[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-fc87bfb1eae70784-gemma-4-multimodal-open-models-excelling-in-reason-summary":3,"summaries-facets-categories":139,"summary-related-fc87bfb1eae70784-gemma-4-multimodal-open-models-excelling-in-reason-summary":3709},{"id":4,"title":5,"ai":6,"body":13,"categories":94,"created_at":95,"date_modified":95,"description":87,"extension":96,"faq":95,"featured":97,"kicker_label":95,"meta":98,"navigation":121,"path":122,"published_at":95,"question":95,"scraped_at":123,"seo":124,"sitemap":125,"source_id":126,"source_name":127,"source_type":128,"source_url":129,"stem":130,"tags":131,"thumbnail_url":95,"tldr":136,"tweet":95,"unknown_tags":137,"__hash__":138},"summaries\u002Fsummaries\u002Ffc87bfb1eae70784-gemma-4-multimodal-open-models-excelling-in-reason-summary.md","Gemma 4: Multimodal Open Models Excelling in Reasoning and Coding",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",6989,2148,12002,0.00244915,{"type":14,"value":15,"toc":86},"minimark",[16,21,46,50,53,57,79,83],[17,18,20],"h2",{"id":19},"architectural-designs-enable-scalable-efficient-deployment","Architectural Designs Enable Scalable, Efficient Deployment",[22,23,24,25,29,30,33,34,37,38,41,42,45],"p",{},"Gemma 4 models use hybrid attention—interleaving local sliding window attention with full global attention, ending in global layers with unified Keys\u002FValues and Proportional RoPE (p-RoPE)—to balance speed, low memory, and long-context handling. Dense models include E2B (2.3B effective\u002F5.1B total params, 35 layers, 512-token window, 128K context, text\u002Fimage\u002Faudio) and E4B (4.5B effective\u002F8B total, 42 layers, same window\u002Fcontext, text\u002Fimage\u002Faudio), both leveraging Per-Layer Embeddings (PLE) for on-device efficiency via token-specific lookups without extra layers. Larger dense 31B has 30.7B params, 60 layers, 1024-token window, 256K context, text\u002Fimage. MoE 26B A4B activates only 3.8B of 25.2B params across 30 layers (8\u002F128 experts +1 shared, 1024-token window, 256K context, text\u002Fimage), matching 4B dense speed. All support 262K vocab, variable image aspect\u002Fresolution via token budget (higher for detail, lower for speed), audio up to 30s (E2B\u002FE4B), video up to 60s at 1fps. Native system role and function-calling power agents; configurable thinking via \u003C|think|>, \u003C|channel>thought\\n\u003C|channel|>. Load via Transformers: ",[26,27,28],"code",{},"AutoProcessor","\u002F",[26,31,32],{},"AutoModelForCausalLM"," with ",[26,35,36],{},"torch.bfloat16",", ",[26,39,40],{},"device_map=\"auto\"","; apply chat template with ",[26,43,44],{},"enable_thinking=True\u002FFalse",", parse responses.",[17,47,49],{"id":48},"benchmark-leadership-in-reasoning-coding-multimodality","Benchmark Leadership in Reasoning, Coding, Multimodality",[22,51,52],{},"Instruction-tuned Gemma 4 outperforms Gemma 3 27B across sizes: 31B hits MMLU Pro 85.2%, AIME 2026 89.2%, LiveCodeBench v6 80.0%, Codeforces ELO 2150, GPQA Diamond 84.3%, Tau2 76.9%, BigBench Extra Hard 74.4%; 26B A4B close at 82.6%\u002F88.3%\u002F77.1%\u002F1718\u002F82.3%\u002F68.2%\u002F64.8%; E4B\u002FE2B at 69.4%\u002F60.0%, 42.5%\u002F37.5%, 52.0%\u002F44.0%, etc. Vision: MMMU Pro 76.9%\u002F73.8%\u002F52.6%\u002F44.2%, MATH-Vision 85.6%\u002F82.4%\u002F59.5%\u002F52.4%, OmniDocBench 0.131\u002F0.149\u002F0.181\u002F0.290 edit distance (lower better). Audio (E4B\u002FE2B): CoVoST 35.54%\u002F33.47%, FLEURS 0.08\u002F0.09. Long-context MRCR v2 128K 66.4%\u002F44.1%\u002F25.4%\u002F19.1% vs. Gemma 3 13.5%; HLE no-tools 19.5%\u002F8.7%, with-search 26.5%\u002F17.2%. Pre-trained on web\u002Fcode\u002Fimages\u002Faudio (cutoff Jan 2025), filtered via dedup, PII removal, toxicity scores, heuristics.",[17,54,56],{"id":55},"best-practices-maximize-reasoning-and-multimodal-outputs","Best Practices Maximize Reasoning and Multimodal Outputs",[22,58,59,60,37,63,37,66,69,70,74,75,78],{},"Sample with ",[26,61,62],{},"temperature=1.0",[26,64,65],{},"top_p=0.95",[26,67,68],{},"top_k=64",". Use standard system\u002Fassistant\u002Fuser roles; libraries handle templates. Multi-turn: append prior exchanges. Modality order: text first, then images\u002Faudio\u002Fvideo. Audio prompts: 'Transcribe ",[71,72,73],"span",{},"audio"," in {LANGUAGE}...' (digits as numerals, no newlines) or transcribe+translate ('{TARGET_LANGUAGE}: ",[71,76,77],{},"translation","'). Thinking mode parses internal reasoning. Deploy E2B\u002FE4B on phones\u002Flaptops (optimized via PLE), 26B A4B\u002F31B on GPUs\u002Fservers for agentic\u002Fcoding tasks.",[17,80,82],{"id":81},"safety-evaluations-show-major-gains-over-prior-models","Safety Evaluations Show Major Gains Over Prior Models",[22,84,85],{},"Rigorous testing (automated\u002Fhuman, no filters) aligns with Google AI principles, minimizing harms like violent\u002Fsexual content, hate, harassment. Gemma 4 cuts policy violations vs. Gemma 3\u002F3n across text\u002Fimage-to-text\u002Fall sizes, low unjustified refusals. Risks (hallucinations, biases, misuse) mitigated via diverse data, safety training; limitations include factual errors, non-English weaknesses, no real-time data post-Jan 2025.",{"title":87,"searchDepth":88,"depth":88,"links":89},"",2,[90,91,92,93],{"id":19,"depth":88,"text":20},{"id":48,"depth":88,"text":49},{"id":55,"depth":88,"text":56},{"id":81,"depth":88,"text":82},[],null,"md",false,{"content_references":99,"triage":116},[100,105,109,113],{"type":101,"title":102,"url":103,"context":104},"other","Gemma 4 Launch Blog","https:\u002F\u002Fblog.google\u002Finnovation-and-ai\u002Ftechnology\u002Fdevelopers-tools\u002Fgemma-4\u002F","mentioned",{"type":101,"title":106,"url":107,"context":108},"Google's AI Principles","https:\u002F\u002Fai.google\u002Fprinciples\u002F","cited",{"type":110,"title":111,"url":112,"context":104},"tool","Hugging Face Gemma 4 Collection","https:\u002F\u002Fhuggingface.co\u002Fcollections\u002Fgoogle\u002Fgemma-4",{"type":110,"title":114,"url":115,"context":104},"Google Gemma GitHub","https:\u002F\u002Fgithub.com\u002Fgoogle-gemma",{"relevance":117,"novelty":118,"quality":117,"actionability":88,"composite":119,"reasoning":120},4,3,3.4,"Category: AI & LLMs. The article discusses the architectural designs and performance benchmarks of the Gemma 4 models, which are relevant to AI product builders interested in integrating advanced LLMs into their products. However, while it provides technical details, it lacks specific actionable insights or frameworks that the audience can directly apply.",true,"\u002Fsummaries\u002Ffc87bfb1eae70784-gemma-4-multimodal-open-models-excelling-in-reason-summary","2026-04-16 03:04:58",{"title":5,"description":87},{"loc":122},"fc87bfb1eae70784","__oneoff__","article","https:\u002F\u002Fai.google.dev\u002Fgemma\u002Fdocs\u002Fcore\u002Fmodel_card_4","summaries\u002Ffc87bfb1eae70784-gemma-4-multimodal-open-models-excelling-in-reason-summary",[132,133,134,135],"llm","open-source","coding","ai-llms","Google DeepMind's Gemma 4 family delivers open-weights multimodal models (2.3B-31B params) with 128K-256K context, topping benchmarks in reasoning (MMLU Pro 85.2%), coding (LiveCodeBench 80%), vision (MMMU Pro 76.9%), and audio, optimized for on-device to server use.",[135],"t0FkuHsnl83-pyAQKuOw-3S79PUunJ9u4JlfZop1GMs",[140,143,146,149,152,155,157,159,161,163,165,167,170,172,174,176,178,180,182,184,186,188,191,194,196,198,201,203,205,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,671,673,675,677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725,727,729,731,733,735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,785,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917,919,921,923,925,927,929,931,933,935,937,939,941,943,945,947,949,951,953,955,957,959,961,963,965,967,969,971,973,975,977,979,981,983,985,987,989,991,993,995,997,999,1001,1003,1005,1007,1009,1011,1013,1015,1017,1019,1021,1023,1025,1027,1029,1031,1033,1035,1037,1039,1041,1043,1045,1047,1049,1051,1053,1055,1057,1059,1061,1063,1065,1067,1069,1071,1073,1075,1077,1079,1081,1083,1085,1087,1089,1091,1093,1095,1097,1099,1101,1103,1105,1107,1109,1111,1113,1115,1117,1119,1121,1123,1125,1127,1129,1131,1133,1135,1137,1139,1141,1143,1145,1147,1149,1151,1153,1155,1157,1159,1161,1163,1165,1167,1169,1171,1173,1175,1177,1179,1181,1183,1185,1187,1189,1191,1193,1195,1197,1199,1201,1203,1205,1207,1209,1211,1213,1215,1217,1219,1221,1223,1225,1227,1229,1231,1233,1235,1237,1239,1241,1243,1245,1247,1249,1251,1253,1255,1257,1259,1261,1263,1265,1267,1269,1271,1273,1275,1277,1279,1281,1283,1285,1287,1289,1291,1293,1295,1297,1299,1301,1303,1305,1307,1309,1311,1313,1315,1317,1319,1321,1323,1325,1327,1329,1331,1333,1335,1337,1339,1341,1343,1345,1347,1349,1351,1353,1355,1357