From a dev perspective this area has a ton of super interesting algorithmic / math / data structure applications, and computational geometry has always been special to me. It's a lot of fun to work on.
If anyone here is interested in this as a user, I'd love for any feedback or comments, here or you can email me directly: tyler@vexlio.com.
Some pages the HN crowd might be interested in:
* https://vexlio.com/blog/making-diagrams-with-syntax-highligh... * https://vexlio.com/solutions/state-diagram-maker/ * https://vexlio.com/blog/speed-up-your-overleaf-workflow-fast...
My notes are basically like Smeegol's precious ring, and to burn them is unfathomable. But initially these notes they were garbage, I initially got into all these PKM systems and used a stripped down Zettelkasten, but then realised that I was focused on creating the system not the outcome. My wonderfully linked notes were never being seen, the notes I was taking was not connected to my current focuses. They were virtually all "maybe I'll use this in the next 10 years" type notes.
I changed my goal away from following a system to focusing on getting meaningful changes in understanding from notes. This means having the ability to recall information, not rely on a second brain. I spent a fair chunk of time reducing my inputs to notes which are focused on my current goals: metacognition, mental health and business. If the note does not fall in these category it is not noted, I still read things for pleasure just noteless. The value of applying what I read in the short-term outweighs notes for possible futures. As possible futures are everchanging and so the likely value of these notes are heavily weighted down. I do have troves of notes which will be transformed when I need them, but these notes have a very high chance of being seen and are related to my goals, but not applicable currently. I delayed transforming these troves until I am applying them, as I will get the most value out of my notes when they are being applied Not someday dreams, but in reality never to seen again notes of yesteryear.
Relying on a second brain is not the same as understanding concepts and applicable learning. An example: When you read an article and come across a word you don't know it stops your train of thought, going to you PKM to find the definition doesn't help. When you know the word it allows you to chunk info and think deeper thoughts about said article. That requires understanding, which you won't get from these PKM systems which focus on input with little concern for output. By having deeper understanding it reveals further planes of thought previously impossible.
Adding a note feels good, it feels like work but it really isn't. PKM has sprung up about making feel good systems but have rarely leads to any meaningful changes or outcomes, such as this blog. To get to deeper thought requires way more than creating a note which is literally one of the first parts in my understanding chain. PKM systems focus on this, but spend very little on the other end- meaningful output.
My "learning stack" - fleeting ideas go into Todoist, ideas are encoded/transformed and go to into Obsidian, at the same time these ideas go into Anki, which I go through multiple times a week. These ideas are further elaborated on and changed in Anki. My pkm is a single step in developing understanding not the destination.
for further anki learning: https://augmentingcognition.com/ltm.html
When the end is in sight how close to completion you are isn't useful as ego plays a bigger part and so needs to be factored in. Percentage completion becomes a non-useful metric as it doesn't help you get to you objective of completion, if anything it's harmful as I tend to beat self up for lack of progress. As to what that metric is useful at the end point I don't know. But closeness to completion doesn't help me finish.
I use ego as a substitute but some human factor needs to be accounted for.
To top it off you have the emotional and ego side at play near the finish line. Is this good enough? This could be done better, etc.
I think that mixture of better taste, more dependent parts and ego make the last part the hardest. I also feel that the finish line being close isn't a strong of a motivator as ego is a demotivator. Whereas, at the start ego has no effect as you don't know anything, you can't be mad because you're new, it's all one big playground.
Outliers will still work hard and become even more valuable, AI won't affect them negatively. I feel non outliers will be affected negatively on average in ability to learn/think.
With no confirming data, I feel those who got that fancy education would do so in any other institution. Just those fancy institutions draw in and filter for intelligent types, not teach them to be intelligent as it's practically a pre-requisite.
Basically, a student's marks depend mostly (only?) on what they can do in a setting where AI is verifiably unavailable. It means less class time for instruction, but students have a tutor in their pocket anyway.
I've also talked with a bunch of teachers and a couple admins about this. They agree it's a huge problem. By the same token, they are using AI to create their lesson plans and assignments! Not fully of course, they edit the output using their expertise. But it's funny to imagine AI completing an AI assignment with the humans just along for the ride.
The point is, if you actually want to know what a student is capable of, you need to watch them doing it. Assigning homework has lost all meaning.
I feel AI has just revealed how poor the teaching is, though I don't expect any meaningful response to be made by teaching establishments. If anything AI will lead to bigger differences in student learning. Those who learn core concepts and to critically think will be become more valuable and the people who just AI everything will become near worthless.
Unis will release some handbook policy changes to the press and will continue to pump out the bell curve of students and get paid.