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Source channel @olddriverGDstudy · Post #38 · Mar 17

#知识 性爱技巧 有男生问我:爱爱时很快就射,怎么办?我回答:节奏放慢,不要着急插入,而要做足“三个半小时”: 1️⃣狂吻半小时,吻得她透不过气,吻得她嘴肿,让她深切感受到你对她的真实激情; 2️⃣玩弄半小时,在嘴上继续狂吻,在胯下开始抠摸,抠摸她的阴蒂,抠摸她阴道内的各个兴奋点,抠得她忍不住喘气呻吟,全身扭动而顾不上让你再吻; 3️⃣舔阴半小时,上边放开她的嘴,下边抽出你的手,趴下身去,把你的脸紧贴她的阴部,狂吻她的阴唇,狂舔她的阴蒂,狂吸并且狂咽她的阴液,让她浑身发抖,在高潮中连连叫床……这时,这时,你才可以在她“进来,进来,快进来”的连声央求下,从容不迫地挺身而出,掰开她的双腿,奋力插将进去,并且一插到底!这样,即使你很快就射,她也已经像死猪一样顾不上说你什么了!

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GitHub Trends

@githubtrending · Post #15143 · 09/14/2025, 12:00 PM

#python#llms#mlx MLX LM is a Python tool that helps you run and fine-tune large language models (LLMs) efficiently on Apple Silicon Macs. It connects easily to thousands of models on Hugging Face, supports model quantization to save memory, and allows distributed training. You can generate text or chat with models via simple commands or Python code. It also offers features like prompt caching and memory optimization for handling long texts, making it faster and less resource-heavy. This means you can run powerful AI models locally on your Mac without needing expensive cloud services, saving cost and improving speed. https://github.com/ml-explore/mlx-lm

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@githubtrending · Post #14655 · 05/01/2025, 01:30 PM

#typescript#electron#llama#llms#lora#mlx#rlhf#transformers Transformer Lab is a free, open-source tool that lets you easily work with large language models on your own computer, offering one-click downloads for popular models like Llama3 and Mistral, fine-tuning across different hardware (including Apple Silicon and GPUs), and features like chatting, training, and evaluating models through a simple interface—saving you from complex setups like CUDA or Python version issues[1][2][5]. https://github.com/transformerlab/transformerlab-app

GitHub Trends

@githubtrending · Post #15614 · 04/13/2026, 11:30 AM

#typescript#ai#cuda#mlx#qwen3_tts#qwen3_tts_ui#voice_ai#voice_clone#whisper Voicebox is a free, open-source voice synthesis studio that lets you clone voices, generate speech in 23 languages, and apply audio effects—all running privately on your computer. You can create realistic voice clones from just seconds of audio, use five different text-to-speech engines for different needs, add effects like reverb and pitch shift, and build multi-voice projects with a timeline editor. The key benefit is complete privacy: your voice data and AI models never leave your machine, unlike cloud-based alternatives. It also includes an API for building voice-powered applications and works across Mac, Windows, and Linux with GPU acceleration support. https://github.com/jamiepine/voicebox

GitHub Trends

@githubtrending · Post #14684 · 05/08/2025, 12:00 PM

#python#apple_silicon#audio_processing#mlx#multimodal#speech_recognition#speech_synthesis#speech_to_text#text_to_speech#transformers MLX-Audio is a powerful tool for converting text into speech and speech into new audio. It works well on Apple Silicon devices, like M-series chips, making it fast and efficient. You can choose from different languages and voices, and even adjust how fast the speech is. It also includes a web interface where you can see audio in 3D and play your own files. This tool is helpful for making audiobooks, interactive media, and personal projects because it's easy to use and provides high-quality audio quickly. https://github.com/Blaizzy/mlx-audio

GitHub Trends

@githubtrending · Post #15600 · 04/04/2026, 11:30 AM

#python#apple_silicon#florence2#idefics#llava#llm#local_ai#mlx#molmo#paligemma#pixtral#vision_framework#vision_language_model#vision_transformer MLX-VLM lets you run, chat with, and fine-tune Vision Language Models (VLMs) plus audio/video models on your Mac using MLX—install easily with `pip install -U mlx-vlm`. Use CLI for quick text/image/audio generation (e.g., `mlx_vlm.generate --model ... --image photo.jpg`), Gradio UI for chats, Python scripts, or a FastAPI server with OpenAI-compatible endpoints supporting multi-images/videos. Features like TurboQuant cut KV cache memory by 76%, and LoRA/QLoRA fine-tuning works on consumer hardware. You benefit by experimenting with powerful multimodal AI locally—fast, memory-efficient, no cloud costs, perfect for Mac users tweaking models affordably. https://github.com/Blaizzy/mlx-vlm