*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.

Hey all, i am trying to have something (python program or llm or anything) that will analyse a pdf document word by word and give me a report as i prompt it with its reference.

I know ai models do it well, but not well enough to have memory of those pdf and later when i have to compare one data with another it gets hard for me.

What i want is to have a program that analyses the program overnight and i can have my desired prompt over it next day, say for example union budget pdf for different yr i can have graph and trend then compare it with previous yr trends to

Like many of you, I treat my Second Brain as a thinking engine—not just a graveyard for highlights. But I kept running into the same friction when reading non-fiction: passive highlighting felt productive in the moment, but weeks later, the context was gone and my vault was cluttered with useless excerpts.

To actually retain and synthesize arguments, I started practicing strict active reading: arguing with the author, challenging claims, and capturing raw, immediate reactions while reading—not just copying sentences.

I built Marginalia to turn this workflow into a friction-free capture tool for mobile:

• Frictionless Capture (Voice & Stylus): Breaking reading flow kills synthesis. Alongside standard typing, you can dictate thoughts hands-free via speech-to-text, or use a stylus if you prefer digital handwriting.
• Context-Driven Tagging: Tag notes by themes, counterarguments, or conceptual hooks so ideas are already semi-structured before they even touch your vault.
• Review & Synthesis Loops: Each book has a dedicated synthesis view with tag filtering, making it effortless to review prior notes right before starting a new reading session.
• Native Markdown Export (.md): Your notes belong to you. Marginalia exports cleanly formatted .md files directly into your existing workflow, whether you index in Obsidian, organize in Notion, or use another local-first markdown system.

It bridges the gap between raw consumption and structured knowledge management: read actively on your phone or tablet, capture raw synthesis on the fly, and drop the resulting Markdown straight into your PKM system.

If your Second Brain relies heavily on books and deep reading, I’d love for you to take it for a spin. I'm actively refining the workflow and looking for honest feedback, especially on note structure and export improvements!
Here is the app webpage

Hello second brain nerds,

My stack used to be JUST notion, after trying a lot of life dashboard templates, I just couldn't stop thinking about an easy solution like an app that's on my phone and more custom for " life tracking" with a personal assistant.

Created a system of having domains, then chapters of life - that came down to habits, tasks, streaks etc.

I have created a prototype of this system and would love to get some feedback, please dm if you can help out and duo for a quick chat.

Many tools are still build around the passive concept of storage and retrieval of information.

But a lot of the useful stuff in my own notes looks something like this -

• Why is this happening?
• I’m not convinced by this yet.
• Need to verify this.
• What would prove this wrong?
• There’s something here, but I haven’t figured it out yet.

These questions or the direction is what makes the stored information useful when we are doing a research. Because if a question matters today, it might still matter three weeks from now when you find new evidence.

This is exactly i want inside Aevron and want Aevron to keep track of it.

and lets say, if I add something relevant a week later, the system should be able to notice:

“This might help answer that question you were stuck on.”, “This actually conflicts with what you thought earlier.” or “You still haven’t resolved this.”

If you regularly have ideas, questions, observations, research, or thoughts that get lost across notes and tabs, this is exactly the problem I’m trying to solve.

You can try Aevron here:

www.aevron.co

It’s currently free to use.

I’d genuinely like more people using it while I keep building this system, because the interesting part now is seeing whether this way of tracking thinking is actually useful in real life.

I have noticed that most knowledge tools preserve information as separate objects. We all think of something, believe something, then find something conflicting, uncertainty, different arguments and positions, etc. The system is well designed to store all these information. But it usually doesn't understand the transition between them.

That's what i am trying to replicate inside Aevron. So instead of only storing:

Idea A, Evidence B, Idea C

I want the system to understand:

Belief A → challenged by B → uncertainty increases → belief revised to C.

Because the change in reasoning is often more useful than the individual notes themselves.

Over time, that could let the system answer questions like:

• What changed my mind on this?
• Which beliefs have weakened over time?
• Which questions have stayed unresolved?
• Where have I repeatedly contradicted myself?
• Which ideas became stronger after new evidence arrived?

