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~ I somehow ended up as a support... Ɛ> High quality version on P-smth <3 Shion Model: dzooworks🔞 Male model: IceDev VA: Misuzugon 🦕 SFX: OpenNSFW 🟣 Available Now #overwatch

DieScope

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119,007 views • 21 days ago •via X (Twitter)

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Officer Qingyi is chasing down a group of vigilantes who stole Random Play's HDD! She managed to catch up to them but they had the leg up on her... SFX OpenNSFW 🟣 Available Now Qingyi & Zhu Yuan VA Misuzugon 🦕 Even Spicier Version on >> <<

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Low quality engagement post This is how each Girl in Overwatch farts on a day-to-day basis. Not after a heavy meal or a night of drinking. These are baseline~ In any case, I wanna hit these ladies fumes like a geek bar! SFX by Lil' 🍊 and OpenNSFW 🟣 Available Now Now, if you'll excuse me, I'm going to go goon to some more fart comps. #Overwatch #meme #fart #fartfetish #girlsfarting #eproctophilia #femalefarts

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GLM 4.6 fans! just soared to the top as the fastest provider in Artificial Analysis for the model. > a 114 TPS and <0.18s TTFT. > That's 2x faster than the next best option on both metrics. Available now in Cline.

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“The quality is consistently high, not occasionally high. Against Wan2.7, it's not just a huge update, it's like a totally new model. Anyone who’s tried any version of Wan before should retry this one." Lovis Odin put Wan3.0 through its paces across T2V, I2V, and R2V — and went beyond testing the model to share his own prompt techniques for getting the most out of it. From exploring Wan3.0’s capabilities to learning how to write better prompts in the era of 30-second video generation, this session is packed with practical insights for creators. Wan3.0 is now available on fal. Try it on fal ai or fal Agent.

“The quality is consistently high, not occasionally high. Against Wan2.7, it's not just a huge update, it's like a totally new model. Anyone who’s tried any version of Wan before should retry this one." Lovis Odin put Wan3.0 through its paces across T2V, I2V, and R2V — and went beyond testing the model to share his own prompt techniques for getting the most out of it. From exploring Wan3.0’s capabilities to learning how to write better prompts in the era of 30-second video generation, this session is packed with practical insights for creators. Wan3.0 is now available on fal. Try it on fal ai or fal Agent.

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𝗥𝘂𝗻 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗚𝗲𝗺𝗺𝗮 𝟰 + 𝗢𝗽𝗲𝗻𝗖𝗹𝗮𝘄 𝗮𝘀 𝗮 𝗳𝗿𝗲𝗲 𝗽𝗿𝗶𝘃𝗮𝘁𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗶𝗻 𝟯 𝘀𝘁𝗲𝗽𝘀. No API bills. No usage limits. No subscription. Nothing leaves your computer. Here's the full setup: → Step 1: Go to Download and install. Update to version 0.2.2.0 or higher. → Step 2: Open terminal. Type: ollama pull gemma4. Downloads the model. Done. → Step 3: Install OpenClaw. Select Ollama as your provider. Point it at port 11434. Pick Gemma 4. That's it. Your AI agent is now running locally. Message it through Telegram, Slack, Discord, or WhatsApp like a coworker. Read files. Write code. Remember context across every conversation. All on your own hardware. Gemma 4 ranked number 3 on the global open model leaderboard on launch day. It beat models with 20 times more parameters. The 26B version activates only 4B parameters at a time so you get near large-model quality at small-model speed. Every AI subscription you're paying for right now could be replaced with this.

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After playing Kingdom Come Deliverance 2 last night, I booted up Starfield with the intention of quickly downloading the recent creation club content. 3 hours later and I was sucked right back into this brilliant game and ended up making a bit more progress on the main game (second playthrough). I've said it before and I'll say it again, love this game. Bethesda RPG and space in one package.....🤌 As an aside, with the latest transformer model, this games already very good image quality is sublime PS, the physics in this game, in low gravity, are brilliant (see vid) #Starfield #pcgaming

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@levelsio

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Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

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🌘 Meet Kimi K2.7 Code HighSpeed! A high-speed mode of our latest open-source multimodal coding model, Kimi K2.7 Code. ⚡️ Up to 6× faster: Around 180 tok/s on coding tasks with median-length inputs, and up to 260 tok/s on shorter-context tasks. 🔷 Rolling out to Kimi Code Beta Program members, Kimi API developers, and Kimi Business users. (Access will remain limited for now due to capacity constraints.) 🔷 No invite needed. Anyone who joins the Beta Program has a chance to get access 👉 Open intelligence should be instant, affordable, and borderless. We'll continue improving the model and expanding access as more capacity becomes available! 🔗 Kimi Code: 🔗 API:

Kimi.ai

571,260 views • 2 months ago

SGLang now supports DSpark, enabling confidence-driven, variable-length verification for speculative decoding 🎉 DSpark addresses a key bottleneck under load: instead of verifying every draft token, it verifies only where the draft model is confident, so the gains hold even as batch size scales. We heavily optimized variable-length verification in SGLang. Across batch sizes 1 to 256, DSpark gives the best throughput/latency tradeoff on DeepSeek-V4-Flash, ahead of both MTP and non-spec. At high concurrency, dynamic scheduling provides up to ~20% higher throughput compared to a fixed budget, while maintaining high verification quality across workloads. With fused kernels and zero-overhead scheduling, DeepSeek-V4-Pro reaches 383.7 tok/s at B=1 on B300. DSpark is now available in SGLang with support for Qwen3 and DeepSeek-V4. Thanks DeepSeek for open-sourcing! Blog with full technical details and commands to run below 👇

