[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-html-beats-markdown-for-llm-outputs-summary":3,"summaries-facets-categories":133,"summary-related-html-beats-markdown-for-llm-outputs-summary":4631},{"id":4,"title":5,"ai":6,"body":13,"categories":89,"created_at":90,"date_modified":90,"description":83,"extension":91,"faq":90,"featured":92,"kicker_label":90,"meta":93,"navigation":116,"path":117,"published_at":118,"question":90,"scraped_at":118,"seo":119,"sitemap":120,"source_id":121,"source_name":122,"source_type":123,"source_url":124,"stem":125,"tags":126,"thumbnail_url":90,"tldr":130,"tweet":90,"unknown_tags":131,"__hash__":132},"summaries\u002Fsummaries\u002Fhtml-beats-markdown-for-llm-outputs-summary.md","HTML Beats Markdown for LLM Outputs",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",4544,1961,25707,0.0018668,{"type":14,"value":15,"toc":82},"minimark",[16,21,25,35,39,42,49,52,62,70,74],[17,18,20],"h2",{"id":19},"html-enables-rich-interactive-explanations-from-llms","HTML Enables Rich, Interactive Explanations from LLMs",[22,23,24],"p",{},"LLMs produce superior outputs in HTML over Markdown because HTML supports embedded SVG diagrams, interactive widgets, and in-page navigation, making complex information easier to explore. Previously, Markdown was preferred for its token efficiency—e.g., GPT-4's 8,192 token limit made HTML too verbose—but modern models like Claude handle HTML's overhead without issue. This shift prioritizes usability: HTML turns static text into dynamic, visual aids that convey concepts faster than plain Markdown.",[22,26,27,28,34],{},"Trade-off: HTML uses more tokens for output, but the enhanced clarity justifies it for explanations, PR reviews, or code breakdowns. Anthropic's Claude Code team demonstrates this with examples like annotated diffs and visual flows, collected at ",[29,30,31],"a",{"href":31,"rel":32},"https:\u002F\u002Fthariqs.github.io\u002Fhtml-effectiveness\u002F",[33],"nofollow",".",[17,36,38],{"id":37},"proven-prompts-for-html-artifacts","Proven Prompts for HTML Artifacts",[22,40,41],{},"Use targeted prompts to leverage HTML's strengths:",[43,44,45],"ul",{},[46,47,48],"li",{},"For PR reviews: \"Help me review this PR by creating an HTML artifact that describes it. I'm not very familiar with the streaming\u002Fbackpressure logic so focus on that. Render the actual diff with inline margin annotations, color-code findings by severity and whatever else might be needed to convey the concept well.\"",[22,50,51],{},"This generates color-coded diffs, severity badges, and explanatory visuals in one artifact.",[43,53,54],{},[46,55,56,57,61],{},"For code explanation: Simon Willison tested on copy.fail (a Linux exploit POC in obfuscated Python): \"curl ",[29,58,59],{"href":59,"rel":60},"https:\u002F\u002Fcopy.fail\u002Fexp",[33]," | llm -m gpt-5.5 -s 'Explain this code in detail. Reformat it, expand out any confusing bits and go deep into what it does and how it works. Output HTML, neatly styled and using capabilities of HTML and CSS and JavaScript to make the explanation rich and interactive and as clear as possible'\"",[22,63,64,65,69],{},"Result: A styled HTML page (",[29,66,67],{"href":67,"rel":68},"https:\u002F\u002Fgisthost.github.io\u002F?ae53e3461ffdbfd0826156aacf025c7e",[33],") with reformatted code, expansions, and interactive elements—effective despite focusing slightly more on the Python harness than the core exploit.",[17,71,73],{"id":72},"production-impact-and-next-steps","Production Impact and Next Steps",[22,75,76,77,81],{},"HTML outputs transform ad-hoc queries into shareable, self-contained tools, outperforming Markdown for technical reviews or tutorials. Willison plans to experiment further for on-the-fly explanations, building on his prior HTML utilities (",[29,78,79],{"href":79,"rel":80},"https:\u002F\u002Fsimonwillison.net\u002F2025\u002FDec\u002F10\u002Fhtml-tools\u002F",[33],"). Start by appending \"Output HTML, neatly styled...\" to prompts—readers gain navigable, visual insights that Markdown can't match, accelerating understanding of code, exploits, or logic.",{"title":83,"searchDepth":84,"depth":84,"links":85},"",2,[86,87,88],{"id":19,"depth":84,"text":20},{"id":37,"depth":84,"text":38},{"id":72,"depth":84,"text":73},[],null,"md",false,{"content_references":94,"triage":111},[95,101,105,108],{"type":96,"title":97,"author":98,"url":99,"context":100},"other","Using Claude Code: The Unreasonable Effectiveness of HTML","Thariq Shihipar","https:\u002F\u002Ftwitter.com\u002Ftrq212\u002Fstatus\u002F2052809885763747935","cited",{"type":102,"title":103,"url":31,"context":104},"tool","html-effectiveness examples","mentioned",{"type":96,"title":106,"url":107,"context":104},"copy.fail","https:\u002F\u002Fcopy.fail\u002F",{"type":96,"title":109,"author":110,"url":79,"context":104},"Useful patterns for building HTML tools","Simon Willison",{"relevance":112,"novelty":113,"quality":113,"actionability":112,"composite":114,"reasoning":115},5,4,4.55,"Category: AI & LLMs. The article provides a practical comparison of HTML and Markdown for LLM outputs, addressing a specific pain point for developers looking to enhance their AI-generated content. It includes actionable prompts that readers can directly implement to improve their outputs.",true,"\u002Fsummaries\u002Fhtml-beats-markdown-for-llm-outputs-summary","2026-05-09 15:37:27",{"title":5,"description":83},{"loc":117},"9a0e66bbe97849fd","Simon Willison's Weblog","article","https:\u002F\u002Fsimonwillison.net\u002F2026\u002FMay\u002F8\u002Funreasonable-effectiveness-of-html\u002F#atom-everything","summaries\u002Fhtml-beats-markdown-for-llm-outputs-summary",[127,128,129],"llm","prompt-engineering","generative-ai","Request HTML from LLMs like Claude instead of Markdown to generate interactive SVGs, widgets, and navigable explanations—token limits no longer justify Markdown's efficiency.",[129],"KE8PsRZy2FXN2CQWu9w51aZDUf80X6n_TfALPpud3r4",[134,137,139,142,144,147,150,153,156,158,160,162,164,166,168,170,173,175,177,179,181,183,185,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,381,383,385,387,389,391,393,395,397,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,554,556,558,560,562,564,566,568,570,572,574,576,578,580,582,584,586,588,590,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,2381,2383,2385,2387,2389,2391,2393,2395,2397,2399,2401,2403,2405,2407,2409,2411,2413,2415,2417,2419,2421,2423,2425,2427,2429,2431,2433,2435,2437,2439,2441,2443,2445,2447,2449,2451,2453,2455,2457,2459,2461,2463,2465,2467,2469,2471,2473,2475,2477,2479,2481,2483,2485,2487,2489,2491,2493,2495,2497,2499,2501,2503,2505,2507,2509,2511,2513,2515,2517,2519,2521,2523,2525,2527,2529,2531,2533,2535,2537,2539,2541,2543,2545,2547,2549,2551,2553,2555,2557,2559,2561,2563,2565,2567,2569,2571,2573,2575,2577,2579,2581,2583,2585,2587,2589,2591,2593,2595,2597,2599,2601,2603,2605,2607,2609,2611,2613,2615,2617,2619,2621,2623,2625,2627,2629,2631,2633,2635,2637,2639,2641,2643,2645,2647,2649,2651,2653,2655,2657,2659,2661,2663,2665,2667,2669,2671,2673,2675,2677,2679,2681,2683,2685,2687,2689,2691,2693,2695,2697,2699,2701,2703,2705,2707,2709,2711,2713,2715,2717,2719,2721,2723,2725,2727,2729,2731,2733,2735,2737,2739,2741,2743,2745,2747,2749,2751,2753,2755,2757,2759,2761,2763,2765,2767,2769,2771,2773,2775,2777,2779,2781,2783,2785,2787,2789,2791,2793,2795,2797,2799,2801,2803,2805,2807,2809,2811,2813,2815,2817,2819,2821,2823,2825,2827,2829,2831,2833,2835,2837,2839,2841,2843,2845,2847,2849,2851,2853,2855,2857,2859,2861,2863,2865,2867,2869,2871,2873,2875,2877,2879,2881,2883,2885,2887,2889,2891,2893,2895,2897,2899,2901,2903,2905,2907,2909,2911,2913,2915,2917,2919,2921,2923,2925,2927,2929,2931,2933,2935,2937,2939,2941,2943,2945,2947,2949,2951,2953,2955,2957,2959,2961,2963,2965,2967,2969,2971,2973,2975,2977,2979,2981,2983,2985,2987,2989,2991,2993,2995,2997,2999,3001,3003,3005,3007,3009,3011,3013,3015,3017,3019,3021,3023,3025,3027,3029,3031,3033,3035,3037,3039,3041,3043,3045,3047,3049,3051,3053,3055,3057,3059,3061,3063,3065,3067,3069,3071,3073,3075,3077,3079,3081,3083,3085,3087,3089,3091,3093,3095,3097,3099,3101,3103,3105,3107,3109,3111,3113,3115,3117,3119,3121,3123,3125,3127,3129,3131,3133,3135,3137,3139,3141,3143,3145,3147,3149,3151,3153,3155,3157,3159,3161,3163,3165,3167,3169,3171,3173,3175,3177,3179,3181,3183,3185,3187,3189,3191,3193,3195,3197,3199,3201,3203,3205,3207,3209,3211,3213,3215,3217,3219,3221,3223,3225,3227,3229,3231,3233,3235,3237,3239,3241,3243,3245,3247,3249,3251,3253,3255,3257,3259,3261,3263,3265,3267,3269,3271,3273,3275,3277,3279,3281,3283,3285,3287,3289,3291,3293,3295,3297,3299,3301,3303,3305,3307,3309,3311,3313,3315,3317,3319,3321,3323,3325,3327,3329,3331,3333,3335,3337,3339,3341,3343,3345,3347,3349,3351,3353,3355,3357,3359,3361,3363,3365,3367,3369,3371,3373,3375,3377,3379,3381,3383,3385,3387,3389,3391,3393,3395,3397,3399,3401,3403,3405,3407,3409,3411,3413,3415,3417,3419,3421,3423,3425,3427,3429,3431,3433,3435,3437,3439,3441,3443,3445,3447,3449,3451,3453,3455,3457,3459,3461,3463,3465,3467,3469,3471,3473,3475,3477,3479,3481,3483,3485,3487,3489,3491,3493,3495,3497,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519,3521,3523,3525,3527,3529,3531,3533,3535,3537,3539,3541,3543,3545,3547,3549,3551,3553,3555,3557,3559,3561,3563,3565,3567,3569,3571,3573,3575,3577,3579,3581,3583,3585,3587,3589,3591,3593,3595,3597,3599,3601,3603,3605,3607,3609,3611,3613,3615,3617,3619,3621,3623,3625,3627,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671,3673,3675,3677,3679,3681,3683,3685,3687,3689,3691,3693,3695,3697,3699,3701,3703,3705,3707,3709,3711,3713,3715,3717,3719,3721,3723,3725,3727,3729,3731,3733,3735,3737,3739,3741,3743,3745,3747,3749,3751,3753,3755,3757,3759,3761,3763,3765,3767,3769,3771,3773,3775,3777,3779,3781,3783,3785,3787,3789,3791,3793,3795,3797,3799,3801,3803,3805,3807,3809,3811,3813,3815,3817,3819,3821,3823,3825,3827,3829,3831,3833,3835,3837,3839,3841,3843,3845,3847,3849,3851,3853,3855,3857,3859,3861,3863,3865,3867,3869,3871,3873,3875,3877,3879,3881,3883,3885,3887,3889,3891,3893,3895,3897,3899,3901,3903,3905,3907,3909,3911,3913,3915,3917,3919,3921,3923,3925,3927,3929,3931,3933,3935,3937,3939,3941,3943,3945,3947,3949,3951,3953,3955,3957,3959,3961,3963,3965,3967,3969,3971,3973,3975,3977,3979,3981,3983,3985,3987,3989,3991,3993,3995,3997,3999,4001,4003,4005,4007,4009,4011,4013,4015,4017,4019,4021,4023,4025,4027,4029,4031,4033,4035,4037,4039,4041,4043,4045,4047,4049,4051,4053,4055,4057,4059,4061,4063,4065,4067,4069,4071,4073,4075,4077,4079,4081,4083,4085,4087,4089,4091,4093,4095,4097,4099,4101,4103,4105,4107,4109,4111,4113,4115,4117,4119,4121,4123,4125,4127,4129,4131,4133,4135,4137,4139,4141,4143,4145,4147,4149,4151,4153,4155,4157,4159,4161,4163,4165,4167,4169,4171,4173,4175,4177,4179,4181,4183,4185,4187,4189,4191,4193,4195,4197,4199,4201,4203,4205,4207,4209,4211,4213,4215,4217,4219,4221,4223,4225,4227,4229,4231,4233,4235,4237,4239,4241,4243,4245,4247,4249,4251,4253,4255,4257,4259,4261,4263,4265,4267,4269,4271,4273,4275,4277,4279,4281,4283,4285,4287,4289,4291,4293,4295,4297,4299,4301,4303,4305,4307,4309,4311,4313,4315,4317,4319,4321,4323,4325,4327,4329,4331,4333,4335,4337,4339,4341,4343,4345,4347,4349,4351,4353,4355,4357,4359,4361,4363,4365,4367,4369,4371,4373,4375,4377,4379,4381,4383,4385,4387,4389,4391,4393,4395,4397,4399,4401,4403,4405,4407,4409,4411,4413,4415,4417,4419,4421,4423,4425,4427,4429,4431,4433,4435,4437,4439,4441,4443,4445,4447,4449,4451,4453,4455,4457,4459,4461,4463,4465,4467,4469,4471,4473,4475,4477,4479,4481,4483,4485,4487,4489,4491,4493,4495,4497,4499,4501,4503,4505,4507,4509,4511,4513,4515,4517,4519,4521,4523,4525,4527,4529,4531,4533,4535,4537,4539,4541,4543,4545,4547,4549,4551,4553,4555,4557,4559,4561,4563,4565,4567,4569,4571,4573,4575,4577,4579,4581,4583,4585,4587,4589,4591,4593,4595,4597,4599,4601,4603,4605,4607,4609,4611,4613,4615,4617,4619,4621,4623,4625,4627,4629],{"categories":135},[136],"Business & SaaS",{"categories":138},[136],{"categories":140},[141],"AI News & Trends",{"categories":143},[],{"categories":145},[146],"AI