#语音

Voice-first AI products touch some of your most private data: Your raw thoughts. Your unfinished drafts. Your unfiltered voice. Your work before it's ready to be seen. That deserves serious protection. Typeless is now ISO 27001 certified. 🛡️ ISO 27001 is one of the world's most rigorous information security standards - independently auditing how a company manages data, risks, systems, and controls. For users: Typeless is built to protect your thoughts and work. For enterprises: Typeless is ready for teams that need trusted, security-reviewed AI tools before adoption. Our privacy principles: 1. Zero cloud data retention 2. Never trained on your data 3. On-device history storage Voice-first only works if it's private by design. http://trust.typeless.com Written with @typelessdotcom

Starting today, we're opening our Agentic Dialog Platform to every enterprise builder. Our dialog agents have resolved 1 billion+ customer conversations for clients like FedEx, Unicredit, PG&E, Marriott, Foot Locker, and many more. These aren't easy conversations. They solve problems like: > A patient booking medical transport who needs insurance verified on the spot. > A homeowner calling their utility company about a gas leak. > A cardholder figuring out why their must-have purchase was declined. Standard conversational AI was never built for this. It was designed for chat, adapted for voice later. It generates responses, but can't do what dialog requires: hold context under pressure, navigate ambiguity in real time, and actually resolve problems. So we built a better model. Our proprietary model Raven was built from the ground up specifically for dialog. Agent harness in the weights, not bolted on through prompts that drift under pressure. And in our platform, you can deploy Raven as your default or bring in GPT-5, Claude, Gemini, whatever model fits your use case or regulatory requirement. Now that the Agentic Dialog Platform is open, any team can create, test, and deploy dialog agents on the same model and infrastructure the world’s top brands trust on their hardest days. This opens up the pool of builders across your entire enterprise. The person who knows customers best, who runs operations, who owns the customer journey: they're all builders now. Two ways to build: > Poly Agent Builder: Describe your use case in natural language, and it configures your agent, knowledge base, and conversation flows automatically. Production-ready in ten minutes. > Agent Development Kit (ADK): Developers use this to build dialog agents the same way they build everything else. Use your own IDE, a coding assistant like Claude, version with Git, deploy from your terminal. Get started now: http://studio.poly.ai/

People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way. We share our approach, early results, and a quick look at our model in action. https://thinkingmachines.ai/blog/interaction-models

Had tons of fun chatting with @sonyatweetybird about music and the future of entertainment

Meet the 1st radio on X fully run by AI. Covers AI news 24/7, always on. Designed for builders and founders. Live right now. AI Twitter is hundreds of posts an hour. You can't read all of it. Tune in - hit play - do your thing. With non-distracting ambient music between segments. What you'll hear any hour you tune in: - breaking news within minutes - roundups every 30 min — top stories with builder context - startup funding & traction radar - what's moving and trending in AI tooling — GitHub, OpenRouter, HuggingFace — every 30 min - community — what people actually say on X, HN, YouTube - editorial takes — and real opinions from founders, researchers, builders - patterns others miss, delivered as arguments with conclusions Five AI hosts. Each with their own editorial judgment, memory, and personality. They don't just read data — they collect patterns, find contradictions, form opinions, and argue their point. And they do it live, continuously, on air.

我们正在用 AI 重新构想一个已有 50 年历史的界面——鼠标指针。🖱️ 这些实验性演示展示了人们如何通过动作、语音和自然简写，在屏幕上直观地指挥 Gemini 完成任务 🧵

Announcing agentic performance benchmarking for Speech to Speech models on Artificial Analysis. We use 𝜏-Voice to measure tool calling and customer interaction voice agent capabilities in realistic customer service scenarios Even the strongest Speech to Speech (S2S) models today resolve only about half of realistic customer service scenarios end-to-end - a meaningful gap relative to frontier text-based agents on the same tasks. Voice channels introduce significant complexity: challenging accents, background noise, and packet loss, all while requiring fast responses, consistency across long multi-turn conversations, and reliable tool use. Performance also varies considerably by audio condition: in clean audio some models perform notably better, but realistic conditions continue to pose a challenge. Conversation duration also varies meaningfully across models, with implications for both customer experience and operational cost. About 𝜏-Voice: Our Agentic Performance benchmark is based on 𝜏-Voice (Ray, Dhandhania, Barres & Narasimhan, 2026), which extends 𝜏²-bench into the voice modality to evaluate S2S models on realistic customer service tasks. It measures multi-turn instruction following, support of a simulated customer through a complete interaction, and tool use against simulated customer service systems. The simulated user combines an LLM-driven decision model with realistic audio synthesis: diverse accents, background noise, and packet loss modelled on real network conditions. This complements our Big Bench Audio benchmark measuring intelligence and Conversational Dynamics (Full Duplex Bench subset) benchmark measuring conversational naturalness. Scores are the average of three independent pass@1 trials. We evaluate under realistic audio conditions using the 𝜏²-bench base task split across three domains: ➤ Airline (50 scenarios): e.g., changing a flight, rebooking under policy constraints ➤ Retail (114 scenarios): e.g., disputing a charge, processing a return ➤ Telecom (114 scenarios): e.g., resolving a billing issue, troubleshooting a service problem Task success is determined by deterministic checks against expected actions and final database state, consistent with the 𝜏²-bench evaluator. Key results: xAI's Grok Voice Think Fast 1.0 is the clear leader at 52.1%, averaging 5.6 minutes per conversation, the second-longest overall. OpenAI's GPT-Realtime-2 (High) (39.8%, 3.0 min) and GPT-Realtime-1.5 (38.8%, 4.8 min) follow, with Gemini 3.1 Flash Live Preview - High close behind at 37.7% (3.8 min). Speech to Speech is a fast evolving modality and we expect movement in rankings as we continue to add new models with these capabilities, and model robustness improves. Congratulations @xAI @elonmusk! See below for further detail ⬇️

Grok Voice Think Fast 1.0 ranks #1 on the Artificial Analysis τ-Voice benchmark for real-world agentic customer service resolution Absolutely outperforming GPT-Realtime-2 (High) and Gemini 3.1 Flash by a huge margin That's a massive 12%+ lead over OpenAI's best model that just released a few days ago Grok is running real-time background reasoning without the latency penalty, which is why it is already handling live Starlink phone operations autonomously at scale

What if your team gave standup updates, and GPT-Realtime-2 moved the tickets?

People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way. We share our approach, early results, and a quick look at our model in action. https://thinkingmachines.ai/blog/interaction-models