[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-ai-critiques-consciousness-bio-progress-nn-fractal-summary":3,"summaries-facets-categories":98,"summary-related-ai-critiques-consciousness-bio-progress-nn-fractal-summary":4503},{"id":4,"title":5,"ai":6,"body":13,"categories":75,"created_at":77,"date_modified":77,"description":68,"extension":78,"faq":77,"featured":79,"kicker_label":77,"meta":80,"navigation":81,"path":82,"published_at":83,"question":77,"scraped_at":77,"seo":84,"sitemap":85,"source_id":86,"source_name":87,"source_type":88,"source_url":89,"stem":90,"tags":91,"thumbnail_url":77,"tldr":95,"tweet":77,"unknown_tags":96,"__hash__":97},"summaries\u002Fsummaries\u002Fai-critiques-consciousness-bio-progress-nn-fractal-summary.md","AI Critiques: Consciousness, Bio Progress, NN Fractals",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",6632,1688,17327,0.00214775,{"type":14,"value":15,"toc":67},"minimark",[16,21,25,28,32,40,47,51,54,57,61,64],[17,18,20],"h2",{"id":19},"brain-waves-solve-binding-problem-but-is-feedback-consciousness","Brain Waves Solve Binding Problem, But Is Feedback Consciousness?",[22,23,24],"p",{},"Max Hodak's theory ties consciousness to 'binding': mode binding (color\u002Fshape into 'red cup') via 40Hz gamma waves for local neuron sync, and moment binding (brain-wide firing as single experience quanta) via 10Hz alpha waves acting like a forward pass. Neurons fire at alpha peaks; alpha shifts cause time dilation. Alpha waves provide feedback control, verifying structured world representations—equating this to consciousness.",[22,26,27],{},"Hodak predicts new physics at fundamental force level (like mass\u002Fcharge), as consciousness either epiphenomenal (odd) or causal (new field). Critique: Effects like wood floating emerge from existing physics without new laws; evolution unlikely stumbled on undetected universal field. Counterexample: Does DRAM memory refresh equal consciousness?",[17,29,31],{"id":30},"llms-unlock-async-math-mastery-but-ai-lacks-model-insight","LLMs Unlock Async Math Mastery, But AI Lacks Model Insight",[22,33,34,35,39],{},"Strogatz's ",[36,37,38],"em",{},"Nonlinear Dynamics and Chaos"," uses phase space plots (trajectories from starting points) for system evolution prediction, far clearer than time-series. Graphical focus yields intuitive examples like budworm outbreaks: dimensionless R\u002FK parameters reveal regimes—low capacity (no growth), bird control, or outbreak—via clever intercepts aligning intuition.",[22,41,42,43,46],{},"LLMs + async lectures (pause\u002Fchatbot clarify) enable college-level grasp impossible live; author bounced from similar course pre-AI, now thrives with adult focus. Yet AI's 'automated cleverness' falters on judgment calls: selecting key dimensions (R\u002FK vs. birds), visualizations unlocking regimes. New frameworks demand human insight into ",[36,44,45],{},"how to think"," about systems; AI applies templates, leaving framework inventors essential.",[17,48,50],{"id":49},"bio-tech-exploded-but-ai-wont-cure-diseases-faster","Bio Tech Exploded, But AI Won't Cure Diseases Faster",[22,52,53],{},"Amodei claims AI delivers century of bio progress in years: breakthroughs (CRISPR, mRNA, CAR-T, 1M-fold sequencing\u002F1K-fold synthesis cost drops) from scrappy intelligence, not data (which intelligence expands via multiplexing\u002FAlphaFold). Clinical trials slow from uncertainty; superior prediction accelerates like COVID mRNA.",[22,55,56],{},"Counter: 30 years of bio tools slowed drug development, not sped it—Alzheimer's amyloid drugs failed despite links. Raw insights insufficient; human trials essential, un-deriskable sans full body sims. 'Million George Church clones' won't suffice. Post-AGI catchup growth loses labor arbitrage. Intelligence malleable long-run (in vitro paradigms, less bureaucracy), but capital historically failed similar factor bypasses.",[17,58,60],{"id":59},"fractal-hyperparam-boundaries-explain-nn-evolution-wins","Fractal Hyperparam Boundaries Explain NN, Evolution Wins",[22,62,63],{},"NN training convergence\u002Fdivergence boundary is fractal, complicating max learning rate via gradient descent iterations. Evolution tuned brain hyperparameters gradient-free, averaging high-convergence regions vs. point gradients trapped by fractals.",[22,65,66],{},"Fractals from iterative functions; applies to chain-of-thought (iterative prompting) and RNNs (hidden state iterations), explaining variance issues.",{"title":68,"searchDepth":69,"depth":69,"links":70},"",2,[71,72,73,74],{"id":19,"depth":69,"text":20},{"id":30,"depth":69,"text":31},{"id":49,"depth":69,"text":50},{"id":59,"depth":69,"text":60},[76],"AI & LLMs",null,"md",false,{},true,"\u002Fsummaries\u002Fai-critiques-consciousness-bio-progress-nn-fractal-summary","2026-04-08 21:21:17",{"title":5,"description":68},{"loc":82},"d7c64f29c70fb8a9","Dwarkesh Patel","article","https:\u002F\u002Funknown","summaries\u002Fai-critiques-consciousness-bio-progress-nn-fractal-summary",[92,93,94],"machine-learning","research","ai-llms","Dwarkesh critiques theories linking consciousness to brain waves, questions AI's bio acceleration despite tech drops (1M-fold sequencing costs), praises LLMs for math learning, and explores fractal NN training landscapes evolution navigated via gradient-free optimization.",[94],"ZHpvaLD9-hZ6IIjMRDW7vGlTk-qLRxtP5P6zG2pQJrQ",[99,102,104,107,109,112,115,118,121,123,125,127,129,131,133,135,137,139,141,143,145,147,149,152,154,156,158,160,162,164,166,168,170,172,174,176,178,180,182,184,186,188,190,192,194,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,494,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2465,2467,2469,2471,2473,2475,2477,2479,2481,2483,2485,2487,2489,2491,2493,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2625,2627,2629,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671,3673,3675,3677,3679,3681,3683,3685,3687,3689,3691,3693,3695,3697,3699,3701,3703,3705,3707,3709,3711,3713,3715,3717,3719,3721,3723,3725,3727,3729,3731,3733,3735,3737,3739,3741,3743,3745,3747,3749,3751,3753,3755,3757,3759,3761,3763,3765,3767,3769,3771,3773,3775,3777,3779,3781,3783,3785,3787,3789,3791,3793,3795,3797,3799,3801,3803,3805,3807,3809,3811,3813,3815,3817,3819,3821,3823,3825,3827,3829,3831,3833,3835,3837,3839,3841,3843,3845,3847,3849,3851,3853,3855,3857,3859,3861,3863,3865,3867,3869,3871,3873,3875,3877,3879,3881,3883,3885,3887,3889,3891,3893,3895,3897,3899,3901,3903,3905,3907,3909,3911,3913,3915,3917,3919,3921,3923,3925,3927,3929,3931,3933,3935,3937,3939,3941,3943,3945,3947,3949,3951,3953,3955,3957,3959,3961,3963,3965,3967,3969,3971,3973,3975,3977,3979,3981,3983,3985,3987,3989,3991,3993,3995,3997,3999,4001,4003,4005,4007,4009,4011,4013,4015,4017,4019,4021,4023,4025,4027,4029,4031,4033,4035,4037,4039,4041,4043,4045,4047,4049,4051,4053,4055,4057,4059,4061,4063,4065,4067,4069,4071,4073,4075,4077,4079,4081,4083,4085,4087,4089,4091,4093,4095,4097,4099,4101,4103,4105,4107,4109,4111,4113,4115,4117,4119,4121,4123,4125,4127,4129,4131,4133,4135,4137,4139,4141,4143,4145,4147,4149,4151,4153,4155,4157,4159,4161,4163,4165,4167,4169,4171,4173,4175,4177,4179,4181,4183,4185,4187,4189,4191,4193,4195,4197,4199,4201,4203,4205,4207,4209,4211,4213,4215,4217,4219,4221,4223,4225,4227,4229,4231,4233,4235,4237,4239,4241,4243,4245,4247,4249,4251,4253,4255,4257,4259,4261,4263,4265,4267,4269,4271,4273,4275,4277,4279,4281,4283,4285,4287,4289,4291,4293,4295,4297,4299,4301,4303,4305,4307,4309,4311,4313,4315,4317,4319,4321,4323,4325,4327,4329,4331,4333,4335,4337,4339,4341,4343,4345,4347,4349,4351,4353,4355,4357,4359,4361,4363,4365,4367,4369,4371,4373,4375,4377,4379,4381,4383,4385,4387,4389,4391,4393,4395,4397,4399,4401,4403,4405,4407,4409,4411,4413,4415,4417,4419,4421,4423,4425,4427,4429,4431,4433,4435,4437,4439,4441,4443,4445,4447,4449,4451,4453,4455,4457,4459,4461,4463,4465,4467,4469,4471,4473,4475,4477,4479,4481,4483,4485,4487,4489,4491,4493,4495,4497,4499,4501],{"categories":100},[101],"Business & SaaS",{"categories":103},[101],{"categories":105},[106],"AI News & Trends",{"categories":108},[],{"categories":110},[111],"AI Automation",{"categories":113},[114],"Marketing & Growth",{"categories":116},[117],"Design & Frontend",{"categories":119},[120],"Software Engineering",{"categories":122},[111],{"categories":124},[],{"categories":126},[117],{"categories":128},[117],{"categories":130},[111],{"categories":132},[117],{"categories":134},[117],{"categories":136},[76],{"categories":138},[117],{"categories":140},[117],{"categories":142},[],{"categories":144},[117],{"categories":146},[117],{"categories":148},[76],{"categories":150},[151],"Developer Productivity",{"categories":153},[76],{"categories":155},[76],{"categories":157},[76],{"categories":159},[106],{"categories":161},[76],{"categories":163},[111],{"categories":165},[101],{"categories":167},[106],{"categories":169},[114],{"categories":171},[],{"categories":173},[],{"categories":175},[111],{"categories":177},[111],{"categories":179},[111],{"categories":181},[114],{"categories":183},[76],{"categories":185},[151],{"categories":187},[106],{"categories":189},[],{"categories":191},[],{"categories":193},[],{"categories":195},[196],"Data Science & Visualization",{"categories":198},[],{"categories":200},[111],{"categories":202},[120],{"categories":204},[111],{"categories":206},[111],{"categories":208},[76],{"categories":210},[114],{"categories":212},[111],{"categories":214},[],{"categories":216},[],{"categories":218},[],{"categories":220},[117],{"categories":222},[117],{"categories":224},[111],{"categories":226},[114],{"categories":228},[151],{"categories":230},[117],{"categories":232},[76],{"categories":234},[120],{"categories":236},[76],{"categories":238},[],{"categories":240},[111],{"categories":242},[76],{"categories":244},[151],{"categories":246},[151],{"categories":248},[],{"categories":250},[114],{"categories":252},[101],{"categories":254},[76],{"categories":256},[101],{"categories":258},[101],{"categories":260},[111],{"categories":262},[114],{"categories":264},[111],{"categories":266},[101],{"categories":268},[111],{"categories":270},[117],{"categories":272},[76],{"categories":274},[117],{"categories":276},[76],{"categories":278},[101],{"categories":280},[76],{"categories":282},[114],{"categories":284},[],{"categories":286},[76],{"categories":288},[101],{"categories":290},[],{"categories":292},[106],{"categories":294},[120],{"categories":296},[],{"categories":298},[76],{"categories":300},[117],{"categories":302},[76],{"categories":304},[117],{"categories":306},[],{"categories":308},[111],{"categories":310},[],{"categories":312},[],{"categories":314},[],{"categories":316},[76],{"categories":318},[],{"categories":320},[76],{"categories":322},[76],{"categories":324},[117],{"categories":326},[76],{"categories":328},[151],{"categories":330},[111],{"categories":332},[114],{"categories":334},[151],{"categories":336},[151],{"categories":338},[151],{"categories":340},[114],{"categories":342},[114],{"categories":344},[76],{"categories":346},[76],{"categories":348},[117],{"categories":350},[101],{"categories":352},[117],{"categories":354},[120],{"categories":356},[101],{"categories":358},[101],{"categories":360},[101],{"categories":362},[117],{"categories":364},[],{"categories":366},[],{"categories":368},[76],{"categories":370},[76],{"categories":372},[120],{"categories":374},[76],{"categories":376},[76],{"categories":378},[],{"categories":380},[76],{"categories":382},[76],{"categories":384},[],{"categories":386},[76],{"categories":388},[106],{"categories":390},[106],{"categories":392},[],{"categories":394},[],{"categories":396},[114],{"categories":398},[114],{"categories":400},[120],{"categories":402},[76],{"categories":404},[],{"categories":406},[],{"categories":408},[111],{"categories":410},[76],{"categories":412},[76],{"categories":414},[],{"categories":416},[76,101],{"categories":418},[76],{"categories":420},[],{"categories":422},[76],{"categories":424},[76],{"categories":426},[],{"categories":428},[],{"categories":430},[111],{"categories":432},[76],{"categories":434},[76],{"categories":436},[111],{"categories":438},[76],{"categories":440},[],{"categories":442},[],{"categories":444},[76],{"categories":446},[],{"categories":448},[76],{"categories":450},[76],{"categories":452},[],{"categories":454},[111],{"categories":456},[117],{"categories":458},[],{"categories":460},[111,461],"DevOps & Cloud",{"categories":463},[76],{"categories":465},[111],{"categories":467},[76],{"categories":469},[],{"categories":471},[],{"categories":473},[],{"categories":475},[],{"categories":477},[76],{"categories":479},[111],{"categories":481},[],{"categories":483},[111],{"categories":485},[],{"categories":487},[76],{"categories":489},[],{"categories":491},[],{"categories":493},[],{"categories":495},[],{"categories":497},[111],{"categories":499},[117],{"categories":501},[76],{"categories":503},[114],{"categories":505},[106],{"categories":507},[101],{"categories":509},[151],{"categories":511},[],{"categories":513},[111],{"categories":515},[111],{"categories":517},[76],{"categories":519},[],{"categories":521},[],{"categories":523},[],{"categories":525},[111],{"categories":527},[],{"categories":529},[111],{"categories":531},[111],{"categories":533},[106],{"categories":535},[111],{"categories":537},[76],{"categories":539},[],{"categories":541},[76],{"categories":543},[],{"categories":545},[106],{"categories":547},[111,548],"Product Strategy",{"categories":550},[120],{"categories":552},[461],{"categories":554},[548],{"categories":556},[76],{"categories":558},[111],{"categories":560},[],{"categories":562},[106],{"categories":564},[106],{"categories":566},[111],{"categories":568},[],{"categories":570},[111],{"categories":572},[76],{"categories":574},[76],{"categories":576},[151],{"categories":578},[76],{"categories":580},[],{"categories":582},[76,120],{"categories":584},[106],{"categories":586},[76],{"categories":588},[106],{"categories":590},[111],{"categories":592},[106],{"categories":594},[],{"categories":596},[120],{"categories":598},[101],{"categories":600},[],{"categories":602},[111],{"categories":604},[111],{"categories":606},[111],{"categories":608},[111],{"categories":610},[101],{"categories":612},[117],{"categories":614},[114],{"categories":616},[],{"categories":618},[111],{"categories":620},[],{"categories":622},[106],{"categories":624},[106],{"categories":626},[106],{"categories":628},[111],{"categories":630},[106],{"categories":632},[76],{"categories":634},[151],{"categories":636},[76],{"categories":638},[120],{"categories":640},[76,151],{"categories":642},[151],{"categories":644},[151],{"categories":646},[151],{"categories":648},[151],{"categories":650},[76],{"categories":652},[],{"categories":654},[],{"categories":656},[114],{"categories":658},[],{"categories":660},[76],{"categories":662},[151],{"categories":664},[76],{"categories":666},[117],{"categories":668},[120],{"categories":670},[],{"categories":672},[76],{"categories":674},[151],{"categories":676},[114],{"categories":678},[106],{"categories":680},[120],{"categories":682},[76],{"categories":684},[],{"categories":686},[120],{"categories":688},[117],{"categories":690},[101],{"categories":692},[101],{"categories":694},[],{"categories":696},[117],{"categories":698},[101],{"categories":700},[106],{"categories":702},[151],{"categories":704},[111],{"categories":706},[111],{"categories":708},[76],{"categories":710},[76],{"categories":712},[106],{"categories":714},[106],{"categories":716},[151],{"categories":718},[106],{"categories":720},[],{"categories":722},[548],{"categories":724},[111],{"categories":726},[106],{"categories":728},[106],{"categories":730},[106],{"categories":732},[76],{"categories":734},[111],{"categories":736},[111],{"categories":738},[101],{"categories":740},[101],{"categories":742},[76],{"categories":744},[106],{"categories":746},[],{"categories":748},[76],{"categories":750},[101],{"categories":752},[111],{"categories":754},[111],{"categories":756},[111],{"categories":758},[117],{"categories":760},[111],{"categories":762},[151],{"categories":764},[106],{"categories":766},[106],{"categories":768},[106],{"categories":770},[106],{"categories":772},[106],{"categories":774},[],{"categories":776},[],{"categories":778},[151],{"categories":780},[106],{"categories":782},[106],{"categories":784},[106],{"categories":786},[],{"categories":788},[76],{"categories":790},[],{"categories":792},[],{"categories":794},[117],{"categories":796},[101],{"categories":798},[],{"categories":800},[106],{"categories":802},[111],{"categories":804},[111],{"categories":806},[111],{"categories":808},[114],{"categories":810},[111],{"categories":812},[],{"categories":814},[106],{"categories":816},[106],{"categories":818},[76],{"categories":820},[],{"categories":822},[114],{"categories":824},[114],{"categories":826},[76],{"categories":828},[106],{"categories":830},[101],{"categories":832},[120],{"categories":834},[76],{"categories":836},[],{"categories":838},[76],{"categories":840},[76],{"categories":842},[120],{"categories":844},[76],{"categories":846},[76],{"categories":848},[76],{"categories":850},[114],{"categories":852},[106],{"categories":854},[76],{"categories":856},[76],{"categories":858},[106],{"categories":860},[111],{"categories":862},[151],{"categories":864},[101],{"categories":866},[76],{"categories":868},[151],{"categories":870},[151],{"categories":872},[],{"categories":874},[114],{"categories":876},[106],{"categories":878},[106],{"categories":880},[151],{"categories":882},[111],{"categories":884},[111],{"categories":886},[111],{"categories":888},[111],{"categories":890},[117],{"categories":892},[76],{"categories":894},[76],{"categories":896},[548],{"categories":898},[76],{"categories":900},[76],{"categories":902},[111],{"categories":904},[101],{"categories":906},[114],{"categories":908},[],{"categories":910},[101],{"categories":912},[101],{"categories":914},[],{"categories":916},[117],{"categories":918},[76],{"categories":920},[],{"categories":922},[],{"categories":924},[106],{"categories":926},[106],{"categories":928},[106],{"categories":930},[106],{"categories":932},[],{"categories":934},[106],{"categories":936},[76],{"categories":938},[76],{"categories":940},[],{"categories":942},[106],{"categories":944},[106],{"categories":946},[101],{"categories":948},[76],{"categories":950},[],{"categories":952},[],{"categories":954},[106],{"categories":956},[106],{"categories":958},[106],{"categories":960},[76],{"categories":962},[106],{"categories":964},[106],{"categories":966},[106],{"categories":968},[106],{"categories":970},[106],{"categories":972},[],{"categories":974},[111],{"categories":976},[76],{"categories":978},[114],{"categories":980},[101],{"categories":982},[111],{"categories":984},[76],{"categories":986},[],{"categories":988},[114],{"categories":990},[106],{"categories":992},[106],{"categories":994},[106],{"categories":996},[106],{"categories":998},[151],{"categories":1000},[120],{"categories":1002},[],{"categories":1004},[76],{"categories":1006},[111],{"categories":1008},[111],{"categories":1010},[111],{"categories":1012},[461],{"categories":1014},[111],{"categories":1016},[76],{"categories":1018},[76],{"categories":1020},[120],{"categories":1022},[461],{"categories":1024},[196],{"categories":1026},[76],{"categories":1028},[196],{"categories":1030},[],{"categories":1032},[114],{"categories":1034},[114],{"categories":1036},[117],{"categories":1038},[461],{"categories":1040},[111],{"categories":1042},[76],{"categories":1044},[76],{"categories":1046},[111],{"categories":1048},[111],{"categories":1050},[111],{"categories":1052},[151],{"categories":1054},[151],{"categories":1056},[111],{"categories":1058},[111],{"categories":1060},[],{"categories":1062},[111],{"categories":1064},[111],{"categories":1066},[76],{"categories":1068},[196],{"categories":1070},[111],{"categories":1072},[111],{"categories":1074},[111],{"categories":1076},[111],{"categories":1078},[101],{"categories":1080},[117],{"categories":1082},[106],{"categories":1084},[120],{"categories":1086},[461],{"categories":1088},[120],{"categories":1090},[196],{"categories":1092},[],{"categories":1094},[120],{"categories":1096},[],{"categories":1098},[],{"categories":1100},[120],{"categories":1102},[76],{"categories":1104},[],{"categories":1106},[],{"categories":1108},[],{"categories":1110},[101],{"categories":1112},[],{"categories":1114},[],{"categories":1116},[196],{"categories":1118},[76],{"categories":1120},[461],{"categories":1122},[76],{"categories":1124},[],{"categories":1126},[111],{"categories":1128},[151],{"categories":1130},[151],{"categories":1132},[114],{"categories":1134},[114],{"categories":1136},[114],{"categories":1138},[461],{"categories":1140},[120],{"categories":1142},[111],{"categories":1144},[101],{"categories":1146},[101],{"categories":1148},[120],{"categories":1150},[117],{"categories":1152},[196],{"categories":1154},[117],{"categories":1156},[],{"categories":1158},[76],{"categories":1160},[111],{"categories":1162},[111],{"categories":1164},[151],{"categories":1166},[111],{"categories":1168},[111],{"categories":1170},[117],{"categories":1172},[117],{"categories":1174},[111],{"categories":1176},[461],{"categories":1178},[76],{"categories":1180},[],{"categories":1182},[114],{"categories":1184},[111],{"categories":1186},[101],{"categories":1188},[111],{"categories":1190},[111],{"categories":1192},[],{"categories":1194},[76],{"categories":1196},[111],{"categories":1198},[111],{"categories":1200},[151],{"categories":1202},[111],{"categories":1204},[76],{"categories":1206},[],{"categories":1208},[111],{"categories":1210},[],{"categories":1212},[117],{"categories":1214},[151],{"categories":1216},[76],{"categories":1218},[120],{"categories":1220},[117],{"categories":1222},[151],{"categories":1224},[196],{"categories