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🚀 AuK is officially here. Nano banana🍌 for audio An open-source foundation model for unified speech generation and editing. Natural-language instructions + reference audio. One interface. Zero-shot TTS. Instruction-controlled generation. Content editing. Whisper-conversion. De-accent. Timbre/style/emotion edit. Speed/Pitch control. Enhancement, denoising, multi-speaker and music separation. Also releasing AuK-Flash: 4-step inference....

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Tencent Hy

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92,572 görüntüleme • 11 gün önce •via X (Twitter)

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🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: 📰 Paper: 🎥 Demo: #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA
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🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: 📰 Paper: 🎥 Demo: #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA

Yukang Chen

59,754 görüntüleme • 4 ay önce

Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale blog: Large-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision research. These models not only generate high fidelity text or image outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are neither filtered nor enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation. In particular, Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to 20 times faster.
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Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale blog: Large-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision research. These models not only generate high fidelity text or image outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are neither filtered nor enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation. In particular, Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to 20 times faster.

AK

429,355 görüntüleme • 3 yıl önce

🚀 VoxCPM 2 is live! 🎉 Another open-source AI #TTS model from China — and one that stands shoulder to shoulder with Qwen3-TTS, while bringing everything into a single unified model. After rapid iterations from V1 (zero-shot cloning) to V1.5 (long-form + fine-tuning), #VoxCPM has consistently pushed quality and usability forward. Now, VoxCPM 2 takes it further: 🔹30+ languages — truly global, truly local. 🔹Infinite voice design — type it, hear it, control it. From a whisper to a booming cinematic voice. 🔹Studio-grade audio — 48kHz ultra-high fidelity with emotional depth 🔹Diffusion-Autoregressive cloning — preserves more acoustic and emotional detail than token-based models like Qwen3-TTS 💡 Big shoutout to Grok — used your multi-image video magic for our launch demo. It’s scarily good at keeping visuals consistent across shots. Elon Elon Musk, this one’s for you. 😉 Check the demo & start cloning your dream voice: 🌐 Hugging Face Space: 🤗 Hugging Face Model: 🤖 ModelScope Model: 💻 GitHub： #TTS #AI #VoiceCloning #GrokImagine #ElonMusk #OpenBMB #VoxCPM
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🚀 VoxCPM 2 is live! 🎉 Another open-source AI #TTS model from China — and one that stands shoulder to shoulder with Qwen3-TTS, while bringing everything into a single unified model. After rapid iterations from V1 (zero-shot cloning) to V1.5 (long-form + fine-tuning), #VoxCPM has consistently pushed quality and usability forward. Now, VoxCPM 2 takes it further: 🔹30+ languages — truly global, truly local. 🔹Infinite voice design — type it, hear it, control it. From a whisper to a booming cinematic voice. 🔹Studio-grade audio — 48kHz ultra-high fidelity with emotional depth 🔹Diffusion-Autoregressive cloning — preserves more acoustic and emotional detail than token-based models like Qwen3-TTS 💡 Big shoutout to Grok — used your multi-image video magic for our launch demo. It’s scarily good at keeping visuals consistent across shots. Elon Elon Musk, this one’s for you. 😉 Check the demo & start cloning your dream voice: 🌐 Hugging Face Space: 🤗 Hugging Face Model: 🤖 ModelScope Model: 💻 GitHub： #TTS #AI #VoiceCloning #GrokImagine #ElonMusk #OpenBMB #VoxCPM

OpenBMB

560,058 görüntüleme • 5 ay önce

Microsoft did it again! Speech AI models have a major limitation. They slice long recordings into tiny chunks, lose track of who's speaking, and forget all context halfway through. This is exactly what Microsoft's VibeVoice solves. It's an open-source family of frontier voice AI models for both speech recognition and speech generation. Here's what it can do: > VibeVoice-ASR processes up to 60 minutes of audio in a single pass. No chunking. It outputs structured transcriptions with who spoke, when they spoke, and what they said. > You can feed it custom hotwords like names, technical jargon, or domain-specific terms. The model uses them to significantly improve accuracy on specialized content. > VibeVoice-TTS generates up to 90 minutes of multi-speaker speech with up to 4 distinct speakers. Natural turn-taking, emotional expression, all in one pass. > VibeVoice-Realtime is a 0.5B streaming TTS model with ~300ms first-audio latency. Small enough to deploy practically anywhere. All of this is powered by continuous speech tokenizers running at just 7.5 Hz. This ultra-low frame rate preserves audio quality while making long sequences computationally feasible. I have shared the link to the GitHub repo in the replies!
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Microsoft did it again! Speech AI models have a major limitation. They slice long recordings into tiny chunks, lose track of who's speaking, and forget all context halfway through. This is exactly what Microsoft's VibeVoice solves. It's an open-source family of frontier voice AI models for both speech recognition and speech generation. Here's what it can do: > VibeVoice-ASR processes up to 60 minutes of audio in a single pass. No chunking. It outputs structured transcriptions with who spoke, when they spoke, and what they said. > You can feed it custom hotwords like names, technical jargon, or domain-specific terms. The model uses them to significantly improve accuracy on specialized content. > VibeVoice-TTS generates up to 90 minutes of multi-speaker speech with up to 4 distinct speakers. Natural turn-taking, emotional expression, all in one pass. > VibeVoice-Realtime is a 0.5B streaming TTS model with ~300ms first-audio latency. Small enough to deploy practically anywhere. All of this is powered by continuous speech tokenizers running at just 7.5 Hz. This ultra-low frame rate preserves audio quality while making long sequences computationally feasible. I have shared the link to the GitHub repo in the replies!

