In Obsidian, the local graph has real uses, but the global one is mostly to see structure in your notes and look cool on social media.

I was researching and prototyping a graph like Obsidian's before Obsidian came out, based on the ideas in "how to take smart notes"

https://www.youtube.com/watch?v=a6yUA46ek6M

I believe the direction of UI I was exploring there has more than what graphs currently have, although I didn't have the time to build it out and I saw that the site has been offline for a while.

It was a working tool though.

Either by introducing new tools, or by proving things that were previously unproven that end up helping in unexpected ways?

That's often how math goes, isn't it?

On top of that, their aftermarket and open source situation is pretty good.

They're not ideal e-readers though, but if you're in the market for a good e-ink device with long-term support and that works well with calibre? Might be worth a look.

I was researching how to predict hallucinations using the literature (fastowski et al, 2025) (cecere et al, 2025) and the general-ish situation is that there are ways to introspect model certainty levels by probing it from the outside to get the same certainty metric that you _would_ have gotten if the model was trained as a bayesian model, ie, it knows what it knows and it knows what it doesn't know.

This significantly improves claim-level false-positive rates (which is measured with the AUARC metric, ie, abstention rates; ie have the model shut up when it is actually uncertain).

This would be great to include as a metric in benchmarks because right now the benchmark just says "it solves x% of benchmarks", whereas the real question real-world developers care about is "it solves x% of benchmarks *reliably*" AND "It creates false positives on y% of the time".

So the answer to your question, we don't know. It might be a cherry picked result, it might be fewer hallucinations (better metacognition) it might be capability to solve more difficult problems (better intelligence).

The benchmarks don't make this explicit.

It reminds me a lot of the YouTube channel "life in jars", he normally makes videos about microbiology and freshwater ecology in... jars!

But on top of that he also had a short series on gaining the trust of and befriending crows in his city.

Good incentive for me to try this out!

On top of that, I have ADHD, which makes it more difficult for me to really relax.

What do you do for relaxation?

Comments URL: https://news.ycombinator.com/item?id=47642541

Points: 22

# Comments: 29

It feels _amazing_ to draw a bird in a single stroke!

Maybe this can give you some inspiration!

https://laura.fm/generative-art/wind/wind.html

I don't particularly think "y7u8888888ftrg34BC" would pass as a crystal clear requirement at my workplace :<

Do you mean something different?

He's looking for a model that works for the story in the media and runs with it.

Your criticism seems to be criticizing the story, not the author's attempt to take it "seriously"

https://scrybble.ink

Thanks a lot for this! I was interested in beads but found the author's approach to software development quite erratic and honestly a bit unprofessional. Yes, LLMs are great, but no they shouldn't be the lead developer.

Beads is an incredibly difficult-to-follow mess for something that is at its core a pretty simple idea. You distilled it to its core, I will absolutely be checking this out :)

It goes much beyond just cellular automata, the thousand pages or so all seem to drive down the same few points:

- "I, Stephen Wolfram, am an unprecedented genius" (not my favorite part of the book) - Simple rules lead to complexity when iterated upon - The invention of field of computation is as big and important of an invention as the field of mathematics

The last one is less explicit, but it's what I took away from it. Computation is of course part of mathematics, but it is a kind of "live" mathematics. Executable mathematics.

Super cool book and absolutely worth reading if you're into this kind of thing.

Has this been studied? This is a very strong claim to make without any references.

What if you take two groups of software developers, one which has 5-10 years of experience in a popular language of choice, let's say C, and then take a group of people who write LISP professionally (maybe clojure? Common lisp? Academics who work with scheme/racket?) and then have scientists who know how to evaluate cognitive effort measure the difference in reading difficulty.

These models are trained on image+text pairs. So if you prompt something like "an apple" you get a conceptual average of all images containing apples. Depending on your dataset, it's likely going to be a photograph of an apple in the center.

The development experience is almost always really smooth and there are more and more tools to further smoothen that experience every day.

There are definitely better tools out there but given how the web ecosystem functions, it could be much worse.

His videos are incredibly interesting and fascinating

These people won't sit still and models will keep getting better as well as cheaper to run.