[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-microgpt-py-full-gpt-in-300-lines-of-pure-python-summary":3,"summaries-facets-categories":95,"summary-related-microgpt-py-full-gpt-in-300-lines-of-pure-python-summary":6999},{"id":4,"title":5,"ai":6,"body":13,"categories":71,"created_at":73,"date_modified":73,"description":65,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":76,"navigation":77,"path":78,"published_at":79,"question":73,"scraped_at":73,"seo":80,"sitemap":81,"source_id":82,"source_name":83,"source_type":84,"source_url":85,"stem":86,"tags":87,"thumbnail_url":73,"tldr":92,"tweet":73,"unknown_tags":93,"__hash__":94},"summaries\u002Fsummaries\u002Fmicrogpt-py-full-gpt-in-300-lines-of-pure-python-summary.md","microgpt.py: Full GPT in 300 Lines of Pure Python",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",11786,1242,8684,0.0029557,{"type":14,"value":15,"toc":64},"minimark",[16,21,38,42,49,53],[17,18,20],"h2",{"id":19},"custom-autograd-engine-powers-end-to-end-training","Custom Autograd Engine Powers End-to-End Training",[22,23,24,25,29,30,33,34,37],"p",{},"Implements automatic differentiation via ",[26,27,28],"code",{},"Value"," class with slots for efficiency. Supports add, mul, pow, log, exp, ReLU, and backward via topological sort on computation graph. Chain rule propagates gradients recursively: ",[26,31,32],{},"child.grad += local_grad * v.grad",". Enables full forward\u002Fbackward without libraries. For a names dataset (32k lines from ",[26,35,36],{},"names.txt","), builds char-level tokenizer: unique chars (vocab_size=~30+1 BOS token). Model params (~10k total): 1 layer, n_embd=16, block_size=16, n_head=4 (head_dim=4). Weights initialized Gaussian std=0.08. Embeddings: wte (vocab x 16), wpe (16 x 16), lm_head (vocab x 16). Per layer: QKV (4x 16x16), Wo (16x16), MLP fc1 (64x16), fc2 (16x64).",[17,39,41],{"id":40},"gpt-architecture-mirrors-gpt-2-essentials","GPT Architecture Mirrors GPT-2 Essentials",[22,43,44,45,48],{},"Forward pass: token+pos embeds → RMSNorm → residual blocks. Attention: raw dot-product (scaled by 1\u002Fsqrt(head_dim)), softmax weights → weighted V sum → Wo projection. Causal via key\u002Fvalue history append (no mask). MLP: RMSNorm → fc1 → ReLU → fc2 → residual. Final lm_head logits → softmax probs. Uses RMSNorm (",[26,46,47],{},"scale = (mean(x^2)+eps)^-0.5",") over LayerNorm, ReLU over GeLU, no biases. Keys\u002Fvalues persist across positions for KV cache simulation. Loss: average -log P(next_token) over sequence (BOS-wrapped docs, up to block_size=16).",[17,50,52],{"id":51},"adam-training-inference-in-1000-steps","Adam Training + Inference in 1000 Steps",[22,54,55,56,60,61,63],{},"Shuffles 32k names, cycles through docs. Per step: tokenize ",[57,58,59],"span",{},"BOS"," + chars + ",[57,62,59],{},", forward all positions (building KV cache), average cross-entropy loss → backward → Adam update (lr=0.01 linear decay to 0, β1=0.85, β2=0.99). Prints loss (drops from ~3 to ~1.5 typically). Inference: start BOS, sample argmax-probs (temp=0.5) until BOS, yields plausible names like 'korsal' after training. Demonstrates: core GPT is simple; libs optimize speed\u002Fscale. Trade-off: slow (minutes on CPU), but reveals every op.",{"title":65,"searchDepth":66,"depth":66,"links":67},"",2,[68,69,70],{"id":19,"depth":66,"text":20},{"id":40,"depth":66,"text":41},{"id":51,"depth":66,"text":52},[72],"AI & LLMs",null,"md",false,{},true,"\u002Fsummaries\u002Fmicrogpt-py-full-gpt-in-300-lines-of-pure-python-summary","2026-04-08 21:21:19",{"title":5,"description":65},{"loc":78},"56d2bdaaa16d5c3b","Andrej Karpathy Gists","article","https:\u002F\u002Funknown","summaries\u002Fmicrogpt-py-full-gpt-in-300-lines-of-pure-python-summary",[88,89,90,91],"llm","python","machine-learning","coding","Trains a tiny GPT on names dataset using custom autograd—no deps, no PyTorch—to generate realistic names, distilling the core transformer algorithm.",[],"3fO1PHuRnDxVHEXFsDwlj_bugbD79pZ1c6UEJVeKQE8",[96,98,101,103,106,108,111,114,116,118,120,122,125,127,129,131,133,136,138,140,142,144,147,150,152,154,156,158,160,162,164,166,168,170,172,174,176,178,180,182,184,186,188,190,192,194,196,199,201,203,205,207,209,211,213,215,217,219,221,223,225,228,230,232,234,236,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,269,271,273,275,277,279,281,283,285,287,289,291,293,295,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,363,365,367,369,371,373,375,377,379,381,383,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,419,421,423,425,427,429,431,433,435,437,439,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,494,496,498,501,503,505,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,547,549,551,553,555,557,559,561,563,565,567,569,572,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,606,608,610,612,614,616,618,620,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798,800,802,804,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,859,861,863,865,867,870,872,874,876,878,880,882,884,886,888,890,892,894,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1402,1404,1406,1408,1410,1412,1414,1416,1418,1420,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1562,1564,1566,1568,1570,1572,1574,1576,1578,1580,1582,1584,1586,1588,1590,1592,1594,1596,1598,1600,1602,1604,1606,1608,1610,1612,1614,1616,1618,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1812,1814,1816,1818,1820,1822,1824,1826,1828,1830,1832,1834,1836,1838,1840,1842,1844,1846,1848,1850,1852,1854,1856,1858,1860,1862,1864,1866,1868,1870,1872,1874,1876,1878,1880,1882,1884,1886,1888,1890,1892,1894,1896,1898,1900,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1962,1964,1966,1968,1970,1972,1974,1976,1978,1980,1982,1984,1986,1988,1990,1992,1994,1996,1998,2000,2002,2004,2006,2008,2010,2012,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032,2034,2036,2038,2040,2042,2044,2046,2048,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2122,2124,2126,2128,2130,2132,2134,2136,2138,2140,2142,2144,2146,2148,2150,2152,2154,2156,2158,2160,2162,2164,2166,2168,2170,2172,2174,2176,2178,2180,2182,2184,2186,2188,2190,2192,2194,2196,2198,2200,2202,2204,2206,2208,2210,2212,2214,2216,2218,2220,2222,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2318,2320,2322,2324,2326,2328,2330,2332,2334,2336,2338,2340,2342,2344,2346,2348,2350,2352,2354,2356,2358,2360,2362,2364,2366,2368,2370,2372,2374,2376,2378,2380,2382,2384,2386,2388,2390,2392,2394,2396,2398,2400,2402,2404,2406,2408,2410,2412,2414,2416,2418,2420,2422,2424,2426,2428,2430,2432,2434,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2465,2467,2469,2471,2473,2475,2477,2480,2482,2484,2486,2488,2490,2492,2494,2496,2498,2500,2502,2504,2506,2508,2510,2512,2514,2516,2518,2520,2522,2524,2526,2528,2530,2532,2534,2536,2538,2540,2542,2544,2546,2548,2550,2552,2554,2556,2558,2560,2562,2564,2566,2568,2570,2572,2574,2576,2578,2580,2582,2584,2586,2588,2590,2592,2594,2596,2598,2600,2602,2604,2606,2608,2610,2612,2614,2616,2618,2620,2622,2624,2626,2628,2630,2632,2634,2636,2638,2640,2642,2644,2646,2648,2650,2652,2654,2656,2658,2660,2662,2664,2666,2668,2670,2672,2674,2676,2678,2680,2682,2684,2686,2688,2690,2692,2694,2696,2698,2700,2702,2704,2706,2708,2710,2712,2714,2716,2718,2720,2722,2724,2726,2728,2730,2732,2734,2736,2738,2740,2742,2744,2746,2748,2750,2752,2754,2756,2758,2760,2762,2764,2766,2768,2770,2772,2774,2776,2778,2780,2782,2784,2786,2788,2790,2792,2794,2796,2798,2800,2802,2804,2806,2808,2810,2812,2814,2816,2818,2820,2822,2824,2826,2828,2830,2832,2834,2836,2838,2840,2842,2844,2846,2848,2850,2852,2854,2856,2858,2860,2862,2864,2866,2868,2870,2872,2874,2876,2878,2880,2882,2884,2886,2888,2890,2892,2894,2896,2898,2900,2902,2904,2906,2908,2910,2912,2914,2916,2918,2920,2922,2924,2926,2928,2930,2932,2934,2936,2938,2940,2942,2944,2946,2948,2950,2952,2954,2956,2958,2960,2962,2964,2966,2968,2970,2972,2974,2976,2978,2980,2982,2984,2986,2988,2990,2992,2994,2996,2998,3000,3002,3004,3006,3008,3010,3012,3014,3016,3018,3020,3022,3024,3026,3028,3030,3032,3034,3036,3038,3040,3042,3044,3046,3048,3050,3052,3054,3056,3058,3060,3062,3064,3066,3068,3070,3072,3074,3076,3078,3080,3082,3084,3086,3088,3090,3092,3094,3096,3098,3100,3102,3104,3106,3108,3110,3112,3114,3116,3118,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3248,3250,3252,3254,3256,3258,3260,3262,3264,3266,3268,3270,3272,3274,3276,3278,3280,3282,3284,3286,3288,3290,3292,3294,3296,3298,3300,3302,3304,3306,3308,3310,3312,3314,3316,3318,3320,3322,3324,3326,3328,3330,3332,3334,3336,3338,3340,3342,3344,3346,3348,3350,3352,3354,3356,3358,3360,3362,3364,3366,3368,3370,3372,3374,3376,3378,3380,3382,3384,3386,3388,3390,3392,3394,3396,3398,3400,3402,3404,3406,3408,3410,3412,3414,3416,3418,3420,3422,3424,3426,3428,3430,3432,3434,3436,3438,3440,3442,3444,3446,3448,3450,3452,3454,3456,3458,3460,3462,3464,3466,3468,3470,3472,3474,3476,3478,3480,3482,3484,3486,3488,3490,3492,3494,3496,3498,3500,3502,3504,3506,3508,3510,3512,3514,3516,3518,3520,3522,3524,3526,3528,3530,3532,3534,3536,3538,3540,3542,3544,3546,3548,3550,3552,3554,3556,3558,3560,3562,3564,3566,3568,3570,3572,3574,3576,3578,3580,3582,3584,3586,3588,3590,3592,3594,3596,3598,3600,3602,3604,3606,3608,3610,3612,3614,3616,3618,3620,3622,3624,3626,3628,3630,3632,3634,3636,3638,3640,3642,3644,3646,3648,3650,3652,3654,3656,3658,3660,3662,3664,3666,3668,3670,3672,3674,3676,3678,3680,3682,3684,3686,3688,3690,3692,3694,3696,3698,3700,3702,3704,3706,3708,3710,3712,3714,3716,3718,3720,3722,3724,3726,3728,3730,3732,3734,3736,3738,3740,3742,3744,3746,3748,3750,3752,3754,3756,3758,3760,3762,3764,3766,3768,3770,3772,3774,3776,3778,3780,3782,3784,3786,3788,3790,3792,3794,3796,3798,3800,3802,3804,3806,3808,3810,3812,3814,3816,3818,3820,3822,3824,3826,3828,3830,3832,3834,3836,3838,3840,3842,3844,3846,3848,3850,3852,3854,3856,3858,3860,3862,3864,3866,3868,3870,3872,3874,3876,3878,3880,3882,3884,3886,3888,3890,3892,3894,3896,3898,3900,3902,3904,3906,3908,3910,3912,3914,3916,3918,3920,3922,3924,3926,3928,3930,3932,3934,3936,3938,3940,3942,3944,3946,3948,3950,3952,3954,3956,3958,3960,3962,3964,3966,3968,3970,3972,3974,3976,3978,3980,3982,3984,3986,3988,3990,3992,3994,3996,3998,4000,4002,4004,4006,4008,4010,4012,4014,4016,4018,4020,4022,4024,4026,4028,4030,4032,4034,4036,4038,4040,4042,4044,4046,4048,4050,4052,4054,4056,4058,4060,4062,4064,4066,4068,4070,4072,4074,4076,4078,4080,4082,4084,4086,4088,4090,4092,4094,4096,4098,4100,4102,4104,4106,4108,4110,4112,4114,4116,4118,4120,4122,4124,4126,4128,4130,4132,4134,4136,4138,4140,4142,4144,4146,4148,4150,4152,4154,4156,4158,4160,4162,4164,4166,4168,4170,4172,4174,4176,4178,4180,4182,4184,4186,4188,4190,4192,4194,4196,4198,4200,4202,4204,4206,4208,4210,4212,4214,4216,4218,4220,4222,4224,4226,4228,4230,4232,4234,4236,4238,4240,4242,4244,4246,4248,4250,4252,4254,4256,4258,4260,4262,4264,4266,4268,4270,4272,4274,4276,4278,4280,4282,4284,4286,4288,4290,4292,4294,4296,4298,4300,4302,4304,4306,4308,4310,4312,4314,4316,4318,4320,4322,4324,4326,4328,4330,4332,4334,4336,4338,4340,4342,4344,4346,4348,4350,4352,4354,4356,4358,4360,4362,4364,4366,4368,4370,4372,4374,4376,4378,4380,4382,4384,4386,4388,4390,4392,4394,4396,4398,4400,4402,4404,4406,4408,4410,4412,4414,4416,4418,4420,4422,4424,4426,4428,4430,4432,4434,4436,4438,4440,4442,4444,4446,4448,4450,4452,4454,4456,4458,4460,4462,4464,4466,4468,4470,4472,4474,4476,4478,4480,4482,4484,4486,4488,4490,4492,4494,4496,4498,4500,4502,4504,4506,4508,4510,4512,4514,4516,4518,4520,4522,4524,4526,4528,4530,4533,4535,4537,4539,4541,4543,4545,4547,4549,4551,4553,4555,4557,4559,4561,4563,4565,4567,4569,4571,4573,4575,4577,4579,4581,4583,4585,4587,4589,4591,4593,4595,4597,4599,4601,4603,4605,4607,4609,4611,4613,4615,4617,4619,4621,4623,4625,4627,4629,4631,4633,4635,4637,4639,4641,4643,4645,4647,4649,4651,4653,4655,4657,4659,4661,4663,4665,4667,4669,4671,4673,4675,4677,4679,4681,4683,4685,4687,4689,4691,4693,4695,4697,4699,4701,4703,4705,4707,4709,4711,4713,4715,4717,4719,4721,4723,4725,4727,4729,4731,4733,4735,4737,4739,4741,4743,4745,4747,4749,4751,4753,4755,4757,4759,4761,4763,4765,4767,4769,4771,4773,4775,4777,4779,4781,4783,4785,4787,4789,4791,4793,4795,4797,4799,4801,4803,4805,4807,4809,4811,4813,4815,4817,4819,4821,4823,4825,4827,4829,4831,4833,4835,4837,4839,4841,4843,4845,4847,4849,4851,4853,4855,4857,4859,4861,4863,4865,4867,4869,4871,4873,4875,4877,4879,4881,4883,4885,4887,4889,4891,4893,4895,4897,4899,4901,4903,4905,4907,4909,4911,4913,4915,4917,4919,4921,4923,4925,4927,4929,4931,4933,4935,4937,4939,4941,4943,4945,4947,4949,4951,4953,4955,4957,4959,4961,4963,4965,4967,4969,4971,4973,4975,4977,4979,4981,4983,4985,4987,4989,4991,4993,4995,4997,4999,5001,5003,5005,5007,5009,5011,5013,5015,5017,5019,5021,5023,5025,5027,5029,5031,5033,5035,5037,5039,5041,5043,5045,5047,5049,5051,5053,5055,5057,5059,5061,5063,5065,5067,5069,5071,5073,5075,5077,5079,5081,5083,5085,5087,5089,5091,5093,5095,5097,5099,5101,5103,5105,5107,5109,5111,5113,5115,5117,5119,5121,5123,5125,5127,5129,5131,5133,5135,5137,5139,5141,5143,5145,5147,5149,5151,5153,5155,5157,5159,5161,5163,5165,5167,5169,5171,5173,5175,5177,5179,5181,5183,5185,5187,5189,5191,5193,5195,5197,5199,5201,5203,5205,5207,5209,5211,5213,5215,5217,5219,5221,5223,5225,5227,5229,5231,5233,5235,5237,5239,5241,5243,5245,5247,5249,5251,5253,5255,5257,5259,5261,5263,5265,5267,5269,5271,5273,5275,5277,5279,5281,5283,5285,5287,5289,5291,5293,5295,5297,5299,5301,5303,5305,5307,5309,5311,5313,5315,5317,5319,5321,5323,5325,5327,5329,5331,5333,5335,5337,5339,5341,5343,5345,5347,5349,5351,5353,5355,5357,5359,5361,5363,5365,5367,5369,5371,5373,5375,5377,5379,5381,5383,5385,5387,5390,5392,5394,5396,5398,5400,5402,5404,5406,5408,5410,5412,5414,5416,5418,5420,5422,5424,5426,5428,5430,5432,5434,5436,5438,5440,5442,5444,5446,5448,5450,5452,5454,5456,5458,5460,5462,5464,5466,5468,5470,5472,5474,5476,5478,5480,5482,5484,5486,5488,5490,5492,5494,5496,5498,5500,5502,5504,5506,5508,5510,5512,5514,5516,5518,5520,5522,5524,5526,5528,5530,5532,5534,5536,5538,5540,5542,5544,5546,5548,5550,5552,5554,5556,5558,5560,5562,5564,5566,5568,5570,5572,5574,5576,5578,5580,5582,5584,5586,5588,5590,5593,5595,5597,5599,5601,5603,5605,5607,5609,5611,5613,5615,5617,5619,5621,5623,5625,5627,5629,5631,5633,5635,5637,5639,5641,5643,5645,5647,5649,5651,5653,5655,5657,5659,5661,5663,5665,5667,5669,5671,5673,5675,5677,5679,5681,5683,5685,5687,5689,5691,5693,5695,5697,5699,5701,5703,5705,5707,5709,5711,5713,5715,5717,5719,5721,5723,5725,5727,5729,5731,5733,5735,5737,5739,5741,5743,5745,5747,5749,5751,5753,5755,5757,5759,5761,5763,5765,5767,5769,5771,5773,5775,5777,5779,5781,5783,5785,5787,5789,5791,5793,5795,5797,5799,5801,5803,5805,5807,5809,5811,5813,5815,5817,5819,5821,5823,5825,5827,5829,5831,5833,5835,5837,5839,5841,5843,5845,5847,5849,5851,5853,5855,5857,5859,5861,5863,5865,5867,5869,5871,5873,5875,5877,5879,5881,5883,5885,5887,5889,5891,5893,5895,5897,5899,5901,5903,5905,5907,5909,5911,5913,5915,5917,5919,5921,5923,5925,5927,5929,5931,5933,5935,5937,5939,5941,5943,5945,5947,5949,5951,5953,5955,5957,5959,5961,5963,5965,5967,5969,5971,5973,5975,5977,5979,5981,5983,5985,5987,5989,5991,5993,5995,5997,5999,6001,6003,6005,6007,6009,6011,6013,6015,6017,6019,6021,6023,6025,6027,6029,6031,6033,6035,6037,6039,6041,6043,6045,6047,6049,6051,6053,6055,6057,6059,6061,6063,6065,6067,6069,6071,6073,6075,6077,6079,6081,6083,6085,6087,6089,6091,6093,6095,6097,6099,6101,6103,6105,6107,6109,6111,6113,6115,6117,6119,6121,6123,6125,6127,6129,6131,6133,6135,6137,6139,6141,6143,6145,6147,6149,6151,6153,6155,6157,6159,6161,6163,6165,6167,6169,6171,6173,6175,6177,6179,6181,6183,6185,6187,6189,6191,6193,6195,6197,6199,6201,6203,6205,6207,6209,6211,6213,6215,6217,6219,6221,6223,6225,6227,6229,6231,6233,6235,6237,6239,6241,6243,6245,6247,6249,6251,6253,6255,6257,6259,6261,6263,6265,6267,6269,6271,6273,6275,6277,6279,6281,6283,6285,6287,6289,6291,6293,6295,6297,6299,6301,6303,6305,6307,6309,6311,6313,6315,6317,6319,6321,6323,6325,6327,6329,6331,6333,6335,6337,6339,6341,6343,6345,6347,6349,6351,6353,6355,6357,6359,6361,6363,6365,6367,6369,6371,6373,6375,6377,6379,6381,6383,6385,6387,6389,6391,6393,6395,6397,6399,6401,6403,6405,6407,6409,6411,6413,6415,6417,6419,6421,6423,6425,6427,6429,6431,6433,6435,6437,6439,6441,6443,6445,6447,6449,6451,6453,6455,6457,6459,6461,6463,6465,6467,6469,6471,6473,6475,6477,6479,6481,6483,6485,6487,6489,6491,6493,6495,6497,6499,6501,6503,6505,6507,6509,6511,6513,6515,6517,6519,6521,6523,6525,6527,6529,6531,6533,6535,6537,6539,6541,6543,6545,6547,6549,6551,6553,6555,6557,6559,6561,6563,6565,6567,6569,6571,6573,6575,6577,6579,6581,6583,6585,6587,6589,6591,6593,6595,6597,6599,6601,6603,6605,6607,6609,6611,6613,6615,6617,6619,6621,6623,6625,6627,6629,6631,6633,6635,6637,6639,6641,6643,6645,6647,6649,6651,6653,6655,6657,6659,6661,6663,6665,6667,6669,6671,6673,6675,6677,6679,6681,6683,6685,6687,6689,6691,6693,6695,6697,6699,6701,6703,6705,6707,6709,6711,6713,6715,6717,6719,6721,6723,6725,6727,6729,6731,6733,6735,6737,6739,6741,6743,6745,6747,6749,6751,6753,6755,6757,6759,6761,6763,6765,6767,6769,6771,6773,6775,6777,6779,6781,6783,6785,6787,6789,6791,6793,6795,6797,6799,6801,6803,6805,6807,6809,6811,6813,6815,6817,6819,6821,6823,6825,6827,6829,6831,6833,6835,6837,6839,6841,6843,6845,6847,6849,6851,6853,6855,6857,6859,6861,6863,6865,6867,6869,6871,6873,6875,6877,6879,6881,6883,6885,6887,6889,6891,6893,6895,6897,6899,6901,6903,6905,6907,6909,6911,6913,6915,6917,6919,6921,6923,6925,6927,6929,6931,6933,6935,6937,6939,6941,6943,6945,6947,6949,6951,6953,6955,6957,6959,6961,6963,6965,6967,6969,6971,6973,6975,6977,6979,6981,6983,6985,6987,6989,6991,6993,6995,6997],{"categories":97},[72],{"categories":99},[100],"Developer