":1226},[151],{"categories":1228},[],{"categories":1230},[76],{"categories":1232},[76],{"categories":1234},[548],{"categories":1236},[120],{"categories":1238},[76,111],{"categories":1240},[111],{"categories":1242},[76],{"categories":1244},[111],{"categories":1246},[111,120],{"categories":1248},[111],{"categories":1250},[76],{"categories":1252},[],{"categories":1254},[151],{"categories":1256},[76],{"categories":1258},[111],{"categories":1260},[76],{"categories":1262},[],{"categories":1264},[120],{"categories":1266},[101],{"categories":1268},[111],{"categories":1270},[],{"categories":1272},[196],{"categories":1274},[120],{"categories":1276},[111],{"categories":1278},[120],{"categories":1280},[],{"categories":1282},[111],{"categories":1284},[],{"categories":1286},[111],{"categories":1288},[],{"categories":1290},[],{"categories":1292},[117],{"categories":1294},[151],{"categories":1296},[76],{"categories":1298},[111],{"categories":1300},[],{"categories":1302},[111],{"categories":1304},[120],{"categories":1306},[76],{"categories":1308},[76],{"categories":1310},[120],{"categories":1312},[120],{"categories":1314},[151],{"categories":1316},[101],{"categories":1318},[],{"categories":1320},[76],{"categories":1322},[76],{"categories":1324},[76],{"categories":1326},[111],{"categories":1328},[76],{"categories":1330},[],{"categories":1332},[117],{"categories":1334},[76],{"categories":1336},[111],{"categories":1338},[],{"categories":1340},[76],{"categories":1342},[],{"categories":1344},[76],{"categories":1346},[],{"categories":1348},[],{"categories":1350},[],{"categories":1352},[76],{"categories":1354},[76],{"categories":1356},[76],{"categories":1358},[76],{"categories":1360},[],{"categories":1362},[76],{"categories":1364},[76],{"categories":1366},[76],{"categories":1368},[],{"categories":1370},[76],{"categories":1372},[],{"categories":1374},[114],{"categories":1376},[76],{"categories":1378},[],{"categories":1380},[],{"categories":1382},[],{"categories":1384},[76],{"categories":1386},[106],{"categories":1388},[106],{"categories":1390},[],{"categories":1392},[111],{"categories":1394},[76],{"categories":1396},[],{"categories":1398},[76],{"categories":1400},[76],{"categories":1402},[106],{"categories":1404},[],{"categories":1406},[76],{"categories":1408},[106],{"categories":1410},[111],{"categories":1412},[76],{"categories":1414},[],{"categories":1416},[],{"categories":1418},[],{"categories":1420},[111],{"categories":1422},[111],{"categories":1424},[111],{"categories":1426},[111],{"categories":1428},[76],{"categories":1430},[117],{"categories":1432},[117],{"categories":1434},[111],{"categories":1436},[111],{"categories":1438},[151],{"categories":1440},[548],{"categories":1442},[151],{"categories":1444},[151],{"categories":1446},[76],{"categories":1448},[111],{"categories":1450},[76],{"categories":1452},[151],{"categories":1454},[76],{"categories":1456},[111],{"categories":1458},[111],{"categories":1460},[111],{"categories":1462},[111],{"categories":1464},[111],{"categories":1466},[76],{"categories":1468},[151],{"categories":1470},[151],{"categories":1472},[114],{"categories":1474},[111],{"categories":1476},[],{"categories":1478},[111],{"categories":1480},[],{"categories":1482},[106],{"categories":1484},[76],{"categories":1486},[],{"categories":1488},[101],{"categories":1490},[117],{"categories":1492},[117],{"categories":1494},[111],{"categories":1496},[111],{"categories":1498},[76],{"categories":1500},[76],{"categories":1502},[106],{"categories":1504},[106],{"categories":1506},[461],{"categories":1508},[111],{"categories":1510},[106],{"categories":1512},[],{"categories":1514},[76],{"categories":1516},[111],{"categories":1518},[111],{"categories":1520},[111],{"categories":1522},[111],{"categories":1524},[76],{"categories":1526},[76],{"categories":1528},[76],{"categories":1530},[76],{"categories":1532},[111],{"categories":1534},[111],{"categories":1536},[111],{"categories":1538},[111],{"categories":1540},[],{"categories":1542},[117],{"categories":1544},[76],{"categories":1546},[76],{"categories":1548},[76],{"categories":1550},[],{"categories":1552},[114],{"categories":1554},[],{"categories":1556},[151],{"categories":1558},[],{"categories":1560},[111],{"categories":1562},[151],{"categories":1564},[117],{"categories":1566},[151],{"categories":1568},[],{"categories":1570},[151],{"categories":1572},[151],{"categories":1574},[],{"categories":1576},[117],{"categories":1578},[111],{"categories":1580},[111],{"categories":1582},[151],{"categories":1584},[76],{"categories":1586},[76],{"categories":1588},[],{"categories":1590},[106],{"categories":1592},[],{"categories":1594},[114],{"categories":1596},[],{"categories":1598},[117],{"categories":1600},[106],{"categories":1602},[117],{"categories":1604},[117],{"categories":1606},[117],{"categories":1608},[117],{"categories":1610},[117],{"categories":1612},[117],{"categories":1614},[117],{"categories":1616},[117],{"categories":1618},[117],{"categories":1620},[117],{"categories":1622},[],{"categories":1624},[111],{"categories":1626},[117],{"categories":1628},[76],{"categories":1630},[76],{"categories":1632},[117],{"categories":1634},[117],{"categories":1636},[117],{"categories":1638},[117],{"categories":1640},[117],{"categories":1642},[117],{"categories":1644},[117],{"categories":1646},[76,117],{"categories":1648},[117],{"categories":1650},[117],{"categories":1652},[117],{"categories":1654},[117],{"categories":1656},[],{"categories":1658},[117],{"categories":1660},[117],{"categories":1662},[117],{"categories":1664},[117],{"categories":1666},[117],{"categories":1668},[117],{"categories":1670},[117],{"categories":1672},[117],{"categories":1674},[117],{"categories":1676},[117,76],{"categories":1678},[117],{"categories":1680},[117],{"categories":1682},[],{"categories":1684},[106],{"categories":1686},[],{"categories":1688},[76],{"categories":1690},[],{"categories":1692},[111],{"categories":1694},[461],{"categories":1696},[548],{"categories":1698},[111],{"categories":1700},[111],{"categories":1702},[],{"categories":1704},[111],{"categories":1706},[],{"categories":1708},[111],{"categories":1710},[],{"categories":1712},[],{"categories":1714},[76],{"categories":1716},[76],{"categories":1718},[76],{"categories":1720},[106],{"categories":1722},[106],{"categories":1724},[106],{"categories":1726},[106],{"categories":1728},[],{"categories":1730},[106],{"categories":1732},[],{"categories":1734},[106],{"categories":1736},[76],{"categories":1738},[106],{"categories":1740},[106],{"categories":1742},[106],{"categories":1744},[106],{"categories":1746},[76],{"categories":1748},[106],{"categories":1750},[111],{"categories":1752},[],{"categories":1754},[111],{"categories":1756},[106],{"categories":1758},[76],{"categories":1760},[106],{"categories":1762},[106],{"categories":1764},[106],{"categories":1766},[76],{"categories":1768},[76],{"categories":1770},[76],{"categories":1772},[],{"categories":1774},[],{"categories":1776},[76],{"categories":1778},[106],{"categories":1780},[],{"categories":1782},[76],{"categories":1784},[111],{"categories":1786},[76],{"categories":1788},[111],{"categories":1790},[111],{"categories":1792},[76],{"categories":1794},[],{"categories":1796},[],{"categories":1798},[111],{"categories":1800},[111],{"categories":1802},[111],{"categories":1804},[111],{"categories":1806},[111],{"categories":1808},[111],{"categories":1810},[111],{"categories":1812},[111],{"categories":1814},[],{"categories":1816},[111],{"categories":1818},[111],{"categories":1820},[111],{"categories":1822},[76],{"categories":1824},[76],{"categories":1826},[76],{"categories":1828},[106],{"categories":1830},[76],{"categories":1832},[76],{"categories":1834},[76],{"categories":1836},[111],{"categories":1838},[114],{"categories":1840},[114],{"categories":1842},[114],{"categories":1844},[111],{"categories":1846},[],{"categories":1848},[76],{"categories":1850},[],{"categories":1852},[],{"categories":1854},[76],{"categories":1856},[],{"categories":1858},[111],{"categories":1860},[117],{"categories":1862},[151],{"categories":1864},[196],{"categories":1866},[76],{"categories":1868},[111],{"categories":1870},[117],{"categories":1872},[],{"categories":1874},[111],{"categories":1876},[114,101],{"categories":1878},[111],{"categories":1880},[111],{"categories":1882},[461],{"categories":1884},[120],{"categories":1886},[114],{"categories":1888},[151],{"categories":1890},[76],{"categories":1892},[],{"categories":1894},[76],{"categories":1896},[],{"categories":1898},[76],{"categories":1900},[76],{"categories":1902},[111],{"categories":1904},[],{"categories":1906},[76],{"categories":1908},[111],{"categories":1910},[76],{"categories":1912},[151],{"categories":1914},[111],{"categories":1916},[76],{"categories":1918},[76,151],{"categories":1920},[151],{"categories":1922},[],{"categories":1924},[76],{"categories":1926},[76],{"categories":1928},[76],{"categories":1930},[],{"categories":1932},[],{"categories":1934},[111],{"categories":1936},[114],{"categories":1938},[106],{"categories":1940},[111],{"categories":1942},[76],{"categories":1944},[106],{"categories":1946},[],{"categories":1948},[151],{"categories":1950},[106],{"categories":1952},[],{"categories":1954},[196],{"categories":1956},[114],{"categories":1958},[101],{"categories":1960},[106],{"categories":1962},[76],{"categories":1964},[111],{"categories":1966},[76],{"categories":1968},[111],{"categories":1970},[111],{"categories":1972},[106],{"categories":1974},[151],{"categories":1976},[117],{"categories":1978},[101],{"categories":1980},[76],{"categories":1982},[76],{"categories":1984},[],{"categories":1986},[],{"categories":1988},[76],{"categories":1990},[],{"categories":1992},[76],{"categories":1994},[106],{"categories":1996},[],{"categories":1998},[111],{"categories":2000},[151],{"categories":2002},[106],{"categories":2004},[151],{"categories":2006},[111],{"categories":2008},[76],{"categories":2010},[],{"categories":2012},[111],{"categories":2014},[111],{"categories":2016},[117],{"categories":2018},[111],{"categories":2020},[117],{"categories":2022},[111],{"categories":2024},[111],{"categories":2026},[117],{"categories":2028},[],{"categories":2030},[],{"categories":2032},[117],{"categories":2034},[117],{"categories":2036},[117],{"categories":2038},[120],{"categories":2040},[151],{"categories":2042},[151],{"categories":2044},[111],{"categories":2046},[106],{"categories":2048},[151],{"categories":2050},[151],{"categories":2052},[114],{"categories":2054},[117],{"categories":2056},[111],{"categories":2058},[111],{"categories":2060},[76],{"categories":2062},[151],{"categories":2064},[76],{"categories":2066},[],{"categories":2068},[461],{"categories":2070},[548],{"categories":2072},[],{"categories":2074},[],{"categories":2076},[111],{"categories":2078},[106],{"categories":2080},[114],{"categories":2082},[114],{"categories":2084},[196],{"categories":2086},[117],{"categories":2088},[196],{"categories