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Your AI Voice Partner: Smart, Empathetic & Useful Open-sourced now!!! Introducing Fun-Audio-Chat — a new end-to-end voice model, more than just chat. · An empathetic companion that understands tone and emotion. · A productivity helper that follows voice commands to get things done. · Leader in multiple benchmarks (OpenAudioBench,... show more

Tongyi Lab

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256,132 просмотров • 8 месяцев назад •via X (Twitter)

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🔊🔛🔥 ... Groq Inc launched voice generation today. GroqCloud now has realtime transcription, LLMs, *and* text-to-speech. You can build super-responsive, ultra low-latency voice agents end-to-end entirely on Groq! At Daily, we're big fans of Groq's fast, low-latency inference. Pipecat AI supports all the Groq models, including the new voice model. Lately, we've been obsessively playing a voice chat game that Mark Backman wrote. (My high score is 6, so far.) Here's Mark, with Groq's `Celeste-PlayAI` voice.

🔊🔛🔥 ... Groq Inc launched voice generation today. GroqCloud now has realtime transcription, LLMs, and text-to-speech. You can build super-responsive, ultra low-latency voice agents end-to-end entirely on Groq! At Daily, we're big fans of Groq's fast, low-latency inference. Pipecat AI supports all the Groq models, including the new voice model. Lately, we've been obsessively playing a voice chat game that Mark Backman wrote. (My high score is 6, so far.) Here's Mark, with Groq's `Celeste-PlayAI` voice.

kwindla

31,843 просмотров • 1 год назад

New course: Add voice to your AI agents and applications, built with Vocal Bridge (disclosure: an AI Fund portfolio company) and taught by its CEO Ashwyn Sharma. Voice applications historically required making a hard tradeoff: using fast voice-to-voice models that sacrifice reliability, or accurate speech-to-text pipelines that add latency. This course teaches you how to build voice agents that are both reliable and fast. You'll build three types of voice-enabled applications: a voice-interactive game where voice commands and mouse clicks work together over a single channel, an agent that gains a voice in about 10 lines of code without touching its prompts or tools, and an agent that places outbound phone calls using a make_phone_call function. Skills you'll gain: - Add a voice layer to an existing agent without rewriting your prompts, RAG pipeline, or tools - Give an agent the ability to place outbound calls and stream transcripts back live - Set up voice evaluation to score calls, catch regressions, and improve quality before deployment Join and add voice to your agents without overhauling your architecture:

New course: Add voice to your AI agents and applications, built with Vocal Bridge (disclosure: an AI Fund portfolio company) and taught by its CEO Ashwyn Sharma. Voice applications historically required making a hard tradeoff: using fast voice-to-voice models that sacrifice reliability, or accurate speech-to-text pipelines that add latency. This course teaches you how to build voice agents that are both reliable and fast. You'll build three types of voice-enabled applications: a voice-interactive game where voice commands and mouse clicks work together over a single channel, an agent that gains a voice in about 10 lines of code without touching its prompts or tools, and an agent that places outbound phone calls using a make_phone_call function. Skills you'll gain: - Add a voice layer to an existing agent without rewriting your prompts, RAG pipeline, or tools - Give an agent the ability to place outbound calls and stream transcripts back live - Set up voice evaluation to score calls, catch regressions, and improve quality before deployment Join and add voice to your agents without overhauling your architecture:

Andrew Ng

92,555 просмотров • 2 месяцев назад

NVIDIA just removed the biggest friction point in Voice AI 🤯 They've open-sourced PersonaPlex-7B, a full-duplex conversational model that can listen and speak at the same time. Instead of waiting for you to finish talking, it uses a dual-stream architecture to process and respond in real-time. 100% Open-Source and Free.

NVIDIA just removed the biggest friction point in Voice AI 🤯 They've open-sourced PersonaPlex-7B, a full-duplex conversational model that can listen and speak at the same time. Instead of waiting for you to finish talking, it uses a dual-stream architecture to process and respond in real-time. 100% Open-Source and Free.

