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Introducing Octave 2: our next-generation multilingual text-to-speech model What’s new: - Fluent in 11+ languages - 40% faster (<200ms latency⁠⁠) & 50% cheaper than Octave 1 - Multi-speaker conversation - More reliable pronunciation - New voice conversion & phoneme editing capabilities For the month of October, we’re offering 50%...

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Hume AI

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BUILD 🔥: Microsoft is preparing new image and voice models for the announcement on June 2. > MAI Voice 2, a multilingual model supporting 15 news languages and a wider range of emotional spectrum (check voice samples in the article) > MAI Transcribe 1.5, a new model for speech-to-text use cases. > MAI Image 2.5, already announced last week, is now available on LM Arena in preview. Compared to MAI Image 2, it supports file uploads and can be used for image editing.
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BUILD 🔥: Microsoft is preparing new image and voice models for the announcement on June 2. > MAI Voice 2, a multilingual model supporting 15 news languages and a wider range of emotional spectrum (check voice samples in the article) > MAI Transcribe 1.5, a new model for speech-to-text use cases. > MAI Image 2.5, already announced last week, is now available on LM Arena in preview. Compared to MAI Image 2, it supports file uploads and can be used for image editing.

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Learn about Google’s new SOTA image model, Gemini 2.5 Flash, its key capabilities, and what’s next on the roadmap with some of the team behind the model Nicole Brichtova Kaushik Shivakumar Mostafa Dehghani Robert Riachi with Logan Kilpatrick. Timecodes: 0:37 New model introduction 01:21 Demo: Image editing 03:44 Text rendering capabilities 04:44 Beyond human preference evals 06:44 Text rendering as a proxy for quality 08:38 Positive transfer between modalities 11:25 Demo: multi-turn, context aware image generation 13:54 Pixel-perfect editing and character consistency 15:51 Interleaved image generation 17:59 Specialized vs. native models 19:52 Understanding nuanced prompts 20:59 User feedback shaping model development 22:37 Improvements in character consistency 24:17 More natural looking images from team collaboration 26:41 What’s next for image generation models
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Learn about Google’s new SOTA image model, Gemini 2.5 Flash, its key capabilities, and what’s next on the roadmap with some of the team behind the model Nicole Brichtova Kaushik Shivakumar Mostafa Dehghani Robert Riachi with Logan Kilpatrick. Timecodes: 0:37 New model introduction 01:21 Demo: Image editing 03:44 Text rendering capabilities 04:44 Beyond human preference evals 06:44 Text rendering as a proxy for quality 08:38 Positive transfer between modalities 11:25 Demo: multi-turn, context aware image generation 13:54 Pixel-perfect editing and character consistency 15:51 Interleaved image generation 17:59 Specialized vs. native models 19:52 Understanding nuanced prompts 20:59 User feedback shaping model development 22:37 Improvements in character consistency 24:17 More natural looking images from team collaboration 26:41 What’s next for image generation models

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QT FUNDED

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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
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We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:
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Tencent Hy

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Today, we’re introducing new tools and model updates to help you build, deploy, and scale Voice AI applications.🎙️ 🆕 Speech Understanding: Turn transcripts into actionable data with speaker identification, custom formatting & translation 🆕 LLM Gateway: One API for your voice-to-intelligence pipeline, with GPT, Claude, Gemini & more 🆕 Voice AI Guardrails: End-to-end protection for safe, compliant content Model Upgrades: ✅ 99 languages with auto code-switching ✅ 64% fewer speaker counting errors ✅ 57% better accuracy on critical terms with 1,000-word context Real results: 🔥 Calabrio: 80% boost in customer satisfaction, 22% revenue increase 🔥 Siro: "10/10 customers say 'wow, that insight was crisp'" Build & scale Voice AI in minutes! Try it in our Playground or check the docs. 🚀
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Today, we’re introducing new tools and model updates to help you build, deploy, and scale Voice AI applications.🎙️ 🆕 Speech Understanding: Turn transcripts into actionable data with speaker identification, custom formatting & translation 🆕 LLM Gateway: One API for your voice-to-intelligence pipeline, with GPT, Claude, Gemini & more 🆕 Voice AI Guardrails: End-to-end protection for safe, compliant content Model Upgrades: ✅ 99 languages with auto code-switching ✅ 64% fewer speaker counting errors ✅ 57% better accuracy on critical terms with 1,000-word context Real results: 🔥 Calabrio: 80% boost in customer satisfaction, 22% revenue increase 🔥 Siro: "10/10 customers say 'wow, that insight was crisp'" Build & scale Voice AI in minutes! Try it in our Playground or check the docs. 🚀