Akshay 🚀

45,256 görüntüleme • 5 ay önce

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:
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We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

Tencent Hy

89,392 görüntüleme • 1 yıl önce

We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:
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We’re excited to announce the release and open-source of HunyuanImage 3.0 — the largest and most powerful open-source text-to-image model to date, with over 80 billion total parameters, of which 13 billion are activated per token during inference.The effect is completely comparable to the industry’s flagship closed-source model.🚀🚀🚀 HunyuanImage 3.0 originates from our internally developed native multimodal large language model, with fine-tuning and post-training focused on text-to-image generation. This unique foundation gives the model a powerful set of capabilities: ✅Reason with world knowledge ✅Understand complex, thousand-word prompts ✅Generate precise text within images Different from traditional DiT architecture image generation models, HunyuanImage 3.0’s MoE architecture uses a Transfusion-based approach to deeply couple Diffusion and LLM training for a single, powerful system. Built on Hunyuan-A13B, HunyuanImage 3.0 was trained on a massive dataset: 5 billion image-text pairs, video frames, interleaved image-text data, and 6 trillion tokens of text corpora. This hybrid training across multimodal generation, understanding, and LLM capabilities allows the model to seamlessly integrate multiple tasks. Whether you're an illustrator, designer, or creator, this is built to slash your workflow from hours to minutes. HunyuanImage 3.0 can generate intricate text, detailed comics, expressive emojis, and lively, engaging illustrations for educational content. The current release focuses solely on text-to-image generation and future updates will include image-to-image, image editing, multi-turn interaction, and more. 👉🏻Try it now: 🔗GitHub: 🤗Hugging Face:

Tencent Hy

413,096 görüntüleme • 11 ay önce

I just built an open NotebookLM clone! Here's what it can do for you: - Process multi-modal data - Scrape websites and YouTube videos - Create a unified knowledge base - Lets you do RAG over it - Remember every conversation - Generate a podcast 🎙️ The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step! So by the end of this video, you'll learn how to: ↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs ↳ Store everything in a vector database for fast retrieval ↳ Add a memory layer that remembers conversations and preferences for a personalized experience ↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model The podcast generation feature is my favorite part! There's something powerful about turning written content into conversational audio that you can listen to while doing something else. The entire code is 100% open-source. I've shared a link in the replies! ____ Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this. Cheers! :)
20:04

I just built an open NotebookLM clone! Here's what it can do for you: - Process multi-modal data - Scrape websites and YouTube videos - Create a unified knowledge base - Lets you do RAG over it - Remember every conversation - Generate a podcast 🎙️ The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step! So by the end of this video, you'll learn how to: ↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs ↳ Store everything in a vector database for fast retrieval ↳ Add a memory layer that remembers conversations and preferences for a personalized experience ↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model The podcast generation feature is my favorite part! There's something powerful about turning written content into conversational audio that you can listen to while doing something else. The entire code is 100% open-source. I've shared a link in the replies! ____ Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this. Cheers! :)

Akshay 🚀

83,187 görüntüleme • 11 ay önce

Seedance 2.0 is allowing us to enter a new era of music video creation. Here is how I created HONEY. It was a quick test to see how well this workflow holds up. 🐝 1 - Write your song and generate the music with Suno 5.5. 2 - Use an image generator of your choice. For HONEY I combined both Grok Imagine for aesthetics and Nano Banana Pro for refined editing. 3 - In Capcut I import my audio and just save out a blank video video containing the audio. This step is important because this video file containing audio will now be used with Seedance 2.0 as a video reference with Omni. This allows the AI to apply automatic and realistic lipsync and movement to the music, it's extremely powerful! 4 - Once I have a both my image and video with audio as reference, I use Seedance 2.0 Omni and upload my starting image and then the video reference with the audio. 5 - From here I'm simply prompting like normal, specifying what's happening in my scene with detailed instructions, mentioning multi shots and camera angle changes and then specifying that the person is singing along to the song. I type out the lyrics that are present to have better lipsync accuracy. 6 - Once I have generated a video and like the result, I do video to video, so i upload that video that just got generated and type "The scene continues" and prompt new actions to take place. This allows you to expand on a narrative. These new shots can be used as B-ROLL and since I uploaded my video as reference I have full consistency of everything it saw in the video. This is also extremely powerful. 7 - This is actually the most difficult part. Edit in Capcut. This is where you need to understand pacing and shot selection from all the scenes you generated to bring it all together. You must be strategic with the editing. Goodluck! I'll probably record a video tutorial at some point as it's easier to see what is being done.
0:46

Seedance 2.0 is allowing us to enter a new era of music video creation. Here is how I created HONEY. It was a quick test to see how well this workflow holds up. 🐝 1 - Write your song and generate the music with Suno 5.5. 2 - Use an image generator of your choice. For HONEY I combined both Grok Imagine for aesthetics and Nano Banana Pro for refined editing. 3 - In Capcut I import my audio and just save out a blank video video containing the audio. This step is important because this video file containing audio will now be used with Seedance 2.0 as a video reference with Omni. This allows the AI to apply automatic and realistic lipsync and movement to the music, it's extremely powerful! 4 - Once I have a both my image and video with audio as reference, I use Seedance 2.0 Omni and upload my starting image and then the video reference with the audio. 5 - From here I'm simply prompting like normal, specifying what's happening in my scene with detailed instructions, mentioning multi shots and camera angle changes and then specifying that the person is singing along to the song. I type out the lyrics that are present to have better lipsync accuracy. 6 - Once I have generated a video and like the result, I do video to video, so i upload that video that just got generated and type "The scene continues" and prompt new actions to take place. This allows you to expand on a narrative. These new shots can be used as B-ROLL and since I uploaded my video as reference I have full consistency of everything it saw in the video. This is also extremely powerful. 7 - This is actually the most difficult part. Edit in Capcut. This is where you need to understand pacing and shot selection from all the scenes you generated to bring it all together. You must be strategic with the editing. Goodluck! I'll probably record a video tutorial at some point as it's easier to see what is being done.

Travis Davids

19,236 görüntüleme • 5 ay önce

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.
1:24

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.

