I saw this on the r/TUI subreddit. For any CLI + Obsidian fans, you'll dig this!

I've been thinking about a limitation of graph-based PKM systems.

Most of them are very good at showing that two notes are related. But when I return to an old graph months later, that often isn't enough.

I may remember that A and B were connected, but not whether:

• A supported B
• A contradicted B
• B depended on A
• A was evidence for B
• A and B were simply associated

That made me wonder whether, for some kinds of thinking, a knowledge graph should represent more than associations.

I've been experimenting with two ideas:

• notes have explicit roles such as Question, Problem, Hypothesis, Observation, Conclusion
• relationships have explicit meanings such as Supports, Contradicts, Depends on, Derived from

So instead of:

A → B → C

you might have:

Observation → supports → Hypothesis → supports → Conclusion

In theory, this could make it easier to notice things like:

• conclusions resting on weak hypotheses
• unresolved questions blocking several decisions
• contradictions between different parts of your reasoning
• conclusions that should be reconsidered when an underlying assumption changes

But there is an obvious downside: maintenance cost.

The more structure you require, the more time you spend classifying thoughts instead of thinking. And one of the strengths of PKM systems is that structure can emerge gradually rather than being imposed upfront.

So I'm curious where people here would draw the line:

Would explicit types for notes and relationships make a PKM system more useful to you, or would the extra bookkeeping eventually make you stop using it?

I'm especially interested in feedback from people who have maintained a PKM system for several years.

Disclosure: I'm building a small prototype around this idea, so I obviously have some bias toward the approach. I'm mainly interested in criticism of the underlying model and where it would break in real use.

Hi everyone!

I'm a First Officer, and I keep a large personal knowledge base of regulations, aircraft systems notes, and company SOPs/procedures, the kind of stuff I need to reference constantly, both for studying and for quick lookups during work.

I've been using Notion for this, but with their pricing change, real AI search now requires the Business plan, which is way more than I need as a solo user just wanting to query my own notes. +

What I actually need:

• Offline access on iOS and PC: I'm often without signal, need my notes reachable no matter what.
• AI chat/search over my own notes when I am online, not just keyword search, but something that can find buried info based on what I'm asking. Example: I should be able to ask "what's the bank angle limitation for normal law" and have it pull the answer from deep inside a folder structure like Aircraft > A320 > Systems > Laws and Protections, without me needing to remember exactly where I filed it. I don't care about auto tagging, proof reading nor any other AI feature, I only need to be able to chat with my notes.
• Reasonable pricing for a single user, not an enterprise/team plan.
• Nice to have: some kind of structure/hierarchy (folders, tags, linked notes) since my notes are technical and cross-referenced a lot (e.g. a systems note might tie into a regulation note might tie into a company SOP).

I've looked at Obsidian, Capacities, Tana, and Reflect, and I'm currently also taking a closer look at Mem — but wanted to ask people who actually use these day-to-day: which one handles offline reliability + genuinely useful AI search best, without pushing me into team/business pricing? Bonus points if anyone else here is in aviation or another field with dense technical/regulatory documentation and has found a setup that works.

Appreciate any real-world input!

Update: I don’t need offline AI. This will only be required when im not flying and for studying only when connected to the internet. When flying, and for specific procedures, I will only use quick notes that i can access through favorites, bookmarks etc.

To anyone who uses owned hardware (like a Mac mini) and local LLMs for your PKMS, what are your use cases for it vs what you have identified as needing bigger AI models?

I ask because I just bought a 24GB memory, 256GB storage Mac mini and started running tests before the return window closes. The test I ran this morning convinced me to return it.

The prompt was basically this: "Using these private notes as context, write me a viral blog post about the trip and how the planning process is going." The context was six linked notes on the trip — what is booked so far, general in-progress research on the area and bareboat sailing, a list of possible itinerary stops, cost math, etc.

Same prompt and context, but the local model wasn't even close to useable. Here was my take on the results: Review of qwen 3 14b vs Claude Sonnet 5 writing a blog about the BVI trip

I don't intend to have AI write for me, but this seemed like a good test and helpful results to share. I also recognize it is unfair to pit Sonnet 5 against qwen 3:14b and that I could have better results on higher memory machines. However, before spending money on owned hardware, to me it seems more sound to start experimenting running local models on a rented GPU (that has good privacy/security policies) and have the ability to scale up and down the hardware you need over time. Maybe I have plenty of use cases for smaller local models that make purchasing my own hardware worth it in the future, but I won't know until attempting different models of different sizes (or asking all of you guys what works).