> One read-only command that inventories every AI agent, harness, MCP server, plugin, and AI extension on a machine
If this tool is returning even a single hit from this, you're probably using these agents wrong. You really want to run these in a way so they cannot touch your system drive/general filesystem that you use to do real work on. Even SOTA models at the end of their context limit behave REALLY illogical and does mistakes frequently. Don't run them straight on your machine unless you have backups and confirmed your backups work.
I run it on a physically dedicated machine, it has a root there (through sudo nopassword) and I keep some of my stuff there too. I don't do it for security reasons, I just want it to run when my laptop is closed and I'm outside or what not.
Haven't seen any close calls so far. The thing just behaves. Nothing of the horror stories of eremerefing the whole home directory or a database. Am I just lucky?
I don't think you're just lucky, depends heavily on the model + reasoning effort. I've never had any of the GPT models do anything of the sorts when using the higher reasoning efforts, but sometimes when I play around with local models, even "big" ones (as big as you can fit with 96GB VRAM) sometimes forgets/misses to define $ID then do "rm -rf data/$ID" for example, deleting more than they intended.
>sometimes forgets/misses to define $ID then do "rm -rf data/$ID"
Right, that's kinda the same failure mode as delete from table something and hitting enter before you write the condition or writing the wrong one. If you reach the point where you opened the terminal to do it this way you already lost.
> If you reach the point where you opened the terminal to do it this way you already lost.
I'm not sure what this means, the model and agent harness is the ones "opening the terminal and running this" (via a exec_shell tool or whatever), they do mistakes like this sometimes. Sometimes the scope is bigger, sometimes less, but anything below SOTA + higher reasoning efforts seems to fall into these mistakes sometimes.
This is the correct response, but it also feels as though ≈nobody is sandboxing their agents/harnesses in practice. Or at least, a good majority isn’t.
Seems like something that could also have enterprise applications for Shadow AI within an organization. Wouldn't be surprised if some of the observability/governance companies pick this up to use in their stack.
I need to wire a pixel-display to show emoji faces of the agents and how they roll their eyes and all of that. I have the display, I have the prompt to pick a mood from emotional vocabulary, a custom harness and all of that. I just can't figure out how to make the eyes move on a statically pre-rendered emoji.
> One read-only command that inventories every AI agent, harness, MCP server, plugin, and AI extension on a machine
If this tool is returning even a single hit from this, you're probably using these agents wrong. You really want to run these in a way so they cannot touch your system drive/general filesystem that you use to do real work on. Even SOTA models at the end of their context limit behave REALLY illogical and does mistakes frequently. Don't run them straight on your machine unless you have backups and confirmed your backups work.
"Even SOTA models at the end of their context limit behave REALLY illogical "
The trick is not go to that limit, but stay under 50% or even better 25% of context length. But backups are a smart thing anyway.
I run it on a physically dedicated machine, it has a root there (through sudo nopassword) and I keep some of my stuff there too. I don't do it for security reasons, I just want it to run when my laptop is closed and I'm outside or what not.
Haven't seen any close calls so far. The thing just behaves. Nothing of the horror stories of eremerefing the whole home directory or a database. Am I just lucky?
I don't think you're just lucky, depends heavily on the model + reasoning effort. I've never had any of the GPT models do anything of the sorts when using the higher reasoning efforts, but sometimes when I play around with local models, even "big" ones (as big as you can fit with 96GB VRAM) sometimes forgets/misses to define $ID then do "rm -rf data/$ID" for example, deleting more than they intended.
>sometimes forgets/misses to define $ID then do "rm -rf data/$ID"
Right, that's kinda the same failure mode as delete from table something and hitting enter before you write the condition or writing the wrong one. If you reach the point where you opened the terminal to do it this way you already lost.
> If you reach the point where you opened the terminal to do it this way you already lost.
I'm not sure what this means, the model and agent harness is the ones "opening the terminal and running this" (via a exec_shell tool or whatever), they do mistakes like this sometimes. Sometimes the scope is bigger, sometimes less, but anything below SOTA + higher reasoning efforts seems to fall into these mistakes sometimes.
This is the correct response, but it also feels as though ≈nobody is sandboxing their agents/harnesses in practice. Or at least, a good majority isn’t.
Seems like something that could also have enterprise applications for Shadow AI within an organization. Wouldn't be surprised if some of the observability/governance companies pick this up to use in their stack.
Wes McKinney has a project for agent visibility: https://www.agentsview.io/
I need to wire a pixel-display to show emoji faces of the agents and how they roll their eyes and all of that. I have the display, I have the prompt to pick a mood from emotional vocabulary, a custom harness and all of that. I just can't figure out how to make the eyes move on a statically pre-rendered emoji.