Kingnet AI

46,437 görüntüleme • 1 yıl önce

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource
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🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource

Bo Wang

199,790 görüntüleme • 2 yıl önce

🚀 Sol-H3: MiniMax (official) H3 Video Generation Faster Than Playback 🤩 Five seconds of world. 1.653 seconds to infer. We’re releasing Sol-H3, our fastest end-to-end MiniMax-H3 inference stack yet. On one 8× NVIDIA B300 Blackwell system, it generates five seconds of 1344×768 video with stereo audio in 1.653 seconds. Across 1×, 4×, and 8× B300, Sol-H3 reaches up to a 15.54× speedup versus Base H3. Compared with 50-step Base H3 Dense on the same 8× B300 system, the four-step Sol-H3 profile delivers: • 5s: 18.250s → 1.653s (11.04×) • 10s: 50.660s → 3.732s (13.57×) • 15s: 99.513s → 6.612s (15.05×) Sol-H3 also scales across GPU counts: • 4× B300: 2.918s / 6.993s / 12.542s for 5s / 10s / 15s (12.11–15.54×) • 1× B300: 13.745s / 37.813s / 52.260s for 5s / 10s / 15s (9.45–14.29×) All figures are medians of three measured runs after one warmup at 1344×768 and 24 FPS with stereo audio. Base H3 uses 50 scheduler points (49 DiT forwards); Sol-H3 uses four DiT forwards, so this is a full-profile comparison—not an attention-only runtime change. Sol-H3 uses Dense attention on 1× B300 and SOL with INT8 QKV / FP8 output transport on 4× / 8×. Timing includes text encoding, DiT denoising, and video/audio VAE decoding; model loading, compilation warmup, and final MP4 encoding are excluded. Sol-H3 brings Sol-Engine × Sol-Attn into one full-stack runtime: • dynamic sparse attention with no retraining • fused norm, RoPE, MLP, and sparse-attention setup • fused INT8 QKV / FP8 output communication across 8 GPUs • parallel, batched VAE decoding • precomputed AdaLN caching Inside the stack: • sparse-attention setup: 1.206 → 0.285 ms (−76.4%) • VAE decode: 7.55 → 0.602 s • ~24 GB memory freed per GPU Any MiniMax-H3 few-step LoRA can plug into the same engine, and the code is deployment-friendly under Apache 2.0. For us, the bigger milestone is crossing from “fast generation” into “faster than playback.” That opens the path toward continuous 24 FPS generation and truly interactive video systems. We’re excited to partner with reactor to release Sol-H3 and make it available as an API day-0. Try it now on Reactor: 🔗 Amazing team effort—full credits in the blog. LoveSy Junsong_Chen yitong li Haopeng Li Haocheng Xi Song Han
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🚀 Sol-H3: MiniMax (official) H3 Video Generation Faster Than Playback 🤩 Five seconds of world. 1.653 seconds to infer. We’re releasing Sol-H3, our fastest end-to-end MiniMax-H3 inference stack yet. On one 8× NVIDIA B300 Blackwell system, it generates five seconds of 1344×768 video with stereo audio in 1.653 seconds. Across 1×, 4×, and 8× B300, Sol-H3 reaches up to a 15.54× speedup versus Base H3. Compared with 50-step Base H3 Dense on the same 8× B300 system, the four-step Sol-H3 profile delivers: • 5s: 18.250s → 1.653s (11.04×) • 10s: 50.660s → 3.732s (13.57×) • 15s: 99.513s → 6.612s (15.05×) Sol-H3 also scales across GPU counts: • 4× B300: 2.918s / 6.993s / 12.542s for 5s / 10s / 15s (12.11–15.54×) • 1× B300: 13.745s / 37.813s / 52.260s for 5s / 10s / 15s (9.45–14.29×) All figures are medians of three measured runs after one warmup at 1344×768 and 24 FPS with stereo audio. Base H3 uses 50 scheduler points (49 DiT forwards); Sol-H3 uses four DiT forwards, so this is a full-profile comparison—not an attention-only runtime change. Sol-H3 uses Dense attention on 1× B300 and SOL with INT8 QKV / FP8 output transport on 4× / 8×. Timing includes text encoding, DiT denoising, and video/audio VAE decoding; model loading, compilation warmup, and final MP4 encoding are excluded. Sol-H3 brings Sol-Engine × Sol-Attn into one full-stack runtime: • dynamic sparse attention with no retraining • fused norm, RoPE, MLP, and sparse-attention setup • fused INT8 QKV / FP8 output communication across 8 GPUs • parallel, batched VAE decoding • precomputed AdaLN caching Inside the stack: • sparse-attention setup: 1.206 → 0.285 ms (−76.4%) • VAE decode: 7.55 → 0.602 s • ~24 GB memory freed per GPU Any MiniMax-H3 few-step LoRA can plug into the same engine, and the code is deployment-friendly under Apache 2.0. For us, the bigger milestone is crossing from “fast generation” into “faster than playback.” That opens the path toward continuous 24 FPS generation and truly interactive video systems. We’re excited to partner with reactor to release Sol-H3 and make it available as an API day-0. Try it now on Reactor: 🔗 Amazing team effort—full credits in the blog. LoveSy Junsong_Chen yitong li Haopeng Li Haocheng Xi Song Han

