Thanks! Checked out your project — really impressive work. The way I see it, our projects are complementary: FireClaw sanitizes inputs (is this content trying to hijack the agent?), yours governs outputs (should the agent take this action?). Together that's defense-in-depth.
We just shipped /api/scan in v1.1.0 which could plug into your policy evaluation — scan content before it enters the decision pipeline. Also now on Docker and npm (npx fireclaw) for easier integration.
Happy to brainstorm integration. Feel free to open an issue on our repo or reach out on GitHub.
FireClaw = prompt injection firewall for AI agents. Proxy architecture, not just detection. 4-stage pipeline, no bypass mode, community threat feed.
The thing that surprised us most during research: nobody is doing this. There are great pattern detectors (Rebuff, LLM Guard, etc.) but they all work post-hoc — the content has already entered the agent's context by the time you detect injection. FireClaw intercepts it before that happens.
The Pi appliance was honestly just for fun at first, but it turns out having a physical box with a screen showing "3 threats blocked today" is surprisingly reassuring. The OLED does an animated fire claw when it catches something.
Happy to answer any questions about the architecture, the canary token system, or the threat feed privacy model.
Great idea. The cruise line space has the same dynamic — prices fluctuate constantly after you book, and if you catch a lower fare the cruise lines will often give you onboard credit or other perks. But they don't proactively tell you about it, so you have to keep checking yourself. An agent that continuously monitors for these kinds of opportunities is exactly the right approach.
I watched a news piece about this and alot of people are calling for more testing before sending a crew up. Every mission has risks but there seems to be real concern about the vehicle's re-entry into Earth's atmosphere. Blessings and good luck to the whole team.
I feel bad for all the employees that were affected. Atlassian years ago started as an innovative company, look at their original mission statement. "Open company, no bullshit" and Don't F&%# the customer. They truly had great products that complemented each other. The problem now, is alot of the tools are not need, or can be made with some vibe coding.
This project looks super cool. I love having the idea of having openclaw on a low powered device. I am working on something and should have it out next week. It was designed to run on a raspberry pi and would be a great companion to your project. I will post back when it is live and would love for you to take a look.
Great site, I have an M2 and M3pro and was thinking about getting and Ultra M4 and wanted to know if it was going to be worth it. Now I can see exactly what models I can run locally.
We just shipped /api/scan in v1.1.0 which could plug into your policy evaluation — scan content before it enters the decision pipeline. Also now on Docker and npm (npx fireclaw) for easier integration.
Happy to brainstorm integration. Feel free to open an issue on our repo or reach out on GitHub.