[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-abcfb8d399396bcd-shackleton-framework-pivot-failing-ai-plans-in-4-p-summary":3,"summaries-facets-categories":127,"summary-related-abcfb8d399396bcd-shackleton-framework-pivot-failing-ai-plans-in-4-p-summary":3712},{"id":4,"title":5,"ai":6,"body":13,"categories":86,"created_at":88,"date_modified":88,"description":80,"extension":89,"faq":88,"featured":90,"kicker_label":88,"meta":91,"navigation":108,"path":109,"published_at":110,"question":88,"scraped_at":111,"seo":112,"sitemap":113,"source_id":114,"source_name":115,"source_type":116,"source_url":117,"stem":118,"tags":119,"thumbnail_url":88,"tldr":124,"tweet":88,"unknown_tags":125,"__hash__":126},"summaries\u002Fsummaries\u002Fabcfb8d399396bcd-shackleton-framework-pivot-failing-ai-plans-in-4-p-summary.md","Shackleton Framework: Pivot Failing AI Plans in 4 Phases",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",7842,2383,14750,0.00273725,{"type":14,"value":15,"toc":79},"minimark",[16,21,25,28,32,35,38,42,49,55,58,64,67,73,76],[17,18,20],"h2",{"id":19},"three-traps-dooming-ai-projects-to-sunk-cost-failure","Three Traps Dooming AI Projects to Sunk-Cost Failure",[22,23,24],"p",{},"AI builders cling to failing plans through plan attachment (persisting post-change per Kahneman & Tversky's commitment bias, JSTOR 1738360), sunk-cost paralysis (weighing past investments over future value, ignoring that costs are irrecoverable), and means-end confusion (obsessing over shiny agents like architectures before outcomes). Signals: 'One more tweak,' 'I've spent X weeks,' or describing system specs before problems. Shackleton's crew pumped a crushing Endurance hull for 10 months because the tangible ship felt like progress, despite divergence from the mission—mirroring how AI 'productivity' masks drift.",[22,26,27],{},"Copy-paste diagnostic prompt identifies your trap: Describe project, AI flags evidence of 1) plan bias, 2) sunk costs, or 3) tool-love vs. problem-solving.",[17,29,31],{"id":30},"binary-diagnostic-splits-fix-from-pivot","Binary Diagnostic Splits Fix from Pivot",[22,33,34],{},"Core question forces clarity: 'If rebuilt from scratch now, would you build the same?' No hedging—yes means triage 2-3 bugs by severity; no means kill plan. Author applied to GREENHOUSE agent (v1 sorting system with \u002Fplant & \u002Fsignal commands): User bore cognitive load of classifying inputs first, patches bloated it worse. Answer 'no' led to one-entry-point v4 rebuild in one evening—simpler, faster, agent-handled sorting.",[22,36,37],{},"Prompt: Describe build, get pushed to yes\u002Fno, then targeted fixes or wreckage mode. This separates execution tweaks from directional death, preventing weeks of futile iteration.",[17,39,41],{"id":40},"_4-phase-shackleton-framework-rebuilds-from-survivors","4-Phase Shackleton Framework Rebuilds from Survivors",[22,43,44,48],{},[45,46,47],"strong",{},"Phase 1: Acknowledge ice","—Run diagnostic above.",[22,50,51,54],{},[45,52,53],{},"Phase 2: Inventory survivors","—Sort into SURVIVED (problem understanding, audience needs, research, frameworks) vs. SANK (tools, files, architectures). Survivors transfer; e.g., GREENHOUSE kept idea-tending insights, ditched commands.",[22,56,57],{},"Prompt: List died plan, get exhaustive columns—pushes forgotten gems like context files.",[22,59,60,63],{},[45,61,62],{},"Phase 3: Excavate real mission","—Push past 'built X for Y' via 'Why matter?' chains. GREENHOUSE v1: Not sorting, but 'create conditions for ideas to grow autonomously' vs. filing-cabinet death.",[22,65,66],{},"Prompt: State project\u002Fpurpose, iterate to irreducible goal.",[22,68,69,72],{},[45,70,71],{},"Phase 4: Draft rebuild brief","—One-pager: Mission sentence, carry-forward assets, abandon list + why, simplest v1, one key lesson. Builds leaner using wreckage (e.g., Shackleton's lifeboats + instruments for 1,800-mile survival).",[22,74,75],{},"Prompt structures it. All 5 prompts in RobotsOS; foundation thinking survives pivots, enabling evening rebuilds vs. days.",[22,77,78],{},"Outcomes: Plans for failure upfront—structure files\u002Fthinking as transferable. Apply Phase 1 tonight to evening project for mode-shift clarity.",{"title":80,"searchDepth":81,"depth":81,"links":82},"",2,[83,84,85],{"id":19,"depth":81,"text":20},{"id":30,"depth":81,"text":31},{"id":40,"depth":81,"text":41},[87],"Product Strategy",null,"md",false,{"content_references":92,"triage":103},[93,99],{"type":94,"title":95,"author":96,"url":97,"context":98},"tool","GREENHOUSE agent","Mia Kiraki","https:\u002F\u002Frobotsatemyhomework.substack.com\u002Fp\u002Fi-built-an-ai-greenhouse-where-scattered","mentioned",{"type":100,"title":101,"author":102,"context":98},"event","photography exhibit featuring images from Shackleton’s voyage","Royal Geographic Society",{"relevance":104,"novelty":105,"quality":105,"actionability":104,"composite":106,"reasoning":107},5,4,4.55,"Category: Product Strategy. The article provides a structured framework for diagnosing and pivoting failing AI projects, which directly addresses the pain points of product-minded builders looking for actionable strategies. The use of a binary diagnostic question and a four-phase framework offers clear, practical steps that can be immediately applied to real-world AI product development.",true,"\u002Fsummaries\u002Fabcfb8d399396bcd-shackleton-framework-pivot-failing-ai-plans-in-4-p-summary","2026-04-15 12:43:59","2026-04-15 15:39:21",{"title":5,"description":80},{"loc":109},"abcfb8d399396bcd","Robots Ate My Homework","article","https:\u002F\u002Frobotsatemyhomework.substack.com\u002Fp\u002Fai-strategy-hit-iceberg-shackleton","summaries\u002Fabcfb8d399396bcd-shackleton-framework-pivot-failing-ai-plans-in-4-p-summary",[120,121,122,123],"prompt-engineering","product-strategy","agents","ai-automation","When AI projects stall, diagnose with one binary question—'Would you rebuild it now?'—then use 4 phases to inventory survivors, uncover the real mission, and rebuild leaner from wreckage, as proven rebuilding GREENHOUSE agent in one evening.",[123],"orJSmfkJdovLe5piUDaSKD8BI1LDNuq7Eabl9I6SEoM",[128,131,134,137,140,142,144,146,148,150,152,154,157,159,161,163,165,167,169,171,173,175,178,181,183,185,188,190,192,195,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,452,454,456,458,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,494,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,554,556,558,560,562,564,566,568,570,572,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,606,608,610,612,614,616,618,620,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798,800,802,804,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,858,860,862,864,866,868,870,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,932,934,936,938,940,942,944,946,948,950,952,954,956,958,960,962,964,966,968,970,972,974,976,978,980,982,984,986,988,990,992,994,996,998,1000,1002,1004,1006,1008,1010,1012,1014,1016,1018,1020,1022,1024,1026,1028,1030,1032,1034,1036,1038,1040,1042,1044,1046,1048,1050,1052,1054,1056,1058,1060,1062,1064,1066,1068,1070,1072,1074,1076,1078,1080,1082,1084,1086,1088,1090,1092,1094,1096,1098,1100,1102,1104,1106,1108,1110,1112,1114,1116,1118,1120,1122,1124,1126,1128,1130,1132,1134,1136,1138,1140,1142,1144,1146,1148,1150,1152,1154,1156,1158,1160,1162,1164,1166,1168,1170,1172,1174,1176,1178,1180,1182,1184,1186,1188,1190,1192,1194,1196,1198,1200,1202,1204,1206,1208,1210,1212,1214,1216,1218,1220,1222,1224,1226,1228,1230,1232,1234,1236,1238,1240,1242,1244,1246,1248,1250,1252,1254,1256,1258,1260,1262,1264,1266,1268,1270,1272,1274,1276,1278,1280,1282,1284,1286,1288,1290,1292,1294,1296,1298,1300,1302,1304,1306,1308,1310,1312,1314,1316,1318,1320,1322,1324,1326,1328,1330,1332,1334,1336,1338,1340,1342,1344,1346,1348,1350,1352,1354,1356,1358,1360,1362,1364,1366,1368,1370,1372,1374,1376,1378,1380,1382,1384,1386,1388,1390,1392,1394,1396,1398,1400,1402,1404,1406,1408,1410,1412,1414,1416,1418,1420,1422,1424,1426,1428,1430,1432,1434,1436,1438,1440,1442,1444,1446,1448,1450,1452,1454,1456,1458,1460,1462,1464,1466,1468,1470,1472,1474,1476,1478,1480,1482,1484,1486,1488,1490,1492,1494,1496,1498,1500,1502,1504,1506,1508,1510,1512,1514,1516,1518,1520,1522,1524,1526,1528,1530,1532,1534,1536,1538,1540,1542,1544,1546,1548,1550,1552,1554,1556,1558,1560,1562,1564,1566,1568,1570,1572,1574,1576,1578,1580,1582,1584,1586,1588,1590,1592,1594,1596,1598,1600,1602,1604,1606,1608,1610,1612,1614,1616,1618,1620,1622,1624,1626,1628,1630,1632,1634,1636,1638,1640,1642,1644,1646,1648,1650,1652,1654,1656,1658,1660,1662,1664,1666,1668,1670,1672,1674,1676,1678,1680,1682,1684,1686,1688,1690,1692,1694,1696,1698,1700,1702,1704,1706,1708,1710,1712,1714,1716,1718,1720,1722,1724,1726,1728,1730,1732,1734,1736,1738,1740,1742,1744,1746,1748,1750,1752,1754,1756,1758,1760,1762,1764,1766,1768,1770,1772,1774,1776,1778,1780,1782,1784,1786,1788,1790,1792,1794,1796,1798,1800,1802,1804,1806,1808,1810,1812,1814,1816,1818,1820,1822,1824,1826,1828,1830,1832,1834,1836,1838,1840,1842,1844,1846,1848,1850,1852,1854,1856,1858,1860,1862,1864,1866,1868,1870,1872,1874,1876,1878,1880,1882,1884,1886,1888,1890,1892,1894,1896,1898,1900,1902,1904,1906,1908,1910,1912,1914,1916,1918,1920,1922,1924,1926,1928,1930,1932,1934,1936,1938,1940,1942,1944,1946,1948,1950,1952,1954,1956,1958,1960,1962,1964,1966,1968,1970,1972,1974,1976,1978,1980,1982,1984,1986,1988,1990,1992,1994,1996,1998,2000,2002,2004,2006,2008,2010,2012,2014,2016,2018,2020,2022,2024,2026,2028,2030,2032,2034,2036,2038,2040,2042,2044,2046,2048,2050,2052,2054,2056,2058,2060,2062,2064,2066,2068,2070,2072,2074,2076,2078,2080,2082,2084,2086,2088,2090,2092,2094,2096,2098,2100,2102,2104,2106,2108,2110,2112,2114,2116,2118,2120,2122,2124,2126,2128,2130,2132,2134,2136,2138,2140,2142,2144,2146,2148,2150,2152,2154,2156,2158,2160,2162,2164,2166,2168,2170,2172,2174,2176,2178,2180,2182,2184,2186,2188,2190,2192,2194,2196,2198,2200,2202,2204,2206,2208,2210,2212,2214,2216,2218,2220,2222,2224,2226,2228,2230,2232,2234,2236,2238,2240,2242,2244,2246,2248,2250,2252,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2282,2284,2286,2288,2290,2292,2294,2296,2298,2300,2302,2304,2306,2308,2310,2312,2314,2316,2318,2320,2322,2324,2326,2328,2330,2332,2334,2336,2338,2340,2342,2344,2346,2348,2350,2352,2354,2356,2358,2360,2362,2364,2366,2368,2370,2372,2374,2376,2378,2380,2382,2384,2386,2388,2390,2392,2394,2396,2398,2400,2402,2404,2406,2408,2410,2412,2414,2416,2418,2420,2422,2424,2426,2428,2430,2432,2434,2436,2438,2440,2442,2444,2446,2448,2450,2452,2454,2456,2458,2460,2462,2464,2466,2468,2470,2472,2474,2476,2478,2480,2482,2484,2486,2488,2490,2492,2494,2496,2498,2500,2502,2504,2506,2508,2510,2512,2514,2516,2518,2520,2522,2524,2526,2528,2530,2532,2534,2536,2538,2540,2542,2544,2546,2548,2550,2552,2554,2556,2558,2560,2562,2564,2566,2568,2570,2572,2574,2576,2578,2580,2582,2584,2586,2588,2590,2592,2594,2596,2598,2600,2602,2604,2606,2608,2610,2612,2614,2616,2618,2620,2622,2624,2626,2628,2630,2632,2634,2636,2638,2640,2642,2644,2646,2648,2650,2652,2654,2656,2658,2660,2662,2664,2666,2668,2670,2672,2674,2676,2678,2680,2682,2684,2686,2688,2690,2692,2694,2696,2698,2700,2702,2704,2706,2708,2710,2712,2714,2716,2718,2720,2722,2724,2726,2728,2730,2732,2734,2736,2738,2740,2742,2744,2746,2748,2750,2752,2754,2756,2758,2760,2762,2764,2766,2768,2770,2772,2774,2776,2778,2780,2782,2784,2786,2788,2790,2792,2794,2796,2798,2800,2802,2804,2806,2808,2810,2812,2814,2816,2818