":2090},[196],{"categories":2092},[111],{"categories":2094},[],{"categories":2096},[],{"categories":2098},[196],{"categories":2100},[120],{"categories":2102},[76],{"categories":2104},[120],{"categories":2106},[196],{"categories":2108},[120],{"categories":2110},[196],{"categories":2112},[101],{"categories":2114},[120],{"categories":2116},[151],{"categories":2118},[76],{"categories":2120},[],{"categories":2122},[196],{"categories":2124},[461],{"categories":2126},[],{"categories":2128},[76],{"categories":2130},[76],{"categories":2132},[],{"categories":2134},[],{"categories":2136},[76],{"categories":2138},[76],{"categories":2140},[106],{"categories":2142},[76],{"categories":2144},[],{"categories":2146},[106],{"categories":2148},[],{"categories":2150},[],{"categories":2152},[106],{"categories":2154},[106],{"categories":2156},[76],{"categories":2158},[76],{"categories":2160},[76],{"categories":2162},[76],{"categories":2164},[76],{"categories":2166},[76],{"categories":2168},[114],{"categories":2170},[],{"categories":2172},[76],{"categories":2174},[],{"categories":2176},[],{"categories":2178},[111],{"categories":2180},[151],{"categories":2182},[],{"categories":2184},[461],{"categories":2186},[76,461],{"categories":2188},[76],{"categories":2190},[],{"categories":2192},[117],{"categories":2194},[117],{"categories":2196},[117],{"categories":2198},[117],{"categories":2200},[117],{"categories":2202},[],{"categories":2204},[],{"categories":2206},[],{"categories":2208},[120],{"categories":2210},[111],{"categories":2212},[101],{"categories":2214},[120],{"categories":2216},[151],{"categories":2218},[117],{"categories":2220},[],{"categories":2222},[114],{"categories":2224},[548],{"categories":2226},[196],{"categories":2228},[196],{"categories":2230},[196],{"categories":2232},[151],{"categories":2234},[548],{"categories":2236},[151],{"categories":2238},[],{"categories":2240},[101],{"categories":2242},[120],{"categories":2244},[76],{"categories":2246},[117],{"categories":2248},[114],{"categories":2250},[120],{"categories":2252},[114],{"categories":2254},[76],{"categories":2256},[117],{"categories":2258},[120],{"categories":2260},[461],{"categories":2262},[76],{"categories":2264},[106],{"categories":2266},[120],{"categories":2268},[],{"categories":2270},[76],{"categories":2272},[120],{"categories":2274},[120],{"categories":2276},[111],{"categories":2278},[],{"categories":2280},[114],{"categories":2282},[114],{"categories":2284},[114],{"categories":2286},[111],{"categories":2288},[76],{"categories":2290},[],{"categories":2292},[101],{"categories":2294},[151],{"categories":2296},[151],{"categories":2298},[196],{"categories":2300},[101],{"categories":2302},[106],{"categories":2304},[196],{"categories":2306},[],{"categories":2308},[106],{"categories":2310},[106],{"categories":2312},[106],{"categories":2314},[76],{"categories":2316},[101],{"categories":2318},[76],{"categories":2320},[],{"categories":2322},[],{"categories":2324},[],{"categories":2326},[120],{"categories":2328},[111],{"categories":2330},[],{"categories":2332},[151],{"categories":2334},[117],{"categories":2336},[],{"categories":2338},[114],{"categories":2340},[],{"categories":2342},[117],{"categories":2344},[76],{"categories":2346},[151],{"categories":2348},[101],{"categories":2350},[],{"categories":2352},[117],{"categories":2354},[117],{"categories":2356},[76],{"categories":2358},[],{"categories":2360},[],{"categories":2362},[120],{"categories":2364},[76],{"categories":2366},[],{"categories":2368},[111],{"categories":2370},[76],{"categories":2372},[],{"categories":2374},[120],{"categories":2376},[111],{"categories":2378},[76],{"categories":2380},[196],{"categories":2382},[76],{"categories":2384},[],{"categories":2386},[196],{"categories":2388},[76],{"categories":2390},[120],{"categories":2392},[76],{"categories":2394},[196],{"categories":2396},[111],{"categories":2398},[76],{"categories":2400},[76],{"categories":2402},[76,111],{"categories":2404},[111],{"categories":2406},[111],{"categories":2408},[111],{"categories":2410},[117],{"categories":2412},[151],{"categories":2414},[76],{"categories":2416},[151],{"categories":2418},[117],{"categories":2420},[76],{"categories":2422},[],{"categories":2424},[],{"categories":2426},[76],{"categories":2428},[76],{"categories":2430},[76],{"categories":2432},[111],{"categories":2434},[76],{"categories":2436},[],{"categories":2438},[76],{"categories":2440},[76],{"categories":2442},[111],{"categories":2444},[111],{"categories":2446},[76],{"categories":2448},[76],{"categories":2450},[],{"categories":2452},[76],{"categories":2454},[],{"categories":2456},[76],{"categories":2458},[76],{"categories":2460},[76],{"categories":2462},[76],{"categories":2464},[76],{"categories":2466},[76],{"categories":2468},[76],{"categories":2470},[],{"categories":2472},[76],{"categories":2474},[106],{"categories":2476},[106],{"categories":2478},[],{"categories":2480},[],{"categories":2482},[76],{"categories":2484},[],{"categories":2486},[76],{"categories":2488},[76,461],{"categories":2490},[],{"categories":2492},[106],{"categories":2494},[],{"categories":2496},[76],{"categories":2498},[],{"categories":2500},[],{"categories":2502},[],{"categories":2504},[76],{"categories":2506},[],{"categories":2508},[76],{"categories":2510},[],{"categories":2512},[76],{"categories":2514},[76],{"categories":2516},[],{"categories":2518},[],{"categories":2520},[76,461],{"categories":2522},[461,76],{"categories":2524},[106],{"categories":2526},[],{"categories":2528},[76],{"categories":2530},[],{"categories":2532},[76],{"categories":2534},[76],{"categories":2536},[],{"categories":2538},[106],{"categories":2540},[76,101],{"categories":2542},[106],{"categories":2544},[120],{"categories":2546},[],{"categories":2548},[111],{"categories":2550},[76],{"categories":2552},[114],{"categories":2554},[76],{"categories":2556},[151],{"categories":2558},[151],{"categories":2560},[461],{"categories":2562},[106],{"categories":2564},[76],{"categories":2566},[461],{"categories":2568},[120],{"categories":2570},[76],{"categories":2572},[151],{"categories":2574},[],{"categories":2576},[76],{"categories":2578},[],{"categories":2580},[],{"categories":2582},[76],{"categories":2584},[],{"categories":2586},[76],{"categories":2588},[120],{"categories":2590},[101],{"categories":2592},[151],{"categories":2594},[114],{"categories":2596},[111],{"categories":2598},[151],{"categories":2600},[],{"categories":2602},[114],{"categories":2604},[],{"categories":2606},[],{"categories":2608},[76],{"categories":2610},[106],{"categories":2612},[114],{"categories":2614},[],{"categories":2616},[76],{"categories":2618},[106],{"categories":2620},[106],{"categories":2622},[114],{"categories":2624},[106],{"categories":2626},[76],{"categories":2628},[106],{"categories":2630},[76],{"categories":2632},[],{"categories":2634},[76],{"categories":2636},[76],{"categories":2638},[76],{"categories":2640},[106],{"categories":2642},[],{"categories":2644},[],{"categories":2646},[117],{"categories":2648},[106],{"categories":2650},[],{"categories":2652},[76],{"categories":2654},[76],{"categories":2656},[76],{"categories":2658},[76],{"categories":2660},[76],{"categories":2662},[76],{"categories":2664},[76],{"categories":2666},[76],{"categories":2668},[76],{"categories":2670},[114],{"categories":2672},[76,117],{"categories":2674},[106],{"categories":2676},[106],{"categories":2678},[76],{"categories":2680},[120],{"categories":2682},[196],{"categories":2684},[76],{"categories":2686},[76],{"categories":2688},[],{"categories":2690},[],{"categories":2692},[76],{"categories":2694},[76],{"categories":2696},[],{"categories":2698},[117],{"categories":2700},[117],{"categories":2702},[151],{"categories":2704},[76],{"categories":2706},[151],{"categories":2708},[76],{"categories":2710},[76],{"categories":2712},[],{"categories":2714},[76],{"categories":2716},[],{"categories":2718},[],{"categories":2720},[76],{"categories":2722},[],{"categories":2724},[],{"categories":2726},[106],{"categories":2728},[],{"categories":2730},[76],{"categories":2732},[76],{"categories":2734},[76],{"categories":2736},[],{"categories":2738},[76],{"categories":2740},[106],{"categories":2742},[548],{"categories":2744},[111],{"categories":2746},[76],{"categories":2748},[],{"categories":2750},[111],{"categories":2752},[76],{"categories":2754},[],{"categories":2756},[76],{"categories":2758},[],{"categories":2760},[111],{"categories":2762},[],{"categories":2764},[],{"categories":2766},[111],{"categories":2768},[111],{"categories":2770},[111],{"categories":2772},[76],{"categories":2774},[],{"categories":2776},[111],{"categories":2778},[111],{"categories":2780},[],{"categories":2782},[],{"categories":2784},[111],{"categories":2786},[76],{"categories":2788},[106],{"categories":2790},[548],{"categories":2792},[114],{"categories":2794},[],{"categories":2796},[117],{"categories":2798},[76],{"categories":2800},[76],{"categories":2802},[101],{"categories":2804},[106],{"categories":2806},[106],{"categories":2808},[106],{"categories":2810},[106],{"categories":2812},[],{"categories":2814},[111],{"categories":2816},[111],{"categories":2818},[111],{"categories":2820},[111],{"categories":2822},[151],{"categories":2824},[76],{"categories":2826},[101],{"categories":2828},[],{"categories":2830},[151],{"categories":2832},[111],{"categories":2834},[117],{"categories":2836},[117],{"categories":2838},[117],{"categories":2840},[117],{"categories":2842},[117],{"categories":2844},[117],{"categories":2846},[76,101],{"categories":2848},[111],{"categories":2850},[101],{"categories":2852},[106],{"categories":2854},[106],{"categories":2856},[151],{"categories":2858},[],{"categories":2860},[],{"categories":2862},[114],{"categories":2864},[],{"categories":2866},[76],{"categories":2868},[114],{"categories":2870},[76],{"categories":2872},[120],{"categories":2874},[111],{"categories":2876},[101],{"categories":2878},[111],{"categories":2880},[120],{"categories":2882},[151],{"categories":2884},[111],{"categories":2886},[],{"categories":2888},[151],{"categories":2890},[],{"categories":2892},[],{"categories":2894},[111],{"categories":2896},[111],{"categories":2898},[111],{"categories":2900},[76],{"categories":2902},[76],{"categories":2904},[76],{"categories":2906},[76],{"categories":2908},[76],{"categories":2910},[],{"categories":2912},[461],{"categories":2914},[76],{"categories":2916},[],{"categories":2918},[],{"categories":2920},[],{"categories":2922},[151],{"categories":2924},[],{"categories":2926},[76],{"categories":2928},[],{"categories":2930},[106],{"categories":2932},[76],{"categories":2934},[106],{"categories":2936},[76],{"categories":2938},[111],{"categories":2940},[],{"categories":2942},[76],{"categories":2944},[76],{"categories":2946},[],{"categories":2948},[196],{"categories":2950},[196],{"categories":2952},[120],{"categories":2954},[117],{"categories":2956},[],{"categories":2958},[76],{"categories":2960},[111],