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@dramago.id: Part 38 - Malam yang penuh hasrat

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Tuesday 03 March 2026 11:26:11 GMT
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حبي يزيد 💛 #احمد_الصياد #Sing_Oldies #طربيات_الزمن_الجميل_🎼🎶🎻♥️🌹 #هواجيس #تصميم_فيديوهات🎶🎤🎬
حبي يزيد 💛 #احمد_الصياد #Sing_Oldies #طربيات_الزمن_الجميل_🎼🎶🎻♥️🌹 #هواجيس #تصميم_فيديوهات🎶🎤🎬
Google has officially launched Gemini 3.8 Flash alongside a new cybersecurity-focused model called Gemini 3.8 Flash Cyber, and the direction is clear: Flash models are no longer just about speed and low cost. They’re being pushed into serious reasoning, coding, autonomous agents and long-running professional workflows. 🧠💻🤖 Google is calling Gemini 3.8 Flash its best reasoning and coding Flash model yet. The model is designed for long-horizon software engineering, autonomous agents and complex multi-step tasks, with improvements over 3.7 Flash in software engineering, agentic work and specialised reasoning. 🔥 And here’s one of the most interesting parts… 💰 Gemini 3.8 Flash launches at the same introductory API pricing as 3.7 Flash: $0.75 per 1M input tokens and $3.75 per 1M output tokens. That puts some serious pressure on much more expensive frontier models, especially for developers running large volumes of agentic or coding workloads. ⚡📉 But 3.8 Flash isn’t simply trying to answer prompts faster. Google says it deliberately “works harder” on difficult problems by taking additional reasoning steps and calling tools repeatedly when needed. Higher effort can therefore improve capability, but may also consume more tokens. For efficiency-first tasks, Google says 3.7 Flash remains supported. 🧠⚙️ The coding side is especially interesting. Google says 3.8 Flash delivers major gains in long-horizon software engineering, where an AI agent needs to understand a task, plan, modify multiple files, use tools, test the result, refine its work and keep going until the job is finished. On DeepSWE v1.1, Google reports that 3.8 Flash performs competitively with significantly more expensive frontier models. 💻🚀 Google also demonstrated what the model can actually build. Examples include a playable DOS-style version of Google Maps from one prompt, an interactive 3D wizard game, real-time topographic visualisations using USGS data, and a “Hardware Anatomy” application capable of generating interactive 3D device teardowns. 🤯🗺️🎮🔧 Then there’s Gemini 3.8 Flash Cyber. 🛡️ This specialised version is focused on defensive cybersecurity, particularly autonomous vulnerability discovery and automated patching. Google reports frontier-level results on CyberGym, more than 70% success on an internal vulnerability-discovery benchmark spanning 20 programming languages, and 47.2% pass@1 on CWE-Bench for patching. These are Google-reported evaluations, so they’re best treated as vendor benchmark results rather than universal rankings. 🔐🐛 Google says its Chrome Security team also saw 2.6x more correct vulnerability patches from Gemini 3.8 Flash Cyber than the larger commercial models it compared against. Unlike normal Gemini 3.8 Flash, however, Flash Cyber isn’t simply open to everyone: access is currently aimed at trusted defenders through Google’s Fairwind Program. 🛡️👨‍💻 The bigger story here is what’s happening across AI generally. We’re moving beyond “Which chatbot gives the nicest answer?” into a world of AI systems that reason, write software, call tools, inspect results, correct themselves and execute complete workflows autonomously. 🤖⚙️ And Gemini 3.8 Flash is Google making a very aggressive play for that market while keeping Flash-level economics. 👀 So the question is… 🔥 Would you choose Gemini 3.8 Flash over GPT-5.6 Sol, Claude Fable 5.1 or Grok 4.5? 👇 Drop your winner in the comments. 💾 SAVE this carousel so you have the Gemini 3.8 breakdown for later. 📤 SHARE it with someone following the AI model race. ➕ FOLLOW TechSerks for AI launches, model comparisons, Claude Code, local AI, coding tools and practical AI guides. #TechSerks #Gemini38 #GeminiAI #GoogleAI #ArtificialIntelligence
