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Introducing Real-time Transcription with Nvidia Parakeet - Same top accuracy as file transcription - Best-in-market 160 ms lips-to-screen latency - 744x more cost-efficient compared to cloud APIs - Available in Argmax Pro SDK starting today! Link in comments

argmax

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60,779 görüntüleme • 1 yıl önce •via X (Twitter)

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Introducing Real-time Transcription with Speakers! - Step change in accuracy, surpassing top cloud APIs - Faster than real-time on Mac and iPhone - Still under 3 watts when all features are enabled Available in Argmax SDK 2.0 for early access! Benchmarks and details in comments.

Introducing Real-time Transcription with Speakers! - Step change in accuracy, surpassing top cloud APIs - Faster than real-time on Mac and iPhone - Still under 3 watts when all features are enabled Available in Argmax SDK 2.0 for early access! Benchmarks and details in comments.

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Introducing Argmax Local Server Run our state-of-the-art real-time transcription server directly on Mac! 0:31 Feature complete for AI Meeting Notes apps 0:49 Migrate from cloud APIs with 1 line of code 1:05 Fastest speech models with top accuracy 1:31 Other apps do not slow down Available starting today with Python, JavaScript, and Rust clients! Details in comments

Introducing Argmax Local Server Run our state-of-the-art real-time transcription server directly on Mac! 0:31 Feature complete for AI Meeting Notes apps 0:49 Migrate from cloud APIs with 1 line of code 1:05 Fastest speech models with top accuracy 1:31 Other apps do not slow down Available starting today with Python, JavaScript, and Rust clients! Details in comments

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Real-time Transcription with Speakers is now generally available on iOS and macOS! Details for installing or simply testing Argmax SDK 2 are in the comments.

Real-time Transcription with Speakers is now generally available on iOS and macOS! Details for installing or simply testing Argmax SDK 2 are in the comments.

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TTSKit now achieves sub-100ms time-to-first-byte for Qwen3-TTS 1.7b on Apple Silicon! Link to the code repo and details in comments.

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Today, we’re introducing Gemini 3.5 Transcribe, our most precise speech-to-text model yet, designed for intelligent voice interactions in Google Antigravity. It pairs screen context and chat history, with your permission, to ensure pinpoint transcription accuracy across file names, agent thoughts, and active documents.

Today, we’re introducing Gemini 3.5 Transcribe, our most precise speech-to-text model yet, designed for intelligent voice interactions in Google Antigravity. It pairs screen context and chat history, with your permission, to ensure pinpoint transcription accuracy across file names, agent thoughts, and active documents.

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Introducing WhisperKit

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Introducing Gemini 3.5 Transcribe, our most precise speech-to-text model yet, designed for intelligent voice interactions! 💬 Available for both real-time speech and audio file transcription, with support for: 💡 smart transcription: rewrite and format text based on intent. 🌍 support for 85+ languages, regional accents and diverse dialects. 📚 custom vocabulary: recognize jargon and specific spellings. 💯 high precision in alphanumerics. 🫂 multi-speaker identification.

Introducing Gemini 3.5 Transcribe, our most precise speech-to-text model yet, designed for intelligent voice interactions! 💬 Available for both real-time speech and audio file transcription, with support for: 💡 smart transcription: rewrite and format text based on intent. 🌍 support for 85+ languages, regional accents and diverse dialects. 📚 custom vocabulary: recognize jargon and specific spellings. 💯 high precision in alphanumerics. 🫂 multi-speaker identification.

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Argmax now runs on Google Tensor TPU, the first-ever SDK to harness this edge inference accelerator! Tensor TPU enabled us to deploy billion-scale transformers reliably on Pixel phones without impacting battery life or resource contention with traditional workloads.

Argmax now runs on Google Tensor TPU, the first-ever SDK to harness this edge inference accelerator! Tensor TPU enabled us to deploy billion-scale transformers reliably on Pixel phones without impacting battery life or resource contention with traditional workloads.