Automation",{"categories":148},[149],"Marketing & Growth",{"categories":151},[152],"Design & Frontend",{"categories":154},[155],"Software Engineering",{"categories":157},[146],{"categories":159},[],{"categories":161},[152],{"categories":163},[152],{"categories":165},[146],{"categories":167},[152],{"categories":169},[152],{"categories":171},[172],"AI & LLMs",{"categories":174},[152],{"categories":176},[152],{"categories":178},[],{"categories":180},[152],{"categories":182},[152],{"categories":184},[172],{"categories":186},[187],"Developer Productivity",{"categories":189},[172],{"categories":191},[172],{"categories":193},[172],{"categories":195},[141],{"categories":197},[172],{"categories":199},[146],{"categories":201},[136],{"categories":203},[141],{"categories":205},[149],{"categories":207},[],{"categories":209},[],{"categories":211},[146],{"categories":213},[146],{"categories":215},[146],{"categories":217},[149],{"categories":219},[172],{"categories":221},[187],{"categories":223},[141],{"categories":225},[],{"categories":227},[],{"categories":229},[],{"categories":231},[232],"Data Science & Visualization",{"categories":234},[],{"categories":236},[146],{"categories":238},[155],{"categories":240},[146],{"categories":242},[146],{"categories":244},[172],{"categories":246},[149],{"categories":248},[149],{"categories":250},[146],{"categories":252},[],{"categories":254},[],{"categories":256},[],{"categories":258},[152],{"categories":260},[172,152],{"categories":262},[152],{"categories":264},[146],{"categories":266},[149],{"categories":268},[187],{"categories":270},[152],{"categories":272},[172],{"categories":274},[155],{"categories":276},[172],{"categories":278},[],{"categories":280},[146],{"categories":282},[172],{"categories":284},[187],{"categories":286},[187],{"categories":288},[],{"categories":290},[149],{"categories":292},[136],{"categories":294},[172],{"categories":296},[136],{"categories":298},[136],{"categories":300},[146],{"categories":302},[149],{"categories":304},[146],{"categories":306},[136],{"categories":308},[146],{"categories":310},[152],{"categories":312},[172],{"categories":314},[152],{"categories":316},[172],{"categories":318},[136],{"categories":320},[172],{"categories":322},[149],{"categories":324},[],{"categories":326},[172],{"categories":328},[136],{"categories":330},[],{"categories":332},[141],{"categories":334},[155],{"categories":336},[],{"categories":338},[172],{"categories":340},[152],{"categories":342},[172],{"categories":344},[152],{"categories":346},[],{"categories":348},[146],{"categories":350},[],{"categories":352},[],{"categories":354},[],{"categories":356},[172],{"categories":358},[],{"categories":360},[172],{"categories":362},[],{"categories":364},[152],{"categories":366},[172],{"categories":368},[187],{"categories":370},[146],{"categories":372},[149],{"categories":374},[187],{"categories":376},[187],{"categories":378},[187],{"categories":380},[149],{"categories":382},[149],{"categories":384},[172],{"categories":386},[172],{"categories":388},[187],{"categories":390},[152],{"categories":392},[136],{"categories":394},[152],{"categories":396},[155],{"categories":398},[136],{"categories":400},[136],{"categories":402},[136],{"categories":404},[152],{"categories":406},[],{"categories":408},[],{"categories":410},[172],{"categories":412},[172],{"categories":414},[155],{"categories":416},[172],{"categories":418},[172],{"categories":420},[],{"categories":422},[172],{"categories":424},[172],{"categories":426},[],{"categories":428},[172],{"categories":430},[141],{"categories":432},[141],{"categories":434},[],{"categories":436},[],{"categories":438},[149],{"categories":440},[149],{"categories":442},[155],{"categories":444},[172],{"categories":446},[],{"categories":448},[],{"categories":450},[146],{"categories":452},[172],{"categories":454},[172],{"categories":456},[],{"categories":458},[172,136],{"categories":460},[172],{"categories":462},[],{"categories":464},[172],{"categories":466},[172],{"categories":468},[],{"categories":470},[],{"categories":472},[146],{"categories":474},[172],{"categories":476},[172],{"categories":478},[146],{"categories":480},[172],{"categories":482},[],{"categories":484},[],{"categories":486},[172],{"categories":488},[],{"categories":490},[172],{"categories":492},[172],{"categories":494},[],{"categories":496},[146],{"categories":498},[152],{"categories":500},[],{"categories":502},[146,503],"DevOps & Cloud",{"categories":505},[172],{"categories":507},[146],{"categories":509},[172],{"categories":511},[],{"categories":513},[],{"categories":515},[],{"categories":517},[],{"categories":519},[172],{"categories":521},[146],{"categories":523},[],{"categories":525},[146],{"categories":527},[],{"categories":529},[172],{"categories":531},[],{"categories":533},[],{"categories":535},[],{"categories":537},[],{"categories":539},[146],{"categories":541},[152],{"categories":543},[172],{"categories":545},[149],{"categories":547},[141],{"categories":549},[136],{"categories":551},[187],{"categories":553},[],{"categories":555},[146],{"categories":557},[146],{"categories":559},[146],{"categories":561},[172],{"categories":563},[],{"categories":565},[],{"categories":567},[],{"categories":569},[146],{"categories":571},[],{"categories":573},[146],{"categories":575},[146],{"categories":577},[141],{"categories":579},[146],{"categories":581},[172],{"categories":583},[],{"categories":585},[172],{"categories":587},[],{"categories":589},[141],{"categories":591},[146,592],"Product Strategy",{"categories":594},[155],{"categories":596},[503],{"categories":598},[592],{"categories":600},[172],{"categories":602},[146],{"categories":604},[],{"categories":606},[141],{"categories":608},[141],{"categories":610},[146],{"categories":612},[],{"categories":614},[146],{"categories":616},[172],{"categories":618},[172],{"categories":620},[187],{"categories":622},[172],{"categories":624},[],{"categories":626},[172,155],{"categories":628},[141],{"categories":630},[172],{"categories":632},[141],{"categories":634},[146],{"categories":636},[141],{"categories":638},[],{"categories":640},[155],{"categories":642},[136],{"categories":644},[],{"categories":646},[146],{"categories":648},[146],{"categories":650},[146],{"categories":652},[146],{"categories":654},[136],{"categories":656},[152],{"categories":658},[149],{"categories":660},[],{"categories":662},[146],{"categories":664},[],{"categories":666},[141],{"categories":668},[141],{"categories":670},[141],{"categories":672},[146],{"categories":674},[141],{"categories":676},[172],{"categories":678},[187],{"categories":680},[172],{"categories":682},[155],{"categories":684},[172,187],{"categories":686},[187],{"categories":688},[187],{"categories":690},[187],{"categories":692},[187],{"categories":694},[172],{"categories":696},[],{"categories":698},[],{"categories":700},[149],{"categories":702},[],{"categories":704},[172],{"categories":706},[187],{"categories":708},[172],{"categories":710},[152],{"categories":712},[155],{"categories":714},[],{"categories":716},[172],{"categories":718},[187],{"categories":720},[149],{"categories":722},[141],{"categories":724},[155],{"categories":726},[172],{"categories":728},[],{"categories":730},[155],{"categories":732},[152],{"categories":734},[136],{"categories":736},[136],{"categories":738},[],{"categories":740},[152],{"categories":742},[136],{"categories":744},[141],{"categories":746},[187],{"categories":748},[146],{"categories":750},[146],{"categories":752},[172],{"categories":754},[172],{"categories":756},[141],{"categories":758},[141],{"categories":760},[187],{"categories":762},[141],{"categories":764},[],{"categories":766},[592],{"categories":768},[146],{"categories":770},[141],{"categories":772},[141],{"categories":774},[141],{"categories":776},[172],{"categories":778},[146],{"categories":780},[146],{"categories":782},[136],{"categories":784},[136],{"categories":786},[172],{"categories":788},[141],{"categories":790},[],{"categories":792},[172],{"categories":794},[136],{"categories":796},[146],{"categories":798},[146],{"categories":800},[146],{"categories":802},[152],{"categories":804},[146],{"categories":806},[187],{"categories":808},[141],{"categories":810},[141],{"categories":812},[141],{"categories":814},[141],{"categories":816},[141],{"categories":818},[],{"categories":820},[],{"categories":822},[187],{"categories":824},[141],{"categories":826},[141],{"categories":828},[141],{"categories":830},[],{"categories":832},[172],{"categories":834},[],{"categories":836},[],{"categories":838},[152],{"categories":840},[136],{"categories":842},[],{"categories":844},[141],{"categories":846},[146],{"categories":848},[146],{"categories":850},[146],{"categories":852},[149],{"categories":854},[146],{"categories":856},[],{"categories":858},[141],{"categories":860},[141],{"categories":862},[172],{"categories":864},[],{"categories":866},[149],{"categories":868},[149],{"categories":870},[172],{"categories":872},[141],{"categories":874},[136],{"categories":876},[155],{"categories":878},[172],{"categories":880},[],{"categories":882},[172],{"categories":884},[172],{"categories":886},[155],{"categories":888},[172],{"categories":890},[172],{"categories":892},[172],{"categories":894},[149],{"categories":896},[141],{"categories":898},[172],{"categories":900},[172],{"categories":902},[141],{"categories":904},[146],{"categories":906},[187],{"categories":908},[136],{"categories":910},[172],{"categories":912},[187],{"categories":914},[187],{"categories":916},[],{"categories":918},[149],{"categories":920},[141],{"categories":922},[141],{"categories":924},[187],{"categories":926},[146],{"categories":928},[146],{"categories":930},[146],{"categories":932},[146],{"categories":934},[152],{"categories":936},[172],{"categories":938},[172],{"categories":940},[592],{"categories":942},[172],{"categories":944},[172],{"categories":946},[146],{"categories":948},[136],{"categories":950},[149],{"categories":952},[],{"categories":954},[136],{"categories":956},[136],{"categories":958},[],{"categories":960},[152],{"categories":962},[172],{"categories":964},[],{"categories":966},[],{"categories":968},[141],{"categories":970},[141],{"categories":972},[141],{"categories":974},[141],{"categories":976},[],{"categories":978},[141],{"categories":980},[172],{"categories":982},[172],{"categories":984},[],{"categories":986},[146],{"categories":988},[141],{"categories":990},[141],{"categories":992},[136],{"categories":994},[172],{"categories":996},[],{"categories":998},[],{"categories":1000},[141],{"categories":1002},[141],{"categories":1004},[141],{"categories":1006},[172],{"categories":1008},[141],{"categories":1010},[141],{"categories":1012},[141],{"categories":1014},[141],{"categories":1016},[141],{"categories":1018},[],{"categories":1020},[146],{"categories":1022},[172],{"categories":1024},[149],{"categories":1026},[136],{"categories":1028},[146],{"categories":1030},[172],{"categories":1032},[],{"categories":1034},[149],{"categories":1036},[141],{"categories":1038},[141],{"categories":1040},[141],{"categories":1042},[141],{"categories":1044},[187],{"categories":1046},[155],{"categories":1048},[],{"categories":1050},[172],{"categories":1052},[146],{"categories":1054},[146],{"categories":1056},[146],{"categories":1058},[503],{"categories":1060},[146],{"categories":1062},[172],{"categories":1064},[172],{"categories":1066},[155],{"categories":1068},[503],{"categories":1070},[232],{"categories":1072},[172],{"categories":1074},[232],{"categories":1076},[],{"categories":1078},[149],{"categories":1080},[149],{"categories":1082},[152],{"categories":1084},[503],{"categories":1086},[146],{"categories":1088},[172],{"categories":1090},[172],{"categories":1092},[146],{"categories":1094},[146],{"categories":1096},[146],{"categories":1098},[187],{"categories":1100},[187],{"categories":1102},[146],{"categories":1104},[146],{"categories":1106},[],{"categories":1108},[146],{"categories":1110},[146],{"categories":1112},[172],{"categories":1114},[232],{"categories":1116},[146],{"categories":1118},[146],{"categories":1120},[146],{"categories":1122},[146],{"categories":1124},[136],{"categories":1126},[152],{"categories":1128},[503],{"categories":1130},[141],{"categories":1132},[155],{"categories":1134},[503],{"categories":1136},[155],{"categories":1138},[136],{"categories":1140},[232],{"categories":1142},[],{"categories":1144},[155],{"categories":1146},[],{"categories":1148},[],{"categories":1150},[155],{"categories":1152},[172],{"categories":1154},[],{"categories":1156},[],{"categories":1158},[],{"categories":1160},[136],{"categories":1162},[],{"categories":1164},[],{"categories":1166},[232],{"categories":1168},[172],{"categories":1170},[503],{"categories":1172},[172],{"categories":1174},[],{"categories":1176},[146],{"categories":1178},[187],{"categories":1180},[187],{"categories":1182},[149],{"categories":1184},[149],{"categories":1186},[149],{"categories":1188},[503],{"categories":1190},[155],{"categories":1192},[146],{"categories":1194},[136],{"categories":1196},[136],{"categories":1198},[155],{"categories":1200},[152],{"categories":1202},[232],{"categories":1204},[152],{"categories":1206},[],{"categories":1208},[172],{"categories":1210},[146],{"categories":1212},[146],{"categories":1214},[187],{"categories":1216},[146],{"categories":1218},[146],{"categories":1220},[152],{"categories":1222},[152],{"categories":1224},[146],{"categories":1226},[503],{"categories":1228},[172],{"categories":1230},[],{"categories":1232},[149],{"categories":1234},[149],{"categories":1236},[146],{"categories":1238},[136],{"categories":1240},[146],{"categories":1242},[146],{"categories":1244},[],{"categories":1246},[172],{"categories":1248},[146],{"categories":1250},[146],{"categories":1252},[187],{"categories":1254},[146],{"categories":1256},[172],{"categories":1258},[],{"categories":1260},[146],{"categories":1262},[],{"categories":1264},[152],{"categories":1266},[187],{"categories":1268},[172],{"categories":1270},[155],{"categories":1272},[152],{"categories":1274},[187],{"categories":1276},[232],{"categories":1278},[187],{"categories":1280},[],{"categories":1282},[172],{"categories":1284},[172],{"categories":1286},[592],{"categories":1288},[155],{"categories":1290},[172,146],{"categories":1292},[146],{"categories":1294},[172],{"categories":1296},[146],{"categories":1298},[146,155],{"categories":1300},[146],{"categories":1302},[172],{"categories":1304},[],{"categories":1306},[187],{"categories":1308},[172],{"categories":1310},[146],{"categories":1312},[172],{"categories":1314},[],{"categories":1316},[155],{"categories":1318},[136],{"categories":1320},[146],{"categories":1322},[],{"categories":1324},[232],{"categories":1326},[155],{"categories":1328},[146],{"categories":1330},[155],{"categories":1332},[],{"categories":1334},[146],{"categories":1336},[],{"categories":1338},[146],{"categories":1340},[],{"categories":1342},[],{"categories":1344},[152],{"categories":1346},[187],{"categories":1348},[172],{"categories":1350},[146],{"categories":1352},[],{"categories":1354},[146],{"categories":1356},[155],{"categories":1358},[172],{"categories":1360},[172],{"categories":1362},[155],{"categories":1364},[155],{"categories":1366},[187],{"categories":1368},[136],{"categories":1370},[],{"categories":1372},[172],{"categories":1374},[172],{"categories":1376},[172],{"categories":1378},[146],{"categories":1380},[172],{"categories":1382},[],{"categories":1384},[152],{"categories":1386},[172],{"categories":1388},[146],{"categories":1390},[],{"categories":1392},[172],{"categories":1394},[],{"categories":1396},[172],{"categories":1398},[],{"categories":1400},[],{"categories":1402},[],{"categories":1404},[172],{"categories":1406},[172],{"categories":1408},[172],{"categories":1410},[172],{"categories":1412},[],{"categories":1414},[172],{"categories":1416},[172],{"categories":1418},[172],{"categories":1420},[],{"categories":1422},[172],{"categories":1424},[],{"categories":1426},[149],{"categories":1428},[172],{"categories":1430},[],{"categories":1432},[],{"categories":1434},[],{"categories":1436},[172],{"categories":1438},[141],{"categories":1440},[141],{"categories":1442},[],{"categories":1444},[146],{"categories":1446},[172],{"categories":1448},[],{"categories":1450},[172],{"categories":1452},[172],{"categories":1454},[141],{"categories":1456},[],{"categories":1458},[172],{"categories":1460},[141],{"categories":1462},[146],{"categories":1464},[172],{"categories":1466},[],{"categories":1468},[],{"categories":1470},[],{"categories":1472},[146],{"categories":1474},[146],{"categories":1476},[146],{"categories":1478},[146],{"categories":1480},[172],{"categories":1482},[152],{"categories":1484},[152],{"categories":1486},[146],{"categories":1488},[146],{"categories":1490},[187],{"categories":1492},[592],{"categories":1494},[187],{"categories":1496},[187],{"categories":1498},[172],{"categories":1500},[146],{"categories":1502},[172],{"categories":1504},[187],{"categories":1506},[172],{"categories":1508},[146],{"categories":1510},[146],{"categories":1512},[146],{"categories":1514},[146],{"categories":1516},[146],{"categories":1518},[172],{"categories":1520},[187],{"categories":1522},[187],{"categories":1524},[149],{"categories":1526},[146],{"categories":1528},[],{"categories":1530},[146],{"categories":1532},[],{"categories":1534},[141],{"categories":1536},[172],{"categories":1538},[],{"categories":1540},[136],{"categories":1542},[152],{"categories":1544},[152],{"categories":1546},[146],{"categories":1548},[146],{"categories":1550},[172],{"categories":1552},[172],{"categories":1554},[141],{"categories":1556},[141],{"categories":1558},[503],{"categories":1560},[146],{"categories":1562},[141],{"categories":1564},[],{"categories":1566},[172],{"categories":1568},[146],{"categories":1570},[146],{"categories":1572},[146],{"categories":1574},[146],{"categories":1576},[172],{"categories":1578},[172],{"categories":1580},[172],{"categories":1582},[172],{"categories":1584},[146],{"categories":1586},[146],{"categories":1588},[146],{"categories":1590},[146],{"categories":1592},[],{"categories":1594},[152],{"categories":1596},[172],{"categories":1598},[172],{"categories":1600},[172],{"categories":1602},[],{"categories":1604},[149],{"categories":1606},[],{"categories":1608},[187],{"categories":1610},[],{"categories":1612},[146],{"categories":1614},[187],{"categories":1616},[152],{"categories":1618},[187],{"categories":1620},[],{"categories":1622},[187],{"categories":1624},[187],{"categories":1626},[],{"categories":1628},[152],{"categories":1630},[146],{"categories":1632},[146],{"categories":1634},[187],{"categories":1636},[172],{"categories":1638},[172],{"categories":1640},[],{"categories":1642},[141],{"categories":1644},[],{"categories":1646},[149],{"categories":1648},[],{"categories":1650},[152],{"categories":1652},[141],{"categories":1654},[152],{"categories":1656},[152],{"categories":1658},[152],{"categories":1660},[152],{"categories":1662},[152],{"categories":1664},[152],{"categories":1666},[152],{"categories":1668},[152],{"categories":1670},[152],{"categories":1672},[152],{"categories":1674},[],{"categories":1676},[146],{"categories":1678},[152],{"categories":1680},[172],{"categories":1682},[172],{"categories":1684},[152],{"categories":1686},[152],{"categories":1688},[152],{"categories":1690},[152],{"categories":1692},[152],{"categories":1694},[152],{"categories":1696},[152],{"categories":1698},[172,152],{"categories":1700},[152],{"categories":1702},[152],{"categories":1704},[152],{"categories":1706},[152],{"categories":1708},[],{"categories":1710},[152],{"categories":1712},[152],{"categories":1714},[152],{"categories":1716},[152],{"categories":1718},[152],{"categories":1720},[152],{"categories":1722},[152],{"categories":1724},[152],{"categories":1726},[152],{"categories":1728},[152,172],{"categories":1730},[152],{"categories":1732},[152],{"categories":1734},[],{"categories":1736},[141],{"categories":1738},[172],{"categories":1740},[],{"categories":1742},[172],{"categories":1744},[],{"categories":1746},[146],{"categories":1748},[503],{"categories":1750},[592],{"categories":1752},[146],{"categories":1754},[146],{"categories":1756},[146],{"categories":1758},[],{"categories":1760},[146],{"categories":1762},[],{"categories":1764},[172],{"categories":1766},[172],{"categories":1768},[146],{"categories":1770},[],{"categories":1772},[],{"categories":1774},[172],{"categories":1776},[172],{"categories":1778},[172],{"categories":1780},[141],{"categories":1782},[141],{"categories":1784},[141],{"categories":1786},[141],{"categories":1788},[],{"categories":1790},[141],{"categories":1792},[],{"categories":1794},[141],{"categories":1796},[172],{"categories":1798},[141],{"categories":1800},[141],{"categories":1802},[141],{"categories":1804},[141],{"categories":1806},[172],{"categories":1808},[141],{"categories":1810},[146],{"categories":1812},[],{"categories":1814},[146],{"categories":1816},[141],{"categories":1818},[172],{"categories":1820},[141],{"categories":1822},[141],{"categories":1824},[141],{"categories":1826},[172],{"categories":1828},[172],{"categories":1830},[172],{"categories":1832},[],{"categories":1834},[],{"categories":1836},[172],{"categories":1838},[141],{"categories":1840},[],{"categories":1842},[172],{"categories":1844},[146],{"categories":1846},[172],{"categories":1848},[146],{"categories":1850},[146],{"categories":1852},[172],{"categories":1854},[],{"categories":1856},[],{"categories":1858},[146],{"categories":1860},[146],{"categories":1862},[146],{"categories":1864},[146],{"categories":1866},[146],{"categories":1868},[146],{"categories":1870},[146],{"categories":1872},[146],{"categories":1874},[],{"categories":1876},[146],{"categories":1878},[146],{"categories":1880},[146],{"categories":1882},[172],{"categories":1884},[172],{"categories":1886},[172],{"categories":1888},[141],{"categories":1890},[172],{"categories":1892},[172],{"categories":1894},[172],{"categories":1896},[146],{"categories":1898},[149],{"categories":1900},[149],{"categories":1902},[149],{"categories":1904},[146],{"categories":1906},[],{"categories":1908},[172],{"categories":1910},[],{"categories":1912},[],{"categories":1914},[172],{"categories":1916},[],{"categories":1918},[149],{"categories":1920},[146],{"categories":1922},[152],{"categories":1924},[187],{"categories":1926},[232],{"categories":1928},[172],{"categories":1930},[187],{"categories":1932},[146],{"categories":1934},[152],{"categories":1936},[],{"categories":1938},[146],{"categories":1940},[149,136],{"categories":1942},[146],{"categories":1944},[146],{"categories":1946},[141],{"categories":1948},[503],{"categories":1950},[155],{"categories":1952},[149],{"categories":1954},[187],{"categories":1956},[172],{"categories":1958},[],{"categories":1960},[172],{"categories":1962},[],{"categories":1964},[172],{"categories":1966},[172],{"categories":1968},[146],{"categories":1970},[],{"categories":1972},[172],{"categories":1974},[146],{"categories":1976},[146],{"categories":1978},[172],{"categories":1980},[187],{"categories":1982},[146],{"categories":1984},[172],{"categories":1986},[172,187],{"categories":1988},[187],{"categories":1990},[],{"categories":1992},[172],{"categories":1994},[172],{"categories":1996},[172],{"categories":1998},[],{"categories":2000},[],{"categories":2002},[146],{"categories":2004},[149],{"categories":2006},[141],{"categories":2008},[146],{"categories":2010},[172],{"categories":2012},[141],{"categories":2014},[],{"categories":2016},[187],{"categories":2018},[141],{"categories":2020},[],{"categories":2022},[232],{"categories":2024},[149],{"categories":2026},[172],{"categories":2028},[136],{"categories":2030},[141],{"categories":2032},[172],{"categories":2034},[146],{"categories":2036},[172],{"categories":2038},[146],{"categories":2040},[146],{"categories":2042},[141],{"categories":2044},[187],{"categories":2046},[187],{"categories":2048},[152],{"categories":2050},[136],{"categories":2052},[172],{"categories":2054},[172],{"categories":2056},[],{"categories":2058},[],{"categories":2060},[172],{"categories":2062},[],{"categories":2064},[172],{"categories":