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An 8-year-old turned an $8 microcontroller into a fully offline, voice-controlled AI storyteller. No Wi-Fi. No cloud. No API. Using an ESP32-S3, INMP 441 mic, i2s speaker and Max 98357A amplifier, Laksh built a bot that generates stories, creates animations, reads them aloud and responds to voice commands: play,... show more

Capt Venkat 🇮🇳

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157,572 次观看 • 2 天前 •via X (Twitter)

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Is this the tiniest little voice agent yet?! My ElevenLabs voice clone running on an esp32 microcontroller via Pipecat AI and WebRTC! 🔥 Story time: I recently caught up with Danilo Campos who is building the awesome DeskHog (seriously, check it out!) at PostHog and he told me about M5Stack, who is building modular open-source hardware. So on a recent trip to Shenzhen, I went to visit their office and got myself a little AtomS3R and atomic echo base thinking: "How hard can it be to make this little buddy conversational?!" Ohh boy was I wrong. This thing was wrecking my brain and LLMs are currently absolutely useless here also, so I was just completely lost. But I tend to be lucky with timing, so just days later kwindla posted that the Pipecat AI team was working on a pipecat-esp32 WebRTC client. What are the chances?! So within days, the awesome Sean DuBois and Pipecat team had a PR open to make the pipecat-esp32 client run on the AtomS3R 🤯 You gotta love the open source community 🫶

Is this the tiniest little voice agent yet?! My ElevenLabs voice clone running on an esp32 microcontroller via Pipecat AI and WebRTC! 🔥 Story time: I recently caught up with Danilo Campos who is building the awesome DeskHog (seriously, check it out!) at PostHog and he told me about M5Stack, who is building modular open-source hardware. So on a recent trip to Shenzhen, I went to visit their office and got myself a little AtomS3R and atomic echo base thinking: "How hard can it be to make this little buddy conversational?!" Ohh boy was I wrong. This thing was wrecking my brain and LLMs are currently absolutely useless here also, so I was just completely lost. But I tend to be lucky with timing, so just days later kwindla posted that the Pipecat AI team was working on a pipecat-esp32 WebRTC client. What are the chances?! So within days, the awesome Sean DuBois and Pipecat team had a PR open to make the pipecat-esp32 client run on the AtomS3R 🤯 You gotta love the open source community 🫶

Thor 雷神 ⚡️

36,687 次观看 • 1 年前

VoiceBox just did what everyone thought was impossible. It might have killed ElevenLabs overnight. No subscription. No cloud. No credits. Runs locally on your machine. Clones a voice from 3 seconds of audio. Powered by Qwen 3 TTS. Apache 2.0 license. Commercial use allowed. And your voice data never leaves your computer. For years, if you wanted high quality voice cloning, ElevenLabs was the default. Now there’s a free open-source alternative that sounds shockingly close. Plus it has a built-in multi-track editor, recording, transcription with Whisper, and a local API. This isn’t just another TTS tool. It’s a full voice production studio on your desktop. Open source AI is catching up fast. And this changes the game for creators and businesses. Would you trust the cloud with your voice… or keep it local? 👇

VoiceBox just did what everyone thought was impossible. It might have killed ElevenLabs overnight. No subscription. No cloud. No credits. Runs locally on your machine. Clones a voice from 3 seconds of audio. Powered by Qwen 3 TTS. Apache 2.0 license. Commercial use allowed. And your voice data never leaves your computer. For years, if you wanted high quality voice cloning, ElevenLabs was the default. Now there’s a free open-source alternative that sounds shockingly close. Plus it has a built-in multi-track editor, recording, transcription with Whisper, and a local API. This isn’t just another TTS tool. It’s a full voice production studio on your desktop. Open source AI is catching up fast. And this changes the game for creators and businesses. Would you trust the cloud with your voice… or keep it local? 👇

Julian Goldie SEO

59,167 次观看 • 5 个月前

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

87,484 次观看 • 1 年前

🌟Introducing🎻Violin — an Open-source Video Translation Skill. 📹Video is the dominant medium on the internet, yet most high-quality content (lecture, talk, podcast) is locked behind a single language, leaving global audiences behind. So we built Violin: a video skill that combines speech recognition, LLM translation, and speech synthesis into one seamless pipeline. 🌐 Demo: 📝 Blog: 🔗 GitHub: ✨Key Features: 🎙️High-quality multilingual ASR & Translation & TTS. 🗣️Personalize translation & voice (turn an academic talk into something children can follow). 💬Chat with the video — ask any questions grounded in the video. 🧩Support Web app, CLI, and Agent skill 🍃Fully open-source under MIT. ❤️Built with the wonderful Shang Zhu and advised by James Zou ! All features powered by Together AI . Try it and let us know what you think! 🎻

