Rida Al Barazi rbarazi

General Background and Overview

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

UPDATE a fork of this gist has been used as a starting point for a community-maintained "awesome" list: machine-learning-with-ruby Please look here for the most up-to-date info!

Resources for Machine Learning in Ruby

Gems

liblinear-ruby: Ruby interface to LIBLINEAR using SWIG

Switching from Atom to VS Code

Install Extensions
Keyboard Shortcuts
Command Palette Commands
Misc.
Adding Language Specific Editor Rules

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

🆕 Update: See more extensive repo here: https://github.com/marckohlbrugge/unofficial-37signals-coding-style-guide

The Unofficial 37signals/DHH Rails Style Guide

About This Document

This style guide was generated by Claude Code through deep analysis of the Fizzy codebase - 37signals' open-source project management tool.

Why Fizzy matters: While 37signals has long advocated for "vanilla Rails" and opinionated software design, their production codebases (Basecamp, HEY, etc.) have historically been closed source. Fizzy changes that. For the first time, developers can study a real 37signals/DHH-style Rails application - not just blog posts and conference talks, but actual production code with all its patterns, trade-offs, and deliberate omissions.

	def select2 text, options
	page.find("#s2id_#{options[:from]} a").click
	find(:xpath, "//body").find("input.select2-input").set(text)
	page.execute_script(%\|$("input.select2-input:visible").keyup();\|)
	find(:xpath, '//body').find('ul.select2-results li', text: text).click
	end

	# Hack for Capybara to use SSL connections using selenium.
	#
	### Usage:
	# Require this from rails_helper.rb
	#
	### Steps to generate a SSL certificate on a Linux box:
	# 0. Starting from 'Rails.root'
	# 1. Generate private key. Type in some password.
	# $ openssl genrsa -des3 -out private.key 4096
	# 2. Generate certificate sign request

	1 - Create a private GitHub/Bitbucket or similar git repo. Here I assume the repo is:

	https://github.com/calkan/bash_history.git

	2 - Create .history directory and initialize it for the repo:

	mkdir $HOME/.history
	cd $HOME/.history
	git init
	touch README.md

	# Run a python script using FB Prophet in a Docker container
	# Build image: docker build -f Dockerfile-Debian -t forecast:R1 .

	FROM python:3.4.6-wheezy
	MAINTAINER Stefan Proell <stefan.proell@cropster.com>

	RUN apt-get -y update && apt-get install -y \
	python3-dev \
	libpng-dev \
	apt-utils \

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet