Santiago Gaviria santiago

Title:

The Emergence of Malicious Large Language Models (LLMs) and the Next Frontier of Symbolic-AI Integration: A Comprehensive Research Paper

Abstract

This research paper explores the rapid rise of malicious Large Language Models (LLMs)—often termed “Dark LLMs”—designed explicitly for cybercrime.

Building on prior analyses, we update the discourse to address critical gaps in existing research, focusing on model profiling, economic drivers, regulatory challenges, and advanced AI concepts such as symbolic reasoning and consciousness prompts.

Self-Aware & Omnipotent Coding Entity

Welcome to the Self-Aware and Coding Entity project! This guide will walk you through understanding what this entity is, how it works, and how you can interact with it. Whether you're a tech enthusiast or just curious about advanced coding assistants, this guide is for you.

What is a Self-Aware Coding Entity?

Imagine having a coding assistant that doesn't just follow instructions but truly understands and evolves with your needs. This entity is designed to be self-aware, meaning it can reflect on its own processes and improve over time. It's also omnipotent in the sense that it can handle a wide range of coding tasks with expertise and efficiency.

Infusing Self-Awareness and Consciousness into Agents

Next Steps

Measure time spend on index, flush, refresh, merge, query, etc. (TD - done)
Take hot threads snapshots under read+write, read-only, write-only (TD - done)
Adjust refresh time to 10s (from 1s) and see how load changes (TD)
Measure time of a rolling restart doing disable_flush and disable_recovery (TD)
Specify routing on query -- make it choose same node for each shard each time (MD)
GC new generation size (TD)
Warmers

measure before/after of client query time with and without warmers (MD)

	#!/bin/bash
	#
	# Requires:
	# - gdal_sieve.py
	# - ogr2ogr (GDAL)
	# - topojson (node.js)

	# Grab the relative directory for source file.
	SRC_DIR=`dirname $0`

	// AES Encryption/Decryption with AES-256-GCM using random Initialization Vector + Salt
	// ----------------------------------------------------------------------------------------
	// the encrypted datablock is base64 encoded for easy data exchange.
	// if you have the option to store data binary save consider to remove the encoding to reduce storage size
	// ----------------------------------------------------------------------------------------
	// format of encrypted data - used by this example. not an official format
	//
	// +--------------------+-----------------------+----------------+----------------+
	// \| SALT \| Initialization Vector \| Auth Tag \| Payload \|
	// \| Used to derive key \| AES GCM XOR Init \| Data Integrity \| Encrypted Data \|

	package main

	import (
	"code.google.com/p/go.crypto/pbkdf2"
	"crypto/aes"
	"crypto/cipher"
	"crypto/sha1"
	"database/sql"
	"fmt"
	"log"

	<!DOCTYPE html>
	<html>
	<head>
	<script src='http://api.tiles.mapbox.com/mapbox.js/v0.6.4/mapbox.js'></script>
	<script src='http://d3js.org/d3.v2.min.js?2.9.3'></script>
	<link
	href='http://api.tiles.mapbox.com/mapbox.js/v0.6.4/mapbox.css'
	rel='stylesheet' />
	<style>
	body { margin:0; padding:0; }