AssemblyAI

18,105 Aufrufe • vor 9 Monaten

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Better/faster/cheaper voice AI turn detection with Gemini 2.0 The code that determines when the agent should respond to the user is some of the most important code in your voice AI agent. The technical terms for this job are "turn detection" or "phrase endpointing." If the voice AI responds before the user has finished their thought, the conversation is choppy and unproductive. If the AI waits too long, the conversation is slow and frustrating. There are a number of ways to approach this. You can: 1. Use a fast "voice activity detection" model to detect pauses in speech. Respond when the user pauses. 2. Use a specialized phrase endpointing model that operates on transcribed text, pattern-matching on text semantics. 3. Train a specialized phrase endpointing model that operates directly on audio. 4. Leverage the native audio capabilities of a SOTA LLM like Gemini 2.0. We've benchmarked all four of these, and Gemini 2.0 currently beats other approaches. Using Gemini is also cheaper than transcribing the audio separately using a transcription service or model. Here's a short video showing Gemini phrase endpointing in two scenarios. First, correctly handling pauses in natural conversation. Second, requesting a phone number (which is a common activity in a use case like customer support). You can see the Completeness check lines in the terminal output, printed each time Gemini processes a chunk of audio.

kwindla

64,184 Aufrufe • vor 1 Jahr

Last month, we released Lyria 3, enabling you to create tracks with lyrics from text, image, or video prompts. Now, we’re introducing Lyria 3 Pro, which expands upon our music generation model to offer additional advanced capabilities. What’s really special about this upgrade is that the model now understands the architecture of music. This makes it possible to prompt for intros, verses, choruses and bridges + generate songs with more complex transitions. You can also create tracks up to 3 minutes long, a big change from previous models that were limited to 30 second tracks. Use Lyria 3 Pro to build upon your existing creativity. We’re excited for your beats to drop 🎶
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Last month, we released Lyria 3, enabling you to create tracks with lyrics from text, image, or video prompts. Now, we’re introducing Lyria 3 Pro, which expands upon our music generation model to offer additional advanced capabilities. What’s really special about this upgrade is that the model now understands the architecture of music. This makes it possible to prompt for intros, verses, choruses and bridges + generate songs with more complex transitions. You can also create tracks up to 3 minutes long, a big change from previous models that were limited to 30 second tracks. Use Lyria 3 Pro to build upon your existing creativity. We’re excited for your beats to drop 🎶

Google AI

81,399 Aufrufe • vor 4 Monaten

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
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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

22,949 Aufrufe • vor 5 Monaten

We’re still buzzing from my running mate Nicole Shanahan’s amazing speech in Oakland last month and the new energy she’s brought to our campaign. We’re here to radically change the political conversation and to put issues on the map that both parties have neglected. While about one in 5 voters already supports us, to get on the ballot in all 50 states and the District of Columbia and take the White House, we need financial support. The April 30th FEC deadline is around the corner, and your contribution today means more than ever. DONATE NOW ➡️ #KennedyShanahan24
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We’re still buzzing from my running mate Nicole Shanahan’s amazing speech in Oakland last month and the new energy she’s brought to our campaign. We’re here to radically change the political conversation and to put issues on the map that both parties have neglected. While about one in 5 voters already supports us, to get on the ballot in all 50 states and the District of Columbia and take the White House, we need financial support. The April 30th FEC deadline is around the corner, and your contribution today means more than ever. DONATE NOW ➡️ #KennedyShanahan24

Robert F. Kennedy Jr

257,159 Aufrufe • vor 2 Jahren

Starting today, open source is leading the way. Introducing Llama 3.1: Our most capable models yet. Today we’re releasing a collection of new Llama 3.1 models including our long awaited 405B. These models deliver improved reasoning capabilities, a larger 128K token context window and improved support for 8 languages among other improvements. Llama 3.1 405B rivals leading closed source models on state-of-the-art capabilities across a range of tasks in general knowledge, steerability, math, tool use and multilingual translation. The models are available to download now directly from Meta or Hugging Face. With today’s release the ecosystem is also ready to go with 25+ partners rolling out our latest models — including Amazon Web Services, NVIDIA, Databricks, Groq Inc, Dell Technologies, Microsoft Azure and Google Cloud ready on day one. More details in the full announcement ➡️ Download Llama 3.1 models ➡️ With these releases we’re setting the stage for unprecedented new opportunities and we can’t wait to see the innovation our newest models will unlock across all levels of the AI community.
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Starting today, open source is leading the way. Introducing Llama 3.1: Our most capable models yet. Today we’re releasing a collection of new Llama 3.1 models including our long awaited 405B. These models deliver improved reasoning capabilities, a larger 128K token context window and improved support for 8 languages among other improvements. Llama 3.1 405B rivals leading closed source models on state-of-the-art capabilities across a range of tasks in general knowledge, steerability, math, tool use and multilingual translation. The models are available to download now directly from Meta or Hugging Face. With today’s release the ecosystem is also ready to go with 25+ partners rolling out our latest models — including Amazon Web Services, NVIDIA, Databricks, Groq Inc, Dell Technologies, Microsoft Azure and Google Cloud ready on day one. More details in the full announcement ➡️ Download Llama 3.1 models ➡️ With these releases we’re setting the stage for unprecedented new opportunities and we can’t wait to see the innovation our newest models will unlock across all levels of the AI community.

AI at Meta

1,268,895 Aufrufe • vor 2 Jahren

Open science is how we continue to push technology forward and today at Meta FAIR we’re sharing eight new AI research artifacts including new models, datasets and code to inspire innovation in the community. More in the video from Joelle Pineau. This work is another important step towards our goal of achieving Advanced Machine Intelligence (AMI). What we’re releasing: • Meta Spirit LM: An open source language model for seamless speech and text integration. • Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. Plus a new developer suite to make it easier for developers to build with SAM 2. • Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance. • SALSA: New code to enable researchers to benchmark AI-based attacks in support of validating security for post-quantum cryptography. • Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale. • Meta Open Materials: New open source models and the largest dataset of its kind to accelerate AI-driven discovery of new inorganic materials. • MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder with coverage across 80 languages. • Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations. Access to state-of-the-art AI creates opportunities for everyone. We’re excited to share this work and look forward to seeing the community innovation that results from it. Details and access to everything released by FAIR today ➡️
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Open science is how we continue to push technology forward and today at Meta FAIR we’re sharing eight new AI research artifacts including new models, datasets and code to inspire innovation in the community. More in the video from Joelle Pineau. This work is another important step towards our goal of achieving Advanced Machine Intelligence (AMI). What we’re releasing: • Meta Spirit LM: An open source language model for seamless speech and text integration. • Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. Plus a new developer suite to make it easier for developers to build with SAM 2. • Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance. • SALSA: New code to enable researchers to benchmark AI-based attacks in support of validating security for post-quantum cryptography. • Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale. • Meta Open Materials: New open source models and the largest dataset of its kind to accelerate AI-driven discovery of new inorganic materials. • MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder with coverage across 80 languages. • Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations. Access to state-of-the-art AI creates opportunities for everyone. We’re excited to share this work and look forward to seeing the community innovation that results from it. Details and access to everything released by FAIR today ➡️

