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@WildSiphon
WildSiphon / DockerDesktop.yaml
Created July 5, 2025 11:00
Docker Desktop direct download links
4.0.0:
Windows: https://desktop.docker.com/win/main/amd64/67817/Docker%20Desktop%20Installer.exe
Mac with Intel chip: https://desktop.docker.com/mac/main/amd64/67817/Docker.dmg
Mac with Apple chip: https://desktop.docker.com/mac/main/arm64/67817/Docker.dmg
release_date: '2021-08-31'
4.0.1:
Windows: https://desktop.docker.com/win/main/amd64/68347/Docker%20Desktop%20Installer.exe
Mac with Intel chip: https://desktop.docker.com/mac/main/amd64/68347/Docker.dmg
Mac with Apple chip: https://desktop.docker.com/mac/main/arm64/68347/Docker.dmg
release_date: '2021-09-13'
@magjac
magjac / index.html
Last active April 17, 2026 04:04
d3-graphviz Basic Example
<!DOCTYPE html>
<meta charset="utf-8">
<body>
<script src="//d3js.org/d3.v7.min.js"></script>
<script src="https://unpkg.com/@hpcc-js/wasm@2.20.0/dist/graphviz.umd.js"></script>
<script src="https://unpkg.com/d3-graphviz@5.6.0/build/d3-graphviz.js"></script>
<div id="graph" style="text-align: center;"></div>
<script>
d3.select("#graph").graphviz()
@magjac
magjac / index.html
Last active April 17, 2026 04:04
d3-graphviz Demo
<!DOCTYPE html>
<meta charset="utf-8">
<body>
<script src="//d3js.org/d3.v7.min.js"></script>
<script src="https://unpkg.com/@hpcc-js/wasm@2.20.0/dist/graphviz.umd.js"></script>
<script src="https://unpkg.com/d3-graphviz@5.6.0/build/d3-graphviz.js"></script>
<div id="graph" style="text-align: center;"></div>
<script>
var dotIndex = 0;
i
me
my
myself
we
our
ours
ourselves
you
your
@ctlllll
ctlllll / longest_chinese_tokens_gpt4o.py
Created May 13, 2024 19:53
Longest Chinese tokens in gpt4o
import tiktoken
import langdetect
T = tiktoken.get_encoding("o200k_base")
length_dict = {}
for i in range(T.n_vocab):
try:
length_dict[i] = len(T.decode([i]))
except:

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.

@darconeous
darconeous / tesla-key-card-protocol.md
Last active April 17, 2026 04:02
Tesla Key Card Protocol

Tesla Key Card Protocol

Researched by Robert Quattlebaum darco@deepdarc.com.

Last updated 2020-02-03.

Image of Tesla Key Card Image of Tesla Model 3 Key Fob

@alganet
alganet / c89cc.sh
Last active April 17, 2026 03:55
c89cc.sh - standalone C89/ELF64 compiler in pure portable shell
#!/bin/sh
# ISC License
# Copyright (c) 2026 Alexandre Gomes Gaigalas <alganet@gmail.com>
# Permission to use, copy, modify, and/or distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in all copies.
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES