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🚨 BREAKING: Call centers just met their biggest threat yet. ElevenLabs Speech Engine just changed how the $40B support industry operates → 70+ languages, natural real time voices → Books, updates, closes tickets mid-conversation → Plugs into GPT, Claude, Gemini any LLM you run → $0.08/min, startups get $4K... show more

Hasan

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70,816 views • 3 months ago •via X (Twitter)

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🚨BREAKING : Call centers are officially dead. ElevenLabs Agents quietly wiped out the $40B customer support industry. → Sounds human in 70+ languages → Books, updates, closes tickets mid-call → Plugs into GPT, Claude, Gemini, any LLM → $0.08/min, startups get $4K free Revolut, Cisco, Deliveroo already switched. You're next 🧵

🚨BREAKING : Call centers are officially dead. ElevenLabs Agents quietly wiped out the $40B customer support industry. → Sounds human in 70+ languages → Books, updates, closes tickets mid-call → Plugs into GPT, Claude, Gemini, any LLM → $0.08/min, startups get $4K free Revolut, Cisco, Deliveroo already switched. You're next 🧵

Hasan

417,141 views • 3 months ago

5 startup ideas you can build and resell using only ElevenLabs Agents each one costs $0.08/min to run and replaces $2-5k/mo in human labor Let's break them down ↓ 1. AI Receptionist for Local Businesses dentists, salons, clinics, they all pay $2-3k/mo for someone to answer phones build a voice agent that: - answers calls 24/7 - books appointments - handles FAQs - speaks the client's language who ALREADY uses it: ~31% of local service businesses who STILL needs it: ~69% (your market) white-label it, charge $300-500/mo per client your cost per client: ~$30/mo in minutes 2. Multilingual Customer Support ElevenLabs agents speak 70+ languages natively e-commerce brands selling internationally need support in 5-10 languages minimum one agent replaces a 5-person multilingual team who ALREADY uses it: ~36% of e-commerce businesses who STILL needs it: ~64% and most of them are mid-market brands scaling globally sell 24/7 coverage, mark up the minutes, charge per-seat 3. AI Sales Qualifier (SDR Replacement) voice agent calls inbound leads, asks 5-10 qualifying questions, books meetings directly into the sales team's calendar startups pay $4-6k/mo per SDR you charge $1.5k/mo for an agent that works 24/7 and never misses a lead who ALREADY uses it: ~27% of mid-market teams who STILL needs it: ~73% and 22% already fully replaced human SDRs plug it into any CRM like HubSpot, Salesforce, Pipedrive 4. Restaurant Order-Taking Agent phone ordering for restaurants, pizzerias, takeout spots the agent takes the order, upsells sides and drinks, confirms, pushes to the POS who ALREADY uses it: ~34% of restaurants who STILL needs it: ~66% (expected to hit 50%+ in major cities this year) build one integration template → sell to 100+ restaurants at $200/mo each that's $20k/mo from one vertical 5. Real Estate Showing Scheduler agents answer property inquiry calls, give listing details, qualify buyers, and book viewings (all mid-call) realtors spend hours on phone scheduling who ALREADY uses it: ~18% use voice AI specifically who STILL needs it: ~82% while 82% of agents already use some form of AI, almost none have voice agents charge per listing or flat monthly integrates with their calendar + CRM -------- How to build any of these: - sign up for ElevenLabs (startups get $4k free credits) - pick your niche - build the agent with their no-code platform - connect it to GPT or Claude for the brain - plug in scheduling/CRM via API - white-label it under your brand you don't need to build AI, you need to sell AI to people who don't know it exists yet reply "ELEVEN" + RT and i'll send you a free guide so you can build this too

Ronin

775,226 views • 3 months ago

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

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

Charly Wargnier

59,144 views • 7 months ago

🚨BREAKING: ANTHROPIC WILL PAY YOU $85K TO WORK WITH AI. NO DEGREE NEEDED. APPLICATIONS CLOSE IN 48 HOURS. 1000 fellows will spend 12 months inside nonprofits building AI tools and automations using Claude, including intake systems, databases, and workflows. After the year ends, the fellow leaves but the tools stay running on Claude. For many small nonprofits with no technical team, switching away later can be difficult. The program costs Anthropic $150M. That's just 0.02% of their $965B valuation. It places fellows at 400+ nonprofits. How to apply: 1. 18+, US work authorization (no visa sponsorship), under 2 years of full-time experience 2. Apply at Cohort 1 closes July 17. Next cohorts open in 2027. 3. They look for people who already use AI regularly 4. You get matched with nonprofits based on fit and location 5. Paid training first, then 12 months at $85k + Claude access Just know this program also helps get Claude deeply into these organizations for the long term.

🚨BREAKING: ANTHROPIC WILL PAY YOU $85K TO WORK WITH AI. NO DEGREE NEEDED. APPLICATIONS CLOSE IN 48 HOURS. 1000 fellows will spend 12 months inside nonprofits building AI tools and automations using Claude, including intake systems, databases, and workflows. After the year ends, the fellow leaves but the tools stay running on Claude. For many small nonprofits with no technical team, switching away later can be difficult. The program costs Anthropic $150M. That's just 0.02% of their $965B valuation. It places fellows at 400+ nonprofits. How to apply: 1. 18+, US work authorization (no visa sponsorship), under 2 years of full-time experience 2. Apply at Cohort 1 closes July 17. Next cohorts open in 2027. 3. They look for people who already use AI regularly 4. You get matched with nonprofits based on fit and location 5. Paid training first, then 12 months at $85k + Claude access Just know this program also helps get Claude deeply into these organizations for the long term.

Evan Luthra

30,744 views • 1 month ago

🚨 THIS IS INSANE A developer has released a free AI voice studio capable of cloning almost any voice in just a few seconds. It's called Voicebox. And it has already surpassed 50,500 stars on GitHub. Here's how it works: 1.- Upload a short audio sample and clone a voice in seconds. 2.- Generate speech in 23 different languages using multiple TTS engines. 3.- Run everything locally for greater privacy, and you can integrate it into your own applications. It also includes voice dictation, support for AI agents, and MCP integration. All in a single open-source tool. Save this tool. I've left the link in the next comment 👇

🚨 THIS IS INSANE A developer has released a free AI voice studio capable of cloning almost any voice in just a few seconds. It's called Voicebox. And it has already surpassed 50,500 stars on GitHub. Here's how it works: 1.- Upload a short audio sample and clone a voice in seconds. 2.- Generate speech in 23 different languages using multiple TTS engines. 3.- Run everything locally for greater privacy, and you can integrate it into your own applications. It also includes voice dictation, support for AI agents, and MCP integration. All in a single open-source tool. Save this tool. I've left the link in the next comment 👇

Warren Lee

25,566 views • 7 days ago

Grok Voice Agent is absolutely insane Watch how it handles a dinner reservation flawlessly - felt exactly like talking to a real human host This is what AI-powered customer support looks like in 2026: → First response in under 1 second - 5× faster than rivals → 5 ultra-natural voices that talk, think, and act on your behalf → Full-duplex WebSocket for real-time, zero-lag conversations → Built-in live Web & 𝕏 search + MCP support → Auto-detects & switches between 20+ languages mid-conversation → 24/7 - never sleeps, never calls in sick And the base price is just $0.05/min For $3/hour, you get a 24/7 worker that never sleeps, never burns out, and treats every single customer with infinite patience Build voice agents for your business right now and replace your entire support queue Customer support is about to change forever xAI's voice agent is hands down the most impressive AI voice agent out there right now

Grok Voice Agent is absolutely insane Watch how it handles a dinner reservation flawlessly - felt exactly like talking to a real human host This is what AI-powered customer support looks like in 2026: → First response in under 1 second - 5× faster than rivals → 5 ultra-natural voices that talk, think, and act on your behalf → Full-duplex WebSocket for real-time, zero-lag conversations → Built-in live Web & 𝕏 search + MCP support → Auto-detects & switches between 20+ languages mid-conversation → 24/7 - never sleeps, never calls in sick And the base price is just $0.05/min For $3/hour, you get a 24/7 worker that never sleeps, never burns out, and treats every single customer with infinite patience Build voice agents for your business right now and replace your entire support queue Customer support is about to change forever xAI's voice agent is hands down the most impressive AI voice agent out there right now