AI at Meta

150,222 Aufrufe • vor 1 Jahr

Carney: Now, above all, we will build Canadian. The new security threats reaching our shores mean we’re making the biggest investments in our military in generations. But up until now, more than 70 cents of every dollar of military capital spending by our government has gone to the United States. With our new Defense Investment Agency, we’ll make sure we build more of our security and defense capabilities here at home—creating new careers in aerospace, shipbuilding, cyber, and AI for Canadian engineers, technicians, and scientists. It’s the same thing with steel. Consider that while 80% of the steel Europe buys is manufactured in Europe, and 75% of the steel the U.S. buys is made in the U.S., only 40% of the steel we use is made here. That doesn’t make sense—not when we have the people, the resources, and the companies to build right here in Canada. So, our upcoming budget will move forward with our Buy Canadian policy—prioritizing Canadian steel, aluminum, lumber, manufactured goods, and technology for Canadian projects. With new orders for homes, infrastructure, and defense equipment becoming orders for Canadian suppliers, we will be our own best customer
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Carney: Now, above all, we will build Canadian. The new security threats reaching our shores mean we’re making the biggest investments in our military in generations. But up until now, more than 70 cents of every dollar of military capital spending by our government has gone to the United States. With our new Defense Investment Agency, we’ll make sure we build more of our security and defense capabilities here at home—creating new careers in aerospace, shipbuilding, cyber, and AI for Canadian engineers, technicians, and scientists. It’s the same thing with steel. Consider that while 80% of the steel Europe buys is manufactured in Europe, and 75% of the steel the U.S. buys is made in the U.S., only 40% of the steel we use is made here. That doesn’t make sense—not when we have the people, the resources, and the companies to build right here in Canada. So, our upcoming budget will move forward with our Buy Canadian policy—prioritizing Canadian steel, aluminum, lumber, manufactured goods, and technology for Canadian projects. With new orders for homes, infrastructure, and defense equipment becoming orders for Canadian suppliers, we will be our own best customer

Acyn

239,493 Aufrufe • vor 9 Monaten

🚀 Next Phase Unlocked: Remittix Bonus Update Following the incredible momentum that got us past the soft cap, we’re excited to keep things moving with a brand new limited-time offer. As we set our sights on the 20 million dollar milestone, we’re introducing a 40% bonus on all token purchases. This offer is available when you use the promo code WALLET40 during your transaction. Also, our 10% referral bonus remains active, giving you the chance to earn even more by bringing others into the Remittix ecosystem. With tokens still priced at $0.0895, this is another strong opportunity for new and returning investors to get in before the next price adjustment. Let’s keep the momentum going. The journey is far from over. Warm Regards, Remittix Team
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🚀 Next Phase Unlocked: Remittix Bonus Update Following the incredible momentum that got us past the soft cap, we’re excited to keep things moving with a brand new limited-time offer. As we set our sights on the 20 million dollar milestone, we’re introducing a 40% bonus on all token purchases. This offer is available when you use the promo code WALLET40 during your transaction. Also, our 10% referral bonus remains active, giving you the chance to earn even more by bringing others into the Remittix ecosystem. With tokens still priced at $0.0895, this is another strong opportunity for new and returning investors to get in before the next price adjustment. Let’s keep the momentum going. The journey is far from over. Warm Regards, Remittix Team

Remittix

10,381 Aufrufe • vor 11 Monaten

🚀 Today Photoroom is announcing the next generation of AI photo-editing features. 🚀 We’re dropping 5 new AI features, including a text-to-image model, generative fill, and our new-and-improved AI backgrounds, plus launching the world’s first foundation model trained specifically for product photography. 🖼️ AI Images: Use AI to generate high-quality images from scratch. Create anything you can imagine, at the click of a button. 💗 AI Expand: Use generative AI to realistically expand the scene around your image. 🎑 AI Backgrounds: Generate stunningly realistic AI backgrounds for product photos. ⏳ AI Fill: Take total control of your AI images, by customizing an item or area of the picture. 🔄 Auto-rotate: Correct your camera angle with AI for more professional results. 🔎 AI Upscale: Get better quality images by enhancing the resolution of your images. If our 6x feature drop wasn’t enough, we also just closed our series B funding round, to the tune of $43 million 🎤 #Photoroom #generativeAI #AI #productphotography #ecommerce
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🚀 Today Photoroom is announcing the next generation of AI photo-editing features. 🚀 We’re dropping 5 new AI features, including a text-to-image model, generative fill, and our new-and-improved AI backgrounds, plus launching the world’s first foundation model trained specifically for product photography. 🖼️ AI Images: Use AI to generate high-quality images from scratch. Create anything you can imagine, at the click of a button. 💗 AI Expand: Use generative AI to realistically expand the scene around your image. 🎑 AI Backgrounds: Generate stunningly realistic AI backgrounds for product photos. ⏳ AI Fill: Take total control of your AI images, by customizing an item or area of the picture. 🔄 Auto-rotate: Correct your camera angle with AI for more professional results. 🔎 AI Upscale: Get better quality images by enhancing the resolution of your images. If our 6x feature drop wasn’t enough, we also just closed our series B funding round, to the tune of $43 million 🎤 #Photoroom #generativeAI #AI #productphotography #ecommerce

