Run local LLMs on your phone with Observer: offline agents for monitoring and logging
Observer is an open-source iOS app that lets you run local LLMs on your phone in a completely offline configuration. It uses a simple n-second agent loop: it captures an image via camera (using multimodal models), processes it with a local LLM, and then executes actions based on the model's response. The agent can start and stop itself, matching Anthropic's definition of an agent (not just a workflow).
Key features
- 100% offline — no sign-in required, no network calls for inference.
- Multimodal model support: the agent sees images and responds with text.
- Discord notifications work offline (other channels like WhatsApp, Email, SMS, Voice Calling, and Telegram are unavailable in offline mode due to no auth).
- Logs events and can write descriptions to the agent's memory.
- Customizable loop interval (e.g., every n seconds).
Limitations
Because the offline mode has no authentication, push notifications via WhatsApp, Email, SMS, Voice Calling, and Telegram will not work. However, Discord notifications function perfectly.
Availability
- iOS: Available now on the App Store.
- Android: Expected release in ~3 days (after the two-week testing period).
How it works (from the tutorial)
The tutorial (linked below) walks through setting up Observer in offline mode. The core loop is:
capture image → multimodal LLM inference → action (log / notify via Discord) → repeat every n seconds
The author demonstrates using the app to monitor anything (e.g., recording when something happens, writing descriptions to memory).
Get started
GitHub: github.com/Roy3838/Observer
App Store: Observer AI on the App Store
Who it's for: Developers who want to run private, offline AI agents on their phone for monitoring, logging, or alerting — without cloud dependencies.
📖 Read the full source: r/LocalLLaMA
👀 See Also
read-once: A Claude Code Hook That Prevents Redundant File Reads
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Karpathy's autoresearch project: AI agents run overnight LLM training experiments
Andrej Karpathy released a minimal autoresearch project where an AI agent edits train.py, runs 5-minute nanochat training experiments, checks if val_bpb improved, and repeats overnight on a single GPU.
Claude Code's Illusion of Finished Work: Why Reviewing the Agent's Path Matters More Than the Diff
Claude Code can produce a clean diff, passing tests, and a good summary—yet still miss real behavior, security concerns, or architecture constraints. The author argues that reviewing the chain of actions (plans, files read, commands run, test output) is now essential, not just the final diff.
OpenPlawd: OpenClaw Skill for Automated Plaud Meeting Notes
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