A Twitter thread laying out concrete steps to build a working AI agent from scratch, skipping theory and focusing on actual implementation you can follow in sequence.

• Provides a step-by-step guide designed for beginners with no prior agent-building experience
• Emphasizes practical, hands-on instructions rather than conceptual background
• Aims to produce a functional agent that operates independently without constant user intervention

This post highlights the first book that pulls together language modeling, inference optimization, reinforcement learning, system scaling, agentic AI, retrieval-augmented generation, memory, environments, and benchmarks in one volume. It then points you to paperswithcode.co’s “most cited” list and recommends reading the top ten papers, coding them, and writing about your findings.

• A single book reportedly covers language modeling, inference optimization, RL, system scaling, agentic AI, RAG, memory, and benchmarks together—rare breadth even after five years of rapid AI progress.
• Recommended self-study path: go to paperswithcode.co's "most cited" list and work through the top ten papers.
• Suggested pace is one to two papers per week, each time reading, breaking down the math, building a toy implementation, and writing up findings.

This article breaks down Andrej Karpathy’s zero-dependency, 243-line GPT implementation in plain Python. It explains how each part—tokenizer, autograd engine, embeddings, attention mechanism, residual connections, and MLP—mirrors a full-scale transformer on a tiny dataset of baby names.

• Karpathy's microGPT implements a full GPT—tokenizer, autograd engine, transformer, training loop—in just 243 lines of pure Python with zero external dependencies beyond os, math, random and argparse.
• A ~40-line custom autograd engine (Value class) replicates PyTorch's backward-pass mechanics via topological graph traversal.
• The toy model trains on baby names using a tiny architecture (16-dim embeddings, seq length 8, single layer, 4 attention heads) totaling about 4,000 parameters.
• The same core transformer math—embeddings, RMSNorm, attention, MLP—scales up unchanged to power trillion-parameter models like GPT-4.

Learn how to create a code review agent using the Claude Agent SDK, which allows developers to build custom AI agents capable of analyzing codebases for bugs and security issues. The guide provides step-by-step instructions, from setting up the environment to implementing structured output and handling permissions.

• The Claude Agent SDK can be used to build a custom code review agent that scans codebases for bugs and security issues
• The guide walks through the full build process: environment setup, structured output implementation, and permission handling