u/fatcatgirl1111

Perhaps this is something widely understood, but it only dawned on me yesterday: that perhaps all of us are just children who are trying to fill some of the painful gaps that we experienced as adults.

Love is too broad of a term, so I would break it down into a few:

1. Recognition, can you see me?
2. Paying attention, can you hear me?
3. Are you proud of me?
4. Do you accept me even when I am different and don't fit your norms?

I thought that these were the four major questions or potential gaps that end up unfolding, which then shape us as adults.

A second thought I had was the flip: our parents then look to fill those gaps in their children as they age, wanting to be recognized, wanting attention from their children, wanting to make their children proud, and wanting acceptance from their children. Curious if this is just a wildly widely accepted phenomenon or actually an interesting thought exercise, and if there are any other elements I'm missing

Well, if there was ever a time for the world to wake up to the idea of a second brain / knowledge base software/ PKM, whatever you might call it, I truly believe the time is now!

I was having lunch this morning while watching Bloomberg Tech, and all over the news is talk of all the AI models being recalled, which really seeded this writing of this post.

I did some digging and was surprised to find out that there were 255 AI model releases in the first three months of 2026!! That's roughly three a day. (If you asked me to guess, I would have said something like 50.)

The "best" model changed at least four times while you were deciding which one to commit to. We / the world keeps treating "which model" as the important question, refreshing the leaderboards, reading the comparison threads, migrating workflows every time a new version drops.

Meanwhile, the layer that actually carries your work forward, your knowledge, your context (the second brain, the knowledge base software) holding everything you've read and understood, sits ignored.

We're optimizing the one variable that's becoming a commodity.

Not sure who else in this community is coming to a similar realization as me, but I am sharing my thoughts below. Curious to know your take on models, what's a commodity, and how you are treating your knowledge today.

The treadmill

You who are hopping around model shopping , have a think about what model-chasing actually costs you. This comes down to picking a single platform to lock yourself into, whether that's Claude or OpenAI (whatever you might decide is worth uploading your documents to for having a memory with), and then going a bit deeper if you're nerdy enough into learning the quirks.

You re-tune your prompts. You move your work over. And critically, you leave something behind. The conversations, the things you read and saved, the highlights, the slowly accumulated understanding of your domain that lived inside that tool. Gone, or stranded, every time you jump.

(Now I'm very aware of memory software you can use to keep all your memory in one place, but I'm not even talking about memory here. I'm talking about actual knowledge that you store in your traditional knowledge-based software or second brain, whatever you might be using at the time.)

Your knowledge base is the asset (all hail the PKMs!)

This is where it clicked for me. Here's the asymmetry that should reorganize how you think about all of this. The model is rented. You don't own it. You can't keep it. It will be deprecated, replaced, or quietly upgraded whether you like it or not.

Your context is owned. The things you've read, saved, connected, and returned to, that's yours. It doesn't expire when a new model drops. It doesn't need migrating. It gets more valuable over time, not less, because knowledge compounds and a good model is just a fresh rental you point at it.

The reframe

To the PKM non believers out there - Stop asking "which model is best." (Or don't. I mean, it's fine to know which model to use for what, but the point I'm making is that we're over-indexing on the model and not the context!) Start asking "where does my context live, and do I actually own it?" Because as models multiply and get swapped under you, a knowledge layer that isn't tied to any single provider becomes more valuable, not less. You're no longer rebuilding from scratch every release cycle. You point the new rental at the same owned foundation and keep going. The churn that exhausts everyone else becomes a non-event for you. That's the whole game. Not a better model. A foundation that outlasts every model.

Where this points

This is why knowledge base software is interesting, not because it picks models for you, but because it's built on the right side of this asymmetry. I think this is finally the awakening of the second brain, more than just the few of us hanging out in this group.

That famous tweet from Andrej Karpathy on the LLM wiki pointed to the second brain. I think now the idea of models being table stakes, coming and going, is hopefully having people think more about context than their actual knowledge.

The things you read and save become a context layer that's yours and stays yours, independent of whatever model happens to be on top this week. The model sits on top and changes constantly. Your knowledge base underneath stays put and compounds.

The second-brain landscape (pick the one you'll actually own)

You're hanging out in this group, so if you're not yet convinced that you need a second brain, I hope this post at least nods you towards it. If you're looking for one, here's my list.

I won't say what I'm using, because I really don't want this to be biased, but just bring this idea to the surface.

The point of this post isn't a single tool, it's owning your context layer. Here's a rundown of the main options, since they make different tradeoffs on ownership, linking, and AI.

If you need local-first knowledge base software

• Obsidian. Local-first Markdown files you fully own, plus a huge plugin ecosystem. Best if you want maximum control and zero lock-in, at the cost of setup effort.
• Logseq. Open-source, local-first, outliner-style with strong block-linking. Great for daily notes and networked thought.
• Anytype. Local-first, encrypted, open-source Notion alternative for people who want ownership and databases.

If you need powerful AI-first knowledge base software, or AI second brains

• Recall. a self-organizing AI knowledge base for YouTube videos, podcasts, PDFs, and your own notes. Everything summarized and organized for you. They have a model picker and MCP
• Mem. AI-native notes with automatic organization, lighter on manual linking. this is one of the original second brains, now more focused on being a thinking partner
• Tana. Supernodes plus AI for power users who want structured, queryable knowledge. if you're already taking voice notes, this one's for you. The voice-saved notes are the big win here. You can make this the center of your knowledge instead of just obsessing over the model.

If you need editors, note takers

• Notion. The most flexible all-in-one workspace (docs plus databases). Cloud-hosted, so ownership and export are weaker, but unbeatable for structured team knowledge.
• Capacities. Object-based note-taking that treats notes as typed objects rather than files. A good middle ground between structure and networked notes.

The model sits on top and changes constantly. Your knowledge base underneath stays put and compounds, whichever of these you choose. The only mistake is not building the layer at all. Some of these tools come with a model picker and an MCP. Those are the critical pieces.

If this post convinces you to choose some knowledge base software or a second brain? Please let me know. I'd love to know and stay in the loop of your journey.

Well, if there was ever a time for the world to wake up to the idea of a second brain / knowledge base software/ PKM, whatever you might call it, I truly believe the time is now!

I was having lunch this morning while watching Bloomberg Tech, and all over the news is talk of all the AI models being recalled, which really seeded this writing of this post.

I did some digging and was surprised to find out that there were 255 AI model releases in the first three months of 2026!! That's roughly three a day. (If you asked me to guess, I would have said something like 50.)

The "best" model changed at least four times while you were deciding which one to commit to. We / the world keeps treating "which model" as the important question, refreshing the leaderboards, reading the comparison threads, migrating workflows every time a new version drops.

Meanwhile, the layer that actually carries your work forward, your knowledge, your context (the second brain, the knowledge base software) holding everything you've read and understood, sits ignored.

We're optimizing the one variable that's becoming a commodity.

Not sure who else in this community is coming to a similar realization as me, but I am sharing my thoughts below. Curious to know your take on models, what's a commodity, and how you are treating your knowledge today.

The treadmill

You who are hopping around model shopping , have a think about what model-chasing actually costs you. This comes down to picking a single platform to lock yourself into, whether that's Claude or OpenAI (whatever you might decide is worth uploading your documents to for having a memory with), and then going a bit deeper if you're nerdy enough into learning the quirks.

You re-tune your prompts. You move your work over. And critically, you leave something behind. The conversations, the things you read and saved, the highlights, the slowly accumulated understanding of your domain that lived inside that tool. Gone, or stranded, every time you jump.

(Now I'm very aware of memory software you can use to keep all your memory in one place, but I'm not even talking about memory here. I'm talking about actual knowledge that you store in your traditional knowledge-based software or second brain, whatever you might be using at the time.)

Your knowledge base is the asset (all hail the PKMs!)

This is where it clicked for me. Here's the asymmetry that should reorganize how you think about all of this. The model is rented. You don't own it. You can't keep it. It will be deprecated, replaced, or quietly upgraded whether you like it or not.

Your context is owned. The things you've read, saved, connected, and returned to, that's yours. It doesn't expire when a new model drops. It doesn't need migrating. It gets more valuable over time, not less, because knowledge compounds and a good model is just a fresh rental you point at it.

The reframe

To the PKM non believers out there - Stop asking "which model is best." (Or don't. I mean, it's fine to know which model to use for what, but the point I'm making is that we're over-indexing on the model and not the context!) Start asking "where does my context live, and do I actually own it?" Because as models multiply and get swapped under you, a knowledge layer that isn't tied to any single provider becomes more valuable, not less. You're no longer rebuilding from scratch every release cycle. You point the new rental at the same owned foundation and keep going. The churn that exhausts everyone else becomes a non-event for you. That's the whole game. Not a better model. A foundation that outlasts every model.

Where this points

This is why knowledge base software is interesting, not because it picks models for you, but because it's built on the right side of this asymmetry. I think this is finally the awakening of the second brain, more than just the few of us hanging out in this group.

That famous tweet from Andrej Karpathy on the LLM wiki pointed to the second brain. I think now the idea of models being table stakes, coming and going, is hopefully having people think more about context than their actual knowledge.

The things you read and save become a context layer that's yours and stays yours, independent of whatever model happens to be on top this week. The model sits on top and changes constantly. Your knowledge base underneath stays put and compounds.

The second-brain landscape (pick the one you'll actually own)

You're hanging out in this group, so if you're not yet convinced that you need a second brain, I hope this post at least nods you towards it. If you're looking for one, here's my list.

I won't say what I'm using, because I really don't want this to be biased, but just bring this idea to the surface.

The point of this post isn't a single tool, it's owning your context layer. Here's a rundown of the main options, since they make different tradeoffs on ownership, linking, and AI.

If you need local-first knowledge base software

• Obsidian. Local-first Markdown files you fully own, plus a huge plugin ecosystem. Best if you want maximum control and zero lock-in, at the cost of setup effort.
• Logseq. Open-source, local-first, outliner-style with strong block-linking. Great for daily notes and networked thought.
• Anytype. Local-first, encrypted, open-source Notion alternative for people who want ownership and databases.

If you need powerful AI-first knowledge base software, or AI second brains

• Recall. a self-organizing AI knowledge base for YouTube videos, podcasts, PDFs, and your own notes. Everything summarized and organized for you. They have a model picker and MCP
• Mem. AI-native notes with automatic organization, lighter on manual linking. this is one of the original second brains, now more focused on being a thinking partner
• Tana. Supernodes plus AI for power users who want structured, queryable knowledge. if you're already taking voice notes, this one's for you. The voice-saved notes are the big win here. You can make this the center of your knowledge instead of just obsessing over the model.

If you need editors, note takers

• Notion. The most flexible all-in-one workspace (docs plus databases). Cloud-hosted, so ownership and export are weaker, but unbeatable for structured team knowledge.
• Capacities. Object-based note-taking that treats notes as typed objects rather than files. A good middle ground between structure and networked notes.

The model sits on top and changes constantly. Your knowledge base underneath stays put and compounds, whichever of these you choose. The only mistake is not building the layer at all. Some of these tools come with a model picker and an MCP. Those are the critical pieces.

If this post convinces you to choose some knowledge base software or a second brain? Please let me know. I'd love to know and stay in the loop of your journey.

I'm curious about Ashwagandha, especially when it comes to taking it in the evening when I feel a little bit wired and irritable from the day. I've seen a lot of caution and the need to take it for two weeks. I'm unclear about the break between taking Ashwagandha.

If anyone in the community is actively taking Ashwagandha and can say that they've had a real impact, I would love to learn about your protocol and why you are taking it as well.

