Discover gists
| #!/usr/bin/env bash | |
| set -euo pipefail | |
| DOMAIN="${1:-example.com}" | |
| RELEASE="${CC_RELEASE:-cc-main-2026-jan-feb-mar}" | |
| CACHE="${HOME}/.cache/cc-backlinks/${RELEASE}" | |
| BASE="https://data.commoncrawl.org/projects/hyperlinkgraph/${RELEASE}/domain" | |
| VERTICES="${CACHE}/domain-vertices.txt.gz" | |
| EDGES="${CACHE}/domain-edges.txt.gz" |
Useful links:
- Calling codes: https://en.wikipedia.org/wiki/List_of_country_calling_codes#Overview
- List of SMS verification sites: https://www.reddit.com/r/FREEMEDIAHECKYEAH/wiki/storage/#wiki_sms_verification_sites
As some of these sites are a bit sketchy, make sure to keep privacy & security measures. Learn more here: https://www.privacyguides.org/en/desktop-browsers/
UPDATE - I now just use https://5sim.net and choose England as the country (it works the best for me)!
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| # annotated by dave eddy (@yousuckatprogramming) | |
| # explained - https://youtu.be/D0sG2fj0G4Y | |
| # borrowed heavily from https://grml.org | |
| # Begin blinking text mode | |
| # I just use bold red here since my terminal has blinking disabled | |
| export LESS_TERMCAP_mb=$'\e[1;31m' | |
| # Begin bold text mode | |
| export LESS_TERMCAP_md=$'\e[1;31m' |
These scripts allows you to use vim in a remote machine via ssh, and being capable of sharing the local clipboard with vim's 0 register (which is the one used by default by yank/paste).
To make it work:
-
In your local machine, run
clipboard-server.py, which uses flask to create a simple REST API exposing only the/endpoint, listening onlocalhost:11223GET /will retrieve the system clipboard of the machine in which the server is run, in form of a JSON with the field"content".