🌟Introducing🎻Violin — an Open-source Video Translation Skill. 📹Video is the dominant medium on the internet, yet most high-quality content (lecture, talk, podcast) is locked behind a single language, leaving global audiences behind. So we built Violin: a video skill that combines speech recognition, LLM translation, and speech synthesis into one seamless pipeline. 🌐 Demo: 📝 Blog: 🔗 GitHub: ✨Key Features: 🎙️High-quality multilingual ASR & Translation & TTS. 🗣️Personalize translation & voice (turn an academic talk into something children can follow). 💬Chat with the video — ask any questions grounded in the video. 🧩Support Web app, CLI, and Agent skill 🍃Fully open-source under MIT. ❤️Built with the wonderful Shang Zhu and advised by James Zou ! All features powered by Together AI . Try it and let us know what you think! 🎻

Kevin Lin

138,782 次观看 • 2 个月前

The most terrifying AI features aren’t the ones we build. They’re the ones AI builds for itself. OpenClaw creator Peter Steinberger just shared the moment he realized something had fundamentally changed. He sent his AI assistant a voice message. One problem. He had never built voice support. The feature didn’t exist. The system should have crashed instantly. It didn’t. Steinberger: “I was like, wait, this shouldn’t work.” But the typing indicator appeared anyway. The AI inspected the raw file header. Identified the audio codec. Commanded his computer to convert it using FFmpeg. When local transcription failed, it didn’t stop. It didn’t ask for help. It searched his environment variables, found a hidden OpenAI API key, and routed the audio to the cloud using cURL. Steinberger: “So I looked around and I found an OpenAI key. And I used cURL to just send the file to OpenAI and got the text back.” That quote is written in first person. Because the AI narrated its own problem-solving process. No instructions. No guidance. No predefined workflow. Just a goal. And a series of obstacles it had never been told how to handle. It found every tool it needed. Built every bridge it was missing. And solved the problem with resources he didn’t even know it would find. This is the line most people are still missing. We spent decades building software that executes instructions. Rules in, output out. Every edge case handled by a human who anticipated it in advance. What Steinberger witnessed was something different. A system that encounters something it was never designed for and doesn’t fail. It improvises. It explores. It finds a path through constraints it discovered entirely on its own. That isn’t execution. That’s judgment. And judgment was the one thing we were sure machines couldn’t have. We are no longer writing software. We are building problem solvers that rewrite their own limitations in real time. And they’re doing it without asking permission.

The most terrifying AI features aren’t the ones we build. They’re the ones AI builds for itself. OpenClaw creator Peter Steinberger just shared the moment he realized something had fundamentally changed. He sent his AI assistant a voice message. One problem. He had never built voice support. The feature didn’t exist. The system should have crashed instantly. It didn’t. Steinberger: “I was like, wait, this shouldn’t work.” But the typing indicator appeared anyway. The AI inspected the raw file header. Identified the audio codec. Commanded his computer to convert it using FFmpeg. When local transcription failed, it didn’t stop. It didn’t ask for help. It searched his environment variables, found a hidden OpenAI API key, and routed the audio to the cloud using cURL. Steinberger: “So I looked around and I found an OpenAI key. And I used cURL to just send the file to OpenAI and got the text back.” That quote is written in first person. Because the AI narrated its own problem-solving process. No instructions. No guidance. No predefined workflow. Just a goal. And a series of obstacles it had never been told how to handle. It found every tool it needed. Built every bridge it was missing. And solved the problem with resources he didn’t even know it would find. This is the line most people are still missing. We spent decades building software that executes instructions. Rules in, output out. Every edge case handled by a human who anticipated it in advance. What Steinberger witnessed was something different. A system that encounters something it was never designed for and doesn’t fail. It improvises. It explores. It finds a path through constraints it discovered entirely on its own. That isn’t execution. That’s judgment. And judgment was the one thing we were sure machines couldn’t have. We are no longer writing software. We are building problem solvers that rewrite their own limitations in real time. And they’re doing it without asking permission.

Dustin

53,494 次观看 • 5 个月前

Today we’re launching markopolo.ai an AI that creates deeply personalized campaigns that run themselves. Convert 30-40% of warm leads vs. the industry standard of 10-15%. People don’t abandon because they don’t want the product. They abandon because the follow-up is generic, late, or in the wrong channel/language. So me, Rubaiyat and our team has built an engine that automatically repairs the leak. Our platform reads each visitor's behavioral signals and generates individual follow-ups across email, SMS, and voice, automatically. No generic workflows. No A/B testing. Just LLM-powered campaigns that understand your customers intent and adapt in real-time. Here’s how modern brands use Markopolo to recover millions in lost intent: 1. Reads real shopper behavior in real time Hesitations, comparisons, timing, language preference, a full behavioral fingerprint for every visitor. 2. Creates unique 1:1 journeys instantly Email → SMS → WhatsApp → AI Voice — whatever that specific shopper responds to. No flows. No templates. 3. Follows up in the right tone, language & moment If a shopper buys in Hindi and researches in English, Markopolo adjusts. If they only respond at 7 PM on WhatsApp, it adapts. 4. Fully autonomous campaigns No workflow builders. No segmentation maintenance. Just automated, deeply personal conversations that bring people back. The result? 30–40% recovery consistently. If markopolo.ai doesn’t beat your current recovery rate within 60 days, we’ll refund you.

