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. submitted by /u/fatcatgirl1111
Originally posted by u/fatcatgirl1111 on r/ArtificialInteligence