Productivity",{"categories":102},[72],{"categories":104},[105],"Business & SaaS",{"categories":107},[72],{"categories":109},[110],"AI Automation",{"categories":112},[113],"Product Strategy",{"categories":115},[110],{"categories":117},[72],{"categories":119},[100],{"categories":121},[110],{"categories":123},[124],"Software Engineering",{"categories":126},[72],{"categories":128},[105],{"categories":130},[],{"categories":132},[72],{"categories":134},[135],"Inference & Serving",{"categories":137},[72],{"categories":139},[72],{"categories":141},[110],{"categories":143},[],{"categories":145},[146],"AI News & Trends",{"categories":148},[149],"Data Science & Visualization",{"categories":151},[110],{"categories":153},[72],{"categories":155},[72],{"categories":157},[105],{"categories":159},[100],{"categories":161},[72],{"categories":163},[110],{"categories":165},[146],{"categories":167},[72],{"categories":169},[110],{"categories":171},[110],{"categories":173},[72],{"categories":175},[72],{"categories":177},[110],{"categories":179},[72],{"categories":181},[72],{"categories":183},[72],{"categories":185},[110],{"categories":187},[146],{"categories":189},[72],{"categories":191},[72],{"categories":193},[72],{"categories":195},[],{"categories":197},[198],"Design & Frontend",{"categories":200},[149],{"categories":202},[146],{"categories":204},[72],{"categories":206},[72],{"categories":208},[72],{"categories":210},[],{"categories":212},[72],{"categories":214},[72],{"categories":216},[110],{"categories":218},[124],{"categories":220},[72],{"categories":222},[110],{"categories":224},[72],{"categories":226},[227],"Marketing & Growth",{"categories":229},[198],{"categories":231},[72],{"categories":233},[110],{"categories":235},[72],{"categories":237},[72],{"categories":239},[124],{"categories":241},[72],{"categories":243},[],{"categories":245},[],{"categories":247},[198],{"categories":249},[72],{"categories":251},[110],{"categories":253},[100],{"categories":255},[124],{"categories":257},[110],{"categories":259},[198],{"categories":261},[113],{"categories":263},[72],{"categories":265},[124],{"categories":267},[268],"DevOps & Cloud",{"categories":270},[110],{"categories":272},[113],{"categories":274},[146],{"categories":276},[72],{"categories":278},[],{"categories":280},[72],{"categories":282},[72],{"categories":284},[],{"categories":286},[110],{"categories":288},[124],{"categories":290},[],{"categories":292},[124],{"categories":294},[72],{"categories":296},[297],"Governance & Standards",{"categories":299},[105],{"categories":301},[],{"categories":303},[],{"categories":305},[72],{"categories":307},[72],{"categories":309},[110],{"categories":311},[72],{"categories":313},[72],{"categories":315},[110],{"categories":317},[72],{"categories":319},[72],{"categories":321},[72],{"categories":323},[],{"categories":325},[124],{"categories":327},[],{"categories":329},[],{"categories":331},[72],{"categories":333},[124],{"categories":335},[],{"categories":337},[124],{"categories":339},[72],{"categories":341},[72],{"categories":343},[227],{"categories":345},[72],{"categories":347},[72],{"categories":349},[72],{"categories":351},[198],{"categories":353},[198],{"categories":355},[72],{"categories":357},[124],{"categories":359},[110],{"categories":361},[362],"GovTech & Public-Sector Adoption",{"categories":364},[124],{"categories":366},[72],{"categories":368},[72],{"categories":370},[72],{"categories":372},[110],{"categories":374},[110],{"categories":376},[149],{"categories":378},[72],{"categories":380},[146],{"categories":382},[110],{"categories":384},[385],"Legal AI Tools",{"categories":387},[72],{"categories":389},[110],{"categories":391},[72],{"categories":393},[227],{"categories":395},[110],{"categories":397},[113],{"categories":399},[72],{"categories":401},[124],{"categories":403},[362],{"categories":405},[],{"categories":407},[110],{"categories":409},[],{"categories":411},[105],{"categories":413},[110],{"categories":415},[110],{"categories":417},[418],"RAG & Retrieval",{"categories":420},[105],{"categories":422},[72],{"categories":424},[124],{"categories":426},[124],{"categories":428},[268],{"categories":430},[198],{"categories":432},[110],{"categories":434},[72],{"categories":436},[72],{"categories":438},[],{"categories":440},[441],"Agents & Orchestration",{"categories":443},[124],{"categories":445},[72],{"categories":447},[],{"categories":449},[110],{"categories":451},[105],{"categories":453},[],{"categories":455},[72],{"categories":457},[],{"categories":459},[72],{"categories":461},[100],{"categories":463},[124],{"categories":465},[105],{"categories":467},[72],{"categories":469},[110],{"categories":471},[72],{"categories":473},[146],{"categories":475},[72],{"categories":477},[],{"categories":479},[72],{"categories":481},[],{"categories":483},[72],{"categories":485},[124],{"categories":487},[72],{"categories":489},[110],{"categories":491},[149],{"categories":493},[],{"categories":495},[72],{"categories":497},[198],{"categories":499},[500],"Models & Frontier Labs",{"categories":502},[],{"categories":504},[198],{"categories":506},[507],"Regulation & Governance of AI",{"categories":509},[113],{"categories":511},[110],{"categories":513},[],{"categories":515},[72],{"categories":517},[72],{"categories":519},[110],{"categories":521},[110],{"categories":523},[146],{"categories":525},[72],{"categories":527},[105],{"categories":529},[72],{"categories":531},[110],{"categories":533},[],{"categories":535},[124],{"categories":537},[110],{"categories":539},[72],{"categories":541},[113],{"categories":543},[72],{"categories":545},[546],"AI Policy & Regulation",{"categories":548},[],{"categories":550},[72],{"categories":552},[110],{"categories":554},[113],{"categories":556},[110],{"categories":558},[72],{"categories":560},[72],{"categories":562},[72],{"categories":564},[110],{"categories":566},[],{"categories":568},[149],{"categories":570},[571],"Evals & Reliability",{"categories":573},[72],{"categories":575},[72],{"categories":577},[],{"categories":579},[100],{"categories":581},[362],{"categories":583},[546],{"categories":585},[72],{"categories":587},[105],{"categories":589},[72],{"categories":591},[110],{"categories":593},[72],{"categories":595},[110],{"categories":597},[441],{"categories":599},[72],{"categories":601},[124],{"categories":603},[72],{"categories":605},[],{"categories":607},[198],{"categories":609},[],{"categories":611},[72],{"categories":613},[362],{"categories":615},[72],{"categories":617},[72],{"categories":619},[72],{"categories":621},[],{"categories":623},[72],{"categories":625},[198],{"categories":627},[124],{"categories":629},[],{"categories":631},[72],{"categories":633},[],{"categories":635},[110],{"categories":637},[72],{"categories":639},[198],{"categories":641},[],{"categories":643},[72],{"categories":645},[72],{"categories":647},[149],{"categories":649},[110],{"categories":651},[72],{"categories":653},[105],{"categories":655},[110],{"categories":657},[72],{"categories":659},[72],{"categories":661},[124],{"categories":663},[198],{"categories":665},[72],{"categories":667},[110],{"categories":669},[],{"categories":671},[124],{"categories":673},[110],{"categories":675},[149],{"categories":677},[],{"categories":679},[72],{"categories":681},[146],{"categories":683},[72],{"categories":685},[],{"categories":687},[72],{"categories":689},[72],{"categories":691},[72],{"categories":693},[105,227],{"categories":695},[],{"categories":697},[124],{"categories":699},[72],{"categories":701},[72],{"categories":703},[110],{"categories":705},[72],{"categories":707},[],{"categories":709},[],{"categories":711},[72],{"categories":713},[198],{"categories":715},[72],{"categories":717},[],{"categories":719},[72],{"categories":721},[268],{"categories":723},[],{"categories":725},[110],{"categories":727},[146],{"categories":729},[72],{"categories":731},[72],{"categories":733},[198],{"categories":735},[],{"categories":737},[146],{"categories":739},[72],{"categories":741},[135],{"categories":743},[72],{"categories":745},[72],{"categories":747},[110],{"categories":749},[146],{"categories":751},[500],{"categories":753},[72],{"categories":755},[227],{"categories":757},[],{"categories":759},[110],{"categories":761},[105],{"categories":763},[124],{"categories":765},[72],{"categories":767},[110],{"categories":769},[],{"categories":771},[72,268],{"categories":773},[72],{"categories":775},[72],{"categories":777},[72],{"categories":779},[110],{"categories":781},[72,124],{"categories":783},[149],{"categories":785},[72],{"categories":787},[72],{"categories":789},[72],{"categories":791},[124],{"categories":793},[72],{"categories":795},[110],{"categories":797},[110],{"categories":799},[546],{"categories":801},[227],{"categories":803},[72],{"categories":805},[110],{"categories":807},[72],{"categories":809},[72],{"categories":811},[110],{"categories":813},[],{"categories":815},[110],{"categories":817},[72],{"categories":819},[72],{"categories":821},[110],{"categories":823},[72],{"categories":825},[72,105],{"categories":827},[72],{"categories":829},[105],{"categories":831},[],{"categories":833},[198],{"categories":835},[198],{"categories":837},[72],{"categories":839},[],{"categories":841},[],{"categories":843},[72],{"categories":845},[146],{"categories":847},[],{"categories":849},[100],{"categories":851},[72],{"categories":853},[124],{"categories":855},[72],{"categories":857},[858],"Generative UI & Design-to-Code",{"categories":860},[72],{"categories":862},[72],{"categories":864},[198],{"categories":866},[72],{"categories":868},[869],"Algorithmic Accountability",{"categories":871},[110],{"categories":873},[124],{"categories":875},[146],{"categories":877},[198],{"categories":879},[72],{"categories":881},[],{"categories":883},[113],{"categories":885},[72],{"categories":887},[72],{"categories":889},[72],{"categories":891},[72],{"categories":893},[110],{"categories":895},[896],"MLOps & Infrastructure",{"categories":898},[72],{"categories":900},[72],{"categories":902},[72],{"categories":904},[72],{"categories":906},[72],{"categories":908},[124],{"categories":910},[146],{"categories":912},[72],{"categories":914},[113],{"categories":916},[100],{"categories":918},[72],{"categories":920},[110],{"categories":922},[268],{"categories":924},[72],{"categories":926},[105],{"categories":928},[72],{"categories":930},[198],{"categories":932},[72],{"categories":934},[72],{"categories":936},[110],{"categories":938},[],{"categories":940},[],{"categories":942},[72],{"categories":944},[135],{"categories":946},[198],{"categories":948},[146],{"categories":950},[149],{"categories":952},[],{"categories":954},[72],{"categories":956},[72],{"categories":958},[105],{"categories":960},[110],{"categories":962},[72],{"categories":964},[72],{"categories":966},[72],{"categories":968},[72],{"categories":970},[146],{"categories":972},[135],{"categories":974},[72],{"categories":976},[198],{"categories":978},[72],{"categories":980},[],{"categories":982},[110],{"categories":984},[124],{"categories":986},[],{"categories":988},[72],{"categories":990},[72],{"categories":992},[110],{"categories":994},[124],{"categories":996},[72],{"categories":998},[149],{"categories":1000},[198],{"categories":1002},[],{"categories":1004},[72],{"categories":1006},[],{"categories":1008},[72],{"categories":1010},[],{"categories":1012},[72],{"categories":1014},[72],{"categories":1016},[113],{"categories":1018},[105],{"categories":1020},[110],{"categories":1022},[110],{"categories":1024},[],{"categories":1026},[72],{"categories":1028},[100],{"categories":1030},[72],{"categories":1032},[72],{"categories":1034},[105],{"categories":1036},[146],{"categories":1038},[100],{"categories":1040},[],{"categories":1042},[72],{"categories":1044},[],{"categories":1046},[72],{"categories":1048},[],{"categories":1050},[146],{"categories":1052},[146],{"categories":1054},[],{"categories":1056},[441],{"categories":1058},[72],{"categories":1060},[198],{"categories":1062},[124],{"categories":1064},[],{"categories":1066},[385],{"categories":1068},[110],{"categories":1070},[105],{"categories":1072},[],{"categories":1074},[],{"categories":1076},[100],{"categories":1078},[149],{"categories":1080},[],{"categories":1082},[227],{"categories":1084},[110],{"categories":1086},[105],{"categories":1088},[110],{"categories":1090},[72],{"categories":1092},[105],{"categories":1094},[72],{"categories":1096},[124],{"categories":1098},[],{"categories":1100},[135],{"categories":1102},[113],{"categories":1104},[72],{"categories":1106},[198],{"categories":1108},[124],{"categories":1110},[105],{"categories":1112},[72],{"categories":1114},[124],{"categories":1116},[72],{"categories":1118},[110],{"categories":1120},[105],{"categories":1122},[72],{"categories":1124},[72],{"categories":1126},[72],{"categories":1128},[72],{"categories":1130},[72],{"categories":1132},[],{"categories":1134},[],{"categories":1136},[124],{"categories":1138},[149],{"categories":1140},[113],{"categories":1142},[72],{"categories":1144},[110],{"categories":1146},[124],{"categories":1148},[124],{"categories":1150},[72],{"categories":1152},[],{"categories":1154},[146],{"categories":1156},[113],{"categories":1158},[113],{"categories":1160},[124],{"categories":1162},[72],{"categories":1164},[571],{"categories":1166},[268],{"categories":1168},[],{"categories":1170},[110],{"categories":1172},[72],{"categories":1174},[],{"categories":1176},[100],{"categories":1178},[],{"categories":1180},[72],{"categories":1182},[72],{"categories":1184},[72],{"categories":1186},[198],{"categories":1188},[227],{"categories":1190},[72],{"categories":1192},[124],{"categories":1194},[72],{"categories":1196},[110],{"categories":1198},[],{"categories":1200},[124],{"categories":1202},[72],{"categories":1204},[100],{"categories":1206},[],{"categories":1208},[105],{"categories":1210},[72],{"categories":1212},[72],{"categories":1214},[146],{"categories":1216},[72,268],{"categories":1218},[72],{"categories":1220},[1221],"Design Systems for AI",{"categories":1223},[72],{"categories":1225},[72],{"categories":1227},[146],{"categories":1229},[72],{"categories":1231},[72],{"categories":1233},[72],{"categories":1235},[105],{"categories":1237},[72],{"categories":1239},[72],{"categories":1241},[72],{"categories":1243},[],{"categories":1245},[72],{"categories":1247},[72],{"categories":1249},[105],{"categories":1251},[72],{"categories":1253},[],{"categories":1255},[110],{"categories":1257},[110],{"categories":1259},[124],{"categories":1261},[146],{"categories":1263},[124],{"categories":1265},[72],{"categories":1267},[198],{"categories":1269},[146],{"categories":1271},[149],{"categories":1273},[72],{"categories":1275},[72],{"categories":1277},[110],{"categories":1279},[100],{"categories":1281},[546],{"categories":1283},[72],{"categories":1285},[110],{"categories":1287},[72],{"categories":1289},[124],{"categories":1291},[124],{"categories":1293},[],{"categories":1295},[],{"categories":1297},[72],{"categories":1299},[110],{"categories":1301},[113],{"categories":1303},[],{"categories":1305},[105],{"categories":1307},[72],{"categories":1309},[],{"categories":1311},[198],{"categories":1313},[124],{"categories":1315},[110],{"categories":1317},[124],{"categories":1319},[198],{"categories":1321},[72],{"categories":1323},[72],{"categories":1325},[198],{"categories":1327},[],{"categories":1329},[],{"categories":1331},[146],{"categories":1333},[110],{"categories":1335},[110],{"categories":1337},[72],{"categories":1339},[72],{"categories":1341},[72],{"categories":1343},[72],{"categories":1345},[105],{"categories":1347},[72],{"categories":1349},[72],{"categories":1351},[],{"categories":1353},[124],{"categories":1355},[124],{"categories":1357},[72],{"categories":1359},[124],{"categories":1361},[105],{"categories":1363},[],{"categories":1365},[72],{"categories":1367},[72],{"categories":1369},[72],{"categories":1371},[72],{"categories":1373},[72],{"categories":1375},[110],{"categories":1377},[100],{"categories":1379},[105],{"categories":1381},[72],{"categories":1383},[110],{"categories":1385},[146],{"categories":1387},[110],{"categories":1389},[135],{"categories":1391},[227],{"categories":1393},[72],{"categories":1395},[110],{"categories":1397},[72],{"categories":1399},[72],{"categories":1401},[72],{"categories":1403},[],{"categories":1405},[198],{"categories":1407},[],{"categories":1409},[72],{"categories":1411},[72],{"categories":1413},[],{"categories":1415},[72],{"categories":1417},[124],{"categories":1419},[105],{"categories":1421},[1422],"Visual & Generative Media",{"categories":1424},[110],{"categories":1426},[],{"categories":1428},[72],{"categories":1430},[72],{"categories":1432},[124],{"categories":1434},[268],{"categories":1436},[72],{"categories":1438},[149],{"categories":1440},[546],{"categories":1442},[124],{"categories":1444},[227],{"categories":1446},[72],{"categories":1448},[198],{"categories":1450},[72],{"categories":1452},[72],{"categories":1454},[124],{"categories":1456},[110],{"categories":1458},[72],{"categories":1460},[],{"categories":1462},[],{"categories":1464},[110],{"categories":1466},[124],{"categories":1468},[100],{"categories":1470},[110],{"categories":1472},[500],{"categories":1474},[72],{"categories":1476},[113],{"categories":1478},[72],{"categories":1480},[105],{"categories":1482},[],{"categories":1484},[72],{"categories":1486},[113],{"categories":1488},[72],{"categories":1490},[72],{"categories":1492},[72],{"categories":1494},[113],{"categories":1496},[72],{"categories":1498},[72],{"categories":1500},[227],{"categories":1502},[72],{"categories":1504},[441],{"categories":1506},[72],{"categories":1508},[110],{"categories":1510},[72],{"categories":1512},[72],{"categories":1514},[72],{"categories":1516},[72],{"categories":1518},[198],{"categories":1520},[110],{"categories":1522},[],{"categories":1524},[110],{"categories":1526},[],{"categories":1528},[268],{"categories":1530},[124],{"categories":1532},[],{"categories":1534},[500],{"categories":1536},[72],{"categories":1538},[110],{"categories":1540},[110],{"categories":1542},[72],{"categories":1544},[198,72],{"categories":1546},[100],{"categories":1548},[72],{"categories":1550},[198],{"categories":1552},[],{"categories":1554},[72],{"categories":1556},[100],{"categories":1558},[72],{"categories":1560},[1561],"Medical Imaging & Radiology",{"categories":1563},[72],{"categories":1565},[72],{"categories":1567},[72],{"categories":1569},[198],{"categories":1571},[110],{"categories":1573},[124],{"categories":1575},[],{"categories":1577},[72],{"categories":1579},[72],{"categories":1581},[72],{"categories":1583},[],{"categories":1585},[],{"categories":1587},[72],{"categories":1589},[72],{"categories":1591},[441],{"categories":1593},[72],{"categories":1595},[100],{"categories":1597},[72],{"categories":1599},[72],{"categories":1601},[],{"categories":1603},[110],{"categories":1605},[72],{"categories":1607},[113],{"categories":1609},[124],{"categories":1611},[72],{"categories":1613},[110],{"categories":1615},[441],{"categories":1617},[72],{"categories":1619},[110],{"categories":1621},[72],{"categories":1623},[72],{"categories":1625},[72],{"categories":1627},[198],{"categories":1629},[110],{"categories":1631},[268],{"categories":1633},[198],{"categories":1635},[105],{"categories":1637},[110],{"categories":1639},[146],{"categories":1641},[72],{"categories":1643},[72],{"categories":1645},[113],{"categories":1647},[72],{"categories":1649},[72],{"categories":1651},[72],{"categories":1653},[72],{"categories":1655},[110],{"categories":1657},[72],{"categories":1659},[124],{"categories":1661},[124],{"categories":1663},[72],{"categories":1665},[113],{"categories":1667},[],{"categories":1669},[146],{"categories":1671},[],{"categories":1673},[113],{"categories":1675},[110],{"categories":1677},[72],{"categories":1679},[110],{"categories":1681},[1221],{"categories":1683},[1221],{"categories":1685},[198],{"categories":1687},[72],{"categories":1689},[72],{"categories":1691},[72],{"categories":1693},[110],{"categories":1695},[124],{"categories":1697},[198],{"categories":1699},[110],{"categories":1701},[146],{"categories":1703},[],{"categories":1705},[72],{"categories":1707},[],{"categories":1709},[72],{"categories":1711},[72],{"categories":1713},[72],{"categories":1715},[72],{"categories":1717},[110],{"categories":1719},[1720],"Contract Review & E-Discovery",{"categories":1722},[72],{"categories":1724},[198],{"categories":1726},[72],{"categories":1728},[100],{"categories":1730},[72],{"categories":1732},[146],{"categories":1734},[72],{"categories":1736},[72],{"categories":1738},[227],{"categories":1740},[124],{"categories":1742},[72],{"categories":1744},[72],{"categories":1746},[110],{"categories":1748},[110],{"categories":1750},[869],{"categories":1752},[72],{"categories":1754},[72],{"categories":1756},[110],{"categories":1758},[110],{"categories":1760},[72],{"categories":1762},[72],{"categories":1764},[72],{"categories":1766},[110],{"categories":1768},[72],{"categories":1770},[72],{"categories":1772},[441],{"categories":1774},[418],{"categories":1776},[72],{"categories":1778},[110],{"categories":1780},[72],{"categories":1782},[1783],"Law-Firm Practice & Adoption",{"categories":1785},[72],{"categories":1787},[110],{"categories":1789},[198],{"categories":1791},[72],{"categories":1793},[72],{"categories":1795},[72],{"categories":1797},[],{"categories":1799},[],{"categories":1801},[124],{"categories":1803},[72],{"categories":1805},[],{"categories":1807},[110],{"categories":1809},[100],{"categories":1811},[268],{"categories":1813},[72],{"categories":1815},[],{"categories":1817},[100],{"categories":1819},[105],{"categories":1821},[72],{"categories":1823},[227],{"categories":1825},[],{"categories":1827},[105],{"categories":1829},[110],{"categories":1831},[105],{"categories":1833},[],{"categories":1835},[72],{"categories":1837},[113],{"categories":1839},[72],{"categories":1841},[124],{"categories":1843},[],{"categories":1845},[],{"categories":1847},[],{"categories":1849},[],{"categories":1851},[72],{"categories":1853},[113],{"categories":1855},[110],{"categories":1857},[268],{"categories":1859},[72],{"categories":1861},[100],{"categories":1863},[124],{"categories":1865},[72],{"categories":1867},[72],{"categories":1869},[124],{"categories":1871},[113],{"categories":1873},[72],{"categories":1875},[72],{"categories":1877},[72],{"categories":1879},[896],{"categories":1881},[72],{"categories":1883},[124],{"categories":1885},[72],{"categories":1887},[227],{"categories":1889},[124],{"categories":1891},[105],{"categories":1893},[72],{"categories":1895},[72],{"categories":1897},[72],{"categories":1899},[198],{"categories":1901},[72],{"categories":1903},[72],{"categories":1905},[72],{"categories":1907},[72],{"categories":1909},[105],{"categories":1911},[110],{"categories":1913},[72,100],{"categories":1915},[441],{"categories":1917},[72],{"categories":1919},[72],{"categories":1921},[124],{"categories":1923},[124],{"categories":1925},[198],{"categories":1927},[110],{"categories":1929},[110],{"categories":1931},[124],{"categories":1933},[72],{"categories":1935},[72],{"categories":1937},[72],{"categories":1939},[],{"categories":1941},[],{"categories":1943},[72],{"categories":1945},[149],{"categories":1947},[72],{"categories":1949},[110],{"categories":1951},[],{"categories":1953},[72],{"categories":1955},[72],{"categories":1957},[124],{"categories":1959},[149],{"categories":1961},[146],{"categories":1963},[198],{"categories":1965},[72],{"categories":1967},[110],{"categories":1969},[72],{"categories":1971},[124],{"categories":1973},[],{"categories":1975},[110],{"categories":1977},[72],{"categories":1979},[72],{"categories":1981},[72],{"categories":1983},[72],{"categories":1985},[],{"categories":1987},[110],{"categories":1989},[72],{"categories":1991},[72],{"categories":1993},[72],{"categories":1995},[],{"categories":1997},[110],{"categories":1999},[72],{"categories":2001},[72],{"categories":2003},[105],{"categories":2005},[72],{"categories":2007},[72],{"categories":2009},[],{"categories":2011},[100],{"categories":2013},[72],{"categories":2015},[72],{"categories":2017},[72],{"categories":2019},[198],{"categories":2021},[72],{"categories":2023},[124],{"categories":2025},[72],{"categories":2027},[100],{"categories":2029},[72],{"categories":2031},[124],{"categories":2033},[227],{"categories":2035},[110],{"categories":2037},[110],{"categories":2039},[72],{"categories":2041},[72],{"categories":2043},[72,198],{"categories":2045},[72],{"categories":2047},[110],{"categories":2049},[146],{"categories":2051},[72],{"categories":2053},[146],{"categories":2055},[110],{"categories":2057},[198],{"categories":2059},[72],{"categories":2061},[],{"categories":2063},[124],{"categories":2065},[268],{"categories":2067},[198],{"categories":2069},[124],{"categories":2071},[72],{"categories":2073},[113],{"categories":2075},[72],{"categories":2077},[72],{"categories":2079},[110],{"categories":2081},[],{"categories":2083},[],{"categories":2085},[72],{"categories":2087},[],{"categories":2089},[],{"categories":2091},[113],{"categories":2093},[124],{"categories":2095},[72],{"categories":2097},[110],{"categories":2099},[110],{"categories":2101},[105],{"categories":2103},[110],{"categories":2105},[268],{"categories":2107},[72],{"categories":2109},[72],{"categories":2111},[72],{"categories":2113},[135],{"categories":2115},[72],{"categories":2117},[72],{"categories":2119},[72],{"categories":2121},[124],{"categories":2123},[110],{"categories":2125},[72],{"categories":2127},[72],{"categories":2129},[124],{"categories":2131},[385],{"categories":2133},[110],{"categories":2135},[869],{"categories":2137},[],{"categories":2139},[198],{"categories":2141},[1783],{"categories":2143},[124],{"categories":2145},[],{"categories":2147},[],{"categories":2149},[72],{"categories":2151},[110],{"categories":2153},[],{"categories":2155},[],{"categories":2157},[72],{"categories":2159},[227],{"categories":2161},[72],{"categories":2163},[227],{"categories":2165},[110],{"categories":2167},[72],{"categories":2169},[72],{"categories":2171},[124],{"categories":2173},[113],{"categories":2175},[],{"categories":2177},[72],{"categories":2179},[72],{"categories":2181},[124],{"categories":2183},[1720],{"categories":2185},[198],{"categories":2187},[198],{"categories":2189},[72],{"categories":2191},[110],{"categories":2193},[100],{"categories":2195},[72],{"categories":2197},[72],{"categories":2199},[72],{"categories":2201},[72],{"categories":2203},[198],{"categories":2205},[198],{"categories":2207},[110],{"categories":2209},[110],{"categories":2211},[110],{"categories":2213},[72],{"categories":2215},[72],{"categories":2217},[],{"categories":2219},[72],{"categories":2221},[],{"categories":2223},[2224],"Interaction & Product Design",{"categories":2226},[72],{"categories":2228},[110],{"categories":2230},[124],{"categories":2232},[297],{"categories":2234},[146],{"categories":2236},[124],{"categories":2238},[72],{"categories":2240},[72],{"categories":2242},[72],{"categories":2244},[124],{"categories":2246},[72],{"categories":2248},[100],{"categories":2250},[110],{"categories":2252},[72],{"categories":2254},[],{"categories":2256},[110],{"categories":2258},[110],{"categories":2260},[110],{"categories":2262},[],{"categories":2264},[124],{"categories":2266},[72],{"categories":2268},[110],{"categories":2270},[100],{"categories":2272},[2224],{"categories":2274},[72],{"categories":2276},[100],{"categories":2278},[100],{"categories":2280},[],{"categories":2282},[110],{"categories":2284},[124],{"categories":2286},[],{"categories":2288},[110],{"categories":2290},[146],{"categories":2292},[72],{"categories":2294},[110],{"categories":2296},[72],{"categories":2298},[110],{"categories":2300},[110],{"categories":2302},[72],{"categories":2304},[72],{"categories":2306},[146],{"categories":2308},[149],{"categories":2310},[72],{"categories":2312},[113],{"categories":2314},[124],{"categories":2316},[2317],"Coding Agents & Dev Productivity",{"categories":2319},[146],{"categories":2321},[198],{"categories":2323},[72],{"categories":2325},[72],{"categories":2327},[],{"categories":2329},[72],{"categories":2331},[869],{"categories":2333},[],{"categories":2335},[72],{"categories":2337},[72],{"categories":2339},[268],{"categories":2341},[72],{"categories":2343},[146],{"categories":2345},[],{"categories":2347},[],{"categories":2349},[72],{"categories":2351},[],{"categories":2353},[110],{"categories":2355},[72],{"categories":2357},[],{"categories":2359},[124],{"categories":2361},[124],{"categories":2363},[72],{"categories":2365},[149],{"categories":2367},[],{"categories":2369},[72],{"categories":2371},[72],{"categories":2373},[72],{"categories":2375},[149],{"categories":2377},[124],{"categories":2379},[110],{"categories":2381},[],{"categories":2383},[],{"categories":2385},[72],{"categories":2387},[72],{"categories":2389},[110],{"categories":2391},[110],{"categories":2393},[362],{"categories":2395},[124],{"categories":2397},[124],{"categories":2399},[110],{"categories":2401},[146],{"categories":2403},[146],{"categories":2405},[110],{"categories":2407},[110],{"categories":2409},[72],{"categories":2411},[100],{"categories":2413},[2224],{"categories":2415},[113],{"categories":2417},[72,268],{"categories":2419},[149],{"categories":2421},[],{"categories":2423},[198],{"categories":2425},[110],{"categories":2427},[124],{"categories":2429},[100],{"categories":2431},[72],{"categories":2433},[110],{"categories":2435},[2436],"The Designer's Role & Craft",{"categories":2438},[198],{"categories":2440},[],{"categories":2442},[110],{"categories":2444},[72],{"categories":2446},[110],{"categories":2448},[110],{"categories":2450},[72],{"categories":2452},[227],{"categories":2454},[72],{"categories":2456},[124],{"categories":2458},[72],{"categories":2460},[198],{"categories":2462},[72],{"categories":2464},[],{"categories":2466},[110],{"categories":2468},[198],{"categories":2470},[113],{"categories":2472},[72],{"categories":2474},[72],{"categories":2476},[72],{"categories":2478},[2479],"AI UX