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stumbled onto something genuinely impressive today - real-time interactive AI avatars with 180ms latency let me break down why I think this is one of the more underrated AI products right now 👇 most AI agents are either a chat box or a robotic voice. anam gives them a... face. photorealistic. micro-expressions. no looping, no mouth-dubbing nonsense. the numbers are hard to ignore: → +44% engagement when your agent has a face vs text/voice only → CARA-3 (their latest model) is #1 across ALL avatar benchmarks in 2025 → 33% faster than the next-best competitor → 180ms avg response time, literally faster than a blink → 70+ languages with native voices → plug in your own LLM, your own voice, your own avatar image use cases they're already disrupting: customer support, sales, medical services, language tutoring, skill training and it's actually developer-friendly. clean SDK, LiveKit + Pipecat integrations, no-code option if you don't want to touch the API if you're building AI products or advising companies on AI strategy, this is one to watchshow more

Vyom

11,579 subscribers

268,158 просмотров • 5 месяцев назад •via X (Twitter)

Образование Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 29

Фото профиля Mark Richards

Mark Richards5 месяцев назад

That was so wild I had to record it. it literally felt like a natural, bilingual zoom call. (PS. zero affiliation. I hadn't even heard of them until i saw your post)

Фото профиля Dan Benyamin (Æ)

Dan Benyamin (Æ)5 месяцев назад

The founders are ex-synthesia, wonder how that's working out 😅

Фото профиля Vyom

Vyom5 месяцев назад

Why did something before? I’m not aware !

Фото профиля Prasad Pilla

Prasad Pilla5 месяцев назад

this is such a well thought website

Фото профиля Yev Marusenko

Yev Marusenko5 месяцев назад

Infrastructure tool or they are consumer app?

Фото профиля Vyom

Vyom5 месяцев назад

Consumer

Фото профиля Yev Marusenko

Yev Marusenko5 месяцев назад

Oh surprising actually. Thought it’s for devs. But yea this will be more common soon.

Фото профиля Carsten

Carsten5 месяцев назад

Website design is fantastic at least !!!

Фото профиля AI Mastery Guide

AI Mastery Guide5 месяцев назад

44% more engagement just from giving your agent a face is a number that should make every customer support team stop and rethink their setup

Фото профиля sarwech

sarwech5 месяцев назад

How does it compare to Tavus?

Фото профиля Vyom

Vyom5 месяцев назад

Never heard of Tavus

Фото профиля sarwech

sarwech5 месяцев назад

It’s probably the leader in this space

Фото профиля Muhammad Anas

Muhammad Anas5 месяцев назад

Giving AI agents a realistic face & real-time interaction changes the experience completely. Feels way more human than another chatbot window.

Фото профиля Kanhaiya Yadav

Kanhaiya Yadav5 месяцев назад

Crazy 😳

Фото профиля Ivan Flugelman — VVSVS™

Ivan Flugelman — VVSVS™5 месяцев назад

you should check @Ojin_ai unless this is an…unannounced paid partnership

Фото профиля Habibi ❎ (💰//acc) | 𐤊

Habibi ❎ (💰//acc) | 𐤊5 месяцев назад

@yzzztv_official

Фото профиля Jesse Lau

Jesse Lau5 месяцев назад

First time I have been blown away by one of these.

Фото профиля Ron Shah

Ron Shah5 месяцев назад

this site is mad buggy.

Фото профиля Gavin Payne

Gavin Payne5 месяцев назад

very cool

Фото профиля Ahmed Goda

Ahmed Goda5 месяцев назад

Hey, I think would make a much stronger brand for an avatar platform than generic brandable names

Фото профиля Mo Ayob

Mo Ayob5 месяцев назад

This is interesting. Do you know if they do digital cloning ?

Фото профиля Percy Kintu

Percy Kintu5 месяцев назад

Now give them a real phone number plus memory and it’s over

Фото профиля Rhy

Rhy5 месяцев назад

gave it a try but the outcome fell short of the marketing hype around it

Фото профиля TJ

TJ5 месяцев назад

Plugging my boy @interact_studio

Фото профиля NAITIVE

NAITIVE5 месяцев назад

:)

Фото профиля Vadim | Sollas

Vadim | Sollas5 месяцев назад

Nice soundtrack

Фото профиля NIHAL JANI

NIHAL JANI5 месяцев назад

This is good but if you want your own avatar, new gemini omni isnt bad.its expressive too. Dont know about latency though.

