brandonasuncion

If you look at it that way, I agree. But then the same thing is done for executing Go, which is listed with the other compiled languages.

As a small note, Swift is a compiled language. It uses LLVM as a backend, same as Rust and Clang (C/C++/ObjC). It's currently listed under "Web & typed scripting".

Claims of being "10x faster performance than existing solutions" using a HNSW written entirely in Python is enough to raise alarm bells, vibecoded or not.

I was reading into it and came across your blog post. It's a good followup read.

It has better avalanching compared to indexing by `(i * prime) % n`, but at the tradeoff of `n` being restricted to powers of 2.

I use "random" in quotation marks because it communicates the topic effectively and is very straight to the point. Colloquially, when people say "random" in the context of algorithms, it's generally understood to mean "pseudorandom" or "quasirandom". The trick is essentially an LCG using coprime numbers.

The blog post uses "random" in quotations as well in its title.

I call it O(1) because the ordering is fully determined just by choosing an arbitrary prime number. It's constant time to "reorder" the elements and also constant time to get the i'th element in the permutation. This is in contrast to something like the the Fisher-Yates shuffle which permutes the elements in O(n) time.

I've tried different L-Theanine supplements, and there's definitely a difference in quality across companies... which could help explain the variance in experiences.

Unfortunately, there isn't much regulation for supplements in general. Some companies do 3rd party purity testing, though it's not always the case.

Honestly, I'm welcoming this move to Notion. It's much less cluttered than Medium.

Hi there!

I made the initial suggestion to look into Robin Hood hashing when it was first posted on Reddit.

Glad to see it make its way into the repo!

Is it possible to finetune an LLM on the factual content without altering its linguistic characteristics?

With Stable Diffusion, you're able to use LoRAs to introduce specific characters, objects, concepts, etc. while maintaining the same visual qualities of the base model.

Why can't something similar be done with an LLM?

Btw, the quantized models are on Huggingface, so step 1 and steps 3-6 can be avoided. (The link is 3/4ths into the llama.cpp README)

Also,

  Android 1.0
  Android 1.1
  Android Cupcake
  Android Donut
  Android Eclair
  Android Froyo
  Android Gingerbread
  Android Honeycomb
  Android Ice Cream Sandwich
  Android Jelly Bean
  Android KitKat
  Android Lollipop
  Android Marshmallow
  Android Nougat
  Android Oreo
  Android Pie
  Android 10
  Android 11
  Android 12
  Android 12L
  Android 13
  Android 14
  Android 15

I really like the font aesthetically, and I'd use it as a coding font. However, the brackets and parentheses look too similar at smaller sizes, and arrows (ie. -> and =>) don't quite line up properly.