Predicting what people want to consume is hard, especially if # of impressions is your success measure. More broadly, I have been endlessly surprised by how users use my products and what in particular they liked. You do tend to get better at feeling this out from seeing people interacting with your product, but you stand to be bewildered, forever. If only you saw in what circumstances people read your posts!
At the same time, some might say it's about the area under the curve. If 10 folks get their mind blown by in-depth treatment of some curious topic, it's roughly same amount of utils as 1e5 impressions on some silly quip if you ask me.
I, for one, am perpetually grateful to lcamtuf. I have been looking up to him for like 15 years, and he has shaped me profoundly by showing what level of focus, productivity and insight is possible. You wouldn't think that someone's life trajectory can get changed by super detailed CNC lore write up, but here we are, years later (: Thanks!!
Also, if you're reading this lcamtuf, I would like to put one vote in favour of re-instating the American essays. Pls don't pull a Kafka on us! I did read your "choosing how to be remembered" post, but still, the fact that you took the American essays down feeds right into the topic of this current post. I found them positively entertaining and insightful.
wait, aren't "they" going to gather evidence on user of "my account" and associate the downloads from this domain to "me" and come after me and ask hard questions?
TL; DR, you might be riding too high on dopamine / layering too many dopaminergic activities. You might need to lower your base dopamine level.
2) for dealing with youtube specifically, block youtube recommended. Like youtube becomes only search bar, no recommended, no follow up videos etc. Complete game-changer for me.
I think I've ran into some criticisms of Huberman for often citing animal studies as equally applicable to human studies. Tbf, I like Huberman generally, but I do also think he's very good at making every single thing he says "science based" as if the existence of a single finding from a one-off study on mice is somehow iron clad confirmation and proof in favor of his specific recommendation.
But his general recommendations are usually pretty good and he has good guests sometimes.
+ it is completely free, no ads, no nothing paid, not snooping your data
+ you don't have to unlock your phone - you can just type what you are doing inside the notification itself / choose from list of your usual activities
For modern music, the definitive resource (in my experience) is RYM: https://rateyourmusic.com/. It has several advantages:
1) extremely broad coverage - it is almost "list of all music ever"
2) the community has rated all of this music and "has good taste"
3) nice sorting and search capability
How to use:
a) For overview of most important albums which is at the same time a reasonable overview of modern music history, use the default sorting https://rateyourmusic.com/customchart
b) after you get into specific sub-genre's, use the sorting. For example, you discover you like Japanese 90s city-pop? Here's the query for you https://rateyourmusic.com/customchart?page=1&chart_type=top&...
c) for music discovery, browse recommendation lists of people there. It is a true gold-mine of great music
For completness, to rollingstone.com and npr, I add pitchfork.com which is arguably the most important "independent" medium
Seconding RYM, imo the best place to find music worth listening if you don't have time to go through everything. The community is also very good in curating lists for various tastes.
I find the inverse surprising: that many algorithms that work on real-life robots _do_ _provide_ error bounds and their optimality / convergence properties are proven in the papers that introduce them.
A great example of this is motion planning, where papers both on sample-based methods (such as SST), and on search based (descendants of the A* family) argue at length the theoretical optimality and convergence properties.
On another note, I think requiring more theoretical analysis as a guarantee of safety could partially be an AI-winter meme rather than practical solution. Point in case: do people run a quick check of aerodynamics maths before boarding a flight? No - they rely mostly on the engineering and regulatory process that gradually made passenger flights safer.
It seems you are taking about theoretical error bounds, that is proofs in papers with assumptions on input probabilities etc. These don't always apply to actual implementations, in physical real robots. There is a huge gap in safety between practices in aerospace engineering and robotics.
While I appreciate the efforts of authors and believe in long term mission, they seem to not mention anywhere some key shortcomings of Ray, while marketing it pretty hard (eg see the paper).
I have used ray (a year ago) in one of the advertised basic applications: parallelising the environments for RL. It was unusable back then, as it was clogging up the memory.
The plasma store which is backend for arrow was never cleaned which made the computation stop after 3 hours
At the same time, some might say it's about the area under the curve. If 10 folks get their mind blown by in-depth treatment of some curious topic, it's roughly same amount of utils as 1e5 impressions on some silly quip if you ask me.
I, for one, am perpetually grateful to lcamtuf. I have been looking up to him for like 15 years, and he has shaped me profoundly by showing what level of focus, productivity and insight is possible. You wouldn't think that someone's life trajectory can get changed by super detailed CNC lore write up, but here we are, years later (: Thanks!!
Also, if you're reading this lcamtuf, I would like to put one vote in favour of re-instating the American essays. Pls don't pull a Kafka on us! I did read your "choosing how to be remembered" post, but still, the fact that you took the American essays down feeds right into the topic of this current post. I found them positively entertaining and insightful.