,2820,2822,2824,2826,2828,2830,2832,2834,2836,2838,2840,2842,2844,2846,2848,2850,2852,2854,2856,2858,2860,2862,2864,2866,2868,2870,2872,2874,2876,2878,2880,2882,2884,2886,2888,2890,2892,2894,2896,2898,2900,2902,2904,2906,2908,2910,2912,2914,2916,2918,2920,2922,2924,2926,2928,2930,2932,2934,2936,2938,2940,2942,2944,2946,2948,2950,2952,2954,2956,2958,2960,2962,2964,2966,2968,2970,2972,2974,2976,2978,2980,2982,2984,2986,2988,2990,2992,2994,2996,2998,3000,3002,3004,3006,3008,3010,3012,3014,3016,3018,3020,3022,3024,3026,3028,3030,3032,3034,3036,3038,3040,3042,3044,3046,3048,3050,3052,3054,3056,3058,3060,3062,3064,3066,3068,3070,3072,3074,3076,3078,3080,3082,3084,3086,3088,3090,3092,3094,3096,3098,3100,3102,3104,3106,3108,3110,3112,3114,3116,3118,3120,3122,3124,3126,3128,3130,3132,3134,3136,3138,3140,3142,3144,3146,3148,3150,3152,3154,3156,3158,3160,3162,3164,3166,3168,3170,3172,3174,3176,3178,3180,3182,3184,3186,3188,3190,3192,3194,3196,3198,3200,3202,3204,3206,3208,3210,3212,3214,3216,3218,3220,3222,3224,3226,3228,3230,3232,3234,3236,3238,3240,3242,3244,3246,3248,3250,3252,3254,3256,3258,3260,3262,3264,3266,3268,3270,3272,3274,3276,3278,3280,3282,3284,3286,3288,3290,3292,3294,3296,3298,3300,3302,3304,3306,3308,3310,3312,3314,3316,3318,3320,3322,3324,3326,3328,3330,3332,3334,3336,3338,3340,3342,3344,3346,3348,3350,3352,3354,3356,3358,3360,3362,3364,3366,3368,3370,3372,3374,3376,3378,3380,3382,3384,3386,3388,3390,3392,3394,3396,3398,3400,3402,3404,3406,3408,3410,3412,3414,3416,3418,3420,3422,3424,3426,3428,3430,3432,3434,3436,3438,3440,3442,3444,3446,3448,3450,3452,3454,3456,3458,3460,3462,3464,3466,3468,3470,3472,3474,3476,3478,3480,3482,3484,3486,3488,3490,3492,3494,3496,3498,3500,3502,3504,3506,3508,3510,3512,3514,3516,3518,3520,3522,3524,3526,3528,3530,3532,3534,3536,3538,3540,3542,3544,3546,3548,3550,3552,3554,3556,3558,3560,3562,3564,3566,3568,3570,3572,3574,3576,3578,3580,3582,3584,3586,3588,3590,3592,3594,3596,3598,3600,3602,3604,3606,3608,3610,3612,3614,3616,3618,3620,3622,3624,3626,3628,3630,3632,3634,3636,3638,3640,3642,3644,3646,3648,3650,3652,3654,3656,3658,3660,3662,3664,3666,3668,3670,3672,3674,3676,3678,3680,3682,3684,3686,3688,3690,3692,3694,3696,3698,3700,3702,3704,3706,3708,3710],{"categories":129},[130],"Developer Productivity",{"categories":132},[133],"Business & SaaS",{"categories":135},[136],"AI & LLMs",{"categories":138},[139],"AI Automation",{"categories":141},[87],{"categories":143},[136],{"categories":145},[130],{"categories":147},[133],{"categories":149},[],{"categories":151},[136],{"categories":153},[],{"categories":155},[156],"AI News & Trends",{"categories":158},[139],{"categories":160},[156],{"categories":162},[139],{"categories":164},[139],{"categories":166},[136],{"categories":168},[136],{"categories":170},[156],{"categories":172},[136],{"categories":174},[],{"categories":176},[177],"Design & Frontend",{"categories":179},[180],"Data Science & Visualization",{"categories":182},[156],{"categories":184},[],{"categories":186},[187],"Software Engineering",{"categories":189},[136],{"categories":191},[139],{"categories":193},[194],"Marketing & Growth",{"categories":196},[136],{"categories":198},[139],{"categories":200},[],{"categories":202},[],{"categories":204},[177],{"categories":206},[139],{"categories":208},[130],{"categories":210},[177],{"categories":212},[136],{"categories":214},[139],{"categories":216},[156],{"categories":218},[],{"categories":220},[],{"categories":222},[139],{"categories":224},[187],{"categories":226},[],{"categories":228},[133],{"categories":230},[],{"categories":232},[],{"categories":234},[139],{"categories":236},[139],{"categories":238},[136],{"categories":240},[],{"categories":242},[187],{"categories":244},[],{"categories":246},[],{"categories":248},[],{"categories":250},[136],{"categories":252},[194],{"categories":254},[177],{"categories":256},[177],{"categories":258},[136],{"categories":260},[139],{"categories":262},[136],{"categories":264},[136],{"categories":266},[139],{"categories":268},[139],{"categories":270},[180],{"categories":272},[156],{"categories":274},[139],{"categories":276},[194],{"categories":278},[139],{"categories":280},[87],{"categories":282},[],{"categories":284},[139],{"categories":286},[],{"categories":288},[139],{"categories":290},[187],{"categories":292},[177],{"categories":294},[136],{"categories":296},[],{"categories":298},[],{"categories":300},[139],{"categories":302},[],{"categories":304},[136],{"categories":306},[],{"categories":308},[130],{"categories":310},[187],{"categories":312},[133],{"categories":314},[156],{"categories":316},[136],{"categories":318},[],{"categories":320},[136],{"categories":322},[],{"categories":324},[187],{"categories":326},[180],{"categories":328},[],{"categories":330},[136],{"categories":332},[177],{"categories":334},[],{"categories":336},[177],{"categories":338},[139],{"categories":340},[],{"categories":342},[139],{"categories":344},[156],{"categories":346},[133],{"categories":348},[136],{"categories":350},[],{"categories":352},[139],{"categories":354},[136],{"categories":356},[87],{"categories":358},[],{"categories":360},[136],{"categories":362},[139],{"categories":364},[139],{"categories":366},[],{"categories":368},[180],{"categories":370},[136],{"categories":372},[],{"categories":374},[130],{"categories":376},[133],{"categories":378},[136],{"categories":380},[139],{"categories":382},[187],{"categories":384},[136],{"categories":386},[],{"categories":388},[],{"categories":390},[136],{"categories":392},[],{"categories":394},[177],{"categories":396},[],{"categories":398},[136],{"categories":400},[],{"categories":402},[139],{"categories":404},[136],{"categories":406},[177],{"categories":408},[],{"categories":410},[136],{"categories":412},[136],{"categories":414},[133],{"categories":416},[139],{"categories":418},[136],{"categories":420},[177],{"categories":422},[139],{"categories":424},[],{"categories":426},[],{"categories":428},[156],{"categories":430},[],{"categories":432},[136],{"categories":434},[133,194],{"categories":436},[],{"categories":438},[136],{"categories":440},[],{"categories":442},[],{"categories":444},[136],{"categories":446},[],{"categories":448},[136],{"categories":450},[451],"DevOps & Cloud",{"categories":453},[],{"categories":455},[156],{"categories":457},[177],{"categories":459},[],{"categories":461},[156],{"categories":463},[156],{"categories":465},[136],{"categories":467},[194],{"categories":469},[],{"categories":471},[133],{"categories":473},[],{"categories":475},[136,451],{"categories":477},[136],{"categories":479},[136],{"categories":481},[139],{"categories":483},[136,187],{"categories":485},[180],{"categories":487},[136],{"categories":489},[194],{"categories":491},[139],{"categories":493},[139],{"categories":495},[],{"categories":497},[139],{"categories":499},[136,133],{"categories":501},[],{"categories":503},[177],{"categories":505},[177],{"categories":507},[],{"categories":509},[],{"categories":511},[156],{"categories":513},[],{"categories":515},[130],{"categories":517},[187],{"categories":519},[136],{"categories":521},[177],{"categories":523},[139],{"categories":525},[187],{"categories":527},[156],{"categories":529},[177],{"categories":531},[],{"categories":533},[136],{"categories":535},[136],{"categories":537},[136],{"categories":539},[156],{"categories":541},[130],{"categories":543},[136],{"categories":545},[139],{"categories":547},[451],{"categories":549},[177],{"categories":551},[139],{"categories":553},[],{"categories":555},[],{"categories":557},[177],{"categories":559},[156],{"categories":561},[180],{"categories":563},[],{"categories":565},[136],{"categories":567},[136],{"categories":569},[133],{"categories":571},[136],{"categories":573},[136],{"categories":575},[156],{"categories":577},[],{"categories":579},[139],{"categories":581},[187],{"categories":583},[],{"categories":585},[136],{"categories":587},[136],{"categories":589},[139],{"categories":591},[],{"categories":593},[],{"categories":595},[136],{"categories":597},[],{"categories":599},[133],{"categories":601},[139],{"categories":603},[],{"categories":605},[130],{"categories":607},[136],{"categories":609},[133],{"categories":611},[156],{"categories":613},[],{"categories":615},[],{"categories":617},[],{"categories":619},[156],{"categories":621},[156],{"categories":623},[],{"categories":625},[],{"categories":627},[133],{"categories":629},[],{"categories":631},[],{"categories":633},[130],{"categories":635},[],{"categories":637},[194],{"categories":639},[139],{"categories":641},[133],{"categories":643},[139],{"categories":645},[187],{"categories":647},[],{"categories":649},[87],{"categories":651},[177],{"categories":653},[187],{"categories":655},[136],{"categories":657},[139],{"categories":659},[133],{"categories":661},[136],{"categories":663},[],{"categories":665},[],{"categories":667},[187],{"categories":669},[180],{"categories":671},[87],{"categories":673},[139],{"categories":675},[136],{"categories":677},[],{"categories":679},[451],{"categories":681},[],{"categories":683},[139],{"categories":685},[],{"categories":687},[],{"categories":689},[136],{"categories":691},[177],{"categories":693},[194],{"categories":695},[139],{"categories":697},[],{"categories":699},[130],{"categories":701},[],{"categories":703},[156],{"categories":705},[136,451],{"categories":707},[156],{"categories":709},[136],{"categories":711},[133],{"categories":713},[136],{"categories":715},[],{"categories":717},[133],{"categories":719},[],{"categories":721},[187],{"categories":723},[177],{"categories":725},[156],{"categories":727},[180],{"categories":729},[130],{"categories":731},[136],{"categories":733},[187],{"categories":735},[],{"categories":737},[],{"categories":739},[87],{"categories":741},[],{"categories":743},[136],{"categories":745},[],{"categories":747},[177],{"categories":749},[177],{"categories":751},[177],{"categories":753},[],{"categories":755},[],{"categories":757},[156],{"categories":759},[139],{"categories":761},[136],{"categories":763},[136],{"categories":765},[136],{"categories":767},[133],{"categories":769},[136],{"categories":771},[],{"categories":773},[187],{"categories":775},[187],{"categories":777},[133],{"categories":779},[],{"categories":781},[136],{"categories":783},[136],{"categories":785},[133],{"categories":787},[156],{"categories":789},[194],{"categories":791},[139],{"categories":793},[],{"categories":795},[177],{"categories":797},[],{"categories":799},[136],{"categories":801},[],{"categories":803},[133],{"categories":805},[139],{"categories":807},[],{"categories":809},[451],{"categories":811},[180],{"categories":813},[187],{"categories":815},[194],{"categories":817},[187],{"categories":819},[139],{"categories":821},[],{"categories":823},[],{"categories":825},[139],{"categories":827},[130],{"categories":829},[139],{"categories":831},[87],{"categories":833},[133],{"categories":835},[],{"categories":837},[136],{"categories":839},[87],{"categories":841},[136],{"categories":843},[136],{"categories":845},[194],{"categories":847},[177],{"categories":849},[139],{"categories":851},[],{"categories":853},[],{"categories":855},[451],{"categories":857},[187],{"categories":859},[],{"categories":861},[139],{"categories":863},[136],{"categories":865},[177,136],{"categories":867},[130],{"categories":869},[],{"categories":871},[136],{"categories":873},[130],{"categories":875},[177],{"categories":877},[139],{"categories":879},[187],{"categories":881},[],{"categories":883},[136],{"categories":885},[],{"categories":887},[130],{"categories":889},[],{"categories":891},[139],{"categories":893},[87],{"categories":895},[136],{"categories":897},[136],{"categories":899},[177],{"categories":901},[139],{"categories":903},[451],{"categories":905},[177],{"categories":907},[139],{"categories":909},[136],{"categories":911},[136],{"categories":913},[136],{"categories":915},[156],{"categories":917},[],{"categories":919},[87],{"categories":921},[139],{"categories":923},[177],{"categories":925},[139],{"categories":927},[187],{"categories":929},[177],{"categories":931},[139],{"categories":933},[156],{"categories":935},[],{"categories":937},[136],{"categories":939},[177],{"categories":941},[136],{"categories":943},[130],{"categories":945},[156],{"categories":947},[136],{"categories":949},[194],{"categories":951},[136],{"categories":953},[136],{"categories":955},[139],{"categories":957},[139],{"categories":959},[136],{"categories":961},[139],{"categories":963},[177],{"categories":965},[136],{"categories":967},[],{"categories":969},[],{"categories":971},[187],{"categories":973},[],{"categories":975},[130],{"categories":977},[451],{"categories":979},[],{"categories":981},[130],{"categories":983},[133],{"categories":985},[194],{"categories":987},[],{"categories":989},[133],{"categories":991},[],{"categories":993},[],{"categories":995},[],{"categories":997},[],{"categories":999},[],{"categories":1001},[136],{"categories":1003},[139],{"categories":1005},[451],{"categories":1007},[130],{"categories":1009},[136],{"categories":1011},[187],{"categories":1013},[87],{"categories":1015},[136],{"categories":1017},[194],{"categories":1019},[136],{"categories":1021},[136],{"categories":1023},[136],{"categories":1025},[136,130],{"categories":1027},[187],{"categories":1029},[187],{"categories":1031},[177],{"categories":1033},[136],{"categories":1035},[],{"categories":1037},[],{"categories":1039},[],{"categories":1041},[187],{"categories":1043},[180],{"categories":1045},[156],{"categories":1047},[177],{"categories":1049},[],{"categories":1051},[136],{"categories":1053},[136],{"categories":1055},[],{"categories":1057},[],{"categories":1059},[139],{"categories":1061},[136],{"categories":1063},[133],{"categories":1065},[],{"categories":1067},[130],{"categories":1069},[136],{"categories":1071},[130],{"categories":1073},[136],{"categories":1075},[187],{"categories":1077},[194],{"categories":1079},[136,177],{"categories":1081},[156],{"categories":1083},[177],{"categories":1085},[],{"categories":1087},[451],{"categories":1089},[177],{"categories":1091},[139],{"categories":1093},[],{"categories":1095},[],{"categories":1097},[],{"categories":1099},[],{"categories":1101},[187],{"categories":1103},[139],{"categories":1105},[139],{"categories":1107},[451],{"categories":1109},[136],{"categories":1111},[136],{"categories":1113},[136],{"categories":1115},[],{"categories":1117},[177],{"categories":1119},[],{"categories":1121},[],{"categories":1123},[139],{"categories":1125},[],{"categories":1127},[],{"categories":1129},[194],{"categories":1131},[194],{"categories":1133},[139],{"categories":1135},[],{"categories":1137},[136],{"categories":1139},[136],{"categories":1141},[187],{"categories":1143},[177],{"categories":1145},[177],{"categories":1147},[139],{"categories":1149},[130],{"categories":1151},[136],{"categories":1153},[177],{"categories":1155},[177],{"categories":1157},[139],{"categories":1159},[139],{"categories":1161},[136],{"categories":1163},[],{"categories":1165},[],{"categories":1167},[136],{"categories":1169},[139],{"categories":1171},[156],{"categories":1173},[187],{"categories":1175},[130],{"categories":1177},[136],{"categories":1179},[],{"categories":1181},[139],{"categories":1183},[139],{"categories":1185},[],{"categories":1187},[130],{"categories":1189},[136],{"categories":1191},[130],{"categories":1193},[130],{"categories":1195},[],{"categories":1197},[],{"categories":1199},[139],{"categories":1201},[139],{"categories":1203},[136],{"categories":1205},[136],{"categories":1207},[156],{"categories":1209},[180],{"categories":1211},[87],{"categories":1213},[156],{"categories":1215},[177],{"categories":1217},[],{"categories":1219},[156],{"categories":1221},[],{"categories":1223},[],{"categories":1225},[],{"categories":1227},[],{"categories":1229},[187],{"categories":1231},[180],{"categories":1233},[],{"categories":1235},[136],{"categories":1237},[136],{"categories":1239},[180],{"categories":1241},[187],{"categories":1243},[],{"categories":1245},[],{"categories":1247},[139],{"categories":1249},[156],{"categories":1251},[156],{"categories":1253},[139],{"categories":1255},[130],{"categories":1257},[136,451],{"categories":1259},[],{"categories":1261},[177],{"categories":1263},[130],{"categories":1265},[139],{"categories":1267},[177],{"categories":1269},[],{"categories":1271},[139],{"categories":1273},[139],{"categories":1275},[136],{"categories":1277},[194],{"categories":1279},[187],{"categories":1281},[177],{"categories":1283},[],{"categories":1285},[139],{"categories":1287},[136],{"categories":1289},[139],{"categories":1291},[139],{"categories":1293},[139],{"categories":1295},[194],{"categories":1297},[139],{"categories":1299},[136],{"categories":1301},[],{"categories":1303},[194],{"categories":1305},[156],{"categories":1307},[139],{"categories":1309},[],{"categories":1311},[],{"categories":1313},[136],{"categories":1315},[139],{"categories":1317},[156],{"categories":1319},[139],{"categories":1321},[],{"categories":1323},[],{"categories":1325},[],{"categories":1327},[139],{"categories":1329},[],{"categories":1331},[],{"categories":1333},[180],{"categories":1335},[136],{"categories":1337},[180],{"categories":1339},[156],{"categories":1341},[136],{"categories":1343},[136],{"categories":1345},[139],{"categories":1347},[136],{"categories":1349},[],{"categories":1351},[],{"categories":1353},[451],{"categories":1355},[],{"categories":1357},[],{"categories":1359},[130],{"categories":1361},[],{"categories":1363},[],{"categories":1365},[],{"categories":1367},[],{"categories":1369},[187],{"categories":1371},[156],{"categories":1373},[194],{"categories":1375},[133],{"categories":1377},[136],{"categories":1379},[136],{"categories":1381},[133],{"categories":1383},[],{"categories":1385},[177],{"categories":1387},[139],{"categories":1389},[133],{"categories":1391},[136],{"categories":1393},[136],{"categories":1395},[130],{"categories":1397},[],{"categories":1399},[130],{"categories":1401},[136],{"categories":1403},[194],{"categories":1405},[139],{"categories":1407},[156],{"categories":1409},[133],{"categories":1411},[136],{"categories":1413},[139],{"categories":1415},[],{"categories":1417},[136],{"categories":1419},[130],{"categories":1421},[136],{"categories":1423},[],{"categories":1425},[156],{"categories":1427},[136],{"categories":1429},[],{"categories":1431},[133],{"categories":1433},[136],{"categories":1435},[],{"categories":1437},[],{"categories":1439},[],{"categories":1441},[136],{"categories":1443},[],{"categories":1445},[451],{"categories":1447},[136],{"categories":1449},[],{"categories":1451},[136],{"categories":1453},[136],{"categories":1455},[136],{"categories":1457},[136,451],{"categories":1459},[136],{"categories":1461},[136],{"categories":1463},[177],{"categories":1465},[139],{"categories":1467},[],{"categories":1469},[139],{"categories":1471},[136],{"categories":1473},[136],{"categories":1475},[136],{"categories":1477},[130],{"categories":1479},[130],{"categories":1481},[187],{"categories":1483},[177],{"categories":1485},[139],{"categories":1487},[],{"categories":1489},[136],{"categories":1491},[156],{"categories":1493},[136],{"categories":1495},[133],{"categories":1497},[],{"categories":1499},[451],{"categories":1501},[177],{"categories":1503},[177],{"categories":1505},[139],{"categories":1507},[156],{"categories":1509},[139],{"categories":1511},[136],{"categories":1513},[],{"categories":1515},[136],{"categories":1517},[],{"categories":1519},[],{"categories":1521},[136],{"categories":1523},[136],{"categories":1525},[136],{"categories":1527},[139],{"categories":1529},[136],{"categories":1531},[],{"categories":1533},[180],{"categories":1535},[139],{"categories":1537},[],{"categories":1539},[],{"categories":1541},[136],{"categories":1543},[156],{"categories":1545},[],{"categories":1547},[177],{"categories":1549},[451],{"categories":1551},[156],{"categories":1553},[187],{"categories":1555},[187],{"categories":1557},[156],{"categories":1559},[156],{"categories":1561},[451],{"categories":1563},[],{"categories":1565},[156],{"categories":1567},[136],{"categories":1569},[130],{"categories":1571},[156],{"categories":1573},[],{"categories":1575},[180],{"categories":1577},[156],{"categories":1579},[187],{"categories":1581},[156],{"categories":1583},[451],{"categories":1585},[136],{"categories":1587},[136],{"categories":1589},[],{"categories":1591},[133],{"categories":1593},[],{"categories":1595},[],{"categories":1597},[136],{"categories":1599},[136],{"categories":1601},[136],{"categories":1603},[136],{"categories":1605},[],{"categories":1607},[180],{"categories":1609},[130],{"categories":1611},[],{"categories":1613},[136],{"categories":1615},[136],{"categories":1617},[451],{"categories":1619},[451],{"categories":1621},[],{"categories":1623},[139],{"categories":1625},[156],{"categories":1627},[156],{"categories":1629},[136],{"categories":1631},[139],{"categories":1633},[],{"categories":1635},[177],{"categories":1637},[136],{"categories":1639},[136],{"categories":1641},[],{"categories":1643},[],{"categories":1645},[451],{"categories":1647},[136],{"categories":1649},[187],{"categories":1651},[133],{"categories":1653},[136],{"categories":1655},[],{"categories":1657},[139],{"categories":1659},[130],{"categories":1661},[130],{"categories":1663},[],{"categories":1665},[136],{"categories":1667},[177],{"categories":1669},[139],{"categories":1671},[],{"categories":1673},[136],{"categories":1675},[136],{"categories":1677},[139],{"categories":1679},[],{"categories":1681},[139],{"categories":1683},[187],{"categories":1685},[],{"categories":1687},[136],{"categories":1689},[],{"categories":1691},[136],{"categories":1693},[],{"categories":1695},[136],{"categories":1697},[136],{"categories":1699},[],{"categories":1701},[136],{"categories":1703},[156],{"categories":1705},[136],{"categories":1707},[136],{"categories":1709},[130],{"categories":1711},[136],{"categories":1713},[156],{"categories":1715},[139],{"categories":1717},[],{"categories":1719},[136],{"categories":1721},[194],{"categories":1723},[],{"categories":1725},[],{"categories":1727},[],{"categories":1729},[130],{"categories":1731},[156],{"categories":1733},[139],{"categories":1735},[136],{"categories":1737},[177],{"categories":1739},[139],{"categories":1741},[],{"categories":1743},[139],{"categories":1745},[],{"categories":1747},[136],{"categories":1749},[139],{"categories":1751},[136],{"categories":1753},[],{"categories":1755},[136],{"categories":1757},[136],{"categories":1759},[156],{"categories":1761},[177],{"categories":1763},[139],{"categories":1765},[177],{"categories":1767},[133],{"categories":1769},[],{"categories":1771},[],{"categories":1773},[136],{"categories":1775},[130],{"categories":1777},[156],{"categories":1779},[],{"categories":1781},[],{"categories":1783},[187],{"categories":1785},[177],{"categories":1787},[],{"categories":1789},[136],{"categories":1791},[],{"categories":1793},[194],{"categories":1795},[136],{"categories":1797},[451],{"categories":1799},[187],{"categories":1801},[],{"categories":1803},[139],{"categories":1805},[136],{"categories":1807},[139],{"categories":1809},[139],{"categories":1811},[136],{"categories":1813},[],{"categories":1815},[130],{"categories":1817},[136],{"categories":1819},[133],{"categories":1821},[187],{"categories":1823},[177],{"categories":1825},[],{"categories":1827},[],{"categories":1829},[],{"categories":1831},[139],{"categories":1833},[177],{"categories":1835},[156],{"categories":1837},[136],{"categories":1839},[156],{"categories":1841},[177],{"categories":1843},[],{"categories":1845},[177],{"categories":1847},[156],{"categories":1849},[133],{"categories":1851},[136],{"categories":1853},[156],{"categories":1855},[194],{"categories":1857},[],{"categories":1859},[],{"categories":1861},[180],{"categories":1863},[136,187],{"categories":1865},[156],{"categories":1867},[136],{"categories":1869},[139],{"categories":1871},[139],{"categories":1873},[136],{"categories":1875},[],{"categories":1877},[187],{"categories":1879},[136],{"categories":1881},[180],{"categories":1883},[139],{"categories":1885},[194],{"categories":1887},[451],{"categories":1889},[],{"categories":1891},[130],{"categories":1893},[139],{"categories":1895},[139],{"categories":1897},[187],{"categories":1899},[136],{"categories":1901},[136],{"categories":1903},[],{"categories":1905},[],{"categories":1907},[],{"categories":1909},[451],{"categories":1911},[156],{"categories":1913},[136],{"categories":1915},[136],{"categories":1917},[136],{"categories":1919},[],{"categories":1921},[180],{"categories":1923},[133],{"categories":1925},[],{"categories":1927},[139],{"categories":1929},[451],{"categories":1931},[],{"categories":1933},[177],{"categories":1935},[177],{"categories":1937},[],{"categories":1939},[187],{"categories":1941},[177],{"categories":1943},[136],{"categories":1945},[],{"categories":1947},[156],{"categories":1949},[136],{"categories":1951},[177],{"categories":1953},[139],{"categories":1955},[156],{"categories":1957},[],{"categories":1959},[139],{"categories":1961},[177],{"categories":1963},[136],{"categories":1965},[],{"categories":1967},[136],{"categories":1969},[136],{"categories":1971},[451],{"categories":1973},[156],{"categories":1975},[180],{"categories":1977},[180],{"categories":1979},[],{"categories":1981},[],{"categories":1983},[],{"categories":1985},[139],{"categories":1987},[187],{"categories":1989},[187],{"categories":1991},[],{"categories":1993},[],{"categories":1995},[136],{"categories":1997},[],{"categories":1999},[139],{"categories":2001},[136],{"categories":2003},[],{"categories":2005},[136],{"categories":2007},[133],{"categories":2009},[136],{"categories":2011},[194],{"categories":2013},[139],{"categories":2015},[136],{"categories":2017},[187],{"categories":2019},[156],{"categories":2021},[139],{"categories":2023},[],{"categories":2025},[156],{"categories":2027},[139],{"categories":2029},[139],{"categories":2031},[],{"categories":2033},[133],{"categories":2035},[139],{"categories":2037},[],{"categories":2039},[136],{"categories":2041},[130],{"categories":2043},[156],{"categories":2045},[451],{"categories":2047},[139],{"categories":2049},[139],{"categories":2051},[130],{"categories":2053},[136],{"categories":2055},[],{"categories":2057},[],{"categories":2059},[177],{"categories":2061},[136,133],{"categories":2063},[],{"categories":2065},[130],{"categories":2067},[180],{"categories":2069},[136],{"categories":2071},[187],{"categories":2073},[136],{"categories":2075},[139],{"categories":2077},[136],{"categories":2079},[136],{"categories":2081},[156],{"categories":2083},[139],{"categories":2085},[],{"categories":2087},[],{"categories":2089},[139],{"categories":2091},[136],{"categories":2093},[451],{"categories":2095},[],{"categories":2097},[136],{"categories":2099},[139],{"categories":2101},[],{"categories":2103},[136],{"categories":2105},[194],{"categories":2107},[180],{"categories":2109},[139],{"categories":2111},[136],{"categories":2113},[451],{"categories":2115},[],{"categories":2117},[136],{"categories":2119},[194],{"categories":2121},[177],{"categories":2123},[136],{"categories":2125},[],{"categories":2127},[194],{"categories":2129},[156],{"categories":2131},[136],{"categories":2133},[136],{"categories":2135},[130],{"categories":2137},[],{"categories":2139},[],{"categories":2141},[177],{"categories":2143},[136],{"categories":2145},[180],{"categories":2147},[194],{"categories":2149},[194],{"categories":2151},[156],{"categories":2153},[],{"categories":2155},[],{"categories":2157},[136],{"categories":2159},[],{"categories":2161},[136,187],{"categories":2163},[156],{"categories":2165},[139],{"categories":2167},[187],{"categories":2169},[136],{"categories":2171},[130],{"categories":2173},[],{"categories":2175},[],{"categories":2177},[130],{"categories":2179},[194],{"categories":2181},[136],{"categories":2183},[],{"categories":2185},[177,136],{"categories":2187},[451],{"categories":2189},[130],{"categories":2191},[],{"categories":2193},[133],{"categories":2195},[133],{"categories":2197},[136],{"categories":2199},[187],{"categories":2201},[139],{"categories":2203},[156],{"categories":2205},[194],{"categories":2207},[177],{"categories":2209},[136],{"categories":2211},[136],{"categories":2213},[136],{"categories":2215},[130],{"categories":2217},[136],{"categories":2219},[139],{"categories":2221},[156],{"categories":2223},[],{"categories":2225},[],{"categories":2227},[180],{"categories":2229},[187],{"categories":2231},[136],{"categories":2233},[177],{"categories":2235},[180],{"categories":2237},[136],{"categories":2239},[136],{"categories":2241},[139],{"categories":2243},[139],{"categories":2245},[136,133],{"categories":2247},[],{"categories":2249},[177],{"categories":2251},[],{"categories":2253},[136],{"categories":2255},[156],{"categories":2257},[130],{"categories":2259},[130],{"categories":2261},[139],{"categories":2263},[136],{"categories":2265},[133],{"categories":2267},[187],{"categories":2269},[194],{"categories":2271},[],{"categories":2273},[156],{"categories":2275},[136],{"categories":2277},[136],{"categories":2279},[156],{"categories":2281},[187],{"categories":2283},[136],{"categories":2285},[139],{"categories":2287},[156],{"categories":2289},[136],{"categories":2291},[177],{"categories":2293},[136],{"categories":2295},[136],{"categories":2297},[451],{"categories":2299},[87],{"categories":2301},[139],{"categories":2303},[136],{"categories":2305},[156],{"categories":2307},[139],{"categories":2309},[194],{"categories":2311},[136],{"categories":2313},[],{"categories":2315},[136],{"categories":2317},[],{"categories":2319},[],{"categories":2321},[],{"categories":2323},[133],{"categories":2325},[136],{"categories":2327},[139],{"categories":2329},[156],{"categories":2331},[156],{"categories":2333},[156],{"categories":2335},[156],{"categories":2337},[],{"categories":2339},[130],{"categories":2341},[139],{"categories":2343},[156],{"categories":2345},[130],{"categories":2347},[139],{"categories":2349},[136],{"categories":2351},[136,139],{"categories":2353},[139],{"categories":2355},[451],{"categories":2357},[156],{"categories":2359},[156],{"categories":2361},[139],{"categories":2363},[136],{"categories":2365},[],{"categories":2367},[156],{"categories":2369},[194],{"categories":2371},[130],{"categories":2373},[136],{"categories":2375},[136],{"categories":2377},[],{"categories":2379},[187],{"categories":2381},[],{"categories":2383},[130],{"categories":2385},[139],{"categories":2387},[156],{"categories":2389},[136],{"categories":2391},[156],{"categories":2393},[130],{"categories":2395},[156],{"categories":2397},[156],{"categories":2399},[],{"categories":2401},[133],{"categories":2403},[139],{"categories":2405},[156],{"categories":2407},[156],{"categories":2409},[156],{"categories":2411},[156],{"categories":2413},[156],{"categories":2415},[156],{"categories":2417},[156],{"categories":2419},[156],{"categories":2421},[156],{"categories":2423},[156],{"categories":2425},[180],{"categories":2427},[130],{"categories":2429},[136],{"categories":2431},[136],{"categories":2433},[],{"categories":2435},[136,130],{"categories":2437},[],{"categories":2439},[139],{"categories":2441},[156],{"categories":2443},[139],{"categories":2445},[136],{"categories":2447},[136],{"categories":2449},[136],{"categories":2451},[136],{"categories":2453},[136],{"categories":2455},[139],{"categories":2457},[133],{"categories":2459},[177],{"categories":2461},[156],{"categories":2463},[136],{"categories":2465},[],{"categories":2467},[],{"categories":2469},[139],{"categories":2471},[177],{"categories":2473},[136],{"categories":2475},[],{"categories":2477},[],{"categories":2479},[194],{"categories":2481},[136],{"categories":2483},[],{"categories":2485},[],{"categories":2487},[130],{"categories":2489},[133],{"categories":2491},[136],{"categories":2493},[133],{"categories":2495},[177],{"categories":2497},[],{"categories":2499},[156],{"categories":2501},[],{"categories":2503},[177],{"categories":2505},[136],{"categories":2507},[194],{"categories":2509},[],{"categories":2511},[194],{"categories":2513},[],{"categories":2515},[],{"categories":2517},[139],{"categories":2519},[],{"categories":2521},[133],{"categories":2523},[130],{"categories":2525},[177],{"categories":2527},[187],{"categories":2529},[],{"categories":2531},[],{"categories":2533},[136],{"categories":2535},[130],{"categories":2537},[194],{"categories":2539},[],{"categories":2541},[139],{"categories":2543},[139],{"categories":2545},[156],{"categories":2547},[136],{"categories":2549},[139],{"categories":2551},[136],{"categories":2553},[139],{"categories":2555},[136],{"categories":2557},[87],{"categories":2559},[156],{"categories":2561},[],{"categories":2563},[194],{"categories":2565},[187],{"categories":2567},[139],{"categories":2569},[],{"categories":2571},[136],{"categories":2573},[139],{"categories":2575},[133],{"categories":2577},[130],{"categories":2579},[136],{"categories":2581},[177],{"categories":2583},[187],{"categories":2585},[187],{"categories":2587},[136],{"categories":2589},[180],{"categories":2591},[136],{"categories":2593},[139],{"categories":2595},[133],{"categories":2597},[139],{"categories":2599},[136],{"categories":2601},[136],{"categories":2603},[139],{"categories":2605},[156],{"categories":2607},[],{"categories":2609},[130],{"categories":2611},[136],{"categories":2613},[139],{"categories":2615},[136],{"categories":2617},[136],{"categories":2619},[],{"categories":2621},[177],{"categories":2623},[133],{"categories":2625},[156],{"categories":2627},[136],{"categories":2629},[136],{"categories":2631},[177],{"categories":2633},[194],{"categories":2635},[180],{"categories":2637},[136],{"categories":2639},[156],{"categories":2641},[136],{"categories":2643},[139],{"categories":2645},[451],{"categories":2647},[136],{"categories":2649},[139],{"categories":2651},[180],{"categories":2653},[],{"categories":2655},[139],{"categories":2657},[187],{"categories":2659},[177],{"categories":2661},[136],{"categories":2663},[130],{"categories":2665},[133],{"categories":2667},[187],{"categories":2669},[],{"categories":2671},[139],{"categories":2673},[136],{"categories":2675},[],{"categories":2677},[156],{"categories":2679},[],{"categories":2681},[156],{"categories":2683},[136],{"categories":2685},[139],{"categories":2687},[139],{"categories":2689},[139],{"categories":2691},[],{"categories":2693},[],