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@almereyda
almereyda / falsehoods.md
Created April 14, 2016 15:11 — forked from wboykinm/falsehoods.md
Falsehoods programmers believe about addresses

Falsehoods programmers believe about addresses

This is an anchor-linked version of the excellent, amazing original opus magnum by Michael Tandy.

An address will start with, or at least include, a building number.

Counterexample: Royal Opera House, Covent Garden, London, WC2E 9DD, United Kingdom.

When there is a building number, it will be all-numeric.

Counterexample: 1A Egmont Road, Middlesbrough, TS4 2HT

@nckroy
nckroy / faleshoods-programmers-believe-about-names.md
Last active August 27, 2026 10:00
Falsehoods Programmers Believe About Names

Falsehoods Programmers Believe About Names


By Patrick McKenzie (patio11) (original location: https://www.kalzumeus.com/2010/06/17/falsehoods-programmers-believe-about-names/)

John Graham-Cumming wrote an article today complaining about how a computer system he was working with described his last name as having invalid characters. It of course does not, because anything someone tells you is their name is — by definition — an appropriate identifier for them. John was understandably vexed about this situation, and he has every right to be, because names are central to our identities, virtually by definition.

I have lived in Japan for several years, programming in a professional capacity, and I have broken many systems by the simple expedient of being introduced into them. (Most people call me Patrick McKenzie, but I’ll acknowledge as correct any of six different “full” names, any many systems I deal with will accept precisely none of them.) Similarly, I’ve worked with Big Freaki

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.

@k16shikano
k16shikano / SKILL.md
Last active August 27, 2026 09:56
japanese-tech-writing/SKILL
name japanese-tech-writing
description 日本語の技術文書・書籍原稿の文章規範。整形(一文一行、引用ブロック、脚注、コラム記法)、段落と論証の構成(パラグラフライティング)、論証の厳密さ(ツッコミどころの除去)、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。

日本語技術文書の文章規範

日本語で技術的な原稿(書籍の章、記事、解説文)を書く・推敲するときは、以下の規範に従う。

整形

@eliesgalvira
eliesgalvira / true-name.md
Last active August 27, 2026 09:50
A discipline for naming things well.
name true-name
description Name things well. Use when the user wants a name, rename, name critique, candidate list, or true-name search for a product, company, project, package, feature, system, tool, or library.

True Name

A discipline for naming things well.

Do not start with candidates. First find the shape of the thing: what it is, who it is for, what world it implies, and what constraints it must survive.

Cute Sparkles

These cool and girly glitters come with lots of flowers, hearts, bows and ribbons! These decorations can suit all sorts of happy and soft aesthetic moods ~ ✨(◕▿◕✿)

✧・゚: *✧・゚:*    *:・゚✧*:・゚✧
.・゜゜・  ・゜゜・.
。・゚゚・  ・゚゚・。
༶•┈┈⛧┈♛ ♛┈⛧┈┈•༶
✧༺♥༻∞  ∞༺♥༻✧

*✿❀ ❀✿*

@glanchow
glanchow / config
Created February 23, 2021 09:12
Use a keyboard key to paste X primary selection buffer from i3
# It's annoying to grab the mouse just to click the paste button
# .config/i3/config
# Paste
# Mac users may have to use click 3
bindsym XF86Launch3 exec xdotool click 2
@sundowndev
sundowndev / GoogleDorking.md
Last active August 27, 2026 09:47
Google dork cheatsheet

Google dork cheatsheet

Search filters

Filter Description Example
allintext Searches for occurrences of all the keywords given. allintext:"keyword"
intext Searches for the occurrences of keywords all at once or one at a time. intext:"keyword"
inurl Searches for a URL matching one of the keywords. inurl:"keyword"
allinurl Searches for a URL matching all the keywords in the query. allinurl:"keyword"
intitle Searches for occurrences of keywords in title all or one. intitle:"keyword"