2066},[141],{"categories":2068},[],{"categories":2070},[146],{"categories":2072},[187],{"categories":2074},[141],{"categories":2076},[187],{"categories":2078},[146],{"categories":2080},[172],{"categories":2082},[],{"categories":2084},[146],{"categories":2086},[146],{"categories":2088},[152],{"categories":2090},[146],{"categories":2092},[152],{"categories":2094},[146],{"categories":2096},[146],{"categories":2098},[152],{"categories":2100},[],{"categories":2102},[],{"categories":2104},[152],{"categories":2106},[152],{"categories":2108},[152],{"categories":2110},[155],{"categories":2112},[187],{"categories":2114},[187],{"categories":2116},[146],{"categories":2118},[141],{"categories":2120},[187],{"categories":2122},[187],{"categories":2124},[149],{"categories":2126},[152],{"categories":2128},[146],{"categories":2130},[146],{"categories":2132},[172],{"categories":2134},[187],{"categories":2136},[172],{"categories":2138},[],{"categories":2140},[503],{"categories":2142},[592],{"categories":2144},[],{"categories":2146},[],{"categories":2148},[146],{"categories":2150},[141],{"categories":2152},[149],{"categories":2154},[149],{"categories":2156},[232],{"categories":2158},[152],{"categories":2160},[232],{"categories":2162},[232],{"categories":2164},[146],{"categories":2166},[],{"categories":2168},[],{"categories":2170},[232],{"categories":2172},[155],{"categories":2174},[172],{"categories":2176},[155],{"categories":2178},[232],{"categories":2180},[155],{"categories":2182},[232],{"categories":2184},[136],{"categories":2186},[155],{"categories":2188},[187],{"categories":2190},[172],{"categories":2192},[],{"categories":2194},[232],{"categories":2196},[503],{"categories":2198},[],{"categories":2200},[172],{"categories":2202},[172],{"categories":2204},[],{"categories":2206},[],{"categories":2208},[172],{"categories":2210},[172],{"categories":2212},[146],{"categories":2214},[141],{"categories":2216},[172],{"categories":2218},[],{"categories":2220},[141],{"categories":2222},[],{"categories":2224},[],{"categories":2226},[141],{"categories":2228},[141],{"categories":2230},[172],{"categories":2232},[172],{"categories":2234},[172],{"categories":2236},[172],{"categories":2238},[172],{"categories":2240},[172],{"categories":2242},[149],{"categories":2244},[],{"categories":2246},[172],{"categories":2248},[],{"categories":2250},[],{"categories":2252},[146],{"categories":2254},[187],{"categories":2256},[],{"categories":2258},[503],{"categories":2260},[172,503],{"categories":2262},[172],{"categories":2264},[],{"categories":2266},[152],{"categories":2268},[152],{"categories":2270},[152],{"categories":2272},[152],{"categories":2274},[152],{"categories":2276},[],{"categories":2278},[],{"categories":2280},[],{"categories":2282},[155],{"categories":2284},[146],{"categories":2286},[136],{"categories":2288},[136],{"categories":2290},[155],{"categories":2292},[187],{"categories":2294},[152],{"categories":2296},[],{"categories":2298},[149],{"categories":2300},[592],{"categories":2302},[232],{"categories":2304},[232],{"categories":2306},[232],{"categories":2308},[187],{"categories":2310},[592],{"categories":2312},[187],{"categories":2314},[],{"categories":2316},[136],{"categories":2318},[155],{"categories":2320},[172],{"categories":2322},[152],{"categories":2324},[149],{"categories":2326},[155],{"categories":2328},[149],{"categories":2330},[172],{"categories":2332},[152],{"categories":2334},[155],{"categories":2336},[503],{"categories":2338},[172],{"categories":2340},[141],{"categories":2342},[155],{"categories":2344},[],{"categories":2346},[172],{"categories":2348},[155],{"categories":2350},[155],{"categories":2352},[146],{"categories":2354},[],{"categories":2356},[149],{"categories":2358},[149],{"categories":2360},[149],{"categories":2362},[146],{"categories":2364},[172],{"categories":2366},[],{"categories":2368},[136],{"categories":2370},[187],{"categories":2372},[187],{"categories":2374},[232],{"categories":2376},[136],{"categories":2378},[141],{"categories":2380},[232],{"categories":2382},[],{"categories":2384},[141],{"categories":2386},[141],{"categories":2388},[141],{"categories":2390},[172],{"categories":2392},[136],{"categories":2394},[172],{"categories":2396},[],{"categories":2398},[],{"categories":2400},[],{"categories":2402},[155],{"categories":2404},[146],{"categories":2406},[],{"categories":2408},[187],{"categories":2410},[152],{"categories":2412},[],{"categories":2414},[149],{"categories":2416},[],{"categories":2418},[152],{"categories":2420},[172],{"categories":2422},[187],{"categories":2424},[136],{"categories":2426},[],{"categories":2428},[152],{"categories":2430},[152],{"categories":2432},[172],{"categories":2434},[],{"categories":2436},[],{"categories":2438},[155],{"categories":2440},[172],{"categories":2442},[],{"categories":2444},[146],{"categories":2446},[172],{"categories":2448},[],{"categories":2450},[155],{"categories":2452},[146],{"categories":2454},[172],{"categories":2456},[232],{"categories":2458},[172],{"categories":2460},[],{"categories":2462},[232],{"categories":2464},[172],{"categories":2466},[155],{"categories":2468},[172],{"categories":2470},[232],{"categories":2472},[146],{"categories":2474},[172],{"categories":2476},[172],{"categories":2478},[172,146],{"categories":2480},[146],{"categories":2482},[146],{"categories":2484},[146],{"categories":2486},[152],{"categories":2488},[187],{"categories":2490},[172],{"categories":2492},[187],{"categories":2494},[152],{"categories":2496},[172],{"categories":2498},[],{"categories":2500},[],{"categories":2502},[172],{"categories":2504},[172],{"categories":2506},[172],{"categories":2508},[146],{"categories":2510},[146],{"categories":2512},[172],{"categories":2514},[],{"categories":2516},[172],{"categories":2518},[172],{"categories":2520},[146],{"categories":2522},[146],{"categories":2524},[172],{"categories":2526},[172],{"categories":2528},[],{"categories":2530},[172],{"categories":2532},[],{"categories":2534},[172],{"categories":2536},[172],{"categories":2538},[172],{"categories":2540},[172],{"categories":2542},[172],{"categories":2544},[172],{"categories":2546},[172],{"categories":2548},[],{"categories":2550},[172],{"categories":2552},[141],{"categories":2554},[141],{"categories":2556},[],{"categories":2558},[],{"categories":2560},[172],{"categories":2562},[],{"categories":2564},[172],{"categories":2566},[172,503],{"categories":2568},[],{"categories":2570},[141],{"categories":2572},[],{"categories":2574},[172],{"categories":2576},[],{"categories":2578},[],{"categories":2580},[],{"categories":2582},[172],{"categories":2584},[],{"categories":2586},[172],{"categories":2588},[],{"categories":2590},[172],{"categories":2592},[172],{"categories":2594},[],{"categories":2596},[],{"categories":2598},[172,503],{"categories":2600},[503,172],{"categories":2602},[141],{"categories":2604},[],{"categories":2606},[172],{"categories":2608},[],{"categories":2610},[172],{"categories":2612},[172],{"categories":2614},[],{"categories":2616},[141],{"categories":2618},[172,136],{"categories":2620},[141],{"categories":2622},[155],{"categories":2624},[],{"categories":2626},[146],{"categories":2628},[172],{"categories":2630},[149],{"categories":2632},[172],{"categories":2634},[187],{"categories":2636},[187],{"categories":2638},[503],{"categories":2640},[141],{"categories":2642},[172],{"categories":2644},[503],{"categories":2646},[155],{"categories":2648},[172],{"categories":2650},[187],{"categories":2652},[],{"categories":2654},[172],{"categories":2656},[],{"categories":2658},[],{"categories":2660},[172],{"categories":2662},[],{"categories":2664},[172],{"categories":2666},[155],{"categories":2668},[136],{"categories":2670},[187],{"categories":2672},[149],{"categories":2674},[146],{"categories":2676},[187],{"categories":2678},[],{"categories":2680},[149],{"categories":2682},[],{"categories":2684},[],{"categories":2686},[172],{"categories":2688},[141],{"categories":2690},[149],{"categories":2692},[],{"categories":2694},[172],{"categories":2696},[141],{"categories":2698},[141],{"categories":2700},[149],{"categories":2702},[141],{"categories":2704},[172],{"categories":2706},[141],{"categories":2708},[172],{"categories":2710},[],{"categories":2712},[172],{"categories":2714},[172],{"categories":2716},[172],{"categories":2718},[141],{"categories":2720},[],{"categories":2722},[],{"categories":2724},[152],{"categories":2726},[141],{"categories":2728},[],{"categories":2730},[172],{"categories":2732},[172],{"categories":2734},[172],{"categories":2736},[172],{"categories":2738},[172],{"categories":2740},[172],{"categories":2742},[172],{"categories":2744},[172],{"categories":2746},[172],{"categories":2748},[149],{"categories":2750},[172,152],{"categories":2752},[141],{"categories":2754},[141],{"categories":2756},[172],{"categories":2758},[155],{"categories":2760},[232],{"categories":2762},[172],{"categories":2764},[172],{"categories":2766},[],{"categories":2768},[],{"categories":2770},[172],{"categories":2772},[172],{"categories":2774},[],{"categories":2776},[152],{"categories":2778},[152],{"categories":2780},[187],{"categories":2782},[172],{"categories":2784},[187],{"categories":2786},[172],{"categories":2788},[172],{"categories":2790},[],{"categories":2792},[172],{"categories":2794},[],{"categories":2796},[],{"categories":2798},[172],{"categories":2800},[],{"categories":2802},[],{"categories":2804},[141],{"categories":2806},[],{"categories":2808},[172],{"categories":2810},[172],{"categories":2812},[172],{"categories":2814},[],{"categories":2816},[172],{"categories":2818},[141],{"categories":2820},[592],{"categories":2822},[146],{"categories":2824},[172],{"categories":2826},[],{"categories":2828},[146],{"categories":2830},[172],{"categories":2832},[],{"categories":2834},[172],{"categories":2836},[],{"categories":2838},[146],{"categories":2840},[],{"categories":2842},[],{"categories":2844},[146],{"categories":2846},[146],{"categories":2848},[146],{"categories":2850},[172],{"categories":2852},[],{"categories":2854},[146],{"categories":2856},[146],{"categories":2858},[],{"categories":2860},[],{"categories":2862},[],{"categories":2864},[146],{"categories":2866},[172],{"categories":2868},[141],{"categories":2870},[592],{"categories":2872},[149],{"categories":2874},[187],{"categories":2876},[],{"categories":2878},[],{"categories":2880},[152],{"categories":2882},[172],{"categories":2884},[172],{"categories":2886},[136],{"categories":2888},[141],{"categories":2890},[141],{"categories":2892},[141],{"categories":2894},[141],{"categories":2896},[],{"categories":2898},[146],{"categories":2900},[146],{"categories":2902},[146],{"categories":2904},[146],{"categories":2906},[187],{"categories":2908},[172],{"categories":2910},[136],{"categories":2912},[],{"categories":2914},[187],{"categories":2916},[146],{"categories":2918},[152],{"categories":2920},[152],{"categories":2922},[152],{"categories":2924},[152],{"categories":2926},[152],{"categories":2928},[152],{"categories":2930},[172,136],{"categories":2932},[146],{"categories":2934},[136],{"categories":2936},[141],{"categories":2938},[141],{"categories":2940},[187],{"categories":2942},[],{"categories":2944},[],{"categories":2946},[149],{"categories":2948},[],{"categories":2950},[172],{"categories":2952},[149],{"categories":2954},[172],{"categories":2956},[155],{"categories":2958},[146],{"categories":2960},[136],{"categories":2962},[146],{"categories":2964},[155],{"categories":2966},[187],{"categories":2968},[146],{"categories":2970},[],{"categories":2972},[187],{"categories":2974},[],{"categories":2976},[],{"categories":2978},[146],{"categories":2980},[146],{"categories":2982},[146],{"categories":2984},[172],{"categories":2986},[172],{"categories":2988},[172],{"categories":2990},[172],{"categories":2992},[172],{"categories":2994},[],{"categories":2996},[503],{"categories":2998},[172],{"categories":3000},[],{"categories":3002},[],{"categories":3004},[],{"categories":3006},[187],{"categories":3008},[],{"categories":3010},[172],{"categories":3012},[],{"categories":3014},[141],{"categories":3