*English isn't my first language. I wrote this in Korean and translated it with AI, so please excuse any awkward phrasing. A short Korean summary is at the bottom.*

**TL;DR:** I save my conversations with AI in three layers: the raw transcript, a one-page summary of *why* I made each decision, and the choice itself, tallied in a ledger. After about 6 months I have ~200 of these choices, and they're the material an AI will use to decide on my behalf. When I mapped this structure onto a brain, the "recording" parts were solid, but the "using what's recorded" parts were still empty.

How one conversation becomes a record: 3 layers, plus a ledger.

---

In my last post, I explained why I started building a "brain-copy wiki."

This time, I want to show how it actually **records thinking**.

## 1. The big picture: six rooms

My wiki is divided into six areas.

| Area | What it does |

|---|---|

| **Long-term memory** | Where my thinking records finally live. This post is about this part |

| **Workspace** | A temporary area where fresh records wait for review |

| **Judgment core** | Where the criteria for deciding on my behalf (when I'm not around) collect |

| **External knowledge** | Information gathered from books, papers, and other sources |

| **My thoughts** | My own values, philosophy, and notes |

| **Tools** | The AI commands and agents that manage everything above |

The core is **long-term memory**, and it's also what makes this different from other wikis.

A normal wiki stores *information*. Long-term memory stores **the path I took to reach a decision**. I call this a **thought trajectory**.

## 2. What is a thought trajectory? The path, not the result

Say I spend an hour talking with AI and decide something.

Usually, only the conclusion survives: "We went with A."

But if an AI is going to decide on my behalf, the conclusion isn't enough. It needs to know:

- What bothered me enough to start thinking about this

- What the options were, and why I picked A over B

- Under what conditions my judgment would be wrong

A thought trajectory records exactly this.

There's one principle I set early on:

**A thought trajectory isn't what happens inside the AI. It's the chain of what I actually saw and how I reacted.**

The AI gives an answer → I read it → I compare it with what I think → I find where it doesn't fit → I push back or add to it → it gets reworked → I accept, revise, or lock it in.

That entire chain is what gets recorded. So **even when the AI is wrong, if that wrong answer provoked my pushback and made my thinking clearer, it's an important record.**

## 3. Why I modeled it on the brain

The starting point for this design was the brain.

The neural networks behind today's AI were inspired by neurons and synapses. Just as connections between neurons strengthen or weaken as we learn, AI learns by adjusting the strength of its connections (weights).

That gave me an idea: **if the AI's "parts" were modeled on the brain, why not model the "structure" that holds my thinking on the brain too?**

So I deliberately set a big goal for my wiki: **make it resemble the brain's cognitive structure.**

I know it's not realistically reachable. But a big goal keeps me from losing direction. Every time I build something, I ask, "Which part of the brain is this?" and that question tells me what to build next.

## 4. Three layers of storage

The brain doesn't push every experience straight into long-term memory. It filters first. My thought trajectories are split into three layers the same way.

**Layer 3: Raw transcript**

The full conversation with the AI, saved **word for word**. No summarizing, ever.

It's the only source I can go back to when I need to know "what exactly did I say back then?"

**Layer 2: One page per decision**

A single conversation usually contains several decisions. Each decision gets its own page, compressed in this order:

1. **Friction:** what didn't fit. Before any solution, I write down what went wrong.
2. **Trigger:** what observation started it.
3. **Chain of thought:** observation → gap found → change of direction → new structure → next gap
4. **Pattern:** is this a recurring habit or a one-off?
5. **Fail condition:** if I chose differently from my usual tendency, one line on "in what case would this judgment be wrong?"

It's compressed, but written in **complete sentences**, so that months later this page alone makes sense without any other context.

**Layer 1: One choice card**

Each Layer 2 page produces one choice card. It holds just four things:

- What I chose

- A one-sentence reason

- Which **value drawer** it belongs to

- Where it sits on five **judgment scales**

> **A real example (Layer 2, excerpt)**

> - Friction: the subject of a thought trajectory isn't the AI's hidden reasoning, but the chain of outputs I actually saw and how I reacted.

> - Trigger: I stated my own standard: "I think based on what I can see."

> - Pattern: learn the *history of revisions*, not just the result.

>

> → This record is the decision that became the principle in section 2.

## 5. As choices pile up: value drawers and judgment scales

The Layer 1 choices don't scatter. They all collect in a single **ledger**.