Before I even talk about NotebookLM alternatives, I have to give praise to the incredible work that NotebookLM has done. I don't think there's a better free product that gives you the AI features that NotebookLM does. This has also done incredible work for the PKM community and for spreading the power of what a focus on your own knowledge can do for learning, joy, and even just general life purpose.

NOW - I write this because there are a bunch of NotebookLM alternatives" listicles floating around right now, and most of them just rank tools without telling you why you'd actually leave. I am extremely passionate about the PKM space, after being extremely overwhelmed by social media and feeling like I had no real interested I turned a new chapter when I discovered the wonderful world of personal knowledge management. I have since tested over 35 different tools and workflows and have now settled on one that works for me. I have been collecting over 3400 notes from my health research and AI trends to journals, poems and recipes. 

The topic of NotebookLM gets me really excited because as mentioned, I feel like it showcased the value of knowledge management to a lot more people and the power of AI.

So here's the short version. I hope down here you find something valuable. If you do, please share your workflows with me. I find it incredibly inspiring. I will break it down into local-first alternatives, full AI knowledge bases, and the DIY Karpathy LLM wiki version.

First, why are people even looking for a NotebookLM alternative?

NotebookLM is genuinely good and a super powerful free tool if you are doing bounded research. BUT it feels more like a teaser for what knowledge management could be:

1. Source caps (Which can be increased on paid plans) but always per notebook, never one lifelong library. 
2. Chat is scoped to one notebook. No chatting across everything you've ever saved. This really is a deal-breaker when it comes to having a full, lifelong knowledge management system. You'll have one notebook for health and another notebook for recipes, but they'll never really connect.
3. No rich note-taking. Again, this is a huge gap when it comes to using NotebookLM for journals, having tables, to-do lists, and full rich text editing.
4. Gemini only. You're locked to Google's model. No swapping in Claude, GPT, or a specialized frontier model when a task needs different reasoning. Honestly, Gemini 3.5 Flash is already really powerful, but sometimes you can't help getting FOMO when GPT5.5 drops or there's a new model from Claude.
5. Google ecosystem lock-in. A Google account is required, and it's built around Drive, Docs, and Gemini, which is a non-starter if you want to learn outside that stack.
6. Privacy and cloud-only. Everything uploads to Google's cloud. There's no local or offline mode, which is a hard blocker for anyone handling sensitive, proprietary, or client data.
7. Capture and export friction. No browser extension for one-click saving from the open web, weak rich-text note-taking with no real tables, to-dos, or code blocks, and historically rigid export. I will say I find the mobile app to work pretty well, though, so that's definitely a plus.
8. No retention layer. No spaced repetition or knowledge graph across your full archive. It's a notebook, not a second brain. Now, maybe the retention layer isn't for everyone, but man, once you discover the power of spaced repetition or see how your knowledge connects in a knowledge graph, it's just a whole other level of knowledge management.

***Important note: the chat is scoped to only one notebook, and the Gemini can only be dealt with as a workaround using an MCP to, let's say, Claude. Those are super hacky workarounds and actually break the whole intention of this Google-first product.

So if you are actually looking for proper lifelong knowledge management, depending on what your restrictions are, here are my personal suggestions. might be missing something, so again, please comment below

1. Local-first alternatives for privacy and offline control

For the privacy-conscious crowd. These run on your machine.

• Open Notebook is the closest open-source clone of the NotebookLM experience. Self-hosted, lets you query with current AI models, and even does its own podcast and audio generation.
• InsightsLM is open-source and self-hosted, grounding every AI response exclusively in your own documents.
• SurfSense is an open-source AI research agent that connects your LLM to internal sources like Slack, Drive, and Notion plus live search, with no cloud or vendor lock-in.I've been seeing a bunch of Reddit posts on SurfSense. Feels like an exciting product and a great space.

The reality is, though, that there's a pretty big trade-off when it comes to having a local first set up. You trade A polished setup along with top-tier models. I think you really need to think about what material you are actually saving, how big a concern privacy really is. how important the latest AI models are to your workflow

2. Full AI knowledge bases for the lifelong library

This is the category for people who don't want a notebook. They want a single, growing knowledge base / second brain that compounds over years and that you can chat with across everything.