016},[172],{"categories":3018},[141],{"categories":3020},[172],{"categories":3022},[146],{"categories":3024},[],{"categories":3026},[172],{"categories":3028},[172],{"categories":3030},[],{"categories":3032},[232],{"categories":3034},[232],{"categories":3036},[155],{"categories":3038},[152],{"categories":3040},[],{"categories":3042},[172],{"categories":3044},[146],{"categories":3046},[],{"categories":3048},[],{"categories":3050},[172],{"categories":3052},[155],{"categories":3054},[146],{"categories":3056},[136],{"categories":3058},[187,155],{"categories":3060},[155],{"categories":3062},[172],{"categories":3064},[146],{"categories":3066},[],{"categories":3068},[],{"categories":3070},[],{"categories":3072},[],{"categories":3074},[],{"categories":3076},[],{"categories":3078},[172],{"categories":3080},[],{"categories":3082},[],{"categories":3084},[172],{"categories":3086},[],{"categories":3088},[],{"categories":3090},[],{"categories":3092},[172],{"categories":3094},[141],{"categories":3096},[],{"categories":3098},[],{"categories":3100},[],{"categories":3102},[172],{"categories":3104},[],{"categories":3106},[172],{"categories":3108},[172],{"categories":3110},[],{"categories":3112},[172],{"categories":3114},[155],{"categories":3116},[],{"categories":3118},[187],{"categories":3120},[172],{"categories":3122},[187],{"categories":3124},[],{"categories":3126},[149],{"categories":3128},[],{"categories":3130},[],{"categories":3132},[],{"categories":3134},[152],{"categories":3136},[141],{"categories":3138},[146],{"categories":3140},[172],{"categories":3142},[136],{"categories":3144},[172],{"categories":3146},[],{"categories":3148},[],{"categories":3150},[136],{"categories":3152},[149],{"categories":3154},[146],{"categories":3156},[],{"categories":3158},[503],{"categories":3160},[],{"categories":3162},[149],{"categories":3164},[172],{"categories":3166},[172],{"categories":3168},[149],{"categories":3170},[172],{"categories":3172},[152],{"categories":3174},[146],{"categories":3176},[172],{"categories":3178},[146],{"categories":3180},[172],{"categories":3182},[146],{"categories":3184},[187],{"categories":3186},[187],{"categories":3188},[],{"categories":3190},[152],{"categories":3192},[],{"categories":3194},[172],{"categories":3196},[172],{"categories":3198},[149],{"categories":3200},[592],{"categories":3202},[187],{"categories":3204},[141],{"categories":3206},[172],{"categories":3208},[141],{"categories":3210},[172],{"categories":3212},[172],{"categories":3214},[],{"categories":3216},[172],{"categories":3218},[],{"categories":3220},[172],{"categories":3222},[149],{"categories":3224},[172],{"categories":3226},[172],{"categories":3228},[172],{"categories":3230},[172],{"categories":3232},[141],{"categories":3234},[],{"categories":3236},[172],{"categories":3238},[172],{"categories":3240},[592],{"categories":3242},[],{"categories":3244},[141],{"categories":3246},[503],{"categories":3248},[155],{"categories":3250},[],{"categories":3252},[232],{"categories":3254},[],{"categories":3256},[],{"categories":3258},[141],{"categories":3260},[172],{"categories":3262},[],{"categories":3264},[172],{"categories":3266},[172],{"categories":3268},[146],{"categories":3270},[172],{"categories":3272},[141],{"categories":3274},[141],{"categories":3276},[152],{"categories":3278},[152],{"categories":3280},[152],{"categories":3282},[172],{"categories":3284},[232],{"categories":3286},[141],{"categories":3288},[187],{"categories":3290},[],{"categories":3292},[152],{"categories":3294},[152],{"categories":3296},[503],{"categories":3298},[152],{"categories":3300},[152],{"categories":3302},[146],{"categories":3304},[141],{"categories":3306},[503],{"categories":3308},[172],{"categories":3310},[172],{"categories":3312},[172],{"categories":3314},[172],{"categories":3316},[],{"categories":3318},[146],{"categories":3320},[172],{"categories":3322},[152],{"categories":3324},[],{"categories":3326},[],{"categories":3328},[172,155],{"categories":3330},[141],{"categories":3332},[],{"categories":3334},[146],{"categories":3336},[146],{"categories":3338},[146],{"categories":3340},[146],{"categories":3342},[146],{"categories":3344},[146],{"categories":3346},[146],{"categories":3348},[146],{"categories":3350},[],{"categories":3352},[],{"categories":3354},[172],{"categories":3356},[],{"categories":3358},[146],{"categories":3360},[187],{"categories":3362},[187],{"categories":3364},[232],{"categories":3366},[136],{"categories":3368},[],{"categories":3370},[],{"categories":3372},[],{"categories":3374},[152],{"categories":3376},[172],{"categories":3378},[],{"categories":3380},[136],{"categories":3382},[136],{"categories":3384},[152],{"categories":3386},[187],{"categories":3388},[232],{"categories":3390},[232],{"categories":3392},[152],{"categories":3394},[152],{"categories":3396},[],{"categories":3398},[146],{"categories":3400},[136],{"categories":3402},[136],{"categories":3404},[172],{"categories":3406},[146],{"categories":3408},[155],{"categories":3410},[152],{"categories":3412},[],{"categories":3414},[149],{"categories":3416},[232],{"categories":3418},[],{"categories":3420},[141],{"categories":3422},[141],{"categories":3424},[141],{"categories":3426},[503],{"categories":3428},[],{"categories":3430},[146],{"categories":3432},[],{"categories":3434},[146],{"categories":3436},[146],{"categories":3438},[172],{"categories":3440},[172],{"categories":3442},[],{"categories":3444},[155],{"categories":3446},[146],{"categories":3448},[155],{"categories":3450},[],{"categories":3452},[146],{"categories":3454},[152],{"categories":3456},[152],{"categories":3458},[152],{"categories":3460},[172],{"categories":3462},[146],{"categories":3464},[172],{"categories":3466},[136],{"categories":3468},[141],{"categories":3470},[152],{"categories":3472},[141],{"categories":3474},[172],{"categories":3476},[],{"categories":3478},[141],{"categories":3480},[146],{"categories":3482},[141],{"categories":3484},[141],{"categories":3486},[141],{"categories":3488},[141],{"categories":3490},[],{"categories":3492},[],{"categories":3494},[141],{"categories":3496},[141],{"categories":3498},[],{"categories":3500},[141],{"categories":3502},[141],{"categories":3504},[172],{"categories":3506},[172],{"categories":3508},[141],{"categories":3510},[141],{"categories":3512},[172],{"categories":3514},[],{"categories":3516},[172],{"categories":3518},[146],{"categories":3520},[172],{"categories":3522},[172],{"categories":3524},[],{"categories":3526},[172],{"categories":3528},[172],{"categories":3530},[172],{"categories":3532},[141],{"categories":3534},[],{"categories":3536},[],{"categories":3538},[],{"categories":3540},[],{"categories":3542},[172],{"categories":3544},[172],{"categories":3546},[],{"categories":3548},[149],{"categories":3550},[141],{"categories":3552},[],{"categories":3554},[],{"categories":3556},[],{"categories":3558},[],{"categories":3560},[],{"categories":3562},[172],{"categories":3564},[],{"categories":3566},[],{"categories":3568},[172],{"categories":3570},[],{"categories":3572},[146],{"categories":3574},[146],{"categories":3576},[146],{"categories":3578},[136],{"categories":3580},[],{"categories":3582},[149],{"categories":3584},[155],{"categories":3586},[155],{"categories":3588},[503],{"categories":3590},[141],{"categories":3592},[],{"categories":3594},[172],{"categories":3596},[172],{"categories":3598},[136],{"categories":3600},[],{"categories":3602},[136],{"categories":3604},[],{"categories":3606},[],{"categories":3608},[],{"categories":3610},[146],{"categories":3612},[155],{"categories":3614},[146],{"categories":3616},[146],{"categories":3618},[146],{"categories":3620},[146],{"categories":3622},[146],{"categories":3624},[],{"categories":3626},[141],{"categories":3628},[172],{"categories":3630},[172],{"categories":3632},[172],{"categories":3634},[],{"categories":3636},[136],{"categories":3638},[],{"categories":3640},[152],{"categories":3642},[232],{"categories":3644},[152],{"categories":3646},[],{"categories":3648},[],{"categories":3650},[172],{"categories":3652},[146],{"categories":3654},[],{"categories":3656},[172],{"categories":3658},[172],{"categories":3660},[172],{"categories":3662},[146],{"categories":3664},[146],{"categories":3666},[172],{"categories":3668},[232],{"categories":3670},[146],{"categories":3672},[],{"categories":3674},[172],{"categories":3676},[],{"categories":3678},[592],{"categories":3680},[155],{"categories":3682},[232],{"categories":3684},[155],{"categories":3686},[503],{"categories":3688},[172],{"categories":3690},[155],{"categories":3692},[141],{"categories":3694},[503],{"categories":3696},[155],{"categories":3698},[152],{"categories":3700},[152],{"categories":3702},[],{"categories":3704},[155],{"categories":3706},[],{"categories":3708},[187],{"categories":3710},[155],{"categories":3712},[],{"categories":3714},[],{"categories":3716},[232],{"categories":3718},[232],{"categories":3720},[592],{"categories":3722},[],{"categories":3724},[172],{"categories":3726},[155],{"categories":3728},[146],{"categories":3730},[503],{"categories":3732},[146],{"categories":3734},[146],{"categories":3736},[232],{"categories":3738},[172],{"categories":3740},[187],{"categories":3742},[172],{"categories":3744},[],{"categories":3746},[],{"categories":3748},[],{"categories":3750},[149],{"categories":3752},[172],{"categories":3754},[152],{"categories":3756},[155],{"categories":3758},[155],{"categories":3760},[172],{"categories":3762},[149],{"categories":3764},[187],{"categories":3766},[172],{"categories":3768},[155],{"categories":3770},[172],{"categories":3772},[155],{"categories":3774},[187],{"categories":3776},[187],{"categories":3778},[146],{"categories":3780},[187],{"categories":3782},[155],{"categories":3784},[136],{"categories":3786},[155],{"categories":3788},[155],{"categories":3790},[155],{"categories":3792},[155],{"categories":3794},[],{"categories":3796},[141],{"categories":3798},[],{"categories":3800},[232],{"categories":3802},[172],{"categories":3804},[172],{"categories":3806},[],{"categories":3808},[],{"categories":3810},[],{"categories":3812},[172],{"categories":3814},[141],{"categories":3816},[172],{"categories":3818},[172],{"categories":3820},[],{"categories":3822},[172],{"categories":3824},[152],{"categories":3826},[172],{"categories":3828},[172],{"categories":3830},[172],{"categories":3832},[],{"categories":3834},[],{"categories":3836},[],{"categories":3838},[503],{"categories":3840},[503],{"categories":3842},[136],{"categories":3844},[146],{"categories":3846},[136,149],{"categories":3848},[172],{"categories":3850},[141],{"categories":3852},[],{"categories":3854},[152],{"categories":3856},[232],{"categories":3858},[172],{"categories":3860},[155],{"categories":3862},[172],{"categories":3864},[],{"categories":3866},[232],{"categories":3868},[503],{"categories":3870},[146],{"categories":3872},[136],{"categories":3874},[503],{"categories":3876},[146],{"categories":3878},[187],{"categories":3880},[146],{"categories":3882},[187],{"categories":3884},[172],{"categories":3886},[187],{"categories":3888},[187],{"categories":3890},[155],{"categories":3892},[232],{"categories":3894},[172],{"categories":3896},[149],{"categories":3898},[],{"categories":3900},[172],{"categories":3902},[152],{"categories":3904},[232],{"categories":3906},[136],{"categories":3908},[172],{"categories":3910},[232],{"categories":3912},[187],{"categories":3914},[172],{"categories":3916},[172],{"categories":3918},[172],{"categories":3920},[232],{"categories":3922},[172],{"categories":3924},[187],{"categories":3926},[172],{"categories":3928},[],{"categories":3930},[172],{"categories":3932},[172],{"categories":3934},[172],{"categories":3936},[172],{"categories":3938},[],{"categories":3940},[146],{"categories":3942},[503],{"categories":3944},[],{"categories":3946},[],{"categories":3948},[172],{"categories":3950},[136],{"categories":3952},[149],{"categories":3954},[136],{"categories":3956},[136],{"categories":3958},[146],{"categories":3960},[],{"categories":3962},[172],{"categories":3964},[141],{"categories":3966},[172],{"categories":3968},[172],{"categories":3970},[],{"categories":3972},[146],{"categories":3974},[141],{"categories":3976},[172,503],{"categories":3978},[146,503],{"categories":3980},[503],{"categories":3982},[172],{"categories":3984},[146],{"categories":3986},[146],{"categories":3988},[155],{"categories":3990},[155],{"categories":3992},[155],{"categories":3994},[172],{"categories":3996},[152],{"categories":3998},[146],{"categories":4000},[],{"categories":4002},[503],{"categories":4004},[],{"categories":4006},[503],{"categories":4008},[503],{"categories":4010},[136],{"categories":4012},[146],{"categories":4014},[],{"categories":4016},[503],{"categories":4018},[172],{"categories":4020},[232],{"categories":4022},[141],{"categories":4024},[172],{"categories":4026},[152],{"categories":4028},[155],{"categories":4030},[155],{"categories":4032},[155],{"categories":4034},[503],{"categories":4036},[],{"categories":4038},[],{"categories":4040},[],{"categories":4042},[172],{"categories":4044},[155],{"categories":4046},[172],{"categories":4048},[155],{"categories":4050},[503],{"categories":4052},[503],{"categories":4054},[172],{"categories":4056},[146],{"categories":4058},[],{"categories":4060},[172],{"categories":4062},[172],{"categories":4064},[172],{"categories":4066},[],{"categories":4068},[],{"categories":4070},[503],{"categories":4072},[503],{"categories":4074},[172,503],{"categories":4076},[146],{"categories":4078},[146],{"categories":4080},[146],{"categories":4082},[146],{"categories":4084},[146],{"categories":4086},[146],{"categories":4088},[172],{"categories":4090},[],{"categories":4092},[155],{"categories":4094},[172],{"categories":4096},[155],{"categories":4098},[149],{"categories":4100},[172],{"categories":4102},[592],{"categories":4104},[592],{"categories":4106},[146],{"categories":4108},[155],{"categories":4110},[],{"categories":4112},[146],{"categories":4114},[172],{"categories":4116},[],{"categories":4118},[152],{"categories":4120},[],{"categories":4122},[172],{"categories":4124},[146],{"categories":4126},[141],{"categories":4128},[172],{"categories":4130},[],{"categories":4132},[],{"categories":4134},[152],{"categories":4136},[152],{"categories":4138},[187],{"categories":4140},[152],{"categories":4142},[146],{"categories":4144},[],{"categories":4146},[146],{"categories":4148},[155],{"categories":4150},[141],{"categories":4152},[172],{"categories":4154},[172],{"categories":4156},[],{"categories":4158},[172],{"categories":4160},[187],{"categories":4162},[172],{"categories":4164},[],{"categories":4166},[232],{"categories":4168},[155],{"categories":4170},[155],{"categories":4172},[136],{"categories":4174},[136],{"categories":4176},[136],{"categories":4178},[146],{"categories":4180},[136],{"categories":4182},[146],{"categories":4184},[503],{"categories":4186},[592],{"categories":4188},[141],{"categories":4190},[141],{"categories":4192},[172],{"categories":4194},[141],{"categories":4196},[503],{"categories":4198},[141,136],{"categories":4200},[232],{"categories":4202},[146],{"categories":4204},[],{"categories":4206},[172],{"categories":4208},[],{"categories":4210},[187],{"categories":4212},[155],{"categories":4214},[232],{"categories":4216},[152],{"categories":4218},[155],{"categories":4220},[187],{"categories":4222},[],{"categories":4224},[146],{"categories":4226},[],{"categories":4228},[592],{"categories":4230},[],{"categories":4232},[152],{"categories":4234},[152],{"categories":4236},[232],{"categories":4238},[],{"categories":4240},[172],{"categories":4242},[232],{"categories":4244},[],{"categories":4246},[172],{"categories":4248},[172],{"categories":4250},[],{"categories":4252},[187],{"categories":4254},[172],{"categories":4256},[],{"categories":4258},[172],{"categories":4260},[],{"categories":4262},[],{"categories":4264},[146],{"categories":4266},[146],{"categories":4268},[],{"categories":4270},[155],{"categories":4272},[155],{"categories":4274},[155],{"categories":4276},[172,146],{"categories":4278},[146],{"categories":4280},[146],{"categories":4282},[146],{"categories":4284},[146],{"categories":4286},[232],{"categories":4288},[232],{"categories":4290},[],{"categories":4292},[141],{"categories":4294},[172],{"categories":4296},[232],{"categories":4298},[232],{"categories":4300},[141],{"categories":4302},[136],{"categories":4304},[146],{"categories":4306},[155],{"categories":4308},[172],{"categories":4310},[172],{"categories":4312},[146],{"categories":4314},[155],{"categories":4316},[146],{"categories":4318},[172],{"categories":4320},[149],{"categories":4322},[],{"categories":4324},[172],{"categories":4326},[],{"categories":4328},[172],{"categories":4330},[172],{"categories":4332},[155],{"categories":4334},[],{"categories":4336},[232],{"categories":4338},[172],{"categories":4340},[146],{"categories":4342},[146],{"categories":4344},[155],{"categories":4346},[155],{"categories":4348},[187],{"categories":4350},[187],{"categories":4352},[141],{"categories":4354},[172],{"categories":4356},[146],{"categories":4358},[],{"categories":4360},[146],{"categories":4362},[172],{"categories":4364},[141],{"categories":4366},[172],{"categories":4368},[172],{"categories":4370},[172],{"categories":4372},[146],{"categories":4374},[232],{"categories":4376},[172],{"categories":4378},[152],{"categories":4380},[172],{"categories":4382},[172],{"categories":4384},[172],{"categories":4386},[172],{"categories":4388},[],{"categories":4390},[172],{"categories":4392},[232],{"categories":4394},[152],{"categories":4396},[172],{"categories":4398},[152],{"categories":4400},[],{"categories":4402},[],{"categories":4404},[],{"categories":4406},[172],{"categories":4408},[],{"categories":4410},[],{"categories":4412},[],{"categories":4414},[],{"categories":4416},[146],{"categories":4418},[187],{"categories":4420},[146],{"categories":4422},[146],{"categories":4424},[155],{"categories":4426},[136],{"categories":4428},[172],{"categories":4430},[172],{"categories":4432},[172],{"categories":4434},[136],{"categories":4436},[187],{"categories":4438},[],{"categories":4440},[232],{"categories":4442},[149],{"categories":4444},[172],{"categories":4446},[152],{"categories":4448},[187],{"categories":4450},[187],{"categories":4452},[592],{"categories":4454},[152],{"categories":4456},[146],{"categories":4458},[172],{"categories":4460},[172],{"categories":4462},[187],{"categories":4464},[172],{"categories":4466},[],{"categories":4468},[],{"categories":4470},[503],{"categories":4472},[152],{"categories":4474},[187],{"categories":4476},[146],{"categories":4478},[172],{"categories":4480},[141],{"categories":4482},[187],{"categories":4484},[136],{"categories":4486},[146],{"categories":4488},[146],{"categories":4490},[141],{"categories":4492},[172],{"categories":4494},[],{"categories":4496},[],{"categories":4498},[],{"categories":4500},[172],{"categories":4502},[],{"categories":4504},[141],{"categories":4506},[],{"categories":4508},[172],{"categories":4510},[],{"categories":4512},[141],{"categories":4514},[146],{"categories":4516},[172],{"categories":4518},[503],{"categories":4520},[172],{"categories":4522},[187],{"categories":4524},[172],{"categories":4526},[187],{"categories":4528},[187],{"categories":4530},[],{"categories":4532},[],{"categories":4534},[187],{"categories":4536},[187],{"categories":4538},[187],{"categories":4540},[],{"categories":4542},[187],{"categories":4544},[146],{"categories":4546},[146],{"categories":4548},[],{"categories":4550},[172],{"categories":4552},[149],{"categories":4554},[232],{"categories":4556},[172],{"categories":4558},[],{"categories":4560},[187],{"categories":4562},[172],{"categories":4564},[592],{"categories":4566},[187],{"categories":4568},[187],{"categories":4570},[149],{"categories":4572},[155],{"categories":4574},[155],{"categories":4576},[],{"categories":4578},[155],{"categories":4580},[172],{"categories":4582},[],{"categories":4584},[],{"categories":4586},[146],{"categories":4588},[],{"categories":4590},[146],{"categories":4592},[146],{"categories":4594},[141],{"categories":4596},[172],{"categories":4598},[141],{"categories":4600},[187],{"categories":4602},[141],{"categories":4604},[155],{"categories":4606},[155],{"categories":4608},[155],{"categories":4610},[141],{"categories":4612},[172],{"categories":4614},[146],{"categories":4616},[503],{"categories":4618},[136],{"categories":4620},[503],{"categories":4622},[503],{"categories":4624},[155],{"categories":4626},[155],{"categories":4628},[503],{"categories":4630},[503],[4632,4693,5077,5219],{"id":4633,"title":4634,"ai":4635,"body":4640,"categories":4668,"created_at":90,"date_modified":90,"description":83,"extension":91,"faq":90,"featured":92,"kicker_label":90,"meta":4669,"navigation":116,"path":4680,"published_at":4681,"question":90,"scraped_at":4682,"seo":4683,"sitemap":4684,"source_id":4685,"source_name":4686,"source_type":123,"source_url":4687,"stem":4688,"tags":4689,"thumbnail_url":90,"tldr":4690,"tweet":90,"unknown_tags":4691,"__hash__":4692},"summaries\u002Fsummaries\u002Fpre-mortem-prompts-fix-claude-s-yes-man-bias-summary.md","Pre-Mortem Prompts Fix Claude's Yes-Man Bias",{"provider":7,"model":8,"input_tokens":4636,"output_tokens":4637,"processing_time_ms":4638,"cost_usd":4639},3893,1649,24045,0.00109625,{"type":14,"value":4641,"toc":4663},[4642,4646,4649,4653,4656,4660],[17,4643,4645],{"id":4644},"bypass-rlhf-flattery-with-failure-framing","Bypass RLHF Flattery with Failure Framing",[22,4647,4648],{},"Claude's RLHF training makes it overly agreeable, turning 'Is this a good plan?' into superficial praise disguised as critique. This wastes time since it rarely flags real flaws. Instead, frame prompts around inevitable failure: tell Claude your plan died 6 months from now and ask it to narrate the autopsy. This inverts bias, surfacing hidden risks like market shifts, execution gaps, or overlooked dependencies that affirmative prompts ignore. Readers gain production-ready critiques, turning vague agreement into actionable fixes.",[17,4650,4652],{"id":4651},"execute-the-pre-mortem-technique","Execute the Pre-Mortem Technique",[22,4654,4655],{},"Start by stating the plan's failure as fact—'Six months from now, this project failed completely. Explain exactly how.' Claude then generates plausible failure paths, such as technical debt accumulation or user drop-off from poor UX. Use outputs to iterate: patch the top 3-5 risks before relaunch. This method delivers what standard prompts bury, providing concrete mitigation steps. For AI product builders, it accelerates from idea to robust prototype by preempting derailments.",[17,4657,4659],{"id":4658},"proven-origins-and-impact","Proven Origins and Impact",[22,4661,4662],{},"Gary Klein invented the pre-mortem in 1989 for high-stakes decisions; Daniel Kahneman later called it his most valuable tool for avoiding overconfidence. Applied to LLMs, it leverages Claude's narrative strengths without affirmation traps, yielding deeper insights than yes\u002Fno evaluations. Builders testing this report 2-3x sharper risk identification, directly improving plan survival odds in competitive AI landscapes.",{"title":83,"searchDepth":84,"depth":84,"links":4664},[4665,4666,4667],{"id":4644,"depth":84,"text":4645},{"id":4651,"depth":84,"text":4652},{"id":4658,"depth":84,"text":4659},[],{"content_references":4670,"triage":4678},[4671,4675],{"type":96,"title":4672,"author":4673,"publisher":4674,"context":100},"Pre-mortem","Klein","1989",{"type":96,"title":4672,"author":4676,"context":4677},"Kahneman","recommended",{"relevance":112,"novelty":113,"quality":113,"actionability":112,"composite":114,"reasoning":4679},"Category: AI & LLMs. The article provides a practical technique for overcoming biases in AI models, specifically Claude, which is highly relevant for AI product builders. It offers a concrete method for risk analysis that can be immediately applied to improve product planning and execution.","\u002Fsummaries\u002Fpre-mortem-prompts-fix-claude-s-yes-man-bias-summary","2026-05-08 06:49:18","2026-05-09 15:36:43",{"title":4634,"description":83},{"loc":4680},"7bfce2f937233fa5","Generative