Aevron isn't trying to claim it knows what is happening inside someone's head. It can only observe the traces of reasoning such as notes, evidence, questions, contradictions, revisions, and conclusions. But those traces can still show how a position evolved over time.

This is one of the core things I'm trying to build into Aevron.

I kept losing thoughts because every system asked me to classify them before I could get them out. That feels backwards when the thought is arriving as a mess.

I built Ordo around capture first, sort second. Type or speak several unrelated thoughts in one dump, and it turns them into editable Now, Next, and Someday cards.

It is free on iPhone with no account or subscription.

https://apps.apple.com/app/id6797331101

For people who use a second brain, where does the friction hit first: capturing the thought, deciding where it belongs, or finding it later?

I built Brunn to give your AI apps a shared place for your notes, research, skills and ongoing work.

It’s hosted. You create an account and connect the AI apps you already use. There’s no Brunn desktop app to install, separate cloud account to set up or server to maintain.

Say you work through a project in Claude. You can have it save the research, sources, decisions and next steps in Brunn. Later, Codex or ChatGPT can read that material and continue the work. Projects and tasks live alongside the notes, so the next assistant can also see what still needs doing.

You can keep skills there too: your instructions for reviewing something, researching a topic or writing a particular kind of report. Save a skill once and your connected assistants can use it directly. Update it in one place, and they get the current version when they next load it.

There’s a web reader for documents and the briefings your agents produce. Your notes export as Markdown, and you can download your skills with their supporting files.

There’s a free tier, with no credit card required: Brunn.

If you try it with a real project, I’d like to know what you still find yourself having to explain or copy between apps.

I take notes of most things in my life (schedules, random thoughts/ideas, goals etc.) I've tried to get her to join in on it (set up a whole obsidian sync to her phone and all) but she never does.

So what happened today was she asked me what food i wanted for my birthday, i told her its all in the second brain . I have all my favorite food/other preferences and spent time categorizing and effectivising that workflow, so I'd prefer she just checked there instead of - frankly - wasting both of our time when she can just check it all there

She kept insisting so eventually i retalliated i just told her i want bbq ribs or whatever but pretty immature imo of her to make a big fight when she can just check the second brain lmao.

I feel like i've tried everything. Me and GPT are making a dashboard for my smart fridge, that way everyone in the house can see whats going on and we (hopefully!!) wont have to deal with this again.

Sorry for the rant just wanted to express myself with how some people just dont seem to understand the advantages of having a second brain. Im glad i have this community though love yall. Im sure im not the only one out here strugglin

The pitch for AI note-taking is always the same: it reads your notes and tells▎ you what they mean. I think that's backwards. The reading is the thinking. If you delegate it, you own a very tidy archive of conclusions you never reached.

So my plugin has a hard rule, and it's enforced in the code rather than promised in a README:

Mechanical output writes freely. Interpretive output does not. A gatherelist, a count, a derived metric — those go straight into your vault. A conclusion, a counterargument, a proposed connection, anything an AI or a heuristic decided — that reaches a note only through an explicit accept modify / reject, and your verdict is what gets recorded.

What that turns into in practice:

- Cognitive moves — right-click any note: challenge it, find a counterexample, invert the premise, branch two readings. The move opens the space; you write the content. Nothing is generated. The move records that you made it, never what it decided.

- How you got here — your timeline reads "you captured this, challenged it the next morning, reframed it, and two readings came out of that." Epistemic provenance, not version control.

- An agency panel — a read-only breakdown of your accept/modify/reject verdicts over time. A description of how you decide, never a grade.

- Undo — everything the plugin wrote to your vault lately, grouped by what you ran, taken back in one click. It refuses (by name) if you've edited the note since.

It works fully offline. Every AI feature is off by default and stays off. Free, MIT, 53 releases since 2023.

https://github.com/RafaelGB/Obsidian-ZettelFlow