• Recall is the standout here, and it's built specifically as an AI knowledge base rather than a notebook.The best part is it works really well with saved online content plus proper rich note-taking. No per-notebook source caps thanks to one growing library, chat across your entire archive, a browser extension for one-click capture from YouTube, podcasts, PDFs, and articles, automatic organization as you save (Can't sing the praises enough for this browser extension. Honestly, I think I'm not even an extension person, and this is one of the best things I've done in the past two years.) and a retention layer with listen mode and knowledge that compounds. There's a free tier with unlimited saves, and Plus from around $10 a month for full AI summaries, library-wide chat, and multi-model Is on the max plan. We also have an API and an MCP to plug into existing workflows. The one thing it doesn't replicate is NotebookLM's output generation: two-host podcast audio( though listen mode on your own summaries covers most of that review habit) But there's definitely a gap in infographics and video creation.
• Mem This is the OG second brain. They've taken a couple of pivots and it is currently better if your week runs on meetings and calendar context rather than a study library. It's AI-native notes you write and refine with AI right in the editor, with smart search that surfaces past notes and meeting context fast, automatic collections and templates for recurring workflows, and calendar integrations that tie notes to your schedule. Free tier, paid from around $10 a month. Reach for it when work rhythm matters more than a personal learning archive.
• Notion is the pick when shared team wikis and project docs matter more than personal learning at library scale. I have mixed feelings about Notion. I feel like they are more the original, rich, block-style editor, which I love. Their templates are incredible, but I feel the focus on enterprise has neglected the consumer space. It's the most flexible authoring environment of the group, databases, docs, and wikis all in one, with AI layered on top, and it scales cleanly across a team. Free tier, Plus from around $10 a month. The catch is that it leans toward notes you write yourself rather than content you capture from the web, and it has no library-wide grounded chat in the NotebookLM sense.

3. The DIY Karpathy LLM wiki for technical tinkerers

For people who want maximum control, plain-text ownership - You're interested in knowledge management. I'm 100% sure you've been seeing this massive trend the pattern Andrej Karpathy popularized.

The core idea is that instead of you maintaining notes and occasionally asking AI about them, the LLM builds and maintains the knowledge base for you. The architecture has three layers.

1. raw/ holds immutable source documents like articles, papers, repos, and images that you ingest, often via the Obsidian Web Clipper.
2. wiki/ is a structured, interlinked set of .md files the LLM compiles from raw sources, a living curated layer that sits between you and the raw material.
3. A schema like CLAUDE.md or AGENTS.md tells the agent how the wiki is organized and what workflows to follow when ingesting, answering, or maintaining.

In practice that's Obsidian plus Claude Code, or any capable coding agent, pointed at a folder of markdown. You get local files, total model freedom, and a knowledge base that reasons over your stuff, not the open internet. The catch, as skeptics on r/ObsidianMD note, is that it's essentially an AI-maintained zettelkasten. Powerful, but you own all the setup and upkeep. 

You could also strike the best of both worlds if you want. Instead of using Obsidian (which is local-first and has the benefit, but is very markdown-heavy), you could pair it with something like Notion or Recall. That way, you get a really powerful workflow in Claude plus a very strong AI knowledge base in Recall or Notion.

If you read this, I genuinely hope it's helpful, and I hope that your learning journey blossoms. I'd love to hear more about it in the comments below.

I thought from the silent retreat things would have changed, but they actually haven't. After summer, I slipped back into bad habits, and I'm trying to meditate again. Is it so bad to want some balance and to have some fun? What's the price to pay? How to regain mindfulness again while just being socially normal

I have been researching natural appetite suppressants that actually work, and Yerba Mate came up. I've just bought it and have been using it for the last few days. I genuinely think my appetite is not as active as it usually is, but I also don't know if it's in my head.

Has anyone else been taking it? Have any side effects or thoughts?