Enze Xie

204,891 görüntüleme • 14 gün önce

🚨 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. 👇
0:44

🚨 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

221,715 görüntüleme • 5 ay önce

I tested Tencent’s Hy4 preview model in WorkBuddy on a real frontend build, not a benchmark screenshot. I gave it one practical brief: create an original neon courier game in a single HTML file, with Canvas rendering, keyboard controls, collision detection, scoring, a countdown timer, a boost mechanic, sound effects, and reliable restart logic. The point was not to see whether it could describe a game. I wanted to see whether it could turn a creative idea into a coherent, playable result while handling the details that often break a quick prototype: state changes, movement, collisions, feedback, layout, and interaction flow. Tencent’s Hy4 preview is designed for stronger code understanding, generation, and editing, together with document and information processing, workflow automation, web and game development, cross-tool collaboration, and complex multi-step task completion. That makes it relevant not only for answering questions, but also for taking a task from brief to working output. The current preview version is faster and more effective than the first release. After the latest upgrade, the workflow takes fewer conversation rounds, uses fewer tokens, and reaches useful results more quickly. The difference is most noticeable in the handoff between idea, implementation, revision, and final testing: there is less waiting and less back-and-forth before the result becomes usable. For context, the reference materials describe Tencent’s Hy4 preview as a 770B-parameter model with 49B active parameters, a 1M-token context window, and Apache 2.0 weights. The practical question is how those capabilities translate into real work. In this case, I’m testing whether the model can produce a finished, playable game rather than just a promising code fragment. The one-minute video shows the complete process: selecting Tencent’s Hy4 preview model in WorkBuddy, submitting the build prompt, reviewing the generated result, playing the game, and checking the controls, collisions, score, timer, boost effect, sound, and restart flow. Try your own coding, frontend, document, or automation task in WorkBuddy: Tencent Hy Workbuddy Tencent AI
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I tested Tencent’s Hy4 preview model in WorkBuddy on a real frontend build, not a benchmark screenshot. I gave it one practical brief: create an original neon courier game in a single HTML file, with Canvas rendering, keyboard controls, collision detection, scoring, a countdown timer, a boost mechanic, sound effects, and reliable restart logic. The point was not to see whether it could describe a game. I wanted to see whether it could turn a creative idea into a coherent, playable result while handling the details that often break a quick prototype: state changes, movement, collisions, feedback, layout, and interaction flow. Tencent’s Hy4 preview is designed for stronger code understanding, generation, and editing, together with document and information processing, workflow automation, web and game development, cross-tool collaboration, and complex multi-step task completion. That makes it relevant not only for answering questions, but also for taking a task from brief to working output. The current preview version is faster and more effective than the first release. After the latest upgrade, the workflow takes fewer conversation rounds, uses fewer tokens, and reaches useful results more quickly. The difference is most noticeable in the handoff between idea, implementation, revision, and final testing: there is less waiting and less back-and-forth before the result becomes usable. For context, the reference materials describe Tencent’s Hy4 preview as a 770B-parameter model with 49B active parameters, a 1M-token context window, and Apache 2.0 weights. The practical question is how those capabilities translate into real work. In this case, I’m testing whether the model can produce a finished, playable game rather than just a promising code fragment. The one-minute video shows the complete process: selecting Tencent’s Hy4 preview model in WorkBuddy, submitting the build prompt, reviewing the generated result, playing the game, and checking the controls, collisions, score, timer, boost effect, sound, and restart flow. Try your own coding, frontend, document, or automation task in WorkBuddy: Tencent Hy Workbuddy Tencent AI

Rebecca Adson

61,820 görüntüleme • 4 gün önce

If you are running local LLMs without N-gram speculative decoding, you are wasting massive amounts of compute. Whether your AI is editing a document, outputting structured JSON, or rewriting boilerplate templates, a huge chunk of the text it generates is highly repetitive or already exists right there in the prompt. Standard decoding wastes expensive GPU compute cycles "re thinking" every single token. By adding one hidden flag in llama.cpp, you can instantly fast forward through the repetition. Zero draft models. Zero extra VRAM. And virtually zero compute overhead. Google Colab hands you an enterprise grade NVIDIA Tesla T4 GPU with 16GB of VRAM for free. It’s the perfect Ubuntu Linux sandbox to build a bleeding edge inference engine from scratch. Recently, I showed you how to double your local speeds using MTP (Multi Token Prediction). But MTP requires a secondary neural network draft model. That eats into your precious VRAM (slightly though) and burns extra compute for every guess it makes. N-gram Speculative Decoding gives you a massive speed boost for exactly 0 memory cost and minimal compute. And it's faster than MTP when it works. Here is how it actually works under the hood: Standard autoregressive decoding is slow because it predicts one token at a time. If you ask an agent to format a long JSON object or update one line in an HTML file, it runs heavy matrix multiplications to calculate the probability of every single bracket, space, and letter from scratch. N-gram changes the game. It acts as a lightweight caching system. Instead of running heavy neural network math to guess the next word, it uses a simple hash table. Whenever the LLM starts outputting a sequence of tokens that already exists anywhere in its context window, N-gram instantly recognizes the pattern. Because it is just doing lightning fast string matching, the compute cost is practically zero. It "fast forwards" through the text, drafting the boilerplate instantly from memory, and the main model just verifies it in parallel. Pure speed. Using quantized GGUFs from Unsloth via HuggingFace, I spun up DeepMind’s massive Gemma 4 26B A4B QAT MoE on a free Colab instance to test this. Just look at the raw benchmark data on code editing task: Without N-gram: [ Prompt: 638.6 t/s | Generation: 45.9 t/s ] With N-gram: [ Prompt: 601.9 t/s | Generation: 107.1 t/s ] Here is the exact llama.cpp CLI command to activate it. Notice we don't even need the --model-draft flag: ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -cnv -n 6000 -c 12000 -ngl 99 -fa on --spec-type ngram-mod Stop waiting for your GPU to re calculate words it already knows. I’ve built a free, interactive, cell by cell Google Colab notebook that lets you test this live in your browser. You can literally chat with the model and watch the text generation speed absolutely fly on the second turn when you ask it to edit a file. There are additional parameters for ngram-mod that you can tune once you get it working with the single flag. Link to the free Colab Notebook is in the comments below. It walks you through the entire stack: pulling pre built llama.cpp CUDA binaries for Linux, fetching GGUFs from HuggingFace, and spinning up the inference engine with ngram-mod from scratch. Let me know if you have already tried ngram-mod
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If you are running local LLMs without N-gram speculative decoding, you are wasting massive amounts of compute. Whether your AI is editing a document, outputting structured JSON, or rewriting boilerplate templates, a huge chunk of the text it generates is highly repetitive or already exists right there in the prompt. Standard decoding wastes expensive GPU compute cycles "re thinking" every single token. By adding one hidden flag in llama.cpp, you can instantly fast forward through the repetition. Zero draft models. Zero extra VRAM. And virtually zero compute overhead. Google Colab hands you an enterprise grade NVIDIA Tesla T4 GPU with 16GB of VRAM for free. It’s the perfect Ubuntu Linux sandbox to build a bleeding edge inference engine from scratch. Recently, I showed you how to double your local speeds using MTP (Multi Token Prediction). But MTP requires a secondary neural network draft model. That eats into your precious VRAM (slightly though) and burns extra compute for every guess it makes. N-gram Speculative Decoding gives you a massive speed boost for exactly 0 memory cost and minimal compute. And it's faster than MTP when it works. Here is how it actually works under the hood: Standard autoregressive decoding is slow because it predicts one token at a time. If you ask an agent to format a long JSON object or update one line in an HTML file, it runs heavy matrix multiplications to calculate the probability of every single bracket, space, and letter from scratch. N-gram changes the game. It acts as a lightweight caching system. Instead of running heavy neural network math to guess the next word, it uses a simple hash table. Whenever the LLM starts outputting a sequence of tokens that already exists anywhere in its context window, N-gram instantly recognizes the pattern. Because it is just doing lightning fast string matching, the compute cost is practically zero. It "fast forwards" through the text, drafting the boilerplate instantly from memory, and the main model just verifies it in parallel. Pure speed. Using quantized GGUFs from Unsloth via HuggingFace, I spun up DeepMind’s massive Gemma 4 26B A4B QAT MoE on a free Colab instance to test this. Just look at the raw benchmark data on code editing task: Without N-gram: [ Prompt: 638.6 t/s | Generation: 45.9 t/s ] With N-gram: [ Prompt: 601.9 t/s | Generation: 107.1 t/s ] Here is the exact llama.cpp CLI command to activate it. Notice we don't even need the --model-draft flag: ./llama-cli -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -cnv -n 6000 -c 12000 -ngl 99 -fa on --spec-type ngram-mod Stop waiting for your GPU to re calculate words it already knows. I’ve built a free, interactive, cell by cell Google Colab notebook that lets you test this live in your browser. You can literally chat with the model and watch the text generation speed absolutely fly on the second turn when you ask it to edit a file. There are additional parameters for ngram-mod that you can tune once you get it working with the single flag. Link to the free Colab Notebook is in the comments below. It walks you through the entire stack: pulling pre built llama.cpp CUDA binaries for Linux, fetching GGUFs from HuggingFace, and spinning up the inference engine with ngram-mod from scratch. Let me know if you have already tried ngram-mod