,1359,1361,1363,1365,1367,1369,1371,1373,1375,1377,1379,1381,1383,1385,1387,1389,1391,1393,1395,1397,1399,1401,1403,1405,1407,1409,1411,1413,1415,1417,1419,1421,1423,1425,1427,1429,1431,1433,1435,1437,1439,1441,1443,1445,1447,1449,1451,1453,1455,1457,1459,1461,1463,1465,1467,1469,1471,1473,1475,1477,1479,1481,1483,1485,1487,1489,1491,1493,1495,1497,1499,1501,1503,1505,1507,1509,1511,1513,1515,1517,1519,1521,1523,1525,1527,1529,1531,1533,1535,1537,1539,1541,1543,1545,1547,1549,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585,1587,1589,1591,1593,1595,1597,1599,1601,1603,1605,1607,1609,1611,1613,1615,1617,1619,1621,1623,1625,1627,1629,1631,1633,1635,1637,1639,1641,1643,1645,1647,1649,1651,1653,1655,1657,1659,1661,1663,1665,1667,1669,1671,1673,1675,1677,1679,1681,1683,1685,1687,1689,1691,1693,1695,1697,1699,1701,1703,1705,1707,1709,1711,1713,1715,1717,1719,1721,1723,1725,1727,1729,1731,1733,1735,1737,1739,1741,1743,1745,1747,1749,1751,1753,1755,1757,1759,1761,1763,1765,1767,1769,1771,1773,1775,1777,1779,1781,1783,1785,1787,1789,1791,1793,1795,1797,1799,1801,1803,1805,1807,1809,1811,1813,1815,1817,1819,1821,1823,1825,1827,1829,1831,1833,1835,1837,1839,1841,1843,1845,1847,1849,1851,1853,1855,1857,1859,1861,1863,1865,1867,1869,1871,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893,1895,1897,1899,1901,1903,1905,1907,1909,1911,1913,1915,1917,1919,1921,1923,1925,1927,1929,1931,1933,1935,1937,1939,1941,1943,1945,1947,1949,1951,1953,1955,1957,1959,1961,1963,1965,1967,1969,1971,1973,1975,1977,1979,1981,1983,1985,1987,1989,1991,1993,1995,1997,1999,2001,2003,2005,2007,2009,2011,2013,2015,2017,2019,2021,2023,2025,2027,2029,2031,2033,2035,2037,2039,2041,2043,2045,2047,2049,2051,2053,2055,2057,2059,2061,2063,2065,2067,2069,2071,2073,2075,2077,2079,2081,2083,2085,2087,2089,2091,2093,2095,2097,2099,2101,2103,2105,2107,2109,2111,2113,2115,2117,2119,2121,2123,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187,2189,2191,2193,2195,2197,2199,2201,2203,2205,2207,2209,2211,2213,2215,2217,2219,2221,2223,2225,2227,2229,2231,2233,2235,2237,2239,2241,2243,2245,2247,2249,2251,2253,2255,2257,2259,2261,2263,2265,2267,2269,2271,2273,2275,2277,2279,2281,2283,2285,2287,2289,2291,2293,2295,2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Each attempt used 100M tokens ($12,500 for Mythos, $125k total for 10 runs), with no diminishing returns observed: models kept improving as budgets increased. This shifts security economics to raw compute power, akin to cryptocurrency's proof-of-work or a low-temperature lottery—success depends on outspending attackers on token-fueled exploit discovery. Harden systems by allocating more tokens to red-teaming than adversaries will for attacks; cleverness yields no edge.",[17,3728,3730],{"id":3729},"open-source-outpaces-custom-reimplementations","Open Source Outpaces Custom Reimplementations",[22,3732,3733],{},"Despite AI maximalists like Karpathy advocating LLM-based reimplementation of dependencies to avoid supply chain risks (e.g., LiteLLM, Axios incidents), open source remains superior. Linus's Law expands: enough eyeballs plus corporate token budgets on OSS libraries make bugs shallow and security robust. Custom \"yoinked\" code can't match collective investment; attackers prioritize high-value OSS targets but defenders' pooled resources still win on spend.",[17,3735,3737],{"id":3736},"add-autonomous-hardening-to-dev-cycles","Add Autonomous Hardening to Dev Cycles",[22,3739,3740],{},"Evolve coding into three phases separated by human vs. money limits: (1) Development for fast iteration with intuition\u002Ffeedback; (2) Review for docs\u002Frefactors\u002Fbest practices (e.g., Anthropic's $15-20 Claude tool); (3) Hardening via autonomous exploit hunting until budget exhausts. This makes security continuous and budget-optimized, unlike rare manual audits. Code stays cheap until secure—costs fix via exploit market value, demanding more tokens than foes regardless of inference optimizations.",{"title":87,"searchDepth":88,"depth":88,"links":3742},[3743,3744,3745],{"id":3722,"depth":88,"text":3723},{"id":3729,"depth":88,"text":3730},{"id":3736,"depth":88,"text":3737},[],{"content_references":3748,"triage":3775},[3749,3752,3755,3760,3764,3767,3771],{"type":110,"title":3750,"url":3751,"context":108},"Mythos","https:\u002F\u002Fred.anthropic.com\u002F2026\u002Fmythos-preview\u002F",{"type":110,"title":3753,"url":3754,"context":104},"Glasswing","https:\u002F\u002Fwww.anthropic.com\u002Fglasswing",{"type":3756,"title":3757,"author":3758,"url":3759,"context":108},"report","Our