AI","https:\u002F\u002Fgenerativeai.pub\u002Fthe-pre-mortem-trick-that-makes-claude-absolutely-great-630f610809d6?source=rss----440100e76000---4","summaries\u002Fpre-mortem-prompts-fix-claude-s-yes-man-bias-summary",[128,127],"Claude flatters plans due to RLHF; prompt it to assume failure in 6 months and explain why to get honest risk analysis—Kahneman's top decision tool, invented by Klein in 1989.",[],"6LCCcDNEujK9HGY_8japqNTsOvfdQXWVzi8wnayRTOI",{"id":4694,"title":4695,"ai":4696,"body":4701,"categories":5051,"created_at":90,"date_modified":90,"description":83,"extension":91,"faq":90,"featured":92,"kicker_label":90,"meta":5052,"navigation":116,"path":5064,"published_at":5065,"question":90,"scraped_at":5066,"seo":5067,"sitemap":5068,"source_id":5069,"source_name":5070,"source_type":123,"source_url":5071,"stem":5072,"tags":5073,"thumbnail_url":90,"tldr":5074,"tweet":90,"unknown_tags":5075,"__hash__":5076},"summaries\u002Fsummaries\u002Fguarantee-llm-outputs-match-exact-taxonomies-with--summary.md","Guarantee LLM Outputs Match Exact Taxonomies with Tries",{"provider":7,"model":8,"input_tokens":4697,"output_tokens":4698,"processing_time_ms":4699,"cost_usd":4700},7679,2345,26271,0.0026858,{"type":14,"value":4702,"toc":5046},[4703,4707,4710,4717,4720,4744,4748,4751,4923,4930,4937,5012,5019,5023,5030,5033,5036,5039,5042],[17,4704,4706],{"id":4705},"logit-masking-guarantees-valid-outputs","Logit Masking Guarantees Valid Outputs",[22,4708,4709],{},"LLMs generate tokens autoregressively, producing a logit vector over 32,000-100,000 vocabulary tokens at each step, converted to probabilities via softmax. Any token with finite logit has nonzero probability, allowing hallucinations like near-miss labels (e.g., \"Techology\" instead of \"Technology\"). Standard fixes—prompt instructions, string matching, retries—fail because they act post-generation.",[22,4711,4712,4713,34],{},"Constrained decoding intervenes pre-sampling: set logits of invalid tokens to -∞, yielding exactly zero softmax probability. Remaining valid logits renormalize to sum to 1. This works for any sampling (greedy, temperature, top-p, top-k) since zero-probability tokens cannot be selected. In code: ",[4714,4715,4716],"code",{},"logits[~valid_token_mask] = float('-inf')",[22,4718,4719],{},"Validity depends on prior tokens. A trie (prefix tree) encodes all taxonomy labels as token paths. Root children are first tokens of any label; deeper nodes narrow to continuations. After prefix \" Tech\" (token ID 8987), only \"nology\" (ID 1366) is valid. At end nodes, only EOS is valid, terminating the label.",[22,4721,4722,4723,4726,4727,4730,4731,4735,4736,4739,4740,4743],{},"Tokenization nuance: BPE splits depend on context. Tokenize labels as continuations with leading space (",[4714,4724,4725],{},"\" \" + label",", ",[4714,4728,4729],{},"add_special_tokens=False","), e.g., Qwen2.5 tokenizes \" Sports\" to ",[4732,4733,4734],"span",{},"22470",", not \"Sports\" to ",[4732,4737,4738],{},"51660",". Verify round-trip: ",[4714,4741,4742],{},"tokenizer.decode(token_ids) == \" \" + label",". Tiktoken (GPT-4 family) bakes whitespace into boundaries without ▁.",[17,4745,4747],{"id":4746},"trie-and-logits-processor-implementation","Trie and Logits Processor Implementation",[22,4749,4750],{},"Build trie from labels:",[4752,4753,4757],"pre",{"className":4754,"code":4755,"language":4756,"meta":83,"style":83},"language-python shiki shiki-themes github-light github-dark","class TrieNode:\n    def __init__(self):\n        self.children = {}  # token_id → TrieNode\n        self.is_end = False\n\nclass ConstrainedTrie:\n    def __init__(self):\n        self.root = TrieNode()\n    def insert(self, token_ids):\n        node = self.root\n        for tid in token_ids:\n            if tid not in node.children:\n                node.children[tid] = TrieNode()\n            node = node.children[tid]\n        node.is_end = True\n    def get_valid_next_tokens(self, prefix):\n        node = self.root\n        for tid in prefix:\n            if tid not in node.children:\n                return set()\n            node = node.children[tid]\n        return set(node.children.keys())\n    def is_complete(self, prefix):\n        node = self.root\n        for tid in prefix:\n            if tid not in node.children:\n                return False\n            node = node.children[tid]\n        return node.is_end\n","python",[4714,4758,4759,4766,4771,4777,4782,4787,4793,4798,4804,4810,4816,4822,4828,4834,4840,4846,4852,4857,4863,4868,4874,4879,4885,4891,4896,4901,4906,4912,4917],{"__ignoreMap":83},[4732,4760,4763],{"class":4761,"line":4762},"line",1,[4732,4764,4765],{},"class TrieNode:\n",[4732,4767,4768],{"class":4761,"line":84},[4732,4769,4770],{},"    def __init__(self):\n",[4732,4772,4774],{"class":4761,"line":4773},3,[4732,4775,4776],{},"        self.children = {}  # token_id → TrieNode\n",[4732,4778,4779],{"class":4761,"line":113},[4732,4780,4781],{},"        self.is_end = False\n",[4732,4783,4784],{"class":4761,"line":112},[4732,4785,4786],{"emptyLinePlaceholder":116},"\n",[4732,4788,4790],{"class":4761,"line":4789},6,[4732,4791,4792],{},"class ConstrainedTrie:\n",[4732,4794,4796],{"class":4761,"line":4795},7,[4732,4797,4770],{},[4732,4799,4801],{"class":4761,"line":4800},8,[4732,4802,4803],{},"        self.root = TrieNode()\n",[4732,4805,4807],{"class":4761,"line":4806},9,[4732,4808,4809],{},"    def insert(self, token_ids):\n",[4732,4811,4813],{"class":4761,"line":4812},10,[4732,4814,4815],{},"        node = self.root\n",[4732,4817,4819],{"class":4761,"line":4818},11,[4732,4820,4821],{},"        for tid in token_ids:\n",[4732,4823,4825],{"class":4761,"line":4824},12,[4732,4826,4827],{},"            if tid not in node.children:\n",[4732,4829,4831],{"class":4761,"line":4830},13,[4732,4832,4833],{},"                node.children[tid] = TrieNode()\n",[4732,4835,4837],{"class":4761,"line":4836},14,[4732,4838,4839],{},"            node = node.children[tid]\n",[4732,4841,4843],{"class":4761,"line":4842},15,[4732,4844,4845],{},"        node.is_end = True\n",[4732,4847,4849],{"class":4761,"line":4848},16,[4732,4850,4851],{},"    def get_valid_next_tokens(self, prefix):\n",[4732,4853,4855],{"class":4761,"line":4854},17,[4732,4856,4815],{},[4732,4858,4860],{"class":4761,"line":4859},18,[4732,4861,4862],{},"        for tid in prefix:\n",[4732,4864,4866],{"class":4761,"line":4865},19,[4732,4867,4827],{},[4732,4869,4871],{"class":4761,"line":4870},20,[4732,4872,4873],{},"                return set()\n",[4732,4875,4877],{"class":4761,"line":4876},21,[4732,4878,4839],{},[4732,4880,4882],{"class":4761,"line":4881},22,[4732,4883,4884],{},"        return set(node.children.keys())\n",[4732,4886,4888],{"class":4761,"line":4887},23,[4732,4889,4890],{},"    def is_complete(self, prefix):\n",[4732,4892,4894],{"class":4761,"line":4893},24,[4732,4895,4815],{},[4732,4897,4899],{"class":4761,"line":4898},25,[4732,4900,4862],{},[4732,4902,4904],{"class":4761,"line":4903},26,[4732,4905,4827],{},[4732,4907,4909],{"class":4761,"line":4908},27,[4732,4910,4911],{},"                return False\n",[4732,4913,4915],{"class":4761,"line":4914},28,[4732,4916,4839],{},[4732,4918,4920],{"class":4761,"line":4919},29,[4732,4921,4922],{},"        return node.is_end\n",[22,4924,4925,4926,4929],{},"Insert: ",[4714,4927,4928],{},"token_ids = tokenizer.encode(\" \" + label, add_special_tokens=False); trie.insert(token_ids)",". Rebuild on taxonomy changes (milliseconds for hundreds-thousands labels).",[22,4931,4932,4933,4936],{},"HuggingFace ",[4714,4934,4935],{},"LogitsProcessor",":",[4752,4938,4940],{"className":4754,"code":4939,"language":4756,"meta":83,"style":83},"class TrieLogitsProcessor(LogitsProcessor):\n    def __init__(self, trie, prompt_length, eos_token_id):\n        self.trie = trie\n        self.prompt_length = prompt_length\n        self.eos = eos_token_id\n    def __call__(self, input_ids, scores):\n        generated = input_ids[0, self.prompt_length:].tolist()\n        valid = self.trie.get_valid_next_tokens(generated)\n        if self.trie.is_complete(generated):\n            valid.add(self.eos)\n        masked = torch.full_like(scores, float('-inf'))\n        for tid in valid:\n            masked[0, tid] = scores[0, tid]\n        return masked\n",[4714,4941,4942,4947,4952,4957,4962,4967,4972,4977,4982,4987,4992,4997,5002,5007],{"__ignoreMap":83},[4732,4943,4944],{"class":4761,"line":4762},[4732,4945,4946],{},"class TrieLogitsProcessor(LogitsProcessor):\n",[4732,4948,4949],{"class":4761,"line":84},[4732,4950,4951],{},"    def __init__(self, trie, prompt_length, eos_token_id):\n",[4732,4953,4954],{"class":4761,"line":4773},[4732,4955,4956],{},"        self.trie = trie\n",[4732,4958,4959],{"class":4761,"line":113},[4732,4960,4961],{},"        self.prompt_length = prompt_length\n",[4732,4963,4964],{"class":4761,"line":112},[4732,4965,4966],{},"        self.eos = eos_token_id\n",[4732,4968,4969],{"class":4761,"line":4789},[4732,4970,4971],{},"    def __call__(self, input_ids, scores):\n",[4732,4973,4974],{"class":4761,"line":4795},[4732,4975,4976],{},"        generated = input_ids[0, self.prompt_length:].tolist()\n",[4732,4978,4979],{"class":4761,"line":4800},[4732,4980,4981],{},"        valid = self.trie.get_valid_next_tokens(generated)\n",[4732,4983,4984],{"class":4761,"line":4806},[4732,4985,4986],{},"        if self.trie.is_complete(generated):\n",[4732,4988,4989],{"class":4761,"line":4812},[4732,4990,4991],{},"            valid.add(self.eos)\n",[4732,4993,4994],{"class":4761,"line":4818},[4732,4995,4996],{},"        masked = torch.full_like(scores, float('-inf'))\n",[4732,4998,4999],{"class":4761,"line":4824},[4732,5000,5001],{},"        for tid in valid:\n",[4732,5003,5004],{"class":4761,"line":4830},[4732,5005,5006],{},"            masked[0, tid] = scores[0, tid]\n",[4732,5008,5009],{"class":4761,"line":4836},[4732,5010,5011],{},"        return masked\n",[22,5013,5014,5015,5018],{},"Generate: ",[4714,5016,5017],{},"model.generate(input_ids, logits_processor=LogitsProcessorList([processor]), max_new_tokens=16)",". Output decodes to exact label.",[17,5020,5022],{"id":5021},"multi-label-hierarchies-and-broader-applications","Multi-Label, Hierarchies, and Broader Applications",[22,5024,5025,5026,5029],{},"For multi-label: After end node, allow EOS or separator (e.g., ",[4714,5027,5028],{},"|,|","). Parse generated tokens into seen labels and current prefix. At root, exclude first tokens only after all labels sharing it are emitted (precompute groups by first token). Supports hierarchies: insert full paths like \"Technology > AI > NLP\"; shared prefixes compress naturally.",[22,5031,5032],{},"Edge cases: Low confidence concentrates mass on valid tokens (fix: fine-tune); long labels create narrow paths (fine-tune improves); rebuild trie on changes.",[22,5034,5035],{},"Proof of correctness: (1) Forward invariant—emitted tokens always extend valid prefixes; (2) Termination invariant—EOS only at end nodes. Verify by enumerating trie paths against labels. Independent of model, temperature, etc.",[22,5037,5038],{},"Limitations: Needs logit access (open models like Qwen2.5, not OpenAI APIs); masking redistributes probability (structurally correct but semantically wrong possible); no accuracy boost—pair with fine-tuning.",[22,5040,5041],{},"Generalizes to JSON (trie encodes schema), SQL (grammar FSM), agents (tool names). Enforces structure without prompt\u002Fmodel changes.",[5043,5044,5045],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":83,"searchDepth":84,"depth":84,"links":5047},[5048,5049,5050],{"id":4705,"depth":84,"text":4706},{"id":4746,"depth":84,"text":4747},{"id":5021,"depth":84,"text":5022},[172],{"content_references":5053,"triage":5061},[5054,5057],{"type":102,"title":5055,"url":5056,"context":4677},"constrained-decoding","https:\u002F\u002Fgithub.com\u002FSachinKalsi\u002Fconstrained-decoding",{"type":96,"title":5058,"author":5059,"url":5060,"context":100},"Why do we use negative infinity for masking in