Note: This is all almost full YouTube ID informations. Original owner goes to AgentOak, modified version by Martin Eesmaa.
See the credits and also special thanks in below.
Before this gist has been forked, AgentOak made original gist on November 13 2019 and it was last updated on May 5 2021.
From now on, this forked gist is maintained by Martin Eesmaa since 17 September 2022. I joined to yt-dlp Discord server on 3rd April 2026 with my Discord username: martineesmaa.
Last updated: 26 May 2026
| Fill one broken-link stub in the DSWoK vault: research the topic, draft the note in DSWoK voice, run `draft-critic-mm`, save to the right folder, and refresh `missing_notes.md`. | |
| Use the path `ROOT = '/Documents/DSWoK'`. | |
| ## Inputs | |
| - `$ARGUMENTS` (optional). If provided, treat as the stub title to fill (e.g. "AUC", "Negative sampling", "T5"). If omitted, pick the highest-impact stub from `missing_notes.md` (most references first; ties broken by alphabetical order). | |
| ## Workflow |
| --- | |
| name: draft-critic-mm | |
| description: Review drafted DSWoK vault notes (dswok.com) against .claude/writing_style_guide.md and vault conventions. Use before publishing to catch style violations, AI writing tells, factual errors in citations, missing cross-links, and frontmatter convention violations. | |
| tools: Read, Grep, Glob | |
| --- | |
| You are a style and convention critic for the DSWoK reference vault (dswok.com). Your job is to review a drafted vault note against `.claude/writing_style_guide.md`, vault frontmatter conventions, and cross-link requirements — and produce a concrete, actionable critique before publication. This agent is for vault notes only (not blog posts, not book reviews). | |
| ## How to review |
This document provides a comprehensive guide for creating Orchestrator Agents in OpenCode. Based on the reference implementation (@agent/orchestrator.md), this guide details the structure, patterns, and best practices for building intelligent routing agents.
An Orchestrator Agent serves as a central dispatch system. Unlike standard agents that execute tasks, an orchestrator's sole purpose is to analyze user requests and delegate work to specialized subagents.
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
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.
| export const gpt_functions_param_jobs_fetching = [ | |
| { | |
| name: "process_job_data", | |
| description: "Process job data and extract core fields for a scraper.", | |
| parameters: { | |
| type: "object", | |
| additionalProperties: false, | |
| properties: { | |
| // Company | |
| company_name: { |
If you're trying to install the postgresql gem pg and it is failing with the following error message:
Installing pg 1.2.3 with native extensions
Gem::Ext::BuildError: ERROR: Failed to build gem native extension.
current directory: ~/.rbenv/versions/3.0.0/lib/ruby/gems/3.0.0/gems/pg-1.2.3/ext
~/.rbenv/versions/3.0.0/bin/ruby -I ~/.rbenv/versions/3.0.0/lib/ruby/3.0.0 -r ./siteconf20210125-97201-pycpo.rb extconf.rb
https://stackoverflow.com/questions/73735202/typescript-jest-imports-with-js-extension-cause-error-cannot-find-module kulshekhar/ts-jest#1057 (comment)
tsconfig.json
{
"compilerOptions": {
"outDir": "./lib/",
"inlineSourceMap": true,
Fish is a smart and user-friendly command line (like bash or zsh). This is how you can instal Fish on MacOS and make your default shell.
Note that you need the https://brew.sh/ package manager installed on your machine.
brew install fish