{"categories":2695},[136],{"categories":2697},[136],{"categories":2699},[],{"categories":2701},[177],{"categories":2703},[139],{"categories":2705},[194],{"categories":2707},[130],{"categories":2709},[],{"categories":2711},[],{"categories":2713},[156],{"categories":2715},[187],{"categories":2717},[136],{"categories":2719},[136],{"categories":2721},[136],{"categories":2723},[187],{"categories":2725},[156],{"categories":2727},[177],{"categories":2729},[136],{"categories":2731},[136],{"categories":2733},[136],{"categories":2735},[156],{"categories":2737},[136],{"categories":2739},[156],{"categories":2741},[139],{"categories":2743},[139],{"categories":2745},[187],{"categories":2747},[139],{"categories":2749},[136],{"categories":2751},[187],{"categories":2753},[177],{"categories":2755},[],{"categories":2757},[139],{"categories":2759},[],{"categories":2761},[],{"categories":2763},[],{"categories":2765},[133],{"categories":2767},[136],{"categories":2769},[139],{"categories":2771},[130],{"categories":2773},[139],{"categories":2775},[194],{"categories":2777},[],{"categories":2779},[139],{"categories":2781},[],{"categories":2783},[130],{"categories":2785},[139],{"categories":2787},[],{"categories":2789},[139],{"categories":2791},[136],{"categories":2793},[156],{"categories":2795},[136],{"categories":2797},[139],{"categories":2799},[156],{"categories":2801},[139],{"categories":2803},[187],{"categories":2805},[177],{"categories":2807},[130],{"categories":2809},[],{"categories":2811},[139],{"categories":2813},[177],{"categories":2815},[451],{"categories":2817},[156],{"categories":2819},[136],{"categories":2821},[177],{"categories":2823},[130],{"categories":2825},[],{"categories":2827},[139],{"categories":2829},[139],{"categories":2831},[136],{"categories":2833},[],{"categories":2835},[139],{"categories":2837},[87],{"categories":2839},[156],{"categories":2841},[139],{"categories":2843},[133],{"categories":2845},[],{"categories":2847},[136],{"categories":2849},[87],{"categories":2851},[136],{"categories":2853},[139],{"categories":2855},[156],{"categories":2857},[130],{"categories":2859},[451],{"categories":2861},[136],{"categories":2863},[136],{"categories":2865},[136],{"categories":2867},[156],{"categories":2869},[133],{"categories":2871},[136],{"categories":2873},[177],{"categories":2875},[156],{"categories":2877},[451],{"categories":2879},[136],{"categories":2881},[],{"categories":2883},[],{"categories":2885},[451],{"categories":2887},[180],{"categories":2889},[139],{"categories":2891},[139],{"categories":2893},[156],{"categories":2895},[136],{"categories":2897},[130],{"categories":2899},[177],{"categories":2901},[139],{"categories":2903},[136],{"categories":2905},[194],{"categories":2907},[136],{"categories":2909},[139],{"categories":2911},[],{"categories":2913},[136],{"categories":2915},[136],{"categories":2917},[156],{"categories":2919},[130],{"categories":2921},[],{"categories":2923},[136],{"categories":2925},[136],{"categories":2927},[187],{"categories":2929},[177],{"categories":2931},[136,139],{"categories":2933},[194,133],{"categories":2935},[136],{"categories":2937},[],{"categories":2939},[139],{"categories":2941},[],{"categories":2943},[187],{"categories":2945},[136],{"categories":2947},[156],{"categories":2949},[],{"categories":2951},[139],{"categories":2953},[],{"categories":2955},[177],{"categories":2957},[139],{"categories":2959},[130],{"categories":2961},[139],{"categories":2963},[136],{"categories":2965},[451],{"categories":2967},[194],{"categories":2969},[133],{"categories":2971},[133],{"categories":2973},[130],{"categories":2975},[130],{"categories":2977},[136],{"categories":2979},[139],{"categories":2981},[136],{"categories":2983},[136],{"categories":2985},[130],{"categories":2987},[136],{"categories":2989},[194],{"categories":2991},[156],{"categories":2993},[136],{"categories":2995},[139],{"categories":2997},[136],{"categories":2999},[],{"categories":3001},[187],{"categories":3003},[],{"categories":3005},[139],{"categories":3007},[130],{"categories":3009},[],{"categories":3011},[451],{"categories":3013},[136],{"categories":3015},[],{"categories":3017},[156],{"categories":3019},[139],{"categories":3021},[187],{"categories":3023},[136],{"categories":3025},[139],{"categories":3027},[187],{"categories":3029},[139],{"categories":3031},[156],{"categories":3033},[130],{"categories":3035},[156],{"categories":3037},[187],{"categories":3039},[136],{"categories":3041},[177],{"categories":3043},[136],{"categories":3045},[136],{"categories":3047},[136],{"categories":3049},[136],{"categories":3051},[139],{"categories":3053},[136],{"categories":3055},[139],{"categories":3057},[136],{"categories":3059},[130],{"categories":3061},[136],{"categories":3063},[139],{"categories":3065},[177],{"categories":3067},[130],{"categories":3069},[139],{"categories":3071},[177],{"categories":3073},[],{"categories":3075},[136],{"categories":3077},[136],{"categories":3079},[187],{"categories":3081},[],{"categories":3083},[139],{"categories":3085},[194],{"categories":3087},[136],{"categories":3089},[156],{"categories":3091},[194],{"categories":3093},[139],{"categories":3095},[133],{"categories":3097},[133],{"categories":3099},[136],{"categories":3101},[130],{"categories":3103},[],{"categories":3105},[136],{"categories":3107},[],{"categories":3109},[130],{"categories":3111},[136],{"categories":3113},[139],{"categories":3115},[139],{"categories":3117},[],{"categories":3119},[187],{"categories":3121},[187],{"categories":3123},[194],{"categories":3125},[177],{"categories":3127},[],{"categories":3129},[136],{"categories":3131},[130],{"categories":3133},[136],{"categories":3135},[187],{"categories":3137},[130],{"categories":3139},[156],{"categories":3141},[156],{"categories":3143},[],{"categories":3145},[156],{"categories":3147},[139],{"categories":3149},[177],{"categories":3151},[180],{"categories":3153},[136],{"categories":3155},[],{"categories":3157},[156],{"categories":3159},[187],{"categories":3161},[133],{"categories":3163},[136],{"categories":3165},[130],{"categories":3167},[451],{"categories":3169},[130],{"categories":3171},[],{"categories":3173},[],{"categories":3175},[156],{"categories":3177},[],{"categories":3179},[139],{"categories":3181},[139],{"categories":3183},[139],{"categories":3185},[],{"categories":3187},[136],{"categories":3189},[],{"categories":3191},[156],{"categories":3193},[130],{"categories":3195},[177],{"categories":3197},[136],{"categories":3199},[156],{"categories":3201},[156],{"categories":3203},[],{"categories":3205},[156],{"categories":3207},[130],{"categories":3209},[136],{"categories":3211},[],{"categories":3213},[139],{"categories":3215},[139],{"categories":3217},[130],{"categories":3219},[],{"categories":3221},[],{"categories":3223},[],{"categories":3225},[177],{"categories":3227},[139],{"categories":3229},[136],{"categories":3231},[],{"categories":3233},[],{"categories":3235},[],{"categories":3237},[177],{"categories":3239},[],{"categories":3241},[130],{"categories":3243},[],{"categories":3245},[],{"categories":3247},[177],{"categories":3249},[136],{"categories":3251},[156],{"categories":3253},[],{"categories":3255},[194],{"categories":3257},[156],{"categories":3259},[194],{"categories":3261},[136],{"categories":3263},[],{"categories":3265},[],{"categories":3267},[139],{"categories":3269},[],{"categories":3271},[],{"categories":3273},[139],{"categories":3275},[136],{"categories":3277},[],{"categories":3279},[139],{"categories":3281},[156],{"categories":3283},[194],{"categories":3285},[180],{"categories":3287},[139],{"categories":3289},[139],{"categories":3291},[],{"categories":3293},[],{"categories":3295},[],{"categories":3297},[156],{"categories":3299},[],{"categories":3301},[],{"categories":3303},[177],{"categories":3305},[130],{"categories":3307},[],{"categories":3309},[133],{"categories":3311},[194],{"categories":3313},[136],{"categories":3315},[187],{"categories":3317},[130],{"categories":3319},[180],{"categories":3321},[133],{"categories":3323},[187],{"categories":3325},[],{"categories":3327},[],{"categories":3329},[139],{"categories":3331},[130],{"categories":3333},[177],{"categories":3335},[130],{"categories":3337},[139],{"categories":3339},[451],{"categories":3341},[139],{"categories":3343},[],{"categories":3345},[136],{"categories":3347},[156],{"categories":3349},[187],{"categories":3351},[],{"categories":3353},[177],{"categories":3355},[156],{"categories":3357},[130],{"categories":3359},[139],{"categories":3361},[136],{"categories":3363},[133],{"categories":3365},[139,451],{"categories":3367},[139],{"categories":3369},[187],{"categories":3371},[136],{"categories":3373},[180],{"categories":3375},[194],{"categories":3377},[139],{"categories":3379},[],{"categories":3381},[139],{"categories":3383},[136],{"categories":3385},[133],{"categories":3387},[],{"categories":3389},[],{"categories":3391},[136],{"categories":3393},[180],{"categories":3395},[136],{"categories":3397},[],{"categories":3399},[156],{"categories":3401},[],{"categories":3403},[156],{"categories":3405},[187],{"categories":3407},[139],{"categories":3409},[136],{"categories":3411},[194],{"categories":3413},[187],{"categories":3415},[],{"categories":3417},[156],{"categories":3419},[136],{"categories":3421},[],{"categories":3423},[136],{"categories":3425},[139],{"categories":3427},[136],{"categories":3429},[139],{"categories":3431},[136],{"categories":3433},[136],{"categories":3435},[136],{"categories":3437},[136],{"categories":3439},[133],{"categories":3441},[],{"categories":3443},[87],{"categories":3445},[156],{"categories":3447},[136],{"categories":3449},[],{"categories":3451},[187],{"categories":3453},[136],{"categories":3455},[136],{"categories":3457},[139],{"categories":3459},[156],{"categories":3461},[136],{"categories":3463},[136],{"categories":3465},[133],{"categories":3467},[139],{"categories":3469},[177],{"categories":3471},[],{"categories":3473},[180],{"categories":3475},[136],{"categories":3477},[],{"categories":3479},[156],{"categories":3481},[194],{"categories":3483},[],{"categories":3485},[],{"categories":3487},[156],{"categories":3489},[156],{"categories":3491},[194],{"categories":3493},[130],{"categories":3495},[139],{"categories":3497},[139],{"categories":3499},[136],{"categories":3501},[133],{"categories":3503},[],{"categories":3505},[],{"categories":3507},[156],{"categories":3509},[180],{"categories":3511},[187],{"categories":3513},[139],{"categories":3515},[177],{"categories":3517},[180],{"categories":3519},[180],{"categories":3521},[],{"categories":3523},[156],{"categories":3525},[136],{"categories":3527},[136],{"categories":3529},[187],{"categories":3531},[],{"categories":3533},[156],{"categories":3535},[156],{"categories":3537},[156],{"categories":3539},[],{"categories":3541},[139],{"categories":3543},[136],{"categories":3545},[],{"categories":3547},[130],{"categories":3549},[133],{"categories":3551},[],{"categories":3553},[136],{"categories":3555},[136],{"categories":3557},[],{"categories":3559},[187],{"categories":3561},[],{"categories":3563},[],{"categories":3565},[],{"categories":3567},[],{"categories":3569},[136],{"categories":3571},[156],{"categories":3573},[],{"categories":3575},[],{"categories":3577},[136],{"categories":3579},[136],{"categories":3581},[136],{"categories":3583},[180],{"categories":3585},[136],{"categories":3587},[180],{"categories":3589},[],{"categories":3591},[180],{"categories":3593},[180],{"categories":3595},[451],{