noud

KISS. In my home folder (*nix based):

  bin/
    ...all personal bash scripts can be found here...
  documents/
    audiobooks/
    backups/
    books/
    personal/
    projects/
    videos/
    work/
  downloads/
  hosts/
    ...sshfs to all important folders on several servers, no sync...
  images/
  music/
  share/
    ...for locally installed software, no sync...
  temp/
    ...remove all files in this folder once a month, no sync...

I had exactly the same feeling when reading this blog. Sure, the techniques used to find the solutions are really interesting. But the claim more than they achieve. The problem statements are not available in Lean, and the time limit is 2 x 4.5 hours. Not 3 days.

The article claims they have another model that can work without formal languages, and that it looks very promising. But they don't mention how well that model performed. Would that model also perform at silver medal level?

Also note, that if the problems are provided in a formal language, you can always find the solution in finite amount of time (provided the solution exists). You can brute-force over all possible solutions until you find the solution that proofs the statement. This may take a very long time, but it will find the solutions eventually. You will always solve all the problems and win the IMO at gold medal level. Alphaproof seems to do something similar, but takes smarter decisions which possible solutions to try and which once to skip. What would be the reason they don't achieve gold?

Not only did these techniques save me money, but they also saved me a considerable amount of time. I've employed these same strategies at work and private to navigate away from requests and projects in which I have no interest.

I 100% do exactly the same. I gave up following what's new in Artificial Intelligence (Machine Learning?) years ago. 99% of it is distraction, and not worth my time to find that last 1% of useful information. Instead, I focus on improving my foundations: statistical inference, linear algebra, calculus, classical machine learning (e.g., regression, boosting, component analysis, ...), programming, domain knowledge, social skills, ... I only learn a new technique if I cannot solve it with my usual toolbox (which is not very often).

I'm way more productive, have to work less hard, and I'm not distracted. Sure, I don't do that fancy new thing, but at the end of the day (or earlier) I get the job done. And I'm judged on what I do, and how it brings money into the company, not how I do it.

Another benefit working mostly with a box of boring, old tools, is that it will likely still be relevant in the next 30 years. You never know how long that new popular thing will remain popular and useful. But I'm pretty sure we'll still fit datasets with linear/logistic regressions, optimize processes with linear programming, or do straightforward A/B testing for the next few decades (if not centuries or millennia).

But can it run Doom?

I installed solar panels and AC.

Well, of course, I could put a version number on top of each page... but hey, what's the fun about that?

I used this library very often when writing a new paper. When the paper was a draft I would put coffee stains on the pages. In this way, I always knew if I was looking at a draft or the final version.

Or Maté?

I think it's "only" a quote from a movie called Margin Call [1]. But my grandma could have said it. ;)

[1] https://www.imdb.com/title/tt1615147/quotes/?item=qt1531207&...

Zelda, link's awakening... one of the best Gameboy games ever made.

I use lists, a lot of them. I have Daily, Weekly, and Yearly lists (all on paper). I don't have them to be productive, I have them so that I don't have to remember the things on the lists. I want my head to be empty of all the things I cannot do right now. For me, the best way to achieve that, is to write the things I want to do in the future. That way, they get out of my head and I can focus on my daily business.

For me, the same trick also works for other things that are on my mind. Especially the things that would usually keep me up at night. I write them down, so that they get out of my head.

My recommendation would be to first learn vanilla Javascript. Become experience in it (I'm assuming you're not a Javascript expert yet). Build a couple of websites with vanilla Javascript. Then, move to a framework. First, it's much easier to learn these frameworks when your foundations are strong. Secondly, you'll better understand how the framework works under the hood. Thirdly, you understand better why a framework is used, and what it tries to solve what vanilla Javascript cannot (easily) do.

p.s. You'll be surprised how far you can get building a website with vanilla Javascript without the use of any advanced framework.

Last week:

mon: 6, tue: 3, wed: 4, thu: 4, fri: 4 (only did 3).

