How to Make an LLM: A Practical Guide for Developers
From tokenization to alignment, learn the full pipeline for building a language model, with realistic code, compute tradeoffs, and when fine-tuning beats training.
From tokenization to alignment, learn the full pipeline for building a language model, with realistic code, compute tradeoffs, and when fine-tuning beats training.
Learn to build a retrieval-augmented generation app in Python: chunk documents, create embeddings, store them in a vector database, and wire retrieval into an LLM call.
Learn how to build LLM-powered applications on the JVM with LangChain4j: chains, RAG pipelines, tool-using agents, and production tips for Java developers.
Install LangGraph, build and run your first state graph, add loops and tools, then deploy with checkpointing and production-ready serving options.
Learn how to integrate the OpenAI API step by step: set up your key, make your first chat completion call, stream responses, and build a production-ready endpoint.
Learn how to integrate an AI API into a web app: secure key handling, your first request, streaming responses, cost control, retries, and choosing providers.
Learn how to integrate AI into your API testing workflow. This guide shows developers how LLMs can generate test cases, draft payloads, and accelerate debugging.
A serious design thought experiment: what structural properties make a language hard for AI assistants, why obscurity fails, and what it teaches about language design.
The viral headline got it wrong: the native TypeScript compiler targets Go, not Rust. Learn why, what role AI actually plays in migrations, and how to try the preview.
Learn how to deploy Firecrawl on your own infrastructure with Docker Compose, from configuring Redis and Playwright to understanding what differs from the hosted API.