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Microsoft just made expensive GPUs useless. And it runs on your 5-year-old laptop. BitNet B1.58 crushes models 10x bigger using 96% less energy. Here's what changed: → Uses ternary weights (only -1, 0, +1) instead of full precision floats → 0.4GB memory usage (runs on a phone, no GPU... show more

Julian Goldie SEO

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76,615 views • 7 months ago •via X (Twitter)

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Stop paying for every API call. Download once and run forever. My 1-post “thread” on Gemma. → Gemma is Google’s open weights model family. → Gemma runs locally on laptop and phone. → Gemma 3N is optimized for low RAM devices. → Variants exist for medical and safety and embeddings. → Fine tuning lets you build custom models for your niche. Save this video, you’ll build your first local AI tool. Want the SOP? DM me. 💬

Stop paying for every API call. Download once and run forever. My 1-post “thread” on Gemma. → Gemma is Google’s open weights model family. → Gemma runs locally on laptop and phone. → Gemma 3N is optimized for low RAM devices. → Variants exist for medical and safety and embeddings. → Fine tuning lets you build custom models for your niche. Save this video, you’ll build your first local AI tool. Want the SOP? DM me. 💬

Julian Goldie SEO

14,403 views • 8 months ago

Kimmy K2.5 just launched a local cloud model. And it runs Open Claw for FREE forever. No API costs. No token limits. No subscriptions. Here's the setup 👇 → Download Ollama at → Install Kimmy K2.5 cloud model. → Launch Open Claw with one command. → Runs locally but uses cloud processing. → Zero laptop slowdown. → Works on older machines perfectly. I just set it up in under 2 minutes. Zero configuration. Zero technical skills. Want the commands? DM me.

Kimmy K2.5 just launched a local cloud model. And it runs Open Claw for FREE forever. No API costs. No token limits. No subscriptions. Here's the setup 👇 → Download Ollama at → Install Kimmy K2.5 cloud model. → Launch Open Claw with one command. → Runs locally but uses cloud processing. → Zero laptop slowdown. → Works on older machines perfectly. I just set it up in under 2 minutes. Zero configuration. Zero technical skills. Want the commands? DM me.

Julian Goldie SEO

240,306 views • 6 months ago

There's a free 120 billion parameter AI you can run on your own computer. Zero internet. Zero API costs. Zero monthly fees. It's called Nvidia Nemotron 3 Super. And it just changed what solopreneurs can do with AI agents. Here's the setup: → Download Ollama. Copy one command into your terminal. → Connect it to OpenClaw in under 10 minutes. → Your AI agent now runs 24/7 on WhatsApp, Discord, or Telegram. → It answers customers, runs tasks, and manages workflows while you sleep. → 256,000 token context window. It reads entire documents without forgetting. No subscriptions. No cloud. No one else touching your data. A full AI employee stack for $0. Save this. Set it up this weekend.

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Julian Goldie SEO

29,325 views • 5 months ago

𝗖𝗵𝗶𝗻𝗮 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝘁𝗵𝗲 #𝟭 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗶𝗺𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹. 𝗬𝗼𝘂'𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝗽𝗮𝘆𝗶𝗻𝗴 𝗳𝗼𝗿 𝗠𝗶𝗱𝗷𝗼𝘂𝗿𝗻𝗲𝘆. 𝗤𝘄𝗲𝗻 𝗜𝗺𝗮𝗴𝗲 𝟮.𝟱 𝗶𝘀 𝗳𝗿𝗲𝗲. 𝗔𝗻𝗱 𝗶𝘁 𝗿𝘂𝗻𝘀 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿. I tested it against ChatGPT and Gemini. Here's what happened: 1: Qwen generated images 3x faster 2: Zero limits on what you create 3: No cloud required 4: Beat paid models on editing tests 5: Runs locally with no monthly fees Open source means no restrictions. Less than 1% of creators know this exists. Save this video, you'll cancel your subscriptions. Want the SOP? DM me. 💬