I also have a weekly TODO list with more general tasks. It contained 4.

In my experience I often cannot do more than 5 tasks a day. So I try to limit it to 5 tasks if possible.

The Black Swan: The Impact of the Highly Improbable from Nassim Taleb. I'm rereading the book for the third time. Previous two times were in 2015 and 2019.

I still have a working X60s from 2006. Really liked that laptop. Unfortunately, for modern standards, the battery life is not great, a couple of hours at most. And the resolution is just a little bit too low, 1024 × 768.

Of course I don't know. This is just pure speculation. But perhaps it's just not that hard to build a trading platform with just a couple of (very good) developers?

Not having separated large teams also has its benefits. Keep everything as simple as possible reduces complexity, which could make it more safe as well.

I found this presentation useful on modern c quite useful: https://www.youtube.com/watch?v=QpAhX-gsHMs

Good luck!

What OpenAI really wants

1. Get lots of users; 2. monetize everything; 3. go public; 4. sell all shares and get super rich?

LAMP + Wordpress, works fine for me.

Am I the only one using vanilla vim with a minimal configuration file (<50 lines)? I set tabs to 4 spaces, put syntax highlighting on, set background to dark, add fuzzy finding, and configure netrw (default file browsing).

Very easy to setup. Moreover, when I'm on a different computer and open (vanilla) vim without any configurations. I know that everything is more or less the same as my default vim environment. I think it's not that hard to learn developing in the default vim environment, with some minor tweaks.

Also related, How to Do 90% of What Plugins Do (With Just Vim): https://www.youtube.com/watch?v=XA2WjJbmmoM

I once asked a girl for a date who responded with "I cannot make it, because I have to polish my nails tonight." When I asked if she could make it tomorrow, she answered that she also had to polish her nails tomorrow.

I would have been fine if she just said "no".

There is absolutely no reason to have 8 cheesy indirect ways of saying no when, "I'm very sorry, but I cannot do it" (or something in these lines) is a perfectly fine way to say no in almost every case.

I'm not coding for 30 years (yet), but I have some code from 10 years ago that is still running and being used today.

The fun part: all code that had "fancy new technology" in it has been replaced over the years. The only code that has survived was written from scratch without any other dependencies. Most of it being vanilla Javascript (doing quite advanced calculations)!

Rather off-topic question: but what makes rust toxic in your opinion?

You have the Digital Database for Screening Mammography (DDSM). It's a relatively old database with pre-digital images. But it should be a good start.

http://www.eng.usf.edu/cvprg/mammography/database.html

Good luck!

Who is wiser? One who doesn't know much, but knows what he doesn't know and points this out when asked. Or the one who knows a lot, but doesn't know what he doesn't know, and (confidently) makes up something when he doesn't know? (also see Apology from Plato)

I'm not disappointed that GPT doesn't know the answers to questions in my domain of expertise. I'm disappointed that GPT doesn't know that it isn't an expert in my domain, and makes up a confident but wrong answers.

ChatGPT4 blew my mind when I asked it some specific details about my PhD thesis. It was a relative simple question, but you needed to know some domain knowledge to give the correct answer. ChatGPT not only gave a wrong answer, it completely made up a story was beyond repair. It is concerning (blew my mind) how confident ChatGPT can provide a completely wrong answer.

The good news is that it's reversible. When you stop feeding antibiotics to animals, the amount of resistance bacteria in the gut seems to reduce as well.

Additionally, good management practices (clean pens, good hygiene, low stress) and proper feed could make the use of antibiotics in animals (almost) obsolete.

I once build a machine learning library from scratch in rust. No external libraries allowed. It could do linear regression, lasso, and ridge. It also contained some statistical inference features, like p-values, AIC, feature selection. Basically chapter 3 of Hastie [1]. It's more difficult than you might think, as you have to figure out how to do efficient matrix manipulations, deal with floating point overflows, multi-threading, unit testing, bench marking, etc.

[1] https://hastie.su.domains/Papers/ESLII.pdf