evaluation of Claude Mythos preview’s cyber capabilities","AI Security Institute","https:\u002F\u002Fwww.aisi.gov.uk\u002Fblog\u002Four-evaluation-of-claude-mythos-previews-cyber-capabilities",{"type":3761,"title":3762,"url":3763,"context":108},"paper","The Last Ones","https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.11214",{"type":110,"title":3765,"url":3766,"context":104},"LiteLLM","https:\u002F\u002Fdocs.litellm.ai\u002Fblog\u002Fsecurity-update-march-2026",{"type":3756,"title":3768,"author":3769,"url":3770,"context":104},"How we caught the Axios supply chain attack","Elastic Security Labs","https:\u002F\u002Fwww.elastic.co\u002Fsecurity-labs\u002Fhow-we-caught-the-axios-supply-chain-attack",{"type":110,"title":3772,"url":3773,"context":3774},"Code Review","https:\u002F\u002Fcode.claude.com\u002Fdocs\u002Fen\u002Fcode-review","recommended",{"relevance":118,"novelty":118,"quality":117,"actionability":118,"composite":3776,"reasoning":3777},3.25,"Category: AI & LLMs. The article discusses the application of AI in cybersecurity, specifically how LLMs can be used for exploit discovery, which aligns with the audience's interest in AI engineering. It provides some novel insights into the economics of security but lacks detailed actionable steps for implementation.","\u002Fsummaries\u002F142a3fb09c400ccd-cybersecurity-spend-more-tokens-than-attackers-summary","2026-04-14 14:42:00","2026-04-16 03:15:43",{"title":3712,"description":87},{"loc":3778},"142a3fb09c400ccd","https:\u002F\u002Fwww.dbreunig.com\u002F2026\u002F04\u002F14\u002Fcybersecurity-is-proof-of-work-now.html","summaries\u002F142a3fb09c400ccd-cybersecurity-spend-more-tokens-than-attackers-summary",[132,3787,133,134],"ai-tools","AI turns security into proof-of-work: defenders must burn more tokens finding exploits (e.g., 100M tokens\u002F$12.5k per Mythos run) than attackers do to exploit them.",[],"LBYylNkkIRYVuujdG4Xq4oLr0CxwsWFlTzDp28hZU9U",{"id":3792,"title":3793,"ai":3794,"body":3799,"categories":3841,"created_at":95,"date_modified":95,"description":3842,"extension":96,"faq":95,"featured":97,"kicker_label":95,"meta":3843,"navigation":121,"path":3844,"published_at":3845,"question":95,"scraped_at":3846,"seo":3847,"sitemap":3848,"source_id":3849,"source_name":3850,"source_type":3851,"source_url":3852,"stem":3853,"tags":3854,"thumbnail_url":95,"tldr":3856,"tweet":95,"unknown_tags":3857,"__hash__":3858},"summaries\u002Fsummaries\u002F25496226ff5ae55c-gemma-4-matches-top-models-with-2-5x-token-efficie-summary.md","Gemma 4 Matches Top Models with 2.5x Token Efficiency",{"provider":7,"model":8,"input_tokens":3795,"output_tokens":3796,"processing_time_ms":3797,"cost_usd":3798},7423,1463,10298,0.00174885,{"type":14,"value":3800,"toc":3835},[3801,3805,3808,3811,3815,3818,3822,3825,3828,3832],[17,3802,3804],{"id":3803},"gemma-4-architecture-prioritizes-intelligence-per-parameter","Gemma 4 Architecture Prioritizes Intelligence per Parameter",[22,3806,3807],{},"Google's Gemma 4 series includes four models under Apache 2.0: 2B for mobile\u002Fedge, 4B with multimodal for edge, 26B (activates ~3.8B params during inference for efficiency), and 31B dense flagship. All support 256K context, 140+ languages, multi-step reasoning, math\u002Fplanning, agentic tool use, JSON outputs, and coding. The 26B runs at 300 tokens\u002Fsec on Mac M2 Ultra (several years old), enabling real-time local use that outperforms larger models by focusing on efficiency over size—26B rivals 20x larger models in select tasks.",[22,3809,3810],{},"Cloud pricing for 31B: $0.14\u002FM input tokens, $0.40\u002FM output tokens. Access via Google AI Studio (free testing), API, OpenRouter, Kilo CLI (best for agent\u002Ftool use, $25 free credits), Ollama, Hugging Face, or LM Studio.",[17,3812,3814],{"id":3813},"efficiency-trumps-raw-intelligence-over-qwen-35-27b","Efficiency Trumps Raw Intelligence Over Qwen 3.5 27B",[22,3816,3817],{},"Gemma 4 31B scores 31 on intelligence index (vs. Qwen's 42), but uses 2.5x fewer output tokens for equivalent tasks, cutting costs and speeding generations—making the intelligence gap irrelevant for production. Benchmarks: #3 on LM Arena (open models), 85.2 MMLU Pro, excels GPQA\u002Fmath, 80% LiveCodeBench. Strong multimodal reasoning. Trade-off: Qwen edges benchmarks but burns more tokens; Gemma wins real workflows via speed\u002Fcost.",[17,3819,3821],{"id":3820},"production-ready-frontend-and-agent-outputs","Production-Ready Frontend and Agent Outputs",[22,3823,3824],{},"In Kilo CLI agent tests, 31B generated MacOS-style UI (loading screen, toolbar, apps like calculator\u002Fterminal\u002Fsettings; rated 7.5-8\u002F10 for size, clones real components despite non-functional edges). 