Darren Poon dyhpoon

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.

Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide

⚠️ this is now stupidly out of date

Computers

13" Macbook Pro 3.3 GHz i7 (late 2016)
Microsoft Surface Book (2016)

Peripherals

Dell up3216q 32" monitor

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

Writing a reddit bot with Go and OAuth2

Preface
Quick Start
Implicit Grant Authorization
See Also

Preface

##分布式系统(Distributed System)资料

#####希望转载的朋友，你可以不用联系我．但是一定要保留原文链接，因为这个项目还在继续也在不定期更新．希望看到文章的朋友能够学到更多．

《Reconfigurable Distributed Storage for Dynamic Networks》

介绍:这是一篇介绍在动态网络里面实现分布式系统重构的paper.论文的作者(导师)是MIT读博的时候是做分布式系统的研究的,现在在NUS带学生,不仅仅是分布式系统,还有无线网络.如果感兴趣可以去他的主页了解.

	web:
	image: 'gitlab/gitlab-ce:11.4.7-ce.0'
	restart: always
	hostname: 'gitlab.example.com'
	environment:
	GITLAB_OMNIBUS_CONFIG: \|
	external_url 'http://gitlab.example.com'
	redis['bind']='127.0.0.1'
	redis['port']=6379
	gitlab_rails['initial_root_password']=File.read('/steg0_initial_root_password')

	# This tells kubecfg to read its config from the local directory
	export KUBECONFIG=./kubeconfig

	# Looking at the cluster
	kubectl get nodes
	kubectl get pods --namespace=kube-system

	# Running a single pod
	kubectl run --generator=run-pod/v1 --image=gcr.io/kuar-demo/kuard-amd64:1 kuard
	kubectl get pods

	import React from 'react'
	import { BrowserRouter as Router, Route, Link, Redirect, matchPath } from 'react-router-dom'
	import { Motion } from 'data-driven-motion' // https://github.com/tkh44/data-driven-motion

	const WOBBLY_SPRING = { stiffness: 200, damping: 15, precision: 0.1 }

	const AnimationExample = () => (
	<Router>
	<div>
	<ul>