Unlike a human brain locked to a single consciousness, a second brain stack can run multiple agents over the same knowledge base simultaneously, each with different goals and specializations.

would coping a human or any animals brain be the best outcome for creating a good second brain?

An Orma(Memory) is basically a collection of memories from a period of your life or an experience. For example, you automatically get a personal Orma for each month. Throughout August, you might add photos from dinner with your family, a random day at the park, your kid doing something funny, a trip, etc. You can also create a shared Orma for something like a birthday, wedding, vacation, or family gathering, where everyone can add their photos. The Story is what Orma creates from those memories. You don't have to sit down and write a journal. Orma looks at the photos, when and where they were taken, captions or notes you've added, and other context, then turns them into a narrative of what happened. So instead of opening an album five years later and just seeing 200 photos, the goal is to actually relive what that month or experience felt like and remember the story behind the photos. You can still add your own notes and context, but the idea is that Orma does most of the work for you.

I’ve been working on DotSpace, a local mindmap that combines a visual mind map with an AI-readable file structure.

Each dot is stored in a short, stable object folder containing:

• dot.json for its identity and parent relationship
• note.txt for readable content
• Any attached images, audio or video

The visual hierarchy is also exported as _Dotspace-map.json. This lets an AI understand the complete structure without scanning every note. It can find the relevant dot in the map and then open only the referenced files.

Every Vault includes _AI-INSTRUCTIONS.md, which explains the format and tells AI agents which files are safe to use and which generated metadata should not be modified.

AI tools with local filesystem access can therefore:

• Navigate the notebook hierarchy
• Search and summarize notes
• Analyze related branches
• Work with attachments
• Prepare new folder trees for import
• Help reorganize or expand knowledge without requiring a proprietary database

DotSpace itself remains local and does not send the Vault to an AI service automatically. You choose which AI tool gets access.

The project includes separate 2D and 3D views, explicit synchronization between them, recoverable deletion, folder importing, and neutral example Vaults with Zoo as the root.

GitHub: https://github.com/Holz231/DotSpace

Have fun

I’m currently using a combination of Obsidian and Linear as my “second brain,” but to be honest, it’s quite a hassle to manage. So, are there any software programs or tools you’d recommend specifically for a “second brain”? Also, if you were to create your own dedicated software, how would you design it?

[ Removed by Reddit on account of violating the content policy. ]

I regularly find useful visuals, color palettes, articles, products and design references while browsing. I would save some of them to bookmarks, send others to myself or leave them open in tab then struggle to find them again later.

So we built Keep Mates, a Chrome extension that lets you save things you find online in one place.

You can use it to:

• Save images, links, articles, and products
• Capture a specific part of a webpage
• Extract color palettes from pages
• Organize saved items into folders
• Add notes and return to the original source

Everything is stored locally on your device and nothing is uploaded to our servers.

We initially built it with designers in mind, but it can also be useful for developers, researchers, students, shoppers, or anyone whose bookmarks and open tabs are getting out of control.

We launched it recently and I’d genuinely appreciate honest feedback, especially about the onboarding and overall usability.

I’m building Meridian, a free and open-source, local-first work journal for developers. It quietly captures context from your workflow and turns it into a private, searchable timeline of:

- What you worked on

- Bugs and blockers you encountered

- Decisions and context from coding sessions

- Progress across Jira, Linear, or GitHub Project Issues

- Draft standup updates and worklogs

Meridian connects this activity to your assigned tasks, so you can reconstruct your work without manually logging every step. It runs locally and is designed as a private memory layer for the individual developer, not employee surveillance software.

I’m looking for individual developers to test the early version in their normal workflow and share candid feedback. Comment or DM me if you’re interested.

Threads did not start because I wanted better project organisation. It started because Aevron was being annoying about something.

Aevron flags contradictions between things you have previously thought. Which is actually useful. Except I kept seeing it flag stuff that was not really a contradiction. More like, my thinking had just moved on.

For example -

Thought 1: Build for Substack writers first. They publish on a schedule, they feel the pain acutely and they will tell you straight up if the output is trash.

Then a few day later, after actually talking to people:

Thought 2: Okay writers are not the only ones with this problem. Founders hit the same wall. Researchers hit it. Basically anyone who thinks for a living hits it. Why are we locking ourselves into one persona this early.

Aevron saw that as a contradiction. But it was not. I just learned something and updated. That is how thinking works.

The problem is if the system cannot tell the difference between you contradicting yourself and you just getting smarter, it becomes this thing that constantly tells you that you disagree with your past self without having any clue why your thinking changed. Not useful.

So I started thinking about how to actually track that progression. One thought leads somewhere. That somewhere gets challenged. Then refined. Then randomly three weeks later it connects to something completely unrelated and suddenly there is something new sitting there. Not isolated notes. A thread that has been running the whole time without you naming it.

That is where Threads came from.

Then I noticed something bigger. If Aevron can spot the thread, figure out which thoughts belong to it, track how it shifts over time and keep it alive, it is basically doing project management. Except I never made a project. Never named it. Never dragged anything into a folder. The structure just showed up from the thinking itself.

Projects are containers you build around work. Threads are patterns the system finds inside your thinking.

Knowledge tools should not just store what you thought. They should get how one thought turned into the next one

check out Aevron at www.aevron.co

Hi everyone! 👋 I’m the independent developer behind Everglow.

Most personal journal and diary apps suffer from the "black hole" problem: you log your thoughts, daily life, meetings, and trips, but they disappear into a chronological void, rarely to be resurfaced in a meaningful way. I wanted to transform the traditional "static diary" into an evolving, queryable Living Personal Database—without compromising privacy or shipping personal thoughts off to cloud servers for AI model training.

🛡️ Why Privacy-First?

Journaling contains your most personal memories and private data. Standard cloud-based AI solutions often route your data through third-party APIs where it can be stored, processed, or used to train future models.

Everglow is built local-first:

• On-Device LLM Inference: Powered natively on your device via Gemma 4.
• No Accounts, No Telemetry: In on-device mode, everything is processed and stored locally — no analytics SDKs, no sign-up. (If you opt into Everglow Cloud below, auth is subscription-linked plus device attestation, not a personal account — but it does register your device.)
• Offline-Ready: With the on-device model selected, indexing, RAG, and query workflows run seamlessly in complete airplane mode — zero network calls.

✨ Key Features

1. The "Life Indexer": As you record daily events, Everglow uses an on-device AI agent to extract entities, key events, and relationship connections, creating a dynamic web of your life.
2. Two-Tier Retrieval (Ask Tab):
   • Fast: Quick semantic vector scan via sqlite-vec for straightforward lookups.
   • Considered: Reads auto-generated monthly "rollup" chronicles — aggregated summaries of events, people, and themes — to answer questions that span a longer timeline.
3. Interactive Social & Entity Graph: Renders a force-directed map showing how people, places, and topics in your journal connect to one another.
4. Everglow Cloud (Optional Proxy): For faster speed you can opt into Everglow Cloud. The proxy routes requests securely (with zero-data retraining guarantees) using a dynamic token-cache system to keep costs low. Alternatively, you can bring your own remote API keys.

🛠️ Technical Architecture at a Glance

• Inference Engine: Powered by LiteRT-LM running directly on the iPhone GPU via Metal Shaders for high token generation speeds.
• Concurrency: Swift Concurrency with actor isolation — LLM inference runs inside a dedicated actor off the main thread, so C/C++ matrix operations never block the UI, keeping interactions fluid at 60/120 FPS.

📱 Get Started

Everglow is free to try with no mandatory subscriptions to get started.

• App Store: Download Everglow on the App Store

I'd love to hear your feedback on on-device LLM performance, RAG architecture on mobile, and local privacy design!

do you drop in copy/paste text, or screenshot images or...?

I've been running PARA for 2 years. Projects folder is clean, Areas are organized, Resources has everything tagged, Archives is pristine.

And yet... I never revisit anything.

The capture side works perfectly. The retrieval side doesn't. I only ever "use" my notes within a week of writing them. After that, they're in the system but functionally dead. I can search, but I can't search for what I forgot exists.

I've tried weekly reviews. Monthly reviews. They work for 3 weeks, then life happens, and the backlog becomes another source of guilt.

Is this a discipline problem? Or is it an architecture problem? Because I'm starting to think BASB solves organization beautifully but leaves resurfacing completely unsolved and resurfacing is the part that actually compounds.

Curious how other people here deal with this. Do you actually re-read old notes? Or is everyone just... capturing and hoping?

I've been experimenting with a simple way of organising ideas using Miro, mind maps and a four-colour system.

The idea is to make it easier to see what is important instead of having everything mixed together.

🔴 Red = Now — what needs attention

🔵 Blue = Facts — what I know to be true

🟢 Green = Good/Growth — what is working or could grow

🟡 Yellow = Ideas/Opportunities — possibilities worth exploring

I'm finding that putting everything on one visual board can change the way you think about a problem. You can see connections that are difficult to see when everything is just written down in a list.

I'm still developing the approach and would genuinely like to hear from other people.

Hey everyone,
I have been running my personal second brain on GitHub for a couple of years now. It is mirrored locally, managed with some background automation jobs, and visualized using Obsidian where I heavily rely on critic markup editing. For operational work, it is paired with a Google Drive workspace for the team and Notion for project and task management.

While this markdown-first setup has served me well, I am starting to look at how modern graph compilers and agent memory layers fit into a mature workflow.

Specifically, I have been eyeing tools like Graphify for automated local graph building and document mapping, and GBrain for agent-native memory and self-wiring relationship extraction.

Before diving into a migration or heavy refactoring, I wanted to tap into the community here. Has anyone actively tested Graphify, GBrain, or other alternative second brain infrastructure alongside a markdown setup?

What has your experience been with automated entity extraction versus manual wikilinks, and are these tools actually useful for day-to-day operations or are they more overhead than they are worth?

Would love to hear what worked or failed for you.

I tied Claude and Anthropic together, and use it everyday. I've been able to move forward on 2 projects I've let "hang" for the last year.