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Greetings. I've just pressed the option key and now I am holding it, which is why you see that small reddish capsule at the top of my screen, flashing in synchrony with my voice volume, etcetera. Anyway, I hope that didn't somehow clip the audio. The reason I'm doing this is to show off the fact that I've integrated NVIDIA's new parakeet, or relatively new parakeet, voice transcription speech to text model into Hex, my open source free Mac audio transcription tool. And Parakeet is ridiculously fast, so I will stop rambling in just a moment and release the option key, and it will transcribe and paste into the text box. So yeah, that was very fast. Faster than Whisper. Certainly faster and more accurate than Mac dictation. Uh so yeah, give it a shot and enjoy. The download link to Hex will be uh pasted beneath this transcription.

Greetings. I've just pressed the option key and now I am holding it, which is why you see that small reddish capsule at the top of my screen, flashing in synchrony with my voice volume, etcetera. Anyway, I hope that didn't somehow clip the audio. The reason I'm doing this is to show off the fact that I've integrated NVIDIA's new parakeet, or relatively new parakeet, voice transcription speech to text model into Hex, my open source free Mac audio transcription tool. And Parakeet is ridiculously fast, so I will stop rambling in just a moment and release the option key, and it will transcribe and paste into the text box. So yeah, that was very fast. Faster than Whisper. Certainly faster and more accurate than Mac dictation. Uh so yeah, give it a shot and enjoy. The download link to Hex will be uh pasted beneath this transcription.

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Listen, speak, handle interruptions, and call tools in one live conversation. 🎙️ Introducing NVIDIA-NemotronLabs-VoiceChat-11B, NVIDIA’s end-to-end full-duplex model for real-time voice agents. 🤖 🏆 It ranks #2 among open full-duplex models on both VoiceBench and Full-Duplex-Bench 1.0. ⚡ Natural turn-taking responds in ~448 ms, while barge-in lets users interrupt the model with ~480 ms latency. 🛠️ The first open full-duplex model with live tool calling. One unified system handles speech understanding, generation, transcription, and tool execution while keeping the conversation flowing. 🧠 Built with a Fast Conformer, Nemotron Nano v2, and a TTS decoder, trained on ~550K hours of real and synthetic speech. 📜 Research use only. OpenMDW 1.1.

Listen, speak, handle interruptions, and call tools in one live conversation. 🎙️ Introducing NVIDIA-NemotronLabs-VoiceChat-11B, NVIDIA’s end-to-end full-duplex model for real-time voice agents. 🤖 🏆 It ranks #2 among open full-duplex models on both VoiceBench and Full-Duplex-Bench 1.0. ⚡ Natural turn-taking responds in ~448 ms, while barge-in lets users interrupt the model with ~480 ms latency. 🛠️ The first open full-duplex model with live tool calling. One unified system handles speech understanding, generation, transcription, and tool execution while keeping the conversation flowing. 🧠 Built with a Fast Conformer, Nemotron Nano v2, and a TTS decoder, trained on ~550K hours of real and synthetic speech. 📜 Research use only. OpenMDW 1.1.

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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.

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.

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Real-time transcription just got a significant upgrade. Universal-3-Pro is now available for streaming — bringing AssemblyAI's most accurate speech model to live audio for the first time. Developers building voice agents, live captioning tools, and real-time analytics pipelines now get three things they've been asking for: 🔹 Best-in-class word error and entity detection across streaming ASR benchmarks 🔹 Real-time speaker labels — know who said what, as it happens 🔹 Superior entity detection for names, places, orgs, and specialized terminology in real-time 🔹 Code-switching and global language coverage built-in

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🚀 New Generally Available Whisper drop: The fastest, most accurate, and cost-effective transcription with over 1000x real-time factor for production AI workloads. 🚀 Our new Generally Available Whisper implementation delivers: 🏎️ Over 1000x real-time factor ✨ The lowest word error rate 💪 Production-grade reliability 🧩 Custom scaling and hardware per processing step 👉 See how in our blog: Reach out to get record-breaking performance for your mission-critical AI workloads!

🚀 New Generally Available Whisper drop: The fastest, most accurate, and cost-effective transcription with over 1000x real-time factor for production AI workloads. 🚀 Our new Generally Available Whisper implementation delivers: 🏎️ Over 1000x real-time factor ✨ The lowest word error rate 💪 Production-grade reliability 🧩 Custom scaling and hardware per processing step 👉 See how in our blog: Reach out to get record-breaking performance for your mission-critical AI workloads!

