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For those of unfamiliar with Racket, could you go over what it takes to run a Racket app on iOS?

I just had a look at the creators's other project and this one his hilarious! https://www.unoptimal.com/push-off

Two friends receive a notification and the first one to tap it wins!


The pattern that this relies on is called "pre-pooping" because of this blog post: https://cglab.ca/~abeinges/blah/everyone-poops/

How do people design emulators? Is there a good resource on how you can learn to do this?

This YouTube video by Modern Vintage Gamer might be an interesting watch, for people interested in PS4 emulators: https://www.youtube.com/watch?v=KZvSEdFGyxE

This is quite interesting! While we're on the topic, does anyone know of a similar set of exercises for learning Golang?

I maintain a list of Rust tips and tricks for people who are looking to dig a bit deeper: https://geeklaunch.io/blog/rust-pro-tips-collection/

I don't know a word or phrase for this, but I really enjoy any examples of "thinking outside the box" like this because it's something I struggle with in my professional career. Learning not only the right ways to solve problems, but figuring out the questions to ask that make solving the problems you have easier or even in some cases possible. In this case, it's hey, we don't need exact numbers if we can define a probabilistic range given defined parameters. Other problems are gonna have other questions. I guess my hope is that if I see enough examples I'll be able to eventually internalize the thought process and apply it correctly.

Last month an amazing biographical podcast came out describing his personal journey to starting rentech, and the factors that make the business so competitive.

Certainly worth a listen https://www.acquired.fm/episodes/renaissance-technologies


I think Veritasium made a really good video talking about some of the differential equations governing option pricing [1] which I found really fascinating. Patrick Boyle's video about Jim Simons' history is really interesting too [2].

Also just reading about Jim Simons' being an already-very-successful mathematician dropping everything to start a hedge fund and ending up extremely successful at the end of it was a bit of a wakeup call. Clearly this was an extremely smart dude (he was the chair of the math department at Stony Brook!), and so if this is interesting enough for someone like him, then it's probably something worth looking into.

I read through a book on basic trading strategies and I thought it was pretty interesting [3], though I've gone in a pretty different direction from what they taught.

[1] https://youtu.be/A5w-dEgIU1M

[2] https://youtu.be/xkbdZb0UPac

[3] https://www.amazon.com/Machine-Learning-Algorithmic-Trading-...


Yeah there's a famous but outdated book called A Practical Guide to Quantitative Finance Interviews by Xinfeng Zhou that gives you some idea of what questions they like to ask.

His whole RenTech story was fascinating.

Effectively an outsider in finance who gathered a bunch of other outsiders (aka big mathematicians), and decided to start a hedge fund that takes zero interest in the actual companies and trades solely on math. Which makes sense, since none of the main people involved in its creation had any corporate or finance experience, but tons of math experience and knowledge.

This is oversimplifying it like crazy, but I recommend anyone to anyone with even a passing curiosity for this look up the details (or read “The Man Who Solved The Market”, which is documenting the beginnings and growth of RenTech, as well as that of Simons; very enjoyable read).


Stanford's NLP Group has a good list of more specialized NLP coursers ( as well as CS224N, basically their CS388) - https://nlp.stanford.edu/teaching/

CS 124: From Languages to Information

CS224n: NLP with DL from Stanford

CS224U: Natural Language Understanding (Lecture Videos)

CS224S: Spoken Language Processing

CS276 : Information Retrieval and Web Search

CS324 - Large Language Models

LING 289: History of Computational Linguistics

Some others are below https://nasmith.github.io/NLP-winter22/about/

https://www.cs.princeton.edu/courses/archive/fall22/cos597G/

https://self-supervised.cs.jhu.edu/fa2022/ (has a list of other NLP courses at the bottom)

http://demo.clab.cs.cmu.edu/NLP/ (has a list of other NLP courses at the bottom)

I found it useful to compare various school's NLP courses when doing my own learning for different view points.


This presentation from Deep Mind outlines some foundational ML books: https://drive.google.com/file/d/1lPePNMGMEKoaDvxiftc8hcy-rFp...

For the impatient, look into slide #123. Essentially, the recommendations are Murphy, Gelman, Barber, and Deisenroth.

Note these slides have a Bayesian bias. In spite of that, Murphy is a great DL book. Besides, going through GLMs is a great way to get into DL.


There's a ton of really good Rust content on the Zed blog. As somebody new to Rust, I've learned a lot from it. For example, how they do ownership in a GUI setting, in a language for which even writing a doubly-linked list is a challenge:

https://zed.dev/blog/gpui-ownership


If anyone's looking for a solid MIPS assembly IDE, I wrote this one ~18 years ago in undergrad http://mipscope.cs.brown.edu

wow I'm getting old


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