workflow:
$ rails g model NameOfModel
invoke active_record
create db/migrate/YYYYMMDDHHMMSS_create_name_of_models.rb| MIT License | |
| Copyright (c) 2018 Noel Bundick | |
| Permission is hereby granted, free of charge, to any person obtaining a copy | |
| of this software and associated documentation files (the "Software"), to deal | |
| in the Software without restriction, including without limitation the rights | |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| copies of the Software, and to permit persons to whom the Software is | |
| furnished to do so, subject to the following conditions: |
| #!/usr/bin/env python | |
| # Reflects the requests from HTTP methods GET, POST, PUT, and DELETE | |
| # Written by Nathan Hamiel (2010) | |
| from http.server import HTTPServer, BaseHTTPRequestHandler | |
| from optparse import OptionParser | |
| class RequestHandler(BaseHTTPRequestHandler): | |
| def do_GET(self): |
| import React from "react"; | |
| import { | |
| StyleSheet, | |
| Text, | |
| View, | |
| ScrollView, | |
| Animated, | |
| SafeAreaView, | |
| Dimensions | |
| } from "react-native"; |
How To Add bitcoinjs-lib Version 4.0.2 To A React Native Project
Add/Link the following dependencies:
yarn add bitcoinjs-lib@4.0.2 react-native-randombytes buffer-reverse buffer@5yarn add --dev rn-nodeifyreact-native link react-native-randombytes"postinstall": "rn-nodeify --install buffer,stream,assert,events,crypto,vm --hack && cd node_modules/bs58 && yarn add base-x@3.0.4 && cd ../../"Before we begin:
HID iCLASS Credentials tech primer
What does all this stuff on my card mean?!
Key Terms:
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.