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GW Key Manipulator Fixer Control Basics 2 #Maya Ababel model by SkyMedic Gnome Voice Leo Greystone🔞🎙️

Gnome Wizard

20,344 subscribers

127,399 просмотров • 3 месяцев назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 8

Фото профиля JFJ🇺🇸

JFJ🇺🇸3 месяцев назад

@SkyMedicpro @LeoGreystoneVA See this is all we'll and cool but im not buying Maya.

Фото профиля Gnome Wizard

Gnome Wizard3 месяцев назад

@LeoGreystoneVA

Фото профиля Goblin Shark

Goblin Shark3 месяцев назад

@SkyMedicpro @LeoGreystoneVA I appreciate this wizard, i would like to see more of this wizard.

Фото профиля Breadman🍞

Breadman🍞3 месяцев назад

@SkyMedicpro @LeoGreystoneVA no idea how this started but indeed was it impressive

Фото профиля Lordship of Uncreation (Busy)

Lordship of Uncreation (Busy)3 месяцев назад

@SkyMedicpro @LeoGreystoneVA m m m

Фото профиля Meguido

Meguido3 месяцев назад

@SkyMedicpro @LeoGreystoneVA It looks amazing! Please tell me you have another place from where I can buy it. Gumroad don't let me buy from them 😩

Фото профиля Gnome Wizard

Gnome Wizard3 месяцев назад

@SkyMedicpro @LeoGreystoneVA I accept Paypal and Payoneer payments, is it suitable for you? Write to me in private messages, I will send the script, after payment

Фото профиля Souper Hot

Souper Hot3 месяцев назад

@SkyMedicpro @LeoGreystoneVA the way you are moving those hips, reminds me of Mohawk Hudson Cbat song

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Punching Donut 🥊🍩

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fallow me on patreon and suscribestar or newgrounds too, i'm posting there some thing too cast: #17 by: Leo Greystone🔞🎙️ #18 by: 💖 SilkyMilk - Adult VA 💖 Cell by: RB After Dark (NSFW COMMISSION OPEN) Music by: Mono Memory

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Breeze TTS 2 by BreezeBlue AI dropped on Hugging Face as the #1 open weights TTS model on Artificial Analysis It does 🎨 Voice Design (prompt a voice) 🎙️ Voice Reference (upload a voice) 🎛️ Voice Direction (upload & prompt a voice) vibe it on Spaces ▶️

Breeze TTS 2 by BreezeBlue AI dropped on Hugging Face as the #1 open weights TTS model on Artificial Analysis It does 🎨 Voice Design (prompt a voice) 🎙️ Voice Reference (upload a voice) 🎛️ Voice Direction (upload & prompt a voice) vibe it on Spaces ▶️

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Today we’re releasing Chroma 1.0 → the world first open-source, end-to-end, real-time speech-to-speech model → with personalized voice cloning Trained by FlashLabs. Deployed on FlashAI👉 An open research-grade alternative to the OpenAI Realtime model. Voice Test dubbing Elon Musk and Lex Fridman: 🔥What’s real (evals and benchmarks attached): ⚡ 2× faster than real-time ) 📘 SGLang Cookbook: 📄 Paper + benchmarks: 🤗 Models: 💻 Inference code: 🔁 RT if you believe real-time AI should be open 💬 Reply if you’re building on Conversational Voice AI products

Today we’re releasing Chroma 1.0 → the world first open-source, end-to-end, real-time speech-to-speech model → with personalized voice cloning Trained by FlashLabs. Deployed on FlashAI👉 An open research-grade alternative to the OpenAI Realtime model. Voice Test dubbing Elon Musk and Lex Fridman: 🔥What’s real (evals and benchmarks attached): ⚡ 2× faster than real-time ) 📘 SGLang Cookbook: 📄 Paper + benchmarks: 🤗 Models: 💻 Inference code: 🔁 RT if you believe real-time AI should be open 💬 Reply if you’re building on Conversational Voice AI products

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1,249,347 просмотров • 8 месяцев назад

