Don't trust AI agents

I move the security boundary one or two layers up: the Unix user (on main machine I run them as a `agent` user, so they can't read or write my files), or even better, just give it a separate machine. (VPSes are now popular for this purpose, as are Mac Minis. My choice is $50 Thinkpad :)

That said I am a fan of Nanoclaw, and especially the philosophy of "it should be small enough to understand, modify and extend itself." I think that's a very good idea, for many reasons.

The idea of giving different agents access to different subsets of information is interesting. That's the Principle of Least Privilege. That seems like a decent idea. Each individual agent can get prompt injected, but the blast radius is limited to what that specific agent has access to.

Still, I find it amusing that people are running this with strict rulesets, in Docker, on a VM, and then they hook it up to their GMail account (and often with random discount LLMs to boot!). It's like, we need to be clear about what the actual threat model is there. It comes down to trust and privacy.

You can start by thinking, "if the LLM were perfectly reliable (not susceptible to random error or prompt injection) and perfectly private (running on my own hardware)", what would you be comfortable letting it do. And then you remove these hypothetical perfect qualities one by one to arrive at what we have now: slightly dodgy, moderately prompt-injectable cloud services. Each one changing the picture in a slightly different way.

I don't really see a solution to the Security/Privacy Convenience tension, except "wait for them to get smarter" (mostly done) and "accept loss of privacy" (also mostly done, sadly!)

>Nobody has reviewed OpenClaw’s 400,000 lines.

Including the author, who brags he doesn't read his own code. Indeed, it would be physically impossible for him to do so!

https://steipete.me/posts/2025/shipping-at-inference-speed

As mentioned elsewhere in the thread, there is very clearly an obsession with quantity over quality. Not a new phenomenon by any means: people were already complaining about this in the 19th century! But it has reached a new absurd height with this latest trend.

It's not about Peter. Of course, he's a great programmer. The point is that you can follow nicely written tutorials and have your own in a very short period of time. It's not particularly difficult to build a Lisp.

I don't know if it is incompetence - if anything i doubt it, someone else pointed out that pg also used that metric and i don't think pg is incompetent. However at the same time i think it is misleading at best.

My impression is that, as someone else wrote, we do not have an actual metric for such things as productivity or quality or what have you, but some people do want to communicate that they feel (regardless of if that matches reality) using an LLM is better/faster/easier and they latch to the (wrong) assumption about more LoC == better/faster that non-programmers already believed for years (intentionally or not, they may also deluding themselves) as that is an easy path to convince them that the new toys have value that applies to the non-programmers too (note that i explicitly ignore the perspective of the "toymakers" as those have further incentives to promote their products).

Personally i also have about 2 decades of professional experience (more if counting non-professional) and i've been toying with LLMs now and then. I do find them interesting and when i use them for coding tasks, i absolutely find useful cases for them, i like to have them (where possible) write all sorts of code that i could write myself but i just don't feel like doing so and i do find them useful for stuff i'm not particularly interested in exploring but want to have anyway (usually Python stuff) and i'm sure i'll find more uses for them in the future. Depending on the case and specifics i may even say that in very particular situations i can do things faster using LLMs (though it is not a given and personally that is not much of a requirement nor something i have anywhere high in my interest when it comes to using LLMs - i'd rather have them produce better code slower, than dummy/pointless/repetitive code faster).

However one thing i never thought about was how "great" it is that they generate a lot of lines of code per whatever time interval. If anything i'd prefer it if they generated less line of code and i'd consider an LLM (or any other AI-ish system) "smarter" if they could figure out how to do that without needing hand holding from me. Because of this, i just can't see LoCs as anything but a very bad metric - which is the same as when the code is written by humans.

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