26B produced comparable complex UIs with strict rules, multiple typographies, dynamic animations—run locally, iterable for refinement.",[22,3826,3827],{},"Demos: F1 donut simulator (physics\u002Fmotion\u002F3D in browser, creative but not Qwen-level); 360° product viewer (rotation\u002Fzoom\u002Fhotspots\u002Fstate management\u002Fshadows\u002Fcolor changes); SVGs (animated butterfly strong, PS5 controller\u002FPS5 painting decent structure\u002Fambience); Airbnb clone (icons\u002Fformatting near-perfect); cardboard game (physics\u002Finteractions\u002Fturns\u002Fscoring\u002Fstate). Mobile: On-device agent chains tools for multi-step tasks (data pull\u002Fprocess\u002Fvisualize), no cloud.",[17,3829,3831],{"id":3830},"multimodal-and-local-agent-edge","Multimodal and Local Agent Edge",[22,3833,3834],{},"Multimodal 4B\u002Fothers parse images for patterns\u002Fcontext (e.g., compare multiples, synthesize insights beyond description). Mobile Gemini app runs Gemma 4 agent skills locally: tool selection\u002Fordering\u002Foutput combination for queries. Enables on-device function calling, visual reasoning—shifts AI to faster\u002Fcheaper\u002Flocal systems over cloud-heavy giants.",{"title":87,"searchDepth":88,"depth":88,"links":3836},[3837,3838,3839,3840],{"id":3803,"depth":88,"text":3804},{"id":3813,"depth":88,"text":3814},{"id":3820,"depth":88,"text":3821},{"id":3830,"depth":88,"text":3831},[],"Gemma 4 is honestly one of the craziest open model drops we’ve seen. In this video, I put Google’s latest models through real tests not just benchmarks, but actual workflows. We’re talking frontend generation, agentic tool use, multimodal reasoning, and even running these models locally at speeds that shouldn’t be possible.\n\n🔗 My Links:\nSponsor a Video or Do a Demo of Your Product, Contact me: intheworldzofai@gmail.com\n🔥 Become a Patron (Private Discord): https:\u002F\u002Fpatreon.com\u002FWorldofAi\n🧠 Follow me on Twitter: https:\u002F\u002Ftwitter.com\u002Fintheworldofai \n🚨 Subscribe To The SECOND Channel: https:\u002F\u002Fwww.youtube.com\u002F@UCYwLV1gDwzGbg7jXQ52bVnQ \n👩🏻‍🏫 Learn to code with Scrimba – from fullstack to AI https:\u002F\u002Fscrimba.com\u002F?via=worldofai (20% OFF)\n🚨 Subscribe To The FREE AI Newsletter For Regular AI Updates: https:\u002F\u002Fintheworldofai.com\u002F\n👾 Join the World of AI Discord! : https:\u002F\u002Fdiscord.gg\u002FNPf8FCn4cD\n\nSomething coming soon :) https:\u002F\u002Fwww.skool.com\u002Fworldofai-automation\n\n[Must Watch]:\nClaude Code Computer Use Can Control Your ENTIRE Computer! Automate Your Life!: https:\u002F\u002Fyoutu.be\u002FKiywNP4b0aw?si=HuJnvik0AgLjIkCb\nTurn Antigravity Into AN AI Autonomous Engineering Team! Automate Your Code with Subagents!: https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yuaBPLNdNSU\nGemini 3.5? NEW Gemini Stealth Model Is POWERFUL & Fast! (Fully Tested): https:\u002F\u002Fyoutu.be\u002F1abLcL33eKA?si=H50xRhJxVYM7HFPK\n\n📌 LINKS & RESOURCES\nBlog Post: https:\u002F\u002Fblog.google\u002Finnovation-and-ai\u002Ftechnology\u002Fdevelopers-tools\u002Fgemma-4\u002F\nAPI: https:\u002F\u002Faistudio.google.com\u002Fu\u002F1\u002Fprompts\u002Fnew_chat\nKilo: https:\u002F\u002Fkilo.ai\u002Fcli\nOllama: https:\u002F\u002Follama.com\u002Flibrary\u002Fgemma4\nHuggingFace: https:\u002F\u002Fhuggingface.co\u002Fcollections\u002Fgoogle\u002Fgemma-4\nOpenRouter: https:\u002F\u002Fopenrouter.ai\u002Fgoogle\u002Fgemma-4-31b-it\nhttps:\u002F\u002Fx.com\u002Fstevibe\u002Fstatus\u002F2040039108748177706\nhttps:\u002F\u002Fx.com\u002Fggerganov\u002Fstatus\u002F2039752638384709661\n\nThe biggest surprise? It’s not just about being powerful it’s about being efficient. Gemma 4 is hitting near frontier-level performance while using way fewer tokens and running on real hardware like a Mac Studio.