Building a local-first AI tutor for my daughter (and 10–14 year-olds in Austrian schools) with Gemma 4

The article describes the creation of "Lernbuddy," a local-first AI study companion for children aged 10–14, built using Gemma 4 E4B. The app runs entirely on-device with no network calls, ensuring student data like homework and personal information never leaves the device. It features AI-generated flashcards, a spaced-repetition scheduler, and bilingual support, all implemented as a .NET MAUI application.

This is a submission for the Gemma 4 Challenge: Build with Gemma 4 My daughter is 13. Like most students her age in Austria, she has an iPad. Like most parents, I'm uncomfortable about her typing homework into ChatGPT — not because the answers are wrong, but because everything she types disappears into a cloud I don't control. Names, schools, half-formed thoughts, the topics where she's struggling. Stuff I want to stay on her device. So I built Lernbuddy. It's a study companion for ages 10–14 that runs entirely on-device with Gemma 4 E4B. No network calls. No telemetry. The model lives next to the kid's flashcards and her review history, on the same disk, and that's it. Three things, all driven by Gemma 4 E4B: irregular verbs , European capitals → the model pulls from general knowledge and creates a deck. Every card lands in a preview where you can edit, delete, or add more before they go in the database.correct / almost / incorrect — spelling errors and paraphrasing are tolerated. It even writes a short personalized summary at the end: "You handled past participles well. 'choose' and 'speak' got mixed up — they'll come back tomorrow." Behind the quiz sits a small SM-2 spaced-repetition scheduler. Cards she gets wrong come back the next day; cards she gets right keep slipping further out. A streak counter and a per-topic "✓ 12 solid · … 4 to practise" badge make progress visible without dashboards. The whole thing is a .NET 9 MAUI app — one codebase, four targets Windows, Android, iOS, macCatalyst , bilingual from day one German is the primary audience, English the default for international demos . Inference goes through Microsoft.Extensions.AI 's IChatClient interface. The concrete implementation wraps LLamaSharp running unsloth/gemma-4-E4B-it-GGUF Q4 K M quant, ~4.6 GB . Gemma 4's chat template — <|turn role\n...<turn| — gets built by hand; the embedded jinja in the GGUF carries tool-calling logic the kid will never need. A singleton InferenceStatus service publishes "Loading model… 4.6 GB", "Generating… 23 tokens 4.1/s " to a small banner that's visible on every page. When prompt-processing takes 30 seconds before the first token CPU is slow on long prompts , the banner still ticks. No dead spinners — that matters more than raw throughput when you're 11 and waiting. Long source texts get split by paragraph and sentence into ~500-character chunks. Cards appear rolling instead of waiting two minutes for one mega-prompt. Total throughput is slightly worse; perceived time is much better. The project's privacy contract starts with "local first": no PII in prompts, parent-PIN gate on any future cloud mode, model download only over WiFi with explicit consent. The current app makes zero outbound network calls after install. The only thing that ever leaves the device is what you type into a Gemini API form — and we don't have that form. By design. That's the differentiator. There are dozens of "ChatGPT for kids" apps; almost none of them can credibly say "your child's homework never leaves their device." I went down three runtime rabbit-holes before things stuck. ONNX Runtime GenAI doesn't support the Gemma 4 multi-file ONNX split yet. transformers.js + WebGPU inside a MAUI HybridWebView is theoretically beautiful and practically a stack of four bleeding-edge components silently failing in ways nothing surfaces. LLamaSharp + GGUF works — except LLamaSharp doesn't ship iOS-arm64 natives. So I built llama.cpp myself for iOS on a Mac, statically linked against the resulting .a files via MAUI's NativeReference mechanism, and pointed DllImport "llama" at Internal via a runtime resolver. The lesson: for a multi-platform local-LLM .NET project in 2026, pick the runtime that has the deepest community, not the one with the prettiest abstraction. llama.cpp has a community measured in thousands; the .NET wrappers around it inherit that depth almost for free. Code: https://github.com/gpiwonka/Lernbuddy more: https://piwonka.cc/Lernbuddy License: MIT. Built solo, on the side, between a day job and a tired father's evenings. PRs welcome — especially from teachers who can point out what's missing from a learning-science perspective. The submission is for the Gemma 4 Challenge. The motivation is more personal.