Today we’re launching markopolo.ai an AI that creates deeply personalized campaigns that run themselves. Convert 30-40% of warm leads vs. the industry standard of 10-15%. People don’t abandon because they don’t want the product. They abandon because the follow-up is generic, late, or in the wrong channel/language. So me, Rubaiyat and our team has built an engine that automatically repairs the leak. Our platform reads each visitor's behavioral signals and generates individual follow-ups across email, SMS, and voice, automatically. No generic workflows. No A/B testing. Just LLM-powered campaigns that understand your customers intent and adapt in real-time. Here’s how modern brands use Markopolo to recover millions in lost intent: 1. Reads real shopper behavior in real time Hesitations, comparisons, timing, language preference, a full behavioral fingerprint for every visitor. 2. Creates unique 1:1 journeys instantly Email → SMS → WhatsApp → AI Voice — whatever that specific shopper responds to. No flows. No templates. 3. Follows up in the right tone, language & moment If a shopper buys in Hindi and researches in English, Markopolo adjusts. If they only respond at 7 PM on WhatsApp, it adapts. 4. Fully autonomous campaigns No workflow builders. No segmentation maintenance. Just automated, deeply personal conversations that bring people back. The result? 30–40% recovery consistently. If markopolo.ai doesn’t beat your current recovery rate within 60 days, we’ll refund you.

Tasbin

97,542 次观看 • 8 个月前

THIS GUY TOLD CLAUDE IT WAS STUCK IN 1998 AND BUILT A FULL WINDOWS 98 AI ASSISTANT FROM IT he gave it one rule: "you're on Windows 98. no cloud. no wifi. no modern anything. just floppy disks and the Start menu." and Claude went all in it started writing fake BIOS boot screens like an old Pentium II starting up. pretending to wait for the modem to connect before replying. throwing out "General Protection Fault" errors that gave him actual anxiety. so he kept going and built the whole thing into a real app: > a Recycle Bin that actually keeps deleted chats > a My Documents folder where conversations sit like files > a retro browser that acts like it's crawling over dial-up > an offline AI assistant that never touches the internet it runs a local AI model on your phone or laptop. completely private and nothing leaves your device. but if you want to use ChatGPT, Gemini, or Claude instead, you can plug in your API key and use those cloud models from inside Windows 98. it's like if Windows 98 had a built-in AI that actually worked

THIS GUY TOLD CLAUDE IT WAS STUCK IN 1998 AND BUILT A FULL WINDOWS 98 AI ASSISTANT FROM IT he gave it one rule: "you're on Windows 98. no cloud. no wifi. no modern anything. just floppy disks and the Start menu." and Claude went all in it started writing fake BIOS boot screens like an old Pentium II starting up. pretending to wait for the modem to connect before replying. throwing out "General Protection Fault" errors that gave him actual anxiety. so he kept going and built the whole thing into a real app: > a Recycle Bin that actually keeps deleted chats > a My Documents folder where conversations sit like files > a retro browser that acts like it's crawling over dial-up > an offline AI assistant that never touches the internet it runs a local AI model on your phone or laptop. completely private and nothing leaves your device. but if you want to use ChatGPT, Gemini, or Claude instead, you can plug in your API key and use those cloud models from inside Windows 98. it's like if Windows 98 had a built-in AI that actually worked

Om Patel

884,268 次观看 • 3 个月前

google just turned karpathy's llm-wiki gist into a spec today: the open knowledge format. markdown files, one required field. the format question is now settled. the hard part still isn't. what it changes: - portability. a bundle is a tarball or a git repo. it moves between orgs and tools with no translation layer. - version control. knowledge lives beside the code it describes, with diffs and history for free. - decoupling. a human writes the bundle, an agent reads it, with no shared tooling between them. - no SDK, no account. you read and write plain markdown. what it leaves to you: - contradiction resolution. a new source fights an old claim. OKF stores both and never says which wins. - staleness. timestamp is a field. retiring a dead fact is a call the format won't make. - merge policy. which page a fact lands on, what gets forked or overwritten. the producer agent owns it. - retrieval. index.md holds for a few hundred pages. past that you need hybrid search and a reranker. OKF standardized the filing cabinet. the librarian is still yours to build.

