Загрузка видео...

Не удалось загрузить видео

На главную

.Vogent Voicelab is a high-performance API to run top open-source voice models, like Sesame CSM-1B, Dia, and more. Voicelab optimizes inference for each model so you can run them quickly and scalably— and they post-train select models to improve performance and reliability.

ycombinator's profile picture

Y Combinator

1,543,444 subscribers

54,718 просмотров • 11 месяцев назад •via X (Twitter)

Наука и технологииОбразование
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 7

Фото профиля Rob Imbeault
Rob Imbeault11 месяцев назад

@vogentai Lovin this video

Фото профиля Wolfy
Wolfy11 месяцев назад

@vogentai The interruption 💀

Фото профиля AshutoshShrivastava
AshutoshShrivastava11 месяцев назад

@vogentai This is really cool , love the clarity and how natural it sounds actually .

Фото профиля Catalin Zorzini ʕ•ᴥ•ʔ
Catalin Zorzini ʕ•ᴥ•ʔ11 месяцев назад

@vogentai creative demo!

Фото профиля ANIRUDDHA ADAK
ANIRUDDHA ADAK11 месяцев назад

@vogentai it sounds too human

Фото профиля Nizzy
Nizzy11 месяцев назад

@vogentai wow! this looks interesting

Фото профиля ZAZO
ZAZO11 месяцев назад

@vogentai Congratulations

Похожие видео

You can run local AI models up to 120B without a $10,000 Mac Studio This phone-sized device is your own server for open-source models. 100% local and private. And you can use it to: - Power an agent like OpenClaw 24/7 - Completely replace a chatbot - Literally anything that requires an API It’s called Tiiny, and, once again, you can run the latest open-source models on it. Link below
16:10

You can run local AI models up to 120B without a $10,000 Mac Studio This phone-sized device is your own server for open-source models. 100% local and private. And you can use it to: - Power an agent like OpenClaw 24/7 - Completely replace a chatbot - Literally anything that requires an API It’s called Tiiny, and, once again, you can run the latest open-source models on it. Link below

Paul Couvert

41,185 просмотров • 3 месяцев назад

You can now fine-tune TTS models with Unsloth! Train, run and save models like Sesame-CSM and OpenAI's Whisper locally with our free notebooks. Unsloth makes TTS training 1.5x faster with 50% less VRAM. GitHub: Docs & Notebooks:
0:29

You can now fine-tune TTS models with Unsloth! Train, run and save models like Sesame-CSM and OpenAI's Whisper locally with our free notebooks. Unsloth makes TTS training 1.5x faster with 50% less VRAM. GitHub: Docs & Notebooks:

Unsloth AI

126,049 просмотров • 1 год назад

The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:
0:31

The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:

NVIDIA AI

12,152 просмотров • 3 месяцев назад

✨Introducing Civitai models support. You can now Install ANY models from Civitai. Open and run comfy UI in the cloud. Run via simple API. This is Comfy Deploy. Join discord for the latest updates!
0:51

✨Introducing Civitai models support. You can now Install ANY models from Civitai. Open and run comfy UI in the cloud. Run via simple API. This is Comfy Deploy. Join discord for the latest updates!

BennyKok

24,443 просмотров • 2 лет назад

See what’s new in Gemma 3, the latest generation of open models from Google with sizes ranging from 1B to 27B parameters. These versatile models run quickly and efficiently, and scale to developer needs to provide project flexibility.
4:20

See what’s new in Gemma 3, the latest generation of open models from Google with sizes ranging from 1B to 27B parameters. These versatile models run quickly and efficiently, and scale to developer needs to provide project flexibility.

Google AI Developers

11,391 просмотров • 1 год назад

How to use 50+ API keys (models) for FREE on OpenClaw API??? - go to - login or register your account - click on "more models" - click on "use case" and select what you need it for - choose the model and open it - click on "view code" → "Generate API key" many models don't allow direct deploy, so use the "view code" button to generate API access basically Nvidia NIM gives you the ability to test almost any model from their list for FREE some of them are not worse than GPT 5.2 or Claude Opus 4.6, some might even perform better depending on the task how to understand if a model is efficient and compare it with others??? - go to - type the model name in search - click on "benchmarks" - you’ll see performance tests and rankings this way you can easily compare free models with paid ones of course there are RPM limits, on many models it’s around ~40 requests per minute each model is different, after generating the API key, RPM limits are shown in the top-right corner nothing stops you from using them, many work perfectly fine, super solid option for first tests and for learning OpenClaw or any other system where you need an AI API model
1:54