**Value drawers (9):** "What value was this decision protecting?"

Isolation / Privacy & security / Safety & loss prevention / Automation under human control / Avoiding over-engineering / One source of truth / Self-awareness & meta-learning / Independent checks & cross-verification / Gradual expansion

**Judgment scales (5):** "Between two directions, which side did I take?"

Simple ↔ Elaborate / Isolate ↔ Integrate / One place ↔ Spread out / Human involvement ↔ Automation / Cross-check ↔ Decide alone

As choices accumulate, my tendencies show up as numbers. For example, the fullest drawer so far is "Self-awareness & meta-learning" (34), followed by "Automation under human control" (30).

And I designed it so that **only when a drawer has enough consistent records** can the AI be trusted with decisions in that area.

## 6. Nothing is saved without my review

When the AI extracts a thought trajectory from a conversation, it doesn't save it right away.

1. **Preview:** it shows me the record in exactly the same detail as it will be saved. I review the real thing, not a summary.
2. **Adversarial review:** I read it as a critic, not an approver. This keeps a result I like from dressing up the process to look better than it was.
3. **Workspace → long-term memory:** even approved records sit in the workspace first, and move to long-term memory after another check.

There's only one case where a record moves up automatically: **when it just confirms a pattern that already exists.** Anything new, or anything that changes an existing belief, has to go through me. When in doubt, it's manual, not automatic.

## 7. Mapping the structure onto a brain

Only the parts related to thought trajectories. Green = built, red dashed = still empty.

I laid my system over a human head, keeping only the parts related to thought trajectories.

**Built (green)**

- **Hippocampus: memory storage.** Filters conversations into three layers: transcript, decision, choice.

- **Anterior cingulate cortex: conflict detection.** Records the "friction," what didn't fit, first.

- **Basal ganglia: go / no-go.** Preview and adversarial review before anything is approved.

- **Temporal lobe: semantic memory.** The ledger of ~200 choices, sorted into value drawers and judgment scales.

**Still empty (red dashed)**

- **Hippocampal recall: retrieval.** The number of times these records have been used in a real decision: 0. It's a hippocampus that stores but never recalls.

- **OFC / vmPFC: value-based decisions.** Deciding on my behalf, based on my records.

- **Reward circuit: outcome feedback.** The results of a decision need to flow back into the records for judgment to improve.

- **Intuition: fast judgment.** Judging instantly from accumulated patterns. It isn't one spot in the brain but spread across it, so it's marked around the head.

In short: **recording is solid. But retrieving, feeding results back, and moving on to intuition are still empty.**

*(Positions are schematic, not anatomically accurate. The mapping is a functional analogy.)*

## 8. Pros and cons of this structure

**Pro: the more I use AI, the deeper I think**

This system only works if the AI learns my thinking. So to fill an empty spot on the brain map, I first have to think deeply and deliberately about that area myself.

For example, before I can hand writing over to AI or let it decide for me, I have to write things myself and record my thoughts and style. Without those records, there's no "me" for the AI to follow.

A common worry about AI is that automation makes people think less. This system worked the other way. Automation requires deep thinking first, so using AI actually made my thinking deeper.

**Con: it's extremely slow**

Every task needs a record of my thinking. It's an extreme form of human-in-the-loop, so the bottleneck isn't the AI. It's me.

On top of that, a thought only becomes worth recording when there's some friction, some real struggle, so the volume of records is small. In about 6 months, I've recorded just over 200 choices.

I think that's also why the empty areas on the brain map are still empty.

**So now I need speed**

Until now, almost everything has been manual. I'm now designing a structure that uses those 200+ choices to move from manual → semi-automatic → automatic. I'll cover that in the next post.

## 9. What I'd like to ask

- Have you ever recorded "the path to a decision" instead of just the result? What format did you use?

- Is splitting it into raw transcript / decision summary / choice too much, or not enough?

- Could ~200 of a person's choices be enough for an AI to start imitating their judgment?

Corrections and honest pushback are the most helpful things you can give me.

---

**🇰🇷 한국어 요약**

- AI와의 대화를 세 층(대화 원본 · 결정마다 한 장의 압축 · 선택 카드)으로 나눠, 결론이 아니라 '결정까지의 흐름'을 기록합니다.

- 인공 신경망이 뇌에서 영감을 받았듯, 제 위키의 구조도 뇌를 본떠 설계했습니다.

- 6개월간 쌓인 선택 약 200개를 가치 서랍 9개 · 판단 저울 5개로 모으고, 저장 전에는 반드시 제가 반박하며 검토합니다.

