[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-8ccff9c28a5e07d2-run-vibevoice-stt-locally-on-mac-in-one-uv-command-summary":3,"summaries-facets-categories":249,"summary-related-8ccff9c28a5e07d2-run-vibevoice-stt-locally-on-mac-in-one-uv-command-summary":3835},{"id":4,"title":5,"ai":6,"body":13,"categories":200,"created_at":201,"date_modified":201,"description":19,"extension":202,"faq":201,"featured":203,"kicker_label":201,"meta":204,"navigation":231,"path":232,"published_at":201,"question":201,"scraped_at":233,"seo":234,"sitemap":235,"source_id":236,"source_name":237,"source_type":238,"source_url":239,"stem":240,"tags":241,"thumbnail_url":201,"tldr":246,"tweet":201,"unknown_tags":247,"__hash__":248},"summaries\u002Fsummaries\u002F8ccff9c28a5e07d2-run-vibevoice-stt-locally-on-mac-in-one-uv-command-summary.md","Run VibeVoice STT Locally on Mac in One uv Command",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",5072,2597,26872,0.0022904,{"type":14,"value":15,"toc":195},"minimark",[16,20,25,37,47,61,150,161,165,168,181,184,188,191],[17,18,19],"p",{},"This link post demonstrates running Microsoft's VibeVoice, a Whisper-style speech-to-text model with built-in speaker diarization, locally on Apple Silicon. Released January 21, 2026, and MIT-licensed, it uses the 5.71GB 4-bit MLX-quantized version of the 17.3GB original for efficient inference.",[21,22,24],"h2",{"id":23},"one-liner-command-delivers-full-transcription","One-Liner Command Delivers Full Transcription",[17,26,27,28,32,33,36],{},"Install and run via ",[29,30,31],"code",{},"uv"," and ",[29,34,35],{},"mlx-audio",":",[38,39,44],"pre",{"className":40,"code":42,"language":43},[41],"language-text","uv run --with mlx-audio mlx_audio.stt.generate \\\n  --model mlx-community\u002FVibeVoice-ASR-4bit \\\n  --audio lenny.mp3 --output-path lenny \\\n  --format json --verbose --max-tokens 32768\n","text",[29,45,42],{"__ignoreMap":46},"",[17,48,49,50,32,53,56,57,60],{},"This handles ",[29,51,52],{},".mp3",[29,54,55],{},".wav"," inputs. Default ",[29,58,59],{},"--max-tokens 8192"," covers ~25min audio; increase to 32768 for up to ~59min (model limit trims longer files). Outputs JSON array of segments like:",[38,62,66],{"className":63,"code":64,"language":65,"meta":46,"style":46},"language-json shiki shiki-themes github-light github-dark","{\n  \"text\": \"And an open question for me is...\",\n  \"start\": 13.85,\n  \"end\": 19.5,\n  \"duration\": 5.65,\n  \"speaker_id\": 0\n}\n","json",[29,67,68,77,94,107,120,133,144],{"__ignoreMap":46},[69,70,73],"span",{"class":71,"line":72},"line",1,[69,74,76],{"class":75},"sVt8B","{\n",[69,78,80,84,87,91],{"class":71,"line":79},2,[69,81,83],{"class":82},"sj4cs","  \"text\"",[69,85,86],{"class":75},": ",[69,88,90],{"class":89},"sZZnC","\"And an open question for me is...\"",[69,92,93],{"class":75},",\n",[69,95,97,100,102,105],{"class":71,"line":96},3,[69,98,99],{"class":82},"  \"start\"",[69,101,86],{"class":75},[69,103,104],{"class":82},"13.85",[69,106,93],{"class":75},[69,108,110,113,115,118],{"class":71,"line":109},4,[69,111,112],{"class":82},"  \"end\"",[69,114,86],{"class":75},[69,116,117],{"class":82},"19.5",[69,119,93],{"class":75},[69,121,123,126,128,131],{"class":71,"line":122},5,[69,124,125],{"class":82},"  \"duration\"",[69,127,86],{"class":75},[69,129,130],{"class":82},"5.65",[69,132,93],{"class":75},[69,134,136,139,141],{"class":71,"line":135},6,[69,137,138],{"class":82},"  \"speaker_id\"",[69,140,86],{"class":75},[69,142,143],{"class":82},"0\n",[69,145,147],{"class":71,"line":146},7,[69,148,149],{"class":75},"}\n",[17,151,152,153,156,157,160],{},"Load