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4/10（金）70分ジョグ 🗒️15.01km 🔧Free 👟ヴェロシティニトロ4 明日30...
4/10（金）70分ジョグ 🗒️15.01km 🔧Free 👟ヴェロシティニトロ4 明日30kmほど走るので今日は軽めに #Gemini3 #まるお製作所RC https://t.co/NcqqIDPCpp
昨日も #ジェミラン に参加 1000レペティション×3本 いけると思って臨んだが盛大にたれる...
昨日も #ジェミラン に参加 1000レペティション×3本 いけると思って臨んだが盛大にたれる おかわりのペーランでいつも脹脛攣るし 5000の17'30はとてつもなく高い壁に思えるな 今はひたすら筋力強化か #Gemini3 #まるお製作所RC https://t.co/lqcHN0AucF
𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗚𝗲𝗺𝗺𝗮 𝟰 𝗷𝘂𝘀𝘁 𝗹𝗮𝗻𝗱𝗲�...
𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗚𝗲𝗺𝗺𝗮 𝟰 𝗷𝘂𝘀𝘁 𝗹𝗮𝗻𝗱𝗲𝗱 𝗮𝗻𝗱 𝗶𝘁 𝗿𝘂𝗻𝘀 𝗳𝗿𝗲𝗲 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗹𝗮𝗽𝘁𝗼𝗽 𝘄𝗶𝘁𝗵 𝘇𝗲𝗿𝗼 𝗶𝗻𝘁𝗲𝗿𝗻𝗲𝘁. #3 open model in the world. Built from the same research as Gemini 3. Apache 2.0. Setup in 4 steps: → Download Ollama — one installer, Mac, Windows, Linux → Run "ollama pull gemma4" — downloads automatically → Run "ollama run gemma4" — talking to it locally in seconds → Open Web UI or LM Studio if you want a browser interface Coding score jumped from 110 to 2,150 vs Gemma 3. Reasoning benchmark went from 19% to 74%. Your data never leaves your machine. No rate limits. No monthly bill. No surprises. Want the full setup guide? DM me.
كثر أداة ذكاء اصطناعي حالياً مفيدة للباحثين وطلبة...
كثر أداة ذكاء اصطناعي حالياً مفيدة للباحثين وطلبة الدراسات العليا هي اداة Ask5، فهي تجمع اقوى 5 نماذج LLMs لانجاز المهام خاصة الكتابة و هذه النماذج Gemini - 2.5- Pro , GPT, Grok , Claude , Deepseek ، مع تحديثات مستمرة لهذه الادوات. https://t.co/5MacQG5Ttt👇👇 https://t.co/26fNruWxca
App idea: Todo app BUT you upload a voice recordi...
App idea: Todo app BUT you upload a voice recording (or type) about how you feel and what you are going to do today Then AI will built a personalized UI for your todo based on your mood/what you said/weather/... (maybe gemini 3.1 flash light because of the speed) Let it show as a widget
GLM-5.1 weights just dropped. 🎉 This is a strong...
GLM-5.1 weights just dropped. 🎉 This is a strong model. > Excels at coding, just under Claude Opus 4.6 and above Gemini 3.1 Pro. > Built to work across longer multi-step agentic workflows. > At 754B it's quite a bit smaller than the frontier models it's competing against https://t.co/kscxIU5smp
THANK YOU FOR 7 MILLION VIEWS ON YOUTUBE 🔥 เรียกว...
THANK YOU FOR 7 MILLION VIEWS ON YOUTUBE 🔥 เรียกว่ารักได้ไหม (Is This Love?) - GEMINI, FOURTH #IsThisLove_GeminiFourth7M 💗 📺 WATCH MUSIC VIDEO NOW! 📎https://t.co/ZEJUgINKXF #IsThisLove_GeminiFourth #Gemini_NT #Fourthnattawat #RISERMUSIC https://t.co/fsocNuXVUN
🚨 CONCERNING: Stanford just published a paper tha...
🚨 CONCERNING: Stanford just published a paper that should alarm every company building multi-agent AI. When thinking tokens are matched, single agents beat debate systems, parallel role systems, ensemble agents, and sequential pipelines. The multi-agent advantage is a compute accounting artifact not an architectural breakthrough. Stanford tested single agents against five different multi-agent architectures across three model families Qwen3, DeepSeek-R1, and Gemini 2.5 on multi-hop reasoning tasks. The key variable: thinking tokens held constant across every comparison. When compute is equal, single agents match or outperform every multi-agent design tested. Every time. The reason is mathematical, not empirical. Multi-agent systems pass information between agents as messages. Every message is a compressed, lossy version of the full context. The Data Processing Inequality proves that no downstream agent can recover information discarded in that compression. A single agent with access to the full context is information-theoretically guaranteed to perform at least as well as any multi-agent system operating on summaries of that context. Stanford then ran the numbers. Results across all models and budgets: → Single agent average accuracy at 1000 tokens: 0.418 → Sequential pipeline: 0.379 → Subtask-parallel: 0.369 → Parallel roles: 0.381 → Debate: 0.388 → Ensemble: 0.333 Not one multi-agent architecture beat the single agent at any matched budget above 100 tokens. The pattern held across Qwen3, DeepSeek, Gemini 2.5 Flash, and Gemini 2.5 Pro. It held across two different benchmarks. It held across six different token budgets from 100 to 10,000. Stanford also found a significant measurement artifact in the Gemini API. When you request 10,000 thinking tokens, the API reports 1,687 tokens used. The visible thought text contains an average of 251 words — roughly 359 tokens. That's a 4.7x inflation factor. Multi-agent systems produce more visible thought text than single agents under the same requested budget because multiple agent calls generate multiple thought blocks. This makes multi-agent systems look like they're reasoning more when they're just generating more text. Every benchmark that didn't control for this is measuring compute, not architecture. There is one regime where multi-agent systems become competitive: corrupted context. When 70% of the reasoning context is replaced with random tokens, sequential pipelines start outperforming single agents. When misleading information is injected into the context, multi-agent decomposition helps filter it. But under normal conditions with clean context and matched compute — single agents win. Most reported multi-agent gains come from one of two sources: → Unaccounted compute multi-agent systems simply use more tokens → Context degradation single agents struggle when context is noisy or corrupted Neither is an architectural advantage. Neither justifies the complexity. The question every AI team should ask before building a multi-agent pipeline: Are you controlling for thinking tokens? If not, you're not measuring whether your architecture works. You're measuring whether more compute helps. It always does.