X Freeze

591,415 views • 4 months ago

The next billion-dollar AI companies won’t be built around an LLM. They’ll be built around the HARNESS. Everyone is asking: “Which AI model should I use?” I think that’s the wrong question. An LLM is like a wild black stallion—immensely powerful, constantly improving, but running free. The opportunity is building the harness that lets you control that power and actually win the horse race. Your AI application shouldn’t be the model. It should orchestrate models. Claude today. GPT tomorrow. Gemini, Grok, Qwen, or whatever comes next. Swap the engine without rebuilding the car. Crypto already taught us this lesson. People interact with things like USDC and Tether without caring nearly as much which underlying blockchain happens to settle the transaction. The application layer abstracts the infrastructure. AI is heading the same direction. LLMs are infrastructure. The harness is the product. The winners will build AI harnesses around real human problems—businesses, workflows, coding, research, finance, education, everyday life—and make the underlying intelligence interchangeable. Everyone is trying to capture this incredibly powerful new thing called AI. Stop trying to build a better horse. Build the harness that lets you ride any horse. 🐎🤖 I just made a video explaining why I think this is where some of the biggest opportunities in AI are hiding.

The next billion-dollar AI companies won’t be built around an LLM. They’ll be built around the HARNESS. Everyone is asking: “Which AI model should I use?” I think that’s the wrong question. An LLM is like a wild black stallion—immensely powerful, constantly improving, but running free. The opportunity is building the harness that lets you control that power and actually win the horse race. Your AI application shouldn’t be the model. It should orchestrate models. Claude today. GPT tomorrow. Gemini, Grok, Qwen, or whatever comes next. Swap the engine without rebuilding the car. Crypto already taught us this lesson. People interact with things like USDC and Tether without caring nearly as much which underlying blockchain happens to settle the transaction. The application layer abstracts the infrastructure. AI is heading the same direction. LLMs are infrastructure. The harness is the product. The winners will build AI harnesses around real human problems—businesses, workflows, coding, research, finance, education, everyday life—and make the underlying intelligence interchangeable. Everyone is trying to capture this incredibly powerful new thing called AI. Stop trying to build a better horse. Build the harness that lets you ride any horse. 🐎🤖 I just made a video explaining why I think this is where some of the biggest opportunities in AI are hiding.

Jared Tate ©️

10,083 views • 17 days ago

What if your voice AI could interrupt you the moment it figured out your question - sometimes even before you finished asking it? Last week, I sat down with Neil, CEO of Gradium and co-founder of Kyutai , to talk about the future of speech-to-speech models and why he believes today's cascaded voice systems will soon look "archaic and brittle." Some highlights from our conversation: 🎯 How Kyutai built Moshi—a full duplex conversational AI with "negative latency"—in 6 months with just 4-6 people (while big tech teams had 10-20x the resources) 🧠 Why speech-to-speech models lose intelligence compared to their text counterparts (and what's being done about it) 📱 Pocket TTS: The first voice cloning model that runs on your phone's CPU—not GPU, CPU 🤖 Why robotics and spatial audio represent the next frontier (hint: current voice systems completely break in these environments) 👶 The efficiency gap: Babies learn to speak fluently from <5,000 hours of audio. Current models train on millions of hours. We're doing something wrong. My favorite vision from Neil? The first truly contrarian AI that interrupts you mid-sentence to tell you why you're wrong. Not just more natural conversation—but actually useful for testing ideas and playing devil's advocate. Full episode and detailed blog post linked in the comments 👇 What's your take - will speech-to-speech replace cascaded systems, or will modularity keep cascaded architectures dominant even as naturalness improves?

What if your voice AI could interrupt you the moment it figured out your question - sometimes even before you finished asking it? Last week, I sat down with Neil, CEO of Gradium and co-founder of Kyutai , to talk about the future of speech-to-speech models and why he believes today's cascaded voice systems will soon look "archaic and brittle." Some highlights from our conversation: 🎯 How Kyutai built Moshi—a full duplex conversational AI with "negative latency"—in 6 months with just 4-6 people (while big tech teams had 10-20x the resources) 🧠 Why speech-to-speech models lose intelligence compared to their text counterparts (and what's being done about it) 📱 Pocket TTS: The first voice cloning model that runs on your phone's CPU—not GPU, CPU 🤖 Why robotics and spatial audio represent the next frontier (hint: current voice systems completely break in these environments) 👶 The efficiency gap: Babies learn to speak fluently from <5,000 hours of audio. Current models train on millions of hours. We're doing something wrong. My favorite vision from Neil? The first truly contrarian AI that interrupts you mid-sentence to tell you why you're wrong. Not just more natural conversation—but actually useful for testing ideas and playing devil's advocate. Full episode and detailed blog post linked in the comments 👇 What's your take - will speech-to-speech replace cascaded systems, or will modularity keep cascaded architectures dominant even as naturalness improves?