JSON into Datasette Lite (",[29,154,155],{},"https:\u002F\u002Flite.dssette.io\u002F?json=URL",") to facet by ",[29,158,159],{},"speaker_id"," and browse turns—accurately distinguishes speakers, even voice changes in intros.",[21,162,164],{"id":163},"m5-max-performance-fast-for-local-use","M5 Max Performance: Fast for Local Use",[17,166,167],{},"On 128GB M5 Max MacBook Pro, 99.8min podcast (trimmed to 59min) took 524.79s total:",[169,170,171,175,178],"ul",{},[172,173,174],"li",{},"Prompt: 26,615 tokens at 50.718 t\u002Fs",[172,176,177],{},"Generation: 20,248 tokens at 38.585 t\u002Fs",[172,179,180],{},"Peak reported: 30.44GB RAM (Activity Monitor showed 61.5GB prefill, 18GB generation)",[17,182,183],{},"That's 8min 45s for ~1hr audio, enabling quick local prototyping without cloud costs.",[21,185,187],{"id":186},"handling-long-audio-requires-splitting","Handling Long Audio Requires Splitting",[17,189,190],{},"Model caps at ~59min; for longer files, split with 1min overlaps to align speaker IDs and avoid cut-off words. Align segments post-processing to merge full transcripts.",[192,193,194],"style",{},"html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}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":46,"searchDepth":79,"depth":79,"links":196},[197,198,199],{"id":23,"depth":79,"text":24},{"id":163,"depth":79,"text":164},{"id":186,"depth":79,"text":187},[],null,"md",false,{"content_references":205,"triage":228},[206,211,215,218,221,225],{"type":207,"title":208,"url":209,"context":210},"tool","microsoft\u002FVibeVoice","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FVibeVoice","mentioned",{"type":207,"title":35,"author":212,"url":213,"context":214},"Prince Canuma","https:\u002F\u002Fgithub.com\u002FBlaizzy\u002Fmlx-audio","recommended",{"type":207,"title":216,"url":217,"context":210},"mlx-community\u002FVibeVoice-ASR-4bit","https:\u002F\u002Fhuggingface.co\u002Fmlx-community\u002FVibeVoice-ASR-4bit",{"type":207,"title":219,"url":220,"context":210},"microsoft\u002FVibeVoice-ASR","https:\u002F\u002Fhuggingface.co\u002Fmicrosoft\u002FVibeVoice-ASR\u002Ftree\u002Fmain",{"type":222,"title":223,"url":224,"context":210},"podcast","podcast appearance with Lenny Rachitsky","https:\u002F\u002Fsimonwillison.net\u002F2026\u002FApr\u002F2\u002Flennys-podcast\u002F",{"type":207,"title":226,"url":227,"context":214},"Datasette Lite","https:\u002F\u002Flite.datasette.io\u002F",{"relevance":122,"novelty":109,"quality":109,"actionability":122,"composite":229,"reasoning":230},4.55,"Category: AI & LLMs. The article provides a practical guide on running a specific AI model for speech-to-text transcription, addressing the needs of developers looking to implement AI features in their products. It includes a concrete command for execution and performance metrics, making it immediately actionable for the target audience.",true,"\u002Fsummaries\u002F8ccff9c28a5e07d2-run-vibevoice-stt-locally-on-mac-in-one-uv-command-summary","2026-05-03 17:01:59",{"title":5,"description":19},{"loc":232},"8ccff9c28a5e07d2","Simon Willison's Weblog","article","https:\u002F\u002Fsimonwillison.net\u002F2026\u002FApr\u002F27\u002Fvibevoice\u002F#atom-everything","summaries\u002F8ccff9c28a5e07d2-run-vibevoice-stt-locally-on-mac-in-one-uv-command-summary",[242,243,244,245],"python","ai-tools","mlx","speech-to-text","Transcribe up to 59min audio with Microsoft's MIT-licensed VibeVoice model using mlx-audio: uv one-liner on M5 Max Mac processes 1hr podcast in 524s (8:45min) at 30-61GB RAM peak, outputs speaker-diarized JSON