My quiz Reels have no video editor behind them. GSAP animates a plain HTML page, Chrome records the tab at 60 fps, and ffmpeg makes it Instagram-ready. Here is how each piece works, with code.
How an LLM Works — From Input to Output How an LLM Works: From Input to Output A practical, numerical journey through tokenization, embeddings, attention, Q/K/V, multi-head attention, feed-forward networks, and billions of parameters....
A practical Java design for compacting long LLM conversations while preserving system instructions, recent context, tool-call boundaries, persistent memory, concurrency safety, streaming behavior, and failure atomicity.
I Was Burning $1,000 a Week on Claude Code — So I Built a Dashboard to See Where It Went A local, zero-config tool that turns Claude Code’s raw transcripts into cost you can...
Implementing EMA Crossover in Java — A Practical Guide Implementing EMA Crossover in Java A hands-on guide to building a fast/slow EMA bullish signal — math, code, and a runnable example. The EMA crossover...
Disclaimer: This article describes an unofficial practice site. The AI Booklet (theaibooklet.com) is not affiliated with, endorsed by, or sponsored by Anthropic. Always confirm exam details on Anthropic’s official certification pages before you register....
If you’ve looked at your Claude API usage and wondered why input tokens seem suspiciously low — you’re not imagining things. The label is misleading. Here’s what’s actually...
Deep agents in Java with LangChain4j A ready-made harness for planning, workspace files, sub-agents, and optional skills—so you ship a tool-using orchestrator instead of hand-wiring prompts and schemas. If you follow LangChain’s deepagents idea—planning,...
Building middleware in Java with LangChain4j Cross-cutting behavior around assistants—logging, retries, safety rails, and memory compaction—without rewriting your business logic. If you build agents with LangChain4j, you already wire a ChatModel, optional tools, and...
If you’re building an LLM-powered application (like a code review system), you’ve probably noticed this pattern: Yet every request reprocesses the entire prompt. That’s wasteful. Prompt caching fixes this. This guide explains: 🧠 What...
Modern agents keep a chat history of user messages, assistant replies, and tool calls. That history is bounded by a context window. When it gets long, frameworks can compress older turns so the model still has useful signal without...
