Updating My Telos: Using AI to Excavate Identity from a Year of Journals
In early January 2025, I sat down with an AI assistant and tried to answer a deceptively simple question: Who am I, and what am I trying to do?
The result was my first Telos file—a structured document attempting to capture my missions, beliefs, goals, and values. It was useful. It helped AI assistants understand my context. But almost a year later, looking at that file feels like reading someone else’s autobiography. Not wrong, exactly. Just… incomplete. Static. A snapshot that stopped developing the moment I finished writing it.
So I decided to update it. But this time, I wanted evidence. Not just my current feelings about myself, but patterns extracted from a year of actual lived experience.
What Is a Telos File?
The concept comes from Daniel Miessler, who created a structured format for capturing personal identity in a way that’s useful for both humans and AI systems. The term “telos” is Greek—it means purpose, aim, or ultimate objective. Aristotle used it to describe the final cause of something: not just what it is, but what it’s for.
Miessler’s Telos format includes sections like:
- Problems — Issues in the world you want to address
- Missions — Core purpose statements
- Goals — Measurable objectives
- Beliefs — Fundamental worldview elements
- Models — Mental frameworks for understanding reality
- Background — History and key experiences
The idea is that if you can articulate these clearly, AI assistants can serve you better. They understand not just what you’re asking for in the moment, but why you’re asking and what you’re ultimately trying to achieve.
My First Telos: The Conversational Approach
When I built my initial Telos file in January 2025, I used what felt like the obvious method: I talked to an AI about who I am. We had a long conversation. I answered questions. The AI helped me structure my responses into Miessler’s format.
The result was reasonable. Seven missions ranging from “physical adventure and exploration” to “building practical AI systems.” Twelve beliefs about people, growth, and sustainability. Goals for sailing, relationships, health, and career.
But here’s what I’ve learned after almost a year of living with that document:
The conversational approach captures who you think you are. It’s filtered through your self-image, your aspirational identity, the story you tell yourself. It’s not wrong—but it’s incomplete. And it drifts out of date almost immediately.
My January Telos has goals about transitioning to a ProdSec role. That happened—and then evolved into something entirely different. It mentions wanting to “develop a functional AI assistant.” What I actually built was a sophisticated multi-agent system with parallel execution, a machine-readable daemon API, and production-grade infrastructure. The scale of evolution wasn’t captured anywhere.
The Evidence-Based Approach: Mining a Year of Morning Pages
So for version 2, I tried something different. Instead of asking myself who I am now, I asked: What does a year of honest journal entries reveal about who I actually am?
I’ve written morning pages almost daily for years—stream-of-consciousness journaling first thing in the morning, before the internal editor wakes up. This is the unfiltered stuff. Frustrations, anxieties, half-formed ideas, relationship dynamics, work stress, moments of clarity. The real material.
The challenge: I have hundreds of entries across 11 months. Reading them all myself would take days and I’d inevitably impose my current narrative on past entries.
The solution: parallel AI agents.
The Architecture: Map-Reduce for Personal Identity
Here’s what I built:
- 11 parallel agents — One for each month of 2025
- Each agent reads all daily notes for its month, extracts themes, values, relationships, emotional patterns, and identity-revealing content
- Each agent produces a comprehensive monthly report with specific quotes and evidence
- A consolidation agent synthesizes all 11 reports into:
- An annual narrative synthesis (for human reading)
- A structured YAML file (for AI consumption)
The whole thing ran in about 10 minutes. 241 daily notes, analyzed in parallel, consolidated into identity insights.
What the Evidence Revealed
The annual synthesis surfaced patterns I hadn’t consciously recognized:
The Core Identity: “Pioneer-builder at the frontier.” Not just using AI tools, but operating where patterns don’t yet exist—discovering what’s possible and building systems to demonstrate it.
The Evolution Arc: From vulnerability management burnout (Jan-May) to AI innovation champion (Jun-Aug) to autonomous SOC architect (Sep-Nov). A year of deliberate repositioning that I was living through but hadn’t named.
The Real Values Hierarchy: Autonomy and self-determination at the top. Not surprising in retrospect—the journal entries showed my strongest negative reactions when autonomy was threatened.
The Tensions I’m Actually Living: Builder identity vs. organizational constraints. Financial provider role vs. personal exploration. Professional success alongside deferred self-care.
A Quest I’d Forgotten I Named: “Build frameworks for collaborative human-AI partnership at organizational scale.” I apparently articulated this in July. Reading it back in December, it feels right—but I’d lost track of it in the day-to-day.
What’s Different About Evidence-Based Identity Work
The conversational approach asks: What do you want to be true about yourself?
The evidence-based approach asks: What is actually true, based on how you’ve lived?
Both have value. The aspirational version matters—it pulls you forward. But the evidence-based version grounds you. It shows patterns you didn’t choose consciously. It reveals what you actually do when no one’s watching, when you’re tired, when you’re frustrated.
Some examples from my synthesis:
- I said I valued teaching. The evidence showed I valued being validated for my ideas—actual pedagogy exhausted me.
- I said I wanted work-life balance. The evidence showed I consistently chose building over rest, then crashed.
- I said health was a priority. The evidence showed health was perpetually deferred for “more important” things.
This isn’t failure. It’s data. Now I can update my Telos to reflect who I actually am, not just who I wish I were.
What Comes Next
The morning pages analysis is complete. But the Telos update isn’t finished—it’s just informed.
Next step: a conversational interview. But this time, Claude has access to the synthesis. Instead of asking “What are your values?” the questions become: “The evidence suggests autonomy is your highest value. Your January Telos had it listed fourth. Should we revise? What else might be misaligned?”
It’s a different kind of conversation when both parties have done the homework.
After that: updating the daemon API—my machine-readable identity endpoint—to reflect the whole person, not just the professional portfolio.
The Meta-Observation
There’s something recursive about using AI to excavate identity so that AI can serve you better. But that’s the point. These tools are only as useful as the context we give them. And the context we give them is only as good as our self-understanding.
The Telos file isn’t sacred. It’s a working document. A current-best-guess at who you are and what you’re trying to do. The goal isn’t to get it perfect—it’s to get it useful.
And “useful” means updating it when you change. Which is always.
This post is part of an ongoing series about building personal AI infrastructure. Previously: Building My Daemon.