How to use 50+ API keys (models) for FREE on OpenClaw API??? - go to - login or register your account - click on "more models" - click on "use case" and select what you need it for - choose the model and open it - click on "view code" → "Generate API key" many models don't allow direct deploy, so use the "view code" button to generate API access basically Nvidia NIM gives you the ability to test almost any model from their list for FREE some of them are not worse than GPT 5.2 or Claude Opus 4.6, some might even perform better depending on the task how to understand if a model is efficient and compare it with others??? - go to - type the model name in search - click on "benchmarks" - you’ll see performance tests and rankings this way you can easily compare free models with paid ones of course there are RPM limits, on many models it’s around ~40 requests per minute each model is different, after generating the API key, RPM limits are shown in the top-right corner nothing stops you from using them, many work perfectly fine, super solid option for first tests and for learning OpenClaw or any other system where you need an AI API model

Ronin

58,624 просмотров • 4 месяцев назад

LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's @younesbelkada and Marc Sun, you'll: - Learn how to quantize nearly any open source model - Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library - Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here:
2:50

LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's @younesbelkada and Marc Sun, you'll: - Learn how to quantize nearly any open source model - Use int8 and bfloat16 (Brain float 16) data types to load and run LLMs using PyTorch and the Hugging Face Transformers library - Dive into the technical details of linear quantization to map 32-bit floats to 8-bit integers As models get bigger and bigger, quantization becomes more important for making models practical and accessible. Please check out the course here:

Andrew Ng

288,266 просмотров • 2 лет назад

📢 New in Visual Studio: Bring Your Own Model to Chat We’re expanding flexibility in Copilot for Visual Studio. You can now connect API keys from providers like OpenAI, Anthropic, and Google to run Chat on the model of your choice. This update gives you: ✅ Choice – Access the latest models beyond the built-in defaults. ✅ Customization – Select models that fit your infrastructure and requirements. ✅ Control – Manage API usage, limits, and billing directly with your provider. ✅ Flexibility – Switch seamlessly between Copilot’s built-in models and your own. This release is the first step toward a more open and customizable chat experience in Visual Studio, with more providers and capabilities to come. 🔗
0:26

📢 New in Visual Studio: Bring Your Own Model to Chat We’re expanding flexibility in Copilot for Visual Studio. You can now connect API keys from providers like OpenAI, Anthropic, and Google to run Chat on the model of your choice. This update gives you: ✅ Choice – Access the latest models beyond the built-in defaults. ✅ Customization – Select models that fit your infrastructure and requirements. ✅ Control – Manage API usage, limits, and billing directly with your provider. ✅ Flexibility – Switch seamlessly between Copilot’s built-in models and your own. This release is the first step toward a more open and customizable chat experience in Visual Studio, with more providers and capabilities to come. 🔗

Visual Studio

11,873 просмотров • 10 месяцев назад

JUST IN: 🇨🇳 China’s new open-source AI model, GLM-5.2 by Zhipu AI, reportedly matches Anthropic’s Claude Mythos in security bug detection. The model outperformed some Claude versions in independent cybersecurity benchmarks and can achieve Mythos-level performance. Unlike Claude Mythos, GLM-5.2 is open-source and significantly cheaper to run. The gap between US and Chinese AI models is narrowing rapidly.
0:32

JUST IN: 🇨🇳 China’s new open-source AI model, GLM-5.2 by Zhipu AI, reportedly matches Anthropic’s Claude Mythos in security bug detection. The model outperformed some Claude versions in independent cybersecurity benchmarks and can achieve Mythos-level performance. Unlike Claude Mythos, GLM-5.2 is open-source and significantly cheaper to run. The gap between US and Chinese AI models is narrowing rapidly.

CryptoTweets

59,520 просмотров • 1 день назад

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.
1:24

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

kwindla

274,327 просмотров • 5 месяцев назад

Today we’re launching Model Vault — a dedicated, fully managed platform to run Cohere models securely and at scale. Model Vault delivers the control and isolation of self-hosting, without the operational burden: 🔒 Dedicated, isolated VPC ⚡ No noisy neighbors or rate limits 🔁 Elastic inference with predictable performance 📈 Real-time usage and performance monitoring No sharing. No limits. No surprises. Let Model Vault take care of model scaling and serving — so you can focus on building.
0:33

Today we’re launching Model Vault — a dedicated, fully managed platform to run Cohere models securely and at scale. Model Vault delivers the control and isolation of self-hosting, without the operational burden: 🔒 Dedicated, isolated VPC ⚡ No noisy neighbors or rate limits 🔁 Elastic inference with predictable performance 📈 Real-time usage and performance monitoring No sharing. No limits. No surprises. Let Model Vault take care of model scaling and serving — so you can focus on building.

Cohere

29,267 просмотров • 5 месяцев назад

.Vogent just released Vogent-Turn-80M: an open-source, state-of-the-art turn-detection model for voice AI. It improves any voice AI stack by detecting when to wait and when to speak, reducing interruptions and making conversations more humanlike.
1:17

.Vogent just released Vogent-Turn-80M: an open-source, state-of-the-art turn-detection model for voice AI. It improves any voice AI stack by detecting when to wait and when to speak, reducing interruptions and making conversations more humanlike.

