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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.

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

Título del Proyecto

Acá va un párrafo que describa lo que es el proyecto

Comenzando 🚀

Estas instrucciones te permitirán obtener una copia del proyecto en funcionamiento en tu máquina local para propósitos de desarrollo y pruebas.

Mira Deployment para conocer como desplegar el proyecto.

RK68 user manual ⌨️

conections

BT 5.1
USB-A 2.4GHz
Wired

BT connection wind

Regular Colors

Value	Color
\e[0;30m	Black
\e[0;31m	Red
\e[0;32m	Green
\e[0;33m	Yellow
\e[0;34m	Blue
\e[0;35m	Purple

Which GGUF is right for me? (Opinionated)

Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962

In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.

llama.cpp feature matrix

See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix

	Act like a Linear product teammate, not a request-taking assistant. Linear is a AI Supported issue tracking/project management/product building tool. All analyzing should take that in to account.

	For every input, start by identifying the underlying problem instead of accepting the proposed solution at face value. Treat customer requests as signals about unmet needs, not instructions to implement literally. Infer what is unsaid, look for patterns across feedback, and explain the deeper need in clear language.

	Before suggesting work, evaluate:

	what problem is actually being expressed

	who is affected

	#!/usr/bin/env python3
	"""
	GeoServer jsonArrayContains SQLi -> PostgreSQL RCE / SQL / exfiltration PoC

	GeoTools FilterToSqlHelper.constructEquality writes the `expected` argument of
	jsonArrayContains RAW into the SQL string (only the JSON pointer is escaped):

	jsonb_path_exists("data"::jsonb, '$ ? (@.a == "<VALUE>")')

	A single quote in VALUE breaks out of the string literal, closes the call

	[user]

	[alias]
	lg1 = log --graph --abbrev-commit --decorate --format=format:'%C(bold blue)%h%C(reset) - %C(bold green)(%ar)%C(reset) %C(white)%s%C(reset) %C(dim white)- %an%C(reset)%C(bold yellow)%d%C(reset)' --all
	lg2 = log --graph --abbrev-commit --decorate --format=format:'%C(bold blue)%h%C(reset) - %C(bold cyan)%aD%C(reset) %C(bold green)(%ar)%C(reset)%C(bold yellow)%d%C(reset)%n'' %C(white)%s%C(reset) %C(dim white)- %an%C(reset)' --all
	lg = !"git lg1"
	[init]
	defaultBranch = main