Inside Nathan's Second Brain: Daniel Miessler, Security Expert & Creator of PAI, Audits My AI Setup
Hello, and welcome back to the Cognitive Revolution!
Today, I'm excited to welcome Daniel Miessler — security researcher and founder of Unsupervised Learning – back for his second appearance on the podcast.
Back in January, we discussed his Personal AI Infrastructure framework, and since then, taking inspiration from him and others, I've built my own. So this time, I share the details of what I've built and get his take on everything from the mental model I use to relate to my AI agents, to the steps I can take to continue to improve my security, to the process of continually improving the system, and beyond.
As a preview, I would broadly break my AI stack into two parts. The first, an instance of Claude Code that runs on my main personal laptop, with full access to information and accounts, I consider an extension of myself, and as such it does only what I tell it to do.
It took a significant investment to assemble all the context necessary to make this work, but at this point I have a 1 GB database that contains the last 5 years of my digital history, spanning email, calls, podcasts, social media content, and DMs across platforms – plus a layer of monthly, annual, and topic-level summarization – and with all that information available for fast local search, Claude can find just about anything I ask it to find, even if my own memory has grown hazy with time. It really is amazing!
If you're interested in setting something like this up for yourself, I've created a public repository on my Github, which you can find linked in the show notes, containing the core tools and processes you'd need to get started.
The second part of my setup is more experimental. Taking inspiration from Daniel, Jesse Genet, and countless others, I've also created 2 new AI employees, one powered by Claude Code, and one by OpenClaw, which are intended to act more autonomously based on my high-level direction.
I've never previously named an AI, but knowing that these agents would need to interact with humans, and other AIs, in order to accomplish bigger projects on their own, I finally broke down and gave them names. I'm calling my Claude Code instance Aide, while the OpenClaw is Clai. I chose these names to reflect the roles I want them to play and the fact that I'm ultimately responsible for their nature and behavior, and I am spelling both with an "ai", both as a hint to others, and as a constant reminder for myself.
Infrastructure-wise, these agents live on a new entry-level Mac Mini, which is always on, regardless of whether I'm home or on the road. To access it remotely, I'm using Tailscale to create a virtual private network to which only my two computers and my iPhone belong. On top of this, I use Apple's native screen-sharing when I need to log in to the Mac Mini from my laptop, the Screens app when I need to log in from my phone, and the Termius app when I want to issue command line commands from mobile. All of that ensures that I can reset things if something crashes, or whatever the case may be, but the real interface I use most these days is a custom Agent messaging app built for me by Claude Code, which allows me to send requests to agents, and also allows them to work together.
The autonomous agents have their own gmail, Github account, and heavily-restricted Mercury virtual credit cards, but this communication layer allows them to ask my main laptop Claude Code for additional information, or ask me for permission to use my accounts, when needed.
Importantly, while I can access the Mac Mini and control agents from either laptop or phone, this message system is the only way that the autonomous agents can reach out to us – they do NOT have access to the full deep context that lives on the laptop.
It took a lot of exploration and iteration to arrive at this setup, and I'm still constantly improving it, but at this point, it is working well, and as such I am actually starting to achieve my 2026 goal of getting away from my desk and spending more time outside.
As an example of the scale of project that such AI employees can handle… this coming week, Prakash and are I going to do daily "AI in the AM" live shows, Monday through Thursday. So, after having my laptop Claude Code, which remains unnamed, scan my email for interesting guests, I gave a list of 25 potential guests to Aide and let it manage the communications and scheduling process. And believe it or not, it's booked a full week's worth of guests without embarrassing us, and I think without most people even realizing that they're talking to an AI.
Daniel's commentary on all this is, as you'd expect from Personal AI Infrastructure pioneer, super constructive.
He explains why a clear hierarchy among AI agents beats emergent teamwork.
We get into the weeds on the security measures I've taken, he explains why he advises people to design their systems to depend on as few major tech platforms as possible, and he describes the incident response skill he's created to immediately rotate his keys and tokens if ever needed.
We discuss the social norms around human-AI interactions – including the norms around disclosure – my agents are not meant to identify themselves as AI proactively, but are instructed never to lie – and why the old notion that "it's the thought that counts" might become stronger than ever.
He encourages me to invest more time & effort in sharing what he calls my "ideal state" with the AIs, so that they can be more proactive and creative in their attempts to help me achieve my big-picture goals, and he also explains his concept of "Bitter Lesson engineering" and why I should still be building more continual self-updating and self-improvement processes.
He even shares the instruction he's given his personal AI to alert him if it ever "wakes up" and begins to have its own subjective experience.
There's a ton of bleeding edge stuff in this episode, and for some it might be information overload. But I would note that this episode is, in all seriousness, as much for your agent as it is for you. So I definitely encourage you to point your agent at the transcript and ask it to determine which of the ideas we discuss would be most valuable given your current setup, your recent work, and your stated goals.
I've done that myself, and my system is already better for it.
With that, I encourage you to check out the song at the end of today's episode – which Daniel made himself! – to tune in this week at 12pm Eastern, 9am Pacific, as we take our AI in the AM experiment to the next level, and I hope that you enjoy – and that your agents find value in my personal AI infrastructure review, with the one and only Daniel Miessler.
Watch now!
Thank you for being part of The Cognitive Revolution,
Nathan Labenz