[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-c18618b173a65ae5-mastering-step-plots-in-matplotlib-summary":3,"summaries-facets-categories":124,"summary-related-c18618b173a65ae5-mastering-step-plots-in-matplotlib-summary":7028},{"id":4,"title":5,"ai":6,"body":13,"categories":89,"created_at":91,"date_modified":91,"description":84,"extension":92,"faq":91,"featured":93,"kicker_label":91,"meta":94,"navigation":106,"path":107,"published_at":108,"question":91,"scraped_at":109,"seo":110,"sitemap":111,"source_id":112,"source_name":113,"source_type":114,"source_url":115,"stem":116,"tags":117,"thumbnail_url":91,"tldr":121,"tweet":91,"unknown_tags":122,"__hash__":123},"summaries\u002Fsummaries\u002Fc18618b173a65ae5-mastering-step-plots-in-matplotlib-summary.md","Mastering Step Plots in Matplotlib",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",3962,471,2523,0.001697,{"type":14,"value":15,"toc":83},"minimark",[16,21,25,48,52,60,80],[17,18,20],"h2",{"id":19},"when-to-choose-step-plots-over-line-plots","When to Choose Step Plots Over Line Plots",[22,23,24],"p",{},"Standard line plots connect data points with diagonal lines, implying a continuous, gradual transition between values. This is misleading for data representing discrete state changes. Step plots (or staircase plots) are the correct choice for scenarios where values remain constant until an instantaneous shift occurs. Common use cases include:",[26,27,28,36,42],"ul",{},[29,30,31,35],"li",{},[32,33,34],"strong",{},"Inventory Management:"," Tracking stock levels that decrease by integer amounts.",[29,37,38,41],{},[32,39,40],{},"Financial Data:"," Visualizing interest rate changes that occur on specific dates.",[29,43,44,47],{},[32,45,46],{},"Signal Processing:"," Representing binary or discrete signals that toggle between 'on' and 'off' states.",[17,49,51],{"id":50},"implementation-approaches-in-matplotlib","Implementation Approaches in Matplotlib",[22,53,54,55,59],{},"Matplotlib provides flexible ways to render these plots, allowing developers to control how the 'step' is aligned relative to the data points. The core function is ",[56,57,58],"code",{},"plt.step()",", which supports four distinct alignment configurations:",[26,61,62,68,74],{},[29,63,64,67],{},[32,65,66],{},"'pre' (default):"," The step occurs before the data point. The interval value is determined by the y-value of the current point.",[29,69,70,73],{},[32,71,72],{},"'post' (default):"," The step occurs after the data point. The interval value is determined by the y-value of the previous point.",[29,75,76,79],{},[32,77,78],{},"'mid' (default):"," The step occurs exactly halfway between two consecutive x-values.",[22,81,82],{},"By selecting the appropriate alignment, you ensure the visualization accurately reflects the timing and nature of the state change in your dataset. Using the correct alignment is critical for maintaining data integrity in technical reporting.",{"title":84,"searchDepth":85,"depth":85,"links":86},"",2,[87,88],{"id":19,"depth":85,"text":20},{"id":50,"depth":85,"text":51},[90],"Data Science & Visualization",null,"md",false,{"content_references":95,"triage":101},[96],{"type":97,"title":98,"url":99,"context":100},"tool","Matplotlib","https:\u002F\u002Fmatplotlib.org\u002F","recommended",{"relevance":102,"novelty":85,"quality":103,"actionability":102,"composite":104,"reasoning":105},3,4,3.05,"Category: Data Science & Visualization. The article discusses the use of step plots in data visualization, which is relevant to the audience's interest in analytics and visualization techniques. While it provides practical implementation details for Matplotlib, the concept of step plots is not particularly novel in the field.",true,"\u002Fsummaries\u002Fc18618b173a65ae5-mastering-step-plots-in-matplotlib-summary","2026-05-18 17:48:00","2026-05-18 19:00:33",{"title":5,"description":84},{"loc":107},"c18618b173a65ae5","Level Up Coding","article","https:\u002F\u002Flevelup.gitconnected.com\u002Fhow-to-easily-make-a-good-looking-step-plot-in-matplotlib-4fea229cdd26?source=rss----5517fd7b58a6---4","summaries\u002Fc18618b173a65ae5-mastering-step-plots-in-matplotlib-summary",[118,119,120],"data-visualization","python","matplotlib","Step plots are superior to standard line plots for visualizing incremental state changes, such as inventory levels, interest rates, or discrete signals, where transitions are abrupt rather than gradual.",[120],"U5wKM-dXzhzxVtLgIU0HqQFXH98CID4XnX9jTrMdDh4",[125,128,131,133,136,138,141,144,146,148,150,152,155,157,159,161,163,166,168,170,172,174,177,179,181,183,185,187,189,191,193,195,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,228,230,232,234,236,238,240,242,244,246,248,250,252,254,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,298,300,302,304,306,308,310,312,314,316,318,320,322,324,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,381,383,385,387,389,392,394,396,398,400,402,404,406,408,410,412,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,448,450,452,454,456,458,460,462,464,466,468,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,530,532,534,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,576,578,580,582,584,586,588,590,592,594,596,598,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,888,890,892,894,896,899,901,903,905,907,909,911,913,915,917,919,921,923,926,928,930,932,934,936,938,940,942,944,946,948,950,952,954,956,958,960,962,964,966,968,970,972,974,976,978,980,982,984,986,988,990,992,994,996,998,1000,1002,1004,1006,1008,1010,1012,1014,1016,1018,1020,1022,1024,1026,1028,1030,1032,1034,1036,1038,1040,1042,1044,1046,1048,1050,1052,1054,1056,1058,1060,1062,1064,1066,1068,1070,1072,1074,1076,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1478,1480,1482,1484,1486,1488,1490,1492,1494,1496,1498,1500,1502,1504,1506,1508,1510,1512,1514,1516,1518,1520,1522,1524,1526,1528,1530,1532,1534,1536,1538,1540,1542,1544,1546,1548,1550,1552,1554,1556,1558,1560,1562,1564,1566,1568,1570,1572,1574,1576,1578,1580,1582,1584,1586,1588,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1778,1780,1782,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2282,2284,2286,2288,2290,2292,2294,2296,2298,2300,2302,2304,2306,2308,2310,2312,2314,2316,2318,2320,2322,2324,2326,2328,2330,2332,2334,2336,2338,2340,2342,2344,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2466,2468,2470,2472,2474,2476,2478,2480,2482,2484,2486,2488,2490,2492,2494,2496,2498,2500,2502,2504,2506,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2625,2627,2629,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3150,3152,3154,3156,3158,3160,3162,3164,3166,3168,3170,3172,3174,3176,3178,3180,3182,3184,3186,3188,3190,3192,3194,3196,3198,3200,3202,3204,3206,3208,3210,3212,3214,3216,3218,3220,3222,3224,3226,3228,3230,3232,3234,3236,3238,3240,3242,3244,3246,3248,3250,3252,3254,3256,3258,3260,3262,3264,3266,3268,3270,3272,3274,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671,3673,3675,3677,3679,3681,3683,3685,3687,3689,3691,3693,3695,3697,3699,3701,3703,3705,3707,3709,3711,3713,3715,3717,3719,3721,3723,3725,3727,3729,3731,3733,3735,3737,3739,3741,3743,3745,3747,3749,3751,3753,3755,3757,3759,3761,3763,3765,3767,3769,3771,3773,3775,3777,3779,3781,3783,3785,3787,3789,3791,3793,3795,3797,3799,3801,3803,3805,3807,3809,3811,3813,3815,3817,3819,3821,3823,3825,3827,3829,3831,3833,3835,3837,3839,3841,3843,3845,3847,3849,3851,3853,3855,3857,3859,3861,3863,3865,3867,3869,3871,3873,3875,3877,3879,3881,3883,3885,3887,3889,3891,3893,3895,3897,3899,3901,3903,3905,3907,3909,3911,3913,3915,3917,3919,3921,3923,3925,3927,3929,3931,3933,3935,3937,3939,3941,3943,3945,3947,3949,3951,3953,3955,3957,3959,3961,3963,3965,3967,3969,3971,3973,3975,3977,3979,3981,3983,3985,3987,3989,3991,3993,3995,3997,3999,4001,4003,4005,4007,4009,4011,4013,4015,4017,4019,4021,4023,4025,4027,4029,4031,4033,4035,4037,4039,4041,4043,4045,4047,4049,4051,4053,4055,4057,4059,4061,4063,4065,4067,4069,4071,4073,4075,4077,4079,4081,4083,4085,4087,4089,4091,4093,4095,4097,4099,4101,4103,4105,4107,4109,4111,4113,4115,4117,4119,4121,4123,4125,4127,4129,4131,4133,4135,4137,4139,4141,4143,4145,4147,4149,4151,4153,4155,4157,4159,4161,4163,4165,4167,4169,4171,4173,4175,4177,4179,4181,4183,4185,4187,4189,4191,4193,4195,4197,4199,4201,4203,4205,4207,4209,4211,4213,4215,4217,4219,4221,4223,4225,4227,4229,4231,4233,4235,4237,4239,4241,4243,4245,4247,4249,4251,4253,4255,4257,4259,4261,4263,4265,4267,4269,4271,4273,4275,4277,4279,4281,4283,4285,4287,4289,4291,4293,4295,4297,4299,4301,4303,4305,4307,4309,4311,4313,4315,4317,4319,4321,4323,4325,4327,4329,4331,4333,4335,4337,4339,4341,4343,4345,4347,4349,4351,4353,4355,4357,4359,4361,4363,4365,4367,4369,4371,4373,4375,4377,4379,4381,4383,4385,4387,4389,4391,4393,4395,4397,4399,4401,4403,4405,4407,4409,4411,4413,4415,4417,4419,4421,4423,4425,4427,4429,4431,4433,4435,4437,4439,4441,4443,4445,4447,4449,4451,4453,4455,4457,4459,4461,4463,4465,4467,4469,4471,4473,4475,4477,4479,4481,4483,4485,4487,4489,4491,4493,4495,4497,4499,4501,4503,4505,4507,4509,4511,4513,4515,4517,4519,4521,4523,4525,4527,4529,4531,4533,4535,4537,4539,4541,4543,4545,4547,4549,4551,4553,4555,4557,4559,4562,4564,4566,4568,4570,4572,4574,4576,4578,4580,4582,4584,4586,4588,4590,4592,4594,4596,4598,4600,4602,4604,4606,4608,4610,4612,4614,4616,4618,4620,4622,4624,4626,4628,4630,4632,4634,4636,4638,4640,4642,4644,4646,4648,4650,4652,4654,4656,4658,4660,4662,4664,4666,4668,4670,4672,4674,4676,4678,4680,4682,4684,4686,4688,4690,4692,4694,4696,4698,4700,4702,4704,4706,4708,4710,4712,4714,4716,4718,4720,4722,4724,4726,4728,4730,4732,4734,4736,4738,4740,4742,4744,4746,4748,4750,4752,4754,4756,4758,4760,4762,4764,4766,4768,4770,4772,4774,4776,4778,4780,4782,4784,4786,4788,4790,4792,4794,4796,4798,4800,4802,4804,4806,4808,4810,4812,4814,4816,4818,4820,4822,4824,4826,4828,4830,4832,4834,4836,4838,4840,4842,4844,4846,4848,4850,4852,4854,4856,4858,4860,4862,4864,4866,4868,4870,4872,4874,4876,4878,4880,4882,4884,4886,4888,4890,4892,4894,4896,4898,4900,4902,4904,4906,4908,4910,4912,4914,4916,4918,4920,4922,4924,4926,4928,4930,4932,4934,4936,4938,4940,4942,4944,4946,4948,4950,4952,4954,4956,4958,4960,4962,4964,4966,4968,4970,4972,4974,4976,4978,4980,4982,4984,4986,4988,4990,4992,4994,4996,4998,5000,5002,5004,5006,5008,5010,5012,5014,5016,5018,5020,5022,5024,5026,5028,5030,5032,5034,5036,5038,5040,5042,5044,5046,5048,5050,5052,5054,5056,5058,5060,5062,5064,5066,5068,5070,5072,5074,5076,5078,5080,5082,5084,5086,5088,5090,5092,5094,5096,5098,5100,5102,5104,5106,5108,5110,5112,5114,5116,5118,5120,5122,5124,5126,5128,5130,5132,5134,5136,5138,5140,5142,5144,5146,5148,5150,5152,5154,5156,5158,5160,5162,5164,5166,5168,5170,5172,5174,5176,5178,5180,5182,5184,5186,5188,5190,5192,5194,5196,5198,5200,5202,5204,5206,5208,5210,5212,5214,5216,5218,5220,5222,5224,5226,5228,5230,5232,5234,5236,5238,5240,5242,5244,5246,5248,5250,5252,5254,5256,5258,5260,5262,5264,5266,5268,5270,5272,5274,5276,5278,5280,5282,5284,5286,5288,5290,5292,5294,5296,5298,5300,5302,5304,5306,5308,5310,5312,5314,5316,5318,5320,5322,5324,5326,5328,5330,5332,5334,5336,5338,5340,5342,5344,5346,5348,5350,5352,5354,5356,5358,5360,5362,5364,5366,5368,5370,5372,5374,5376,5378,5380,5382,5384,5386,5388,5390,5392,5394,5396,5398,5400,5402,5404,5406,5408,5410,5412,5414,5416,5419,5421,5423,5425,5427,5429,5431,5433,5435,5437,5439,5441,5443,5445,5447,5449,5451,5453,5455,5457,5459,5461,5463,5465,5467,5469,5471,5473,5475,5477,5479,5481,5483,5485,5487,5489,5491,5493,5495,5497,5499,5501,5503,5505,5507,5509,5511,5513,5515,5517,5519,5521,5523,5525,5527,5529,5531,5533,5535,5537,5539,5541,5543,5545,5547,5549,5551,5553,5555,5557,5559,5561,5563,5565,5567,5569,5571,5573,5575,5577,5579,5581,5583,5585,5587,5589,5591,5593,5595,5597,5599,5601,5603,5605,5607,5609,5611,5613,5615,5617,5619,5622,5624,5626,5628,5630,5632,5634,5636,5638,5640,5642,5644,5646,5648,5650,5652,5654,5656,5658,5660,5662,5664,5666,5668,5670,5672,5674,5676,5678,5680,5682,5684,5686,5688,5690,5692,5694,5696,5698,5700,5702,5704,5706,5708,5710,5712,5714,5716,5718,5720,5722,5724,5726,5728,5730,5732,5734,5736,5738,5740,5742,5744,5746,5748,5750,5752,5754,5756,5758,5760,5762,5764,5766,5768,5770,5772,5774,5776,5778,5780,5782,5784,5786,5788,5790,5792,5794,5796,5798,5800,5802,5804,5806,5808,5810,5812,5814,5816,5818,5820,5822,5824,5826,5828,5830,5832,5834,5836,5838,5840,5842,5844,5846,5848,5850,5852,5854,5856,5858,5860,5862,5864,5866,5868,5870,5872,5874,5876,5878,5880,5882,5884,5886,5888,5890,5892,5894,5896,5898,5900,5902,5904,5906,5908,5910,5912,5914,5916,5918,5920,5922,5924,5926,5928,5930,5932,5934,5936,5938,5940,5942,5944,5946,5948,5950,5952,5954,5956,5958,5960,5962,5964,5966,5968,5970,5972,5974,5976,5978,5980,5982,5984,5986,5988,5990,5992,5994,5996,5998,6000,6002,6004,6006,6008,6010,6012,6014,6016,6018,6020,6022,6024,6026,6028,6030,6032,6034,6036,6038,6040,6042,6044,6046,6048,6050,6052,6054,6056,6058,6060,6062,6064,6066,6068,6070,6072,6074,6076,6078,6080,6082,6084,6086,6088,6090,6092,6094,6096,6098,6100,6102,6104,6106,6108,6110,6112,6114,6116,6118,6120,6122,6124,6126,6128,6130,6132,6134,6136,6138,6140,6142,6144,6146,6148,6150,6152,6154,6156,6158,6160,6162,6164,6166,6168,6170,6172,6174,6176,6178,6180,6182,6184,6186,6188,6190,6192,6194,6196,6198,6200,6202,6204,6206,6208,6210,6212,6214,6216,6218,6220,6222,6224,6226,6228,6230,6232,6234,6236,6238,6240,6242,6244,6246,6248,6250,6252,6254,6256,6258,6260,6262,6264,6266,6268,6270,6272,6274,6276,6278,6280,6282,6284,6286,6288,6290,6292,6294,6296,6298,6300,6302,6304,6306,6308,6310,6312,6314,6316,6318,6320,6322,6324,6326,6328,6330,6332,6334,6336,6338,6340,6342,6344,6346,6348,6350,6352,6354,6356,6358,6360,6362,6364,6366,6368,6370,6372,6374,6376,6378,6380,6382,6384,6386,6388,6390,6392,6394,6396,6398,6400,6402,6404,6406,6408,6410,6412,6414,6416,6418,6420,6422,6424,6426,6428,6430,6432,6434,6436,6438,6440,6442,6444,6446,6448,6450,6452,6454,6456,6458,6460,6462,6464,6466,6468,6470,6472,6474,6476,6478,6480,6482,6484,6486,6488,6490,6492,6494,6496,6498,6500,6502,6504,6506,6508,6510,6512,6514,6516,6518,6520,6522,6524,6526,6528,6530,6532,6534,6536,6538,6540,6542,6544,6546,6548,6550,6552,6554,6556,6558,6560,6562,6564,6566,6568,6570,6572,6574,6576,6578,6580,6582,6584,6586,6588,6590,6592,6594,6596,6598,6600,6602,6604,6606,6608,6610,6612,6614,6616,6618,6620,6622,6624,6626,6628,6630,6632,6634,6636,6638,6640,6642,6644,6646,6648,6650,6652,6654,6656,6658,6660,6662,6664,6666,6668,6670,6672,6674,6676,6678,6680,6682,6684,6686,6688,6690,6692,6694,6696,6698,6700,6702,6704,6706,6708,6710,6712,6714,6716,6718,6720,6722,6724,6726,6728,6730,6732,6734,6736,6738,6740,6742,6744,6746,6748,6750,6752,6754,6756,6758,6760,6762,6764,6766,6768,6770,6772,6774,6776,6778,6780,6782,6784,6786,6788,6790,6792,6794,6796,6798,6800,6802,6804,6806,6808,6810,6812,6814,6816,6818,6820,6822,6824,6826,6828,6830,6832,6834,6836,6838,6840,6842,6844,6846,6848,6850,6852,6854,6856,6858,6860,6862,6864,6866,6868,6870,6872,6874,6876,6878,6880,6882,6884,6886,6888,6890,6892,6894,6896,6898,6900,6902,6904,6906,6908,6910,6912,6914,6916,6918,6920,6922,6924,6926,6928,6930,6932,6934,6936,6938,6940,6942,6944,6946,6948,6950,6952,6954,6956,6958,6960,6962,6964,6966,6968,6970,6972,6974,6976,6978,6980,6982,6984,6986,6988,6990,6992,6994,6996,6998,7000,7002,7004,7006,7008,7010,7012,7014,7016,7018,7020,7022,7024,7026],{"categories":126},[127],"AI & LLMs",{"categories":129},[130],"Developer Productivity",{"categories":132},[127],{"categories":134},[135],"Business & SaaS",{"categories":137},[127],{"categories":139},[140],"AI Automation",{"categories":142},[143],"Product Strategy",{"categories":145},[140],{"categories":147},[127],{"categories":149},[130],{"categories":151},[140],{"categories":153},[154],"Software Engineering",{"categories":156},[127],{"categories":158},[135],{"categories":160},[],{"categories":162},[127],{"categories":164},[165],"Inference & Serving",{"categories":167},[127],{"categories":169},[127],{"categories":171},[140],{"categories":173},[],{"categories":175},[176],"AI News & Trends",{"categories":178},[90],{"categories":180},[140],{"categories":182},[127],{"categories":184},[127],{"categories":186},[135],{"categories":188},[130],{"categories":190},[127],{"categories":192},[140],{"categories":194},[176],{"categories":196},[127],{"categories":198},[140],{"categories":200},[140],{"categories":202},[127],{"categories":204},[127],{"categories":206},[140],{"categories":208},[127],{"categories":210},[127],{"categories":212},[127],{"categories":214},[140],{"categories":216},[176],{"categories":218},[127],{"categories":220},[127],{"categories":222},[127],{"categories":224},[],{"categories":226},[227],"Design & Frontend",{"categories":229},[90],{"categories":231},[176],{"categories":233},[127],{"categories":235},[127],{"categories":237},[127],{"categories":239},[],{"categories":241},[127],{"categories":243},[127],{"categories":245},[140],{"categories":247},[154],{"categories":249},[127],{"categories":251},[140],{"categories":253},[127],{"categories":255},[256],"Marketing & Growth",{"categories":258},[227],{"categories":260},[127],{"categories":262},[140],{"categories":264},[127],{"categories":266},[127],{"categories":268},[154],{"categories":270},[127],{"categories":272},[],{"categories":274},[],{"categories":276},[227],{"categories":278},[127],{"categories":280},[140],{"categories":282},[130],{"categories":284},[154],{"categories":286},[140],{"categories":288},[227],{"categories":290},[143],{"categories":292},[127],{"categories":294},[154],{"categories":296},[297],"DevOps & Cloud",{"categories":299},[140],{"categories":301},[143],{"categories":303},[176],{"categories":305},[127],{"categories":307},[],{"categories":309},[127],{"categories":311},[127],{"categories":313},[],{"categories":315},[140],{"categories":317},[154],{"categories":319},[],{"categories":321},[154],{"categories":323},[127],{"categories":325},[326],"Governance & Standards",{"categories":328},[135],{"categories":330},[],{"categories":332},[],{"categories":334},[127],{"categories":336},[127],{"categories":338},[140],{"categories":340},[127],{"categories":342},[127],{"categories":344},[140],{"categories":346},[127],{"categories":348},[127],{"categories":350},[127],{"categories":352},[],{"categories":354},[154],{"categories":356},[],{"categories":358},[],{"categories":360},[127],{"categories":362},[154],{"categories":364},[],{"categories":366},[154],{"categories":368},[127],{"categories":370},[127],{"categories":372},[256],{"categories":374},[127],{"categories":376},[127],{"categories":378},[127],{"categories":380},[227],{"categories":382},[227],{"categories":384},[127],{"categories":386},[154],{"categories":388},[140],{"categories":390},[391],"GovTech & Public-Sector Adoption",{"categories":393},[154],{"categories":395},[127],{"categories":397},[127],{"categories":399},[127],{"categories":401},[140],{"categories":403},[140],{"categories":405},[90],{"categories":407},[127],{"categories":409},[176],{"categories":411},[140],{"categories":413},[414],"Legal AI Tools",{"categories":416},[127],{"categories":418},[140],{"categories":420},[127],{"categories":422},[256],{"categories":424},[140],{"categories":426},[143],{"categories":428},[127],{"categories":430},[154],{"categories":432},[391],{"categories":434},[],{"categories":436},[140],{"categories":438},[],{"categories":440},[135],{"categories":442},[140],{"categories":444},[140],{"categories":446},[447],"RAG & Retrieval",{"categories":449},[135],{"categories":451},[127],{"categories":453},[154],{"categories":455},[154],{"categories":457},[297],{"categories":459},[227],{"categories":461},[140],{"categories":463},[127],{"categories":465},[127],{"categories":467},[],{"categories":469},[470],"Agents & Orchestration",{"categories":472},[154],{"categories":474},[127],{"categories":476},[],{"categories":478},[140],{"categories":480},[135],{"categories":482},[],{"categories":484},[127],{"categories":486},[],{"categories":488},[127],{"categories":490},[130],{"categories":492},[154],{"categories":494},[135],{"categories":496},[127],{"categories":498},[140],{"categories":500},[127],{"categories":502},[176],{"categories":504},[127],{"categories":506},[],{"categories":508},[127],{"categories":510},[],{"categories":512},[127],{"categories":514},[154],{"categories":516},[127],{"categories":518},[140],{"categories":520},[90],{"categories":522},[],{"categories":524},[127],{"categories":526},[227],{"categories":528},[529],"Models & Frontier Labs",{"categories":531},[],{"categories":533},[227],{"categories":535},[536],"Regulation & Governance of AI",{"categories":538},[143],{"categories":540},[140],{"categories":542},[],{"categories":544},[127],{"categories":546},[127],{"categories":548},[140],{"categories":550},[140],{"categories":552},[176],{"categories":554},[127],{"categories":556},[135],{"categories":558},[127],{"categories":560},[140],{"categories":562},[],{"categories":564},[154],{"categories":566},[140],{"categories":568},[127],{"categories":570},[143],{"categories":572},[127],{"categories":574},[575],"AI Policy & Regulation",{"categories":577},[],{"categories":579},[127],{"categories":581},[140],{"categories":583},[143],{"categories":585},[140],{"categories":587},[127],{"categories":589},[127],{"categories":591},[127],{"categories":593},[140],{"categories":595},[],{"categories":597},[90],{"categories":599},[600],"Evals & Reliability",{"categories":602},[127],{"categories":604},[127],{"categories":606},[],{"categories":608},[130],{"categories":610},[391],{"categories":612},[575],{"categories":614},[127],{"categories":616},[135],{"categories":618},[127],{"categories":620},[140],{"categories":622},[127],{"categories":624},[140],{"categories":626},[470],{"categories":628},[127],{"categories":630},[154],{"categories":632},[127],{"categories":634},[],{"categories":636},[227],{"categories":638},[],{"categories":640},[127],{"categories":642},[391],{"categories":644},[127],{"categories":646},[127],{"categories":648},[127],{"categories":650},[],{"categories":652},[127],{"categories":654},[227],{"categories":656},[154],{"categories":658},[],{"categories":660},[127],{"categories":662},[],{"categories":664},[140],{"categories":666},[127],{"categories":668},[227],{"categories":670},[],{"categories":672},[127],{"categories":674},[127],{"categories":676},[90],{"categories":678},[140],{"categories":680},[127],{"categories":682},[135],{"categories":684},[140],{"categories":686},[127],{"categories":688},[127],{"categories":690},[154],{"categories":692},[227],{"categories":694},[127],{"categories":696},[140],{"categories":698},[],{"categories":700},[154],{"categories":702},[140],{"categories":704},[90],{"categories":706},[],{"categories":708},[127],{"categories":710},[176],{"categories":712},[127],{"categories":714},[],{"categories":716},[127],{"categories":718},[127],{"categories":720},[127],{"categories":722},[135,256],{"categories":724},[],{"categories":726},[154],{"categories":728},[127],{"categories":730},[127],{"categories":732},[140],{"categories":734},[127],{"categories":736},[],{"categories":738},[],{"categories":740},[127],{"categories":742},[227],{"categories":744},[127],{"categories":746},[],{"categories":748},[127],{"categories":750},[297],{"categories":752},[],{"categories":754},[140],{"categories":756},[176],{"categories":758},[127],{"categories":760},[127],{"categories":762},[227],{"categories":764},[],{"categories":766},[176],{"categories":768},[127],{"categories":770},[165],{"categories":772},[127],{"categories":774},[127],{"categories":776},[140],{"categories":778},[176],{"categories":780},[529],{"categories":782},[127],{"categories":784},[256],{"categories":786},[],{"categories":788},[140],{"categories":790},[135],{"categories":792},[154],{"categories":794},[127],{"categories":796},[140],{"categories":798},[],{"categories":800},[127,297],{"categories":802},[127],{"categories":804},[127],{"categories":806},[127],{"categories":808},[140],{"categories":810},[127,154],{"categories":812},[90],{"categories":814},[127],{"categories":816},[127],{"categories":818},[127],{"categories":820},[154],{"categories":822},[127],{"categories":824},[140],{"categories":826},[140],{"categories":828},[575],{"categories":830},[256],{"categories":832},[127],{"categories":834},[140],{"categories":836},[127],{"categories":838},[127],{"categories":840},[140],{"categories":842},[],{"categories":844},[140],{"categories":846},[127],{"categories":848},[127],{"categories":850},[140],{"categories":852},[127],{"categories":854},[127,135],{"categories":856},[127],{"categories":858},[135],{"categories":860},[],{"categories":862},[227],{"categories":864},[227],{"categories":866},[127],{"categories":868},[],{"categories":870},[],{"categories":872},[127],{"categories":874},[176],{"categories":876},[],{"categories":878},[130],{"categories":880},[127],{"categories":882},[154],{"categories":884},[127],{"categories":886},[887],"Generative UI & Design-to-Code",{"categories":889},[127],{"categories":891},[127],{"categories":893},[227],{"categories":895},[127],{"categories":897},[898],"Algorithmic Accountability",{"categories":900},[140],{"categories":902},[154],{"categories":904},[176],{"categories":906},[227],{"categories":908},[127],{"categories":910},[],{"categories":912},[143],{"categories":914},[127],{"categories":916},[127],{"categories":918},[127],{"categories":920},[127],{"categories":922},[140],{"categories":924},[925],"MLOps & Infrastructure",{"categories":927},[127],{"categories":929},[127],{"categories":931},[127],{"categories":933},[127],{"categories":935},[127],{"categories":937},[154],{"categories":939},[176],{"categories":941},[127],{"categories":943},[143],{"categories":945},[130],{"categories":947},[127],{"categories":949},[140],{"categories":951},[297],{"categories":953},[127],{"categories":955},[135],{"categories":957},[127],{"categories":959},[227],{"categories":961},[127],{"categories":963},[127],{"categories":965},[140],{"categories":967},[],{"categories":969},[],{"categories":971},[127],{"categories":973},[165],{"categories":975},[227],{"categories":977},[176],{"categories":979},[90],{"categories":981},[],{"categories":983},[127],{"categories":985},[127],{"categories":987},[135],{"categories":989},[140],{"categories":991},[127],{"categories":993},[127],{"categories":995},[127],{"categories":997},[127],{"categories":999},[176],{"categories":1001},[165],{"categories":1003},[127],{"categories":1005},[227],{"categories":1007},[127],{"categories":1009},[],{"categories":1011},[140],{"categories":1013},[154],{"categories":1015},[],{"categories":1017},[127],{"categories":1019},[127],{"categories":1021},[140],{"categories":1023},[154],{"categories":1025},[127],{"categories":1027},[90],{"categories":1029},[227],{"categories":1031},[],{"categories":1033},[127],{"categories":1035},[],{"categories":1037},[127],{"categories":1039},[],{"categories":1041},[127],{"categories":1043},[127],{"categories":1045},[143],{"categories":1047},[135],{"categories":1049},[140],{"categories":1051},[140],{"categories":1053},[],{"categories":1055},[127],{"categories":1057},[130],{"categories":1059},[127],{"categories":1061},[127],{"categories":1063},[135],{"categories":1065},[176],{"categories":1067},[130],{"categories":1069},[],{"categories":1071},[127],{"categories":1073},[],{"categories":1075},[127],{"categories":1077},[],{"categories":1079},[176],{"categories":1081},[176],{"categories":1083},[],{"categories":1085},[470],{"categories":1087},[127],{"categories":1089},[227],{"categories":1091},[154],{"categories":1093},[],{"categories":1095},[414],{"categories":1097},[140],{"categories":1099},[135],{"categories":1101},[],{"categories":1103},[],{"categories":1105},[130],{"categories":1107},[90],{"categories":1109},[],{"categories":1111},[256],{"categories":1113},[140],{"categories":1115},[135],{"categories":1117},[140],{"categories":1119},[127],{"categories":1121},[135],{"categories":1123},[127],{"categories":1125},[154],{"categories":1127},[],{"categories":1129},[165],{"categories":1131},[143],{"categories":1133},[127],{"categories":1135},[227],{"categories":1137},[154],{"categories":1139},[135],{"categories":1141},[127],{"categories":1143},[154],{"categories":1145},[127],{"categories":1147},[140],{"categories":1149},[135],{"categories":1151},[127],{"categories":1153},[127],{"categories":1155},[127],{"categories":1157},[127],{"categories":1159},[127],{"categories":1161},[],{"categories":1163},[],{"categories":1165},[154],{"categories":1167},[90],{"categories":1169},[143],{"categories":1171},[127],{"categories":1173},[140],{"categories":1175},[154],{"categories":1177},[154],{"categories":1179},[127],{"categories":1181},[],{"categories":1183},[176],{"categories":1185},[143],{"categories":1187},[143],{"categories":1189},[154],{"categories":1191},[127],{"categories":1193},[600],{"categories":1195},[297],{"categories":1197},[],{"categories":1199},[140],{"categories":1201},[127],{"categories":1203},[],{"categories":1205},[130],{"categories":1207},[],{"categories":1209},[127],{"categories":1211},[127],{"categories":1213},[127],{"categories":1215},[227],{"categories":1217},[256],{"categories":1219},[127],{"categories":1221},[154],{"categories":1223},[127],{"categories":1225},[140],{"categories":1227},[],{"categories":1229},[154],{"categories":1231},[127],{"categories":1233},[130],{"categories":1235},[],{"categories":1237},[135],{"categories":1239},[127],{"categories":1241},[127],{"categories":1243},[176],{"categories":1245},[127,297],{"categories":1247},[127],{"categories":1249},[1250],"Design Systems for AI",{"categories":1252},[127],{"categories":1254},[127],{"categories":1256},[176],{"categories":1258},[127],{"categories":1260},[127],{"categories":1262},[127],{"categories":1264},[135],{"categories":1266},[127],{"categories":1268},[127],{"categories":1270},[127],{"categories":1272},[],{"categories":1274},[127],{"categories":1276},[127],{"categories":1278},[135],{"categories":1280},[127],{"categories":1282},[],{"categories":1284},[140],{"categories":1286},[140],{"categories":1288},[154],{"categories":1290},[176],{"categories":1292},[154],{"categories":1294},[127],{"categories":1296},[227],{"categories":1298},[176],{"categories":1300},[90],{"categories":1302},[127],{"categories":1304},[127],{"categories":1306},[140],{"categories":1308},[130],{"categories":1310},[575],{"categories":1312},[127],{"categories":1314},[140],{"categories":1316},[127],{"categories":1318},[154],{"categories":1320},[154],{"categories":1322},[],{"categories":1324},[],{"categories":1326},[127],{"categories":1328},[140],{"categories":1330},[143],{"categories":1332},[],{"categories":1334},[135],{"categories":1336},[127],{"categories":1338},[],{"categories":1340},[227],{"categories":1342},[154],{"categories":1344},[140],{"categories":1346},[154],{"categories":1348},[227],{"categories":1350},[127],{"categories":1352},[127],{"categories":1354},[227],{"categories":1356},[],{"categories":1358},[],{"categories":1360},[176],{"categories":1362},[140],{"categories":1364},[140],{"categories":1366},[127],{"categories":1368},[127],{"categories":1370},[127],{"categories":1372},[127],{"categories":1374},[135],{"categories":1376},[127],{"categories":1378},[127],{"categories":1380},[],{"categories":1382},[154],{"categories":1384},[154],{"categories":1386},[127],{"categories":1388},[154],{"categories":1390},[135],{"categories":1392},[],{"categories":1394},[127],{"categories":1396},[127],{"categories":1398},[127],{"categories":1400},[127],{"categories":1402},[127],{"categories":1404},[140],{"categories":1406},[130],{"categories":1408},[135],{"categories":1410},[127],{"categories":1412},[140],{"categories":1414},[176],{"categories":1416},[140],{"categories":1418},[165],{"categories":1420},[256],{"categories":1422},[127],{"categories":1424},[140],{"categories":1426},[127],{"categories":1428},[127],{"categories":1430},[127],{"categories":1432},[],{"categories":1434},[227],{"categories":1436},[],{"categories":1438},[127],{"categories":1440},[127],{"categories":1442},[],{"categories":1444},[127],{"categories":1446},[154],{"categories":1448},[135],{"categories":1450},[1451],"Visual & Generative Media",{"categories":1453},[140],{"categories":1455},[],{"categories":1457},[127],{"categories":1459},[127],{"categories":1461},[154],{"categories":1463},[297],{"categories":1465},[127],{"categories":1467},[90],{"categories":1469},[575],{"categories":1471},[154],{"categories":1473},[256],{"categories":1475},[127],{"categories":1477},[227],{"categories":1479},[127],{"categories":1481},[127],{"categories":1483},[154],{"categories":1485},[140],{"categories":1487},[127],{"categories":1489},[],{"categories":1491},[],{"categories":1493},[140],{"categories":1495},[154],{"categories":1497},[130],{"categories":1499},[140],{"categories":1501},[529],{"categories":1503},[127],{"categories":1505},[143],{"categories":1507},[127],{"categories":1509},[135],{"categories":1511},[],{"categories":1513},[127],{"categories":1515},[143],{"categories":1517},[127],{"categories":1519},[127],{"categories":1521},[127],{"categories":1523},[143],{"categories":1525},[127],{"categories":1527},[127],{"categories":1529},[256],{"categories":1531},[127],{"categories":1533},[470],{"categories":1535},[127],{"categories":1537},[140],{"categories":1539},[127],{"categories":1541},[127],{"categories":1543},[127],{"categories":1545},[127],{"categories":1547},[227],{"categories":1549},[140],{"categories":1551},[],{"categories":1553},[140],{"categories":1555},[],{"categories":1557},[297],{"categories":1559},[154],{"categories":1561},[],{"categories":1563},[529],{"categories":1565},[127],{"categories":1567},[140],{"categories":1569},[140],{"categories":1571},[127],{"categories":1573},[227,127],{"categories":1575},[130],{"categories":1577},[127],{"categories":1579},[227],{"categories":1581},[],{"categories":1583},[127],{"categories":1585},[130],{"categories":1587},[127],{"categories":1589},[1590],"Medical