Patterns",{"categories":2481},[110],{"categories":2483},[110],{"categories":2485},[110],{"categories":2487},[110],{"categories":2489},[227],{"categories":2491},[149],{"categories":2493},[72],{"categories":2495},[110],{"categories":2497},[72],{"categories":2499},[1221],{"categories":2501},[],{"categories":2503},[227],{"categories":2505},[110],{"categories":2507},[146],{"categories":2509},[124],{"categories":2511},[72],{"categories":2513},[110],{"categories":2515},[],{"categories":2517},[],{"categories":2519},[72],{"categories":2521},[72],{"categories":2523},[110],{"categories":2525},[72],{"categories":2527},[110],{"categories":2529},[362],{"categories":2531},[198],{"categories":2533},[72],{"categories":2535},[146],{"categories":2537},[124],{"categories":2539},[72],{"categories":2541},[110],{"categories":2543},[110],{"categories":2545},[],{"categories":2547},[72],{"categories":2549},[],{"categories":2551},[72],{"categories":2553},[],{"categories":2555},[72],{"categories":2557},[72],{"categories":2559},[72],{"categories":2561},[110],{"categories":2563},[124],{"categories":2565},[],{"categories":2567},[],{"categories":2569},[149],{"categories":2571},[135],{"categories":2573},[72],{"categories":2575},[72],{"categories":2577},[72],{"categories":2579},[149],{"categories":2581},[72],{"categories":2583},[72],{"categories":2585},[146],{"categories":2587},[72],{"categories":2589},[72],{"categories":2591},[72],{"categories":2593},[110],{"categories":2595},[72],{"categories":2597},[110],{"categories":2599},[72],{"categories":2601},[72],{"categories":2603},[72],{"categories":2605},[110],{"categories":2607},[],{"categories":2609},[72],{"categories":2611},[],{"categories":2613},[72],{"categories":2615},[72],{"categories":2617},[268],{"categories":2619},[72],{"categories":2621},[],{"categories":2623},[],{"categories":2625},[198],{"categories":2627},[896],{"categories":2629},[110],{"categories":2631},[100],{"categories":2633},[2436],{"categories":2635},[],{"categories":2637},[],{"categories":2639},[72],{"categories":2641},[],{"categories":2643},[],{"categories":2645},[124],{"categories":2647},[146],{"categories":2649},[227],{"categories":2651},[110],{"categories":2653},[105],{"categories":2655},[72],{"categories":2657},[72],{"categories":2659},[105],{"categories":2661},[],{"categories":2663},[198],{"categories":2665},[113],{"categories":2667},[72],{"categories":2669},[72],{"categories":2671},[110],{"categories":2673},[105],{"categories":2675},[72],{"categories":2677},[72],{"categories":2679},[100],{"categories":2681},[72],{"categories":2683},[72],{"categories":2685},[],{"categories":2687},[100],{"categories":2689},[72],{"categories":2691},[227],{"categories":2693},[110],{"categories":2695},[146],{"categories":2697},[72],{"categories":2699},[124],{"categories":2701},[72],{"categories":2703},[72],{"categories":2705},[105],{"categories":2707},[72],{"categories":2709},[72],{"categories":2711},[72],{"categories":2713},[110],{"categories":2715},[72],{"categories":2717},[],{"categories":2719},[72],{"categories":2721},[124],{"categories":2723},[100],{"categories":2725},[72],{"categories":2727},[72],{"categories":2729},[72],{"categories":2731},[],{"categories":2733},[72],{"categories":2735},[441],{"categories":2737},[110],{"categories":2739},[105],{"categories":2741},[146],{"categories":2743},[72],{"categories":2745},[72],{"categories":2747},[],{"categories":2749},[105],{"categories":2751},[105],{"categories":2753},[72],{"categories":2755},[72],{"categories":2757},[113],{"categories":2759},[72],{"categories":2761},[72],{"categories":2763},[72],{"categories":2765},[72],{"categories":2767},[124],{"categories":2769},[124],{"categories":2771},[72],{"categories":2773},[],{"categories":2775},[124],{"categories":2777},[72],{"categories":2779},[124],{"categories":2781},[110],{"categories":2783},[546],{"categories":2785},[],{"categories":2787},[],{"categories":2789},[72],{"categories":2791},[146],{"categories":2793},[],{"categories":2795},[268],{"categories":2797},[72],{"categories":2799},[72],{"categories":2801},[72],{"categories":2803},[198],{"categories":2805},[858],{"categories":2807},[],{"categories":2809},[72],{"categories":2811},[72],{"categories":2813},[72],{"categories":2815},[124],{"categories":2817},[72],{"categories":2819},[72],{"categories":2821},[72,268],{"categories":2823},[72],{"categories":2825},[72],{"categories":2827},[198],{"categories":2829},[110],{"categories":2831},[],{"categories":2833},[110],{"categories":2835},[110],{"categories":2837},[72],{"categories":2839},[72],{"categories":2841},[72],{"categories":2843},[72],{"categories":2845},[149],{"categories":2847},[72],{"categories":2849},[2479],{"categories":2851},[100],{"categories":2853},[149],{"categories":2855},[100],{"categories":2857},[124],{"categories":2859},[198],{"categories":2861},[110],{"categories":2863},[72],{"categories":2865},[],{"categories":2867},[105],{"categories":2869},[72],{"categories":2871},[72],{"categories":2873},[146],{"categories":2875},[72],{"categories":2877},[72],{"categories":2879},[72],{"categories":2881},[110],{"categories":2883},[72],{"categories":2885},[72],{"categories":2887},[72],{"categories":2889},[105],{"categories":2891},[],{"categories":2893},[268],{"categories":2895},[72],{"categories":2897},[362],{"categories":2899},[198],{"categories":2901},[198],{"categories":2903},[124],{"categories":2905},[110],{"categories":2907},[72],{"categories":2909},[105],{"categories":2911},[146],{"categories":2913},[72],{"categories":2915},[72],{"categories":2917},[72],{"categories":2919},[198],{"categories":2921},[110],{"categories":2923},[110],{"categories":2925},[72],{"categories":2927},[72],{"categories":2929},[500],{"categories":2931},[110],{"categories":2933},[],{"categories":2935},[72],{"categories":2937},[72],{"categories":2939},[72],{"categories":2941},[],{"categories":2943},[],{"categories":2945},[72],{"categories":2947},[72],{"categories":2949},[110],{"categories":2951},[72],{"categories":2953},[72],{"categories":2955},[72],{"categories":2957},[124],{"categories":2959},[72],{"categories":2961},[72],{"categories":2963},[110],{"categories":2965},[72],{"categories":2967},[72],{"categories":2969},[72],{"categories":2971},[72],{"categories":2973},[72],{"categories":2975},[],{"categories":2977},[124],{"categories":2979},[149],{"categories":2981},[72],{"categories":2983},[110],{"categories":2985},[110],{"categories":2987},[72],{"categories":2989},[72],{"categories":2991},[],{"categories":2993},[],{"categories":2995},[72],{"categories":2997},[72],{"categories":2999},[72],{"categories":3001},[146],{"categories":3003},[149],{"categories":3005},[],{"categories":3007},[72],{"categories":3009},[198],{"categories":3011},[72],{"categories":3013},[268],{"categories":3015},[1783],{"categories":3017},[146],{"categories":3019},[124],{"categories":3021},[72],{"categories":3023},[124],{"categories":3025},[124],{"categories":3027},[72],{"categories":3029},[72],{"categories":3031},[124],{"categories":3033},[146],{"categories":3035},[146],{"categories":3037},[268],{"categories":3039},[110],{"categories":3041},[],{"categories":3043},[146],{"categories":3045},[72],{"categories":3047},[110],{"categories":3049},[100],{"categories":3051},[124],{"categories":3053},[72],{"categories":3055},[146],{"categories":3057},[],{"categories":3059},[72],{"categories":3061},[124],{"categories":3063},[124],{"categories":3065},[149],{"categories":3067},[72],{"categories":3069},[146],{"categories":3071},[72],{"categories":3073},[124],{"categories":3075},[110],{"categories":3077},[110],{"categories":3079},[146],{"categories":3081},[110],{"categories":3083},[268],{"categories":3085},[110],{"categories":3087},[72],{"categories":3089},[72],{"categories":3091},[72],{"categories":3093},[72],{"categories":3095},[124],{"categories":3097},[72],{"categories":3099},[],{"categories":3101},[110],{"categories":3103},[105],{"categories":3105},[124],{"categories":3107},[],{"categories":3109},[],{"categories":3111},[72],{"categories":3113},[110],{"categories":3115},[72],{"categories":3117},[72],{"categories":3119},[3120],"Frameworks & Tooling",{"categories":3122},[72],{"categories":3124},[72],{"categories":3126},[124],{"categories":3128},[72],{"categories":3130},[72],{"categories":3132},[],{"categories":3134},[149],{"categories":3136},[149],{"categories":3138},[100],{"categories":3140},[72],{"categories":3142},[110],{"categories":3144},[72],{"categories":3146},[198],{"categories":3148},[],{"categories":3150},[1783],{"categories":3152},[72],{"categories":3154},[124],{"categories":3156},[72],{"categories":3158},[268],{"categories":3160},[268],{"categories":3162},[],{"categories":3164},[110],{"categories":3166},[110],{"categories":3168},[72],{"categories":3170},[72],{"categories":3172},[146],{"categories":3174},[110],{"categories":3176},[146],{"categories":3178},[72],{"categories":3180},[110],{"categories":3182},[],{"categories":3184},[198],{"categories":3186},[72],{"categories":3188},[72],{"categories":3190},[],{"categories":3192},[72],{"categories":3194},[110],{"categories":3196},[72],{"categories":3198},[72],{"categories":3200},[72],{"categories":3202},[],{"categories":3204},[124],{"categories":3206},[72],{"categories":3208},[124],{"categories":3210},[268],{"categories":3212},[72],{"categories":3214},[72],{"categories":3216},[72],{"categories":3218},[124],{"categories":3220},[105],{"categories":3222},[72],{"categories":3224},[1783],{"categories":3226},[],{"categories":3228},[110],{"categories":3230},[100],{"categories":3232},[72],{"categories":3234},[100],{"categories":3236},[72],{"categories":3238},[],{"categories":3240},[110],{"categories":3242},[72],{"categories":3244},[72],{"categories":3246},[3247],"AI Design Tooling",{"categories":3249},[198],{"categories":3251},[72],{"categories":3253},[72],{"categories":3255},[124],{"categories":3257},[198],{"categories":3259},[72],{"categories":3261},[72],{"categories":3263},[124],{"categories":3265},[146],{"categories":3267},[113],{"categories":3269},[124],{"categories":3271},[72],{"categories":3273},[72],{"categories":3275},[72],{"categories":3277},[110],{"categories":3279},[72],{"categories":3281},[],{"categories":3283},[110],{"categories":3285},[72],{"categories":3287},[72],{"categories":3289},[110],{"categories":3291},[72],{"categories":3293},[72],{"categories":3295},[72],{"categories":3297},[110],{"categories":3299},[],{"categories":3301},[110],{"categories":3303},[3120],{"categories":3305},[72],{"categories":3307},[72],{"categories":3309},[110],{"categories":3311},[110],{"categories":3313},[124],{"categories":3315},[124],{"categories":3317},[72],{"categories":3319},[],{"categories":3321},[124],{"categories":3323},[72],{"categories":3325},[72],{"categories":3327},[110],{"categories":3329},[105],{"categories":3331},[72],{"categories":3333},[],{"categories":3335},[72],{"categories":3337},[72],{"categories":3339},[2224],{"categories":3341},[],{"categories":3343},[72],{"categories":3345},[72],{"categories":3347},[72],{"categories":3349},[72],{"categories":3351},[198],{"categories":3353},[72],{"categories":3355},[],{"categories":3357},[72],{"categories":3359},[72],{"categories":3361},[72],{"categories":3363},[72],{"categories":3365},[227],{"categories":3367},[146],{"categories":3369},[72],{"categories":3371},[72],{"categories":3373},[1783],{"categories":3375},[100],{"categories":3377},[72],{"categories":3379},[72],{"categories":3381},[149],{"categories":3383},[72],{"categories":3385},[72],{"categories":3387},[146],{"categories":3389},[110],{"categories":3391},[],{"categories":3393},[72],{"categories":3395},[72],{"categories":3397},[198],{"categories":3399},[72],{"categories":3401},[227],{"categories":3403},[110],{"categories":3405},[72],{"categories":3407},[110],{"categories":3409},[],{"categories":3411},[],{"categories":3413},[],{"categories":3415},[100],{"categories":3417},[146],{"categories":3419},[110],{"categories":3421},[72],{"categories":3423},[72],{"categories":3425},[72],{"categories":3427},[72],{"categories":3429},[385],{"categories":3431},[198],{"categories":3433},[110],{"categories":3435},[72],{"categories":3437},[],{"categories":3439},[110],{"categories":3441},[110],{"categories":3443},[],{"categories":3445},[72],{"categories":3447},[110],{"categories":3449},[72],{"categories":3451},[],{"categories":3453},[72],{"categories":3455},[72],{"categories":3457},[72],{"categories":3459},[146],{"categories":3461},[198],{"categories":3463},[110],{"categories":3465},[198],{"categories":3467},[110],{"categories":3469},[72],{"categories":3471},[105],{"categories":3473},[],{"categories":3475},[],{"categories":3477},[72],{"categories":3479},[72],{"categories":3481},[72],{"categories":3483},[100],{"categories":3485},[110],{"categories":3487},[146],{"categories":3489},[],{"categories":3491},[198],{"categories":3493},[],{"categories":3495},[124],{"categories":3497},[72],{"categories":3499},[124],{"categories":3501},[198],{"categories":3503},[124],{"categories":3505},[72],{"categories":3507},[],{"categories":3509},[72],{"categories":3511},[72],{"categories":3513},[],{"categories":3515},[72],{"categories":3517},[72],{"categories":3519},[227],{"categories":3521},[72],{"categories":3523},[72],{"categories":3525},[268],{"categories":3527},[124],{"categories":3529},[72],{"categories":3531},[],{"categories":3533},[110],{"categories":3535},[72],{"categories":3537},[100],{"categories":3539},[500],{"categories":3541},[72],{"categories":3543},[72],{"categories":3545},[110],{"categories":3547},[72],{"categories":3549},[110],{"categories":3551},[72],{"categories":3553},[72],{"categories":3555},[72],{"categories":3557},[72],{"categories":3559},[],{"categories":3561},[72],{"categories":3563},[100],{"categories":3565},[72],{"categories":3567},[105],{"categories":3569},[124],{"categories":3571},[198],{"categories":3573},[],{"categories":3575},[72],{"categories":3577},[],{"categories":3579},[110],{"categories":3581},[72],{"categories":3583},[],{"categories":3585},[110],{"categories":3587},[72],{"categories":3589},[124],{"categories":3591},[198],{"categories":3593},[146],{"categories":3595},[72],{"categories":3597},[146],{"categories":3599},[110],{"categories":3601},[198],{"categories":3603},[72],{"categories":3605},[],{"categories":3607},[72],{"categories":3609},[135],{"categories":3611},[110],{"categories":3613},[72],{"categories":3615},[198],{"categories":3617},[146],{"categories":3619},[105],{"categories":3621},[124],{"categories":3623},[72],{"categories":3625},[72],{"categories":3627},[72],{"categories":3629},[72],{"categories":3631},[146],{"categories":3633},[227],{"categories":3635},[],{"categories":3637},[],{"categories":3639},[149],{"categories":3641},[441],{"categories":3643},[72],{"categories":3645},[110],{"categories":3647},[72,124],{"categories":3649},[146],{"categories":3651},[72],{"categories":3653},[72],{"categories":3655},[72],{"categories":3657},[72],{"categories":3659},[72],{"categories":3661},[72],{"categories":3663},[72],{"categories":3665},[110],{"categories":3667},[72],{"categories":3669},[110],{"categories":3671},[72],{"categories":3673},[72],{"categories":3675},[72],{"categories":3677},[],{"categories":3679},[72],{"categories":3681},[1221],{"categories":3683},[124],{"categories":3685},[198],{"categories":3687},[72],{"categories":3689},[72],{"categories":3691},[72],{"categories":3693},[149],{"categories":3695},[110],{"categories":3697},[227],{"categories":3699},[268],{"categories":3701},[],{"categories":3703},[124],{"categories":3705},[72],{"categories":3707},[105],{"categories":3709},[110],{"categories":3711},[100],{"categories":3713},[110],{"categories":3715},[72],{"categories":3717},[110],{"categories":3719},[110],{"categories":3721},[113],{"categories":3723},[124],{"categories":3725},[72],{"categories":3727},[72],{"categories":3729},[],{"categories":3731},[],{"categories":3733},[],{"categories":3735},[268],{"categories":3737},[72],{"categories":3739},[146],{"categories":3741},[72],{"categories":3743},[72],{"categories":3745},[72],{"categories":3747},[72],{"categories":3749},[],{"categories":3751},[72],{"categories":3753},[149],{"categories":3755},[105],{"categories":3757},[110],{"categories":3759},[72],{"categories":3761},[],{"categories":3763},[72],{"categories":3765},[110],{"categories":3767},[72],{"categories":3769},[268],{"categories":3771},[],{"categories":3773},[198],{"categories":3775},[198],{"categories":3777},[72],{"categories":3779},[110],{"categories":3781},[],{"categories":3783},[124],{"categories":3785},[72],{"categories":3787},[198],{"categories":3789},[72],{"categories":3791},[105],{"categories":3793},[110],{"categories":3795},[72],{"categories":3797},[],{"categories":3799},[146],{"categories":3801},[72],{"categories":3803},[72],{"categories":3805},[72],{"categories":3807},[198],{"categories":3809},[110],{"categories":3811},[146],{"categories":3813},[],{"categories":3815},[110],{"categories":3817},[105],{"categories":3819},[110],{"categories":3821},[198],{"categories":3823},[72],{"categories":3825},[72],{"categories":3827},[72],{"categories":3829},[441],{"categories":3831},[72],{"categories":3833},[110],{"categories":3835},[],{"categories":3837},[72],{"categories":3839},[72],{"categories":3841},[268],{"categories":3843},[146],{"categories":3845},[149],{"categories":3847},[546],{"categories":3849},[149],{"categories":3851},[149],{"categories":3853},[72],{"categories":3855},[],{"categories":3857},[],{"categories":3859},[],{"categories":3861},[110],{"categories":3863},[72],{"categories":3865},[110],{"categories":3867},[110],{"categories":3869},[124],{"categories":3871},[72],{"categories":3873},[418],{"categories":3875},[124],{"categories":3877},[110],{"categories":3879},[72],{"categories":3881},[72],{"categories":3883},[72],{"categories":3885},[72],{"categories":3887},[72],{"categories":3889},[110],{"categories":3891},[72],{"categories":3893},[],{"categories":3895},[],{"categories":3897},[72],{"categories":3899},[],{"categories":3901},[72],{"categories":3903},[110],{"categories":3905},[198],{"categories":3907},[72],{"categories":3909},[72],{"categories":3911},[],{"categories":3913},[110],{"categories":3915},[72],{"categories":3917},[72],{"categories":3919},[113],{"categories":3921},[72],{"categories":3923},[198],{"categories":3925},[72],{"categories":3927},[110],{"categories":3929},[105],{"categories":3931},[72],{"categories":3933},[72],{"categories":3935},[227],{"categories":3937},[110],{"categories":3939},[72],{"categories":3941},[72],{"categories":3943},[858],{"categories":3945},[72],{"categories":3947},[110],{"categories":3949},[72],{"categories":3951},[124],{"categories":3953},[72],{"categories":3955},[500],{"categories":3957},[198],{"categories":3959},[],{"categories":3961},[72],{"categories":3963},[72],{"categories":3965},[146],{"categories":3967},[441],{"categories":3969},[110],{"categories":3971},