segments.",[244,245],"Q9tnX8SIMUxa-_WyoFfW0YtnSs3O4mbyQR0Om301BMo",[250,253,256,259,262,265,267,269,271,273,275,277,280,282,284,286,288,290,292,294,296,298,301,304,306,308,311,313,315,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,494,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,554,556,558,560,562,564,566,568,570,572,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2297,2299,2301,2303,2305,2307,2309,2311,2313,2315,2317,2319,2321,2323,2325,2327,2329,2331,2333,2335,2337,2339,2341,2343,2345,2347,2349,2351,2353,2355,2357,2359,2361,2363,2365,2367,2369,2371,2373,2375,2377,2379,23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Job-Relevant Python AI Libraries for 2026 Hires",{"provider":7,"model":8,"input_tokens":3840,"output_tokens":3841,"processing_time_ms":3842,"cost_usd":3843},3647,887,8684,0.0011504,{"type":14,"value":3845,"toc":3866},[3846,3850,3853,3857,3860],[21,3847,3849],{"id":3848},"shift-from-generalists-to-production-specialists","Shift from Generalists to Production Specialists",[17,3851,3852],{},"Python boasts 1.19 million LinkedIn job listings, but roles target engineers mastering specific libraries for real systems, not broad knowledge. Beginners fail interviews by learning demo-focused tools that don't scale to production codebases or job descriptions. Success requires picking libraries tied to target AI fields, as the landscape evolves rapidly without clear guidance.",[21,3854,3856],{"id":3855},"evaluate-libraries-by-field-and-impact","Evaluate Libraries by Field and Impact",[17,3858,3859],{},"Prioritize the 5 libraries repeatedly appearing in production and hiring: each serves distinct AI domains (details cut off in source). Use this framework to select: match tool to your career path, verify job relevance via listings, and build deep systems—not superficial demos—to signal hireability. This guide kickstarts targeted learning over scattered exploration.",[17,3861,3862],{},[3863,3864,3865],"em",{},"Content is introductory and truncated before listing libraries, limiting specifics; core lesson is tool-job alignment for employability.",{"title":46,"searchDepth":79,"depth":79,"links":3867},[3868,3869],{"id":3848,"depth":79,"text":3849},{"id":3855,"depth":79,"text":3856},[258],{},"\u002Fsummaries\u002Fmaster-job-relevant-python-ai-libraries-for-2026-h-summary","2026-04-08 21:21:18",{"title":3838,"description":46},{"loc":3872},"61e00e4895cc707e","Python in Plain English","https:\u002F\u002Funknown","summaries\u002Fmaster-job-relevant-python-ai-libraries-for-2026-h-summary",[242,243],"AI interviews fail on non-production tools; employers seek deep expertise in 5 specific Python libraries amid 1.19M job listings demanding real-system builders.",[],"0LgeuClXQ8J58L2XMZhdZL5qUlFwRqEnwm5plw1-sMg",{"id":3885,"title":3886,"ai":3887,"body":3892,"categories":4136,"created_at":201,"date_modified":201,"description":46,"extension":202,"faq":201,"featured":203,"kicker_label":201,"meta":4137,"navigation":231,"path":4149,"published_at":4150,"question":201,"scraped_at":4151,"seo":4152,"sitemap":4153,"source_id":4154,"source_name":4155,"source_type":238,"source_url":4156,"stem":4157,"tags":4158,"thumbnail_url":201,"tldr":4160,"tweet":201,"unknown_tags":4161,"__hash__":4162},"summaries\u002Fsummaries\u002Fc879b50ed964f64d-stealth-cloakbrowser-automation-in-colab-with-pers-summary.md","Stealth CloakBrowser Automation in Colab with Persistence",{"provider":7,"model":8,"input_tokens":3888,"output_tokens":3889,"processing_time_ms":3890,"cost_usd":3891},9090,2229,32481,0.00291,{"type":14,"value":3893,"toc":4130},[3894,3898,3955,3974,3978,4008,4023,4027,4053,4057,4106],[21,3895,3897],{"id":3896},"colab-setup-and-async-isolation-for-reliable-launches","Colab