Alok

31,765 görüntüleme • 2 ay önce

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.
0:47

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.

m0h

87,330 görüntüleme • 1 ay önce

GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre
1:45

GeoLibre v1.3.0 is here! GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. This release packs in 50+ pull requests of new capabilities. A few highlights: - GIS in your pocket. A native Android build with offline tile caching and download-a-region support, so you can take your maps into the field with no signal. - AI, built in. A natural-language GIS assistant that turns plain-English requests into real geoprocessing, plus an AI segmentation toolbox powered by SamGeo and SAM 3 for extracting features from imagery. - Automate everything with Python. A full scripting API and an in-app Python Console, with new helpers for local rasters, choropleths, marker clusters, split-map comparisons, legends, and colorbars. - Map together, live. Real-time multi-user collaboration so you can open a project and edit the map with others at the same time. - Tell stories with maps. A scroll-driven story map builder and presenter that exports interactive narrative maps to standalone HTML. - A much bigger analysis toolbox. Reproject, explode, and aggregate tools, IDW and kriging interpolation, zonal statistics, a raster calculator, a Spatial Statistics toolbox, and network analysis with isochrones, service areas, and OD cost matrices, plus batch runs and model/pipeline chaining. - Smarter raster and SQL. Single-band pseudocolor classification, RGB band combinations, a no-backend client-side raster fallback, Apache Sedona as a SQL Workspace engine, and transparent S3, GCS, and Azure URL support in queries. - More ways to add, view, and share. New Shapefile and GeoPackage export, glTF/GLB 3D model layers, multi-provider batch and reverse geocoding, collapsible layer groups, and a macOS Homebrew cask. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #Android #GeoLibre

Qiusheng Wu

18,657 görüntüleme • 3 ay önce

🐆 Rapid-MLX v0.12 is here. We’ve officially evolved from a simple chat app into a full-fledged, on-device AI studio for Apple Silicon! 🖥️✨ We didn't just push the MLX inference engine to its limits and expand support for a massive lineup of local open-source models—we are alpha-launching the highly anticipated Desktop Version. (A huge shoutout to the IoTeX community for grinding through the closed beta with us. Your feedback was incredible and helped shape this beast.) Here are the game-changing features you can run on your Mac right now, 100% free and 100% offline 👇 🚀 Blazing Fast Local LLMs Run anything from 4B up to Qwen3.5-122B completely offline. No guessing games—we recommend models matched perfectly to your Mac's actual RAM. Rich chat includes syntax highlighting, markdown tables, and honest tok/s metrics. 🎨 Local Image Generation A brand new Images tab to render directly on your machine. Pick a model (FLUX.2-klein, Z-Image-Turbo), prompt, and refine. Everything lands in a visual filmstrip. 👁️ Vision & Live Web Tools Attach an image and chat about it with local vision. Need real-time data? Our built-in web tools (weather, search, page-fetch) run mid-answer with strict, transparent privacy controls. 🤖 Plug-and-Play Coding Agents Wire up Claude Code, Codex, Cline, or Continue in seconds. One copy-paste from the Launch tab spins up a local OpenAI/Anthropic-compatible endpoint. 🔒 Private by Design Everything runs on-device. Signed, notarized, and entirely local. Your data stays yours. Turn your Mac into an AI powerhouse today. ⚡️
0:37

🐆 Rapid-MLX v0.12 is here. We’ve officially evolved from a simple chat app into a full-fledged, on-device AI studio for Apple Silicon! 🖥️✨ We didn't just push the MLX inference engine to its limits and expand support for a massive lineup of local open-source models—we are alpha-launching the highly anticipated Desktop Version. (A huge shoutout to the IoTeX community for grinding through the closed beta with us. Your feedback was incredible and helped shape this beast.) Here are the game-changing features you can run on your Mac right now, 100% free and 100% offline 👇 🚀 Blazing Fast Local LLMs Run anything from 4B up to Qwen3.5-122B completely offline. No guessing games—we recommend models matched perfectly to your Mac's actual RAM. Rich chat includes syntax highlighting, markdown tables, and honest tok/s metrics. 🎨 Local Image Generation A brand new Images tab to render directly on your machine. Pick a model (FLUX.2-klein, Z-Image-Turbo), prompt, and refine. Everything lands in a visual filmstrip. 👁️ Vision & Live Web Tools Attach an image and chat about it with local vision. Need real-time data? Our built-in web tools (weather, search, page-fetch) run mid-answer with strict, transparent privacy controls. 🤖 Plug-and-Play Coding Agents Wire up Claude Code, Codex, Cline, or Continue in seconds. One copy-paste from the Launch tab spins up a local OpenAI/Anthropic-compatible endpoint. 🔒 Private by Design Everything runs on-device. Signed, notarized, and entirely local. Your data stays yours. Turn your Mac into an AI powerhouse today. ⚡️

raullen

34,148 görüntüleme • 1 ay önce

Sam Altman made the case for open-source harnesses in July. a month later, someone shipped it, and it's more efficient than most managed harnesses. here is the problem it was aimed at: a large share of your agent's token bill is the model rereading things it already read. that isn't the model's doing. the runtime around it decides what goes into every prompt and how often the model gets called. for example, an agent queries a CRM at step four and gets back 400 rows. those rows get piled up in the conversation history. by step nineteen, the model has to read those rows fifteen times unnecessarily, and every token read is billed at input rates. it happened because your harness assembled that prompt on every turn and kept the rows in it. that gives you two levers: how much context the harness carries forward, and how often it calls the model. there are four practical ways to keep the prompt from growing unnecessarily: → load tool schemas on demand. a server with 100 tools doesn't need to put all 100 into every prompt when the agent only calls two. → offload large results to disk. turn a large response into a short preview and a file path instead of replaying the entire result on every turn. → delegate to subagents. let a subagent spend thirty tool calls in its own context and return one summary to the root agent. → run toolchains in code. one script calls three tools, joins the results, and returns a table instead of three turns each dragging a full response. but reducing context is only half the job. you also need to control how often the model gets called. a good harness should avoid unnecessary planning, verification, and reflection when the work can be completed in fewer steps. TrueFoundry's open-source agent harness, TrueForge, is built around both of those controls. it sits between the model and the tools, deciding what goes into every prompt and when another model call is actually needed. it also breaks token usage down across the harness, skills, instructions, tools, and messages. DevRev's Enterprise-Bench is where this gets tested, on multi-step tasks of the kind where an agent pulls records from one system and reconciles them against another. TrueFoundry ran TrueForge there against Claude Managed Agents, both on the same model, and both finished the same number of tasks. the tie is the part that matters, because it means the gap underneath is not a quality tradeoff. TrueForge reached that score on close to a third of the tokens, with roughly 40% fewer trips back to the model. for the same result, that comes out around 2.7x cheaper than Claude Managed Agents. swapping in an open model made it sharper still. TrueForge with GLM-5.2 scored a little higher than either setup above, and the entire benchmark run cost about $3 at list prices. being open source matters beyond the license here. the model underneath can be swapped without rewriting the agent, and the whole thing can run inside your own environment when the data cannot leave it. all of this comes down to the runtime around the model, the context it carries, the tools it exposes, and how many times it goes back to the model. that is what a production harness actually owns. the full task list, the per-run numbers, and the MIT-licensed code are on GitHub: (don't forget to star 🌟) you can read more about the same in the article quoted below. thanks to the TrueForge team for working with me on this one.
0:39

Sam Altman made the case for open-source harnesses in July. a month later, someone shipped it, and it's more efficient than most managed harnesses. here is the problem it was aimed at: a large share of your agent's token bill is the model rereading things it already read. that isn't the model's doing. the runtime around it decides what goes into every prompt and how often the model gets called. for example, an agent queries a CRM at step four and gets back 400 rows. those rows get piled up in the conversation history. by step nineteen, the model has to read those rows fifteen times unnecessarily, and every token read is billed at input rates. it happened because your harness assembled that prompt on every turn and kept the rows in it. that gives you two levers: how much context the harness carries forward, and how often it calls the model. there are four practical ways to keep the prompt from growing unnecessarily: → load tool schemas on demand. a server with 100 tools doesn't need to put all 100 into every prompt when the agent only calls two. → offload large results to disk. turn a large response into a short preview and a file path instead of replaying the entire result on every turn. → delegate to subagents. let a subagent spend thirty tool calls in its own context and return one summary to the root agent. → run toolchains in code. one script calls three tools, joins the results, and returns a table instead of three turns each dragging a full response. but reducing context is only half the job. you also need to control how often the model gets called. a good harness should avoid unnecessary planning, verification, and reflection when the work can be completed in fewer steps. TrueFoundry's open-source agent harness, TrueForge, is built around both of those controls. it sits between the model and the tools, deciding what goes into every prompt and when another model call is actually needed. it also breaks token usage down across the harness, skills, instructions, tools, and messages. DevRev's Enterprise-Bench is where this gets tested, on multi-step tasks of the kind where an agent pulls records from one system and reconciles them against another. TrueFoundry ran TrueForge there against Claude Managed Agents, both on the same model, and both finished the same number of tasks. the tie is the part that matters, because it means the gap underneath is not a quality tradeoff. TrueForge reached that score on close to a third of the tokens, with roughly 40% fewer trips back to the model. for the same result, that comes out around 2.7x cheaper than Claude Managed Agents. swapping in an open model made it sharper still. TrueForge with GLM-5.2 scored a little higher than either setup above, and the entire benchmark run cost about $3 at list prices. being open source matters beyond the license here. the model underneath can be swapped without rewriting the agent, and the whole thing can run inside your own environment when the data cannot leave it. all of this comes down to the runtime around the model, the context it carries, the tools it exposes, and how many times it goes back to the model. that is what a production harness actually owns. the full task list, the per-run numbers, and the MIT-licensed code are on GitHub: (don't forget to star 🌟) you can read more about the same in the article quoted below. thanks to the TrueForge team for working with me on this one.

