
learn-agent
A collection of notes on building coding agents, derived from the production development of the desktop agent Reina, with each mechanism simplified into a zero-dependency, single-file Node.js demo.
It offers an all-in-one, free, open-source tool for learning blackjack strategy, simulating bankroll outcomes, and comparing multiple casino games without requiring any installation.
CasCal v2026 is a browser-based casino mathematics and practice application combining blackjack advice, bankroll planning, and simulation-based learning in a single HTML and JavaScript project.
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A collection of notes on building coding agents, derived from the production development of the desktop agent Reina, with each mechanism simplified into a zero-dependency, single-file Node.js demo.

A comprehensive step-by-step tutorial series that teaches AI agents from scratch, covering function calling, agent loops, ReAct, reflection, plan-and-execute, multi-agent systems, orchestration, evaluation, and related concepts like RAG, GraphRAG, and LangChain.
INTACT is an end-to-end JEPA world model that learns an isomorphic intent-to-action interface, enabling search-free direct control for goal-conditioned robot tasks with high success rates after just one epoch of training.