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#!/usr/bin/env bash
#===========================================================================
# Works only with the official image available in the Mac App Store.
# Make sure you download the official installer before running this script.
#===========================================================================

hdiutil create -o /tmp/Catalina.cdr -size 9000m -layout SPUD -fs HFS+J
hdiutil attach /tmp/Catalina.cdr.dmg -noverify -mountpoint /Volumes/install_build
sudo /Applications/Install\ macOS\ Catalina.app/Contents/Resources/createinstallmedia  --volume /Volumes/install_build --nointeraction
hdiutil detach "/Volumes/Install macOS Catalina"

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// Package main is a sample macOS-app-bundling program to demonstrate how to
// automate the process described in this tutorial:
//
// https://medium.com/@mattholt/packaging-a-go-application-for-macos-f7084b00f6b5
//
// Bundling the .app is the first thing it does, and creating the DMG is the
// second. Making the DMG is optional, and is only done if you provide
// the template DMG file, which you have to create beforehand.
//
// Example use:

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const std = @import("std");
const net = std.net;
const fs = std.fs;
const os = std.os;

pub const io_mode = .evented;

pub fn main() anyerror!void {
    var general_purpose_allocator = std.heap.GeneralPurposeAllocator(.{}){};
    const allocator = &general_purpose_allocator.allocator;

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from datasets import load_dataset
from sentence_transformers.losses import CosineSimilarityLoss

from setfit import SetFitModel, SetFitTrainer

dataset = load_dataset("yelp_polarity")
print(dataset)

# Select N examples per class (8 in this case)
train_ds = dataset["train"].shuffle(seed=42).select(range(8 * 2))

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"""
This script combines two datasets to generate a file with all found patterns. 
"""

import srsly 
from prodigy.components.db import connect
import spacy 

nlp = spacy.blank("en")

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"""
This script combines two datasets to generate a file with all found patterns. 
"""

import srsly 
from prodigy.components.db import connect
import spacy 

nlp = spacy.blank("en")

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# must have conda installed
git clone https://github.com/joonspk-research/generative_agents.git
cd generative_agents
# open visual studio code, open gen agents folder
# within vscode, go to reverie/backend_server
# create new file utils.py
# copy/paste contents from github (below)

###
# Copy and paste your OpenAI API Key

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Getting GCP access token from a service account key

Use your service account's key JSON file to get an access token to call Google APIs.

Good for seeing how things work, including the creation of JWT token.

To create a JWT token, you can replace create-jwt-token.sh script with tools like step.

If you just want to get an access token for a service account,

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/**
 * Get a Google auth token given service user credentials. This function
 * is a very slightly modified version of the one found at
 * https://community.cloudflare.com/t/example-google-oauth-2-0-for-service-accounts-using-cf-worker/258220
 * 
 * @param {string} user   the service user identity, typically of the 
 *   form [user]@[project].iam.gserviceaccount.com
 * @param {string} key    the private key corresponding to user
 * @param {string} scope  the scopes to request for this token, a 
 *   listing of available scopes is provided at

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5 entertaining things you can find with the GitHub Search API

Let's have some command-line fun with curl, [jq][1], and the [new GitHub Search API][2].

Today we're looking for:

• The hottest repositories created in the last week.
• The oldest user accounts with zero followers. (So sad.)
• Issue trends by language.
• Clojure projects defying convention.