Hi everyone! 👋 I’m the independent developer behind Everglow.

Most personal journal and diary apps suffer from the "black hole" problem: you log your thoughts, daily life, meetings, and trips, but they disappear into a chronological void, rarely to be resurfaced in a meaningful way. I wanted to transform the traditional "static diary" into an evolving, queryable Living Personal Database—without compromising privacy or shipping personal thoughts off to cloud servers for AI model training.

🛡️ Why Privacy-First?

Journaling contains your most personal memories and private data. Standard cloud-based AI solutions often route your data through third-party APIs where it can be stored, processed, or used to train future models.

Everglow is built local-first:

• On-Device LLM Inference: Powered natively on your device via Gemma 4.
• No Accounts, No Telemetry: In on-device mode, everything is processed and stored locally — no analytics SDKs, no sign-up. (If you opt into Everglow Cloud below, auth is subscription-linked plus device attestation, not a personal account — but it does register your device.)
• Offline-Ready: With the on-device model selected, indexing, RAG, and query workflows run seamlessly in complete airplane mode — zero network calls.

✨ Key Features

1. The "Life Indexer": As you record daily events, Everglow uses an on-device AI agent to extract entities, key events, and relationship connections, creating a dynamic web of your life.
2. Two-Tier Retrieval (Ask Tab):
   • Fast: Quick semantic vector scan via sqlite-vec for straightforward lookups.
   • Considered: Reads auto-generated monthly "rollup" chronicles — aggregated summaries of events, people, and themes — to answer questions that span a longer timeline.
3. Interactive Social & Entity Graph: Renders a force-directed map showing how people, places, and topics in your journal connect to one another.
4. Everglow Cloud (Optional Proxy): For faster speed you can opt into Everglow Cloud. The proxy routes requests securely (with zero-data retraining guarantees) using a dynamic token-cache system to keep costs low. Alternatively, you can bring your own remote API keys.

🛠️ Technical Architecture at a Glance

• Inference Engine: Powered by LiteRT-LM running directly on the iPhone GPU via Metal Shaders for high token generation speeds.
• Concurrency: Swift Concurrency with actor isolation — LLM inference runs inside a dedicated actor off the main thread, so C/C++ matrix operations never block the UI, keeping interactions fluid at 60/120 FPS.

📱 Get Started

Everglow is free to try with no mandatory subscriptions to get started.

• App Store: Download Everglow on the App Store

I'd love to hear your feedback on on-device LLM performance, RAG architecture on mobile, and local privacy design!