Imaging & Radiology",{"categories":1592},[127],{"categories":1594},[127],{"categories":1596},[127],{"categories":1598},[227],{"categories":1600},[140],{"categories":1602},[154],{"categories":1604},[],{"categories":1606},[127],{"categories":1608},[127],{"categories":1610},[127],{"categories":1612},[],{"categories":1614},[],{"categories":1616},[127],{"categories":1618},[127],{"categories":1620},[470],{"categories":1622},[127],{"categories":1624},[130],{"categories":1626},[127],{"categories":1628},[127],{"categories":1630},[],{"categories":1632},[140],{"categories":1634},[127],{"categories":1636},[143],{"categories":1638},[154],{"categories":1640},[127],{"categories":1642},[140],{"categories":1644},[470],{"categories":1646},[127],{"categories":1648},[140],{"categories":1650},[127],{"categories":1652},[127],{"categories":1654},[127],{"categories":1656},[227],{"categories":1658},[140],{"categories":1660},[297],{"categories":1662},[227],{"categories":1664},[135],{"categories":1666},[140],{"categories":1668},[176],{"categories":1670},[127],{"categories":1672},[127],{"categories":1674},[143],{"categories":1676},[127],{"categories":1678},[127],{"categories":1680},[127],{"categories":1682},[127],{"categories":1684},[140],{"categories":1686},[127],{"categories":1688},[154],{"categories":1690},[154],{"categories":1692},[127],{"categories":1694},[143],{"categories":1696},[],{"categories":1698},[176],{"categories":1700},[],{"categories":1702},[143],{"categories":1704},[140],{"categories":1706},[127],{"categories":1708},[140],{"categories":1710},[1250],{"categories":1712},[1250],{"categories":1714},[227],{"categories":1716},[127],{"categories":1718},[127],{"categories":1720},[127],{"categories":1722},[140],{"categories":1724},[154],{"categories":1726},[227],{"categories":1728},[140],{"categories":1730},[176],{"categories":1732},[],{"categories":1734},[127],{"categories":1736},[],{"categories":1738},[127],{"categories":1740},[127],{"categories":1742},[127],{"categories":1744},[127],{"categories":1746},[140],{"categories":1748},[1749],"Contract Review & E-Discovery",{"categories":1751},[127],{"categories":1753},[227],{"categories":1755},[127],{"categories":1757},[130],{"categories":1759},[127],{"categories":1761},[176],{"categories":1763},[127],{"categories":1765},[127],{"categories":1767},[256],{"categories":1769},[154],{"categories":1771},[127],{"categories":1773},[127],{"categories":1775},[140],{"categories":1777},[140],{"categories":1779},[898],{"categories":1781},[127],{"categories":1783},[127],{"categories":1785},[140],{"categories":1787},[140],{"categories":1789},[127],{"categories":1791},[127],{"categories":1793},[127],{"categories":1795},[140],{"categories":1797},[127],{"categories":1799},[127],{"categories":1801},[470],{"categories":1803},[447],{"categories":1805},[127],{"categories":1807},[140],{"categories":1809},[127],{"categories":1811},[1812],"Law-Firm Practice & Adoption",{"categories":1814},[127],{"categories":1816},[140],{"categories":1818},[227],{"categories":1820},[127],{"categories":1822},[127],{"categories":1824},[127],{"categories":1826},[],{"categories":1828},[],{"categories":1830},[154],{"categories":1832},[127],{"categories":1834},[],{"categories":1836},[140],{"categories":1838},[130],{"categories":1840},[297],{"categories":1842},[127],{"categories":1844},[],{"categories":1846},[130],{"categories":1848},[135],{"categories":1850},[127],{"categories":1852},[256],{"categories":1854},[],{"categories":1856},[135],{"categories":1858},[140],{"categories":1860},[135],{"categories":1862},[],{"categories":1864},[127],{"categories":1866},[143],{"categories":1868},[127],{"categories":1870},[154],{"categories":1872},[],{"categories":1874},[],{"categories":1876},[],{"categories":1878},[],{"categories":1880},[127],{"categories":1882},[143],{"categories":1884},[140],{"categories":1886},[297],{"categories":1888},[127],{"categories":1890},[130],{"categories":1892},[154],{"categories":1894},[127],{"categories":1896},[127],{"categories":1898},[154],{"categories":1900},[143],{"categories":1902},[127],{"categories":1904},[127],{"categories":1906},[127],{"categories":1908},[925],{"categories":1910},[127],{"categories":1912},[154],{"categories":1914},[127],{"categories":1916},[256],{"categories":1918},[154],{"categories":1920},[135],{"categories":1922},[127],{"categories":1924},[127],{"categories":1926},[127],{"categories":1928},[227],{"categories":1930},[127],{"categories":1932},[127],{"categories":1934},[127],{"categories":1936},[127],{"categories":1938},[135],{"categories":1940},[140],{"categories":1942},[127,130],{"categories":1944},[470],{"categories":1946},[127],{"categories":1948},[127],{"categories":1950},[154],{"categories":1952},[154],{"categories":1954},[227],{"categories":1956},[140],{"categories":1958},[140],{"categories":1960},[154],{"categories":1962},[127],{"categories":1964},[127],{"categories":1966},[127],{"categories":1968},[],{"categories":1970},[],{"categories":1972},[127],{"categories":1974},[90],{"categories":1976},[127],{"categories":1978},[140],{"categories":1980},[],{"categories":1982},[127],{"categories":1984},[127],{"categories":1986},[154],{"categories":1988},[90],{"categories":1990},[176],{"categories":1992},[227],{"categories":1994},[127],{"categories":1996},[140],{"categories":1998},[127],{"categories":2000},[154],{"categories":2002},[],{"categories":2004},[140],{"categories":2006},[127],{"categories":2008},[127],{"categories":2010},[127],{"categories":2012},[127],{"categories":2014},[],{"categories":2016},[140],{"categories":2018},[127],{"categories":2020},[127],{"categories":2022},[127],{"categories":2024},[],{"categories":2026},[140],{"categories":2028},[127],{"categories":2030},[127],{"categories":2032},[135],{"categories":2034},[127],{"categories":2036},[127],{"categories":2038},[],{"categories":2040},[130],{"categories":2042},[127],{"categories":2044},[127],{"categories":2046},[127],{"categories":2048},[227],{"categories":2050},[127],{"categories":2052},[154],{"categories":2054},[127],{"categories":2056},[130],{"categories":2058},[127],{"categories":2060},[154],{"categories":2062},[256],{"categories":2064},[140],{"categories":2066},[140],{"categories":2068},[127],{"categories":2070},[127],{"categories":2072},[127,227],{"categories":2074},[127],{"categories":2076},[140],{"categories":2078},[176],{"categories":2080},[127],{"categories":2082},[176],{"categories":2084},[140],{"categories":2086},[227],{"categories":2088},[127],{"categories":2090},[],{"categories":2092},[154],{"categories":2094},[297],{"categories":2096},[227],{"categories":2098},[154],{"categories":2100},[127],{"categories":2102},[143],{"categories":2104},[127],{"categories":2106},[127],{"categories":2108},[140],{"categories":2110},[],{"categories":2112},[],{"categories":2114},[127],{"categories":2116},[],{"categories":2118},[],{"categories":2120},[143],{"categories":2122},[154],{"categories":2124},[127],{"categories":2126},[140],{"categories":2128},[140],{"categories":2130},[135],{"categories":2132},[140],{"categories":2134},[297],{"categories":2136},[127],{"categories":2138},[127],{"categories":2140},[127],{"categories":2142},[165],{"categories":2144},[127],{"categories":2146},[127],{"categories":2148},[127],{"categories":2150},[154],{"categories":2152},[140],{"categories":2154},[127],{"categories":2156},[127],{"categories":2158},[154],{"categories":2160},[414],{"categories":2162},[140],{"categories":2164},[898],{"categories":2166},[],{"categories":2168},[227],{"categories":2170},[1812],{"categories":2172},[154],{"categories":2174},[],{"categories":2176},[],{"categories":2178},[127],{"categories":2180},[140],{"categories":2182},[],{"categories":2184},[],{"categories":2186},[127],{"categories":2188},[256],{"categories":2190},[127],{"categories":2192},[256],{"categories":2194},[140],{"categories":2196},[127],{"categories":2198},[127],{"categories":2200},[154],{"categories":2202},[143],{"categories":2204},[],{"categories":2206},[127],{"categories":2208},[127],{"categories":2210},[154],{"categories":2212},[1749],{"categories":2214},[227],{"categories":2216},[227],{"categories":2218},[127],{"categories":2220},[140],{"categories":2222},[130],{"categories":2224},[127],{"categories":2226},[127],{"categories":2228},[127],{"categories":2230},[127],{"categories":2232},[227],{"categories":2234},[227],{"categories":2236},[140],{"categories":2238},[140],{"categories":2240},[140],{"categories":2242},[127],{"categories":2244},[127],{"categories":2246},[],{"categories":2248},[127],{"categories":2250},[],{"categories":2252},[2253],"Interaction & Product Design",{"categories":2255},[127],{"categories":2257},[140],{"categories":2259},[154],{"categories":2261},[326],{"categories":2263},[176],{"categories":2265},[154],{"categories":2267},[127],{"categories":2269},[127],{"categories":2271},[127],{"categories":2273},[154],{"categories":2275},[127],{"categories":2277},[130],{"categories":2279},[140],{"categories":2281},[127],{"categories":2283},[],{"categories":2285},[140],{"categories":2287},[140],{"categories":2289},[140],{"categories":2291},[],{"categories":2293},[154],{"categories":2295},[127],{"categories":2297},[140],{"categories":2299},[130],{"categories":2301},[2253],{"categories":2303},[127],{"categories":2305},[130],{"categories":2307},[130],{"categories":2309},[],{"categories":2311},[140],{"categories":2313},[154],{"categories":2315},[],{"categories":2317},[140],{"categories":2319},[176],{"categories":2321},[127],{"categories":2323},[140],{"categories":2325},[127],{"categories":2327},[140],{"categories":2329},[140],{"categories":2331},[127],{"categories":2333},[127],{"categories":2335},[176],{"categories":2337},[90],{"categories":2339},[127],{"categories":2341},[143],{"categories":2343},[154],{"categories":2345},[2346],"Coding Agents & Dev Productivity",{"categories":2348},[176],{"categories":2350},[227],{"categories":2352},[127],{"categories":2354},[127],{"categories":2356},[],{"categories":2358},[127],{"categories":2360},[898],{"categories":2362},[],{"categories":2364},[127],{"categories":2366},[127],{"categories":2368},[297],{"categories":2370},[127],{"categories":2372},[176],{"categories":2374},[],{"categories":2376},[],{"categories":2378},[127],{"categories":2380},[],{"categories":2382},[140],{"categories":2384},[127],{"categories":2386},[],{"categories":2388},[154],{"categories":2390},[154],{"categories":2392},[127],{"categories":2394},[90],{"categories":2396},[],{"categories":2398},[127],{"categories":2400},[127],{"categories":2402},[127],{"categories":2404},[90],{"categories":2406},[154],{"categories":2408},[140],{"categories":2410},[],{"categories":2412},[],{"categories":2414},[127],{"categories":2416},[127],{"categories":2418},[140],{"categories":2420},[140],{"categories":2422},[391],{"categories":2424},[154],{"categories":2426},[154],{"categories":2428},[140],{"categories":2430},[176],{"categories":2432},[176],{"categories":2434},[140],{"categories":2436},[140],{"categories":2438},[127],{"categories":2440},[130],{"categories":2442},[2253],{"categories":2444},[143],{"categories":2446},[127,297],{"categories":2448},[90],{"categories":2450},[],{"categories":2452},[227],{"categories":2454},[140],{"categories":2456},[154],{"categories":2458},[130],{"categories":2460},[127],{"categories":2462},[140],{"categories":2464},[2465],"The Designer's Role & Craft",{"categories":2467},[227],{"categories":2469},[],{"categories":2471},[140],{"categories":2473},[127],{"categories":2475},[140],{"categories":2477},[140],{"categories":2479},[127],{"categories":2481},[256],{"categories":2483},[127],{"categories":2485},[154],{"categories":2487},[127],{"categories":2489},[227],{"categories":2491},[127],{"categories":2493},[],{"categories":2495},[140],{"categories":2497},[227],{"categories":2499},[143],{"categories":2501},[127],{"categories":2503},[127],{"categories":2505},[127],{"categories":2507},[2508],"AI UX Patterns",{"categories":2510},[140],{"categories":2512},[140],{"categories":2514},[140],{"categories":2516},[140],{"categories":2518},[256],{"categories":2520},[90],{"categories":2522},[127],{"categories":2524},[140],{"categories":2526},[127],{"categories":2528},[1250],{"categories":2530},[],{"categories":2532},[256],{"categories":2534},[140],{"categories":2536},[176],{"categories":2538},[154],{"categories":2540},[127],{"categories":2542},[140],{"categories":2544},[],{"categories":2546},[],{"categories":2548},[127],{"categories":2550},[127],{"categories":2552},[140],{"categories":2554},[127],{"categories":2556},[140],{"categories":2558},[391],{"categories":2560},[227],{"categories":2562},[127],{"categories":2564},[176],{"categories":2566},[154],{"categories":2568},[127],{"categories":2570},[140],{"categories":2572},[140],{"categories":2574},[],{"categories":2576},[127],{"categories":2578},[],{"categories":2580},[127],{"categories":2582},[],{"categories":2584},[127],{"categories":2586},[127],{"categories":2588},[127],{"categories":2590},[140],{"categories":2592},[154],{"categories":2594},[],{"categories":2596},[],{"categories":2598},[90],{"categories":2600},[165],{"categories":2602},[127],{"categories":2604},[127],{"categories":2606},[127],{"categories":2608},[90],{"categories":2610},[127],{"categories":2612},[127],{"categories":2614},[176],{"categories":2616},[127],{"categories":2618},[127],{"categories":2620},[127],{"categories":2622},[140],{"categories":2624},[127],{"categories":2626},[140],{"categories":2628},[127],{"categories":2630},[127],{"categories":2632},[127],{"categories":2634},[140],{"categories":2636},[],{"categories":2638},[127],{"categories":2640},[],{"categories":2642},[127],{"categories":2644},[127],{"categories":2646},[297],{"categories":2648},[127],{"categories":2650},[],{"categories":2652},[],{"categories":2654},[227],{"categories":2656},[925],{"categories":2658},[140],{"categories":2660},[130],{"categories":2662},[2465],{"categories":2664},[],{"categories":2666},[],{"categories":2668},[127],{"categories":2670},[],{"categories":2672},[],{"categories":2674},[154],{"categories":2676},[176],{"categories":2678},[256],{"categories":2680},[140],{"categories":2682},[135],{"categories":2684},[127],{"categories":2686},[127],{"categories":2688},[135],{"categories":2690},[],{"categories":2692},[227],{"categories":2694},[143],{"categories":2696},[127],{"categories":2698},[127],{"categories":2700},[140],{"categories":2702},[135],{"categories":2704},[127],{"categories":2706},[127],{"categories":2708},[130],{"categories":2710},[127],{"categories":2712},[127],{"categories":2714},[],{"categories":2716},[130],{"categories":2718},[127],{"categories":2720},[256],{"categories":2722},[140],{"categories":2724},[176],{"categories":2726},[127],{"categories":2728},[154],{"categories":2730},[127],{"categories":2732},[127],{"categories":2734},[135],{"categories":2736},[127],{"categories":2738},[127],{"categories":2740},[127],{"categories":2742},[140],{"categories":2744},[127],{"categories":2746},[],{"categories":2748},[127],{"categories":2750},[154],{"categories":2752},[130],{"categories":2754},[127],{"categories":2756},[127],{"categories":2758},[127],{"categories":2760},[],{"categories":2762},[127],{"categories":2764},[470],{"categories":2766},[140],{"categories":2768},[135],{"categories":2770},[176],{"categories":2772},[127],{"categories":2774},[127],{"categories":2776},[],{"categories":2778},[135],{"categories":2780},[135],{"categories":2782},[127],{"categories":2784},[127],{"categories":2786},[143],{"categories":2788},[127],{"categories":2790},[127],{"categories":2792},[127],{"categories":2794},[127],{"categories":2796},[154],{"categories":2798},[154],{"categories":2800},[127],{"categories":2802},[],{"categories":2804},[154],{"categories":2806},[127],{"categories":2808},[154],{"categories":2810},[140],{"categories":2812},[575],{"categories":2814},[],{"categories":2816},[],{"categories":2818},[127],{"categories":2820},[176],{"categories":2822},[],{"categories":2824},[297],{"categories":2826},[127],{"categories":2828},[127],{"categories":2830},[127],{"categories":2832},[227],{"categories":2834},[887],{"categories":2836},[],{"categories":2838},[127],{"categories":2840},[127],{"categories":2842},[127],{"categories":2844},[154],{"categories":2846},[127],{"categories":2848},[127],{"categories":2850},[127,297],{"categories":2852},[127],{"categories":2854},[127],{"categories":2856},[227],{"categories":2858},[140],{"categories":2860},[],{"categories":2862},[140],{"categories":2864},[140],{"categories":2866},[127],{"categories":2868},[127],{"categories":2870},[127],{"categories":2872},[127],{"categories":2874},[90],{"categories":2876},[127],{"categories":2878},[2508],{"categories":2880},[130],{"categories":2882},[90],{"categories":2884},[130],{"categories":2886},[154],{"categories":2888},[227],{"categories":2890},[140],{"categories":2892},[127],{"categories":2894},[],{"categories":2896},[135],{"categories":2898},[127],{"categories":2900},[127