dataset genes). Regress out ",[29,7614,7554],{},[29,7616,7539],{}," (",[29,7619,7620],{},"sc.pp.regress_out","). Scale (",[29,7623,7624],{},"sc.pp.scale(max_value=10)","). These steps isolate biological variance, regressing technical noise for accurate modeling.",[17,7627,7629],{"id":7628},"dimensionality-reduction-leiden-clustering-and-marker-based-annotation-reveals-cell-types","Dimensionality Reduction, Leiden Clustering, and Marker-Based Annotation Reveals Cell Types",[22,7631,7632,7633,7636,7637,7640,7641,7644,7645,7648,7649,7652],{},"PCA (",[29,7634,7635],{},"sc.tl.pca(svd_solver=\"arpack\")",", check ",[29,7638,7639],{},"n_pcs=50"," variance). Neighbors (",[29,7642,7643],{},"sc.pp.neighbors(n_neighbors=10, n_pcs=40)","). Embeddings: UMAP (",[29,7646,7647],{},"sc.tl.umap","), t-SNE (",[29,7650,7651],{},"sc.tl.tsne(n_pcs=40)",").",[22,7654,7655,7656,7659,7660,7663,7664,7251,7667,7670],{},"Cluster with Leiden (",[29,7657,7658],{},"sc.tl.leiden(resolution=0.5, flavor=\"igraph\", n_iterations=2)","). Rank markers (",[29,7661,7662],{},"sc.tl.rank_genes_groups(method=\"wilcoxon\")",", top 10\u002Fcluster via Wilcoxon). Annotate using PBMC markers: B-cell (CD79A,MS4A1), CD8 T (CD8A,CD8B), CD4 T (IL7R,CD4), NK (GNLY,NKG7), CD14 Mono (CD14,LYZ), FCGR3A Mono (FCGR3A,MS4A7), Dendritic (FCER1A,CST3), Mega (PPBP). Confirm via ",[29,7665,7666],{},"sc.pl.dotplot",[29,7668,7669],{},"sc.pl.stacked_violin(groupby=\"leiden\")",". Visualizes 8-9 clusters matching immune subsets.",[17,7672,7674],{"id":7673},"paga-trajectories-pseudotime-and-custom-scores-enable-developmental-insights","PAGA Trajectories, Pseudotime, and Custom Scores Enable Developmental Insights",[22,7676,7677,7678,7681,7682,7685,7686,7689,7690,7693,7694,7697,7698,7701,7702,7705],{},"Graph-based trajectories: ",[29,7679,7680],{},"sc.tl.paga(groups=\"leiden\")",", threshold=0.1, init UMAP (",[29,7683,7684],{},"sc.tl.umap(init_pos=\"paga\")","). Diffusion maps (",[29,7687,7688],{},"sc.tl.diffmap","), recompute neighbors on ",[29,7691,7692],{},"X_diffmap",", root at cluster 0 (",[29,7695,7696],{},"adata.uns[\"iroot\"]","), pseudotime (",[29,7699,7700],{},"sc.tl.dpt","). Plot ",[29,7703,7704],{},"dpt_pseudotime"," on UMAP.",[22,7707,7708,7709,7251,7712,7715,7716,7719],{},"Custom score: IFN-response genes (ISG15,IFI6,IFIT1,IFIT3,MX1,OAS1,STAT1,IRF7) via ",[29,7710,7711],{},"sc.tl.score_genes(score_name=\"IFN_score\")",[29,7713,7714],{},"cmap=\"viridis\"",". Save full AnnData (",[29,7717,7718],{},"adata.write(\"pbmc3k_analyzed.h5ad\")",") with embeddings, clusters, scores for reuse. Extends basic clustering to infer progression and response states.",{"title":64,"searchDepth":77,"depth":77,"links":7721},[7722,7723,7724,7725],{"id":7524,"depth":77,"text":7525},{"id":7586,"depth":77,"text":7587},{"id":7628,"depth":77,"text":7629},{"id":7673,"depth":77,"text":7674},[193],{"content_references":7728,"triage":7740},[7729,7732,7735,7737],{"type":200,"title":7730,"url":7731,"context":203},"Scanpy","https:\u002F\u002Fgithub.com\u002Fscverse\u002Fscanpy",{"type":7733,"title":7734,"context":203},"dataset","PBMC-3k",{"type":200,"title":7736,"context":203},"Scrublet",{"type":205,"title":7738,"url":7739,"context":208},"Full Codes with Notebook","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FData%20Science\u002Fscanpy_pbmc3k_single_cell_rnaseq_analysis_Marktechpost.ipynb",{"relevance":83,"novelty":77,"quality":216,"actionability":83,"composite":7741,"reasoning":7742},3.05,"Category: Data Science & Visualization. The article provides a detailed overview of building a single-cell RNA-seq analysis pipeline using Scanpy, which is relevant for data scientists working with biological data. However, it primarily focuses on a specific use case without broader implications or insights that could apply to a wider audience.","\u002Fsummaries\u002Fa59df2d47dafe018-scanpy-pipeline-for-pbmc-scrna-seq-clustering-traj-summary","2026-05-08 21:32:12","2026-05-09 15:37:24",{"title":7514,"description":64},{"loc":7743},"a59df2d47dafe018","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F08\u002Fhow-to-build-a-single-cell-rna-seq-analysis-pipeline-with-scanpy-for-pbmc-clustering-annotation-and-trajectory-discovery\u002F","summaries\u002Fa59df2d47dafe018-scanpy-pipeline-for-pbmc-scrna-seq-clustering-traj-summary",[230,7508,63],"Process PBMC-3k data with Scanpy: filter cells (min 200 genes, \u003C2500 genes, \u003C5% mt), remove Scrublet doublets, select HVGs (min_mean=0.0125, max_mean=3, min_disp=0.5), Leiden cluster at res=0.5, annotate via markers, infer PAGA\u002FDPT trajectories, score IFN response.",[],"jTCku7xsp8M-LiBcwiNLzHzB68G5RjE-UBMIb_cET-c"]