{"categories":2962},[],{"categories":2964},[],{"categories":2966},[76],{"categories":2968},[120],{"categories":2970},[111],{"categories":2972},[101],{"categories":2974},[151,120],{"categories":2976},[120],{"categories":2978},[76],{"categories":2980},[111],{"categories":2982},[],{"categories":2984},[],{"categories":2986},[],{"categories":2988},[],{"categories":2990},[],{"categories":2992},[],{"categories":2994},[76],{"categories":2996},[],{"categories":2998},[],{"categories":3000},[76],{"categories":3002},[],{"categories":3004},[],{"categories":3006},[],{"categories":3008},[76],{"categories":3010},[106],{"categories":3012},[],{"categories":3014},[],{"categories":3016},[],{"categories":3018},[76],{"categories":3020},[],{"categories":3022},[76],{"categories":3024},[76],{"categories":3026},[],{"categories":3028},[76],{"categories":3030},[120],{"categories":3032},[],{"categories":3034},[151],{"categories":3036},[151],{"categories":3038},[],{"categories":3040},[114],{"categories":3042},[],{"categories":3044},[],{"categories":3046},[],{"categories":3048},[117],{"categories":3050},[106],{"categories":3052},[111],{"categories":3054},[76],{"categories":3056},[101],{"categories":3058},[76],{"categories":3060},[],{"categories":3062},[],{"categories":3064},[101],{"categories":3066},[114],{"categories":3068},[111],{"categories":3070},[],{"categories":3072},[461],{"categories":3074},[],{"categories":3076},[114],{"categories":3078},[76],{"categories":3080},[76],{"categories":3082},[114],{"categories":3084},[76],{"categories":3086},[117],{"categories":3088},[111],{"categories":3090},[76],{"categories":3092},[111],{"categories":3094},[76],{"categories":3096},[111],{"categories":3098},[151],{"categories":3100},[151],{"categories":3102},[117],{"categories":3104},[],{"categories":3106},[76],{"categories":3108},[76],{"categories":3110},[114],{"categories":3112},[548],{"categories":3114},[151],{"categories":3116},[106],{"categories":3118},[76],{"categories":3120},[106],{"categories":3122},[76],{"categories":3124},[76],{"categories":3126},[],{"categories":3128},[76],{"categories":3130},[],{"categories":3132},[76],{"categories":3134},[114],{"categories":3136},[76],{"categories":3138},[76],{"categories":3140},[76],{"categories":3142},[],{"categories":3144},[76],{"categories":3146},[76],{"categories":3148},[548],{"categories":3150},[],{"categories":3152},[106],{"categories":3154},[461],{"categories":3156},[120],{"categories":3158},[],{"categories":3160},[196],{"categories":3162},[],{"categories":3164},[],{"categories":3166},[106],{"categories":3168},[76],{"categories":3170},[],{"categories":3172},[76],{"categories":3174},[76],{"categories":3176},[111],{"categories":3178},[76],{"categories":3180},[106],{"categories":3182},[106],{"categories":3184},[117],{"categories":3186},[117],{"categories":3188},[117],{"categories":3190},[76],{"categories":3192},[196],{"categories":3194},[106],{"categories":3196},[151],{"categories":3198},[],{"categories":3200},[117],{"categories":3202},[117],{"categories":3204},[461],{"categories":3206},[117],{"categories":3208},[117],{"categories":3210},[111],{"categories":3212},[106],{"categories":3214},[461],{"categories":3216},[76],{"categories":3218},[76],{"categories":3220},[76],{"categories":3222},[76],{"categories":3224},[],{"categories":3226},[111],{"categories":3228},[76],{"categories":3230},[117],{"categories":3232},[],{"categories":3234},[],{"categories":3236},[106],{"categories":3238},[],{"categories":3240},[111],{"categories":3242},[111],{"categories":3244},[111],{"categories":3246},[111],{"categories":3248},[111],{"categories":3250},[111],{"categories":3252},[111],{"categories":3254},[111],{"categories":3256},[],{"categories":3258},[],{"categories":3260},[76],{"categories":3262},[],{"categories":3264},[111],{"categories":3266},[151],{"categories":3268},[151],{"categories":3270},[196],{"categories":3272},[101],{"categories":3274},[],{"categories":3276},[],{"categories":3278},[],{"categories":3280},[117],{"categories":3282},[76],{"categories":3284},[],{"categories":3286},[101],{"categories":3288},[101],{"categories":3290},[117],{"categories":3292},[151],{"categories":3294},[196],{"categories":3296},[117],{"categories":3298},[117],{"categories":3300},[],{"categories":3302},[111],{"categories":3304},[101],{"categories":3306},[101],{"categories":3308},[76],{"categories":3310},[111],{"categories":3312},[120],{"categories":3314},[117],{"categories":3316},[],{"categories":3318},[114],{"categories":3320},[196],{"categories":3322},[106],{"categories":3324},[106],{"categories":3326},[106],{"categories":3328},[461],{"categories":3330},[],{"categories":3332},[111],{"categories":3334},[],{"categories":3336},[111],{"categories":3338},[111],{"categories":3340},[76],{"categories":3342},[76],{"categories":3344},[120],{"categories":3346},[111],{"categories":3348},[120],{"categories":3350},[],{"categories":3352},[111],{"categories":3354},[117],{"categories":3356},[117],{"categories":3358},[117],{"categories":3360},[76],{"categories":3362},[111],{"categories":3364},[76],{"categories":3366},[101],{"categories":3368},[106],{"categories":3370},[117],{"categories":3372},[106],{"categories":3374},[76],{"categories":3376},[],{"categories":3378},[106],{"categories":3380},[111],{"categories":3382},[106],{"categories":3384},[106],{"categories":3386},[106],{"categories":3388},[106],{"categories":3390},[],{"categories":3392},[],{"categories":3394},[106],{"categories":3396},[106],{"categories":3398},[],{"categories":3400},[106],{"categories":3402},[106],{"categories":3404},[76],{"categories":3406},[76],{"categories":3408},[106],{"categories":3410},[106],{"categories":3412},[76],{"categories":3414},[],{"categories":3416},[76],{"categories":3418},[111],{"categories":3420},[76],{"categories":3422},[76],{"categories":3424},[],{"categories":3426},[76],{"categories":3428},[76],{"categories":3430},[76],{"categories":3432},[106],{"categories":3434},[],{"categories":3436},[],{"categories":3438},[],{"categories":3440},[],{"categories":3442},[76],{"categories":3444},[76],{"categories":3446},[],{"categories":3448},[114],{"categories":3450},[106],{"categories":3452},[],{"categories":3454},[],{"categories":3456},[],{"categories":3458},[],{"categories":3460},[],{"categories":3462},[76],{"categories":3464},[],{"categories":3466},[],{"categories":3468},[76],{"categories":3470},[],{"categories":3472},[111],{"categories":3474},[111],{"categories":3476},[111],{"categories":3478},[101],{"categories":3480},[],{"categories":3482},[114],{"categories":3484},[120],{"categories":3486},[120],{"categories":3488},[461],{"categories":3490},[106],{"categories":3492},[],{"categories":3494},[76],{"categories":3496},[76],{"categories":3498},[101],{"categories":3500},[],{"categories":3502},[101],{"categories":3504},[],{"categories":3506},[],{"categories":3508},[],{"categories":3510},[120],{"categories":3512},[111],{"categories":3514},[111],{"categories":3516},[111],{"categories":3518},[111],{"categories":3520},[111],{"categories":3522},[],{"categories":3524},[106],{"categories":3526},[76],{"categories":3528},[76],{"categories":3530},[76],{"categories":3532},[],{"categories":3534},[101],{"categories":3536},[],{"categories":3538},[117],{"categories":3540},[196],{"categories":3542},[117],{"categories":3544},[],{"categories":3546},[],{"categories":3548},[76],{"categories":3550},[111],{"categories":3552},[],{"categories":3554},[76],{"categories":3556},[76],{"categories":3558},[76],{"categories":3560},[111],{"categories":3562},[111],{"categories":3564},[76],{"categories":3566},[196],{"categories":3568},[111],{"categories":3570},[],{"categories":3572},[76],{"categories":3574},[],{"categories":3576},[548],{"categories":3578},[120],{"categories":3580},[196],{"categories":3582},[120],{"categories":3584},[461],{"categories":3586},[76],{"categories":3588},[120],{"categories":3590},[106],{"categories":3592},[461],{"categories":3594},[120],{"categories":3596},[117],{"categories":3598},[117],{"categories":3600},[],{"categories":3602},[120],{"categories":3604},[],{"categories":3606},[151],{"categories":3608},[120],{"categories":3610},[],{"categories":3612},[196],{"categories":3614},[196],{"categories":3616},[548],{"categories":3618},[],{"categories":3620},[76],{"categories":3622},[120],{"categories":3624},[461],{"categories":3626},[111],{"categories":3628},[111],{"categories":3630},[196],{"categories":3632},[76],{"categories":3634},[151],{"categories":3636},[76],{"categories":3638},[],{"categories":3640},[],{"categories":3642},[],{"categories":3644},[114],{"categories":3646},[76],{"categories":3648},[117],{"categories":3650},[120],{"categories":3652},[120],{"categories":3654},[76],{"categories":3656},[114],{"categories":3658},[151],{"categories":3660},[76],{"categories":3662},[120],{"categories":3664},[76],{"categories":3666},[120],{"categories":3668},[151],{"categories":3670},[151],{"categories":3672},[111],{"categories":3674},[151],{"categories":3676},[120],{"categories":3678},[101],{"categories":3680},[120],{"categories":3682},[120],{"categories":3684},[120],{"categories":3686},[120],{"categories":3688},[],{"categories":3690},[106],{"categories":3692},[],{"categories":3694},[196],{"categories":3696},[76],{"categories":3698},[76],{"categories":3700},[],{"categories":3702},[],{"categories":3704},[],{"categories":3706},[76],{"categories":3708},[106],{"categories":3710},[76],{"categories":3712},[76],{"categories":3714},[],{"categories":3716},[76],{"categories":3718},[117],{"categories":3720},[76],{"categories":3722},[76],{"categories":3724},[76],{"categories":3726},[],{"categories":3728},[],{"categories":3730},[],{"categories":3732},[461],{"categories":3734},[461],{"categories":3736},[101],{"categories":3738},[111],{"categories":3740},[101,114],{"categories":3742},[76],{"categories":3744},[106],{"categories":3746},[],{"categories":3748},[117],{"categories":3750},[196],{"categories":3752},[76],{"categories":3754},[120],{"categories":3756},[76],{"categories":3758},[],{"categories":3760},[196],{"categories":3762},[461],{"categories":3764},[111],{"categories":3766},[101],{"categories":3768},[461],{"categories":3770},[111],{"categories":3772},[151],{"categories":3774},[111],{"categories":3776},[151],{"categories":3778},[76],{"categories":3780},[151],{"categories":3782},[151],{"categories":3784},[120],{"categories":3786},[196],{"categories":3788},[76],{"categories":3790},[114],{"categories":3792},[],{"categories":3794},[76],{"categories":3796},[117],{"categories":3798},[196],{"categories":3800},[101],{"categories":3802},[76],{"categories":3804},[196],{"categories":3806},[151],{"categories":3808},[76],{"categories":3810},[76],{"categories":3812},[196],{"categories":3814},[76],{"categories":3816},[151],{"categories":3818},[76],{"categories":3820},[],{"categories":3822},[76],{"categories":3824},[76],{"categories":3826},[76],{"categories":3828},[76],{"categories":3830},[],{"categories":3832},