learnxinyminutes-docs VS mathlib4

> Sure, maybe for some esoteric edge cases, but 5 mins on https://learnxinyminutes.com/ should get you 80% of the way there, and an afternoon looking at big projects or guidelines/examples should you another 18% of the way.

Not for C++, and even for other languages, it's not the language that's hard, it's the idioms.

Python written by experts can be well-nigh incomprehensible (you can save typing out exactly one line if you use list-comprehensions everywhere!).

Someone who knows Javascript well still needs to know all the nooks and crannies of the popular frameworks.

Java with the most popular frameworks (Spring/Boot/etc) can be impossible for a non-Java programmer to reason about (where's all this fucking magic coming from? Where is it documented? What are the other magic words I can put into comments?)

C# is turning into a C++ wannabe as far as comprehension complexity goes.

Right now, the quickest onboarding I've seen by far are Go codebases.

The knowledge tree required to contribute to a codebase can exists on a Deep axis and a Wide axis. C++ goes Deep and Wide. Go and C are the only projects I've seen that goes neither deep nor wide.

Learn x in y minutes: Concise tutorials to learn various programming languages and tools quickly.

StackOverflow's making their own competing LLM for all this stuff.

IMO, one of the biggest problems with the way people use LLMs right now, is that they're being treated as a single oracle: to know Java, it must be trained on examples of Java.

It would be much better if their language comprehension abilities were kept separated from their knowledge (and there are development efforts in this direction), so in this example it would be trained to be able to be able to read a Java tutorial rather than by actually reading a Java tutorial, so when the overall system is asked to write something in Java, the language model within the system decides to do this by opening https://learnxinyminutes.com and combining the user query with the webpage.

I think this will help make the models more compact, which is a benefit all by itself, but it would also mean that knowledge can be updated much more easily.

Someone would have to actually do this in order to see if those benefits are worth the extra cost of having to load a potentially huge a tutorial into the context window, and likewise the extent to which a more compact training set makes the language comprehension worse.

The project was created and is maintained by Adam Bard, but is open sourced with over 1.7k contributors since 2013

https://github.com/adambard/learnxinyminutes-docs

So you have studied programming for at least 5 years, what kinds of programs have you written? Apparently you have already applied your skills, since you have "created a good reputation among developers"? Why a time-frame of 20 years, why not 20 months or 20 weeks? Heck, you can learn a lot in even 20 days!

Once you have learned a few languages, libraries and frameworks then learning new stuff becomes much easier. At that point I'd recommend to check the website https://learnxinyminutes.com. Meanwhile, continue asking questions here and elsewhere :)

An other tip, if you are into computer science and algorithms stuff I recommend you try to solve problems which are posted at https://codegolf.stackexchange.com. You don't need to try solving them in less than X characters, but just to get them solved by any means necessary. And don't take too much bad influence from the posted solutions.

If you want a brief overview, go to https://learnxinyminutes.com/ and look for Javascript. I guess it should be roughly the time it took to learn C++ or possibly less, but JS has its own quirks. Often learning a second language is difficult as the first.

Very light compared to the other resources people have linked for you, but I love https://learnxinyminutes.com/

We essentially implemented this matrix version in Lean/mathlib to both compute the fibonacci number and generate an efficient proof for the calculation.

https://github.com/leanprover-community/mathlib4/blob/master...

In practice this isn't very useful (the definition of Nat.fib unfolds quick enough and concrete large fibonacci numbers don't often appear in proofs) but still it shaves a bit of time off the calculation and the proof verification.

As an aside, there are a number of Turing machines defined in Lean's mathlib. https://github.com/leanprover-community/mathlib4/blob/2c3ee3...

Thanks,

and there is Subobject, which looks like the subobject classifier.

https://github.com/leanprover-community/mathlib4/blob/master...

I just wonder if there are any irl meetups of people involved with formalizing mathematics, I thought that it would be a cool hobby to pick up (with some background in math and programming) but the existing libraries, like MathLib, TypeTopology or UniMath look a bit intimidating...

What is a good proof in either unimath or mathlib4 or somewhere else to get started with formal proofs? Like some well known result without too many dependencies, but still nothing trivial like propositional logic?

For searching using search terms for theorems in mathlib, there is the mathlib documentation page (for Lean 3 https://leanprover-community.github.io/mathlib_docs/ and Lean 4 https://leanprover-community.github.io/mathlib4_docs/). To find theorems by type, I find the best way is to use the `library_search` tactic from inside Lean itself.

Hello, I'd like to start learning Lean 4. I'm already reading the book, but I'm really curious to study real-life parallel. So I looked into mathlib4, but there seem to be a lot of dependencies between the the files. So I wonder the following: