| // Use Gists to store code you would like to remember later on | |
| console.log(window); // log the "window" object to the console |
| @ECHO OFF | |
| SETLOCAL | |
| REM Speed up by checking for bin directory directly | |
| IF NOT EXIST node_modules\.bin GOTO FINDBIN | |
| SET BIN=.\node_modules\.bin | |
| GOTO RUN | |
| :FINDBIN | |
| REM Find the current bin directory from npm, storing the result in 'BIN' |
| /** | |
| * Deep diff between two object, using lodash | |
| * @param {Object} object Object compared | |
| * @param {Object} base Object to compare with | |
| * @return {Object} Return a new object who represent the diff | |
| */ | |
| function difference(object, base) { | |
| function changes(object, base) { | |
| return _.transform(object, function(result, value, key) { | |
| if (!_.isEqual(value, base[key])) { |
| /** | |
| * Implementation of the Critical Path Method (CPM) with variation | |
| * @see http://en.wikipedia.org/wiki/Critical_path_method | |
| * | |
| * Shows all the critical Paths, returns a subset of the graph | |
| * containing the critical activities | |
| */ | |
| /** | |
| * Activity Class |
| parseMustache = (str, obj) -> | |
| str.replace /{{\s*([\w\.]+)\s*}}/g, (tag, match) -> | |
| nodes = match.split(".") | |
| current = obj | |
| for node in nodes | |
| try | |
| current = current[node] | |
| catch | |
| return "" | |
| current |
refer to: https://stackoverflow.com/questions/39371772/how-to-install-anaconda-on-raspberry-pi-3-model-b
{USER}: pi
Install Miniconda 3:
wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-armv7l.sh
sudo md5sum Miniconda3-latest-Linux-armv7l.sh # (optional) check md5
sudo /bin/bash Miniconda3-latest-Linux-armv7l.sh # -> change default directory to /home/pi/miniconda3
| const net = require('net'); // 引入網路 (Net) 模組 | |
| const HOST = '10.10.10.168'; | |
| const PORT = 9100; | |
| let zpl = ` | |
| ^XA | |
| ^FO150,40^BY3 | |
| ^BCN,110,Y,N,N | |
| ^FD123456^FS | |
| ^XZ |
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.
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.

