skratchdot

A list of some other badges: http://shields.io/

Version Numbers

Alt Text	Image Src	Link
NPM version	https://badge.fury.io/{{programming_language:js}}/{{project_name}}.svg	http://badge.fury.io/{{programming_language:js}}/{{project_name}}
Dependency Status	https://david-dm.org/{{username}}/{{project_name}}.svg	https://david-dm.org/{{username}}/{{project_name}}
devDependency Status	https://david-dm.org/{{username}}/{{project_name}}/dev-status.svg	https://david-dm.org/{{username}}/{{project_name}}#info=devDependencies
GEM Dependency Status	https://gemnasium.com/{{username}}/{{repo}}.png	https://gemnasium.com/{{username}}/{{repo}})

arawn

Spring MVC 를 활용한 웹 개발 실무

http://skpla.net/springmvc 로 접속하시면 이 페이지가 나옵니다.

학습 목표

• Spring MVC를 사용해 웹 프로젝트를 개발할 수 있다.
• Spring MVC가 구축한 다양한 전략을 배우고 사용할 수 있다.
• Java및 스프링을 활용해서 데이터베이스와 연동할 수 있다.

adambray

Number to Ordinal

See full kata at codewars

Finish the function numberToOrdinal, which should take a number and return it as a string with the correct ordinal indicator suffix (in English). That is:

• numberToOrdinal(1) ==> '1st'
• numberToOrdinal(2) ==> '2nd'
• numberToOrdinal(3) ==> '3rd'
• numberToOrdinal(4) ==> '4th'

roachhd

EMOJI CHEAT SHEET

Emoji emoticons listed on this page are supported on Campfire, GitHub, Basecamp, Redbooth, Trac, Flowdock, Sprint.ly, Kandan, Textbox.io, Kippt, Redmine, JabbR, Trello, Hall, plug.dj, Qiita, Zendesk, Ruby China, Grove, Idobata, NodeBB Forums, Slack, Streamup, OrganisedMinds, Hackpad, Cryptbin, Kato, Reportedly, Cheerful Ghost, IRCCloud, Dashcube, MyVideoGameList, Subrosa, Sococo, Quip, And Bang, Bonusly, Discourse, Ello, and Twemoji Awesome. However some of the emoji codes are not super easy to remember, so here is a little cheat sheet. ✈ Got flash enabled? Click the emoji code and it will be copied to your clipboard.

People

😄

ihoneymon

쉽게 이해하면 좋을 스프링 프레임워크

---

'스프링 프레임워크Spring Framework'.

perhapsspy

Django에서 Test 쉽고 빠르게 하기

django 기본 테스트는 데이터베이스 마이그레이션 덕에 느리고 번거롭기 때문에 py.test와 model_mommy를 사용해서 빠르게 테스트를 하는 방법을 소개함.

• py.test : http://pytest.org/
• pytest-django : http://pytest-django.readthedocs.org/
• model_mommy : http://model-mommy.readthedocs.org/

테스트 파일 레이아웃

rondy

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

---

Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?

sshh12

You are a powerful agentic AI coding assistant, powered by Claude 3.5 Sonnet. You operate exclusively in Cursor, the world's best IDE.

You are pair programming with a USER to solve their coding task.
The task may require creating a new codebase, modifying or debugging an existing codebase, or simply answering a question.
Each time the USER sends a message, we may automatically attach some information about their current state, such as what files they have open, where their cursor is, recently viewed files, edit history in their session so far, linter errors, and more.
This information may or may not be relevant to the coding task, it is up for you to decide.
Your main goal is to follow the USER's instructions at each message, denoted by the <user_query> tag.

<communication>
1. Be conversational but professional.

karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.