arstechnica.com
Summarize this content to 100 words:
An open source AI assistant called Moltbot (formerly “Clawdbot”) recently crossed 69,000 stars on GitHub after a month, making it one of the fastest-growing AI projects of 2026. Created by Austrian developer Peter Steinberger, the tool lets users run a personal AI assistant and control it through messaging apps they already use. While some say it feels like the AI assistant of the future, running the tool as currently designed comes with serious security risks.
Among the dozens of unofficial AI bot apps that never rise above the fray, Moltbot is perhaps most notable for its proactive communication with the user. The assistant works with WhatsApp, Telegram, Slack, Discord, Google Chat, Signal, iMessage, Microsoft Teams, and other platforms. It can reach out to users with reminders, alerts, or morning briefings based on calendar events or other triggers. The project has drawn comparisons to Jarvis, the AI assistant from the Iron Man films, for its ability to actively attempt to manage tasks across a user’s digital life.
However, we’ll tell you up front that there are plenty of drawbacks to the still-hobbyist software: While the organizing assistant code runs on a local machine, the tool effectively requires a subscription to Anthropic or OpenAI for model access (or using an API key). Users can run local AI models with the bot, but they are currently less effective at carrying out tasks than the best commercial models. Claude Opus 4.5, which is Anthropic’s flagship large language model (LLM), is a popular choice.
A screenshot of talking with Clawdbot / Moltbot taken from its GitHub page.
Credit:
Moltbot
A screenshot of talking with Clawdbot / Moltbot taken from its GitHub page.
Credit:
Moltbot
Setting up Moltbot requires configuring a server, managing authentication, and understanding sandboxing for even a slice of security in a system that basically demands access to every facet of your digital life. Heavy use can rack up significant API costs, since agentic systems make many calls behind the scenes and use up a lot of tokens.