SGLang now supports DSpark, enabling confidence-driven, variable-length verification for speculative decoding 🎉 DSpark addresses a key bottleneck under load: instead of verifying every draft token, it verifies only where the draft model is confident, so the gains hold even as batch size scales. We heavily optimized variable-length verification in SGLang. Across batch sizes 1 to 256, DSpark gives the best throughput/latency tradeoff on DeepSeek-V4-Flash, ahead of both MTP and non-spec. At high concurrency, dynamic scheduling provides up to ~20% higher throughput compared to a fixed budget, while maintaining high verification quality across workloads. With fused kernels and zero-overhead scheduling, DeepSeek-V4-Pro reaches 383.7 tok/s at B=1 on B300. DSpark is now available in SGLang with support for Qwen3 and DeepSeek-V4. Thanks DeepSeek for open-sourcing! Blog with full technical details and commands to run below 👇

LMSYS Org

170,203 views • 2 months ago

SAKANA FUGU ULTRA vs. CLAUDE OPUS 4.8 RESULTS Prompt: "build a really high quality single html file crossy road game with three.js" Sakana Fugu Ultra: - Tokens Used: ~89k ($7.32) - Time Elapsed: 22 minutes - Issues: inverted directional turn, wonky camera, no sfx, not identical to Crossy Road game Claude Opus 4.8 Ultracode: - Tokens Used: ~940k (~$37.85) - Time elapsed: 79 minutes - Issues: got stuck twice in a retry loop (had to prompt to self-correct), wrong character position after restart, difficult from the start (whereas Fugu's version got significantly more difficult as you progress, which is the correct behaviour) I think in terms of application functionality, quality, and design, Opus won. In terms of model speed and performance, Fugu on Opencode won. What are your thoughts? Comment down below who you think won! ⬇️

SAKANA FUGU ULTRA vs. CLAUDE OPUS 4.8 RESULTS Prompt: "build a really high quality single html file crossy road game with three.js" Sakana Fugu Ultra: - Tokens Used: ~89k ($7.32) - Time Elapsed: 22 minutes - Issues: inverted directional turn, wonky camera, no sfx, not identical to Crossy Road game Claude Opus 4.8 Ultracode: - Tokens Used: ~940k (~$37.85) - Time elapsed: 79 minutes - Issues: got stuck twice in a retry loop (had to prompt to self-correct), wrong character position after restart, difficult from the start (whereas Fugu's version got significantly more difficult as you progress, which is the correct behaviour) I think in terms of application functionality, quality, and design, Opus won. In terms of model speed and performance, Fugu on Opencode won. What are your thoughts? Comment down below who you think won! ⬇️

Mark Santos

684,261 views • 2 months ago

researchers gave a tiny local model human-style memory and its context limit basically stopped existing a team from MBZUAI, Princeton and Weizmann took a 1B model and rebuilt how it reads. instead of attending to everything at once, the model reads in 1,024 token chunks and passes the important stuff forward through an associative memory, the same way you carry the plot of a book between chapters without rereading them. the design mirrors human memory on purpose. full attention inside a chunk works as short-term memory. the module that carries information between chunks works as long-term memory. they even trained it like a person, starting with short easy texts and raising the difficulty gradually, because memory thrown into the deep end learns nothing. the numbers back it up. the normal model burns 40GB of GPU memory on a long document and collapses hard past its limit, dropping from 0.86 to 0.32 accuracy. the memory version holds 0.71 at double that length while using a flat 12GB no matter how long the input gets. it also needs about 30% fewer FLOPs. the part i keep thinking about is that nobody scaled anything here. they didn't build a bigger model, didn't stretch the window, didn't add compute. they looked at how a brain handles a long day and copied the architecture. a model small enough to run on a consumer gpu now survives documents its own architecture used to choke on. we keep treating intelligence as a compute problem. sometimes it's a memory problem.

researchers gave a tiny local model human-style memory and its context limit basically stopped existing a team from MBZUAI, Princeton and Weizmann took a 1B model and rebuilt how it reads. instead of attending to everything at once, the model reads in 1,024 token chunks and passes the important stuff forward through an associative memory, the same way you carry the plot of a book between chapters without rereading them. the design mirrors human memory on purpose. full attention inside a chunk works as short-term memory. the module that carries information between chunks works as long-term memory. they even trained it like a person, starting with short easy texts and raising the difficulty gradually, because memory thrown into the deep end learns nothing. the numbers back it up. the normal model burns 40GB of GPU memory on a long document and collapses hard past its limit, dropping from 0.86 to 0.32 accuracy. the memory version holds 0.71 at double that length while using a flat 12GB no matter how long the input gets. it also needs about 30% fewer FLOPs. the part i keep thinking about is that nobody scaled anything here. they didn't build a bigger model, didn't stretch the window, didn't add compute. they looked at how a brain handles a long day and copied the architecture. a model small enough to run on a consumer gpu now survives documents its own architecture used to choke on. we keep treating intelligence as a compute problem. sometimes it's a memory problem.

Alex Veremeyenko

16,147 views • 1 month ago