Brooke Hopkins

13,000 views • 6 months ago

She rebuilt Claude with a second brain, structured 3 folders, connected it to n8n, and runs her business for $200/month instead of $15,000. Most people just drop links into Claude and wonder why it keeps giving generic answers. the problem was never the prompts, it was about the context all the time. so a girl built 3 folders and gave them to Claude: - wiki: frameworks from the smartest people alive - business: what she's building, who it's for, how she makes money - self: her voice, her principles, what she says no to Together, they form a brain that actually thinks, without any slop, and you get only sharp and specific output. then she connected that brain to n8n and let it run: - support tickets ✓ - invoices ✓ - weekly reports ✓ - lead qualification ✓ As a result, she managed to make her whole business fully autonomous for $200. Full breakdown with every workflow and prompt is in the article below 👇

She rebuilt Claude with a second brain, structured 3 folders, connected it to n8n, and runs her business for $200/month instead of $15,000. Most people just drop links into Claude and wonder why it keeps giving generic answers. the problem was never the prompts, it was about the context all the time. so a girl built 3 folders and gave them to Claude: - wiki: frameworks from the smartest people alive - business: what she's building, who it's for, how she makes money - self: her voice, her principles, what she says no to Together, they form a brain that actually thinks, without any slop, and you get only sharp and specific output. then she connected that brain to n8n and let it run: - support tickets ✓ - invoices ✓ - weekly reports ✓ - lead qualification ✓ As a result, she managed to make her whole business fully autonomous for $200. Full breakdown with every workflow and prompt is in the article below 👇

Spivach

13,546 views • 2 months ago

Elon Musk just made one if the biggest moves in taking over the programming industry “SpaceX just bought Cursor for $60 billion. Do you realize how big this is? SpaceX went public — the biggest IPO in history. $75 billion raised, almost a $2 trillion valuation and the first thing to do with that money? Buy the most popular AI coding tool on the planet. Here's why that changes everything. Elon now owns 3 layers: the compute, Colossus data centers, the models, Grok through xAI, and now the tool that developers actually use every day. It's the full stack. And here's what makes Cursor different from Claude Code or Codex. Cursor is model agnostic. You can run Claude in it, GPT, Gemini, whatever model you want. It's not locked to any one company, and now it has SpaceX's resources behind it. Cursor said they were bottlenecked by compute. Well, that bottleneck has just been removed. $4 billion in annual revenue, over half the Fortune 500 already uses it, and now it's backed by a $2 trillion company. OpenAI has Codex, Anthropic has Claude Code, and now Elon has Cursor.” Let me break this down in simple terms Elon Musk now controls more of the full AI picture: - Massive computers, power (data centers like Colossus) - Smart AI models (Grok from xAI) - The actual tool millions of developers use every day (Cursor) For every day users this means Faster and smarter apps and websites in the future. More developers using powerful AI tools means new apps, games, websites, and features get built quicker and cheaper. This means better video games, smoother streaming, smarter phone apps and better programs For Developers they can describe what they want in plain English (“make a feature that does X”) and the AI handles more of the heavy lifting

Elon Musk just made one if the biggest moves in taking over the programming industry “SpaceX just bought Cursor for $60 billion. Do you realize how big this is? SpaceX went public — the biggest IPO in history. $75 billion raised, almost a $2 trillion valuation and the first thing to do with that money? Buy the most popular AI coding tool on the planet. Here's why that changes everything. Elon now owns 3 layers: the compute, Colossus data centers, the models, Grok through xAI, and now the tool that developers actually use every day. It's the full stack. And here's what makes Cursor different from Claude Code or Codex. Cursor is model agnostic. You can run Claude in it, GPT, Gemini, whatever model you want. It's not locked to any one company, and now it has SpaceX's resources behind it. Cursor said they were bottlenecked by compute. Well, that bottleneck has just been removed. $4 billion in annual revenue, over half the Fortune 500 already uses it, and now it's backed by a $2 trillion company. OpenAI has Codex, Anthropic has Claude Code, and now Elon has Cursor.” Let me break this down in simple terms Elon Musk now controls more of the full AI picture: - Massive computers, power (data centers like Colossus) - Smart AI models (Grok from xAI) - The actual tool millions of developers use every day (Cursor) For every day users this means Faster and smarter apps and websites in the future. More developers using powerful AI tools means new apps, games, websites, and features get built quicker and cheaper. This means better video games, smoother streaming, smarter phone apps and better programs For Developers they can describe what they want in plain English (“make a feature that does X”) and the AI handles more of the heavy lifting