],{"categories":2902},[176],{"categories":2904},[127],{"categories":2906},[127],{"categories":2908},[127],{"categories":2910},[140],{"categories":2912},[127],{"categories":2914},[127],{"categories":2916},[127],{"categories":2918},[135],{"categories":2920},[],{"categories":2922},[297],{"categories":2924},[127],{"categories":2926},[391],{"categories":2928},[227],{"categories":2930},[227],{"categories":2932},[154],{"categories":2934},[140],{"categories":2936},[127],{"categories":2938},[135],{"categories":2940},[176],{"categories":2942},[127],{"categories":2944},[127],{"categories":2946},[127],{"categories":2948},[227],{"categories":2950},[140],{"categories":2952},[140],{"categories":2954},[127],{"categories":2956},[127],{"categories":2958},[529],{"categories":2960},[140],{"categories":2962},[],{"categories":2964},[127],{"categories":2966},[127],{"categories":2968},[127],{"categories":2970},[],{"categories":2972},[],{"categories":2974},[127],{"categories":2976},[127],{"categories":2978},[140],{"categories":2980},[127],{"categories":2982},[127],{"categories":2984},[127],{"categories":2986},[154],{"categories":2988},[127],{"categories":2990},[127],{"categories":2992},[140],{"categories":2994},[127],{"categories":2996},[127],{"categories":2998},[127],{"categories":3000},[127],{"categories":3002},[127],{"categories":3004},[],{"categories":3006},[154],{"categories":3008},[90],{"categories":3010},[127],{"categories":3012},[140],{"categories":3014},[140],{"categories":3016},[127],{"categories":3018},[127],{"categories":3020},[],{"categories":3022},[],{"categories":3024},[127],{"categories":3026},[127],{"categories":3028},[127],{"categories":3030},[176],{"categories":3032},[90],{"categories":3034},[],{"categories":3036},[127],{"categories":3038},[227],{"categories":3040},[127],{"categories":3042},[297],{"categories":3044},[1812],{"categories":3046},[176],{"categories":3048},[154],{"categories":3050},[127],{"categories":3052},[154],{"categories":3054},[154],{"categories":3056},[127],{"categories":3058},[127],{"categories":3060},[154],{"categories":3062},[176],{"categories":3064},[176],{"categories":3066},[297],{"categories":3068},[140],{"categories":3070},[],{"categories":3072},[176],{"categories":3074},[127],{"categories":3076},[140],{"categories":3078},[130],{"categories":3080},[154],{"categories":3082},[127],{"categories":3084},[176],{"categories":3086},[],{"categories":3088},[127],{"categories":3090},[154],{"categories":3092},[154],{"categories":3094},[90],{"categories":3096},[127],{"categories":3098},[176],{"categories":3100},[127],{"categories":3102},[154],{"categories":3104},[140],{"categories":3106},[140],{"categories":3108},[176],{"categories":3110},[140],{"categories":3112},[297],{"categories":3114},[140],{"categories":3116},[127],{"categories":3118},[127],{"categories":3120},[127],{"categories":3122},[127],{"categories":3124},[154],{"categories":3126},[127],{"categories":3128},[],{"categories":3130},[140],{"categories":3132},[135],{"categories":3134},[154],{"categories":3136},[],{"categories":3138},[],{"categories":3140},[127],{"categories":3142},[140],{"categories":3144},[127],{"categories":3146},[127],{"categories":3148},[3149],"Frameworks & Tooling",{"categories":3151},[127],{"categories":3153},[127],{"categories":3155},[154],{"categories":3157},[127],{"categories":3159},[127],{"categories":3161},[],{"categories":3163},[90],{"categories":3165},[90],{"categories":3167},[130],{"categories":3169},[127],{"categories":3171},[140],{"categories":3173},[127],{"categories":3175},[227],{"categories":3177},[],{"categories":3179},[1812],{"categories":3181},[127],{"categories":3183},[154],{"categories":3185},[127],{"categories":3187},[297],{"categories":3189},[297],{"categories":3191},[],{"categories":3193},[140],{"categories":3195},[140],{"categories":3197},[127],{"categories":3199},[127],{"categories":3201},[176],{"categories":3203},[140],{"categories":3205},[176],{"categories":3207},[127],{"categories":3209},[140],{"categories":3211},[],{"categories":3213},[227],{"categories":3215},[127],{"categories":3217},[127],{"categories":3219},[],{"categories":3221},[127],{"categories":3223},[140],{"categories":3225},[127],{"categories":3227},[127],{"categories":3229},[127],{"categories":3231},[],{"categories":3233},[154],{"categories":3235},[127],{"categories":3237},[154],{"categories":3239},[297],{"categories":3241},[127],{"categories":3243},[127],{"categories":3245},[127],{"categories":3247},[154],{"categories":3249},[135],{"categories":3251},[127],{"categories":3253},[1812],{"categories":3255},[],{"categories":3257},[140],{"categories":3259},[130],{"categories":3261},[127],{"categories":3263},[130],{"categories":3265},[127],{"categories":3267},[],{"categories":3269},[140],{"categories":3271},[127],{"categories":3273},[127],{"categories":3275},[3276],"AI Design Tooling",{"categories":3278},[227],{"categories":3280},[127],{"categories":3282},[127],{"categories":3284},[154],{"categories":3286},[227],{"categories":3288},[127],{"categories":3290},[127],{"categories":3292},[154],{"categories":3294},[176],{"categories":3296},[143],{"categories":3298},[154],{"categories":3300},[127],{"categories":3302},[127],{"categories":3304},[127],{"categories":3306},[140],{"categories":3308},[127],{"categories":3310},[],{"categories":3312},[140],{"categories":3314},[127],{"categories":3316},[127],{"categories":3318},[140],{"categories":3320},[127],{"categories":3322},[127],{"categories":3324},[127],{"categories":3326},[140],{"categories":3328},[],{"categories":3330},[140],{"categories":3332},[3149],{"categories":3334},[127],{"categories":3336},[127],{"categories":3338},[140],{"categories":3340},[140],{"categories":3342},[154],{"categories":3344},[154],{"categories":3346},[127],{"categories":3348},[],{"categories":3350},[154],{"categories":3352},[127],{"categories":3354},[127],{"categories":3356},[140],{"categories":3358},[135],{"categories":3360},[127],{"categories":3362},[],{"categories":3364},[127],{"categories":3366},[127],{"categories":3368},[2253],{"categories":3370},[],{"categories":3372},[127],{"categories":3374},[127],{"categories":3376},[127],{"categories":3378},[127],{"categories":3380},[227],{"categories":3382},[127],{"categories":3384},[],{"categories":3386},[127],{"categories":3388},[127],{"categories":3390},[127],{"categories":3392},[127],{"categories":3394},[256],{"categories":3396},[176],{"categories":3398},[127],{"categories":3400},[127],{"categories":3402},[1812],{"categories":3404},[130],{"categories":3406},[127],{"categories":3408},[127],{"categories":3410},[90],{"categories":3412},[127],{"categories":3414},[127],{"categories":3416},[176],{"categories":3418},[140],{"categories":3420},[],{"categories":3422},[127],{"categories":3424},[127],{"categories":3426},[227],{"categories":3428},[127],{"categories":3430},[256],{"categories":3432},[140],{"categories":3434},[127],{"categories":3436},[140],{"categories":3438},[],{"categories":3440},[],{"categories":3442},[],{"categories":3444},[130],{"categories":3446},[176],{"categories":3448},[140],{"categories":3450},[127],{"categories":3452},[127],{"categories":3454},[127],{"categories":3456},[127],{"categories":3458},[414],{"categories":3460},[227],{"categories":3462},[140],{"categories":3464},[127],{"categories":3466},[],{"categories":3468},[140],{"categories":3470},[140],{"categories":3472},[],{"categories":3474},[127],{"categories":3476},[140],{"categories":3478},[127],{"categories":3480},[],{"categories":3482},[127],{"categories":3484},[127],{"categories":3486},[127],{"categories":3488},[176],{"categories":3490},[227],{"categories":3492},[140],{"categories":3494},[227],{"categories":3496},[140],{"categories":3498},[127],{"categories":3500},[135],{"categories":3502},[],{"categories":3504},[],{"categories":3506},[127],{"categories":3508},[127],{"categories":3510},[127],{"categories":3512},[130],{"categories":3514},[140],{"categories":3516},[176],{"categories":3518},[],{"categories":3520},[227],{"categories":3522},[],{"categories":3524},[154],{"categories":3526},[127],{"categories":3528},[154],{"categories":3530},[227],{"categories":3532},[154],{"categories":3534},[127],{"categories":3536},[],{"categories":3538},[127],{"categories":3540},[127],{"categories":3542},[],{"categories":3544},[127],{"categories":3546},[127],{"categories":3548},[256],{"categories":3550},[127],{"categories":3552},[127],{"categories":3554},[297],{"categories":3556},[154],{"categories":3558},[127],{"categories":3560},[],{"categories":3562},[140],{"categories":3564},[127],{"categories":3566},[130],{"categories":3568},[529],{"categories":3570},[127],{"categories":3572},[127],{"categories":3574},[140],{"categories":3576},[127],{"categories":3578},[140],{"categories":3580},[127],{"categories":3582},[127],{"categories":3584},[127],{"categories":3586},[127],{"categories":3588},[],{"categories":3590},[127],{"categories":3592},[130],{"categories":3594},[127],{"categories":3596},[135],{"categories":3598},[154],{"categories":3600},[227],{"categories":3602},[],{"categories":3604},[127],{"categories":3606},[],{"categories":3608},[140],{"categories":3610},[127],{"categories":3612},[],{"categories":3614},[140],{"categories":3616},[127],{"categories":3618},[154],{"categories":3620},[227],{"categories":3622},[176],{"categories":3624},[127],{"categories":3626},[176],{"categories":3628},[140],{"categories":3630},[227],{"categories":3632},[127],{"categories":3634},[],{"categories":3636},[127],{"categories":3638},[165],{"categories":3640},[140],{"categories":3642},[127],{"categories":3644},[227],{"categories":3646},[176],{"categories":3648},[135],{"categories":3650},[154],{"categories":3652},[127],{"categories":3654},[127],{"categories":3656},[127],{"categories":3658},[127],{"categories":3660},[176],{"categories":3662},[256],{"categories":3664},[],{"categories":3666},[],{"categories":3668},[90],{"categories":3670},[470],{"categories":3672},[127],{"categories":3674},[140],{"categories":3676},[127,154],{"categories":3678},[176],{"categories":3680},[127],{"categories":3682},[127],{"categories":3684},[127],{"categories":3686},[127],{"categories":3688},[127],{"categories":3690},[127],{"categories":3692},[127],{"categories":3694},[140],{"categories":3696},[127],{"categories":3698},[140],{"categories":3700},[127],{"categories":3702},[127],{"categories":3704},[127],{"categories":3706},[],{"categories":3708},[127],{"categories":3710},[1250],{"categories":3712},[154],{"categories":3714},[227],{"categories":3716},[127],{"categories":3718},[127],{"categories":3720},[127],{"categories":3722},[90],{"categories":3724},[140],{"categories":3726},[256],{"categories":3728},[297],{"categories":3730},[],{"categories":3732},[154],{"categories":3734},[127],{"categories":3736},[135],{"categories":3738},[140],{"categories":3740},[130],{"categories":3742},[140],{"categories":3744},[127],{"categories":3746},[140],{"categories":3748},[140],{"categories":3750},[143],{"categories":3752},[154],{"categories":3754},[127],{"categories":3756},[127],{"categories":3758},[],{"categories":3760},[],{"categories":3762},[],{"categories":3764},[297],{"categories":3766},[127],{"categories":3768},[176],{"categories":3770},[127],{"categories":3772},[127],{"categories":3774},[127],{"categories":3776},[127],{"categories":3778},[],{"categories":3780},[127],{"categories":3782},[90],{"categories":3784},[135],{"categories":3786},[140],{"categories":3788},[127],{"categories":3790},[],{"categories":3792},[127],{"categories":3794},[140],{"categories":3796},[127],{"categories":3798},[297],{"categories":3800},[],{"categories":3802},[227],{"categories":3804},[227],{"categories":3806},[127],{"categories":3808},[140],{"categories":3810},[],{"categories":3812},[154],{"categories":3814},[127],{"categories":3816},[227],{"categories":3818},[127],{"categories":3820},[135],{"categories":3822},[140],{"categories":3824},[127],{"categories":3826},[],{"categories":3828},[176],{"categories":3830},[127],{"categories":3832},[127],{"categories":3834},[127],{"categories":3836},[227],{"categories":3838},[140],{"categories":3840},[176],{"categories":3842},[],{"categories":3844},[140],{"categories":3846},[135],{"categories":3848},[140],{"categories":3850},[227],{"categories":3852},[127],{"categories":3854},[127],{"categories":3856},[127],{"categories":3858},[470],{"categories":3860},[127],{"categories":3862},[140],{"categories":3864},[],{"categories":3866},[127],{"categories":3868},[127],{"categories":3870},[297],{"categories":3872},[176],{"categories":3874},[90],{"categories":3876},[575],{"categories":3878},[90],{"categories":3880},[90],{"categories":3882},[127],{"categories":3884},[],{"categories":3886},[],{"categories":3888},[],{"categories":3890},[140],{"categories":3892},[127],{"categories":3894},[140],{"categories":3896},[140],{"categories":3898},[154],{"categories":3900},[127],{"categories":3902},[447],{"categories":3904},[154],{"categories":3906},[140],{"categories":3908},[127],{"categories":3910},[127],{"categories":3912},[127],{"categories":3914},[127],{"categories":3916},[127],{"categories":3918},[140],{"categories":3920},[127],{"categories":3922},[],{"categories":3924},[],{"categories":3926},[127],{"categories":3928},[],{"categories":3930},[127],{"categories":3932},[140],{"categories":3934},[227],{"categories":3936},[127],{"categories":3938},[127],{"categories":3940},[],{"categories":3942},[140],{"categories":3944},[127],{"categories":3946},[127],{"categories":3948},[143],{"categories":3950},[127],{"categories":3952},[227],{"categories":3954},[127],{"categories":3956},[140],{"categories":3958},[135],{"categories":3960},[127],{"categories":3962},[127],{"categories":3964},[256],{"categories":3966},[140],{"categories":3968},[127],{"categories":3970},[127],{"categories":3972},[887],{"categories":3974},[127],{"categories":3976},[140],{"categories":3978},[127],{"categories":3980},[154],{"categories":3982},[127],{"categories":3984},[529],{"categories":3986},[227],{"categories":3988},[],{"categories":3990},[127],{"categories":3992},[127],{"categories":3994},[176],{"categories":3996},[470],{"categories":3998},[140],{"categories":4000},[127],{"categories":4002},[],{"categories":4004},[176],{"categories":4006},[391],{"categories":4008},[140],{"categories":4010},[140],{"categories":4012},[140],{"categories":4014},[127],{"categories":4016},[127],{"categories":4018},[140],{"categories":4020},[],{"categories":4022},[135],{"categories":4024},[127],{"categories":4026},[135],{"categories":4028},[140],{"categories":4030},[],{"categories":4032},[154],{"categories":4034},[127],{"categories":4036},[127],{"categories":4038},[130],{"categories":4040},[127],{"categories":4042},[176],{"categories":4044},[297],{"categories":4046},[165],{"categories":4048},[140],{"categories":4050},[140],{"categories":4052},[127],{"categories":4054},[127],{"categories":4056},[140],{"categories":4058},[127],{"categories":4060},[130],{"categories":4062},[],{"categories":4064},[140],{"categories":4066},[127],{"categories":4068},[127],{"categories":4070},[127],{"categories":4072},[140],{"categories":4074},[127],{"categories":4076},[],{"categories":4078},[127],{"categories":4080},[],{"categories":4082},[227],{"categories":4084},[140],{"categories":4086},[127,135],{"categories":4088},[140],{"categories":4090},[127],{"categories":4092},[],{"categories":4094},[130],{"categories":4096},[90],{"categories":4098},[135],{"categories":4100},[127],{"categories":4102},[154],{"categories":4104},[127],{"categories":4106},[127],{"categories":4108},[140],{"categories":4110},[127],{"categories":4112},[127],{"categories":4114},[127],{"categories":4116},[176],{"categories":4118},[1250],{"categories":4120},[140],{"categories":4122},[127],{"categories":4124},[],{"categories":4126},[],{"categories":4128},[127],{"categories":4130},[140],{"categories":4132},[127],{"categories":4134},[127],{"categories":4136},[297],{"categories":4138},[],{"categories":4140},[127],{"categories":4142},[140],{"categories":4144},[165],{"categories":4146},[140],{"categories":4148},[470],{"categories":4150},[],{"categories":4152},[414],{"categories":4154},[140],{"categories":4156},[127],{"categories":4158},[127],{"categories":4160},[256],{"categories":4162},[140],{"categories":4164},[127],{"categories":4166},[90],{"categories":4168},[143],{"categories":4170},[140],{"categories":4172},[127],{"categories":4174},[470],{"categories":4176},[127],{"categories":4178},[297],{"categories":4180},[135],{"categories":4182},[],{"categories":4184},[127],{"categories":4186},[127],{"categories":4188},[256],{"categories":4190},[227],{"categories":4192},[127],{"categories":4194},[127],{"categories":4196},[127],{"categories":4198},[],{"categories":4200},[256],{"categories":4202},[176],{"categories":4204},[127],{"categories":4206},[127],{"categories":4208},[127],{"categories":4210},[575],{"categories":4212},[130],{"categories":4214},[127],{"categories":4216},[143],{"categories":4218},[127],{"categories":4220},[],{"categories