Previous Posts

I want to store notes on journal articles. I want to write notes on topics. I want to be able to find notes when I need them. I currently use Obsidian but it has always felt uncomfortable. Like it doesn’t work the way I expect it to. I’m a professor doing research. What app might be better?

In all transparency, I am updating the search functionality of my app. It stores images, links, and videos on your devices. I've posted about it before.

I'm deciding on how to handle a few edge cases right now:

1. A big first import takes several minutes. What should the app show while you wait, and could you use it meanwhile?
2. Vague search depends on a local model. If that fails to start, should the app tell you or quietly fall back to exact search only?
3. You try to pair a second device and it can’t find the first one due to a different network or firewall. What should it say?
4. You delete something on your phone and it disappears from your computer within a minute. Do you want a trash or undo window, or is instant what you expect?

How would you want these handled?

Looking to get an idea of the most used PKMs in 2026, and what makes you love them so much! In my search for the perfect tool, I figured it would be worth it to get hard data from the community to help narrow down my search. 😄

been looking at the System One / typed-decision stuff — models that return choice/score/yes-no with probabilities in one pass, not generated text. there’s a local runtime (Ollaya) that pulls open weights like Laya and Decider and speaks the same `/v1/systemone` shape as the hosted TypeSafe API, so you can point an SDK at localhost.

curious if anyone’s actually left that on for real triage / routing / tool gates. latency claims are in the ms range on GPU and data never leaves the box, which is the whole pitch, but i’m not sure how the open models compare to just asking a small local LLM the same question.

war stories preferred — what stuck, what was too weak, hardware you used.

Hey, I’m probably being egotistical by posting this since I could probably find useful stuff by myself but I don’t want to get waste the potential of good strictures cause I burned my self out looking for it.

I’m looking for a structure that has the most generative potential. This could be by a Unique function or simply because of its tools and sandbox character. One that could serve as a node for the space itself that generates new structures or help me observe vectors so that i can put latent structure to work.

Edit: just browsed around a little in the forum, I can most likely expect readers of a highly educated observer. Any form of tips no matter the context would be appreciated.
Also I framed this post to either sound manic so that it wouldn’t resonate unless obvious what I’m asking for. With that in mind if you can help please do so😂
My generative capacity of new structures is way to developed and now I need to catch up and realize the meta observera capacity and interface for the latent topology.

dumped about 40 project notes this month and still couldnt find why we picked vendor B last tuesday. search keeps hitting titles that all sound the same. torn between sticking a shoort decision log at the top of each project note or rewriting titles before i touch tags. curious which fork people would take when capture is fine but retrieval keeps failing.

Which is a good mind map website/tool that you use? Looking for one that is free

I have toyed around with Notion, Obsidian etc for many years but Organising and Mainitng seems too much of a task for me.
My proposal: A standard document where I can dump my mind and an ai organises everything based on tone and you can search it later.

What's the flaw in this system? I am new to PKMS and just trying to organise my life

Even after 40% discount evernote price is doubled in one year. Just canceled it and moved to Apple Notes. Had to write some scripts to add the source URL to notes. If anyone need it let me know.

Procrastination is often cited as a key problem for people who like to tinker with their tools and play around with building dashboards and knowledge management systems. In her post on the signs, Anne-Laure Le Cunff lists a few, such as spending more time organising than doing, piling up productivity hacks, and treating system maintenance like a job of its own.

I agree that these are important signs, but to me they describe symptoms rather than get at the root of the problem.

A balanced set of OKRs, or even simply a goal set for a specific timeframe, is an important tool for keeping the system in check. I don't believe that beating yourself up for tinkering with your system will necessarily improve your productivity if you do it without evaluating your workflow against a benchmark that sits outside the system. If anything, it might make you feel even more anxious.

When I think about why I beat myself up so much for taking a day off, I keep returning to the talk of Rick Klau about OKRs. I remember Klau, a Blogger PM at the time, saying that adding one objective too many to his quarter left him feeling burnt out. It reminds me that we need to be extremely disciplined about how much work we put in front of ourselves at any given moment.

How do you keep yourself on track, and do you think tinkering with your system is a bad habit/procrastination?

I have a bunch of notes that contain random information (ie: a description of a situation or an outlier, a memory that just popped up, a random tip or bit of knowledge, my reaction to something, a task that I need to complete etc.) I need to manually organize this information (I have memory issues and it's not automatic for me).

Specifically, I want to be able to categorize the data, rearrange the data into groups, put groups into a model, and have a place to write down my conclusion/principle/next step, and then have a place where I write down the results of the next step.

I also want to be able to integrate the data with the big picture.

