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A different game in Squid Game☂️ Models: x_RedEyes 🔞 | (OPEN COMMISSIONS) Audio: Evilaudio Voice: DelaliciousVA #r34 #nsfw #r34nsfw #3dartnsfw #rule34 #squidgame3 ##SquidGame #squidgamensfw

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139,818 görüntüleme • 1 yıl önce •via X (Twitter)

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I tested GLM 5.2 on three real builds. A horror house game. A 3D stealth game. A Remotion marketing video. The results are exactly what I expect from Chinese open source coding models right now. The horror house has broken game logic. Collect all three keys, complete the objective, and the front door still won't open. The stealth game shipped with lighting so dark you can't see your own character. The Remotion video is rough. GLM 5.2 is faster than 5.1 and 10x cheaper than the frontier. But Opus 4.8 and GPT 5.5 are still in a different league for serious coding work. Chinese models keep missing the attention to detail that matters. I MISS FABLE 5 ALREADY. Full review live now.
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I tested GLM 5.2 on three real builds. A horror house game. A 3D stealth game. A Remotion marketing video. The results are exactly what I expect from Chinese open source coding models right now. The horror house has broken game logic. Collect all three keys, complete the objective, and the front door still won't open. The stealth game shipped with lighting so dark you can't see your own character. The Remotion video is rough. GLM 5.2 is faster than 5.1 and 10x cheaper than the frontier. But Opus 4.8 and GPT 5.5 are still in a different league for serious coding work. Chinese models keep missing the attention to detail that matters. I MISS FABLE 5 ALREADY. Full review live now.

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24,681 görüntüleme • 3 ay önce

VoiceBox just did what everyone thought was impossible. It might have killed ElevenLabs overnight. No subscription. No cloud. No credits. Runs locally on your machine. Clones a voice from 3 seconds of audio. Powered by Qwen 3 TTS. Apache 2.0 license. Commercial use allowed. And your voice data never leaves your computer. For years, if you wanted high quality voice cloning, ElevenLabs was the default. Now there’s a free open-source alternative that sounds shockingly close. Plus it has a built-in multi-track editor, recording, transcription with Whisper, and a local API. This isn’t just another TTS tool. It’s a full voice production studio on your desktop. Open source AI is catching up fast. And this changes the game for creators and businesses. Would you trust the cloud with your voice… or keep it local? 👇
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VoiceBox just did what everyone thought was impossible. It might have killed ElevenLabs overnight. No subscription. No cloud. No credits. Runs locally on your machine. Clones a voice from 3 seconds of audio. Powered by Qwen 3 TTS. Apache 2.0 license. Commercial use allowed. And your voice data never leaves your computer. For years, if you wanted high quality voice cloning, ElevenLabs was the default. Now there’s a free open-source alternative that sounds shockingly close. Plus it has a built-in multi-track editor, recording, transcription with Whisper, and a local API. This isn’t just another TTS tool. It’s a full voice production studio on your desktop. Open source AI is catching up fast. And this changes the game for creators and businesses. Would you trust the cloud with your voice… or keep it local? 👇