[72],{"categories":3973},[],{"categories":3975},[146],{"categories":3977},[362],{"categories":3979},[110],{"categories":3981},[110],{"categories":3983},[110],{"categories":3985},[72],{"categories":3987},[72],{"categories":3989},[110],{"categories":3991},[],{"categories":3993},[105],{"categories":3995},[72],{"categories":3997},[105],{"categories":3999},[110],{"categories":4001},[],{"categories":4003},[124],{"categories":4005},[72],{"categories":4007},[72],{"categories":4009},[100],{"categories":4011},[72],{"categories":4013},[146],{"categories":4015},[268],{"categories":4017},[135],{"categories":4019},[110],{"categories":4021},[110],{"categories":4023},[72],{"categories":4025},[72],{"categories":4027},[110],{"categories":4029},[72],{"categories":4031},[100],{"categories":4033},[],{"categories":4035},[110],{"categories":4037},[72],{"categories":4039},[72],{"categories":4041},[72],{"categories":4043},[110],{"categories":4045},[72],{"categories":4047},[],{"categories":4049},[72],{"categories":4051},[],{"categories":4053},[198],{"categories":4055},[110],{"categories":4057},[72,105],{"categories":4059},[110],{"categories":4061},[72],{"categories":4063},[],{"categories":4065},[100],{"categories":4067},[149],{"categories":4069},[105],{"categories":4071},[72],{"categories":4073},[124],{"categories":4075},[72],{"categories":4077},[72],{"categories":4079},[110],{"categories":4081},[72],{"categories":4083},[72],{"categories":4085},[72],{"categories":4087},[146],{"categories":4089},[1221],{"categories":4091},[110],{"categories":4093},[72],{"categories":4095},[],{"categories":4097},[],{"categories":4099},[72],{"categories":4101},[110],{"categories":4103},[72],{"categories":4105},[72],{"categories":4107},[268],{"categories":4109},[],{"categories":4111},[72],{"categories":4113},[110],{"categories":4115},[135],{"categories":4117},[110],{"categories":4119},[441],{"categories":4121},[],{"categories":4123},[385],{"categories":4125},[110],{"categories":4127},[72],{"categories":4129},[72],{"categories":4131},[227],{"categories":4133},[110],{"categories":4135},[72],{"categories":4137},[149],{"categories":4139},[113],{"categories":4141},[110],{"categories":4143},[72],{"categories":4145},[441],{"categories":4147},[72],{"categories":4149},[268],{"categories":4151},[105],{"categories":4153},[],{"categories":4155},[72],{"categories":4157},[72],{"categories":4159},[227],{"categories":4161},[198],{"categories":4163},[72],{"categories":4165},[72],{"categories":4167},[72],{"categories":4169},[],{"categories":4171},[227],{"categories":4173},[146],{"categories":4175},[72],{"categories":4177},[72],{"categories":4179},[72],{"categories":4181},[546],{"categories":4183},[100],{"categories":4185},[72],{"categories":4187},[113],{"categories":4189},[72],{"categories":4191},[],{"categories":4193},[],{"categories":4195},[198],{"categories":4197},[72],{"categories":4199},[149],{"categories":4201},[227],{"categories":4203},[110],{"categories":4205},[72],{"categories":4207},[72],{"categories":4209},[227],{"categories":4211},[146],{"categories":4213},[72],{"categories":4215},[],{"categories":4217},[72],{"categories":4219},[72],{"categories":4221},[],{"categories":4223},[72],{"categories":4225},[72],{"categories":4227},[571],{"categories":4229},[72],{"categories":4231},[72],{"categories":4233},[110],{"categories":4235},[124],{"categories":4237},[441],{"categories":4239},[72],{"categories":4241},[72],{"categories":4243},[72],{"categories":4245},[],{"categories":4247},[72,124],{"categories":4249},[146],{"categories":4251},[110],{"categories":4253},[124],{"categories":4255},[110],{"categories":4257},[896],{"categories":4259},[124],{"categories":4261},[124],{"categories":4263},[110],{"categories":4265},[72],{"categories":4267},[100],{"categories":4269},[],{"categories":4271},[],{"categories":4273},[110],{"categories":4275},[72],{"categories":4277},[124],{"categories":4279},[72],{"categories":4281},[100],{"categories":4283},[124],{"categories":4285},[124],{"categories":4287},[72],{"categories":4289},[227],{"categories":4291},[72],{"categories":4293},[124],{"categories":4295},[72],{"categories":4297},[],{"categories":4299},[72],{"categories":4301},[72],{"categories":4303},[198,72],{"categories":4305},[268],{"categories":4307},[100],{"categories":4309},[72],{"categories":4311},[],{"categories":4313},[72],{"categories":4315},[72],{"categories":4317},[105],{"categories":4319},[72],{"categories":4321},[105],{"categories":4323},[72],{"categories":4325},[72],{"categories":4327},[362],{"categories":4329},[72],{"categories":4331},[105],{"categories":4333},[124],{"categories":4335},[149],{"categories":4337},[110],{"categories":4339},[72],{"categories":4341},[124],{"categories":4343},[72],{"categories":4345},[72],{"categories":4347},[146],{"categories":4349},[227],{"categories":4351},[198],{"categories":4353},[72],{"categories":4355},[72],{"categories":4357},[72],{"categories":4359},[72],{"categories":4361},[100],{"categories":4363},[72],{"categories":4365},[110],{"categories":4367},[110],{"categories":4369},[124],{"categories":4371},[146],{"categories":4373},[124],{"categories":4375},[124],{"categories":4377},[72],{"categories":4379},[72],{"categories":4381},[],{"categories":4383},[],{"categories":4385},[149],{"categories":4387},[72],{"categories":4389},[124],{"categories":4391},[72],{"categories":4393},[198],{"categories":4395},[441],{"categories":4397},[385],{"categories":4399},[362],{"categories":4401},[72],{"categories":4403},[72],{"categories":4405},[72],{"categories":4407},[149],{"categories":4409},[72],{"categories":4411},[72],{"categories":4413},[72],{"categories":4415},[72],{"categories":4417},[72],{"categories":4419},[72],{"categories":4421},[72],{"categories":4423},[110],{"categories":4425},[100],{"categories":4427},[110],{"categories":4429},[72,105],{"categories":4431},[],{"categories":4433},[198],{"categories":4435},[],{"categories":4437},[113],{"categories":4439},[72],{"categories":4441},[146],{"categories":4443},[100],{"categories":4445},[72],{"categories":4447},[100],{"categories":4449},[110],{"categories":4451},[149],{"categories":4453},[110],{"categories":4455},[113],{"categories":4457},[110],{"categories":4459},[72],{"categories":4461},[72],{"categories":4463},[105],{"categories":4465},[110],{"categories":4467},[124],{"categories":4469},[227],{"categories":4471},[72],{"categories":4473},[72],{"categories":4475},[],{"categories":4477},[146],{"categories":4479},[72],{"categories":4481},[72],{"categories":4483},[72],{"categories":4485},[72],{"categories":4487},[72],{"categories":4489},[72],{"categories":4491},[124],{"categories":4493},[146],{"categories":4495},[124],{"categories":4497},[124],{"categories":4499},[72],{"categories":4501},[72],{"categories":4503},[72],{"categories":4505},[72],{"categories":4507},[385],{"categories":4509},[72],{"categories":4511},[110],{"categories":4513},[146],{"categories":4515},[72],{"categories":4517},[72],{"categories":4519},[72],{"categories":4521},[110],{"categories":4523},[72],{"categories":4525},[72],{"categories":4527},[72],{"categories":4529},[3120],{"categories":4531},[4532],"Clinical AI",{"categories":4534},[198],{"categories":4536},[72],{"categories":4538},[72],{"categories":4540},[72],{"categories":4542},[72],{"categories":4544},[268],{"categories":4546},[2479],{"categories":4548},[72],{"categories":4550},[113],{"categories":4552},[198],{"categories":4554},[72],{"categories":4556},[110],{"categories":4558},[72],{"categories":4560},[72],{"categories":4562},[146],{"categories":4564},[72],{"categories":4566},[110],{"categories":4568},[124],{"categories":4570},[227],{"categories":4572},[72],{"categories":4574},[72],{"categories":4576},[105],{"categories":4578},[72],{"categories":4580},[72],{"categories":4582},[500],{"categories":4584},[72],{"categories":4586},[],{"categories":4588},[110],{"categories":4590},[72],{"categories":4592},[124],{"categories":4594},[100],{"categories":4596},[72],{"categories":4598},[],{"categories":4600},[],{"categories":4602},[72],{"categories":4604},[],{"categories":4606},[105],{"categories":4608},[72],{"categories":4610},[72],{"categories":4612},[110],{"categories":4614},[72],{"categories":4616},[146],{"categories":4618},[146],{"categories":4620},[146],{"categories":4622},[146],{"categories":4624},[],{"categories":4626},[100],{"categories":4628},[110],{"categories":4630},[146],{"categories":4632},[72],{"categories":4634},[571],{"categories":4636},[113],{"categories":4638},[110],{"categories":4640},[72],{"categories":4642},[100],{"categories":4644},[72],{"categories":4646},[110],{"categories":4648},[72],{"categories":4650},[72],{"categories":4652},[72],{"categories":4654},[72,110],{"categories":4656},[110],{"categories":4658},[268],{"categories":4660},[146],{"categories":4662},[110],{"categories":4664},[146],{"categories":4666},[110],{"categories":4668},[72],{"categories":4670},[],{"categories":4672},[146],{"categories":4674},[227],{"categories":4676},[100],{"categories":4678},[72],{"categories":4680},[72],{"categories":4682},[],{"categories":4684},[124],{"categories":4686},[],{"categories":4688},[100],{"categories":4690},[110],{"categories":4692},[146],{"categories":4694},[72],{"categories":4696},[146],{"categories":4698},[100],{"categories":4700},[146],{"categories":4702},[146],{"categories":4704},[],{"categories":4706},[105],{"categories":4708},[110],{"categories":4710},[146],{"categories":4712},[146],{"categories":4714},[146],{"categories":4716},[146],{"categories":4718},[146],{"categories":4720},[146],{"categories":4722},[146],{"categories":4724},[146],{"categories":4726},[146],{"categories":4728},[146],{"categories":4730},[149],{"categories":4732},[100],{"categories":4734},[72],{"categories":4736},[72],{"categories":4738},[110],{"categories":4740},[110],{"categories":4742},[],{"categories":4744},[72],{"categories":4746},[72,100],{"categories":4748},[],{"categories":4750},[110],{"categories":4752},[72],{"categories":4754},[146],{"categories":4756},[110],{"categories":4758},[896],{"categories":4760},[72],{"categories":4762},[72],{"categories":4764},[72],{"categories":4766},[72],{"categories":4768},[72],{"categories":4770},[362],{"categories":4772},[72],{"categories":4774},[72],{"categories":4776},[110],{"categories":4778},[72],{"categories":4780},[72],{"categories":4782},[105],{"categories":4784},[113],{"categories":4786},[110],{"categories":4788},[110],{"categories":4790},[],{"categories":4792},[110],{"categories":4794},[198],{"categories":4796},[146],{"categories":4798},[72],{"categories":4800},[],{"categories":4802},[113],{"categories":4804},[],{"categories":4806},[124],{"categories":4808},[72],{"categories":4810},[110],{"categories":4812},[198],{"categories":4814},[72],{"categories":4816},[],{"categories":4818},[72],{"categories":4820},[72],{"categories":4822},[],{"categories":4824},[227],{"categories":4826},[72],{"categories":4828},[110],{"categories":4830},[],{"categories":4832},[],{"categories":4834},[146],{"categories":4836},[100],{"categories":4838},[72],{"categories":4840},[72],{"categories":4842},[105],{"categories":4844},[72],{"categories":4846},[72],{"categories":4848},[110],{"categories":4850},[72],{"categories":4852},[105],{"categories":4854},[105],{"categories":4856},[198],{"categories":4858},[],{"categories":4860},[72],{"categories":4862},[146],{"categories":4864},[],{"categories":4866},[72],{"categories":4868},[72],{"categories":4870},[198],{"categories":4872},[72],{"categories":4874},[72],{"categories":4876},[227],{"categories":4878},[72],{"categories":4880},[268],{"categories":4882},[],{"categories":4884},[110],{"categories":4886},[72],{"categories":4888},[227],{"categories":4890},[124],{"categories":4892},[],{"categories":4894},[72],{"categories":4896},[],{"categories":4898},[110],{"categories":4900},[198],{"categories":4902},[124],{"categories":4904},[],{"categories":4906},[3120],{"categories":4908},[105],{"categories":4910},[100],{"categories":4912},[72],{"categories":4914},[149],{"categories":4916},[110],{"categories":4918},[198],{"categories":4920},[72],{"categories":4922},[124],{"categories":4924},[],{"categories":4926},[],{"categories":4928},[72],{"categories":4930},[100],{"categories":4932},[72],{"categories":4934},[227],{"categories":4936},[],{"categories":4938},[110],{"categories":4940},[110],{"categories":4942},[72],{"categories":4944},[110],{"categories":4946},[72],{"categories":4948},[146],{"categories":4950},[124],{"categories":4952},[72],{"categories":4954},[110],{"categories":4956},[113],{"categories":4958},[72],{"categories":4960},[72],{"categories":4962},[72],{"categories":4964},[110],{"categories":4966},[72],{"categories":4968},[113],{"categories":4970},[227],{"categories":4972},[146],{"categories":4974},[],{"categories":4976},[227],{"categories":4978},[72],{"categories":4980},[],{"categories":4982},[124],{"categories":4984},[110],{"categories":4986},[],{"categories":4988},[72],{"categories":4990},[72],{"categories":4992},[72],{"categories":4994},[72],{"categories":4996},[72],{"categories":4998},[110],{"categories":5000},[105],{"categories":5002},[100],{"categories":5004},[110],{"categories":5006},[72],{"categories":5008},[198],{"categories":5010},[124],{"categories":5012},[124],{"categories":5014},[72],{"categories":5016},[149],{"categories":5018},[110],{"categories":5020},[72],{"categories":5022},[72],{"categories":5024},[110],{"categories":5026},[72],{"categories":5028},[72],{"categories":5030},[110],{"categories":5032},[105],{"categories":5034},[72],{"categories":5036},[198],{"categories":5038},[124],{"categories":5040},[110],{"categories":5042},[72],{"categories":5044},[113],{"categories":5046},[72],{"categories":5048},[110],{"categories":5050},[72],{"categories":5052},[72],{"categories":5054},[146],{"categories":5056},[72],{"categories":5058},[],{"categories":5060},[100],{"categories":5062},[72],{"categories":5064},[72],{"categories":5066},[72],{"categories":5068},[124],{"categories":5070},[124],{"categories":5072},[72],{"categories":5074},[124],{"categories":5076},[72],{"categories":5078},[110],{"categories":5080},[72],{"categories":5082},[72],{"categories":5084},[72],{"categories":5086},[72],{"categories":5088},[72],{"categories":5090},[],{"categories":5092},[72],{"categories":5094},[198],{"categories":5096},[110],{"categories":5098},[105],{"categories":5100},[146],{"categories":5102},[72],{"categories":5104},[110],{"categories":5106},[72],{"categories":5108},[110],{"categories":5110},[72],{"categories":5112},[72],{"categories":5114},[198],{"categories":5116},[110],{"categories":5118},[72],{"categories":5120},[227],{"categories":5122},[72],{"categories":5124},[149],{"categories":5126},[72],{"categories":5128},[72],{"categories":5130},[146],{"categories":5132},[72],{"categories":5134},[72],{"categories":5136},[72],{"categories":5138},[72],{"categories":5140},[110],{"categories":5142},[268],{"categories":5144},[72],{"categories":5146},[124],{"categories":5148},[110],{"categories":5150},[149],{"categories":5152},[],{"categories":5154},[110],{"categories":5156},[124],{"categories":5158},[72],{"categories":5160},[72],{"categories":5162},[2317],{"categories":5164},[198],{"categories":5166},[297],{"categories":5168},[72],{"categories":5170},[72],{"categories":5172},[72],{"categories":5174},[72],{"categories":5176},[100],{"categories":5178},[72],{"categories":5180},[72],{"categories":5182},[124],{"categories":5184},[105],{"categories":5186},[72],{"categories":5188},[124],{"categories":5190},[72],{"categories":5192},[],{"categories":5194},[110],{"categories":5196},[110],{"categories":5198},[72],{"categories":5200},[72],{"categories":5202},[72],{"categories":5204},[149],{"categories":5206},[],{"categories":5208},[146],{"categories":5210},[],{"categories":5212},[146],{"categories":5214},[72],{"categories":5216},[72],{"categories":5218},[110],{"categories":5220},[72],{"categories":5222},[110],{"categories":5224},[110],{"categories":5226},[],{"categories":5228},[72],{"categories":5230},[146],{"categories":5232},[72],{"categories":5234},[],{"categories":5236},[72],{"categories":5238},[72],{"categories":5240},[],{"categories":5242},[72],{"categories":5244},[72],{"categories":5246},[198],{"categories":5248},[124],{"categories":5250},[110],{"categories":5252},[72],{"categories":5254},[72],{"categories":5256},[72],{"categories":5258},[72],{"categories":5260},[227],{"categories":5262},[72],{"categories":5264},[72],{"categories":5266},[72],{"categories":5268},[100],{"categories":5270},[72],{"categories":5272},[72],{"categories":5274},[],{"categories":5276},[72],{"categories":5278},[72],{"categories":5280},[72],{"categories":5282},[],{"categories":5284},[100],{"categories":5286},[72],{"categories":5288},[72],{"categories":5290},[146],{"categories":5292},[124],{"categories":5294},[113],{"categories":5296},[110],{"categories":5298},[441],{"categories":5300},[72],{"categories":5302},[72],{"categories":5304},[72],{"categories":5306},[124],{"categories":5308},[146],{"categories":5310},[198],{"categories":5312},[72],{"categories":5314},[72],{"categories":5316},[72],{"categories":5318},[72],{"categories":5320},[146],{"categories":5322},[72],{"categories":5324},[198],{"categories":5326},[72],{"categories":5328},[72],{"categories":5330},[146],{"categories":5332},[198],{"categories":5334},[72],{"categories":5336},[146],{"categories":5338},[72],{"categories":5340},[110],{"categories":5342},[110],{"categories":5344},[110],{"categories":5346},[124],{"categories":5348},[146],{"categories":5350},[110],{"categories":5352},[110],{"categories":5354},[72],{"categories":5356},[124],{"categories":5358},[198],{"categories":5360},[72],{"categories":5362},[72],{"categories":5364},[110],{"categories":5366},[72],{"categories":5368},[],{"categories":5370},[110],{"categories":5372},[],{"categories":5374},[72],{"categories":5376},[72],{"categories":5378},[],{"categories":5380},[],{"categories":5382},[110],{"categories":5384},[105],{"categories":5386},[110],{"categories":5388},[5389],"Liability & Ethics",{"categories":5391},[72],{"categories":5393},[72],{"categories":5395},[72],{"categories":5397},[110],{"categories":5399},[100],{"categories":5401},[110],{"categories":5403},[105],{"categories":5405},[227],{"categories":5407},[110],{"categories":5409},[72],{"categories":5411},[72],{"categories":5413},[],{"categories":5415},[546],{"categories":5417},[110],{"categories":5419},[],{"categories":5421},[72],{"categories":5423},[100],{"categories":5425},[110],{"categories":5427},[],{"categories":5429},[110],{"categories":5431},[72],{"categories":5433},[72],{"categories":5435},[124],{"categories":5437},[72],{"categories":5439},[146],{"categories":5441},[72],{"categories":5443},[72],{"categories":5445},[113],{"categories":5447},[110],{"categories":5449},[72],{"categories":5451},[72],{"categories":5453},[72],{"categories":5455},[146],{"categories":5457},[110],{"categories":5459},[124],{"categories":5461},[198],{"categories":5463},[100],{"categories":5465},[72],{"categories":5467},[72],{"categories":5469},[72],{"categories":5471},[],{"categories":5473},[110],{"categories":5475},[110],{"categories":5477},[110],{"categories":5479},[441],{"categories":5481},[198],{"categories":5483},[110],{"categories":5485},[268],{"categories":5487},[124],{"categories":5489},[146],{"categories":5491},[72],{"categories":5493},[198],{"categories":5495},[72],{"categories":5497},[100],{"categories":5499},[],{"categories":5501},[110],{"categories":5503},[72],{"categories":5505},[72],{"categories":5507},[72],{"categories":5509},[72],{"categories":5511},[110],{"categories":5513},[72],{"categories":5515},[72],{"categories":5517},[198],{"categories":5519},[],{"categories":5521},[110],{"categories":5523},[113],{"categories":5525},[146],{"categories":5527},[110],{"categories":5529},[105],{"categories":5531},[],{"categories":5533},[72],{"categories":5535},[72],{"categories":5537},[113],{"categories":5539},[72],{"categories":5541},[110],{"categories":5543},[146],{"categories":5545},[100],{"categories":5547},[268],{"categories":5549},[72],{"categories":5551},[72],{"categories":5553},[72],{"categories":5555},[146],{"categories":5557},[105],{"categories":5559},[72],{"categories":5561},[198],{"categories":5563},[146],{"categories":5565},[268],{"categories":5567},[72],{"categories":5569},[110],{"categories":5571},[],{"categories":5573},[500],{"categories":5575},[],{"categories":5577},[72],{"categories":5579},[268],{"categories":5581},[72],{"categories":5583},[149],{"categories":5585},[72],{"categories":5587},[110],{"categories":5589},[110],{"categories":5591},[5592],"Design News & Tools",{"categories":5594},[72],{"categories":5596},[72],{"categories":5598},[146],{"categories":5600},[72],{"categories":5602},[72],{"categories":5604},[100],{"categories":5606},[110],{"categories":5608},[72],{"categories":5610},[198],{"categories":5612},[110],{"categories":5614},[110],{"categories":5616},[198],{"categories":5618},[72],{"categories":5620},[72],{"categories":5622},[441],{"categories":5624},[110],{"categories":5626},[72],{"categories":5628},[72],{"categories":5630},[441],{"categories":5632},[72],{"categories":5634},[227],{"categories":5636},[72],{"categories":5638},[110],{"categories":5640},[],{"categories":5642},[72],{"categories":5644},[72],{"categories":5646},[72],{"categories":5648},[146],{"categories":5650},[72],{"categories":5652},[100],{"categories":5654},[],{"categories":5656},[72],{"categories":5658},[72],{"categories":5660},[72],{"categories":5662},[124],{"categories":5664},[571],{"categories":5666},[124],{"categories":5668},[198],{"categories":5670},[72],{"categories":5672},[72,110],{"categories":5674},[227,105],{"categories":5676},[124],{"categories":5678},[72],{"categories":5680},[72],{"categories":5682},[72],{"categories":5684},[72],{"categories":5686},[],{"categories":5688},[110],{"categories":5690},[72],{"categories":5692},[],{"categories":5694},[72],{"categories":5696},[124],{"categories":5698},[72],{"categories":5700},[124],{"categories":5702},[],{"categories":5704},[110],{"categories":5706},[72],{"categories":5708},[105],{"categories":5710},[72],{"categories":5712},[146],{"categories":5714},[72],{"categories":5716},[],{"categories":5718},[110],{"categories":5720},[72],{"categories":5722},[],{"categories":5724},[198],{"categories":5726},[72],{"categories":5728},[72],{"categories":5730},[110],{"categories":5732},[72],{"categories":5734},[72],{"categories":5736},[100],{"categories":5738},[110],{"categories":5740},[72],{"categories":5742},[],{"categories":5744},[72],{"categories":5746},[268],{"categories":5748},[227],{"categories":5750},[105],{"categories":5752},[105],{"categories":5754},[72],{"categories":5756},[100],{"categories":5758},[100],{"categories":5760},[72],{"categories":5762},[110],{"categories":5764},[72],{"categories":5766},[72],{"categories":5768},[72],{"categories":5770},[72],{"categories":5772},[124],{"categories":5774},[72],{"categories":5776},[100],{"categories":5778},[72],{"categories":5780},[72],{"categories":5782},[110],{"categories":5784},[72],{"categories":5786},[227],{"categories":5788},[72],{"categories":5790},[146],{"categories":5792},[72],{"categories":5794},[72],{"categories":5796},[110],{"categories":5798},[113],{"categories":5800},[72],{"categories":5802},[72],{"categories":5804},[110],{"categories":5806},[],{"categories":5808},[124],{"categories":5810},[],{"categories":5812},[124],{"categories":5814},[110],{"categories":5816},[100],{"categories":5818},[72],{"categories":5820},[],{"categories":5822},[149],{"categories":5824},[268],{"categories":5826},[72],{"categories":5828},[124],{"categories":5830},[72],{"categories":5832},[],{"categories":5834},[146],{"categories":5836},[110],{"categories":5838},[124],{"categories":5840},[198],{"categories":5842},[105],{"categories":5844},[72],{"categories":5846},[72],{"categories":5848},[110],{"categories":5850},[124],{"categories":5852},[110],{"categories":5854},[146],{"categories":5856},[72],{"categories":5858},[113],{"categories":5860},[100],{"categories":5862},[113],{"categories":5864},[146],{"categories":5866},[72],{"categories":5868},[124],{"categories":5870},[72],{"categories":5872},[198],{"categories":5874},[105],{"categories":5876},[72],{"categories":5878},[72],{"categories":5880},[72],{"categories":5882},[72],{"categories":5884},[72],{"categories":5886},[72],{"categories":5888},[110],{"categories":5890},[72],{"categories":5892},[110],{"categories":5894},[72],{"categories":5896},[72],{"categories":5898},[100],{"categories":5900},[72],{"categories":5902},[110],{"categories":5904},[110],{"categories":5906},[198],{"categories":5908},[110],{"categories":5910},[110],{"categories":5912},[72],{"categories":5914},[100],{"categories":5916},[110],{"categories":5918},[198],{"categories":5920},[],{"categories":5922},[72],{"categories":5924},[149],{"categories":5926},[441],{"categories":5928},[72],{"categories":5930},[110],{"categories":5932},[72],{"categories":5934},[72],{"categories":5936},[124],{"categories":5938},[72],{"categories":5940},[],{"categories":5942},[72],{"categories":5944},[110],{"categories":5946},[72],{"categories":5948},[227],{"categories":5950},[72],{"categories":5952},[124],{"categories":5954},[72],{"categories":5956},[146],{"categories":5958},[110],{"categories":5960},[72],{"categories":5962},[227],{"categories":5964},[110],{"categories":5966},[105],{"categories":5968},[105],{"categories":5970},[72],{"categories":5972},[72],{"categories":5974},[72],{"categories":5976},[72],{"categories":5978},[72],{"categories":5980},[72],{"categories":5982},[100],{"categories":5984},[],{"categories":5986},[72],{"categories":5988},[72],{"categories":5990},[110],{"categories":5992},[110],{"categories":5994},[72],{"categories":5996},[72],{"categories":5998},[72],{"categories":6000},[72],{"categories":6002},[72],{"categories":6004},[124],{"categories":6006},[],{"categories":6008},[100],{"categories":6010},[72],{"categories":6012},[72],{"categories":6014},[110],{"categories":6016},[110],{"categories":6018},[],{"categories":6020},[124],{"categories":6022},[124],{"categories":6024},[72],{"categories":6026},[227],{"categories":6028},[105],{"categories":6030},[198],{"categories":6032},[],{"categories":6034},[72],{"categories":6036},[110],{"categories":6038},[100],{"categories":6040},[72],{"categories":6042},[72],{"categories":6044},[124],{"categories":6046},[100],{"categories":6048},[72],{"categories":6050},[72],{"categories":6052},[146],{"categories":6054},[149],{"categories":6056},[72],{"categories":6058},[146],{"categories":6060},[110],{"categories":6062},[72],{"categories":6064},[],{"categories":6066},[146],{"categories":6068},[110],{"categories":6070},[198],{"categories":6072},[149],{"categories":6074},[72],{"categories":6076},[72],{"categories":6078},[],{"categories":6080},[110],{"categories":6082},[110],{"categories":6084},[110],{"categories":6086},[3120],{"categories":6088},[146],{"categories":6090},[72],{"categories":6092},[124],{"categories":6094},[72],{"categories":6096},[72],{"categories":6098},[72],{"categories":6100},[72],{"categories":6102},[72],{"categories":6104},[105],{"categories":6106},[72],{"categories":6108},[100],{"categories":6110},[1783],{"categories":6112},[268],{"categories":6114},[100],{"categories":6116},[],{"categories":6118},[72],{"categories":6120},[],{"categories":6122},[146],{"categories":6124},[110],{"categories":6126},[198],{"categories":6128},[72],{"categories":6130},[72],{"categories":6132},[72],{"categories":6134},[146],{"categories":6136},[],{"categories":6138},[110],{"categories":6140},[72],{"categories":6142},[110],{"categories":6144},[110],{"categories":6146},[],{"categories":6148},[72],{"categories":6150},[],{"categories":6152},[146],{"categories":6154},[100],{"categories":6156},[198],{"categories":6158},[72],{"categories":6160},[110],{"categories":6162},[146],{"categories":6164},[72],{"categories":6166},[146],{"categories":6168},[],{"categories":6170},[146],{"categories":6172},[72],{"categories":6174},[100],{"categories":6176},[441],{"categories":6178},[110],{"categories":6180},[72],{"categories":6182},[],{"categories":6184},[124],{"categories":6186},[110],{"categories":6188},[113],{"categories":6190},[110],{"categories":6192},[100],{"categories":6194},[72],{"categories":6196},[72],{"categories":6198},[],{"categories":6200},[],{"categories":6202},[],{"categories":6204},[198],{"categories":6206},[72],{"categories":6208},[110],{"categories":6210},[72],{"categories":6212},[72],{"categories":6214},[],{"categories":6216},[],{"categories":6218},[],{"categories":6220},[72],{"categories":6222},[110],{"categories":6224},[198],{"categories":6226},[72],{"categories":6228},[],{"categories":6230},[110],{"categories":6232},[72],{"categories":6234},[72],{"categories":6236},[100],{"categories":6238},[],{"categories":6240},[],{"categories":6242},[72],{"categories":6244},[72],{"categories":6246},[110],{"categories":6248},[198],{"categories":6250},[72],{"categories":6252},[146],{"categories":6254},[],{"categories":6256},[72],{"categories":6258},[72],{"categories":6260},[227],{"categories":6262},[146],{"categories":6264},[227],{"categories":6266},[149],{"categories":6268},[72],{"categories":6270},[72],{"categories":6272},[],{"categories":6274},[],{"categories":6276},[110],{"categories":6278},[],{"categories":6280},[72],{"categories":6282},[441],{"categories":6284},[72],{"categories":6286},[72],{"categories":6288},[72],{"categories":6290},[72],{"categories":6292},[],{"categories":6294},[110],{"categories":6296},[72],{"categories":6298},[72],{"categories":6300},[],{"categories":6302},[110],{"categories":6304},[72],{"categories":6306},[146],{"categories":6308},[72],{"categories":6310},[227],{"categories":6312},[105],{"categories":6314},[113],{"categories":6316},[72],{"categories":6318},[72],{"categories":6320},[110],{"categories":6322},[149],{"categories":6324},[110],{"categories":6326},[110],{"categories":6328},[],{"categories":6330},[72],{"categories":6332},[110],{"categories":6334},[],{"categories":6336},[72],{"categories":6338},[],{"categories":6340},[146],{"categories":6342},[105],{"categories":6344},[],{"categories":6346},[72],{"categories":6348},[72],{"categories":6350},[72],{"categories":6352},[],{"categories":6354},[110],{"categories":6356},[198],{"categories":6358},[100],{"categories":6360},[72],{"categories":6362},[],{"categories":6364},[105],{"categories":6366},[227],{"categories":6368},[72],{"categories":6370},[124],{"categories":6372},[100],{"categories":6374},[149],{"categories":6376},[105],{"categories":6378},[124],{"categories":6380},[110],{"categories":6382},[124],{"categories":6384},[],{"categories":6386},[72],{"categories":6388},[113],{"categories":6390},[72],{"categories":6392},[],{"categories":6394},[110],{"categories":6396},[100],{"categories":6398},[198],{"categories":6400},[72],{"categories":6402},[100],{"categories":6404},[110],{"categories":6406},[268],{"categories":6408},[72],{"categories":6410},[72],{"categories":6412},[72],{"categories":6414},[72],{"categories":6416},[72],{"categories":6418},[100],{"categories":6420},[72],{"categories":6422},[124],{"categories":6424},[149],{"categories":6426},[110],{"categories":6428},[],{"categories":6430},[72],{"categories":6432},[72],{"categories":6434},[72],{"categories":6436},[124],{"categories":6438},[110],{"categories":6440},[146],{"categories":6442},[124],{"categories":6444},[72],{"categories":6446},[113],{"categories":6448},[],{"categories":6450},[198],{"categories":6452},[124],{"categories":6454},[146],{"categories":6456},[72],{"categories":6458},[100],{"categories":6460},[110],{"categories":6462},[72],{"categories":6464},[72],{"categories":6466},[110],{"categories":6468},[113],{"categories":6470},[72],{"categories":6472},[110],{"categories":6474},[72],{"categories":6476},[105],{"categories":6478},[110],{"categories":6480},[110,268],{"categories":6482},[72],{"categories":6484},[72],{"categories":6486},[110],{"categories":6488},[124],{"categories":6490},[72],{"categories":6492},[72],{"categories":6494},[149],{"categories":6496},[110],{"categories":6498},[227],{"categories":6500},[110],{"categories":6502},[105],{"categories":6504},[],{"categories":6506},[110],{"categories":6508},[72],{"categories":6510},[105],{"categories":6512},[],{"categories":6514},[],{"categories":6516},[124],{"categories":6518},[72],{"categories":6520},[72],{"categories":6522},[110],{"categories":6524},[149],{"categories":6526},[227],{"categories":6528},[72],{"categories":6530},[72],{"categories":6532},[72],{"categories":6534},[110],{"categories":6536},[],{"categories":6538},[110],{"categories":6540},[146],{"categories":6542},[72],{"categories":6544},[110],{"categories":6546},[110],{"categories":6548},[72],{"categories":6550},[],{"categories":6552},[146],{"categories":6554},[124],{"categories":6556},[3120],{"categories":6558},[100],{"categories":6560},[124],{"categories":6562},[72],{"categories":6564},[110],{"categories":6566},[72],{"categories":6568},[72],{"categories":6570},[227],{"categories":6572},[124],{"categories":6574},[149],{"categories":6576},[],{"categories":6578},[146],{"categories":6580},[72],{"categories":6582},[72],{"categories":6584},[],{"categories":6586},[110],{"categories":6588},[72],{"categories":6590},[72],{"categories":6592},[72],{"categories":6594},[72],{"categories":6596},[110],{"categories":6598},[72