Setup and Async Isolation for Reliable Launches",[17,3899,3900,3901,3904,3905,3908,3909,3912,3913,3916,3917,3920,3921,3920,3924,3927,3928,3931,3932,3935,3936,3920,3939,3942,3943,3946,3947,3950,3951,3954],{},"Install CloakBrowser via ",[29,3902,3903],{},"pip install cloakbrowser playwright pandas beautifulsoup4",", then ",[29,3906,3907],{},"playwright install-deps chromium"," for runtime dependencies. Prepare stealth binary with ",[29,3910,3911],{},"ensure_binary()"," and verify via ",[29,3914,3915],{},"binary_info()",". Colab's existing asyncio loop blocks Playwright sync APIs like ",[29,3918,3919],{},"launch()",", ",[29,3922,3923],{},"launch_context()",[29,3925,3926],{},"launch_persistent_context()","—wrap them in ",[29,3929,3930],{},"ThreadPoolExecutor"," to run in a separate thread: ",[29,3933,3934],{},"executor.submit(fn).result()",". This enables headless launches with ",[29,3937,3938],{},"headless=True",[29,3940,3941],{},"humanize=True"," (anti-detection), and args like ",[29,3944,3945],{},"--no-sandbox --disable-dev-shm-usage",". Working dir ",[29,3948,3949],{},"\u002Fcontent\u002Fcloakbrowser_advanced_tutorial"," stores screenshots, ",[29,3952,3953],{},"storage_state.json",", and profile dirs.",[17,3956,3957,3958,3961,3962,3965,3966,3969,3970,3973],{},"Basic launch: ",[29,3959,3960],{},"browser = launch(...)","; ",[29,3963,3964],{},"page.goto('https:\u002F\u002Fexample.com', wait_until='domcontentloaded', timeout=60000)"," extracts title, body preview",[69,3967,3968],{},":300",", URL. Always ",[29,3971,3972],{},"safe_close()"," in finally blocks to avoid leaks.",[21,3975,3977],{"id":3976},"custom-contexts-for-realistic-browser-simulation","Custom Contexts for Realistic Browser Simulation",[17,3979,3980,3981,3984,3985,3988,3989,3992,3993,3920,3996,3999,4000,4003,4004,4007],{},"Use ",[29,3982,3983],{},"launch_context(headless=True, humanize=True, viewport={'width':1365,'height':768}, locale='en-US', timezone_id='America\u002FNew_York', color_scheme='light', extra_http_headers={'Accept-Language':'en-US,en;q=0.9', 'X-Tutorial-Run':'cloakbrowser-colab'})",". Navigate to data:URL test pages for safe interaction: fill form ",[29,3986,3987],{},"#name","=\"CloakBrowser Colab User\", ",[29,3990,3991],{},"#message","=\"We are testing...\", click ",[29,3994,3995],{},"#submit",[29,3997,3998],{},"wait_for_timeout(1000)",". Save ",[29,4001,4002],{},"context.storage_state(path='storage_state.json')","; screenshot ",[29,4005,4006],{},"full_page=True"," to PNG.",[17,4009,4010,4011,4014,4015,4018,4019,4022],{},"Restore in new context: ",[29,4012,4013],{},"launch_context(..., storage_state='storage_state.json')","; verify localStorage like ",[29,4016,4017],{},"tutorial_name"," persists via ",[29,4020,4021],{},"page.evaluate(\"() => localStorage.getItem('tutorial_name')\")",". Demonstrates session continuity without full profile overhead.",[21,4024,4026],{"id":4025},"persistent-profiles-across-restarts","Persistent Profiles Across Restarts",[17,4028,4029,4032,4033,4036,4037,4040,4041,4044,4045,4048,4049,4052],{},[29,4030,4031],{},"launch_persistent_context(str(PROFILE_DIR), ...)"," creates dir-based profiles surviving ",[29,4034,4035],{},"ctx.close()"," and relaunches. First run: ",[29,4038,4039],{},"page.evaluate(\"localStorage.setItem('persistent_profile_demo', 'saved_across_browser_restarts')\")","; second run confirms value and timestamp ",[29,4042,4043],{},"new Date().toISOString()"," match, proving ",[29,4046,4047],{},"persisted_successfully: true",". Use viewport=1280x720 for persistence demo. Clear dir with ",[29,4050,4051],{},"shutil.rmtree(PROFILE_DIR)"," before tests. Profiles handle localStorage automatically, ideal for long-running automations.",[21,4054,4056],{"id":4055},"stealth-signal-inspection-and-content-extraction","Stealth Signal Inspection and Content Extraction",[17,4058,4059,4060,4063,4064,3920,4067,3920,4070,3920,4073,3920,4076,3920,4079,3920,4082,3920,4085,3920,4088,3920,4091,3920,4094,4097,4098,4101,4102,4105],{},"Test page JavaScript collects 15+ signals: ",[29,4061,4062],{},"navigator.webdriver"," (false for stealth), ",[29,4065,4066],{},"userAgent",[29,4068,4069],{},"platform",[29,4071,4072],{},"languages",[29,4074,4075],{},"hardwareConcurrency",[29,4077,4078],{},"deviceMemory",[29,4080,4081],{},"pluginsLength",[29,4083,4084],{},"chromeObjectPresent:true",[29,4086,4087],{},"timezone",[29,4089,4090],{},"screen:{width,height,colorDepth=24,pixelDepth=24}",[29,4092,4093],{},"viewport:{innerWidth,innerHeight,devicePixelRatio}",[29,4095,4096],{},"webglVendor\u002FRenderer"," (masked), ",[29,4099,4100],{},"localStorageWorks:true",". Extract via ",[29,4103,4104],{},"page.evaluate('() => collectSignals()')",".",[17,4107,4108,4109,3920,4112,3920,4115,4118,4119,3920,4122,4125,4126,4129],{},"Capture rendered content: ",[29,4110,4111],{},"page.title()",[29,4113,4114],{},"locator('h1').inner_text(timeout=15000)",[29,4116,4117],{},"page.content()",". Parse static HTML with BeautifulSoup: ",[29,4120,4121],{},"soup.title.get_text()",[29,4123,4124],{},"soup.find('h1')",", links list ",[29,4127,4128],{},"[{text,href}]",". Compare rendered vs static reveals JS effects. Pandas table summarizes: signals (e.g., webdriver=false, pluginsLength=null), persistence true, outputs like screenshot_path. Builds production-ready pipelines evading detection while extracting parseable data.",{"title":46,"searchDepth":79,"depth":79,"links":4131},[4132,4133,4134,4135],{"id":3896,"depth":79,"text":3897},{"id":3976,"depth":79,"text":3977},{"id":4025,"depth":79,"text":4026},{"id":4055,"depth":79,"text":4056},[261],{"content_references":4138,"triage":4146},[4139,4142],{"type":207,"title":4140,"url":4141,"context":210},"CloakBrowser","https:\u002F\u002Fgithub.com\u002FCloakHQ\u002FCloakBrowser",{"type":4143,"title":4144,"url":4145,"context":210},"other","cloakbrowser_colab_browser_automation_tutorial_Marktechpost.ipynb","https:\u002F\u002Fgithub.com\u002FMarktechpost\u002FAI-Agents-Projects-Tutorials\u002Fblob\u002Fmain\u002FAI%20Agents%20Codes\u002Fcloakbrowser_colab_browser_automation_tutorial_Marktechpost.ipynb",{"relevance":109,"novelty":96,"quality":109,"actionability":109,"composite":4147,"reasoning":4148},3.8,"Category: AI Automation. The article provides a practical guide on setting up browser automation using CloakBrowser in Google Colab, which is relevant for developers looking to implement automation in their AI-powered products. It includes specific code snippets and configurations that can be directly applied, addressing the audience's need for actionable content.","\u002Fsummaries\u002Fc879b50ed964f64d-stealth-cloakbrowser-automation-in-colab-with-pers-summary","2026-05-08 00:14:49","2026-05-08 11:28:21",{"title":3886,"description":46},{"loc":4149},"c879b50ed964f64d","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F05\u002F07\u002Fbuild-a-cloakbrowser-automation-workflow-with-stealth-chromium-persistent-profiles-and-browser-signal-inspection\u002F","summaries\u002Fc879b50ed964f64d-stealth-cloakbrowser-automation-in-colab-with-pers-summary",[242,4159,243],"automation","Run Playwright-style stealth Chromium automation in Google Colab by isolating sync APIs in a worker