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after three months of building, i'm officially sharing my app sono 🥳 sono is my 100% offline transcription and summarization app! you can use it to: - transcribe voice notes, real-time audio, videos in 99 languages - share transcripts as txt, pdf, docx, or srt file - get ai summary and chat i built this app because i didn't want my recordings in the cloud + didn't want to pay recurring subscriptions sono is just $4.99 one-time while most ai notetakers charge $100+ annually would mean a lot if u try it out!

after three months of building, i'm officially sharing my app sono 🥳 sono is my 100% offline transcription and summarization app! you can use it to: - transcribe voice notes, real-time audio, videos in 99 languages - share transcripts as txt, pdf, docx, or srt file - get ai summary and chat i built this app because i didn't want my recordings in the cloud + didn't want to pay recurring subscriptions sono is just $4.99 one-time while most ai notetakers charge $100+ annually would mean a lot if u try it out!

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I ran the same task on Claude Code and DeepSeek's new agent harness. One cost $150. The other cost $2. Today we're launching (AgentSky), the "OpenRouter for Agents" — one API → Claude Code, Codex, DeepSeek, Kimi, OpenCode, and every major agent in the cloud. And Agent Playground on top: race them on your own task, with your real tools (GitHub, Gmail, more), side by side in a browser: time, cost, tokens burnt. Guess which one was $2.

I ran the same task on Claude Code and DeepSeek's new agent harness. One cost $150. The other cost $2. Today we're launching (AgentSky), the "OpenRouter for Agents" — one API → Claude Code, Codex, DeepSeek, Kimi, OpenCode, and every major agent in the cloud. And Agent Playground on top: race them on your own task, with your real tools (GitHub, Gmail, more), side by side in a browser: time, cost, tokens burnt. Guess which one was $2.

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AI transcribing 1 hour of audio in less than 30 seconds! Think about that! That's lightning-fast, especially when it can do that with very few mistakes. The team Deepgram released their new Nova 2 model. It's another leap forward: • 36% more accurate than OpenAI Whisper • 5-40x faster than every other alternative • 30% fewer errors than the competitors in real-time transcriptions I like to travel and can't wait for headphones with integrated live transcription and translation. We are getting close! By the way, this is the cheapest transcription service out in the market now (if you discount Whisper, which is open-source.) It costs $0.0043 to transcribe a minute of audio. When you sign up to access their API, you get $200 of credit for free, which will let you transcribe around 45,000 minutes of audio! Here is the link: I wanted to record a quick video showing their live transcription in Spanish and English. You can see it attached here. This post was sponsored by Deepgram.

AI transcribing 1 hour of audio in less than 30 seconds! Think about that! That's lightning-fast, especially when it can do that with very few mistakes. The team Deepgram released their new Nova 2 model. It's another leap forward: • 36% more accurate than OpenAI Whisper • 5-40x faster than every other alternative • 30% fewer errors than the competitors in real-time transcriptions I like to travel and can't wait for headphones with integrated live transcription and translation. We are getting close! By the way, this is the cheapest transcription service out in the market now (if you discount Whisper, which is open-source.) It costs $0.0043 to transcribe a minute of audio. When you sign up to access their API, you get $200 of credit for free, which will let you transcribe around 45,000 minutes of audio! Here is the link: I wanted to record a quick video showing their live transcription in Spanish and English. You can see it attached here. This post was sponsored by Deepgram.

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Introducing Composer — the first AI Agent for document processing. Get to production-grade accuracy, autonomously in minutes. In our early beta, some teams hit 99% accuracy on complex document tasks in under 10 minutes. Composer is an agent built to optimize schemas the same way a human would (but way faster). Instead of tuning prompts by hand, you point Composer at your eval set inside Extend. Composer will: - analyze where your schema falls short - propose targeted improvements - run multiple experiments in parallel - surface diffs, accuracy gains, and traces behind each change With this launch, Extend is the only product on the market that helps you reach production-grade accuracy this fast. Composer is live for all Extend customers today! Try it out at the link in comments below.

