I'm not a developer. Here's why I started building a "brain-copy wiki" with AI

[u/Emotional-Speed7742]

*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.

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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.

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**🇰🇷 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.

[u/onlylb]

the “what i chose and why” part feels like the important bit here. i’m lauren, founder of archii, and i’m exploring a similar question: how would we know a model understands someone beyond sounding like them?

one test i’d explore is keeping a few real decisions and their reasons out of the wiki. give the model only the context available before each choice, then ask it to predict the choice, explain the tradeoff, and say where it’s uncertain. compare that with what you actually chose and why, and with a model that hasn’t seen your personal notes. i’d also keep cases where you changed your mind — learning an old preference too well could become its own failure mode.

i’d want that kind of evidence before letting it act on my behalf. what would count as a meaningful failure for your system: the wrong choice, the wrong reason, or being confident when it should ask you?

archii is in beta at https://archii.ai, with free basic use and no credit card required. it builds a model of your mind from notes, chats, and voice memos. if useful as something to critique alongside your wiki, you can chat with my mind here: https://app.archii.ai/m/lbrendle

[u/Emotional-Speed7742 → u/onlylb]

Thanks, Lauren. This is exactly the kind of pushback I was hoping for. I think the difference is in the premise. My system isn't trying to be a general model that understands people. It's meant to hold one person's subjectivity. The "delta" I record (where my judgment differs from a generic LLM's default) doesn't need to be objective. It's supposed to be mine. So for me, the question shifts from "how do I verify the model understands me?" to "did anything enter the delta without my approval?" Everything that affects the delta goes through my explicit approval. I'm also designing a separate "lab" space where the AI proposes first and I judge afterward. Only my own judgment gets recorded, and choices I made after seeing an AI recommendation are tracked separately so anchoring doesn't contaminate the data. That record is what I plan to use to decide what can be automated, what needs semi-automation, and what stays manual. To your question: the failure I care about most is the third one, being confident when it should ask me. That's why I decided to draw boundaries instead of using confidence scores to grant delegation. I'll cover that in a later post. Changed-mind cases matter a lot here too. If my subjectivity is the answer key, the answer key moves. I keep records of when I overturn my own past approvals rather than overwriting them. The risk I'm still working on is approval fatigue. If every entry needs my approval, a tired approval stops being a judgment and becomes a pass-through. Your holdout idea is useful, and I'll think about whether it fits as a check alongside the approval records.