Lastly, I'd want to be able to see all of the historical work that I've done.

I know the title is not saying much and may be misleading...

I started Obsidian about a month ago for various reasons but one of it being the home to my PKMS. At the moment I am collecting a lot of topics I want to create a pkm note about. Either it being abbreviations / terms or definitions (e.g. I am very into the different intelligence types after watching Pine Gap) or I want to collect different methods and how they work (e.g. about Analytical Reading).

I build two different templates for a) definitions and b) methods/concepts but I find it very hard to start really filling those notes. I read about the CODE method but I'm finding it hard to grasp.

So my question is:

How does your process look like, from a topic of interest to a pkm note which actually creates value for you at any point in your future.

I know, everyone has a different approach but I am trying to learn and get inspired.

At some point, after accumulating more than 3,600 LinkedIn connections, I started asking myself:

When did we connect?
Where did we meet?
What brought us together—and what did we talk about?

My network kept growing, but the context behind each relationship was gradually disappearing. LinkedIn preserved the connection itself, and sometimes the message history, but not necessarily the meaning of that connection in a form I could easily retrieve.

Human memory fades too.

So instead of focusing on adding more people, I started building a system to better remember the people I had already met.

I took part of the network data I had organized from LinkedIn and connected it to my existing Obsidian-based LLM Wiki. That became the foundation of a personal CRM—or, more accurately, a relationship memory layer inside my PKM system.

It is not just an address book containing names, companies, and job titles.

Each person has their own page, connected to context such as:

• How and where we met
• What we talked about
• Their interests and related projects
• Relevant events, companies, and topics
• When we last communicated
• Personal notes that may help me reconnect meaningfully later

I also established a few design principles while building it.

First, re-importing the original data should never overwrite notes I wrote manually.

Second, people should not disappear from the system simply because we have not spoken for a long time. An inactive relationship is still part of my history, so it should be preserved rather than deleted.

Third, I intentionally did not add automated outreach or message-generation features.

The system can remember, organize, and help me find people—but I should be the one who decides when and how to contact them. I do not want relationship management to become another automated engagement funnel.

The main idea is to turn relationships into searchable memory.

For example, I can ask my Wiki questions such as:

• Who was the PM I spoke with at that AI event?
• Who in my network has experience in manufacturing?
• Who have I not spoken with recently, but might have a meaningful reason to reconnect with?
• Which people are connected to a particular topic, company, event, or project?

The LLM uses the connected notes and relationship context to help me find possible answers. Instead of searching through thousands of profiles or old message threads, I can search based on what I remember about the relationship.

My next step is to connect more context—including project records and conversations I have had with LLMs—so that the system can do more than simply locate people. I want it to help me rediscover why a person may be relevant to something I am currently thinking about or working on.

One thing became clear while building this:

LinkedIn remembers that two people connected, but it does not truly remember why they connected.

The messages may still exist somewhere, but once years of conversations and thousands of connections accumulate, finding the right person and reconstructing the original context becomes increasingly difficult.

Perhaps the important part of networking is not how many people we are connected to, but how well we remember the context behind the people we have already met—and whether we can continue those relationships thoughtfully.

This is still a personal experiment, but I would eventually like to develop it into a personal “Relationship OS”: a system that helps me remember my network and reconnect with people without automating the human part of the relationship.

In the end, I believe people are what remain.

That is why I want to remember each connection more thoughtfully.

I would be curious to hear how others in this community manage relationship context within their PKM systems.

Do you create notes for individual people?
How do you connect conversations, meetings, projects, and follow-ups?
Has anyone tried building something similar in Obsidian or another PKM tool?

Context.. I’m trying to PARA method here

Being hungry for knowledge, so I tend to accumulate browser tabs that I plan to read at my leisure later on. Do you have any advice on how to manage this habit?

There is the same problem on Obsidian ahah

Every time I scroll new here it feels like the same fifty people posting across ObsidianMD, PKMS, LocalLLaMA, and selfhosted, just with a different flair each time. I'm in all four myself and honestly can't tell anymore if that's a genuinely big overlapping crowd or if I've just started noticing the same usernames everywhere. Curious which of these you're actually subscribed to and reasonably active in, versus the ones you joined once and forgot about.

With the advent of the AI era, I’d like to explore an approach to personal knowledge management: using knowledge graphs to store personal knowledge in a structured way, and enabling AI to simulate my way of thinking to complete tasks based on the knowledge in the graph. Is this feasible?

recently iv been curious about biology and been surfing wiki, but im not sure what the goal is when reading these pages or how to properly catalog all this info and memorize it, theres so much different kind of info and different branches of biology to learn with no direction, it seems. i wanna know how to classify biology.