Wall Street Apes

213,589 views • 2 months ago

This repo is going viral. Why? Claude Fable 5 just came back online. GPT-5.6 is on the way. Every major AI lab is racing to ship the next frontier model. But here's the question nobody's asking: how much do you actually trust the agent you're about to deploy? Someone built a way to answer that in 5 minutes instead of guessing. iFixAi reads your agent's real tools, rules, and permissions, builds a full test around them, and grades it using a completely different vendor's model. Nothing ever scores its own homework. Most teams already have red teaming in place. What they don't have is red teaming combined with operational assurance, so the checks that catch a problem before launch never actually run again once the agent is live. Here's what changes with this: - Every startup shipping an AI agent gets an independent trust score before a single customer touches it - Enterprises can finally compare Fable 5, GPT-5.6, and every other model on the exact same fixture, apples to apples - CI pipelines catch an agent's behavior silently degrading release over release - Regulators get a standardized, reproducible audit trail instead of just trusting a vendor's word 45 inspections. A to F grade. Any model, any provider. 100% open source.

This repo is going viral. Why? Claude Fable 5 just came back online. GPT-5.6 is on the way. Every major AI lab is racing to ship the next frontier model. But here's the question nobody's asking: how much do you actually trust the agent you're about to deploy? Someone built a way to answer that in 5 minutes instead of guessing. iFixAi reads your agent's real tools, rules, and permissions, builds a full test around them, and grades it using a completely different vendor's model. Nothing ever scores its own homework. Most teams already have red teaming in place. What they don't have is red teaming combined with operational assurance, so the checks that catch a problem before launch never actually run again once the agent is live. Here's what changes with this: - Every startup shipping an AI agent gets an independent trust score before a single customer touches it - Enterprises can finally compare Fable 5, GPT-5.6, and every other model on the exact same fixture, apples to apples - CI pipelines catch an agent's behavior silently degrading release over release - Regulators get a standardized, reproducible audit trail instead of just trusting a vendor's word 45 inspections. A to F grade. Any model, any provider. 100% open source.

Abdul Șhakoor

29,216 views • 1 month ago

A 35-year-old marketer from Hong Kong quit his job and got deeply into AI. Using Claude, he made $360,000 in just one month. He built a perfect BTC price simulation engine with MiroFish. Claude as algorithm’s brain + MiroFish simulation engine allowed him to earn $5,000-$15,000 in profit per trade. He left a funny easter egg about his previous profession in his Polymarket nickname - he called himself Marketing101. His algorithm instantly detects any mispricing in crypto markets and enters trade immediately. $366k all-time profit. Constantly fading the crowd because his simulation reads the market better than everyone else. He’s using closed order book data + private OTC desks. Already elite alpha. Then the real magic happens: 10,000 simulation cycles of how the market will react. This isn’t "guessing where the chart will go" This is engineered money. Pure fusion of AI + MiroFish + insane math on exclusive data. Giving This Free for 24 hours. To get it: 1. Comment what ever think about it. ( Mandatory ) 2. Like and Retweet this post 3. Follow me Marry Evan

A 35-year-old marketer from Hong Kong quit his job and got deeply into AI. Using Claude, he made $360,000 in just one month. He built a perfect BTC price simulation engine with MiroFish. Claude as algorithm’s brain + MiroFish simulation engine allowed him to earn $5,000-$15,000 in profit per trade. He left a funny easter egg about his previous profession in his Polymarket nickname - he called himself Marketing101. His algorithm instantly detects any mispricing in crypto markets and enters trade immediately. $366k all-time profit. Constantly fading the crowd because his simulation reads the market better than everyone else. He’s using closed order book data + private OTC desks. Already elite alpha. Then the real magic happens: 10,000 simulation cycles of how the market will react. This isn’t "guessing where the chart will go" This is engineered money. Pure fusion of AI + MiroFish + insane math on exclusive data. Giving This Free for 24 hours. To get it: 1. Comment what ever think about it. ( Mandatory ) 2. Like and Retweet this post 3. Follow me Marry Evan

Marry Evan

22,793 views • 3 months ago

Meta just dropped the biggest Ad Library update ever: you can now sort any brand's ads by highest impressions 🤯 Which means you can see exactly which competitor ads are getting the most views. Daniel ⛰️ from ACTIV said "if someone can create an AI scraper to review the top 10 ads every week, I'd pay for that." So I built it with Claude Code. Here's how it works: → Import any brand with their Ad Library URL → Apify scrapes their top-performing ads automatically → Click into any ad for full breakdown (headline, copy, CTA) → Gemini watches the video or analyzes the image → Returns asset type, visual format, messaging angle, hook tactic, offer type No manual research, no watching videos one by one, no messy spreadsheets. Here's what I built: - 50+ DTC brands already loaded (AG1, Caraway, Chomps, Dr. Squatch, Gruns, Jones Road, Magic Spoon, Ridge) - Weekly auto-scrape to refresh top ads - AI analysis on any ad in one click - Bookmark system to save winners - Filter by brand, category, media type, or AI tags Built 100% in Claude Code, hosted on Replit. I recorded a full step-by-step showing exactly how I built this. Want to build your own version? I'm giving away the prompts I used + the Claude Code starter template. To get access for free: > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

Mike Futia

83,948 views • 6 months ago

🚨 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

221,264 views • 4 months ago

$THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this$

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this

Yarchi

56,177 views • 2 months ago

🚨 SPACEX: THE BIGGEST EXIT LIQUIDITY IN HISTORY SpaceX is up 70% from IPO. Everyone is FOMOing hard. But almost nobody is talking about the real reason. Only 4% of shares are available to the public. So they can paint any price they want. The playbook never changes: Hold 96% in your pocket. Drop a few crumbs into the market. Pull the chart wherever you want. But now it’s wrapped as "the biggest IPO in history." And Wall Street has already found their FORCED buyers. On June 13, SPCX was rushed into the MSCI indices. This means every passive fund worldwide is now obligated to buy it. Not because they believe in Elon Musk. Because that's how the system works: If it's in the index, you must buy it. We're talking about $15–20 trillion in passive funds. Massive forced demand + tiny float = perfect pump. But the lock-ups are coming: August: first 20% unlock November: another big wave Day 180: everything unlocked In other words, early investors will exit in 1–2 months, not in 6 months like most people think. And who will they sell to? Retail and those funds that were just forced into the stock. No one is arguing that SpaceX isn't a real company with a real future. Moon bases, Mars, orbital data centers: all of this might happen. But not anytime soon. Right now, something else is happening. The US stock market is going through a wild overvaluation of everything. SpaceX is just the biggest example. They are selling a dream about space today, at a price that won't make sense for another 15 to 20 years. And as always, the crowd will buy the top just to fund the exit. Remember, I've called every major turn for the last 10 years, including short BTC from $111K in October. My next call will be the biggest one this cycle. Turn on notifications. Most people will follow me too late.

🚨 SPACEX: THE BIGGEST EXIT LIQUIDITY IN HISTORY SpaceX is up 70% from IPO. Everyone is FOMOing hard. But almost nobody is talking about the real reason. Only 4% of shares are available to the public. So they can paint any price they want. The playbook never changes: Hold 96% in your pocket. Drop a few crumbs into the market. Pull the chart wherever you want. But now it’s wrapped as "the biggest IPO in history." And Wall Street has already found their FORCED buyers. On June 13, SPCX was rushed into the MSCI indices. This means every passive fund worldwide is now obligated to buy it. Not because they believe in Elon Musk. Because that's how the system works: If it's in the index, you must buy it. We're talking about $15–20 trillion in passive funds. Massive forced demand + tiny float = perfect pump. But the lock-ups are coming: August: first 20% unlock November: another big wave Day 180: everything unlocked In other words, early investors will exit in 1–2 months, not in 6 months like most people think. And who will they sell to? Retail and those funds that were just forced into the stock. No one is arguing that SpaceX isn't a real company with a real future. Moon bases, Mars, orbital data centers: all of this might happen. But not anytime soon. Right now, something else is happening. The US stock market is going through a wild overvaluation of everything. SpaceX is just the biggest example. They are selling a dream about space today, at a price that won't make sense for another 15 to 20 years. And as always, the crowd will buy the top just to fund the exit. Remember, I've called every major turn for the last 10 years, including short BTC from $111K in October. My next call will be the biggest one this cycle. Turn on notifications. Most people will follow me too late.