Meta is back! Muse Spark scores 52 on the Artifici...
Meta is back! Muse Spark scores 52 on the Artificial Analysis Intelligence Index, behind only Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6. Muse Spark is the first new release since Llama 4 in April 2025 and also Meta's first release that is not open weights Muse Spark is a new model from @Meta evaluated on Artificial Analysis. We were given early access by Meta to independently benchmark the model. It is the first frontier-class model from Meta since Llama 4 Maverick was released in April 2025, and notably the first @AIatMeta model that is not being released as open weights. The release follows Meta's reorganization of its AI efforts under Meta Superintelligence Labs, and signals that Meta is re-entering the frontier race after roughly a year of relative quiet. For context, Llama 4 Maverick and Scout scored 18 and 13 respectively on the Artificial Analysis Intelligence Index as non-reasoning models at the time of their release, while Muse Spark scores 52. Muse Spark essentially closes the gap between to the frontier in a single release. The model is not open source and is not yet accessible via an API but Meta has shared they expect this to come soon. Meta is also integrating Muse Spark into their first party products including their Meta AI chat product, Facebook, Instagram and Threads. Key takeaways from our benchmarks: ➤ Muse Spark scores 52 on the Artificial Analysis Intelligence Index, placing it within the top 5 models we have benchmarked. It sits ahead of Claude Sonnet 4.6, GLM-5.1, MiniMax-M2.7, Grok 4.20 and behind Gemini 3.1 Pro Preview, GPT-5.4 and Claude Opus 4.6 ➤ Muse Spark is notably token efficient for its intelligence level. It used 58M output tokens to run the Intelligence Index, comparable to Gemini 3.1 Pro Preview (57M) and notably lower than Claude Opus 4.6 (Adaptive Reasoning, max effort, 157M), GPT-5.4 (xhigh, 120M) and GLM-5 (110M) ➤ Muse Spark is the second-most capable vision model we have benchmarked. It scores 80.5% on MMMU-Pro, behind only Gemini 3.1 Pro Preview (82.4%) ➤ Muse Spark performs strongly on reasoning and instruction-following evaluations. It scores 39.9% on HLE, trailing only Gemini 3.1 Pro Preview (44.7%) and GPT-5.4 (xhigh, 41.6%). The model also achieved 5th highest in CritPT with a score of 11%, an eval that is focused on difficult physics research questions. This is substantially above above Gemini 3 Flash (9%) and Claude 4.6 Sonnet (3%) ➤ Agentic performance does not stand out. On GDPval-AA, our evalaution focused on real world work tasks, Muse Spark scores 1427, behind both Claude Sonnet 4.6 at 1648 and GPT-5.4 at 1676, but ahead of Gemini 3.1 Pro Preview at 1320. On On TerminalBench Hard, Muse Spark trails Claude Sonnet 4.6, GPT-5.4, and Gemini 3.1 Pro. Muse Spark joins others in achieving a high τ²-Bench Telecom score of 92% Key model details: ➤ Modalities: Multimodal including text and vision input, text output ➤ License: Proprietary, Meta's first frontier model not released as open weights ➤ Availability: No public API at the time of publishing. Meta expects to provide API access soon. Meta has started integration into their first party AI offering Meta AI and inside Facebook, Instagram, and Threads
ICYMI: Google is hosting Google Cloud Next 26 even...
ICYMI: Google is hosting Google Cloud Next 26 event on April 22-24 in Las Vegas. Loads of updates are expected across Gemini, Vertex AI, Agents, Vibe Coding, Generative UI, and AI Cloud. > Going to be a fun next few months 👀 https://t.co/FvOqXWQdRk
AI can't build complex websites, they said. So I...
AI can't build complex websites, they said. So I vibe coded a hyper realistic 3D tunnel gallery using @threejs with Gemini 3.1 pro in @GoogleAIStudio in < 1 hour. It runs on Catmull Rom splines with a treadmill style sliding geometry, custom fragment shaders with depth based fog, and tight optimizations like texture disposal, disabled frustum checks, and capped DPR to keep it smooth even on long infinite scrolls. All of this is seriously hard to learn and build manually, but AI pulls it together so well! Live: https://t.co/wprYfooauI Code: https://t.co/1P9G0l8UC6
พินัมจุน rm ♡สมาชิกBTS ฮยองไลน์ เข้ามาไลค์น้องเจมใ...
พินัมจุน rm ♡สมาชิกBTS ฮยองไลน์ เข้ามาไลค์น้องเจมใน instagrams กับโพส 932k ด้วย ฮรือละน้องเจมใช้เพลง swim ของบังทัน 환영해요! ka 😭😭😭 જ ◞ #Gemini_NT #เจมีไนน์ https://t.co/xu49ZFBluA