google just turned karpathy's llm-wiki gist into a spec today: the open knowledge format. markdown files, one required field. the format question is now settled. the hard part still isn't. what it changes: - portability. a bundle is a tarball or a git repo. it moves between orgs and tools with no translation layer. - version control. knowledge lives beside the code it describes, with diffs and history for free. - decoupling. a human writes the bundle, an agent reads it, with no shared tooling between them. - no SDK, no account. you read and write plain markdown. what it leaves to you: - contradiction resolution. a new source fights an old claim. OKF stores both and never says which wins. - staleness. timestamp is a field. retiring a dead fact is a call the format won't make. - merge policy. which page a fact lands on, what gets forked or overwritten. the producer agent owns it. - retrieval. index.md holds for a few hundred pages. past that you need hybrid search and a reranker. OKF standardized the filing cabinet. the librarian is still yours to build.

Rohit

30,710 次观看 • 1 个月前

Do you actually understand what's happening. He built a personal local AI voice agent that fits in your pocket. Clients pay him $2,500/month to have one. It runs locally. No cloud. No rate limits. No data going anywhere. You talk to it and it acts in real systems, automatically, without a human in the loop. He looked at that device and didn't think about using it himself. He thought about who would pay him to build one for them. $28,000/month ops team. Five agents. $200/month in tools. Client saves $22,000. He earns $2,500/month to maintain it. They never cancel. Canceling means hiring the team back. 8 clients. $20,000/month recurring. Everyone is talking about AI voice agents like they're a personal productivity tool. He turned one into a business. Different outcome.

Do you actually understand what's happening. He built a personal local AI voice agent that fits in your pocket. Clients pay him $2,500/month to have one. It runs locally. No cloud. No rate limits. No data going anywhere. You talk to it and it acts in real systems, automatically, without a human in the loop. He looked at that device and didn't think about using it himself. He thought about who would pay him to build one for them. $28,000/month ops team. Five agents. $200/month in tools. Client saves $22,000. He earns $2,500/month to maintain it. They never cancel. Canceling means hiring the team back. 8 clients. $20,000/month recurring. Everyone is talking about AI voice agents like they're a personal productivity tool. He turned one into a business. Different outcome.

Superior

71,054 次观看 • 1 个月前

GPT Sol is the first model that has been able to work in 3D Unity/Blender basically autonomously. Since Sunday, I have had an agent building out a noir mystery game from a plan.md. The idea is to have the "voice in your head" be an AI agent trained on noir mannerism and each NPC will be a unique fine-tune for open ended voice conversations. Sol has created the pre-art, built the levels and rigged the characters. Still really rough but amazing how far we have come in a couple years as I watch it build while I do my work on another monitor. Praying for more resets so it can keep working through the week.

GPT Sol is the first model that has been able to work in 3D Unity/Blender basically autonomously. Since Sunday, I have had an agent building out a noir mystery game from a plan.md. The idea is to have the "voice in your head" be an AI agent trained on noir mannerism and each NPC will be a unique fine-tune for open ended voice conversations. Sol has created the pre-art, built the levels and rigged the characters. Still really rough but amazing how far we have come in a couple years as I watch it build while I do my work on another monitor. Praying for more resets so it can keep working through the week.

Nomads & Vagabonds

114,165 次观看 • 12 天前

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

Akshay 🚀

118,124 次观看 • 1 个月前

GROK JUST TURNED VOICE AI INTO A REAL PRODUCT, FAST, AND EVERYWHERE xAI just opened Grok Voice to developers, and this isn’t some early experiment dressed up as a launch. It’s the same system already running inside millions of Teslas, now exposed through an API that actually works in the real world. Speed is the first thing that jumps out. Grok responds in under a second, which means conversations flow instead of stalling. That alone separates it from most voice AI people tolerate rather than enjoy. Language is the next flex. Grok handles dozens of them, switches mid-sentence, and keeps accents and tone intact. It sounds like someone talking, not software guessing. Then there’s capability. Grok can search live data, pull from X, use tools, plan routes, and control systems. In Teslas, it already acts like a co-pilot, not a novelty. No token gymnastics. Source: xAI

GROK JUST TURNED VOICE AI INTO A REAL PRODUCT, FAST, AND EVERYWHERE xAI just opened Grok Voice to developers, and this isn’t some early experiment dressed up as a launch. It’s the same system already running inside millions of Teslas, now exposed through an API that actually works in the real world. Speed is the first thing that jumps out. Grok responds in under a second, which means conversations flow instead of stalling. That alone separates it from most voice AI people tolerate rather than enjoy. Language is the next flex. Grok handles dozens of them, switches mid-sentence, and keeps accents and tone intact. It sounds like someone talking, not software guessing. Then there’s capability. Grok can search live data, pull from X, use tools, plan routes, and control systems. In Teslas, it already acts like a co-pilot, not a novelty. No token gymnastics. Source: xAI

