[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODQwNjE","title","PHOENIX - BUILDING PERSISTENT AI","state","published","contactAuthorMethod","email","aboutText","

Every conversation with a language model ends the same way: it forgets you. Not gradually, not partially — completely. This book builds a system that does not forget.

Building Persistent AI teaches you to design and implement Phoenix V2, a complete cognitive architecture that decouples persistent intelligence from the reasoning engine. Memory, affective state, and identity live in a local SQLite file you own — not in the model. When the provider updates, reprices, or retires a model, your assistant's memory and personality survive.

You will build five specialized agents coordinated by a Blackboard orchestrator, a semantic memory system with embedding-based retrieval, a Pleasure–Arousal–Dominance emotion engine that persists across sessions, a SubconsciousEngine that consolidates and reflects during idle cycles, and an incremental learning layer that adapts to you without retraining.

The stack is TypeScript on Node.js. No GPU required. The entire system was developed and runs on a 2008-era notebook. The companion paper is available open access at https://doi.org/10.5281/zenodo.22645361

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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