[111],{"categories":3834},[461],{"categories":3836},[],{"categories":3838},[],{"categories":3840},[76],{"categories":3842},[101],{"categories":3844},[114],{"categories":3846},[101],{"categories":3848},[101],{"categories":3850},[111],{"categories":3852},[],{"categories":3854},[76],{"categories":3856},[106],{"categories":3858},[76],{"categories":3860},[76],{"categories":3862},[],{"categories":3864},[111],{"categories":3866},[106],{"categories":3868},[76,461],{"categories":3870},[111,461],{"categories":3872},[461],{"categories":3874},[76],{"categories":3876},[111],{"categories":3878},[111],{"categories":3880},[120],{"categories":3882},[120],{"categories":3884},[120],{"categories":3886},[76],{"categories":3888},[117],{"categories":3890},[111],{"categories":3892},[],{"categories":3894},[461],{"categories":3896},[],{"categories":3898},[461],{"categories":3900},[461],{"categories":3902},[101],{"categories":3904},[111],{"categories":3906},[],{"categories":3908},[461],{"categories":3910},[76],{"categories":3912},[106],{"categories":3914},[76],{"categories":3916},[117],{"categories":3918},[120],{"categories":3920},[120],{"categories":3922},[120],{"categories":3924},[461],{"categories":3926},[],{"categories":3928},[],{"categories":3930},[],{"categories":3932},[76],{"categories":3934},[120],{"categories":3936},[76],{"categories":3938},[120],{"categories":3940},[461],{"categories":3942},[461],{"categories":3944},[76],{"categories":3946},[111],{"categories":3948},[],{"categories":3950},[76],{"categories":3952},[76],{"categories":3954},[76],{"categories":3956},[],{"categories":3958},[],{"categories":3960},[461],{"categories":3962},[461],{"categories":3964},[76,461],{"categories":3966},[111],{"categories":3968},[111],{"categories":3970},[111],{"categories":3972},[111],{"categories":3974},[111],{"categories":3976},[111],{"categories":3978},[],{"categories":3980},[120],{"categories":3982},[76],{"categories":3984},[120],{"categories":3986},[114],{"categories":3988},[76],{"categories":3990},[548],{"categories":3992},[548],{"categories":3994},[111],{"categories":3996},[120],{"categories":3998},[],{"categories":4000},[111],{"categories":4002},[76],{"categories":4004},[],{"categories":4006},[117],{"categories":4008},[],{"categories":4010},[76],{"categories":4012},[111],{"categories":4014},[106],{"categories":4016},[76],{"categories":4018},[],{"categories":4020},[],{"categories":4022},[117],{"categories":4024},[117],{"categories":4026},[151],{"categories":4028},[117],{"categories":4030},[111],{"categories":4032},[],{"categories":4034},[111],{"categories":4036},[106],{"categories":4038},[76],{"categories":4040},[76],{"categories":4042},[],{"categories":4044},[76],{"categories":4046},[151],{"categories":4048},[76],{"categories":4050},[],{"categories":4052},[196],{"categories":4054},[120],{"categories":4056},[120],{"categories":4058},[101],{"categories":4060},[101],{"categories":4062},[101],{"categories":4064},[111],{"categories":4066},[101],{"categories":4068},[111],{"categories":4070},[461],{"categories":4072},[548],{"categories":4074},[106],{"categories":4076},[106],{"categories":4078},[106],{"categories":4080},[461],{"categories":4082},[106,101],{"categories":4084},[196],{"categories":4086},[111],{"categories":4088},[],{"categories":4090},[76],{"categories":4092},[],{"categories":4094},[120],{"categories":4096},[196],{"categories":4098},[117],{"categories":4100},[120],{"categories":4102},[151],{"categories":4104},[],{"categories":4106},[111],{"categories":4108},[],{"categories":4110},[548],{"categories":4112},[],{"categories":4114},[117],{"categories":4116},[117],{"categories":4118},[196],{"categories":4120},[],{"categories":4122},[76],{"categories":4124},[196],{"categories":4126},[],{"categories":4128},[76],{"categories":4130},[76],{"categories":4132},[],{"categories":4134},[151],{"categories":4136},[76],{"categories":4138},[],{"categories":4140},[76],{"categories":4142},[],{"categories":4144},[],{"categories":4146},[111],{"categories":4148},[111],{"categories":4150},[],{"categories":4152},[120],{"categories":4154},[120],{"categories":4156},[120],{"categories":4158},[76,111],{"categories":4160},[111],{"categories":4162},[111],{"categories":4164},[111],{"categories":4166},[196],{"categories":4168},[196],{"categories":4170},[],{"categories":4172},[106],{"categories":4174},[76],{"categories":4176},[196],{"categories":4178},[196],{"categories":4180},[106],{"categories":4182},[101],{"categories":4184},[111],{"categories":4186},[120],{"categories":4188},[76],{"categories":4190},[76],{"categories":4192},[111],{"categories":4194},[120],{"categories":4196},[111],{"categories":4198},[76],{"categories":4200},[114],{"categories":4202},[],{"categories":4204},[76],{"categories":4206},[],{"categories":4208},[76],{"categories":4210},[76],{"categories":4212},[120],{"categories":4214},[],{"categories":4216},[196],{"categories":4218},[76],{"categories":4220},[111],{"categories":4222},[111],{"categories":4224},[120],{"categories":4226},[151],{"categories":4228},[151],{"categories":4230},[106],{"categories":4232},[76],{"categories":4234},[111],{"categories":4236},[],{"categories":4238},[111],{"categories":4240},[76],{"categories":4242},[106],{"categories":4244},[76],{"categories":4246},[76],{"categories":4248},[76],{"categories":4250},[111],{"categories":4252},[196],{"categories":4254},[76],{"categories":4256},[117],{"categories":4258},[76],{"categories":4260},[76],{"categories":4262},[76],{"categories":4264},[76],{"categories":4266},[],{"categories":4268},[76],{"categories":4270},[196],{"categories":4272},[117],{"categories":4274},[76],{"categories":4276},[117],{"categories":4278},[],{"categories":4280},[],{"categories":4282},[],{"categories":4284},[76],{"categories":4286},[],{"categories":4288},[],{"categories":4290},[],{"categories":4292},[],{"categories":4294},[111],{"categories":4296},[151],{"categories":4298},[111],{"categories":4300},[111],{"categories":4302},[120],{"categories":4304},[101],{"categories":4306},[76],{"categories":4308},[76],{"categories":4310},[76],{"categories":4312},[101],{"categories":4314},[151],{"categories":4316},[],{"categories":4318},[196],{"categories":4320},[114],{"categories":4322},[76],{"categories":4324},[117],{"categories":4326},[151],{"categories":4328},[151],{"categories":4330},[548],{"categories":4332},[111],{"categories":4334},[76],{"categories":4336},[76],{"categories":4338},[151],{"categories":4340},[76],{"categories":4342},[],{"categories":4344},[],{"categories":4346},[461],{"categories":4348},[117],{"categories":4350},[151],{"categories":4352},[76],{"categories":4354},[106],{"categories":4356},[151],{"categories":4358},[101],{"categories":4360},[111],{"categories":4362},[111],{"categories":4364},[106],{"categories":4366},[76],{"categories":4368},[],{"categories":4370},[],{"categories":4372},[],{"categories":4374},[76],{"categories":4376},[],{"categories":4378},[106],{"categories":4380},[],{"categories":4382},[76],{"categories":4384},[],{"categories":4386},[106],{"categories":4388},[111],{"categories":4390},[76],{"categories":4392},[461],{"categories":4394},[76],{"categories":4396},[151],{"categories":4398},[76],{"categories":4400},[151],{"categories":4402},[151],{"categories":4404},[],{"categories":4406},[],{"categories":4408},[151],{"categories":4410},[151],{"categories":4412},[151],{"categories":4414},[],{"categories":4416},[151],{"categories":4418},[111],{"categories":4420},[111],{"categories":4422},[],{"categories":4424},[76],{"categories":4426},[114],{"categories":4428},[196],{"categories":4430},[76],{"categories":4432},[],{"categories":4434},[151],{"categories":4436},[76],{"categories":4438},[548],{"categories":4440},[151],{"categories":4442},[151],{"categories":4444},[114],{"categories":4446},[120],{"categories":4448},[120],{"categories":4450},[],{"categories":4452},[120],{"categories":4454},[76],{"categories":4456},[],{"categories":4458},[],{"categories":4460},[111],{"categories":4462},[],{"categories":4464},[111],{"categories":4466},[111],{"categories":4468},[106],{"categories":4470},[76],{"categories":4472},[106],{"categories":4474},[151],{"categories":4476},[106],{"categories":4478},[120],{"categories":4480},[120],{"categories":4482},[120],{"categories":4484},[106],{"categories":4486},[76],{"categories":4488},[111],{"categories":4490},[461],{"categories":4492},[101],{"categories":4494},[461],{"categories":4496},[461],{"categories":4498},[120],{"categories":4500},[461],{"categories":4502},[461],[4504,4553,4740,4810],{"id":4505,"title":4506,"ai":4507,"body":4512,"categories":4540,"created_at":77,"date_modified":77,"description":68,"extension":78,"faq":77,"featured":79,"kicker_label":77,"meta":4541,"navigation":81,"path":4542,"published_at":4543,"question":77,"scraped_at":77,"seo":4544,"sitemap":4545,"source_id":4546,"source_name":4547,"source_type":88,"source_url":89,"stem":4548,"tags":4549,"thumbnail_url":77,"tldr":4550,"tweet":77,"unknown_tags":4551,"__hash__":4552},"summaries\u002Fsummaries\u002Fstatic-embeddings-fail-on-context-dependent-meanin-summary.md","Static Embeddings Fail on Context-Dependent Meaning",{"provider":7,"model":8,"input_tokens":4508,"output_tokens":4509,"processing_time_ms":4510,"cost_usd":4511},5723,1321,9367,0.00178245,{"type":14,"value":4513,"toc":4535},[4514,4518,4521,4525,4528,4532],[17,4515,4517],{"id":4516},"static-embeddings-breakthrough-and-core-limitation","Static Embeddings' Breakthrough and Core Limitation",[22,4519,4520],{},"Word2Vec transformed NLP by assigning words stable vectors based on their 'neighbors' in training data, placing similar concepts like 'king'-'queen' or 'Paris'-'London' near each other in semantic space. This represented relationships, not just frequencies, turning words into positions with preserved meaning. However, it assumes one vector per word captures its overall sense—a blended average across uses—which loses precision for polysemous words. 'Bank' gets a single vector mixing riverbank and financial institution traits, preventing clean disambiguation: \"She sat on the bank\" (river edge) vs. \"She went to the bank\" (loan office). Same for 'light' (illumination\u002Fweight), 'bat' (animal\u002Fsports gear), 'duck' (bird\u002Faction), and 'cold' (temperature\u002Fillness\u002Fdistance). Impact: Models make shallow decisions in translation, QA, summarization, search, and dialogue, as they can't activate the exact sense.",[17,4522,4524],{"id":4523},"context-activates-and-shapes-meaning","Context Activates and Shapes Meaning",[22,4526,4527],{},"Words aren't self-contained; they trigger potential meanings refined by surrounding context. 'He is cold' could mean temperature or emotional distance, but 'The weather is cold' collapses ambiguity to temperature. Static vectors capture general neighborhoods but not sentence-specific interpretation—'Apple' as fruit or company shifts with \"She sliced the apple\" vs. \"Apple launched a product.