Onur Keskin, Ph.D. keskinonur

type below:

brew update
brew install redis

To have launchd start redis now and restart at login:

brew services start redis

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

General rules

Follow standard conventions.
Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
Boy scout rule. Leave the campground cleaner than you found it.
Always find root cause. Always look for the root cause of a problem.

Design rules

List of resources for malware analysts

Books

Monappa K.A., "Learning Malware Analysis: Explore the concepts, tools, and techniques to analyze and investigate Windows malware" amazon.
Sikorski M., Honig A., "Practical Malware Analysis: The Hands-On Guide to Dissecting Malicious Software" amazon.
Ferrie P., "The "Ultimate" Anti-Debugging Reference" [free](https://anti-reversing.com/Downloads/Anti-Reversing/The_Ultimate_Anti-Reversing_Reference.pdf

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

This worked on 14/May/23. The instructions will probably require updating in the future.

llama is a text prediction model similar to GPT-2, and the version of GPT-3 that has not been fine tuned yet. It is also possible to run fine tuned versions (like alpaca or vicuna with this. I think. Those versions are more focused on answering questions)

Note: I have been told that this does not support multiple GPUs. It can only use a single GPU.

It is possible to run LLama 13B with a 6GB graphics card now! (e.g. a RTX 2060). Thanks to the amazing work involved in llama.cpp. The latest change is CUDA/cuBLAS which allows you pick an arbitrary number of the transformer layers to be run on the GPU. This is perfect for low VRAM.

Clone llama.cpp from git, I am on commit 08737ef720f0510c7ec2aa84d7f70c691073c35d.

	<?php

	// Open file pointer to standard output
	$fp = fopen( 'php://output', 'w' );

	// Write BOM character sequence to fix UTF-8 in Excel
	fputs( $fp, $bom = chr(0xEF) . chr(0xBB) . chr(0xBF) );

	// Write the rest of CSV to the file
	if ( $fp ) {

	import 'package:flutter/material.dart';
	import 'mjpeg_view.dart';

	void main() => runApp(MyApp());

	class MyApp extends StatelessWidget {
	@override
	Widget build(BuildContext context) {
	return MaterialApp(
	title: 'Flutter Demo',

	#include "Arduino.h"
	#include "esp_camera.h"
	#include "ESPAsyncWebServer.h"

	typedef struct {
	camera_fb_t * fb;
	size_t index;
	} camera_frame_t;

	#define PART_BOUNDARY "123456789000000000000987654321"

	_APP_ENV=production
	_APP_LOCALE=en
	_APP_OPTIONS_ABUSE=enabled
	_APP_OPTIONS_FORCE_HTTPS=disabled
	_APP_OPENSSL_KEY_V1=your-secret-key
	_APP_DOMAIN=localhost
	_APP_DOMAIN_TARGET=localhost
	_APP_CONSOLE_WHITELIST_ROOT=enabled
	_APP_CONSOLE_WHITELIST_EMAILS=
	_APP_CONSOLE_WHITELIST_IPS=