],{"categories":6600},[72],{"categories":6602},[72],{"categories":6604},[113],{"categories":6606},[72],{"categories":6608},[110],{"categories":6610},[72],{"categories":6612},[72],{"categories":6614},[72],{"categories":6616},[72],{"categories":6618},[72],{"categories":6620},[72],{"categories":6622},[72],{"categories":6624},[105],{"categories":6626},[],{"categories":6628},[113],{"categories":6630},[146],{"categories":6632},[110],{"categories":6634},[72],{"categories":6636},[124],{"categories":6638},[],{"categories":6640},[124],{"categories":6642},[124],{"categories":6644},[110],{"categories":6646},[124],{"categories":6648},[72],{"categories":6650},[72],{"categories":6652},[72],{"categories":6654},[110],{"categories":6656},[124],{"categories":6658},[72],{"categories":6660},[72],{"categories":6662},[72],{"categories":6664},[110],{"categories":6666},[146],{"categories":6668},[72],{"categories":6670},[72],{"categories":6672},[72],{"categories":6674},[105],{"categories":6676},[72],{"categories":6678},[110],{"categories":6680},[198],{"categories":6682},[],{"categories":6684},[72],{"categories":6686},[149],{"categories":6688},[110],{"categories":6690},[72],{"categories":6692},[72],{"categories":6694},[],{"categories":6696},[72],{"categories":6698},[72],{"categories":6700},[146],{"categories":6702},[72],{"categories":6704},[72],{"categories":6706},[110],{"categories":6708},[227],{"categories":6710},[],{"categories":6712},[],{"categories":6714},[124],{"categories":6716},[72],{"categories":6718},[72],{"categories":6720},[146],{"categories":6722},[72],{"categories":6724},[124],{"categories":6726},[146],{"categories":6728},[72],{"categories":6730},[72],{"categories":6732},[227],{"categories":6734},[149],{"categories":6736},[72],{"categories":6738},[72],{"categories":6740},[100],{"categories":6742},[110],{"categories":6744},[72],{"categories":6746},[72],{"categories":6748},[110],{"categories":6750},[105],{"categories":6752},[110],{"categories":6754},[124],{"categories":6756},[72],{"categories":6758},[105],{"categories":6760},[],{"categories":6762},[72],{"categories":6764},[149],{"categories":6766},[72],{"categories":6768},[72],{"categories":6770},[],{"categories":6772},[146],{"categories":6774},[72],{"categories":6776},[110],{"categories":6778},[149],{"categories":6780},[72],{"categories":6782},[124],{"categories":6784},[124],{"categories":6786},[124],{"categories":6788},[72],{"categories":6790},[110],{"categories":6792},[110],{"categories":6794},[72],{"categories":6796},[110],{"categories":6798},[72],{"categories":6800},[72],{"categories":6802},[198],{"categories":6804},[149],{"categories":6806},[149],{"categories":6808},[],{"categories":6810},[146],{"categories":6812},[72],{"categories":6814},[72],{"categories":6816},[124],{"categories":6818},[],{"categories":6820},[146],{"categories":6822},[146],{"categories":6824},[146],{"categories":6826},[],{"categories":6828},[110],{"categories":6830},[72],{"categories":6832},[],{"categories":6834},[100],{"categories":6836},[105],{"categories":6838},[],{"categories":6840},[72],{"categories":6842},[72],{"categories":6844},[],{"categories":6846},[124],{"categories":6848},[],{"categories":6850},[],{"categories":6852},[],{"categories":6854},[],{"categories":6856},[72],{"categories":6858},[146],{"categories":6860},[],{"categories":6862},[],{"categories":6864},[72],{"categories":6866},[72],{"categories":6868},[72],{"categories":6870},[149],{"categories":6872},[72],{"categories":6874},[149],{"categories":6876},[],{"categories":6878},[149],{"categories":6880},[149],{"categories":6882},[268],{"categories":6884},[110],{"categories":6886},[124],{"categories":6888},[],{"categories":6890},[],{"categories":6892},[149],{"categories":6894},[124],{"categories":6896},[124],{"categories":6898},[124],{"categories":6900},[],{"categories":6902},[100],{"categories":6904},[124],{"categories":6906},[124],{"categories":6908},[100],{"categories":6910},[124],{"categories":6912},[105],{"categories":6914},[124],{"categories":6916},[124],{"categories":6918},[124],{"categories":6920},[149],{"categories":6922},[146],{"categories":6924},[146],{"categories":6926},[72],{"categories":6928},[124],{"categories":6930},[149],{"categories":6932},[268],{"categories":6934},[149],{"categories":6936},[149],{"categories":6938},[149],{"categories":6940},[],{"categories":6942},[105],{"categories":6944},[],{"categories":6946},[268],{"categories":6948},[124],{"categories":6950},[124],{"categories":6952},[124],{"categories":6954},[110],{"categories":6956},[146,105],{"categories":6958},[149],{"categories":6960},[],{"categories":6962},[],{"categories":6964},[149],{"categories":6966},[],{"categories":6968},[149],{"categories":6970},[146],{"categories":6972},[110],{"categories":6974},[],{"categories":6976},[124],{"categories":6978},[72],{"categories":6980},[198],{"categories":6982},[],{"categories":6984},[72],{"categories":6986},[],{"categories":6988},[146],{"categories":6990},[100],{"categories":6992},[149],{"categories":6994},[],{"categories":6996},[124],{"categories":6998},[146],[7000,7085,7474,7567],{"id":7001,"title":7002,"ai":7003,"body":7008,"categories":7059,"created_at":73,"date_modified":73,"description":65,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":7060,"navigation":77,"path":7072,"published_at":7073,"question":73,"scraped_at":7074,"seo":7075,"sitemap":7076,"source_id":7077,"source_name":7078,"source_type":84,"source_url":7079,"stem":7080,"tags":7081,"thumbnail_url":73,"tldr":7082,"tweet":73,"unknown_tags":7083,"__hash__":7084},"summaries\u002Fsummaries\u002Fadd9ec06f3d8b78d-decoder-only-transformers-drive-gpt-scaling-summary.md","Decoder-Only Transformers Drive GPT Scaling",{"provider":7,"model":8,"input_tokens":7004,"output_tokens":7005,"processing_time_ms":7006,"cost_usd":7007},8457,1685,17671,0.00202705,{"type":14,"value":7009,"toc":7053},[7010,7014,7017,7020,7024,7027,7030,7034,7037,7040,7044,7047,7050],[17,7011,7013],{"id":7012},"self-attention-enables-parallel-long-range-dependencies","Self-Attention Enables Parallel Long-Range Dependencies",[22,7015,7016],{},"Transformers replace RNNs' sequential processing, which suffers vanishing gradients beyond 50-100 words, with self-attention that computes direct relationships between all token pairs simultaneously. For a token like \"it\" in \"The cat sat on the mat and looked at the fishbowl because it was hungry,\" every prior word votes on relevance via query-key dot products scaled by embed_size^{-0.5}, softmax-normalized, and applied to values. This parallelization trains across thousands of GPUs.",[22,7018,7019],{},"GPT's decoder-only design strips away the encoder, applying a causal mask to block future tokens, forcing rich representations solely from predicting the next token. GPT-1 (117M params, 12 layers) showed modest NLP scores, but GPT-2 (1.5B params) gained zero-shot abilities like summarization via prompting. GPT-3 (175B params, 96 layers) added in-context learning from prompt examples without fine-tuning. Deeper layers progress from syntax (early) to reasoning and world models (late). This simplicity scales better than encoder-decoder setups, avoiding cross-attention overhead.",[17,7021,7023],{"id":7022},"moe-and-test-time-compute-scale-beyond-dense-models","MoE and Test-Time Compute Scale Beyond Dense Models",[22,7025,7026],{},"Dense models activate all parameters per token, making trillions unaffordable. Mixture of Experts (MoE) routes each token to 2-8 specialized experts out of 128+, activating ~5% of weights—e.g., DeepSeek-V3 uses 37B active out of 671B total, trained for $5.6M on 2,048 H800 GPUs, matching GPT-4. Multi-Head Latent Attention (MLA) compresses KV cache to cut memory bandwidth. Tradeoffs include expert collapse (router overloads few experts) and full-model memory needs despite sparse activation.",[22,7028,7029],{},"o1 introduced test-time compute: generate internal reasoning chains (30s for hard problems), backtrack dead ends, and refine via RL on verifiable rewards like math solutions. This outperforms larger instant-response models, decoupling ability from size. GPT-5 routes simple queries fast (System 1) and complex ones deeply (System 2). Open models like DeepSeek-R1 replicate this.",[17,7031,7033],{"id":7032},"multimodal-fusion-and-real-world-impacts","Multimodal Fusion and Real-World Impacts",[22,7035,7036],{},"Early fusion embeds vision tokens from Vision Transformers (e.g., MetaCLIP) into the same space as text, enabling unified attention across modalities—no separate captioning. Models like LLaMA 4, Qwen-VL handle charts, 3D spatial reasoning (GLM-4.5V's rotated positional encoding). This yields native cross-modal reasoning, e.g., diagnosing X-rays directly.",[22,7038,7039],{},"Applications: Harvey AI (RAG + fine-tuned GPT-4) cuts legal review 40-60%; GPT-4.1 hits 54.6% on SWE-bench (21.4pp over GPT-4o), ingesting 1M-token codebases; 75% medical accuracy accelerates drug discovery. Open weights (LLaMA, DeepSeek) ensure data sovereignty.",[17,7041,7043],{"id":7042},"implement-mini-gpt-from-scratch-in-pytorch","Implement Mini-GPT from Scratch in PyTorch",[22,7045,7046],{},"Build a character-level GPT: Tokenizer maps unique chars to indices (vocab_size ~50). SelfAttention computes QKV projections, scores = (Q @ K.T) * scale, weights = softmax(scores), out = weights @ V. TransformerBlock adds residual attention + FFN (4x expand, ReLU), LayerNorm post each.",[22,7048,7049],{},"MiniGPT stacks NUM_LAYERS=2 blocks on token + positional embeddings (BLOCK_SIZE=32), outputs logits via linear to vocab_size. Train on dataset.txt: batch BATCH_SIZE=16 sequences, predict next token with CrossEntropyLoss, Adam at 3e-4, 20 EPOCHS. Generation: sample from last-token softmax via multinomial, append up to 100 tokens from context like \"AI is\".",[22,7051,7052],{},"Project structure: data\u002Fdataset.txt, model\u002F{tokenizer,attention,transformer,gpt}.py, train.py saves model.pth, generate.py loads\u002Finfers. Config: EMBED_SIZE=64, NUM_HEADS=4 (implied in attention). This replicates core logic scalably.",{"title":65,"searchDepth":66,"depth":66,"links":7054},[7055,7056,7057,7058],{"id":7012,"depth":66,"text":7013},{"id":7022,"depth":66,"text":7023},{"id":7032,"depth":66,"text":7033},{"id":7042,"depth":66,"text":7043},[72],{"content_references":7061,"triage":7067},[7062],{"type":7063,"title":7064,"author":7065,"context":7066},"paper","Attention Is All You Need","Ashish Vaswani’s team","cited",{"relevance":7068,"novelty":7069,"quality":7068,"actionability":66,"composite":7070,"reasoning":7071},4,3,3.4,"Category: AI & LLMs. The article provides a detailed explanation of the architecture behind GPT models, which is relevant for developers looking to integrate AI features. However, while it offers insights into model design, it lacks practical applications or frameworks that the audience can directly implement.","\u002Fsummaries\u002Fadd9ec06f3d8b78d-decoder-only-transformers-drive-gpt-scaling-summary","2026-04-18 19:32:29","2026-04-19 01:22:04",{"title":7002,"description":65},{"loc":7072},"add9ec06f3d8b78d","Python in Plain English","https:\u002F\u002Fpython.plainenglish.io\u002Fthe-architecture-behind-gpt-models-de61992c088a?source=rss----78073def27b8---4","summaries\u002Fadd9ec06f3d8b78d-decoder-only-transformers-drive-gpt-scaling-summary",[88,89,90,91],"GPT models use decoder-only transformers with causal masking for next-token prediction, enabling emergent zero-shot and in-context learning when scaled massively, now enhanced by MoE for efficiency and reasoning chains.",[],"FYS789V3fqVrHXGVHROYyiskRFH6nT84_QPvh_I63p0",{"id":7086,"title":7087,"ai":7088,"body":7093,"categories":7437,"created_at":73,"date_modified":73,"description":65,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":7438,"navigation":77,"path":7461,"published_at":7462,"question":73,"scraped_at":7463,"seo":7464,"sitemap":7465,"source_id":7466,"source_name":7467,"source_type":84,"source_url":7468,"stem":7469,"tags":7470,"thumbnail_url":73,"tldr":7471,"tweet":73,"unknown_tags":7472,"__hash__":7473},"summaries\u002Fsummaries\u002F11694bb2ea4dab37-train-gpt-2-llm-from-scratch-on-laptop-summary.md","Train GPT-2 LLM from Scratch on Laptop",{"provider":7,"model":8,"input_tokens":7089,"output_tokens":7090,"processing_time_ms":7091,"cost_usd":7092},8437,3044,42622,0.0031869,{"type":14,"value":7094,"toc":7429},[7095,7099,7102,7105,7111,7122,7126,7129,7132,7173,7176,7181,7184,7187,7191,7194,7197,7237,7240,7264,7267,7302,7305,7310,7313,7317,7320,7322,7347,7350,7355,7358,7364,7368,7371,7374,7385,7388,7393,7397],[17,7096,7098],{"id":7097},"why-local-llm-training-reveals-core-mechanics","Why Local LLM Training Reveals Core Mechanics",[22,7100,7101],{},"Training an LLM from scratch locally demystifies the process, showing 80% of what big labs do without cloud-scale resources. Angelos Perivolaropoulos, who leads speech-to-text at ElevenLabs (creators of top benchmark model Scribe v2), emphasizes starting with basics: no pre-trained weights, pure PyTorch. This tiny GPT-2 variant (vocab=65 chars, context=256, 6 layers) trains fast on laptops, exposing tokenizer choices, architecture blocks, and training loops as the real differentiators between models like GPT-3 vs. GPT-4.",[22,7103,7104],{},"Key principle: Focus on bi-grams (token pairs). Small vocab (65) yields ~4k bi-grams, coverable by Shakespeare dataset; larger (50k like GPT-2) needs trillions of tokens to converge. \"If you have a model with 200,000 tokens, you need 200,000 tokens squared at least data to train from scratch.\"",[7106,7107,7108],"blockquote",{},[22,7109,7110],{},"\"We're going to work purely on torch... this is like 80% of the way there to create a model from scratch.\"",[22,7112,7113,7114,7117,7118,7121],{},"Prerequisites: Python 3.12, 16GB RAM (scales down), MPS\u002FCUDA\u002FCPU support. Use UV for env: ",[26,7115,7116],{},"uv sync",". Colab alternative: ",[26,7119,7120],{},"!pip install torch numpy datasets tiktoken",". Dataset: Shakespeare (tiny text corpus, downloadable via repo).",[17,7123,7125],{"id":7124},"tokenizer-character-level-for-tiny-models","Tokenizer: Character-Level for Tiny Models",[22,7127,7128],{},"Start here – LLMs process vectors, not text. Character-level tokenizer maps 65 chars (A-Z, a-z, punctuation, space, newline) to integers via simple dict\u002Fenumerate. Converts strings to int tensors; embedding layer maps to vectors (dim=384).",[22,7130,7131],{},"Steps:",[7133,7134,7135,7143,7160,7170],"ol",{},[7136,7137,7138,7139,7142],"li",{},"Load data: ",[26,7140,7141],{},"text = open('input.txt', 'r').read()"," (Shakespeare).",[7136,7144,7145,7146,7149,7150,7149,7153,7149,7156,7159],{},"Build vocab: ",[26,7147,7148],{},"chars = sorted(list(set(text)))","; ",[26,7151,7152],{},"stoi = {ch:i for i,ch in enumerate(chars)}",[26,7154,7155],{},"itos = {i:ch for i,ch in enumerate(chars)}",[26,7157,7158],{},"vocab_size = len(chars)",".",[7136,7161,7162,7163,7166,7167,7159],{},"Encode: ",[26,7164,7165],{},"def encode(s): return [stoi[c] for c in s]","; batch via ",[26,7168,7169],{},"torch.tensor",[7136,7171,7172],{},"Decode: Reverse for output.",[22,7174,7175],{},"Trade-off: Low vocab trains fast on small data but poor scaling – model struggles with long-range correlations (e.g., 'sky' + 'is' + 'bl' vs. semantic tokens). For code: Falls to chars for rare vars; BPE (train on data patterns like 'for', 'enumerate') better for prod but needs massive data.",[7106,7177,7178],{},[22,7179,7180],{},"\"Character level because it's much easier to train... 65*65 = 4,225 possible bi-grams... our dataset should include all bi-grams multiple times.