Simplifying AI

278,970 просмотров • 7 месяцев назад

Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by dsa (Co-founder & CEO of LiveKit), Shayne (Developer Advocate, LiveKit), and Nedelina Teneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from , a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here:

Andrew Ng

87,711 просмотров • 1 год назад

"To that". Those two words are what Nothing needs to focus on right now. Google just announced Ramble. Basically Nothing's Essential Voice. But. There’s a catch that makes it much more useful: contextual editing. Once you stop talking, you can hit the mic again and refine the text just by saying "to that" at the end. Example: 1. I dictate a rough message to a client. 2. Text appears. 3. "Add some professional flair to that!" 4. Email is instantly ready. Probably is not going to be just a "to that", but the flexibility to either add more text or edit what you just said makes this "AI voice" experience feel complete. I have a potential easy solution: 1. You need to SELECT THE TEXT you want to edit 2. Hit the Essential Voice button (or Key) 3. Done.

"To that". Those two words are what Nothing needs to focus on right now. Google just announced Ramble. Basically Nothing's Essential Voice. But. There’s a catch that makes it much more useful: contextual editing. Once you stop talking, you can hit the mic again and refine the text just by saying "to that" at the end. Example: 1. I dictate a rough message to a client. 2. Text appears. 3. "Add some professional flair to that!" 4. Email is instantly ready. Probably is not going to be just a "to that", but the flexibility to either add more text or edit what you just said makes this "AI voice" experience feel complete. I have a potential easy solution: 1. You need to SELECT THE TEXT you want to edit 2. Hit the Essential Voice button (or Key) 3. Done.

Francesco Sgnaolin

26,818 просмотров • 3 месяцев назад

Tired of voice AI that sounds like a glitchy robot and still needs you to manage phones, infra, numbers, and endless setup? FlashAI 2.0 is officially here – no infra headaches, no phone number provisioning, no devops drama. Just plug in and go. - 100% human-level voice (emotions, pauses, interruptions handled naturally) - Ultra-low latency –> feels real-time - Powered by FlashLabs' proprietary end-to-end speech infrastructure (no stitched APIs) Old voice AI: robotic, laggy, suspicious-sounding toaster that scares customers away. FlashAI 2.0: sounds 100% human, smarter, faster – and when things get tricky, seamless human-in-the-loop handover keeps everything safe & reliable (AI redirects to your team instantly). Ditch the robotic fails and setup nightmares. Level up your calls in 2026. First 100 registrations get priority access + free credits! Who's ready to make voice AI actually work? Reply with your worst bot horror story Register now: Demo here: #VoiceAI #AIAgents #FlashAI #FlashLabs

Tired of voice AI that sounds like a glitchy robot and still needs you to manage phones, infra, numbers, and endless setup? FlashAI 2.0 is officially here – no infra headaches, no phone number provisioning, no devops drama. Just plug in and go. - 100% human-level voice (emotions, pauses, interruptions handled naturally) - Ultra-low latency –> feels real-time - Powered by FlashLabs' proprietary end-to-end speech infrastructure (no stitched APIs) Old voice AI: robotic, laggy, suspicious-sounding toaster that scares customers away. FlashAI 2.0: sounds 100% human, smarter, faster – and when things get tricky, seamless human-in-the-loop handover keeps everything safe & reliable (AI redirects to your team instantly). Ditch the robotic fails and setup nightmares. Level up your calls in 2026. First 100 registrations get priority access + free credits! Who's ready to make voice AI actually work? Reply with your worst bot horror story Register now: Demo here: #VoiceAI #AIAgents #FlashAI #FlashLabs

FlashLabs

514,365 просмотров • 6 месяцев назад

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.

