Interactive roadmaps, guides and other educational content to help developers grow in their careers.

Newsletter to help busy software engineers become good at system design 👇

🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴

Azure OpenAI 기반의 Prompt Engineering 방법을 배울 수 있는 샘플을 제공합니다.

🇬🇧 영어 면접 준비

"프로그래밍 러스트: 빠르고 안전한 시스템 개발, 개정2판" 예제 코드

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

A curated awesome list of lists of interview questions. Feel free to contribute! 🎓

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

The Abstraction and Reasoning Corpus

Sample notebooks and prompts for LLM evaluation

🛫 리트코드 스터디

Code for paper: "QuIP: 2-Bit Quantization of Large Language Models With Guarantees"

An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.

Lightweight but robust Githru for VSCode Extension

Foundation Model Evaluations Library

[NeurIPS 2024 Spotlight] Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.

FOSSLight Scanner

Function graph tracer for C/C++/Rust/Python

Raft distributed consensus algorithm implemented in Rust.

Survey: A collection of AWESOME papers and resources on the large language model (LLM) related recommender system topics.

Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…

AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation

가짜연구소 인과추론팀 블로그입니다!

Causal Inference for The Brave and True 책의 한국어 번역 자료입니다.

An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.