":4222},[],{"categories":4224},[227],{"categories":4226},[127],{"categories":4228},[90],{"categories":4230},[256],{"categories":4232},[140],{"categories":4234},[127],{"categories":4236},[127],{"categories":4238},[256],{"categories":4240},[176],{"categories":4242},[127],{"categories":4244},[],{"categories":4246},[127],{"categories":4248},[127],{"categories":4250},[],{"categories":4252},[127],{"categories":4254},[127],{"categories":4256},[600],{"categories":4258},[127],{"categories":4260},[127],{"categories":4262},[140],{"categories":4264},[154],{"categories":4266},[470],{"categories":4268},[127],{"categories":4270},[127],{"categories":4272},[127],{"categories":4274},[],{"categories":4276},[127,154],{"categories":4278},[176],{"categories":4280},[140],{"categories":4282},[154],{"categories":4284},[140],{"categories":4286},[925],{"categories":4288},[154],{"categories":4290},[154],{"categories":4292},[140],{"categories":4294},[127],{"categories":4296},[130],{"categories":4298},[],{"categories":4300},[],{"categories":4302},[140],{"categories":4304},[127],{"categories":4306},[154],{"categories":4308},[127],{"categories":4310},[130],{"categories":4312},[154],{"categories":4314},[154],{"categories":4316},[127],{"categories":4318},[256],{"categories":4320},[127],{"categories":4322},[154],{"categories":4324},[127],{"categories":4326},[],{"categories":4328},[127],{"categories":4330},[127],{"categories":4332},[227,127],{"categories":4334},[297],{"categories":4336},[130],{"categories":4338},[127],{"categories":4340},[],{"categories":4342},[127],{"categories":4344},[127],{"categories":4346},[135],{"categories":4348},[127],{"categories":4350},[135],{"categories":4352},[127],{"categories":4354},[127],{"categories":4356},[391],{"categories":4358},[127],{"categories":4360},[135],{"categories":4362},[154],{"categories":4364},[90],{"categories":4366},[140],{"categories":4368},[127],{"categories":4370},[154],{"categories":4372},[127],{"categories":4374},[127],{"categories":4376},[176],{"categories":4378},[256],{"categories":4380},[227],{"categories":4382},[127],{"categories":4384},[127],{"categories":4386},[127],{"categories":4388},[127],{"categories":4390},[130],{"categories":4392},[127],{"categories":4394},[140],{"categories":4396},[140],{"categories":4398},[154],{"categories":4400},[176],{"categories":4402},[154],{"categories":4404},[154],{"categories":4406},[127],{"categories":4408},[127],{"categories":4410},[],{"categories":4412},[],{"categories":4414},[90],{"categories":4416},[127],{"categories":4418},[154],{"categories":4420},[127],{"categories":4422},[227],{"categories":4424},[470],{"categories":4426},[414],{"categories":4428},[391],{"categories":4430},[127],{"categories":4432},[127],{"categories":4434},[127],{"categories":4436},[90],{"categories":4438},[127],{"categories":4440},[127],{"categories":4442},[127],{"categories":4444},[127],{"categories":4446},[127],{"categories":4448},[127],{"categories":4450},[127],{"categories":4452},[140],{"categories":4454},[130],{"categories":4456},[140],{"categories":4458},[127,135],{"categories":4460},[],{"categories":4462},[227],{"categories":4464},[],{"categories":4466},[143],{"categories":4468},[127],{"categories":4470},[176],{"categories":4472},[130],{"categories":4474},[127],{"categories":4476},[130],{"categories":4478},[140],{"categories":4480},[90],{"categories":4482},[140],{"categories":4484},[143],{"categories":4486},[140],{"categories":4488},[127],{"categories":4490},[127],{"categories":4492},[135],{"categories":4494},[140],{"categories":4496},[154],{"categories":4498},[256],{"categories":4500},[127],{"categories":4502},[127],{"categories":4504},[],{"categories":4506},[176],{"categories":4508},[127],{"categories":4510},[127],{"categories":4512},[127],{"categories":4514},[127],{"categories":4516},[127],{"categories":4518},[127],{"categories":4520},[154],{"categories":4522},[176],{"categories":4524},[154],{"categories":4526},[154],{"categories":4528},[127],{"categories":4530},[127],{"categories":4532},[127],{"categories":4534},[127],{"categories":4536},[414],{"categories":4538},[127],{"categories":4540},[140],{"categories":4542},[176],{"categories":4544},[127],{"categories":4546},[127],{"categories":4548},[127],{"categories":4550},[140],{"categories":4552},[127],{"categories":4554},[127],{"categories":4556},[127],{"categories":4558},[3149],{"categories":4560},[4561],"Clinical AI",{"categories":4563},[227],{"categories":4565},[127],{"categories":4567},[127],{"categories":4569},[127],{"categories":4571},[127],{"categories":4573},[297],{"categories":4575},[2508],{"categories":4577},[127],{"categories":4579},[143],{"categories":4581},[227],{"categories":4583},[127],{"categories":4585},[140],{"categories":4587},[127],{"categories":4589},[127],{"categories":4591},[176],{"categories":4593},[127],{"categories":4595},[140],{"categories":4597},[154],{"categories":4599},[256],{"categories":4601},[127],{"categories":4603},[127],{"categories":4605},[135],{"categories":4607},[127],{"categories":4609},[127],{"categories":4611},[529],{"categories":4613},[127],{"categories":4615},[],{"categories":4617},[140],{"categories":4619},[127],{"categories":4621},[154],{"categories":4623},[130],{"categories":4625},[127],{"categories":4627},[],{"categories":4629},[],{"categories":4631},[127],{"categories":4633},[],{"categories":4635},[135],{"categories":4637},[127],{"categories":4639},[127],{"categories":4641},[140],{"categories":4643},[127],{"categories":4645},[176],{"categories":4647},[176],{"categories":4649},[176],{"categories":4651},[176],{"categories":4653},[],{"categories":4655},[130],{"categories":4657},[140],{"categories":4659},[176],{"categories":4661},[127],{"categories":4663},[600],{"categories":4665},[143],{"categories":4667},[140],{"categories":4669},[127],{"categories":4671},[130],{"categories":4673},[127],{"categories":4675},[140],{"categories":4677},[127],{"categories":4679},[127],{"categories":4681},[127],{"categories":4683},[127,140],{"categories":4685},[140],{"categories":4687},[297],{"categories":4689},[176],{"categories":4691},[140],{"categories":4693},[176],{"categories":4695},[140],{"categories":4697},[127],{"categories":4699},[],{"categories":4701},[176],{"categories":4703},[256],{"categories":4705},[130],{"categories":4707},[127],{"categories":4709},[127],{"categories":4711},[],{"categories":4713},[154],{"categories":4715},[],{"categories":4717},[130],{"categories":4719},[140],{"categories":4721},[176],{"categories":4723},[127],{"categories":4725},[176],{"categories":4727},[130],{"categories":4729},[176],{"categories":4731},[176],{"categories":4733},[],{"categories":4735},[135],{"categories":4737},[140],{"categories":4739},[176],{"categories":4741},[176],{"categories":4743},[176],{"categories":4745},[176],{"categories":4747},[176],{"categories":4749},[176],{"categories":4751},[176],{"categories":4753},[176],{"categories":4755},[176],{"categories":4757},[176],{"categories":4759},[90],{"categories":4761},[130],{"categories":4763},[127],{"categories":4765},[127],{"categories":4767},[140],{"categories":4769},[140],{"categories":4771},[],{"categories":4773},[127],{"categories":4775},[127,130],{"categories":4777},[],{"categories":4779},[140],{"categories":4781},[127],{"categories":4783},[176],{"categories":4785},[140],{"categories":4787},[925],{"categories":4789},[127],{"categories":4791},[127],{"categories":4793},[127],{"categories":4795},[127],{"categories":4797},[127],{"categories":4799},[391],{"categories":4801},[127],{"categories":4803},[127],{"categories":4805},[140],{"categories":4807},[127],{"categories":4809},[127],{"categories":4811},[135],{"categories":4813},[143],{"categories":4815},[140],{"categories":4817},[140],{"categories":4819},[],{"categories":4821},[140],{"categories":4823},[227],{"categories":4825},[176],{"categories":4827},[127],{"categories":4829},[],{"categories":4831},[143],{"categories":4833},[],{"categories":4835},[154],{"categories":4837},[127],{"categories":4839},[140],{"categories":4841},[227],{"categories":4843},[127],{"categories":4845},[],{"categories":4847},[127],{"categories":4849},[127],{"categories":4851},[],{"categories":4853},[256],{"categories":4855},[127],{"categories":4857},[140],{"categories":4859},[],{"categories":4861},[],{"categories":4863},[176],{"categories":4865},[130],{"categories":4867},[127],{"categories":4869},[127],{"categories":4871},[135],{"categories":4873},[127],{"categories":4875},[127],{"categories":4877},[140],{"categories":4879},[127],{"categories":4881},[135],{"categories":4883},[135],{"categories":4885},[227],{"categories":4887},[],{"categories":4889},[127],{"categories":4891},[176],{"categories":4893},[],{"categories":4895},[127],{"categories":4897},[127],{"categories":4899},[227],{"categories":4901},[127],{"categories":4903},[127],{"categories":4905},[256],{"categories":4907},[127],{"categories":4909},[297],{"categories":4911},[],{"categories":4913},[140],{"categories":4915},[127],{"categories":4917},[256],{"categories":4919},[154],{"categories":4921},[],{"categories":4923},[127],{"categories":4925},[],{"categories":4927},[140],{"categories":4929},[227],{"categories":4931},[154],{"categories":4933},[],{"categories":4935},[3149],{"categories":4937},[135],{"categories":4939},[130],{"categories":4941},[127],{"categories":4943},[90],{"categories":4945},[140],{"categories":4947},[227],{"categories":4949},[127],{"categories":4951},[154],{"categories":4953},[],{"categories":4955},[],{"categories":4957},[127],{"categories":4959},[130],{"categories":4961},[127],{"categories":4963},[256],{"categories":4965},[],{"categories":4967},[140],{"categories":4969},[140],{"categories":4971},[127],{"categories":4973},[140],{"categories":4975},[127],{"categories":4977},[176],{"categories":4979},[154],{"categories":4981},[127],{"categories":4983},[140],{"categories":4985},[143],{"categories":4987},[127],{"categories":4989},[127],{"categories":4991},[127],{"categories":4993},[140],{"categories":4995},[127],{"categories":4997},[143],{"categories":4999},[256],{"categories":5001},[176],{"categories":5003},[],{"categories":5005},[256],{"categories":5007},[127],{"categories":5009},[],{"categories":5011},[154],{"categories":5013},[140],{"categories":5015},[],{"categories":5017},[127],{"categories":5019},[127],{"categories":5021},[127],{"categories":5023},[127],{"categories":5025},[127],{"categories":5027},[140],{"categories":5029},[135],{"categories":5031},[130],{"categories":5033},[140],{"categories":5035},[127],{"categories":5037},[227],{"categories":5039},[154],{"categories":5041},[154],{"categories":5043},[127],{"categories":5045},[90],{"categories":5047},[140],{"categories":5049},[127],{"categories":5051},[127],{"categories":5053},[140],{"categories":5055},[127],{"categories":5057},[127],{"categories":5059},[140],{"categories":5061},[135],{"categories":5063},[127],{"categories":5065},[227],{"categories":5067},[154],{"categories":5069},[140],{"categories":5071},[127],{"categories":5073},[143],{"categories":5075},[127],{"categories":5077},[140],{"categories":5079},[127],{"categories":5081},[127],{"categories":5083},[176],{"categories":5085},[127],{"categories":5087},[],{"categories":5089},[130],{"categories":5091},[127],{"categories":5093},[127],{"categories":5095},[127],{"categories":5097},[154],{"categories":5099},[154],{"categories":5101},[127],{"categories":5103},[154],{"categories":5105},[127],{"categories":5107},[140],{"categories":5109},[127],{"categories":5111},[127],{"categories":5113},[127],{"categories":5115},[127],{"categories":5117},[127],{"categories":5119},[],{"categories":5121},[127],{"categories":5123},[227],{"categories":5125},[140],{"categories":5127},[135],{"categories":5129},[176],{"categories":5131},[127],{"categories":5133},[140],{"categories":5135},[127],{"categories":5137},[140],{"categories":5139},[127],{"categories":5141},[127],{"categories":5143},[227],{"categories":5145},[140],{"categories":5147},[127],{"categories":5149},[256],{"categories":5151},[127],{"categories":5153},[90],{"categories":5155},[127],{"categories":5157},[127],{"categories":5159},[176],{"categories":5161},[127],{"categories":5163},[127],{"categories":5165},[127],{"categories":5167},[127],{"categories":5169},[140],{"categories":5171},[297],{"categories":5173},[127],{"categories":5175},[154],{"categories":5177},[140],{"categories":5179},[90],{"categories":5181},[],{"categories":5183},[140],{"categories":5185},[154],{"categories":5187},[127],{"categories":5189},[127],{"categories":5191},[2346],{"categories":5193},[227],{"categories":5195},[326],{"categories":5197},[127],{"categories":5199},[127],{"categories":5201},[127],{"categories":5203},[127],{"categories":5205},[130],{"categories":5207},[127],{"categories":5209},[127],{"categories":5211},[154],{"categories":5213},[135],{"categories":5215},[127],{"categories":5217},[154],{"categories":5219},[127],{"categories":5221},[],{"categories":5223},[140],{"categories":5225},[140],{"categories":5227},[127],{"categories":5229},[127],{"categories":5231},[127],{"categories":5233},[90],{"categories":5235},[],{"categories":5237},[176],{"categories":5239},[],{"categories":5241},[176],{"categories":5243},[127],{"categories":5245},[127],{"categories":5247},[140],{"categories":5249},[127],{"categories":5251},[140],{"categories":5253},[140],{"categories":5255},[],{"categories":5257},[127],{"categories":5259},[176],{"categories":5261},[127],{"categories":5263},[],{"categories":5265},[127],{"categories":5267},[127],{"categories":5269},[],{"categories":5271},[127],{"categories":5273},[127],{"categories":5275},[227],{"categories":5277},[154],{"categories":5279},[140],{"categories":5281},[127],{"categories":5283},[127],{"categories":5285},[127],{"categories":5287},[127],{"categories":5289},[256],{"categories":5291},[127],{"categories":5293},[127],{"categories":5295},[127],{"categories":5297},[130],{"categories":5299},[127],{"categories":5301},[127],{"categories":5303},[],{"categories":5305},[127],{"categories":5307},[127],{"categories":5309},[127],{"categories":5311},[],{"categories":5313},[130],{"categories":5315},[127],{"categories":5317},[127],{"categories":5319},[176],{"categories":5321},[154],{"categories":5323},[143],{"categories":5325},[140],{"categories":5327},[470],{"categories":5329},[127],{"categories":5331},[127],{"categories":5333},[127],{"categories":5335},[154],{"categories":5337},[176],{"categories":5339},[227],{"categories":5341},[127],{"categories":5343},[127],{"categories":5345},[127],{"categories":5347},[127],{"categories":5349},[176],{"categories":5351},[127],{"categories":5353},[227],{"categories":5355},[127],{"categories":5357},[127],{"categories":5359},[176],{"categories":5361},[227],{"categories":5363},[127],{"categories":5365},[176],{"categories":5367},[127],{"categories":5369},[140],{"categories":5371},[140],{"categories":5373},[140],{"categories":5375},[154],{"categories":5377},[176],{"categories":5379},[140],{"categories":5381},[140],{"categories":5383},[127],{"categories":5385},[154],{"categories":5387},[227],{"categories":5389},[127],{"categories":5391},[127],{"categories":5393},[140],{"categories":5395},[127],{"categories":5397},[],{"categories":5399},[140],{"categories":5401},[],{"categories":5403},[127],{"categories":5405},[127],{"categories":5407},[],{"categories":5409},[],{"categories":5411},[140],{"categories":5413},[135],{"categories":5415},[140],{"categories":5417},[5418],"Liability & Ethics",{"categories":5420},[127],{"categories":5422},[127],{"categories":5424},[127],{"categories":5426},[140],{"categories":5428},[130],{"categories":5430},[140],{"categories":5432},[135],{"categories":5434},[256],{"categories":5436},[140],{"categories":5438},[127],{"categories":5440},[127],{"categories":5442},[],{"categories":5444},[575],{"categories":5446},[140],{"categories":5448},[],{"categories":5450},[127],{"categories":5452},[130],{"categories":5454},[140],{"categories":5456},[],{"categories":5458},[140],{"categories":5460},[127],{"categories":5462},[127],{"categories":5464},[154],{"categories":5466},[127],{"categories":5468},[176],{"categories":5470},[127],{"categories":5472},[127],{"categories":5474},[143],{"categories":5476},[140],{"categories":5478},[127],{"categories":5480},[127],{"categories":5482},[127],{"categories":5484},[176],{"categories":5486},[140],{"categories":5488},[154],{"categories":5490},[227],{"categories":5492},[130],{"categories":5494},[127],{"categories":5496},[127],{"categories":5498},[127],{"categories":5500},[],{"categories":5502},[140],{"categories":5504},[140],{"categories":5506},[140],{"categories":5508},[470],{"categories":5510},[227],{"categories":5512},[140],{"categories":5514},[297],{"categories":5516},[154],{"categories":5518},[176],{"categories":5520},[127],{"categories":5522},[227],{"categories":5524},[127],{"categories":5526},[130],{"categories":5528},[],{"categories":5530},[140],{"categories":5532},[127],{"categories":5534},[127],{"categories":5536},[127],{"categories":5538},[127],{"categories":5540},[140],{"categories":5542},[127],{"categories":5544},[127],{"categories":5546},[227],{"categories":5548},[],{"categories":5550},[140],{"categories":5552},[143],{"categories":5554},[176],{"categories":5556},[140],{"categories":5558},[135],{"categories":5560},[],{"categories":5562},[127],{"categories":5564},[127],{"categories":5566},[143],{"categories":5568},[127],{"categories":5570},[140],{"categories":5572},[176],{"categories":5574},[130],{"categories":5576},[297],{"categories":5578},[127],{"categories":5580},[127],{"categories":5582},[127],{"categories":5584},[176],{"categories":5586},[135],{"categories":5588},[127],{"categories":5590},[227],{"categories":5592},[176],{"categories":5594},[297],{"categories":5596},[127],{"categories":5598},[140],{"categories":5600},[],{"categories":5602},[529],{"categories":5604},[],{"categories":5606},[127],{"categories":5608},[297],{"categories":5610},[127],{"categories":5612},[90],{"categories":5614},[127],{"categories":5616},[140],{"categories":5618},[140],{"categories":5620},[5621],"Design