"categories":3597},[139],{"categories":3599},[187],{"categories":3601},[],{"categories":3603},[],{"categories":3605},[180],{"categories":3607},[187],{"categories":3609},[187],{"categories":3611},[187],{"categories":3613},[],{"categories":3615},[130],{"categories":3617},[187],{"categories":3619},[187],{"categories":3621},[130],{"categories":3623},[187],{"categories":3625},[133],{"categories":3627},[187],{"categories":3629},[187],{"categories":3631},[187],{"categories":3633},[180],{"categories":3635},[156],{"categories":3637},[156],{"categories":3639},[136],{"categories":3641},[187],{"categories":3643},[180],{"categories":3645},[451],{"categories":3647},[180],{"categories":3649},[180],{"categories":3651},[180],{"categories":3653},[],{"categories":3655},[133],{"categories":3657},[],{"categories":3659},[451],{"categories":3661},[187],{"categories":3663},[187],{"categories":3665},[187],{"categories":3667},[139],{"categories":3669},[156,133],{"categories":3671},[180],{"categories":3673},[],{"categories":3675},[],{"categories":3677},[180],{"categories":3679},[],{"categories":3681},[180],{"categories":3683},[156],{"categories":3685},[139],{"categories":3687},[],{"categories":3689},[187],{"categories":3691},[136],{"categories":3693},[177],{"categories":3695},[],{"categories":3697},[136],{"categories":3699},[],{"categories":3701},[156],{"categories":3703},[130],{"categories":3705},[180],{"categories":3707},[],{"categories":3709},[187],{"categories":3711},[156],[3713,3791,3969,4048],{"id":3714,"title":3715,"ai":3716,"body":3721,"categories":3757,"created_at":88,"date_modified":88,"description":80,"extension":89,"faq":88,"featured":90,"kicker_label":88,"meta":3758,"navigation":108,"path":3778,"published_at":3779,"question":88,"scraped_at":3780,"seo":3781,"sitemap":3782,"source_id":3783,"source_name":3784,"source_type":116,"source_url":3785,"stem":3786,"tags":3787,"thumbnail_url":88,"tldr":3788,"tweet":88,"unknown_tags":3789,"__hash__":3790},"summaries\u002Fsummaries\u002Fcfb8096e9f38c707-gm-cuts-600-it-jobs-to-hire-ai-native-engineers-summary.md","GM Cuts 600 IT Jobs to Hire AI-Native Engineers",{"provider":7,"model":8,"input_tokens":3717,"output_tokens":3718,"processing_time_ms":3719,"cost_usd":3720},5344,2365,29761,0.0022288,{"type":14,"value":3722,"toc":3751},[3723,3727,3730,3734,3737,3741,3744,3748],[17,3724,3726],{"id":3725},"workforce-rebuild-signals-true-enterprise-ai-shift","Workforce Rebuild Signals True Enterprise AI Shift",[22,3728,3729],{},"GM eliminated over 10% of its IT department—roughly 600 salaried roles—to create space for hires skilled in building AI systems from scratch. This isn't a net headcount cut; the company continues recruiting, but prioritizes AI-native expertise over legacy IT skills. Past cuts include 1,000 software jobs in August 2024 as GM refocused on AI and quality. The result: teams capable of designing systems, training models, and engineering pipelines that integrate AI deeply, rather than treating it as a mere productivity overlay.",[17,3731,3733],{"id":3732},"high-demand-ai-skills-for-production","High-Demand AI Skills for Production",[22,3735,3736],{},"Targeted roles emphasize practical AI engineering: AI-native development, data engineering\u002Fanalytics, cloud engineering, agent and model development, prompt engineering, and new AI workflows. These skills enable end-to-end AI ownership—building agents that act autonomously, fine-tuning models for specific tasks, and creating reliable data pipelines. GM's push counters superficial AI adoption by demanding engineers who deliver scalable, enterprise-grade AI, avoiding the pitfalls of bolting tools onto outdated stacks.",[17,3738,3740],{"id":3739},"leadership-overhaul-drives-change","Leadership Overhaul Drives Change",[22,3742,3743],{},"Since hiring Sterling Anderson (ex-Aurora co-founder) as chief product officer in May 2025, GM consolidated its software teams and saw exits from three execs: Baris Cetinok (SVP software product), Dave Richardson (SVP engineering), and Barak Turovsky (ex-chief AI officer). New AI leaders include Behrad Toghi (ex-Apple AI lead) and Rashed Haq (ex-Cruise head of AI\u002Frobotics as VP autonomous vehicles). This executive pivot accelerates GM's transition to AI-centric operations over 18 months of white-collar reductions.",[17,3745,3747],{"id":3746},"broader-lesson-for-enterprises","Broader Lesson for Enterprises",[22,3749,3750],{},"GM's moves preview large-scale AI adoption: deliberate workforce swaps to match skills with demands like agent development and AI workflows. Enterprises ignoring this risk obsolescence, as demand surges for teams that engineer AI natively rather than experiment superficially.",{"title":80,"searchDepth":81,"depth":81,"links":3752},[3753,3754,3755,3756],{"id":3725,"depth":81,"text":3726},{"id":3732,"depth":81,"text":3733},{"id":3739,"depth":81,"text":3740},{"id":3746,"depth":81,"text":3747},[156],{"content_references":3759,"triage":3774},[3760,3765,3768,3771],{"type":3761,"title":3762,"url":3763,"context":3764},"other","GM to cut hundreds of white-collar workers in push to trim costs","https:\u002F\u002Fwww.bloomberg.com\u002Fnews\u002Farticles\u002F2026-05-11\u002Fgm-to-cut-hundreds-of-white-collar-workers-in-push-to-trim-costs?srnd=homepage-americas","cited",{"type":3761,"title":3766,"url":3767,"context":98},"GM cuts 1000 software jobs as it prioritizes quality and AI","https:\u002F\u002Ftechcrunch.com\u002F2024\u002F08\u002F19\u002Fgm-cuts-1000-software-jobs-as-it-prioritizes-quality-and-ai\u002F",{"type":3761,"title":3769,"url":3770,"context":98},"GM taps Aurora co-founder for new chief product officer role","https:\u002F\u002Ftechcrunch.com\u002F2025\u002F05\u002F12\u002Fgm-taps-aurora-co-founder-for-new-chief-product-officer-role\u002F",{"type":3761,"title":3772,"url":3773,"context":98},"GM tech executive shakeup continues on software team","https:\u002F\u002Ftechcrunch.com\u002F2025\u002F11\u002F26\u002Fgm-tech-executive-shakeup-continues-on-software-team\u002F",{"relevance":105,"novelty":3775,"quality":105,"actionability":81,"composite":3776,"reasoning":3777},3,3.4,"Category: AI & LLMs. The article discusses GM's strategic shift towards hiring AI-native engineers, which addresses the audience's interest in practical AI engineering roles and skills. However, while it provides insights into workforce changes, it lacks specific actionable steps for individuals looking to adapt to this trend.","\u002Fsummaries\u002Fcfb8096e9f38c707-gm-cuts-600-it-jobs-to-hire-ai-native-engineers-summary","2026-05-11 23:04:10","2026-05-12 15:01:35",{"title":3715,"description":80},{"loc":3778},"cfb8096e9f38c707","TechCrunch — AI","https:\u002F\u002Ftechcrunch.com\u002F2026\u002F05\u002F11\u002Fgm-just-laid-off-hundreds-of-it-workers-to-hire-those-with-stronger-ai-skills\u002F","summaries\u002Fcfb8096e9f38c707-gm-cuts-600-it-jobs-to-hire-ai-native-engineers-summary",[122,120,123],"GM laid off 600 IT workers (10% of department) to recruit specialists in agent\u002Fmodel development, prompt engineering, data pipelines—showing enterprises must rebuild teams for production AI, not just add tools.",[123],"bXfzgtCwoh_-YNtEMdHw66e6UU8DOcbZrMMd767kajo",{"id":3792,"title":3793,"ai":3794,"body":3799,"categories":3935,"created_at":88,"date_modified":88,"description":80,"extension":89,"faq":88,"featured":90,"kicker_label":88,"meta":3936,"navigation":108,"path":3956,"published_at":3957,"question":88,"scraped_at":3958,"seo":3959,"sitemap":3960,"source_id":3961,"source_name":3962,"source_type":116,"source_url":3963,"stem":3964,"tags":3965,"thumbnail_url":88,"tldr":3966,"tweet":88,"unknown_tags":3967,"__hash__":3968},"summaries\u002Fsummaries\u002F80b0ce4c14ece886-consumer-ai-s-anticipation-gap-blocks-true-assista-summary.md","Consumer AI's Anticipation Gap Blocks True Assistants",{"provider":7,"model":8,"input_tokens":3795,"output_tokens":3796,"processing_time_ms":3797,"cost_usd":3798},8851,2312,36608,0.00290355,{"type":14,"value":3800,"toc":3927},[3801,3805,3808,3811,3814,3818,3821,3824,3827,3830,3834,3837,3840,3843,3846,3850,3853,3875,3878,3882,3885,3888,3891,3895],[17,3802,3804],{"id":3803},"reactive-agents-create-a-new-management-layer","Reactive Agents Create a New Management Layer",[22,3806,3807],{},"Nate B. Jones argues that despite capable AI, consumer agents have become \"one more thing to manage,\" turning users into stressed project managers. Current agents demand users identify tasks, craft prompts, grant permissions, supervise outputs, and handle failures—more work than doing tasks manually for short jobs like booking a reservation. This contrasts with chatbots' success, which leveraged Google's query-box mental model for minimal behavioral shift. Agents lack this: users don't naturally think \"which life admin task to delegate today?\"",[22,3809,3810],{},"Jones highlights enterprise progress like OpenAI's Symphfony, an open-source protocol addressing human attention bottlenecks in coding agents. Engineers faced constant session checks, nudges, and restarts; Symphfony shifts management to issue trackers where agents pull tasks and humans review. Even AWS now offers managed agents with identities, logs, and controls. Yet for consumers, no equivalent exists—life lacks GitHub. Messy calendars, inboxes, family logistics, and uncanceled commitments defy clean boards.",[22,3812,3813],{},"\"The frontier where we need to go next is can AI do useful work without pulling me into a new management layer.\" This quote underscores why frontier products hit walls: attention exhaustion from tabs, sessions, notifications, and partial tasks.",[17,3815,3817],{"id":3816},"coding-agents-succeed-where-consumer-life-fails","Coding Agents Succeed Where Consumer Life Fails",[22,3819,3820],{},"Coding crossed proactivity thresholds via clean verification: code compiles, tests pass\u002Ffail, evals confirm. Stripe data shows exponential agent-driven business starts; GitHub braces for 30x repo growth from agents. Computer use (e.g., Codeex) is solved, enabling reliable action.",[22,3822,3823],{},"Consumer tasks lack this. No \"compiler for taste\" verifies if a flight, restaurant, email, or meeting summary is \"right\"—success is subjective, errors costly (wrong booking cascades). Tasks like \"book a trip\" explode into budgets, preferences, calendars, hotels, cars—why Expedia employs thousands. Users can't even name tasks amid email-calendar-text-Slack chaos.",[22,3825,3826],{},"\"Consumer life doesn't have any of that. Did the agent book the right flight? I don't know... There's not a compiler for taste. There's not a test suite for life admin yet.\"",[22,3828,3829],{},"Demand exists: ChatGPT proved it, Gemini ubiquity confirms, non-devs attempt OpenClaw installs (though risky for family data). Yet post-install, common query: \"What do I do with it?\" Lines formed in China to uninstall.",[17,3831,3833],{"id":3832},"defining-the-anticipation-gap","Defining the Anticipation Gap",[22,3835,3836],{},"The core problem: agents react to user invocation; true assistants anticipate. Users want AI spotting flight delays first, flagging school permission slips against calendars\u002Fgrocery lists, drafting tense replies, or converting long lists to deliveries—surfacing in context without recall.",[22,3838,3839],{},"\"A tool waits for you to remember it. An assistant reduces the number of things you have to remember.