Akshay 🚀

76,998 görüntüleme • 1 ay önce

UC Berkeley just open-sourced FreeToken. (2–4x faster local LLM inference than Ollama) the results are wild: - Qwen3.6-35B on an 8GB GPU at 39.3 tokens/s - DeepSeek-V4-Flash 284B on a 32GB GPU at 22 tokens/s - GLM-5.2 753B on a 96GB GPU at 14.9 tokens/s a 35B model at 16-bit precision needs about 70GB just for its weights. even at 4 bits it is close to 18GB, and FreeToken serves it on an 8GB GPU. let me explain how: all three models mentioned above are Mixture-of-Experts, and that is what FreeToken takes advantage of. each layer holds hundreds of separate experts plus a small router that picks a few of them per token. Qwen3.6-35B activates roughly 3B of its 35B parameters per token. DeepSeek-V4-Flash picks 6 of 256 experts per layer, so 13B of its 284B run at a time. so compute was never the bottleneck. the weights a single step touches fit comfortably on a consumer GPU. every expert the router might pick still has to exist somewhere. they sit in system RAM, and the GPU keeps a cache of the ones the model has been using recently. so everything comes down to what happens when the router picks an expert that is not on the GPU. there are two ways to serve that miss: 1. copy it over PCIe and run it on the GPU 2. run it on the CPU, where it already lives both read from the same system memory, so they compete for one pool of bandwidth instead of adding to each other. existing engines pick one option and freeze it when the model loads. but routing changes on every token, so a fixed choice misses most of what the model asks for. FreeToken measures both bandwidths on your machine and splits each step's misses between the two paths in proportion. the GPU and CPU results then merge exactly, with no approximation. two machines with the same GPU can end up wanting opposite strategies, which I did not expect. a 5090 in a gaming desktop should push nearly everything over PCIe, while an 8GB laptop is better off computing most misses on the CPU. none of that is readable off a spec sheet, so the engine profiles it once per machine. the second half of the design is about agents. coding agents constantly rewrite their own history, and every edit normally forces thousands of tokens back through prefill. FreeToken saves its checkpoints at the exact boundaries agent frameworks cut on, so it only reprocesses the new part. its slowest first token stays under 44 seconds, while llama.cpp peaks at 232 and KTransformers at 946. it serves the OpenAI and Anthropic APIs under Apache 2.0, so Claude Code and Codex can point at it directly. releasing weights publicly decides who can download a model, not who can afford to run one. frontier open models keep shipping, and running them still assumes a rented cluster. meanwhile there are over a hundred million consumer machines with discrete GPUs sitting mostly idle. closing that gap was never a hardware problem, and work like this is what turns open weights into something you can actually use. paper: repo: almost every idea in this post, from why memory bandwidth decides the outcome to why moving weights costs more than computing on them, comes straight out of how a GPU is built. I wrote a detailed primer on that. the article is quoted below.
0:42

UC Berkeley just open-sourced FreeToken. (2–4x faster local LLM inference than Ollama) the results are wild: - Qwen3.6-35B on an 8GB GPU at 39.3 tokens/s - DeepSeek-V4-Flash 284B on a 32GB GPU at 22 tokens/s - GLM-5.2 753B on a 96GB GPU at 14.9 tokens/s a 35B model at 16-bit precision needs about 70GB just for its weights. even at 4 bits it is close to 18GB, and FreeToken serves it on an 8GB GPU. let me explain how: all three models mentioned above are Mixture-of-Experts, and that is what FreeToken takes advantage of. each layer holds hundreds of separate experts plus a small router that picks a few of them per token. Qwen3.6-35B activates roughly 3B of its 35B parameters per token. DeepSeek-V4-Flash picks 6 of 256 experts per layer, so 13B of its 284B run at a time. so compute was never the bottleneck. the weights a single step touches fit comfortably on a consumer GPU. every expert the router might pick still has to exist somewhere. they sit in system RAM, and the GPU keeps a cache of the ones the model has been using recently. so everything comes down to what happens when the router picks an expert that is not on the GPU. there are two ways to serve that miss: 1. copy it over PCIe and run it on the GPU 2. run it on the CPU, where it already lives both read from the same system memory, so they compete for one pool of bandwidth instead of adding to each other. existing engines pick one option and freeze it when the model loads. but routing changes on every token, so a fixed choice misses most of what the model asks for. FreeToken measures both bandwidths on your machine and splits each step's misses between the two paths in proportion. the GPU and CPU results then merge exactly, with no approximation. two machines with the same GPU can end up wanting opposite strategies, which I did not expect. a 5090 in a gaming desktop should push nearly everything over PCIe, while an 8GB laptop is better off computing most misses on the CPU. none of that is readable off a spec sheet, so the engine profiles it once per machine. the second half of the design is about agents. coding agents constantly rewrite their own history, and every edit normally forces thousands of tokens back through prefill. FreeToken saves its checkpoints at the exact boundaries agent frameworks cut on, so it only reprocesses the new part. its slowest first token stays under 44 seconds, while llama.cpp peaks at 232 and KTransformers at 946. it serves the OpenAI and Anthropic APIs under Apache 2.0, so Claude Code and Codex can point at it directly. releasing weights publicly decides who can download a model, not who can afford to run one. frontier open models keep shipping, and running them still assumes a rented cluster. meanwhile there are over a hundred million consumer machines with discrete GPUs sitting mostly idle. closing that gap was never a hardware problem, and work like this is what turns open weights into something you can actually use. paper: repo: almost every idea in this post, from why memory bandwidth decides the outcome to why moving weights costs more than computing on them, comes straight out of how a GPU is built. I wrote a detailed primer on that. the article is quoted below.

