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Source channel @githubtrending · Post #15148 · Sep 17

#cplusplus Monad is a fast, scalable Layer 1 blockchain fully compatible with Ethereum's EVM, allowing you to run Ethereum smart contracts without changes. It improves speed by separating consensus (agreement on transaction order) from execution (processing transactions), enabling parallel transaction execution and reaching 10,000 transactions per second with 1-second finality. Monad uses a custom EVM and a special database (MonadDb) optimized for parallel state access, reducing delays. This means you get much faster, cheaper transactions while keeping Ethereum compatibility, making it easier for developers and users to adopt and benefit from high performance and scalability. https://github.com/category-labs/monad

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

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#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

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#python #apple_silicon #florence2 #idefics #llava #llm #local_ai #mlx #molmo #paligemma #pixtral #vision_framework #vision_language_model #vision_transformer