\n\nI also break down:\n• 31B vs 26B performance\n• Real coding + UI generation tests\n• Agent workflows running locally\n• Multimodal capabilities in action\n• Whether it actually beats Qwen in real usage\n\nIf you’re into open-source AI, local LLMs, or building with agents, this is a huge shift you need to understand.\n\n[Time Stamp]:\n0:00 - Introduction\n1:16 - Running 3005\u002Fs on Mac M2\n1:53 - Benchmarks\n3:14 - How To Use\n4:12 - MacOS Demo\n5:52 - Frontend Demo 31B vs 26B\n7:19 - F1 Donut Sim Demo\n8:06 - Product Page Demo\n8:49 - SVG Demo\n9:32 - AirBNB Demo\n9:50 - Game Dev Demo\n10:21 - Mobile Demo\n11:31 - Multimodal Demo\n\n#AI #Gemma4 #OpenModels #LocalAI #LLM #GoogleAI #AIAgents #MachineLearning #Tech\n\ntags:\ngemma 4, google gemma 4, gemma 4 test, gemma 4 review, open ai models, open source llm, local ai models, gemma 4 vs qwen, ai agent workflows, multimodal ai demo, frontend generation ai, ai coding test, llm efficiency, best open model 2026, google ai release",{},"\u002Fsummaries\u002F25496226ff5ae55c-gemma-4-matches-top-models-with-2-5x-token-efficie-summary","2026-04-04 06:05:20","2026-04-05 16:14:49",{"title":3793,"description":3842},{"loc":3844},"25496226ff5ae55c","WorldofAI","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=KW5SFt3rgKo","summaries\u002F25496226ff5ae55c-gemma-4-matches-top-models-with-2-5x-token-efficie-summary",[132,133,135,3855],"ai-agents","Google's Gemma 4 31B open model scores 85.2 on MMLU Pro and 80% on LiveCodeBench, runs at 300 tokens\u002Fsec on Mac M2 Ultra, and uses 2.5x fewer output tokens than Qwen 3.5 27B for similar tasks.",[135,3855],"tmdyRvfLR8dkiR__Cc4Yf2tTCDgdOaQzpD5-mZNya5c",{"id":3860,"title":3861,"ai":3862,"body":3867,"categories":3907,"created_at":95,"date_modified":95,"description":3908,"extension":96,"faq":95,"featured":97,"kicker_label":95,"meta":3909,"navigation":121,"path":3910,"published_at":3911,"question":95,"scraped_at":3912,"seo":3913,"sitemap":3914,"source_id":3915,"source_name":3916,"source_type":3851,"source_url":3917,"stem":3918,"tags":3919,"thumbnail_url":95,"tldr":3921,"tweet":95,"unknown_tags":3922,"__hash__":3923},"summaries\u002Fsummaries\u002F86da1e7358e9a36c-gemma-4-apache-2-0-multimodal-models-for-any-use-summary.md","Gemma 4: Apache 2.0 Multimodal Models for Any Use",{"provider":7,"model":8,"input_tokens":3863,"output_tokens":3864,"processing_time_ms":3865,"cost_usd":3866},7051,1268,12745,0.00157695,{"type":14,"value":3868,"toc":3901},[3869,3873,3876,3880,3883,3887,3890,3894],[17,3870,3872],{"id":3871},"apache-20-license-enables-unrestricted-commercial-deployment","Apache 2.0 License Enables Unrestricted Commercial Deployment",[22,3874,3875],{},"Gemma 4's standout feature is its pure Apache 2.0 license, allowing full modification, fine-tuning, and commercial deployment without custom restrictions like non-compete clauses. This addresses past frustrations with Gemma 3's limited license, positioning it competitively against Llama or Qwen. Built from Gemini 3 research, these models trickle flagship innovations into open weights, enabling builders to create production AI features like local coding assistants or on-device agents without legal hurdles.",[17,3877,3879],{"id":3878},"native-multimodality-and-reasoning-boost-agentic-workflows","Native Multimodality and Reasoning Boost Agentic Workflows",[22,3881,3882],{},"All four models integrate vision, audio, long chain-of-thought reasoning, and function calling at the architecture level—not bolted-on prompts. Reasoning spans text, images, and audio (on edge models), improving benchmarks like MMU Pro and Sweetbench Pro. Function calling supports multi-turn agentic flows with multiple tools, outperforming instruction-following hacks. Edge models (E2B, E4B) handle ASR, speech-to-text translation (e.g., English to Japanese), and interleaved multi-image inputs for video or OCR. Workstation models excel in code generation, completion, correction across 140 pre-trained and 35 fine-tuned languages.",[17,3884,3886],{"id":3885},"optimized-architectures-for-edge-efficiency-and-workstation-power","Optimized Architectures for Edge Efficiency and Workstation Power",[22,3888,3889],{},"Workstation tier: 31B dense model (fewer layers, value normalization, optimized attention for 256K context) and 26B MoE (128 tiny experts, 3.8B-4B active + shared expert, mimicking 27B intelligence at 4B compute cost). Both support 