\" Past software bridged smaller gaps: push notifications (messages), recommendations (content), autocomplete\u002Fsmart replies (search\u002Femail)—narrow, bounded, reversible. Agents span domains with real actions (e.g., Stripe's agent wallets for purchases), demanding higher bars: know when to interrupt\u002Fshut up, act in guardrails, lighten load.",[22,3841,3842],{},"Fake proactivity annoys: agents assuming all calendar events real, nudging ghosts. Breakthrough needs intuition for relevance.",[22,3844,3845],{},"\"The breakaway consumer agent has to figure out how to appear in the situation when they're needed without being asked.\"",[17,3847,3849],{"id":3848},"product-bets-reveal-paths-forward","Product Bets Reveal Paths Forward",[22,3851,3852],{},"Jones evaluates consumer bets:",[3854,3855,3856,3863,3869],"ul",{},[3857,3858,3859,3862],"li",{},[45,3860,3861],{},"Clicky.so",": Builds on computer use; plain-English requests spawn screen-corner \"little guys\" for tasks. Cool UX (mom-friendly), multi-instance possible, but reactive and battery-draining—not proactive.",[3857,3864,3865,3868],{},[45,3866,3867],{},"Poke, Clueless, Cowork",": Varied bets on proactivity; specifics reveal gaps in context grasp.",[3857,3870,3871,3874],{},[45,3872,3873],{},"Chronicle",": Clue to future via better anticipation patterns.",[22,3876,3877],{},"Delegation to humans works via shared taste\u002Fhistory\u002Fjudgment (e.g., EA booking dinner knowing vibe\u002Fbudget). Software lacks this; users bear translation\u002Fsupervision burden.",[17,3879,3881],{"id":3880},"the-permission-ladder-enables-safe-autonomy","The Permission Ladder Enables Safe Autonomy",[22,3883,3884],{},"Proactivity scales via ladder: read → suggest → draft → act-with-confirmation → autonomous. Success requires context understanding to interrupt meaningfully, not spam. Labs\u002Fbuilders must prioritize; leaders awaiting lab miracles delay—make workflows predictable now.",[22,3886,3887],{},"Test agents by load lifted: do they reduce mental overhead? Jones urges trying despite flaws, watching for true relief.",[22,3889,3890],{},"\"I want the agent that sees the school email and says, 'This permission slip needs a signature by Friday.' and it looks at my messy calendar... and quietly asks, 'I can handle the next step. Want me to?'\"",[17,3892,3894],{"id":3893},"key-takeaways","Key Takeaways",[3854,3896,3897,3900,3903,3906,3909,3912,3915,3918,3921,3924],{},[3857,3898,3899],{},"Make personal workflows predictable (e.g., issue trackers) to enable agent anticipation today.",[3857,3901,3902],{},"Prioritize products surfacing in context over reactive invocation—anticipate needs like flight delays or tense threads.",[3857,3904,3905],{},"Build permission ladders: start read-only, escalate to autonomous only after proven reliability.",[3857,3907,3908],{},"Consumer lacks coding's verification; invest in evals for subjective success (taste, judgment).",[3857,3910,3911],{},"Avoid fake proactivity from bad data; grasp messy life context to interrupt only when vital.",[3857,3913,3914],{},"Evaluate agents by load reduction: if they add management, discard.",[3857,3916,3917],{},"Consumer opportunity: simple UX like Clicky.so's \"little guys,\" but make proactive.",[3857,3919,3920],{},"Demand secure, non-technical options—OpenClaw risks deter masses.",[3857,3922,3923],{},"Labs: close anticipation gap; builders: pull enterprise patterns (Symphfony) to personal use.",[3857,3925,3926],{},"True assistants lighten life; tools demand recall.",{"title":80,"searchDepth":81,"depth":81,"links":3928},[3929,3930,3931,3932,3933,3934],{"id":3803,"depth":81,"text":3804},{"id":3816,"depth":81,"text":3817},{"id":3832,"depth":81,"text":3833},{"id":3848,"depth":81,"text":3849},{"id":3880,"depth":81,"text":3881},{"id":3893,"depth":81,"text":3894},[136],{"content_references":3937,"triage":3954},[3938,3943,3946,3948,3950],{"type":3761,"title":3939,"author":3940,"publisher":3941,"url":3942,"context":98},"Consumer AI Anticipation Gap","Nate B Jones","Nates Newsletter Substack","https:\u002F\u002Fnatesnewsletter.substack.com\u002Fp\u002Fconsumer-ai-anticipation-gap",{"type":94,"title":3944,"author":3945,"context":98},"Symphfony","OpenAI developers",{"type":94,"title":3861,"url":3947,"context":98},"https:\u002F\u002Fclicky.so",{"type":94,"title":3949,"context":98},"OpenClaw",{"type":3951,"title":3952,"url":3953,"context":98},"podcast","AI News & Strategy Daily with Nate B. Jones","https:\u002F\u002Fpodcasts.apple.com\u002Fus\u002Fpodcast\u002Fai-news-strategy-daily-with-nate-b-jones\u002Fid1877109372",{"relevance":105,"novelty":3775,"quality":105,"actionability":81,"composite":3776,"reasoning":3955},"Category: AI Automation. The article discusses the limitations of current consumer AI agents and highlights the need for proactive systems, which aligns with the audience's interest in AI automation. However, while it presents some insights into the challenges faced, it lacks specific actionable steps for product builders to implement improvements.","\u002Fsummaries\u002F80b0ce4c14ece886-consumer-ai-s-anticipation-gap-blocks-true-assista-summary","2026-05-05 14:00:58","2026-05-05 16:03:57",{"title":3793,"description":80},{"loc":3956},"59926b5e62a2f4d7","AI News & Strategy Daily | Nate B Jones","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Z0HizICooiw","summaries\u002F80b0ce4c14ece886-consumer-ai-s-anticipation-gap-blocks-true-assista-summary",[122,121,123],"Consumer AI agents are reactive tools forcing users to manage prompts and tasks; the frontier is proactive anticipation that notices issues and acts without prompting, but lacks due to messy life data and no 'compiler for taste'.",[123],"XaY3IE-7ijhcHEXaGUE5w90ooFqc-fnI545ZVXxTWUs",{"id":3970,"title":3971,"ai":3972,"body":3977,"categories":4034,"created_at":88,"date_modified":88,"description":80,"extension":89,"faq":88,"featured":90,"kicker_label":88,"meta":4035,"navigation":108,"path":4036,"published_at":4037,"question":88,"scraped_at":88,"seo":4038,"sitemap":4039,"source_id":4040,"source_name":4041,"source_type":116,"source_url":4042,"stem":4043,"tags":4044,"thumbnail_url":88,"tldr":4045,"tweet":88,"unknown_tags":4046,"__hash__":4047},"summaries\u002Fsummaries\u002Fprecise-prompting-ai-s-reckoning-for-vague-leaders-summary.md","Precise Prompting: AI's Reckoning for Vague Leaders",{"provider":7,"model":8,"input_tokens":3973,"output_tokens":3974,"processing_time_ms":3975,"cost_usd":3976},6083,1329,13492,0.00185845,{"type":14,"value":3978,"toc":4029},[3979,3983,3986,3989,3993,3996,3999,4002,4006,4009,4026],[17,3980,3982],{"id":3981},"ai-agents-demand-clarity-humans-forgave","AI Agents Demand Clarity Humans Forgave",[22,3984,3985],{},"AI agents like Copilot reject vague instructions such as \"forward this and pls fix,\" instead firing back clarifying questions on objectives, constraints, stakeholders, outcomes, and tone. This exposes execution gaps that star juniors once bridged, turning prompts into rigorous strategy memos. In one case, a 25-page client deck review—lacking metrics or context—yielded a polished proposal with novel FX volatility scenarios after 14 minutes of precise input, reclaiming 8 hours. Leaders who thrived on ambiguity now face a \"mirror that never blinks,\" as agents amplify the cost of poor articulation, echoing Drucker's management by objectives and Arendt's emphasis on precise action to enable autonomous execution.",[22,3987,3988],{},"Vague delegation like \"do xxx by EOD\" fails at scale with agents, unlike humans who inferred intent across time zones. The 4D AI Fluency Framework's Description-Discernment Loop requires iterative refinement, which impatient executives abandon, mistaking it for junior hand-holding.",[17,3990,3992],{"id":3991},"data-proves-precision-unlocks-massive-gains","Data Proves Precision Unlocks Massive Gains",[22,3994,3995],{},"McKinsey's 2025 survey shows 62% of organizations experimenting with agents, but only 23% scaling successfully by redesigning workflows for precise human-AI collaboration. Anthropic's analysis of 100,000+ Claude conversations reveals 80% reduction in task time for 90+ minute complex work. PwC's survey notes 79% adoption with 66% productivity lifts, but only for structured inputs. Wharton's model projects 1.5% added U.S. productivity\u002FGDP growth by 2035 if leadership overcomes these bottlenecks.",[22,3997,3998],{},"Risks include \"metacognitive laziness,\" where over-reliance erodes clear thinking, per 2025 research. Citrini Research's 2026 scenario warns of \"ghost GDP\" from agent output leading to 38% S&P 500 drawdown and 10.2% unemployment by 2028—mitigated only if leaders master precise instructions.",[22,4000,4001],{},"Sector wins demand discipline: tech teams spec full user stories and edge cases; finance institutes run \"prompt audits\" slashing rework; healthcare mirrors shift handoffs; PepsiCo gained 20% throughput and 10-15% capex cuts via physics-level parameters in manufacturing digital twins.",[17,4003,4005],{"id":4004},"executives-pull-ahead-by-modeling-precision","Executives Pull Ahead by Modeling Precision",[22,4007,4008],{},"Top performers treat prompting as core competency:",[3854,4010,4011,4014,4017,4020,4023],{},[3857,4012,4013],{},"Share strongest prompts publicly in channels for critique, spreading humility faster than training.",[3857,4015,4016],{},"Embed prompt quality in performance reviews alongside OKRs; one tech firm cut rework 20% in a quarter.",[3857,4018,4019],{},"Replace calls with agent-ready written briefs to sharpen upstream thinking.",[3857,4021,4022],{},"Use agents visibly for board preps and strategy memos to set standards.",[3857,4024,4025],{},"Audit calendars: if >20% time clarifies ambiguity, you're the bottleneck.",[22,4027,4028],{},"Agents raise clarity's value without replacing vision or empathy. Prompting sharpens strategic thinking, scales results sans headcount growth, and averts displacement—positioning disciplined leaders for the productivity boom.",{"title":80,"searchDepth":81,"depth":81,"links":4030},[4031,4032,4033],{"id":3981,"depth":81,"text":3982},{"id":3991,"depth":81,"text":3992},{"id":4004,"depth":81,"text":4005},[],{},"\u002Fsummaries\u002Fprecise-prompting-ai-s-reckoning-for-vague-leaders-summary","2026-04-08 21:21:17",{"title":3971,"description":80},{"loc":4036},"2878573ba35b2426","Towards AI","https:\u002F\u002Funknown","summaries\u002Fprecise-prompting-ai-s-reckoning-for-vague-leaders-summary",[120,122,123],"AI agents expose decades of sloppy delegation by refusing to decode vagueness, forcing executives to master precise prompting for 80% faster task completion and scaled leverage.",[123],"c5ut29XfN-3ImwxJwdRZs0ziEOpqqVk2TD_Qe488-i4",{"id":4049,"title":4050,"ai":4051,"body":4056,"categories":4160,"created_at":88,"date_modified":88,"description":4161,"extension":89,"faq":88,"featured":90,"kicker_label":88,"meta":4162,"navigation":108,"path":4163,"published_at":4164,"question":88,"scraped_at":4165,"seo":4166,"sitemap":4167,"source_id":4168,"source_name":4169,"source_type":4170,"source_url":4171,"stem":4172,"tags":4173,"thumbnail_url":88,"tldr":4174,"tweet":88,"unknown_tags":4175,"__hash__":4176},"summaries\u002Fsummaries\u002Fe0b7501b8853447e-agent-harnesses-unlock-scalable-ai-teams-beyond-cl-summary.md","Agent Harnesses Unlock Scalable AI Teams Beyond Claude Code",{"provider":7,"model":8,"input_tokens":4052,"output_tokens":4053,"processing_time_ms":4054,"cost_usd":4055},8713,2152,20088,0.00252775,{"type":14,"value":4057,"toc":4153},[4058,4062,4065,4068,4071,4075,4078,4081,4084,4087,4090,4094,4097,4100,4103,4106,4109,4113,4116,4119,4122,4124],[17,4059,4061],{"id":4060},"agent-harness-the-real-product-behind-claude-codes-success","Agent Harness: The Real Product Behind Claude Code's Success",[22,4063,4064],{},"Claude Code hit $2.5B ARR in months by prioritizing the agent harness over models alone. This harness delivers deterministic code execution, token caching, orchestration, specialized prompts, skills, and model routing—without it, agents fail at scale. IndyDevDan argues models commoditize fast, so harness engineering captures value: customize for domains like security UIs to rival Anthropic's first-mover edge.",[22,4066,4067],{},"He rejected single-agent \"vibe coding\" (ad-hoc prompting in tools like Claude Code) for structured teams. Tradeoff: vibe coding suits quick prototypes but crumbles on repetition; harnesses demand upfront engineering but enable horizontal scaling. Pi Coding Agent (pi.dev) became his base—open-source GitHub repo (disler\u002Fpi-vs-claude-code) shows setup from zero—extended with three-tier architecture: one orchestrator (prompt engineers\u002Fdelegates), multiple leads (plan\u002Fdelegate), hyper-specialized workers (execute).",[22,4069,4070],{},"\"Without the agent harness, there are no agents, no agentic coding. And that means there is no agentic engineering.