Damn, no wonder my pet RL project barely ever worked

Rules number 4 and 6

I think you’re going to have a hard time completing this assignment. Just based on the fact you were unable to read the sub’s rules. I don’t think whatever we link you you’ll be able to read the readme of.

Why. Why. Why are you trying to go the hard way on purpose. Why.

The comments might differ in style, but they do address the issue. Engaging in thoughtful discourse can enrich conversations, even if the perspectives expressed aren’t in alignment with one’s own.

While dissenting voices may have faced challenges historically, acknowledging their existence doesn’t discount the progress made in recognizing diverse perspectives over time.

Absolutely, there have been instances of controversy and concerns regarding the reviewing process at various conferences. However, it’s crucial to note that while these incidents do occur, they might not necessarily represent the entire system. Many conferences continuously strive to improve their review processes and address these issues. While acknowledging these problems is essential, it’s also important to engage constructively in efforts to make the system better, perhaps by actively participating in discussions or proposing reforms, rather than solely highlighting the flaws.

Reddit posts are problematic.

I’ve been an active participant in the Machine Learning subreddit for quite some time now, and lately, I’ve noticed a trend that’s been concerning. While the subreddit serves as an incredible hub for knowledge sharing and discussions around ML, there’s a growing issue with the quality and reliability of some posts.

Numerous submissions lack proper context, thorough explanations, or credible sources, making it challenging for newcomers and even seasoned practitioners to discern accurate information from misinformation. This trend isn’t just about incomplete explanations; it also extends to the validity of claims made in these posts.

It’s important to acknowledge that not all content falls into this category—there are incredible insights shared regularly. However, the influx of hasty, ill-explained, or unverified information is diluting the overall value the subreddit offers to the community.

In a field as intricate as machine learning, accuracy and credibility are paramount. Misleading or incomplete information can misguide newcomers and even experts, leading to misconceptions or wasted efforts in pursuit of understanding or implementing certain techniques.

Thus, after observing this trend over some time, I firmly believe that there is indeed a problematic issue with the quality and reliability of several Reddit posts within the Machine Learning subreddit. It’s a plea to the community to uphold standards of clarity, depth, and substantiation in discussions and submissions to maintain the subreddit’s integrity and credibility.

At least have the decency of reading a sub’s rules before posting…

Around 88k gross including bonuses, I think around 6 years of experience, around 3-4 that’s actually DS/ML within that. In Hungary, where the average salary for a medior Python dev is 50k and the overall average salary in the country is 19k.

A “prompt engineer”'s job is iterally to be able to write concise but specific sentences. E.g. Googling skills.

Vs. Infra engineer that needs to understand complex systems and maintain an extremely complicated set of tools so that they work without an issue for hundreds or thousands of users.

You decide

I’ll do it for 100 bucks