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 просмотров • 5 месяцев назад

Function calling is a powerful way to extend the capabilities of LLMs and AI agents by letting them use external tools. Our new short course Function calling and Data Extraction with LLMs, created with @NexusflowX and taught by Jiantao Jiao and Venkat, demonstrates how to prompt LLMs to form calls to external functions. You'll work with NexusRavenV2-13B, a 13B parameter open-source model that excels in function calling tasks while still being small enough to host locally. Learn to use function calling to extract structured data from unstructured text and access web APIs, and build an end-to-end application that processes customer service transcripts. You'll learn how to build LLM-powered applications that can analyze feedback, automate data entry, and enhance search. Please get started here:

Function calling is a powerful way to extend the capabilities of LLMs and AI agents by letting them use external tools. Our new short course Function calling and Data Extraction with LLMs, created with @NexusflowX and taught by Jiantao Jiao and Venkat, demonstrates how to prompt LLMs to form calls to external functions. You'll work with NexusRavenV2-13B, a 13B parameter open-source model that excels in function calling tasks while still being small enough to host locally. Learn to use function calling to extract structured data from unstructured text and access web APIs, and build an end-to-end application that processes customer service transcripts. You'll learn how to build LLM-powered applications that can analyze feedback, automate data entry, and enhance search. Please get started here:

Andrew Ng

110,630 просмотров • 2 лет назад

Tesla plans to have fully integrated native voice control with commands that can safely control the vehicle "Currently, Tesla vehicles do have Grok, and you can chat with it We have our own audio model that's not meant for natural language interaction right now; it's more for understanding the noises around the car, like sirens and emergency vehicles In the future, we will have fully integrated voice control commands that can control the vehicle. It's just a bit too early for that. It opens up an entire area of testing that we have to do for example, you shouldn't be able to tell the car to crash, and it shouldn't crash There's a lot of safety that needs to be done to protect against adversary attacks using voice, and it's just not worth the trouble right now" - Ashok Elluswamy (VP of AI at Tesla)

Tesla plans to have fully integrated native voice control with commands that can safely control the vehicle "Currently, Tesla vehicles do have Grok, and you can chat with it We have our own audio model that's not meant for natural language interaction right now; it's more for understanding the noises around the car, like sirens and emergency vehicles In the future, we will have fully integrated voice control commands that can control the vehicle. It's just a bit too early for that. It opens up an entire area of testing that we have to do for example, you shouldn't be able to tell the car to crash, and it shouldn't crash There's a lot of safety that needs to be done to protect against adversary attacks using voice, and it's just not worth the trouble right now" - Ashok Elluswamy (VP of AI at Tesla)

X Freeze

37,849 просмотров • 6 месяцев назад

Text-to-speech is moving way too fast. Just a few days ago, I tweeted about PersonaPlex-7B, NVIDIA's new open source TTS ( And today, Qwen just open-sourced Qwen3-TTS 🤯 It’s a revolutionary text-to-speech model built for control. Not just about generating speech, but about shaping how it sounds directly from language. You can guide the pace, the tone, and the expressiveness straight from text, without touching audio graphs or hand-tuning parameters. That’s the real shift! What makes Qwen3-TTS stand out is how practical it already is: → voice cloning from just a few seconds of audio → voice creation without any reference sample → support for 10 languages out of the box → end-to-end latency down to ~97ms → works in both streaming and non-streaming setups The models come in two sizes (0.6B and 1.7B), so you can trade off quality and hardware cost depending on your setup. You can work with curated voices, designed voices, or cloned ones, and it integrates cleanly with vLLM for production use. It also ships as a simple Python package you can pip install. If you’re building real-time voice systems, this removes a lot of friction! 100% free and open source. I put the repo in the 🧵↓

Text-to-speech is moving way too fast. Just a few days ago, I tweeted about PersonaPlex-7B, NVIDIA's new open source TTS ( And today, Qwen just open-sourced Qwen3-TTS 🤯 It’s a revolutionary text-to-speech model built for control. Not just about generating speech, but about shaping how it sounds directly from language. You can guide the pace, the tone, and the expressiveness straight from text, without touching audio graphs or hand-tuning parameters. That’s the real shift! What makes Qwen3-TTS stand out is how practical it already is: → voice cloning from just a few seconds of audio → voice creation without any reference sample → support for 10 languages out of the box → end-to-end latency down to ~97ms → works in both streaming and non-streaming setups The models come in two sizes (0.6B and 1.7B), so you can trade off quality and hardware cost depending on your setup. You can work with curated voices, designed voices, or cloned ones, and it integrates cleanly with vLLM for production use. It also ships as a simple Python package you can pip install. If you’re building real-time voice systems, this removes a lot of friction! 100% free and open source. I put the repo in the 🧵↓