\"",[22,7182,7183],{},"Common mistake: Using full GPT-2 vocab (50k) – embedding table alone ~19M params (3x model size), won't converge. Future-proof: Train BPE tokenizer on your corpus for real LLMs.",[22,7185,7186],{},"Quality check: Ensure all bi-grams covered; test encode\u002Fdecode round-trip.",[17,7188,7190],{"id":7189},"causal-transformer-stack-simple-blocks","Causal Transformer: Stack Simple Blocks",[22,7192,7193],{},"GPT-2 base: Decoder-only, causal self-attention. Don't need PhD-level math – implement blocks, learn why via experimentation.",[22,7195,7196],{},"Core blocks (per layer):",[7198,7199,7200,7211,7217,7227],"ul",{},[7136,7201,7202,7206,7207,7210],{},[7203,7204,7205],"strong",{},"Multi-head self-attention",": Computes token relationships (QKV matrices). Causal mask prevents future peeking: ",[26,7208,7209],{},"mask = torch.tril(torch.ones(block_size, block_size))",". Heads (e.g., n_head=6) parallelize; concat + proj.",[7136,7212,7213,7216],{},[7203,7214,7215],{},"MLP\u002FFeed-forward",": Processes attended features into logits.",[7136,7218,7219,7222,7223,7226],{},[7203,7220,7221],{},"Residuals",": Add input to output (",[26,7224,7225],{},"x + sublayer(x)",") – gradients flow directly, stabilizes deep stacks.",[7136,7228,7229,7232,7233,7236],{},[7203,7230,7231],{},"LayerNorm",": Normalizes activations pre-sublayer (",[26,7234,7235],{},"ln(x) * sublayer(ln(x)) + x","); prevents exploding\u002Fvanishing.",[22,7238,7239],{},"Model params:",[7198,7241,7242,7248,7253,7258],{},[7136,7243,7244,7247],{},[26,7245,7246],{},"n_embd=384"," (embed dim)",[7136,7249,7250],{},[26,7251,7252],{},"n_head=6",[7136,7254,7255],{},[26,7256,7257],{},"n_layer=6",[7136,7259,7260,7263],{},[26,7261,7262],{},"block_size=256"," (context)",[22,7265,7266],{},"Implementation skeleton (PyTorch nn.Module):",[7133,7268,7269,7275,7281,7288,7299],{},[7136,7270,7271,7272,7159],{},"Embed: ",[26,7273,7274],{},"self.tok_emb = nn.Embedding(vocab_size, n_embd)",[7136,7276,7277,7278,7159],{},"Pos embed: ",[26,7279,7280],{},"self.position_embedding_table = nn.Embedding(block_size, n_embd)",[7136,7282,7283,7284,7287],{},"Layers: Stack ",[26,7285,7286],{},"TransformerBlock"," (attention + MLP + norms).",[7136,7289,7290,7291,7294,7295,7298],{},"Final: ",[26,7292,7293],{},"ln_f = LayerNorm(n_embd)"," → ",[26,7296,7297],{},"lm_head = nn.Linear(n_embd, vocab_size)"," (no bias, tie to embed? Optional).",[7136,7300,7301],{},"Forward: Add pos embeds, loop layers, project logits.",[22,7303,7304],{},"Principle: Stack identical layers; residuals\u002Fnorms enable scaling depth. Big labs optimize attention for 1M+ context (e.g., avoid O(n²) blowup) but base works.",[7106,7306,7307],{},[22,7308,7309],{},"\"Attention is what makes transformers different... they can attend to previous tokens and understand relationships.\"",[22,7311,7312],{},"Mistake: No causal mask → cheats by seeing future. Test: Forward pass on sample, check shapes (batch, seq, vocab).",[17,7314,7316],{"id":7315},"training-loop-where-performance-wins","Training Loop: Where Performance Wins",[22,7318,7319],{},"Pre-training core: Next-token prediction (cross-entropy loss). Smarter loops separate GPT-3\u002F4 (e.g., Gemini 3 → 3.1 doubles benchmarks via tuning).",[22,7321,7131],{},[7133,7323,7324,7331,7334,7340],{},[7136,7325,7326,7327,7330],{},"Data: Split train\u002Fval; generate batches ",[26,7328,7329],{},"get_batch('train')"," → (B,T) ints.",[7136,7332,7333],{},"Optimize: AdamW, lr=1e-3 (warmup? Basic: constant).",[7136,7335,7336,7337,7159],{},"Loop: ",[26,7338,7339],{},"for i in range(max_iters): xb,yb = get_batch(); logits,p = model(xb); loss = F.cross_entropy(logits.view(-1,vocab_size), yb.view(-1)); optimizer.zero_grad(); loss.backward(); optimizer.step()",[7136,7341,7342,7343,7346],{},"Eval: Perplexity on val (",[26,7344,7345],{},"torch.exp(loss)",").",[22,7348,7349],{},"Batch size: 4-64 (RAM-limited); steps: 5k+ for convergence. Estimate iters: dataset_tokens \u002F (batch * block_size).",[7106,7351,7352],{},[22,7353,7354],{},"\"The training loop is generally the most important part... what you use with the same base model makes the big difference.\"",[22,7356,7357],{},"Trade-off: Small context (256) fast but forgets long deps; crank on bigger GPU.",[22,7359,7360,7361,7159],{},"Inference: Simple ",[26,7362,7363],{},"while True: generate next token via top-k\u002F1 sample",[17,7365,7367],{"id":7366},"hardware-trade-offs-and-extensions","Hardware Trade-offs and Extensions",[22,7369,7370],{},"Local constraints force smart choices: 16GB RAM → tiny model (millions params). Colab GPUs free for this scale.",[22,7372,7373],{},"Scaling path:",[7198,7375,7376,7379,7382],{},[7136,7377,7378],{},"Bigger data\u002FGPU: BPE tokenizer, 16k context.",[7136,7380,7381],{},"Week-long train: Proper LLM.",[7136,7383,7384],{},"Compete: Optimize loss faster.",[22,7386,7387],{},"No deep theory needed initially: \"I had no clue how transformers worked... you learn as you push through.\"",[7106,7389,7390],{},[22,7391,7392],{},"\"Transformers have been commoditized... optimizations on the base idea.\"",[17,7394,7396],{"id":7395},"key-takeaways","Key Takeaways",[7198,7398,7399,7402,7405,7408,7414,7417,7420,7423,7426],{},[7136,7400,7401],{},"Use character-level tokenizer (65 vocab) for tiny local LLMs; covers bi-grams with small data like Shakespeare.",[7136,7403,7404],{},"Implement causal transformer via 4 blocks: attention (masked), MLP, residual, LayerNorm – stack 6 layers.",[7136,7406,7407],{},"Training: Next-token CE loss, AdamW; monitor val perplexity; 5k iters suffices.",[7136,7409,7410,7411,7413],{},"Start with ",[26,7412,7116],{},"; test on Colab if no GPU\u002FRAM.",[7136,7415,7416],{},"Trade-off explicitly: Char tok fast\u002Fcheap but unscalable; BPE for prod needs data.",[7136,7418,7419],{},"Fork repo, beat baseline loss – extend to code tokenizer or longer context.",[7136,7421,7422],{},"Embeddings dominate small models; GPT-2 vocab would 3x size.",[7136,7424,7425],{},"Residuals\u002FLayerNorm stabilize; causal mask essential.",[7136,7427,7428],{},"Bi-grams rule data needs: vocab² minimum tokens.",{"title":65,"searchDepth":66,"depth":66,"links":7430},[7431,7432,7433,7434,7435,7436],{"id":7097,"depth":66,"text":7098},{"id":7124,"depth":66,"text":7125},{"id":7189,"depth":66,"text":7190},{"id":7315,"depth":66,"text":7316},{"id":7366,"depth":66,"text":7367},{"id":7395,"depth":66,"text":7396},[72],{"content_references":7439,"triage":7457},[7440,7445,7448,7452,7454],{"type":7441,"title":7442,"author":7443,"context":7444},"other","nanoGPT","Andrej Karpathy","mentioned",{"type":7446,"title":7447,"context":7444},"dataset","Shakespeare",{"type":7449,"title":7450,"context":7451},"tool","UV","recommended",{"type":7449,"title":7453,"context":7444},"tiktoken",{"type":7449,"title":7455,"author":7456,"context":7444},"Scribe v2","ElevenLabs",{"relevance":7458,"novelty":7068,"quality":7068,"actionability":7458,"composite":7459,"reasoning":7460},5,4.55,"Category: AI & LLMs. This article provides a hands-on workshop for training a GPT-2 model from scratch, which directly addresses the audience's need for practical applications in AI engineering. It includes specific steps and code snippets for building a tokenizer and training loop, making it immediately actionable for developers.","\u002Fsummaries\u002F11694bb2ea4dab37-train-gpt-2-llm-from-scratch-on-laptop-summary","2026-05-04 18:30:06","2026-05-05 16:04:36",{"title":7087,"description":65},{"loc":7461},"45eb198f2256f249","AI Engineer","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UsB70Tf5zcE","summaries\u002F11694bb2ea4dab37-train-gpt-2-llm-from-scratch-on-laptop-summary",[88,89,91],"Hands-on workshop: Build tokenizer, causal transformer, training loop in PyTorch to train tiny GPT-2 on Shakespeare locally (16GB RAM) or Colab – reveals core engineering without cloud.",[],"5Ukfnhm75lyKlN6uDUvolBl8QzKyNJepG6aCp8NwNTQ",{"id":7475,"title":7476,"ai":7477,"body":7482,"categories":7537,"created_at":73,"date_modified":73,"description":65,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":7538,"navigation":77,"path":7554,"published_at":7555,"question":73,"scraped_at":7556,"seo":7557,"sitemap":7558,"source_id":7559,"source_name":7560,"source_type":84,"source_url":7561,"stem":7562,"tags":7563,"thumbnail_url":73,"tldr":7564,"tweet":73,"unknown_tags":7565,"__hash__":7566},"summaries\u002Fsummaries\u002F79f82c07ea7441fe-trl-code-guide-sft-to-grpo-llm-alignment-on-t4-gpu-summary.md","TRL Code Guide: SFT to GRPO LLM Alignment on T4 GPU",{"provider":7,"model":8,"input_tokens":7478,"output_tokens":7479,"processing_time_ms":7480,"cost_usd":7481},9458,2615,35753,0.00269195,{"type":14,"value":7483,"toc":7531},[7484,7488,7495,7499,7509,7513,7519,7523],[17,7485,7487],{"id":7486},"lora-and-trl-setup-enables-post-training-on-limited-hardware","LoRA and TRL Setup Enables Post-Training on Limited Hardware",[22,7489,7490,7491,7494],{},"Use LoRA (r=8, alpha=16, dropout=0.05, targets=",[57,7492,7493],{},"'q_proj','k_proj','v_proj','o_proj'",") with TRL trainers to adapt Qwen\u002FQwen2.5-0.5B-Instruct on T4 GPU (16GB). Common args across stages: num_train_epochs=1, gradient_checkpointing=True, bf16 if supported else fp16, logging_steps=10, report_to=\"none\", save_strategy=\"no\". Install stack: torchao>=0.16, trl>=0.20, transformers>=4.45, peft>=0.13, bitsandbytes. Helpers like chat_generate apply chat template, generate with temp=0.7\u002Ftop_p=0.9. Cleanup VRAM with gc.collect() + torch.cuda.empty_cache() between stages to fit in Colab.",[17,7496,7498],{"id":7497},"sft-and-rm-build-imitation-and-reward-signals","SFT and RM Build Imitation and Reward Signals",[22,7500,7501,7502,7505,7506,7508],{},"For Supervised Fine-Tuning, load trl-lib\u002FCapybara (train",[57,7503,7504],{},":300","), use SFTConfig(per_device_train_batch_size=2, gradient_accumulation_steps=4, learning_rate=2e-4, max_length=768). Trainer imitates high-quality chat responses; post-train inference on \"Explain bias-variance tradeoff in two sentences\" yields coherent output. Reward Modeling on trl-lib\u002Fultrafeedback_binarized (train",[57,7507,7504],{},") uses RewardConfig(batch_size=2, accum_steps=2, lr=1e-4, max_length=512), LoRA task_type=\"SEQ_CLS\". Trains to score chosen vs. rejected pairs, producing a preference-based reward without explicit RL.",[17,7510,7512],{"id":7511},"dpo-skips-rm-for-direct-preference-alignment","DPO Skips RM for Direct Preference Alignment",[22,7514,7515,7516,7518],{},"DPOTrainer on same ultrafeedback_binarized",[57,7517,7504],{}," simplifies via implicit rewards: DPOConfig(batch_size=1, accum_steps=4, lr=5e-6, beta=0.1, max_length=512, max_prompt_length=256). Beta controls KL-divergence from reference policy, preventing mode collapse. Optimizes policy to prefer chosen over rejected responses directly, reducing steps vs. traditional RM+PPO.",[17,7520,7522],{"id":7521},"grpo-uses-custom-rewards-to-sharpen-reasoning","GRPO Uses Custom Rewards to Sharpen Reasoning",[22,7524,7525,7526,7530],{},"GRPOTrainer generates num_generations=4 completions per prompt (max_prompt_length=128, max_completion_length=96, max_steps=15), ranks via reward_funcs. Custom dataset: 200 synthetic math problems (e.g., \"Solve 17 + 28 =\", gold=eval). Rewards: correctness_reward (1.0 if last extracted number matches gold else 0), brevity_reward (max(0,1-len(c)\u002F200)",[7527,7528,7529],"em",{},"0.2). GRPOConfig(lr=1e-5, batch=2, accum=2). Inference on \"17+28?\", \"9","7?\", \"100-47?\" produces accurate, concise answers like final numbers, improving verifiable task performance over base.",{"title":65,"searchDepth":66,"depth":66,"links":7532},[7533,7534,7535,7536],{"id":7486,"depth":66,"text":7487},{"id":7497,"depth":66,"text":7498},{"id":7511,"depth":66,"text":7512},{"id":7521,"depth":66,"text":7522},[72],{"content_references":7539,"triage":7552},[7540,7543,7545,7547,7549],{"type":7449,"title":7541,"url":7542,"context":7444},"TRL","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftrl",{"type":7446,"title":7544,"context":7444},"trl-lib\u002FCapybara",{"type":7446,"title":7546,"context":7444},"trl-lib\u002Fultrafeedback_binarized",{"type":7449,"title":7548,"context":7444},"Qwen\u002FQwen2.5-0.5B-Instruct",{"type":7441,"title":7550,"url":7551,"context":7451},"trl_llm_post_training_sft_dpo_grpo_marktechpost.py","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FLLM%20Projects\u002Ftrl_llm_post_training_sft_dpo_grpo_marktechpost.py",{"relevance":7458,"novelty":7068,"quality":7068,"actionability":7458,"composite":7459,"reasoning":7553},"Category: AI & LLMs. The article provides a detailed guide on using TRL and LoRA for LLM post-training, addressing practical applications for developers looking to implement AI features. It includes specific configurations and techniques that can be directly applied in production, making it highly actionable.","\u002Fsummaries\u002F79f82c07ea7441fe-trl-code-guide-sft-to-grpo-llm-alignment-on-t4-gpu-summary","2026-05-01 20:52:08","2026-05-03 17:01:49",{"title":7476,"description":65},{"loc":7554},"79f82c07ea7441fe","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F01\u002Fa-coding-guide-on-llm-post-training-with-trl-from-supervised-fine-tuning-to-dpo-and-grpo-reasoning\u002F","summaries\u002F79f82c07ea7441fe-trl-code-guide-sft-to-grpo-llm-alignment-on-t4-gpu-summary",[88,89,90],"Train Qwen2.5-0.5B via SFT, RM, DPO, GRPO using TRL+LoRA on Colab T4: configs include r=8 LoRA, 300-sample datasets, epochs=1, small batches\u002Faccum for memory efficiency, custom math rewards boost reasoning.",[],"py8Fe1-Noi99CHywKy61Q363dqRBmUxl6tZ9TDJOp3E",{"id":7568,"title":7569,"ai":7570,"body":7576,"categories":7596,"created_at":73,"date_modified":73,"description":65,"extension":74,"faq":73,"featured":75,"kicker_label":73,"meta":7597,"navigation":77,"path":7606,"published_at":7607,"question":73,"scraped_at":7607,"seo":7608,"sitemap":7609,"source_id":7610,"source_name":7611,"source_type":84,"source_url":7602,"stem":7612,"tags":7613,"thumbnail_url":73,"tldr":7615,"tweet":73,"unknown_tags":7616,"__hash__":7617},"summaries\u002Fsummaries\u002Faf56b2ee53845e43-optimizing-code-models-with-function-level-executi-summary.md","Optimizing Code Models with Function-Level Execution Feedback",{"provider":7,"model":7571,"input_tokens":7572,"output_tokens":7573,"processing_time_ms":7574,"cost_usd":7575},"google\u002Fgemini-3.1-flash-lite",4047,456,2829,0.00169575,{"type":14,"value":7577,"toc":7592},[7578,7582,7585,7589],[17,7579,7581],{"id":7580},"moving-beyond-binary-execution-feedback","Moving Beyond Binary Execution Feedback",[22,7583,7584],{},"Traditional methods for aligning code-generation models often rely on binary feedback—whether a generated script passes or fails a set of unit tests. This paper introduces a more granular approach: Function-Level Execution Feedback. Instead of treating an entire code block as a single success or failure, this method decomposes the code into individual functions and evaluates them independently. By isolating the execution success of specific functions, the model receives more precise signals during the preference optimization process, allowing it to learn which specific segments of code are problematic rather than discarding an entire generation due to a single localized error.",[17,7586,7588],{"id":7587},"enhancing-preference-optimization","Enhancing Preference Optimization",[22,7590,7591],{},"By integrating this function-level feedback into the training pipeline, the researchers demonstrate that models can more effectively navigate the search space of potential solutions. The core argument is that binary feedback is too noisy; a model might generate a highly functional, complex algorithm that fails only because of a minor syntax error in a helper function. By providing feedback at the function level, the optimization process can reward the model for the correct logic in the primary function while penalizing only the specific sub-component that failed. This leads to more stable training dynamics and higher-quality code generation, as the model learns to prioritize functional correctness at a modular level, reducing the likelihood of cascading errors in larger codebases.",{"title":65,"searchDepth":66,"depth":66,"links":7593},[7594,7595],{"id":7580,"depth":66,"text":7581},{"id":7587,"depth":66,"text":7588},[72],{"content_references":7598,"triage":7603},[7599],{"type":7063,"title":7600,"author":7601,"url":7602,"context":7066},"Function-Level Execution Feedback for Code Preference Optimization","Various","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.23632",{"relevance":7068,"novelty":7068,"quality":7068,"actionability":7069,"composite":7604,"reasoning":7605},3.8,"Category: AI & LLMs. The article discusses a novel approach to optimizing code generation models using function-level execution feedback, which addresses a specific pain point of improving AI model performance. It presents new insights into how granular feedback can enhance training dynamics, although it lacks detailed actionable steps for implementation.","\u002Fsummaries\u002Faf56b2ee53845e43-optimizing-code-models-with-function-level-executi-summary","2026-08-27 03:13:03",{"title":7569,"description":65},{"loc":7606},"af56b2ee53845e43","arXiv cs.AI","summaries\u002Faf56b2ee53845e43-optimizing-code-models-with-function-level-executi-summary",[88,90,91,7614],"research","Improving code generation models by using granular, function-level execution feedback rather than binary pass\u002Ffail signals to guide preference optimization.",[],"YwFImh86Z9S6bSotdwhdDGzlle98nQbm_BCMLxmbOy0"]