WhaleTwits

54,628 views • 2 months ago

🚨 SPACEX: THE BIGGEST EXIT LIQUIDITY IN HISTORY SpaceX is up 70% from IPO. Everyone is FOMOing hard. But almost nobody is talking about the real reason. Only 4% of shares are available to the public. So they can paint any price they want. The playbook never changes: Hold 96% in your pocket. Drop a few crumbs into the market. Pull the chart wherever you want. But now it’s wrapped as "the biggest IPO in history." And Wall Street has already found their FORCED buyers. On June 13, SPCX was rushed into the MSCI indices. This means every passive fund worldwide is now obligated to buy it. Not because they believe in Elon Musk. Because that's how the system works: If it's in the index, you must buy it. We're talking about $15–20 trillion in passive funds. Massive forced demand + tiny float = perfect pump. But the lock-ups are coming: August: first 20% unlock November: another big wave Day 180: everything unlocked In other words, early investors will exit in 1–2 months, not in 6 months like most people think. And who will they sell to? Retail and those funds that were just forced into the stock. No one is arguing that SpaceX isn't a real company with a real future. Moon bases, Mars, orbital data centers: all of this might happen. But not anytime soon. Right now, something else is happening. The US stock market is going through a wild overvaluation of everything. SpaceX is just the biggest example. They are selling a dream about space today, at a price that won't make sense for another 15 to 20 years. And as always, the crowd will buy the top just to fund the exit. Remember, I've called every major turn for the last 10 years, including short BTC from $111K in October. My next call will be the biggest one this cycle. Turn on notifications. Most people will follow me too late.

🚨 SPACEX: THE BIGGEST EXIT LIQUIDITY IN HISTORY SpaceX is up 70% from IPO. Everyone is FOMOing hard. But almost nobody is talking about the real reason. Only 4% of shares are available to the public. So they can paint any price they want. The playbook never changes: Hold 96% in your pocket. Drop a few crumbs into the market. Pull the chart wherever you want. But now it’s wrapped as "the biggest IPO in history." And Wall Street has already found their FORCED buyers. On June 13, SPCX was rushed into the MSCI indices. This means every passive fund worldwide is now obligated to buy it. Not because they believe in Elon Musk. Because that's how the system works: If it's in the index, you must buy it. We're talking about $15–20 trillion in passive funds. Massive forced demand + tiny float = perfect pump. But the lock-ups are coming: August: first 20% unlock November: another big wave Day 180: everything unlocked In other words, early investors will exit in 1–2 months, not in 6 months like most people think. And who will they sell to? Retail and those funds that were just forced into the stock. No one is arguing that SpaceX isn't a real company with a real future. Moon bases, Mars, orbital data centers: all of this might happen. But not anytime soon. Right now, something else is happening. The US stock market is going through a wild overvaluation of everything. SpaceX is just the biggest example. They are selling a dream about space today, at a price that won't make sense for another 15 to 20 years. And as always, the crowd will buy the top just to fund the exit. Remember, I've called every major turn for the last 10 years, including short BTC from $111K in October. My next call will be the biggest one this cycle. Turn on notifications. Most people will follow me too late.

MARMOT

715,884 views • 2 months ago

Claude Code + Shopify AI is f*cking cracked 🤯 Shopify just dropped an official AI Toolkit that connects Claude Code directly to your store. One prompt → Claude reads your products, rewrites your descriptions for AI shopping, and pushes the updates live. All from the terminal. All inside Claude Code. Perfect for DTC brands on Shopify who are still manually editing product pages, writing descriptions in Google Docs, and copy-pasting into the Shopify admin one product at a time. Claude Code + the Shopify AI Toolkit fixes the entire workflow: → Install the official Shopify plugin in Claude Code → Authenticate to your store → Claude reads your entire product catalog → Rewrites every description to be optimized for AI shopping → Pushes the updates directly to your store automatically → Validates every API call against Shopify's official docs before executing No Shopify admin tab-switching. No copy-pasting from a Google Doc. No hiring a copywriter to rewrite 50 product pages. What you get: → Claude Code connected directly to your live Shopify store → Product descriptions optimized for how ChatGPT, Gemini, and Perplexity recommend products → Bulk updates across your entire catalog from a single prompt → Full access to Shopify's GraphQL API — products, themes, inventory, orders, everything Claude can read and write → An official plugin built by Shopify that auto-updates as new features ship I put together a full playbook with the plugin install, the store authentication walkthrough, 5 DTC workflows to run on day one, and the exact prompts I used. Want it for free? > Like this post > Comment "SHOP" And I'll send it over (must be following so I can DM)

