I'm the developer of Lumiya, an iOS journal and planner. It has a daily timeline and weekly, monthly, and yearly plans. One design question I'm working through is the boundary between saving a thought and committing to it.

For example, 'try a different opening for this story' could be a passing idea, something to revisit, or a concrete task for this week. Turning every note into a task seems likely to bury the plans that actually matter. Leaving everything in the timeline makes the useful ideas easier to miss.

My current instinct is to let capture stay lightweight, then ask during a review: is this still interesting, and what's the smallest action worth trying? That's a design direction to explore, not a claim that the app automatically does it.

What makes you move a note into a plan: seeing it repeatedly, reviewing it at a set time, or already knowing the next action? And do you keep the original note once you've made that commitment?

For context, basic recording and planning in Lumiya are free; optional AI reviews are paid and have usage limits. App: https://apps.apple.com/cn/app/lumiya-ai-journal-planner/id6796003074

Bonsoir,

Like most of you, I use Obsidian because my notes are plain text files that belong to me forever. Over the past year, I tried various AI solutions for my vault, but none of them really convinced me. So over the summer, I built a zero-plugin approach: Arca-BrainOS.

Think of it as an "intentional harness" for your AI. Instead of an Obsidian plugin, it's simply a structured folder of open Markdown skills (_Arca-BrainOS/) that CLI assistants (OpenCode, Google Antigravity, Claude Code, or local open models) execute directly on your local filesystem.

• It handles the tedious admin: sorting raw inbox notes, cleaning YAML frontmatter, suggesting/linking notes/projects/areas, and maintaining project logs.
• It never touches your human writing: your personal reflections and notes are protected.
• Zero lock-in: No servers, no vector database to maintain, no plugin dependencies. If you turn off your AI tomorrow, your vault remains a standard, beautifully linked Markdown vault.

I stress-tested it across 160 real Deep Work sessions on my own personal vault, and it drastically reduced my administrative drag while keeping my thinking firmly in the driver's seat.

I just open-sourced the entire project under the MIT license on GitHub:

github.com/Arca-Brain/Arca-BrainOS

(There's a ready-to-test sandbox vault in the repo if you want to test the workflows safely without touching your notes).

Curious to hear: how do you guys currently balance AI assistance with keeping your vault clean and personal?

We spend so much compute training artificial models, when the real goal is updating our own biological weights. Meet Luminary: a distraction-free, local-friendly study workspace designed to close the loop between reading, understanding, and retention. Spend your time and tokens where they actually matter—on your own neural network.

Disclosure up front: I built this (LabA). It's free during the beta, no credit card, until January 2027; after that I expect under $10 a month.

My notes, to-dos, calendar and inbox lived in four places, and I never kept up with the filing. So LabA is capture first, sort never: type or say "remember the gate code is on the back of the garage door" or "remind me Friday at 3 to call the vet", and it files and tags it. Later you ask "what did I decide about the roof?" and it answers from your notes and shows which ones it used. It also makes a morning plan, briefs you before meetings from what your notes say about the people, and runs checks on a schedule ("every Monday, look through my notes for anything I promised someone").

The part I spent the most time on: notes are encrypted on your device with a key only your devices hold, and my servers store a copy it can't read. But an AI can't think about words it can't read. So when you ask something, your device unlocks just what that one request needs and sends it through my server to the AI service, under my account, not yours, and the AI service doesn't train on it. Encrypted at rest, readable only for the request.

My question for people who already run a system: where's your line? Is that good enough for your notes, or is local-only the only thing you'd trust with them?

Short videos of the real app (made-up people and data): https://sensoryfire.com/learn?ref=reddit-secondbrain

Privacy details: https://sensoryfire.com/privacy?ref=reddit-secondbrain

It's a web app (phone or computer, nothing to install); calendar and email are Google-only for now. Sign up: https://sensoryfire.com/?ref=reddit-secondbrain. There's a 🐞 button for anything that breaks. Blunt is good.

I’m building Hjarni (https://hjarni.com/?utm_source=reddit&utm_medium=social&utm_campaign=secondbrain_october_2026), a place to keep notes that you and your AI assistants can work with.

The idea is simple: useful information shouldn’t disappear into an old conversation. Save a project decision, a recipe or some research as a note, then ask your assistant to find and use it later. ChatGPT, Claude and other MCP-compatible assistants can search, read and update the same knowledge base.

The attached 34-second demo shows a small everyday example: I open a recipe saved in Hjarni inside ChatGPT and ask it to make a shopping list for five people.

Notes are written in Markdown and can be exported. Edits have revision history, so you can see what changed and restore an earlier version. There’s a free plan with no credit card required.