256K context, native aspect-ratio vision encoders for document understanding. Edge tier: E2B\u002FE4B with 128K context, compressed audio encoder (305M params, 87MB vs. prior 681M\u002F390MB, 40ms frames for responsive transcription) and 150M vision encoder (vs. 300-350M). QAT checkpoints preserve quality at low precision; run edge on T4 GPUs or phones, workstations on H100\u002FRTX 6000 or serverless Cloud Run with G4 GPUs (96GB VRAM).",[17,3891,3893],{"id":3892},"hands-on-usage-yields-immediate-results","Hands-On Usage Yields Immediate Results",[22,3895,3896,3897,3900],{},"Enable thinking via chat template (",[26,3898,3899],{},"enable_thinking=true",") for better outputs on tasks like deep learning use cases in finance. Process images\u002Fvideos with autoprocessor for detailed scene breakdowns (e.g., \"girl on beach with dog\"). Audio demos transcribe dual voices accurately or translate speech end-to-end. Base and instruction-tuned versions on Hugging Face suit fine-tuning; expect strong results from solid base models, outperforming Gemma 3's 32K context, outdated encoders, and text\u002Fvision-only limits.",{"title":87,"searchDepth":88,"depth":88,"links":3902},[3903,3904,3905,3906],{"id":3871,"depth":88,"text":3872},{"id":3878,"depth":88,"text":3879},{"id":3885,"depth":88,"text":3886},{"id":3892,"depth":88,"text":3893},[],"In this video, we look at the launch of the Gemma 4 family of models. These are 4 models 2 small and 2 larger models which combine not only multilingual but multimodality features.\n\nBlog: https:\u002F\u002Fblog.google\u002Finnovation-and-ai\u002Ftechnology\u002Fdevelopers-tools\u002Fgemma-4\u002F\nColab: https:\u002F\u002Fdripl.ink\u002FEYT7h\nHF Collection: https:\u002F\u002Fhuggingface.co\u002Fcollections\u002Fgoogle\u002Fgemma-4\n\nTwitter: https:\u002F\u002Fx.com\u002FSam_Witteveen \n\n🕵️ Interested in building LLM Agents? Fill out the form below\nBuilding LLM Agents Form: https:\u002F\u002Fdrp.li\u002FdIMes\n\n👨‍💻Github:\nhttps:\u002F\u002Fgithub.com\u002Fsamwit\u002Fllm-tutorials\n\n⏱️Time Stamps:\n00:00 Intro\n00:15 Gemma 4 License\n00:55 Quick Orientation\n01:01 Gemma 4: 2 Model Tiers\n03:05 Gemma 4: Thinking, Audio, Image & Video, Function Calling\n03:27 Reasoning\n04:06 Function Calling\n04:56 Audio Support\n05:38 Image and Video\n06:23 Model Comparison\n06:47 Gemma 4 Model Sizes\n07:59 Workstation models\n09:27 Edge models\n11:30 Demo\n16:40 Gemma 4 Availability",{},"\u002Fsummaries\u002F86da1e7358e9a36c-gemma-4-apache-2-0-multimodal-models-for-any-use-summary","2026-04-02 16:10:00","2026-04-03 21:19:03",{"title":3861,"description":3908},{"loc":3910},"86da1e7358e9a36c","Sam Witteveen","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5aqF1HVpjdc","summaries\u002F86da1e7358e9a36c-gemma-4-apache-2-0-multimodal-models-for-any-use-summary",[132,133,3920,135],"agents","Google's Gemma 4 releases four models under true Apache 2.0 license with native vision, audio, reasoning, and function calling—run commercially on edge devices or workstations without restrictions.",[135],"zAKDo4l96eOkOjOyHzJJh-IWvoeXYXcQTCqkKbXguuQ",{"id":3925,"title":3926,"ai":3927,"body":3932,"categories":3972,"created_at":95,"date_modified":95,"description":87,"extension":96,"faq":95,"featured":97,"kicker_label":95,"meta":3973,"navigation":121,"path":3991,"published_at":95,"question":95,"scraped_at":3992,"seo":3993,"sitemap":3994,"source_id":3995,"source_name":127,"source_type":128,"source_url":3996,"stem":3997,"tags":3998,"thumbnail_url":95,"tldr":3999,"tweet":95,"unknown_tags":4000,"__hash__":4001},"summaries\u002Fsummaries\u002F661d1f4b66cfe71b-glm-5-leads-open-source-in-coding-reasoning-agents-summary.md","GLM-5 Leads Open-Source in Coding, Reasoning, Agents",{"provider":7,"model":8,"input_tokens":3928,"output_tokens":3929,"processing_time_ms":3930,"cost_usd":3931},7657,1725,8371,0.00188705,{"type":14,"value":3933,"toc":3967},[3934,3938,3941,3944,3947,3951,3954,3957,3961,3964],[17,3935,3937],{"id":3936},"scale-pre-training-and-rl-for-superior-competence","Scale Pre-Training and RL for Superior Competence",[22,3939,3940],{},"GLM-5 advances from GLM-4.5's 355B params (32B active) and 23T tokens to 744B params (40B active) with 28.5T pre-training tokens, using DeepSeek Sparse Attention (DSA) to maintain long-context capacity at lower deployment costs. Post-training leverages slime, an asynchronous RL