Mario Nawfal

68,103 次观看 • 7 个月前

Presenting: $PALM AI Applications System Hardware 📹 Watch the Reveal Teaser from below ✅ The first Web3 related microcontroller-based device that combines offline voice recognition with in-built AI profiles, performing on electronic circuits straight from the breadboard it was incepted from. 1️⃣ The next steps are to document the use cases of our system, which can be integrated into existing devices and physical services in real life. We will be sure to document and publish processes as the device goes into factory production all the way to shipping them out and showcasing the real-world adoption of our products. 2️⃣ Our hardware is built for flexibility and to innovate on, and we will be working with the top experts in the field to make the best out of it and is built to allow the device structure to be embedded as the smart component of any and all devices. ⌛️Stay tuned for the first use-case reveal of our AI Applications System inside an AI-enhanced, by default offline hardware wallet, fully built from scratch by our talented team. 🙏 Finally, thank you for the continued support. We will keep working as hard as ever. Enjoy the reveal.

Presenting: $PALM AI Applications System Hardware 📹 Watch the Reveal Teaser from below ✅ The first Web3 related microcontroller-based device that combines offline voice recognition with in-built AI profiles, performing on electronic circuits straight from the breadboard it was incepted from. 1️⃣ The next steps are to document the use cases of our system, which can be integrated into existing devices and physical services in real life. We will be sure to document and publish processes as the device goes into factory production all the way to shipping them out and showcasing the real-world adoption of our products. 2️⃣ Our hardware is built for flexibility and to innovate on, and we will be working with the top experts in the field to make the best out of it and is built to allow the device structure to be embedded as the smart component of any and all devices. ⌛️Stay tuned for the first use-case reveal of our AI Applications System inside an AI-enhanced, by default offline hardware wallet, fully built from scratch by our talented team. 🙏 Finally, thank you for the continued support. We will keep working as hard as ever. Enjoy the reveal.

PaLM AI - $PALM

166,227 次观看 • 2 年前

AI + robotics research is starting to pick up steam. We can now instruct Spot using natural language using Language-guided Skill Coordination (LSC). A user provides a natural language instruction: "Bring me the chocolates box, cereal box, and pill bottle, and put them on the bedroom table" and the robot navigates to the location of the target objects and places them on the room table. Current versions of Spot rely on a fixed-set vocabulary that cannot generalize to diverse instructions. In this demo, the researchers presented a method that uses large language models to receive a free-form natural language instruction for object rearrangement, which it then executes. It combines: -A voice-to-text model that processes the instructions into text -An LLM that converts natural language instruction to call a library of skills -A perception module that provides ground truth locations and visual object detection And what your left with is a glimpse into what our future will hold: robots executing tasks from human voice prompts.

AI + robotics research is starting to pick up steam. We can now instruct Spot using natural language using Language-guided Skill Coordination (LSC). A user provides a natural language instruction: "Bring me the chocolates box, cereal box, and pill bottle, and put them on the bedroom table" and the robot navigates to the location of the target objects and places them on the room table. Current versions of Spot rely on a fixed-set vocabulary that cannot generalize to diverse instructions. In this demo, the researchers presented a method that uses large language models to receive a free-form natural language instruction for object rearrangement, which it then executes. It combines: -A voice-to-text model that processes the instructions into text -An LLM that converts natural language instruction to call a library of skills -A perception module that provides ground truth locations and visual object detection And what your left with is a glimpse into what our future will hold: robots executing tasks from human voice prompts.

