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@marckohlbrugge
marckohlbrugge / GUIDE.md
Created December 14, 2025 02:52

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.

How this was created: Claude Code analyzed the entire codebase - routes, controllers, models, concerns, views, JavaScript, CSS, tests, and configuration. The goal was to extract not just what patterns are used, but why - inferring philosophy from implementation choices.

@jlia0
jlia0 / agent loop
Last active December 13, 2025 09:06
Manus tools and prompts
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
@rain-1
rain-1 / LLM.md
Last active December 4, 2025 11:51
LLM Introduction: Learn Language Models

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.

@the0neWhoKnocks
the0neWhoKnocks / Switching from Atom to VS Code.md
Last active May 28, 2024 10:07
Switching from Atom to VS Code
@stefanproell
stefanproell / Dockerfile
Last active December 30, 2021 14:25
Run a Facebook Prophet Forecast in a Docker Container
# 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 <[email protected]>
RUN apt-get -y update && apt-get install -y \
python3-dev \
libpng-dev \
apt-utils \
@calkan
calkan / gist:eaad0bc4458da16a72dd
Last active January 5, 2021 02:09
Michael Hoffman's crazy bash_history backer upper on git
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
@Papierkorb
Papierkorb / ssl_hack.rb
Created June 10, 2015 16:46
SSL with Capybara and Selenium
# 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
@gbuesing
gbuesing / ml-ruby.md
Last active December 10, 2025 03:21
Resources for Machine Learning in Ruby

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

@debasishg
debasishg / gist:8172796
Last active November 16, 2025 01:54
A collection of links for streaming algorithms and data structures

General Background and Overview

  1. 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.
  2. Models and Issues in Data Stream Systems
  3. 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
  4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
  5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&amp;rep=rep1&amp;t
@andreierdoss
andreierdoss / capybara_select2.rb
Last active December 21, 2015 13:39 — forked from strukturedkaos/gist:3801641
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