VoxCPM 2 just dropped by OpenBMB Only 2B-param open-source TTS (Text-to-Speech) model built for production-grade multilingual voice work. Apache-2.0 license, Can run on only 8GB VRAM. • Eliminates the "robotic" feel of traditional TTS, delivering prosody and emotional depth suitable for high-stakes professional environments like filmmaking, gaming, animation, and audiobooks. • 30-language multilingual: no language tag needed, just type in a supported language and generate directly. • Voice design: create a brand-new voice from a text description alone, like age, tone, pace, or emotion. No reference audio required. Describe the desired voice characteristics (gender, age, tone, emotion, pace …) in Control Instruction, and VoxCPM2 will craft a unique voice from your description alone. • Controllable cloning: clone from a short clip, then steer delivery style without losing the speaker’s core voice. • Ultimate cloning: use reference audio + transcript for continuation-style cloning that keeps the tiny vocal details. • 48kHz output: takes 16kHz reference audio and produces studio-quality speech without an external upsampler. • Real-time ready: around 0.3 RTF on RTX 4090, even lower with Nano-VLLM. • Commercial use: Apache-2.0 licensed. Developer-Friendly Infrastructure: - Native Torch Inference: Direct support for PyTorch-based workflows. - Training Flexibility: Supports both full-parameter and LoRA fine-tuning for specific domain adaptation. - Production Readiness: Compatible with voxcpm-nanovllm for large-scale, high-concurrency deployment.

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Cleo 3.0 is live. The world’s first AI financial assistant just took a giant leap forward! Here’s what’s new: 1. Agentic architecture. Cleo can now complete complex tasks on her own by choosing the right tools and figuring out when to use them. 2. Proactive reasoning. Using an advanced reasoning model, Cleo reviews your transactions and surfaces insights before you even ask. 3. Memory. Cleo remembers key context, like your budgeting goals and financial stressors, so every chat builds on the last. 4. Voice. You can talk to Cleo like a real person. She talks back. This update outperformed top models from OpenAI, Anthropic, and Google on real-world financial tasks, but the real test is how Cleo works for you. See how we’re changing the game ➡️

Cleo

1,317,094 просмотров • 1 год назад

We partnered with artists, designers, and builders to create new AI tools that solve real problems in their creative workflows. Here’s what’s new: — Introducing Google Pics in Google Workspace: A brand-new image creation & editing tool. Move and resize objects, add text, and translate just by hovering and clicking — Big updates to صافي النيه😉: 1) You can now create with Gemini Omni Flash in Google Flow 2) Google Flow Agent is a multi-step creative partner that reasons and plans complex tasks with you. 3) Google Flow tools are custom tools you can “vibe code” for animations, video effects, text layering & more — Design live with Stitch by Google: Now, you can use text or voice prompts to edit layouts in real time then export those designs straight to code — More creative control in صافي النيه😉Music: Edit songs section by section, remix the style of full songs, and create music videos with our new Gemini Omni Flash model

Google AI

13,954,807 просмотров • 4 месяцев назад

QVAC SDK 0.12.0 is now live, bringing longer context, increased memory optimisation, new modalities, and broader ecosystem support directly to your device. Key Features and Updates: - TurboQuant KV-Cache Quantization: Fit much longer context in the same memory. TurboQuant, an algorithm from Google Research, compresses the KV cache by up to 5x, near-lossless. - Text-to-Video: Generate video from a text prompt, fully local, with the new wan2.1 model in the Diffusion addon - Apple Metal Performance for Flux2-klein: Diffusion on Apple Silicon now matches MLX performance, the native benchmark for Apple GPUs - Robot Control (new VLA addon): A GGML-based Vision-Language-Action addon brings fast, efficient robot control to edge devices - Coding Assistant / Harness Support: QVAC now works with OpenCode and OpenClaw as a local provider. A new @qvac/ai-sdk-provider package automates model registry and provider integration - Cross-Platform Voice: Text-to-speech and Parakeet transcription moved from ONNX to the GGML engine for better CPU and GPU support on macOS, iOS, Windows, Linux, and Android. Parakeet also adds long-term streaming diarization (tracking who spoke when on live audio) - Faster Lightweight Visual Classification: A new GGML-based Classification addon delivers millisecond-level classification, useful where a vision-language model (VLM) would be unnecessarily slow - Under the Hood: Fabric synced to llama.cpp v8828 (from v8189), plus GPU acceleration added to image-upscale models for faster results Full release notes:

