wolfi3 usmanakram232

NOTE: Moved to the Postgres Wiki

Extension Ecosystem: Jobs and Tools

This document sketches an idealized Postgres extension ecosystem.

Why?

The past year has seen a surge in interest in Postgres extension distribution. A number of people have noted in particular the challenges in finding and

Anti-hype LLM reading list

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.

Foundational Concepts

Pre-Transformer Models

Rethinking Boundaries: Merging End-to-End Testing and Implementation with Macro Abstractions

In traditional software development, there's a clear boundary separating end-to-end testing and implementation. This conventional mindset, however, faces a challenge with the introduction of a unique software development paradigm proposing the unification of these two aspects.

Underpinning this concept, a Domain-Specific Language (DSL) is used. This language serves to sketch out a detailed sequence of actions and responses - acting as test specifications. Interestingly, at the same time, these sequences also provide a functional model of the system's behavior and thus can serve as an efficient implementation.

Let's dive deeper with an example. Consider you are crafting multiple test cases for a basic addition operation in a calculator application. After designing a few tests, you spot a consistent pattern of steps - you enter two numbers, add them together, and finally, you expect a result.

Here is how it would

Automating Daily Reports, because fuck it, really...

Each day at our company, developers are required to document their activities, painstakingly jotting down their daily work and future plans. A monotonous chore that I just really dislike.

So now, there's a scribe for that :

Code

The problem

Initial situation

Using Fedora 38, say you want to update the package albert from manuelschneid3r, hosted at https://software.opensuse.org/download.html?project=home:manuelschneid3r&package=albert.

And with dnf update, you get the following output:

~ sudo dnf update
Dependencies resolved.

20 million digits of pi in under a minute with Julia

I recently discovered a relatively obscure algorithm for calculating the digits of pi: https://en.wikipedia.org/wiki/Gauss–Legendre_algorithm. Well, at least obscure compared to Chudnovsky's. Wikipedia notes that it is "memory-intensive" but is it really? Let's compare to the MPFR pi function:

function gauss_legendre(prec)
    setprecision(BigFloat, prec, base=10)
    GC.enable(false)

	#!/usr/bin/env python

	import json, sys
	from urllib.request import urlopen


	def green(str): return f'\033[92m{str}\033[0m'
	def orange(str): return f'\033[38;5;214m{str}\033[0m'
	def red(str): return f'\033[91m{str}\033[0m'
	def yellow(str): return f'\033[93m{str}\033[0m'

	void setup() {
	size(800, 800);
	background(255);
	noFill();

	translate(width * 0.5, height * 0.5); // center of the curve.
	hilbert_curve(6, width);
	}

	void hilbert_curve(int n, float size) {

	# Makefile to convert images (JPG or PNG) to a grayscale 4-bit PDF with a custom message, contrast adjustment, 8 levels of gray, and a background image using ImageMagick

	# Output PDF file name
	OUTPUT = output.pdf

	# File containing the list of image filenames
	IMAGE_LIST_FILE = slides.txt

	# Temporary directory for images with messages
	TEMP_DIR = temp_msg

	2023/7/11 08:59
	GPT-4 Architecture, Infrastructure, Training Dataset, Costs, Vision, MoE
	GPT-4 Architecture, Infrastructure,
	Training Dataset, Costs, Vision, MoE
	Demystifying GPT-4: The engineering tradeoffs that led OpenAI to their
	architecture.
	DYLAN PATEL AND GERALD WONG
	2023 年 7 月 11 日 ∙ PAID
	38
	8