Charly Wargnier

59,144 просмотров • 7 месяцев назад

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:

Andrew Ng

118,254 просмотров • 10 месяцев назад

Smart Turn v2: open source, native audio turn detection in 14 languages. New checkpoint of the open source, open data, open training code, semantic VAD model on Hugging Face, fal, and Pipecat AI. - 3x faster inference (12ms on an L40) - 14 languages (13 more than v1, which was english-only) - New synthetic data set `chirp_3_all` with ~163k audio samples - 99% accuracy on held out `human_5_all` test data Good turn detection is critical for voice agents. This model "understands" both semantic and audio patterns, and mitigates the voice AI trade-off between unwanted turn latency vs the agent interrupting people before they are finished speaking. Training scripts for both Modal and local training are in the repo. We want to make it as easy as possible to contribute to or customize this model! Here's a demo running the smart-turn model with default settings, aimed at generally hitting 400ms total turn detection time. You can tune things to be faster, too. You can help by contributing data, doing architecture expermints, or cleaning open source data! Keep reading ...

Smart Turn v2: open source, native audio turn detection in 14 languages. New checkpoint of the open source, open data, open training code, semantic VAD model on Hugging Face, fal, and Pipecat AI. - 3x faster inference (12ms on an L40) - 14 languages (13 more than v1, which was english-only) - New synthetic data set `chirp_3_all` with ~163k audio samples - 99% accuracy on held out `human_5_all` test data Good turn detection is critical for voice agents. This model "understands" both semantic and audio patterns, and mitigates the voice AI trade-off between unwanted turn latency vs the agent interrupting people before they are finished speaking. Training scripts for both Modal and local training are in the repo. We want to make it as easy as possible to contribute to or customize this model! Here's a demo running the smart-turn model with default settings, aimed at generally hitting 400ms total turn detection time. You can tune things to be faster, too. You can help by contributing data, doing architecture expermints, or cleaning open source data! Keep reading ...

kwindla

42,246 просмотров • 1 год назад

Today we're thrilled to announce the first end-to-end platform for Reinforcement Fine-Tuning. With just a dozen labeled data points, you can outperform #OpenAI o1 and #DeepSeekR1 on complex tasks. Built on the #GRPO methodology that DeepSeek-R1 popularized, our platform delivers exceptional results. In our real-world PyTorch to Triton transpilation case study, we achieved 3x higher accuracy than OpenAI o1 and DeepSeek-R1 when writing GPU code. Check out the thread below to learn how you can adapt an #opensource #LLM to your use cases with unmatched efficiency. #rft

Today we're thrilled to announce the first end-to-end platform for Reinforcement Fine-Tuning. With just a dozen labeled data points, you can outperform #OpenAI o1 and #DeepSeekR1 on complex tasks. Built on the #GRPO methodology that DeepSeek-R1 popularized, our platform delivers exceptional results. In our real-world PyTorch to Triton transpilation case study, we achieved 3x higher accuracy than OpenAI o1 and DeepSeek-R1 when writing GPU code. Check out the thread below to learn how you can adapt an #opensource #LLM to your use cases with unmatched efficiency. #rft

Predibase by Rubrik

1,022,507 просмотров • 1 год назад

ChatGPT: Opens a new tab, you copy-paste. Siri: Transcribes text, you still do the work. Lemon: Completes the entire task while you keep working. Traditional voice assistants are convenient to use. But they cannot complete tasks for you. That's why Lemon stands out. Lemon is an AI agent that completes tasks from voice commands, without opening new tabs or switching apps. Press the fn key. Say what you need done. Watch it happen: ➟ "Reply to Sarah's email and tell her I'll have the report by Friday" — sent ➟ "Create a project brief for the new client" — done ➟ "Search for the latest stats on AI adoption" — on your screen ➟ "Make this email sound more professional" — edited ➟ "Give me three angles for this campaign" — ideas generated ➟ "Write this paragraph in a more casual tone" — rewritten No new tabs. No manual steps. No switching back and forth. Lemon works across every app on your Mac. The results: ✓ Users save 2+ hours daily ✓ Reply to emails 12x faster ✓ Open 70% fewer tabs ✓ Type 5x faster than traditional workflows Your voice isn't just for dictation anymore. It's a faster way to complete your work tasks. 📌 Try Lemon (it's free for 30 days): 🔄 Repost to help others work faster — #AI #Agent #LemonPartnership