Julian Goldie SEO

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Today we're announcing the open-source release of HunyuanVideo-Foley, our new end-to-end Text-Video-to-Audio (TV2A) framework for generating high-fidelity audio.🚀 This tool empowers creators in video production, filmmaking, and game development to generate professional-grade audio that precisely aligns with visual dynamics and semantic context, addressing key challenges in V2A generation.🔊 Key Innovations: 🔹Exceptional Generalization: Trained on a massive 100k-hour multimodal dataset, the model generates contextually-aware soundscapes for a wide range of scenes, from natural landscapes to animated shorts. 🔹Balanced Multimodal Response: Our innovative multimodal diffusion transformer (MMDiT) architecture ensures the model balances video and text cues, generating rich, layered sound effects that capture every detail—from the main subject to subtle background elements. 🔹High-Fidelity Audio: Using a Representation Alignment (REPA) loss function and a powerful Audio VAE, we've improved generation stability and producing professional-grade audio, free of noise and inconsistencies. HunyuanVideo-Foley achieves SOTA on multiple benchmarks, surpassing all open-source models in audio quality, visual-semantic alignment, and temporal alignment. 👉Try it now: 🌐Project Page: 🔗Code: 📄Technical Report: 🤗Hugging Face:
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Today we're announcing the open-source release of HunyuanVideo-Foley, our new end-to-end Text-Video-to-Audio (TV2A) framework for generating high-fidelity audio.🚀 This tool empowers creators in video production, filmmaking, and game development to generate professional-grade audio that precisely aligns with visual dynamics and semantic context, addressing key challenges in V2A generation.🔊 Key Innovations: 🔹Exceptional Generalization: Trained on a massive 100k-hour multimodal dataset, the model generates contextually-aware soundscapes for a wide range of scenes, from natural landscapes to animated shorts. 🔹Balanced Multimodal Response: Our innovative multimodal diffusion transformer (MMDiT) architecture ensures the model balances video and text cues, generating rich, layered sound effects that capture every detail—from the main subject to subtle background elements. 🔹High-Fidelity Audio: Using a Representation Alignment (REPA) loss function and a powerful Audio VAE, we've improved generation stability and producing professional-grade audio, free of noise and inconsistencies. HunyuanVideo-Foley achieves SOTA on multiple benchmarks, surpassing all open-source models in audio quality, visual-semantic alignment, and temporal alignment. 👉Try it now: 🌐Project Page: 🔗Code: 📄Technical Report: 🤗Hugging Face:

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Huge drop from Tencent! AuK - turns speech gen/editing into a text-prompt playground. You can control literally anything with natural language. - zero-shot voice cloning, no reference transcript - swap an accent, inject anger, fix background noise, or change a single spoken word in a track. - full TTS, vocal separation - studio-grade cleanup The scale behind this is wild: trained on ~2 million hours of data. - Ridiculously low error rates - 2.65% uses Qwen3-Omni/ASR/ForcedAligner. also AuK-Flash for lightning-fast inference. for dubbing, podcasting, game audio... weights are fully open-source!
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Huge drop from Tencent! AuK - turns speech gen/editing into a text-prompt playground. You can control literally anything with natural language. - zero-shot voice cloning, no reference transcript - swap an accent, inject anger, fix background noise, or change a single spoken word in a track. - full TTS, vocal separation - studio-grade cleanup The scale behind this is wild: trained on ~2 million hours of data. - Ridiculously low error rates - 2.65% uses Qwen3-Omni/ASR/ForcedAligner. also AuK-Flash for lightning-fast inference. for dubbing, podcasting, game audio... weights are fully open-source!

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Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!
2:46

Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!

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Text to Sound Effects is here. Our newest AI Audio model generates sound effects, short instrumental tracks, soundscapes, and a wide variety of character voices, all from a text prompt. Available now for all users. Everyone from content creators, video game developers, to film and television studios, uses sound effects to create rich and immersive content. Now, in addition to AI voiceovers, you can generate all of the sounds you need with just a prompt. Everything you hear in this video was generated by ElevenLabs sound and voice models. In this thread, we shared some additional clips that help show off the range of this new model.

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All aboard the Mary Celeste, General Elizabeth Blythe has joined the crew! How are y'all liking the new Subverse: 1.2 OUT NOW on Steam!🔞 patch?! I am honored to voice such a layered dommy mommy and excited for us all to see a different side (...all her Butcha-U Approved sides, in fact... 😏🔥) of Blythe. 🖤🦾 Mayhaps Blythe also has a lil' musical surprise coming up, so stay tuned for that... 🎵 Huge props to the incredible voice cast and all the hardworking talent at Studio FOW for making this game truly something special. 💖 Also. BLYTHE #subverse #blythe
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All aboard the Mary Celeste, General Elizabeth Blythe has joined the crew! How are y'all liking the new Subverse: 1.2 OUT NOW on Steam!🔞 patch?! I am honored to voice such a layered dommy mommy and excited for us all to see a different side (...all her Butcha-U Approved sides, in fact... 😏🔥) of Blythe. 🖤🦾 Mayhaps Blythe also has a lil' musical surprise coming up, so stay tuned for that... 🎵 Huge props to the incredible voice cast and all the hardworking talent at Studio FOW for making this game truly something special. 💖 Also. BLYTHE #subverse #blythe