Claude Code + Shopify AI is f*cking cracked 🤯 Shopify just dropped an official AI Toolkit that connects Claude Code directly to your store. One prompt → Claude reads your products, rewrites your descriptions for AI shopping, and pushes the updates live. All from the terminal. All inside Claude Code. Perfect for DTC brands on Shopify who are still manually editing product pages, writing descriptions in Google Docs, and copy-pasting into the Shopify admin one product at a time. Claude Code + the Shopify AI Toolkit fixes the entire workflow: → Install the official Shopify plugin in Claude Code → Authenticate to your store → Claude reads your entire product catalog → Rewrites every description to be optimized for AI shopping → Pushes the updates directly to your store automatically → Validates every API call against Shopify's official docs before executing No Shopify admin tab-switching. No copy-pasting from a Google Doc. No hiring a copywriter to rewrite 50 product pages. What you get: → Claude Code connected directly to your live Shopify store → Product descriptions optimized for how ChatGPT, Gemini, and Perplexity recommend products → Bulk updates across your entire catalog from a single prompt → Full access to Shopify's GraphQL API — products, themes, inventory, orders, everything Claude can read and write → An official plugin built by Shopify that auto-updates as new features ship I put together a full playbook with the plugin install, the store authentication walkthrough, 5 DTC workflows to run on day one, and the exact prompts I used. Want it for free? > Like this post > Comment "SHOP" And I'll send it over (must be following so I can DM)

Mike Futia

113,785 views • 4 months ago

THE WAY YOU LOOK AT AI GAME DEVELOPMENT WILL CHANGE AFTER YOU WATCH THIS VIDEO "The AI Slop is Coming for Games." That's right. Introducing... Slopet League Everything you see here was built by Opus 5 and GPT 5.6 Sol in _____🤫. This is not an html page, it is a functional game with a fair bit of polish working inside a real game engine. This has to be some kind of trick. NO. It downloaded a map. NO. Imported assets. Only the Octane meshes, I have other cars that were generated that look just as good. The balls, the map, the effects, the physics, animations, etc were all generated from scratch by Opus 5. GPT 5.6 Sol handled the car meshes. This is only the beginning. I made this in about a day and a half, and most of that time I was waiting on Opus to finish generation. Maybe 25 prompts. I will share my process, and many photos, proofs, evidence of this as we go. This is just a taste, I'm extremely busy this week and working on real life stuff so I'm not going to put any more time into this until next week, but believe me I'm gonna polish this into something beautiful. If you're interested, please share, bookmark, follow, and comment below. I want the world to see just how powerful the language models are TODAY. It's only going to get better from here. Buckle up folks. Oh, and What a save!

THE WAY YOU LOOK AT AI GAME DEVELOPMENT WILL CHANGE AFTER YOU WATCH THIS VIDEO "The AI Slop is Coming for Games." That's right. Introducing... Slopet League Everything you see here was built by Opus 5 and GPT 5.6 Sol in _____🤫. This is not an html page, it is a functional game with a fair bit of polish working inside a real game engine. This has to be some kind of trick. NO. It downloaded a map. NO. Imported assets. Only the Octane meshes, I have other cars that were generated that look just as good. The balls, the map, the effects, the physics, animations, etc were all generated from scratch by Opus 5. GPT 5.6 Sol handled the car meshes. This is only the beginning. I made this in about a day and a half, and most of that time I was waiting on Opus to finish generation. Maybe 25 prompts. I will share my process, and many photos, proofs, evidence of this as we go. This is just a taste, I'm extremely busy this week and working on real life stuff so I'm not going to put any more time into this until next week, but believe me I'm gonna polish this into something beautiful. If you're interested, please share, bookmark, follow, and comment below. I want the world to see just how powerful the language models are TODAY. It's only going to get better from here. Buckle up folks. Oh, and What a save!

am.will

131,568 views • 26 days ago