thread; customize contexts with viewport=1365x768, persist localStorage via storage_state.json or profile dirs, and inspect undetectable signals like webdriver=false.",[],"Y9iC3gaig6qKNxPwyF1kKVZnI6KfFfGW8VsDdCZTcug",{"id":4164,"title":4165,"ai":4166,"body":4171,"categories":4226,"created_at":201,"date_modified":201,"description":46,"extension":202,"faq":201,"featured":203,"kicker_label":201,"meta":4227,"navigation":231,"path":4256,"published_at":4257,"question":201,"scraped_at":4258,"seo":4259,"sitemap":4260,"source_id":4261,"source_name":4262,"source_type":238,"source_url":4263,"stem":4264,"tags":4265,"thumbnail_url":201,"tldr":4267,"tweet":201,"unknown_tags":4268,"__hash__":4269},"summaries\u002Fsummaries\u002F352a655761b08b28-35b-models-on-rtx-4090-turboquant-kv-compression-u-summary.md","35B Models on RTX 4090: TurboQuant KV Compression Unlocks 32K Context",{"provider":7,"model":8,"input_tokens":4167,"output_tokens":4168,"processing_time_ms":4169,"cost_usd":4170},6914,2052,13171,0.00190195,{"type":14,"value":4172,"toc":4220},[4173,4177,4180,4184,4187,4191,4198,4204,4210,4213,4217],[21,4174,4176],{"id":4175},"q4_k_m-quantization-delivers-90-95-quality-at-60-original-size","Q4_K_M Quantization Delivers 90-95% Quality at 60% Original Size",[17,4178,4179],{},"Q4_K_M GGUF compresses model weights to ~0.6 GB per billion parameters (7B → 4GB, 32B → 19GB, 70B → 40GB) by storing weights in 4 bits with K-quant block grouping and M-medium mixed precision for sensitive layers. This preserves 90-95% of FP16 accuracy, making it the default for local runs on HuggingFace\u002FOllama. Dense 35B models need 21-22GB VRAM for weights alone on RTX 4090 (24GB total), leaving ~2GB for KV cache—insufficient beyond short contexts. MoE 35B (e.g., Qwen2.5-35B-A3B) activates only 3B params\u002Ftoken, fitting in ~20GB with 1.2GB KV at 64K context due to fewer active heads, reducing TurboQuant's necessity.",[21,4181,4183],{"id":4182},"turboquant-stacks-on-weights-for-long-context-memory-wins","TurboQuant Stacks on Weights for Long-Context Memory Wins",[17,4185,4186],{},"TurboQuant compresses KV cache to 2-4 bits at inference (PolarQuant + QJL, e.g., bits=3) without touching weights, enabling dense models like Mistral Small 3.1 24B or Qwen2.5-32B (64 layers, 8 GQA heads, head_dim=128) to handle 32K context on 24GB VRAM. Formula: 2 × layers × heads × head_dim × seq_len × bytes\u002Felement. Without it, 16K context KV hits ~4GB (total ~24GB borderline); with turbo3, drops to ~1.2GB, freeing space for 32K (~2.4GB). Fused Triton kernels compute attention on compressed KV, speeding up >8K contexts (major at 32K+). Asymmetric K@3bits\u002FV@2bits saves more with zero quality loss empirically.",[21,4188,4190],{"id":4189},"three-paths-to-turboquant-on-24gb-gpus-today","Three Paths to TurboQuant on 24GB GPUs Today",[17,4192,4193,4197],{},[4194,4195,4196],"strong",{},"PyPI turboquant-kv",": Wrap HF Transformers (load_in_4bit) with TurboQuantModel(bits=3).enable_decoder_fused_attention() for Python scripts; handles 512+ new tokens on long inputs.",[17,4199,4200,4203],{},[4194,4201,4202],{},"vLLM fork (0xSero\u002Fturboquant)",": install_turboquant_vllm(bits=3, head_dim=128) before LLM(model, gpu_memory_utilization=0.92); prebuilt codebooks for d=128\u002F256 at 2\u002F3\u002F4 bits; server-friendly.",[17,4205,4206,4209],{},[4194,4207,4208],{},"llama.cpp fork (turboquant_plus)",": Build with CUDA, run llama-server -m model-Q4_K_M.gguf --cache-type-k turbo3 --cache-type-v turbo2 -c 32768 -ngl 99. Turbo4 ≈ q8_0 quality, turbo3 best tradeoff, turbo2 extreme. Fits 32K on Qwen2.5-32B (19GB weights + \u003C4GB KV).",[17,4211,4212],{},"Quality holds ≥8B models; speedups context-dependent (\u003C2K: memory only). Experimental—await Google impl (Q2-Q3 2026), llama.cpp #20969, vLLM #38171 merges.",[21,4214,4216],{"id":4215},"optimal-stack-q4_k_m-gguf-turboquant_plus-turbo32","Optimal Stack: Q4_K_M GGUF + turboquant_plus turbo3\u002F2",[17,4218,4219],{},"Download Q4_K_M GGUF, use llama.cpp fork at 16-32K context. Achieves reliable 35B dense inference where defaults crash; 128K impossible (KV still GBs post-compression).",{"title":46,"searchDepth":79,"depth":79,"links":4221},[4222,4223,4224,4225],{"id":4175,"depth":79,"text":4176},{"id":4182,"depth":79,"text":4183},{"id":4189,"depth":79,"text":4190},{"id":4215,"depth":79,"text":4216},[258],{"content_references":4228,"triage":4253},[4229,4232,4235,4239,4241,4244,4247,4250],{"type":4143,"title":4230,"url":4231,"context":210},"GGUF","https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fgguf",{"type":4143,"title":4233,"url":4234,"context":210},"AWQ","https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Fquantization\u002Fawq",{"type":4143,"title":4236,"url":4237,"context":4238},"VRAM Requirements for AI Models","https:\u002F\u002Fwillitrunai.com\u002Fblog\u002Fvram-requirements-for-ai-models","cited",{"type":207,"title":4240,"context":214},"turboquant-kv",{"type":207,"title":4242,"url":4243,"context":214},"0xSero\u002Fturboquant","https:\u002F\u002Fgithub.com\u002F0xSero\u002Fturboquant.git",{"type":207,"title":4245,"url":4246,"context":214},"turboquant_plus","https:\u002F\u002Fgithub.com\u002FTheTom\u002Fturboquant_plus.git",{"type":4143,"title":4248,"url":4249,"context":210},"llama.cpp discussion #20969","https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\u002Fdiscussions\u002F20969",{"type":4143,"title":4251,"url":4252,"context":210},"vLLM issue #38171","https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm\u002Fissues\u002F38171",{"relevance":122,"novelty":109,"quality":109,"actionability":109,"composite":4254,"reasoning":4255},4.35,"Category: AI & LLMs. The article provides in-depth technical insights on running large language models efficiently, addressing the pain point of integrating AI features into products. It offers specific implementation paths for using TurboQuant, which is actionable for developers looking to optimize AI model performance.","\u002Fsummaries\u002F352a655761b08b28-35b-models-on-rtx-4090-turboquant-kv-compression-u-summary","2026-04-15 12:31:01","2026-04-15 15:39:14",{"title":4165,"description":46},{"loc":4256},"352a655761b08b28","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Frunning-a-35b-model-locally-with-turboquant-whats-actually-possible-right-now-1ac5327430b0?source=rss----98111c9905da---4","summaries\u002F352a655761b08b28-35b-models-on-rtx-4090-turboquant-kv-compression-u-summary",[4266,243,242],"llm","Stack Q4_K_M weight quantization with TurboQuant's 3-bit KV cache compression to run dense 35B models at 32K context on 24GB VRAM, fitting weights (20GB) + KV cache (under 4GB) with room to spare—use llama.cpp forks today.",[],"sGRm9blteDamXpWHWdqmPUk1Seq5Nct_nBmywP1bFT0",{"id":4271,"title":4272,"ai":4273,"body":4278,"categories":4306,"created_at":201,"date_modified":201,"description":46,"extension":202,"faq":201,"featured":203,"kicker_label":201,"meta":4307,"navigation":231,"path":4308,"published_at":4309,"question":201,"scraped_at":201,"seo":4310,"sitemap":4311,"source_id":4312,"source_name":4313,"source_type":238,"source_url":3878,"stem":4314,"tags":4315,"thumbnail_url":201,"tldr":4317,"tweet":201,"unknown_tags":4318,"__hash__":4319},"summaries\u002Fsummaries\u002Fgenerate-videos-by-slerp-walking-stable-diffusion--summary.md","Generate