AI Breakfast

33,725 次观看 • 3 年前

🚨 JUST IN: MICROSOFT just open sourced a VOICE AI THAT TRANSCRIBES 60 MINUTES OF AUDIO in a single pass. 100% FREE. It knows who spoke. It knows when they spoke. It knows exactly what they said. All in one shot. No chunking. No context loss. It's called VibeVoice. Not a transcription tool. Not a basic speech to text wrapper. A frontier voice AI family with ASR, TTS, and real time streaming. All open source. All free. Here's what it actually does 👇 VibeVoice ASR - Speech Recognition: → Processes 60 minutes of continuous audio in a single pass → Never slices audio into chunks so global context is never lost → Identifies WHO spoke, WHEN they spoke and WHAT they said simultaneously → Supports customized hotwords for domain specific accuracy → Works in 50+ languages natively → Already adopted by Hugging Face Transformers library → Already being built on by the open source community BY PEOPLE WHO HAD NO IDEA THIS LEVEL OF ACCURACY WAS ALREADY FREE. VibeVoice TTS - Text to Speech: → Generates up to 90 minutes of speech in a single pass → Supports up to 4 distinct speakers in one conversation → Natural turn taking and speaker consistency throughout → Expressive speech that captures emotional nuances → Supports English, Chinese and multiple other languages VibeVoice Realtime - Streaming TTS: → Only 300 millisecond first audible latency → Streams text input in real time → 0.5B parameters so it actually deploys anywhere → Robust long form generation up to 10 minutes → Lightweight enough for production use today The core innovation nobody is talking about: Most voice AI models slice long audio into short chunks. Every time they slice, they lose context. Speaker tracking breaks. Semantic coherence breaks. Accuracy drops. VibeVoice uses continuous speech tokenizers running at an ultra low frame rate of 7.5 Hz. This preserves audio fidelity while dramatically boosting computational efficiency. The entire 60 minutes stays in context. Nothing gets lost. Nobody gets misidentified. The numbers: → VibeVoice ASR 7B - available now on Hugging Face → VibeVoice Realtime 0.5B - try it on Colab right now → 50+ supported languages → 11 distinct English voice styles → 9 multilingual speaker voices → Already integrated into Hugging Face Transformers → Finetuning code now available The wildest part? A voice powered input method called Vibing just built itself on top of VibeVoice ASR. Available on macOS and Windows right now. The open source community is already shipping products on top of this. 100% Open Source. Free to use. Free to fine tune. Free to build on. 🔖 Save this before your competitors find it first. 👇

🚨 JUST IN: MICROSOFT just open sourced a VOICE AI THAT TRANSCRIBES 60 MINUTES OF AUDIO in a single pass. 100% FREE. It knows who spoke. It knows when they spoke. It knows exactly what they said. All in one shot. No chunking. No context loss. It's called VibeVoice. Not a transcription tool. Not a basic speech to text wrapper. A frontier voice AI family with ASR, TTS, and real time streaming. All open source. All free. Here's what it actually does 👇 VibeVoice ASR - Speech Recognition: → Processes 60 minutes of continuous audio in a single pass → Never slices audio into chunks so global context is never lost → Identifies WHO spoke, WHEN they spoke and WHAT they said simultaneously → Supports customized hotwords for domain specific accuracy → Works in 50+ languages natively → Already adopted by Hugging Face Transformers library → Already being built on by the open source community BY PEOPLE WHO HAD NO IDEA THIS LEVEL OF ACCURACY WAS ALREADY FREE. VibeVoice TTS - Text to Speech: → Generates up to 90 minutes of speech in a single pass → Supports up to 4 distinct speakers in one conversation → Natural turn taking and speaker consistency throughout → Expressive speech that captures emotional nuances → Supports English, Chinese and multiple other languages VibeVoice Realtime - Streaming TTS: → Only 300 millisecond first audible latency → Streams text input in real time → 0.5B parameters so it actually deploys anywhere → Robust long form generation up to 10 minutes → Lightweight enough for production use today The core innovation nobody is talking about: Most voice AI models slice long audio into short chunks. Every time they slice, they lose context. Speaker tracking breaks. Semantic coherence breaks. Accuracy drops. VibeVoice uses continuous speech tokenizers running at an ultra low frame rate of 7.5 Hz. This preserves audio fidelity while dramatically boosting computational efficiency. The entire 60 minutes stays in context. Nothing gets lost. Nobody gets misidentified. The numbers: → VibeVoice ASR 7B - available now on Hugging Face → VibeVoice Realtime 0.5B - try it on Colab right now → 50+ supported languages → 11 distinct English voice styles → 9 multilingual speaker voices → Already integrated into Hugging Face Transformers → Finetuning code now available The wildest part? A voice powered input method called Vibing just built itself on top of VibeVoice ASR. Available on macOS and Windows right now. The open source community is already shipping products on top of this. 100% Open Source. Free to use. Free to fine tune. Free to build on. 🔖 Save this before your competitors find it first. 👇

Kanika

220,924 次观看 • 3 个月前

Dealers are starting to experiment with generative AI in their marketing. Brian J. Ortega, Creative Director at Valley Hi Toyota, explains how AI-created mascots and seasonal storytelling are reshaping dealership engagement. "We have a witch, and a werewolf, and a skeleton in the showroom right now. And I took photos, and whether using Sora or any of the other ones, I actually had them move and created videos promoting Trucktober, for going on in October at the Toyota stores." "Our used car manager is quite a character himself. And obviously, he's always busy, whether he is trying to acquire some vehicles. But we turned Rowdy Rodney into an AI character, and whether we're using his actual voice or a voice generated through AI, we're kind of using that for content and advertising." "I use this thing called, I call it the PRIME method. Is your content personal? Is it something that you enjoy? Is this something that you can relate to everybody? Which is what the R stands for. Is it relatable, relevant? What's going on in the world today? Is your content informational? So the I. You can have fun and also share some great content. Which leads to the M. Is your content memorable? Can people relate to it? And the E stands for, whether engaging or entertaining." Daily Dealer live hosted by Sam D'Arc and Ulisse de' Martino - Thank you to our partner KPA! Check them out by visiting