Akshay 🚀

343,270 görüntüleme • 1 ay önce

10 free github repos that can replace major SaaS with subscriptions. all free. open-sourced. some are MIT licensed. — 1️⃣ openscreen — replaces screen studio ($29/mo) - a clean macOS/windows/linux screen recorder for polished demos. - blur, cursor highlighting, annotations, export to mp4 or gif at any aspect ratio. - doesn't try to clone every feature, just nails the basics for quick walkthroughs you'd post on X. — 2️⃣ voicebox — replaces elevenlabs ($22/mo) + wisprflow ($15/mo) - local-first AI voice studio. - clone voices from 3 seconds of audio, generate speech across 7 TTS engines in 23 languages, - dictate into any text field with a global hotkey. - nothing leaves your machine. - runs on apple silicon, cuda, rocm. — 3️⃣ openshorts — replaces opus clip ($19/mo) + submagic ($16/mo) - free AI video platform. - clip generator turns long youtube videos into 9:16 shorts with auto-subtitles and face tracking (runs on free gemini + elevenlabs tiers). - also includes AI UGC video generation with actors — that part is pay-per-use via fal. ai (~$0.65-2 per video). docker self-host. — 4️⃣ freellmapi — replaces chatgpt pro + claude pro ($20/mo each) - stacks 14 free AI provider tiers (google, groq, cerebras, openrouter, github models + 9 more) behind one openai-compatible endpoint. ~800M tokens/month. - smart router with failover, sticky sessions, encrypted key storage. ships with a dashboard. — 5️⃣ playwright-mcp — replaces browserbase ($39/mo) + browser use ($25/mo) - microsoft's official MCP server that gives any AI agent full browser control. - uses accessibility trees, not screenshots — deterministic and token-efficient. - works with claude code, cursor, windsurf, codex out of the box. — 6️⃣ vibe-trading — replaces tradingview premium ($60/mo) - natural-language finance research agent. - 7 backtest engines across stocks, crypto, futures, forex. - 75 specialist skills (factor analysis, options strategy, ML strategy). - 29 multi-agent swarm presets. - 21 of 22 MCP tools work with zero API keys. — 7️⃣ CalCom — replaces calendly ($12/mo) + savvycal ($12/mo) - the open-source scheduling infrastructure. - one-on-ones, group events, round-robin, team booking, - payment collection (stripe), routing forms, workflows. - integrates with google/outlook/apple calendar, zoom, meet, teams. - self-host in 10 minutes with docker. 40k stars. — 8️⃣ whisper — replaces otter ($17/mo) - openAI's open-source speech-to-text model. - transcribe audio in 99 languages, translate to english, generate timestamps. - runs locally on cpu or gpu. - the actual model behind most "AI transcription" SaaS tools you're paying for. — 9️⃣ postiz — replaces buffer ($15/mo) - AI-powered social media scheduler. - cross-post to X, linkedin, instagram, tiktok, threads, bluesky, mastodon, youtube, pinterest. - AI captions and hashtags. - analytics dashboard. team workspaces. 31k stars and rising. — 🔟 vaultwarden — replaces 1password ($8/mo) - unofficial bitwarden-compatible server written in rust. - works with every official bitwarden client (mobile, desktop, browser). - unlimited users, unlimited vaults, full enterprise feature set. - runs on a $5 VPS or your home server. — disclaimer: open-source ≠ 1:1 replacement. you'll trade polish for ownership, hand-holding for control, and a credit card for a github version. for builders, prototypers, and indie hackers — that's the whole point. for everyone else, the paid tools still have their place. bookmark this. share with one friend bleeding subscription fees. ~m0h
0:31

10 free github repos that can replace major SaaS with subscriptions. all free. open-sourced. some are MIT licensed. — 1️⃣ openscreen — replaces screen studio ($29/mo) - a clean macOS/windows/linux screen recorder for polished demos. - blur, cursor highlighting, annotations, export to mp4 or gif at any aspect ratio. - doesn't try to clone every feature, just nails the basics for quick walkthroughs you'd post on X. — 2️⃣ voicebox — replaces elevenlabs ($22/mo) + wisprflow ($15/mo) - local-first AI voice studio. - clone voices from 3 seconds of audio, generate speech across 7 TTS engines in 23 languages, - dictate into any text field with a global hotkey. - nothing leaves your machine. - runs on apple silicon, cuda, rocm. — 3️⃣ openshorts — replaces opus clip ($19/mo) + submagic ($16/mo) - free AI video platform. - clip generator turns long youtube videos into 9:16 shorts with auto-subtitles and face tracking (runs on free gemini + elevenlabs tiers). - also includes AI UGC video generation with actors — that part is pay-per-use via fal. ai (~$0.65-2 per video). docker self-host. — 4️⃣ freellmapi — replaces chatgpt pro + claude pro ($20/mo each) - stacks 14 free AI provider tiers (google, groq, cerebras, openrouter, github models + 9 more) behind one openai-compatible endpoint. ~800M tokens/month. - smart router with failover, sticky sessions, encrypted key storage. ships with a dashboard. — 5️⃣ playwright-mcp — replaces browserbase ($39/mo) + browser use ($25/mo) - microsoft's official MCP server that gives any AI agent full browser control. - uses accessibility trees, not screenshots — deterministic and token-efficient. - works with claude code, cursor, windsurf, codex out of the box. — 6️⃣ vibe-trading — replaces tradingview premium ($60/mo) - natural-language finance research agent. - 7 backtest engines across stocks, crypto, futures, forex. - 75 specialist skills (factor analysis, options strategy, ML strategy). - 29 multi-agent swarm presets. - 21 of 22 MCP tools work with zero API keys. — 7️⃣ CalCom — replaces calendly ($12/mo) + savvycal ($12/mo) - the open-source scheduling infrastructure. - one-on-ones, group events, round-robin, team booking, - payment collection (stripe), routing forms, workflows. - integrates with google/outlook/apple calendar, zoom, meet, teams. - self-host in 10 minutes with docker. 40k stars. — 8️⃣ whisper — replaces otter ($17/mo) - openAI's open-source speech-to-text model. - transcribe audio in 99 languages, translate to english, generate timestamps. - runs locally on cpu or gpu. - the actual model behind most "AI transcription" SaaS tools you're paying for. — 9️⃣ postiz — replaces buffer ($15/mo) - AI-powered social media scheduler. - cross-post to X, linkedin, instagram, tiktok, threads, bluesky, mastodon, youtube, pinterest. - AI captions and hashtags. - analytics dashboard. team workspaces. 31k stars and rising. — 🔟 vaultwarden — replaces 1password ($8/mo) - unofficial bitwarden-compatible server written in rust. - works with every official bitwarden client (mobile, desktop, browser). - unlimited users, unlimited vaults, full enterprise feature set. - runs on a $5 VPS or your home server. — disclaimer: open-source ≠ 1:1 replacement. you'll trade polish for ownership, hand-holding for control, and a credit card for a github version. for builders, prototypers, and indie hackers — that's the whole point. for everyone else, the paid tools still have their place. bookmark this. share with one friend bleeding subscription fees. ~m0h

m0h

253,490 görüntüleme • 4 ay önce