Bounded Me
A private metric for extractable structure, geometry, and loop stability.
I keep getting tempted by clean definitions.
Information as entropy. Intelligence as compression. Learning as “matching a distribution.”
Those definitions are elegant, but they don’t cash out in my actual life.
In my life, “information” is not what exists in the world. It’s what I can pull into myself under constraints: time, attention, energy, compute, mood, calendar, context. I don’t experience the universe. I experience the slice I can process.
So the question isn’t “how much information is there?” It’s “what structure is extractable for me, right now, with my limits?”
That’s the only definition that stays stable across days where I’m sharp, days where I’m foggy, and days where I’m pretending.
1) Bounded me
When I say “I learned something,” what I mean is:
- I found a pattern that survived contact with my schedule.
- It became cheap to recall.
- It started generating predictions or choices with low friction.
So “information” isn’t a property of the thing I read. It’s a property of the interaction between that thing and my constraints.
Some days, the same paragraph is dead text. Other days, it turns into a lever.
That makes me suspect that a lot of my confusion comes from treating my mind like an unbounded observer. I keep asking what’s “true” in the abstract, when what matters is what I can distill into internal programs.
A good piece of writing for me isn’t one that contains the most novelty. It’s one that increases the amount of structure I can reliably extract later.
That’s the bar: does it reduce future compute for me?
One more private corollary: sometimes computation itself creates something I can learn. Not “new truth,” but new extractable structure for bounded-me.
2) “Memory” as geometry (not storage)
I don’t want a second brain that is a library.
A library gives you retrieval. It doesn’t give you shape.
What I actually need is a geometry: a coordinate system where ideas have distance, direction, neighbors, borders—so that reasoning becomes movement instead of lookup.
This is the difference between:
- storing facts, and
- arranging facts so they become navigable.
When my thinking is good, it’s because I’ve built a local geometry:
- I know where the “hard” parts are,
- I know what’s adjacent,
- I can move from one concept to another without re-deriving everything from scratch.
When my thinking is bad, I’m trapped in flat text. Everything is equally far away.
So when I write, I’m not trying to “publish.” I’m trying to carve geometry into my own space.
The draft fails when it reads like notes about other people’s ideas. The draft succeeds when it changes the topology of my own.
3) The fractal edge in my own training
There’s a boundary in my life that feels like training stability.
On one side: I’m compounding. It converges. The loop is tight. Small inputs become durable progress. On the other side: everything diverges. I’m “busy,” but nothing converges.
And that boundary isn’t a clean line.
It’s sensitive. Weirdly sensitive.
If I zoom in, the difference between a good week and a trash week can be:
- one meeting at the wrong time,
- one small breach of sleep,
- one subtle context switch I didn’t respect,
- one “I’ll just do this quickly” that turns into a lost evening.
From far away, it looks like discipline. Up close, it looks like dynamical systems.
So I want to treat my own productivity like trainability: not moral, not identity—just stability conditions.
Not “why am I like this?” But “what hyperparameters keep me in the convergent basin?”
Writing is part of that. A piece is not just output; it’s a stabilizer if it makes my next actions less chaotic.
4) Information as flow through the loop
I like the idea that information isn’t only stored. It flows.
In my life, “flow” is literal:
- it flows into attention,
- it flows into notes,
- it flows into decisions,
- it flows into habits,
- it flows into products,
- it flows back as feedback.
Most of the time, I lose information not because it wasn’t there, but because it leaked at a transition:
- I read something but didn’t integrate it.
- I had an insight but didn’t attach it to a decision.
- I made a decision but didn’t make it executable.
- I executed but didn’t close the loop with feedback.
So the real unit of improvement isn’t “better memory.” It’s lower-leakage loops.
This is why “writing from inside my head” matters. Because writing is the part of the loop where I convert diffuse impressions into a structured object I can re-enter later.
If the writing is for others, I optimize for legibility. If the writing is for me, I optimize for re-entry.
The point is: future-me can jump back in and regain the geometry quickly.
5) A private metric: did the piece increase extractable structure?
I need a way to evaluate whether this piece is “good,” without referencing audience.
So here’s the metric:
After writing it, do I notice that:
- I make faster decisions in the same domain?
- I can explain the core tension to myself in fewer steps?
- I reuse the concept naturally without prompting?
- I feel a drop in “mental compilation time”?
If yes, the piece increased extractable structure. If no, it’s just text.
This is also a test for working with AI.
If the AI produces clean paragraphs that don’t change my internal geometry, that’s not “me.” That’s a ghostwriter.
The role I want is different: co-authoring as instrumentation. The AI helps me scan the space, but the piece must be a faithful compression of my loop.
Which means the draft has to contain my constraints, not just my references.
New (minimal) add: I’ve started treating “me + AI” as a point in a coupling space: how much human–AI exchange exists, how much human feedback control I retain, and how much complexity I’m importing. When exchange rises but feedback control weakens, the loop doesn’t get smarter—it gets tighter and wrong. It amplifies errors, dependencies, and delusions. My default target is high control, moderate exchange: fast scanning, slow believing.
6) The actual thesis (for me)
Information is not a number.
Information is structural signal that (1) a bounded learner can extract, (2) often crystallizes into geometry, (3) survives chaotic training dynamics, and (4) behaves like flow through a loop.
It’s what my loop can extract under constraints—then keep stable—then reuse.
My job is to build geometry, reduce leakage, and stay in the convergent basin.
This is why I keep building systems. Not because I love tools. Because my mind is bounded, and I want to increase what’s extractable without increasing fragility.
Writing is one of my best tools for that. But only when it’s written from inside my head.
Not what sounds right. What changes what I do next.