News & Tools",{"categories":5623},[127],{"categories":5625},[127],{"categories":5627},[176],{"categories":5629},[127],{"categories":5631},[127],{"categories":5633},[130],{"categories":5635},[140],{"categories":5637},[127],{"categories":5639},[227],{"categories":5641},[140],{"categories":5643},[140],{"categories":5645},[227],{"categories":5647},[127],{"categories":5649},[127],{"categories":5651},[470],{"categories":5653},[140],{"categories":5655},[127],{"categories":5657},[127],{"categories":5659},[470],{"categories":5661},[127],{"categories":5663},[256],{"categories":5665},[127],{"categories":5667},[140],{"categories":5669},[],{"categories":5671},[127],{"categories":5673},[127],{"categories":5675},[127],{"categories":5677},[176],{"categories":5679},[127],{"categories":5681},[130],{"categories":5683},[],{"categories":5685},[127],{"categories":5687},[127],{"categories":5689},[127],{"categories":5691},[154],{"categories":5693},[600],{"categories":5695},[154],{"categories":5697},[227],{"categories":5699},[127],{"categories":5701},[127,140],{"categories":5703},[256,135],{"categories":5705},[154],{"categories":5707},[127],{"categories":5709},[127],{"categories":5711},[127],{"categories":5713},[127],{"categories":5715},[],{"categories":5717},[140],{"categories":5719},[127],{"categories":5721},[],{"categories":5723},[127],{"categories":5725},[154],{"categories":5727},[127],{"categories":5729},[154],{"categories":5731},[],{"categories":5733},[140],{"categories":5735},[127],{"categories":5737},[135],{"categories":5739},[127],{"categories":5741},[176],{"categories":5743},[127],{"categories":5745},[],{"categories":5747},[140],{"categories":5749},[127],{"categories":5751},[],{"categories":5753},[227],{"categories":5755},[127],{"categories":5757},[127],{"categories":5759},[140],{"categories":5761},[127],{"categories":5763},[127],{"categories":5765},[130],{"categories":5767},[140],{"categories":5769},[127],{"categories":5771},[],{"categories":5773},[127],{"categories":5775},[297],{"categories":5777},[256],{"categories":5779},[135],{"categories":5781},[135],{"categories":5783},[127],{"categories":5785},[130],{"categories":5787},[130],{"categories":5789},[127],{"categories":5791},[140],{"categories":5793},[127],{"categories":5795},[127],{"categories":5797},[127],{"categories":5799},[127],{"categories":5801},[154],{"categories":5803},[127],{"categories":5805},[130],{"categories":5807},[127],{"categories":5809},[127],{"categories":5811},[140],{"categories":5813},[127],{"categories":5815},[256],{"categories":5817},[127],{"categories":5819},[176],{"categories":5821},[127],{"categories":5823},[127],{"categories":5825},[140],{"categories":5827},[143],{"categories":5829},[127],{"categories":5831},[127],{"categories":5833},[140],{"categories":5835},[],{"categories":5837},[154],{"categories":5839},[],{"categories":5841},[154],{"categories":5843},[140],{"categories":5845},[130],{"categories":5847},[127],{"categories":5849},[],{"categories":5851},[90],{"categories":5853},[297],{"categories":5855},[127],{"categories":5857},[154],{"categories":5859},[127],{"categories":5861},[],{"categories":5863},[176],{"categories":5865},[140],{"categories":5867},[154],{"categories":5869},[227],{"categories":5871},[135],{"categories":5873},[127],{"categories":5875},[127],{"categories":5877},[140],{"categories":5879},[154],{"categories":5881},[140],{"categories":5883},[176],{"categories":5885},[127],{"categories":5887},[143],{"categories":5889},[130],{"categories":5891},[143],{"categories":5893},[176],{"categories":5895},[127],{"categories":5897},[154],{"categories":5899},[127],{"categories":5901},[227],{"categories":5903},[135],{"categories":5905},[127],{"categories":5907},[127],{"categories":5909},[127],{"categories":5911},[127],{"categories":5913},[127],{"categories":5915},[127],{"categories":5917},[140],{"categories":5919},[127],{"categories":5921},[140],{"categories":5923},[127],{"categories":5925},[127],{"categories":5927},[130],{"categories":5929},[127],{"categories":5931},[140],{"categories":5933},[140],{"categories":5935},[227],{"categories":5937},[140],{"categories":5939},[140],{"categories":5941},[127],{"categories":5943},[130],{"categories":5945},[140],{"categories":5947},[227],{"categories":5949},[],{"categories":5951},[127],{"categories":5953},[90],{"categories":5955},[470],{"categories":5957},[127],{"categories":5959},[140],{"categories":5961},[127],{"categories":5963},[127],{"categories":5965},[154],{"categories":5967},[127],{"categories":5969},[],{"categories":5971},[127],{"categories":5973},[140],{"categories":5975},[127],{"categories":5977},[256],{"categories":5979},[127],{"categories":5981},[154],{"categories":5983},[127],{"categories":5985},[176],{"categories":5987},[140],{"categories":5989},[127],{"categories":5991},[256],{"categories":5993},[140],{"categories":5995},[135],{"categories":5997},[135],{"categories":5999},[127],{"categories":6001},[127],{"categories":6003},[127],{"categories":6005},[127],{"categories":6007},[127],{"categories":6009},[127],{"categories":6011},[130],{"categories":6013},[],{"categories":6015},[127],{"categories":6017},[127],{"categories":6019},[140],{"categories":6021},[140],{"categories":6023},[127],{"categories":6025},[127],{"categories":6027},[127],{"categories":6029},[127],{"categories":6031},[127],{"categories":6033},[154],{"categories":6035},[],{"categories":6037},[130],{"categories":6039},[127],{"categories":6041},[127],{"categories":6043},[140],{"categories":6045},[140],{"categories":6047},[],{"categories":6049},[154],{"categories":6051},[154],{"categories":6053},[127],{"categories":6055},[256],{"categories":6057},[135],{"categories":6059},[227],{"categories":6061},[],{"categories":6063},[127],{"categories":6065},[140],{"categories":6067},[130],{"categories":6069},[127],{"categories":6071},[127],{"categories":6073},[154],{"categories":6075},[130],{"categories":6077},[127],{"categories":6079},[127],{"categories":6081},[176],{"categories":6083},[90],{"categories":6085},[127],{"categories":6087},[176],{"categories":6089},[140],{"categories":6091},[127],{"categories":6093},[],{"categories":6095},[176],{"categories":6097},[140],{"categories":6099},[227],{"categories":6101},[90],{"categories":6103},[127],{"categories":6105},[127],{"categories":6107},[],{"categories":6109},[140],{"categories":6111},[140],{"categories":6113},[140],{"categories":6115},[3149],{"categories":6117},[176],{"categories":6119},[127],{"categories":6121},[154],{"categories":6123},[127],{"categories":6125},[127],{"categories":6127},[127],{"categories":6129},[127],{"categories":6131},[127],{"categories":6133},[135],{"categories":6135},[127],{"categories":6137},[130],{"categories":6139},[1812],{"categories":6141},[297],{"categories":6143},[130],{"categories":6145},[],{"categories":6147},[127],{"categories":6149},[],{"categories":6151},[176],{"categories":6153},[140],{"categories":6155},[227],{"categories":6157},[127],{"categories":6159},[127],{"categories":6161},[127],{"categories":6163},[176],{"categories":6165},[],{"categories":6167},[140],{"categories":6169},[127],{"categories":6171},[140],{"categories":6173},[140],{"categories":6175},[],{"categories":6177},[127],{"categories":6179},[],{"categories":6181},[176],{"categories":6183},[130],{"categories":6185},[227],{"categories":6187},[127],{"categories":6189},[140],{"categories":6191},[176],{"categories":6193},[127],{"categories":6195},[176],{"categories":6197},[],{"categories":6199},[176],{"categories":6201},[127],{"categories":6203},[130],{"categories":6205},[470],{"categories":6207},[140],{"categories":6209},[127],{"categories":6211},[],{"categories":6213},[154],{"categories":6215},[140],{"categories":6217},[143],{"categories":6219},[140],{"categories":6221},[130],{"categories":6223},[127],{"categories":6225},[127],{"categories":6227},[],{"categories":6229},[],{"categories":6231},[],{"categories":6233},[227],{"categories":6235},[127],{"categories":6237},[140],{"categories":6239},[127],{"categories":6241},[127],{"categories":6243},[],{"categories":6245},[],{"categories":6247},[],{"categories":6249},[127],{"categories":6251},[140],{"categories":6253},[227],{"categories":6255},[127],{"categories":6257},[],{"categories":6259},[140],{"categories":6261},[127],{"categories":6263},[127],{"categories":6265},[130],{"categories":6267},[],{"categories":6269},[],{"categories":6271},[127],{"categories":6273},[127],{"categories":6275},[140],{"categories":6277},[227],{"categories":6279},[127],{"categories":6281},[176],{"categories":6283},[],{"categories":6285},[127],{"categories":6287},[127],{"categories":6289},[256],{"categories":6291},[176],{"categories":6293},[256],{"categories":6295},[90],{"categories":6297},[127],{"categories":6299},[127],{"categories":6301},[],{"categories":6303},[],{"categories":6305},[140],{"categories":6307},[],{"categories":6309},[127],{"categories":6311},[470],{"categories":6313},[127],{"categories":6315},[127],{"categories":6317},[127],{"categories":6319},[127],{"categories":6321},[],{"categories":6323},[140],{"categories":6325},[127],{"categories":6327},[127],{"categories":6329},[],{"categories":6331},[140],{"categories":6333},[127],{"categories":6335},[176],{"categories":6337},[127],{"categories":6339},[256],{"categories":6341},[135],{"categories":6343},[143],{"categories":6345},[127],{"categories":6347},[127],{"categories":6349},[140],{"categories":6351},[90],{"categories":6353},[140],{"categories":6355},[140],{"categories":6357},[],{"categories":6359},[127],{"categories":6361},[140],{"categories":6363},[],{"categories":6365},[127],{"categories":6367},[],{"categories":6369},[176],{"categories":6371},[135],{"categories":6373},[],{"categories":6375},[127],{"categories":6377},[127],{"categories":6379},[127],{"categories":6381},[],{"categories":6383},[140],{"categories":6385},[227],{"categories":6387},[130],{"categories":6389},[127],{"categories":6391},[],{"categories":6393},[135],{"categories":6395},[256],{"categories":6397},[127],{"categories":6399},[154],{"categories":6401},[130],{"categories":6403},[90],{"categories":6405},[135],{"categories":6407},[154],{"categories":6409},[140],{"categories":6411},[154],{"categories":6413},[],{"categories":6415},[127],{"categories":6417},[143],{"categories":6419},[127],{"categories":6421},[],{"categories":6423},[140],{"categories":6425},[130],{"categories":6427},[227],{"categories":6429},[127],{"categories":6431},[130],{"categories":6433},[140],{"categories":6435},[297],{"categories":6437},[127],{"categories":6439},[127],{"categories":6441},[127],{"categories":6443},[127],{"categories":6445},[127],{"categories":6447},[130],{"categories":6449},[127],{"categories":6451},[154],{"categories":6453},[90],{"categories":6455},[140],{"categories":6457},[],{"categories":6459},[127],{"categories":6461},[127],{"categories":6463},[127],{"categories":6465},[154],{"categories":6467},[140],{"categories":6469},[176],{"categories":6471},[154],{"categories":6473},[127],{"categories":6475},[143],{"categories":6477},[],{"categories":6479},[227],{"categories":6481},[154],{"categories":6483},[176],{"categories":6485},[127],{"categories":6487},[130],{"categories":6489},[140],{"categories":6491},[127],{"categories":6493},[127],{"categories":6495},[140],{"categories":6497},[143],{"categories":6499},[127],{"categories":6501},[140],{"categories":6503},[127],{"categories":6505},[135],{"categories":6507},[140],{"categories":6509},[140,297],{"categories":6511},[127],{"categories":6513},[127],{"categories":6515},[140],{"categories":6517},[154],{"categories":6519},[127],{"categories":6521},[127],{"categories":6523},[90],{"categories":6525},[140],{"categories":6527},[256],{"categories":6529},[140],{"categories":6531},[135],{"categories":6533},[],{"categories":6535},[140],{"categories":6537},[127],{"categories":6539},[135],{"categories":6541},[],{"categories":6543},[],{"categories":6545},[154],{"categories":6547},[127],{"categories":6549},[127],{"categories":6551},[140],{"categories":6553},[90],{"categories":6555},[256],{"categories":6557},[127],{"categories":6559},[127],{"categories":6561},[127],{"categories":6563},[140],{"categories":6565},[],{"categories":6567},[140],{"categories":6569},[176],{"categories":6571},[127],{"categories":6573},[140],{"categories":6575},[140],{"categories":6577},[127],{"categories":6579},[],{"categories":6581},[176],{"categories":6583},[154],{"categories":6585},[3149],{"categories":6587},[130],{"categories":6589},[154],{"categories":6591},[127],{"categories":6593},[140],{"categories":6595},[127],{"categories":6597},[127],{"categories":6599},[256],{"categories":6601},[154],{"categories":6603},[90],{"categories":6605},[],{"categories":6607},[176],{"categories":6609},[127],{"categories":6611},[127],{"categories":6613},[],{"categories":6615},[140],{"categories":6617},[127],{"categories":6619},[127],{"categories":6621},[127],{"categories":6623},[127],{"categories":6625},[140],{"categories":6627},[127],{"categories":6629},[127],{"categories":6631},[127],{"categories":6633},[143],{"categories":6635},[127],{"categories":6637},[140],{"categories":6639},[127],{"categories":6641},[127],{"categories":6643},[127],{"categories":6645},[127],{"categories":6647},[127],{"categories":6649},[127],{"categories":6651},[127],{"categories":6653},[135],{"categories":6655},[],{"categories":6657},[143],{"categories":6659},[176],{"categories":6661},[140],{"categories":6663},[127],{"categories":6665},[154],{"categories":6667},[],{"categories":6669},[154],{"categories":6671},[154],{"categories":6673},[140],{"categories":6675},[154],{"categories":6677},[127],{"categories":6679},[127],{"categories":6681},[127],{"categories":6683},[140],{"categories":6685},[154],{"categories":6687},[127],{"categories":6689},[127],{"categories":6691},[127],{"categories":6693},[140],{"categories":6695},[176],{"categories":6697},[127],{"categories":6699},[127],{"categories":6701},[127],{"categories":6703},[135],{"categories":6705},[127],{"categories":6707},[140],{"categories":6709},[227],{"categories":6711},[],{"categories":6713},[127],{"categories":6715},[90],{"categories":6717},[140],{"categories":6719},[127],{"categories":6721},[127],{"categories":6723},[],{"categories":6725},[127],{"categories":6727},[127],{"categories":6729},[176],{"categories":6731},[127],{"categories":6733},[127],{"categories":6735},[140],{"categories":6737},[256],{"categories":6739},[],{"categories":6741},[],{"categories":6743},[154],{"categories":6745},[127],{"categories":6747},[127],{"categories":6749},[176],{"categories":6751},[127],{"categories":6753},[154],{"categories":6755},[176],{"categories":6757},[127],{"categories":6759},[127],{"categories":6761},[256],{"categories":6763},[90],{"categories":6765},[127],{"categories":6767},[127],{"categories":6769},[130],{"categories":6771},[140],{"categories":6773},[127],{"categories":6775},[127],{"categories":6777},[140],{"categories":6779},[135],{"categories":6781},[140],{"categories":6783},[154],{"categories":6785},[127],{"categories":6787},[135],{"categories":6789},[],{"categories":6791},[127],{"categories":6793},[90],{"categories":6795},[127],{"categories":6797},[127],{"categories":6799},[],{"categories":6801},[176],{"categories":6803},[127],{"categories":6805},[140],{"categories":6807},[90],{"categories":6809},[127],{"categories":6811},[154],{"categories":6813},[154],{"categories":6815},[154],{"categories":6817},[127],{"categories":6819},[140],{"categories":6821},[140],{"categories":6823},[127],{"categories":6825},[140],{"categories":6827},[127],{"categories":6829},[127],{"categories":6831},[227],{"categories":6833},[90],{"categories":6835},[90],{"categories":6837},[],{"categories":6839},[176],{"categories":6841},[127],{"categories":6843},[127],{"categories":6845},[154],{"categories":6847},[],{"categories":6849},[176],{"categories":6851},[176],{"categories":6853},[176],{"categories":6855},[],{"categories":6857},[140],{"categories