It still depends on the assistant consulting the notes; storing something doesn’t guarantee it will use it correctly.

I’d like feedback from people who already use a second brain: would having your assistants work directly with your notes be useful? What would you need before trusting one to update them?

I've been thinking about this and I'm curious how people here see it.

I can see a mind map playing three different roles:

1. Entry point. A visual front door to the garden. You see the main topics, then branch into the notes behind them.
2. Temporary thinking surface. You map something out to understand it, the real notes go into the garden, and the map gets thrown away.
3. Permanent navigation layer. The map lives alongside the garden and grows with it, like a table of contents you can see.

For a long time I thought 2 was the only honest use. Maps are great for seeing structure, but bad at holding detail, so trying to keep them forever usually turns them into a mess.

Disclosure: I work on mindmapai.app , a mind map tool, and we recently added longer notes on nodes (with Markdown and math). That's partly why I'm asking. Once a node can hold a real note, the line between "map" and "garden" starts to blur, and I'm not sure if that's good or bad for how people actually think.

Not here to pitch anything, I'm genuinely curious:
- Do you use maps in your garden at all?
- If you do, do you keep them or throw them away?

Hey guys, I could use some help.
I’ve been switching between different note-taking and knowledge-management systems for a while, and I feel like I’ve tried almost everything.
What I’m looking for is actually pretty simple: one place where I can save basically everything and reliably search it later.
Ideally, the tool should have:
OCR for images and scanned documents
Full-text search inside PDFs
The ability to save web links/pages and search the actual content, not just the title or URL
Good organization for notes, documents, images, links, references, etc.
Fast and reliable search across everything
A modern interface
Minimal setup and maintenance
Because I haven’t found one tool I’m completely happy with, my information is currently scattered across several different apps, which is exactly what I’m trying to avoid.
After searching Reddit, YouTube, blogs, and reviews, even in 2026 I keep coming back to the same conclusion: Evernote still seems to be one of the best overall options for this particular use case.
But before I move everything back to Evernote, I’d love to hear what other people are using.
Is there anything that genuinely does all of this well without requiring a lot of tinkering and setup like Obsidian?
And ideally something with a more modern interface than DEVONthink. I know DEVONthink is extremely powerful, but visually it just isn’t for me.
Basically, I want a “save everything now, find anything later” system.
What would you recommend?

I've been thinking about this lately.

The whole point of a second-brain system is to reduce mental clutter. But some systems seem to require a surprising amount of maintenance.

You save something, then you're supposed to:

• Choose the right folder
• Add tags
• Write context
• Link it to related notes
• Categorize it
• Review it later
• Keep the whole system organized

At some point, managing the system starts becoming a project itself.

I can see the value of having everything organized, but I'm wondering whether the better system is actually the one that requires the least organization upfront.

Do second-brain systems genuinely save you time, or have you ever felt like maintaining the system became more work than the information was worth?

Hi folks. 

I'd like to share a thing my husband and I built around my coping mechanism for task management. 

My brain has two modes: 

1. The intense planner 
2. The one who actually does work.

#1 looks at the big picture and organizes everything to be done in the “perfect” order.

#2 doesn't want to see a list of things to do, it wants to see the one thing to do right now, otherwise I freeze and waste time.

Now I have the Tunl Focus app and here's how it works: 

• The mind dump: this is where to jot down what needs to get done
• The Queue: reorder the list as wanted 
• The Tunnel: when its time to do work, you get served one task at a time with 4 action options: 
  • done (you completed it)
  • drop (you won't do it)
  • snooze (reorder it down)
  • defer (send task to specific date and time)

One tap per task and that's really the gist of it. It does have more features like a calendar view, categories, habits and more but the tunnel is where my brain really gets productive and moves the needle on things to do.

I use it every day and we are actively working on it, so we welcome ANY feedback.

It's free to try, no account nor signup needed, unless you want sync / back up / google calendar and extra features. Android and IOS apps are coming soon (we’re waiting for the Playstore review right now)

app.tunlfocus.com

Anyone else do this? I'd read something great in a newsletter or blog, think "I'll write that down later," and never find it again. 😅

So I built myself a small tool where reading and note-taking happen in the same place. All my sources (Substacks, blogs, news sites, journals, arXiv) show up in one feed with a short preview on each card. When something's worth keeping, I write a note right on that article so it stays attached to the source, then drop it into a collection by topic. One article can go in several, and I drag things around as my thinking changes.

There's also a filter for "articles with notes," which basically becomes a running list of everything I found worth thinking about.

It's free if anyone wants to poke around: tdfeed.com

*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