𝔾𝕚𝕟𝕒 𝔾𝕒𝕝𝕠𝕣𝕖✨🔞

120,565 görüntüleme • 3 yıl önce

Voice used to be AI’s forgotten modality - now it's having its big moment: rapid innovation, big funding rounds, major agentic applications My conversation with Neil Zeghidour, top AI researcher in the field (Google DeepMind, Meta, kyutai) and now CEO of Gradium This is a reference episode on all things voice AI 🔥 00:00 Intro 01:21 Voice AI’s big moment, and why we’re still early 03:34 Why voice lagged behind text/image/video 06:06 The convergence era: transformers for every modality 07:40 Beyond Her: always-on assistants, wake words, voice-first devices 11:01 Voice vs text: where voice fits (even for coding) 12:56 Neil’s origin story: from finance to machine learning, with help from Yann LeCun and Soumith Chintala 18:35 Neural codecs (SoundStream): compression as the unlock 22:30 Kyutai: open research, small elite teams, moving fast 31:32 Why big labs haven’t “won” voice AI4 34:01 On-device voice: where it works, why compact models matter 46:37 The last mile: real-world robustness, pronunciation, uptime 41:35 Benchmarking voice: why metrics fail, how they actually test 47:03 Cascades vs speech-to-speech: trade-offs + what’s next 54:05 Hardest frontier: noisy rooms, factories, multi-speaker chaos 1:00:50 New languages + dialects: what transfers, what doesn’t 1:02:54 Hardware & compute: why voice isn’t a 10,000-GPU game 1:07:27 What data do you need to train voice models 1:09:02 Deepfakes + privacy: why watermarking isn’t a solution 1:12:30 Voice + vision: multimodality, screen awareness, video+audio 1:14:43 Voice cloning vs voice design: where the market goes 1:16:32 Paris/Europe AI: talent density, underdog energy, what’s next
1:22:49

Voice used to be AI’s forgotten modality - now it's having its big moment: rapid innovation, big funding rounds, major agentic applications My conversation with Neil Zeghidour, top AI researcher in the field (Google DeepMind, Meta, kyutai) and now CEO of Gradium This is a reference episode on all things voice AI 🔥 00:00 Intro 01:21 Voice AI’s big moment, and why we’re still early 03:34 Why voice lagged behind text/image/video 06:06 The convergence era: transformers for every modality 07:40 Beyond Her: always-on assistants, wake words, voice-first devices 11:01 Voice vs text: where voice fits (even for coding) 12:56 Neil’s origin story: from finance to machine learning, with help from Yann LeCun and Soumith Chintala 18:35 Neural codecs (SoundStream): compression as the unlock 22:30 Kyutai: open research, small elite teams, moving fast 31:32 Why big labs haven’t “won” voice AI4 34:01 On-device voice: where it works, why compact models matter 46:37 The last mile: real-world robustness, pronunciation, uptime 41:35 Benchmarking voice: why metrics fail, how they actually test 47:03 Cascades vs speech-to-speech: trade-offs + what’s next 54:05 Hardest frontier: noisy rooms, factories, multi-speaker chaos 1:00:50 New languages + dialects: what transfers, what doesn’t 1:02:54 Hardware & compute: why voice isn’t a 10,000-GPU game 1:07:27 What data do you need to train voice models 1:09:02 Deepfakes + privacy: why watermarking isn’t a solution 1:12:30 Voice + vision: multimodality, screen awareness, video+audio 1:14:43 Voice cloning vs voice design: where the market goes 1:16:32 Paris/Europe AI: talent density, underdog energy, what’s next

Matt Turck

22,980 görüntüleme • 6 ay önce

Did Google just mass-murdered every voice AI startup irrelevant? Google just shipped Gemini 3.1 Flash Live. Real-time voice AND vision. Sub-second latency. 90+ languages. And the one thing no voice AI has cracked until now: it filters background noise in real-world environments. Not demo environments. Real ones. Traffic outside. TV left on. Drill running next door. That's the wall every voice AI startup has been quietly hitting for three years. The demos worked. The deployments didn't. Because the real world is loud and messy and the models couldn't tell your voice from the noise around it. Google just called that problem solved. And it's not sitting in a research paper. It's already running in production apps. Stitch is using it to let designers talk to their canvas in real-time. Ato is using it to give elderly people a companion who actually listens. Wit's End built an RPG game master with it that delivers theatrical dialogue indistinguishable from a human voice actor. Three live products. Three different industries. All shipping now. Every voice AI startup just woke up to a new baseline. The question isn't whether to build with this. The question is how fast.
0:57

