Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

# ----------------------------------------------------------------------------- | |
# AI-powered Git Commit Function | |
# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It: | |
# 1) gets the current staged changed diff | |
# 2) sends them to an LLM to write the git commit message | |
# 3) allows you to easily accept, edit, regenerate, cancel | |
# But - just read and edit the code however you like | |
# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/ | |
gcm() { |
Here's how I configured a GitHub Action so that a new version issued by GitHub's release interface will build a Dockerfile, tag it with the version number and upload it to Google Artifact Registry.
Before you attempt the steps below, you need the following:
#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
import os | |
import sys | |
import argparse | |
import datetime | |
def main(): | |
console_prefix = "$ " |
This is a set up for projects which want to check in only their source files, but have their gh-pages branch automatically updated with some compiled output every time they push.
A file below this one contains the steps for doing this with Travis CI. However, these days I recommend GitHub Actions, for the following reasons:
#!/bin/sh | |
TABLE_SCHEMA=$1 | |
TABLE_NAME=$2 | |
mytime=`date '+%y%m%d%H%M'` | |
hostname=`hostname | tr 'A-Z' 'a-z'` | |
file_prefix="trimax$TABLE_NAME$mytime$TABLE_SCHEMA" | |
bucket_name=$file_prefix | |
splitat="4000000000" | |
bulkfiles=200 |