𝗖𝗵𝗶𝗻𝗮 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝘁𝗵𝗲 #𝟭 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗶𝗺𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹. 𝗬𝗼𝘂'𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝗽𝗮𝘆𝗶𝗻𝗴 𝗳𝗼𝗿 𝗠𝗶𝗱𝗷𝗼𝘂𝗿𝗻𝗲𝘆. 𝗤𝘄𝗲𝗻 𝗜𝗺𝗮𝗴𝗲 𝟮.𝟱 𝗶𝘀 𝗳𝗿𝗲𝗲. 𝗔𝗻𝗱 𝗶𝘁 𝗿𝘂𝗻𝘀 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿. I tested it against ChatGPT and Gemini. Here's what happened: 1: Qwen generated images 3x faster 2: Zero limits on what you create 3: No cloud required 4: Beat paid models on editing tests 5: Runs locally with no monthly fees Open source means no restrictions. Less than 1% of creators know this exists. Save this video, you'll cancel your subscriptions. Want the SOP? DM me. 💬

Julian Goldie SEO

53,003 views • 7 months ago

OpenClaw + Qwen 3.5 just unlocked “Claude-level” local agents. No API bills, no cloud, no data leaving your machine. Here’s the setup flow. → Install the Ollama pre-release (needed for the newest Qwen build). → Pull Qwen 3.5 locally (it’s a big download). → Launch OpenClaw with the Qwen model. → Run one real workflow: publish, message, schedule. If you can run it locally, you own the whole stack. Save this video, you’ll set up a free private agent. Want the SOP? DM me. 💬

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Julian Goldie SEO

163,586 views • 5 months ago

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Julian Goldie SEO

27,006 views • 6 months ago

AMD JUST KILLED THE $200 - A - MONTH AI SUBSCRIPTION - A 65-WATT BOX WITH 128GB OF UNIFIED MEMORY THAT RUNS 70B MODELS LOCALLY. that clip is amd's ryzen ai max+ 395 mini box, held up on stage. the whole pitch is one spec: 128gb of high-speed unified memory. why it matters: cpu, gpu and npu share the same 128gb pool, so a model that used to need a rack of vram just fits. what it actually runs: llama 70b in full precision on the built-in gpu open models up to ~100b parameters, locally, no cloud all of it in a mini pc pulling around 65 watts that's the part that ends the subscription: your prompts, your data, your model, on a box on your desk, not metered by the token. honest line: this runs open models, not gpt-5 or claude opus. for chat, coding help and agents on your own data it's more than enough. for the absolute frontier, the cloud still wins. and it isn't free: the box runs ~$1,500 and up. but that's a few months of a $200 sub, then it's yours forever. no monthly ai bill, no tokens metered, no prompt leaving the room. the uncomfortable part isn't that amd made a fast box. it's that "you need the cloud for ai" was a pricing choice, and unified memory just made it optional. follow RetroChainer for the hardware that turns rent into ownership, and bookmark this before your next ai renewal.

AMD JUST KILLED THE $200 - A - MONTH AI SUBSCRIPTION - A 65-WATT BOX WITH 128GB OF UNIFIED MEMORY THAT RUNS 70B MODELS LOCALLY. that clip is amd's ryzen ai max+ 395 mini box, held up on stage. the whole pitch is one spec: 128gb of high-speed unified memory. why it matters: cpu, gpu and npu share the same 128gb pool, so a model that used to need a rack of vram just fits. what it actually runs: llama 70b in full precision on the built-in gpu open models up to ~100b parameters, locally, no cloud all of it in a mini pc pulling around 65 watts that's the part that ends the subscription: your prompts, your data, your model, on a box on your desk, not metered by the token. honest line: this runs open models, not gpt-5 or claude opus. for chat, coding help and agents on your own data it's more than enough. for the absolute frontier, the cloud still wins. and it isn't free: the box runs ~$1,500 and up. but that's a few months of a $200 sub, then it's yours forever. no monthly ai bill, no tokens metered, no prompt leaving the room. the uncomfortable part isn't that amd made a fast box. it's that "you need the cloud for ai" was a pricing choice, and unified memory just made it optional. follow RetroChainer for the hardware that turns rent into ownership, and bookmark this before your next ai renewal.