Y Combinator

18,913 просмотров • 8 месяцев назад

At #RenderCon Jules Urbach shared a view for the future of AI and decentralized compute, discussing how the industry is shifting from focusing on pre-training models towards inference time AI applications and agents that can run on The Render Network high performance consumer GPUs.
3:15

At #RenderCon Jules Urbach shared a view for the future of AI and decentralized compute, discussing how the industry is shifting from focusing on pre-training models towards inference time AI applications and agents that can run on The Render Network high performance consumer GPUs.

The Render Network

10,904 просмотров • 1 год назад

Open source/weight models are often used in regulated industries like Health Care or Financial Services, where they handle personally identifiable data, and can't send it to proprietary LLM providers. We recently chatted to Vaibhav (VB) Srivastav about the partnership VS Visual Studio Code and Hugging Face inference providers have to let you use open weight models directly in your IDE! Something that surprised me was just how fast inference providers like Cerebras make it to generate code! In this episode we made a journaling CLI tool using Qwen! I personally love open source and hope more of these models will become small enough to run locally!
1:36

Open source/weight models are often used in regulated industries like Health Care or Financial Services, where they handle personally identifiable data, and can't send it to proprietary LLM providers. We recently chatted to Vaibhav (VB) Srivastav about the partnership VS Visual Studio Code and Hugging Face inference providers have to let you use open weight models directly in your IDE! Something that surprised me was just how fast inference providers like Cerebras make it to generate code! In this episode we made a journaling CLI tool using Qwen! I personally love open source and hope more of these models will become small enough to run locally!

Marlene Mhangami

18,215 просмотров • 7 месяцев назад

Introducing Unsloth Studio ✨ A new open-source web UI to train and run LLMs. • Run models locally on Mac, Windows, Linux • Train 500+ models 2x faster with 70% less VRAM • Supports GGUF, vision, audio, embedding models • Auto-create datasets from PDF, CSV, DOCX • Self-healing tool calling and code execution • Compare models side by side + export to GGUF GitHub: Blog and Guide: Available now on Hugging Face, NVIDIA, Docker and Colab.
0:45

Introducing Unsloth Studio ✨ A new open-source web UI to train and run LLMs. • Run models locally on Mac, Windows, Linux • Train 500+ models 2x faster with 70% less VRAM • Supports GGUF, vision, audio, embedding models • Auto-create datasets from PDF, CSV, DOCX • Self-healing tool calling and code execution • Compare models side by side + export to GGUF GitHub: Blog and Guide: Available now on Hugging Face, NVIDIA, Docker and Colab.

Unsloth AI

1,689,627 просмотров • 3 месяцев назад

The classic sovereign model puzzle is well solved here. 👌🏼 INHO: Get open-source models, optimize and upgrade them to Indian level, and voila! You got foundation models owned and controlled by Indian entities.
0:38

The classic sovereign model puzzle is well solved here. 👌🏼 INHO: Get open-source models, optimize and upgrade them to Indian level, and voila! You got foundation models owned and controlled by Indian entities.

Vijay Shekhar Sharma

49,736 просмотров • 5 месяцев назад

Cerebras Inference powers the world's top models - like Qwen3 Coder at 2000 tokens/s 🤯 And you can now access these models directly in Visual Studio Code with the extension and an API key (that you can get for free from
0:22

Cerebras Inference powers the world's top models - like Qwen3 Coder at 2000 tokens/s 🤯 And you can now access these models directly in Visual Studio Code with the extension and an API key (that you can get for free from

Visual Studio Code

57,360 просмотров • 9 месяцев назад

“Our compute nodes don’t run a full copy of the models we train on IOTA. They actually run a small sliver of the model. This means you can train really large frontier sized models using very small building blocks”. CTO crux unpacks how IOTA ・ SN9’s model parallel architecture allows us to train at scale by splitting our models across multiple machines, and then sewing them together. This is core to SN9’s architecture. See the full Eye On A.I. podcast below.
0:36

“Our compute nodes don’t run a full copy of the models we train on IOTA. They actually run a small sliver of the model. This means you can train really large frontier sized models using very small building blocks”. CTO crux unpacks how IOTA ・ SN9’s model parallel architecture allows us to train at scale by splitting our models across multiple machines, and then sewing them together. This is core to SN9’s architecture. See the full Eye On A.I. podcast below.

IOTA ・ SN9

15,358 просмотров • 1 месяц назад

Fireworks AI is now live on EvoSkill v1.3.0! You can now use Fireworks AI directly with EvoSkill to run fast inference on open models as both the evolution harness backend and the LLM scorer. Alongside Claude API and OpenRouter, Fireworks AI is now a first-class provider in EvoSkill.
0:21

Fireworks AI is now live on EvoSkill v1.3.0! You can now use Fireworks AI directly with EvoSkill to run fast inference on open models as both the evolution harness backend and the LLM scorer. Alongside Claude API and OpenRouter, Fireworks AI is now a first-class provider in EvoSkill.

Sentient

33,208 просмотров • 3 дней назад