Vinh Nguyen vinhnx

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.

Terminals should generate the 256-color palette from the user's base16 theme.

If you've spent much time in the terminal, you've probably set a custom base16 theme. They work well. You define a handful of colors in one place and all your programs use them.

The drawback is that 16 colors is limiting. Complex and color-heavy programs struggle with such a small palette.

File Editing Engines Audit: Codex vs Gemini-CLI vs OpenCode

This document analyzes how file editing works in three AI coding assistants, highlighting the unique approaches and tricks each uses.

Executive Summary

Feature	Codex	Gemini-CLI	OpenCode

	#!/usr/bin/env python3
	"""
	Detects whether the terminal is using a light or dark theme and automatically
	adjusts 256-color palette indices so that colors render consistently regardless
	of the active theme. This is especially useful when a terminal (e.g. Ghostty)
	generates its 256-color palette to match the current theme — or when it doesn't,
	and we need to compensate by flipping the indices ourselves.
	"""

	import os

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	---
	name: frontend-design
	description: Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
	license: Complete terms in LICENSE.txt
	---

	This skill guides creation of distinctive, production-grade frontend interfaces that avoid generic "AI slop" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.

	The user provides frontend requirements: a component, page, application, or interface to build. They may include context about the purpose, audience, or technical constraints.

	import os
	import json
	import subprocess
	from anthropic import Anthropic

	# Tool definitions
	TOOLS = [
	{
	"name": "list_files",
	"description": "List files and directories at a given path",

	# train_grpo.py
	#
	# See https://github.com/willccbb/verifiers for ongoing developments
	#
	"""
	citation:

	@misc{brown2025grpodemo,
	title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
	author={Brown, William},

	unfocused-split-opacity = 1
	custom-shader=~/.config/ghostty/focus-pane.glsl

	You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

	## Core Principles

	1. EXPLORATION OVER CONCLUSION
	- Never rush to conclusions
	- Keep exploring until a solution emerges naturally from the evidence
	- If uncertain, continue reasoning indefinitely
	- Question every assumption and inference