Фото профиля Parag Arora

Parag Arora5 месяцев назад

Stumble upon too someday :)

Фото профиля Adam Delehanty

Adam Delehanty5 месяцев назад

“Genuinely” is slop language

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Lenny Rachitsky

176,860 просмотров • 9 месяцев назад

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.

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

64,261 просмотров • 1 год назад

Impressive use of #chatgpt to prepare a PowerPoint Presentation! Prompt: “I want you to write me VBA code for a PowerPoint presentation about the history of AI. You are to fill in all the text with your own knowledge, no placeholders. I need 5 slides.” Crt: Avin Foo #AI #tech

Impressive use of #chatgpt to prepare a PowerPoint Presentation! Prompt: “I want you to write me VBA code for a PowerPoint presentation about the history of AI. You are to fill in all the text with your own knowledge, no placeholders. I need 5 slides.” Crt: Avin Foo #AI #tech

Pascal Bornet

108,721 просмотров • 3 лет назад

Introducing PhoneLLM, an open model for voice agents. GPT 5.6 Terra performance on typical voice agent tasks at 1/3 the latency and 1/18 the cost. For voice agents, we need models that are both very low latency and very good at tool calling and instruction following. There's a trade-off here, and we often have to compromise on either latency or capability when building voice agents. With PhoneLLM (and the training and data stack that made this model possible) we're fixing this problem. For the last couple of years, most of the effort in frontier model development has gone towards leveraging test-time compute. Which is awesome! Models of all shapes and sizes are available that perform really, really well ... if you have "thinking" turned on for your model. But if you need your agent to respond at voice conversation speed, you can't use thinking models. PhoneLLM is a full-weights fine-tune of NVIDIA Nemotron Nano 30B. We trained on a wide range of real-world telephone and customer support use cases. The training focused on taking the excellent Nano 30B base capabilities and teaching the model to do typical voice agent tasks with thinking disabled. The results are really good: accurate tool calling and concise, on-topic responses in long conversations. And fast: TTFAT measured server-side is <100ms if you run PhoneLLM on a lightly loaded B200. :-) But seriously, when we characterize model latency, we do it with full, end-to-end, batched request simulations using real Pipecat voice agent pipelines. You can serve more than 80 concurrent agents on a single B200 with P95 end-to-end TTFAT <600ms. Including network overhead. That's an LLM cost-per-minute around $0.0025. (1/4 of a cent.) At a latency lower than any third-party API offers today. More details about this model, including weights on Hugging Face, how to spin it up with one click on Modal, and a starter project repo you can clone, are in the thread ...

Introducing PhoneLLM, an open model for voice agents. GPT 5.6 Terra performance on typical voice agent tasks at 1/3 the latency and 1/18 the cost. For voice agents, we need models that are both very low latency and very good at tool calling and instruction following. There's a trade-off here, and we often have to compromise on either latency or capability when building voice agents. With PhoneLLM (and the training and data stack that made this model possible) we're fixing this problem. For the last couple of years, most of the effort in frontier model development has gone towards leveraging test-time compute. Which is awesome! Models of all shapes and sizes are available that perform really, really well ... if you have "thinking" turned on for your model. But if you need your agent to respond at voice conversation speed, you can't use thinking models. PhoneLLM is a full-weights fine-tune of NVIDIA Nemotron Nano 30B. We trained on a wide range of real-world telephone and customer support use cases. The training focused on taking the excellent Nano 30B base capabilities and teaching the model to do typical voice agent tasks with thinking disabled. The results are really good: accurate tool calling and concise, on-topic responses in long conversations. And fast: TTFAT measured server-side is <100ms if you run PhoneLLM on a lightly loaded B200. :-) But seriously, when we characterize model latency, we do it with full, end-to-end, batched request simulations using real Pipecat voice agent pipelines. You can serve more than 80 concurrent agents on a single B200 with P95 end-to-end TTFAT <600ms. Including network overhead. That's an LLM cost-per-minute around $0.0025. (1/4 of a cent.) At a latency lower than any third-party API offers today. More details about this model, including weights on Hugging Face, how to spin it up with one click on Modal, and a starter project repo you can clone, are in the thread ...

kwindla

335,479 просмотров • 2 месяцев назад