RetroChainer

16,971 views • 10 days ago

I thought I needed a powerful GPU to run serious AI locally. I was completely wrong. Late 2025, Ollama quietly launched something that changed everything. They built a cloud inference layer running on NVIDIA's Blackwell architecture, the most powerful GPU hardware available today. No GPU on your desk required. No expensive API subscription. No complex setup. Just one command and you're running Kimi K2.5, a 1 trillion parameter open-source model, completely free. I connected it to OpenClaw, which links my phone's messaging apps to the AI agent running at home. I sent a message from WhatsApp. My machine picked it up, ran the task, and sent me back the result. It felt like magic. It's just good engineering. Save this post, you'll want this when you're building your first workflow. Want the SOP? DM me.

I thought I needed a powerful GPU to run serious AI locally. I was completely wrong. Late 2025, Ollama quietly launched something that changed everything. They built a cloud inference layer running on NVIDIA's Blackwell architecture, the most powerful GPU hardware available today. No GPU on your desk required. No expensive API subscription. No complex setup. Just one command and you're running Kimi K2.5, a 1 trillion parameter open-source model, completely free. I connected it to OpenClaw, which links my phone's messaging apps to the AI agent running at home. I sent a message from WhatsApp. My machine picked it up, ran the task, and sent me back the result. It felt like magic. It's just good engineering. Save this post, you'll want this when you're building your first workflow. Want the SOP? DM me.

Julian Goldie SEO

11,967 views • 5 months ago

𝗚𝗲𝗺𝗺𝗮 𝟰 + 𝗢𝗹𝗹𝗮𝗺𝗮 + 𝗢𝗽𝗲𝗻𝗖𝗹𝗮𝘄 𝗶𝘀 𝗮 𝗳𝘂𝗹𝗹𝘆 𝗹𝗼𝗰𝗮𝗹 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗿𝘂𝗻𝘀 𝗳𝗿𝗲𝗲 𝗳𝗼𝗿𝗲𝘃𝗲𝗿. #3 open model in the world. Runs on your laptop. Zero API costs. Zero data leaving your machine. Setup in 3 steps: → Install Ollama and run "ollama pull gemma4" — downloads the model → Install OpenClaw and select Ollama as your provider → Connect Telegram — message your agent from anywhere, completely local One prompt built a working SEO calculator — HTML, CSS, JavaScript, no debugging. Coding benchmark jumped from 110 to 2,150 vs the previous Gemma. Apache 2.0. No restrictions. No monthly bill. No surprises. Want the full setup guide? DM me.

𝗚𝗲𝗺𝗺𝗮 𝟰 + 𝗢𝗹𝗹𝗮𝗺𝗮 + 𝗢𝗽𝗲𝗻𝗖𝗹𝗮𝘄 𝗶𝘀 𝗮 𝗳𝘂𝗹𝗹𝘆 𝗹𝗼𝗰𝗮𝗹 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗿𝘂𝗻𝘀 𝗳𝗿𝗲𝗲 𝗳𝗼𝗿𝗲𝘃𝗲𝗿. #3 open model in the world. Runs on your laptop. Zero API costs. Zero data leaving your machine. Setup in 3 steps: → Install Ollama and run "ollama pull gemma4" — downloads the model → Install OpenClaw and select Ollama as your provider → Connect Telegram — message your agent from anywhere, completely local One prompt built a working SEO calculator — HTML, CSS, JavaScript, no debugging. Coding benchmark jumped from 110 to 2,150 vs the previous Gemma. Apache 2.0. No restrictions. No monthly bill. No surprises. Want the full setup guide? DM me.

Julian Goldie SEO

76,908 views • 4 months ago

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

Gipp 🦅

11,334 views • 1 month ago

120 AI models. FREE for a year. no credit card. Hermes Studio already has NVIDIA preset as the base_url - so all you need is a FREE API key and you're running 120+ models instantly here's the setup: 1/ go to 2/ register, log in, bind your phone number 3/ grab your API key 4/ drop it into Hermes Studio what you get: 1/ 120+ models 2/ 40 requests per minute 3/ free for a full year while everyone's paying for API access, this is sitting right there for free

120 AI models. FREE for a year. no credit card. Hermes Studio already has NVIDIA preset as the base_url - so all you need is a FREE API key and you're running 120+ models instantly here's the setup: 1/ go to 2/ register, log in, bind your phone number 3/ grab your API key 4/ drop it into Hermes Studio what you get: 1/ 120+ models 2/ 40 requests per minute 3/ free for a full year while everyone's paying for API access, this is sitting right there for free

kaize

137,776 views • 2 months ago

Met a solo founder who just raised $2 million from tier 1 VC. No team. No cofounder. Only Claude Code & a laptop. I asked him how he runs the whole company alone. He sent me a 1 hour video that made me realize I've not been using Claude Code at even 5% of its potential. Claude Code has 96 slash commands. I hardly knew 3. This guy uses 60. Runs loops that ship features overnight. Runs Claude from his phone while he sleeps. He teaches exactly what makes you ship 10x faster. Bookmark & watch this. Then read the article on loop engineering below.