\" Sequence order amplifies this: 'dog bites man' vs. 'man bites dog' inverts meaning despite identical words. Language unfolds sequentially, requiring models to carry 'unfolding memory' where prior words influence later ones. Without this, representation stays isolated, ignoring how context dynamically selects and updates meaning.",[17,4529,4531],{"id":4530},"transition-to-dynamic-sequence-models","Transition to Dynamic Sequence Models",[22,4533,4534],{},"This gap exposed that language understanding demands more than static semantics—models need to process evolving streams, remembering prior context to shape interpretation. Static embeddings enabled word-level relationships; contextual representations enable sentence-level dynamics. This pressure birthed recurrent models with hidden states for sequence memory, leading to LSTMs, encoder-decoders, attention, and transformers. Outcomes: Machines track precise, unfolding meaning, enabling robust downstream tasks. Word2Vec marked words becoming representable; the next era gave meanings 'motion' through context.",{"title":68,"searchDepth":69,"depth":69,"links":4536},[4537,4538,4539],{"id":4516,"depth":69,"text":4517},{"id":4523,"depth":69,"text":4524},{"id":4530,"depth":69,"text":4531},[],{},"\u002Fsummaries\u002Fstatic-embeddings-fail-on-context-dependent-meanin-summary","2026-04-08 21:21:18",{"title":4506,"description":68},{"loc":4542},"71ab26e32ef8c9d0","Towards AI","summaries\u002Fstatic-embeddings-fail-on-context-dependent-meanin-summary",[92,94],"Word2Vec captured general word relationships but couldn't handle polysemy or sequence, like 'bank' shifting from river to finance based on context—forcing NLP to dynamic models.",[94],"wRvRTpKiycxG5K5fn9XYJnSIjMgKwb1BwcGEYi9Rcms",{"id":4554,"title":4555,"ai":4556,"body":4561,"categories":4703,"created_at":77,"date_modified":77,"description":68,"extension":78,"faq":77,"featured":79,"kicker_label":77,"meta":4704,"navigation":81,"path":4726,"published_at":4727,"question":77,"scraped_at":4728,"seo":4729,"sitemap":4730,"source_id":4731,"source_name":4732,"source_type":88,"source_url":4733,"stem":4734,"tags":4735,"thumbnail_url":77,"tldr":4737,"tweet":77,"unknown_tags":4738,"__hash__":4739},"summaries\u002Fsummaries\u002Fdeepmind-s-diffusion-model-training-secrets-summary.md","DeepMind's Diffusion Model Training Secrets",{"provider":7,"model":8,"input_tokens":4557,"output_tokens":4558,"processing_time_ms":4559,"cost_usd":4560},8585,2469,25172,0.00266035,{"type":14,"value":4562,"toc":4695},[4563,4567,4570,4573,4579,4583,4586,4589,4592,4595,4600,4604,4607,4610,4613,4618,4622,4625,4628,4631,4634,4639,4643,4646,4649,4652,4657,4661],[17,4564,4566],{"id":4565},"data-curation-drives-quality-over-model-tweaks","Data Curation Drives Quality Over Model Tweaks",[22,4568,4569],{},"High-quality generative models for audiovisual data hinge on meticulous data curation, often more impactful than architectural or optimization changes. Sander emphasizes that research incentives historically discouraged data scrutiny—favoring standard datasets for benchmarks—but scaling demands unlearning this. Time on data yields better returns than hyperparameter tuning, though details remain proprietary as \"secret sauce.\" Poor data leads to artifacts; curation filters noise, balances distributions, and ensures diversity, enabling models like Veo to produce coherent video.",[22,4571,4572],{},"Tradeoff: Curation is labor-intensive and unpublished, but essential for production-scale results where off-the-shelf datasets fail.",[4574,4575,4576],"blockquote",{},[22,4577,4578],{},"\"time spent on improving the data is sometimes a better investment of that time than actually sort of trying to tweak the model and trying to make the optimizer better or things like that.\" (Sander on why data curation outpaces model iteration, highlighting a shift from academic norms.)",[17,4580,4582],{"id":4581},"latent-representations-unlock-scalable-training","Latent Representations Unlock Scalable Training",[22,4584,4585],{},"Raw pixels are infeasible at scale: 30s 1080p 30fps video spans gigabytes per example. Instead, train autoencoders to compress into latents—retaining grid topology but slashing tensor size by 100x via reduced resolution (e.g., 256x256 RGB → 32x32x4 latents, as in Stable Diffusion) and extra channels for high-frequency details.",[22,4587,4588],{},"Process: Encoder squeezes input through bottleneck; decoder reconstructs. Latents preserve semantics and structure for neural nets' inductive biases, unlike semantic-obliterating codecs (JPEG\u002FH.265). Visualization via principal components (from EQ-VAE paper) shows latents abstract local texture, not content—e.g., animal shapes remain discernible.",[22,4590,4591],{},"Decision chain: Rejected pixel-direct training (works small-scale but OOMs) and standard codecs (lose topology). Chose learned autoencoders for 2-order magnitude efficiency, enabling video modeling. Train diffusion on latents, decode samples post-generation.",[22,4593,4594],{},"Tradeoffs: Lossy (discards fine details selectively); simpler than pro codecs but topology-preserving boosts generative fidelity.",[4574,4596,4597],{},[22,4598,4599],{},"\"the latent are not really making abstraction of any semantic content of the image they're basically just sort of um abstracting the local texture and very fine grain structure right that's sort of the information that's sort of compressed and that's removed to some degree.\" (Sander explaining latent design preserves perceptual structure for modeling.)",[17,4601,4603],{"id":4602},"diffusion-mechanics-denoising-as-guided-optimization","Diffusion Mechanics: Denoising as Guided Optimization",[22,4605,4606],{},"Diffusion models corrupt data via gradual Gaussian noise addition, then train denoisers to reverse it for sampling. Intuition: From noisy XT, predict average clean X0 (blurry, as ill-posed—many originals map to one noisy input). Take small step toward it, add trace new noise to correct errors, repeat T steps shrinking uncertainty from broad region to point sample.",[22,4608,4609],{},"Analogy: Like SGD in pixel space—local updates prevent overshooting. Autoregression (sequence prediction) fits language but awkwardly rasterizes images\u002Fvideos; diffusion's parallel refinement suits spatiotemporal data.",[22,4611,4612],{},"Why chosen: Edges out autoregression on audiovisual tasks per parameter budget; flexible sampling.",[4574,4614,4615],{},[22,4616,4617],{},"\"We're only going to take a small step and then ask a model again basically. Right? You can compare this to how uh optimization of neural networks works.\" (Sander likening diffusion sampling to optimizers, revealing iterative local refinement core.)",[17,4619,4621],{"id":4620},"spectral-autoregression-coarse-to-fine-magic","Spectral Autoregression: Coarse-to-Fine Magic",[22,4623,4624],{},"Fourier analysis reveals why diffusion thrives on images\u002Fvideos: Natural spectra follow power laws (log-log straight lines on ImageNet samples). Noise is flat-spectrum; corruption drowns high frequencies first (details), then low (structure).",[22,4626,4627],{},"Denoising predicts low→high frequencies naturally—\"spectral autoregression.\" Start coarse (semantics), refine details; perceptually weights key scales. Enables global structure before textures, outperforming one-shot autoregression.",[22,4629,4630],{},"Observation: Image + noise spectrum hugs image until noise dominates. Sampling inverts: low-freq sketch → high-freq polish.",[22,4632,4633],{},"Tradeoffs: Multi-step (vs. autoregressive single-pass), but parallelizable and controllable; error accumulation mitigated by re-noising.",[4574,4635,4636],{},[22,4637,4638],{},"\"diffusion is basically spectral auto reggression, right? Because it's essentially allowing you to generate images from coarse defined, right? You start with the low frequencies and then you gradually add higher and higher frequencies.\" (Sander's key insight tying frequency dynamics to generation intuition.)",[17,4640,4642],{"id":4641},"architectures-scaling-sampling-distillation-control","Architectures, Scaling, Sampling, Distillation & Control",[22,4644,4645],{},"Denoisers use U-Nets (originally for segmentation)—simple noisy-to-clean predictors. Scaling touches briefly: Massive compute for latents\u002Fvideos. Sampling flexibility > autoregression: Variable steps, guidance.",[22,4647,4648],{},"Distillation accelerates: Train student to mimic teacher in fewer steps (not size reduction). Control signals (text, etc.) steer via classifiers\u002Fgradients, making models \"do our bidding.\"",[22,4650,4651],{},"Progression: Pixels → latents → diffusion → optimized sampling\u002Fcontrol. Failures implied: Early pixel training OOM'd; uncurated data flops.",[4574,4653,4654],{},[22,4655,4656],{},"\"there's there's there sort of more stuff you can do with diffusion models than you can do with autogressive models.\" (Sander on diffusion's sampling regime advantages for practical use.)",[17,4658,4660],{"id":4659},"key-takeaways","Key Takeaways",[4662,4663,4664,4668,4671,4674,4677,4680,4683,4686,4689,4692],"ul",{},[4665,4666,4667],"li",{},"Prioritize data curation over model tweaks—it's the highest-ROI step for scale.",[4665,4669,4670],{},"Use learned autoencoders for latents: Compress 100x while preserving grid topology and semantics.",[4665,4672,4673],{},"View diffusion as spectral autoregression: Low-to-high freq generation matches perceptual priorities.",[4665,4675,4676],{},"Sample iteratively: Small denoise steps + re-noise prevent error accumulation, like SGD in latent space.",[4665,4678,4679],{},"Reject standard codecs; design primitives preserve inductive biases for convnets.",[4665,4681,4682],{},"For video, latents handle time redundancy best—feasible where pixels fail.",[4665,4684,4685],{},"Distill for speed: Fewer steps without quality loss.",[4665,4687,4688],{},"Leverage diffusion's control flexibility (guidance) for conditioned generation.",[4665,4690,4691],{},"Analyze spectra: Power laws explain natural media structure exploitation.",[4665,4693,4694],{},"Check sander.ai for diffusion intuition blogs.",{"title":68,"searchDepth":69,"depth":69,"links":4696},[4697,4698,4699,4700,4701,4702],{"id":4565,"depth":69,"text":4566},{"id":4581,"depth":69,"text":4582},{"id":4602,"depth":69,"text":4603},{"id":4620,"depth":69,"text":4621},{"id":4641,"depth":69,"text":4642},{"id":4659,"depth":69,"text":4660},[76],{"content_references":4705,"triage":4721},[4706,4710,4714,4718],{"type":4707,"title":4708,"context":4709},"dataset","ImageNet","mentioned",{"type":4711,"title":4712,"context":4713},"paper","EQVE","cited",{"type":4715,"title":4716,"url":4717,"context":4709},"other","sander.ai","https:\u002F\u002Fsander.ai",{"type":4719,"title":4720,"context":4709},"tool","Stable Diffusion",{"relevance":4722,"novelty":4722,"quality":4723,"actionability":69,"composite":4724,"reasoning":4725},3,4,3.05,"Category: