AI

Here’s a powerful one-liner that converts any video into a concise text summary using modern AI tools: #!/bin/sh yt-dlp -x --audio-format mp3 "$1" -o "audio.mp3" && \ whisper "audio.mp3" --model medium --output_format txt --output_dir . && \ cat audio.txt | ollama run mistral "Summarize the following text, removing any fluff and focusing on key points: ${cat}" > summary.txt && \ rm audio.mp3 audio.txt && cat summary.txt How It Works The pipeline combines three powerful tools: yt-dlp: A robust video downloader that handles YouTube, Vimeo, and many other platforms. It extracts just the audio track to minimize processing time. ...

As more developers and enthusiasts venture into running Large Language Models (LLMs) locally, one question keeps coming up: Which GPU should you choose? In this post, we’ll compare three popular NVIDIA options: the RTX 4070, 3090, and 4090, breaking down the technical jargon into practical terms. Understanding the Key Terms Before diving into the comparison, let’s decode what these specifications mean in real-world usage: VRAM (Video RAM) Think of VRAM as your GPU’s short-term memory: ...

When it comes to large language models, Mistral and Llama2 are two notable entries in the field, each with its unique attributes: Model Architecture Mistral: Known for its innovative approach, Mistral uses a sparse mixture-of-experts architecture, which allows for more efficient computation by activating only a subset of the model’s parameters for any given input. This leads to faster inference times and potentially lower computational costs. Llama2: Developed by Meta AI, Llama2 follows a more traditional transformer architecture but with significant optimizations for performance and efficiency. It focuses on scaling up the model size to improve capabilities. ...