QVAC SDK 0.12.0 is now live, bringing longer context, increased memory optimisation, new modalities, and broader ecosystem support directly to your device. Key Features and Updates: - TurboQuant KV-Cache Quantization: Fit much longer context in the same memory. TurboQuant, an algorithm from Google Research, compresses the KV cache by up to 5x, near-lossless. - Text-to-Video: Generate video from a text prompt, fully local, with the new wan2.1 model in the Diffusion addon - Apple Metal Performance for Flux2-klein: Diffusion on Apple Silicon now matches MLX performance, the native benchmark for Apple GPUs - Robot Control (new VLA addon): A GGML-based Vision-Language-Action addon brings fast, efficient robot control to edge devices - Coding Assistant / Harness Support: QVAC now works with OpenCode and OpenClaw as a local provider. A new @qvac/ai-sdk-provider package automates model registry and provider integration - Cross-Platform Voice: Text-to-speech and Parakeet transcription moved from ONNX to the GGML engine for better CPU and GPU support on macOS, iOS, Windows, Linux, and Android. Parakeet also adds long-term streaming diarization (tracking who spoke when on live audio) - Faster Lightweight Visual Classification: A new GGML-based Classification addon delivers millisecond-level classification, useful where a vision-language model (VLM) would be unnecessarily slow - Under the Hood: Fabric synced to llama.cpp v8828 (from v8189), plus GPU acceleration added to image-upscale models for faster results Full release notes:

QVAC

9,932,369 просмотров • 4 месяцев назад

Today, we laid the physics foundation for our ARC fusion power plant. ⚛️ With 5 deeply researched papers — validated by independent peer review and published in the Journal of Plasma Physics — we’ve shown we've nailed the scientific basics of producing copious amounts of fusion power. The collective assessment from our 58 co-authors? This machine will work. No scientific breakthroughs are required to bring this clean, secure, abundant source of energy to the grid. Here is how we're handling fusion's biggest challenges: ⚡ Powering the Grid: Using extremely strong magnets, ARC will confine the plasma long enough to generate 1.1 GW of fusion power. We'll convert that into 400 MW of continuous net electricity — enough to power ~280,000 average American homes. 🛡️ Handling Heat Exhaust: To control a superhot, unruly cloud of charged particles, we're utilizing proven methods to safely handle the heat exhaust that acts as a key practical constraint for tokamaks. ✅ Managing Disruptions: We aren't trying to build an operationally perfect machine. We are pragmatically designing ARC to safely handle disruptions and keep the plasma stable for top performance. 🏗️ Proving the Approach: We're building on decades of tokamak research and supercomputer simulations. And we're proving our approach right now with SPARC, the tokamak we are actively building in Massachusetts. With our transparency, you don’t have to take our assertions on faith. We are really pushing fusion forward.

Today, we laid the physics foundation for our ARC fusion power plant. ⚛️ With 5 deeply researched papers — validated by independent peer review and published in the Journal of Plasma Physics — we’ve shown we've nailed the scientific basics of producing copious amounts of fusion power. The collective assessment from our 58 co-authors? This machine will work. No scientific breakthroughs are required to bring this clean, secure, abundant source of energy to the grid. Here is how we're handling fusion's biggest challenges: ⚡ Powering the Grid: Using extremely strong magnets, ARC will confine the plasma long enough to generate 1.1 GW of fusion power. We'll convert that into 400 MW of continuous net electricity — enough to power ~280,000 average American homes. 🛡️ Handling Heat Exhaust: To control a superhot, unruly cloud of charged particles, we're utilizing proven methods to safely handle the heat exhaust that acts as a key practical constraint for tokamaks. ✅ Managing Disruptions: We aren't trying to build an operationally perfect machine. We are pragmatically designing ARC to safely handle disruptions and keep the plasma stable for top performance. 🏗️ Proving the Approach: We're building on decades of tokamak research and supercomputer simulations. And we're proving our approach right now with SPARC, the tokamak we are actively building in Massachusetts. With our transparency, you don’t have to take our assertions on faith. We are really pushing fusion forward.

