https://github.com/asmuelle/inward

A voice-first journaling and reflection companion, verifiably airplane-mode functional — spoken thoughts are transcribed, reflected on, and stored only on the phone. Zero network in the journaling path.
https://github.com/asmuelle/inward

android edge-ai foundation-models ios journaling on-device-inference privacy reflection swift

Last synced: about 19 hours ago
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A voice-first journaling and reflection companion, verifiably airplane-mode functional — spoken thoughts are transcribed, reflected on, and stored only on the phone. Zero network in the journaling path.

• Host: GitHub
• URL: https://github.com/asmuelle/inward
• Owner: asmuelle
• Created: 2026-06-11T16:50:34.000Z (26 days ago)
• Default Branch: main
• Last Pushed: 2026-07-01T07:43:48.000Z (6 days ago)
• Last Synced: 2026-07-01T09:23:22.093Z (6 days ago)
• Topics: android, edge-ai, foundation-models, ios, journaling, on-device-inference, privacy, reflection, swift
• Language: Swift
• Homepage: https://asmuelle.github.io/inward/
• Size: 3.8 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Agents: AGENTS.md

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README

          
# Inward

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> A voice-first journaling and CBT-reframing companion that is verifiably airplane-mode functional — spoken thoughts are transcribed, reflected, and stored only on the phone.

## Concept

A voice-first journaling and CBT-reframing companion that is verifiably airplane-mode functional — spoken thoughts are transcribed, reflected, and stored only on the phone.

##  Edge AI 

* On-device Whisper-class ASR for voice entries

* AFM 3 Core with Dynamic Profiles running listener

* CBT-reframe (cognitive distortions via @Generable structured output), and weekly-review modes

* Spotlight local RAG retrieves past entries for longitudinal patterns

* a LoRA adapter trained on reflective-questioning style as an uncopyable differentiator.

* Android: Gemini Nano Summarization/Rewriting/Prompt APIs on flagships, Gemma 3n E2B via LiteRT-LM elsewhere. 

## Tech Stack

* iOS (primary, iOS 26.4+): Swift/SwiftUI

* SpeechAnalyzer + SpeechTranscriber for on-device ASR (whisper.cpp small as fallback for older devices)

* FoundationModels AFM 3 Core via LanguageModelSession with @Generable structured output for distortion-tagging and reflection prompts, using the new context-size/token-count APIs to chunk under the 8K window with hierarchical entry summaries

* Core Spotlight + NLContextualEmbedding (or a small Core ML embedding model) + sqlite-vec for local RAG over past entries; GRDB/SwiftData with SQLCipher and NSFileProtectionComplete; skip the custom LoRA adapter at launch (version-lock retraining tax) in favor of a prompt-engineered persona with few-shot exemplars; client-side-encrypted export to Files/iCloud Drive. Android (downscoped, flagships first): Kotlin/Jetpack Compose

* ML Kit GenAI Summarization + Rewriting APIs on the AICore-supported device list, Prompt API only for short single-entry reflections (respect 4K-in/255-out limits); Gemma 3n E2B-it int4 via LiteRT-LM / MediaPipe LLM Inference as an opt-in download for non-AICore devices; on-device SpeechRecognizer or whisper.cpp for ASR

* Room + SQLCipher

* EmbeddingGemma-class embeddings via LiteRT + sqlite-vec for local retrieval. Both platforms: no analytics SDKs, no network calls in the journaling path (verifiable via iOS App Privacy Report)