Met a solo founder who just raised $2 million from tier 1 VC. No team. No cofounder. Only Claude Code & a laptop. I asked him how he runs the whole company alone. He sent me a 1 hour video that made me realize I've not been using Claude Code at even 5% of its potential. Claude Code has 96 slash commands. I hardly knew 3. This guy uses 60. Runs loops that ship features overnight. Runs Claude from his phone while he sleeps. He teaches exactly what makes you ship 10x faster. Bookmark & watch this. Then read the article on loop engineering below.

Roan

34,827 views • 1 month ago

The era of Stable Intelligence is here 🤖 Tether’s QVAC Fabric just released the world’s first cross-platform 1-bit LLM LoRA fine-tuning framework. QVAC Fabric extends Microsoft's ultra-efficient BitNet architecture, allowing fine-tuning and inference of LLMs directly on your smartphone—no NVIDIA GPU/CUDA lock-in or expensive server required. The Breakthrough: - Total Sovereignty: LoRA fine-tune ultra-efficient models locally on any smartphone, including iPhones, Pixel phones, Samsung Galaxy phones and any desktop/laptop operating systems using Vulkan and Metal backends. - Extreme Efficiency: 1-bit architecture uses up to 90% less memory and runs up to 11x faster than traditional models. - Universal Access: What used to require a data center now runs on the chip in your pocket. Own your intelligence. The era of stable, local AI is here. 📱🧠 Read the full details on Hugging Face and grab the binaries to build on your own hardware.

The era of Stable Intelligence is here 🤖 Tether’s QVAC Fabric just released the world’s first cross-platform 1-bit LLM LoRA fine-tuning framework. QVAC Fabric extends Microsoft's ultra-efficient BitNet architecture, allowing fine-tuning and inference of LLMs directly on your smartphone—no NVIDIA GPU/CUDA lock-in or expensive server required. The Breakthrough: - Total Sovereignty: LoRA fine-tune ultra-efficient models locally on any smartphone, including iPhones, Pixel phones, Samsung Galaxy phones and any desktop/laptop operating systems using Vulkan and Metal backends. - Extreme Efficiency: 1-bit architecture uses up to 90% less memory and runs up to 11x faster than traditional models. - Universal Access: What used to require a data center now runs on the chip in your pocket. Own your intelligence. The era of stable, local AI is here. 📱🧠 Read the full details on Hugging Face and grab the binaries to build on your own hardware.

QVAC

108,780 views • 5 months ago

What Lisa Su actually held on stage: A mini PC the size of a lunchbox running Qwen3-235B locally, with no cloud and no discrete GPU Inside: the Ryzen AI Max+ 395, 128GB unified memory, 110GB usable as VRAM on Linux The first x86 chip that handles 200+ billion parameters on a single die AMD claims it beats the RTX 5080 by several times on memory-bound models — because the 5080 simply cannot fit them $1,400 to $2,500 once. cloud bills run $200 to $400 a month It pays for itself in a few months, then costs nothing per request This is not a faster GPU. it is the first real argument that your AI does not belong in someone else's data center

What Lisa Su actually held on stage: A mini PC the size of a lunchbox running Qwen3-235B locally, with no cloud and no discrete GPU Inside: the Ryzen AI Max+ 395, 128GB unified memory, 110GB usable as VRAM on Linux The first x86 chip that handles 200+ billion parameters on a single die AMD claims it beats the RTX 5080 by several times on memory-bound models — because the 5080 simply cannot fit them $1,400 to $2,500 once. cloud bills run $200 to $400 a month It pays for itself in a few months, then costs nothing per request This is not a faster GPU. it is the first real argument that your AI does not belong in someone else's data center