Videos by Slerp-Walking Stable Diffusion Latents",{"provider":7,"model":8,"input_tokens":4274,"output_tokens":4275,"processing_time_ms":4276,"cost_usd":4277},10775,1430,16123,0.00284735,{"type":14,"value":4279,"toc":4301},[4280,4284,4287,4291,4294,4298],[21,4281,4283],{"id":4282},"latent-space-walking-creates-hypnotic-videos","Latent Space Walking Creates Hypnotic Videos",[17,4285,4286],{},"Sample two random latents (shape 1x4x64x64 for 512x512 images), then use spherical linear interpolation (slerp) across 200 steps from init1 to init2. For each interpolated latent, run diffusion conditioned on a fixed text prompt (e.g., \"blueberry spaghetti\") with classifier-free guidance: concatenate unconditional and conditional embeddings, predict noise with UNet, apply guidance_scale=7.5, and denoise over num_inference_steps=50 using LMSDiscreteScheduler. Decode final latents via VAE to produce one frame per step. Repeat pairs up to max_frames=10000, saving JPEGs at 90% quality. Stitch with ffmpeg -r 10 -f image2 -s 512x512 -i frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p output.mp4. This random walk yields surreal, morphing visuals without prompt changes.",[21,4288,4290],{"id":4289},"custom-diffuse-handles-guidance-and-schedulers","Custom Diffuse Handles Guidance and Schedulers",[17,4292,4293],{},"Bypass pipeline for fine control: compute unconditional embeddings from empty prompt, cat with conditional (1x77x768). Set timesteps with offset=1 if supported, eta=0.0 for DDIM compatibility. For each timestep, double latents for CFG, predict noise_pred, scale as uncond + guidance_scale*(text - uncond), step scheduler to prev_sample. Scale latents by 1\u002F0.18215 before VAE decode, clamp\u002Fpost-process to uint8 numpy. Supports LMSDiscreteScheduler (multiplies latents by sigmas initially, divides model input by sqrt(sigma^2 +1)). Slerp avoids straight-line artifacts in high-D latent space using arccos(dot) for theta, blending with sin terms if dot \u003C 0.9995.",[21,4295,4297],{"id":4296},"setup-params-and-optimizations","Setup, Params, and Optimizations",[17,4299,4300],{},"Requires Hugging Face access token for CompVis\u002Fstable-diffusion-v1-3-diffusers (or v1-4), diffusers library, torch, einops, PIL, fire (pip install fire), ~10GB VRAM for 512x512. Run: python stablediffusionwalk.py --prompt \"blueberry spaghetti\" --name outdir --num_steps 200 --num_inference_steps 50 --guidance_scale 7.5 --seed 1337 --max_frames 10000. Wrap diffuse in torch.autocast('cuda') for half-precision speedup. Higher inference steps (100-200) improve quality; guidance 3-10 tunes adherence. Users extended to prompt interpolation, fp16 models (fix dtype mismatches by upgrading diffusers\u002Ftransformers\u002Fscipy), or pipeline simplifications (pipe(prompt, latents=init, ...)).",{"title":46,"searchDepth":79,"depth":79,"links":4302},[4303,4304,4305],{"id":4282,"depth":79,"text":4283},{"id":4289,"depth":79,"text":4290},{"id":4296,"depth":79,"text":4297},[310],{},"\u002Fsummaries\u002Fgenerate-videos-by-slerp-walking-stable-diffusion-summary","2026-04-08 21:21:20",{"title":4272,"description":46},{"loc":4308},"9fd1fce56d7f77a1","Andrej Karpathy Gists","summaries\u002Fgenerate-videos-by-slerp-walking-stable-diffusion--summary",[242,243,4316],"machine-learning","Interpolate random latents with slerp under a fixed prompt to create smooth, hypnotic videos from Stable Diffusion frames (50 inference steps, 7.5 guidance, 200 steps per pair).",[],"VddoAG9zJ0Akb8dH2o3dDgU_wO7ggV90n9VzfWlSvPE"]