ChatGPT: Opens a new tab, you copy-paste. Siri: Transcribes text, you still do the work. Lemon: Completes the entire task while you keep working. Traditional voice assistants are convenient to use. But they cannot complete tasks for you. That's why Lemon stands out. Lemon is an AI agent that completes tasks from voice commands, without opening new tabs or switching apps. Press the fn key. Say what you need done. Watch it happen: ➟ "Reply to Sarah's email and tell her I'll have the report by Friday" — sent ➟ "Create a project brief for the new client" — done ➟ "Search for the latest stats on AI adoption" — on your screen ➟ "Make this email sound more professional" — edited ➟ "Give me three angles for this campaign" — ideas generated ➟ "Write this paragraph in a more casual tone" — rewritten No new tabs. No manual steps. No switching back and forth. Lemon works across every app on your Mac. The results: ✓ Users save 2+ hours daily ✓ Reply to emails 12x faster ✓ Open 70% fewer tabs ✓ Type 5x faster than traditional workflows Your voice isn't just for dictation anymore. It's a faster way to complete your work tasks. 📌 Try Lemon (it's free for 30 days): 🔄 Repost to help others work faster — #AI #Agent #LemonPartnership

Andrew Bolis

91,782 просмотров • 6 месяцев назад

Another Hailuo AI (MiniMax) H3 test, this time looking at audio reference and performance (A little NSFW language on end of this one!). I recorded the dialogue as an audio track and fed that in as a reference with a image reference of the man. In the text prompts I gave some direction to the actor (Like the look to camera at the end). I think the model did a brilliant job of matching the performance to the words and emotion that were in the audio ref. Unlike Seedance 2.0 I didn't have to feed in black video or write the words in the prompt to get the words correct. It seemed to use the exact audio from the reference I provided, which is what you want when using voice actors. Things are getting real... fast

Another Hailuo AI (MiniMax) H3 test, this time looking at audio reference and performance (A little NSFW language on end of this one!). I recorded the dialogue as an audio track and fed that in as a reference with a image reference of the man. In the text prompts I gave some direction to the actor (Like the look to camera at the end). I think the model did a brilliant job of matching the performance to the words and emotion that were in the audio ref. Unlike Seedance 2.0 I didn't have to feed in black video or write the words in the prompt to get the words correct. It seemed to use the exact audio from the reference I provided, which is what you want when using voice actors. Things are getting real... fast

Uncanny Harry AI

14,036 просмотров • 24 дней назад

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. :-)

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

40,593 просмотров • 3 месяцев назад

Voice In My Head, a collaboration with Kyle McDonald. With the proliferation of generated content, AI now seeps constantly into our consciousness. What happens when it begins to intervene directly into your thoughts? Where the people you interact with, the things you do, are guided by an AI enhanced voice that speaks to you the way you’d like to be spoken to. Voice In My Head explores the implications for AI (ChatGPT) to listen and intervene in your social experience in real-time, augmenting your personality. The piece begins with an onboarding session where you place a bud in your ear and the voice asks you to reflect on the inner voice you were born with. What if it could be more caring? Less obsessive? Less judgmental? More helpful? What if you could change your inner monologue? As you respond to the onboarding questions, it clones the sound of your voice and uses it to speak to you. Then you go out into the world, as the voice follows along and offers commentary and direction. The resulting performance calls into question how natural vs synthetic each person’s thoughts actually are. Do any of us have our own point of view? Trailer directed by David Leonard.