AI & LLMs. The article discusses the importance of data curation in training generative models, which is relevant to AI product builders. However, while it provides insights into model training, it lacks specific actionable steps for implementation, making it less practical for the audience.","\u002Fsummaries\u002Fdeepmind-s-diffusion-model-training-secrets-summary","2026-04-21 19:33:38","2026-04-26 17:03:29",{"title":4555,"description":68},{"loc":4726},"39480ff9882fcab8","AI Engineer","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xOP1PM8fwnk","summaries\u002Fdeepmind-s-diffusion-model-training-secrets-summary",[92,4736,94],"deep-learning","Sander from DeepMind reveals data curation trumps model tweaks, latent autoencoders enable scale, diffusion denoises via spectral autoregression for superior audiovisual generation.",[94],"mIwY3o1eQYYYmtO9TUZO1GQjO0tN6UKDY-TwcdUeVhk",{"id":4741,"title":4742,"ai":4743,"body":4748,"categories":4784,"created_at":77,"date_modified":77,"description":68,"extension":78,"faq":77,"featured":79,"kicker_label":77,"meta":4785,"navigation":81,"path":4796,"published_at":4797,"question":77,"scraped_at":4798,"seo":4799,"sitemap":4800,"source_id":4801,"source_name":4802,"source_type":88,"source_url":4803,"stem":4804,"tags":4805,"thumbnail_url":77,"tldr":4807,"tweet":77,"unknown_tags":4808,"__hash__":4809},"summaries\u002Fsummaries\u002Fai-transformers-match-patients-to-cancer-treatment-summary.md","AI Transformers Match Patients to Cancer Treatments, Fixing 95% Failures",{"provider":7,"model":8,"input_tokens":4744,"output_tokens":4745,"processing_time_ms":4746,"cost_usd":4747},5651,1599,14419,0.00142285,{"type":14,"value":4749,"toc":4778},[4750,4754,4757,4761,4764,4768,4771,4775],[17,4751,4753],{"id":4752},"cancer-trial-failures-stem-from-poor-patient-tumor-matching","Cancer Trial Failures Stem from Poor Patient-Tumor Matching",[22,4755,4756],{},"Cancer comprises hundreds or thousands of unique diseases, each with distinct biology, leading to a 95% clinical trial failure rate despite $20-30B annual investment and hundreds of trials yearly. Many \"failed\" treatments actually work but on mismatched patients—those without the right tumor biology. Better matching via biomarkers improves success dramatically, potentially saving millions of lives using existing safe drugs that stalled in trials. Translation from lab (e.g., mouse models, cell lines) to clinic fails because standard care lacks rich tumor profiling; ~0% of patients get whole-plex spatial transcriptomics, the richest readout.",[17,4758,4760],{"id":4759},"noetiks-multimodal-data-pipeline-creates-virtual-cells","Noetik's Multimodal Data Pipeline Creates \"Virtual Cells\"",[22,4762,4763],{},"Noetik spent two years collecting thousands of real human tumors, generating hundreds of millions of images across four modalities: spatial transcriptomics (1000+ channels), spatial proteomics, H&E imaging, and whole exome sequencing. This data trains massive self-supervised models forming \"virtual cells\" with deep cancer biology understanding, distinguishing tumor types (even novel ones) and simulating patient responses to treatments. Scaling laws show no limits, outperforming synthetic data sources.",[17,4765,4767],{"id":4766},"tario-2-predicts-rich-tumor-maps-from-routine-he-slides","TARIO-2 Predicts Rich Tumor Maps from Routine H&E Slides",[22,4769,4770],{},"TARIO-2, an autoregressive transformer trained on the world's largest tumor spatial transcriptomics datasets, predicts ~19,000-gene spatial maps directly from H&E assays every patient already receives. This unlocks precise cohort selection for trials, reviving safe-but-ineffective drugs by identifying responsive subgroups. Unlike discovery-focused AI (often turning tools into drug companies), Noetik licenses platforms; GSK's $50M deal plus undisclosed long-term commitments validates this, signaling pharma's appetite for AI software over single drugs.",[17,4772,4774],{"id":4773},"why-this-beats-hype-platform-licensing-over-drug-discovery","Why This Beats Hype: Platform Licensing Over Drug Discovery",[22,4776,4777],{},"Big Pharma shifts from in-house AI development to licensing (e.g., Boltz, Isomorphic) because cohort selection addresses the core lab-to-clinic bottleneck. Noetik's approach guides discovery toward trial-successful drugs while matching existing ones, offering billions in savings and faster approvals without new molecules.",{"title":68,"searchDepth":69,"depth":69,"links":4779},[4780,4781,4782,4783],{"id":4752,"depth":69,"text":4753},{"id":4759,"depth":69,"text":4760},{"id":4766,"depth":69,"text":4767},{"id":4773,"depth":69,"text":4774},[76],{"content_references":4786,"triage":4794},[4787,4790],{"type":4711,"title":4788,"url":4789,"context":4713},"Clinical trial failure rate in oncology","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-025-64552-2",{"type":4791,"title":4792,"url":4793,"context":4709},"podcast","Boltz episode","https:\u002F\u002Fwww.latent.space\u002Fp\u002Fboltz",{"relevance":4722,"novelty":4722,"quality":4723,"actionability":69,"composite":4724,"reasoning":4795},"Category: AI & LLMs. The article discusses a specific application of AI in improving cancer treatment outcomes, which aligns with the audience's interest in practical AI applications. However, it lacks actionable steps for product builders to implement similar AI solutions.","\u002Fsummaries\u002Fai-transformers-match-patients-to-cancer-treatment-summary","2026-04-15 00:31:14","2026-04-21 15:27:03",{"title":4742,"description":68},{"loc":4796},"f8363d42b74365e2","Latent Space (Swyx + Alessio)","https:\u002F\u002Fwww.latent.space\u002Fp\u002Fnoetik","summaries\u002Fai-transformers-match-patients-to-cancer-treatment-summary",[92,4806,94],"startups","95% of cancer trials fail due to poor patient-tumor-treatment matching; Noetik's TARIO-2 autoregressive transformer predicts 19,000-gene spatial maps from standard H&E slides, enabling precise cohort selection and GSK's $50M licensing deal.",[94],"euZ8rgV-vJ93EXz6zKrjJvfr19zhILEESoQ2eIlLPXY",{"id":4811,"title":4812,"ai":4813,"body":4818,"categories":4846,"created_at":77,"date_modified":77,"description":68,"extension":78,"faq":77,"featured":79,"kicker_label":77,"meta":4847,"navigation":81,"path":4861,"published_at":4862,"question":77,"scraped_at":4863,"seo":4864,"sitemap":4865,"source_id":4866,"source_name":4867,"source_type":88,"source_url":4868,"stem":4869,"tags":4870,"thumbnail_url":77,"tldr":4871,"tweet":77,"unknown_tags":4872,"__hash__":4873},"summaries\u002Fsummaries\u002Fgenerative-ai-prediction-to-creation-via-scale-summary.md","Generative AI: Prediction to Creation via Scale",{"provider":7,"model":8,"input_tokens":4814,"output_tokens":4815,"processing_time_ms":4816,"cost_usd":4817},5405,1255,26427,0.00168585,{"type":14,"value":4819,"toc":4841},[4820,4824,4827,4831,4834,4838],[17,4821,4823],{"id":4822},"core-shift-from-ai-critics-to-creators","Core Shift: From AI Critics to Creators",[22,4825,4826],{},"Traditional machine learning excels at prediction and analysis—categorizing data, forecasting outcomes like customer churn or disease detection from images—but cannot generate novel content. Generative AI learns data patterns to produce new outputs: text, images, music, or code. Use the analogy: traditional AI is a critic evaluating thousands of paintings for value; generative AI paints originals by statistically mimicking learned styles. This leap enables tools like predictive text (early form) to evolve into story-writing chatbots, with modern models predicting next tokens over vast contexts from internet-scale training.",[17,4828,4830],{"id":4829},"historical-foundations-markov-chains-to-neural-scale","Historical Foundations: Markov Chains to Neural Scale",[22,4832,4833],{},"Generative roots trace to 1906 when Andrey Markov invented Markov chains, modeling sequences by predicting the next event (e.g., word) from 1-2 predecessors—basis for basic autocomplete like suggesting 'morning' after 'good.' These simple models fail at long coherent text due to short memory. Deep learning revolutionized this via neural networks mimicking brain synapses, trained on billions of data points to capture complex dependencies. A model viewing 50 million cat images learns feline patterns; scaled to language\u002Faudio\u002Fimages, it generates plausible continuations. Modern LLMs conceptually extend Markov prediction but with billions of parameters for nuanced, context-aware outputs.",[17,4835,4837],{"id":4836},"scale-drives-emergent-capabilities","Scale Drives Emergent Capabilities",[22,4839,4840],{},"Capabilities emerge from massive datasets, compute, and parameters—tuned like brain synapses for intricate connections. Private investment hit $33.9 billion globally in 2024 (18.7% YoY increase per Stanford HAI's 2025 AI Index Report), funding infrastructure for sophisticated models. This scale pushes beyond functionality to human-like creativity, transforming generative AI from academic niche to industry force, as seen in everyday tools like recommendation engines.",{"title":68,"searchDepth":69,"depth":69,"links":4842},[4843,4844,4845],{"id":4822,"depth":69,"text":4823},{"id":4829,"depth":69,"text":4830},{"id":4836,"depth":69,"text":4837},[],{"content_references":4848,"triage":4858},[4849,4853],{"type":4715,"title":4850,"author":4851,"url":4852,"context":4713},"Explained: Generative AI","Massachusetts Institute of Technology (MIT)","https:\u002F\u002Fnews.mit.edu\u002F2023\u002Fexplained-generative-ai-1109",{"type":4854,"title":4855,"author":4856,"url":4857,"context":4713},"report","2025 AI Index Report","Stanford HAI","https:\u002F\u002Fhai.stanford.edu\u002Fai-index\u002F2025-ai-index-report",{"relevance":4723,"novelty":4722,"quality":4723,"actionability":69,"composite":4859,"reasoning":4860},3.4,"Category: AI & LLMs. The article discusses the evolution of generative AI and its capabilities, which aligns with the audience's interest in AI engineering and practical applications. However, it lacks specific actionable insights or frameworks that the audience could implement in their work.","\u002Fsummaries\u002Fgenerative-ai-prediction-to-creation-via-scale-summary","2026-05-06 03:09:39","2026-05-06 16:13:37",{"title":4812,"description":68},{"loc":4861},"3e3a5ba66a18008e","Generative AI","https:\u002F\u002Fgenerativeai.pub\u002Fthe-foundations-of-generative-ai-from-concepts-to-reality-f01e6edb1181?source=rss----440100e76000---4","summaries\u002Fgenerative-ai-prediction-to-creation-via-scale-summary",[92,4736,94],"Generative AI shifts machines from analyzing data (traditional AI's strength) to creating new content like text or images, powered by Markov chains, deep learning, and massive datasets\u002Fcompute yielding $33.9B investment in 2024.",[94],"GvX8j_yRY2zbD3HP6w3ySHzTTfsXLb9btVaRg5HiEB0"]