A collection of algorithms and data structures
- algorithms
- linear-algebra
- graph-theory
- search-algorithms
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A collection of algorithms and data structures
Jargon from the functional programming world in simple terms!
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 model family and using them on various provider services
TranslatedQuestions to ask the interviewer at the end of a technical interview
Clean Architecture Solution Template: A proven Clean Architecture Template for ASP.NET Core 10
A book for Learning the Foundations of LLMs
📚 Playground and cheatsheet for learning Python. Collection of Python scripts that are split by topics and contain code examples with explanations.
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
All the resources you need to get to Senior Engineer and beyond
Everything you need to prepare for your technical interview
TranslatedThe Ultimate Guide to Making Money with AI Side Hustles: Learn how to leverage AI for some cool side gigs and rake in some extra cash. Check out the English version for more insights.
A collection of full time roles in SWE, Quant, and PM for new grads.
FastAPI Best Practices and Conventions we used at our startup
Algorithms and Data Structures implemented in Go for beginners, following best practices.
Build the best interview map. The current content includes JS, network, browser related, performance optimization, security, framework, Git, data structure, algorithm, etc.
💿 Free software that works great, and also happens to be open-source Python.
A collection of ZSH frameworks, plugins, themes and tutorials.
:sparkles::sparkles:Latest Advances on Multimodal Large Language Models
LeetCode Problems' Solutions
Gitbook
Chrome Extensions Samples
Optimizing compiler for React
A collection of various deep learning architectures, models, and tips
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.