":6859},[127],{"categories":6861},[],{"categories":6863},[130],{"categories":6865},[135],{"categories":6867},[],{"categories":6869},[127],{"categories":6871},[127],{"categories":6873},[],{"categories":6875},[154],{"categories":6877},[],{"categories":6879},[],{"categories":6881},[],{"categories":6883},[],{"categories":6885},[127],{"categories":6887},[176],{"categories":6889},[],{"categories":6891},[],{"categories":6893},[127],{"categories":6895},[127],{"categories":6897},[127],{"categories":6899},[90],{"categories":6901},[127],{"categories":6903},[90],{"categories":6905},[],{"categories":6907},[90],{"categories":6909},[90],{"categories":6911},[297],{"categories":6913},[140],{"categories":6915},[154],{"categories":6917},[],{"categories":6919},[],{"categories":6921},[90],{"categories":6923},[154],{"categories":6925},[154],{"categories":6927},[154],{"categories":6929},[],{"categories":6931},[130],{"categories":6933},[154],{"categories":6935},[154],{"categories":6937},[130],{"categories":6939},[154],{"categories":6941},[135],{"categories":6943},[154],{"categories":6945},[154],{"categories":6947},[154],{"categories":6949},[90],{"categories":6951},[176],{"categories":6953},[176],{"categories":6955},[127],{"categories":6957},[154],{"categories":6959},[90],{"categories":6961},[297],{"categories":6963},[90],{"categories":6965},[90],{"categories":6967},[90],{"categories":6969},[],{"categories":6971},[135],{"categories":6973},[],{"categories":6975},[297],{"categories":6977},[154],{"categories":6979},[154],{"categories":6981},[154],{"categories":6983},[140],{"categories":6985},[176,135],{"categories":6987},[90],{"categories":6989},[],{"categories":6991},[],{"categories":6993},[90],{"categories":6995},[],{"categories":6997},[90],{"categories":6999},[176],{"categories":7001},[140],{"categories":7003},[],{"categories":7005},[154],{"categories":7007},[127],{"categories":7009},[227],{"categories":7011},[],{"categories":7013},[127],{"categories":7015},[],{"categories":7017},[176],{"categories":7019},[130],{"categories":7021},[90],{"categories":7023},[],{"categories":7025},[154],{"categories":7027},[176],[7029,7324,7485,7556],{"id":7030,"title":7031,"ai":7032,"body":7038,"categories":7299,"created_at":91,"date_modified":91,"description":84,"extension":92,"faq":91,"featured":93,"kicker_label":91,"meta":7300,"navigation":106,"path":7310,"published_at":7311,"question":91,"scraped_at":7312,"seo":7313,"sitemap":7314,"source_id":7315,"source_name":7316,"source_type":114,"source_url":7317,"stem":7318,"tags":7319,"thumbnail_url":91,"tldr":7321,"tweet":91,"unknown_tags":7322,"__hash__":7323},"summaries\u002Fsummaries\u002Fe3a7d313e4f27d00-momentum-dampens-gd-zigzags-via-gradient-averaging-summary.md","Momentum Dampens GD Zigzags via Gradient Averaging",{"provider":7,"model":7033,"input_tokens":7034,"output_tokens":7035,"processing_time_ms":7036,"cost_usd":7037},"x-ai\u002Fgrok-4.1-fast",8869,1948,36530,0.0027253,{"type":14,"value":7039,"toc":7294},[7040,7044,7061,7064,7117,7120,7124,7130,7138,7141,7193,7196,7200,7207,7283,7290],[17,7041,7043],{"id":7042},"anisotropic-surfaces-force-gd-zigzags","Anisotropic Surfaces Force GD Zigzags",[22,7045,7046,7047,7051,7052,7056,7057,7060],{},"Real-world loss surfaces often have uneven curvature—flat in one direction (e.g., 0.05 x²) and steep in another (e.g., 5 y²)—yielding a Hessian with eigenvalues 0.1 and 10 (condition number 100). Gradients are ",[7048,7049,7050],"span",{},"0.1x, 10y",". With learning rate lr=0.18 (near stability limit 2\u002Fλ_max=0.2), steep direction factor |1-10",[7053,7054,7055],"em",{},"0.18|=0.8 causes 20% overshoot per step (oscillations), while flat direction |1-0.1","0.18|=0.982 advances just 1.8% (near-stagnation). Starting at ",[7048,7058,7059],{},"-4,1.5",", vanilla GD: θ ← θ - lr ∇L(θ) zigzags slowly, hitting loss\u003C0.001 in 185 steps (final loss 1.5e-5 after 300 steps).",[22,7062,7063],{},"Implement as:",[7065,7066,7069],"pre",{"className":7067,"code":7068,"language":119,"meta":84,"style":84},"language-python shiki shiki-themes github-light github-dark","def grad(x, y): return np.array([0.1 * x, 10 * y])\ndef gradient_descent(start, lr, steps=300):\n    path = [np.array(start, dtype=float)]\n    pos = np.array(start, dtype=float)\n    for _ in range(steps):\n        pos = pos - lr * grad(*pos)\n        path.append(pos.copy())\n    return np.array(path)\n",[56,7070,7071,7078,7083,7088,7093,7099,7105,7111],{"__ignoreMap":84},[7048,7072,7075],{"class":7073,"line":7074},"line",1,[7048,7076,7077],{},"def grad(x, y): return np.array([0.1 * x, 10 * y])\n",[7048,7079,7080],{"class":7073,"line":85},[7048,7081,7082],{},"def gradient_descent(start, lr, steps=300):\n",[7048,7084,7085],{"class":7073,"line":102},[7048,7086,7087],{},"    path = [np.array(start, dtype=float)]\n",[7048,7089,7090],{"class":7073,"line":103},[7048,7091,7092],{},"    pos = np.array(start, dtype=float)\n",[7048,7094,7096],{"class":7073,"line":7095},5,[7048,7097,7098],{},"    for _ in range(steps):\n",[7048,7100,7102],{"class":7073,"line":7101},6,[7048,7103,7104],{},"        pos = pos - lr * grad(*pos)\n",[7048,7106,7108],{"class":7073,"line":7107},7,[7048,7109,7110],{},"        path.append(pos.copy())\n",[7048,7112,7114],{"class":7073,"line":7113},8,[7048,7115,7116],{},"    return np.array(path)\n",[22,7118,7119],{},"High lr speeds flat progress but oscillates steep; low lr stabilizes but crawls flat—core GD trade-off.",[17,7121,7123],{"id":7122},"momentum-velocity-cancels-oscillations-builds-speed","Momentum Velocity Cancels Oscillations, Builds Speed",[22,7125,7126,7127,7129],{},"Momentum tracks velocity v (exponential moving average of gradients): v ← β v + (1-β) ∇L(θ); θ ← θ - lr v. Consistent gradients (flat direction) accumulate for larger steps; opposing gradients (steep oscillations) cancel, damping zigzags. From ",[7048,7128,7059],{}," with lr=0.18:",[26,7131,7132,7135],{},[29,7133,7134],{},"β=0.9: smooth path, loss\u003C0.001 in 159 steps (final 1e-6).",[29,7136,7137],{},"β=0.99: excessive accumulation overshoots, final loss 0.487 (circles minimum).",[22,7139,7140],{},"Code:",[7065,7142,7144],{"className":7067,"code":7143,"language":119,"meta":84,"style":84},"def momentum_gd(start, lr, beta, steps=300):\n    path = [np.array(start, dtype=float)]\n    pos = np.array(start, dtype=float)\n    v = np.zeros(2)\n    for _ in range(steps):\n        g = grad(*pos)\n        v = beta * v + (1 - beta) * g\n        pos = pos - lr * v\n        path.append(pos.copy())\n    return np.array(path)\n",[56,7145,7146,7151,7155,7159,7164,7168,7173,7178,7183,7188],{"__ignoreMap":84},[7048,7147,7148],{"class":7073,"line":7074},[7048,7149,7150],{},"def momentum_gd(start, lr, beta, steps=300):\n",[7048,7152,7153],{"class":7073,"line":85},[7048,7154,7087],{},[7048,7156,7157],{"class":7073,"line":102},[7048,7158,7092],{},[7048,7160,7161],{"class":7073,"line":103},[7048,7162,7163],{},"    v = np.zeros(2)\n",[7048,7165,7166],{"class":7073,"line":7095},[7048,7167,7098],{},[7048,7169,7170],{"class":7073,"line":7101},[7048,7171,7172],{},"        g = grad(*pos)\n",[7048,7174,7175],{"class":7073,"line":7107},[7048,7176,7177],{},"        v = beta * v + (1 - beta) * g\n",[7048,7179,7180],{"class":7073,"line":7113},[7048,7181,7182],{},"        pos = pos - lr * v\n",[7048,7184,7186],{"class":7073,"line":7185},9,[7048,7187,7110],{},[7048,7189,7191],{"class":7073,"line":7190},10,[7048,7192,7116],{},[22,7194,7195],{},"β weights history: β→0 mimics GD; β=0.9 balances smoothing\u002Fspeed; β→1 risks divergence.",[17,7197,7199],{"id":7198},"β-tuning-via-convergence-sweep","β Tuning via Convergence Sweep",[22,7201,7202,7203,7206],{},"Sweep β=",[7048,7204,7205],{},"0.0,0.5,0.7,0.85,0.90,0.95,0.99"," to loss\u003C0.001 (max 500 steps):",[7208,7209,7210,7223],"table",{},[7211,7212,7213],"thead",{},[7214,7215,7216,7220],"tr",{},[7217,7218,7219],"th",{},"β",[7217,7221,7222],{},"Steps to converge",[7224,7225,7226,7235,7243,7251,7259,7267,7275],"tbody",{},[7214,7227,7228,7232],{},[7229,7230,7231],"td",{},"0.00",[7229,7233,7234],{},"185 (vanilla GD)",[7214,7236,7237,7240],{},[7229,7238,7239],{},"0.50",[7229,7241,7242],{},"170",[7214,7244,7245,7248],{},[7229,7246,7247],{},"0.70",[7229,7249,7250],{},"165",[7214,7252,7253,7256],{},[7229,7254,7255],{},"0.85",[7229,7257,7258],{},"161",[7214,7260,7261,7264],{},[7229,7262,7263],{},"0.90",[7229,7265,7266],{},"159 (sweet spot)",[7214,7268,7269,7272],{},[7229,7270,7271],{},"0.95",[7229,7273,7274],{},"158",[7214,7276,7277,7280],{},[7229,7278,7279],{},"0.99",[7229,7281,7282],{},">500 (diverges)",[22,7284,7285,7286,7289],{},"Inverted U: β=0.9-0.95 optimal (faster by ~15-20% vs GD); too high prioritizes stale velocity. Visualize trajectories (first 55 steps on contours) and log-loss curves confirm: GD slow\u002Foscillatory, good β direct\u002Ffast, high β bouncy\u002Ffailed. Loss surface: def loss(x,y): return 0.05",[7053,7287,7288],{},"x**2 + 5","y**2.",[7291,7292,7293],"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":84,"searchDepth":85,"depth":85,"links":7295},[7296,7297,7298],{"id":7042,"depth":85,"text":7043},{"id":7122,"depth":85,"text":7123},{"id":7198,"depth":85,"text":7199},[90],{"content_references":7301,"triage":7307},[7302],{"type":7303,"title":7304,"url":7305,"context":7306},"other","Momentum_Gradient_Descent.ipynb","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FData%20Science\u002FMomentum_Gradient_Descent.ipynb","mentioned",{"relevance":103,"novelty":102,"quality":103,"actionability":103,"composite":7308,"reasoning":7309},3.8,"Category: AI & LLMs. The article discusses gradient descent and momentum in machine learning, addressing practical concerns about convergence speed and oscillations, which are relevant to AI developers. It provides actionable Python code examples for implementing gradient descent and momentum, making it useful for practitioners.","\u002Fsummaries\u002Fe3a7d313e4f27d00-momentum-dampens-gd-zigzags-via-gradient-averaging-summary","2026-05-05 07:26:29","2026-05-05 16:09:53",{"title":7031,"description":84},{"loc":7310},"e3a7d313e4f27d00","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F05\u002Fwhy-gradient-descent-zigzags-and-how-momentum-fixes-it\u002F","summaries\u002Fe3a7d313e4f27d00-momentum-dampens-gd-zigzags-via-gradient-averaging-summary",[7320,119,118],"machine-learning","On anisotropic loss surfaces (condition number 100), vanilla GD zigzags and takes 185 steps to converge (loss \u003C0.001); momentum with β=0.9 converges in 159 steps by canceling steep-direction oscillations while accelerating flat directions—but β=0.99 diverges.",[],"XRkn18Lid7OsOHXT1dP1s2Nh4f4rEKAHvOL4X3Y6phw",{"id":7325,"title":7326,"ai":7327,"body":7332,"categories":7459,"created_at":91,"date_modified":91,"description":84,"extension":92,"faq":91,"featured":93,"kicker_label":91,"meta":7460,"navigation":106,"path":7472,"published_at":7473,"question":91,"scraped_at":7474,"seo":7475,"sitemap":7476,"source_id":7477,"source_name":7316,"source_type":114,"source_url":7478,"stem":7479,"tags":7480,"thumbnail_url":91,"tldr":7482,"tweet":91,"unknown_tags":7483,"__hash__":7484},"summaries\u002Fsummaries\u002F0cdee908eb39d657-stream-parse-tasktrove-dataset-for-ai-task-insight-summary.md","Stream Parse TaskTrove Dataset for AI Task Insights",{"provider":7,"model":7033,"input_tokens":7328,"output_tokens":7329,"processing_time_ms":7330,"cost_usd":7331},9713,1943,26130,0.0028916,{"type":14,"value":7333,"toc":7454},[7334,7338,7393,7400,7404,7411,7425,7429,7436],[17,7335,7337],{"id":7336},"build-streaming-parser-for-compressed-task-binaries","Build Streaming Parser for Compressed Task Binaries",[22,7339,7340,7341,7344,7345,7348,7349,7352,7353,7356,7357,7360,7361,7364,7365,7368,7369,7372,7373,7376,7377,7380,7381,7384,7385,7388,7389,7392],{},"Handle TaskTrove's ",[56,7342,7343],{},"task_binary"," fields—gzip-compressed blobs up to p95= some KB—without downloading the full dataset by using ",[56,7346,7347],{},"datasets.load_dataset(..., streaming=True)",". Convert blobs to bytes via ",[56,7350,7351],{},"to_bytes()"," which decodes base64 strings or lists. Decompress if gzip header (",[56,7354,7355],{},"b'\\x1f\\x8b'","), then auto-detect format in ",[56,7358,7359],{},"parse_task()",": prioritize ",[56,7362,7363],{},"tarfile.open()"," for archives (extract files as str\u002Fbytes), fall back to ",[56,7366,7367],{},"ZipFile",", then ",[56,7370,7371],{},"json.loads()"," (or JSONL line-by-line), plain text decode, or binary. This yields dicts with ",[56,7374,7375],{},"format",", ",[56,7378,7379],{},"files"," (for archives), ",[56,7382,7383],{},"content",", plus ",[56,7386,7387],{},"raw_size","\u002F",[56,7390,7391],{},"compressed_size",". Example: first sample decompresses from compressed bytes to raw, revealing tar with JSON metadata and .py code files.",[22,7394,7395,7396,7399],{},"Use ",[56,7397,7398],{},"show_task()"," to preview: breakdown by extension (e.g., .json, .py), truncate JSON to 1500 chars, code to 600. Trade-off: Streaming processes samples in real-time but requires robust error handling for malformed blobs (e.g., UnicodeDecodeError keeps as bytes).",[17,7401,7403],{"id":7402},"uncover-dataset-structure-via-counters-and-plots","Uncover Dataset Structure via Counters and Plots",[22,7405,7406,7407,7410],{},"Extract source from ",[56,7408,7409],{},"path"," prefix (split on last '-'): top 15 sources dominate test split (e.g., count thousands each). Track compressed sizes: log-scale histogram shows median p50 KB, p95 ~higher KB—most tasks compact, outliers bulkier. Inspect 200 samples: common filenames (e.g., task.json, README.md top counts), JSON keys (e.g., instruction, tests frequent). Full listings reveal 5-10 files per tar\u002Fzip typically.",[22,7412,7413,7414,7417,7418,7376,7421,7424],{},"Aggregate in ",[56,7415,7416],{},"TaskTroveExplorer.summary(limit=1000)",": group by source for n tasks, mean compressed\u002Fraw KB (log y-scale bar chart top 12), mean files. Enables quick profiling—e.g., some sources average 10+ KB raw, others leaner. Polars DataFrame slice of 500 tasks captures ",[56,7419,7420],{},"source",[56,7422,7423],{},"is_verified",", sizes, instruction preview for downstream modeling.",[17,7426,7428],{"id":7427},"detect-verifiers-and-export-rl-ready-tasks","Detect Verifiers and Export RL-Ready Tasks",[22,7430,7431,7432,7435],{},"Flag evaluation-ready tasks with ",[56,7433,7434],{},"has_verifier()",": scan filenames for 'verifier'\u002F'judge'\u002F'grader', JSON keys like 'verifier_config'\u002F'rubric'\u002F'test_patch', or content strings. Multi-signal boosts recall—e.g., verified tasks have dedicated verifier.py or JSON. Per-source rates vary (bar chart: green high % usable for RL); hunt first verified sample to inspect (e.g., grader JSON with tests).",[22,7437,7438,7441,7442,7445,7446,7449,7450,7453],{},[56,7439,7440],{},"TaskTroveExplorer"," class unifies: ",[56,7443,7444],{},"iter()"," filters sources, ",[56,7447,7448],{},"sample(n=5)"," parses + adds metadata, ",[56,7451,7452],{},"export()"," writes dirs with files\u002FJSON. Saves Parquet slice (500 rows, ~KB): boosts workflows by filtering verified tasks (sum across sources). Full pipeline scales to validation split; lists HF repo subdirs for all sources (~dozens).",{"title":84,"searchDepth":85,"depth":85,"links":7455},[7456,7457,7458],{"id":7336,"depth":85,"text":7337},{"id":7402,"depth":85,"text":7403},{"id":7427,"depth":85,"text":7428},[90],{"content_references":7461,"triage":7469},[7462,7466],{"type":7463,"title":7464,"url":7465,"context":7306},"dataset","TaskTrove","https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fopen-thoughts\u002FTaskTrove",{"type":7303,"title":7467,"url":7468,"context":100},"Full Codes with Notebook","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Ftasktrove_exploration_pipeline_marktechpost.py",{"relevance":7095,"novelty":103,"quality":103,"actionability":103,"composite":7470,"reasoning":7471},4.35,"Category: Data Science & Visualization. The article provides a detailed guide on streaming and parsing a specific dataset, which is highly relevant for developers looking to integrate AI features using real-world data. It includes practical code examples and techniques for handling large datasets, making it actionable for the target audience.","\u002Fsummaries\u002F0cdee908eb39d657-stream-parse-tasktrove-dataset-for-ai-task-insight-summary","2026-05-03 21:26:42","2026-05-04 16:13:43",{"title":7326,"description":84},{"loc":7472},"0cdee908eb39d657","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F03\u002Fa-coding-implementation-to-explore-and-analyze-the-tasktrove-dataset-with-streaming-parsing-visualization-and-verifier-detection\u002F","summaries\u002F0cdee908eb39d657-stream-parse-tasktrove-dataset-for-ai-task-insight-summary",[119,7481,118],"data-science","Stream multi-GB TaskTrove dataset without full download; parse gzip-compressed tar\u002Fzip\u002FJSON binaries to analyze