HighwayofLife

Coding Rules and Instructions

1. Test-Driven Development (TDD) with pytest: Always write a failing test before writing implementation code (Red-Green-Refactor). Use pytest and pytest-fixtures for test setup, execution, and teardown.
2. KISS (Keep It Simple, Stupid): Favor the simplest solution that meets the requirements.
3. DRY (Don't Repeat Yourself): Avoid code duplication. Extract reusable logic into functions or classes.
4. Standard Libraries and Tools: Utilize standard Python libraries (like datetime for date/time, requests for HTTP requests, and logging) and external libraries, including BeautifulSoup4 for HTML parsing, to avoid reinventing the wheel. Favor well-maintained and widely-used libraries.
5. YAGNI (You Ain't Gonna Need It): Don't implement features or functionality unless they are currently required.
6. SOLID Principles & Extensibility: Adhere to SOLID principles, promoting maintainability, testability, and future extension. Consider potential future requi

veekaybee

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowhere. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture

alukach

# .github/workflows/app.yaml
name: My Python Project
on: push

jobs:
  test:
    runs-on: ubuntu-latest
    timeout-minutes: 10

    services:

zillwc

const KEY_OP_ALGO = 'AES-GCM';
const KEY_WRAPPING_ALGO = 'AES-KW';
const KEY_DERIVATION_ALGO = 'PBKDF2';

const encode = (t) => (new TextEncoder()).encode(t);
const decode = (t) => (new TextDecoder()).decode(t);
const bytesToArrayBuffer = (b) => new Uint8Array(b);
const areBuffersEqual = (buf1, buf2) => {
  const a1 = bytesToArrayBuffer(buf1);
  const a2 = bytesToArrayBuffer(buf2);

acdlite

// getComponent is a function that returns a promise for a component
// It will not be called until the first mount
function asyncComponent(getComponent) {
  return class AsyncComponent extends React.Component {
    static Component = null;
    state = { Component: AsyncComponent.Component };

    componentWillMount() {
      if (!this.state.Component) {
        getComponent().then(Component => {

takluyver

#!/usr/bin/env python3
"""
To run: python3 nb2to3.py notebook-or-directory
"""
# Authors: Thomas Kluyver, Fernando Perez
# See: https://gist.github.com/takluyver/c8839593c615bb2f6e80

import argparse
import pathlib
from nbformat import read, write

amatellanes

/* Useful celery config.

app = Celery('tasks',
             broker='redis://localhost:6379',
             backend='redis://localhost:6379')

app.conf.update(
    CELERY_TASK_RESULT_EXPIRES=3600,
    CELERY_QUEUES=(
        Queue('default', routing_key='tasks.#'),

kevin-smets

Default

Powerlevel10k

19h

console.reset = function () {
  return process.stdout.write('\033c');
}

rgreenjr

-- show running queries (pre 9.2)
SELECT procpid, age(clock_timestamp(), query_start), usename, current_query 
FROM pg_stat_activity 
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

-- show running queries (9.2)
SELECT pid, age(clock_timestamp(), query_start), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%'