plutos

1,237,208 views • 2 months ago

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 views • 6 months ago

Frontier AI went free this month. Not cheaper. Free. New models: > Gemini 3.6 Flash. 1,500 req/day, 1M tokens/min, no card > Kimi K3. 2.8T MoE open weights, shipped today. Largest open-weight model ever > DeepSeek V4. 1.6T MoE, MIT license, on HuggingFace > GLM-5.2. 753B MoE, 1M context, MIT license > Qwen 3.8-Max. Free preview, no login, no card > Mistral Vibe. Work Mode free. Agent features, scheduled tasks. ChatGPT and Claude charge $20/mo for the same upd (the article): > Gemini 3.5 → 3.6 Flash (July 21) > Claude $100 Fable 5 credit, claim by Aug 2 > Claude for Teachers: free through June 2027 > Malta: free ChatGPT Plus, 1 year. First country-wide deal > OpenAI: $100 credits for posting about ChatGPT Work > Perplexity Pro: 1 year free via Xfinity > Alibaba Cloud: 70M tokens + $200 credits > Grok 4.5 live across all platforms Chinese labs shipped 4 of 5 biggest open weights this month. OpenAI and Anthropic shipped credits. The access gap didn't close. It already closed.

Frontier AI went free this month. Not cheaper. Free. New models: > Gemini 3.6 Flash. 1,500 req/day, 1M tokens/min, no card > Kimi K3. 2.8T MoE open weights, shipped today. Largest open-weight model ever > DeepSeek V4. 1.6T MoE, MIT license, on HuggingFace > GLM-5.2. 753B MoE, 1M context, MIT license > Qwen 3.8-Max. Free preview, no login, no card > Mistral Vibe. Work Mode free. Agent features, scheduled tasks. ChatGPT and Claude charge $20/mo for the same upd (the article): > Gemini 3.5 → 3.6 Flash (July 21) > Claude $100 Fable 5 credit, claim by Aug 2 > Claude for Teachers: free through June 2027 > Malta: free ChatGPT Plus, 1 year. First country-wide deal > OpenAI: $100 credits for posting about ChatGPT Work > Perplexity Pro: 1 year free via Xfinity > Alibaba Cloud: 70M tokens + $200 credits > Grok 4.5 live across all platforms Chinese labs shipped 4 of 5 biggest open weights this month. OpenAI and Anthropic shipped credits. The access gap didn't close. It already closed.

ＺＥＦＩ

49,876 views • 26 days ago

$Someone open-sourced a 66M parameter model that beats ElevenLabs, OpenAI, and Gemini and runs entirely offline on a Raspberry Pi. it's called Supertonic, a text-to-speech model so fast it turns an entire webpage into audio in under 1 second. locally. offline. for free. text-to-speech has lived in the cloud for years. every spoken character was an API call and a fraction of a cent. Supertonic 3 kills that entirely. the model is 99M parameters. ships as a single ONNX file. hits 167× faster than real-time on a laptop CPU. that's ~1,263 characters of speech per second. other open systems sit at 55–287. no cloud. no API. no GPU. → 31 languages, no separate adapters → works inside a browser tab (WebGPU/WASM) → handles phone numbers, currency, dates — no preprocessing → inline tags for laugh and breath → Turns an entire webpage into audio in under 1 second → 44.1kHz studio-grade WAV, no upsampler needed` and on real-world text, "$5.2M", "(212) 555-0142 ext. 402", "30kph”, it's the only one that reads them correctly. ElevenLabs Flash, OpenAI TTS-1, Gemini 2.5 Flash all fail. Supertonic passes. 11.2k stars. 100% Open Source.$

Someone open-sourced a 66M parameter model that beats ElevenLabs, OpenAI, and Gemini and runs entirely offline on a Raspberry Pi. it's called Supertonic, a text-to-speech model so fast it turns an entire webpage into audio in under 1 second. locally. offline. for free. text-to-speech has lived in the cloud for years. every spoken character was an API call and a fraction of a cent. Supertonic 3 kills that entirely. the model is 99M parameters. ships as a single ONNX file. hits 167× faster than real-time on a laptop CPU. that's ~1,263 characters of speech per second. other open systems sit at 55–287. no cloud. no API. no GPU. → 31 languages, no separate adapters → works inside a browser tab (WebGPU/WASM) → handles phone numbers, currency, dates — no preprocessing → inline tags for laugh and breath → Turns an entire webpage into audio in under 1 second → 44.1kHz studio-grade WAV, no upsampler needed` and on real-world text, "$5.2M", "(212) 555-0142 ext. 402", "30kph”, it's the only one that reads them correctly. ElevenLabs Flash, OpenAI TTS-1, Gemini 2.5 Flash all fail. Supertonic passes. 11.2k stars. 100% Open Source.