Did Google just mass-murdered every voice AI startup irrelevant? Google just shipped Gemini 3.1 Flash Live. Real-time voice AND vision. Sub-second latency. 90+ languages. And the one thing no voice AI has cracked until now: it filters background noise in real-world environments. Not demo environments. Real ones. Traffic outside. TV left on. Drill running next door. That's the wall every voice AI startup has been quietly hitting for three years. The demos worked. The deployments didn't. Because the real world is loud and messy and the models couldn't tell your voice from the noise around it. Google just called that problem solved. And it's not sitting in a research paper. It's already running in production apps. Stitch is using it to let designers talk to their canvas in real-time. Ato is using it to give elderly people a companion who actually listens. Wit's End built an RPG game master with it that delivers theatrical dialogue indistinguishable from a human voice actor. Three live products. Three different industries. All shipping now. Every voice AI startup just woke up to a new baseline. The question isn't whether to build with this. The question is how fast.

Vaibhav Sisinty

59,350 görüntüleme • 5 ay önce

The kyutai fully end-to-end audio model demo of today is a huge deal that many people missed in the room Mostly irrelevant are the facts that: - they come a few week after OpenAI ChatGPT-4o - the demo was less polished than the 4o one (in terms of voice quality, voice timing…) Relevant: - the model training pipeline and model archi are simple and hugely scalable, with a tiny 8+ people team like Kyutai building it in 4 months. Synthetic data is a huge enabler here - laser focus on local devices: Moshi will soon be everywhere. Frontier model builders have low incentive to let you run smaller models locally (price per token…) but non-profits like Kyutai have very different incentives. The Moshi demo is already online while the OpenAI 4o one is still in limbo. - going under 300 ms of latency while keeping Llama 8B or above quality of answers is a key enabler in terms of interactivity, it’s game changing, This feeling when the model answer your question before you even finished asking is quite crazy or when you interrupt the model while it’s talking and it react… Predictive coding in a model, instantly updated model of what you’re about to say... Basically they nailed the fundamentals. It’s here. This interactive voice tech will be everywhere. It will soon be an obvious commodity.
1:10

The kyutai fully end-to-end audio model demo of today is a huge deal that many people missed in the room Mostly irrelevant are the facts that: - they come a few week after OpenAI ChatGPT-4o - the demo was less polished than the 4o one (in terms of voice quality, voice timing…) Relevant: - the model training pipeline and model archi are simple and hugely scalable, with a tiny 8+ people team like Kyutai building it in 4 months. Synthetic data is a huge enabler here - laser focus on local devices: Moshi will soon be everywhere. Frontier model builders have low incentive to let you run smaller models locally (price per token…) but non-profits like Kyutai have very different incentives. The Moshi demo is already online while the OpenAI 4o one is still in limbo. - going under 300 ms of latency while keeping Llama 8B or above quality of answers is a key enabler in terms of interactivity, it’s game changing, This feeling when the model answer your question before you even finished asking is quite crazy or when you interrupt the model while it’s talking and it react… Predictive coding in a model, instantly updated model of what you’re about to say... Basically they nailed the fundamentals. It’s here. This interactive voice tech will be everywhere. It will soon be an obvious commodity.

Thomas Wolf

339,646 görüntüleme • 2 yıl önce

GLM-5.1 > Claude Code (Opus 4.6)? I'm tripping or CC has become very bad but built a Three.js racing game to eval and it's extremely impressive. Thoughts: - One-shot car physics with real drift mechanics (this is hard) - My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen - 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters - All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio! - Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed You are going to hear about this model a lot in the next months - open source let's go 🚀🚀
0:25

GLM-5.1 > Claude Code (Opus 4.6)? I'm tripping or CC has become very bad but built a Three.js racing game to eval and it's extremely impressive. Thoughts: - One-shot car physics with real drift mechanics (this is hard) - My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen - 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters - All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio! - Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed You are going to hear about this model a lot in the next months - open source let's go 🚀🚀