Google has officially launched Gemini 3.8 Flash alongside a new cybersecurity-focused model called Gemini 3.8 Flash Cyber, and the direction is clear: Flash models are no longer just about speed and low cost. They’re being pushed into serious reasoning, coding, autonomous agents and long-running professional workflows. 🧠💻🤖 Google is calling Gemini 3.8 Flash its best reasoning and coding Flash model yet. The model is designed for long-horizon software engineering, autonomous agents and complex multi-step tasks, with improvements over 3.7 Flash in software engineering, agentic work and specialised reasoning. 🔥 And here’s one of the most interesting parts… 💰 Gemini 3.8 Flash launches at the same introductory API pricing as 3.7 Flash: $0.75 per 1M input tokens and $3.75 per 1M output tokens. That puts some serious pressure on much more expensive frontier models, especially for developers running large volumes of agentic or coding workloads. ⚡📉 But 3.8 Flash isn’t simply trying to answer prompts faster. Google says it deliberately “works harder” on difficult problems by taking additional reasoning steps and calling tools repeatedly when needed. Higher effort can therefore improve capability, but may also consume more tokens. For efficiency-first tasks, Google says 3.7 Flash remains supported. 🧠⚙️ The coding side is especially interesting. Google says 3.8 Flash delivers major gains in long-horizon software engineering, where an AI agent needs to understand a task, plan, modify multiple files, use tools, test the result, refine its work and keep going until the job is finished. On DeepSWE v1.1, Google reports that 3.8 Flash performs competitively with significantly more expensive frontier models. 💻🚀 Google also demonstrated what the model can actually build. Examples include a playable DOS-style version of Google Maps from one prompt, an interactive 3D wizard game, real-time topographic visualisations using USGS data, and a “Hardware Anatomy” application capable of generating interactive 3D device teardowns. 🤯🗺️🎮🔧 Then there’s Gemini 3.8 Flash Cyber. 🛡️ This specialised version is focused on defensive cybersecurity, particularly autonomous vulnerability discovery and automated patching. Google reports frontier-level results on CyberGym, more than 70% success on an internal vulnerability-discovery benchmark spanning 20 programming languages, and 47.2% pass@1 on CWE-Bench for patching. These are Google-reported evaluations, so they’re best treated as vendor benchmark results rather than universal rankings. 🔐🐛 Google says its Chrome Security team also saw 2.6x more correct vulnerability patches from Gemini 3.8 Flash Cyber than the larger commercial models it compared against. Unlike normal Gemini 3.8 Flash, however, Flash Cyber isn’t simply open to everyone: access is currently aimed at trusted defenders through Google’s Fairwind Program. 🛡️👨‍💻 The bigger story here is what’s happening across AI generally. We’re moving beyond “Which chatbot gives the nicest answer?” into a world of AI systems that reason, write software, call tools, inspect results, correct themselves and execute complete workflows autonomously. 🤖⚙️ And Gemini 3.8 Flash is Google making a very aggressive play for that market while keeping Flash-level economics. 👀 So the question is… 🔥 Would you choose Gemini 3.8 Flash over GPT-5.6 Sol, Claude Fable 5.1 or Grok 4.5? 👇 Drop your winner in the comments. 💾 SAVE this carousel so you have the Gemini 3.8 breakdown for later. 📤 SHARE it with someone following the AI model race. ➕ FOLLOW TechSerks for AI launches, model comparisons, Claude Code, local AI, coding tools and practical AI guides. #TechSerks #Gemini38 #GeminiAI #GoogleAI #ArtificialIntelligence
#j #fyp #foryoupage
#j #fyp #foryoupage
Only ascending from here @WKR CO #aesthetic #gymaesthetics #gymedit
Only ascending from here @WKR CO #aesthetic #gymaesthetics #gymedit
Để đây làm kỉ niệm😛 #summervibes #viral #fyp #xuhuong
Để đây làm kỉ niệm😛 #summervibes #viral #fyp #xuhuong
#fyp #trending #foryoupage
#fyp #trending #foryoupage

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