infrastructure (github.com\u002FTHUDM\u002Fslime), to boost throughput for fine-grained iterations, bridging pre-trained competence to excellence without RL's typical inefficiency.",[22,3942,3943],{},"This yields best-in-class open-source results: Humanity's Last Exam (30.5, 50.4 w\u002Ftools), AIME 2026 I (92.7%), HMMT Nov 2025 (96.9%), GPQA-Diamond (86.0%). Coding excels on SWE-bench Verified (77.8%), Multilingual (73.3%), Terminal-Bench 2.0 (56.2\u002F60.7 verified), CyberGym (43.2%). Agent benchmarks include BrowseComp (62.0\u002F75.9 w\u002Fcontext mgmt), τ²-Bench (89.7%), MCP-Atlas (67.8%), Tool-Decathlon (39.2), nearing Claude Opus 4.5 and Gemini 3.0 Pro.",[22,3945,3946],{},"On CC-Bench-V2 internal suite, GLM-5 outperforms GLM-4.7 in frontend, backend, long-horizon tasks, closing gap to Claude Opus 4.5. Vending Bench 2 simulates year-long vending machine ops; GLM-5 ends with $4,432 balance (#1 open-source, near Claude's $4,967).",[17,3948,3950],{"id":3949},"agentic-engineering-for-real-deliverables","Agentic Engineering for Real Deliverables",[22,3952,3953],{},"GLM-5 shifts from 'vibe coding' to agentic systems engineering, handling complex, multi-turn tasks like generating production-ready .docx\u002F.pdf\u002F.xlsx files (PRDs, spreadsheets, reports) from prompts. Example: Student council football sponsorship proposal includes structured sections (intro, event details, fund use, tiers, benefits), visual elements (image placeholders, tables), color palette (navy\u002Fgold), ensuring print-ready output without edits.",[22,3955,3956],{},"Z.ai's Agent mode supports PDF\u002FWord\u002FExcel creation, multi-turn collaboration for deliverables. Outperforms priors on long-horizon planning\u002Fresource mgmt, as in Vending Bench 2's operational simulation.",[17,3958,3960],{"id":3959},"deploy-anywhere-integrate-seamlessly","Deploy Anywhere, Integrate Seamlessly",[22,3962,3963],{},"Open-source (MIT) on HuggingFace (zai-org\u002FGLM-5), ModelScope, GitHub (zai-org\u002FGLM-5). API via api.z.ai, BigModel.cn, Z.ai chat\u002Fagent modes. Local inference: vLLM\u002FSGLang; non-NVIDIA chips (Ascend, Moore Threads) via quantization.",[22,3965,3966],{},"Coding agents: Claude Code\u002FOpenClaw (update to 'GLM-5', higher quota use), OpenCode\u002FKilo\u002FRoo\u002FCline\u002FDroid. Z Code IDE for multi-agent collab. Gradual rollout to GLM Coding Plan subs; free trial on Z.ai.",{"title":87,"searchDepth":88,"depth":88,"links":3968},[3969,3970,3971],{"id":3936,"depth":88,"text":3937},{"id":3949,"depth":88,"text":3950},{"id":3959,"depth":88,"text":3960},[],{"content_references":3974,"triage":3988},[3975,3978,3981,3985],{"type":3761,"title":3976,"url":3977,"context":104},"GLM-5 Tech Report","https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.15763",{"type":110,"title":3979,"url":3980,"context":104},"slime","https:\u002F\u002Fgithub.com\u002FTHUDM\u002Fslime",{"type":3982,"title":3983,"url":3984,"context":104},"dataset","Terminal-Bench 2.0 Verified","https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fzai-org\u002Fterminal-bench-2-verified",{"type":101,"title":3986,"url":3987,"context":104},"Vending Bench 2","https:\u002F\u002Fandonlabs.com\u002Fevals\u002Fvending-bench-2",{"relevance":117,"novelty":118,"quality":117,"actionability":118,"composite":3989,"reasoning":3990},3.6,"Category: AI & LLMs. The article discusses GLM-5's advancements in coding and reasoning capabilities, which directly relates to AI engineering and the development of AI-powered products. It provides insights into the model's performance benchmarks and practical applications, such as generating production-ready documents, which can help developers understand how to leverage these capabilities in their projects.","\u002Fsummaries\u002F661d1f4b66cfe71b-glm-5-leads-open-source-in-coding-reasoning-agents-summary","2026-04-16 03:07:03",{"title":3926,"description":87},{"loc":3991},"661d1f4b66cfe71b","https:\u002F\u002Fz.ai\u002Fblog\u002Fglm-5","summaries\u002F661d1f4b66cfe71b-glm-5-leads-open-source-in-coding-reasoning-agents-summary",[132,3920,134,133],"GLM-5 scales to 744B params (40B active) and 28.5T tokens, tops open-source benchmarks like SWE-bench (77.8%) and Vending Bench 2 ($4,432 balance), enabling complex engineering and long-horizon agents while cutting deployment costs via DSA.",[],"EVOJLevCvMfkN4EQZAjuWO1tjnTop9-wKWMYLgh9bSg"]