Victor M

202,410 görüntüleme • 5 ay önce

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."
1:10

America has the edge in the world’s most powerful AI models, no question. But China is playing a different game: efficiency, scale, and global deployment. Kyle Chan from Brookings China explains: "Does winning mean developing the world's most powerful AI models? If yes, then the us has the decisive lead. American ai models are widely recognized as the best in the world on virtually every metric you can think of, from math and reasoning to coding and AI agents, American private companies continue to set the pace at the technological frontier supported by deep capital markets, a dynamic innovation ecosystem, and the best talent from around the world. China's AI models are improving quickly but continue to lag behind. Chinese AI companies face real constraints on compute due to our export controls and their own limited capital resources, so instead they're prioritizing other goals. They're focused on efficiency, building models that are cheaper to train and run. They're focused on adoption using an open source strategy to win users around the world, and they're focused on integration with other platforms and services. On sheer performance, I would expect the gap between our models and theirs to persist or even widen over time. But model performance is not everything."

Select Committee on China

60,135 görüntüleme • 4 ay önce

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!
1:20

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!

@levelsio

72,338 görüntüleme • 4 ay önce

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐊𝐚𝐭𝐚𝐧𝐚 𝐈𝐧𝐮: 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐖𝐞𝐛𝟑 𝐆𝐚𝐦𝐢𝐧𝐠 & What's coming next? Many people are still discovering what we’re building at Katana Inu, so here’s a quick overview of our vision: Katana Inu is a fun-to-play, pioneering Web3 game that goes beyond the ordinary. We’re creating a dynamic gaming experience that bridges Web2 and Web3 players in a thrilling universe where teams, communities, and memes come together with eSport and tournaments! Katana Inu is a place where gaming and memes collide! 𝐀𝐭 𝐊𝐚𝐭𝐚𝐧𝐚 𝐈𝐧𝐮, 𝐰𝐞’𝐫𝐞 𝐚𝐥𝐥 𝐚𝐛𝐨𝐮𝐭 𝐫𝐞𝐚𝐥 𝐯𝐚𝐥𝐮𝐞: Bringing popular chains, meme icons, and communities into the gaming world as 3D models – think Dogecoin, Shiba Inu, Solana, Polygon and more. Just like other Web2 games integrates popular characters, we’re doing the same with the best of Web3. In the future, we even plan to incorporate anime and pop-culture characters, expanding our world to be a true Web3 cultural hub. Our gameplay is designed for Team vs Team and Battle Royale modes, where players experience a unique mix of fast-paced, sword-and-spell combat. Katana Inu is truly one-of-a-kind, offering Web2 and Web3 gamers alike an engaging world and a reason to keep coming back. 𝐌𝐮𝐥𝐭𝐢𝐜𝐡𝐚𝐢𝐧 𝐑𝐞𝐰𝐚𝐫𝐝𝐬 & 𝐁𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 As a multichain game, Katana Inu collaborates across multiple blockchains, including Solana, Polygon, Immutable, Beam, and Avalanche, giving players access to rewards on different chains while driving real transactions and value into these ecosystems. In addition, we’re implementing a burn system within tournaments and various activities, adding value for players and token holders alike. 𝐎𝐮𝐫 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 We are actively developing our game and platform and here is what's ahead for Katana Inu: • A New Telegram Game with $KATA Airdrops. • Upgrading from UE4.27 to UE5 by December 2024. • Developing the Battle Royale mode through Q1 2025 with Closed Beta testing sas soon as Q4 2024. • Full Closed Beta in Q4 2024, with Open Beta planned for early Q1 2025. • Integrate In-Game wallet system for a seamless player experience. • Launching on Steam and Epic Games to reach a broader audience. • Great Open-Beta Cinematic Trailer with Legends of the Web3 space. • Improving the Game with better quality • Improving the Animations and Fights abilities • On top of that, we’re constantly developing epic 3D models, cinematic Trailers, Skins, and Animations to create a visually captivating world. 𝐁𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧 𝐆𝐚𝐦𝐢𝐧𝐠 𝐇𝐮𝐛 Our Web3 Game Launcher will work to onboard even more web2 and web3 games, positioning Katana Inu as the go-to hub for blockchain gaming. 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 & 𝐆𝐫𝐨𝐰𝐭𝐡 We’re also excited to bring the community into the action! Look out for eSports, tournaments, gameplay sessions, and collaborations with our team, plus influencer marketing and new partnerships to grow our player base. Additionally, we’re preparing for a Series A equity raise to support Katana Inu’s continued growth and innovation. Thanks for being part of the $KATA Fam! Follow us for more updates as we build the future of Web3 gaming – Katana Inu is just getting started. 🥷 |
0:53

