Recent submissions

Comment · 2 days ago
its great that you're interested in this topic, good luck)
Comment · 2 days ago
Thank you. What do you mean by 'citations'? Each item has a 'Stroq show' feature that displays the basis for it
Comment · 2 days ago
Everything is performed programmatically, without using a model
Comment · 3 days ago
Thanks, the owl's name is Hoo) Stroq reads sessions logs and command text directly from the computer. There are no LLM models involved
Comment · 3 days ago
Yes, the rules dont catch everything but since this is an open-source project, anyone can add new rules, and I'll be looking for new approaches. as for the llm, it reads the same text as the agent, so it can be fooled as well)
Comment · 3 days ago
oh no, I hadn't made that connection until now lol
Comment · 3 days ago
Yes, that’s correct. the rules already flag this kind of text
Comment · 3 days ago
yes
Comment · 3 days ago
to be honest, in the first scenario, the hook will only detect 'nom test' command itself. in the second scenario 'stroq inspect' will identify the hook before the folder is opened and issue a warning. thanks for these examples :) I'll use them to improve product
Comment · 3 days ago
You’re right the command from my comment only generates a report and doesn’t actually prevent anything. stroq init sets up hooks that prompt for confirmation before a dangerous command is executed—but that’s a filter, not a sandbox
Comment · 3 days ago
Thanks) good idea
Comment · 3 days ago
I've built project that helps to protect your computer from dangerous commands that your ai agent might execute. Have you noticed how often dont even check what our agents are actually running? Thats why I created Stroq. Try it locally npx @stroq/cli

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