\" This quote underscores why leaks confirm harnesses as the moat—Anthropic pioneered it, but you replicate fractions of ARR via specialization.",[17,4072,4074],{"id":4073},"three-tier-multi-model-orchestration-for-infinite-uis","Three-Tier Multi-Model Orchestration for Infinite UIs",[22,4076,4077],{},"Dan's harness generates branded UIs endlessly within constraints, targeting Aegis: an agentic security command center monitoring threats in real-time. Before: one-off UIs per prompt. After: system tracks brands (Aegis, Agentics, Indean), apps (observability, dashboard), branches (mobile\u002Fdesktop), producing nodes like threat timelines, false positives, coverage, performance logs.",[22,4079,4080],{},"Orchestrator ingests single input, crafts \"till done\" lists (not to-dos), delegates via reusable meta-prompts it generates. Leads read files, scaffold, prompt workers—no direct work from leaders. Workers: view generators, animation specialists, soft\u002Fhard validators, brand analysts (demo used reduced set). Runs parallel teams (A\u002FB\u002FC) on Claude Sonnet 4.6, Minimax 2.7, Step 3.5 Flash—compares live.",[22,4082,4083],{},"Key mechanism: shared context files, mental models (7K tokens auto-tracked via 75-line skill—agents document ideas\u002Fwork autonomously). Multi-team config defines composition; expertise files evolve without intervention. Input scales O(1) despite agent count, enabling 1M+ context Sonnet\u002FOpus.",[22,4085,4086],{},"\"When you stop vibe coding and you start agentic engineering teams of agents in your agent harness, you can solve problem classes, not just one-off tasks.\" Here, Dan contrasts task-solving (e.g., single UI) with class-solving (infinite branded variants), showing repo with 3+ brands, multiple UIs per app.",[22,4088,4089],{},"Tradeoffs surfaced live: open models (Minimax\u002FStep) failed mid-demo (no response on timeline stacks), forcing leads to break rules and self-write—Sonnet succeeded. Solution: model rotation in harness. Proves redundancy value; orchestrator reroutes to reliable teams.",[17,4091,4093],{"id":4092},"agentic-security-as-massive-opportunity","Agentic Security as Massive Opportunity",[22,4095,4096],{},"Aegis prototypes blend AI agents with security amid rising exploits: autonomous cybercrime, Claude RCE, OpenClaw crisis, InversePrompt, agentic attack chains (links provided). Black hats prompt-exploits apps easily—agents counter via real-time threat watching.",[22,4098,4099],{},"Dan's teams built operational UIs: scrollable nodes, forked designs (primary\u002Factivity logs), full prototypes. Horizontal scaling: parallel teams deploy post-setup. Uses Claude Code 80% as meta-builder—\"building the system that builds the system,\" not direct product work.",[22,4101,4102],{},"\"80% of the time I'm spinning up cloud code agents to not work on the actual product or the actual system. I'm using cloud code as a meta builder, a meta agent.\" This reveals workflow: Claude for harness evolution, Pi teams for production UIs—hybrid maximizes leverage.",[22,4104,4105],{},"Evolution: Builds on prior videos (CEO\u002Flead\u002FUI agents trilogy). Agents learn via observation-action-learn-iterate cycles, mental models. Northstar: agents operating products end-to-end, better than humans.",[22,4107,4108],{},"\"The agentic security space is going to be one of the most important business opportunities for engineers, specifically for agentic engineers for the next few years.\" Ties UI scale to business: agents + security = defensible moats amid hacks.",[17,4110,4112],{"id":4111},"building-trust-through-scale-and-control","Building Trust Through Scale and Control",[22,4114,4115],{},"Harness ownership enables custom file structures, skills, prompts—beyond Claude's commands\u002Fplugins. Agents step out-of-domain? X-flagged via system prompts. Pi + harness outperforms single Claude\u002FGemini instances on domains.",[22,4117,4118],{},"Demo failures highlighted resilience: multiple models\u002Fteams ensure completion. Theme for 2026: trust agents for larger work via iteration. Rejected blank-slate parallels for persistent memory teams.",[22,4120,4121],{},"\"You observe, you act, you learn, and then you iterate.\" Dan frames agent teams mimicking human execution, key to absurd results at scale.",[17,4123,3894],{"id":3893},[3854,4125,4126,4129,4132,4135,4138,4141,4144,4147,4150],{},[3857,4127,4128],{},"Engineer custom agent harnesses on Pi Coding Agent for domain control—deterministic orchestration beats commoditized models.",[3857,4130,4131],{},"Use three tiers: orchestrator (meta-prompts\u002Fdelegate), leads (plan), workers (specialize)—scale input O(1).",[3857,4133,4134],{},"Run multi-model teams (Sonnet\u002FMinimax\u002FStep) in parallel; add rotation for reliability.",[3857,4136,4137],{},"Target problem classes like infinite branded UIs—track via mental models (auto 7K tokens).",[3857,4139,4140],{},"Claude Code as 80% meta-builder: build systems, then deploy specialized teams.",[3857,4142,4143],{},"Prioritize agentic security: counter exploits with real-time UIs—huge opportunity.",[3857,4145,4146],{},"Hybrid tools: Pi for execution, Claude for evolution—avoid all-in on one.",[3857,4148,4149],{},"Build trust via OALI cycles (observe-act-learn-iterate) and redundancy.",[3857,4151,4152],{},"Own prompts\u002Fskills\u002Ftools: push beyond mainstream Claude for edge.",{"title":80,"searchDepth":81,"depth":81,"links":4154},[4155,4156,4157,4158,4159],{"id":4060,"depth":81,"text":4061},{"id":4073,"depth":81,"text":4074},{"id":4092,"depth":81,"text":4093},{"id":4111,"depth":81,"text":4112},{"id":3893,"depth":81,"text":3894},[],"The Claude Code leak just told us EVERYTHING we need to know. While every other tech channel covers the features and the Mythos model, we're focused on the REAL signal: The Claude Code Agent Harness.\n\n💡 MASTER AGENTIC CODING\nUnlock your Pi Agent Teams: https:\u002F\u002Fagenticengineer.com\u002Ftactical-agentic-coding?y=RairMJflUSA\n\n\n🎥 VIDEO REFERENCES\n\n- Pi Coding Agent: https:\u002F\u002Fpi.dev\u002F\n- Agent Teams: https:\u002F\u002Fyoutu.be\u002FM30gp1315Y4\n- PI CEO Agents: https:\u002F\u002Fyoutu.be\u002FTqjmTZRL31E\n- Learn Pi From Zero: https:\u002F\u002Fgithub.com\u002Fdisler\u002Fpi-vs-claude-code\u002Ftree\u002Fmain\n- Claude Code: https:\u002F\u002Fwww.anthropic.com\u002Fclaude-code\n\n❌ AI Agent Hacks\n\n- Autonomous AI Cybercrime: https:\u002F\u002Fwww.cybersecuritydive.com\u002Fnews\u002Fcybercrime-ai-ransomware-mcp-malwarebytes\u002F811360\u002F\n- Claude Code RCE: https:\u002F\u002Fthehackernews.com\u002F2026\u002F02\u002Fclaude-code-flaws-allow-remote-code.html\n- OpenClaw Agent Crisis: https:\u002F\u002Fwww.reco.ai\u002Fblog\u002Fopenclaw-the-ai-agent-security-crisis-unfolding-right-now\n- InversePrompt vs Claude: https:\u002F\u002Fcymulate.com\u002Fblog\u002Fcve-2025-547954-54795-claude-inverseprompt\u002F\n- Agentic Attack Chains: https:\u002F\u002Fwww.helpnetsecurity.com\u002F2026\u002F03\u002F12\u002Fagentic-attack-chains-infostealers-criminal-markets\u002F\n\n🔥 The Claude Code leak revealed that a $2.5B ARR product is built on one thing: the agent harness. Without it, there are no agents, no agentic coding, and no agentic engineering. In this video, we break down why harness engineering is one of the most valuable skills an agentic engineer can learn in 2026, and how you can build your own specialized agent harness to capture fractions of that massive value.\n\n🛠️ Watch as we deploy infinite UI agent teams with a three-tier multi-agent orchestration architecture: one orchestrator, multiple team leads, and hyper-specialized workers running different models like Claude Sonnet 4.6, Minimax 2.7, and Step 3.5 Flash side by side. Our orchestrator doesn't write code, it prompt engineers and delegates. This is tactical agentic coding at scale, not vibe coding.\n\n🚀 We dive deep into the PyCoding agent and show how a customized agent harness lets you solve entire problem classes, not just one-off tasks. See how we built a system to generate infinite UIs within a consistent brand design for Aegis, an agentic security command center. The combination of AI agents and security is going to be one of the biggest business opportunities for engineers in the coming years.\n\n💡 Key takeaways:\n\nAgent Harness: The deterministic code, token caching, agent orchestration, prompts, skills, and model control that powers everything.\n\nMulti-Agent Orchestration: Scale horizontally with agent teams that observe, act, learn, and iterate. We showcase minimax vs stepfun vs claude sonnet 4.6.\n\nHarness Engineering: Stop vibe coding, start engineering teams of agents in your agent harness.\n\nCompute Scaling: Use Claude Code as a meta builder 80% of the time, building the system that builds the system.\n\nAgentic Security: The intersection of AI agents and security is where the next wave of massive opportunity lives.\n\n🌟 The theme of 2026 is increasing the trust you have in your agents to do larger scales of work over time. \n\nStay focused and keep building.\nDan\n\n📖 Chapters\n00:00 Claude Code Leak SIGNAL\n02:10 Infinite UI Agents\n05:40 The Multi-Team Prompt\n14:37 Control the Harness - Control your Results\n23:05 Aegis UI Agent Prototypes\n26:25 Agentic Horizon\n30:56 Solve Problem Classes Not Tasks\n\n#agenticcoding #aiagents #agenticengineering",{},"\u002Fsummaries\u002Fe0b7501b8853447e-agent-harnesses-unlock-scalable-ai-teams-beyond-cl-summary","2026-04-06 13:00:00","2026-04-06 16:38:59",{"title":4050,"description":4161},{"loc":4163},"e0b7501b8853447e","IndyDevDan","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=RairMJflUSA","summaries\u002Fe0b7501b8853447e-agent-harnesses-unlock-scalable-ai-teams-beyond-cl-summary",[122,120,123],"Claude Code's leak reveals agent harnesses as the core of $2.5B ARR agentic coding—build custom ones on Pi to run multi-model teams solving UI classes at scale, not tasks.",[123],"Bvbr49JwqZ7xmgAkpowNGZaZgFVok8BWQ-1m35oEsTU"]