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐊𝐚𝐭𝐚𝐧𝐚 𝐈𝐧𝐮: 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐖𝐞𝐛𝟑 𝐆𝐚𝐦𝐢𝐧𝐠 & What's coming next? Many people are still discovering what we’re building at Katana Inu, so here’s a quick overview of our vision: Katana Inu is a fun-to-play, pioneering Web3 game that goes beyond the ordinary. We’re creating a dynamic gaming experience that bridges Web2 and Web3 players in a thrilling universe where teams, communities, and memes come together with eSport and tournaments! Katana Inu is a place where gaming and memes collide! 𝐀𝐭 𝐊𝐚𝐭𝐚𝐧𝐚 𝐈𝐧𝐮, 𝐰𝐞’𝐫𝐞 𝐚𝐥𝐥 𝐚𝐛𝐨𝐮𝐭 𝐫𝐞𝐚𝐥 𝐯𝐚𝐥𝐮𝐞: Bringing popular chains, meme icons, and communities into the gaming world as 3D models – think Dogecoin, Shiba Inu, Solana, Polygon and more. Just like other Web2 games integrates popular characters, we’re doing the same with the best of Web3. In the future, we even plan to incorporate anime and pop-culture characters, expanding our world to be a true Web3 cultural hub. Our gameplay is designed for Team vs Team and Battle Royale modes, where players experience a unique mix of fast-paced, sword-and-spell combat. Katana Inu is truly one-of-a-kind, offering Web2 and Web3 gamers alike an engaging world and a reason to keep coming back. 𝐌𝐮𝐥𝐭𝐢𝐜𝐡𝐚𝐢𝐧 𝐑𝐞𝐰𝐚𝐫𝐝𝐬 & 𝐁𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 As a multichain game, Katana Inu collaborates across multiple blockchains, including Solana, Polygon, Immutable, Beam, and Avalanche, giving players access to rewards on different chains while driving real transactions and value into these ecosystems. In addition, we’re implementing a burn system within tournaments and various activities, adding value for players and token holders alike. 𝐎𝐮𝐫 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 We are actively developing our game and platform and here is what's ahead for Katana Inu: • A New Telegram Game with $KATA Airdrops. • Upgrading from UE4.27 to UE5 by December 2024. • Developing the Battle Royale mode through Q1 2025 with Closed Beta testing sas soon as Q4 2024. • Full Closed Beta in Q4 2024, with Open Beta planned for early Q1 2025. • Integrate In-Game wallet system for a seamless player experience. • Launching on Steam and Epic Games to reach a broader audience. • Great Open-Beta Cinematic Trailer with Legends of the Web3 space. • Improving the Game with better quality • Improving the Animations and Fights abilities • On top of that, we’re constantly developing epic 3D models, cinematic Trailers, Skins, and Animations to create a visually captivating world. 𝐁𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧 𝐆𝐚𝐦𝐢𝐧𝐠 𝐇𝐮𝐛 Our Web3 Game Launcher will work to onboard even more web2 and web3 games, positioning Katana Inu as the go-to hub for blockchain gaming. 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 & 𝐆𝐫𝐨𝐰𝐭𝐡 We’re also excited to bring the community into the action! Look out for eSports, tournaments, gameplay sessions, and collaborations with our team, plus influencer marketing and new partnerships to grow our player base. Additionally, we’re preparing for a Series A equity raise to support Katana Inu’s continued growth and innovation. Thanks for being part of the $KATA Fam! Follow us for more updates as we build the future of Web3 gaming – Katana Inu is just getting started. 🥷 |

Katana Inu

103,982 görüntüleme • 1 yıl önce