sources, sizes (median  p50 KB compressed), filenames, and detect verifiers for RL-ready tasks via multi-signal heuristics.",[],"vJBe85PNXCRjjCrLU1WGvZnO0Dhqgjb6ThGkJ-rMnRQ",{"id":7486,"title":7487,"ai":7488,"body":7493,"categories":7529,"created_at":91,"date_modified":91,"description":84,"extension":92,"faq":91,"featured":93,"kicker_label":91,"meta":7530,"navigation":106,"path":7543,"published_at":7544,"question":91,"scraped_at":7545,"seo":7546,"sitemap":7547,"source_id":7548,"source_name":7549,"source_type":114,"source_url":7550,"stem":7551,"tags":7552,"thumbnail_url":91,"tldr":7553,"tweet":91,"unknown_tags":7554,"__hash__":7555},"summaries\u002Fsummaries\u002F6e4b4d5944c58d66-etl-pipeline-turns-messy-hr-data-into-star-schema-summary.md","ETL Pipeline Turns Messy HR Data into Star Schema Insights",{"provider":7,"model":7033,"input_tokens":7489,"output_tokens":7490,"processing_time_ms":7491,"cost_usd":7492},7468,1638,25555,0.0022901,{"type":14,"value":7494,"toc":7523},[7495,7499,7502,7506,7509,7513,7516,7520],[17,7496,7498],{"id":7497},"restructure-flat-data-into-star-schema-for-efficient-analysis","Restructure Flat Data into Star Schema for Efficient Analysis",[22,7500,7501],{},"Raw HR datasets arrive as wide, redundant tables that slow queries and complicate scaling. Transform them into a star schema: one central fact table for employee records (EmpID, Age, tenure_years, is_attrition, foreign keys like department_id) surrounded by dimension tables (department, position, salary with qcut-segmented levels: Low\u002FMedium\u002FHigh for equal distribution groups). This reduces redundancy, speeds queries, and adds business meaning—e.g., salary_level enables quick counts of high-salary employees. Use pd.read_csv for extraction, then merge unique values back with surrogate keys (index + 1) to link facts to dimensions, creating maintainable analytical workloads over monolithic tables.",[17,7503,7505],{"id":7504},"clean-and-engineer-features-robustly-from-unreliable-raw-data","Clean and Engineer Features Robustly from Unreliable Raw Data",[22,7507,7508],{},"Don't trust provided fields—derive them. Strip column whitespace to prevent code breaks. Convert strings to datetime with errors='coerce' for DateofHire, DateofTermination, DOB (format='%m\u002F%d\u002F%y'). Compute Age as (today - DOB).days \u002F\u002F 365, tenure_years as (today - DateofHire).days \u002F 365, is_attrition as DateofTermination.notna(), is_active as opposite. Fill missing Salary and Age with medians (outlier-resistant over means). These steps turn inconsistent inputs into reliable features for downstream analysis and ML, emphasizing derivation over assumption.",[17,7510,7512],{"id":7511},"extract-actionable-hr-insights-post-transformation","Extract Actionable HR Insights Post-Transformation",[22,7514,7515],{},"Query structured data reveals: Managers show no strong performance impact—most employees rate 'Fully Meets' across leaders, with minor 'Exceeds' variations (e.g., Ketsia Liebig, Brandon Miller) and rare 'PIP\u002FNeeds Improvement'. Diversity: 60% White, 26% Black\u002FAfrican American, 9% Asian; gender balanced at 56.6% female vs. 43.4% male. Recruitment: Diversity Job Fair yields 100% Black hires; Indeed\u002FLinkedIn balanced; Google Search varied but White-dominant; avoid Online Web Application\u002FOther (100% White). Stacked crosstabs and countplots highlight channels driving diversity, prioritizing targeted sources over uniform ones.",[17,7517,7519],{"id":7518},"predict-attrition-at-71-accuracy-with-key-drivers-identified","Predict Attrition at 71% Accuracy with Key Drivers Identified",[22,7521,7522],{},"Leverage cleaned fact table merges (absences, salary dims) for RandomForestClassifier on age, tenure_years, absences, Salary (filled medians). Train\u002Ftest split (80\u002F20) yields 71% accuracy, 59% precision\u002Frecall for attrition (confusion: 32 true stay, 13 true leave, 9 misses each). Feature importances: tenure (47%), Salary (23%), absences moderate, age lowest—focus retention on long-tenured, low-salary employees with absences to cut churn.",{"title":84,"searchDepth":85,"depth":85,"links":7524},[7525,7526,7527,7528],{"id":7497,"depth":85,"text":7498},{"id":7504,"depth":85,"text":7505},{"id":7511,"depth":85,"text":7512},{"id":7518,"depth":85,"text":7519},[90],{"content_references":7531,"triage":7540},[7532,7536],{"type":7463,"title":7533,"author":7534,"url":7535,"context":7306},"Human Resources Data Set","rhuebner","https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Frhuebner\u002Fhuman-resources-data-set",{"type":7303,"title":7537,"author":7538,"url":7539,"context":7306},"ETL-HR-Analytics-Project","jihanKamilah","https:\u002F\u002Fgithub.com\u002FjihanKamilah\u002FETL-HR-Analytics-Project",{"relevance":7095,"novelty":102,"quality":103,"actionability":103,"composite":7541,"reasoning":7542},4.15,"Category: Data Science & Visualization. The article provides a detailed guide on building an ETL pipeline to transform messy HR data into a star schema, addressing practical applications for data analysis, which is highly relevant for product builders. It includes specific techniques for data cleaning and feature engineering, making it actionable for the audience.","\u002Fsummaries\u002F6e4b4d5944c58d66-etl-pipeline-turns-messy-hr-data-into-star-schema-summary","2026-04-29 17:03:37","2026-05-03 17:01:04",{"title":7487,"description":84},{"loc":7543},"6e4b4d5944c58d66","Learning Data","https:\u002F\u002Fmedium.com\u002Flearning-data\u002Fthis-is-what-real-data-looks-like-and-how-i-turned-it-into-insights-3d520e7da561?source=rss----eec44e936bf1---4","summaries\u002F6e4b4d5944c58d66-etl-pipeline-turns-messy-hr-data-into-star-schema-summary",[7481,7320,118,119],"Build a scalable ETL pipeline to restructure flat HR data into a star schema fact\u002Fdimension tables, enabling analysis of manager performance, diversity (60% White, 56.6% female), recruitment channels, and 71% accurate attrition prediction where tenure drives 47% of decisions.",[],"3NZcd4HtDiYwUcyaMlD-6kxaFLU1SsvoWhathCU7avY",{"id":7557,"title":7558,"ai":7559,"body":7564,"categories":7808,"created_at":91,"date_modified":91,"description":84,"extension":92,"faq":91,"featured":93,"kicker_label":91,"meta":7809,"navigation":106,"path":7820,"published_at":7821,"question":91,"scraped_at":7822,"seo":7823,"sitemap":7824,"source_id":7825,"source_name":7549,"source_type":114,"source_url":7826,"stem":7827,"tags":7828,"thumbnail_url":91,"tldr":7830,"tweet":91,"unknown_tags":7831,"__hash__":7832},"summaries\u002Fsummaries\u002F90a024f8fc9fd261-automate-weekly-pdf-reports-with-python-etl-pipeli-summary.md","Automate Weekly PDF Reports with Python ETL Pipeline",{"provider":7,"model":7033,"input_tokens":7560,"output_tokens":7561,"processing_time_ms":7562,"cost_usd":7563},8933,2254,17256,0.00289095,{"type":14,"value":7565,"toc":7803},[7566,7570,7573,7623,7638,7654,7664,7667,7671,7674,7719,7722,7725,7728,7732,7735,7738,7794,7797,7800],[17,7567,7569],{"id":7568},"merge-raw-datasets-into-actionable-business-data","Merge Raw Datasets into Actionable Business Data",[22,7571,7572],{},"Start by loading six Olist e-commerce CSVs (orders, customers, items, payments, products, reviews) with pandas.read_csv, then merge on keys like customer_id, order_id, product_id:",[7065,7574,7576],{"className":7067,"code":7575,"language":119,"meta":84,"style":84},"def load_data():\n    return {\n        \"orders\": pd.read_csv(\"data\u002Folist_orders_dataset.csv\"),\n        # ... other datasets\n    }\n\ndf = data[\"orders\"].merge(data[\"customers\"], on=\"customer_id\", how=\"left\") \\\n    .merge(data[\"items\"], on=\"order_id\", how=\"left\") \\\n    # ... other merges\n",[56,7577,7578,7583,7588,7593,7598,7603,7608,7613,7618],{"__ignoreMap":84},[7048,7579,7580],{"class":7073,"line":7074},[7048,7581,7582],{},"def load_data():\n",[7048,7584,7585],{"class":7073,"line":85},[7048,7586,7587],{},"    return {\n",[7048,7589,7590],{"class":7073,"line":102},[7048,7591,7592],{},"        \"orders\": pd.read_csv(\"data\u002Folist_orders_dataset.csv\"),\n",[7048,7594,7595],{"class":7073,"line":103},[7048,7596,7597],{},"        # ... other datasets\n",[7048,7599,7600],{"class":7073,"line":7095},[7048,7601,7602],{},"    }\n",[7048,7604,7605],{"class":7073,"line":7101},[7048,7606,7607],{"emptyLinePlaceholder":106},"\n",[7048,7609,7610],{"class":7073,"line":7107},[7048,7611,7612],{},"df = data[\"orders\"].merge(data[\"customers\"], on=\"customer_id\", how=\"left\") \\\n",[7048,7614,7615],{"class":7073,"line":7113},[7048,7616,7617],{},"    .merge(data[\"items\"], on=\"order_id\", how=\"left\") \\\n",[7048,7619,7620],{"class":7073,"line":7185},[7048,7621,7622],{},"    # ... other merges\n",[22,7624,7625,7626,7629,7630,7633,7634,7637],{},"Convert timestamps to datetime for time-based calcs: df",[7048,7627,7628],{},"\"order_purchase_timestamp\""," = pd.to_datetime(...). Compute delivery delays as (delivered - estimated).dt.days > 0 for is_delayed. Derive revenue = price + freight_value, profit = price - freight_value. Aggregate metrics like revenue_current = df",[7048,7631,7632],{},"\"revenue\"",".sum(), orders_current = df",[7048,7635,7636],{},"\"order_id\"",".nunique(), AOV = revenue \u002F orders.",[22,7639,7640,7641,7644,7645,7647,7648,7644,7651,7653],{},"Group by month for trends: monthly = df.groupby(\"month\").agg({\"revenue\": \"sum\", \"order_id\": \"nunique\"}); monthly",[7048,7642,7643],{},"\"growth\""," = monthly",[7048,7646,7632],{},".pct_change() * 100; monthly",[7048,7649,7650],{},"\"moving_avg\"",[7048,7652,7632],{},".rolling(3).mean().",[22,7655,7656,7657,7663],{},"Simulate weekly reporting with cutoff: df_sim = df",[7048,7658,7659,7660,7662],{},"df",[7048,7661,7628],{}," \u003C= cutoff_date",", advancing cutoff_date = start_date + pd.Timedelta(days=7 * run_count) via state.txt to mimic live cycles without reprocessing all history.",[22,7665,7666],{},"This standardization ensures consistent metric definitions across runs, turning scattered CSVs into a unified view of who bought what, payment amounts, delivery times, and satisfaction.",[17,7668,7670],{"id":7669},"add-rule-based-insights-and-build-pdf-reports","Add Rule-Based Insights and Build PDF Reports",[22,7672,7673],{},"Metrics alone fail without context—use simple if-conditions to interpret:",[7065,7675,7677],{"className":7067,"code":7676,"language":119,"meta":84,"style":84},"def generate_insights(metrics):\n    insights = []\n    if metrics[\"profit_current\"] \u003C metrics[\"revenue_current\"]:\n        insights.append(\"Revenue growing but profit margin thin, high logistics costs.\")\n    growth_volatility = metrics[\"monthly\"][\"growth\"].std()\n    if growth_volatility > 50:\n        insights.append(\"Revenue growth highly volatile, unstable performance.\")\n    # ...\n",[56,7678,7679,7684,7689,7694,7699,7704,7709,7714],{"__ignoreMap":84},[7048,7680,7681],{"class":7073,"line":7074},[7048,7682,7683],{},"def generate_insights(metrics):\n",[7048,7685,7686],{"class":7073,"line":85},[7048,7687,7688],{},"    insights = []\n",[7048,7690,7691],{"class":7073,"line":102},[7048,7692,7693],{},"    if metrics[\"profit_current\"] \u003C metrics[\"revenue_current\"]:\n",[7048,7695,7696],{"class":7073,"line":103},[7048,7697,7698],{},"        insights.append(\"Revenue growing but profit margin thin, high logistics costs.\")\n",[7048,7700,7701],{"class":7073,"line":7095},[7048,7702,7703],{},"    growth_volatility = metrics[\"monthly\"][\"growth\"].std()\n",[7048,7705,7706],{"class":7073,"line":7101},[7048,7707,7708],{},"    if growth_volatility > 50:\n",[7048,7710,7711],{"class":7073,"line":7107},[7048,7712,7713],{},"        insights.append(\"Revenue growth highly volatile, unstable performance.\")\n",[7048,7715,7716],{"class":7073,"line":7113},[7048,7717,7718],{},"    # ...\n",[22,7720,7721],{},"Generate PDF with ReportLab: create executive summary (e.g., 2018 revenue \u003C 2017, orders down, AOV stable, 9.36% delay rate, 3.91 avg review score), KPI trends (Jan 2018 revenue\u002Fprofit >600% over 2017 but slowing; AOV 2-14% lower, driven by transaction volume), top products (relogios_presentes\u002Fbeleza_saude ~510K revenue each), delivery (SE state 33% delays, casa_conforto_2 60%; overall -10.76 avg delay days = early deliveries), payments (credit card 75%, boleto 19.1%), reviews (5-stars dominant, avg 3.91).",[22,7723,7724],{},"Key patterns: thin margins from costs; volatile growth; new-customer reliance; delays hurt scores; SP top region; credit users spend more.",[22,7726,7727],{},"Code charts with matplotlib (plt.savefig(\"revenue_chart.png\")), insert via Image(width=450,height=220), tables via Table(table_data). Central pipeline: data → transform → metrics → insights → generate_report().",[17,7729,7731],{"id":7730},"schedule-email-delivery-with-github-actions","Schedule Email Delivery with GitHub Actions",[22,7733,7734],{},"Automate email: use smtplib.SMTP_SSL('smtp.gmail.com',465), login via os.getenv(\"EMAIL_SENDER\u002FPASSWORD\"), attach PDF, dynamic subject. Secure creds in GitHub Secrets (EMAIL_SENDER, EMAIL_PASSWORD, EMAIL_RECEIVER).",[22,7736,7737],{},"Deploy via .github\u002Fworkflows\u002Fauto-report.yml:",[7065,7739,7743],{"className":7740,"code":7741,"language":7742,"meta":84,"style":84},"language-yaml shiki shiki-themes github-light github-dark","on:\n  schedule:\n    - cron: '0 1 * * 1'  # Mondays 1AM UTC\njobs:\n  # setup env, pip install, run main.py\n","yaml",[56,7744,7745,7755,7763,7782,7789],{"__ignoreMap":84},[7048,7746,7747,7751],{"class":7073,"line":7074},[7048,7748,7750],{"class":7749},"sj4cs","on",[7048,7752,7754],{"class":7753},"sVt8B",":\n",[7048,7756,7757,7761],{"class":7073,"line":85},[7048,7758,7760],{"class":7759},"s9eBZ","  schedule",[7048,7762,7754],{"class":7753},[7048,7764,7765,7768,7771,7774,7778],{"class":7073,"line":102},[7048,7766,7767],{"class":7753},"    - ",[7048,7769,7770],{"class":7759},"cron",[7048,7772,7773],{"class":7753},": ",[7048,7775,7777],{"class":7776},"sZZnC","'0 1 * * 1'",[7048,7779,7781],{"class":7780},"sJ8bj","  # Mondays 1AM UTC\n",[7048,7783,7784,7787],{"class":7073,"line":103},[7048,7785,7786],{"class":7759},"jobs",[7048,7788,7754],{"class":7753},[7048,7790,7791],{"class":7073,"line":7095},[7048,7792,7793],{"class":7780},"  # setup env, pip install, run main.py\n",[22,7795,7796],{},"Triggers workflow: installs deps, executes pipeline (advances run_count), generates\u002Fsends report. No local runs—wake to delivered emails. Full loop: cron → ETL → PDF → email → state update for next cutoff.",[22,7798,7799],{},"Trade-offs: Relies on GitHub free tier (2k min\u002Fmonth); Gmail app passwords needed; rule-insights basic (extend with ML if needed). Scales to live data sources by swapping CSVs for APIs\u002FDBs.",[7291,7801,7802],{},"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);}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .s9eBZ, html code.shiki .s9eBZ{--shiki-default:#22863A;--shiki-dark:#85E89D}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}",{"title":84,"searchDepth":85,"depth":85,"links":7804},[7805,7806,7807],{"id":7568,"depth":85,"text":7569},{"id":7669,"depth":85,"text":7670},{"id":7730,"depth":85,"text":7731},[90],{"content_references":7810,"triage":7818},[7811,7815],{"type":7463,"title":7812,"author":7813,"url":7814,"context":7306},"Brazilian Ecommerce Public Dataset by Olist","Olist","https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Folistbr\u002Fbrazilian-ecommerce",{"type":7303,"title":7816,"author":7538,"url":7817,"context":100},"Weekly-Business-Report-Automation","https:\u002F\u002Fgithub.com\u002FjihanKamilah\u002FWeekly-Business-Report-Automation\u002F",{"relevance":7095,"novelty":102,"quality":103,"actionability":7095,"composite":7470,"reasoning":7819},"Category: AI Automation. The article provides a detailed guide on automating weekly reports using a Python ETL pipeline, which directly addresses the audience's need for practical automation solutions. It includes specific code examples and actionable steps, making it highly relevant and immediately applicable for those building AI-powered products.","\u002Fsummaries\u002F90a024f8fc9fd261-automate-weekly-pdf-reports-with-python-etl-pipeli-summary","2026-04-21 13:31:02","2026-04-21 15:26:14",{"title":7558,"description":84},{"loc":7820},"90a024f8fc9fd261","https:\u002F\u002Fmedium.com\u002Flearning-data\u002Fi-was-tired-of-weekly-reports-so-i-automated-the-entire-thing-f63f88de59ce?source=rss----eec44e936bf1---4","summaries\u002F90a024f8fc9fd261-automate-weekly-pdf-reports-with-python-etl-pipeli-summary",[119,7829,7481,118],"automation","Load\u002Fmerge e-commerce datasets, compute revenue\u002Fprofit\u002FAOV\u002Fgrowth metrics, generate PDF with matplotlib\u002FReportLab charts and rule-based insights, email via smtplib, schedule weekly via GitHub Actions cron.",[],"ui9FcZ6uGNS1FckDV1aa-U2QNtxJCoCeuihBdEY63jo"]