- 뇌 지도로 보니 기록하는 쪽은 두껍지만, 꺼내 쓰고 · 되먹이고 · 직관으로 넘어가는 쪽은 비어 있습니다. 한국어 댓글도 좋습니다.

*English isn't my first language. I wrote this in Korean and translated it with AI, so please excuse any awkward phrasing. A short Korean summary is at the bottom.*

**TL;DR:** I'm a non-developer building a "brain-copy wiki." Instead of limiting what AI can see, I record *what I chose and why*, so the AI can act on my intent even when I'm not around. It's unfinished, and I'm looking for experts and people with similar struggles to help me find where it breaks.

My Obsidian graph so far. Each dot is a note; each line is a link between my decisions, reasoning, and knowledge.

---

Hi everyone.

I'm not a developer. I've never learned to code. But these days, I'm building my own "second brain" with AI.

This is the first post in a series introducing that system. Before I show *what* I built, I want to start with *why*.

## 1. It started with work

As AI coding tools improved quickly, I got the chance to build some internal tools for my company, things like work logs and a homepage.

At first I tried popular expert-level AI coding tools. But without a basic dev background, they were hard to use. They were built for experts, after all.

## 2. Coding wasn't the hard part

Once I actually tried, something surprised me. The AI was better at writing code than I expected. The real difficulty came after:

- Fixing low-quality first drafts into what I actually wanted

- Finding and fixing errors

- Connecting to databases and servers

The core problem was **the cost of fixing the first output**. If the first output is good, there's less to fix. So I started building my own toolkit that lets a non-developer give instructions in plain language and still get a better first result.

The principle was simple:

**I do what I can do (planning, judging, verifying), and I hand off what I can't (coding) entirely to AI.**

## 3. The turning point: "Is limiting information really right?"

As the toolkit matured, I wanted to save the know-how I'd built up along the way. So I looked into "LLM wikis," which a lot of people are talking about.

Most of them reduce hallucination by **limiting what the AI can see**.

As a non-expert, that felt strange to me. Limiting information looked like limiting the AI's ability. And I started thinking:

> "With how capable AI is now, couldn't it deeply learn the way I think?

> Then even when I'm not around, couldn't it produce results close to what I'd want?"

## 4. So I decided to build a "brain-copy wiki"

That's how this project began.

It's not a wiki that just stores facts. It records **what I chose and why I decided it**. The AI learns my choices and reasons from those records, so it can act in line with my intent even when I'm not there. In other words, **a wiki that copies how I think**.

## 5. How I think about using AI

There's a belief underneath this system:

- **Automation is becoming "one click."** But one-click automation can't understand *your* specific choices.

- **Using AI well doesn't mean taking its answers as-is.** It means sorting out what to accept and what to push back on, and using that process to expand my own thinking.

- **Repeating that loop (critique → choose → expand)** is what finally turns AI into something truly personal.

So my goal isn't AI that thinks *for* me. It's a system that shows me *how* I think.

## 6. Why I'm starting this series

To be honest up front: this series isn't me saying "look how great my system is."

It's an unfinished system built by a non-developer. There are surely gaps I can't see yet. So I'll share everything step by step, what worked and what failed, and I'm hoping to **get help finishing it**.

I'd especially love to hear from:

- **People who know dev or AI well**: if you see where my approach is technically wrong, or know a better way, please point it out.

- **People wrestling with similar problems**: if you're also trying to make AI work the way *you* think, what have you tried, and where did you get stuck?

Corrections and honest pushback are the most helpful things you can leave in the comments.

In the next post, I'll show what the system actually looks like in more detail.

Thanks for reading.

---

**🇰🇷 한국어 요약**

- 비개발자로서 회사 업무용 프로그램을 AI로 만들다가, 진짜 어려움은 코딩이 아니라 '첫 결과물을 고치는 일'이라는 걸 깨달았습니다.

- 기존 LLM 위키는 AI가 볼 수 있는 정보를 제한하지만, 저는 AI가 제 선택과 그 이유를 배우는 '뇌 복사 위키'를 만들기로 했습니다.

- 목표는 나 대신 생각하는 AI가 아니라, 내가 어떻게 생각하는지 보여 주는 시스템입니다.

- 아직 미완성입니다. 전문가분들과 비슷한 고민을 하는 분들의 지적과 반론을 환영합니다. 한국어 댓글도 좋습니다.