Photoroom

22,236 Aufrufe • vor 2 Jahren

Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)
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Cerebras inference is very fast. So fast that it changes how we think about configuring our LLMs for voice agent use cases. Kimi K2.6 is a 1T parameter reasoning model that Cerebras serves at 650 - 1,000 tokens per second (end-to-end throughput), with time to first token metrics as low as 150ms (latency). These numbers are two to three times faster than other similarly capable models. The biggest lever we get from this kind of speed is that we can use the model in reasoning mode, and still have excellent "time to first non-thinking token." This solves a big pain point we have in 2026 for voice agent use cases. Almost all recent innovation in post-training has focused on making models good at reasoning ("test time compute"). This is great, but it makes the user-facing model latency much, much slower. Which is a problem for conversational voice agents. We can run Kimi K2.6 with reasoning turned on, and get responses faster than other models produce with reasoning disabled. On my 30-turn voice agent benchmark, Kimi K2.6 with reasoning enabled ties GPT 5.1 and Haiku 4.5 with reasoning disabled, and is still about 200ms seconds faster! On my primary task agent benchmark, Kimi K2.6 is now the #2 model. It ranks just behind Gemini 3.5 Flash in "high" reasoning mode, and tied with GLM 5, Sonnet 4.6, and GPT 5.4 with reasoning set to "low." But Kimi K2.6 completes each turn in the agent loop in under 500ms. The other four models are all at least 3x slower. (Models only qualify for this benchmark if they can complete task turns at a P50 <4s.) A couple of other things that this speed buys us, for production voice agents: - Tool calls happen fast enough that we don't have to work around tool call latency in our pipeline design. - We can prompt the model to output structured data at the beginning of a response, followed by plain text for voice generation. This opens up possibilities like asking the model to do complex classification/generation tasks that influence the rest of the pipeline. For example, the model could create a detailed style prompt for a steerable TTS model, for each individual conversation turn. And, of course, you can use Kimi K2.6 with reasoning turned off. Cerebras calls this "instant" mode. Here's a video of a Cerebras Kimi K2.6 voice agent with voice-to-voice response time, measured at the client, under 500ms. This is the true response latency as perceived by the user, including all network and audio codec overhead, transcription and turn detection, Kimi K2.6 token generation, and voice generation. 500ms is, effectively, instant. So the Cerebras naming for this mode is a propos. :-)

kwindla

40,319 Aufrufe • vor 2 Monaten

AI commercials are becoming the standard. They’re faster, more affordable, and creatively limitless. This is the new wave of advertising. At NFINITY Studios, we turn complex ideas into clear, compelling stories. Visually striking. Easy to understand. Built for impact. $NFNT x $VRA Working together with PLRL by Verasity, we’re taking the next step. We’ll use their player to reach broader audiences and push our content further. But more importantly; we’re opening the doors. Creators in the Verasity ecosystem will be able to use NFINITY for free. They’ll learn what’s possible with AI video. They’ll become part of the next generation of creators. Because NFINITY isn’t just a tool. We’re here to reshape how stories are told; and who gets to tell them. This is how we revolutionize the commercial space. By making it accessible. By making it meaningful. The age of AI creators has begun.
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AI commercials are becoming the standard. They’re faster, more affordable, and creatively limitless. This is the new wave of advertising. At NFINITY Studios, we turn complex ideas into clear, compelling stories. Visually striking. Easy to understand. Built for impact. $NFNT x $VRA Working together with PLRL by Verasity, we’re taking the next step. We’ll use their player to reach broader audiences and push our content further. But more importantly; we’re opening the doors. Creators in the Verasity ecosystem will be able to use NFINITY for free. They’ll learn what’s possible with AI video. They’ll become part of the next generation of creators. Because NFINITY isn’t just a tool. We’re here to reshape how stories are told; and who gets to tell them. This is how we revolutionize the commercial space. By making it accessible. By making it meaningful. The age of AI creators has begun.

NFINITY AI

96,354 Aufrufe • vor 1 Jahr