dori4n

Office 2019 ISO images

updated 2021-06-23

Version 2105 (Build 16.0.14026.20302)

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English

dori4n

Office 2016 ISO images

Updated 2018-03-23

Version 1802 (Build 16.0.9029.2167)

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English

dori4n

Office 2024 ISO images

updated 2024-09-20

Version 2409? (Build 16.0.17928.20148)

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English

dori4n

Office 2021 ISO images

updated 2023-07-14

Version 2304? (Build 16.0.16327.20264)

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bathivinod

Let's Encrypt SSL with Cloudflare DNS Validation

Requirements Recap

✅ Your Requirements:

• 🔒 Ports 80/443 are firewalled to Cloudflare IPs only
• ☁️ You're behind Cloudflare, so Let's Encrypt (Certbot) cannot reach port 80 directly
• 🌐 You're using Apache, not Nginx
• You already have a self-signed or manually managed cert in use

gone

import requests

cookies = dict(CMSESSION="YOUR SESSION TOKEN HERE")

for x in range(150):
    incoming = requests.get('http://creativemarket.com/op/download/%s' % x, cookies=cookies)
    if 'content-disposition' not in incoming.headers:
        print "%s not a valid file" % x
        continue
    file_name = incoming.headers['content-disposition'].split("\"")[1]

s1204IT

JOYSOUND

0009JOYSOUND

27844FADDDE5B8423E8EB84BA9EDCE3CEA92C3FCDB2879C796B7B97A39E5786C

0008JOYSOUND

5C468DDEF3C92CC5BE93536F84C178D5D887BFC6196A51FB83ED4AA5A8C4C2D5

kfasick

<#
.SYNOPSIS
    PowerShell replacement for the `code-insiders.cmd` launcher.

.DESCRIPTION
    Sets the same environment variables used by the original batch launcher, resolves
    the `Code - Insiders.exe` and the CLI script (`resources\app\out\cli.js`) relative
    to this script's location (one level up), forwards all arguments, and exits with
    the executable's exit code.

karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Shrutiind02

Round	Human Score	AI Score
Round 1	1%	99%
Round 2	18%	82%
Round 3	60%	40%
Round 4	99%	1%