Introducing Composer — the first AI Agent for document processing. Get to production-grade accuracy, autonomously in minutes. In our early beta, some teams hit 99% accuracy on complex document tasks in under 10 minutes. Composer is an agent built to optimize schemas the same way a human would (but way faster). Instead of tuning prompts by hand, you point Composer at your eval set inside Extend. Composer will: - analyze where your schema falls short - propose targeted improvements - run multiple experiments in parallel - surface diffs, accuracy gains, and traces behind each change With this launch, Extend is the only product on the market that helps you reach production-grade accuracy this fast. Composer is live for all Extend customers today! Try it out at the link in comments below.

Kushal Byatnal

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AMD might have disrupted Nvidia's entire cloud GPU rental business. In January at CES, AMD CEO Lisa Su demonstrated a $1,499 mini PC running the same class of AI model that currently costs companies $2,500 to $3,000 every month to rent from Nvidia-powered cloud servers. AMD's own branded version opened pre-orders this month at $3,999. Third party manufacturers have been selling the same chip since 2025 starting at $1,499. Here is exactly why this is dangerous for Nvidia. Nvidia's $75 billion quarterly revenue is built almost entirely on one business model, companies rent access to Nvidia GPUs through cloud providers like AWS and Lambda Labs to run AI. They pay monthly. Nvidia gets paid every time someone runs an AI model in the cloud. That recurring rental income is what turned Nvidia into a $5 trillion company. The AMD box eliminates that monthly fee permanently. One AI consultant switched from $2,800 per month in Nvidia cloud rental costs to $8 per month in electricity. The hardware paid for itself in 11 days. Over 8 months he generated $47,000 running the same AI workloads that previously left him paying Nvidia's ecosystem $2,800 every single month. Multiply that across thousands of enterprise customers and the revenue erosion becomes structural. Every business that buys this box stops paying cloud rental fees forever. Lawyers, doctors, banks, accountants, and financial advisors, businesses with sensitive data that cannot legally go to a cloud server represent billions in annual cloud GPU fees that Nvidia is now at risk of losing permanently. The threat is also closing in from the top. Google signed deals worth tens of billions with Anthropic and Meta to replace Nvidia with its own chips. Amazon built its own AI chips across AWS. Apple trained its AI on Google's chips, not Nvidia's. Custom silicon has grown from 21% of the AI chip market in 2025 to 28% in 2026. Nvidia's rental model only worked because serious AI compute had no alternative.

AMD might have disrupted Nvidia's entire cloud GPU rental business. In January at CES, AMD CEO Lisa Su demonstrated a $1,499 mini PC running the same class of AI model that currently costs companies $2,500 to $3,000 every month to rent from Nvidia-powered cloud servers. AMD's own branded version opened pre-orders this month at $3,999. Third party manufacturers have been selling the same chip since 2025 starting at $1,499. Here is exactly why this is dangerous for Nvidia. Nvidia's $75 billion quarterly revenue is built almost entirely on one business model, companies rent access to Nvidia GPUs through cloud providers like AWS and Lambda Labs to run AI. They pay monthly. Nvidia gets paid every time someone runs an AI model in the cloud. That recurring rental income is what turned Nvidia into a $5 trillion company. The AMD box eliminates that monthly fee permanently. One AI consultant switched from $2,800 per month in Nvidia cloud rental costs to $8 per month in electricity. The hardware paid for itself in 11 days. Over 8 months he generated $47,000 running the same AI workloads that previously left him paying Nvidia's ecosystem $2,800 every single month. Multiply that across thousands of enterprise customers and the revenue erosion becomes structural. Every business that buys this box stops paying cloud rental fees forever. Lawyers, doctors, banks, accountants, and financial advisors, businesses with sensitive data that cannot legally go to a cloud server represent billions in annual cloud GPU fees that Nvidia is now at risk of losing permanently. The threat is also closing in from the top. Google signed deals worth tens of billions with Anthropic and Meta to replace Nvidia with its own chips. Amazon built its own AI chips across AWS. Apple trained its AI on Google's chips, not Nvidia's. Custom silicon has grown from 21% of the AI chip market in 2025 to 28% in 2026. Nvidia's rental model only worked because serious AI compute had no alternative.

Bull Theory

26,765 görüntüleme • 3 ay önce

QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.

QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.

QVAC

20,932,195 görüntüleme • 3 ay önce