youngprogrammer

Atlantic and New Yorker my two favorite publications for long form articles. Although the Atlantic has got a bit too political for my tastes

At a 3 minute shot, I’d rather use the same time to do a pour over

You might as well play "who can memorize the most openings and lines"

It can get complicated quickly if you're actually using it in a production system. At my prev enterprise saas company we had feature flags that could be turned on per customer / per environment (dev, staging, prod) with permission + logging model such that our support team could also toggle flags with history of who turned on what. We also had "per user" feature flags for certain test users at companies and had DSL rules to evaluate the features

Fish sauce is delicious but had to stop using it since it's high in histamine (gives me a stuffy nose) and potentially carcinogenic due to its high levels of nitrosamines

industrialized overfitting is basically what ML researchers do

should go a bit earlier with word2vec, NMT, seq2seq, attention, self attention

Little late to this thread but from my list:

LLM (foundational papers)

* Attention is all you need - transformers + self attention

* BERT - first masked LM using transformers + self attention

* GPT3 - big LLM decoder (Basis of gpt4 and most LLM)

* Instruct GPT or TKInstruct (instruction tuning enables improved zero shot learning)

* Chain of Thought (improve performance via prompting)

some other papers which are become trendy depending on your interest

* RLHF - RL using human feedback

* Lora - make models smaller

* MoE - kind of ensembling

* self instruct - self label data

* constitutional ai - self alignment

* tree of thought - like CoT but a tree

* FastAttention,Longformer - optimized attention mechanisms

* React - agents

They probably didn't have a Gantt chart to help them figure out the dependencies to properly plan it on their roadmap

Anecdote: Our caterer in silicon valley said there was a supply issue for tofu.