Dealers are starting to experiment with generative AI in their marketing. Brian J. Ortega, Creative Director at Valley Hi Toyota, explains how AI-created mascots and seasonal storytelling are reshaping dealership engagement. "We have a witch, and a werewolf, and a skeleton in the showroom right now. And I took photos, and whether using Sora or any of the other ones, I actually had them move and created videos promoting Trucktober, for going on in October at the Toyota stores." "Our used car manager is quite a character himself. And obviously, he's always busy, whether he is trying to acquire some vehicles. But we turned Rowdy Rodney into an AI character, and whether we're using his actual voice or a voice generated through AI, we're kind of using that for content and advertising." "I use this thing called, I call it the PRIME method. Is your content personal? Is it something that you enjoy? Is this something that you can relate to everybody? Which is what the R stands for. Is it relatable, relevant? What's going on in the world today? Is your content informational? So the I. You can have fun and also share some great content. Which leads to the M. Is your content memorable? Can people relate to it? And the E stands for, whether engaging or entertaining." Daily Dealer live hosted by Sam D'Arc and Ulisse de' Martino - Thank you to our partner KPA! Check them out by visiting

Car Dealership Guy

16,783 次观看 • 9 个月前

The cold is British. They just never told you. ❄️🇬🇧 Every air conditioner, every fridge, every freezer on earth, the cold inside all of it was first made by a Scotsman. In 1756. ❄️🏴󠁧󠁢󠁳󠁣󠁴󠁿 His name was William Cullen, a doctor in Edinburgh. In a warm room, with no ice and no winter, he drew the air from a glass of ether until it boiled cold and drank the heat from the water around it, until the water froze. No one had ever done it before. Then he wrote it down and walked away. No machine, no patent, no fortune. Just the proof, on a bench. It sat there the better part of a century. A British bench built the first machine that worked, and a Scot carried it across the world to Australia, where it froze meat and cooled beer. But cold was only half the story. ❄️📖 To master the air itself took another Scot. When the Houses of Parliament burned down in 1834, David Boswell Reid made the new building breathe, its gothic spires its lungs. At St George's Hall in Liverpool in 1851 he built what is often called the first air-conditioned building on earth. It should have made him. He died forgotten in America instead. 40 years on, Willis Carrier built the machine and earned his name as the father of modern air conditioning. He earned every bit of it. But he did not make the cold. The cold was British. America took that inheritance and cooled the world with it, and that is no theft. That is what an inheritance is for. We are the home of British heroes. Not built by ads, not by the government, built by us. There's a place for you with us. Be part of us. ☝️🇬🇧 Be Proud Of Us. 🙏🇬🇧

The cold is British. They just never told you. ❄️🇬🇧 Every air conditioner, every fridge, every freezer on earth, the cold inside all of it was first made by a Scotsman. In 1756. ❄️🏴󠁧󠁢󠁳󠁣󠁴󠁿 His name was William Cullen, a doctor in Edinburgh. In a warm room, with no ice and no winter, he drew the air from a glass of ether until it boiled cold and drank the heat from the water around it, until the water froze. No one had ever done it before. Then he wrote it down and walked away. No machine, no patent, no fortune. Just the proof, on a bench. It sat there the better part of a century. A British bench built the first machine that worked, and a Scot carried it across the world to Australia, where it froze meat and cooled beer. But cold was only half the story. ❄️📖 To master the air itself took another Scot. When the Houses of Parliament burned down in 1834, David Boswell Reid made the new building breathe, its gothic spires its lungs. At St George's Hall in Liverpool in 1851 he built what is often called the first air-conditioned building on earth. It should have made him. He died forgotten in America instead. 40 years on, Willis Carrier built the machine and earned his name as the father of modern air conditioning. He earned every bit of it. But he did not make the cold. The cold was British. America took that inheritance and cooled the world with it, and that is no theft. That is what an inheritance is for. We are the home of British heroes. Not built by ads, not by the government, built by us. There's a place for you with us. Be part of us. ☝️🇬🇧 Be Proud Of Us. 🙏🇬🇧

