I am working on a LLM-powered newsletter (https://distilleddaily.news/) that aims to solve this exact problem for myself (and targeting ML engineers, data scientists and AI researchers).

I've only started last week and have launched among friends, and so only source HackerNews for now but will quickly include curated sources: Twitter popular users' tweets (@hwchase17, @hardmaru), popular blogs (@chipro, @lilianweng), popular research papers and more.

I'm currently prioritising features to build and so would love to hear your thoughts!

I'm currently working on this! Currently using traditional computer vision methods (e.g. canny edge detection) which already works quite well for most websites or applications, but am working towards curating a dataset for deep learning. I'm keen to chat!

Let me know if you want a good place to host and iterate on the dataset, I'm working on a version control system optimized for deep learning datasets. Free to host public datasets right now, just looking for feedback.

It's called Oxen and is super fast at versioning image data.

https://github.com/Oxen-AI/oxen-release

If we train recursively-restricted reinforcement learning agents, could there be interesting differences in the behaviors that emerge? Could it even be used as a method for exploration?

Some set-up considerations: 1) Actions must be discrete, or at least binned for restriction, 2) The number of times to restrict is limited by the size of the action space

I would imagine for CartPole, the balancing would become more wobbly, while still somewhat successfully balancing. But in more complicated environments, it could result in much more different behaviors because the states visited (and trajectories) could be different.

I reflected upon how I approached many past opportunities, and realised that I only truly enjoy the process if the nature of work is interesting to me because I'm mostly interest-driven. I also found that enjoying the process is already rewarding regardless of the outcome, and at the same time have much more control over the process than the outcome. :)

Hi, I'm the author of the post. I wrote it on Notion to seek feedback from a few friends, but seems like one friend shared the Notion link here :) Hope that my website would provide a better reading experience instead - https://jetnew.io/blog/2021/100-lessons/

Thanks for this! I will take a look at how to improve the veracity and quality of time parameters and distributions for my model.

Hi, I'm the creator of this project. Seems like someone shared it here. This is a school project and a current work-in-progress, and I'm open to any feedback available. The usefulness of a simulator is heavily dependent on how well it approximates to reality, which I have yet to do. It is currently a baseline experiment without any reference to current research on COVID-19, but stay tuned as I'm working on it! And thanks for sharing :)

It sounds like you and I are thinking along the same line: https://github.com/joemccall86/cap5600-project2.

I am also taking AI this semester and was considering expanding upon something like this for my masters' capstone project. The application of machine learning to this type of situation is going to be very interesting to say the least.

Just wanted to say good luck!