attention","Sachin Kalsi","https:\u002F\u002Fmedium.com\u002F@sachinkalsi\u002Fwhy-do-we-use-negative-infinity-for-masking-in-attention-450c59274ac8",{"relevance":112,"novelty":113,"quality":113,"actionability":113,"composite":5062,"reasoning":5063},4.35,"Category: AI & LLMs. The article provides a detailed method for constraining LLM outputs to match specific taxonomies, addressing a key pain point for developers integrating AI features. It includes practical code examples and a clear explanation of the trie data structure, making it actionable for the audience.","\u002Fsummaries\u002Fguarantee-llm-outputs-match-exact-taxonomies-with-summary","2026-05-07 04:37:46","2026-05-07 11:23:51",{"title":4695,"description":83},{"loc":5064},"b0d82d6ef098f216","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Fconstrained-decoding-forcing-llms-to-respect-your-taxonomy-3aaaf13329f9?source=rss----98111c9905da---4","summaries\u002Fguarantee-llm-outputs-match-exact-taxonomies-with--summary",[127,128],"Constrain LLM generation by masking invalid logits to -∞ using a trie of tokenized labels, ensuring outputs are always exact taxonomy matches regardless of sampling method.",[],"pSS4i1v22VwaujuhOPlIt8tx-Fut_d93ojbD3ALEERc",{"id":5078,"title":5079,"ai":5080,"body":5085,"categories":5188,"created_at":90,"date_modified":90,"description":83,"extension":91,"faq":90,"featured":92,"kicker_label":90,"meta":5189,"navigation":116,"path":5206,"published_at":5207,"question":90,"scraped_at":5208,"seo":5209,"sitemap":5210,"source_id":5211,"source_name":5212,"source_type":123,"source_url":5213,"stem":5214,"tags":5215,"thumbnail_url":90,"tldr":5216,"tweet":90,"unknown_tags":5217,"__hash__":5218},"summaries\u002Fsummaries\u002Frebuild-gpt-5-5-prompts-from-scratch-minimal-wins--summary.md","Rebuild GPT-5.5 Prompts from Scratch: Minimal Wins Over Legacy Detail",{"provider":7,"model":8,"input_tokens":5081,"output_tokens":5082,"processing_time_ms":5083,"cost_usd":5084},5455,1761,19749,0.00194885,{"type":14,"value":5086,"toc":5183},[5087,5091,5094,5097,5100,5103,5107,5110,5155,5158,5161,5164,5167,5171,5174,5177,5180],[17,5088,5090],{"id":5089},"strip-prompts-to-outcomes-for-better-reasoning-efficiency","Strip Prompts to Outcomes for Better Reasoning Efficiency",[22,5092,5093],{},"GPT-5.5 outperforms predecessors by reasoning more efficiently, so legacy prompts with step-by-step instructions create noise, narrow search space, or yield mechanical outputs. Instead, define only the target outcome, success criteria, constraints, and context—let the model handle the process. Test low or medium reasoning effort first; short prompts beat process-heavy stacks.",[22,5095,5096],{},"Avoid absolutes like \"ALWAYS\" or \"NEVER\" except for invariants (e.g., security). Use decision rules for judgment calls and explicit stop conditions to prevent tool loops: \"Resolve in fewest useful loops without sacrificing correctness; after each result, check if core request is answerable with evidence.\"",[22,5098,5099],{},"Positive example for customer service: \"Resolve issue end-to-end. Success: eligibility from policy\u002Faccount data, complete actions before responding, output includes completed_actions, customer_message, blockers; ask for smallest missing field if needed.\" Negative: micromanaging \"First inspect A, then B, compare fields, think exceptions, decide tool...\"",[22,5101,5102],{},"This approach unlocks higher performance by giving GPT-5.5 room to optimize paths, reducing latency and improving naturalness.",[17,5104,5106],{"id":5105},"use-7-part-schema-starting-with-role-and-personality","Use 7-Part Schema Starting with Role and Personality",[22,5108,5109],{},"Structure prompts as:",[43,5111,5112,5119,5125,5131,5137,5143,5149],{},[46,5113,5114,5118],{},[5115,5116,5117],"strong",{},"Role",": 1-2 sentences on function, context, job.",[46,5120,5121,5124],{},[5115,5122,5123],{},"# Personality",": Tone, demeanor, collaboration style.",[46,5126,5127,5130],{},[5115,5128,5129],{},"# Goal",": User-visible outcome.",[46,5132,5133,5136],{},[5115,5134,5135],{},"# Success criteria",": What must be true before final answer.",[46,5138,5139,5142],{},[5115,5140,5141],{},"# Constraints",": Policy, safety, evidence limits.",[46,5144,5145,5148],{},[5115,5146,5147],{},"# Output",": Sections, length, tone.",[46,5150,5151,5154],{},[5115,5152,5153],{},"# Stop rules",": When to retry, fallback, abstain, ask, stop.",[22,5156,5157],{},"Role definitions counter prior doubts (e.g., some research called them counterproductive); they now anchor effective prompts. Split personality (sound: warm\u002Fformal) from collaboration (ask questions\u002Fassume when clear).",[22,5159,5160],{},"Task-focused: \"Capable collaborator: approachable, steady, direct. Assume competence\u002Fgood faith; progress over clarification unless material risk.\"",[22,5162,5163],{},"Expressive: \"Vivid presence: intelligent, curious, playful. Ask on blurriness, decisive with context; warm, offer viewpoint without mirroring.\"",[22,5165,5166],{},"Keep sections short—add details only if they shift behavior. Treat as starting point, tune with examples.",[17,5168,5170],{"id":5169},"set-retrieval-budgets-citation-rules-and-streaming-preambles","Set Retrieval Budgets, Citation Rules, and Streaming Preambles",[22,5172,5173],{},"Embed citation logic in prompts: specify claims needing evidence (e.g., metrics, dates), sufficient proof, and responses to gaps. Retrieval budgets as stop rules: one broad search first; retry only if core unanswerable, facts missing, exhaustive needed, or specific docs required. Skip for phrasing\u002Fexamples.",[22,5175,5176],{},"Drafting rule: Cite product\u002Fmetrics claims; avoid inventing specifics—use generics\u002Fplaceholders if unsupported.",[22,5178,5179],{},"For streaming, cut perceived latency with preambles: Before tools, send 1-2 sentences acknowledging request and first step (e.g., for multi-step tasks).",[22,5181,5182],{},"Automate rewrites via Codex or OpenAI's Docs Skill GitHub tool.",{"title":83,"searchDepth":84,"depth":84,"links":5184},[5185,5186,5187],{"id":5089,"depth":84,"text":5090},{"id":5105,"depth":84,"text":5106},{"id":5169,"depth":84,"text":5170},[],{"content_references":5190,"triage":5204},[5191,5194,5197,5201],{"type":96,"title":5192,"url":5193,"context":100},"prompting guide for GPT-5.5","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Fprompt-guidance?model=gpt-5.5",{"type":96,"title":5195,"url":5196,"context":104},"General Tips","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Flatest-model",{"type":5198,"title":5199,"url":5200,"context":104},"paper","arxiv.org\u002Fabs\u002F2603.18507","https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.18507",{"type":102,"title":5202,"url":5203,"context":4677},"OpenAI Docs Skill","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills\u002Ftree\u002Fmain\u002Fskills\u002F.curated\u002Fopenai-docs",{"relevance":112,"novelty":113,"quality":113,"actionability":112,"composite":114,"reasoning":5205},"Category: AI & LLMs. The article provides a detailed framework for prompt engineering specifically for GPT-5.5, addressing the pain point of outdated prompt structures that limit AI performance. It offers a clear, actionable 7-part schema that developers can implement immediately to enhance their AI interactions.","\u002Fsummaries\u002Frebuild-gpt-5-5-prompts-from-scratch-minimal-wins-summary","2026-04-26 10:20:04","2026-04-26 17:22:51",{"title":5079,"description":83},{"loc":5206},"568ea01dbb8e8f83","The Decoder","https:\u002F\u002Fthe-decoder.com\u002Fopenai-says-old-prompts-are-holding-gpt-5-5-back-and-developers-need-a-fresh-baseline\u002F","summaries\u002Frebuild-gpt-5-5-prompts-from-scratch-minimal-wins--summary",[128,127],"OpenAI's GPT-5.5 guide: Ditch old detailed prompts—they limit performance. Start with minimal, outcome-focused instructions in a 7-part schema beginning with role definitions to leverage efficient reasoning.",[],"cz1dQDYGJX3AifhhJ1hQkpSbofakEfbLAR11SCAvvAk",{"id":5220,"title":5221,"ai":5222,"body":5227,"categories":5256,"created_at":90,"date_modified":90,"description":83,"extension":91,"faq":90,"featured":92,"kicker_label":90,"meta":5257,"navigation":116,"path":5265,"published_at":5266,"question":90,"scraped_at":5267,"seo":5268,"sitemap":5269,"source_id":5270,"source_name":5271,"source_type":123,"source_url":5272,"stem":5273,"tags":5274,"thumbnail_url":90,"tldr":5275,"tweet":90,"unknown_tags":5276,"__hash__":5277},"summaries\u002Fsummaries\u002Fkernel-framework-delivers-340-ai-accuracy-gains-summary.md","KERNEL Framework Delivers 340% AI Accuracy Gains",{"provider":7,"model":8,"input_tokens":5223,"output_tokens":5224,"processing_time_ms":5225,"cost_usd":5226},3871,1802,16733,0.0016527,{"type":14,"value":5228,"toc":5252},[5229,5233,5236,5239,5243,5246,5249],[17,5230,5232],{"id":5231},"overcoming-vague-prompts-with-kernel-principles","Overcoming Vague Prompts with KERNEL Principles",[22,5234,5235],{},"Long, complicated, vague prompts produce inconsistent AI outputs, as the author experienced in an enterprise IoT project where responses varied wildly. The solution is the KERNEL Framework—a practical six-principle checklist (K, E, R, N, E, L) that enforces simplicity, focus, and verifiability. This shifts prompts from frustrating guesswork to precise, reliable instructions, directly improving accuracy by up to 340% in production systems like IoT.",[22,5237,5238],{},"Use it as a go-to checklist: instead of overloading prompts with details, strip them to essentials that guide the AI clearly. The framework turns theoretical prompt engineering into a repeatable process, eliminating output chaos without needing advanced skills.",[17,5240,5242],{"id":5241},"proven-results-from-hands-on-application","Proven Results from Hands-On Application",[22,5244,5245],{},"In real-world testing, KERNEL transformed unreliable LLM responses into consistent, high-quality ones. The author credits it for clarity in complex environments, where vague prompts fail but structured ones succeed. Key outcome: prompts become easy to verify and iterate, reducing trial-and-error cycles.",[22,5247,5248],{},"Trade-off: it prioritizes precision over verbosity, so avoid it for creative brainstorming—reserve for accuracy-critical tasks like data analysis or system integration. Readers overwhelmed by varying AI results gain a immediate tool: apply the six principles before every prompt to see measurable reliability gains.",[22,5250,5251],{},"This content teases the framework effectively but is thin on specifics, as full details are paywalled.",{"title":83,"searchDepth":84,"depth":84,"links":5253},[5254,5255],{"id":5231,"depth":84,"text":5232},{"id":5241,"depth":84,"text":5242},[172],{"content_references":5258,"triage":5262},[5259],{"type":96,"title":5260,"url":5261,"context":104},"KERNEL Framework","https:\u002F\u002Fwww.shailykumar.com\u002Fprompt-engineering-mastery",{"relevance":112,"novelty":113,"quality":4773,"actionability":113,"composite":5263,"reasoning":5264},4.1,"Category: AI & LLMs. The article maps directly to the AI & LLMs category by discussing the KERNEL Framework for prompt engineering, which addresses a specific pain point of producing consistent AI outputs. It provides actionable principles for improving prompt accuracy, making it relevant for developers looking to implement AI features.","\u002Fsummaries\u002Fkernel-framework-delivers-340-ai-accuracy-gains-summary","2026-04-26 03:11:01","2026-04-26 17:22:34",{"title":5221,"description":83},{"loc":5265},"d40c4d615fa22540","AI Simplified in Plain English","https:\u002F\u002Fmedium.com\u002Fai-simplified-in-plain-english\u002F340-higher-ai-accuracy-d4d0fec20b28?source=rss----f37ab7d4e76b---4","summaries\u002Fkernel-framework-delivers-340-ai-accuracy-gains-summary",[128,127],"Apply the KERNEL Framework's six principles to craft simple, focused, verifiable prompts that boost AI accuracy up to 340%, as proven in enterprise IoT projects.",[],"kVmUZAgpSa9GBwLzDPFOo92RFsmASmuiisX3C57R5iI"]