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オアシス21の豊田合成リンクにて松村香織最後？のオアシストークショーが開催されました👏平日の夜、寒い中沢山の方が来てくれてびっくり😳本当にありがとうございました🙇💗ミニライブも盛りがっていて良かったです☺✨なんか私の秘密沢山暴露されちゃったー😂💣そして #UHAGE# 広まれ～👨‍🦲

#オアシス21# トークショー&LIVE 寒い中来てくれて ありがとう😊🌷 みんなー！ #UHAGE# 知ってるかい？？ りょうはのUHAに 香織さんのHAGEで。 #UHAGE# 香織さん大好きなんだよなあ。💓 かおりさーん😊💓💓

DeepSeek-V4-Flash-0731 is Ollama's fastest growing model ever in token usage. We are scaling capacity in US & Europe. On Ollama, this model runs with high performance (100tps+) and zero data retention. Your data stays yours. ollama run deepseek-v4-flash:0731-cloud

Which AI model actually fits the job? You won't know until you experiment. The Alibaba Cloud Token Plan makes it easier to try, test and compare, with: · One credit pool across Qwen, Wan, HappyHorse, DeepSeek and GLM · Text, audio, image and video capabilities · Clearer visibility into your AI usage And it’s easy to start, with your first month priced at just $4. Explore the Token Plans: #AlibabaCloud# #TokenPlan# #Qwen# #Wan# #HappyHorse# #DeepSeek# #GLM# #MultiModelAI# #GenerativeAI# #AIWorkflow#

Why manage separate AI tools for the script, the image, and the video? The Alibaba Cloud Token Plan gives you one shared credit pool across supported models and tools, with visibility into usage and access to newer models like Qwen3.8-Max-Preview, HappyHorse1.1, DeepSeek V4, and GLM-5.2. One plan for every modality, to build more, spend less. Get started from just $4 in your first month. Explore the Token Plans: #AlibabaCloud# #TokenPlan# #Qwen# #Wan# #HappyHorse# #GenerativeAI# #AIContent# #MultimodalAI#

We have reset usage limits for all Codex and ChatGPT Work users. Last night around 2am to 4am we suffered an almost global outage. All well and recovered, but you know what comes next. We learn. We reset. Enjoy.

holy opus 5... 🤯 At first I was VERY disapointed, the model is ridiculously slow and the output was not impressive. i was using it WRONG. fable 5 can take a shit prompt and give you gold, but it will also burn all your tokens. opus is the opposite. use loops, graphs, reviewers, orchestrators, and agents working in parallel. ( this subway surfers game took over 3 hours ) Your output will be 10x better. The crazy part is, even with all these agents working together I have PLENTY of usage left on my first claude plan. The skill ceiling is insanely high, if you put time into a good agentic engineering strategy you can achieve BETTER output than fable. Opus 5 is a true daily driver.

Former $CRWV employee on why neoclouds are far more exposed to GPU generation cycles than hyperscalers ( $MSFT, $AMZN, $GOOGL ): - The expert describes GPU utilization tracking at hyperscale as a continuous and disciplined process built around two lenses. The first is infrastructure utilization, covering GPU occupancy, idle time, and memory utilization, noting that 95% booked usage can still mask inefficiency if jobs stall or batches have idle gaps. The second is outcome utilization, asking whether the compute is actually generating business value, measured by metrics such as tokens trained per dollar, time to reach target accuracy, and tokens per second per GPU. - The expert sees a meaningful difference between hyperscalers and neoclouds on GPU investment economics. Hyperscalers like $MSFT, $GOOGL, and $AMZN can tolerate a 3-5 year payback period given their ability to monetize the same infrastructure across multiple revenue streams. Neoclouds like $CRWV operate on a tighter 2-3 year window. - With higher financing costs and direct dependence on infrastructure cash yield, neoclouds are far more sensitive to utilization and GPU residual risk. The biggest risk is GPU generation cycles, where a slow payback means newer chips could erode pricing power before the asset has paid itself off. - The expert explains that for hyperscalers, roughly 60% of GPU capacity is allocated to external monetization, including GPU rentals, managed AI services, enterprise inference workloads, and startup model training. The remaining 40% is used internally, and of that internal portion, the majority is still indirectly monetized through products like M365 Copilot or GitHub. Around 40% is dedicated to pure R&D. - The GPU pricing mix has shifted meaningfully over the past few years. In 2023, around 70-80% of revenue was hourly as customers paid a premium just to get access to scarce GPUs. By 2025 that had moved to roughly 50% hourly and 30-50% committed, and the expert expects 2026 to tip further toward committed at around 65% for hyperscalers as AI matures and inference becomes more predictable. Neoclouds are moving in the same direction but more slowly. - By 2027-2030, the expert sees committed contracts settling at 55-65% as the norm, with hourly pricing remaining but losing its scarcity premium as more supply comes online.