Learn it. Build it. Ship it for others.

TensorZero is an open-source LLMOps platform that unifies an LLM gateway, observability, evaluation, optimization, and experimentation.

This repo is meant to serve as a guide for Machine Learning/AI technical interviews.

High-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale

An AI-powered data science team of agents to help you perform common data science tasks 10X faster.

Warcraft III Peon voice notifications (+ more!) for Claude Code, Codex, IDEs, and any AI agent. Stop babysitting your terminal. Employ a Peon today.

A curated collection of practical AI projects implementing OCR systems, RAG, AI agents, and other AI use cases.

📚 Two books on harness engineering — the design philosophies behind Claude Code & Codex: constraints, query loops, context governance, multi-agent verification. harness-books.agentway.dev

Your Cheat Sheet for AI Engineering Interview – Questions and Answers.

How real engineers run Claude Code and Codex: spec-driven planning, enforced TDD, persistent memory, and quality enforcement on all levels. Make your agents production-ready.

A list of AI memory projects

A curated list of best ai tools

AI-Native 工程师招聘面试官手册 · Builder / Reviewer 双岗评分 · AI 驾驶能力实操考核 · 易哈佛医疗

A curated collection of resources for 🌌 Azure OpenAI, 🦙 LLMs (+RAG, Agents). Monthly Updates.

Give you decision-ready references for the most common AI engineering problems

All my YouTube tutorials. Everything here is free - but please follow me on YouTube if you find these helpful. ❤️

Progressive UI from LLM

Learn to build from scratch an AI PR reviewer integrated with GitHub, Slack and Asana that scales within your organization.

GRACE (Graph-RAG Anchored Code Engineering): open Agent Skills for contract-driven AI code generation with semantic markup, knowledge graphs, and support for Claude Code, Codex CLI, and Kilo Code.

A curated list of battle-tested tools, frameworks, and best practices for building scalable, production-grade Retrieval-Augmented Generation (RAG) systems.