From your first widgetto fully-offline AI.
Three books, one reading path. Build a real Flutter app, give it production AI, then run that AI privately on the device — with every line of code verified for June 2026.
- 3 books
- 850+ pages
- Verified June 2026
- Flutter 3.44 · Dart 3.12
A deliberate path — not a pile of tutorials.
Each book hands off to the next. You build one real app — Habito — and carry it from a clean architecture, to production AI features, to AI that runs entirely on the device.
The Complete Flutter Blueprint
From Visual Prototypes to Seamless Store Distribution
The end-to-end craft of shipping a real Flutter app — from a design file to a polished build in the store.
Building AI-Powered Flutter Apps
From First Token to Production
Add real AI to a Flutter app and take it all the way to production — streaming chat, RAG, tools and agents, on-device and hybrid, then the unglamorous craft that keeps it shipping: evals, cost, and safety.
On-Device AI for Flutter
Private, Offline, and Free at Inference
Run real AI on the phone — no servers, no API bills, nothing leaves the device.
Read straight through, or drop into the book you need — every chapter tells you up front what it assumes.
Three books. Read in any order.
Premium, fixed-layout PDFs that render code exactly the way you wrote it — built around one app you actually ship.
The Complete Flutter Blueprint
From Visual Prototypes to Seamless Store Distribution
The end-to-end craft of shipping a real Flutter app — from a design file to a polished build in the store. Twenty-nine chapters and a full capstone, with a locked, modern stack and nothing left hand-wavy.
- Translate a design into a clean widget tree, the right way
- A locked production stack: Riverpod, Drift, dio, go_router
- Build Habito — a real habit tracker — end to end
- Test, profile, and ship to both stores with confidence
Building AI-Powered Flutter Apps
From First Token to Production
Add real AI to a Flutter app and take it all the way to production — streaming chat, RAG, tools and agents, on-device and hybrid, then the unglamorous craft that keeps it shipping: evals, cost, and safety.
- Your first real token, then a streaming chat UI that doesn't jank
- Embeddings, RAG, and multimodal that actually ground answers
- Agents, tools, and MCP — with guardrails that hold
- Evals, caching, cost, and safety: the production half nobody writes about
On-Device AI for Flutter
Private, Offline, and Free at Inference
Run real AI on the phone — no servers, no API bills, nothing leaves the device. Local LLMs, offline RAG, vision and speech, performance under heat and memory pressure, and a capstone that works in airplane mode.
- Run Gemma on a phone with flutter_gemma and LiteRT
- Fully-offline RAG — a private knowledge base that never phones home
- Survive real hardware: memory, battery, and thermal throttling
- Ship a fully-offline feature that works on a plane, at zero cost
The AI-Flutter Stack
The complete vertical: build a real Flutter app, give it production AI, then run that AI privately on the device. One reading path, first widget to airplane mode.
- The Complete Flutter Blueprint· 523 pp
- Building AI-Powered Flutter Apps· 186 pp
- On-Device AI for Flutter· 143 pp
Written like production code — reviewed like it too.
In a market flooded with AI-generated filler, these are built for developers who have to make it work on Monday.
Verified for June 2026
Every package version, API name, and signature fact-checked against the live docs — not a model's memory. The AI stack moves fast; this keeps up.
Runnable, never hand-wavy
Code that compiles against the real SDKs — firebase_ai, flutter_gemma, LiteRT — and is adversarially reviewed for correctness.
One app, three books
You build Habito once and carry it the whole way — architecture, cloud AI, then fully-offline AI. The thread never drops.
Diagrams that explain
30+ hand-built diagrams — runtime maps, RAG loops, routing trees — so the hard ideas land before the code does.
Honestly offline
On-device means on-device: airplane-mode-provable, zero API cost. The book tells you exactly where it works and where it doesn't.
Honest about limits
Where small models fail, where RAG beats fine-tuning, where the cloud still wins. No hype — the trade-offs you actually ship against.
A reading experience made for developers.
Fixed-layout PDFs that never mangle a code block, plus a free in-browser reader for samples. No tiny re-flowed snippets, no broken indentation — the way technical books should read.
- Code that renders exactly as written
- Built for long, dark-mode reading
- Reads cleanly on any screen
The Case for On-Device AI
The model that answers your user’s question doesn’t have to live in a data center. It can live in their pocket — running on the phone they’re already holding, answering with no network, no API bill, and nothing leaving the device.
That single shift changes the economics and the ethics of a feature at once. Private by construction. The only honest test is the simplest one: turn on airplane mode and see if it still works.
Most AI-development content is either toy demos or already out of date. NIM Books exists to fix that: deeply practical, ruthlessly current, and anchored in Flutter — the craft of shipping real apps with AI that actually holds up in production.
Every book is built the same way the code is: researched against live sources, written for a developer who has to make it work, and reviewed until it’s right.
- Book 4The AI Backend in Dartthe server every AI app needs
- Book 5Ship a Real AI SaaSNext.js + Vercel AI SDK
- Book 6Production AI Agentstools, MCP, orchestration, evals
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