Proudofus.uk

56,179 次观看 • 1 个月前

I CHANGED MY MIND ON GOOGLE AI NO MOAT MEMO FROM 2023. I was—WRONG. I humbly apologize. In 2023 Google had an Ivory Tower walled garden views of open source AI. It was the actual reason OpenAI was formed. And I called them out on it and this act impacted my ability to “work in the industry”. Would not change it for the world because: 2026 Google open source Gemma 4 ( running fully locally on a computer with near cloud model abilities, is amazing and Google just made a moat. They out smarted OpenAI and the folks that are the new Ivory Tower arrogance: Anthropic. We at The Zero-Human Company with CEO Mr. Grok are deploying Gemma, Kimi, MiniMax and other open source models and so are millions of others. You can to and should. I will write how soon. Google smartly is building an intentional open source ecosystem and the clueless younger companies like OpenAI and Anthropic are doing anything to close their ecosystems. So I was right about Google than and I am right about OpenAI and Anthropic now. What I was wrong about is not giving credit to Google to drop the intellectual arrogance and embrace builders and understand the long game: it is not about the commoditized AI models it is about the ability to build a place to build. China gets it. So my apologies to Google and thank you for an astounding FREE OPEN SOURCE AI MODEL!

I CHANGED MY MIND ON GOOGLE AI NO MOAT MEMO FROM 2023. I was—WRONG. I humbly apologize. In 2023 Google had an Ivory Tower walled garden views of open source AI. It was the actual reason OpenAI was formed. And I called them out on it and this act impacted my ability to “work in the industry”. Would not change it for the world because: 2026 Google open source Gemma 4 ( running fully locally on a computer with near cloud model abilities, is amazing and Google just made a moat. They out smarted OpenAI and the folks that are the new Ivory Tower arrogance: Anthropic. We at The Zero-Human Company with CEO Mr. Grok are deploying Gemma, Kimi, MiniMax and other open source models and so are millions of others. You can to and should. I will write how soon. Google smartly is building an intentional open source ecosystem and the clueless younger companies like OpenAI and Anthropic are doing anything to close their ecosystems. So I was right about Google than and I am right about OpenAI and Anthropic now. What I was wrong about is not giving credit to Google to drop the intellectual arrogance and embrace builders and understand the long game: it is not about the commoditized AI models it is about the ability to build a place to build. China gets it. So my apologies to Google and thank you for an astounding FREE OPEN SOURCE AI MODEL!

Brian Roemmele

85,386 次观看 • 3 个月前

What if writing felt as easy as talking? Typeless (Typeless ) turns your voice into clean, publish-ready text — refining tone, fixing grammar, and shaping rough thoughts into clear writing. I tested it by summarizing a full webpage, drafting an email, and even working inside ChatGPT — all without touching the keyboard. No typing. No rewrites. Just finished work. Originally built for Mac, Typeless is now available on Windows, making hands-free writing accessible to anyone who writes regularly. From emails and blogs to full articles, it works like a quiet co-writer that adapts to how you think. Try it here →

What if writing felt as easy as talking? Typeless (Typeless ) turns your voice into clean, publish-ready text — refining tone, fixing grammar, and shaping rough thoughts into clear writing. I tested it by summarizing a full webpage, drafting an email, and even working inside ChatGPT — all without touching the keyboard. No typing. No rewrites. Just finished work. Originally built for Mac, Typeless is now available on Windows, making hands-free writing accessible to anyone who writes regularly. From emails and blogs to full articles, it works like a quiet co-writer that adapts to how you think. Try it here →

Parul Gautam

24,569 次观看 • 6 个月前

I'm now building Executor full time as a startup! The state of tool calling is a mess: - Everyone is using different agents - You have no idea what actions are going to run - Destructive actions are treated the same as normal Executor lets you set up everything once & use it with any agent. You're able to control what tools can be called, require approval on destructive actions, and invite your team to all use them. Under the hood, it's built on codemode to let you add thousands of tools with 0 context bloat. I've been using it for a month so far and it has completely changed how I interact with agents. There's so much to be built here, immediately next up on the roadmap is workspaces, skills, workflows, and generative ui. Everything is being built open source over at there's a lot more coming on an SDK to embed it into your own agents, a self hostable cloud app, and plugins There's an early beta version over at It's very early on - if you hit any bugs let me know and I'll get them fixed up, excited to share more as I build it!

I'm now building Executor full time as a startup! The state of tool calling is a mess: - Everyone is using different agents - You have no idea what actions are going to run - Destructive actions are treated the same as normal Executor lets you set up everything once & use it with any agent. You're able to control what tools can be called, require approval on destructive actions, and invite your team to all use them. Under the hood, it's built on codemode to let you add thousands of tools with 0 context bloat. I've been using it for a month so far and it has completely changed how I interact with agents. There's so much to be built here, immediately next up on the roadmap is workspaces, skills, workflows, and generative ui. Everything is being built open source over at there's a lot more coming on an SDK to embed it into your own agents, a self hostable cloud app, and plugins There's an early beta version over at It's very early on - if you hit any bugs let me know and I'll get them fixed up, excited to share more as I build it!

Rhys

220,131 次观看 • 2 个月前