Edi Santoso repodevs

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowhere. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture

Python Guide

This python guide is intended to those who already know python and want a mind refresh or looking for a specific syntax.
-as this guide doesn't elaborate in explaining thing that a programmer would already know -like: variable, function, csv, json-

additionally, this guide is not intended to be a replacement for reading official Python documentation.

guide version: 2.1.0 | python version: 3.10

| Part of Version | Explanation |

Tuning ZFS + Postgres to outperform EXT4 + Postgres

Please refer to ZFS 2-3x slower than EXT4 to see how ZFS defaults + Postgres defaults severely underperform EXT4 defaults + Postgres defaults (and also to know more about the system on which these benchmarks were performed). This page documents how to tune ZFS + Postgres to give better performance for the tpcb-like benchmark.

BIG FAT WARNING

Please do not copy these settings blindly because I am myself not clear on why/how these settings had the impact they did. For example, I cannot explain why full_page_writes=off independently did not give that much boost, nor did an optimized PG configuration. However, putting both of them together gave a 2-4x boost compared to baseline numbers.

Benchmark results

AWS 학습 링크집 시리즈

AWS 트레이닝 및 자격증 소개 http://bit.ly/aws-tnc-intro
AWS 학습 자료집 http://bit.ly/aws-study-resource
AWS 공인 솔루션스 아키텍트 - 어소시에이트 수험 가이드 http://bit.ly/sacertguide
AWS 공인 개발자 - 어소시에이트 수험 가이드 http://bit.ly/devcertguide
AWS 보안 관련 컨텐츠 모음집 http://bit.ly/secontents
AWS 기반 빅데이터 학습자료집 http://bit.ly/bdonaws
AWS 딥러닝 학습 자료 모음집 http://bit.ly/dlonaws
2019년 re:Invent 에서 공개된 AI/ML관련 서비스 소개 http://bit.ly/2019-ml-recap

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	func main() {

	s := time.Now()
	args := os.Args[1:]
	if len(args) != 6 { // for format LogExtractor.exe -f "From Time" -t "To Time" -i "Log file directory location"
	fmt.Println("Please give proper command line arguments")
	return
	}
	startTimeArg := args[1]
	finishTimeArg := args[3]

	FROM postgres:15-alpine
	LABEL maintainer="Jordan Gould <[email protected]>"
	# Based on https://github.com/andreaswachowski/docker-postgres/blob/master/initdb.sh

	ENV PG_JOBMON_VERSION v1.4.1
	ENV PG_PARTMAN_VERSION v4.7.1

	# Install pg_jobmon
	RUN set -ex \
	\

	##############################################################################
	#
	# Author: Miku Laitinen / Avoin.Systems
	# Copyright 2019 Avoin.Systems
	#
	# This program is free software: you can redistribute it and/or modify
	# it under the terms of the GNU Affero General Public License as
	# published by the Free Software Foundation, either version 3 of the
	# License, or (at your option) any later version.
	#

	package main

	import (
	"github.com/jinzhu/gorm"
	_ "github.com/jinzhu/gorm/dialects/mysql"
	"github.com/satori/go.uuid"
	"fmt"
	)

	type Email struct {

	#!/bin/bash

	# variables
	# listening address for jupyter. set to localhost if only accessing locally.
	read -p $'\n\e[32mServer static IP address\e[m: ' ipAddress

	# Set this either as parent or direct working directory of the project you want to work on.
	read -p $'\n\e[32mProject Parent Directory\e[m: ' WorkingDirectory

	echo -e "\n...\e[32mGenerating random token\e[m...\n"