Voice In My Head, a collaboration with Kyle McDonald. With the proliferation of generated content, AI now seeps constantly into our consciousness. What happens when it begins to intervene directly into your thoughts? Where the people you interact with, the things you do, are guided by an AI enhanced voice that speaks to you the way you’d like to be spoken to. Voice In My Head explores the implications for AI (ChatGPT) to listen and intervene in your social experience in real-time, augmenting your personality. The piece begins with an onboarding session where you place a bud in your ear and the voice asks you to reflect on the inner voice you were born with. What if it could be more caring? Less obsessive? Less judgmental? More helpful? What if you could change your inner monologue? As you respond to the onboarding questions, it clones the sound of your voice and uses it to speak to you. Then you go out into the world, as the voice follows along and offers commentary and direction. The resulting performance calls into question how natural vs synthetic each person’s thoughts actually are. Do any of us have our own point of view? Trailer directed by David Leonard.

Lauren Lee McCarthy

96,073 просмотров • 2 лет назад

Internet Computer's Tech Powers Autonomous Enterprise Superintelligence Internet Computer (DFINITY Foundation)'s "Cloud Engine" architecture is transitioning the ecosystem from a static smart contract platform to a living "AIware" environment. This paradigm shift enables decentralized software to rebuild itself in real time by integrating AI agents directly into applications' persistent memory. Unlike legacy IT stacks where software is "set in stone," this end-to-end solution allows corporate logic to evolve fluidly, bypassing traditional development roadmaps and eliminating the $240B annual cost of technical debt and vendor lock-in. ICP tech is literally a new paradigm here that provides an end-to-end solution: open source, program is the database, native AIware, tamperproof, and it runs on your choice of cloud, according to ICP CBO Pierre.

Internet Computer's Tech Powers Autonomous Enterprise Superintelligence Internet Computer (DFINITY Foundation)'s "Cloud Engine" architecture is transitioning the ecosystem from a static smart contract platform to a living "AIware" environment. This paradigm shift enables decentralized software to rebuild itself in real time by integrating AI agents directly into applications' persistent memory. Unlike legacy IT stacks where software is "set in stone," this end-to-end solution allows corporate logic to evolve fluidly, bypassing traditional development roadmaps and eliminating the $240B annual cost of technical debt and vendor lock-in. ICP tech is literally a new paradigm here that provides an end-to-end solution: open source, program is the database, native AIware, tamperproof, and it runs on your choice of cloud, according to ICP CBO Pierre.

BSCN

22,917 просмотров • 2 месяцев назад

Walmart updated their new AI call system for when customer call in It now says, “This call and your voice may be recorded for quality or other business purposes” You won’t believe what their “other business serves” are… It is not limited to AI training, voice cloning datasets, emotion tracking, behavioral analysis, customer profiling, speech recognition systems So when you call into Walmart they are literally recording. then storing your voice. It can possibly be used for things like “voice cloning” and more This is horrifying and it’s really happening Here are some of the more controversial uses: - behavioral profiling - emotion detection - cross-linking voice data with customer profiles - long-term biometric storage - AI model training without clear opt-in transparency (You are literally just calling in to talk to customer service) Even worse, modern AI systems can extract a lot more information from speech than people realize: - accent - emotional state - stress - probable age range - speaking habits - identity likelihood - purchase intent - deception indicators (though these systems are often unreliable) All this and voice cloning…. Some states have started to pass legislation to stop this but it’s still in the early stages so companies are getting away with it

Walmart updated their new AI call system for when customer call in It now says, “This call and your voice may be recorded for quality or other business purposes” You won’t believe what their “other business serves” are… It is not limited to AI training, voice cloning datasets, emotion tracking, behavioral analysis, customer profiling, speech recognition systems So when you call into Walmart they are literally recording. then storing your voice. It can possibly be used for things like “voice cloning” and more This is horrifying and it’s really happening Here are some of the more controversial uses: - behavioral profiling - emotion detection - cross-linking voice data with customer profiles - long-term biometric storage - AI model training without clear opt-in transparency (You are literally just calling in to talk to customer service) Even worse, modern AI systems can extract a lot more information from speech than people realize: - accent - emotional state - stress - probable age range - speaking habits - identity likelihood - purchase intent - deception indicators (though these systems are often unreliable) All this and voice cloning…. Some states have started to pass legislation to stop this but it’s still in the early stages so companies are getting away with it

Wall Street Apes

113,181 просмотров • 3 месяцев назад