Commonwealth Fusion Systems

11,413 просмотров • 4 месяцев назад

NordSpace is proud to announce another major orbital launch program milestone. Alongside our ongoing orbital hardware development, we have successfully completed the high-fidelity trajectory and flight dynamics model for our Tundra orbital rocket after 2 years of intense design effort, thousands of simulations, and hundreds of physical tests. This high-fidelity model is far more than a simulation milestone or simply selecting the vehicle's capabilities. It is the cornerstone of the entire vehicle design loop, the foundation required for regulatory commercial flight approval, and the only way to confidently predict and guarantee the performance required to achieve orbit. It integrates detailed aerodynamics, structural loads, propulsion constraints, flight mechanics, guidance, navigation, and control, and much more into a single unified framework that will guide every major design decision moving forward. Crossing this milestone gives us the ability to continue to rapidly advance the engineering efforts behind our orbital launch architecture from engines and tanks, to GNC and GSE. Uniquely, we're focused on a lot more than just a light-lift vehicle. Our entire architecture is based upon selecting key technologies and design pathways that result in the most efficient progression from Tundra (500 kg to LEO, 350 kg to SSO) to Titan, our reusable medium lift vehicle (5,000 kg to LEO, 3,500 kg to SSO). Our architecture even allows for the option to extend Tundra without major modifications to a Tundra+ variant, allowing for 1,100 kg to LEO and 850 kg to SSO. It is crucial to balance the thousands of variables and parameters that go into selecting a scalable and flexible architecture which benefits from the flight heritage of decades of rocket development before us, while also ensuring the capability is globally competitive and domestically relevant to Canada. Our Atlantic Spaceport Complex (ASX) in Newfoundland and Labrador will be experiencing significant growth, investment and construction over the coming year as it prepares to host Tundra's launches. Owning and operating our own infrastructure and manufacturing facilities, end-to-end, enables the maximum level of flexibility and efficiency we need to meet Canada's timelines to develop sovereign space launch. Let's launch the north! Transport Canada National Defence Defence Research and Development Canada Canadian Space Agency

NordSpace is proud to announce another major orbital launch program milestone. Alongside our ongoing orbital hardware development, we have successfully completed the high-fidelity trajectory and flight dynamics model for our Tundra orbital rocket after 2 years of intense design effort, thousands of simulations, and hundreds of physical tests. This high-fidelity model is far more than a simulation milestone or simply selecting the vehicle's capabilities. It is the cornerstone of the entire vehicle design loop, the foundation required for regulatory commercial flight approval, and the only way to confidently predict and guarantee the performance required to achieve orbit. It integrates detailed aerodynamics, structural loads, propulsion constraints, flight mechanics, guidance, navigation, and control, and much more into a single unified framework that will guide every major design decision moving forward. Crossing this milestone gives us the ability to continue to rapidly advance the engineering efforts behind our orbital launch architecture from engines and tanks, to GNC and GSE. Uniquely, we're focused on a lot more than just a light-lift vehicle. Our entire architecture is based upon selecting key technologies and design pathways that result in the most efficient progression from Tundra (500 kg to LEO, 350 kg to SSO) to Titan, our reusable medium lift vehicle (5,000 kg to LEO, 3,500 kg to SSO). Our architecture even allows for the option to extend Tundra without major modifications to a Tundra+ variant, allowing for 1,100 kg to LEO and 850 kg to SSO. It is crucial to balance the thousands of variables and parameters that go into selecting a scalable and flexible architecture which benefits from the flight heritage of decades of rocket development before us, while also ensuring the capability is globally competitive and domestically relevant to Canada. Our Atlantic Spaceport Complex (ASX) in Newfoundland and Labrador will be experiencing significant growth, investment and construction over the coming year as it prepares to host Tundra's launches. Owning and operating our own infrastructure and manufacturing facilities, end-to-end, enables the maximum level of flexibility and efficiency we need to meet Canada's timelines to develop sovereign space launch. Let's launch the north! Transport Canada National Defence Defence Research and Development Canada Canadian Space Agency

NordSpace 🇨🇦

27,412 просмотров • 10 месяцев назад

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

135,556 просмотров • 3 месяцев назад