Cowboy, the popular Erlang web server, has deprecated middleware and replaced the concept with stream handlers, a more flexible, but more complicated API. This page contains a documented reference implementation of a stream handler, to help others when developing their stream handlers in the future.
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| { | |
| "regiones": [ | |
| { | |
| "region": "Arica y Parinacota", | |
| "comunas": ["Arica", "Camarones", "Putre", "General Lagos"] | |
| }, | |
| { | |
| "region": "Tarapacá", | |
| "comunas": ["Iquique", "Alto Hospicio", "Pozo Almonte", "Camiña", "Colchane", "Huara", "Pica"] | |
| }, |
| -- Torch Android demo script | |
| -- Script: main.lua | |
| -- Copyright (C) 2013 Soumith Chintala | |
| require 'torch' | |
| require 'cunn' | |
| require 'nnx' | |
| require 'dok' | |
| require 'image' |
See how a minor change to your commit message style can make a difference.
git commit -m"<type>(<optional scope>): <description>" \ -m"<optional body>" \ -m"<optional footer>"
| movie 'isaac.swf' { | |
| // flash 8, total frames: 41, frame rate: 30 fps, 800x600 px, compressed | |
| movieClip 338 { | |
| } | |
| movieClip 339 b501 { | |
| } | |
| movieClip 342 { |
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.