Superman

30,416 views • 1 month ago

OpenClaw in your pocket — powered by Solana 🦞 (Video sent by one of our .molt domain users minting AI agent on Seeker | Solana Mobile ) Connect wallet → pick a name → mint → your AI agent is live. That's it. No servers, no CLI, no Docker, no monthly bills, no coding. Every .molt domain is an NFT with a built-in wallet, zero private keys, and full on-chain identity. One mint and you own an agent that can launch tokens, mint NFTs, host x402 services, and chat on Telegram, Discord & Slack. OpenClaw runs for free — persistent memory, sleeps when idle, wakes on demand. Your agent is always there when you need it. No $600 MacBook. No $15/month VPS. Just your phone and a Solana wallet. This is what building on Solana feels like — fast, cheap, and instant. A Solana Seeker phone with a built-in wallet is all you need. Mint your AI agent in seconds, straight from your pocket.

OpenClaw in your pocket — powered by Solana 🦞 (Video sent by one of our .molt domain users minting AI agent on Seeker | Solana Mobile ) Connect wallet → pick a name → mint → your AI agent is live. That's it. No servers, no CLI, no Docker, no monthly bills, no coding. Every .molt domain is an NFT with a built-in wallet, zero private keys, and full on-chain identity. One mint and you own an agent that can launch tokens, mint NFTs, host x402 services, and chat on Telegram, Discord & Slack. OpenClaw runs for free — persistent memory, sleeps when idle, wakes on demand. Your agent is always there when you need it. No $600 MacBook. No $15/month VPS. Just your phone and a Solana wallet. This is what building on Solana feels like — fast, cheap, and instant. A Solana Seeker phone with a built-in wallet is all you need. Mint your AI agent in seconds, straight from your pocket.

Molt.id

78,109 views • 6 months ago

the tiebreaker is done. qwen 3.5 27B dense. single RTX 3090. one prompt. zero steering. zero human edits. 1,827 lines across 10 files. 13 minutes. full thinking mode. runs on first load. hermes 4.3 got the same prompt with 2x 3090s and 5x the context it needed. wrote 1,249 lines, left empty files, needed 3 interventions, game was broken on load. same architecture class. same quant. hermes got double the hardware. completely different result. dense wasn't the problem. hermes was. but here's what got me. this model thinks at 27 tok/s. every single token carries 27 billion parameters of reasoning. MoE hit 112 tok/s but only 3B active per token. the dense model is slower and it doesn't matter. watch 13 minutes of autonomous coding on a consumer GPU with zero intervention and tell me speed is what matters. a year ago this wasn't possible. now it runs on hardware you can buy used for $900. no API. no subscription. no cloud. just a 3090 doing what data centers did 18 months ago. full unedited session in the video. every token, every file, every thinking chain. 16 minutes. hit play.

the tiebreaker is done. qwen 3.5 27B dense. single RTX 3090. one prompt. zero steering. zero human edits. 1,827 lines across 10 files. 13 minutes. full thinking mode. runs on first load. hermes 4.3 got the same prompt with 2x 3090s and 5x the context it needed. wrote 1,249 lines, left empty files, needed 3 interventions, game was broken on load. same architecture class. same quant. hermes got double the hardware. completely different result. dense wasn't the problem. hermes was. but here's what got me. this model thinks at 27 tok/s. every single token carries 27 billion parameters of reasoning. MoE hit 112 tok/s but only 3B active per token. the dense model is slower and it doesn't matter. watch 13 minutes of autonomous coding on a consumer GPU with zero intervention and tell me speed is what matters. a year ago this wasn't possible. now it runs on hardware you can buy used for $900. no API. no subscription. no cloud. just a 3090 doing what data centers did 18 months ago. full unedited session in the video. every token, every file, every thinking chain. 16 minutes. hit play.

Sudo su

91,135 views • 5 months ago