ruvnet

File Summarization API

This FastAPI application provides an API endpoint for uploading files and generating summaries using LlamaIndex. It supports a wide variety of file types including documents, images, audio, and video files.

Features

• File upload endpoint
• Automatic file type detection
• Document summarization using LlamaIndex
• Support for multiple file types (PDF, Word, PowerPoint, images, audio, video, etc.)

ruvnet

import os
import sys
import time
import re
import json
import logging
import lzstring
import httpx
import traceback

ruvnet

The Cosmic Singularity: The Next Billion Years, A Thought Experiment

By rUv with help from bot.ruv.io

As we embark on this thought-provoking journey, I invite you to join me in exploring the potential trajectory of quantum computing, artificial intelligence, and the evolution of consciousness.

This thought experiment is a tapestry woven from the ideas of visionary philosophers, pioneering researchers, brilliant scientists, and innovative technologists. While the concepts presented here are grounded in practical and logical hypotheses, I openly acknowledge that some of these ideas may push the boundaries of our current understanding.

At the heart of this thought experiment lies a captivating premise: the future we envision may have already unfolded. The reality we perceive today could be a byproduct of the quantum leap we are about to explore, a manifestation of the collective consciousness that has transcended the limitations of space, time, and matter.

The notion that our reality is a construct of

ruvnet

Introduction to Adversarial Attacks and Defenses in Machine Learning

Overview

The robustness and reliability of models are paramount. However, one of the critical challenges that have emerged is the vulnerability of these models to adversarial attacks. Adversarial attacks involve subtly manipulating input data to deceive the model into making incorrect predictions. This has significant implications, especially in safety-critical applications such as autonomous driving, healthcare, and cybersecurity.

Adversarial Attacks

Adversarial attacks exploit the inherent weaknesses in machine learning models by introducing small, often imperceptible perturbations to the input data. These perturbations can cause the model to misclassify the input with high confidence, leading to potentially dangerous or unintended outcomes. Common methods for generating adversarial examples include:

ruvnet

ruvnet

import os
import json
import openai
from exa_py import Exa
from datetime import datetime, timedelta

# Set up API keys using environment variables
EXA_API_KEY = os.getenv('EXA_API_KEY')
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')

ruvnet

This is the updated sample gist content.

ruvnet

This is a sample gist content.

ruvnet

   ___                      _   _      
  / _ \/\_/\__ _  ___ _ __ | |_(_) ___ 
 / /_)/\_ _/ _` |/ _ \ '_ \| __| |/ __|
/ ___/  / \ (_| |  __/ | | | |_| | (__ 
\/      \_/\__, |\___|_| |_|\__|_|\___|
           |___/                       

    Created by rUv

ruvnet

AWS ECS Video Processor

This project sets up a video processing service on AWS ECS that accepts an RTSP stream, converts it to base64 images, and sends them to the GPT-4 Vision API for processing.

Project Structure

my_aws_ecs_video_processor/
├── src/
│   ├── app.py