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Aru AI local-first AI assistant with semantic memory in browser SQLite

Aru AI local-first AI assistant with semantic memory in browser SQLite

by AruLab·Feb 24, 2026·2 points·0 comments

AI Analysis

●●SolidDark HorseShip It

Semantic memory extraction in browser SQLite, but local AI assistants are crowded.

Strengths
  • Semantic memory module intelligently extracts and injects user context only when relevant — reduces context bloat vs naive full-history injection.
  • True client-side architecture with encrypted SQLite storage means genuine privacy claim, not marketing theater.
  • Canvas/artifact system with Chart.js and code generation provides tangible output beyond chat bubbles.
Weaknesses
  • Local-first browser AI assistants are well-explored territory (Jan.ai, Ollama WebUI, Gpt4All desktop). Semantic memory is the differentiator, but execution evidence is limited.
  • PWA distribution + BYOM pattern means adoption friction — users must bring their own inference stack or API key, unlike ChatGPT's frictionless onboarding.
Category
Target Audience

Privacy-conscious users wanting persistent AI memory without cloud data collection; developers building local-first AI UX.

Similar To

Jan.ai · Ollama WebUI · Khoj (local semantic search)

Post Description

Hi HN,

For the past year, I’ve been building a personal AI assistant from scratch. I was frustrated by two things: cloud-based LLMs using my conversations for training, and the lack of persistent, cross-chat memory in most UIs.

I wrote Aru Ai entirely in Vanilla JS as a PWA. There is no backend, no telemetry, and no data collection. Everything lives in your browser.

Here is how it works under the hood:

Local Storage: All chats, settings, and vector embeddings are stored locally in a SQLite database on your device. Bring Your Own Model: It connects directly to Gemini, OpenRouter, or local models via Ollama/LM Studio. Semantic Memory Module: It extracts facts about you (e.g., allergies, preferences) and dynamically injects them into the context window only when relevant. Canvas & Artifacts: Built-in support to generate code, charts (Chart.js), documents, and mini-games in a dedicated canvas. You can save these to a local library. Heuristic Module: A small math-based logic system that adjusts the AI's "mood" (sarcasm, humor, tone) based on how you interact with it. It also features different modes (Child/Teen/Adult) protected by a database password to restrict output and prevent API changes.

It’s completely free. The code isn't open-source yet as I am planning a major refactoring, but I wanted to share the functional PWA with the community to get some feedback on the architecture and the local-first approach.

Link: https://chat.aru-lab.space/

I'd be happy to answer any questions about running SQLite in the browser, semantic extraction, or building complex PWAs without frameworks.

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